esa-sceva/satcom-chunk-collection huggingface dataset:
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 3 Implementation Results and Analysis  
### Read Access Time  
The RAT is defined as the interval between the activation of the WL and the establishment of a specified voltage difference (e.g., 50 mV) between the BL and the BLB. The RAT is illustrated in Figure 5. The RAT is influenced by several circuit-level parameters, among which the read current through the access transistor and the pull-down transistor is dominant. A higher CR, defined as the ratio of the pull-down transistor strength to that of the access transistor, results in a stronger pull-down strength. The more rapid discharge of the bit line voltage is enabled, thus reducing the RAT. In contrast, an increased number of access transistors or the inclusion of PMOS transistors in the read path can lead to a higher bit line capacitance and voltage division effects, which may introduce additional read delay. As shown in Figure 6, the WRTU-16T achieves a fast read performance that incorporates electrically optimized PMOS/NMOS hybrid access transistors and a dedicated read-decoupled structure, which is isolated from the bit lines. The RAT of WRTU-16T is 52.95 ps, which is faster than the other SRAMs.  
Figure 4: Waveform of SEU recovery of WRTU-16T.
## 3 Bit Error Rate of the OAM Link and Channel Capacity  
In this section, the series expansion method of RCDPPGV random spiral mode is used to obtain the mathematical model of bit error rate and channel capacity.
# Shafting Torsional Vibration Analysis of 1000 MW Unit under Electrical Short-Circuit Fault
## 2 Solving of Torsional Vibration Natural Frequency of Shafting  
The natural frequency analysis of the torsional vibration of the turbo-generator set is the basic content of torsional vibration research in engineering. The Block Lanczos method for solving torsional natural frequency can also meet the needs of practical engineering in terms of accuracy and calculation convergence rate. The research object is decomposed into a multi-stage and multi-degree-of-freedom spring-damping system, and the turbine generator set can be simplified as shown in Figure 1.  
Under the premise of the normal operation of the oil system, the friction damping of the turbine generator rotor is very small, and the damping effect can be ignored. The second-order differential equation of dynamics is  
\[[M]\Big{\{}\ddot{U}\Big{\}}+[K]\{U\}=0 \tag{1}\]  
The differential form solution of Formula (18) can be expressed as  
\[[K]Q=\omega^{2}MQ \tag{2}\]  
Figure 1: Turbo-generator with multiple degrees of freedom system.  
Factorization is performed by the Cholesky method, and is rewritten as  
\[AY=vY \tag{3}\]  
Equation (20) can be expressed as \([K]=\text{DD}^{T}\), where D is the lower triangular matrix (\(v=1/\omega^{2}\); \(A=\text{D}^{-1}\text{MD}^{-T}\); \(Y=\text{D}^{T}Q\)), converting matrix \(A\) to the lower triangular matrix.  
\[V^{T}AVB=vV^{T}VB \tag{4}\]  
where \(VB=\text{E}\), satisfying \(V^{T}=V^{-1}\), where the matrix \(V^{T}AV=T\) is a triangular matrix that can be expressed as  
\[V^{T}AVB=\left[\begin{array}{cccccccc}a_{1}&b_{1}&0&&&&\dots&&0\\ b_{1}&a_{2}&b_{2}&0&&&&\\ 0&b_{2}&a_{3}&b_{3}&0&&&&\\ &&&&\ddots&&&&\\ \vdots&&&&a_{n-3}&b_{n-2}&0\\ &&&b_{n-2}&a_{n-1}&b_{n-1}\\ 0&\dots&&0&b_{n-1}&a_{n}\end{array}\right] \tag{5}\]  
After calculation, the eigenvectors \(Q\) and eigenvalues \(\omega^{2}=1/v\) of the characteristic equation shown in Equation (1) correspond to \(\omega\) (natural frequency) and \(Q\) (vibration mode vector) of the studied elastic system, respectively.  
The first three low-order torsional vibrations in practical engineering comprise the main monitoring range. As can be seen from Table 1, the natural frequency of the first-order torsional vibration of the unit is 14.5 Hz, the natural frequency of the second-order torsional vibration is 26.5 Hz, the natural frequency of the third-order torsional vibration is 34 Hz, and the natural frequency of the fourth-order torsional vibration is 61.9 Hz. Therefore, the rotor system precludes the natural frequency of the torsional vibration at the working frequency of 50 Hz, so there is no power frequency resonance.
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
## 4 Algorithm for the Set Coverage Problem
### Minimum Coverage Control Algorithm
#### 4.2.1 Genetic Operation
Subsequently, unlike the conventional mutation stage that selects some gene randomly to change its value, we propose a novel mutation operation to accelerate the speed of the convergence. Through finding redundant or unnecessary active nodes and mutating its gene value from 1 to 0, the number of active sensor nodes can be considerably reduced.
## 3 Survey Results
### Communication
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Nr** & **Codename** & **Report rate, 1/h** & **Payload size, B** & **Radio range, m** & **Speed, kbps** & **Connectivity type** \\ \hline
10 & Trove game &? &? &? & 38.4 & connected \\
11 & Elder RFID &? & 19 &? & 38.4 & connected \\
12 & Murphy potatoes & 6 & 22 & 76.8 & connected \\
13 & Firevac & 200 &? & 400 & 38.4 & intermittent \\
14 & AlarmNet & configurable & 29 &? & 38.4 & connected \\
15 & Ecuador Volcano & depends on events & 16 & 1,000 & 250 & connected \\
16 & Pet game & configurable &? & 100 & 250 & connected \\
17 & Plug & 720 & 21 &? &? & connected \\
18 & B-Live & - &? &? &? & connected \\
19 & Biomonotion & 360,000 & 16 & 15 & 1,000 & connected \\
20 & dD-N & depends on queries &? & 66 & 250 & connected \\
21 & Fireighting &? &? & 20 & 250 & connected \\
22 & Rehabil &? & 12 & 30 & 250 & connected \\
23 & CargoNet & depends on events &? &? & 250 & sporadic \\
24 & Fence monitor &? &? & 300 & 76.8 & connected \\
25 & BikeNet & opportunistic &? & 20 & 250 & sporadic \\
26 & BriMon & 62 & 116 & 125 & 250 & sporadic \\
27 & IP net &? &? & 300 & 19.2 & connected \\
28 & Smart home &? &? & 75–100 outdoor/20–30 indoor & 250 & connected \\
29 & SVATS &? &? & 400 & 38.4 & connected \\
30 & Hitchhiker &? & 24 & 500 & 76.8 & connected \\
31 & Daily morning & 180,000 & 2? & 100 & 250 & connected \\
32 & Heritage & 6 &? & 125 & 250 & intermittent \\
33 & AC meter & 60 default (configurable) &? & 125 & 250 & connected \\
34 & Coal mine &? & 7 & 4 m forced, 20 m max & 38.4 & intermittent \\
35 & ITS & varies & 5*n &? & 250 & connected \\
36 & Underwater & 900 & 11 &? & 0.3 & intermittent \\
37 & PipeProbe & 72,000 &? & 10 & 1,000 & connected \\
38 & Badgers & 2,380+ & 10 & 1,000 & 250 & connected \\
39 & Helens volcano & configurable &? & 9,600 & 250 & connected \\
40 & Tunnels & 120 &? &? & 250 & connected \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._Eighty percent of deployments consider the network to be connected without interruptions (Figure 6)--any node can communicate to other nodes at any time (not counting delays imposed by Media Access Control (MAC) protocols). Only 12% experience interruptions, and 8% of networks have only opportunistic connectivity. Default networking protocols should support connected networks. Opportunistic connection support is optional.
## 3 Preliminary Design
### Estimated Cooling Power  
The installed cooling power is 11.5 kW, and a temperature range of \(-25\)\({}^{\circ}\)C to \(+5\)\({}^{\circ}\)C is required, with a measurement instrumentation maximum uncertainty of \(\pm 0.5\)\({}^{\circ}\)C [77].  
The cooling power of the chamber needs to compensate for heat losses through the walls of the chamber to maintain the temperature difference with the outside, but it must also compensate for the heat generated by the drive system, as well as the latent heat (released by the drops when they freeze) and the sensible heat (needed to cool the water drops to the chamber ambient temperature).  
The sensible heat will be equal to the amount of water sprayed by the \(c_{p}\) of the water, times the temperature difference between the sprayed water and the ambient temperature; therefore  
\[\dot{Q}_{sensible}=\dot{m}_{water}c_{p,water}\Delta T \tag{17}\]  
The latent heat will be equal to the amount of water sprayed by the \(c_{latent}\) of the water; therefore  
\[\dot{Q}_{latent}=\dot{m}_{water}c_{latent,water} \tag{18}\]  
The amount of water per unit time (mass flow rate) will be equal to the flow rate in the tunnel per LWC, by the very definition of LWC (mass of water per volume of air).  
\[\dot{m}_{water}=V_{t}A_{t}LWC \tag{19}\]  
The power generated by the fan operation will be constant with the LWC, only depending on the speed to be reached in the test section.  
According to these estimates (see Figure 11), a maximum LWC of 1.2 g/m\({}^{3}\) can be used for the maximum tunnel speed (70 m/s) and a chamber temperature of \(-20\)\({}^{\circ}\)C. The latent heat is significantly higher than the sensible heat. The maximum LWC can also be obtained for the installed cooling power, which will vary with the tunnel speed and the temperature to be reached (see Figure 12).  
\[LWC_{max}=\frac{P_{cooling}-P_{int}}{V_{t}A_{t}\Big{(}\dot{Q}_{sensible}+ \dot{Q}_{latent}\Big{)}}=\frac{P_{cooling}-\frac{1}{2}\rho_{air}(T)A_{t}V_{t} ^{3}}{V_{t}A_{t}\big{(}c_{p,water}\Delta T+c_{latent,water}\big{)}} \tag{20}\]  
Figure 10: Minimum droplet diameter for impingement with different scales of SIVA wing aerofoil.
## 3 Methods
### Loss Function  
To address class imbalance and improve the segmentation of challenging cases, we propose a novel hybrid loss function that incorporates CE loss, dice loss, and HD [42] loss.  
CE measures the difference between two probability distributions for a given random variable or set of events. It is widely used for object classification and works well for segmentation because segmentation is a voxel-level classification. CE loss is defined as follows:  
\[\mathcal{L}_{CE}(y,\ p)=\left\{\begin{array}{ll}-log\ p\ \ \ \,&y=1\\ -log\ (1-p),\ \ y=0\end{array}\right. \tag{4}\]  
where \(y=0\) or \(1\) represents the ground truth, and \(p\) denotes the prediction probability when \(y=1\). The dice coefficient is widely used to measure the similarity between two images in computer vision. Dice loss is defined as follows:  
\[\mathcal{L}_{Dice}=1-\frac{2*\sum_{i=1}^{N}p_{i}y_{i}}{\sum_{i=1}^{N}p_{i}^{2} +\sum_{i=1}^{N}y_{i}^{2}} \tag{5}\]  
where \(N\) denotes the number of all predicted voxels, \(p_{i}\) indicates the prediction probability at voxel \(i\), and \(y_{i}\) refers to the ground truth at voxel \(i\). HD is used to measure the distance  
Figure 3: The proposed gradient-enhanced encoder scheme and the output examples of feature maps from different encoders across different stages of the encoding process following the U-Net structure.  
between two sets of points and therefore allows the measurement of localization similarity. HD loss is defined as follows:  
\[\mathcal{L}_{HD}(X,Y)=\underset{x\in X}{\text{max}}\;\underset{y\in Y}{\text{ min}}\;\left\|x-y\right\|_{2} \tag{6}\]  
where \(y\in Y\) denotes the ground truth, and \(x\in X\) indicates the predicted binary segmentation with a threshold of 0.5. Then, the proposed total loss function in this study is defined as follows:  
\[\mathcal{L}_{total}=\alpha\mathcal{L}_{Dice}+\beta\mathcal{L}_{CE}+\gamma \mathcal{L}_{HD} \tag{7}\]  
where \(\alpha\), \(\beta\), and \(\gamma\) are the weights of dice loss, CE loss, and HD loss, respectively. In this study, \(\alpha=\beta=\gamma=1\). We incorporate the deep supervision technique [43, 44] into our decoder for better training efficiency. The hybrid loss function is designed to work synergistically with the gradient-enhanced encoder scheme. The gradient-enhanced encoders extract fine-grained boundary details and structural features, aligning well with the objectives of the hybrid loss function. HD loss especially focuses on boundary precision, benefiting from the boundary features learned by the gradient-enhanced encoders. Dice loss ensures global shape consistency, and CE loss improves voxel-level accuracy. By leveraging both global and local features, the hybrid loss function can effectively address the class imbalance problem and improve the segmentation performance of challenging cases.
## 4 Results and Discussion
### Change in Permeability of Asphalt Mixture During Blockage
#### 4.2.2 The Porosity of the PAC Marshall Specimen Varies with the Number of Blockages  
Similarly, the ratio of the reduction of the void ratio of the test piece after each cycle to the initial void ratio is defined as the reduction of the void ratio. In order to more intuitively describe the trend of void rate reduction during the silting process, the void rate reduction rate in the whole process is plotted as a line graph, as shown in Figure 11.  
It can be seen from Figures 10 and 11 that the porosity of the test piece gradually decreases with the increase in the number of siltings, and the decrease is similar to the change in the quality of the test piece, both from a large change to a flattening, and corresponding to the silting process. The porosity of the test piece dropped from about 20% at the beginning to about 8%, which basically meant a loss of the water permeability function.  
2.3 The Variation of the Permeability Coefficient of the PAC Marshall Specimen with the Number of Blockages  
Similarly, calculate the loss rate of the water permeability coefficient of the test piece, as shown in Figure 12.  
Figure 11: The change of void ratio reduction rate with the number of clogging cycles.  
Figure 12: The change of the water permeability coefficient loss rate with the number of clogging cycles.  
By observing the line graph of the loss rate of the permeability coefficient, it can be easily seen that the attenuation trend of the permeability coefficient is also in line with the overall silting process. The change trend of the permeability coefficient loss rate is first large, then small, and finally stabilized. The final permeability coefficient is reduced to about 0.07 cm/s, and the permeability function only retains less than 20% of the initial value, and the permeability function is basically lost.  
The blockage process simulation of the permeable Marshall specimen in the laboratory was used to explore the changes in the weight, porosity and permeability coefficient of the specimen during the blockage. The three indicators can reflect to a certain extent the changes in the permeability of the permeable pavement during the blockage process. The above three indicators are only suitable for the simple evaluation of the blockage of the specimen in the indoor test.
# MLMD--A Malware-Detecting Antivirus Tool Based on the XGBoost Machine Learning Algorithm
## 3 Design Methods
### Evaluation of the Models
#### 3.6.1 Evaluation Metrics  
The metrics we used are derived and can be calculated from the _confusion matrix_ shown in Figure 4, according to which the classification result can be evaluated as necessary. It contains the following values [31]:  
* True negative (TN)--the number of correctly identified benign samples;
* True positive (TP)--the number of correctly identified malicious samples;
* False positive (FP)--the number of incorrectly identified malicious samples;
* False negative (FN)--the number of incorrectly identified benign samples.  
Classification Accuracy expresses the number of samples--compared to the total number of samples--that were classified correctly. It is expressed as follows:  
\[\text{Accuracy}=\frac{TP+TN}{TP+FP+TN+FN} \tag{1}\]  
Precision expresses how many samples of all positive samples were correctly predicted as positive. It is the first part of F1 score. It is expressed as follows:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{2}\]  
Figure 4: The confusion matrix.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline n\_estimators & 10; 50; 100; 150; 200; 250; 300 \\ max\_depth & 3; 5; 7; 9; 11 \\ learning\_rate & 0.001; 0.01; 0.1; 0.2; 0.3 \\ colsample\_bytree & 0.3; 0.7; 1 \\ subsample & 0.5; 1 \\ scale\_pos\_weight & 0.3 \\ gamma & 0; 1; 2; 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Tested hyperparameters of the _XGBClassifier_ classifier.  
Sensitivity (aka Recall)  
Sensitivity (recall) expresses the ability of the model to correctly classify positive (in our case: malicious) samples, that is, it expresses how many samples of all positive samples were correctly classified as positive. A model with high sensitivity succeeds well in finding all the positive cases in the data. It is the second part of F1 score. It is expressed as follows:  
\[\text{Sensitivity}=\text{Recall}=\frac{TP}{TP+FN} \tag{3}\]  
Specificity expresses the ability of the model to correctly classify negative (in our case: benign) samples, that is, it expresses how many samples of all negative samples were correctly classified as negative. It is expressed as follows:  
\[\text{Specificity}=\frac{TN}{TN+FP} \tag{4}\]  
Precision and recall are the two most common metrics that take into account class imbalance. F1 score expresses the harmonic mean between recall and precision values. The goal of the F1 score is to combine the precision and recall metrics into a single metric. It is expressed as follows:  
\[\text{F1 score}=\frac{2*\text{Precision}*\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{5}\]  
The area under the curve (AUC) is an aggregated measure of the performance of a binary classifier on all possible threshold values of the receiver operating characteristics curve (ROC). It is calculated as the area under this ROC. The AUC ranges from 0 to 1. The bigger the AUC score, the better our classifier is.
## 1 Introduction
The aim is to validate the real-time functionality of the proposed hybrid V2X technology selection algorithm. The remainder of the paper is organized as follows. In Section 2, the related work in the domain of hybrid V2X is discussed, followed by the description of short-range and long-range V2X communication in Section 3. The system model and problem statement is presented in Section 4. An intelligent hybrid V2X technology selection algorithm is proposed in Section 5. Section 6 discusses the experimental V2X testbed setup consisting of DSRC, C-V2X PC5 and 5G capabilities, and the algorithm implementation. This is followed by the experimental results and their analysis in Section 7, while the conclusion and future work directions are drawn in Section 8.
# Re-Engineered Word Embeddings for Improved Document-Level Sentiment Analysis
## 2 Preliminary Word Embedding Generation
### Word Embedding  
After the tokenization, data are ready to be vectorized for word embedding generation. It should be noted that in this study, the word embeddings were created based on the available data (i.e., without using a pretrained model), and embeddings for training and test sets were generated separately. Therefore, for the case when a word in the test set is not in the embedding vocabulary, it will return a row of NaNs. It has been found that with the proposed re-engineering mechanism, such situation did not impact the performance.  
The Word2 Vec algorithm employed in this work uses the continuous skip-gram model architecture to learn the word embeddings, instead of the continuous bag-of-words (CBOW). In the CBOW architecture, the model predicts the current word from a window of surrounding context words. In the continuous skip-gram architecture, in contrast, the model uses the current word to predict the surrounding window of context words. The skip-gram architecture weighs the nearby context words more heavily than more distant context words [17; 18]. According to the authors' note in [19], CBOW is faster while skip-gram does a better job for infrequent words. In addition, for a medium-sized corpus (less than 10 million words),  
Figure 1: Flow chart diagram of the proposed classification system for sentiment analysis with four main processing modules.  
the skip-gram model for Word2 Vec tended to perform well with a relatively short feature vector (less than 100 dimensions) [20]. This makes the skip-gram model a good fit for the proposed study in this work.  
A number of other important parameters need to be clarified while using the adopted Word2 Vec algorithm to create the word embeddings. The dimension of word embedding has been set to 100, i.e., for each unique word, a vector with 100 dimensions will be generated. This parameter was set empirically to balance the computational complexity and performance. The size of the context window is set to 10, which has been found to be effective for the skip-gram model [20]. Other parameter settings, such as loss function, initial learning rate, and number of epochs for training are set with the default values [21].  
Once the tokens have been converted into vectors using Word2 Vec, ML classifiers can begin to perform the sentiment analysis. In the next section, the novel word embedding re-engineering mechanism is presented in detail.
## 3 Material and Methods
### Model Evaluation and Performance Metric  
Two trained models were compared based on the model evaluation and mean per joint position error (MPJPE) values for their quantitative analysis. The model evaluation results were based on the inference results from the selected validation results. TensorRT (tensor runtime), a deep learning inference optimizer and runtime library developed by the NVIDIA engine, was used to acquire the optimized inference results. It helped in optimizing the inference process by applying various techniques such as layer fusion, precision calibration, and kernel autotuning, resulting in reduced memory consumption, increased throughput, and minimized latency during the inference [12]. The evaluation results were compared based on average precision computed over the different thresholds of IoU (intersection over union). These metrics are defined as  
* Intersection over union (IOU): values range from 0 to 1, where a value of 1 indicates a perfect match between the predicted and ground truth bounding boxes, while a value of 0 indicates no overlap at all.
* Precision: measures the proportion of true positive detections out of all positive predictions made by the system.
* Recall: measures the proportion of true positive detections out of all relevant items present in the dataset.  
Furthermore, mean per joint position error (MPJPE) values were computed for both models based on the inference results for the test data sample. The mean per joint position error (MPJPE) was calculated by using the formula [29].  
\[\text{E}_{\text{MPJPE}}\left(f,\ S\right)=\frac{1}{Ns}{\sum_{i=1}^{Ns}||m \underset{f,\ S}{(f)}}(i)-m\underset{gt,\ S}{(f)}(i)|| \tag{2}\]  
where \(N_{s}\) is the number of joints in skeleton \(S\), \(m_{fs}\) is the ground truth position of the \(i\)th joint, and \(m_{gt,s}\) represent the estimated position of joint \(i\). The inference results from the model trained with the proposed dataset were then validated for its accuracy by comparing with the results from the model trained with the COCO 2017 dataset. Two different trained models shown in Section 3.4 were tested with the approximately 2510 number of images to compute MPJPE that was selected from three subjects' data for both two models.