esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Soil Volatilome Profile  
An HS-SPME/GC-MS system was used to acquire the chromatograms of the soil volatilome profiles from different land uses, i.e., forest (F), grassland (G), and agricultural land (A), as displayed in Figure 2. Analyzing this overview of representative total ion  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Properties**} & \multicolumn{4}{c}{**KW Test**} & \multicolumn{4}{c}{**Post Hoc**} \\ \cline{2-7}  & \(\chi^{2}\) & \(p\) & Significance 1  & **F-G** & **G-A** & **A-F** \\ \hline Bulk density & & 10.02 & 0.007 & ** & \(p=0.035\)  & ns & \(p=0.003\)  \\ (g cm\({}^{-3}\)) & & 8.74 & 0.013 & * & \(p=0.024\)  & \(p=0.011\)  & ns \\ Texture & Clay & 1.39 & 0.5 & ns & ns & ns \\  & Sand & 3.37 & 0.185 & ns & ns & ns \\  & 3.4 & 0.183 & ns & ns & ns \\  & OC (\%) & & 5.06 & 0.08 & ns & ns & ns \\  & Ca\({}^{2+}\) & 4.75 & 0.093 & ns & ns & ns \\  & Mg\({}^{2+}\) & 0.74 & 0.689 & ns & ns & ns \\ Water-soluble ions & K\({}^{+}\) & 9.5 & 0.009 & ** & ns & \(p=0.007\) [FOOTNOTE:6]Footnote 6: \(p=0.02\) [FOOTNOTE:6]Footnote 6: \(p=0.  
chromatograms (TICs) for each land use, it was possible to note peaks of higher intensity in the region of the first section of the TIC, with a retention time (t\({}_{\text{R}}\)) of 10 min, followed by the second section, which was between t\({}_{\text{R}}\) values of 10 and 20 min. In the last section, fewer peaks were obtained (t\({}_{\text{R}}\)) between 20 and 33 min (Table S2).  
A total of 106 volatile compounds were detected in the samples from all land uses. These volatile organic compounds belong to different categories (Figure S1), such as hydrocarbons (alkane, alkene), oxygenated compounds (aldehyde, alcohol, acid, ketone, ester, ether), terpenoids (isoprene, monoterenoid, sesquiterpenoid), and N- and S-containing compounds (including benzenoid).  
The dominant compounds in the soil samples studied were oxygenated compounds, which accounted for more than 60% (Figure S1) of the total identified compounds. Higher intensities of oxygenated compounds were detected in the soil samples from forests (77%, Figure S1c), followed by those collected from grasslands (71%, Figure S1b) and those from agricultural land (65%, Figure S1a). Among the oxygenated compounds, alcohols were predominant (30-48), followed by ketones (14-16%). The hydrocarbon content varied from 11 to 16%, depending on land use, with relatively high values identified in the soil samples from agricultural land (Figure S1a). Among the terpenoid compounds, monoterenes were the representative class (Figure S1).
## 2 Analytical Framework
### Topological Parameters and Their Characteristics
#### 2.1.2 Betweenness Centrality (BC)  
Betweenness centrality (BC) depicts the importance of segments within a network by measuring to what extent a particular segment is between the other segments in the network, which reflects the intermediary location of a segment [37]. It captures the pass-by segments of the origin or destination trip distribution and is an excellent proxy for predicting traffic flow distribution in urban areas [38]. The BC of segment \(i\), \(BC_{i}\), can be calculated as follows [39]:  
\[BC_{i}=\frac{1}{(N-1)(N-2)}\sum_{\begin{subarray}{c}j,k\in N\\ j\
eq k\\ k\
eq i\end{subarray}}\frac{p_{jk_{(i)}}}{p_{jk}} \tag{2}\]  
where \(p_{jk}\) is the number of shortest paths between segments \(j\) and \(k\), and \(p_{jk_{(i)}}\) is the number of shortest paths from \(j\) to \(k\) that pass through segment \(i\).
# A Decision Support System for Melanoma Diagnosis from Dermoscopic Images
## 3 Adopted Techniques
### Support Vector Machine  
In machine learning, support vector machines (SVMs) are supervised learning models that analyze data according to the principle of structural risk minimization [42]. They are used for both classification and regression analysis. For classification purposes, SVM tries to find boundaries that separate the different classes of the target variables by maximizing the distance between points closest to the boundaries on either side. The SVM aim is the detection of a hyperplane in a high-dimensional space that distinctly classifies the data points: the procedure finds an optimal hyperplane that classifies new figures based on labeled training data. It is denoted with:  
* \(x\in\mathbb{R}^{n}\)--a vector that represents a pattern to be classified;
* \(y\)--a scalar that represents the class label;
* S = {(x_{i},y_{j});i=1,2,...,l}--a given set of \(l\) training examples.  
The aim is the implementation of a decision function \(f(x)\) able to classify an input pattern \(x\) that is not necessarily from the training set:  
\[f(x)=\sum_{i=1}^{l}\alpha_{i}y_{i}K(x_{i},x)+\alpha_{0} \tag{2}\]  
where \(\alpha_{i}\) are determined through training, and \(K\) (..) is a kernel function. The kernel function transforms data into a higher-dimensional space to make data separation possible. The SVM adoption requires the analysis of three problems: the selection of the kernel function, the detection of the optimal input features subset, and the optimum setting of kernel parameters.  
Various hyperplanes could be chosen for the data separation; the SVM algorithm researches a plane with the maximum distance between the data of the classes with the maximum margin.  
Training an SVM requires the solution of a very large quadratic programming optimization problem. A simple and widely used method for training an SVM is the Sequential Minimal Optimization (SMO) algorithm. It consists of solving the problem by optimizing the minimal subset, including two elements at each iteration. The advantage of the SMO is that it can be implemented simply and analytically.
# Fast Reroute Mechanism for Satellite Networks Based on Segment Routing and Dual Timers Switching
###### Abstract
The remainder of this article is organized as follows: Section 2 introduces the proposed SRDT-FRR scheme based on detailed fault classification, joint fault detection, and backup path management. Section 3 describes the simulation setup, including network topology, fault scenarios, and performance metrics for evaluation. Section 3 also presents the results and discusses the advantages of the proposed scheme in terms of recovery time, resource utilization, and network stability. Finally, Section 4 concludes with a summary of contributions.
## 5 Conclusions  
To study the mechanical behaviors of the supporting structure and surrounding rock of open-buried multi-arch tunnel, the strain and stress of linings and the deformation laws of the surrounding rock were studied by on-site investigation. The following conclusions are obtained:  
1. The application of the secondary lining in the left cavern (open cavern) shows an inhibitory effect on the strain development of the steel arch in the right cavern (concealed cavern). The backfill balances the bias caused by the slope and reduces the strain at the steel arch of the concealed cavern.
2. The process of vault settlement can be divided into three stages: a rapid development stage within 15 d after the construction of the up-step, the continuous development stage before the application of the invert, and the stable convergence stage after the application of the invert. When the invert is applied, the settlement rate of the vault is gradually stabilized. The mid-partition wall and deflecting retaining wall effectively decrease the settlement of the primary lining and present a weakening effect on the bias effect.
3. The peripheral displacement of the tunnel experiences a process of convergence inward and extension outward. The peripheral displacement of the left and right caverns tends to move inward after the construction of the up-step. The deflecting retaining wall and mid-partition wall show a good restraining effect on the tunnel.  
**Author Contributions:** Conceptualization, Y.-L.G. and Y.-L.L.; methodology, Y.-L.G., Y.-L.L., G.-L.Y. and P.-R.Z.; validation, Y.-L.G. and Y.-L.L.; formal analysis, Y.-L.G.; investigation, Y.-L.G. and P.-R.Z.; resources, G.-L.Y.; data curation, Y.-L.G. and Y.-L.L.; writing--original draft preparation, Y.-L.G. and Y.-L.L.; writing--review and editing, Y.-L.L. and Y.-L.G.; visualization, Y.-L.G. and Y.-L.L.; supervision, Y.-L.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research project was funded by the National Natural Science Foundation of China, grant numbers 51878667 and the National Natural Science Foundation of Hunan Province, grant numbers 2021 JJ30830.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study may be available on reasonable request from the corresponding author.  
**Acknowledgments:** The authors would like to acknowledge the Civil Engineering Co. Ltd., China Construction Fifth Engineering Bureau for providing detailed information about the design and construction. Meanwhile, the authors would like to express their gratitude to [PERSON] and [PERSON] for their help during the field investigation.  
**Conflicts of Interest:** The authors declare no conflict of interest.
### LoRaWAN  
LoRaWAN has been standardized by the LoRa Alliance since 2015, and it uses LoRa spread spectrum technology in the unlicensed sub-GHz band. To adapt the tradeoffs between range and data rates, LoRaWAN has six spreading factors (SF7 to SF12), and it offers data rates between 300 bps and 50 kbps. The maximum payload length for each message is 243 bytes. LoRaWAN adopts a unique approach where each message transmitted by an end-device is received by all of the base stations in range [5]. This method enhances the ratio of communication reliability. LoRaWAN provides multiple communication classes that address different latency requirements. These classes are [8]:  
* Class A (bidirectional end-devices): This class has the lowest power consumption for applications requiring short downlink communication after an uplink message.
* Class B: This class introduces scheduled receive slots in addition to random windows and enhances latency predictability.
* Class C: This class continuously opens extra receive windows, suitable for IoT applications with continuous energy and power resources.  
LoRaWAN's ability to support multiple communication classes and exploit redundant reception for localization provides flexibility in meeting diverse IoT application needs.
## 4 Discussion
A plausible explanation is: the high charge states observed in [11] were produced within 10 fs of a single pulse, where one can consider a scenario of internal clock pump-probe experiment [13,18]; at the very short effective delay time, the dissociation at short range of the internuclear separation was probed, leading to large KEs due to stronger Coulomb repulsion; whereas in the current work where an external independent clock was applied, allowing for relatively larger delay times, the potential curve at long range was probed, leading to lower dissociation energy at the end. As an example, for low charge states of iodine, we estimated the KE for I\({}^{4+}\) to be 4.5 eV which is nearly constant for different delay times, indicating that the C\({}^{3+}\) ions are likely to be associated with I\({}^{4+}\). With a similar reasoning, the very high charge states of iodine are likely associated with high charge states of carbon, but due to their small abundance, the contribution of the associated carbon ions is also small. As mentioned above, the KE approaches a constant value at -60 fs delay and the ion yield at 50 fs delay is an odd point in the overall trend. It remains a question whether this tip point around 50 fs for the kinetics is a pulse duration factor or a species-dependent factor. Seeking the answer will be for future investigations with different FEL pulse durations. The pulse duration was not directly measured but derived based on electron bunch duration measurements [15]. The actual pulse duration has been reported to be about half of the cited value [19]. Considering an actual pulse duration of 25 fs for a nominal value of 50 fs, the interference between the pump and probe pulses is not expected to be an issue in the current work.
# TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data
## 3 Background  
### Transformer  
Transformer [11] can effectively capture the long-term correlation within the sequence and realize the end-to-end generation of input to output. It is formed by stacking an encoder and a decoder based on the self-attention mechanism. The encoder compresses the input sequence to generate semantic coding and self-attention matrix. Based on the self-attention matrix, the decoder focuses attention on the effective information of the input sequence, and finally generates the target vector through the residual network and the feed-forward neural network. The input to the transformer is a multidimensional matrix, which can be a multivariate time series under a sliding window. The self-attention mechanism can analyze the context of the sequence, and the encoding-decoding framework uses the encoder output from one modality as the input of the other modality decoder, realizing the conversion between modalities, as shown in Figure 1.  
The attention calculation for all tokens can be expressed as one large matrix calculation using the softmax function, which is useful for training due to computational matrix operation optimizations that quickly compute matrix operations. The matrices \(Q\), \(K\), and \(V\) are defined as the matrices where the \(i\)-th rows are vectors \(q_{i}\), \(k_{i}\), and \(v_{i}\) respectively. These three variable matrices are obtained by projecting the input data through three matrix calculations.  
\[\text{Attention }(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}} \bigg{)}V \tag{1}\]  
Intuitively, the \(Q\) and \(K\) matrices are the operators used to calculate the similarity of the current element in the sequence to the other elements. The \(V\) matrix contains the information contained in the elements themselves. By calculating between these three  
Figure 1: Transformer model proposed in [11].  
variables, each element in the sequence interacts with elements in other positions, and the output can make full use of contextual information. The parallel operation of the self-attention mechanism makes the distance between any word pairs in a sequence as one and can capture long-range contextual information. It can improve the computational efficiency of the model and solve the problems of long-term dependence and excessive network capacity consumption caused by the serial operation mechanism, where each hidden coding of a token must depend on the coding output of the previous token.
## 2 Methodology
### Hilbert-Huang Transform
#### 2.4.3 Feature Selection and Implementation Details  
For each voltage signal, EMD typically yields between six and eight intrinsic mode functions (IMFs), depending on the duration and shape of the disturbance. However, only the first two IMFs are retained for feature extraction. These contain the most relevant high-frequency content associated with transient anomalies. Higher-order IMFs, which reflect slower oscillations and baseline drift, were excluded to reduce redundancy and dimensionality.  
The sifting process follows the standard algorithm with a stopping criterion based on the standard deviation between successive iterations, with a threshold of 0.2. This ensuresconvergence while avoiding excessive iterations. No additional denoising methods were applied, as EMD inherently acts as a signal-adaptive filter by isolating oscillatory modes.  
After obtaining the relevant IMFs, the Hilbert transform is applied to each to compute the instantaneous amplitude \(A(t)\) and frequency \(f(t)\) as defined in Equations (8) and (10). From these, the following statistical features are extracted:  
* Mean of the instantaneous amplitude;
* Standard deviation of the instantaneous amplitude;
* Mean of the instantaneous frequency.  
These features were selected due to their ability to characterize the magnitude and frequency behavior of transient disturbances in a compact form. Preliminary testing with additional features (e.g., entropy, energy, and higher-order moments) showed marginal gains in classification accuracy while increasing the risk of overfitting and computational complexity. Therefore, the chosen features offer a robust and efficient representation for training the neural network classifiers.
# Research on a Collaborative Control Strategy of an Urban Expressway Merging Bottleneck Area
## 2 Improved CTM and Parameter Calibration, Validity Verification
### Improve Merging Cell Basic Diagram  
Improve the basic diagram of merging cell \(i\), as shown in Figure 2.  
In this figure, \(q_{m_{1}}\) and \(q_{m_{2}}\) represent the capacity of a single lane in cell \(i\) before and after the sudden drop of capacity, \(w^{\prime}\) represents the speed of shock wave after the sudden drop of capacity, \(\rho_{m}\) represents the critical density when the sudden drop of capacity occurs.  
Figure 2: Flow-density relationship under sudden drop of merging cell capacity.  
For the merging cell \(i\), due to the double-value phenomenon of its capacity, its sending rate and receiving rate need to be updated as:  
\[s_{i,k}=\left\{\begin{array}{ll}\lambda_{i}v_{f}\rho_{i,k}&\rho_{i,k}<\rho_{m} \\ \min\Bigl{\{}\lambda_{i}v_{f}\rho_{i,k},\lambda_{i}q_{m_{2}}\Bigr{\}}&\rho_{i,k} \geq\rho_{m}\end{array}\right. \tag{8}\]  
\[\left\{r_{i,k}g=\left\{\begin{array}{ll}\min\bigl{\{}\lambda_{i}w\bigl{(}\rho _{j}-\rho_{i,k}\bigr{)},\lambda_{i}q_{m_{1}}\bigr{\}}&\rho_{i,k}<\rho_{m}\\ \min\bigl{\{}\lambda_{i}w^{\prime}\bigl{(}\rho_{j}-\rho_{i,k}\bigr{)},\lambda _{i}q_{m_{2}}\bigr{\}}&\rho_{i,k}\geq\rho_{m}\end{array}\right. \tag{9}\]  
Formulas (8) and (9) are the sending and receiving capacities of merging cell \(i\) into the updating merging model, which can improve the sudden drop of bottleneck capacity.
## 2 System Model
### Power Transfer Manager  
The PTM application is mainly focused on the optimisation of the power transfer process, and, consequently, the battery recharging. As previously introduced, this technique allows the GW nodes to receive the energy from ES; in particular they are equipped with ad-hoc circuitry to recharge their battery. In order to avoid energy waste and optimise the recharge process, a synchronisation procedure between the ES and the specific cluster is necessary. Each IIoT cluster is arranged in a tree topology where a unique root node that is interconnected with outer networks, and, during a proper time interval, all the GW nodes belonging to it can receive energy from ES. This is accomplished by PTA which schedules the cluster charging operations. Every GW node sends to PTA its battery level relying on the on the publish-and-subscribic communications framework, together with its Cluster ID and the current tree level (i.e., rank); this information is used to update the network view and perform the cluster selection. The selection is based on the weighted mean residual battery level of an IIoT cluster, where the weighting factor is inversely proportional to the node rank. Once the cluster has been identified, PTA notifies to the intended cluster nodes the starting of the recharging process by sending a specific publish message.
## 3 Circuit Architecture for Multi-MFC PMS
### Implementation  
The proposed PMS was developed on a two-layer PCB shown in Figure 7. On the top side, a microcontroller was placed along with the low-R switches and the power measurement capacitor. The DC-DC boost converter is found at the bottom side of the PCB. Detailed files of this work can be found in the Supplementary Materials section of this manuscript.  
The collected power is stored in an off-board output supercapacitor that powers the IoT sensor node used to test the system.  
The final algorithm was implemented using MPLAB X IDE and XC8 programmed in the PIC24F16 KA102's non-volatile internal flash memory. It is important to mention that even though the information retrieved by the power ranking algorithm was stored in the volatile data memory, the use of a non-volatile Electrically Erasable Programmable Read-Only Memory (EEPROM) to store such power ranking values is available in case further off-line data analysis is required. The sensor data collected in this work is acquired with the purpose to be transmitted and stored in a remote server in the cloud to enable further and more complex data analysis.  
Even though the proposed system can natively support up to nine MFC devices without any further modification only four MFCs were tested in this work. It is also important to notice that if more  
Figure 7: Printed Circuit Board of the PMS for multi-MFC power extraction, (a) top view; (b) bottom view.  
MFCs are needed to increase the overall power available, the system can easily be adapted by adding more solid-state switches and digital multiplexers as needed.
# Vnode: Low-Overhead Transparent Tracing of Node.js-Based Microservice Architectures
## 2 Basic Concepts
### Node.js Environment  
Node.js, an open source environment, originates from the JavaScript V8 engine it is built on. It has brought the versatility of server-side JavaScript programming since its beginning in 2009. Node.js revolutionized the web ecosystem by enabling developers to use JavaScript for server-side application development. Distinctive for its non-blocking, event-driven nature, Node.js is well suited for high-performance applications. Its architecture is asynchronous and built on a single-thread model. In a Node.js application, a primary process known as the event loop orchestrates the execution of various events in different execution phases.  
Tasks or events submitted for execution are first queued in a specific queue based on the event's nature. The event loop traverses these phases, dequeues the events in each queue, and executes them. For blocking events such as I/O operations, a thread pool is used to delegate execution and avoid blocking the event loop. Node.js is a multi-layered system. Internally, it comprises several components that work together to yield a result. However, at the lower layers of the operating system such as in the kernel space, the Node.js process appears as a black box that makes system calls or generates context switch events. From this perspective, it is challenging to discriminate events being executed. A significant unresolved issue is linking high-level information such as requests and invoked JavaScript functions to actions performed in the V8 engine and the Libuv orchestration layer.  
Current tools only allow for the visualization of high-level information, such as a request's duration or the execution time of a service or JavaScript function. However, when such information is provided by distributed tracing tools, debugging is necessary to trace back to the cause. Debugging such issues in Node.js is extremely challenging since, as a single-thread system, all concurrent request executions are conflated into the same process. Distinguishing which request is causing performance issues is difficult because even if a service shows high latency, it does not necessarily mean the service itself is at fault. For instance, the event loop might have been blocked by the execution of a non-optimized function, thus propagating the error to other pending requests. Therefore, it is important to develop methods suitable to these environments.