esa-sceva/satcom-chunk-collection huggingface dataset:
# Power System Reliability Evaluation Based on Chronological Booth-Balieriaux Method
## 4 Case Study
### Effect of Chronological Characteristic on the Power System Reliability
#### 4.2.4 Renewable Energy Uncertainty  
Renewable energy (RE) output that depends on weather has considerable uncertainty. Sometimes, the output of RE is presumed, like RTS 2020, but generally, it should be predicted.  
For an analysis of RE uncertainty, as shown in Figure 2, it was assumed that the output of RE has a monthly and hourly average output, and the uncertainty follows a normal distribution [36]. The RE models are calculated as follows:  
\[\begin{split} RE_{k,t}(x)&=m_{k,t}+\sum\limits_{i= 1}^{R_{k,t}}P_{k,i,t}\delta(x+Y_{k,i,t})\\ &=\sum\limits_{i=1}^{R_{k}}P_{k,i,t}\;\delta(x+X_{k,i,t}),\end{split} \tag{21}\]  
where \(m_{k,t}\) and \(R_{k,t}\) are the monthly hourly average and the number of cases of the output of RE \(k\), respectively, at time \(t\). \(Y_{k,i,t}\) is the term for uncertainty, and (21) can be expressed as similar to (12). Therefore, RE can be regarded as a generator with many states and does not require maintenance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indices** & **Base Case** & **Temp-Considered Case** & **Change (\%)** \\ \hline LOLH (hour/year) & 0.001898 & 0.011176 & +488.8 \\ \hline EUE (MWh/year) & 0.23380 & 1.47222 & +529.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: IEEE RTS 2020 reliability indices considering the temperature.  
Figure 2: Monthly and hourly output characteristics of renewable energy.  
Table 8 lists the reliability indices according to the uncertainty of RE. In the 0 uncertainty case, the reliability indices appear to be improved because the contribution of RE has increased during the high-demand period compared to the existing system. As with load uncertainty, the reliability indices increase as the uncertainty increases. Hence, the contribution of RE to the system reliability decreases when their output is highly variable and unpredictable because of climate or geographic factors.
# A Review of the T-Stub Components for the Analysis of Bolted Moment Joints
## 2 Analytical Model
### Proposed Analytical Model for T-Stub under Impact Loading Based on EC3  
In 2015, [PERSON] et al. [19] proposed an analytical model based on EC3 and the yield line analytical model developed by [PERSON] et al. [20] to predict the behaviour of T-stubs under impact loads due to blasts or impacts. The proposed method considered the enhancement of the constitutive law of the material by employing a dynamic increase factor (DIF), which will promote the increase of the elastic and ultimate strengths based on the ratio of the strength observed dynamically (\(\sigma_{dyn}\)) and statically (\(\sigma\)):  
\[DIF=\frac{\sigma_{dyn}}{\sigma}, \tag{51}\]  
then according to the Johnson-Cook model [21], Equation (52) is used to describe the \(DIF_{steel}\) for intermediate strain rate values:  
\[\sigma=[A+Be^{n}]\times\left[1+C\times In\dot{\epsilon^{*}}\right]\times[1-(T^{ *})^{m}], \tag{52}\]where \(A\) is the quasi-static yield strength; \(B\) and \(n\) represent the effects of strain hardening; \(m\) is the thermal softening fraction; \(T^{*}\) is a non-dimensional parameter depending on the melting and transition temperatures to take into account the material softening due to a temperature variation (this is not considered in the proposed model); \(\varepsilon\) is the equivalent plastic strain; \(\dot{\varepsilon}^{*}=\dot{\varepsilon}/\dot{\varepsilon}_{0}\) is the dimensionless plastic strain rate, where \(\dot{\varepsilon}\) is the strain rate and \(\dot{\varepsilon}_{0}\) the reference quasi-static strain rate (\(\dot{\varepsilon}_{o}=0.001s^{-1}\)), \(C\) is the strain rate constant. Then DIF could be calculated with Equation (53):  
\[DIF=\left[1+C\times ln\dot{\varepsilon}^{*}\right], \tag{53}\]  
Additionally, the DIF value of the steel (for S355-C = 0.0039) and bolt (Bolt(8.8)-C = 0.0072) could be determined with the aid of Figure 15, where the values of \(C\) are 0.0039 and 0.0072 for steel and bolt, respectively.  
The failure modes that govern the T-stub behaviour are calculated with the same equations of EC3 1-8, see Equations (1), (3) and (5), but \(f_{y}\) and \(f_{ub}\) change to \(f_{y,D}\) and \(f_{ub,D}\), respectively, see Equations (54) and (55). \(DIF_{steel}\) and \(DIF_{bolt}\) could be considered equal to 1.5 and 1.1, for strain rates over 600 \(s^{-1}\), respectively:  
\[f_{y,D}=DIF_{steel}*f_{y}, \tag{54}\]  
\[f_{ub,D}=DIF_{bolt}*f_{ub}, \tag{55}\]
## 2 Problem Description and Formulation
### Formulation  
#### 2.3.1 Downlink Latency Cost (DC)  
The downlink latency cost is determined by the scheduled solution. Each downlink request has a request submission time, and the time from the submission time to the completion time of the request is called the downlink latency time. The downlink latency cost can be calculated as follows,  
\[DC=\sum_{s\in S}\sum_{r\in R_{j}}\omega_{DC}\times(y_{r}-AT(R_{s}^{r})) \tag{2}\]
## 2 Related Works
### Scientific Literature  
Scientific research addresses multi-user collaboration predominantly from a social perspective, emphasizing the role of VR in design review and group decision-making dynamics, ultimately leading to greater efficiency in the process. For instance, in the textile sector, VR/AR platforms have proven to significantly accelerate design workflows [6], while Mixed Reality (MR) facilitates collaborative prototyping among designers, ergonomists, and end users [7].  
Recently, ref. [8] introduced CIDER, an AR system that enables independent and conflict-free editing of shared scenes, analyzing the impact of \"commit\" strategies (forced vs. consensus-based) on collaborative dynamics and usability. In addition, ref. [9] examined how user roles and task complexity influence subjective perception while also exploring technical factors such as latency, which affects Quality of Experience (QoE) in long-range virtual collaboration.  
In the nautical industry, a noteworthy example is BoatAR [10], an AR-based configurator that simplifies remote boat customization. BoatAR has proven to reduce logistics and inventory costs while showcasing the potential of immersive technologies to address operational and economic challenges. Moreover, ref. [11] addresses the functional arrangement of cockpit instrumentation in maneuvering areas, supported by evaluations of operational, ergonomic, and anthropometric aspects.  
Despite these advancements, multi-user product configuration in VR remains under-explored, particularly regarding eco-sustainability and the role of advanced graphics in enhancing emotional engagement. Persistent challenges include high hardware costs and the lack of standardized user interfaces to ensure accessibility and intuitiveness for a broad audience [12]. Moreover, despite technological progress, the inherent complexity of VR tools often requires specialized expertise for effective integration into complex workflows, such as those typical of the nautical industry [13].  
Future research should explore novel collaborative approaches that incorporate realistic graphics and avatar support, enhancing spatial perception and ergonomic assessment in real-world industrial scenarios.
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 3 Feature-Based Modulation Recognition Methods
### Integral Feature Extraction  
In VLC systems, the L-PPM modulation technique is characterized by high energy efficiency. However, the dynamic recognition of its modulation order remains a challenge that has not been effectively addressed. Traditional receivers require a preset fixed L value, which struggles to meet the transmission optimization demands under dynamic channel conditions. In 2022, [PERSON] et al. [22] proposed a machine learning classification framework based on integral feature extraction, which for the first time realized the real-time recognition of multi-order PPM signals. The core innovation of this research lies in the time-domain integral feature-extraction method: targeting the differences in time slot widths of different L-PPM signals. The time slot of 2-PPM is \(T_{2-PPM}=T_{b}/2\), while that of 16-PPM is \(T_{16-PPM}=T_{b}/4\). An integral window of 12\(T\), which is the least common multiple of the periods, is used to calculate the energy accumulation value of the received signal. As the L value increases, the corresponding integral result shows a stepwise decay. For example, the theoretical integral values of 2-PPM and 16-PPM are 6\(AT\) and 0.75\(AT\), respectively. This method suppresses Gaussian noise through energy accumulation while enhancing the feature distinguishability of different L values.  
Experiments compared the performance of four classifiers (as shown in Figure 5). The linear model (LM), which classifies based on geometric distance thresholds, is computationally efficient but its accuracy drops to 77.25% at long distances (3 m). The decision tree (DT) constructs a tree structure using the natural boundary points of features, achieving an accuracy of 97.85% at short distances (2.20 m), but it is sensitive to feature overlap at long distances. The k-nearest neighbors (KNN) method, which employs dynamic neighborhood search, performs best at medium distances (2.32 m), maintaining an accuracy of 79.99% at 3 m. The support vector machine (SVM), which maximizes the classification margin through the hyperplane \(y=w^{T}x+b\), achieves comparable performance to LM at medium distances (2.25-2.86 m), but the complexity of kernel function tuning affects real-time performance. Moreover, this method is highly dependent on the time-slot structure of the signal. The inaccurate estimation of time-slot width during actual hardware deployment may negatively affect recognition performance.
# Wave Polarization Control in Anisotropic Locally Resonant Materials
## 5 Conclusions  
In this work, we studied the in-plane propagation of elastic waves in a ternary locally resonant metamaterial made of a connected stiff matrix and periodic cylindrical inclusions consisting of heavy fibers eccentrically coated by a soft material.  
Two-scale asymptotic homogenization was employed to study the effective dynamic properties of the periodic media. The method provides a frequency-dependent mass density tensor that, in general, is anisotropic. We established that the signature of the effective mass tensor plays a crucial role in the dynamic properties of the media.  
The propagation of elastic waves without attenuation is allowed when the effective mass tensor is positive definite (pass bands) and is forbidden when is negative definite (band gaps). In all the other cases, i.e., when the homogenized mass is indefinite (polarization bands), elastic waves can propagate with a unique polarization vector, whose direction tends to that of the principal positive effective mass as the ratio between the positive and negative effective mass tends to zero.  
Finally, the results obtained through asymptotic homogenization were validated by comparing the predicted polarization bands with several numerical transmission analyses carried out on the real geometry of the metamaterial.  
The results of the present work could help to predict polarization bands for the design and optimization of locally resonant metamaterials that manipulate elastic wave polarization and allow for mode-converting mechanisms. The outcome of this paper could also be generalized to three-dimensional locally resonant metamaterials and metaplates.  
Conceptualization, D.F. and C.C.; methodology, D.F., A.V. and C.C.; software, D.F. and F.M.; validation, F.M.; writing--original draft preparation, D.F.; writing--review and editing, all authors; supervision, A.V. and C.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Ablation Study
#### 3.6.2 IoU Loss Function  
To validate the impact of the loss function module on the detection performance of the model within the proposed improvement methods, ablation experiments were conducted using the YOLOv8n model as the baseline on the SBDS strawberry dataset. As shown in Table 4, replacing the original CIoU loss function with the Shape-IoU loss function led to significant improvements in the precision, recall, and mAP\({}_{0.5}\) of the model. This result indicates that considering the specific shape and size of strawberries when calculating the loss can effectively enhance the model's adaptability for strawberries of varying shapes. Notably, whether Focaler-IoU was coupled with CIoU or with Shape-IoU, positive effects on the precision, recall, and mAP\({}_{0.5}\) were observed. This not only improved the accuracy of the model in recognizing challenging samples but also helped balance the focus of the model across all samples. A further analysis revealed that the combination of Focaler-IoU and Shape-IoU resulted in particularly significant improvements in precision, recall, and mAP\({}_{0.5}\), with increases of 0.3%, 0.3%, and 0.4%, respectively. These experimental results not only validate that the introduction of Shape-IoU and Focaler-IoU can effectively enhance the detection performance of the model but also highlight the superiority of Focaler-shape-IoU in the task of strawberry ripeness detection.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Fusion Structures** & **mAP\({}_{0.5}\)** & **mAP\({}_{0.75}\)** & **mAP\({}_{0.50,95}\)** & **Param. (M)** & **FLOPs (G)** \\ \hline Baseline & 91.0 & 83.5 & 75.1 & 3.0 & 8.1 \\ BiFPN [32] & 91.6 & 84.0 & 75.4 & 2.9 & 7.3 \\ HS-FPN [30] & 91.3 & 83.6 & 75.3 & 2.0 & 7.0 \\ AFPN [31] & 91.4 & 83.8 & 75.4 & 3.2 & 7.4 \\ Ours (MH-FPN) & 92.1 & 84.3 & 75.8 & 3.9 & 11.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparative performance evaluation of different feature fusion architectures on the SBDS dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**LMSA** & **ELA** & **Precision** & **Recall** & **mAP\({}_{0.5}\)** & **mAP\({}_{0.75}\)** & **mAP\({}_{0.50,95}\)** \\ \hline \multirow{4}{*}{\(\surd\)} & & 90.1 & 85.2 & 91.0 & 83.5 & 75.1 \\  & \(\surd\) & 90.5 & 85.7 & 91.5 & 83.8 & 75.4 \\ \cline{1-1}  & \(\surd\) & 90.4 & 85.4 & 91.3 & 83.7 & 75.3 \\ \cline{1-1}  & \(\surd\) & 90.7 & 85.8 & 91.7 & 83.9 & 75.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiments of PHAM on the SBDS dataset.
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
## 4 Proposed Method
### Dataset  
To streamline dataset generation, MediaPipe was used as an automated annotation tool for the YOLO-based dataset. The dataset was structured with image files and annotation text files, each containing bounding box coordinates and 21 hand keypoints per hand in the format [21; 2], where each keypoint is defined by its (x, y) coordinates (Figure 5b).  
A separate dataset was created using video sequences to train the Bi-LSTM model, with a two-minute video recorded for each gesture class. Frame-by-frame annotations were extracted using the YOLOv8-Pose model, collecting hand keypoints, bounding box  
Figure 4: Project flow of the proposed methodology.  
coordinates, and optical flow vectors. Optical flow was computed with the Farneback algorithm, and all features were stored in structured NumPy arrays for sequence modeling.  
To evaluate the feasibility of the proposed method, a small pilot dataset was collected with two visually similar dynamic gesture classes: \"Aty\" and \"Tegi\" (\"Name\" and \"Surname\"), each consisting of 900 images and corresponding YOLO label files. The dataset was then expanded to 13 gesture classes, including 4 static and 9 dynamic signs, totaling 45,851 samples, or around 3527 samples per class.  
Finally, a comprehensive dataset was compiled for full-scale training and evaluation, including the entire Kazakh Sign Language alphabet and 7 common words, with 27 static and 20 dynamic gesture classes. Figure 5a demonstrates the visual difference between dynamic and static signs. Samples from the original, gathered dataset are shown in Figure 5b. The dataset grew to 166,010 samples, or about 3532 samples per class, enabling performance evaluation across a diverse set of gesture types and complexities. Figure 6 illustrates how the the static and dynamic sign ratio in the dataset affects the system's classification accuracy. As the proportion of static signs increases, there is a consistent decline in model performance.
# Special Issue \"Body Sensors Networks for E-Health Applications\"
## 1 Introduction  
The monitoring and analysis of physiological variables through biomedical sensors is fundamental for the diagnosis and monitoring of users and/or patients in the context of e-Health. A biomedical sensor is usually located on the patient to record and analyze physiological signals such as the electrocardiogram, oxygen saturation, blood pressure, body temperature, respiratory rate, heart rate or blood glucose concentration, which can be performed on a 24/7 continuous monitoring scheme (24 h a day, 7 days a week). The biomedical sensors are connected wirelessly to each other or to an external gateway device, forming a body sensors network (BSN). BSNs enable real-time, ubiquitous, pervasive and non-obstructive monitoring of the patient's health status and the detection of emergency situations. They provide a reduction in the cost of medical care, since monitoring is done outside the clinical setting, and results in an improvement of the quality of diagnosis and medical follow-up, making possible the early diagnosis of a possible disease and the cost-effective management of patients outside the hospital.  
These sensors are usually small and lightweight, wearable (placed on the skin or in a garment), but also implantable, to allow non-intrusive monitoring, performed in a seamless way, so that the user can obtain an actual measurement of the physiological variable, without it being affected by the measurement process itself, and avoiding any type of discomfort to the user.  
Despite the advances in BSN, there are still many challenges to be addressed, such as the miniaturization of sensor devices for seamless monitoring, usability and scalability, energy efficiency and energy harvesting to provide greater autonomy, the standardization of low-power wireless communication, design and characterization issues related to antennas in a body environment or the integration of implantable devices.  
This Special Issue collects some of the latest developments in the field of BSN, covering a wide range of related topics, some of which are listed below:* Novel and enhanced sensors for physiological measurement
* Innovative health-sensing technologies and applications
* Implantable and minimally invasive devices and nanosensors
* BSN hardware platforms for e-Health applications
* Artifact correction and enhanced monitoring using information fusion
* New processing algorithms and machine learning in BSNs
* Low-power wireless communication technologies for BSNs
* Design and characterization of antennas in BSNs
* Energy efficiency and energy harvesting for body sensor devices
* Big data challenges and Internet of Things in BSNs
* Future challenges of BSNs in e-Health applications  
From a total of 14 submissions, 9 articles were finally published after a rigorous peer-review process. A brief introduction to the works published in the Special Issue is made below. The works have been grouped according to their subject in different sections, to provide a better structure and facilitate the follow-up to the reader.
# Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter
## 3 The Proposed Method
### Re-Tracking a Disappeared Target Using Multiple Search Windows  
Although it is technically impossible to keep tracking if a target completely disappears in a scene, we assume that the target appears again in the neighborhood of where it disappeared. To re-start tracking a disappeared target, multiple search windows are deployed around the position where the target disappeared. During the re-tracking process, we do not update target appearance. For example, tracking failure due to occlusion is predicted as shown in Figure 5a, then multiple search windows will be deployed as shown in Figure 5b. Response map and peak value is generated from each search window, and the tracker starts tracking again if the target is captured in any of those search windows. We reduced computational loads by giving priority to these search windows, details are explained in the next section. Desired results could be derived by re-tracking the target from a peak point at which condition \(\frac{c_{t(p,d)}}{P_{\text{aver}}}>0.6\) is met in any of multiple search windows.  
Figure 4: Accuracy distribution for distinguishing successfully tracked frames and failed frames according to \(\alpha\) and \(\beta\).
# Mixed Method for Isogeometric Analysis of Coupled Flow and Deformation in Poroelastic Media
## 3 Isogeometric Analysis
### Important Features in Current Context
#### 3.3.1 Continuity  
One of the most distinctive and powerful features of IGA is that the basis functions will be \(C^{p-m}\) continuous across knotspans (analogous to elements in FEA), where \(p\) is the polynomial degree and \(m\) is the multiplicity of the knot. This means that the continuity across knotspans can be controlled by the proper choice of \(p\) and \(m\). The continuity can be decreased by repeating a knot--important to model non-smooth geometry features or to facilitate the application of boundary conditions. For instance, quadratic (\(p=2\)) splines are \(C^{1}\) continuous over non-repeated knots while quadratic Lagrange finite element bases are only \(C^{0}\) continuous. If we consider the quartic (\(p=4\)) basis functions constructed from the open, non-uniform knot vector \(\Xi=\{0,0,0,0,0,1,2,2,3,3,3,4,4,4,4,5,5,5,5\}\), we get different continuities across knotspans as shown in Figure 1.
## 2 Coding Metamaterials for RCS Reduction
### Effect of Multi-Layer Structure for RCS Reduction  
Since the existing literature on the coding metamaterial field focuses only on the conventional lattice forms of coding design structures, this study introduced unique structures, such as multi-layer designs and cuboid designs, by adopting one-bit coding elements. Therefore, the focus of this parametric study was to identify the changes in RCS values when adding multiple layers of coding-based metamaterial structures. A 1-bit 12-lattice design was adopted for this analysis study. The best among the three different coding sequences was selected for this case study. Double and triple layers of the proposed coding sequences were utilized to calculate the RCS values. The one-bit coding metamaterials for both layers exhibited better monostatic and as well bistatic RCS values compared to the single-layer structure, as shown in Figure 12a,b. The bistatic RCS values reached peak points, such as \(-7.87\) dBm\({}^{2}\), \(-9.02\) dBm\({}^{2}\), and \(-7.92\) dBm\({}^{2}\), respectively, for all three coding sequences. Meanwhile, the triple-layered structure also exhibited lower RCS values, which were reduced by 25% from the double-layered structure.  
Figure 12: Cont.
# A Survey on Satellite Communication System Security
## 2 Existing Surveys and Motivation  
In this section, we summarize and compare existing survey papers, presenting their works and limitations. We then introduce the motivation for our research and, in the final part, outline the overall structure of our research.