esa-sceva/satcom-chunk-collection huggingface dataset:
# An Advanced Shear Strength Criterion for Rock Discontinuities Considering Size and Low Shear Rate
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample Type**} & \multirow{2}{*}{\begin{tabular}{c} **Normal** \\ **Stress** \\ **(kPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Shear** \\ **Rate** \\ **(mm/s)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Experimental** \\ **Result** \\ **(kPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Predicted** \\ **Result** \\ **(kPa)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Error** \\ **Value** \\ **(kPa)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Ratio of Error** \\ **Value** \\ **(kPa)** \\ \end{tabular} } \\ \hline \multirow{7}{*}{Sample 1\#} & \multirow{4}{*}{\(100\)} & \(0.05\) & \(123.85\) & \(136.91\) & \(13.06\) & \(10.55\) \\  & & \(0.1\) & \(135.4\) & \(146.81\) & \(11.41\) & \(8.43\) \\  & & \(0.15\) & \(195.7\) & \(153.05\) & \(-42.65\) & \(-21.79\) \\  & & \(0.2\) & \(205.55\) & \(157.71\) & \(-47.84\) & \(-23.28\) \\  & & \(0.3\) & \(202.25\) & \(164.62\) & \(-37.63\) & \(-18.61\) \\  & & \(0.4\) & \(199.5\) & \(169.79\) & \(-29.71\) & \(-14.89\) \\  & & \(0.5\) & \(200.6\) & \(173.98\) & \(-26.62\) & \(-13.27\) \\  & & \(0.6\) & \(211.6\) & \(177.51\) & \(-34.09\) & \(-16.11\) \\  & & \(0.7\) & \(243.9\) & \(180.58\) & \(-63.32\) & \(-25.96\) \\  & & \(0.8\) & \(215.4\) & \(183.31\) & \(-32.09\) & \(-14.90\) \\  & & \(0.9\) & \(250.5\) & \(185.77\) & \(-64.73\) & \(-25.84\) \\  & & \(1.0\) & \(232.4\) & \(188.01\) & \(-44.39\) & \(-19.10\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Theoretically predicted results of peak shear strength for granite discontinuity samples calculated via the new empirical equation and the corresponding error values.
# ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform
## 3 Experiment Setup
### Evaluation Metrics  
To evaluate the classification performance, six classification metrics were used in this study. The metrics are defined by four concepts: true positive (_TP_), true negative (_TN_), false positive (_FP_) and false negative (_FN_), respectively [32].  
* Accuracy Accuracy (_Acc_) is a relatively comprehensive evaluated guideline, directly reflecting the performance. It is the proportion of test examples classified correctly. \[Acc=\frac{TP+TN}{TP+TN+FP+FN},\] (16)
* Error rate Error rate (_Err_) is ratio of incorrect predictions over the total number of instances evaluated. \[Err=\frac{FP+FN}{TP+TN+FP+FN},\] (17)
* Sensitivity Sensitivity (_Sen_), also called Recall, represents the probability that true positive samples can be correctly detected. \[Sen=\frac{TP}{TP+FN},\] (18)
* Specificity Specificity (_Spe_) means the percentage of correctly classified negative instances. \[Spe=\frac{TN}{TN+FP},\] (19)
* Positive predictive rate Positive predictive rate (_Ppr_), also called Precision, means the proportion of positive samples classified correctly. \[Ppr=\frac{TP}{TP+FP},\] (20)
* score_) means relations between data's positive labels and those given by a classifier based on a per-class average. \[F-score=\frac{Ppr\times Sen}{Ppr+Sen}\times 2,\] (21)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Training Set** & **Testing Set** \\ \hline
100,103,111,113,117,121,123,200,202,210,212,213, & 101,109,112,115,116,118,122,124,205,208,209, \\
214,219,221,230,232 & 215,220,222,223,231 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training set and testing set in inter-patient scheme.
# Machine Learning for Network Intrusion Detection--A Comparative Study
## 4 Background
### Algorithms and Techniques
#### 4.2.1 Random Forest  
The random forest (RF) algorithm consists of multiple decision trees. Each of these trees generates a prediction. Then, the algorithm uses these predictions to develop a decision based on the majority of the predicted values. The advantages of RF include its ability to be used for both classification and regression problems, not requiring scaling, and the ability to handle outliers. Disadvantages include that this algorithm requires a lot of computational power because it deals with many decision trees, and subsequently, they take longer to train models.
## 4 Results
### Disturbed Trajectory Tracking  
This section expands on the previous experiment by introducing step disturbances to the UAV's flight along the x, y, and z axes, as illustrated in Figure 7. Figure 8 showcases the tracking performance, revealing that even under these step disturbances, the proposed controller maintains superior tracking capabilities.  
Figure 8: Trajectory tracking performance with disturbance. CB, traditional backstepping [35]; SMC, traditional sliding mode [36]; SAT, sliding mode with saturation [19]; BB, bioinspired backstepping; BSMC, bioinspired sliding mode. (**a**) The three-axis spatial coordinates of the UAV under disturbance conditions (**b**) The three-axis position information of the UAV with respect to time under disturbance conditions.  
Figure 7: Step-wise ascending disturbance signal.  
Figure 9 shows the acceleration curves of the quadrotor UAV along the x, y, and z axes under three different algorithms. Table 3 summarizes the acceleration values on the x, y, and z axes at the moment when disturbances appear in Figure 9. The results indicate that when step disturbances occur, our proposed algorithm produces the smallest acceleration values on each axis. This finding suggests that the proposed algorithm can effectively suppress the overly aggressive control actions that might be generated by the backstepping controller.  
Figure 10a,b illustrate the comparison of thrust and three-axis torque generated by saturated SMC, traditional SMC, and our proposed algorithm. The figures clearly show that our algorithm's total thrust chattering range is [2.08, 1.46], with nearly zero three-axis torque chattering. In contrast, traditional SMC exhibits chattering ranges of [2.24, 1.80] for total thrust and [0.019, -0.019], [0.02, -0.02], and [0.038, -0.031] for the three-axis torque, respectively. Saturated SMC displays thrust and three-axis torque chattering ranges of [2.21, 1.74], [0.017, -0.017], [0.014, -0.014], and [0.001, -0.001], respectively. These data convincingly demonstrate that our improved controller significantly mitigates the chattering problem inherent in SMC while achieving superior disturbance suppression compared to saturated SMC.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Disturbance Occurrence Time (s)**} & \multicolumn{3}{c}{**CB&SOC**} & \multicolumn{3}{c}{**CB&SAT**} & \multicolumn{3}{c}{**BB&BSMC**} \\ \cline{2-10}  & **x** & **y** & **z** & **x** & **y** & **z** & **x** & **y** & **z** \\ \hline
0 & 85.06 & 0.15 & 40.56 & 82.52 & 0.01 & 41.96 & 16.17
# Frequency-Domain and Spatial-Domain MLMVN-Based Convolutional Neural Networks
## 2 MVN and MLMVN Fundamentals
### MLMVN
The entire learning process should continue until the learning error satisfies the angular RMSE criterion [61]:  
\[RMSE=\sqrt{\frac{1}{S}\sum_{s=1}^{S}\frac{1}{N_{M}}\sum_{n=1}^{N_{M}}(\Delta_ {sn})^{2}}\leq\lambda, \tag{15}\]  
where \(\lambda\) is the pre-determined acceptable threshold, \(\Delta_{sn}\) is the error for the \(s^{th}\) learning sample (\(s=\overline{1,S}\)) of the \(n^{th}\) output neuron (\(n=\overline{1,N_{M}}\)), and its calculation depends on the activation function. For the continuous activation function (4), \(\Delta_{sn}\) can be calculated as the difference between the arguments of the desired and actual outputs:  
\[\Delta_{sn}=\min(\arg(D_{sn})-\arg(Y_{sn}),\arg(Y_{sn})-\arg(D_{sn}))mod\ 2\pi, \tag{16}\]  
where \(D_{sn}\) and \(Y_{sn}\) are respectively the desired and actual neuron's outputs for the \(s^{th}\) sample and the \(n^{th}\) output neuron. It is important to mention that weight adjustment for both learning techniques starts from the first layer. After the error backpropagation and adjustment of weights in the first hidden layer, we should update its neurons' outputs, and only then proceed to adjustments of the weights in the second layer. This process should continue until the weights of the last-layer neurons are adjusted.
# A Comparative Study on Violent Sloshing with Complex Baffles Using the ISPH Method
## 3 Model Validation and Convergence Analysis  
In order to validate the accuracy of the sloshing computation by using the ISPH method, the numerical results of the sloshing tank are compared with the experimental data of [PERSON] and [PERSON] [27] and the computations by using the commercial CFD software, i.e., STAR-CCM+. In STAR-CCM+, the 2D viscous incompressible Navier-Stokes equations are solved. The conservation equations for the mass and momentum are based on the Reynolds averaged methods. The standard \(k-\varepsilon\) turbulence model and Eulerian two-phase model are used. The free surface is captured by the volume of fluid (VOF) approach.
## 2 Materials and Methods
### Calibration Protocols
#### 2.3.2 Fluorescence Calibration  
A manual calibration protocol was similarly conducted for fluorescence measurements using fluorescein sodium salt (Sigma-Aldrich, CAS 518-47-8) as the reference fluorophore. Serial 1:2 dilutions were prepared from an initial stock solution of 21.17 uM fluorescein. Fluorescence was recorded using both the Chi.Bio platform (excitation/emission: 457/35 nm and 550/42 nm) and a Cytation 3 Plate Reader (excitation/emission: 488 nm and 530 nm). To standardize fluorescence values across devices and fluorophores, a two-step normalization strategy was employed. First, spectral normalization was applied to convert raw fluorescence outputs into Normalized Fluorescence Radiance (NFR), a unit that accounts for the brightness of the fluorophore used. During the calibration with fluorescein, the Chi.Bio digital output was divided by the known molecular brightness of fluorescein (B\({}_{\text{fluorescein}}=597.5\)) to compute NFR values.  
However, when quantifying fluorescence signals from biological samples expressing GFP, the normalization must reflect the intrinsic brightness of GFP itself. Therefore, for GFP-expressing cells, the NFR was computed by dividing the Chi.Bio fluorescence signal by the molecular brightness of GFP (B\({}_{\text{GFP}}\)), rather than fluorescein. This adjustment ensures that differences in fluorophore brightness are accounted for, enabling meaningful biological interpretation of fluorescence data. In the final calibration step, a nonlinear model was used to relate NFR values to Molecules of Equivalent Fluorescein (MEFL), thereby standardizing measurements in widely accepted units. The calibration model takes the following form:  
\[\text{MEFL}=a\cdot e^{b\cdot x}+c\cdot e^{d\cdot x^{0.995}}, \tag{3}\]  
where \(x\) is the NFR; \(a,b,c,d\) are parameters estimated from calibration data (see Table S3 and Supplementary Figure S7). This dual-exponential function was chosen based on its superior fitting performance compared to linear and polynomial alternatives, which exhibited lower \(R^{2}\) values and higher residuals [44].
## 5 Discussion  
A novel clustering approach for CH selection in LEACH is introduced for identifying energy-efficient algorithms and presenting simulation results demonstrating the effectiveness of the proposed approach.  
* With a predefined number of CHs, a distance threshold-based clustering algorithm was developed in the first level that has a minimum average distance in terms of residual energy level per node. Also, the distance threshold supported communication between nodes to CH and CH to BS.
* The homogeneity in the network was maintained by grid concatenating of cluster formation. Although the position of residual nodes was changing frequently, the defined boundary conditions based on angular distances covered all the nodes within the network.
* The second-level CH selection within clusters added additional computational complexity to the linear but remained within \(\backslash\)(O(N \(\backslash\)cdot k)\(\backslash\)) due to the quadratic term within each cluster. The other parameters including initialization of the cluster, CH selection process, and broadcasting steps were also linearly complex computations.  
Figure 8: Performance comparison between proposed and existing state-of-the-art method: (**a**) network lifetime; and (**b**) network throughput.  
Figure 7: The number of dead nodes after different numbers of information exchange rounds: (**a**) 200 rounds; (**b**) 500 rounds; and (**c**) 1000 rounds.  
This complexity indicates that the algorithm scales linearly with the number of nodes and the average number of nodes per cluster, making it efficient for large networks with well-distributed clusters.  
In conclusion, the performance of the LEACH algorithm is enhanced by introducing angular distance estimation and boundary conditions while selecting CHs in a randomly deployed WSN network.
# Future Trend in Wearable Electronics in the Textile Industry
## 4 Technical Limitations in Wearable Technology
#### Efficiency of power management  
The increasing attention being paid to wearable energy storage devices is triggered by the growing demand for wearable electronics, where besides the energy/power density, lightweight and comfort are also essential requirements. An ideal power device for smart textiles should be in the form of a textile, which can be readily incorporated into a garment without much sacrifice on softness, lightweight, or comfort [58]. In recent research, it was found that printable techniques show promise for the fabrication of power supply devices with practical scalability and versatility, especially for applications in wearable and portable electronics [59].  
A recent study has demonstrated the feasibility of all-inkjet-printed graphene on a rough and porous textile surfaces for wearable electronic applications. Due to the rough and porous textile surface, it is challenging to produce a continuous conductive track onto textiles by using low viscosity graphene-based inkjet printable inks. Therefore, the study proposes an organic nanoparticle-based surface pre-treatment on textiles that reduces the sheet resistance of graphene-based conductive prints [60]. One of the challenges with current reduced graphene-based electronic textiles is that they suffer from poor electrical conductivity and higher power consumption.
## 3 Results and Application of Dangerous Driving Index  
### Weight for Evaluation Items  
In order to employ simulation videos and instantly assess the consistency of the response results, a web-based survey form specifically designed for this study, as shown in Figure 3, is used, as existing survey forms were inadequate for the survey required in this study. When the consistency ratio calculated for the response results of each question is below 0.1, indicating that the response is considered to be inconsistent, a consistency warning notification is displayed, as shown in Figure 3b, so that the respondents could modify their choices.  
A survey was conducted using these web-based forms, targeting 100 participants, with a balanced ratio of approximately 1:1:1 among road traffic professionals, two-wheeled vehicle drivers, and common citizens (non-drivers). Figure 4 shows the weights of two-wheeled vehicle driving evaluation items, as derived from the survey results. The weights of the categories (main criteria) are as follows: traffic violation (0.380), pedestrian threat (0.365), and reckless driving (0.255). This signifies that pedestrian threat is more important than reckless driving, and traffic violation is more important than pedestrian threat. The weights of the evaluation items (sub criteria) were highest for helmet violation (0.158), speed violation (0.124), and pedestrian close driving (0.122), indicating that these should be regarded as important attributes for evaluating two-wheeled vehicle driving.  
Figure 3: Screenshot of web survey form for dangerous driving index calculation: (**a**) example of survey answers; and (**b**) example of consistency alert.  
The risk criteria for the reckless driving category were derived based on the average risk score for each video type. A higher level of the simulation video type corresponds to a greater risk. For rapid acceleration and rapid deceleration, five levels of types were considered, and the respondents were instructed to enter a risk score ranging from 0 to 5. For rapid turn and rapid lane change, there were four levels of risk, and the respondents were required to assign a risk score between 0 and 4. The average of the risk scores was then utilized to calculate the weight of each level. Table 2 shows the average risk score and weight for each level within the reckless driving category.
# Data Compression with a Time Limit
## 3 Lossless Compression
### Lossless Compression of One-Dimensional Data with a Time Limit  
We began testing on a set of 1000 html pages that have similar contents and that were downloaded through the web crawler Wibbi by Stanford University, obtaining a resulting document of size 34 Mb.  
We tested the following compression tools: _gzip_, _xz_, _bzip2_, _arithmetic coding_ all in the default configuration.  
For this experiment, we used a Mac Book Air with 8 Gb of RAM and a 1.7 Ghz Intel Core i7 processor. These, in Table 1, are the results of running the compression tools on this test data set:  
Gzip is the fastest of all the tools, probably because it is implemented in the java.util.zip library and this makes it optimized. Arithmetic Coding, although faster than all the other setups except Gzip, returns a significantly larger output size than the other compressors: about 100% more. The best setup in terms of output size is XZ, even though it takes on this platform the longest time: about 26 s. In this experiment, we decided to limit our optimal mix to only two algorithms and to compress each file with a single compressor. We tested two different time budgets: 22,000 milliseconds and 10,000 milliseconds.  
For the time budget of 22,000 milliseconds, the optimal mix turns out to be composed of Bzip2 and XZ. A total of 632 html pages were compressed by using XZ and the remaining 368 by using Bzip2. The compressed file size for this test was 6,935,454 bytes long and the total time to compress was 22,566 milliseconds, i.e., slightly more that the assigned time budget: an error of about 0.0257%. This depends on the fact that we had chosen to compress each file with a single compressor. By removing this constraint, we can likely adhere precisely to the assigned time budget.  
For the time budget of 10,000 milliseconds, the optimal mix turns out to be composed of Bzip2 and Gzip. A total of 557 html pages were compressed by using Gzip and the remaining 443 by using Bzip2. The compressed file size for this test was 7,401,784 bytes long and the total time to compress was 9703 milliseconds, i.e., slightly less that the assigned time budget.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 2 Overview of the Existing Data Acquisition Techniques for Road Extraction
### Hyperspectral Imaging Technology
#### 2.2.2 Typical Sensors  
According to the installed platforms, hyperspectral sensors can be divided into spaceborne, airborne, UAV [115; 116; 117], car-borne [118], and ground-based [119] imaging systems. Airborne hyperspectral imaging systems were the first hyperspectral imagers to be developed and used for verifying the design of later spaceborne instruments. [PERSON] et al. [80] presented a comprehensive review of airborne hyperspectral imagers, including key design technologies, preprocessing and new applications. Based on this review, we added spaceborne sensors in this paper and summarised the typical hyperspectral imagers in Table 2. Evidently, most hyperspectral sensors, especially the spaceborne hyperspectral imagers, were developed in the USA. In addition, there are more airborne hyperspectral imagers are than spaceborne hyperspectral imagers due to the hardware investment. The future spaceborne program includes the HyspIRI hyperspectral satellite of America [120] and the ENMAP hyperspectral satellite of Germany [121]. Most hyperspectral imagers can acquire data in the visible to near-infrared spectral range owing to the availability of silicon detectors with wide spectral detection ranges. Additionally, shortwave infrared and longwave infrared hyperspectral imagers have emerged in recent years [80].
# Research on Rollover Stability and an Anti-Rollover Warning System for the Vibroseis Truck
## 3 Study on the Warning Index of Rollover for the Vibroseis Truck
### Analysis of the Vehicle Rollover Warning Index  
The conventional rollover warning index uses the lateral load transfer ratio (LTR) as the dynamic warning index for judging the risk of a vehicle rollover. The LTR refers to the absolute ratio of the difference between the sum of the loads of the left-side tires and of the right-side tires to the sum of normal loads of all tires [15; 16]. The range of LTR is \([0,1]\). When the LTR = 0, the normal loads of both sides of the vehicle are nearly equal. The vehicle is in a balanced state. When the LTR approaches 1, one side of the wheels is off the ground, and rollover occurs. This index is easy to calculate in real time. However, it does not cover structural parameters and motion parameters and easily produces false positives.  
The time to rollover (TTR) is a kind of dynamic rollover warning index [17; 18; 19; 20; 21]. A vehicle can adopt different forewarning measures depending on the value of TTR. The TTR refers to the time from the moment to motion until rollover. With good real-time performance, the TTR is a dynamic index and avoids the influence of parameters such as the gravity center. For vehicles with large changes in load and center of mass, it is difficult to determine the parameters' threshold with this method.  
The change of the gravity center of the f vehicle generates an angle (roll angle) which is between the gravity direction and the vertical slope direction. The roll angle is selected as the warning index to judge whether the vehicle will roll or not [22; 23]. When the roll angle is greater than the critical value \(\theta_{max}\), the vehicle undergoes rollover, according to the formula \(\theta>\theta_{max}\). This index is simple and easy to be measured. However, its warning performance is poor, and it cannot accurately predict a dangerous situation in real time.  
The lateral acceleration \(a_{y}\) is produced by the centrifugal force of the vehicle during the steering process, and its value represents the rollover stability of the vehicle [24]. If the lateral acceleration exceeds the critical value amax, the vehicle is undergoes rollover, according to the formula \(a_{y}>a_{max}\). This index is simple, intuitive, and easy to determine in the basis of the vehicle's turning speed. The disadvantage of this method is that this index requires many measurements, and its warning ability is poor, with a low precision.  
Therefore, the commonly used vehicle rollover warning indexes have some disadvantages such as poor measuring reliability, low accuracy, and no real-time performance. Some indexes also do not consider the effect of vehicle load and the gravity center position on critical values. Therefore, exploring a new type of warning index for vibroseis trucks is important for the development of a warning system for vibroseis trucks.