esa-sceva/satcom-chunk-collection huggingface dataset:
# AVSS: Airborne Video Surveillance System
## 6 Evaluation
### Variable Bit Rate Video Streaming  
Functionality of the variable bit rate video streaming was tested by performing video streaming using the proposed system. The streamed video first starts with no object in the scene, and latter in the video couple of people walk and run across the video. Figure 8a depicts the resulting video quality index values, which are calculated based upon the event detection of the video. As the figure shows, the beginning part of the video starts in low video quality index since no objects is present  
Figure 8: Varying bit rate of video streaming: (**a**) Varying VQI based on the anomalous degree of the video frame; (**b**) Frame with low anomalous degree in a low quality video (frame 107); (**c**) Frame with high anomalous degree in a high quality video (frame 609).  
Figure 9: Satellite map of experimental area with position of UAVs.  
and therefore, no event is detected. When the event count is above the threshold, the video quality increases accordingly, and it drops slowly when there is no event detected for a while. Afterwards when people start to walk across the video, an event is detected by the system, and the video quality index increases. Figure 8b,c are captured images of the streamed videos. Figure 8b is an image from the beginning part of the video where video quality index is low. Therefore, the quality of the video is poor and the image is visibly pixelated. Contrarily, Figure 8c is an image from the part where people are walking and running in the video. The system recognized the event and, therefore, the video quality index is increased. The resulting video image shows that the quality of the video is much great and the block boundaries are hard to identify.
## 3 Diffuse Reflectance Spectroscopy: Analyzing Results
### (White) Standard Comparison  
Since the diffuse reflectance is measured against a reference material, it is advisable to take a white standard that is particularly absorption-free in the VIS. From potential materials as outlined in Table 2, we will exemplarily focus on the class of aluminum oxides for their excellent chemical inertness such as water insolubility, i.e., they are non-hygroscopic, and particle size well below 10 um[78; 79]. Investigated white standard materials include _CR30F_ (Baikowski), _AA-03_ (Sumitomo), _ZWSK F2000_ (Imerys Fused Minerals), and, for comparison, the sintered PTFE material _RESTAN_ (Image Engineering) [80; 81; 82; 83]. All powder samples have been prepared according to the procedure described in Section 2.1.1, yielding a pellet thickness of 4.5 mm. The _RESTAN_ sample has been cut out from a bulk-material, rasped into the same dimensions as the powder samples and inserted into a sample holder which was shown in Figure 1.  
Figure 6 shows the detected signal from each white standard as a function of wavelength, normalized to the maximum signal recorded from all samples. The excitation monochromator was operated at a bandwidth of 9 nm whereas the emission monochromator was set to a bandwidth of 1.7 nm. During this sweep, a spectral step size of 1 nm was chosen with an integration time of 1 s. Each sample's diffuse reflectance has been averaged over three scans.  
The curves highlight the central features of the 450 W xenon arc lamp with its broadband emission spectrum throughout the VIS as well as its sharp emission lines between 400 nm to 500 nm (an isolated emission spectrum is shown in Figure 10). The suppression of signals towards the near infrared range can be attributed to the declining spectral sensitivity of the PMT detector for longer wavelengths (also shown in Figure 10). Due to the low signals in this range, the diffuse reflectance beyond 800 nm should be treated critically.  
All investigated white standards exhibit a similar spectral diffuse reflectance behavior with a normalized signal intensity difference of up to 0.1 (absolute value).  
Considering the remarkable (absolute) reflectance of _RESTAN_ on the order of 0.98 throughout the visible region [76], this result highlights the excellent suitability of the investigated aluminum oxide powders. However, as _RESTAN_'s maximum diffuse reflectance may only be obtained for sample thicknesses exceeding 8 mm[76], direct comparison between these 4.5 mm-pellets is not advisable as considerable influence by the sample holder can be assumed in the PTFE material. In this regard, the investigated powders clearly demonstrate to be advantageous for samples at lower thickness whereas PTFE may become a reasonable alternative as a white standard when thickness (and thus, volume) of the sample is not a concern.
## 2 An Isolated, High-Power, Photovoltaic Pumping System (Monitored System)
### Support Structure  
The PV generator contains 171 photovoltaic panels mounted on three independent horizontal single-axis trackers (HSATs) [12; 21], with 57 panels per tracker. Each tracker is oriented along the north-south axis and rotates within a 110\({}^{\circ}\) range.  
Constructed from hot-dip galvanized steel, the tracker structures comply with EN 1991 [22; 23] standards for load resistance (self-weight, wind and snow) and ISO 9223 [24] for corrosion protection.  
A 5 m spacing between trackers was identified as optimal to minimize shading between rows and provide sufficient access for cleaning and routine maintenance.  
Each tracker is equipped with a control unit that manages motor function, solar tracking algorithms and backtracking logic to reduce inter-row shading. The system also includes safety features: stops motion when the rotation of the axis exceeds \(\pm\)55\({}^{\circ}\) or when abnormal power consumption is detected and enters a safe position if the wind speeds exceed set thresholds [12; 21].  
Each HSAT includes an independent controller that communicates wirelessly via ZigBee technology, ensuring reliable data transmission. Each unit is powered autonomously by a photovoltaic module with battery backup. It also supports integration with SCADA systems through built-in alarms and status indicators, facilitating effective operation and maintenance. In extreme wind conditions, the system activates an autonomous safety response to protect both the structure and the panels [12; 21].  
Table 12 from Appendix A presents the main characteristics of the HSAT [21].
## 2 Derivation of Amplification Factors (_A_Fs)
### Database
#### 2.4.3 Correlation Between Site Parameters  
All the parameters considered are not fully independent, as shown by the coefficient of determination (R\({}^{2}\)) presented in Table 2, calculated for each pair of parameters for all soil profiles considered. There exist strong correlations between the pairs (Cv, Cv2), (\(V_{sm}\), Cv), (\(V_{sm}\), \(V_{s30}\)), (\(V_{s30}\), Cv), and (\(V_{s30}\), Cv2), as indicated by the coefficients of determination (R\({}^{2}\)) generally exceeding 0.62. However, very weak correlations, notably for the pairs (Cv, Depth) and (\(V_{sm}\), Depth), and weak correlations for the pairs (Cv, \(f_{0}\)), (\(f_{0}\), \(V_{sm}\)), and (Depth, \(V_{s30}\)), with the R\({}^{2}\) value ranging between 0.25 and 0.37, are observed. Finally, a mitigated correlation, with R\({}^{2}\) = 0.51, is observed for the couple (\(f_{0}\), Depth). These correlation indicators are useful for selecting independent site parameters for the models relating site amplification to site characteristics.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **10\% Fractile** & **50\% Fractile** & **90\% Fractile** \\ \hline Depth (m) & 5 & 50 & 200 \\ \hline \(V_{sm}\) (m/s) & \(V_{sm}\) (m/s) & 100 & 250 & 600 \\ \hline \(V_{s30}\) (m/s) & 100 & 300 & 642 \\ \hline Cv & 1.66 & 4 & 10 \\ \hline Cv2 & 1.33 & 3 & 8 \\ \hline \(f_{0}\) (Hz) & 0.31 & 1.64 & 12.73 \\ \hline \end{tabular}
\end{table}
Table 1: The 10%, 50%, and 90% fractiles of the studied parameters.
## 1 Introduction
However, the temporal resolution of InSAR monitoring methods for key monitoring areas is currently limited by data acquisition and processing constraints. On the other hand, GNSS technology offers the advantage of continuous and all-weather monitoring without being affected by climate or other factors. The monitoring process, including data acquisition, processing, transmission, and analysis, can be easily automated and operated. [PERSON] et al. realized high-precision deformation monitoring by combining GNSS and ground-distance observation under harsh environmental conditions [20]. [PERSON] et al. conducted real-time and high-precision monitoring of landslide displacement based on a GNSS CORS network [21]. [PERSON] et al. carried out research on the evaluation of a real-time, high-precision detection algorithm for landslide displacement using GNSS virtual reference station technology [22]. Given these advantages of GNSS technology, this study proposes a collaborative monitoring approach using SBAS-InSAR and GNSS to investigate the deformation law in the deep excavation channels of the China South-North Water-Diversion Middle-Route Project. This approach aims to achieve surface domain deformation monitoring and the real-time online monitoring of key deformation areas in the study region, thereby enhancing the deformation disaster forecasting and early-warning capabilities. By considering the surface deformation law and geological engineering conditions in the study area, the mechanism of surface deformation was inferred. Additionally, the correlation between soil moisture and three-dimensional surface deformation was carried out by combining GNSS and soil-moisture online-monitoring data. The research results not only revealed the spatial and temporal evolution rules and mechanisms of surface deformation in the channel slope of this deep excavation channel section but also improved the deformation disaster-prevention capability in the study area. Furthermore, the results provided an effective technical means for the safety monitoring of the whole China South-North Water-Diversion Middle-Route Project and similar long-distance water-transmission channel projects.
# Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data
###### Abstract  
Automatic boundary detection of 4D ultrasound (4 DUS) cardiac data is a promising yet challenging application at the intersection of machine learning and medicine. Using recently developed murine 4 DUS cardiac imaging data, we demonstrate here a set of three machine learning models that predict left ventricular wall kinematics along both the endo- and epi-cardial boundaries. Each model is fundamentally built on three key features: (1) the projection of raw US data to a lower dimensional subspace, (2) a smoothing spline basis across time, and (3) a strategic parameterization of the left ventricular boundaries. Model 1 is constructed such that boundary predictions are based on individual short-axis images, regardless of their relative position in the ventricle. Model 2 simultaneously incorporates parallel short-axis image data into their predictions. Model 3 builds on the multi-slice approach of model 2, but assists predictions with a single ground-truth position at end-diastole. To assess the performance of each model, Monte Carlo cross validation was used to assess the performance of each model on unseen data. For predicting the radial distance of the endocardi, models 1, 2, and 3 yielded average R\({}^{2}\) values of 0.41, 0.49, and 0.71, respectively. Monte Carlo simulations of the endocardial wall showed significantly closer predictions when using model 2 versus model 1 at a rate of 48.67%, and using model 3 versus model 2 at a rate of 83.50%. These finding suggest that a machine learning approach where multi-slice data are simultaneously used as input and predictions are aided by a single user input yields the most robust performance. Subsequently, we explore the how metrics of cardiac kinematics compare between ground-truth contours and predicted boundaries. We observed negligible deviations from ground-truth when using predicted boundaries alone, except in the case of early diastolic strain rate, providing confidence for the use of such machine learning models for rapid and reliable assessments of murine cardiac function. To our knowledge, this is the first application of machine learning to murine left ventricular 4 DUS data. Future work will be needed to strengthen both model performance and applicability to different cardiac disease models.  
e acheocardiography; 4D ultrasound; volumetric imaging; murine; left ventricle; myocardium; cardiac kinematics; hypertrophic cardiomyopathy; boundary prediction; machine learning
## 3 Architecture
### Non-Intrusive, Privacy-Preserving Indoor Location  
Indoor location has many applications for structural health monitoring, studying the behavioral patterns of a building's occupants and health-related issues such as locating lost patients with memory and orientation disorders, healthy activities, etc. Most existing solutions for indoor location rely on wearable devices (i.e., location-aware bracelets), which require frequent charging and can generate invalid data in case the device is forgotten. Our approach is a passive system that does not require any maintenance or wearable device. We used off-the-shelf force resistors (FSR model 406), which are emended and centered inside a 30 \(\times\) 30 cm tile of foam. Once force is applied, the foam and FSR deform, which can be measured as a voltage drop by the controller.  
In a wired setting, each tile of foam is shaped like a puzzle piece, which ensures easy assembly. Each tile has two connectors on each face of the square to seamlessly connect to a neighbouring tile. It also includes a small chip for converting the analog signal to a digital that finally allows the collection of sensor readings over a one-wire type protocol. Every three-by-three grid of tiles contains one compute unit, which serves as a controller for the underlying sensors, and a WSN/blockchain node, as depicted in Figures 2-4. Figure 5 illustrates an assembled module in grid mode.  
Figure 2: Bottom side of the foam tile.  
If physical connections are not suitable, a completely wireless configuration is possible, but less cost effective. Figure 6 illustrates a module in full WSN mode.  
Figure 4: Upper side of the foam tile.  
Figure 5: Grid-based connection of individual force sensors.  
Figure 3: A detailed view of the male connector.
## 1 Introduction
The results the obtained shows that the algorithm they proposed was able to extract target pests against a complex farmland background satisfactorily. [PERSON] et al. [29] constructed a pest identification system by integrating feature learning, feature fusion, classification, and position regression of 16 kinds of common pests based on deep learning combineds. The recognition accuracy of the proposed method in light traps, under natural conditions, ranged from 66.00 to 90.00%. [PERSON] et al. [30] proposed an efficient deep CNN model to classify insect species on three publicly available insect datasets [31; 32] and achieved an accuracy of 96.75, 97.47, and 95.97% respectively. However, most of these studies primarily focused on obtaining the quantities of various types of insects. Few studies have been conducted to achieve automatic acquisition of locust type and instar information in field environments. In this paper, we apply CNN for the identification of species and instars information of locusts using our proposed ResNet-Locust-BN model. Based on the ResNet architecture, we integrate the BatchNorm function before each convolution layer to improve network stability, convergence speed, and classification accuracy. Subsequently, locust image data collected in the field were used as sample inputs for training the classifier parameters. We performed comparison experiments to optimize the activation function, initial learning rate, and batch size model parameters of the proposed model. We identified ReLU as the most appropriate activation function for the layers of the proposed model. The initial learning rate and batch size were set to 0.1 and 32, respectively. Results of various experiments performed show that the ResNet-Locust-BN network model can distinguish AM locusts from _Oxya chinensis_ (rice locusts) and cotton locusts with high accuracy. We also observed that the proposed model can identify the growth status information of AM locusts (third-instar, fifth-instar, and adult) with an overall accuracy of 90.16%, which is higher than the accuracies reported for the AlexNet, GoogLeNet, ResNet 18, ResNet 50, and VggNet CNN models. Further, the proposed model showed good robustness, fast convergence, and other advantages. The remaining part of this paper is organized as follows: Section 2 provides the details of the experimental datasets, differences in locust phenotypic characteristics, the architecture of the proposed ResNet-Locust-BN network model, and the experimental process. Section 3 discusses the optimization of the experimental parameters and recognition accuracy of different CNN models. Section 4 provides discussions on comparison of relevant studies, shortcomings, and our intended future work with respect to this study. Finally, Section 5 presents the conclusions of this study.
# Real-Time Location-Positioning Technologies for Managing Cart Operations at a Distribution Facility
## 3 An RFID-Enabled Real-Time Cart-Locating Platform for Mail Facilities
### Reduced Risk of Displacement of Mail Bags to Flights  
Another advantage of this platform is the reduced risk of misplacement of mail bags to flights. The misplacement of mail bags to flights can be avoided in two ways. First, the signal lights on the carts can ensure that the staff members are moving the right carts. Second, the locating system can also warn the staff if carts are entering the ULD area but they are not supposed to be there.  
In the next section, we present a case study which adopted this proposed RFID infrastructure for managing mail bag circulating operations.  
Figure 1: The key components of our proposed platform and its information flows.
## 3 Experimental Results
### Experimental Data Processing
#### 3.1.3 Cycle GAN Data Enhancement Experiment  
To evaluate the image enhancement effect of Cycle GAN data and verify the feasibility of cross-production line sample migration, a total of 1200 original images of various categories were acquired, and new defective samples were generated by image cross-domain conversion. The migration results of some defective samples are shown in Table 2  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Comparison of Cycle GAN Defect** \\  & **Sample before and after Migration** \\ \hline Sc original & \\ \hline Sc generation & \\ \hline In original & \\ \hline In generation & \\ \hline Rs original & \\ \hline Rs generation & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cycle GAN defect sample migration results.  
Figure 9: Image fusion.  
In the example of Table 2, after the migration of Cycle GAN defect samples, samples that can be confused with real ones are generated. Sc, In, and Rs in the table are similar to the original images in terms of the size, position, shape, and texture details of the generated defects, which can be used to expand the sample data.  
SARSN50 was used in the network, and the number of training and testing rounds was 50 epochs. The test accuracy results of each defect category before and after migration are shown in Figure 10 and shown on the right is the total test accuracy curve of defect samples before and after migration.  
The bar chart on the left side of Figure 10 illustrates the accuracy metrics on the test dataset after improving the defect sample migration strategy through Cycle GAN in comparison to the original data. It is evident that the blue bars, representing the test accuracy in six categories of steel strip defects, are very close to the yellow bars. This suggests that, with a slight sacrifice in accuracy, our approach can achieve higher classification prediction scores in completely unknown new domains. After training for 20 epochs, the test accuracy of the migrated image dataset begins to approach that of the original datasets. Under the condition that all parameters are the same, when 50 epochs are used, the test accuracy of the migrated image dataset reaches 88.7%, which is close to the test accuracy of the original dataset of 90.8%, which shows that the newly generated dataset has higher confidence after being enhanced by Cycle GAN and can initially meet the requirements of defect sample generation on new production lines in industrial fields. If the classification accuracy of migrated samples is further improved, a deeper SARSN101 network can be used.
# An Advanced Soil Classification Method Employing the Random Forest Technique in Machine Learning
## 3 Unified Soil Classification System (USCS)
### Use of Plasticity Chart  
The plasticity chart is a crucial tool in geotechnical engineering, used to classify fine-grained soils based on their LL and PI. This chart aids in distinguishing between silts and clays and assessing their plasticity levels, which can be categorized as low, medium, or high. By graphing the LL and PI on the plasticity chart, engineers can ascertain the soil type and its response under different circumstances. The chart typically has the LL on the \(y\)-axis and the PI on the \(x\)-axis, allowing for a clear visualization of the soil's properties.
# Robust Human Pose Estimation Method for Body-to-Body Occlusion Using RGB-D Fusion Neural Network
###### Abstract  
In this study, we propose a novel approach for human pose estimation (HPE) in occluded scenes by progressively fusing features extracted from RGB-D images, which contain RGB and depth images. Conventional bottom-up human pose estimation models that rely solely on RGB inputs often produce erroneous skeletons when parts of a person's body are obscured by another individual, because they struggle to accurately infer body connectivity due to the lack of 3D topological information. To address this limitation, we modify the traditional OpenPose that is a bottom-up HPE model to take a depth image as an additional input, thereby providing explicit 3D spatial cues. Each input modality is processed by a dedicated feature extractor. Each input modality is processed by a dedicated feature extractor. In addition to the two existing modules for each stage--joint connectivity and joint confidence map estimations for the color image--we integrate a new module for estimating joint confidence maps for the depth image into the initial few stages. Subsequently, the confidence maps derived from both depth and RGB modalities are fused at each stage and forwarded to the next, ensuring that 3D topological information from the depth image is effectively utilized for both joint localization and body part association. Subsequently, the confidence maps derived from both depth and RGB modalities are fused at each stage and forwarded to the next to ensure that 3D topological information is effectively utilized for estimating both joint localization and their connectivity. The experimental results on the NTU 120+ RGB-D Dataset verify that our proposed approach achieves a 13.3% improvement in average recall compared to the original OpenPose model. The proposed method can enhance the performance of the bottom-up HPE models for the occlusion scenes.  
deep learning; computer vision; human pose estimation; RGB-D image 2025 15 8746
# The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems
## 2 Methods
### The ESLM-AE PAPR Reduction Scheme  
The neural network, which is an important statistic tool, can be used for describing the relationship between the input and the output. Its parameters can be determined automatically using backpropagation given a particular loss function. The proposed ESLM-AE uses an AE structure combined with ESLM method to mitigate the high PAPR issue of DCO-OFDM signals.