esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Mathematical Models  
Numerical simulations of the above model were carried out using Fluent software. The theoretical equations for three-dimensional steady-state turbulent flow [23] are as follows.  
The continuity equation is:  
\[\frac{\partial u_{i}}{\partial x_{i}}=0 \tag{1}\]  
The momentum equation is:  
\[\frac{\partial(u_{i}u_{j})}{\partial x_{j}}=-\frac{1}{\rho}\frac{\partial P}{ \partial x_{i}}+\frac{\partial}{\partial x_{j}}\Bigg{[}(v+v_{t})\frac{ \partial u_{i}}{\partial x_{j}}\Bigg{]}-\beta g_{i}(T-T_{\infty}) \tag{2}\]  
\(\beta g_{i}(T-T_{\infty})\) derived from fluid density variations, obtained from the Boussinesq hypothesis term. The hypothesis considers the entire system to be non-pressurized flow, except for the buoyancy force term in vertical flow.  
The energy equation is:  
\[\frac{\partial(u_{i}T)}{\partial x_{j}}=\frac{\partial}{\partial x_{j}}\Bigg{[} \Gamma\frac{\partial u_{i}}{\partial x_{j}}\Bigg{]} \tag{3}\]  
The heat transfer process in a combined solar chimney is a complex, dynamic process involving thermal convection and radiation. In order to simplify the theoretical analysis and numerical calculations, the fluid is assumed to flow fully turbulently and to reduce calculation errors where the enhanced wall function method is used near the walls. To simplify the heat transfer process, air infiltration and heat storage on the chimney walls are neglected. The turbulent kinetic energy (\(k\)) and the corresponding dissipation rate (\(\epsilon\)) can be determined as [24]:  
\[\frac{\partial(ku_{i})}{\partial x_{i}}=\frac{1}{\rho}\frac{\partial}{\partial x _{j}}\Bigg{[}\bigg{(}v+\frac{v_{t}}{\sigma_{k}}\bigg{)}\frac{\partial k}{ \partial x_{j}}\Bigg{]}+G_{k}-\epsilon \tag{4}\]  
\[\frac{\partial(\epsilon u_{i})}{\partial x_{i}}=\frac{1}{\rho}\frac{\partial} {\partial x_{j}}\Bigg{[}\bigg{(}v+\frac{v_{t}}{\sigma_{\epsilon}}\bigg{)} \frac{\partial\epsilon}{\partial x_{j}}\Bigg{]}+\frac{\epsilon}{k}(c_{1}G_{k} -c_{2}\epsilon) \tag{5}\]  
\[G_{k}=\frac{v_{t}}{\rho}\frac{\partial u_{i}}{\partial x_{j}}\Bigg{(}\frac{ \partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{\partial x_{i}}\Bigg{)} \tag{6}\]  
where: \(x_{i}\) is the coordinate value, m; \(u_{i}\) is the mean velocity component in the \(x_{i}\) direction, m/s; \(\rho\) is the density of air, kg/m\({}^{3}\); \(P\) is the difference between pressure and ambient pressure, Pa; \(v_{t}\), \(v\) is the coefficient of viscosity for turbulent and laminar flows, m\({}^{2}\)/s; \(g_{i}\) is the acceleration of gravity in the vertical direction, taken as 9.8 m/s\({}^{2}\); \(\beta\) is the coefficient of thermal expansion of air, 1/K; \(T\), \(T_{\infty}\) is the average temperature and the reference point temperature, K; \(\Gamma\) is the generalized diffusion coefficient; \(k\) is turbulent kinetic energy; \(\epsilon\) is the rate of dissipation of turbulent kinetic energy; \(c_{1}\), \(c_{2}\) is an empirical factor; \(G_{k}\) is the turbulent kinetic energy generation term; \(p_{r}\) is the Prandtl number; \(\sigma_{k}\), \(\sigma_{\epsilon}\), \(\sigma_{t}\) take the standard experimental values.
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 6 AI-Based Malware Detection
### Binary Image  
A novel approach to the detection of malicious software is the analysis of the binary representation of said software. Software binary files can be reformatted into an 8-bit sequence and subsequently converted into a grayscale image, which comprises a single channel and has pixel values ranging from 0 to 255. The experiment yielded converted images which highlighted the conspicuous disparities in the structural components of benign and malicious software images. Mirai malware images often tend to have higher density in the central region of the image, providing an example of the general rule that malware images tend to be dense. The disparity between the binary images of multiple software applications has been leveraged to render the detection of malware into a classification problem via image recognition, thereby enabling a Convolutional Neural Network (CNN) to differentiate between benign and malicious software [58]. Following are the key elements of Incident Response Process.
# A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications
## 5 Research Challenges of IRS-Assisted UAV Communications
### Environmental Factors  
Most studies assumed stable UAV flight and a fixed user location. However, in practice, UAVs typically experience channel estimation errors and unstable transmissions, which should not be ignored because of inevitable jitters caused by airflow and vibration [120]. Consequently, the benefits from beamforming design cannot be fully utilized. Environmental factors, such as rain and wind, can change the speed and trajectory of UAVs, which may result in safety issues and performance degradation [121]. Research on these environmental factors remains a challenge.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} \(\epsilon\) & Limit used in the algorithm \\ \(k\) & Dead time period during which the inductor current becomes zero \\ CCM & Continuous Conduction Mode \\ \(\Delta D\) & Step size of the duty ratio \(D\) \\ \(D\) & Duty ratio \\ DCM & Discontinuous Conduction Mode \\ \(f_{\rm s}\) & Switching frequency \\ \(I_{mpt}\) & PV current corresponding to the maximum power point \\ \(I_{p\rm o}\) & Instantaneous photovoltaic current \\ \(I_{\rm sc}\) & Short-circuit current of the PV module \\ I-V & Current vs. voltage characteristics \\ \(L\) & Inductor used in the dc-dc converter \\ \(L_{min}\) & Minimum inductance to ensure CCM mode \\ MPP & Maximum Power Point \\ MPPT & Maximum Power Point Tracking \\ \(P\&O\) & Perturb and Observe MPPT algorithm \\ P-V & Power vs. voltage characteristics \\ PV & Photovoltaic \\ \(R_{L}\) & Load resistance \\ \(\Delta V_{o}\) & Output voltage ripple \\ \(V_{ind}\) & Inductor voltage \\ \(V_{o}\) & Output voltage \\ \(V_{oc}\) & Open-circuit voltage of PV module \\ \(V_{pp\rm(\it CAL)}\) & Estimated value of \(V_{pp}\) \\ \(V_{pp}\) & Instantaneous photovoltaic voltage \\ STC & Standard testing condition (1000 \(\frac{W}{m^{2}}\), 25 \({}^{\circ}\)C) \\ \(T_{on}\) & ON time of the mosfet used in the dc-dc converter \\ \end{tabular}
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **MCC** & **AUC** & **Training Time (s)** \\ \hline GBDT & 0.928426 & 0.671069 & 0.697482 & 0.646786 & 0.920334 & 0.894421 & 196.0811 \\ XGBoost & 0.99862 & 0.99915 & 0.997506 & 0.998322 & 0.998462 & 0.999703 & 678.4637 \\ LightGBM & 0.975294 & 0.934304 & 0.897989 & 0.903689 & 0.950676 & 0.941844 & 15.67894 \\ FCNN & 0.956385 & 0.830573 & 0.780124 & 0.785024 & 0.951467 & 0.99229 & 78.77981 \\ modified CNN & 0.966116 & 0.863868 & 0.826289 & 0.832124 & 0.962253 & 0.996464 & 342.3715 \\ \hline \end{tabular}
\end{table} 
Table 2: Accuracy, precision, recall, F1 score, MCC, AUC and training time for every classifier on the actual UAV data. Figure 6: Receiver Operating Characteristic (ROC) curves of every classifier on the actual UAV flight data. Figure 8: Confusion matrix of XGBoost. Figure 7: Training time of every classifier on the actual data. Figure 9: Confusion matrix of LightGBM. It can be concluded that, compared with the original diagnosis package, the proposed three methods have a significant comprehensive improvement. The most time-consuming XGBoost-based model with outstanding performance, the lightning LightGBM-based model and the balanced modified CNN-based model are reasonably suitable to combine together as a diagnosis package in practice, to handle the UAV compound fault diagnosis mission.
# Integration of a Three-Dimensional-Printed Titanium Implant in Human Tissues: Case Study
## 2 Materials and Methods
### Quantitative Analysis of Tissue Integration  
To evaluate osseointegration, the titanium alloy implant, along with the surrounding tissues, was cut and pathologically analyzed. The retrieved implant and surrounding tissues were fixed in 10% buffered neutralized formalin. The implant with adjacent ulnar fragments was placed in paraffin to generate a 6-um-thick specimen. Specimens of the proximal and distal junctional regions were generated through longitudinal sectioning (Figure 4A-C and Figure 5A-C). Transverse and longitudinal sections were analyzed for the middle implant body (Figure 6A,B) through staining with [PERSON]'s hematoxylin (nuclei staining) and Ponceau Acid Fuchsin solution using Goldner's trichrome method. The tissues are stained red and blue; mature bone is stained blue. The red-stained areas with and without osteocytes were classified as premature bone and fibrous tissue, respectively.  
The porous surface of the implant for each segment of the proximal and distal junctional regions (Proximal [P] 1-5 for the proximal bone implant area; Distal [D] 1-5 for the distal bone-implant area) was examined (Figures 4C and 5C). To quantify integration, the rates of bone-to-implant contact (BIC, %), soft tissue-to-implant contact (SIC, %), and total contact (TC, %) were calculated by dividing the bone, soft tissue, and total contact lengths of each segment on the basis of their corresponding metal surface lengths. Moreover, as the middle shaft specimens were bone-free, only the SIC rate was used to analyze soft tissue integration of the implant (Figure 6A,B).  
Figure 4: Analyzed segments of the proximal region. Micro-computed tomographic images showing (**A**) the longitudinal view and (**B**) axial view, with scout images in the upper right corner. Photomicrograph ((**C**): [PERSON]’s trichrome staining, 1x magnification) of the histological specimen of the longitudinal section showing the analyzed segments (P1–P5).
## 6 Discussion
### Data Correlation and Reliability Discussion  
The results obtained in this landslide area are related to geophysical surveys alone, and no other direct data (as perforation bore-holes) is provided as verification. Using geophysical data, the indirect surveys considered could be an early investigation tool to delineate the characteristics of a sliding mass. It must be related to accurate direct information, but combining methods and techniques can improve the geophysical models [3; 4; 5; 6].  
In this area, the VES information shows a clear separation between shallow materials and the rocky substrate, with resistivity changing values up to four times (>5000 Ohm.m for the rocky substrate). Therefore, the obtained models could be used as a definition of the thickness of the soft material (sedimentary) and altered one (eluvium) with some grade of accuracy, thus defining the surface rupture of the landslide. In the metamorphic studied area, the materials overlaying the rocky substrate are those involved in landslides [44].  
Moreover, seismic surveys provide similar distribution in shallow sediment layers using seismic velocity parameters, i.e., they have corroborated the VES geoelectrical interpretation. Furthermore, the models offer a contrast in impedance values between the sediments and the basement that is up to 2.2, which is a piece of important information in applying the HVSR two-layer model [10; 15; 16; 17; 21].  
The use of few contrast points could be a limitation too. Of course, the more that can be used could help to obtain better accuracy. Nevertheless, the most important thing is to get a variety of thicknesses to adjust the empirical curve (Equations (1) and (3)), as can be  
Figure 13: A distribution map of the \(K_{g}\) values was represented in the investigation area. The maximums values coincide with the area presenting the most significant instability now (marked with dark blue ovals). White crosses refer to the coordinate points (Modified from [22]).  
seen in other investigations (related in [20; 21]). That accuracy and reliability in the final results are related to the precision of geophysical models that were employed [21; 47].  
The \(a\) and \(b\) factors in Equation (3) differed from those obtained by other authors (applied in basin geological areas) such as [18] or [47]. Considering [33; 48], they indicated that \(a\) factor is related to local geological characteristics (ground materials, impedance contrast, and humidity). In contrast, the \(b\) factor is related to sediment thickness and the geometrical shape of the basement. The obtained factors here are close to those obtained in [20], an investigation also made in Ecuador, where geological conditions are more similar to the present one.
# Trusted Energy-Aware Hierarchical Routing (TEAHR) for Wireless Sensor Networks
## 2 Related Studies
### Types of Attacks  
A malicious node can be identified by detecting its trust value in a timely manner. The comprehensive breakdown of the attacks is as follows:  
* Bad-mouthing attack [8]: This attack involves malicious nodes attempting to undermine the credibility of trustworthy nodes or enhance the credibility of malicious nodes through the dissemination of dishonest suggestions.
* Ballot stuffing attack [2]: A Confederate node enhances its reputation by supplying a substantial quantity of successful interaction data to the other side. To address such assaults, it is essential to decrease the weight of the indirect trust value offered by the neighboring Node.
* On-off attack [9; 10]: In an on-off assault, malicious nodes exhibit intermittent performance, alternating between periods of satisfactory and unsatisfactory behavior. Malicious nodes can retain trust values even if they exhibit subpar performance. To effectively counter-switching attacks, older behavioral observations must carry a different level of significance than the more current behavioral observations.
* Selfish attack [11]: The self-node will only delete the consent without reserving the resources to send the trust reply upon receipt of the trust request.
* Sybil attack [12]: The utilization of ID authentication and centralized trust models is a viable strategy to protect against Sybil attacks. These approaches not only enable the accurate identification of nodes but it also facilitate the detection of many false identities associated with malicious nodes through the network sink node or base station (BS).
* Sinkhole attack [13]: The assailant establishes a deceptive aggregation node to divert all data within the vicinity of the fraudulent sink node.
* Reputation time-varying attack [14]: A time-varying attack is a cyberattack strategy that changes its characteristics over time to evade detection and bypass security measures. These attacks exploit the vulnerabilities in systems designed to detect static attack patterns. By constantly changing their tactics, time-varying attacks can remain undetected for more extended periods, potentially causing more damage.
## 2 Materials and Methods
### Basic Assumptions on the Influence of Aging Mechanisms on the EEC Elements  
The expected main aging mechanisms during float charge are corrosion and water loss or dry out of the batteries. Both mechanisms will be severer with higher temperatures and higher positive overpotentials. In undercharge conditions, when battery potentials are below the typical charge reaction potential, sulfation can also occur.  
Corrosion describes an oxidization of the lead grid at the positive electrode. The educts, under charge conditions lead-oxides, have a higher resistance than the original product. In undercharge state also lead-sulfates can evolve at the outer corrosion layer, additionally increasing the resistance. Furthermore, the diameter of the grid as the current collector is decreased which also leads to an ohmic increase. Therefore, corrosion should be mainly seen within the increase of \(R_{\text{i}}\).  
The inner resistance \(R_{\text{i}}\) does increase over SoC due to a reduced conductivity of the lower concentrated acid and a coverage with insulating lead-sulfate [7]. Water loss would lead to a higher concentration of the electrolyte, consequently decreasing the resistance. However, at concentrations above 1.26 g cm\({}^{-3}\) the resistance increases again (see [31] p. 119). In addition, a dry out of the AGM will lead to contact loss between the electrode surface and the separator. Thus, longer reaction paths will also result in higher inner resistances. However, the active surface area that is available for a charge transfer will be influenced by water loss. As a result, this aging mechanism should also be visible in \(R_{\text{ct}}\) as well as in the diffusion processes.  
Figure 2: (**a**) Initially measured impedance spectrum of battery 2-014. The utilized equivalent electrical circuit (EEC) model for the fitting of the spectrum for a full 12 V AGM battery is depicted and the corresponding areas of the spectrum are marked. (**b**) Development of the measurable EIS spectrum from the initial measurement to the check-up after 568 days for battery 2-014. The following frequencies are marked: 1 kHz (\(\Circle\)), 78 Hz (\(\square\)), 10 Hz (\(\Diamond\)), 1 Hz (\(\bigstar\)), and 0.1 Hz (\(\bigstar\)).  
Moreover, the \(R_{\rm ct}\) will increase with coverage of the active surface area by PbSO\({}_{4}\). Such an effect is seen especially at SoC between 50-100% [32]. The non-reactive lead-sulfate is reducing the electrode capacitance and will slow down the charge transfer process. Subsequently, the time constant \(\tau_{\rm ct}\) will increase with a higher rate of sulfation.  
In summary, it could be said, that \(R_{\rm i}\) should be mainly resemble the corrosion mechanism, while water loss and sulfation should be detectable through the charge transfer resistances.
# A Semantic Information-Based Optimized vSLAM in Indoor Dynamic Environments
## 3 Methodology  
### Overview  
The vSLAM tracking module and mapping module will be affected when objects are moving within the field of view of the camera during indoor data acquisition. Therefore, vSLAM should have the ability of robust inter-frame matching and dynamic object detection in dynamic environments. To solve the above problems, we optimized a vSLAM system based on ORB-SLAM2 that run in dynamic environments and generate semantic maps, as shown in Figure 1. Compared with ORB-SLAM2, our system has made the following improvements with the following four modules.  
1. Tracking module. ORB feature points are extracted from the color image. Then, the dynamic regions in the image are acquired based on the dynamic detection module, and the ORB feature points that contained in the regions are removed. Finally, the remaining feature points are matched, and the feature matching set is used to estimate the pose of the camera and to determine whether the current image is a key frame. When the system is in the first frame of operation, the dynamic regions in the image cannot be acquired, so only the first frame is semantic segmented and then the dynamic target is directly acquired, and then the pose estimation is done for the first and second frames to complete the initialization.
2. Motion detection module. It calculates the homography of the current color image frame and the previous image frame. Then, the image of the current frame is transformed using the homography, and the optical flow field is calculated using the dense optical flow method. Finally, the dynamic region for the tracking module is obtained based on the optical flow field. In addition, the semantic masks are combined with the dynamic regions to acquire the dynamic targets for the map building module.
3. Semantic segmentation module. The semantic information is obtained by semantic segmentation of color images using BiSeNet V2 network. Then, the results of semantic segmentation are optimized using region growing algorithm and depth image. Finally, the semantic information in the image is mapped to the point cloud.
4. Map building module. The tracking module selects key frames, and then the depth image and color image of the dynamic target are removed from the key frames to build the point cloud of the current frame and add semantic information. Finally, the mapping module uses the pose information to join the point clouds from keyframes, and if a looping is detected, it performs global pose optimization and outputs a point cloud map and pose information with semantic information.
## 1 Introduction
VSC has been proposed as an appropriate method, both for categorizing the CIE Standard General Skies and for determining daylight illuminance on inclined surfaces with various orientations [17; 32]. The VSC facing a given orientation at a given time under an individual CIE Standard General Sky has its own features and stands for a unique value. So, the use of the CIE standard classification could represent an alternative means of estimating VSC. As it has been pointed out, the determination of daylight availability is a complex issue due to scarce experimental data needed for all methodologies proposed. Therefore, it is mandatory the establishment of the equivalence among the different methodologies for obtaining daylighting through any of the proposed methods and even the possible combination between them when necessary. This study focused on the determination of VSC in Burgos, Spain, for which purpose different alternative proposals for its calculation are compared. Three methods are generally used to calculate VSC: Available experimental data of horizontal global, diffuse, and beam illumination; through the data from experimental sky scanner measurements; and the CIE standard classification for homogeneous skies. The results obtained under different sky conditions were tested in a long-term experimental test campaign over 29 months. The VSC data obtained from the experimental data of global horizontal, diffuse, and beam illuminance were taken as the reference data and two statistical indicators, root mean square deviation (RMSD) and mean bias deviation (MBD), were used as quality indicators of the equivalence of the different approaches. Finally, the daylight conditions in Burgos, Spain, were determined with the VSC calculations for surfaces facing all four cardinal orientations. The paper will be structured as follows. The experimental facility and the measurement campaign as well the quality filters applied to the experimental data will be described in Section 2. The different approaches used for the VSC calculations will be defined in Section 3 and the results of all three methodologies with their statistical indicators will be compared in Section 4. In Section 5, the VSC in Burgos will be calculated for all four cardinal orientations and the results will be compared with the existing knowledge of the CIE standard sky type, analyzing both frequency of occurrence and seasonal and monthly distributions. Finally, the main results and the conclusions of the study will be summarized in Section 6.