esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiservice-Based Traffic Scheduling for 5G Access Traffic Steering, Switching and Splitting
###### Abstract  
As a key enabler of the access traffic steering, switching and splitting (ATSSS) feature, multipath transport can leverage the simultaneous use of several network paths and support seamless failover to improve both communication throughput and resilience. Therefore, a traffic scheduling strategy is necessary to determine the best network path combination that may improve the performance of multipath transport. To address this need, we developed a multiservice-type based transmission (MSTT) traffic scheduling optimization strategy, which involves three steps. First, the user equipment (UE) selects the number of data stream transmission paths, considering the service utility function, and either transmits all data streams via the 3 GPP network or sends two streams, one via the 3 GPP network and the other via the non-3 GPP network. Second, the proposed method is used to select the transmission path for each data stream based on load balancing. Finally, an algorithm for optimizing traffic scheduling is formulated by applying the convex optimization problem to maximize the effective network capacity under a Delay Quality of Service (DQoS) constraint. The proposed traffic scheduling strategy is validated through simulation experiments. The results indicate that user satisfaction and effective capacity realized are always better than when using the always-best-connected and fixed-ratio power-allocation algorithms.  
ATSSS; multipath transport; multiservice; effective network capacity +
Footnote †: journal: _Sensors_  
1
## 4 CO2 Emissions Analysis and Indicators in Sub-Projects
### Shield Excavation  
For shield excavation projects, the factors influencing CO2 emissions are numerous, leading to significant fluctuations in CO2 emissions per unit of length. As depicted in Table 6, We further investigated the distribution patterns of CO2 emissions, examining the emissions per 10,000 yuan of investment across different projects.  
Figure 12 demonstrates that for 15 m diameter shield tunnels, the CO2 emissions per unit of length for Tunnels 1 and 4 differ significantly from those for Tunnels 2 and 3, with a disparity of approximately 33%. In contrast, CO2 emissions for 11.36 m diameter shield tunnels are relatively stable. However, the CO2 emissions per 10,000 yuan of investment  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Length** & **CO2 Emissions** & **CO2 Emissions per Meter** & **CO2 Emissions per Unit Investment** \\  & **(m)** & **(tCO2e)** & **(kgCO2e/m3)** & **(kgCO2e/10,000 yuan)** \\ \hline Tunnel 1 & 2008 & 34,846 & 17,354 & 1882 \\ Tunnel 2 & 1539 & 19,895 & 12,925 & 1837 \\ Tunnel 3 & 2316 & 28,409 & 12,266 & 1871 \\ Tunnel 4 & 520 & 8802 & 16,927 & 1721 \\ Tunnel 5 & 2501 & 24,761 & 9901 & 2238 \\ Tunnel 6 & 768 & 7215 & 9395 & 1659 \\ \hline \hline \end{tabular}
\end{table}
Table 6: CO2 emission analysis table of shield excavation.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Length** & **CO2 Emissions** & **Project Quantity** & **CO2 Emissions per Unit** & **CO2 Emissions per** \\  & **(m)** & **(tCO2e)** & **(m3)** & **Volume** & **Meter** \\  & & & & **(kgCO2e/m3)** & **(kgCO2e/m)** \\ \hline Tunnel 1 & 2008 & 70,588 & 59,465 & 1187 & 35,154 \\ Tunnel 2 & 1539 & 53,707 & 46,149 & 1164 & 34,890 \\ Tunnel 3 & 2316 & 81,243 & 68,886 & 1179 & 35,079 \\ Tunnel 4 & 520 & 18,156 & 155,548 & 1168 & 34,915 \\ Tunnel 5 & 2501 & 55,921 & 44,940 & 1244 & 22,361 \\ Tunnel 6 & 768 & 16,151 & 13,468 & 1199 & 21,030 \\ \hline \hline \end{tabular}
\end{table}
Table 5: CO2 emissions analysis table of shield segment project.  
present the opposite trend: Tunnels 1, 2, 3, and 4 show low variability in this metric, whereas Tunnels 3 and 4 demonstrate a 35% difference in emissions.  
Overall, CO2 emissions per 10,000 yuan of investment are not influenced by the diameter of shield tunnels and are generally consistent, making this metric more suitable for estimation purposes.
## 3 Results and Analysis
### b-Value and \(\gamma\)-Value  
#### 3.2.1 Calculation of AE \(b\)-Value  
The seismic \(b\)-value was initially introduced by [PERSON] [14] and later was progressively utilized to study the evolution of damage and fracture processes in solid materials using the AE \(b\)-value. The calculation of the \(b\)-value is based on Equation (2):  
\[log_{10}N(N\geq M)=a-bM, \tag{2}\]  
where \(M\) is the magnitude of AE signals, and \(a\) is constant. \(N\) is the number of earthquakes with a magnitude greater than or equal to \(M\).  
The relationship between AE amplitude and magnitude, considering that the maximum signal peak amplitude can be expressed in dB as \(A_{dB}=20 log_{10}\left(\frac{V_{max}}{V_{ref}}\right)\), can be calculated using Equation (3):  
\[M=log_{10}\left(\frac{V_{max}}{V_{ref}}\right), \tag{3}\]  
Figure 4: The specimen after testing. (**a**) Specimen 0–1, (**b**) Specimen 0–2, (**c**) Specimen 1–1, (**d**) Specimen 1–2. The red line represents the main fractured direction of the rock.  
Figure 3: Stress vs. time, amplitude vs. time, and cumulated energy vs. time curves of basalt (**a,b**) and granite (**c,d**) specimens subjected to uniaxial compression test.  
where \(V_{ref}\) is a reference amplitude (in this study, taken as 1 \(\mu\)V).  
Therefore, the AE \(b\)-value can be represented by Equation (4):  
\[log_{10}N(N\geq M)=a-b\cdot log_{10}\left(\frac{V_{max}}{V_{ref}}\right) \tag{4}\]  
According to Equation (4), it is evident that the \(b\)-value can be calculated based on the relationship between AE amplitude and their frequency. By linearly fitting \(log_{10}N\) and \(M\), the negative of the slope is the desired AE \(b\)-value. To clearly depict the evolution of the \(b\)-value over time, this study applied time windows to the acquired data. Subsequently, the \(b\)-value for each specific time window was calculated. The detailed calculation steps are illustrated in Figure 5.
## 3 Results and Discussion
### Corrosion Protection Evaluation
#### 3.2.4 Corrosion Inhibition Mechanism
The reason is that their ability to exist in tautomer forms enables them to provide electrons to the anodic sides of the corrosion area resulting in their adsorption onto the metal surface creating a layer that insulates the metal from the aggressive ion penetration [15].
# Estimate of Passive Time Reversal Communication Performance in Shallow Water
## 5 Summary and Discussion  
Passive time reversal can be used in underwater acoustic communications that are severely affected by multipaths because it reduces the ISI and increases the SNR. In general, the time reversal performance totally depends on the behavior of the \(q\)-function, which is the sum of the autocorrelation of the CIR for each channel. High side-lobe level and main-lobe width wider than the symbol duration produce a significant residual ISI, which degrades underwater communication even after the time reversal operation.  
In this paper, the parameter \(E_{q}\) was proposed as a factor to estimate the performance of the time reversal, which is an estimate of how strong tails of the \(q\)-function are distributed outside one symbol duration. Passive time reversal communication experiments were performed at two different sites characterized by either acoustically fast or slow bottom conditions. The parameter \(E_{q}\) was estimated and compared to the BER performance and the output SNR. Although the CIRs estimated at the two sites were significantly different and the estimated \(q\)-functions also differed from each other, the parameter \(E_{q}\) accurately reflected the performance of the passive time reversal communication, showing a strong correlation with the BER and the output SNR. In conclusion, the parameter \(E_{q}\) may be used to determine the following parameters for designing time reversal communication systems.  
* Quick assessment of symbol rate of time reversal communication system
* Determination of receiver array dimension required to meet the BER requirement, including the optimal number of channel elements and their spacing
* Decision of need for additional equalization  
This work was supported by the Agency for Defense Development, Korea, under Grant UD170022 DD, the Ministry of Oceans and Fisheries (in a project titled the development of distributed underwater monitoring & control networks) and the National Research Foundation of Korea, under Grant NRF-2016R1D1A1B03930983.  
[PERSON] conceived and designed the research; [PERSON] played a key role in the data analysis; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments and discussed the results; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
###### Abstract  
Vehicle-to-everything (V2X) communication has been widely studied in recent years for the rapid development of the vehicular traffic. In this paper, a V2X communication system integrating the probabilistic shaping (PS) bitloading multi-input-multi-output (MIMO) visible light communication (VLC) and the millimeter wave radar is proposed. The PS bitloading algorithm utilizes the full spectrum resource of the LED headlights, and the MIMO configuration enhances the performance of the VLC system. Furthermore, the linear and nonlinear interpolation method makes it possible to calculate the SNR at every point from the SNR measured at several discrete calibration points. The SNR table enables the radar to guide the VLC according to the distance, achieving the highest achievable information rate (AIR) at every point. The system supports the V2X communication beyond 1.1 Gb/s up to 5 m. This is the first VLC based V2X sensing and communication system, and the AIR in the system is 1.8484 Gb/s. As far as we know, it is the highest data rate reported in the LED headlight based VLC system.  
V2X communication; sensing and communication system; visible light communication +
Footnote †: journal: Applied Sciences
## 2 Problem Description
### The Adjacency-Matrix Representation of a Multi-Path  
Since the number of paths between any two nodes may provide many choices, this undoubtedly makes the relevant data too large to deal with [24; 25]. Therefore, in this paper, methods were first performed to decrease the dimensions of the pre-collected traffic data, making the next steps more convenient. Firstly, all possible travel paths were represented by three \(n*n\) matrices. Next, the time, speed, distance, fuel consumption and cost values of the different labeled paths A, B and C, respectively, were plugged into the above matrix, where the values of distance and time were derived from the traffic information management system, the fuel consumption was calculated by Equations (5) and (6) and the cost values were calculated by Equations (7)-(10). The driving speed on the arc \((i,j)\) was \(s_{ij}=d_{ij}/t_{ij}\); the specific matrix data are shown in the Appendix A, Table 11. Finally, through the Formulas (1)-(4), operating the matrix of time, velocity, distance, fuel consumption and cost matrix, respectively, with the different path labels A, B and C, the minimum distance matrix, time matrix, fuel consumption matrix and cost matrix in all the graphs could be obtained separately, which could degrade the multi-path problem into a deterministic path problem, which was advantageous for the next calculation and solution.  
\[C_{dis}=\min\{C_{dis}^{A}C_{dis}^{B}C_{dis}^{C}\} \tag{1}\]  
\[C_{time}=\min\{C_{time}^{A}C_{time}^{B}C_{time}^{C}\} \tag{2}\]  
\[C_{fuel}=\min\{C_{fuel}^{A}C_{fuel}^{B}C_{fuel}^{C}\} \tag{3}\]  
\[C_{\text{cost}}=\min\{C_{\text{cost}}^{A}C_{\text{cost}}^{B}C_{\text{cost}}^{C}\} \tag{4}\]  
There are three paths (A, B, C) between two points, and the shortest distance among the three paths is represented by \(C_{dis}\), \(C_{dis}^{A}\), \(C_{dis}^{B}\), \(C_{dis}^{C}\) represent the distances of paths A, B and C respectively. \(C_{time}\) represent the shortest time, \(C_{time}^{A}\), \(C_{time}^{B}\), \(C_{time}^{C}\) represent the time of paths A, B and C respectively. \(C_{fuel}C_{time}\) represent the minimum fuel consumption, \(C_{fuel}^{A}\), \(C_{fuel}^{B}\), \(C_{fuel}^{C}\) represent the fuel consumption of paths A, B and C respectively. \(C_{\text{cost}}^{A}\) represent the minimum cost among the three paths, \(C_{\text{cost}}^{A}\), \(C_{\text{cost}}^{B}\), \(C_{\text{cost}}^{C}\) represent the cost of paths A, B and C, respectively.  
Through the matrix operation mentioned above, the transformation from a multi-path problem to a conventional VRPTW problem was realized, which provided a necessary preparation for solving the problem by using CPLEX in Java.
## 5 Analysis of Parameters Influence
### Ribbed Beam Size  
The ribbed beam size in the original design scheme is 0.4 m \(\times\) 0.4 m. This study set the size of ribbed beam as 0.6 m \(\times\) 0.6 m, 0.8 m \(\times\) 0.8 m, 1.0 m \(\times\) 1.0 m and 1.2 m \(\times\) 1.2 m when other parameters remained unchanged and compared the maximum surface settlement and the maximum lateral deformation of the retaining structure to analyze the influence of ribbed beam size on the support effect.  
Different ribbed beam sizes will change the bending stiffness of the retaining structure, thus affecting the deformation of the retaining structure and the soil loss. From Figure 14a,b, with the increase in ribbed beam size, the maximum surface settlement and the maximum lateral deformation of the retaining structure gradually decrease. When ribbed beam size increased from 0.4 m \(\times\) 0.4 m to 0.6 m \(\times\) 0.6 m, the maximum surface settlement and the maximum lateral deformation of the retaining structure reduced by 23% and 14%, respectively. When ribbed beam size increased from 0.6 m \(\times\) 0.6 m to 0.8 m \(\times\) 0.8 m, the maximum surface settlement and the maximum lateral deformation of the retaining  
Figure 12: Settlement curve of pile top.  
structure reduced by 19% and 6%, respectively. When the ribbed beam size exceeds a certain range, the influence of the ribbed beam size on the maximum lateral deformation of the retaining structure reduces.
# Bio-Inspired Traffic Pattern Generation for Multi-AMR Systems
## 3 Methodology
### Environment Representation and Problem Formulation  
The environment is represented as a weighted grid graph \(\mathcal{G}=(V,E,w)\), where vertices \(V\) correspond to traversable locations and directed edges \(E\subseteq V\times V\) connect adjacent vertices. The weight function \(w:E\rightarrow[\epsilon,1-\epsilon]\) assigns positive real values to edges, representing the cost of moving along that edge, where \(\epsilon\) is a small positive constant that prevents numerical issues at the boundaries. The weights correspond to the concentration of pheromones on the edges in the ACO framework, influencing the path planning decisions of the AMRs, with lower weights indicating more favorable paths. Tasks are defined as tuples \(T=(p_{\text{pickup}},p_{\text{dropoff}},t_{\text{due}})\) specifying a pickup location, a dropoff location, and a due time. New tasks are generated as existing ones are completed and are assigned to the nearest available AMR.  
The optimization objective is to determine the optimal weight configuration \(w^{*}=\{w_{\epsilon}^{*}\}_{\epsilon\in E}\) that maximizes system throughput \(\Theta\), defined as the number of tasks completed per unit time:  
\[\operatorname*{arg\,max}_{w}\Theta(w);w_{\epsilon}\in[\epsilon,1-\epsilon] \quad\forall\epsilon\in E \tag{1}\]  
In this framework, system throughput \(\Theta(w)\) is a composite metric reflecting the effectiveness of AMR operations under the edge weight configuration \(w\). While individual weights influence the immediate routing decisions of AMRs, their collective configuration shapes the emergent behavior of the entire system. The optimization problem presents a substantial search space due to the continuous nature of the weights and the combinatorial nature of weight configurations. Furthermore, the emergent nature of the optimization metric necessitates an optimization approach capable of refining complex behaviors over time.  
Given the complexity of the search space, traditional optimization methods such as gradient descent or linear programming are not well suited for the problem. In contrast, the proposed bio-inspired approach offers several advantages, including scalability in terms of layout size and number of vehicles, adaptability to continuous task execution, and computational efficiency through parallelism.
# Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model
## 4 Case Study of Transmission Line Insulator Defect Detection
### Result Analysis
#### 4.3.4 Influence of Image Sharpening Methods  
In response to the problem of indistinct outline and low contrast caused by the similarity between the insulator target and the background, the Laplace image sharpening method was adopted to sharpen the transmission line insulator images, and compared with other three sharpening methods including sobel, prewitt, and laplacian of gaussian (log). The model detection results with different image sharpening methods are shown in Table 4.  
From Table 4, it can be drawn that the application of image sharpening methods improves the detection ability of the model for insulator defects, with higher \(AP\) values compared to the non-sharpening condition. Among the above four sharpening algorithms, Laplace sharpening has the optimum indexes of insulator location and the highest \(AP\) and \(R\) of insulator defect detection, while the overall \(mAP\) can reach 97.26%, with the highest detection accuracy.
# Inertial Parameter Identification in Robotics: A Survey
###### Abstract  
This work aims at reviewing, analyzing and comparing a range of state-of-the-art approaches to inertial parameter identification in the context of robotics. We introduce \"BIRDy (Benchmark for Identification of Robot Dynamics)\", an open-source Matlab toolbox, allowing a systematic and formal performance assessment of the considered identification algorithms on either simulated or real serial robot manipulators. Seventeen of the most widely used approaches found in the scientific literature are implemented and compared to each other, namely: the Inverse Dynamic Identification Model with Ordinary, Weighted, Iteratively Reweighted and Total Least-Squares (IDIM-OLS, -WLS, -IRLS, -TLS); the Instrumental Variables method (IDIM-IV), the Maximum Likelihood (ML) method; the Direct and Inverse Dynamic Identification Model approach (DIDIM); the Closed-Loop Output Error (CLOE) method; the Closed-Loop Input Error (CLIE) method; the Direct Dynamic Identification Model with Nonlinear Kalman Filtering (DDIM-NKF), the Adaline Neural Network (AdaNN), the Hopfield-Tank Recurrent Neural Network (HTRNN) and eventually a set of Physically Consistent (PC-) methods allowing the enforcement of parameter physicality using Semi-Definite Programming, namely the PC-IDIM-OLS, -WLS, -IRLS, PC-IDIM-IV, and PC-DIDIM. BIRDy is robot-agnostic and features a complete inertial parameter identification pipeline, from the generation of symbolic kinematic and dynamic models to the identification process itself. This includes functionalities for excitation trajectory computation as well as the collection and pre-processing of experiment data. In this work, the proposed methods are first evaluated in simulation, following a Monte Carlo scheme on models of the 6-DoF TX40 and RV2 SQ industrial manipulators, before being tested on the real robot platforms. The robustness, precision, computational efficiency and context of application the different methods are investigated and discussed.  
dynamic parameters identification; performance evaluation and benchmarking +
Footnote †: journal: Applied Sciences  
0
Footnote 0: Publisher’s Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Removal of Color-Document Image Show-Through Based on Self-Supervised Learning
## 4 Experimental Results and Analysis
### Runtime Analysis  
To compare the time consumption, we compared the average runtime of different methods on the S-color0.5 dataset. To illustrate the relationship between runtime and document size, we conducted tests on images of different dimensions: 256 \(\times\) 256, 512 \(\times\) 512, and 1024 \(\times\) 1024 pixels. The average runtimes were 0.1303, 0.2155, and 0.4105, respectively. It is evident that as the document size increases, so does the runtime. Subsequently, we compared our method with the others using a 256 \(\times\) 256-pixel document image as an example. Table 3 lists the implementation frameworks used by various methods and their average runtimes. Among them, DeepOtsu and VDIR use the TensorFlow framework, while the other compared methods, including our proposed method, are based on the PyTorch framework. In terms of average runtime, our proposed method slightly outperforms the other methods in this aspect.
# Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
## 2 Materials and Methods
### Classification Schemes  
The main goal of this study was to determine the stock of individual and composite optical and SAR pictures in terms of classification accuracy. Furthermore, objective insight into the evolution of classification accuracy throughout the course of the growing season gives important information about the predicted accuracy of a classification during a certain phase of the growth season. One of the essential goals of this research is to compare SAR and optical images alone and the combination of these two in the classification process, and during the growing season we will find out the accuracy of classification with the help of these two images, and we can even comment on the accuracy of the classification with the help of these images in a certain period. According to Table 2, 18 classification designs were determined. Sentinel 1 SAR images were used only for the first six designs, Sentinel 2 NDVI images for the second six designs, and Sentinel 1 and Sentinel 2 composite images for the third six designs. In all 18 classification schemes, the performance estimators were OA classification and Kappa Cohen (K) agreement coefficient. The following equation represents the OA calculation:  
\[\text{OA}=\frac{\sum\text{\emph{correct predictions}}}{\text{\emph{total number of predictions}}} \tag{2}\]  
In the Equation (2), the predictions are presented for all validation examples, the expected and real cases are comparable, and the following equation representing the K calculation is also used:  
\[\text{K}=\frac{p_{0}-p_{e}}{1-p_{e}} \tag{3}\]  
where \(p_{0}\) is the relative observed agreement among raters, and \(p_{e}\) is the hypothetical probability of chance agreement.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Code** & **Class** & **Calibration Parcels** & **Validation Parcels** & **Calibration Pixels** & **Validation Pixels** \\ \hline
1 & Potatoes & 613 & 5522 & 121,884 & 3,867,261 \\
2 & Barley & 175 & 1590 & 31,189 & 1,333,238 \\
3 & Rapeseed & 10 & 77 & 1520 & 50,159 \\
4 & Maize & 1894 & 17,063 & 293,880 & 15,614,296 \\
5 & Wheat & 848 & 7652 & 171,864 & 5,700,540 \\
6 & Alfafla & 352 & 3179 & 73,059 & 1,913,304 \\
7 & Grassland & 2614 & 23,549 & 322,425 & 21,200,420 \\ \hline - & Total & 7567 & 67,481 & 1,426,520 & 56,515,322 \\ \hline \end{tabular}
\end{table}
Table 1: Calibration and validation parcels and pixels per class.