esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Evaluation of the Prediction of Gasoline Orders Using Data Augmentation  
A total of 180 monthly data samples from January 2008 to December 2022 were randomly split into 80% training sets and 20% test sets. This ratio is commonly used in many previous studies [31; 32; 33]. In addition, Ref. [34] demonstrated that optimal results can be obtained when using 80% training sets and 20% test sets. To validate the impact of data augmentation, we quantitatively evaluated gasoline order prediction models using three methods: without augmentation, with VAE, and with K-means clustering and VAE together. The models were evaluated by using R-Squared, Root Mean Squared Errors (RMSE), and accuracy. The details of the equations of R-Squared, RMSE, and accuracy are described in Equation (A1) in Appendix A. R-Squared indicates how well the model explains the variability of given datasets. RMSE evaluates the performance of the model by measuring the difference between the actual and predicted values. Accuracy indicates how well the predictions in the model match the actual values. R-Squared and RMSE have values between 0 and 1. Accuracy has a percentage between 0 and 100. A higher R-Squared and accuracy, and a lower RMSE indicate a better performing model. When R-Squared is close to 1, accuracy is close to 100%, and RMSE is close to 0, the model is more successful. Table 3 shows the results. The performance was lower in all evaluation metrics when the datasets were not augmented. When the datasets were augmented using VAE, the accuracy of the test sets improved by 0.25%p compared to that of the non-augmented datasets. However, the significant difference in RMSE between the training and test sets indicates overfitting. Using the K-means clustering algorithm to group clusters and subsequently augmenting the datasets within each cluster resulted in a better representation of the data characteristics, leading to improved generalization performance and accuracy of the model. When the number of datasets per cluster was augmented to 85, the highest performance was shown in all evaluations of the test sets. Figure 4 shows the change in accuracy based on the number of augmented datasets per cluster.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Augmented per Cluster**} & \multirow{2}{*}{**Number of Training Sets**} & \multicolumn{2}{c}{**R-Squared**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{4-9}  & & & **Training** & **Test** & **Training** & **Test** & **Training** & **Test** \\  & & & **Sets** & **Sets** & **Sets** & **Sets** & **Sets** & **Sets** \\ \hline Without & - & 144 & 0.7441 & 0.7162 & 0.4898 & 0.582
# LIransformer: A Transformer-Based Framework for Task Offloading in Vehicular Edge Computing
## 2 Related Work  
### Vehicular Edge Computing  
Task offloading in VEC is the process of transmitting the computing task and related parameters from the service requestor to the service providers through Vehicle-To-Vehicle (V2V) and Vehicle-To-Infrastructure (V2I) communications [1]. [PERSON] et al. [5] summarized the communication issues in task offloading and proposed a novel task offloading scheme that combines edge and cloud resources. An example of vehicular edge computing is shown in Figure 1 below.  
Figure 1: An example of vehicular edge computing.  
The resources of edge servers can be fully utilized to provide better QoS to users via optimizing the task offloading scheme. [PERSON] et al. [4] presented an efficient predictive combination-mode relegation scheme wherein the tasks are adaptively offloaded to the edge servers through direct uploading or predictive relay transmissions. [PERSON] et al. [3] converted global task offloading optimization problem into multiple local optimization problems with a heuristic mobility-aware offloading algorithm (HMAOA) to approximate the optimal offloading scheme. [PERSON] et al. [6] proposed a low-complexity semiparametric predictive model that takes into account the periodic characteristics and spatial/temporal correlations of dynamic road events. Although these methods have shown some improvements, they still fail to achieve an optimal balance between efficiency and accuracy in VEC. Therefore, how to predict vehicle trajectories more accurately while ensuring efficiency is a pressing issue in task offloading at this stage.
# Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards
## 3 Results and Discussion
### Number of END-Nodes  
The developed network has a maximum number of End-nodes that the Coordinator can serve. In some studies, a large number of nodes were reported to participate in wireless networks [8; 10; 23; 25]. However, the vast majority of wireless networks use gateway/data logger to collect process and send the data from all nodes, which are usually powered by the grid and show increased processing capabilities. As already mentioned, in our novel system, the Coordinator is the same \"dummy\" device programmed to serve as the main hub on the network to collect the data packets and send them to the cloud after the required data modification. Therefore, the Coordinator's capabilities were limited. The energy tests produced reliable outcomes for the evaluation of the system's limitations. The network showed satisfactory performance, without any packet losses, connectivity or other issues hosting up to seven End-nodes programmed at a 30-min cycle, or 14 nodes at an hourly data acquiring cycle. Since the maximum field size that the proposed network can serve is 36 ha, setting one node every 2.6 ha is considered adequate for precision agriculture monitoring applications.
## 4 Progress in the Understanding of Illuminated B-O LID Mitigation Processes
### Manipulation of Hydrogen Charge States  
In silicon, interstitial hydrogen is a negative-U impurity that can assume three different charge states, taking on either a positive (H\({}^{+}\)), neutral (H\({}^{0}\)) or negative (H\({}^{-}\)) charge state [109]. The charge state has important implications for the diffusivity and reactivity of the hydrogen [110; 111]. For example, the diffusion of both H\({}^{+}\) and H\({}^{-}\) is impeded by the attraction to ionised dopant atoms. In contrast, H\({}^{0}\) can move freely without the influence of electrostatic effects and has a diffusivity orders of magnitude higher than that of H\({}^{+}\) and H\({}^{-}\)[110; 112; 113; 114].  
The fractional charge state distributions of hydrogen in silicon are determined by the availability of free electrons in the lattice. In thermal equilibrium, this is heavily dependent on the background doping of the silicon. For p-type silicon, due to a deficiency of electrons, almost all hydrogen is H\({}^{+}\). For n-type silicon, due to an excess of electrons, almost all hydrogen is H\({}^{-}\). For both p-type and n-type silicon, H\({}^{0}\) is always a minority charge species, present in only minute concentrations.  
However, the fractional charge state distributions determined by the dominant conductivity of the silicon material can be overcome through the use of minority carrier injection. Minority carrier injection can be achieved by using illumination and can lead to substantial increases in the concentration of highly mobile and reactive H\({}^{0}\)[111; 115]. Some studies have discussed the influence of modulating the charge state of B-O defects and hydrogen for the passivation of B-O defects [116; 117; 118; 87; 102]. One study, in particular, demonstrated a correlation between the theoretically predicted concentrations of H\({}^{0}\) and the regeneration reaction rate using a quasi-Fermi based model [119], highlighting the critical role of manipulating hydrogen charge states to increase the concentration of the minority hydrogen charge species to enable B-O defect passivation. Nevertheless, it remains unclear what species of hydrogen and/or B-O defect is required for the passivation reaction to take place. This uncertainty is partly due to the proximity of the reported donor and acceptor levels of the B-O defect [120] with the energy level at which the majority hydrogen charge state switches from H\({}^{+}\) to H\({}^{-}\) (see Figure 2) [109].  
Figure 2: Energy level diagram for B-O defects and interstitial hydrogen showing the donor (E\({}_{\rm d}\)) and acceptor (E\({}_{\rm a}\)) levels relative to the conduction band (E\({}_{\rm c}\)), valence band or mid-gap (E\({}_{\rm m}\)).
# Broadband Millimeter-Wave 5G Power Amplifier Design in 22 nm CMOS FD-SOI and 40 nm GaN HEMT
## 4 Discussion
Although GaN #2 still requires measurement results for validation, Table 2 shows that both of our GaN PAs achieve some of the best small-signal 3-dB BW as well as good PAE. Additionally, the GaN #1 design achieved excellent measured BW and good linearity with aggressive 5G NR signal modulation and instantaneous \(\sim\)1 GHz signal BW, which have not been reported in prior medium-power mm-Wave GaN PAs [13]. Depending on the application, this GaN technology allows for the \(V_{DD}\) to be varied from 12 V to 4 V to achieve the optimal trade-off of PAE and output power. Although operating this PA at \(V_{DD}\) = 4 V reduces POUT with an increase in cost, being able to vary the supply voltage like this is not as easily feasible for silicon-based stacked broadband mm-Wave PAs. Thus, we conclude our GaN #1 PA exhibits excellent 3-dB BW, peak and linear PAE, and \(P_{OUTLinear}\) with stringent 256-QAM  
modulation and a rather large instantaneous signal BW of 9 \(\times\) 100 MHz across the key 5G FR2 band; it is among the best broadband linear mm-Wave medium-power PAs in the literature. Our 22 nm broadband CMOS SOI PA vs. other state-of-the-art broadband medium-power mm-Wave PAs is also shown in Table 2. Our CMOS PA achieves the best small-signal 3-dB BW in the literature, and it has obtained good broadband linearity/\(PA_{LINEAR}\). It  
\begin{table}
# Anomaly Detection in Automotive Industry Using Clustering Methods--A Case Study
## 2 Clustering Algorithms
### Hierarchical Clustering  
Hierarchical clustering algorithms generate groups at successive levels, in which the current grouping process is created based on the previous hierarchical level. Unlike the partial approach, hierarchical clustering does not need to specify the number of clusters in advance [13].  
However, the computational cost required to perform these methods is high, which is unfeasible for a large data set. Hierarchical approaches create a dendrogram structure, consisting of a tree structure representing the hierarchical sequence of nested partitions of the data set [14]. The procedure creates successive cluster levels, in which the current group is based on the solution obtained in the previous level [40].  
There are two types of hierarchical algorithms: divisive and agglomerative [41; 42]. In the divisive case, the process begins with all data elements in a single cluster divided into smaller clusters based on proximity until the criteria related to the total number of clusters is obtained [41]. In the agglomerative approach, every element is initially an individual cluster.  
Figure 1 shows simulated results of divisive and agglomerative methods.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 4 Localization Techniques
### Device-Based Localization
#### 4.1.1 Smartphone-Based Localization  
Smartphone-based localization is a promising technology for solving localization problems in IoT applications. This approach is mainly classified into three types: Wi-Fi, Camera and Bluetooth [44].
# Temperature Changes in Composite Materials during Photopolymerization
## 2 Materials and Methods
### Flexural Strength Test  
Seventy specimens were prepared for flexural strength test according to the ISO standard 4049:201012, using the Boston (Arkona LFS, Nasutow, Poland) composite resin (shade A2). Rectangular specimens (25 mm \(\times\) 2 mm \(\times\) 2 mm) were produced using a steel mold and placed on a microscope slide to achieve a flat surface. Subsequently, one portion of composite resin was condensed with a dental plugger and flattened by being pressed using another microscope slide. The composite material was then polymerized across a layer of polyethylene film in order to eliminate oxygen inhibition at the surface. Samples were polymerized using a high-powered LED LCU (Mini LED III Supercharged, Acteen Group, Merignac, France) using 7 different modes; the same as for the temperature measurement tests. Ten specimens were used for each mode and duration. Each rectangular sample was polymerized at 4 points. After polymerization, the specimens were released from the mold. Next, the specimens were examined for the presence of air bubbles and defective specimens were excluded from the study. Specimens were then immersed in distilled water at a temperature of 37 \({}^{\circ}\)C for 24 h. Next, the specimens were tested for flexural strength using the Cometech QC-508M2 testing machine (Cometech Testing Machines Co., Taichung City, Taiwan) with the opening width of 20 mm, initial gripping force of 1 N and the crosshead speed of 0.75 mm/min. The specimens were measured to an accuracy of 0.01 mm before the test. The test end was marked by the specimen being crushed.  
Flexural strength was calculated using the following equation:  
\[\text{S}=3\text{FL}/(2\text{BH}^{2})\]  
where F is the maximum load in Newtons exerted on the specimens, L is the distance (20 mm) between the supports, accurate to \(\pm\)0.01 mm, B is the width (2 mm \(\pm\) 0.01 mm) of the specimens measured immediately prior to testing and H is the height (2 mm \(\pm\) 0.01 mm) of the specimens measured immediately prior to testing.
## 2 Results and Discussion
Although no conclusive evidence on the causes of the higher productivity of P50 in the DS were provided from the A-XRD characterization of P75 and P50, its level of insight is hard to achieve with other techniques. In this connection, the variation of the composition of Au-Pd MNPs with their size had already been observed, although only qualitatively, in nano-alloyed bimetallic catalysts supported on carbon. Whereas the largest MNPs were the richest in palladium in samples prepared by sol immobilization [44], the opposite situation was found in materials prepared by impregnation [48]. The atomic Au-Pd ratio as a function of MNPs was quantitatively assessed with TEM-EDX analysis in a bimetallic impregnated catalyst supported on a mesoporous ordered carbon [49]. However, it was impossible to probe MNPs smaller than 5 nm so that no direct quantitative information on the smallest and expectedly more active nanoparticles could be gathered and this prevented a full characterization of the best reported catalyst, which has more than 90% MNPs smaller than 4 nm. Moreover, TEM-EDX analysis can be generally carried out on relatively small MNPs populations, which raises the problem of the representativeness of its results. This problem has been partially overcome by the introduction of a semi-automated HAADF-STEM, which allegedly allows the characterization of several hundreds of MNPs in a short time [50]. Although this method is an improvement in comparison with single particle EDX analysis, it was proved effective for MNPs as large as ca 8 nm and the number of probed MNPs was still below 10\({}^{3}\). We propose that, by contrast, A-XRD analysis allows milligram amounts of P75 and P50 to be probed, providing complementary and statistically representative details on the average sample crystallographic phase composition. This approach noticeably represents an original example of quantitative characterization providing at the same time, structural, morphological, and analytical data of macroscopic, hence fully representative, samples of bi-metallic systems with a relatively low metal content and including MNPs as small as 1 nm.
## 3 Methodology and Algorithms
### Proposed Augmentation Framework via Kinematic Gesture Simulation  
#### 3.2.1 Overview of the Synthetic-Real Gesture Loop  
To address the limitations of existing sign language datasets in terms of anatomical fidelity, inter-signer variability, and scalability, we propose IK-AUG a structured kinematic augmentation framework that bridges real-world motion acquisition and virtual gesture synthesis. As shown in Figure 1, IK-AUG is a multi-stage pipeline that combines inverse kinematics-based hand reconstruction, biomechanically constrained animation, and perturbation-driven augmentation to produce diverse and semantically faithful gesture sequences for downstream sign language recognition (SLR) tasks.  
As detailed in Algorithm 1, the process begins with extracting 3D keypoints \(K\) from raw sign language video \(V\) using a vision-based estimator \(\mathcal{E}\) (e.g., MediaPipe). These keypoints are spatially normalized and mapped into a virtual coordinate system via a transformation module \(\mathcal{T}\), which includes noise filtering, frame interpolation, and re-orientation. A forward-and-backward iterative IK solver \(\mathcal{I}\) reconstructs complete skeletal hand poses \(S\) from sparse landmarks, ensuring biomechanical plausibility. The resulting animation is rendered on a digital hand model within Unity3D (version 2022.3.40f1; Unity Technologies, San Francisco, CA, USA), forming a baseline gesture trajectory.  
To simulate natural motion variability, a multi-modal perturbation operator \(\mathcal{P}_{\Theta}\) is applied, where \(\Theta=\{\epsilon_{xy},\epsilon_{z},\lambda_{IK}\}\) defines parameter ranges for spatial jitter, depth displacement, and solver sensitivity, respectively. Each configuration generates a perturbed variant \(\vec{D}_{i}\) that is exported as both video and coordinate data. Optionally, tactile and kinematic information from multi-sensor gloves \(\mathcal{G}\) can be integrated to further enrich motion fidelity and robustness. The output dataset \(\mathcal{D}_{aug}\) forms a synthetic-real manifold of augmented gesture sequences suitable for deep learning-based recognition and translation systems.  
Figure 1: System overview of the proposed IK-AUG framework. The pipeline begins with 3D keypoint extraction and inverse kinematics-based hand reconstruction (left), followed by biomechanically constrained animation and multi-modal perturbation (right), including depth offset, spatial jitter, and IK sensitivity control. The generated sequences \(\vec{D}_{1}\rightarrow\vec{D}_{N}\) are exported for downstream training. Optional multi-sensor glove input enhances signal richness and motion granularity.
# Directed Clustering of Multivariate Data Based on Linear or Quadratic Latent Variable Models
## 6 Application on Speed-Flow Data
### Directed Clustering
One sees from the table that information provided by the matrix \(W\) is much more fine-grained than that provided by the MAP rule. For instance, we see that the observation at the time stamp 03:35:00 is clearly allocated to mass point 1 (a low-density mass point), whereas the one at 05:35:00 is undecided between the mass points with the lowest and second-lowest density. Due to the presence of 444 time stamps, it is impossible to include all in the table. Therefore, we only show a portion of the full 'league table' in Table 6. Note that the 5-min time periods are not 'ranked' chronologically in the table. The four mass points represent four levels of traffic density, ranging from mass point 1 to mass point 4, corresponding to the time period with the lowest traffic density to the time period with the highest traffic density. Most of the time periods that have been clustered to low traffic density are between 23:00:00 to 05:35:00; most of the time periods between 05:50:00 and 07:15:00 and the time between 19:45:00 and 23:05:00 are assigned to mass point 2. The time periods from the morning rush hour until around 14:00:00 are assigned to the second high traffic density mass point 3, and most of the time between 14:10:00 and 18:35:00 exhibit the highest traffic density. \begin{table}
\begin{tabular}{l c c c} \hline \hline \(K\) & \(\hat{z}_{k}\) & \((q_{k},v_{k})\) & \(d_{k}\) \\ \hline
1 & \(-\)1.14 & \((15.46,51.78)\) & 0.30 \\
2 & \(-\)0.36 & \((52.95,60.62)\) & 0.87 \\
3 & 0.27 & \((79.06,55.75)\) & 1.42 \\
4 & 1.22 & \((111.62,28.02)\) & 3.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: For the dataset from the northbound freeway, the results of the directed clustering algorithm: ordered cluster labels (first column), cluster centers in latent space and data space (second and third columns, respectively), derived density values (final column). \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Time Stamp** & **Posterior Intercept** & \multicolumn{5}{c}{**Mass Points**} & \multicolumn{1}{c}{**MAP**} \\  & & \(k\) & **1** & **2** & **3** & **4** & \\ \cline{3-8}  & & \(\hat{\pi}_{k}\) & **0.18** & **0
## 2 Sensing Method
### Magnetic Field Sensing
#### 2.1.1 Applied Technologies
For magnetic implants with sealed or open cavities, the higher the viscosity of the surrounding environment, the greater the vibration damping and the quicker the vibration attenuation. These motions reflect the biophysical conditions surrounding the implant and the concentration of specific biochemical substances (Figure 1b). By modifying the surface modification to selectively absorb target biochemical substances, this miniaturized system can wirelessly measure the viscosity of cerebrospinal fluid (CSF), intracranial pressure (ICP), and the glucose concentration in cerebrospinal fluid, and present the results in real-time on a mobile terminal. This system lays the foundation for long-term continuous monitoring of various signals related to health conditions. Through surface modification, it may also be extended to wireless sensing of numerous other biochemical substances and biomolecules. Beyond the implantable devices elaborated in detail previously, there exists a diverse range of implantable magnetic field sensors that operate on different principles [17; 18; 19].
# Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks
###### Abstract  
Mobile ad-hoc networks (MANETs) have a decentralized nature that makes them suitable for a variety of applications. The main advantage of a MANET [1] is that its nodes can communicate without any infrastructure. As a result, MANETs are usually deployed in battlefields, natural disasters, etc. MANETs differ from the long-established computer networks, as they have unique characteristics. For example, in a MANET, we observe a frequent link breakage because of node mobility, a high channel-error rate, severe link-layer contentions, and different path properties such as delay, bandwidth, and packet loss rate. Due to these characteristics, the overall performance of a MANET is disturbed in terms of packet delivery ratio, end-to-end delay, network throughput, and network overhead. By applying the principles of MANETs, a vehicular ad-hoc network (VANET) [2] can be established in an ad-hoc mode by vehicles. Using a VANET, vehicles can directly communicate among them, with no supporting infrastructure. Besides, VANETs are employed for road monitoring and infotainment applications, which constitute an integral part of the intelligent transportation system paradigm.  
This Special Issue, entitled \"Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks\", aims to expose the readership to the latest novel works in terms of solutions and techniques for MANETs and VANETs. The Special Issue is composed of five referred papers, covering interesting topics such as power-aware optimization solutions for MANETs, data dissemination in VANETs, adaptive multi-hop broadcast schemes for VANETs, multi-metric routing protocols for VANETs, and incentive mechanisms to encourage the distribution of information in VANETs. The Issue sets out to demonstrate pioneering work in these fields, investigate novel solutions and methods, and discuss future trends in these fields.  
M 2021  
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