esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.1 Least-Cost-Path Method  
The method uses the Least-Cost-Path Analysis (LCPA) tool, implemented in the software ArcGIS Pro [26], to generate the shortest route between the heat source and each of the heat consumers. The methodology uses a two-step approach, which are the streets as the first step and the buildings (heat consumers) as the second one. The method described below was firstly tested on an example test area in order to implement it in a real study area (community of Siechnice and its surroundings) later on.  
Step 1, considers the streets: In the first step, a buffer zone of 2 m is generated around the street network of the analyzed test area. Secondly, a polygon of the streets must be transferred into a raster format in order to be able to perform the next steps. In the third step, a Cost Distance tool is used, which calculates the least accumulative cost distance  
Figure 4: Scheme of the “standardized-replication” method of district heating planning.  
for each cell from or to the least-cost source over a cost surface. This raster effectively illustrates the cost distance from the heat source for each considered cell within it.  
Step 2, considers the buildings: Firstly, a Feature to Point tool is used to generate the centroids of each of the buildings. Next, a tool called Snap is involved using a parameter of the distance set as high to make sure that each of the buildings is dropped on the nearest street available.  
As a final result, a Cost Path is generated, which represents a targeted district heating network. Here, a tool called Cost Path as Polyline is used in order to do this. The input data for this tool is as follows:  
* Raster of feature destination data (result of Step 2);
* Input cost distance (result of Step 1);
* Backlink raster (from Step 1);
* Path type (best cell), matched to each point representing a building (Version 1).  
Since the idea is that the lines are not stratified to each point of the building but only generally run the network to all endpoints (so that there are not as many network endpoints as there are buildings), a further step of selecting only the network endpoints is needed (path line, Version 2).  
In the final step, again the tool called \"Cost Path to Polyline\" is used, but this time the layer that represents only the endpoints is used as input. This results in a simplified network, which does not consist of so many endpoints as before. The following steps are shown in Figure 5, where a workflow of the methodology is implemented in an automated model using the ArcGIS Pro Model Builder application.
## 3 Minimum Transmission Range to \(2\)-Cover a Path on a Polygonal Region  
Given two points \(p\) and \(q\) on a polygonal region \(R\), the objective of this section is to calculate the minimum transmission range of the antennas such that there is a \(2\)-path on \(R\) connecting \(p\) and \(q\), \(P(p,q)\). In Figure 7(a), the black path connecting \(p\) and \(q\) is not a \(2\)-path since some of its points are only covered by one antenna. On the other hand, the yellow path in the same figure is a \(2\)-path between \(p\) and \(q\). Not only it is \(2\)-covered by \(S\) as it exists within \(R\) which makes it the interesting type of \(2\)-path for this section. In the following, whenever \(2\)-path is mentioned, it is understood as a \(2\)-path within \(R\).
# Inverse Kinematics of Large Hydraulic Manipulator Arm Based on ASWO Optimized BP Neural Network
## 2 Forward Modeling and Error Analysis of the Robotic Arm
### Forward and Inverse Kinematic Error Analysis  
The hydraulic manipulator arm is large in size, and its end position error is affected by the intertwining of multiple factors, including mechanical tolerances, software errors, and external load variations.  
In order to accurately localize the robotic arm in real-time, the forward kinematics error compensation model is first constructed. The DH parameter method is used to calculate the theoretical position of the end-effector based on joint values. The actual position of the end-effector is recorded by a total station. The position error of the end-effector is obtained by calculating the difference between these two positions.  
This project utilizes the Leica TS13 automatic total station, which offers an angular accuracy of \(1^{\prime\prime}\) and a distance measurement accuracy of \(1\) mm + \(1.5\) ppm, meeting the accuracy requirements.  
The procedure for data collection using the total station is as follows:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(i\) & \(\theta_{i}\)/o & \(d_{i}\)/mm & \(\alpha_{i}\)/o & \(a_{i}\)/mm & **Joint Range** \\ \hline
1 & \(\theta_{1}\)(0) & 0 & \(-90\) & 200 & \(\theta_{1}\)(\(-40\),\(40\)) \\
2 & \(\theta_{2}\)(90) & 0 & 90 & \(-35\) & \(\theta_{2}\)(\(-50\),\(40\)) \\
3 & 90 & \(d_{3}\)(297) & 0 & 0 & \(d_{3}\)(0,\(200\)) \\
4 & \(\theta_{4}  
Two prisms are placed on the body of the rock drill truck, and the position of these prisms relative to the base coordinate system is measured.  
2. Input the positions of the two prisms into the total station to complete the establishment of the base coordinate system.  
3. A reflective sticker is placed on the actuator at the end of the robotic arm.  
4. Use the total station to record the position of the reflective sticker, thus completing the collection of the actual position of the end effector.  
The joint values and the end position errors are trained as the input and output of the neural network model, respectively, to obtain the mapping relationship between the joint values of the robotic arm and the position errors. The spatial error distance between the actual position and the theoretical position can be determined when the robotic arm is located at different joint angles. The model building process is shown in Figure 3.  
During the operation of the robotic arm, although the forward kinematic error compensation model can accurately locate the actual position of the end of the robotic arm corresponding to a given combination of joint values, it does not completely solve the problem of positioning deviation between the end position and the expected target point. This forward kinematic positioning error, which is known in size but not compensated for, becomes a major factor affecting the accuracy of inverse kinematics. The robotic arm inverse kinematics error is shown in Figure 4.
## 3 Results and Discussion
### Data of the Water Quality Monitoring
Average SS value (2335) in the water samples monitored exceeded common values, COD (440), N\({}_{\rm Kj}\)[47], P\({}_{\rm tot}\) (9.8), pH (6.78), TDS (946) and SUVA (3.18) values were in the range of the typical wastewater contamination, while N\({}_{\rm min}\) (2.5) was slightly lower. Microbiological contamination (consisted of pathogenic microorganisms Total viable count (TVC) > 300 cfu mL\({}^{-1}\)), _E. coli_ (\(5.5\times 10^{3}\)-\(1.0\times 10^{4}\) cfu mL\({}^{-1}\)), intestinal enterococci (\(2.8\times 10^{2}\)-\(1.5\times 10^{4}\) cfu mL\({}^{-1}\)), and coliform bacteria (\(1.6\times 10^{3}\)-\(2.0\times 10^{4}\) cfu mL\({}^{-1}\)). Their overall (2 logs/mL) concentration is typical for standing wastewaters [48; 49]. As this topic isn't the focus of this paper, it will not be described further. Limits of wastewater discharge are based on a condition that no discharge should compromise the current state of a receiving water body [50]. They vary between countries. As shown in Table 1, the average SS concentration exceeded limit values for up to 40-90 times, BODs--38-86 times, COD\({}_{\rm Cr}\)--26 times, COD\({}_{\rm Mn}\)--88 times, N\({}_{\rm Kj}\)--1.8, P\({}_{\rm tot}\)--2.5-19.6 and color--2 times. The main reasons for the root vegetables washing water contamination and its' restriction of possible reuse in the packhouses are suspended solids (SS), biochemical oxygen demand (BOD\({}_{5}\)), chemical oxygen demand (COD), Kjeldahl nitrogen N\({}_{\rm Kjt}\) and total phosphorus P\({}_{\rm tot}\). Washing water of the carrots is more contaminated than that of beetrots, especially after polishing and processing. These findings show that the root vegetables' washing water is greatly contaminated and must be treated before discharge or further usage. SUVA index was in the range of wastewater and natural waters. The third pond of the wastewater treatment system D increased sharply as DOC content was reduced greatly due to sedimentation and microbial decomposition. It is dominated by high molecular weight humic substances as it is typical for natural standing waters [7; 30; 31; 51]. UV/Vis spectra were in accordance with typical absorbance spectra of wastewaters [2; 6; 28]. Two peaks at A240 and A320 indicate increased amounts of organic compounds and suspended solids. In addition, the effect of wastewater treatment of the pond in system D are obvious. It is obvious that absorbance decreases and the spectra of the water in the sampling spot 5 at the D site are close to the spectra of the deionized water (Figure 5).
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 7 Application Fields of Electrospun Chitosan Nanofibers and Chitosan Derivatives  
In the realm of materials research, the integration of nanofibers in product innovation and development is a fast-expanding subject. Chitosan's inherent characteristics, such as its non-toxicity, biodegradability, and antibacterial activity, as well as its ability to be processed into high-surface-area nanofibers, make it suitable for a large selection of future-oriented research and commercial purposes. Because of its cationic character and high crystallinity, processing pure chitosan into nanofibers can be difficult; consequently, it is frequently treated or mixed with various additives to increase its processability and customize its properties according to specific requirements. In addition, bioactive inorganic compounds can be incorporated into these nanofibers to render them more functional.
# Gradient-Based Aerodynamic Optimization of an Airfoil with Morphing Leading and Trailing Edges
## 2 Methodologies
### Flow and Adjoint Solver  
Both flow and adjoint problems are solved in SU2 (Version 6.20), an open-source CFD solver based on a finite volume method (FVM) formulation [31]. Compressible RANS equations govern the flow around the airfoil, and the turbulence model adopted to close the RANS equation is the Spalart-Allmaras (S-A) one-equation model [32].  
Two adjoint approaches, the continuous [33] and discrete approach, have been implemented in SU2 [34]. The former is a problem-based solver by evaluating adjoint through boundary surface integral with the frozen turbulent viscosity assumptions; the latter is a pure code-based adjoint solver by implementing algorithmic differentiation. Readers interested in those adjoint approaches for CFD can refer to [35]. In this paper, the sensitivity is provided by continuous adjoint method.  
For numerical settings of the flow and adjoint solver, the Jameson-Schmidt-Turkel (JST) non-conservative central scheme is adapted for the discretization of both the flow and adjoint convective fluxes. A Venkatakrishnan slope limiter is used with a coefficient of 0.3. The spatial gradient of the flow and adjoint is approximated by means of the Green-Gauss approach. An implicit Euler scheme is used for integration, and the flexible generalized minimum residual (FGMRES) linear solver with incomplete lower upper factorization (ILU) linear preconditioner was chosen.
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 3 Proposed Hybrid Graph Embedding and Adaptive Convolution Networks  
The Hybrid Graph Embedding and Adaptive Convolutional Networks (HGE-ACN) approach is a new framework for efficient sign language sentence recognition (SLSR) using data from wearable glove devices. The method uses multimodal sensor data from a single-handed glove with six-axis inertial and five-finger curvature sensors, capturing spatial-temporal dynamics critical for recognizing complex sentences. The HGE-ACN integrates graph-based embeddings to model dynamic relationships between sequential gestures and employs an adaptive convolutional framework for robust feature extraction. This hybrid HGE-ACN framework addresses variations in signing speed and seamless transitions between gestures, enhancing recognition accuracy and computational efficiency.  
In Figure 1, the wearable glove HGE-ACN system recognizes sign language sentences. It uses six-axis IMU sensors to track hand movement and orientation in three dimensions and finger curvature sensors to quantify finger bending and positioning. The system extracts Inertial Features, Motion Features, and Temporal Sequences from raw sensor data.  
The primary processing block has two main sections: HGE (Hybrid Graph Embedding), which captures geographical graphs, temporal graphs, and feature embedding, and ACN, which dynamically alters processing settings based on input characteristics. The transition model examines sentence sign transitions.  
The final output step incorporates sentence analysis, pattern recognition, and language modeling. Figure 1 depicts that sensor data flows through the system: initial sensor data go to feature extraction, extracted features go to core processing, and both contribute to final recognition. HGE-ACN framework handles sign language recognition difficulties such as multimodal integration, adaptive processing, contextual understanding, and efficiency. The key contributions are key contributions of graph embeddings for spatial-temporal relationship modeling, Adaptive Convolutional Networks for signing variants, style processing for user-independent recognition, and explicit modeling of phrase sign transitions. The system's design covers signing speed, style, and intricate transitions between signs without predefined templates. Real-time sign language recognition, learning tools, assistive technology, and HCI systems are practical applications. The HGE-ACN framework improves wearable-based sign language recognition by eliminating camera-based systems, recognizing complete sentences more accurately, handling natural signing variations, and operating efficiently enough for real-time applications. This design meets the paper's goal of enhancing sign language recognition accuracy and processing economy. The hybrid technique uses graph-based representations and adaptive convolutional processing to handle sign language communication's complexities.
###### Abstract  
Since antennas are strong radar targets, their radar cross section (RCS) reduction and radiation enhancement is of utmost necessity, particularly for stealth platforms. This work proposes the design of a Fabry-Perot Cavity (FPC) antenna which has wideband low monostatic RCS. While in the transmission mode, not only is gain enhancement achieved, but radiation beam is also deflected in the elevation plane. Moreover, the design is low-profile, i.e., the cavity height is \(\sim\)\(\lambda\)/4. A patch antenna designed at 6 GHz serves as the excitation source of the cavity constructed between the metallic ground plane and superstrate. The superstrate structure is formed with absorptive frequency selective surface (AFSS) in conjunction with dual-sided partially reflective surface (PRS). Resistor loaded metallic rings serve as the AFSS, while PRS is constructed from inductive gradated mesh structure on one side to realize phase gradient for beam deflection; the other side has fixed capacitive elements. Results show that wideband RCS reduction was achieved from 4-16 GHz, with average RCS reduction of about 8.5 dB over the reference patch antenna. Off-broadside peak radiation at \(-38^{\circ}\) was achieved, with gain approaching \(\sim\)9.4 dB. Simulation and measurement results are presented.  
F +
Footnote †: journal: Medical Imaging  
_Applied Sciences_  
_Article_  
Quarter Wavelength Fabry-Perot Cavity Antenna with Wideband Low Monostatic Radar Cross Section and Off-Broadside Peak Radiation. _Appl. Sci._**2021**, _11_, 1053. [[https://doi.org/10.3390/app11031053](https://doi.org/10.3390/app11031053)]([https://doi.org/10.3390/app11031053](https://doi.org/10.3390/app11031053))
## 6 Conclusions  
This study proposes an ABAQUS-MATLAB cooperative optimization framework to overcome the limitations of existing commercial topology optimization tools, such as closed architectures, low efficiency in 3D complex structure optimization, and reliance on time-consuming INP/FIL file interactions. The framework employs a hierarchical architecture where MATLAB manages the BESO-based optimization logic (sensitivity analysis, filtering, and variable updates), ABAQUS handles high-precision finite element modeling and solving with GPU/MPI acceleration, and Python scripts enable real-time parameter updates through direct CAE/ODB database access. This integration eliminates text-based file parsing bottlenecks. Validated through a hydraulic transmission test bench adapter support case, the framework achieved a 31% mass reduction while satisfying strength and stiffness requirements, demonstrating its practical value for topology optimization of heavy machinery components. Scalability tests (Section 4.2) further confirmed its capability to handle industrial-scale problems with several hundred thousand-element models through ABAQUS's parallel computing.  
However, there is still some work that needs to be done. Future efforts will focus on enhancing accessibility via open-source FEA tool compatibility (e.g., CalculiX), integrating real-time visualization and AI-driven parameter tuning, and extending the framework to nonlinear materials and multiphysics problems. These advancements aim to bridge the gap between academic research and industrial deployment, positioning the framework as a versatile platform for both theoretical exploration and real-world engineering applications.  
Conceptualization, D.S., X.Y. and H.Y.; data curation, D.S. and H.L.; formal analysis, D.S.; funding acquisition, X.Y.; methodology, D.S., X.Y., H.L. and H.Y.; supervision, H.Y.; visualization, D.S.; writing--original draft, D.S.; writing--review and editing, D.S. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key R&D Program of China (No. 2019 YFB1312704), Enterprise project: High power hydraulic transmission and key component supporting construction project (No. 0611-1600130456C).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 3 Proposed Vehicle Detection and Tracking Scheme  
An effective method for vehicle detection and tracking based on YOLOv5 with Deep SORT is proposed to improve the real-time detection and tracking accuracy. This proposed method was re-implemented with open CV, Tensor Flow, and PyTorch available in an open source machine learning library. Vehicle detection has advanced with emerging deep-learning technology. It makes the detection process easier for vehicles using a series of YOLO models. To achieve better accuracy in detection and tracking, YOLOv5 is the best choice for detecting vehicles in real time. But still, it is a challenging task to track the vehicle due to occlusions, flickering, blur, camera movement, and false detection while  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Authors** & **Year** & **Technique Used** & **Description** & **Advantage** & **Limitation** & **Accuracy** \\ \hline [PERSON] et al. [50] & 2016 & SSD & Single-shot multibox detector & Good balance between accuracy and speed & Can struggle with detecting small objects or occlusions & Moderate to high occlusions & Moderate to high occlusions \\ \hline [PERSON] et al. [51] & 2006 & Kalman filter & Bayesian filtering and prediction & Effective in handling motion prediction & Prone to errors in occluded or non-linear scenarios & Moderate \\ \hline [PERSON] et al. [53] & 2020 & Particle filter & Sequential Monte Carlo method & Robust in handling non-linear motion and occlusion & Requires careful tuning for optimal performance & Moderate to high \\ \hline Proposed work & 2023 & Yolov5, Deep Sort & Vehicle detection and tracking & Working on real-time traffic management & Occlusions, low illumination & 92.18\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._receiving the bounding box from YOLOv5. The Deep SORT algorithm is integrated with the YOLOv5 model to address these issues. Figure 1 illustrates the proposed vehicle detection and tracking system, incorporating YOLOv5 and the Deep SORT algorithm.
## 4 Simulation Results
### Case Study 3: PE-Based Power Systems with High Penetration of Wind Power  
In this scenario, the penetration of wind power is increased in the system. In this way, generators 3, 4, 6, 7, and 8 (see Table 3) are replaced with wind farms with the same nominal apparent power. In this way, the wind turbine penetration is 43.6% (excluding the external power generation, G1). If G5 trips at \(t=50\) s, the system will become unstable, as shown in Figures 9 and 10. The system frequency and some bus voltages after the generator trip are shown in Figures 9 and 10, respectively.  
Figure 9 shows the average frequency of the system, which is the mean value of synchronous generators' speed (frequency in Hz) based on their inertia. After the generator trip at \(t=50\) s, frequency of the system decreases, due to the mismatch of loads and generation. However, at \(t=50.3\) s some generators become unsynchronized, which leads to instability. This type of instability is referred to the rotor angle instability, which is considered as a transient stability issue. Considering Figure 3c, it is worth mentioning that the system transient security is not guaranteed. As the system is not secure for N contingency analysis, there is not need to further assess the N-1 contingencies, due to the fact that the system cannot maintain its stability without any contingency.  
It is worth mentioning that all eigenvalues of the system are on the left-hand-side of the imaginary axis, as shown in Figure 11, which means that the power system is dynamically secure, while its transient stability is not guaranteed; see Figure 3b,c. More specifically, Figure 11 indicates the small-signal stability analysis and does not give any insight into the transient stability assessment.  
Figure 8: Bus 6, 25, and 28 voltage magnitude responses to generator 5 trip at \(t=50\) s for the PE-based power system case study (case study 2).  
Figure 10: Bus 6, 25, and 28 voltage magnitude responses to G5 trip at \(t=50\) s for the system with a high penetration of wind turbine (case study 3).  
Figure 9: The PE-based power system frequency response to the G5 trip at \(t=50\) s for the system with high penetration of wind turbine (case study 3).  
Therefore, while the system can be stable from a small-signal stability point of view (dynamic stability), it might be unstable from a transient stability viewpoint (transient stability).