esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Virtualization: A Solution to Some Challenges in WSN
### Quality of Service (QoS)  
Quality of service (QoS) [111] describes the quality metrics of a wireless sensor. These metrics include the hop time of a message from one node to the other, unbalanced network traffic, network dynamics, data redundancy and energy balance, amongst others. The parameters used to configure a network typically serve to define the bounds within which the network operates in. Usually, a network's quality is derived from the optimal performance of the network and its applications. QoS can be categorized as application-oriented QoS and network-oriented QoS [112]. Application-oriented QoS is concerned with the functions of the service tasks. The metrics of the application services include the locus of radio scope, number of active agents and the exactitude of parameters in the application. Network-specific QoS focuses on the fine grain details of the networking profile in order to ensure that minimal or no packet losses occur during the transmission or reception process. These abstractions normally inject latency and reduce reliability in the network. Virtual links in VWSN may address these challenges by mitigating the balance between the application-oriented QoS performance metrics and the network-oriented metrics. The ability to dynamically assign network components at runtime for application layer usage enables VWSN to contribute to the QoS improvement in WSN. Logical links also bring cohesion to the balance of traffic in WSN.
## 5 Performance Evaluation
### Analysis for Guaranteed Lifetime
When a node continues to perform its duty without the optimized selection of transmission range and active/sleep schedules, the number of alive nodes in the network decreases rapidly, as the plot of FML and EPGLT shows in Figure 6b. On the other hand, the proposed fuzzy controller selects the nodes for the active mode in each round, which are further categorized through the energy-aware three-tiered node selection. Thus, the transmission range and the active/sleep schedules among nodes are selected by defined inference rules that guarantee the pre-configured network lifetime; node failure occurred after the configured lifetime, as shown in the Figure 6b. Furthermore, the figure shows that the nodes start dying after 4000 s in the proposed scheme, where the nodes in FML and EPGLT drain out their energies before the configured time, which brings the network lifetime to an end. Figure 6b shows that the first node died in the FML protocol at 3500 s and in EPGLT at 4000 s, whereas in the fuzzy scheme the first node died after 4500 s.  
Figure 5: Various values of \(\alpha\) to guarantee. (**a**) Network lifetime; (**b**) guaranteed lifetime at the % of energy. The transmission range of sensor nodes can affect the network lifetime. Our scheme is dealing with different transmission ranges for real-time packets; therefore, we evaluate the performance of the proposed fuzzy model under different settings of transmission range. Figure 7a,b shows the results of our proposed approach with a view toward studying the effect of incorporating the energy consumption objective in the guaranteed lifetime process. Recall that the proposed scheme assigns different transmission range relay nodes to routes in real-time and non-real-time data packets. In order to study the energy consumption of the proposed protocol on each active node in a three-tiered model, we examined the different set of nodes over the network lifetime and plotted the results in Figure 7a. The plots showed that the energy consumption in NTR and ETR relay nodes are nearly the same due to the energy and active time-aware schemes applied by the fuzzy controller through fuzzy inference rules in the network to achieve the guaranteed lifetime. The set of active sensors responsible for sensing the network field consumes energy almost at the same rate in all rounds. Figure 6: Network lifetime. (**a**) Guaranteed lifetime; (**b**) working and live nodes in each round. Figure 7: Network lifetime _vs_. energy consumption. (**a**) Energy consumption among nodes; (**b**) energy consumption in rounds. Figure 7b shows the average energy consumption of all nodes at different rounds under a uniform data generation pattern. The higher energy consumption at each round is observed in the proposed scheme more than the other two, but is well balanced in most of the rounds. Since FML and EPGLT do not consider a balanced energy scheme in the routing decision, more nodes have enough residual energy when the first node's energy is drained out, as shown in Figure 7b.
## 2 Materials and Methods
### Phase Shifter  
The phase shifter is used to delay the phase between two lines by adding additional length to the microstrip line, as shown in Figure 5a. The length of the microstrip line is longer than the other line by \(2\Delta L\). The phase shift is given by Equation (6).  
\[\theta=\frac{2\pi\Delta L}{\lambda_{g}} \tag{6}\]  
where \(\theta\) represents phase shift, \(\Delta L\) represents extra length of the microstrip line, and \(\lambda_{g}\) is the wavelength in the microstrip. Figure 5b illustrates the designed phase shifter for the BM network.
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 7 Malware Detection Tools
### Google Rapid Response (GRR)  
Google's security specialists designed the GRR platform, an advanced remote forensics incident response platform. Signs of malware infiltration on desktop machines are commonly discernible. For agent-to-agent communication to occur, both a server architecture and a client program running on the client computer must be present. The GRR system encompasses a Python server architecture dedicated to the monitoring of clients and the establishment of interaction with them, in addition to a Python-based client (or \"agent\") installed on target machines. Once the configuration of the server and proxy have been established, an individual utilizing the GRR platform can obtain the privilege of receiving server-generated transmissions. The subsequent action for the incident response team is to execute a sequence of technological operations on the main computer, comprising assessing the memory, inquiring into diverse configuration alternatives, and engaging with program selections. GRR can be utilized by researchers to expeditiously gather data from a large number of computers, as it has been designed to function in such an environment. to assist in criminal investigations and inquiries, GRR has been implemented to be both user-friendly and versatile, thus promoting the quick assessment of scenarios and enabling remote examination.  
By default, all interactions between the GRR client and server are secured by employing the Advanced Encryption Standard (AES) 256 and transmitted over the Hypertext Transfer Protocol (HTTP). The GRR server comprises a front-end server, a workforce, and a user interface, with flow and search representing fundamental operations. to address matters about limited resources, the GRR server employs flows, a form of finite automata. The transmission of information between the server and the client is the most integral element of a server, as it is responsible for establishing communication. The client must take action to initiate traffic on the GRR server initially. While the server awaits a response from the client, it configures all of its constituents. Once a response has been ascertained,the implementation of the flow state occurs, following which the relevant resource is downloaded. The GRR can be employed to address concerns about the overutilization of resources. Conducting a scan across hundreds of client machines is a process known as \"scoping\". The selection of computers to facilitate the various stages of the workflow is determined via the process of hunting [20; 21; 70].
# Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey
## 3 DNNs in CPS-Based Spatial-Temporal Data  
In this section, the state-of-the-art DNNs used to handle spatial-temporal data in CPS are explored. As shown in Figure 3, a three-dimensional framework is proposed, in which \(X\)-axis indicates the CPS domains (transportation, manufacturing, and others), the \(Y\)-axis displays the targets (spatial-temporal data processing, anomaly detection, predictive maintenance, resource allocation, real-time decision, and multi-modal data fusion, among others), and the \(Z\)-axis reveals the different types of DNNs (i.e., CNNs, RNNs, and GNNs). Note that the purpose of mapping (say \(X_{i}\), \(Y_{i}\), \(Z_{k}\)) is to categorize the existing effort in the designed 3D framework. Recall that, in the \(X\)-axis, denote the transportation CPS as \(X_{1}\), manufacturing CPS as \(X_{2}\), etc.; in the \(Y\)-axis, denote \(Y_{1}\) as data processing, \(Y_{2}\) as anomaly detection, \(Y_{3}\) as predictive maintenance, \(Y_{4}\) as resource allocation, \(Y_{5}\) as real-time decisions, and \(Y_{6}\) as multi-modal data fusion; in \(Z\)-axis, denote CNNs as \(Z_{1}\), RNNs as \(Z_{2}\) and GNN as \(Z_{3}\), respectively. Given that a specific DNN (say RNN) is applied to anomaly detection in transportation CPS, the corresponding effort can be categorized as (\(X_{1}\), \(Y_{2}\), \(Z_{2}\)) in the defined framework.  
Figure 2: Spatial-Temporal Data.  
The proposed 3D framework can be used as a framework to summarize the existing research efforts concerning DNNs in handling spatial-temporal data in CPS. The designed framework can be used to categorize the existing research efforts, and help readers better understand the intersections among different DNNs with different application targets under different CPS. Furthermore, this designed framework is a generic one and can be extended to include more CPS, targets, and DNN techniques.
# A Convex Hull-Based Machine Learning Algorithm for Multipartite Entanglement Classification
## 3 The Performance of the Classifier
### Testing Phase of the Predictors
#### 3.2.2 Algorithm for Finding Critical Points  
For the set of W states \(\mathcal{W}\), which is closed and convex, for an arbitrary state \(\rho\), there must exist and only exist a critical point satisfying that \(\alpha_{\rho}\rho+(1-\alpha_{\rho})I/(d_{A}d_{B})\) is on the boundary of \(\mathcal{W}\). When \(\alpha\leq\alpha_{\rho}\), \(\rho\) is a W state, and when \(\alpha>\alpha_{\rho}\), it is a GHZ state. Here is an iterative algorithm for calculating \(\alpha_{\rho}\) based on the convex hull \(\mathcal{C}\):  
1. Initiate \(p\) as the feature vector of \(\rho\), and set \(\varepsilon=1,\zeta=0.9\);
2. Update \(\alpha_{\rho}\leftarrow\alpha(\mathcal{C},p)\);
3. Now, \(\alpha_{\rho}p=\sum_{i}\lambda_{i}c_{i}\). Pick \(c_{i_{1}}\),..., \(c_{i_{D}}\) to be the critical points satisfying \(\lambda_{i_{k}}>0\). Update \(\mathcal{C}\leftarrow\mathit{conv}(c_{i_{1}}\),..., \(c_{i_{D}})\);
4. For each \(k=1,\ldots D\), suppose \(c_{i_{k}}\) is the feature vector of \(|a_{k}\rangle|b_{k}\rangle\). Sample the neighbor of \(c_{i_{k}}\); that is, to randomly generate two Hermitian operators \(H_{1}\in\mathcal{H}_{A},H_{2}\in\mathcal{H}_{B}\), satisfying \(\|H_{1}\|=1\), \(\|H_{2}\|=1\). Let \(\delta\) be a random number in \([0,\varepsilon]\). Set \(|a_{k^{\prime}}\rangle|b_{k^{\prime}}\rangle=(e^{i\delta H_{1}}\otimes e^{i \delta H_{2}})|a_{k}\rangle|b_{k}\rangle\). Set \(\mathcal{C}\leftarrow\mathit{conv}(\mathcal{C},c_{k^{\prime}})\), where \(c_{k}^{\prime}\) is the feature vector of \(|a_{k^{\prime}}\rangle|b_{k}^{\prime}\rangle\);
5. Update \(\varepsilon\leftarrow\zeta\varepsilon\) and go back to step 2.  
We repeated step 4 10 times to find enough neighbors. The initiation of \(\zeta\) could be adjusted. When \(\zeta\) is closer to 1, the approximation is more precise, and on the contrary, when \(\zeta\) is closer to 0, the speed of approximation gets faster.
# Real-Time Remote Sensing of the _Lobesia botrana_ Moth Using a Wireless Acoustic Detection Sensor
## 3 Results
### Simulated Meth  
The simulation of the moth was performed by using two case scenarios. First, a neural network analysis was configured to find each passing of the moths in the near field of the sensor head. The network was trained in Matlab R2016b using clippings from a long acquisition and each clip was characterized using a set of six features (see Table 1). The parameter of the feedforward network was 6 inputs, 50 hidden layers and 1 output layer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Description** \\ \hline
1 & Signal mean \\
2 & Root mean square \\
3 & Amplitude of the central maxima of the autocorrelation function \\
4 & Position of the first peak in the autocorrelation function \\
5 & Amplitude of the first peak in the autocorrelation function \\
6 & Spectral power from 20 kHz to 40 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Features measured to characterize an event.  
The neural network was trained using a set of 200 clippings from the long signal; 60% was used to train, 20% to validate and the remaining 20% for testing the network. The complete set of signals was analyzed using 4000 windows of detection. Figure 11 shows the mean spectrum over 100 detections obtained from the neural network analysis.  
We performed two separate tests for both frequency bands previously determined (7 kHz to 10 kHz and 20 kHz to 40 kHz). In both tests, an active speaker was set at a distance \(X\) in the axial direction of the microphone and the amplifier was set to achieve 39.5 dB(A) at a distance of 1 cm at 7 kHz. We then displaced the emitter away from microphone until no detection was made by the system (Figure 12). For the first frequency range, we were able to detect the emitter up to 7 cm and for the second case, detection was possible up to 25 cm. It is worth mentioning that if the emitter moved at low speeds, the detection range decreased by approximately a factor of two.
## 3 Methods
### Pre-Processing  
Sentinel-2A tiles were downloaded at Level 1C, which refers to 100 \(\times\) 100 km\({}^{2}\) ortho-images projected to cartographic geometry based on a Digital Elevation Model (DEM). Level 1C products are then transformed to Level-2A Bottom of Atmosphere (BOA) reflectances, using the Sen2 Cor tool. Sentinel-2 bands of 20 m spatial resolution are resampled to 10 m spatial resolution, using the Nearest Neighbor (NN) resampling algorithm. Landsat-8 OLI imagery was acquired in GeoTiff format at Level 1 TP, having been radiometrically calibrated and ortho-rectified. Image pixels have then been converted from digital numbers (DN) to radiances, to Top of Atmosphere (TOA) reflectances, using the Semi-Automatic Classification plugin in the geographic information system application QGIS [27]. DOS-1 atmospheric correction has been additionally applied, converting data to surface reflectances.  
Pan-sharpening was then performed by merging the moderate resolution (30 m) multispectral data with the higher resolution panchromatic (15 m), providing multispectral imagery of higher resolution features. The employed technique implements a Brovey Transform, where the pan-sharpened values of the multispectral bands are calculated as in Equation (1) [27].  
\[\text{MSpan}=\text{MS}\times\frac{\text{PAN}}{1} \tag{1}\]  
Figure 4: Flowchart of overall methodology: imagery acquisition, data pre-processing, feature space creation, crop type classification.  
where I is the intensity, while MS is the multispectral, and PAN is the respective panchromatic pixel values. Intensity weights are a function of the multispectral data and are defined as in Equation (2) [27].  
\[\text{I}=\frac{0.42\times\text{Blue}+0.98\times\text{Green}+0.6\times\text{Red} }{2} \tag{2}\]
###### Abstract  
In this paper, a new time-frequency analysis method--Synchrosqueezing Generalized S-transform (SSGST)--is proposed to meet the needs of high-resolution seismic signal processing and interpretation. The basic wavelet of the generalized S-transform (GST) in the paper is a modulated harmonic wave with four undetermined parameters that can be constructed by adjusting the four parameters to make the GST more suitable for seismic signals processing. The SSGST method squeezes and reconstructs the complex coefficient spectra of GST results along the frequency direction so that the energy distributions on the time-frequency spectra are concentrated around the real instantaneous frequency of the signal; thus, the time-frequency resolution can be improved. Based on mathematical theory, the basic principle of the new transformation method is given, and the mathematical expressions of the positive transformation and lossless inverse transformation of the method are strictly deduced. The experimental results of numerical signals illustrate that the proposed method can correctly decompose signals with different spectral characteristics into a high time-frequency resolution spectrum and can recovery the original signal from the time-frequency spectrum with satisfying reconstructing accuracy. Application on field seismic data shows the superiority of the new method in seismic time-frequency analysis for hydrocarbon detection.  
time-frequency decomposition; synchrosqueezing transform; generalized S-transform; seismic signal; hydrocarbon detection Article
## 2 Materials and Methods
### RNA Extraction and Reverse Transcription-Quantitative Polymerase Chain Reaction (RT-qPCR)  
Total RNA was extracted from 3T3-L1 cells using the TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following to the manufacturer's instructions. The first-strand and complementary DNA (cDNA) (Thermo Fisher Scientific, Inc.) synthesis were performed with Oligo(dT)\({}_{15}\) primers and 1 \(\upmu\)g of total RNA using a reverse transcription system (Thermo Fisher Scientific, Inc.). Primer sequences and product sizes are shown in Table 1. qPCR was conducted in triplicate, the assay was performed on the StepOnePlus Real-Time PCR System with Power SYBR-Green PCR Master Mix (Applied Biosystems; Thermo Fisher Scientific, Inc.). Amplification was performed for each target gene using 1 \(\upmu\)L of cDNA in a 20 \(\upmu\)L reaction mixture, consisting of 10 \(\upmu\)L of Power SYBR Green PCR Master Mix, 2 \(\upmu\)L of primers, and 7 \(\upmu\)L of PCR-grade water. The polymerase chain reactions were performed with a denaturation step of 40 cycles at 95 \({}^{\circ}\)C for 10 min, at 95 \({}^{\circ}\)C for 15 s, and at 60 \({}^{\circ}\)C for 1 min. The relationship between the target gene and GAPDH, serving as a housekeeping gene, was determined using the formula 2-target gene-GAPDH). The relative quantities of transcripts were measured [25].  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Genes** & **Accession No.** & **Forward Primer** & **Revesse Primer** & **Product Size (bp)** \\ \hline C/EBP\(\upalpha\)1  & 80 \\ PPAR2\(\upbeta\)2  & FF0624761 & 5’-TTATACGTGCATATITC & 5’-ACTCTGGCGTGTGTCAGGTGTCAGGT
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 4 Experimental Results and Comparaison  
### Dataset  
In this paper, the dataset used is the 2009-2010 ASSISTments dataset [35], which comprises systematic records of student interactions with an intelligent tutoring system that predates MOOCs and many modern learning analytics features. The dataset includes user IDs, problem IDs, correct or incorrect responses, attempts, time taken, and position of questions. Interestingly, while preprocessing, duplicates were removed. Furthermore, numerical features were standardized in Scikit-Learn using the StandardScaler. For sequential modeling, the user interactions were grouped by user ID and ordered by question position, which generated fixed-length learning sequences (in this case, 10) so that all input dimensions stayed the same. The data were split into a train-test split of 80/20 using a random seed of 42 for reproducibility. These preprocessing steps highlighted connections that were not visible before but opened pathways to train even more sophisticated deep neural network models. The processed sequences will be input into a TSA-GRU network for the prediction of students' response times.  
Once the proposed TSA-GRU model was established, various preprocessing techniques were adopted for the data to attain consistency and quality:* Input Data:
* Student interaction logs with records featuring original, correct, attempt_count, and position.
* The variable to predict is ms_first_response_time.  
* Cleaning: Removing duplicate records.
* Feature Scaling: Standardizing input features and the target using z-score normalization.
* Sequence Generation:
* For each user, a fixed-length sequence (e.g., 10 timesteps) is generated.
* The last value within each sequence is used as the prediction target.
* Variable Split:
* Processed data are split into training and testing sets.
* Data are wrapped in a custom PyTorch Dataset and DataLoader for efficient batching during model training.
## 6 Conclusions  
In this paper the performance of the proposed MMC with NPC sub-modules in PV grid-connected applications was investigated under steady and transient conditions by employing various PWM techniques. The proposed MMC with NPC sub-modules with various SPWMLSC, SPWMPSC, STHILSC, and STHIPSC modulation techniques reports the voltage THDs were 2.58%, 1.63%, 1.26%, and 1.18%, and the current THDs were 12.20%, 6.64%, 10.26%, and 8.84%, respectively. The phase-shifted carrier modulation scheme has features like a low computational burden, can manage fault tolerance, and provides superior voltage balance under abnormal conditions when it comes to the digital controllers. From the results it is clearly revealed that the modulation schemes, such as STHILSC and STHIPSC, give superior performance in terms of lower output voltage and current harmonic distortions. The proposed arrangement achieves any voltage level with lower rating of power switches when compared to the conventional neutral point clamped  
Figure 15: Converter during short circuited condition. (**a**) Line voltages of converter with d-q control and STHILSC, (**b**) line voltages of converter with d-q control and STHIPSC, (**c**) line currents of converter with d-q control and STHILSC, (**d**) line currents of converter with d-q control and STHIPSC, (**e**) STHILSC implemented upper arm phase-a SM-1 capacitor voltage, (**f**) STHIPSC implemented upper arm phase-a SM-1 capacitor voltage, (**g**) STHILSC implemented lower arm phase-a SM-1 capacitor voltage, (**h**) STHIPSC implemented lower arm phase-a SM-1 capacitor voltage, (**i**) primary and secondary phase-a transformer currents when STHILSC is implemented, (**j**) primary and secondary phase-a transformer currents when STHIPSC is implemented.  
converter. This strategy can also significantly reduce the requirement of the high rating capacitor bank due to the presence of the mutual inductance component in the transformers, which is not present in the traditional MMC. Hence it can used for many high-power and medium-voltage grid-connected applications.  
Conceptualization--S.M. and R.V.K; Methodology, Software, Validation--S.M., R.V.K., P.V.B., and T.S.; Investigation, Resources, Writing--Original Draft Preparation, Writing--Review and Editing, Visualization; Supervision; Project Administration, S.M., R.V.K., P.V.B., and T.S. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the support offered by the Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India, under the Grant number: ECR/2017/000316 for this research work.  
Not applicable.  
The authors declare no conflict of interest.