esa-sceva/satcom-chunk-collection huggingface dataset:
# SmartX Multi-View Visibility Framework
## 1 Introduction
First, we enable lightweight, packet-precise, visibility collections from distributed sites. Then, visibility data aggregation, tagging, and integration uses an analytics engine for smooth processing of playground visibility data. Finally, network packet flows are classified using key attributes from multiple layers of visibilities and results are disseminated in the form of interactive visualizations to the playground users. We also identify various tools and provide the software implementation for the proposed solution. In addition, we present the real testbed-scale verifications to highlight the potential of the proposed flow-centric visibility for delivering several types of visibility. In summary, the following key contributions are made in this work. * We list flow-centric visibility requirements of unified visibility framework (SmartX MVF) for SDN-enabled multi-site clouds. These requirements are clearly explained in order to effectively provide a clear mapping with flow-centric visibility design choices. * By leveraging SmartX MVF, we propose a unique integrated design for flow-centric visibility. We provide the prototype implementation of flow-centric visibility for the specific case of OF@TEIN Playground by focusing on the physical-virtual topology of the playground. * The proposed flow-centric visibility is verified as capable of enabling visibility data collection from distributed sites and enables interactive visualization of classified visibility data to support the sustained operation of playground. Also, the results highlight the proposed design potential to process and describe complicated flow information in a simplified presentable manner. The rest of this paper is organized as follows. In Section 2, we provide an overview of OF@TEIN Playground, list flow-centric visibility requirements, and briefly summarize related work. In Section 3, we provide an integrated design and prototype implementation of flow-centric visibility, which is followed by verification results in Section 4. Finally, in Section 5, we conclude the paper.
## 3 Analysis and Results
### Measurement Time  
In addition to the accuracy improvement, another great advantage of the automatic procedure regarding manual measurements is the significant time savings and correspondent economic savings to obtain ultrasonic and electric tomographies. The calculations done in this section assume the manufacturers data specifications [33; 34; 35] for axis movements. These reference values are shown in Table 7.  
An important parameter that greatly influences the automatic measurement time is the selection of the axis increment to take subsequent measurements. Figures 22 and 23 show the relation between axis increments and duration of a tomography, assuming a 250 \(\times\) 250 \(\times\) 600 mm stone block and the axis actuators moving at correspondent constant velocity (see Table 7).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Procedure** & **Grid** & **Total Vein Area [mm\({}^{2}\)]** & **Total Accurately Detect Area [mm\({}^{2}\)]** & **Percentage [\%]** \\ \hline Manual skill & 14 \(\times\) 10 & 15,000.0 & 40.0 \\ Manual best & 14 \(\times\) 15 & 37,538.3 & 22,142.9 & 59.0 \\ Automatic & 20 \(\times\) 41 & 31,646.3 & 84.3 \\ \hline \end{tabular}
\end{table}
Table 6: Electric tomography—accuracy results.  
\begin{table}
\begin{tabular}{c c} \hline
**Axis** & **Automatic System** \\ \hline X & 100 mm/s \\ Z & 70 mm/s \\ Y & 40 mm/s \\ \hline \end{tabular}
\end{table}
Table 7: Actuators speeds.  
Figure 21: Grid 20 \(\times\) 41 (automatic).  
In both graphs, it is seen that the time exponentially increases as the increment size becomes smaller.  
Finally, Figures 24 and 25 show a comparative performance analysis between the manual and the automatic measurement procedures. Both figures consider measured manual times and simulated automatic times (according to manufacturer data specifications--see Table 7). Both measurement procedures (manual and automatic) were performed on the reference stone block (250 \(\times\) 250 \(\times\) 600 mm).  
Figures 24 and 25 clearly show the significant superior performance of the automatic measurement when compared with the manual procedure. The finer the increments, the greater the performance difference, reaching seven hours difference for automatic standard 10 mm axis increments.  
Figure 23: Influence of \(Z\)-axis increments with constant 10 mm X increments (with exponential fitting).  
Figure 22: Influence of \(X\)-axis increments with constant 10 mm Z increments (with exponential fitting).
# Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
## 5 The DOTO Algorithm
### Offload Policy Update  
In order to reduce the correlation between data and improve the learning efficiency, we introduce the experience replay technology, which can effectively improve the generalization ability of the algorithm and accelerate the convergence speed of the algorithm. Firstly, we create an empty memory with limited capacity, and add the wireless channel gain \(c_{t}\) and optimal offloading decision \(u_{t}^{*}\) to the experience replay pool in the \(t-\)th time block. When the memory capacity of the experience replay pool is full, the old data samples will be updated with the newly generated data samples. Therefore, the DNN will only learn relatively new and better offloading decision samples.  
At the next time block, we randomly select a batch of training data samples \(\left\{\left(h_{\rho},u_{\rho}^{*}\right)\ |\rho\in\text{I}_{t}\right\}\) from the experience replay pool to train the DNN, where \(I_{t}\) denotes the total number of time blocks in the experience playback pool. In the DNN, we use the Sigmoid activation function, so it is suitable to use the cross-entropy loss function at this time. The main characteristic of the cross-entropy loss function is that when the error is large, the weight update is fast, and when the error is small, the weight update is relatively slow. The formula is as follows:  
\[Loss(\omega_{t})=-\frac{1}{|I_{t}|}\sum_{\rho\in I_{t}}\left\{(u_{\rho}^{*})^{ \text{T}}\log\Gamma_{\omega_{t}}(c_{t})+(1-u_{\rho}^{*})^{\text{T}}\log[1- \Gamma_{\omega_{t}}(c_{t})]\right\} \tag{19}\]  
where the parameter \(\omega_{t}\) of the DNN reduces the cross-entropy loss by using the Nadam optimization algorithm [41; 42]. After bias correction, the Nadam algorithm will set different adaptive learning rates for different parameters, and the learning rate will have a certain range, so that the parameters are relatively stable. Based on the advantages of the RMSprop algorithm for dealing with non-stationary targets, Nadam also has the characteristics of the Adagrad algorithm for dealing with sparse gradients. It is equivalent to Adam with Nesterov momentum and has great advantages in solving problems with large-scale data or parameters.  
In general, the DNN continuously learns from optimal state actions \((c_{t},u_{t}^{*})\), producing increasingly superior offloading decision outputs over time. It continuously improves the offloading strategy under the mechanism of reinforcement learning until it converges, thus forming an online computing offloading strategy suitable for the MEC system. The DOTO algorithm is shown in Algorithm 1.
## 2 Thermal Analysis and Design Improvement of the WCLL COB Segment TC Region
### The TC Region Reference Design Configuration
#### 2.1.2 Thermal Analysis and Results  
The performed steady-state thermal analysis of the reference TC region configuration under the nominal loading conditions allows predicting its thermal behavior, highlighting the criticalities for the design improvement (Figure 3).  
In particular, as it can be observed in Figure 3, wide regions of the SPs experience a temperature higher than the suggested limit of 550 \({}^{\circ}\)C. Moreover, also within the TC a non-negligible region achieves a temperature close to the limit, whereas good thermal behavior is predicted for the SWs-FW region. In particular, the maximum temperature values predicted for FW-SWs, TC and SPs are equal to 486.1, 531.5 and 609.8 \({}^{\circ}\)C respectively. As for SPs, such a high temperature is calculated for the SPsv, whereas the maximum temperature achieved within SPsh is equal to 591.8 \({}^{\circ}\)C.
## 1 Introduction
To enable highest degree of freedom in controlling the shape of the driving laser pulses, the fiber design, density profile and nonlinear medium have to be matched appropriately as shown in Figure 1. However, in this paper we limit ourselves to the investigation of the shaping capabilities using the molecular gas sulfur hexafluoride (SF\({}_{6}\)) as the nonlinear medium. This molecule is a prominent example for ultrashort pulse generation via molecular phase modulation [13], dual-wavelength pumping [14, 15], filamentation [16], generation of high-energy Raman-shifted Bessel beams [17] and spectral broadening and compression in hollow-core fibers [18]. In this paper we present the generation and shaping of HHG radiation using pulses which were spectrally broadened pulse in a gas-filled HCF and subsequently shaped with a phase only modulator. We will show that using SF\({}_{6}\) as nonlinear medium is advantageous compared to argon because it results in higher XUV yield and improved spectral selectivity. The paper is structured as follows: In Section 2 we describe the experimental setup, discuss the spectral broadening in an HCF for argon and SF\({}_{6}\) and the general scheme of pulse-shaping for HHG. The results of the pulse-shaping experiments are presented in Section 3. Conclusions and possible applications follow in Section 4.
## 2 Materials and Methods
### Electromyography  
This study used an EMG device (TeleMyo 2400, Noraxon, Scottsdale, AZ, USA) to record three muscle group activations (i.e., TES, LES and HMS) on each participant's dominant side. The procedure of EMG data collection mainly referred to the study of [PERSON] et al. [3]. The pairs of Ag/AgCl surface electrodes (lead-off area, \(10\times 10\) mm2; center-to-center electrode distance, 25 mm) were parallel-attached on the skin of muscles with adhesive tape to avoid artifacts after standard skin preparation. Before EMG recording, participants performed standardized muscle-specific maximal voluntary contractions (MVCs) to normalize the signals measured for all testing trials. MVC measurement was conducted as suggested by [PERSON] et al. [30] and the participants were encouraged to exert the maximum contraction during each MVC measurement, and to hold for 5 s each, with a 3 min rest interval between the measurements. For each muscle tested, participants were requested to perform the MVC three times and the highest value among the three recordings was used as the MVC value for normalization purposes [31]. The EMG signals collected from both MVC tests and experimental trials were filtered (band pass 20-600 Hz), sampled (1200 Hz) and full wave rectified to yield integrated EMG (IEMG)  
Figure 2: Schematic of marker locations and body angles utilized in this study.  
data for a period of 5 s. All muscle IEMG values were presented as percentages of the IEMG MVC data (i.e., %MVC). In addition, to identify FRP occurrence, this study defined the FRP occurrence as when the EMG values of the muscles suddenly reduced between two adjacent trunk angles, as suggested by the previous studies [18; 19; 20].
# Rainfall Erosivity Main Features and Their Associated Synoptic Conditions in North-Eastern Romania
## 2 Materials and Methods
### Methods
#### 2.3.2 Self-Organizing Maps (SOM)  
As a type of unsupervised neural network, Self-Organizing Maps (SOMs) are primarily used for visualization and exploratory analysis of large datasets [44]. They employ an unsupervised learning method, and the training of the network is carried out using a competitive learning algorithm [45]. SOM preserves the topological structure of the input data, meaning similar data points are mapped close together. This is particularly valuable in geosciences, where atmospheric systems exhibit continuous, rather than discrete patterns. The method's suitability for synoptic climatology lies in its capacity to handle nonlinear atmospheric data while visualizing transitions between circulation patterns [46].  
In this study, the Self-Organizing Maps (SOM) algorithm was applied to classify atmospheric circulation patterns using a one-dimensional topological structure, implemented via the COST-733 software [47; 48]. Unlike the typical two-dimensional SOM grid, this configuration arranged neurons linearly, with each neuron connected only to its left and right neighbors. The classification was performed using three neurons, and the learning process began with an initial learning rate of 0.1, which decreased linearly to 0.01 over 10,000 iterations, ensuring a gradual refinement of the neural network's weights. This approach leveraged SOM's ability to preserve topological relationships in high-dimensional atmospheric data, clustering similar circulation patterns into neighboring nodes while maintaining the continuous nature of atmospheric states [49]. To assess the robustness of the classification, we performed internal validation by analyzing cluster coherence and the quantization error, ensuring that the resulting patterns were well-separated and meteorologically meaningful.
# Explainable Internet Traffic Classification
## 4 Experimental Results
### Paes-Rcs  
As for the previous cases, and also for the proposed method, we have run a 5-fold cross-validation, and for each fold we have run three trials (each with a different seed of the random number generator). The algorithm has been run with the parameters indicated in Table 12, and for each fold and each trial we have generated an approximation of the optimal Pareto front. In the following, we report the average results of three representative solutions ordered according to decreasing accuracy. Specifically, as discussed in [44], we sorted the FRBCs in each Pareto front approximation in ascending order of accuracy. Then, we extracted the First (the most accurate and the less explainable), the Median, and the Last solution (the less accurate and the most explainable).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Class** & **TPR** & **FPR** \\ \hline \multirow{6}{*}{Statistical} & SSHD & 0.99 & 0.001 \\  & XRDP & 0.99 & 0.001 \\  & DNSMasq & 0.99 & 0.001 \\  & Chrome & 0.97 & 0.001 \\  & Firefox & 0.9 & 0.007 \\  & Amule & 0.98 & 0.001 \\ \hline \multirow{6}{*}{Composite (\(H=3\))} & SSHD & 0.95 & 0.002 \\  & XRDP & 0.74 & 0.002 \\  & DNSMasq & 0.99 & 0.001 \\  & Chrome & 0.86 & 0.007 \\  & Firefox & 0.349 & 0.002 \\  & Amule & 0.94 & 0.003 \\ \hline \multirow{6}{*}{Composite (\(H=5\))} & SSHD & 0.99 & 0.002 \\  & XRDP & 0.99 & 0.02 \\  & DNSMasq & 0.99 & 0.01 \\  & Chrome & 0.94 & 0.02 \\  & Firefox & 0.79 & 0.01 \\  & Amule & 0.98 & 0.001 \\ \hline \multirow{6}{*}{Composite (\(H=10\))} & SSHD & 0.99 & 0.001 \\  & XRDP & 0.99 & 0.001 \\ \cline{1-1}  & DNSMasq & 0.98 & 0.001 \\ \cline{1-1}  & Chrome & 0.94 & 0.02 \\ \cline{1-1}  & Firefox & 0.72 & 0.01 \\ \cline{1-1}  & Amule & 0.97 & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 10: C45: Performance over UPC data set.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **SSHD** & **XRDP** & **DNSMasq** & **Chrome** & **Firefox** & **Amule** \\ \hline
**SSHD** & **7756** & 5 & 1 & 2 & 1 & 4 \\
**XRDP** & 6 & **3017** & 0 & 4 & 2 & 2 \\
**DNSMasq** & 2 & 1 & **1726** & 0 & 0 & 2 \\
**Chrome** & 1 & 0 & 5 & **4404** & 129 & 1 \\
**Firefox** & 0 & 0 & 2 & 183 & **1735** & 4 \\
**Amule** & 10 & 10 & 6 & 14 & 3 & **2319** \\ \hline \hline \end{tabular}
\end{table}
Table 11: C45: confusion matrix (UPC, statistical).  
class on the UniBS and UPC data sets, respectively. Note that, apart with composite feature and \(H=3\), there is not any class that is mostly unrecognized (as for the SVM classifier). Moreover, it is also interesting to see that, differently from the C4.5 classifier, the proposed method is able to correctly classify Chrome, while it presents some issues in the classification of Firefox.  
For a deeper analysis, Tables 17 and 18 report the confusion matrix for the UniBS and UPC case with statistical features, respectively. As expected, these results highlight that, in the UPC case, the most critical case is represented by Firefox, which is often classified as Chrome.
# Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN
## 4 Experimental Evaluation
### Experimental Setup  
To assess the performance of the proposed model, a distributed network of virtual IoT devices is configured using open-source lcarus interlinked with the web-based Beyywise IoT simulator [44; 45]. The WUSTL-EHMS-2020 dataset is used for simulation [46]. This dataset comprises 44 attributes, with 35 of them being network flow metrics, eight representing patients' biometric features, and one dedicated to the label. The configuration of essential parameters is presented in Table 1. Data freshness can be defined as the time elapsed between the creation of the IoT object and the retrieval of this object from the cache store [47]. The data age is the age of the data item, which is the time between the arrival at the router and the generation at the source node, while the period field is used to keep count of the age [48]. Three network topologies, namely Tree, Abilene, and GEANT, have been chosen for the comparative evaluation. To ensure a fair comparison, all algorithms were executed within the same environment. To demonstrate the validity and accuracy of the results, each experiment was repeated 100 times, and the average value of each parameter was employed for a comparative analysis. The parameter used to measure network performance utilization is the cache hit ratio \(CH_{ratio}\), calculated with Equation (2) [49].  
\[CH_{ratio}=\frac{\sum_{k=1}^{k}Hit_{k}}{\sum_{k=1}^{k}{(Hit+Miss)^{\prime}}} \tag{2}\]  
The content retrieval latency \(Content_{Rlatency}\) parameter is used to monitor the traffic load over the network, i.e., the time taken by the interest packet to obtain content from the source node and send it back to the user. Let \(|R|\) be the total number of requests sent by the user; then, the cache retrieval latency can be computed, as shown in Equation (3) [49].  
\[Content_{Rlatency}=\frac{\sum_{i=1}^{|R|}latency\ R_{i}}{|R|} \tag{3}\]  
The stretch shows the measurement of the distance from the content used to the content publisher. The stretch can be computed as shown in Equation (4) [49].  
\[Stretch=\frac{\sum_{i=1}^{R}{Hop-Traveled}}{\sum_{i=1}^{R}{Total-Hop^{\prime}}} \tag{4}\]  
The nominator in Equation (4) shows the number of hops between the content user and the content publisher in terms of the cache hit occurrence. The denominator shows the total number of hops, i.e., hops between the content user and the source.  
Memory consumption shows the measurement of the transmitted content that can be cached while downloading the data path for a time interval. Consumers can download the content from multiple routers. In ICN, memory consumption means the capacity that shows the volume utilized by interest and data contents, as shown in Equation (5).  
\[memory_{consumption}=\frac{U_{m}}{T_{m}}\times\ 100 \tag{5}\]  
where \(U_{m}\) shows the memory utilized by the cached content and \(T_{m}\) shows the cache storage (total memory) of the router and the data delivery path.
# Induced Effect of Gold Nanoparticles (AuNPs) and Halide Ions on Pyridoxine Molecule Stability
## 3 Results and Discussion
### Mechanism of Electrochemical Degradation of Pyridoxine  
The detailed electrooxidation mechanism of pyridoxine on the gold electrode may involve complex multistep/multielectron reactions (Scheme 1).  
Taking into account the electrocatalytic properties of the gold electrode, the pyridoxine molecules are adsorbed by chemisorption phenomena on the metal surface. In the pyridoxine molecule, position 4 has a high reactivity and, participating in successive electrochemical and chemical processes, oxidizes to pyridoxic acid [58; 59]. This mechanism corresponds to a process of electrooxidation by a direct path. In other words, the transfer of electrons takes place directly on the surface of the working electrode.  
This mechanism applies to experimental observations obtained in the presence of an inert support electrolyte, such as a fluoride ion. At the same time, the mechanism ensures the electrochemical degradation of pyridoxine to obtain inorganic final products such as nitrogen dioxide, carbon dioxide and water.  
The pyridoxine molecule contains both heteroatoms with unbound electrons and \(\pi\)-electrons in the pyridine ring. [PERSON] et al. showed that the interactions between the \(\pi\)-electrons of pyridoxine and the gold metal surface are insignificant [59]. The results of FT-IR vibrational spectroscopy showed that the pyridoxine molecule is fixed on the surface of the gold electrode by means of the nitrogen atom. These studies also highlight the intermediate formation in the electrooxidation of pyridoxic acid [59]. Subsequent decomposition of pyridoxic acid indicates intermediates containing C=O and C-O groups. Electrochemical oxidation of pyridoxine through the pyridoxal intermediate takes place on different electrodes, such as pyrolytic graphite electrodes, single-walled carbon nanotubes and multi-walled carbon nanotubes, glassy carbon electrodes, carbon ceramic electrodes and reduced graphene oxide [35; 58]. Electrochemical oxidation occurs through unstable intermediates with a very short lifespan. Even the oxidation of pyridoxine to pyridoxal or pyridoxic acid results in indeterminacy that is not specified by in situ analysis [59].  
Thus, the most stable intermediate species are pyridoxal and pyridoxic acid, which electrochemically decompose in final compounds, step-by-step passing through radicals, ionic or more complex species with short lifespan.  
In the presence of active anions, such as chloride and bromide ions, the electrogeneration of oxidizing active species also involves a homogeneous process of electrochemical oxidation (indirect pathway).  
In the presence of gold nanoparticles, the oxidation mechanism of pyridoxine may also involve the formation of gold ions, as well as the possibility of the formation of mixed gold compounds with pyridoxine or intermediate degradation compounds.
# Borehole Trajectory Optimization Design Based on the SAC Algorithm with a Self-Attention Mechanism
###### Abstract  
Borehole trajectory planning under complex geological conditions poses significant challenges for intelligent drilling systems. To tackle this issue, a novel optimization framework is developed, leveraging the Soft Actor-Critic (SAC) algorithm enhanced by a self-attention mechanism. A three-dimensional heterogeneous geological model is constructed via generative adversarial networks (GANs), incorporating key formation features such as lithology, pressure, and fault zones. A tailored multi-objective reward function is introduced, balancing directional convergence, trajectory smoothness, obstacle avoidance, and formation adaptability. The self-attention mechanism is embedded into both the actor and critic networks to strengthen the agent's capacity for spatial perception and decision stability. The proposed approach enables the agent to adaptively generate control sequences for efficient trajectory planning in highly variable formations. Experimental results demonstrate that the model exhibits superior convergence stability, improved curvature control, and enhanced obstacle avoidance, highlighting its potential for intelligent trajectory planning in challenging drilling environments.  
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