esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Development of a Multilayer Perceptron Model (MLP)  
In the last two decades, artificial neural networks (ANNs) have proven to be one of the most successful technologies, widely used in a large number of applications in various fields. Often referred to simply as a neural network, an artificial neural network is a mathematical model that is inspired by the structure and function of biological neural networks in the brain. Their architecture implies that ANNs are usually designed as three-layer network models of interconnected artificial neurons (input layer, hidden layer, and output layer). Input nodes (neurons) receive input information, intermediate nodes receive signals from input nodes and manipulate the information to provide results to output nodes, and output nodes provide output signals. In this regard, an ANN model can have multiple intermediate layers containing sets of intermediate nodes, and if there are two or more layers, such a model is called a multilayer perceptron (MLP) [72]. MLP models are created due to the existence of a nonlinear data set, which is why there is a need to extend the ANN model construction to multiple layers. This avoids the limitation of models with one hidden layer, i.e., one perceptron, which is only suitable for linearly identifiable data [73; 74]. Such models are based on a supervised procedure, i.e., the network builds a model based on examples in the data with known outputs. MLP must extract this relation exclusively from the presented examples, which together are assumed to implicitly contain the necessary information for this relation. Information flows from the input layer to the output layer through the hidden layer. All neurons from one layer are fully connected to neurons in neighboring layers. These connections are represented in the computing process as weights (connection intensity). Weights play an important role in signal propagation in a network. They contain the neural network's knowledge of the problem-solving relationship. The number of neurons in the input layer depends on the number of independent variables in the model, while the number of neurons in the output layer is equal to the number of dependent variables.
## 3 Results
### Impact of Opening Positions
#### 3.2.2 Results for Building Models with a Fixed 40 mm Inlet Position and Varying Outlet Positions  
In an isolated gable-roofed building model with cross ventilation, where the center of the inlet window opening is fixed at a distance of 40 mm from the ground, the velocity and pressure contours and the distribution of streamlines for the Middle-Lower, Middle-Middle, and Middle-Upper configurations (where the center of the outlet window opening is varied at 20, 40 and 60 mm) are presented in Figures 22-24. Due to the downward inclination of the flow passing through the inlet opening and the lower positioning of the outlet opening, it is observed in the Middle-Lower configuration that the flow exits the building without a significant reduction in inlet velocity. In this model, higher inlet velocities and lower pressure values were obtained compared to the other configurations, which aligns with [PERSON]'s principle. Furthermore, the distribution of pressure across the windward facade remains consistent in all analyzed scenarios. In the isolated building models with Middle-Middle and Middle-Upper configurations, the flow exhibits an upward tendency due to the higher position of the recirculation region above the flow compared to the recirculation region below the flow. When examining the streamline distributions of configurations in which the windward opening is fixed at the middle height level of the building and the leeward opening is located at different heights, it is observed that the incoming jet generally tends to be downward. In all three cases, a clockwise recirculation vortex forms in the lower region of the jet. The center of this vortex is located in the middle section of the building in the Middle-Lower configuration, while it shifts towards the windward opening when the leeward opening is moved to the Middle-Upper level. While the flow structures in the upper region of the jet are similar in the Middle-Middle and Middle-Upper configurations, an additional vortex rotating counterclockwise is observed near the exit opening in the Middle-Lower configuration.  
Figure 22: Dimensionless velocity distributions for a fixed 40 mm inlet opening with a variable outlet opening. (**a**) Middle-Lower, (**b**) Middle-Middle, (**c**) Middle-Upper.
## 3 Results
### Simulated Setup Errors  
The IQM system is intrinsically insensitive to patient positioning, being placed upstream of the patient along the beam line. The SoftDiso provides, therefore, the only tool available to check for the correct patient positioning. The average R-value deviations (absolute values) for all phantom displacements and rotations with respect to the reference position are shown in Figure 5. Given the characteristics of the beams and the irradiation region considered in this study, the R-value measurements show good linearity and a sensitivity of \((0.74\pm 0.12\%)/\)mm to displacements in the anterior direction, where the strictest control of patient positioning is required. In contrast, as expected, the R-value is insensitive to movements in lateral and longitudinal directions, and to rotations, which are less critical in this specific case.  
Figure 4: Deviations from reference values of IQM signal and SoftDiso R-values as functions of MU variations (**top**) and of jaw position variation in close (**bottom left**) and open (**bottom right**) directions.  
Figure 5: Deviations from reference values of the SoftDiso R-value for phantom rotations (**left**) and translations (**right**).  
To further explore the sensitivity of SoftDiso to patient positioning errors, a gamma-index comparison of EPID images with displaced phantom was performed. The results for 2%/2 mm global criteria are presented in Figure 6. To reveal phantom shifts \(\geq 2\) mm, alert thresholds of \(\gamma_{\text{PR}}<89.7\%\pm 0.4\%\) and \(\gamma_{\text{mean}}>0.32\pm 0.04\) were obtained. The \(\gamma_{\text{PR}}\) and \(\gamma_{\text{mean}}\) indexes show good linearity with phantom displacements. Results of the comparison for phantom rotations are reported in Table 2.  
From the measurements we conclude that the sensitivity to small rotations is difficult to asses, with \(\gamma_{\text{PR}}\) remaining within the alert threshold for both rotation angles and \(\gamma_{\text{mean}}\) going slightly above-threshold for the maximum simulated rotation of \(2.8^{\circ}\).  
Figure 6: Measured \(\gamma_{\text{PR}}\) and \(\gamma_{\text{mean}}\) indexes as functions of phantom anterior (**top**); lateral (**middle**) and longitudinal (**bottom**) displacements for both beam 1 and beam 2 used in the simulated treatment plan.
# Lightweight Domestic Pig Behavior Detection Based on YOLOv8
## 4 Experiments and Analysis
### Experimental Results  
The training set inputs of YOLOv8n and the improved method YOLOv8-PigLite are utilized to compare the convergence of loss values. Figure 8 illustrates that the convergence rate of the enhanced algorithm surpasses that of the original model, demonstrating that the methodology proposed in this study not only diminishes model complexity and enhances detection performance but also offers a notable advantage in convergence speed.  
To assess the detection efficacy of the lightweight YOLOv8-PigLite technique, we present a series of graphs depicting loss and performance parameters during the training and validation phases. This study evaluates three categories of losses: bounding-box loss (box_loss), classification loss (cls_loss), and distribution focus loss (dfl_loss). The bounding-box loss assesses the positional difference between the predicted and reference bounding boxes, the classification loss evaluates the variance between the predicted and actual categories, and the distribution focus loss improves the box loss by refining the probability  
Figure 8: Comparison of training effects at each stage of the model.  
Figure 7: Confusion matrix results of the YOLOv8-PigLite model for pig behavior recognition. (**a**) Absolute confusion matrix. (**b**) Normalized confusion matrix.  
distribution of the bounding-box position. The initial three columns of the Figure 9 exhibit a notable alteration in the loss levels by the 50 th iteration. Following 150 iterations, box_loss decreases to 0.41 and cls_loss reduces to 0.24, demonstrating a comparable trend in the validation set and signifying the model's efficacy in target localization and classification tasks. The value of the loss function currently oscillates within a narrow range, indicating convergence and improved outcomes.  
The evaluation metrics in the final two columns of the Figure 9 indicate that, at the onset of training, precision, recall, and mAP exhibit varying degrees of volatility, primarily due to the initial randomization of model parameters and the heterogeneity of the training data. As iterations progress, these metrics exhibit consistent enhancement: precision rises from 0.01 to 0.96, recall advances from 0.09 to 0.95, and, notably, mAP@50 attains a substantial value of 0.98, thereby affirming the model's exceptional accuracy and robustness in detecting domestic pig behavior.
# Graph-Driven Deep Reinforcement Learning for Vehicle Routing Problems with Pickup and Delivery
## 4 Methodology  
In this section, we present the proposed DRL-based approach with an encoder-decoder framework for VRP-PD. We first bring in a graph convolution module as the encoder to embed node features. Then, we introduce a transformer-based decoder for node selection. Finally, we detail the training algorithm to optimize the policy network.
# A Review of Lithium-Sulfur Batteries Based on Metal-Organic Frameworks: Progress and Prospects
## 4 Conclusions and Outlook
* The industrial-scale production of metal-organic framework (MOF) materials holds the potential to vastly expand their applicability in commercial lithium-sulfur batteries (LSBs). To achieve cost reduction, it is crucial to select earth-abundant raw materials and employ synthesis methods that yield high productivity with minimal by-products. Emerging synthesis strategies, such as solid-solid and gas-phase methods, which feature shorter production cycles and higher yields, will play a key role in advancing the industrial manufacturing of MOF materials. Despite numerous challenges, the application of MOFs in the field of LSBs has advanced rapidly, offering a clear pathway for the development of MOF-based materials for next-generation LSBs. Future research endeavors will focus on identifying cost-effective MOF materials suitable for large-scale industrial deployment, as well as pushing forward the progress of commercial LSBs with high energy density and exceptional cycling stability. This comprehensive summary and forward-looking perspective will serve as a crucial scientific foundation for the development of novel MOF-based cathode materials, the large-scale production of functional separators incorporating MOFs, and the strategic design of innovative MOF-based electrolytes. Investigation, Q.Z. and W.S. ; writing--original draft preparation, Q.Z. and W.S. ; writing--review and editing, H.Z. ; supervision, D.M. ; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (22105047) and the Guangxi Natural Science Foundation (2021 GXNSFBA196029). We acknowledge the support from the Guangxi University of Science and Technology Doctoral Fund (19Z10). The authors declare no conflicts of interest.
## 2 Current Status of Development of Geophysical Exploration Techniques
### Exploration Techniques Based on Apparent Resistivity Parameters
#### 2.1.1 Self-Potential Method  
The self-potential method is a geophysical exploration method used to study the distribution pattern of a gradually varying self-potential field generated by a subsurface medium [15]. Unlike in the case of the electrical resistivity tomography method, there is a self-potential field around the earth and rock dams without artificially energizing the ground, and the formation of a self-potential field is dominated by the filtered electric field generated by uniform seepage and concentrated seepage. However, detecting such an electric field requires the deployment of collecting electrodes.  
[PERSON] et al. [16] applied the self-potential method for leakage detection in earth and rock dams. This method was first proposed in 1969. In the hidden danger detection of earth and rock dams, if there is leakage, the potential around the leakage point will be higher than the potential at the leakage point, and the leakage point can be located by detecting the potential of the underground medium. The measurement results are plotted in the form of an isopotential line plan after the detection, and the location of the negative anomaly peak will be the location of the leakage point. The greater the negative anomaly of the natural potential, the more serious the leakage. The self-potential method can be used to evaluate the degree of leakage in earth and rock dams [17]. [PERSON] et al. [18] demonstrated the application of the self-potential method in groundwater exploration with engineering examples. [PERSON] et al. [19] used the self-potential method for a comprehensive interpretation of the water search project in the Bo Yue Cave scenic area of Ling Shui Jiang River as an example. The authors proved the following advantages of the method: strong anti-interference ability, low weight, rapidity, small equipment, and high accuracy. [PERSON] [20] highlighted the anomalous reflection at the natural point position of a dam base seepage section in different periods and performed an anomaly analysis through an example, proving the effectiveness of the self-potential method. However, the self-potential method is susceptible to the interference of stray currents in the measurement area, resulting in suboptimal detection results.
## 4 Results and Analysis  
### Overview of Simulation Results  
The traditional resource allocation models in 5G networks are compared with the simulation results obtained from the proposed ONSSO in this section. The simulation results are presented, analysed, and compared against both the heuristic-based orchestration and static slicing benchmarks The simulation results demonstrate the efficacy of the ONSSO framework in improving both latency and throughput, supporting its viability as a competitive solution for resource allocation in 5G networks.
## 3 Results
### Comparison of Improvements in Maximum Optimization  
We then compared cross-opt TI with the final optimization TI method, where we applied both current adjustment and the free selection of electrode pairs (Intensity opt TI). Figure 5a shows cross-opt TI and the current adjustment concept for cross-opt settings, which was exclusively created for a conceptual understanding. For determining the intensity opt TI parameters, the most optimal settings were obtained by investigating all possible electrode combinations and variable current values fed to the electrodes. The current of each electric field was adjusted by 0.1 mA while the sum of the total currents of E1 and E2 was kept at 2 mA. The final optimization parameters of Intensity opt TI for all subjects are shown in Table 4.  
The left of Figure 5b shows the result of the E-field cross-sectional distribution of cross-opt TI and intensity opt TI. The right of Figure 5b shows the comparison between the difference in PR values of cross-opt TI and intensity opt TI for all models. For visualization purposes, the PR value of cross-opt TI was assigned to be 0 as the reference point, for all subjects. The difference between the PR values of cross-opt TI and parallel-opt TI is shown on the right. Compared with the PR value of 3.7876 \(\pm\) 0.9307 for cross-opt TI in the previous section, the PR value of intensity opt TI here was 4.2061 \(\pm\) 1.1338. To confirm the significance of the average difference in PR values, a paired \(t\)-test was performed. Purple dots represent raw data. The mean and standard deviation of the changes are shown in black. The average PR value of Intensity opt TI was higher than that of Cross opt TI (+11%) (\(p\) < 0.001).  
Figure 5: Comparison of TI stimulation results in electrode-free optimization and final optimization. (**a**) Concept of electrode-free optimization and amplitude-adjustment settings. (**b**) Comparison of cross opt TI and intensity opt TI results, * \(p\) < 0.0001.
## 2 Materials and Methods
### Data  
MODIS EVI data, GPP data, SAVI data, air temperature data and a dummy variable representing spatial heterogeneity were selected as input variables for the study. Rice yield data for Hubei Province from 2000 to 2019 were used for validation. Rice mask layer and Hubei county boundary data were used as auxiliary data. The satellite remote sensing data were collected from April to January, the time when double-season rice is planted and harvested. The following is a description of the relevant data.
## 8 Discussion  
The main objective of the present article was to present a unified and versatile procedure suitable for symbolic computations for the construction of approximate analytic solutions to linear Fredholm integral equations of the second kind with general continuous kernels.  
It was shown how some of the classical methods such as the Direct Computational Method (DCM), the Degenerate Kernel Methods (DKM), the Quadrature Methods (QM) and the Projection Methods (PM) can be incorporated in the proposed procedure. Additionally, it was demonstrated how complicated calculations such as double power series approximation, interpolation in two dimensions and combinations of different types of approximation can be handled in an effective way.  
The technique was tested by solving several examples from the literature. Several approximating schemes for the kernel and the integral operator were used and their accuracy and convergence were evaluated.  
In all cases, explicit solutions of high accuracy for the entire interval \([a,b]\) were obtained, which converge to the true solution as \(n\) increases. The power series approximation and polynomial interpolation of the kernel yield excellent results when the kernel is smooth with no \"peaks\". For continuous kernels with \"peaks\" or kernels of the convolution type, numerical integration of the integral operator is appropriate.  
In this paper, the emphasis was on presenting a novel framework for solving integral equations and on establishing its versatility and reliability in practice. A proper convergence and error analysis is postponed to a sequel paper. In the proposed framework, other numerical methods such as piecewise projection methods, Galerkin methods and wavelets methods can be included. The technique can be extended to two or more dimensions.  
This research received no external funding.  
Not applicable.  
The author would like to thank the anonymous reviewers for their valuable suggestions and comments.  
The author declares no conflict of interest.
## 3 Overview of Hyperspectral Imaging Spectroscopy (HSI)
### The Main Component of the Hyperspectral Imaging System  
The push broom hyperspectral imaging system comprises a camera with a two-dimensional (2D) light detector, a spectrograph, a translation stage, illumination units, and a computer (Figure 1). Each component's characteristics influence the overall accuracy of the system. As a result, measuring and optimizing the system before image capture is critical. For example, ideal illumination should cover a large area uniformly and shield the samples from radiation. When the linear translation stage is moved during an object scan, a three-dimensional (3D) hypercube with (x, y, and z) dimensions is created, where (x and y) indicate spatial positions and (z) represents spectral changes. The images are stacks of hundreds of two-dimensional spatial images taken at various angles.  
The hypercube (Figure 2) is made up of several congruent images that represent intensities at different wavelength bands and are made up of vector pixels (voxels) that contain both two-dimensional spatial information (x rows and y columns) and spectral information (z of wavelength). These hyperspectral data can reveal the physical and chemical properties of the material being tested [71]. Physical and geometric observations of size, orientation, shape, color, and texture, as well as chemical/molecular data such as water, fat, proteins, and other hydrogen-bonded constituents, can all be included in this information [72]. As a result, hyperspectral imaging provides more critical information than traditional NIR spectroscopy.  
Figure 1: A diagram of a hyperspectral imaging system.
## 6 Fuzzy Based Adaptive Resource Allocation
### Experimental Set-up with CNQF Testbed  
This section describes the experiment set up to investigate the efficacy of the FRB system implemented within the CNQF prototype. The experiments use real traffic flows through the testbed shown in Figure 2 and described earlier in Section 4.2. Three classes of traffic flows are generated _i.e._, voice, video and data and assigned to Classes 1, 2 and 3 respectively. Each voice flow is generated with a bitrate of 32 kbps after an exponentially distributed mean inter-arrival time 1/\(\lambda_{1}\) (Poisson arrival rate \(\lambda_{1}\)) between flows while the session is terminated after an exponentially distributed service time of 1/\(\pi_{i}\) seconds. Class 2 video flows are generated at 384 kbps bitrate with 1/\(\lambda_{2}\) exponentially distributed inter-arrival times and exponentially distributed 1/\(\pi_{2}\) seconds lifetime, while Class 3 data flows are generated with 256 kbps bitrate with 1/\(\lambda_{3}\) exponentially distributed mean inter-arrival time and exponentially distributed mean session time of 1/\(\pi_{3}\) seconds.  
Aggregate bandwidth measurements per class are pre-processed and periodically input to the FRB system which then computes \(B_{FLS}^{j}\). The bandwidth measurements are sampled every 10 seconds (the minimum possible due to the SNMP protocol operation on the Linux-based testbed). Subsequently, \(B_{th}^{j}\) is determined from Equation (3) to obtain the maximum current allocations per class that are used by the admission control entity to decide whether to accept or reject an arriving session request. Within the high level admission control policies, B\({}_{1}\) (for voice traffic) is set at 0.3B\({}_{\mathrm{T}}\), while B\({}_{2}\) is set at 0.4B\({}_{\mathrm{T}}\) for video traffic and B\({}_{3}\) = 0.3B\({}_{\mathrm{T}}\) is assigned to data traffic. Br, the maximum total available bandwidth for all the three classes is set at 6400 kbps for the experiments.  
The parameters and rules of the FRB are coded (as described earlier) in an external file which is read by the FRB module in the initial bootstrap phase. Thus, our implementation allows the entire rules and parameters to be re-written and updated offline if desired without disrupting the runtime CNQF admission control functionality. A screen shot of monitoring graphs from the CNQF management user interface depicting instantaneous traffic load per class during the experiments is shown in Figure 12.