esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mutation-Based Multivariate Time-Series Anomaly Generation in ICS
### Attention-Based Variational Recurrent Autoencoder
#### 4.1.1 Evidence Lower Bound  
Variational inference is an approximation method that uses relatively simple distributions to handle complex probability distributions. It is used to optimize a latent variable model for given data, replacing complex distributions with simpler, more computationally feasible ones, thereby reducing computational cost and increasing efficiency. ELBO is a typical objective used in variational inference.  
\[\log p(\mathbf{x}) =KL(q(\mathbf{z})||p(\mathbf{z}|\mathbf{x}))+\mathbb{E}_{q( \mathbf{z})}[\log p(\mathbf{z},\mathbf{x})]-\mathbb{E}_{q(\mathbf{z})}[\log q (\mathbf{z})] \tag{1}\] \[\geq\mathbb{E}_{q(\mathbf{z})}[\log p(\mathbf{z},\mathbf{x})]- \mathbb{E}_{q(\mathbf{z})}[\log q(\mathbf{z})]\] \[=ELBO(q)\]  
Figure 2: Overview of mutation-based ICS time-series anomaly generation.  
Equation (1) is the derivation of ELBO. Originally, we aim to train the model to maximize the loglikelihood \(\log p(\mathbf{x})\), where \(\mathbf{x}\) is the observable data and \(\mathbf{z}\) is the latent variable. \(p(.)\) represents the true distribution, and \(q(.)\) represents the variational distribution. The \(\log p(\mathbf{x})\) can be decomposed into the Kullback-Leibler divergence (KLD) \(KL(q(\mathbf{z})||p(\mathbf{z}|\mathbf{x}))\) and the \(ELBO(q)\). Since the KLD \(KL(q(\mathbf{z})||p(\mathbf{z}|\mathbf{x}))\) is always non-negative, \(\log p(\mathbf{x})\) is always greater than or equal to \(ELBO(q)\).  
Therefore, variational inference seeks to maximize \(ELBO(q)\) instead of directly maximizing \(\log p(\mathbf{x})\). This ELBO fundamentally assumes that the data \(\mathbf{x}\) is i.i.d. Hence, to handle probability distributions over time-series data with variational inference, ELBO must be extended to time-series.  
\[ELBO_{ts}(q) =\mathbb{E}_{q(\mathbf{z},\mathbf{h}_{1:T}|\mathbf{x}_{1:T})}[ \log p(\mathbf{z},\mathbf{h}_{1:T},\mathbf{x}_{1:T})]-\mathbb{E}_{q(\mathbf{ z},\mathbf{h}_{1:T}|\mathbf{x}_{1:T})}[\log q(\mathbf{z},\mathbf{h}_{1:T}| \mathbf{x}_{1:T})] \tag{2}\] \[=\mathbb{E}_{q(\mathbf{z},\mathbf{h}_{1:T}|\mathbf{x}_{1:T})}[ \log p(\mathbf{x}_{1:T}|\mathbf{z})]-KL(q(\mathbf{z}|\mathbf{h}_{1:T})||p( \mathbf{z}|\mathbf{h}_{1:T}))\] \[-KL(q(\mathbf{h}_{1:T}|\mathbf{x}_{1:T})||p(\mathbf{h}_{1:T}))\]  
Equation (2) formulates \(ELBO_{ts}(q)\), which is an extended version of ELBO for time-series data. \(ELBO_{ts}(q)\) not only extends the data to a time-series but also considers an additional sequential latent variable \(\mathbf{h}_{1:T}\) to account for the sequential nature of the data.  
Consequently, maximizing \(ELBO_{ts}(q)\) is equivalent to maximizing the log-likelihood of the time-series data \(\mathbf{x}_{1:T}\) given the latent variable \(\mathbf{z}\), while minimizing the Kullback-Leibler divergence between the true distribution and the variational distribution for both the latent variable \(\mathbf{z}\) and the sequential latent variable \(\mathbf{h}_{1:T}\).
# Approximate Sensory Data Collection: A Survey
## 2 Model-Based Approximate Data Collection Algorithms
### Summary  
All of the aforementioned algorithms try to capture the spatial and temporal correlations among sensory data by mathematical models such as Gaussian distribution, linear regression, etc. Based on these mathematical models, many sensory values can be predicted without transmission, so that a large amount of energy will be saved. However, these methods all have the following drawbacks. Firstly, the mathematical models are too ideal to describe the complicated correlations among sensory data accurately. With the wide use of WSNs and IoT systems, the objects monitored by them becomes more and more intricate, and the correlations among sensory data sampled from the monitoring objects are quite complicated as well, so that they may not follow a specific mathematical model, which has colossal impact on the accuracy of the model-based data collection methods. Secondly, they introduce extra in-network communication costs for guaranteeing the correctness of the prediction models and keeping the consistency of the local prediction model and global prediction model. Meanwhile, these algorithms always have a fixed error bound and cannot automatically adjust according to the user-specified precision requirement.
###### Abstract  
The aim of this study was to evaluate the impact of exercise order in multicomponent training (MCT) on the maximum voluntary contraction (MVC) of older women. A total of 91 older women, ranging in age from 60 to 85 years, were randomly assigned to either Group A or Group B. Group A performed a warm up followed by aerobic training and resistance training, whereas Group B followed a warm up followed by resistance training and aerobic training. A control group (CG) did not engage in any exercise interventions. Statistical analysis was conducted using one-way ANOVA for between-group comparisons, and ANOVA was used for repeated measures. The results revealed that Group A demonstrated significant increases in MVC for knee extensors (KEs) between M1 and M3 (\(p<0.001\)) and between M2 and M3 (\(p<0.001\)). Similarly, Group A exhibited significant increases in MVC for knee flexors (KFs) between M1 and M3 (\(p=0.001\)) and between M2 and M3 (\(p<0.001\)). Both Group A and Group B demonstrated significant increases in MVC for elbow flexors (EFs) between M1 and M3 (\(p<0.001\)). Furthermore, Group B showed a significant increase in hand grip strength (HGS) between M1 and M3 (\(p<0.001\)). Overall, the findings suggest that initiating MCT with aerobic training followed by resistance training is the most effective approach for improving muscle strength in older women.  
women; physical exercise; muscle; sarcopenia; functionality; quality of life 2023
## 5 Simulation Results
However, these overshoots and undershoots appear only for short durations (less than 50 ms), and they quickly converge to the steady-state value. The robustness of the backstepping technique over the PI controller is clearly apparent at \(t=2\) and \(t=4\) s. Thus, despite the variation of the load from 20 to 100 \(\Omega\) and from 100 to 10 \(\Omega\), the backstepping shows high robustness against these variations. On the other hand, the weakness of the conventional PI controller against load variation is clearly shown. It takes 350 ms and 850 ms response times when \(t=2\) and \(t=4\) s, respectively. Therefore, it should be noted that the PI controller may even cause damage to the PEMFC. Figure 13: Load, temperature, and hydrogen variations. Consequently, these figures demonstrate the robustness of the backstepping algorithm against load resistance variations. The PEMFC stack power curves are shown in Figure 16. Compared to the nominal characteristics of the PEMFC, an improvement of 12% in the fuel cell power is obtained using the proposed MPPT control scheme. In addition, although the extracted energies using the two controllers gives almost identical results (with a slight advantage for the backstepping algorithm), it is clear that, when using the backstepping technique, the MPP is obtained with high-performance motion tracking. On the other hand, in addition to its complexity, it is clear that the drawback of the PI controller is its weakness when facing load resistance variation. Consequently, the validity of the backstepping algorithm and its robustness under load resistance variations is clear. Moreover, a magnificent performance, with a quick start-up and high tracking accuracy, is obtained, even for large system parameter variations. Figure 14: PEM fuel cell stack current. Figure 15: PEM fuel cell stack voltage.
# Understanding the Influence of AST-JS for Improving Malicious Webpage Detection
## 1 Introduction
The AST structure defines the program's style, which can be useful to discerning the signature feature of the source code. Due to the complexity of the obfuscation problem, we bring the structure-based analysis of AST-JS to capture the whole structure of AST into a low-dimensional representation that retains the semantic meaning of the original JS code. In this study, we used graph2 vec [11] as an unsupervised learning model to create a vector representation that is then combined with other feature categories. Moreover, we give some analysis of the influence of our proposed feature on prediction results. The aim is to understand AST-JS better and improve malicious webpage detection. In summary, the contributions of this study are as follows:  
* We present a structure-based analysis of AST-JS for enriching webpage content features to improve the performance of malicious webpage detection. * We analyze the importance of AST-JS representation for detecting malicious webpages by demonstrating a feature influence analysis of all feature categories using a Shapley additive explanation (SHAP) method. * We bring the influence analysis to a particular group of webpages to know the benefits of certain features for detecting specific attacks. The organization of this paper proceeds as follows. Section 2 summarizes related works on webpage detection and the use of AST as the representation of JS. Section 3 presents our proposed approach for improving the current webpage detection system using the AST feature. Section 4 gives details of our experimental setup and dataset. Section 5 explains details of our evaluation of the performance with some result discussions. Finally, we conclude our study in Section 6 by giving some limitations and the future direction of our approach.
# Diagnosis by SAM Linked to Machine Vision Systems in Olive Fitting Machines
###### Abstract  
Computer Vision (CV) has proven to be a powerful tool for automation in agri-food industrial processes, offering high-precision solutions tailored to specific working conditions. Recent advancements in Artificial Neural Networks (ANNs) have revolutionized CV applications, enabling systems to autonomously learn and optimize tasks. However, ANN-based approaches often require complex development and lengthy training periods, making their implementation a challenge. In this study, we explore the use of the Segment Anything Model (SAM), a pre-trained neural network developed by META AI in 2023, as an alternative for industrial segmentation tasks in the table olive (_Olea europaea_ L.) processing industry. SAM's ability to segment objects regardless of scene composition makes it a promising tool to improve the efficiency of olive pitting machines (DRRs). These machines, widely employed in industrial processing, frequently experience mechanical inefficiencies, including the \"boat error,\" which arises when olives are improperly oriented, leading to defective pitting and pit splinter contamination. Our approach integrates SAM into n CV workflow to diagnose and quantify boat errors without designing or training an additional task-specific ANN. By analyzing the segmented images, we can determine both the percentage of boat errors and the size distribution of olives during transport. The results validate SAM as a feasible option for industrial segmentation, offering a simpler and more accessible solution compared to traditional ANN-based methods. Moreover, our statistical analysis reveals that improper calibration--manifested as size deviations from the nominal value--does not significantly increase boat error rates. This finding supports the adoption of complementary CV technologies to enhance olive pitting efficiency. Future work could investigate real-time integration and the combination of CV with electromechanical correction systems to fully automate and optimize the pitting process.  
table olive industry; olive pitting; SAM segmentation; industrial process optimization; DRR machines; boat error; Gordal olive; Hojiblanca olive; computer vision +
Footnote †: journal: Applied sciences
## 4 Results and Discussion
### Using Laboratory Results to Predict Parameters Found for Field Tests (at 250% Scale)  
[PERSON] and [PERSON] [11] performed a set of field trials in the open air, detonating 400 g TNT cuboids with a width:height:length ratio of 1:1:2. The experimental layout (gauge distances and structure dimensions) reported herein are geometrically similar, with the field trials representing a scale of 250%. A schematic and photograph of the field tests are shown in Figure 9. The charge mass and dimensions were also scaled using a factor of 250% when assuming a TNT equivalency of 1.2 for PE-4 [24]. The differences between the experiments herein and the field trials [11] exist:  
1. The detonator position was not identical--for the field trials, the explosive was rear detonated at the cross-sectional centre of the bottom of the two 200 g TNT blocks  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Gauge** & **Pi (kPa)** & **ta (ms)** & **t+ (ms)** & **I+ (kPa.ms)** \\ \hline Experiment A 100\% mean [range] & 254 [303; 304;  
(referred to as the rear-below centre). However, in the current work, the PE-4 cuboid was a single moulded part with a centrally located detonator (referred to as middle-centre detonated);
2. Instead of a relatively smooth corner structure manufactured from 12 mm plated steel and a lightweight steel frame, a reinforced concrete structure with a similar corner geometry was used. A slight gap between the side covering wall and the front wall is evident in the photographs of the field trials (Figure 9) [11]. The current work used a steel baseplate for the lab-scale indoor tests rather than earth (this is likely to be less important as the charges were detonated considerably high above the ground).
3. The geometry is not quite identical--in the current tests, the front wall was simplified to facilitate efficient numerical simulations by enabling a half-symmetry assumption (Figure 10). The implications of this assumption are considered in the following discussion.
# Human Activity Recognition Using CSI Information with Nexmon
## 4 The Experimental Setup and CSI Data Collection
### Activity Environment Depiction  
For the CSI data collection, rooms of size of approximate 4 m \(\times\) 4.5 m were used.  
The experimental architecture for Experiment 1 is depicted in the Figure 2a, the data was collected with LOS. There were four activities performed with LOS: EMPTY, STAND, SIT and WALK.  
Experiment 2 was performed in a room of size 3.5 m \(\times\) 4.5 m that is illustrated in Figure 2b. The distance between transmitter and receiver is around 5 m. In this experiment data for the following activities were collected: WALK, SIT, STAND, SIT-DOWN, STAND-UP, and FALL as a significant additional activity. These activities were performed in different parts of the room to avoid dependence of the activity recognition from the location of the performed activity. The area in Figure 2b with green color highlights the area where activity WALK was performed, with color blue depicts the area where activities SIT, STAND, SIT-DOWN, and STAND-UP were performed, and with yellow marked the area where the activity FALL was performed.  
Figure 2c demonstrates the room layout of Experiment 3. The size of the room is 4.5 m \(\times\) 5 m. In this experiment, the data were collected for five activities: EMPTY, LYING, SIT, STAND, and WALK in the LOS scenario. All five activities were performed in different parts of the room.  
The experimental setup for HAR experiments involving fall detection, in particular, was evaluated by the security officer of the Frankfurt University of Applied Sciences, and a risk assessment was established with assistance from a medical doctor classifying our study as a HAR experiment in accordance with the ethical principles of the Declaration of Helsinki [31].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode** & **Maximum Rate** & **Antennas TX/RX** \\ \hline
1 \(\times\) 1 40 MHz & 200 Mbps & 1 TX/1 RX \\ \hline
2 \(\times\) 2 40 MHz & 400 Mbps & 2 TX/2 RX \\ \hline
1 \(\times\) 1 80 MHz & 433 Mbps & 1 TX/1 RX \\ \hline
2 \(\times\) 2 80 MHz & 866 Mbps & 2 TX/2 RX \\ \hline
1 \(\times\) 1 160 MHz & 866 Mbps & 1 TX/1 RX \\ \hline
2 \(\times\) 2 160 MHz & 1.73 Gbps & 2 TX/2 RX \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data rate on 802.11 ac wave2 [30].
# Brillouin Frequency Shift Sensing Technology Used in Railway Strain and Temperature Measurement
## 3 Methods of Test and Verification
### Compensation of Rail Thermal Expansion  
To calculate strain, the temperature distribution of the rail must also be considered. Temperature change is mainly derived from solar thermal radiation and atmospheric temperature. A steel rail is considered to be a high thermal conductivity material, so it was assumed that the three differently-located fibers on the rail experienced the same thermal conditions. Therefore, in the marginal time of measurement, the overall temperature measured by the three sensing fibers could still be regarded as the same. There was no distinction in temperature measured even though the neutral axisymmetric difference of the cross-section of rail was I-shaped.  
However, because the section of the tested rail is within the movable section of the continuous welded rail, if the Brillouin frequency measured by the tested rail was directly used to calculate the temperature and strain values, errors may have occurred due to  
Figure 6: Data collection and internet communication structure.  
ignoring the variation in the thermal expansion of the rail. We added an affecting factor of thermal expansion of the rail to the equation, as shown in Equation (3):  
\[\Delta v_{B}\ =\ C_{\epsilon}\Delta\epsilon+C_{\epsilon}(\alpha\times\Delta T)+C_{T }\Delta T \tag{3}\]  
where \(\alpha\) is thermal expansion coefficient of the rail.
# Towards the Development of Agenda 2063 Geo-Portal to Support Sustainable Development in Africa
## 2 Data  
The success of a geoportal is partly dependent on the availability of the relevant data accessible on the web portal and the frequency at which the data is updated. Several statistical, vector, and raster datasets were sourced from different data providers and uploaded into the geoportal. We obtained statistical socio-economic data from the World Bank, NEPAD-AUDA, and Statistics South Africa to populate the database for this geoportal. The statistical data acquired related to the shared prosperity indicators such as incidence of tuberculosis, the prevalence of HIV, unemployment with intermediate education, the mortality rate for children under five, incidences of malaria, and GNI per capita growth. Data on wealth creation indicators included manufacturing value addition, access to electricity, and improved water sources. Tax revenue data were used as an indicator for transformative capacities indicator. Most of these socio-economic data is updated at least yearly by most countries. Raster and vector data layers were added to provide a spatial and environmental perspective of the situation in various countries. Raster layers consisted of gridded data such as Landsat 8 satellite data acquired from SANSA. The vector datasets layers are comprised of points, line and polygon layers for roads, electricity power lines, administrative boundaries, health service facilities, educational facilities, disaster vulnerability atlas, telecommunication lines, geological maps, rivers, water bodies, and an open street map layer. The vector and raster data were projected to the World Geodetic System (WGS) 84 datum geographic coordinate system. This data are comprised of spatial and non-spatial data. Spatial data included the vector and raster datasets that can be georeferenced to a location on the earth's surface while non-spatial data does not refer to a specific location. While non-spatial data are normally delivered in spreadsheet formats, raster data are supplied in various formats that include GeoTiff, Tiff, JPG, NetCDE, and PNG while vector files are delivered as ESRI shapefiles and other standard textural formats such as KML, GML, GeoJSON, GeoHash, and WKT. The storage, retrieval, visualization, rendering, conversion, and manipulation of vector and raster spatial data and their formats require relational database schemas with GIS functionality as outlined in the architectural design.
# Research on Temperature Field Analysis Method of High-Speed Bearing Chamber-Bearing System
## 2 Model and Method
### Two-Phase Flow Model  
The VOF (Volume of Fluid) model is employed to address immiscible multiphase fluid problems. This model introduces the concept of phase volume fraction, which is used to weigh and average the physicochemical properties of the individual phases. In this way, the properties of the mixed fluid are determined, effectively capturing the combined characteristics of the different phases.  
Through the solution of the phase volume fraction equation, momentum equation, and energy equation, the distribution characteristics of the oil-air two-phase flow can be  
Figure 5: Cross-section diagram of the mesh of bearing chamber-bearing system.  
calculated. This approach allows for a more accurate representation of the interface between the various phases, enhancing the simulation's ability to model complex multiphase phenomena as described in reference [18].  
In the VOF method, each control volume will have a phase volume fraction at each moment after discretizing the computational domain. The volume fraction \(\alpha_{oil}\) can be solved by the following equation:  
\[\frac{1}{\rho_{oil}}\left[\frac{\partial}{\partial t}(\alpha_{oil}\rho_{oil})+ \
abla\bullet\left(\alpha_{oil}\rho_{oil}\overset{\rightarrow}{v}_{oil} \right)=S_{\alpha_{oil}}+\sum_{p=1}^{n}\left(\dot{m}_{\text{oa}}-\dot{m}_{ \text{ao}}\right)\right] \tag{1}\]  
where \(\rho_{oil}\) is the oil density; \(S_{\alpha_{oil}}\) is the source term, which is usually taken as 0; \(\overset{\rightarrow}{v}_{oil}\) is the oil velocity; \(\dot{m}_{oa}\) is the mass transfer rate from the oil phase to the air phase per unit volume; \(\dot{m}_{ao}\) is the mass transfer rate from the air phase to the oil phase per unit volume.  
Meanwhile, the sum of the volume fractions of the oil phase and air phase must always be equal to 1, as shown in the following equation:  
\[\alpha_{oil}+\alpha_{air}=1 \tag{2}\]  
After obtaining \(\alpha_{oil}\) in control volume through Equation (1), the equivalent density and viscosity of oil-air two-phase can be expressed as follows:  
\[\rho =\alpha_{oil}\rho_{oil}+(1-\alpha_{oil})\rho_{air} \tag{3}\] \[\mu =\alpha_{oil}\mu_{oil}+(1-\alpha_{oil})\mu_{air} \tag{4}\]