esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Models and Control Design
### Mathematical Model
\[p_{i}\left(\widetilde{x}\right)=\left[p_{kj}^{i}\right]^{T}+\gamma_{i}I \tag{8}\]  
where \(p_{kj}^{i}\in R^{2N\times 2N}\), \(k=1,\ldots,N\), and \(\gamma_{i}  
The dynamics strategies would be  
\[u_{i}=-\ddot{x}_{i}\left(\sqrt{\alpha_{i}+\beta_{i}^{s}\delta_{i}^{s}(\xi)+\beta_ {i}^{d}\delta_{i}^{d}(\xi)}+\gamma_{i}\right)-\sum_{j=1}^{N}N_{ij}^{i}\big{(} \ddot{x}_{j}-\ddot{\xi}_{j}\big{)} \tag{12}\]  
The derivative of the dynamic's strategies along \(i\) direction would be  
\[\dot{\xi}_{i}=-k\sum_{i=1}^{N}\left(\frac{\ddot{x}_{i}^{T}\ddot{x}_{i}}{2\sqrt {\alpha_{i}+\beta_{i}^{s}\delta_{i}^{s}(\xi)+\beta_{i}^{d}\delta_{i}^{d}(\xi)} }\right)\left(\beta_{i}^{s}\frac{\partial\delta_{i}^{s}(\xi)^{T}}{\partial \xi}+\beta_{i}^{d}\frac{\partial\delta_{i}^{d}(\xi)^{T}}{\partial\xi}\ \right)-R_{i}(\tilde{x}-\xi)\right) \tag{13}\]  
The derivative of the dynamic's strategies along \(j\) direction would be  
Figure 4:0  
The state equation for WMRs in collision avoidance describes the evolution of the system's state over time, incorporating the dynamics of the WMRs and the constraints imposed by collision avoidance. The state equation is derived using unicycle dynamics and incorporates control barrier functions (CBFs) to enforce safety constraints. The control inputs \(v_{i}\) and \(\omega_{i}\) are computed using a quadratic programming (QP) framework, which minimizes a cost function while satisfying the CBF constraints. This approach ensures that the WMRs navigate safely and efficiently in dynamic and complex environments, avoiding collisions with obstacles and other WMRs. Then, the state equation becomes  
\[ \begin{bmatrix}\dot{x}_{i}^{1}\\ \dot{x}_{i}^{2}\end{bmatrix} =-\ddot{x}_{i}\left(\sqrt{\alpha_{i}+\beta_{i}^{s} \delta_{i}^{s}(\xi)+\beta_{i}^{d}\delta_{i}^{d}(\xi)}+\gamma_{i}\right)-\sum _{j=1}^{N}N_{ij}^{i}\big{(}\ddot{x}_{j}-\ddot{\xi}_{j}\big{)} \tag{15}\]  
Velocity and angular velocity states equation becomes  
\[ \begin{bmatrix}V_{i}\\ \omega_{i}\end{bmatrix} = \begin{bmatrix}\cos(\theta_{i})&sin(\theta_{i})\\ -\frac{1}{a}sin(\theta_{i})&\frac{1}{a}cos(\theta_{i})\end{bmatrix}\begin{bmatrix} k(x_{i}^{1}-X_{i}-a_{i}cos(\theta_{i})+\dot{x}_{i}^{1})\\ k(x_{i}^{2}-Y_{i}-a_{i}sin(\theta_{i})+\dot{x}_{i}^{2})\end{bmatrix}  \tag{16}\]  
The coordinates of the center of mass of each robot may be written as  
\[P_{i}=(X_{i}+a_{i}\cos(\theta_{i})+Y_{i}+a_{i}sin(\theta_{i})) \tag{17}\]  
The equations describe the dynamics of each agent's center of mass when the aforementioned relation is differentiated with regard to time. \[\dot{p}_{i}= \begin{bmatrix}\cos(\theta_{i})&-a_{i}sin(\theta_{i})\\ sin(\theta_{i})&a_{i}\cos(\theta_{i})\end{bmatrix} \triangleq T_{i}(\theta_{i} ) \begin{bmatrix}v_{i}\\ \omega_{i}\end{bmatrix}  \tag{18}\]
## 3 Results
### Extracellular and Intracellular Water in the Lower Limbs  
Figure 2A shows the change in the extracellular water/total body water ratio in the lower limbs with and without the compression stocking before and after the 3-hour sitting task. There was a significant interaction of the extracellular water/total body water ratio in the lower limbs between the compression stocking wear status and wear time (baseline value and after the 3-hour sitting task, \(p<0.05\)). The main effect of compression stocking use and wear time were also found (\(p<0.05\), respectively). For limbs without compression stockings, the extracellular water/total body water ratio significantly increased after the 3-hour sitting task (\(0.371\pm 0.008\) to \(0.375\pm 0.009\)\(p<0.05\), Figure 2A), and edema was observed. In contrast, for limbs with the compression stocking, extracellular water did not increase (\(0.370\pm 0.009\) to \(0.371\pm 0.009\), \(p=0.062\), Figure 2A).  
Figure 1: Percent changes of the circumference of the calf before and after 3-hour constant sitting with- or without compression stocking. \({}^{\star}\)\(p<0.05\) between groups.  
Figure 2: (**A**) Extracellular water/total water ratio (ECW/TBW) and (**B**) intracellular water/total water ratio (ICW/TBW) in the lower limbs before and after 3-hour constant sitting with or without compression stocking.  
Changes in the intracellular water/total body water ratio (an indicator of intracellular water in the lower extremities) after the 3-hour sitting task are shown in Figure 2B. There was a significant interaction of intracellular water/total body water ratio in the lower limbs between the compression stocking wear status and wear time (\(p<0.05\), Figure 2B). The main effect of wear time was also found (\(p<0.05\), Figure 2B). However, the main effect of the presence or absence of wearing compression stockings remained insignificant (\(p=0.07\), Figure 2B). Intracellular water in the lower limbs without compression stockings decreased (from \(0.629\pm 0.008\) to \(0.625\pm 0.008\), \(p<0.05\), Figure 2B) whereas intracellular water in the limbs with compression stockings did not change (\(0.630\pm 0.009\) to \(0.629\pm 0.009\), \(p<0.05\), Figure 2B).
# A Portable Non-Motorized Smart IoT Weather Station Platform for Urban Thermal Comfort Studies
## 1 Introduction
[34] customized the globe according to ISO 7726 (1998) and the ASHRAE handbook of fundamentals for GT measurements using S-TMB-M002 HOBO and Pt100 temperature sensors, respectively. Therefore, the proposed weather device offers the following advantages over the existing literature:  
* Portability: It is a battery-operated mobile device measuring 50 cm \(\times\) 11 cm \(\times\) 20 cm and weighing about 750 g.
* Cost-efficient: The proposed weather station costs about \(\copy 80\) per edge device, compared to the commercially available Davis Vantage Pro 2, which is priced at \(\copy 995\). * Scalability: Theoretically, unlimited edge devices can be added to the system. * Real-time remote monitoring: All edge devices can be monitored through the webpage. * Automatic location tagging: Integration of the Global Positioning System (GPS) allows automatic location tagging. * Higher sampling time: The edge device provides a reading every 6 s.  
* GT integration allows for MRT calculation: MRT is one of the main variables for thermal comfort studies. Section 2 presents the design and development of the IoT edge device and the choice of sensors to meet the UHI and outdoor thermal comfort study requirements. Section 3 outlines the validation and performance of the edge device in terms of atmospheric variable measurements. Section 4 shows the test results of preliminary tests, followed by Section 5, the discussion, and conclusion.
## 4 Results and Discussion
### Results of the Secondary Paths Estimation  
In Figure 9, the results of the offline estimation procedure are reported, considering a filter of length M = 1024 and a step size \(\mu\) = 0.01. This evaluation was conducted once before the evaluation of the performance of the adaptive algorithms. Due to the low amplitude of the frequency response function of the control loudspeakers, the secondary paths show that, below 100 Hz, the system is not able to produce a sufficient amount of energy to attenuate the noise below this cut-off frequency. For this reason, the range where the active noise control system works is assumed to be between 100 Hz and a quarter of the sampling frequency (in this case 1600 Hz). In this range, the pressure nodes related to the modal response of the enclosure must be taken into account, because, at these frequencies, they could make the system ineffective due to local minima in the amplitude of the secondary paths. From the graphs, it is also possible to notice a similarity between the two direct paths (from control source \(i\) to error microphone \(i\)), shown on the left-hand side, and the crossed paths (from control source \(i\) to error microphone \(j\)), shown on the right-hand side, meaning a good symmetry of the problem.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **100 Hz** & **500 Hz** &
\begin{tabular}{c} **Pseudo-** \\ **Random** \\ \end{tabular} & **White Noise** \\ \hline External & 0.120 & 0.700 & 0.500 & 0.500 \\ Internal & 1.000 & 5.000 & 5.000 & 5.000 \\ \hline \end{tabular}
\end{table}
Table 1: Step-size parameters \(\mu\) for algorithms with fixed value.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\mu_{b}\) & \(\mu_{max}\) & \(\mu_{min}\) & \(\mu_{in}\) & \(\beta\) \\ \hline
0.050 & 7.000 & 0.010 & 1.000 & 0.001 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters for the FF_FXNLMS and FF_FURLMS algorithms.
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 1 Introduction
The details of the MOTS framework methodology are described in Section 3. Experimental results are shown and analyzed in Section 4. Section 5 discusses the benefits, as well as the limitations, of the method, and it is followed by the conclusions in Section 6.
# Numerical Simulation-Based Study on the Arching Effect in Subsequent Backfill
## 2 Research Methods  
The numerical simulation in this study was conducted based on the mechanical characteristics of the ore-rock mass and backfill material at Dongguanshan Copper Mine, utilizing the Mohr-Coulomb elastoplastic constitutive model in FLAC3D. The primary objective was to investigate the arching effect in subsequent backfilling operations within the mine's panel slopes. The research systematically examined several critical aspects influencing stress distribution in backfilled slopes. First, the impact of interface elements on stress redistribution was analyzed to understand how numerical contact modeling affects simulation results. Second, parametric studies were performed to evaluate how both backfill material properties and interface parameters influence the stress distribution patterns. Third, the role of the backfill inclination angle in modifying stress distributions was examined to account for non-vertical slope configurations. Finally, these findings were synthesized to develop comprehensive conclusions regarding the fundamental laws governing stress distribution in backfilled mine slopes under arching conditions. This integrated approach provides valuable insights for optimizing backfill design and ensuring slope stability in similar mining operations.
## 3. Key Supporting Technologies for the WSN
### Interconnection between WSN and CMS  
In order to transmit the environmental information that the WSN collects to the GMDC stably, the WSN is designed to interconnect with the optical fiber backbone network via the traditional CMS, and the WSN interconnects to the CMS through the sensor node by using a Modbus protocol [18]. In addition, the sink node in the WSN connects to the CMS via a serial port. The sink node has a RS232 interface, while the CMS has a RS485 interface, so RS232/RS485 conversion equipment is used.  
The Modbus protocol is a common language used by electronic controllers. It defines a message structure that controllers will recognize and use, regardless of the type of networks over which they communicate. It describes the process a controller uses to request access to another device, how it will respond to requests from the other devices, and how errors will be detected and reported.  
The Modbus protocol is based on a master/slave or request/reply architecture. For our integrated environment monitoring system, the master is the cable monitoring station--it requests a message. The slave is the WSN, it takes action and responds to the master. In a Modbus network, there is one master and up to 247 slaves, each with a unique slave address from 1 to 247. For saving address, only the sink node is assigned an address for the WSN. The sink node gathers the environment information of the whole WSN, hence the information can be obtained only through the sink node.  
The Modbus protocol supports two transmission modes: ASCII and Remote Terminal Unit (RTU), the RTU mode is used in the interconnection. The RTU format uses binary coding, each byte in a message contains two 4-bit hexadecimal characters, and each byte consists of a start bit, 8 data bits, a bit for even/odd parity, and a stop bit. The main advantage of this mode is that its higher character density allows better data throughput than ASCII for the same baud rate.
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## 2 System Model
### uRLLC Traffic
#### 2.5.4 Wireless RAN Network  
\(T_{bl}^{RAN}\) consists of BS process time, UE process time, frame alignment time, queue time, and transmission time [31; 50; 53]. In this work, uRLLC and eMBB traffic are independent in slices' various services, so the queue of eMBB does not affect uRLLC. The system always has resources to service the uRLLC immediately as it arrives, which leads to the uRLLC UE having no queueing delay. BS and UE process time are bound by three OFDM symbol durations, which are very small and refer to the equipment computing capacity. As for, frame alignment time is upper bounded by the short TTI interval (\(\delta\)), and transmission time \(T_{bl}^{tx}\) is related to the data rate of UE \(l\). In superposition operation, UE \(l\) is paired with eMBB \(e\), if its load \(R_{l}^{min}\) needs to be transmitted within the stipulated period \(\delta\), \(T_{bl}^{tx}=\rho_{bl}\ \delta\), where \(\rho_{bl}\) is the number of mini slots in which uRLLC UE \(l\) pairs with eMBB UE \(e\) and the following condition is satisfied, \(\max\limits_{l\in\mathcal{Z}}\frac{R_{l}^{min}}{R_{l}^{tx}T_{bl}^{tx}}\leq D_{l}\), where \(D_{l}\) indicates the maximum RAN packet delay threshold of uRLLC UE \(l\) and \(R_{bl}=r_{bl}^{min}*\gamma_{be}^{m}\). In case of failure, the packet is subject to additional retransmission delay \(T_{bl}^{RT}\) is a round trip delay of a HARQ retransmission until either it is decoded successfully, or the maximum number of retransmissions is reached. This work considers uRLLC UE \(u\in U\) scheduled with short TTI units composed of two OFDM symbols and Short HARQ RTT. Thus, we can determine the mean transmission packet delay \(T_{bl}^{RAN}\) of a uRLLC UE is as follows [50; 53; 54],  
\[T_{bl}^{RAN}\cong\ T_{bl}^{tx}+T_{bl}^{WT}+T_{bl}^{RTT}, \tag{14}\]  
where \(T_{bl}^{WT}\) is the superimposed mini-slot time for uRLLC UE \(l\) to pair with eMBB UE \(e\). Each eMBB UE may be paired with more than one uRLLC UE, so the eMBB TTI is split into mini-slots; each paired uRLLC UE takes several \(\rho_{bl}\) mini-slots, and one mini slot is spaced to avoid intra-slice interference between uRLLC UEs that are paired.  
To compute the average DL packet transmission delay of this system for uRLLC UE \(l\)[53; 54], i.e.,  
\[\overline{T}_{bl}=\begin{cases}T_{bl}^{RAN},&\text{ if }b=1\\ T_{bl}^{WBH}+T_{bl}^{RAN},&\text{ if }b\
eq 1\end{cases}, \tag{15}\]  
On the other side, the reliability of uRLLC can be improved by making sure that its outage probability is less than a certain threshold, according to [27],  
\[Pr\ (\sum_{l\in\mathcal{Z}}R_{bl}<a_{bl}^{nm}R_{l}^{min})\leq\epsilon, \tag{16}\]  
If a uRLLC UE \(l\) connects with BS \(b\) and paires with an eMBB UE \(e\), it is the duty of this BS and eMBB UE to achieve its QoS. i.e.,  
\[R_{bl}\geq\ R_{l}^{min}\ \&\&\ \overline{T}_{bl}\ \leq T_{l}^{max}, \tag{17}\]
# Axial and Bending Bearing Capacity of Double-Steel-Concrete Composite Shear Walls
## 5 Conclusions  
This paper summarized the experimental results of 49 SC walls subjected to cyclic loading. The parameter analysis using ABAQUS was carried out to supplement the experiments. Based on the analysis of the experimental results and finite element analysis results, the calculation method for the axial and bending bearing capacity of SC walls are proposed. The conclusions of this paper are summarized as follows.  
Figure 11: Equivalence of web plate for the small eccentric SC wall.  
Figure 12: Comparison between test results and calculated results of specimens in the existing literature.  
* The bending failure of SC walls featured the concrete crushing at the column and the average strain distribution of the wall cross section agreed with the plane assumption.
* When studs meet certain construction requirements, the surface steel plates and filled concrete are unified well, and the bond-slip between them is relatively small.
* The ultimate compressive strain of the concrete can be defined as the post-peak strain corresponding to the stress valued \(0.5\mathrm{f}_{\mathrm{cc}}\) in the declining part of the stress-strain curve; the constitutive of the concrete with the confinement of a rectangular steel tube is applied to descript the behavior of concrete.
* The basic assumption for calculating the axial and bending bearing capacity of SC walls is put forward and the calculation formula applying to both small and large eccentric load is deduced. The calculated results are compared with the experimental results, which present good agreements.  
Conceptualization, Q.G.; Data curation, P.Z.; Formal analysis, P.Z. and F.K.; Funding acquisition, Q.G.; Investigation, W.Z. and F.K.; Methodology, W.Z. and Y.Y.; Project administration, Q.G. and YY.; Writing--original draft, F.K.; Writing--review & editing, P.Z. All authors have read and agreed to the published version of the manuscript. This work was funded by National Natural Science Foundation of China (Grant No. 51778032). This work was supported National Natural Science Foundation of China (Grant No. 51778032). The authors greatly appreciate the financial support. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results and Discussion
### Characterization of Optimized ETB-NE
#### 3.4.2 Transmission Electron Microscopy  
The TEM image of the optimized ETB-NE (Figure 4A) shows spherical morphology with a droplet size of less than 200 nm. The droplet size analysis also provided similar results. The morphology of a reported eucalyptus oil NE was comparable to that observed in the present study [35]; however, the ETB-NE has a smaller droplet size compared to that observed in this reported work.
## 4 Implementation and Evaluation
### Threats Resistance  
In terms of security analysis, we used a Speck128 encryption algorithm that is known to be lightweight, fast and secure. As for authentication, it is also analyzed in [26]. Regarding attack types, we used eavesdropping, man-in-the-middle and replay attacks. The FlexCrypt algorithm is used initially in the Trust-CH technique in an effort to lower the cost of authentication, key generation and integrity. Moreover, in an effort to cut costs by depending on the nodes' shared confidence. After being designated as a trusted node, the Trust-CH mechanism may be used to communicate a unified key (k) to the interaction between the nodes and the CH. So even if the attacker utilized brute force, it was impossible for the adversary to find the Consolidated Key (k). This is due to the fact that k is transmitted in encrypted form based on the first algorithm's authentication (FlexCrypt). Moreover, By flooding the network with redundant bogus data, posing as a CH or other node using that node's IDs, or otherwise disrupting it, an attacker might reduce network performance or drain the nodes' power. In our work, we guard against this kind of attack by adding a sequential number to the session key (Sk), guaranteeing the validity of sent data and preventing data repetition.
## 5 Conclusions and Future Work
The main contribution of the proposed framework is the cost-effectiveness in handling the imbalanced malware families while achieving high detection performance and without the need for data augmentation processes or complex features engineering. Extensive simulation experiments based on various evaluation metrics were conducted on the benchmark imbalanced Malimg dataset, which proved the outstanding classification capability and proficiency of the proposed framework. Furthermore, a comprehensive comparative analysis among the proposed work and recent well-known ML and DL-based malware classification algorithms was presented and discussed. The comparison results demonstrated that the proposed framework achieved superior outcomes for all examined classification metrics. For future work, different balanced and imbalanced malware datasets can be tested and explored. Additionally, building and testing a new malware dataset with the recent well-known malicious software is one of our aims to be considered. Moreover, we intend to investigate and examine the detection of cyber-attacks and malicious software in IoT cybersecurity applications. Conceptualization, W.E.-S. and I.A. ; methodology, W.E.-S. and I.A. ; software, W.E.-S. and A.A.; validation, W.E.-S., I.A. and A.A.; formal analysis, W.E.-S. and I.A. ; investigation, W.E.-S., I.A. and A.A.; resources, W.E.-S., I.A. and A.A.; data curation, W.E.-S., I.A. and A.A.; writing--original draft preparation, W.E.-S. and A.A.; writing--I.A. ; visualization, W.E.-S., I.A. and A.A.; supervision, W.E.-S. and I.A. ; project administration, W.E.-S. and I.A. ; funding acquisition, I.A. All authors have read and agreed to the published version of the manuscript. This work was supported by Prince Sultan University, Saudi Arabia, [Grant number: SEED-CCIS-2021-84]. Not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article. The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) of this publication. The authors declare no conflict of interest.
## 4 Results and Discussion
### Level 2: Inferential Statistics
#### 4.2.3 Correlation  
To define the strength of the relationship among two features in the dataset and address RQ2.3 (How the brand, model, vehicle class, cylinder, engine size, transmission type, and fuel type correlate with emissions and consumption of various vehicles?), a correlation algorithm has been introduced to generate correlation coefficients. The most commonly used algorithm of this type in statistics is Pearson correlation, which estimates the direction and strength of a linear relationship among two variables [39]. In this study, the objective of this statistic is to define which parameter has the strongest correlation with the total fuel consumption and CO\({}_{2}\) emission. To achieve this, [PERSON]'s correlation coefficients have been applied and computed between all features through all vehicles and presented in a correlation heat map shown in Figure 12.  
From the heat map in Figure 12, all the correlation coefficients have been calculated, showing the correlation between corresponding parameters on the left and the corresponding parameters at the bottom. The higher the correlation coefficient, the warmer color was presented.  
Moreover, Figures 13 and 14 below reveal the importance of all features on estimating total fuel consumption and CO\({}_{2}\) emissions by using bar charts.  
Figure 11: Total fuel consumption and emissions of each fuel type over time.  
It is seen from Figures 13 and 14 that besides the fuel consumption features in the highway and the city (the two most important features), engine size gives the highest correlation for estimating total fuel consumption, whereas cylinders, year, and smog rating are nearly half as important, compared to engine size. For estimating carbon dioxide emission, engine size, year, and smog rating are important features. This finding contributes as an influential factor in building Machine Learning and Deep Learning models presented in Levels 3 and 4.  
Figure 12: Heatmap of correlation between all dataset parameters.  
Figure 13: Importance of features on predicting total fuel consumption.