esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies
## 5 Performance Evaluation
### Jitter  
Jitter is calculated as the variation in the inter-packet time caused by network congestion, packet loss, loss of synchronization, or by the use of multiple paths from source to destination. It must be noted that due to the use of the TDLS routing protocol, which by default selects the shortest paths between source and destination and performs load balancing across them, all the paths used for data forwarding will have the same number of hops. Therefore, in spite of the use of multiple paths, all the packets will traverse the same number of hops. Consequently, the jitter will not be affected.  
However, there are situations where jitter is more likely to be affected. Analyzing Figure 11, when AO \(=\ 1\) or AO \(=\ 2\) the jitter increases since the _active duration_ is insufficient to perform all  
Figure 10: Inter-packet arrival times for a 6 \(\times\) 6 network.  
slot reservations, resulting in more congestion and consequently in more unpredictable inter-packet arrival times.
# Real-Time Tension Distribution Design for Cable-Driven Parallel Robot
## 7 Discussion: Comparison with Other Tension Distribution Methods  
### Computational Complexity and Calculation Speed  
Realizing real-time control of cable-driven robots requires tackling the problem of computational complexity. In each control loop of the suggested approach for tension distribution, it is necessary to compute Equations (33) and (34), the integral of \(\hat{L}_{a}\hat{S}\), and Equation (28), which comprises the matrix dot product and summation. In addition, there exists no iterative calculation or optimization procedure in either control loop. Therefore, this approach has low enough computational complexity for use in real-time applications.  
Here, we test the computational speed of the proposed algorithm in Matlab2016b with an AMD Ryzen 7 5800H CPU and 3.20 GHz Radeon GPU. Because the closed-form method is the fastest known method for distributing tension, we compared the calculation speed of our method with the closed-form method. We found that the closed-form method requires  
Figure 6: Results of Simulation 3. Parameters and desired pose are selected as \(\alpha_{1}=15\), \(\alpha_{2}=12\), \(K_{d}=0.1\), \(D_{d}=0.1\), \(K_{i}=0\), \(\Gamma=20\), \(q_{d}=[0.6,-0.6,0.6,0,0]^{T}\). (**a**) Tension distribution. (**b**) Tracking error of computed wrench. (**c**) Tracking error of desired pose. (**d**) Variation of \(\hat{a}\).  
24 \(\upmu\)s for one control loop, whereas our method requires 35 \(\upmu\)s. Although our approach is slightly slower than the closed-form method, our proposed algorithm's calculation speed is fast enough to process tension distribution calculations in real-time.
# Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine
## 2 Proposed Methodology
### Feature Extraction--Fuzzy Entropy (FuzzyEn)
Therefore, the feature vector will be equal to the total number frames of the input speech signal. The frame is labeled as speech, if more than half the samples are speech, otherwise the frame is labeled as noise.
# Use of a Heating System to Control the Probiotic Beverage Production in Batch bioreactor
## 3 Results and Discussion  
### Identification of the Mathematical Model  
#### 3.1.1 Identification of the Constant Temperature Model  
In the first phase, we searched the constant temperature model parameters for the analyzed fermentation process in the studied laboratory batch bioreactor. The differential evolution optimization method was used to calculate the parameters of the model [31]. We tried to find the model parameters that will simulate the product response \(P(t)\) as close as possible to the measured CO2 response of the analyzed laboratory reactor. The integral square error objective function was used to evaluate the matching of both responses. Product response \(P(t)\) of the constant temperature model was calculated with Equations (1)-(3). The measured response was obtained from the laboratory bioreactor presented in [27].  
Data of initial concentrations of the microorganisms, substrate, and product were required for simulation with the constant temperature model. These values were measured before the beginning of the fermentation process. Using the optimization method, we were changing the parameters \(\mu_{\text{m}}\), \(K_{\text{m}}\), \(\alpha\), and \(\beta\) of the constant temperature model so long that we obtained a minimal deviation between the responses of the identified model and the measured data in the laboratory bioreactor, i.e., that a minimum of the objective function was obtained. Measured initial values and obtained parameters of the constant temperature model are shown in Table 1.  
The fitting of the measured CO2 response with the simulations obtained by the identified constant temperature model is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Measured Initial Values** & **Identified Parameters** \\ \hline \(X(0)=6.0\times 10^{-4}\) g/L & \(\mu_{\text{m}}=6.0\) h\({}^{-1}\) \\ \(S(0)=3.7\times 10^{-3}\) g/L & \(K_{\text{m}}=0.03\) g/L \\ \(P(0)=2.6\times 10^{-4}\) mol/L & \(\alpha=0.84\) \\  & \(\beta=0.01\) h\({}^{-1}\) \\ \hline \end{tabular}
\end{table}
Table 1: Measured initial values and identified parameters of the constant temperature model.  
Figure 3: Time courses of the dissolved CO2 concentration \(c\) during the milk fermentation with kefir grains at constant temperature \(T=22\)\({}^{\circ}\)C obtained by measurements and with simulations with the identified Constant temperature model.  
The quantitative assessment of the accuracy of the identified Constant temperature model was carried out. The deviation (i.e., error) between the response of the identified model and the response of the laboratory batch bioreactor was calculated (\(c_{\text{error}}=c_{\text{simulation}}-c_{\text{measurement}}\)). Based on the calculated error, the following indexes were determined: the maximum absolute error (\(e_{\text{max}}\)), the average absolute error (\(e_{\text{a}}\)), the integral square error (ISE), and the integral absolute error (IAE). The results are presented in Table 2.
## 4 Results and Discussion
### Building the Ensemble Models and Constructing Landslide Susceptibility Maps  
After determining the landslide conditioning factors for predicting landslides, the next step was to create a landslide susceptibility map using the following three steps. Firstly, the values of each conditioning factor were reflected in the pixel values of the study area. The study area was transformed into pixels to serve as input for the prediction of landslides. Secondly, models were constructed using the prepared training data, and these models were applied to predict the landslide susceptibility for the entire study area. The results predicted by the models were considered to be the landslide susceptibility index. Lastly, the LSI values for the entire study area were input into ArcGIS to generate the landslide susceptibility maps. In this study, three ensemble learning models were utilized for mapping landslide susceptibility: CAET-AdaBoost (the adaptive boosting model with CART-based learners), SVM-AdaBoost (the adaptive boosting model with SVM-based learners) and BLS-AdaBoost (the adaptive boosting model with BLS-based learning). The LSI values predicted by the three integrated learning models were categorized into five classes (very low, low, moderate, high and very high) using the natural break method. The BLS-AdaBoost model resulted (Figure 7) in very low (20.5%), low (19.1%), moderate (19.9%), high (22.1%) and very high (18.5%) landslide susceptibility classes. The SVM-AdaBoost model resulted (Figure 8) in very low (18.3%), low (22.1%), moderate (19.3%), high (23.2%) and very high (17.1%) landslide susceptibility classes. The CART-AdaBoost model resulted (Figure 9) in very low (14.0%), low (32.6%), moderate (29.3%), high (15.8%) and very high (8.2%) landslide susceptibility classes.  
Figure 7: Landslide susceptibility map using BLS-AdaBoost.  
Figure 8: Landslide susceptibility map using SVM-AdaBoost.  
Figure 9: Landslide susceptibility map using CART-AdaBoost.
# A Systematic Review of Responsible Artificial Intelligence Principles and Practice
## 2 Systematic Review Methodology
### Data Extraction and Analysis  
The systematic content analysis of the final selected publications was executed through a dual-stage review process. An initial reviewer performed a detailed examination of all publications to extract relevant information addressing the research questions concerning responsible AI conceptualization, frameworks, principles, and applications. Following this extraction phase, a secondary reviewer validated the extracted data to confirm accuracy and adherence to the established inclusion/exclusion criteria for responsible AI research.  
The information systematically extracted from each publication encompassed:  
1. Publication details: Title, authorship, publication year, and source venue;  
Figure 1: The PRISMA flow diagram for the phases of the systematic review.  
Study characteristics: Publication format (journal article or conference proceeding), publishing house, and bibliometric data;
* Terminology and definitions: Explicit definitions of \"responsible AI,\" \"AI responsibility,\" and associated concepts where available;
* Framework identification: Recognition of responsible AI frameworks, principles, guidelines, or regulatory standards examined;
* Principle mapping: Identification of specific responsible AI principles discussed;
* Application context: Practical implementations, organizational barriers, evaluation methodologies, or governance mechanisms;
* Sectoral focus: Particular industries or application domains where responsible AI concepts are implemented.  
Funding sources and authors' financial disclosures were not extracted because they were judged immaterial to the conceptual focus of this review.  
The following subsections unpack and discuss findings from this final corpus in alignment to the six research questions, Finding 1 corresponds to research question 1, \"What is the current state of responsible AI principles and practice?\", and similarly up to Finding 6 and research question 6.
## 1 Introduction
In this paper, an alternative procedure was investigated for the assessment of the material fatigue properties of a steel rim for off-highway applications. It can be used when standard dynamic radial fatigue tests are not feasible because the test loads are too severe for the tyre resistance. It consists of testing rim sections directly obtained from the finished component, thus keeping the same local geometry and surface conditions. Customized specimens were manufactured from those sections, after a preliminary FEA, to achieve the fatigue crack development in a specific point of interest, which usually corresponds to the most stressed point of the rim during working operations in field or in standard laboratory  
Figure 1: Example of dynamic radial fatigue test (**a**) and dynamic cornering fatigue test (**b**) for off-highway wheels. tests. Dynamic bending tests were completed to create a fatigue curve of the material, which can be used for the durability analysis of the final wheel. A similar procedure was used in previous research for automotive wheels made of different materials, i.e., AISI steel 1008 and aluminum alloy 5454 [8,23]. Here, the focus is on off-highway steel wheels, where the material thickness is bigger to sustain much higher loads in working conditions and, consequently, the effect of the production process on the final product may change. To better assess this aspect, the following experimental steps were further completed:  
* Comparison between rim sections fatigue test results and standard fatigue test results of specimens obtained from the base material, as received prior to the production process;
* Investigation of the residual stress state of both complete rim and rim section specimens;
* Hardness measurement of the rim section specimens to evaluate the influence of the local plastic deformation on the mechanical properties of the material. These aspects are discussed, respectively, in Sections 3.2-3.4.
## 8 Future Challenges for the Enhancement of ED Limitations
### Research Challenges Related to Noise Uncertainty  
The results of the simulations shown in Figures 6, 7, 9, 10 and 13 confirm that the major ED weakness is the reduction of the detection precision when the variation of NU (noise power) rises. As shown in the preceding section, the degradation of the ED performance caused by the NU can be lowered through the DT adaptation (Table 4). Thus far, the literature has presented significant research results dedicated to the topic of DT adaptation for the improvement of the ED technique [3; 8; 10; 16; 45; 47; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64]. To satisfy the requirement of the targeted probability of a false alarm, in Refs. [51; 60; 63; 64], the detection threshold was optimised iteratively. In Ref. [51], an optimal adaptive threshold that utilises the SS error function was proposed. In Ref. [53], the SS based on unknown noise power was adaptively estimated for the different methods of ED. For finding and localising the narrowband signals, in Refs. [54; 130; 55], an algorithm based on double-thresholding was proposed. Generally, references [50; 51; 52; 53] showed the degradation of the sensing performance when the energy detector does not properly adjust its threshold. A well-selected detection threshold can minimise the SS errors, ensure the appropriate detection of a PU, and enable full spectrum utilisation [3]. Several authors have investigated the implementation of DT when there is an NU influence on the sensing process in CR networks. In Refs. [55; 56; 57; 58], the SS performance of ED was presented by considering the NU and different DTs in the channel. Different types of DT methods, more specifically Otsu, Rosin, Kapur and Entropy, were presented and compared in Ref. [15]. The impact on the ED sensing performance of NU and DT as both separate and joint factors was analysed in Ref. [36]. Besides their ratio, it was concluded that the absolute values of DT and NU have a significant impact on sensing performance. In Ref. [45], DTs were arbitrarily generated for each SU in order to increase the detection performance of the predefined NUs. An ED method based on an adaptive threshold, which keeps the false alarm rate at a preferred point under any noise level in an unknown WGNC, was presented in Ref. [59]. The adaptation of the SS threshold in CR by means of a method based on discrete Fourier transform filter bank was proposed in Ref. [60]. In Ref. [8], the adaptive threshold method was proposed as an alternative approach to estimate the threshold as a function of the first and second-order statistics of recorded signals. The simulation results indicate that
# The Influence of Capacitance and Inductance Changes on Frequency Response of Transformer Windings
###### Abstract  
Frequency Response Analysis (FRA) is a test method used for assessment of mechanical condition of transformer active parts. Its biggest problem is the interpretation of test results, namely the relationship between scale of differences between compared curves and the decision for further operation of the given transformer. Very often visible differences between two FRA curves do not mean that there is a deformation in the winding. The cause of the curve shift may come from other elements of the transformer that influence inductive or capacitive parameters. This paper takes under consideration the influence of both capacitance and inductance changes on transformer frequency response (FR). The analysis is performed with the computer model of a transformer and also some experimental results are presented, showing the influence of capacitance and inductance changes on the FR of real transformers. The results of research showed that this influence may lead to misleading effects on the shape of FR characteristics. The paper presents an analysis that can be used in the assessment of FRA measurement, especially in the case of uncertain data comparison results.  
Keywords:transformer winding; FRA; Frequency Response Analysis; modeling +
Footnote †: journal: Applied Sciences
## 5 Experiments
### Comparative Experiments: Proposed Method Versus Traditional Approaches  
To further validate the effectiveness of the proposed algorithm, we compare it with five representative image matching methods (References [3; 4; 5; 6; 7]), and the experimental results are presented in Figure 10. Reference [3] proposes a matching method based on Freeman coding for representing fracture image features, while Reference [4] employs a ConvNeXt based convolutional matching method within a Siamese network framework. Reference [5] introduces a multi-feature matching method based on a residual network; Reference [6] adopts a matching approach based on feature region partitioning; and Reference [7] utilizes a matching method based on fracture surface contour features.  
As illustrated in the results, the proposed network achieves a substantial performance improvement over existing methods on Test Set 1, with average matching accuracy gains compared to References [3; 4; 5; 6; 7] being 11.83%, 9.42%, 7.48%, 11.58%, and 5.19%, respectively. On Test Set 2, the performance gains are similarly significant, reaching 10.57%, 10.86%, 10.51%, 13.51%, and 6.92%, respectively. As shown in Figure 9(a), the proposed method demonstrates particularly strong performance on Test Set 1, attaining a near-optimal matching accuracy by approximately rank 14, whereas the other methods generally do not stabilize until after rank 20, with their overall matching rates consistently lagging behind the proposed approach, thus highlighting a clear performance gap. Moreover, as observed from the expanded dataset evaluation in Figure 9(b), although all methods exhibit some degree of performance degradation, the proposed method demonstrates a marked advantage in generalization capability. Specifically, the decline in matching accuracy is significantly less severe than that of traditional models, indicating greater robustness and adaptability to variations in data distribution and complexity.  
These results collectively validate the effectiveness and robustness of the proposed approach in scenarios characterized by distributional shifts or increased data heterogeneity, thereby demonstrating its strong potential for generalization and real-world applicability.
# Simulated Annealing for Resource Allocation in Downlink NOMA Systems in 5G Networks
## 1 Introduction
### Related Work
Simulated annealing (SA) is one of the metaheuristic optimization algorithms that work dynamically and iteratively to obtain the optimal solution, which can be more realistic and reliable in solving a non-convex optimization problem. The SA algorithm mimics the physical annealing process where it puts the material under a high temperature, then reduces the temperature slowly until a crystalline state is formed with the minimum energy and larger crystal size. The SA starts from a melting phase based on the physical crystal form, then the temperature of the molten crystal is reduced very slowly until the structure of the crystal is formed and stabilized without any defects [22]. The SA algorithm can find the global solution fast and guarantees a convergence upon running enough iterations. This is a significant advantage of the SA, while the gradient descent algorithm obtains the local solution. Moreover, SA is applied to both combinatorial and continuous optimization problems. The NOMA system's resource allocation challenge can be designed and modeled using SA since tuning the parameters is relatively controllable. In NOMA system-based power domain multiplexing, multiple users share the same radio resources, requiring an optimal resource allocation. Heretofore, no literature has reported applying SA to the issues related to resource allocation in the NOMA system. The SA algorithm is used to solve the user pairing and power allocation to maximize the total Throughput in the NOMA system. Moreover, SA is developed to determine the subchannel power and the power ratio for the multiplexed users, where the channel difference is the primary criteria to form the users and subchannels matching scheme. The user pairing scheme is designed using the two types of channel gain difference as a hot and cold configuration that ensures optimal user and subchannel matching. In this work, we propose a novel resource allocation scheme to maximize the Throughput for the downlink 5G NOMA system using SA. We also provide mathematical proof that equal power allocation can be the near-optimal solution for the NOMA system's subchannel power allocation problem. Further, we mathematically prove that the proportional power ratio can be fixed to a value between 0 to 1 that can maximize the Throughput based on the NOMA concept. The remainder of the paper is organized as follows. Section 2 presents the downlink NOMA system model and the mathematical formulation of the resource allocation optimization problem. In Section 3, we provide full detail of the proposed method used for resource allocation. The system model parameters and the analysis of the results are presented in Section 4. Finally, the work conclusion is drawn in Section 5.
## 4 Experimentation  
In this section, we implement the DxT model using real match data from the StatsBomb 360 Open Data dataset. For our experiment, we selected 210 matches, while an additional 85 matches will be used in Section 6 for model validation.  
The dataset used for training consists of 335,002 events, including passes, carries, and shots. It contains various features such as ball positioning, action type, and the positions of off-ball players during each action. The pitch dimensions in this dataset are 120 m in length and 80 m in width.  
To discretize the field, we selected a grid resolution of L = 12 and W = 8, resulting in 1-square-meter grid cells on a 120 \(\times\) 80 m pitch. This resolution offers a balance between spatial granularity and computational efficiency. Finer grids (e.g., 20 \(\times\) 16) may provide more detail but often lead to data sparsity issues, especially in less active zones, while coarser grids (e.g., 6 \(\times\) 4) may fail to capture important spatial dynamics. Additionally, the dataset provides player locations with decimal precision; however, we observed minor inaccuracies in the decimal values--for example, a player recorded at x = 110.7 may, in fact, be closer to 110.6 upon visual inspection. As such, we opted for a grid division that matches the integer meter scale (i.e., dividing the pitch length by 12 and width by 8) since the most reliable aspect of the positional data lies in the integer part of the coordinates. Thisensures that each cell corresponds to a 1 m square, aligned with the practical resolution of the data.  
We will refer to this dataset as the historical dataset, which serves as the foundation for implementing the protocol described in Section 3.2.1.  
In addition to this historical dataset, a separate dataset is used for building the xG model, which is a crucial component in the DxT model implementation.