esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Virtual Network Slicing and Orchestration for Selective MEC Services over Wide-Area SDN
## 5 A Selective MEC Service Model Based on a Multitier Architecture
### MEC Information Policies for MEC Selection  
Regarding the selection of a MEC node for a service request using the hierarchical MEC service architecture, each AP has MEC node candidates to support selective MEC services. The MEC candidates consist of MEC nodes in one of the zone\(-1\) to zone\(-3\) MEC sites mentioned in the subsection above. For the MEC selection, the information corresponding to the each MEC candidate should be maintained, including \(L_{N}^{n}\), \(r_{n}^{r}\), \(r_{n}^{m}\), \(r_{n}^{s}\), \(U(n_{r},\ n)\), and \(R_{A}^{n}\).  
Each \(L_{N}^{n}\) can be derived from Equation (2). As mentioned above, it is assumed that the resource capabilities and resource usages of multitier MEC service entities are known by monitoring each MEC node to obtain the measured dataset of available central processing unit (CPU), memory, and storage resources (\(r_{n}^{r}\), \(r_{n}^{m}\), \(r_{n}^{s}\)).  
In order to reflect the status of network congestion, we consider network utilization between an AP and an MEC node. The data transmission at a shorter distance leads to an overall decrease in network traffic congestion by minimizing the use of network resources in the entire infrastructure. Moreover, there should be no traffic bottleneck and link congestion in a specific connection to improve network utilization. In this regard, the network utilization \(U(r_{s},\ n)\) can be calculated by the product of the utilization of links on the shortest path between \(r_{s}\) and MEC node \(n\). If \(n_{r}\) is selected by the service MEC node, the additional bandwidth is not consumed. Thus, in this case, we assume that the network utilization is 1. Consequently, \(U(r_{s},\ n)\) is formally defined as follows by normalizing the network utilization values:  
\[U(r_{s},\ n)=\left\{\begin{array}{ll}n=n_{r},&1\\ n\
eq n_{r},&\prod\limits_{l(i,\ j)\in P(n_{r},\ n)}\frac{\text{available bandwidth of }l(i,j)}{\text{bandwidth capacity of }l(i,j)}. \end{array}\right. \tag{4}\]  
For the relative comparison in terms of resource availability, each resource availability is normalized on the basis of the maximum value. Each weight of the resource availability can be determined on the basis of service type and requirement. Thus, the normalized resource availability including CPU, memory, and storage capacity in an MEC node \(n\) is expressed as  
\[R_{A}^{n}=\alpha\cdot\frac{r_{n}^{c}}{Max_{n_{k}\in N_{C}^{i}}\left(r_{n_{k}} ^{c}\right)}+\beta\cdot\frac{r_{n}^{m}}{Max_{n_{k}\in N_{C}^{i}}\left(r_{n_{k} }^{m}\right)}+\gamma\cdot\frac{r_{n}^{s}}{Max_{n_{k}\in N_{C}^{i}}\left(r_{n_{k }}^{s}\right)}, \tag{5}\]  
where each weight of the resource availability is a real positive value, and the summation of all weight values is 1 (\(\alpha+\beta+\gamma=1\)).
## 5 Discussion
#### 5.1.2 Resource Allocation Conflicts and Coordination  
Under the conditions of limited resources, the Resource Optimization Path (NH1) and Community Empowerment Path (NH2) emphasize how to enhance the efficiency of disaster prevention and mitigation by optimizing existing resources and empowering communities when financial and professional resources are constrained. These pathways illustrate how the effective management and distribution of resources can be carried out under resource scarcity to support the implementation of H1, H2, and H3. In disaster and risk management, these pathways enhance the overall response capabilities and the resource utilization efficiency through sensible resource allocation and active community participation, ensuring timely and effective psychological crisis intervention and support during major emergencies.
## 4 Numerical Model
### Modeling on PVD Membrane
As compared in Figure 4, the present numerical model of membrane properly represents the actual fabric structure of the woven PVD membrane. The resulting openings bounded by the fibers were of a rectangular size, of which the equivalent diameter \(D\) was set to \(O_{95}\) of the PVD membrane prototype, i.e., \(D=O_{95}=20\)\(\upmu\)m. Thus, the pore-opening size of the membrane model satisfies the corresponding industrial requirement in Table 3, i.e., \(O_{95}=20\)\(\upmu\)m \(<75\)\(\upmu\)m. It is noteworthy that this structure not only controls the equivalent pore-opening size, but also contributes to the permeability coefficient of the membrane. Moreover, the permeability coefficient, calculated by the coupled CFD-DEM solver, is \(k=4.35\times 10^{-4}\) m/s, which meets the industrial manufacturing requirements of PVD membrane in Table 3, i.e., \(k\geq 5\times 10^{-5}\) m/s.
## 3 Results
### Applications Across Industrial Domains
#### 3.3.6 Buildings and HVAC Systems  
A building information modeling and IoT system, as proposed in [69], used SVMs and ANNs to predict chiller degradation, with SVMs yielding the best performance. In [70], autoencoders detected HVAC anomalies via building automation data. As shown in [71], an LSTM model forecast heating system failures up to seven days ahead. Energy use and maintenance needs in active chilled beam systems were predicted using XGBoost and Gaussian process regression, although the study partially relied on synthetic data [72]. A more comprehensive hybrid framework combining SVM, DT, KNN, prophet, and seasonal autoregressive integrated moving average (SARIMA) was applied to hospital HVAC systems using building management system and computerized maintenance management system data [73].
## 3 Results of Data Training and Discussion
### Receiver Operating Characteristics (ROC) and Area under the Curve (AUC)  
The receiver operating characteristic (ROC) curve is a graphical representation of the relationship between the true positive rate (TPR), or recall, and the false positive rate (FPR) at various classification thresholds. The area under the curve (AUC) is a metric used to determine a model's ability to distinguish between classes. In this case, as shown in Figure 4, the AUC values are 0.975 for training, 0.957 for validation, and 0.958 for the test.
## 3 Hybrid Network Implementation Using LoRaWAN and LoRaMESH
### LoRaWAN Class  
In LoRaWAN three classes of operation are defined, of which only Class A must be implemented in all devices compatible with LoRaWAN [125]. Because of this policy, there is a basic set of features that are present in all LoRaWAN final devices, keeping both the architectural complexity and the production cost as low as possible.  
Class A devices operate on a two-way communication between the end device and the gateway. Uplink messages (from the device to the server) can be sent at any time (randomly). The device then opens two reception windows at specified times (1 s and 2 s) after an uplink transmission. If the server does not respond in either of these reception  
Figure 10: LoRaMESH and LoRaWAN Topologies.  
windows, the next opportunity will be after the next uplink transmission from the device. With this, the server can respond in either the first reception window, or the second reception window, but should not use both windows. In Class B, the devices extend Class A by adding programmed reception windows for downlink messages from the server. Using synchronized timers transmitted by the gateway, this way the devices periodically open reception windows. Class C devices extend Class A by keeping reception windows open unless they are transmitting. This allows for low-latency communication, but often consumes more power than Class A devices.
# The Gyrotrons as Promising Radiation Sources for THz Sensing and Imaging
## 1 Introduction
Some of them are well established (for example, the ECRH (electron cyclotron resonance heating) of fusion plasma, materials treatment, radars), while others have been born recently or are currently emerging. The latter include various advanced spectroscopic techniques such as for instance electron spin resonance (ESR), nuclear magnetic resonance with signal enhancement through dynamic nuclear polarization, (NMR-DNP), plasma physics studies, and novel medical technologies. The current state-of-the-art of the development and application of gyrotrons is well represented in the annually updated report [21] as well as in numerous recent review papers (see for example [22; 23; 24; 25; 26]). Here, our overview is focused on the potential of the gyrotrons as appropriate and versatile radiation sources for imaging and sensing. The paper is organized as follows. The next section is a brief introduction to the physical principles of the operation of gyrotrons and discusses the main advantages of the radiation they produce. Some examples of successful usage of the gyrotrons for sensing, imaging, and spectroscopy are given in Section 3. The review ends up with conclusions and an outlook on the anticipated future prospects of the research in the field.
# Critical Review of Techniques for Food Emulsion Characterization
## 3 Techniques for Emulsion Characterization
### Texture Analysis  
Texture profile analysis has been widely utilized within the food industry to determine and enhance the sensory and textural attributes of emulsion-based products [194]. Texture analysis allows the quantification of attributes, such as resilience, chewiness, gumminess, springiness, and cohesiveness across a spectrum of food products [195; 196].  
To modify the texture of emulsion-based products, texture-modifying ingredients can be incorporated into the water or oil phase. Based on molecular origin and functionality, texture-modifying ingredients can be categorized as thickening agents and gelling agents. The thickening agents derive their functionality from extended molecular conformation while gelling agents' action relies on intermolecular cross-linking. However, practical usage often blurs the distinction, as thickening agents can form gels at high concentrations, and gelling agents can increase viscosity without gel formation at low concentrations. Additionally, some biopolymers added into the water phase might act as thickening agents under defined temperature, pH, or ionic strength conditions. Texture modifiers added to food emulsions enhance the product's desired textural qualities and mouthfeel. They also improve emulsion stability by slowing down the movement of droplets [197].  
The devices commonly used for measurements of emulsion textural properties are texture analyzers. Texture analyzers are equipped with a load arm, connected to an appropriate probe. By setting up an appropriate probe type and speed, as well as deformation depth, time, and cycle duration, texture analyzers can generate graphs of the signal of force vs. length work, based on which the textural parameters of the emulsion can be calculated [198]. For instance, firmness is determined as the initial slope of the penetration profile [199]. The penetration test or double compression tests are usually used to characterize the strength of emulsions [95], which can be utilized to measure the emulsion texture profiles during storage. A lack of changes in the texture profile of emulsions confirms their stability in terms of compression parameters. The key limitation of this method is that it provides only _in vitro_ measurements of emulsion sensory properties that need to be further evaluated in human sensory studies.
## 5 Results  
### Test Results Before and After Rehabilitation  
#### 5.1.1 Statistical Analysis of Results  
The results from the studies evaluating the health status of post-stroke patients before and after rehabilitation are presented in tabular form, showing the variance results for the RMA1, RMA2, and RMA3 indices, differentiated by gender (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**RMA 1**} & \multicolumn{2}{c}{**RMA2**} & \multicolumn{2}{c}{**RMA3**} \\  & \multicolumn{2}{c}{**Difference**} & \multicolumn{2}{c}{**Difference**} & \multicolumn{2}{c}{**Difference**} \\ \hline  & **Female** & **Male** & **Female** & **Male** & **Female** & **Male** \\ \hline Mean & 0.964286 & 0.888889 & 0.857143 & 0.611111 & 0.50000 & 1 \\ Variance & 0.850529 & 0.810458 & 0.645503 & 0.486928 & 0.481481 & 0.470588 \\ Observations & 28 & 18 & 28 & 18 & 28 & 18 \\ Degrees of freedom (df) & 27 & 17 & 27 & 17 & 27 & 17 \\ F-statistic (F) & 1.049443 & & 1.325663 & & 1.023148 \\ P(F \(\leq\) f) one-sided & 0.469968 & & 0.275796 & & 0.492730 \\ One-sided F-test & 2.166593 & & 2.166593 & & 2.166593 \\ \hline \hline \end{tabular}
\end{table}
Table 3: F-test: two-sample variance test for the RMA1, RMA2, and RMA3 indicators.  
If we compare the significance of the F-tests [24] in the context of \(p\)-values for all three indicators (RMA1, RMA2, RMA3), we can observe that none of the tests reaches the traditional level of statistical significance (e.g., \(p\) < 0.05). Following are the \(p\)-values for each indicator:  
* RMA1 Indicator--\(p\)-value: 0.469968;
* RMA2 Indicator--\(p\)-value: 0.275796;
* RMA3 Indicator--\(p\)-value: 0.492730.  
None of these \(p\)-values is less than the traditional significance level of 0.05. This means that there are no statistically significant differences between the variances for women before rehabilitation and men for any of the indicators studied.  
For the ADL indicator, an analysis between genders was also conducted using a two-sample variance test (Table 4). The mean value for women is 0.964, while for men it is 0.833. The variances for both groups are 0.258 and 0.147, respectively. The analysis includes 28 observations for women and 18 for men, resulting in degrees of freedom (df) of 27 and 17. The F-statistic value is 1.754, with a one-sided \(p\)-value (P(F \(\leq\) f)) of 0.115. The results are not statistically significant at the 0.05 significance level since the \(p\)-value is greater than this level. The one-sided F-test also does not allow us to reject the null hypothesis.
# Using Machine Learning to Detect Vault (Anti-Forensic) Apps
###### Abstract  
Content hiding, or vault applications (apps), are designed with a secondary, often concealed purpose, such as encrypting and storing files. While these apps may serve legitimate functions, they unequivocally present significant challenges for law enforcement. Conventional methods for tackling this issue, whether static or dynamic, prove inadequate when devices--typically smartphones--cannot be modified. Additionally, these methods frequently require prior knowledge of which apps are classified as vault apps. This research decisively demonstrates that a non-invasive method of app analysis, combined with machine learning, can effectively identify vault apps. Our findings reveal that it is entirely possible to detect an Android vault app with 98% accuracy using a random forest classifier. This clearly indicates that our approach can be instrumental for law enforcement in their efforts to address this critical issue.  
software development; vault apps; content hiding; malware detection; machine learning; Android Article
# Compensation Control Strategy for Orbital Pursuit-Evasion Problem with Imperfect Information
## 6 Results and Discussion
### Examples of Compensation Control Strategy
The pursuer's control deviates from the guaranteed cost strategy due to delayed information, which leads to the system state deviating from the expected trajectory. It thus gives the evader a chance to prolong the interception time. However, the pursuer's time-to-go \(t_{go}^{P}\) does not increase until 206.61 s. The reason is that the pursuer's delayed information misleads the calculation of \(t_{go}^{P}\). When the pursuer plays with compensation for time delay, the time-to-go corresponding to the pursuer and the evader slightly deviates from that of the no delay scenario. The reason is that the compensation method cannot completely eliminate the effect of delayed information. Figure 8: The average relative distance of 100 Monte Carlo runs in the scenario that the pursuer plays (**a**) without time delay; (**b**) without compensation for time delay; and (**c**) with compensation for time delay. Figure 10 shows the average control variable of 100 Monte Carlo runs between the players in three scenarios: the pursuuer plays without time delay, without compensation for time delay, and with compensation for time delay. When the pursuuer plays without time delay, the control trajectories are linear during the confrontation. When the pursuuer plays without compensation for time delay, the evader's control switches at 177.76 s. This phenomenon is caused by a sudden change in \(t_{go}^{E}\). The pursuer's control decreases from 178.77 s due to the state change caused by the evader's control switch. The pursuer's control switches at 206.61 s due to the sudden change in \(t_{go}^{P}\). When the pursuer plays with compensation for time delay, the pursuer's control and the evader's control slightly deviate from the no-delay ones due to the delayed information. Figure 9: The average \(t_{go}\) and \(\Delta t_{go}\) of 100 Monte Carlo runs in the scenario that the pursuer plays (**a**) without time delay; (**b**) without compensation for time delay; and (**c**) with compensation for time delay.
# Agglomerative Clustering and Residual-VLAD Encoding for Human Action Recognition
## 4 Conclusions  
In this work, we worked on two important aspects of Bag-of-Words framework: optimal codebook generation and features encoding. Two deep ConvNet architectures were explored for features extraction, a two-stream 2D Resnet and a 3D Resnet. We have proposed an effective agglomerative clustering approach for codebook formation. This approach provided the most discriminative codewords as many empirical tests were conducted on global and class-specific codebooks. The limitation of this approach is that it takes three steps for generating the final codebook. The R-VLAD feature encoding is proposed which offers compact representation. The R-VLAD is combined with locality-based descriptor to form a hybrid feature vector, it offers an inclusive spatio-temporal representation. The 3D Resnets pre-trained on large video datasets have shown competitive performance compared to the 2D Resnets.  
While this study was focused on 2D Resnet and a single stream 3D Resnet, in the future, two-stream 3D architectures will be explored for learning better video representations. In addition, determining the fusion weights for individual streams and improvement of computational efficiency by embedding the encoding in end-to-end trainable ConvNet.  
Conceptualization, A.M.B., M.H.Y., S.N.; methodology, A.M.B., F.M.; software, A.M.B.; validation, A.M.B., F.M.; investigation, A.M.B., F.M., S.N.; resources, M.H.Y.; writing-original draft preparation, A.M.B.; writing-review and editing, F.M., S.N., M.H.Y., S.V., S.A.V.; visualization, S.N., S.V.; supervision, F.M., M.H.Y., S.A.V.; project administration, M.H.Y., S.V.; funding acquisition, M.H.Y., S.V. All authors have read and agreed to the published version of the manuscript.  
The research work is funded by Higher Education Commission, Pakistan for Swarm Robotics Lab (Sub-Lab: Computer Vision) under National Centre for Robotics and Automation (NCRA).  
Authors acknowledge continuous support from Centre of Computer Vision Research (C\({}^{2}\)VR) and Directorate of Advanced Studies, Research and Technological Development (ASRTD), UET Taxila.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BOW & Bag-of-Words \\ C3D & Convolutional 3D Network \\ ConvNet & Convolutional Neural Network \\ HFV & Hybrid Feature Vector \\ LLC & Locality Constrained Coding \\ MBH & Motion Boundary Histograms \\ R-VLAD & Residual-Vector of Locally Aggregated Descriptors \\ SCN & Spatial Convolutional Network \\ TCN & Temporal Convolutional Network \\ VQ & Vector Quantization \\ \end{tabular}
## 2 Materials and Methods  
### Experimental Setup  
To comprehensively investigate the DC and EMF exposure levels of Wi-Fi 6 networks under varying scenarios, a series of controlled experiments were conducted in a laboratory environment located in the second basement of a building with minimal interference.  
Network Configuration  
The WLAN AP used in this study is an Asus RT AX88U wireless router, which supports Wi-Fi 6, while remaining compatible with legacy Wi-Fi. It operates on dual bands (2.4 GHz and 5 GHz) with a 4 SS MIMO antenna system. This AP supports a maximum bandwidth of 160 MHz, in addition to legacy bandwidths of 20 MHz, 40 MHz, and 80 MHz, and allows for the configuration of various features through its browser-based graphical user interface (GUI). The detailed AP configuration settings are listed in Table 2. To avoid interference and reduce congestion from other devices which operate at 2.4 GHz, the AP was configured to operate exclusively on the 5 GHz band in this study. It was set to 802.11 ax mode only, enabling all Wi-Fi 6 features, including uplink/downlink OFDMA, MIMO, and beamforming. The highest modulation and coding scheme (MCS) was set up to 11, corresponding to 1024-QAM with a 5/6 code rate. These configurations were designed to fully utilize the advanced capabilities of Wi-Fi 6, ensuring optimal performance. Additionally, to evaluate the worst-case exposure scenario, the AP's dynamic transmission (TX) power was set to its maximum, i.e., an effective isotropic radiated power (EIRP) of 23 dBm.  
The AP was connected to a maximum data rate of 1 Gbps Ethernet network in the laboratory and mounted on a stable platform at a fixed height of 1 m, simulating a typical real-world deployment scenario. This study utilized four commonly used consumer electronic devices as UD, including a laptop, a smartphone, a tablet, and a USB Wi-Fi adapter (UWiA). The detailed specifications of these devices are listed in Table 3. Notably, when the UWiA was used, the internal wireless adapter of the laptop hosting the UWiA was disabled to ensure accurate and isolated measurements for this configuration.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**AP Configuration** & \\ \hline Band & 5 GHz \\ Channel & 36, 46, 58, or 114 \\ Center Frequency & 5.18, 5.23, 5.29, or 5.57 GHz \\ Bandwidth & 20, 40, 80, or 160 MHz \\ Wireless Mode & 802.11 ax only \\ Waveform & DL/UL OFDMA + MU-MIMO \\ Beamforming & Enabled \\ Max MCS 1 & 11 (1024-QAM, 5/6 code rate) \\ Max TX Power & 23 dBm \\ \hline \hline \end{tabular} \({}^{\dagger}\) MCS = modulation and coding scheme.  
\end{table}
Table 2: Access point configuration.
## 3 Results and Discussion
### Evaluation Metrics  
The evaluation metrics employed in this paper encompass intersection over union (I), precision (P), recall (R), and the dice coefficient (D). True positive (TP) denotes the accurate prediction of positive samples as positive, false positive (FP) signifies the erroneous  
Figure 3: Comparison of different attentions: (**a**) channel-wise attention, (**b**) spatial-wise attention, and (**c**) full 3-D weights for attention.  
prediction of negative samples as positive, and false negative (FN) indicates the incorrect prediction of positive samples as negative [31].  
\[I=\frac{T_{\text{P}}}{T_{\text{P}}+F_{\text{P}}+F_{\text{N}}} \tag{17}\]  
\[P=\frac{T_{\text{P}}}{T_{\text{P}}+F_{\text{P}}} \tag{18}\]  
\[R=\frac{T_{\text{P}}}{T_{\text{P}}+F_{\text{N}}} \tag{19}\]  
\[D=\frac{2T_{\text{P}}}{2T_{\text{P}}+F_{\text{P}}+F_{\text{N}}} \tag{20}\]