esa-sceva/satcom-chunk-collection huggingface dataset:
# HPC Cloud Architecture to Reduce HPC Workflow Complexity in Containerized Environments
## 3 Platform Design
### Image Management
When the task sent from the worker node is finished, Cancel Consumer is used to deactivate the task, and if it is not the master worker node, the worker node registered using Remove Worker is released. The task queue process for image generation is a scale-down and waits for the next user's request. Figure 10: Image Manager workflow. However, if there are currently active tasks in the auto-scaling worker group, an arbitrary worker among the active workers is first selected using the following steps: add a worker node, activate the task queue using Add Consumer, and send the task by specifying the routing key. When the work is finished, and the worker node is released, it returns to the scale-down state and waits for another request. If there are currently running tasks in all workers, the worker node with the smallest sum of the waiting time is selected and sent. The variables and descriptions to get Equations (1) and (2) for the Get Min (total waiting time) worker are summarized in Table 2 below. Figure 11: Auto-scaling scheduler workflow. Figure 12: Flowchart of the auto-scaling scheduler. Equation (1) explains \(\Phi\)**WT** that calculates the total waiting time for active tasks. After getting a list of currently running tasks, how long each task has been executed up until the present time is calculated using the formula \(\Phi\)**CT\(-\Phi\)**ST**. Here, \(\Phi\)**ST** represents the start time of the current active task, \(\Phi\)**CT** the current time of the system, and n the number of current active tasks. \(\Phi\)**WT** denotes the sum of latencies of active tasks for each worker node that has active tasks. Finally, we can get the worker \(\Phi\)**W** that has a minimum value of the total waiting time of the workers using Equation (2). \[\Phi\text{WT}=\sum_{n=1}^{\infty}\left(\Phi\text{ ET}_{n}-(\Phi\text{ CT }-\text{ ST}_{n})\right) \tag{1}\]  
\[\Phi\text{W}=min(\sum_{N=1}^{\infty}\left(\Phi\text{ WT}_{N}\right)) \tag{2}\]  
However, when these equations are applied to an actual cluster environment, the following limitations exist. We implemented one worker per node; therefore \(N\) also stands for the number of nodes of Image Manager. The higher the value of \(N\), the better, but it is limited by the number of network switch ports connecting nodes in a cluster configuration. Considering the availability of our HPC resources, we tested \(N\) up to 3. We also implemented one task per process; therefore, \(n\) also stands for the number of processes of the host. The maximum value of \(n\) is the number of cores per one node. However, considering each task is not executed completely independently but shares the resource of the host, we actually created 4 tasks and tested them. The optimization of the resource use about n is one more area of further research.
## 4 Experimental Verification
### Experimental Results  
The impulse response is shown in Figure 14a, and the corresponding reversed impulse response is shown in Figure 14b. One pulse sequence representing [0 1 0 1 0 1 0 1 0 1 0 0 0 0 0 0 1 1 0 1 0 1 0 1 0 ] was transmitted through the channel, and Figure 14c shows the received signal. The corresponding focused waveform is shown in Figure 14d. Figure 14e,f show the first 30 ms of Figure 14c,d. From Figure 14f, the received waveform is refocused and the transmitted data can be clearly interpreted. The experimental results verified the effectiveness of this new communication system. Currently, the data transmission rate of the system can reach 67 bits per second (bps) using the stress wave-based transmission.  
Figure 14: Experimental results: (**a**) impulse response; (**b**) time-inversed impulse response; (**c**) received signal; (**d**) focused waveform; (**e**) zoomed-in figure of received signal; (**f**) zoomed-in figure of the focused waveform.
## 3 Results and Discussion
### MIMO Performance Analysis
#### 3.4.4 Channel Capacity Loss (CCL)  
The channel capacity loss is an indicator for understanding the change in capacity due to the MIMO environment. The CCL for the MIMO antenna can be calculated by the equation reported in Equation (7), provided in [26; 27].  
\[(\text{loss})=-\log_{2}det(a) \tag{7}\]  
where  
\[\begin{array}{c}a=\left[\begin{array}{cc}\sigma_{11}&\sigma_{12}\\ \sigma_{21}&\sigma_{22}\end{array}\right]\\ \\ \sigma_{il}=1-\left(\left|S_{ii}\right|^{2}-\left|S_{ij}\right|^{2}\right)\\ \\ \sigma_{il}=-\left(S_{il}^{*}S_{ii}+S_{ii}S_{ii}^{*}\right)\\ \\ a=\left[\begin{array}{cc}\sigma_{11}&\sigma_{12}\\ \sigma_{21}&\sigma_{22}\end{array}\right]\\ \\ \sigma_{il}=1-\left(\left|S_{ii}\right|^{2}-\left|S_{ij}\right|^{2}\right)\\ \\ \sigma_{il}=-\left(S_{ii}^{*}S_{ii}+S_{ii}S_{ii}^{*}\right)\end{array} \tag{8}\]  
The typical value considered as the upper limit for the performance parameter is 0.4. The CCLs based on the simulation and measurements are reported in Figures 22 and 23. The values of the CCL for the simulated and the measured results is below 0.4.
## 4 Application of a Multi-Adaptive Genetic Algorithm to the Problem of Human-Robot Cooperation
### Designing a Multi-Adaptive Genetic Algorithm
#### 4.2.2 Initial Population  
To begin the genetic algorithm, an initial population is randomly generated with randomly assigned genes. The resulting population has a certain level of diversity, but the optimality and fitness of the individuals are random. The size of the initial population greatly affects the efficiency of the algorithm. A small population with limited diversity may require many generations to produce optimal individuals, while a larger population with greater diversity may lead to faster convergence. However, it is important to balance the number of individuals with the number of genes and crossover rate to avoid duplications and save computational resources.  
The initial population is created through a random process, but it is important to ensure that the initial population has sufficient diversity, meaning that the population entropy cannot be too small.  
To represent and calculate population entropy, the solution space is divided into M non-overlapping regions (Q\({}_{1}\), Q\({}_{2}\),..., Q\({}_{M}\)). The probability that an individual in the population belongs to Qi is denoted by \(p_{i}\) (\(i\) = 1, 2,..., M). The population entropy of the \(t\)th generation (\(S(t)\)) can be expressed as:  
\[S(t)=-\sum_{i=1}^{N}p_{i}\ln(p_{i}) \tag{10}\]  
To estimate the population entropy, a range is used based on the fitness values instead of the solution space, where F\({}_{\text{min}}\) represents the minimum fitness value from the initial iteration to the \(t\)th generation, and F\({}_{\text{max}}\) represents the maximum value. An expansion coefficient \(\alpha\) (\(0<\alpha<0.1\)) is used to expand the range ((\(1-\alpha\))F\({}_{\text{min}}\), (\(1+\alpha\))F\({}_{\text{max}}\)). The entire interval is then divided into \(N\) regions, where \(N\) is the number of individuals in the population. The number of individuals whose fitness value falls within the \(i\)th region is denoted by \(l_{i}\) (\(i\) = 1, 2,..., \(N\)). The estimated value of \(p_{i}\) is calculated based on these values.  
\[\hat{p}_{i}=l_{i}/N \tag{11}\]When the estimated value of \(p_{i}\) is substituted into Equation (12), it provides an estimation of the population entropy \(S(t)\):  
\[S(t)=-\sum_{i=1}^{N}\hat{p_{i}}ln\hat{p_{i}} \tag{12}\]
## 3 Results and Discussion
### Indirect Carbon Emissions Assessed Using Three Different Methods
For example, by controlling the dissolved oxygen in the anaerobic section to create good phosphorus-releasing conditions for polyphosphate-accumulating organisms, the biological phosphorus-removing effect can be maximized, and the usage of chemical phosphorus-removing agents can be reduced. The addition of chemicals has an important impact on the indirect carbon emissions of WWTPs and is an indispensable part of greenhouse gas accounting. Although the existing standards provide some reference values for the emission factors of chemicals, there is a lack of unified regulations at the global or national level, resulting in a lack of consistency in the results of different regions and accounting methods. This uncertainty is particularly prominent in the carbon emission accounting of WWTPs, especially because there are many types of chemicals and complex production processes. Selection of emission factors largely depends on data availability and local conditions. Although T/CUWA 50055-2023 has updated the chemical emission factors, it still does not cover all types of chemicals, such as sodium bisulfite, which ranked fourth in chemical carbon emissions in this study, liquid oxygen, a common disinfect raw material, and ferrous sulfate, a common phosphorus-removing agent. At present, the accounting of chemical carbon emissions still relies on limited data sets and lacks detailed data to support the production process and use links of chemicals. In the future, it is particularly important to formulate a unified standard for chemical emission factors, especially for chemicals with high total carbon emissions, such as carbon sources and phosphorus-removing agents. Further refining the emission factors will help improve the accuracy and consistency of the results. Figure 3: Proportion of carbon emissions from different types of chemicals.
###### Abstract  
Nowadays, smartphones are an essential part of people's lives and a sign of a contemporary world. Even that smartphones bring numerous facilities, but they form a wide gate into personal and financial information. In recent years, a substantial increasing rate of malicious efforts to attack smartphone vulnerabilities has been noticed. A serious common threat is the ransomware attack, which locks the system or users' data and demands a ransom for the purpose of decrypting or unlocking them. In this article, a framework based on metaheuristic and machine learning is proposed for the detection of Android ransomware. Raw sequences of the applications API calls and permissions were extracted to capture the ransomware pattern of behaviors and build the detection framework. Then, a hybrid of the Salp Swarm Algorithm (SSA) and Kernel Extreme Learning Machine (KELM) is modeled, where the SSA is used to search for the best subset of features and optimize the KELM hyperparameters. Meanwhile, the KELM algorithm is utilized for the identification and classification of the apps into benign or ransomware. The performance of the proposed (SSA-KELM) exhibits noteworthy advantages based on several evaluation measures, including accuracy, recall, true negative rate, precision, g-mean, and area under the curve of a value of 98%, and a ratio of 2% of false positive rate. In addition, it has a competitive convergence ability. Hence, the proposed SSA-KELM algorithm represents a promising approach for efficient ransomware detection.  
Keywords:Kernel Extreme Learning Machine; Salp Swarm; swarm intelligence; Android; ransomware; machine classification +
Footnote †: journal: Applied Sciences
# A Short-Term Forecasting Method of Inter-Frequency Clock Bias for GPS Block IIF Satellites
## 2 Methods
### Trible-Frequency Uncombined PPP  
The basic model for the original multi-frequency GPS carrier phase and pseudo-range observations from receiver \(r\) to satellite \(s\) in units of length is [27]:  
\[\left\{ \begin{array}{ll}P_{r,j}^{s,G}&=\rho_{r}^{s,G}+c\cdot\left(dt_{r}^{G} -dt^{s,G}\right)+\mathrm{M}_{r}^{s,G}\cdot T_{r}^{s,G}+\gamma_{j}^{G}\cdot I_{ r,1}^{s,G}+\\ c&\cdot\left(b_{r,j}^{s,G}-b_{j}^{s,G}\right)+\varepsilon_{r,j}^{s,G}\\ L_{r,j}^{s,G}&=\lambda_{r}^{G}\sigma_{r,j}^{s,G}\\ &=\rho_{r}^{s,G}+c\cdot\left(dt_{r}^{G}-dt^{s,G}\right)+\mathrm{M}_{r}^{s,G} \cdot T_{r}^{s,G}-\gamma_{j}^{G}\cdot I_{r,1}^{s,G}+\\ &\lambda_{j}^{s,G}(N_{r,j}^{s,G}+B_{r,j}^{s,G}+\partial B_{r,j}^{s,G}-B_{j}^{s, G}-\partial B_{j}^{s,G})+\varepsilon_{r,j}^{s,G}\end{array} \right. \tag{1}\]  
To facilitate the derivation of subsequent formulas, the following expressions are defined:  
\[\left\{ \begin{array}{ll}\alpha_{mn}^{G}&=\frac{{{{\left({f_{m}^{s,G}}\right)} ^{2}}}}{{{\left({f_{m}^{s,G}}\right)}^{2}}-{{\left({f_{m}^{s,G}}\right)}^{2}} }\\ \beta_{mn}^{G}&=-\frac{{{{\left({f_{m}^{s,G}}\right)}^{2}}}}{{{\left({f_{m}^{s, G}}\right)}^{2}}-{{\left({f_{m}^{s,G}}\right)}^{2}}}\\ \mathrm{DCB}_{P_{m}P_{n}}^{s,G}&=b_{m}^{s,G}-b_{m}^{s,G}\\ \mathrm{DCB}_{P_{m}P_{n}}^{s,G}&=b_{m}^{s,G}-b_{r,m}^{s,G}\\ b_{r,r}^{s,G}&=\alpha_{mn}^{G}\cdot b_{m}^{s,G}-\beta_{mn}^{G}\cdot b_{r,n}^{s,G }\\ \par b_{r,I_{mn}^{s,G}}^{s,G}&=\alpha_{mn}^{G}\cdot b_{r,m}^{s,G}-\beta_{mn}^{G }\cdot b_{r,n}^{s,G}\\ \par\partial\mathrm{UPD}_{P_{n}P_{n}}^{s,G}&=\partial B_{P_{m}}^{s,G}- \partial B_{m}^{s,G}\\ \par\partial\mathrm{UPD}_{P_{n}P_{n}}^{s,G}&=\partial B_{r,m}^{s,G}-\partial B _{r,n}^{G}\\ \par\partial B_{r,I_{mn}^{s,G}}^{s,G}&=\alpha_{mn}^{s,G}\cdot\partial B_{m}^{s,G }-\beta_{mn}^{G}\cdot\partial B_{r,n}^{s,G}\\ \par\partial B_{r,I_{mn}^{s,G}}^{s,G}&=\alpha_{mn}^{G}\cdot\partial B_{r,m}^{s, G}-\beta_{mn}^{G}\cdot\partial B_{r,n}^{s,G}\end{array} \right.
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 3 Elements of Frequency Selective Surfaces
### Evaluation of the Four Classes of FSSs  
When being used as the spatial filters, FSS structures should have low dependence on the angle of incidence to ensure operational performance. In order to make the resonance frequency of an FSS more stable with respect to variation of the incidence angle, generally, the inter-element spacing is reduced as compared with the wavelength. A higher level of grating lobes is created with large spacing, and the resonance frequency moves lower with the angle of incidence [111]. Among the mentioned classes of FSSs, group 2 (loop type) elements have a high angle stability. Although some of the elements of this class (three- and four-legged, square, and circular loops) may have different appearances, their operation can be related to dipoles, i.e., a square loop can be understood as two bent dipoles. Consequently, when the two dipoles are at resonance, the loop resonates. Therefore, the mentioned condition is satisfied when the circumference of the loop is one wavelength (\(\lambda\)). However, for better scan angle performance, they must be packed in a small area.  
Table 2 lists the performance of FSSs with different element shapes mainly based on freestanding FSS performance [113]. Ratings: best = 1, second best = 2, third best = 3, least = 4. The performance is compared across 4 categories: Cross Polarization, Stability, Band Separation, and Larger Bandwidth. Each shape--Jerusalem Cross, Dipole, Tripole, Ring, Square Loop, and Cross Dipole--is rated from 1 (best) to 4 (least) in each category. The Square Loop shape consistently performs the best, receiving top ratings (1) in all categories, indicating its superior overall performance. In contrast, the Dipole shape excels in cross-polarization and Band Separation but scores lower in Stability and Larger Bandwidth.  
Figure 8: Examples of the third class of FSSs, the plate type elements [111].
## 3 Results
### Results of Machine Learning Model  
#### 3.2.1 Classification Model  
The performance of the LR, SVM, RF, and LGBM models in the task of classifying the electrophysiological severity of the test data is shown in Table 4. The AUC obtained from the ROC curve was 0.754 (0.752-0.757) for LR, 0.765 (0.763-0.768) for SVM, 0.764 (0.761-0.766) for RF, and 0.802 (0.799-0.804) for LGBM (Figure 2). The LGBM model showed the highest score in all evaluation metrics. When visualizing the feature importance of the LGBM model, preoperative DML, age, and preoperative electrophysiological severity stage were important factors. The SHAP score for the same LGBM model showed that preoperative DML, age, and gender were important factors (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **LR** & **SVM** & **RF** & **LGBM** \\ \hline \multirow{2}{*}{Accuracy} & 0.819 & 0.811 & 0.829 & 0.851 \\  & (0.817-0.821) & (0.809-0.812) & (0.827-0.831) & (0.849-0.853) \\ \hline \multirow{2}{*}{Recall (Sensitivity)} & 0.619 & 0.671 & 0.625 & 0.697 \\  & (0.614-0.623) & (0.666-0.675) & (0.621-0.639) & (0.692-0.701) \\ \hline \multirow{2}{*}{Precision} & 0.667 & 0.630 & 0.694 & 0.724 \\  & (0.663-0.672) & (0.626-0.635) & (0.690-0.699) & (0.720-0.728) \\ \hline \multirow{2}{*}{F-measure} & 0.640 & 0.647 & 0.656 & 0.708 \\  & (0.636-0.643) & (0.644-0.651) & (0.652-0.659) & (0.704-0.711) \\ \hline \multirow{2}{*}{Specificity} & 0.890 & 0.861 & 0.902 & 0.906 \\  & (0.888-0.892) & (0.859-0.863) & (0.900-0.904) & (0.904-0.907) \\ \hline \multirow{2}{*}{Best Parameters} & \multirow{2}{*}{Model intercept} & \multirow{2}{*}{C = 100} & Max depth = 3 & Objective = binary \\  & & & Numbers of & Metric = binary \\ \hline \multirow{2}{*}{of Model} & \multirow{2}{*}{\(=-1.602\)} & \multirow{2}{*}{Gamma = 0.001} & \multirow{2}{*}{Numbers = 30} & \multirow{2}{*}{error} \\  & & & & \\ \hline \end{tabular} The performance of the LR, SVM, RF, and LGBM models in the task of classifying electrophysiological severity, LR: Logistic Regression; SVM: Support Vector Machine; RF: Random Forest; LGBM: LightGBM.  
\end{table}
Table 4: Model performance in electrophysiological severity classification task.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Pre-Surgery (Hands)** & **Post-Surgery (Hands)** \\ \hline Stage 1 & 3 & 113 \\ \hline Stage 2 & 10 & 108 \\ \hline Stage 3 & 59 & 89 \\ \hline Stage 4 & 149 & 68 \\ \hline Stage 5 & 198 & 38 \\ \hline unclassified & 1 & 4 \\ \hline \end{tabular}
\end{table}
Table 2: Distribution of pre-and postoperative electrophysiological severity.  
\begin{table}
\begin{tabular}{l l l} \hline  & **Change In CTSI-JHHS-FS Score** & **Change In CTSI-JHHS-SS Score** \\ \hline Is Female & 1.17 & 0.46 \\ \hline Age & \(-1.37\) & \(-4.87\) \\ \hline preDML & 0.11 & 0.22 \\ \hline preSCV & \(-0.91\) & \(-2.12\) \\ \hline preStage & \(-1.20\) & \(-1.75\) \\ \hline preSS & 25.8 & \(-1.35\) \\ \hline preFS & \(-2.4\) & 32.0 \\ \hline \end{tabular}
\end{table}
Table 3: The t-values for each item in the multiple regression analysis are summarized as an indicator of the contribution of each item in predicting prognosis.
# The Application of Satellite Image Analysis in Oil Spill Detection
## 3 Results
The red pixel covers a large part of the resulting map. They are largely concentrated in the river basins, i.e., streams and channels, as well as around Lake Pysaino (upper left corner). This could mean that the toxins that were draining into the water began to accumulate around the larger reservoirs, slowly degrading the natural environment. Nevertheless, the climate has changed significantly since 1984. Siberia's regions are warming faster, resulting in lake drying. The average annual NDWI2 index from 1985 to 2021 monitors changes related to water content in water bodies over a given period (Figure 10). The results greater than or equal to the value of 0.3 are synonymous with the good water quality of the studied areas. Such results in the study area are visible only between 2001 and 2002, and in 2011 and 2013. The remaining data, obtained from 36 years of research, are below the water detectability. The average annual NDVI index from 1985 to 2021 remains in the range of values from about \(-0.45\) to \(0.05\) for the analysed period of time (Figure 11). The index is based on the contrast between the near-infrared reflection and the visible red absorption. Values in the  
Figure 9: Yearly Water Classification History in Norilsk Area. range of \(-0.1\)-\(0.1\) correspond to barren areas, sand, or snow. Index results below this value are water, and above are vegetations. The analysis of the results shows a practical lack of healthy vegetation in the studied area. For 2020, the result fluctuates towards \(-0.05\). This means that the test area was detected as sterile, with no clearly visible signs of healthy water, which is confirmed by the NDWI2 indicator described above. Data from 14 May 2020 (Figure 12a) and 13 June 2020 (Figure 12b) were used to monitor and study the snow cover of the studied area. The NDSII (Normalized Difference Snow and Ice Index) was used. When analysing data before leakage, it is possible to see large amounts of snow cover, particularly on the left side of the map, showing white pixels. In June, the snowfall declined dramatically. The snowfall in Lake Pyasino is also markedly reduced in the upper and central parts of the two maps. In the months of May and June, Norilsk has normal snow. The sharp decline is largely due to climatic anomalies, making it one of the warmest sources in Siberia's history. Snow defrosting between May and June resulted in a greater amount of water in the soil, which confirms the increase in vegetation. Figure 11: NDVI Values from 1985 to 2021. Figure 10: NDWI2 Values from 1985 to 2021.
# High Performing Facial Skin Problem Diagnosis with Enhanced Mask R-CNN and Super Resolution GAN
## 3 Technical Challenges in Diagnosing Facial Skin Problems
### Challenge #2: Detecting about 20 Different Types of Facial Skin Problems  
There are about 20 different types of commonly known facial skin problems, including acne, hyperpigmentation, scars, birthmarks, spider veins, white spots, rosacea, ingrown hair, moles, wrinkles, dark circles, eye bags, dry skin, oily skin, dull skin, large pores, and black heads. Some of the common skin problems are shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Instances on Facial Photo** & **Average Number of Pixels** & **Occupation Ratio on Photo** \\ \hline _Whole Face_ & 101,000 pixels & 33.06\% \\ \hline _Sections of Face_, i.e., Eye, Nose, & \\ Mouth, and Ear & 3690 pixels & 3.36\% \\ \hline _Facial Skin Problems_ such as & \\ Pore, Mole, and Acne & 25 pixels & 0.02\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparing occupation ratios of face, face section, and facial skin problem areas.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Target Objects in Different Sizes** & **Average of DSC Measurements** \\ \hline Whole Face & 95.6\% \\ \hline Mouth as a _Section of the Face_ & 90.9\% \\ \hline Mole _as a Facial Skin Problem_ & 31.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance measurements of detecting objects in different sizes.  
It is challenging to train a CNN model that can detect all the different types of facial skin problems with a high level of performance. This is because the 20 facial skin problem types are not distinct in their appearances; rather, they have a high similarity. Consequently, training a CNN model from a training set of instances from different classes but a high similarity would result in a low performance of classification.  
Moreover, because facial skin problem areas are quite small in size, training a CNN model for detecting all the skin types with a high performance becomes infeasible.