esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area, Dataset, and Methodology
### Correlation Analysis and Trend Analysis  
#### 2.5.1 Correlation Analysis  
Correlation analysis is a commonly used statistical method for evaluating the degree of linear association between variables [42; 43]. By calculating correlation coefficients, this method reveals the strength and direction of associations between variables. Correlation analysis was conducted to examine the degree of linear association between NDVI and the other factors. The formula used to calculate the correlation coefficient \(r\) between NDVI and each variable (temperature, precipitation, and nighttime light) is given in Formula (1) as follows:  
\[r(X,Y)=\frac{Cov(X,Y)}{\sqrt{Var[X]Var[Y]}} \tag{1}\]  
where Cov(X,Y) represents the covariance between X and Y, and Var[X] and Var[Y] denote their variances. This analysis aimed to uncover interactions between climate, human activities, and vegetation in Aksu.
## 3 Results and Discussion
### RTK-GNSS Dynamic Test  
Table 3 shows the RMS and standard deviation values for the error produced by the GNSS receiver mounted on the off-road vehicle when using RTK-GNSS correction signals. The rover receiver had a horizontal accuracy of 2.5 cm and a vertical accuracy of 3.7 cm when used on a continuous real-time basis. This level of accuracy was expected because the RTK technique can determine the sensor  
position within a few centimeters [19]. The 2.4 cm average for the RMS cross-track error in the RTK correction signal system indicates that the passes were relatively straight, as was desired for the autonomous tractor. Between the rows there was an error with a constant standard deviation; the average of this value for the RTK technique was 1.43 cm. These results demonstrate that the tractor controller was able to track each straight line with a standard deviation of better than 3.5 cm; the vehicle lateral position error never deviated by more than 4 cm when using the RTK technique. Therefore, fully autonomous vehicles could be used for automated precision farming in many other applications, such as site-specific management of weed control in extensive crops, variable-rate application in orchards and vineyards using the appropriate implement, and variable-rate application of fertilizer based on yield maps.  
The mounting location of the GNSS antennas on the autonomous tractor provided a clear view of the entire sky. This maximized the potential for obtaining good satellite geometry, and constant RTK-GPS quality was maintained for the recording of all data.
## 4 Seismic Fragility Assessment  
### Selected Structure  
To investigate the impact of shear deformation in non-ductile RC columns on the seismic fragility of RC structures, a non-conforming building representative of the standard drawings for 1980s school buildings provided by the Ministry of Education in South Korea was selected. The proposed prediction equations for shear spring model parameters, detailed in the previous section, were employed to account for the shear deformation of RC columns. Figure 12 illustrates the plan view and elevation of the selected RC structure. For simplicity, a frame in the short direction was selected, as the aim of this study is to compare the seismic behavior of RC frames with and without considering the shear deformation of RC columns.  
The section details of RC columns are shown in Figure 13. Columns C1, C2, and C3 have rectangular cross-sections with dimensions of 350 mm \(\times\) 500 mm, 350 mm \(\times\) 400 mm, and 350 mm \(\times\) 400 mm, respectively. The quantity and diameter of longitudinal reinforcement vary: C1 contains 8-D19 and 2-D16 rebars, C2 has 12-D19 and 2-D16 rebars, and C3 is reinforced with 8-D16 rebars. These configurations result in longitudinal reinforcement ratios of 1.54% for C1, 2.74% for C2, and 1.13% for C3. Additionally, the volumetric ratios of transverse reinforcement for these three columns are 0.30%, 0.34%, and 0.34%, respectively, indicating relatively low confinement. In accordance with the 1980s specifications for school buildings in South Korea, the compressive strength of concrete and the yield strength of the rebar are assumed to be 21 MPa and 300 MPa, respectively.  
Figure 12: Plan view and elevation of the selected RC structure (unit: mm).  
Figure 13: Section details of RC columns (unit: mm): (**a**) C1; (**b**) C2; and (**c**) C3.
## 6 Discussion  
The proposed kinodynamic-aware obstacle-free trajectory optimization technique revealed promising performance for real-time trajectory optimization, which is necessary for the autonomous navigation of the UAV. It should be noted that, in this paper, by _real-time_ optimization, we did not mean dynamics or high-frequency (e.g., sensor) rate time scales. Rather, we meant formulating and solving the optimization problem in the time frame of a few seconds to ten seconds, which the overhead controller requires to plan the motion primitives for the receding time horizon.  
The developed kinematic model is linear, and thus the proposed trajectory optimization can be easily modified for receding horizon MPC optimization. Although MPC imposes more of a computational burden, more efficient results can be achieved. This is because the optimization problem for the whole step times in the time interval is solved in one go, which converges the solution to the global path. However, modeling obstacle-free paths and LoS channels in the context of MPC requires more research.  
Furthermore, channel modeling is required in the context of UAV-enabled aerial wireless communication for mobile IoVs to be incorporated in trajectory optimization. However, including the channel models with LoS paths in the convex programming imposes a greater computational cost. In this paper, we relaxed channel performance in the path planner and considered the performance of the proposed trajectory optimization technique for kinodynamic navigation exposed to motion constraints, which was verified through simulation results. It is a common procedure in relevant multi-objective optimizations utilizing sequential convex programming that problem objectives are optimized one by one to tackle the computational complexity and nonconvexity of the optimization problem.  
Therefore, in the simulation results section, quantitative and statistics results, which are meaningful when communication performance is considered, were not provided. Furthermore, effective solutions are needed to make the modeling and solving of the trajectory optimization computationally tractable, which maximizes the wireless communication performance and satisfies the QoS index. This issue will be studied in future works in which UAV-enabled RIS-assisted channel modeling will be incorporated into the UAV trajectory optimization problem.
# Emerging Diagnostics in _Clostridioides difficile_ Infection
## 3 Sampling the Community (Ribotyping and Mass Spectrometry)
Conversely, several smaller studies found significant overlap in C\({}_{\text{T}}\) values between symptomatic patients and asymptomatic carriers, making it unreliable to use C\({}_{\text{T}}\) values to distinguish disease from colonization [85]. Another type of multiplex PCR akin to LAMP that does not use thermal cycles and functions at room temperature (target DNA at 37-42 \({}^{\circ}\)C) is recombinase polymerase amplification (RPA). The properties of RPA partly meet the need for a rapid, inexpensive, ultra-sensitive POCT for detecting C. _difficile_ in hospital admission and community settings, where distinguishing symptomatic patients/hosts from asymptomatic carriers is critical. In regard to the ultra-sensitivity requirements for a POCT, the attomole precision of a clustered, regularly interspaced short palindromic repeat (CRISPR)/Cas 12a diagnostic may provide a solution [86]. Cas12a, a CRISPR type V system component, induces the cleavage of non-targeted single-stranded DNA (ssDNA). This activity facilitates the detection of nucleic acids via signal amplification and supports various readouts by incorporating functionalized reporter nucleic acids. Cas12 enzymes require a protospacer-adjacent motif (PAM) site in the target region for dsDNA cleavage to collaterally cleaves ssDNA [87]. Cas12a-based detection has been successfully applied to various pathogens, including SARS-CoV-2 and _Salmonella_[88; 89]. Recent work by [PERSON] et al. combined the advantages of both RPA and Cas12a [90] in detecting tcdA and tcdB via two multiplex RPA-Cas12a-assay formats, namely camera capture UV fluorescence and a visible lateral flow strip (LFS). The strain specificities of both formats were validated against the extracted DNA of 14 diarrhea-causing enteric pathogens, and clinical potential was verified via a commercially available A/B PCR kit. Regarding sensitivity, the authors reported that both formats were equally consistent, exhibiting limit of detection values of 10 copies/uL and 1 copy/uL for tcdA and tcdB, respectively.
# Experimental Study on Two PCM Macro-Encapsulation Designs in a Thermal Energy Storage Tank
## 3 Results and Discussion
### Heat Transfer Rate Evolution and Total Energy Released in the Discharging Process  
The HTR evolution during the discharging process for all the experimental cases is shown in Figure 9. In all the experiments the profiles showed a similar trend. Significantly higher values were obtained during the first 20 min of the process when the heat is mainly transferred from the HTF inside the tank followed by an exponential decrease while the PCM decreases its temperature until minimum values are reached. Furthermore, in this case, due to the characteristics of the experimental facility, at the beginning of the experimentation the inlet temperature of the tank oscillates \(\pm\)2 \({}^{\circ}\)C around the desired temperature, affecting the initial peak of power. However, the inlet temperature stabilizes (T_in standard deviation lower than 0.3 \({}^{\circ}\)C) before the temperature inside the tank reaches the latent range of the PCM. After the initial peak and during the first 1.5 h of operation, ThinICE_2L shows slightly better performance getting up to 0.1 kW more HTR than FlatICE_2L. Moreover, due to the lower amount of PCM in the storage tank with ThinICE slabs, after 1.5 h the HTR delivered by ThinICE_2L decreases faster than the one delivered by FlatICE_2L. At 4 L/min, after the initial peak and during the first 1.5 h, similar results to 2 L/min are obtained. Moreover, after 1.5 h the HTR of ThinICE_4L drastically decreases, therefore for the next 2 h (from 1.5-3.5 h) FlatICE_4L maintains an HTR up to 0.4 kW higher than ThinICE_2L.  
Figure 10 reports the total energy released for each experiment conditions, and the percentage it represents with respect to the energy stored in the charging process (Figure 7) in each case. Analyzing the effects of flow rate, similar to the charging, both experiments with ThinICE slabs showed a comparable energy release, suggesting a correct utilization of the energy stored in the PCM. In the case of FlatICE slabs, experiments at 2 L/min released 10% more energy compared with 4 L/min. This is supported by the fact that in the charging process the tank at 2 L/min manages to store 10% more energy than at 4 L/min (Figure 5). In addition, it is interesting to note that in all cases of the selected operating threshold approximately 85% of the energy stored in the tank was discharged.  
Figure 9: Evolution of the HTF heat transfer rate during the discharging processes of the four study cases presented in this study.
# A Review on Internet of Things for Defense and Public Safety
## 4 Operational Requirements
### Supported Services and Applications  
A number of commercial devices and electronic equipment have been explored to provide the services required, like chat, push-to-talk voice, geo-situational awareness, SRTV (Secure Real-Time Video) or web sharing. A complete list of requirements and application services can be seen in Figure 6. The diagram represents the vision of the Joint Information Environment (JIE), which ensures that DoD military commanders, civilian leadership, warfighters, coalition partners, and other non-DoD mission partners, access information and data provided in an agile DoD-wide information environment. This shared IT infrastructure includes enterprise services, and a Single Security Architecture (SSA). The Mission Partner Environment (MPE) is integrated with and enabled by JIE. It corresponds to an operating environment that enables C2, within an specific coalition, for operational support planning and execution on a network infrastructure at a single security level with a common language. Regarding the small circles of the diagram, they represent the different participants within a specific partnership or coalition (e.g., the intelligence community, U.S. government agencies, allies, and other mission partners, such as industry organizations and Non-Governmental Organizations (NGOs)).  
The U.S. army's Nett Warrior (NW) program [78] has developed ruggedized Android devices. These devices, which are modified from COTS Samsung Galaxy Note II smart phones, provide access to the data-capable Rifleman radio. It aims to connect soldiers in the field with a range of apps, such as Blue Force Tracking, 3-D maps, or an application that shows details on profiles of high-value targets. The devices run an NSA-approved version of the Android operating system, and plan to include applications such as foreign language translation. These programs have been piloted on a limited basis. Broader deployment is hampered by the limited usability, functionality and lack of connectivity. Other commercial devices can be seen in Figure 7.  
The U.S. Air Force (Hurlburt Field, Okaloosa County, FL, USA) has developed apps on commercial iPads. For example, programmers at Scott Air Force Bases created in 2014 an app to plan loads for the KC-10 cargo aircraft [79], winning an award for government innovation. Such an application was designed to automatically gauge pre-flight distribution of cargo in a weight and balance computation, considering the crew, fuel and cargo in a drag-and-drop interface.  
The American Defense Information System Agency's (DISA) Mobility Program has implemented software packages for NSA-approved Android devices. The program includes secure devices that can access a secret classified network, SIPRNET [80]. DMCC-S (DoD Mobility Classified Capability Secret Device) R2.0 is an example (Figure 8) of the new generation of DoD secure mobile communication devices.  
Figure 6: Requirements and application services for commanders.  
Figure 7: DoD Enterprise Mobile Devices Management (MDM) evolution.
# The Optimal Deployment Strategy of Mega-Constellation Based on Markov Decision Process
## 3 Markov Process of Staged Deployment
### Cost-Effectiveness Analysis of Deployment Strategy  
When systems fail, the previous deployment stages cannot operate normally and the investment becomes a sunk cost, with the economic value also included. However, the cost could be accounted for by the failure probability when systems encounter partial malfunctions. Let \(C_{f}\) be the failure cost, \(C_{ex}\) be the cost of development and deployment in the previous stages, and \(V_{co}\) be the economic value brought by the previous deployment; therefore, the cost expression could be denoted by:  
\[C_{f}=\left\{\begin{array}{cc}C_{ex}+V_{eco}&k\geq\varepsilon\\ P\cdot(C_{ex}+V_{eco})&k<\varepsilon\end{array}\right. \tag{13}\]  
During each time step \(t\), let \(C_{tot,t}\) be the total cost, \(C_{tau,t}\) be the cost of an additional launch, \(C_{ord,t}\) be the cost of ordering new satellites, and \(C_{op,t}\) be the operation cost. The total cost is:  
\[C_{tot,t}=C_{f,t}+C_{tau,t}+C_{ord,t}+C_{op,t} \tag{14}\]  
Assuming \(C_{ex}\) is constant, the rest of each part is denoted by:  
\[\begin{array}{l}V_{eco}=\alpha_{eco}\cdot n_{t}\\ C_{tau,t}=\alpha_{tau}\cdot u_{t}\\ C_{ord,t}=\alpha_{tau}\cdot u_{t}\\ C_{op,t}=\alpha_{op}\cdot n_{t}\end{array} \tag{15}\]  
In the above equations, \(\alpha_{*}\) is the coefficient of each item.  
Similar to the cost of each stage, the effectiveness analysis consists of two partsL:normal operation and malfunction. Therefore, the calculation equation is:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(p_{t}\) & \(P(p_{t+1}=0|p_{t}\,)\) & \(P(p_{t+1}=1|p_{t}\,)\) & \(P(p_{t+1}=2|p_{t}\,)\) \\ \hline
0 & \(p_{00}\) & \(p_{01}\) & \(p_{02}\) \\
1 & \(p_{10}\) & \(p_{11}\) & \(p_{12}\) \\
2 & \(p_{20}\) & \(p_{21}\) & \(p_{22}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The transition matrix of Markov process.  
\[E_{tot,t}=\left\{\begin{array}{ll}E_{op^{-1,t}}&k\geq\varepsilon\\ E_{op,t}&k<\varepsilon\end{array}\right. \tag{16}\]  
The net profit function \(NP(n_{t},p_{t};u_{t})\) in each time step \(t\) can be built by:  
\[NP(n_{t},p_{t};u_{t})=E_{tot,t}-C_{tot,t}=E_{tot,t}-\left(C_{f,t}+C_{tan,t}+C_ {end,t}+C_{op,t}\right) \tag{17}\]  
The optimal strategy is the decision which could maximize the net profit, denoted by \(u_{t}^{*}\). The calculation flow is shown in Table 3.
# Detecting Cryptojacking Web Threats: An Approach with Autoencoders and Deep Dense Neural Networks
## 5 Proposed Framework
### Data Collection
Table 2 tabulates the families of cryptojacking scripts, the top-level domains (TDLs), DNS, or IP addresses found, and the number of infected sites involved. In the next step, to aggregate the features at the network and host layer, each infected and benign website was navigated for a period of 120 seconds, simulating a user's browsing; on Ubuntu (Linux) and Windows operating systems. Using the Processing Performance Tool (Windows) and HTOP (Ubuntu-Linux), processing signals from the web browser to memory, disk, and CPU were recorded every 30 seconds. Parallel to this, Wireshark protocol analyzer was focused on inspecting, filtering, and capturing network packets on TCP and HTTP protocols. Each analysis result was mapped with its respective site, class, and stored as a document in a separate collection in the data warehouse, which is the base of operations of the project. The final eighteen features are presented in Table 3, where their exploratory static values are described. \begin{table}
\begin{tabular}{c l c} \hline \hline
**Malicious Script** & **TLD/DNS/IP Targeted** & **Number of Infected Sites** \\ \hline Crypto-loot & crypto-loot. com, cryptoloot. pro, 104. 21. 56. 113 172. 67. 184. 223 & 355 \\ \hline CoinIMP & hostingcloud. *, free-content. * & 1931 \\ \hline Mintr & cnt. statistic. date. *, cdn. static-cnt. bid. *, ad. g-content. bid. *, cdn. jquery-uim. & 692 \\  & download. * minr. pw, cnt. statistic. date. *, ad. g-content. bid. * & 1979 \\ \hline DeepMiner/Coinhive & server. jsecoin. com, load. jsecoin. com, platform. jsecoin. com & 2377 \\ \hline RubyMiner & internetresearch. is, dgnfd564 sdf. com 203. 24. 188. 242 & 822 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Queries of malicious script patterns, top-level domains, and number of detected sites
# Interface Direct Shear Tests on JEZ-1 Mars Regolith Simulant
## 5 Conclusions  
This paper aims at understanding the basic mechanical response of JEZ-1 Martian simulant, the characteristics of soil-soil and soil-steel interface have been investigated by direct shear tests in a laboratory. Based on the obtained results in this study, the main findings are concluded as follows:  
Figure 11: Interface friction angles as a function of normalized interface roughness, compared to internal friction angles of JEZ-1.  
Figure 10: Summary of (**a**) peak and (**b**) residual failure envelopes for the three direct shear tests (soil-soil, smooth, and rough interfaces).  
The compressibility and swelling index of JEZ-1 are quite low;  
Both the peak and residual cohesion of JEZ-1 are \(<\)4 kPa. The peak (\(\varphi_{\text{peak}}\)) and critical (\(\varphi_{\text{cv}}\)) internal friction angles are 40.6\({}^{\circ}\) and 39.7\({}^{\circ}\), respectively;  
The adhesion of the interface between JEZ-1 and smooth/rough steel are lower than 5 kPa;  
The peak interface friction angles (\(\delta_{\text{peak}}\)) are 18.9\({}^{\circ}\) and 36.2\({}^{\circ}\), and the residual interface friction angles (\(\delta_{\text{cv}}\)) are 16.7\({}^{\circ}\) and 33.2\({}^{\circ}\), respectively, for the smooth and rough interfaces;  
The interface friction angles (\(\delta\)) are lower than the internal friction angles (\(\varphi\)) of JEZ-1. The \(\delta\) gets close to the \(\varphi\) when increasing the interface roughness.  
The interface results can provide information on Mars rover design, excavation tool determination as well as infrastructure development. Moreover, in the future, the interface results should be compared to the in-situ values of Jezero Crater Delta that are obtained by the Perseverance Rover. More geotechnical properties of the JEZ-1 Martian simulant need to be further investigated thoroughly.  
Conceptualization, methodology, data curation, writing-original draft preparation, K.Y. and J.L. ([PERSON]); formal analysis, J.L. ([PERSON]) and K.Y.; writing-review and editing, J.L. ([PERSON]), J.L. ([PERSON]), A.-R.V., K.O. and E.D.F.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research Institute of Highway Ministry of Transport.  
Not applicable.  
All data generated or used during the study appear in the submitted article.  
The authors gratefully acknowledge [PERSON], [PERSON] and [PERSON] for their important help.  
The authors declare that they have no conflict of interest.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 4 Edge Node Deployment Algorithm Design
### Improved Dual Population Genetic Algorithm
#### 4.1.1. Gray Coding Chromosome Structure  
Given that genetic algorithms cannot directly handle the parameters of the problem space, it is essential to encode these problem space parameters as chromosomes in the genetic space. In this scenario, each chromosome represents a node deployment location. Due  
Figure 3: The flowchart of the IBGA.  
to the substantial Hamming distance that can arise from binary encoding of neighboring integers, potentially reducing the search efficiency of genetic operators, this study adopts Gray encoding for the chromosome structure. As shown in Figure 4, each chromosome uses Gray coding to represent a solution. The candidate position is represented by a continuous four-digit code, with the edge node number corresponding to the decimal value of this four-digit Gray code.