esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Principal Component Analysis: Factor Extraction Results  
The principal component analysis (PCA) was performed on the datasets to identify the most important data features for use in training the machine learning models. Table 4 shows the variance distribution of the principal components (PCs) (PC1-PC17). According to the eigenvalues, it appears that the cumulative variance of PC1 to PC6 is 91.95%Therefore, the first six principal components were identified as the main model inputs and were sufficient to develop our predictive models. Moreover, Figure 1 presents the scree plot, which is a line plot of the correlation matrix's eigenvalues, arranged from greatest to smallest.  
The top three variables with a value greater than 0.60 in Table 5 were chosen as the main variables of each of the PCs to choose prominent predictor variables for further  
\begin{table}
\begin{tabular}{c c c c} \hline
**PCs** & **Eigenvalues** & **Variance (\%)** & **Cumulative Variance (\%)** \\ \hline
1 & 9.5770 & 56.3356 & 56.3356 \\
2 & 1.8230 & 10.7240 & 67.0597 \\
3 & 1.4733 & 8.6669 & 75.7266 \\
4 & 1.0447 & 6.1458 & 81.8724 \\
5 & 0.9068 & 5.3345 & 87.2069 \\
6 & 0.8063 & 4.7434 & 91.9504 \\
7 & 0.4738 & 2.7875 & 94.7379 \\
8 & 0.2733 & 1.6078 & 96.3458 \\
9 & 0.2175 & 1.2795 & 97.6253 \\
10 & 0.1779 & 1.0468 & 98.67222 \\
11 & 0.0958 & 0.5638 & 99.23608 \\
12 & 0.0778 & 0.4579 & 99.69398 \\
13 & 0.0407 & 0.2396 & 99.93361 \\
14 & 0.0099 & 0.0583 & 99.99193 \\
15 & 0.0013 & 0.0080 & 99.99999 \\
16 & 2.3198 \(\times\) 10\({}^{-6}\) & 1.3646 \(\times\) 10\({}^{-5}\) & 100 \\
17 & 3.6497 \(\times\) 10\({}^{-8}\) & 2.1469 \(\times\) 10\({}^{-7}\) & 100 \\ \hline \end{tabular}
\end{table}
Table 4: Principal components (PCs).  
Figure 1: Scree plot.  
regression analysis [21]. For PC1, the global horizontal irradiation (GHI), beam horizontal irradiation (BHI), and beam normal irradiation (BNI) were used. For PC2, the pressure (P) was identified. For PC3, top of atmosphere radiation (TOA), clear sky diffuse horizontal irradiation (CSDHI), and diffuse horizontal irradiation (DHI) were chosen. For PC4, the wind speed parameter was selected. For PC5, cell temperature (Tcell), PV efficiency (Eff), and ambient temperature (Tamb) were used. Finally, only wind direction was identified for PC6. All 12 variables were considered to be PV power driving factors, allowing them to be used as inputs in the proposed predictive models.
# Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering
## 3 Methodology  
This section of the paper delineates the structure of our proposed ACO model and elucidates the underlying mechanisms, as illustrated in Figure 1. The existing literature underscores the effectiveness of ACO as one of the foremost meta-heuristic models. ACO draws inspiration from the way real ants modify their behavior and navigate to discover the most efficient routes to their resource destinations. The selection of communication channels is contingent upon the presence of highly concentrated pheromone levels and heuristic values. Our framework is fashioned with inspiration from the adaptation and cooperative strategies exhibited by ants. The iterative process underpins the solution generation. During each iteration, ants derive insights from pheromone trails to formulate solutions. The connections play a pivotal role in estimating the pheromone values used for problem-solving. Ants employ a transition rule to visit nodes in a random manner. Continuous learning facilitates the real-time update of pheromone information, and the evaporation rate of pheromones influences the quality of ants' chosen paths. Our approach takes inspiration from evolutionary algorithms to elevate the quality of ant-generated solutions, ultimately yielding the shortest and most optimal paths.  
Let us assume that the vehicular networks are fully connected to the environment. In alignment with the 5G networks, the usage of 5G infrastructure covers the highest communication ranges and coverages. GPS helps to localize the position of vehicles. The continuous changes in topology and mobility look for the standardized conditions achieved from the quantity of cluster formation. Better communication is ensured in the minimum count of clusters. Here, ACO is modified for better CH selections for the V2I model. Each vehicular node is treated as point of solution, and the swarm indicates the set of solutions. At a particular period, an ant portrays the IDs of CHs to the routes. This work intends to establish optimal solutions for VANET clustering, achieved by intelligent node selection and energetic evaporation rate. Every vehicle is equipped with 5G and 802.11p. Based on the formed cluster and the \(V^{Neigh}\), the vehicular networks are organized. The integrity of the proposed framework is achieved in two ways: search space modeling and initialization and the CH selection strategy (Algorithm 1).  
Figure 1: Architecture of the proposed vehicular ad hoc networks (VANETs).
## 4 RF Energy-Harvesting Techniques
### Hybrid Energy Harvesting
For example, a hybrid piezo-thermoelectric energy harvester embedded in a shoe sole could generate electricity from both walking motion and foot-sole temperature, enabling the continuous monitoring of gait or health parameters without requiring battery replacement. The typical antennas used for hybrid RFEH are flexible, transparent, printable, conformal antennas. These advanced antenna designs support the integration of RFEH intohybrid platforms by ensuring mechanical adaptability, esthetic compatibility (e.g., wearables), and low fabrication costs. Their material flexibility allows them to be embedded in curved or irregular surfaces, such as clothing, medical implants, or building materials. Hybrid energy-harvesting systems offer multiple advantages, including enhanced energy capture, achieved by tapping into different ambient energy sources; improved overall efficiency due to the integration of various conversion mechanisms; and greater adaptability for diverse applications such as remote sensors and medical implants. However, the design and implementation of hybrid energy harvesters present certain challenges, including the increased complexity in ensuring compatibility among different energy-harvesting mechanisms, potential increases in size and weight, and higher production costs due to the use of multiple materials and technologies. The integration process demands careful electrical isolation, thermal management, and power conditioning to avoid mutual interference and to optimize overall output. System miniaturization and unified control circuitry remain key areas of ongoing research. Despite these challenges, hybrid energy-harvesting systems represent a promising approach to sustainable and reliable power generation. Ongoing research and technological advancements continue to refine these systems, making them more efficient and cost-effective for real-world applications. Innovative fabrication techniques such as inkjet printing, MEMS integration, and 3D packaging are enabling more compact, scalable, and customizable hybrid harvesters tailored to specific deployment scenarios. Table 5 shows the comparison of the various RFEH techniques described in Section 4.
## 2 Dispersive Fourier Transformation Technique
### Implementation of DFT  
#### 2.2.1 Optical Source  
DFT is usually performed in the coherent regime and restricted to work with a Fourier transform-limited input ultrashort optical pulse only. A Fourier transform-limited pulse, or more commonly known as the transform-limited pulse, is usually an ultrashort optical pulse that has a constant phase (chirp free) across the whole pulse window and maintains the minimum possible time duration for a given spectral bandwidth. The amplitude spectrum of a transform-limited pulse is the Fourier transformation of its temporal envelope, which is, however, not valid for a non-transform-limited optical pulse with a time-dependent phase (chirp). Transform-limited optical pulses are usually generated by mode-locked lasers [60]. An alternative approach to generating transform-limited ultrashort optical pulses is based on electro-optic phase modulation of a continuous-wave laser and subsequent phase-to-intensity conversion and pulse compression in a dispersive medium [61,62]. Optical pulses generated by the latter method have a much higher repetition rate than that of mode-locked lasers, hence enabling higher update rate for real-time measurement. However, to avoid overlaps between consecutive stretched pulses, the effective time window will have to be significantly decreased.
###### Abstract  
Cadmium (Cd) is a widely spread pollutant in the environment and its identification in human breast milk has caused concern. Children are particularly vulnerable, since their detoxification mechanism is not fully developed and their organs still being formed. Human- and animal-based studies demonstrate health issues and adverse pregnancy outcomes related to prenatal and postnatal Cd exposure. However, investigations of the effects, mechanisms, and treatments are still required. Thus, this systematic review compiled studies of Cd effects on lactating mice and rats focusing on experimental features and reported effects. The search was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA), using PubMed, SciELO, and ScienceDirect databases. After identification, screening, and inclusion process 52 research articles were recovered and data, such as animal strain, metal compound, dose or concentration, administration route, exposure time, litter size normalization, lactation days, organs or samples examined, and effects reported were organized in tables. Three general schemes of Cd exposure on dams were identified: pregnancy, lactation, or pregnancy-lactation. The effects evaluated included neurotoxicity, sexual maturation, biochemical parameters, and Cd transfer and retention. Experimental features most common were Cd exposure during the pregnancy-lactation scheme and Cd administration by drinking water, while the most evaluated effect on offspring was Cd-neurotoxicity.  
cadmium; breastfeeding; animal model; toxic effects +
Footnote †: journal: Medical Imaging and Clinical Imaging and Clinical Imaging and Clinical Imaging  
inorganic elements because their detoxification mechanism is not fully developed, and their organs are in formation [6]. Cd is eliminated from organisms principally by the kidneys [7], but newborns present low glomerular filtration and diminished renal tubular secretion, as these only increase and mature after one year of life. In addition, the developing organism experiences processes, such as cell growth, differentiation, and morphogenesis, which may result in adverse developmental effects if they are altered [8]. Human- and animal-based studies demonstrate serious health issues and adverse pregnancy outcomes related to prenatal and postnatal Cd exposure, such as malformations, decrease in fetal growth rate, low birth weight, mortality, and diseases in childhood or later life. However, the mechanisms of Cd-induced toxicity remain unclear [9] and further studies related to mechanisms and effects, as well as possible treatments to decrease toxicity, are required.  
Thus, this systematic review aimed to examine studies of Cd exposure in lactating mice and rats, focusing on the experimental features and the determined effects. Data included were the animal strain, metal compound, dose or concentration, administration route, exposure time, litter size normalization, lactation days, organs or samples examined, and reported effects.
# Robustness of Double-Layer Group-Dependent Combat Network with Cascading Failure
## 3 Cascading Failure Model for DGCN  
Most common interdependence network models are based on a one-to-one dependent pattern, which has no feedback characteristics and is convenient for analysis [9]. Quite a lot of studies on the robustness of interdependent networks are also based on this pattern [23; 24; 25; 26], but such strict dependencies often do not exist in practice. The situation of one-to-many dependency is relatively common, namely, the aforementioned group dependency [27]. If any one of the depended nodes (communication net nodes) fails, the dependent nodes (functional net nodes) fail according to the traditional analysis method. Therefore, group dependency greatly affects the robustness of the interdependent network. As long as a small number of nodes are attacked, the entire interdependent network may collapse, but the network in real life is not so fragile [28; 29]. In addition to wired communication, there are many other communication methods for the communication net of DGCN, such as short-wave communication, ultra-short-wave communication, and satellite communication. Therefore, when the functional net depends on the communication net, as long as a certain percentage of the depended nodes are still working well, the dependent functional nodes will not fail. To build the cascading failure model of DGCN, the failure analyses are as follows:
## 5 Experiments and Results
### Experimental Setup  
The optimisation algorithm was implemented using the jMetalPy package [36]. Our experiments were executed in parallel runs using computing nodes of the HPC facilities of the University of Luxembourg [37], equipped with Intel Xeon Gold 6132 @ 2.6 GHz processors and 128 GB of RAM.
###### Abstract  
Deep neural networks have been widely applied to fiber optic sensor systems, where the detection of external intrusion in metro tunnels is a major challenge; thus, how to achieve the optimal balance between resource consumption and accuracy is a critical issue. To address this issue, we propose a lightweight deep learning model, the Temporal Efficient Residual Network (TEResNet), for the detection of anomalous intrusion. In contrast to the majority of two-dimensional convolutional approaches, which require a deep architecture to encompass both low- and high-frequency domains, our methodology employs temporal convolutions and a compact residual network architecture. This allows the model to incorporate lower-level features into the higher-level feature formation in subsequent layers, leveraging informative features from the lower layers, and thus reducing the number of stacked layers for generating high-level features. As a result, the model achieves a superior performance with a relatively small number of layers. Moreover, the two-dimensional feature map is reduced in size to reduce the computational burden without adding parameters. This is crucial for enabling rapid intrusion detection. Experiments were conducted in the construction environment of the Guangzhou Metro, resulting in the creation of a dataset containing 6948 signal segments, which is publicly accessible. The results demonstrate that TEResNet outperforms the existing intrusion detection methods and advanced deep learning networks, achieving an accuracy of 97.12% and an F1 score of 96.15%. With only 48,009 learnable parameters, it provides an efficient and reliable solution for intrusion detection in metro tunnels, aligning with the growing demand for lightweight and robust information processing systems.  
Keywords:neural network; deep learning; optical fiber sensing; external intrusion detection IEEEexample:BSTcontrolcontrol
## 2 Mathematical Background
### Binary Equivalent Model  
[PERSON] and [PERSON] [16] proposed a method to generate primitive polynomials in \(GF(q^{n})\) using primitive polynomials in \(GF(q)\) based on the relationship previously discovered by [PERSON] and [PERSON] [17] between the \(m\)-sequences produced in \(GF(q^{n})\) and \(GF(q)\), in such a way that an \(m\)-sequence in \(GF(q^{m})\) can be decomposed into \(n\)\(m\)-sequences in \(GF(q)\). More precisely:  
**Theorem 1** (_cf_. [17], th 7).: _Let \(p(x)\) be a primitive polynomial of degree \(m\cdot n\) in \(GF[q,x]\). Let \(f(x)\) be one of the \(n\) primitive polynomials of degree \(m\) in \(GF[q^{n},x]\) into which \(p(x)\) factors when viewed in \(GF[q^{n},x]\). Let \(D=d_{0},d_{1},\cdots\) be an \(m\)-sequence over \(GF(q^{n})\) generated by \(f(x)\). If_  
\[d_{i}=d_{i,0}\lambda_{0}+d_{i,1}\lambda_{1}+\cdots+d_{i,n-1}\lambda_{n}-1 \tag{6}\]  
_where \(\{\lambda_{0},\lambda_{1},\cdots,\lambda_{n-1}\}\) is a basis for \(GF(q^{n})\) over \(GF(q)\), then the sequence \(d_{0,j},d_{1,j},\cdots\) is an \(m\)-sequence of length \(q^{nm}-1\) over \(GF(q)\)._  
As one can observe, the sequence \(d_{0,j},d_{1,j},\cdots\) is composed by the \(j\)-th component of each element \(d_{i}\) in the sequence \(D\). Equivalently, the sequence \(d_{0,j},d_{1,j},\cdots\) can be considered as a decimation sequence obtained from \(D\) giving rise to the following set of decimated sequences, as it is shown in Figure 2:  
\[D^{j}=d_{0,j},d_{1,j},d_{2,j},\cdots, \tag{7}\]  
Furthermore, as it is stated in [16], since the sequences \(D^{j}\), for \(0\leq j\leq n-1\), are generated by the same polynomial \(f(x)\), all of them are shifted versions of the same \(m\)-sequence. Hence, taking \(D^{0}\) as the reference, we can define \(\theta_{j}\) as the shift of \(D^{j}\) respect to \(D^{0}\). In [16], a method to obtain a primitive polynomial \(f(x)\in GF[q^{n},x]\) in terms of a given primitive polynomial \(p(x)\in GF[q,x]\) is proposed by means of the computation of the shifts \(\theta_{j}\) that satisfy the relationship between the \(m\)-sequences in the extended and base fields.  
Figure 1: Linear feedback shift register.
## 6 Traditional and DL Approaches for Object Detection in AVs
### Traditional Approaches for Object Detection in AVs
#### 6.1.1 Traditional Approaches for Vehicle Detection
However, the system's reduced accuracy in rainy and dark conditions suggests a limitation of traditional approaches when dealing with significant environmental variations. The authors in [109] developed a system for concurrent vehicle and weather detection using optical flow and color histogram approaches. While this system demonstrates the utility of traditional methods in distinguishing between raindrops and normal conditions, it also indicates that these methods may struggle with the nuanced recognition tasks required in adverse weather. [PERSON] et al. [110] proposed a method for vehicle detection and tracking at night using HOG features and SVM for object recognition, effectively leveraging traditional machine learning techniques to enhance video surveillance in low-light conditions. [PERSON] et al. [111] achieved a high detection rate of 95.82% with minimal false positives in their multiclass system for nighttime conditions, employing tensor decomposition and object proposal methods. Furthermore, the research in [112] presents a system that integrates rule-based algorithms with a shallow neural network for nighttime vehicle detection, demonstrating significant improvements over traditional methods in terms of detection speed and proactive capabilities. These studies highlight the strengths of both traditional and hybrid approaches in the realm of nighttime vehicle detection, providing a foundation for future advancements in this field. A vehicle tracking and counting system with camera shaking was presented in [113] for difficult sandy weather conditions. The method used particle filter tracking for vehicle detection, integrated headlight analysis, and background subtraction. While the method worked well in difficult situations, such as evenings and sandy weather, with a vibrating camera, it had trouble identifying white automobiles in snowy settings. Similarly, the background subtraction method encountered difficulties when there was movement in the backdrop or vibrations from the camera. A vehicle detection and tracking system for complex traffic was described in [114]. It used a background reduction technique with \"low rank + sparse\" disassembly and performed well in both qualitative and quantitative evaluations. Table 3 tabulates these works.
# Long-Term CBCT Evaluation of Mandibular Third Molar Changes after Distalization in Adolescents
###### Abstract
This approach caused the distal tipping of molars, incisor uprighting, and anti-clockwise rotation of the occlusal plane, correcting lower-lip protrusion [9; 10], but causing the extrusion and mesialization of the maxillary posterior teeth. To avoid this drawback, [PERSON] et al. [11] successfully used the multiloop edgewise archwire technique and Class III elastics with mini-implants for a compromised treatment of Class III malocclusion. During distalization, third molars, which are more likely to become impacted in general, should be the primary concern of clinicians. The eruption space of the third molar is affected by bone remodeling and orthodontic treatment. With the distalization of the maxillary third molars, the use of germectomy prior to applying a pendulum in growing patients has been proposed [12], because non-extraction treatment is significantly associated with increased third-molar impaction rates [13; 14]. Figure 1: Schematic drawing of Class III elastics and a maxillary MCPP for total arch distalization. When [PERSON] et al. [15] evaluated the three-dimensional position of the maxillary molars during retention using cone-beam computed tomography (CBCT), they concluded that second molars can fully erupt and that the third molars in both the groups they studied presented a favorable position after distalization. Recently, [PERSON] et al. [16] evaluated the long-term position of erupted third molars after maxillary total arch distalization using MCPPs in adolescents. They reported that maxillary third molars erupt spontaneously over time in spite of their position after distalization. This is encouraging because prior to mandibular molar distalization, several studies recommended the extraction of the third molar [4; 6; 17; 18]. To the best of our knowledge, no long-term evaluations of the positional changes of mandibular third molars have been reported. In addition, only a few studies have evaluated Class III effects in the mandible using maxillary TSADs as indirect anchorages. Therefore, this study aimed to evaluate mandibular molar changes after distalization using Class III elastic and maxillary MCPPs as indirect anchorages and to use CBCT to specifically analyze the third-molar position after long-term retention in adolescents.
## 2 Materials and Methods
### Plasma Characterizations  
The electrical characteristics of the plasma jet setup were measured using an SDS 8012 oscilloscope (Owon, Zhangzhou, China) in conjunction with a P6015 voltage probe (Tektronix, Beaverton, OR, USA) and a P6021A current probe (Tektronix, Beaverton, OR, USA) to measure the applied voltage and discharge current, respectively. An Aurora 4000 spectrometer (CNI Laser, Jilin, China) was used to record the optical emission spectroscopy (OES) spectrum of the plasma effluent. The spectrometer was connected to an optical fiber positioned perpendicular to the plasma effluent.