esa-sceva/satcom-chunk-collection huggingface dataset:
# Variations in the Equatorial Ionospheric F Region Current during the 2022 Tonga Volcanic Eruption
## 2 Satellite Data  
The Swarm satellite constellation of the European Space Agency (ESA) was launched on 22 November 2013 into near polar orbits. Among three satellites, Swarm-A and Swarm-C fly side by side at altitudes of approximately 470 km, with a longitudinal separation of 1.4\({}^{\circ}\), and Swarm-B flies at a higher altitude of 520 km. The method by which FRC estimations are obtained from dual-satellite observations is briefly introduced in the present work. Using the magnetic field observed by CHAMP, Orsted and Satelite de Aplicaciones Cientificas-C (SAC-C) magnetic satellite data version 6 (CHAOS-6) model, the magnetic field of the core, mantle, and disturbance fields were removed [15]. Using [PERSON]'s circuit law: \(j_{z}=\frac{1}{\mu_{0}5}\oint Bdl\), where \(\mu_{0}\) is the vacuum permeability, \(B\) is magnetic field caused by FRC,\(dl\) is the line element, and \(S\) is the quadrilateral encircled by four observation points of Swarm-A and Swarm-C, the FRC can be calculated. The integration can be expressed as: \(\mu_{0}j_{z}S=(B_{x1}+B_{x2})dl_{1}+\left(B_{y2}+B_{y3}\right)dl_{2}-\left(B_{x3 }+B_{x4}\right)dl_{3}-\left(B_{y4}+B_{y1}\right)dl_{4}\), where \(B_{x}\) is along the satellite orbit, and \(B_{y}\) is from Swarm A to C in the horizontal plane, \(dl\) is the line elements. The subscript 1 (3) and 2 (4) means samples are taken separated by 5 s along the Swarm A (C) orbit. The assumptions are that the currents are constant over the period of 5 s, the time for taking the two samples \(B_{1}\) and \(B_{2}\) or \(B_{3}\) and \(B_{4}\), and the spatial size of the current is larger than the size of the quad. For mitigating the assumption, the magnetic field data have been low pass filtered with a period of 20 s in order to suppress the small-scale variations. For more details of the inversion method, please refer to [PERSON] et al. (2019) [11]. Magnetic coordinates were derived from the quasi-dipole latitude, as outlined by [PERSON] (1995) [16].  
The ICON satellite flies in a low-inclination orbit (27\({}^{\circ}\)) at an azimuth of 575 km [17]. The Michelson Interferometer for Global High-Resolution Thermospheric Imaging (MIGHTI) onboard ICON measures thermospheric winds and temperatures. The 630-nm oxygen red-line observations are collected from altitudes of 160-300 km, while the green-line observations are collected from 90-200 km [18]. The MIGHTI winds cover GLats of \(-13^{\circ}\) and 42\({}^{\circ}\) and all GLons within approximately 1.5 h.
## 7 Methodology and Experimentation
### Experimentation
#### 7.4.1 Job Completion  
This section is divided into two parts: the results regarding human-designed heuristics and those obtained by the RL agent. In Figure 4, we present the average from 20 simulations when using random walk along with the 1500 jobs set; analogously, the same results are given when using random waypoint in Figure 5. In Figures 6 and 7, we present the same 20 simulations but using the entire dataset of jobs.  
As mentioned previously, we performed experiments on two different sets of jobs. The primary goal of the comprehensive dataset of jobs is to highlight that when the simulation runs longer and thus encompasses more events, the proposed human-designed heuristics demonstrate improved performance compared to their original counterparts. The results shown in Table 1 are proof of this since in the set with 1500 jobs, the original human-designed heuristics do not deviate too much from the new variation we present in this paper. For instance, the difference in job completion between BPA and RelBPA is 8.21%, whereas between eSEAS and ReleSEAS it is 10.58%. On the other hand, when using the complete set with 36,000 jobs, the difference between BPA and RelBPA increases to 16.49%, then again for eSEAS and ReleSEAS, the difference rises to 18.83%. In both cases, the difference in job completion between the original human-crafted policy and the new version almost doubles when using the complete set of jobs over the small set. This same behavior can be seen in Table 2, in which the usage of Random Waypoint makes this difference smaller than the results obtained using Random Walk. An additional Random scheduler is added to these results to use as a baseline, which distributes the jobs randomly every time the given event is received.  
Figure 4: Average job completion for each simulation using random walk to generate connection events and the 1500 jobs set.  
Figure 5: Average job completion for each simulation using random waypoint to generate connection events and the 1500 jobs set.
# An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction
## 4 Discussion
### Cross Validation Experiment  
Cross-validation or Monte Carlo simulation method can be used to evaluate the robustness of the model [37]. In this study, a five-fold cross-validation experiment is conducted to further test the robustness of the proposed method (see [38] for details). Since the data are too few, the early stopping strategy is adopted to prevent overfitting, and the maximum batch of early stopping is 30. In this cross validation experiment, the target dataset is divided into six subsets. Then, the first subset is used to predict the second subset, the first two subsets are used as the training set to predict the third subset, and so on in a similar fashion. The average value of the results of all subsets is used as the final evaluation. The experimental results of the cross validation are as follows: MAE = 13.67, RMSE = 17.98, and MAPE = 0.22. The results show that the proposed method can achieve good prediction results, when the training data are too few, which mean that the proposed model has good robustness.
# Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning
## 6 Conclusions  
Static anti-malware products can only identify already registered malware and are incapable of recognizing modified or newly-released malware. Dynamic malware classification examines malicious activities and footprints during execution in a controlled environment but may be resource and time intensive. Deep learning-based techniques can identify complex attack patterns in malware images and, thus, recognize new malware in a confined environment. In this regard, two new frameworks, DFS-MC and DBFS-MC, have been proposed for hybrid learning and feature boosting to identify malware using a large stringent unseen dataset. In the DBFS-MC framework, deep features are generated from the TL-based customized ResNet-18 and DenseNet-201. These deep feature spaces are concatenated for achieving a single boosted feature space and provided to an SVM. The feature boosting merged the potentials of customized ResNet-18 and DenseNet-201 models while leveraging the discrepancies of the single model. Furthermore, it efficiently classified the exceptional malware classes, i.e., Autorun.K, Swizzor.gen:II, Wintrim.BX and Yuner.A, which were previously not addressed. In addition, the 40:60 partitioning scheme of the dataset further increases the robustness. The DBFS-MC framework achieved the highest accuracy (98.61%) and F1-score (0.9630) and improved precision (0.9632) and recall (0.9627). Moreover, our findings revealed that the proposed DBFS-MC framework outperforms TL-based and training from scratch customized CNNs. In the future, the proposed frameworks may be adapted to new malware attacks using the standard benchmark dataset like android and IoT malware. Furthermore, this study may be enhanced by developing an anti-malware application for Microsoft Windows OS that can analyse the traffic on FTP for malware analysis in real-time scenarios.  
Conceptualization, M.A., S.H.K. and T.J.; Formal analysis, M.A. and S.J.H.; Funding acquisition, S.J.H. and M.U.A.; Investigation, A.K.; Methodology, A.K. and U.Z.; Project administration, M.A., M.M. and M.U.A.; Resources, M.A.; Supervision, A.Z.; Validation, M.A. and U.Z.; Visualization, T.J.; Writing--original draft, M.A., S.H.K. and T.J.; Writing--review & editing, S.J.H., M.M., A.Z. and M.U.A. All authors have read and agreed to the published version of the manuscript.  
We acknowledge the Global college of Engineering and Technology, Muscat for funding this research under an internal research funding grant.  
Not applicable.  
All the scripts that are developed for the simulations are available from the corresponding author on reasonable request.  
This work was conducted with the support of the PAEC program. We also thank the Pattern Recognition Lab (PR-Lab) and Pakistan Institute of Engineering, and Applied Sciences (PIEAS), for providing necessary computational resources and a healthy research environment.  
The authors declare no conflict of interest.
## 4 Data Preprocessing  
Real-world databases are highly susceptible to noise, missing data, and outliers, among other disturbances. Examples of noise sources are measuring devices, rounding operations, and transmission mechanisms. Intermittent operation of measuring or transmission devices produces missing data. Periods of missing data can be as short as one reading or as long as several months. Outliers are out-of-range measurements. We can see them as a form of noise but, in some cases, they are authentic peaks (positive or negative) that result from non-characteristic behavior of the underlying system that produces the data. Noise, missing data, and outliers are responsible for data quality deterioration; these problems are more likely to happen when the system handles vast amounts of data (up to gigabytes). Data analytics pipelines such as MIRD must specifically deal with this problem as the lower the input data quality, the lower the quality of the results.  
Before describing the operations of the data-preprocessing component, it is essential to understand how MIRD acquires data. CFE implemented an Internet-of-Things system across most of its assets and equipment. This system includes a PQM at the coupling point of every circuit or quality node. As previously stated, each PQM records more than 30 electrical variables at a 10-min resolution. A relational database called SIMOCE (the Spanish acronym for Sistema de Monitoro de Calidad de la Energia--Power Quality Monitoring System) collects PQM readings, which serve CFE's distribution engineers throughout the country. The data-ingestion component of MIRD uses SIMOCE as its data source. Afterward, the data-preprocessing component consists of four steps. The first step is a sub-sampling operation. PQ measurements, originally recorded at 10-min intervals, are average aggregated to a 1-h resolution, which results in 168 values per week. The second step is to locate missing data; in the absence of data, it applies an imputation process. This operation applies only when a few measurements are missing. The third step is to detect outliers. If they exist, the same missing data imputation process is applied. The preprocessed time series persists in the artifact-storage (Parquet) layer in the fourth step. The following sections explain these steps in detail.
## 3 Results and Discussion  
### The Weather, the Soil Water Content and Crop Development  
The seasonal daily time courses of the main agrometeorological variables--air temperature (Ta), photosynthetically active radiation (PAR), vapor pressure deficit (VPD, kPa) and precipitation (P)--during the experimental period (1 June-30 November) are shown in Figure 1a,b. Ta was 24.6 \(\pm\) 3.4 \({}^{\circ}\)C, with a very hot period in the second half of July when daytime hourly temperatures exceeded 41 \({}^{\circ}\)C. During late spring and early summer, 48.8 mm of precipitation was recorded. The radiation pattern expressed by PAR follows the usual trend for the area, while VPD reached high values corresponding to high Ta in the second part of July.  
The patterns of soil water content, expressed by \(\theta\), are illustrated in Figure 2, along with irrigation and rainfall events for clarity. In the figure, mean-field \(\theta\) values are reported, distinguishing between mulched rows and non-mulched rows, along with the wilting point and field capacity. At the beginning and end of the experimental period, missing values were due to equipment malfunction. After irrigation began, \(\theta\) values evolved differently for \(\theta_{r}\) and \(\theta_{ir}\); while soil water content below the mulched surface remained almost constant and around field capacity (FC), the water content in soil not covered by the films decreased rapidly as evaporation proceeded, even dropping below the WP in the middle part of the growth cycle and more severely after the crop harvesting. In the latter case, the sudden increase in \(\theta_{ir}\) corresponds solely to precipitation events. Mean \(\theta\) values at the field level  
Figure 1: Meteorological conditions during the experimental period (1 June–30 November 2023); (**a**) air temperature, Ta, and Photosynthetically Active Radiation, PAR; (**b**) vapor pressure deficit, VPD, and precipitation, P.  
remained above WP. These high mean \(\theta\) values were due to substantial water supply via irrigation (583 mm), resulting in high relative extractable water (REW) values in the first half of the growth cycle, well above the water stress threshold for food herbaceous crops, which ranges between 0.4 and 0.5 (i.a., [55; 56]).  
The development of the crop plants is shown in Figure 3, in terms of LAI and fresh green biomass. Both variables exhibit similar trends during the growth cycle, showing a slow increase in June, a rapid increase in July with a maximum in early August, followed by a decrease until the harvest of the watermelon fruits. The final yield of freshly harvested fruits for the market was 10.00 kg m\({}^{-2}\).
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 3 Integrated Management Framework
### Plan Executor Module Technical Implementation  
The Plan Executor serves as the orchestrating module that enables autonomous and dynamic operation of the CMS through closed-loop optimization and adaptive scheduling capabilities.
## 8 Technical Challenges for 6G Development
### Terahertz Frequency
#### 8.1.4 Rapid Variations in the Channel and Sporadic Connectivity  
In the terahertz band, the channel coherence time is inversely proportional to the carrier frequency, resulting in a very small coherence time and a large Doppler spread. This leads to faster fluctuations than current cellular networks, exacerbated by higher shadow attenuation. The spatially directional signal transmission of small cells in the terahertz system creates rapid changes in path strength, requiring a fast-adaptive mechanism to address the intermittent connection challenge [42].
# Study on the Vibration Variation of Rock Slope Based on Numerical Simulation and Fitting Analysis
## 4 Numerical Simulation of Slope Effect
### Effect of Height Values on Blasting Vibration  
In this section, vertical and horizontal vibration velocities were selected for analysis. The effect of heights on blast vibration velocity within the same slope was analyzed, and the inference obtained by fitting Equation (2) and fitting Equation (3) was verified to be consistent with the numerical simulation results. In the slope model, feature points have the same horizontal distance (\(D\)) and various height differences (\(H\)) as the data from working condition I. The feature points were named C\({}_{1}\)-C\({}_{6}\) from bottom to top, as shown in Figure 4.  
The comparative analysis of vertical and horizontal vibration velocities is shown in Figure 5.  
As can be seen from Figure 5, when the horizontal distance was constant, vibration velocities in both vertical and horizontal directions showed amplification near the top of the slope. The variation of vibration velocity in the vertical direction was more significant than that in the horizontal direction. In the vertical direction, the simulated minimum blasting vibration velocity was 2.44 cm/s, and the maximum value was 4.52 cm/s, with an increase of 85.24%. In the horizontal direction, the minimum blasting vibration velocity was 1.72 cm/s, and the maximum value was 3.06 cm/s. The increase rate was 77.91%, lower than that in the vertical direction, indicating that the vertical vibration velocity was more affected by the elevation, which is consistent with the law obtained by the fitting analysis.  
Figure 4: The feature points C\({}_{1}\)-C\({}_{6}\).  
Figure 3: The change of blasting vibration duration at the top of the slope.
# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 4 Results
### Contributions of Video Features to UES-SF
#### 4.3.4 Motion Mean and Motion Standard Deviation  
The degree of motion differs significantly between certain video categories, such as action movies, sports, relaxing movies, and dramas. As explained in Section 2.2.2, we used two different features related to the average motion activity and its variability. The linear and quadratic model fits of engagement dimensions to mean motion computed using  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Linear**} & \multicolumn{2}{c}{**Quadratic**} \\ \cline{2-5}  & \(\mathbf{R^{2}}\) & \(\mathbf{p}\) & \(\mathbf{R^{2}}\) & \(\mathbf{p}\) \\ \hline FA & 0.0 & 0.804 & 0.078 & \textless{}0.01 \\ PU & 0.003 & 0.479 & 0.007 & 0.58 \\ AE & 0.0 & 0.973 & 0.074 & \textless{}0.01 \\ RW & 0.0 & 0.998 & 0.054 & 0.017 \\ UES & 0.001 & 0.728 & 0.112 & \textless{}0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Color Variance relation to user engagement scale (short form) for linear and quadratic model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Linear**} & \multicolumn{2}{c}{**Quadratic**} \\ \cline{2-5}  & \(\mathbf{R^{2}}\) & \(\mathbf{p}\) & \(\mathbf{R^{2}}\) & \(\mathbf{p}\) \\ \hline FA & 0.007 & 0.302 & 0.027 & 0.129 \\ PU & 0.006 & 0.361 & 0.009 & 0.511 \\ AE & 0.001 & 0.698 & 0.034 & 0.079 \\ RW & 0.016 & 0.118 & 0.022 & 0.193 \\ UES & 0.005 & 0.405 & 0.023 & 0.182 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Shot Length relation to user engagement scale (short form) for linear and quadratic model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Linear**} & \multicolumn{2}{c}{**Quadratic**} \\ \cline{2-5}  & \(\mathbf{R^{2}}\) & \(\mathbf{p}\) & \(\mathbf{R^{2}}\) & \(\mathbf{p}\) \\ \hline FA & 0.005 & 0.375 & 0.008 & 0.535 \\ PU & 0.006 & 0.33 & 0.016 & 0.311 \\ AE & 0.051 & \textless{}0.01 & 0.054 & 0.018 \\ RW & 0.062 & \textless{}0.01 & 0.067 & \textless{}0.01 \\ UES & 0.037 & 0.018 & 0.037 & 0.062 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Lighting Key in relation to user engagement scale (short form) for the linear and quadratic model.  
optical flow are summarized in Table 8. Table 9 refers to linear and quadratic models of standard deviation of motion. The values of \(p\) for quadratic models of both the mean and standard deviation of the motion were below \(\alpha=0.05\) for the sub-dimension of Perceived Usability (PU). Figure 3c as an example demonstrates a U-shaped relation between the mean value of Motion Activity and Perceived Usability (PU) dimension. The same behaviour could be identified for the case of Motion Standard Deviation. This behaviour indicates optimum values of motion which will lead to the best sub-score of Perceived Usability (PU). This implies that very slow or very fast motion in advertising has a negative impact on this dimension of engagement.
# Accelerating Image Classification using Feature Map Similarity in Convolutional Neural Networks
## 1 Introduction
In this case, the images can be classified through the CNN without using the cache; hence, a well-trained CNN can be used as is. To achieve this, we extract feature vectors from the feature maps generated in the convolution layers. In our proposed structure, the cache is activated after images classified by the CNN are written to the cache. However, there is no need to modify the CNN's structure or retrain the weights. Feature maps are response images that are convolved by a trained kernel or filter and represent features extracted from the image. The CNN's kernel is similar to the Gabor filter [12]. In image classification, studies using intensity from various directions and frequencies extracted from the Gabor filter were used [13; 14; 15]. Likewise, the features extracted by the CNN kernel represent the intensity. For example, two completely different cat images can be classified as cats because the intensities of their eyes, nose, ears, and tail are similar. Owing to the similarity of the CNN kernel and Gabor filters, many studies have been conducted that combined them. [PERSON] et al. [16] used the Gabor filter in different layers of a CNN to reduce the computational complexity of the training phase. The Gabor filter was also used as a preprocessing method for weight training [17; 18; 19]. [PERSON] et al. [12] replaced the Gabor filter with a convolution kernel in a CNN to perform the convolution and train the weights. In general, a CNN can be combined with a Gabor filter to achieve good performance. In this study, we do not use the Gabor filter directly because we extract feature vectors from the pretrained CNN's feature map. However, given that the CNN kernel is generated by training, it does not extract certain features of the input image. This makes it difficult to describe or vectorize the feature maps. However, like the Gabor filter, which shows the intensity of the extracted features, we cache the input image using the intensity in the CNN's feature map. Our main contribution is to locate the cache in the convolution layer of a CNN to make sure it is operable in order to improve the speed of image classification in low-performance devices. In particular, well-trained CNN models can be used without retraining. This structure is not a complete substitute for a CNN's image classification capabilities. Therefore, this auxiliary structure assists the pretrained CNN model. When images corresponding to the same class are continuously input, the classes to which these images are classified are memorized and used when the next image is input.
# Experimental Study on Small-Strain Shear Modulus of Unsaturated Sility-Fine Sand
## 3 Small-Strain Tests for Unsaturated Sand
### Test Program  
Before the test, the soil-water characteristic curve (SWCC) for the silty-fine sand from Hangzhou Bay was measured with a pressure plate extractor and the experimental data were fitted using the Van Genuchten (VG) model, as shown in Figure 7 [33]. The saturation of a specimen was controlled by Equation (2):  
\[S_{r}=S_{c}+\frac{\left(1-S_{c}\right)}{\left[1+\left(\alpha\psi\right)^{n} \right]^{m}} \tag{2}\]  
where \(\psi\) is the matrix suction; \(S_{r}\) is the saturation; \(a\), \(m\) and \(n\) are model parameters (\(a\) = 0.234, \(m\) = 0.116 and \(n\) = 0.234); \(S_{c}\) is the residual saturation (\(S_{c}\) = 0.172).  
According to the relationship between saturation and matrix suction obtained from Equation (2), under the condition of keeping the net stress constant, we could obtain specimens with different saturation by controlling the matrix suction. Then, the combined BE-RC test on sand specimens under the dehumidification path was conducted and the correspondence between matrix suction and saturation during the test is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Matrix suction (kPa)** & 4 & 6 & 8 & 10 & 15 & 40 & 200 & 495 \\ \hline
**Saturation (\%)** & 92 & 65 & 50 & 40 & 30 & 20 & 17 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correspondence between matrix suction and saturation.  
Figure 6: Comparison of \(G_{max}\) between the BE and RC test results.