249 results about "Sample area" patented technology

The invention relates to a soil microorganism recovery index processing method and system and electronic equipment. The method comprises the following steps: collecting a soil sample in a gradient sample area, and measuring soil microbiological indexes and soil attributes; taking the soil microorganism indexes as a mother sequence, taking the soil attributes as subsequences, calculating correlation coefficients and correlation degrees between the subsequences and the mother sequence, and determining key soil attributes influencing soil microorganism recovery; determining a weight value of the key soil attribute; based on the numerical values of the key soil attributes in the original ecological reference area and the damaged unrepaired area, combining the weight values of the key soil attributes, and adopting an improved soil restoration index model to calculate and obtain a soil microorganism restoration index; the technical problem that in existing mining area ecological restoration evaluation, an original ecological system is not used as a benchmark, and the soil microorganism restoration degree cannot be quantitatively represented is solved, and the technical effect of achieving objective, comparable and quantifiable accurate evaluation of the soil ecological function restoration degree is achieved.

An intra prediction method includes: performing filtering processing on template samples corresponding to a target image block to obtain filtered template samples, where the template samples include at least part of reconstructed samples in a neighboring sample region of the target image block; obtaining, based on the filtered template samples, at least one intra prediction mode and an amplitude of the at least one intra prediction mode; determining, based on the amplitude of the at least one intra prediction mode, a target intra prediction mode from the at least one intra prediction mode; and determining, based on the target intra prediction mode, a target prediction value corresponding to the target image block.

The invention relates to a consumable device for sweat-based monitoring, the consumable device comprising a proximal part configured to contact with a user, and further comprising: a) a sweat induction means on the proximal part, comprising: i. a sweat promoter configured to contact the skin of a user, and ii. a microfluidic structure; and b) a sweat interrogation means, located distally to the sweat induction means and comprising: i. a sample area; and ii. a passive fluid pump configured to induce the flow of the liquid through the sample area. The microfluidic structure is configured to convey fluid from the sweat promoter to the sample area. The invention also relates to a sweat-monitoring system comprising: a consumable device as above and a sweat-based monitoring device, the sweat-based monitoring device comprising sensing means and processing means.

The invention relates to the technical field of detection sample sealing, in particular to an instant-drying type slide sample sealing protection device which comprises a slide tray provided with a slide placing groove used for placing a slide; the linear driving module is connected with the slide tray and is used for driving the slide tray to linearly act; the glue adding module comprises a glue dripping head located above the moving path of the slide tray and is used for adding glue to the sample area of the slide; the glue scraping module comprises a scraping blade, and the scraping blade is located above the moving path of the slide tray and used for scraping glue on the slide; and the glue drying module is positioned on one side of the glue scraping module and is used for drying glue on the slide. According to the method, a brand new process is adopted, cover glass does not need to be placed on the slide, the problem of cover glass fragmentation and the problem of mounting medium pasting are avoided, the problem of mutual intersection of smear lamination storage is solved, the storage efficiency of a smear cabinet is improved, and rapid smear sealing can be achieved.

The invention discloses a methane column concentration inversion method, device and equipment, a medium and a program product, relates to the technical field of remote sensing data processing, and is used for accurately determining the methane column concentration and avoiding the problem of low accuracy of a calculation result caused by neglecting actual working condition factors. The method comprises the following steps: acquiring meteorological parameters, methane distribution fitting information, radiance image data observed by a satellite and sample working conditions of a sample area in a natural gas emptying process; processing the radiance image data based on the initial inversion model to obtain an initial inversion result; the initial inversion result comprises the column concentration of methane in a plurality of resolution grids in the space above the sample area; determining correction parameters based on the meteorological parameters, the methane distribution fitting information and the initial inversion result; and correcting the initial inversion model based on the correction parameter to obtain a target inversion model corresponding to the sample working condition.

The invention discloses an intelligent avalanche susceptibility assessment method fused with a deep residual network, and relates to the technical field of geological disaster risk assessment. According to the method, the spatial precision is high, the 12.5 m high-resolution DEM and 10 multi-source evaluation factors are adopted, the influence of the microtopography on the stability of accumulated snow can be accurately recognized, and the avalanche point hit rate reaches 82.5% and is far higher than that of a traditional method (generally lt: 70%); the model is excellent in performance, the interpretability of a shallow model is reserved through a two-stage framework of an integrated learning primary model and a residual network, nonlinear interaction among factors is captured through a deep network, and AUC of ResMLP is equal to 0.8496 and is improved by more than or equal to 4% compared with the primary model; the method is high in generalization stability, the prediction variance is reduced by about 12% on the premise that the training cost is not increased by introducing a TTA mechanism with Gaussian noise added, the method is particularly suitable for a high-altitude small sample area, and the overfitting problem is effectively relieved.

The invention relates to a method and a system for identifying a slow-dip-angle structural plane of a pumped storage upper reservoir, and the method comprises the following steps: selecting a plurality of surveyed pumped storage power station upper reservoir regions as sample regions, and obtaining the exploration sample data of the sample regions; gridding the sample area into a three-dimensional voxel grid, preprocessing exploration sample data, extracting exploration features of each voxel in the three-dimensional voxel grid, adding a sample label indicating whether the voxel belongs to a low-dip-angle structural plane, and putting the sample label into a training sample set; constructing a recognition model based on a machine learning model, and performing iterative training on the recognition model by using the training sample set to obtain a voxel low-dip-angle structural plane probability recognition model; and acquiring exploration data of a reservoir region on the pumped storage power station to be identified, acquiring a prediction probability of whether each voxel in the region belongs to a low-dip-angle structural plane or not by using the voxel low-dip-angle structural plane probability identification model, and further identifying the low-dip-angle structural plane in a target region.

This application relates to a method, apparatus, and equipment for soil erosion analysis. A pre-set model is trained by comprehensively considering soil erosion driving factor data, spatial structure characteristics, and soil erosion modulus data of the sample area. During training, the spatial autocorrelation of the prediction residuals of the soil erosion prediction model is ensured to meet a pre-set non-significant condition, thereby overcoming the problem of existing machine learning models neglecting spatial characteristics. In the prediction stage, the trained model is used in conjunction with the soil erosion driving factor data and spatial structure characteristics of the target prediction area to obtain accurate soil erosion modulus data, thus obtaining reliable soil erosion analysis information.

The invention relates to the technical field of wheat quality detection, and discloses a wheat quality rapid detection system based on a near infrared spectrum. The system comprises five modules: a spectral feature dynamic acquisition module, an environmental parameter coupling analysis module, a spectral precision evaluation module, a detection state control module and a spectral resource optimization module. The spectral characteristic dynamic acquisition module acquires near infrared spectral reflectivity data of a wheat sample, and divides sample areas with different detection attention degrees; the environment parameter coupling analysis module collects growth environment parameters, and integrates the growth environment parameters with the spectral data of the corresponding region to generate an environment coupling analysis data set; the spectral precision evaluation module calculates the real-time detection accuracy according to the difference between the historical reference of the sample area in the data set and the real-time detection quality index; the detection state control module switches the load state of the spectrum acquisition channel in combination with the accuracy and a preset threshold value; and the spectrum resource optimization module allocates resources according to the load state. The system improves detection pertinence, reliability and resource utilization rate, and meets rapid detection requirements.

This invention provides a portable colorimetric detection system for astaxanthin based on a smartphone, belonging to the field of colorimetric detection technology. Its features include: an optical detection platform for accommodating the Yeast lipolyticis fermentation broth sample and providing a standardized optical detection environment; a smartphone with its camera pointed at the sample area of ​​the optical detection platform for acquiring images of the fermentation broth sample; a color analysis module embedded in or connected to the smartphone for extracting color feature parameters from the acquired images; and a quantitative model module pre-stored with a linear regression model based on the color parameters of the fermentation broth and the astaxanthin concentration, the model being constructed based on the (R+G) / 2 parameter, used to calculate the astaxanthin concentration in the sample based on the color feature parameters. The advantages of this invention are: simplified detection steps, convenient and rapid sample detection, and real-time sample monitoring.

The invention discloses a film thickness measurement method based on a nanometer anti-oxidation film, and relates to the technical field of nanometer film thickness measurement, the method comprises the following steps: co-heating a sample containing the nanometer anti-oxidation film and a volatile chelating agent, so that a film layer is converted into a volatile complex through a coordination reaction; condensing and collecting a gaseous product, and filtering and enriching the gaseous product on a filter membrane; measuring the masses of the filter membrane before and after enrichment as b and a respectively, and calculating the mass of a membrane layer product according to a formula m = b-a; according to the known sample area S and the film layer material density rho, the film thickness is calculated according to the formula h = m / (rho * S). According to the method, quantitative separation of the film layer is achieved through chemical coordination-sublimation, measurement errors caused by rough substrates and ultrathin film layers in a traditional method are effectively overcome, and the method has the advantages of being high in accuracy and high in anti-interference performance.

The invention relates to the technical field of rheological testing and material characterization, and provides a local rheological testing system and device based on electromagnetic driving and visual displacement tracking, and the system comprises a magnetic field generation module which is used for generating a stable magnetic field for magnetizing a magnetic needle in a sample area and a periodic gradient magnetic field for driving the magnetic needle, and a driving module, the magnetic field generation module is used for providing corresponding steady-state and alternating current excitation for the magnetic field generation module, and the image acquisition module is used for generating a uniform linear magnetic field gradient in a central area by a gradient field Maxwell configuration coil pair and an external trigger synchronization mechanism through the arrangement of the structures such as the gradient field Maxwell configuration coil pair and the external trigger synchronization mechanism; the driving force acting on the magnetic needle is ensured to be stable and repeatable; an external trigger synchronization mechanism controls driving signals to be strictly synchronized with image acquisition, and magnetic needle displacement and phase difference are accurately extracted by combining image recognition and a sub-pixel positioning algorithm, so that magnetic field fluctuation and time sequence errors are effectively reduced, the rheological parameter inversion precision is improved, and the problems of large errors and high cost in the prior art are solved.

The invention discloses a model training method and device, a target detection method and device, electronic equipment and a storage medium. The training method comprises the following steps: inputting a sample image and sample point cloud data collected for a sample region into a teacher model for target detection to obtain first feature information and a first detection result; inputting the sample image into a student model for target detection to obtain second feature information and a second detection result; determining target loss according to at least two of the first distillation loss, the second distillation loss and the third distillation loss; and at least adjusting parameters of the student model according to the target loss until a training ending condition is met, and taking the student detection model meeting the training ending condition as a target detection model. According to the method provided by the invention, the accuracy of target detection is effectively improved.

The present application belongs to the field of ecology and environmental monitoring technology, and relates to a kind of detection and analysis method for decomposition rate of leguminous sand-fixing shrub litter in field.The present application screens sample area by system grid method, collects and lays out litter bag, synchronously determines initial and current nitrogen content of soil, calculates net increment of soil nitrogen, determines horizontal diffusion boundary of nitrogen, and then obtains soil absorbed nitrogen amount;obtains equivalent dry matter loss amount based on nitrogen balance calibration, and finally determines litter decomposition rate by exponential decay fitting.The method solves the decomposition rate estimation deviation problem caused by nitrogen migration and soil absorption in traditional detection, can more accurately reflect the actual decomposition process in field, improves the reliability of monitoring data, and provides reliable technical support for nutrient cycling research and ecological restoration effect evaluation of desert ecosystem.

The invention discloses a heavy mineral placer prediction method, device and equipment based on paleo-coastline reconstruction, and the method comprises the steps: determining the paleo-sea level relative height of a sample region, and generating a target paleo-coastline through a region growing algorithm; generating an ancient landform elevation model, identifying a plurality of ancient landform units based on the topographic position index, and screening out ancient landform exposure units; performing mineral identification on the ancient landform exposure unit to obtain a heavy mineral abundance map; constructing a hydrodynamic-deposition coupling model, identifying a sediment accumulation area of the sample area, and comparing the sediment accumulation area with the heavy mineral abundance map to obtain a heavy mineral enrichment area; dividing a positive sample area and a negative sample area, and training to obtain a heavy mineral placer prediction model; a mineralization probability distribution diagram of the target area is obtained through prediction, and the heavy mineral target area is determined according to the mineralization probability distribution diagram. The method improves the prediction precision of the mineralization probability of the heavy minerals, thereby improving the efficiency and accuracy of positioning and exploration of heavy mineral placers, and can be applied to the technical field of resource detection.

The present disclosure relates to a model training method, a road generation method, a device, a medium and equipment, which are beneficial to the generation of missing roads in a region. The method comprises: obtaining sample data for training; for each sample data, determining trajectory feature information corresponding to a sample region according to trajectory information in the sample region, and determining road label information corresponding to the sample region according to road information in the sample region; and training a neural network model by using the trajectory feature information and the road feature information to obtain a road generation model, wherein the neural network model comprises a semantic segmentation network and a language model construction network, the semantic segmentation network is used to output a first result for the trajectory feature information of the input sample data, the first result is used to determine whether each sub-region in the sample region of the input sample data is a road, and the language model construction network is used to output a predicted path for the trajectory feature information of the input sample data.

The application relates to a LUCC-based comprehensive atmospheric pollution level evaluation method, and relates to the technical field of data processing.The method comprises the following steps: acquiring regional feature sets, land use data sets and atmospheric pollution concentration sequences of a plurality of sample regions, determining target types corresponding to the sample regions, and dividing the plurality of sample regions into a plurality of sample region groups; for each sample region group, based on the target types corresponding to the sample regions included in the sample region group, the land use data sets and the atmospheric pollution concentration sequences, establishing an atmospheric pollution evaluation model, and based on the regional feature sets of the sample regions included in the sample region group, determining target regional feature factors corresponding to the sample region group; determining a target sample region group according to a regional feature set of a region to be evaluated; and determining the atmospheric pollution level of the region to be evaluated according to the land use data set of the region to be evaluated through the atmospheric pollution evaluation model corresponding to the target sample region group, which has the advantages of improving the efficiency and accuracy of comprehensive atmospheric pollution level evaluation.

This invention relates to the field of data analysis technology, and more specifically, to a method for identifying forest pests and diseases based on data analysis. The method includes: Step 1: acquiring aerial orthophotos of the target forest area and counting trapping points within a set time interval, and associating the trapping counts with the nearest-nearest-assigned sample area units; Step 2: constructing a complex simplex on a forest map grid, consisting of a patch layer, a sample area layer, and a trapping layer, and outputting suspected pest and disease triggering units after filtering out negative examples using a two-stage verifier; Step 3: establishing a pest and disease map database, describing typical patterns using disease type topological fingerprints; performing similarity assessment between the topological evidence package and the disease type topological fingerprints to generate disease type, extent, and confidence level, and labeling the data in layers on the forest map grid. This invention can capture fine-grained structural changes in pests and diseases at an early stage, and improves identification accuracy and interpretability by utilizing multi-source evidence synthesis.

The invention provides a ground feature element extraction method and device based on prior embedding, equipment and a medium, and relates to the technical field of remote sensing image processing, and the method comprises the steps: obtaining an optical satellite image and the region prior information of the optical satellite image; inputting the optical satellite image and the area prior information into a ground feature element extraction model, and obtaining a ground feature element extraction result of the optical satellite image output by the ground feature element extraction model; the ground feature element extraction model is obtained by training based on a sample optical satellite image, sample area prior information corresponding to the sample optical satellite image and a sample ground feature element extraction result corresponding to the sample optical satellite image. By means of the mode, the problem of model misjudgment possibly caused by only depending on image features can be avoided, the generalization ability and robustness of the model can be improved, then the accuracy of model ground feature element extraction is improved, and therefore the application requirement for high-precision ground feature element extraction is met.

The invention discloses a topology enhancement prototype and geometric self-adaption few-sample point cloud semantic segmentation method, which belongs to the technical field of three-dimensional vision, and comprises the following steps of: extracting geometric features of point cloud from a support set and a query set by using a feature encoder; a topology guiding prototype optimization module is introduced, persistent coherence analysis is carried out on point clouds of each category in a support set, multi-scale topology features are extracted, topology descriptors are generated, and the topology descriptors are used for guiding structural adjustment of prototype representation; a difficult sample correction module is designed, a difficult sample area in a query set is identified based on a topology instability index, and fine correction is performed on geometric features in combination with local point density and curvature information; and constructing a topology perception optimization module. According to the method, topology priori is systematically introduced into a few-sample point cloud segmentation task for the first time, and a more stable and fine semantic segmentation result is realized under the condition of not depending on a large amount of annotations.

The invention discloses a spruce height rapid measurement method based on an improved YOLOv11 model and an unmanned aerial vehicle image, and the method comprises the steps: collecting spruce and standard rod unmanned aerial vehicle remote sensing images in a sample region, carrying out the camera distortion correction of the unmanned aerial vehicle remote sensing images, constructing an unmanned aerial vehicle remote sensing image data set, and carrying out the camera distortion correction of the unmanned aerial vehicle remote sensing images; dividing into an unmanned aerial vehicle remote sensing image training set, a test set and a verification set; embedding a global attention mechanism in the YOLOv11 model, constructing an improved YOLOv11 model, and training the unmanned aerial vehicle remote sensing image data set to obtain a target detection model; a front end based on a developer platform application programming interface is deployed on an unmanned aerial vehicle remote controller, complete images of spruces and standard bars are shot through an unmanned aerial vehicle, and the unmanned aerial vehicle remote controller is controlled to transmit the images to a rear-end database; and the rear-end database receives the image and performs camera distortion correction, calls an application programming interface of the target detection model to detect the spruce height and transmits the spruce height back to a front-end page for recording. The spruce height measuring precision and efficiency can be improved, and the cost is reduced.

The utility model provides a biological sample stable preservation system which comprises a sample box body, a water-absorbing breathable carrier used for bearing samples is arranged in the sample box body, the inner space of the sample box body is divided into a sample area and a drying area by the water-absorbing breathable carrier, a drying agent filling layer is arranged in the drying area, and the drying agent filling layer is arranged in the sample box body. The drying agent filling layer and the water-absorbing breathable carrier are arranged in a non-contact mode, and the sample box body is provided with a sealing structure. According to the stable storage system for the biological sample, the biological sample can be quickly dehydrated by adopting the water-absorbing breathable carrier, and then is thoroughly dehydrated by adopting the drying agent filling layer through non-contact water absorption, so that the long-term stability of the biological sample is realized; the biological components in the sample preserved by adopting the preservation system disclosed by the utility model cannot be denatured and inactivated because the biological components are in a completely dehydrated state, the stable preservation of subsequent detection is not influenced, and the preservation system has a wide application prospect in the field of medical detection.

This invention discloses a method for optimizing the design of complex equipment based on a hybrid adaptive sampling surrogate model. This method divides the design space into several Voronoi polygons controlled by sample points. Three indices—leave-one-out error, local nonlinearity, and polygon region size—are used to evaluate the importance of each sample point region. An entropy-weighted distance method is used to select the most suitable sensitive region for adding points. A learning function based on error estimation is then combined to determine new sampling points. This iterative update constructs a high-precision surrogate model, establishing a mapping relationship between design parameters and optimization objectives, thereby solving the design optimization problem. This invention innovatively proposes a multi-index adaptive weight allocation method to select sensitive sample regions and proposes a novel learning function to determine the location of added points. This method can construct a high-precision surrogate model using fewer sample points and can be used for the optimization design of complex equipment, including but not limited to tunnel boring machines.

A method is provided for operating a microscope scanner (1) including a line scanner (3). Pre-scan data is generated for a target (6) including a sample, wherein generating the pre-scan data includes: obtaining a pre-scan image of the target (6) from the pre-scan; and identifying one or more sample-containing regions (24) and one or more sample-free regions (26) of the target (6) from the pre-scan image. Imaging scans are then performed on the scanned areas of the target (6) including one or more sample-containing regions (24) and one or more sample-free regions (26). The scanning speed of the line scanner (3) is adjusted along the image scanning path based on the pre-scan data, such that the target (6) is imaged at a higher scanning speed in one or more sample-free regions (26) than in one or more sample-containing regions (24).

The application relates to the technical field of geotechnical engineering, and particularly discloses a power transmission engineering pipe pile uplift bearing capacity intelligent prediction method and system, the method comprises the following steps: determining the limit uplift bearing capacity of each pipe pile in a preset sample area; acquiring the real-time load and pile head displacement of each pipe pile in an operation stage, and fitting a load response quantity; determining a coefficient distribution map according to the limit uplift bearing capacity of each pipe pile and the load response quantity thereof; receiving a user-inputted to-be-installed area, matching the coefficient distribution map in a dynamic database, and predicting the limit uplift bearing capacity based on the coefficient distribution map and pipe pile installation information; on the basis of predicting the limit uplift bearing capacity, actual use conditions are acquired through a sensor in an actual application stage, a dynamic influence coefficient determined by an area is generated, and the prediction results of subsequent same-type areas are corrected, so that the prediction accuracy is improved at a very low cost.

The invention discloses a fatigue test crack detection method based on thermal imaging. In the periodic fatigue loading process, the surface temperature change of a sample is detected in real time through infrared thermal imaging, and an amplitude diagram and a phase diagram of weak temperature fluctuation are obtained through calculation; segmenting the sample through local phase consistency, prefabricating a gap and a background, and only performing subsequent analysis on a sample area so as to eliminate background interference; after the amplitude diagram is binarized, the boundary displacement influence is eliminated through a simple boundary displacement review strategy and by means of the boundary displacement direction and stress concentration point position relation, complex feature point matching and displacement estimation between images do not need to be conducted through the algorithm, the calculated amount is small, and reliability is high; the crack length is calculated according to the detected movement track of the crack tip feature point, the method does not depend on static crack features, the basic assumption that sample changes inevitably come from damage generation and development is utilized, and crack length evaluation is not affected even if wrong feature points are extracted through single-frame heat map analysis.

The present application relates to the technical field of green view rate supplementing, and particularly relates to an intelligent supplementing path planning method for green view rate supplementing, comprising: dividing a sampling complexity category of a to-be-sampled area; determining a preset green view rate mutation threshold for identifying missing data in each initial image; determining whether to correct a data update rate of GNSS RTK based on an aggregation degree of the missing data; determining a supplementing point based on the missing data, generating a supplementing path based on coordinates corresponding to each supplementing point identified; supplementing each supplementing point by using a UAV, determining an anchor green view rate difference based on supplementing images acquired in real time, determining whether supplementing of the supplementing point is qualified based on the anchor green view rate difference, supplementing a next supplementing point when supplementing of the supplementing point is qualified; and fusing each supplementing image and each initial image to generate a green view rate panorama. When data is missing, sampling parameters are determined, and the supplementing efficiency is improved.

The utility model discloses a sample pretreatment device for gene detection of 10-50g food samples, which belongs to the field of gene detection of food and meat products and comprises a lower platform, and first to sixth food sample pretreatment units are sequentially arranged on the lower platform. Each food sample pretreatment unit comprises a pretreatment container and a rotary cutting unit, the volume of the pretreatment container is 50ml, the rotary cutting unit comprises a rotating shaft and a blade group for rotary cutting, the blade group for rotary cutting is connected to one end of the rotating shaft, the blade group for rotary cutting is located in a sample area in the pretreatment container, and the blade group for rotary cutting is connected to the other end of the rotating shaft. And the other end of the rotating shaft is positioned above the outside of the pretreatment container. According to the utility model, the pretreatment of 10-50g of food samples can be treated in a classified manner, and the problem of cross contamination is avoided.

The utility model relates to a radio-frequency excitation control device for tumor hypoxia research, which comprises a mounting frame, a radio-frequency signal excitation module is mounted on the upper side of the left part of the mounting frame, a radio-frequency signal control module is mounted on the upper side of the right part of the mounting frame, and the radio-frequency signal control module is electrically connected with the radio-frequency signal excitation module. An electric push rod is mounted on the right side of the mounting frame, a lifting table is connected to the telescopic end of the electric push rod, a radio frequency coil is connected to the lifting table, and the radio frequency coil is also electrically connected with the radio frequency signal control module. The electric push rod drives the lifting platform to vertically move, the radio frequency coil is driven to accurately adjust to a target height, and a radio frequency field uniformly covers a sample area, so that test requirements of samples of different specifications are met, an electromagnetic environment is closed by the protective frame, test safety is favorably improved, influence of electromagnetic diffusion on the test is prevented, and test efficiency is improved. Meanwhile, an operator can observe the test condition in real time through the transparent structure in the middle of the protective frame.

A system and method for preparing a sample area of a bale in order to more accurately evaluate the plant material incorporated into the bale. A baler receives, compresses, shapes, and secures material into the bale. A cutter mechanism cuts a portion of the material in the bale into similarly-sized particles. A mixer mechanism mixes the particles into a homogenous aggregate. A compression mechanism compresses the homogenous aggregate into the bale. An NIR testing system receives and analyzes near-infrared radiation reflected by the homogenous aggregate, and generates evaluation data reflecting properties of the material. The cutter may include knives mounted in a compression chamber of the baler. The mixer may be a relief feature on a center rail, the compressor may be a projecting feature on the center rail, and an NIR sensor may be mounted to the center rail so as to press against the surface of the bale.