esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis and Findings of Research Questions
#### 4.1.2. Smartphone User Activities Based on Service Modality  
Research has been conducted on smartphone users where such devices were found to store personal contacts and images [107]. Additionally, instructors might use their computers to store academic materials, while numerous businesses have implemented the BYOD approach, permitting employees to link their own devices to the business network [94; 108]. The BYOD approach plays a fundamental role in user behavior. The trend towards bringing personal devices to work has gained traction in organizations, particularly in rapidly growing nations such as Brazil, Russia, and India [109]. Nevertheless, this development has negative aspects in terms of maintaining the integrity and secrecy of sensitive data due to the mobility of devices and the ubiquitous network enabling data to be viewed from anywhere [110]. Modern vehicles are connected to the outside world through Wi-Fi or a third generation/fourth generation (3G/4G) network [95]. It delivers endless benefits to drivers in terms of services and smart functionality. Nonetheless, it poses significant threats to security and privacy, potentially jeopardizing passengers' safety. When the user visits an attacker-controlled website [98], the JavaScript code embedded in the website page starts listening to the gesture and rotation sensors without the user's consent. Research by [111] showed how users interact with mobile devices and voice assistant systems through voice access technologies. Several studies found that malicious links could be transmitted through email or an \"in-app\" advertisement within another application, such as a popular game [12; 97; 93]. Moreover, mobile advertising systems display adverts for local restaurants to consumers using GPS, enabling users to schedule bookings through smartphones. Mobile devices also store diverse data, including geographic locations and contact information, while offering robust functionality such as SMS, phone calls, and 3G or 4G connections [99]. Such information can be abused with sinister motivations.
## 3 Results and Discussion  
### Porosimetry  
One of the aspects that affect the \(\psi_{m}\) is the capillary forces (i.e., the pore size distribution) [11]. Thus, porosimetry tests were performed to evaluate the suction changes for the natural soil (0 mg/kg) and the arsenic-contaminated soils (25 mg/kg and 50 mg/kg). The results of porosimetry are shown in Figure 3a, which relates the relative pressure and adsorbed volume. According to the International Union of Pure and Applied Chemistry (IUPAC, Research Triangle Park, NC, USA), these results indicate a type IV isotherm with a hysteresis type H3 characteristic of mesoporous solids, such as clays and solid aggregates, related to parallel plates, respectively.  
The BET method was used to evaluate the specific surface area of the soils, while the BJH method was applied to evaluate the pore size distribution in the soil samples. The pores diameter can be classified as macropores (\(\varphi>0.05\)\(\upmu\)m), mesopores (0.05 \(\upmu<\varphi>0.002\)\(\upmu\)m), and micropores (\(\varphi<0.002\)\(\upmu\)m). According to the specific surface area, the natural soil corresponded to a montmorillonite clay mineral. The BET area decreased (16% and 9% for 25 mg/kg and 50 mg/kg, respectively) as well as the mesoporous volume after contamination (as seen in Figure 3a), suggesting that the arsenic modified the soil structure.  
Figure 3b indicates the contribution of the pore size to the total volume of pores. The peaks observed in Figure 3b correspond to the most predominant pore sizes for each soil sample analyzed with the nitrogen adsorption isotherm method. The pore size distribution of the natural soil shows a peak at a pore size of about 5.6282 nm. As arsenic concentration increases to 50 mg/kg, the mesopores reduce their adsorbed volume (as shown in Table 3), and the peak displaces to a larger pore size of about 7.8141 nm. This indicates that the porous solid behaves as a coarse material as the arsenic concentration increases.  
Figure 3: BET-N\({}_{2}\) analysis of the natural and arsenic-contaminated soils: (**a**) isotherms for arsenic-contaminated samples and (**b**) pore-diameter distribution.  
This is explained because the specific soil area combines all surface areas of all particles in the soil mass, which generates an inverse relationship between the particle size and specific surface area. So, the bigger the particle size, the smaller the specific surface [29]. Moreover, adsorption is defined as the accumulation of material above the surface [30], and the results indicate that the decrease in the adsorption potential lowers the specific surface because the adsorbate (N2) volume required to fill the soil pores is smaller than the natural condition, which results in a change of position of the isotherms.  
According to these results, it can be inferred that the matric suction reduces after arsenic contamination due to structural changes generated in the soil mass. This is explained to be due to the inverse relationship of suction and the radius of curvature of the contractile skin in the Laplace equation (i.e., when suction decreases, the radius of curvature increases).
# Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8
## 3 Method
### WloU Loss Function  
Due to the influence of weather changes, geological movements, and human activities, the morphological characteristics of old landslides have significantly changed compared to their initial formation. Climate warming and accelerated vegetation growth have led to extensive vegetation cover on the slopes and walls of old landslides. Consequently, when constructing the old landslide dataset, a large number of low-quality samples can adversely affect the detection performance. To avoid the detrimental impact of low-quality samples on the model's training process and to emphasize the significance of high-quality old landslide samples, this study replaced the boundary box loss function, Complete-IoU loss function, in the original YOLOv8 model with the Wise-IoU loss function. This adjustment  
Figure 7: CBAM attention mechanism.  
is intended to improve the model's detection performance when working with historical landslide datasets.  
There are three versions of WIoU, representing three different ways of constructing loss functions. WIoUv1 constructs the attention-based boundary box loss, while WIoUv2 and WIoUv3 add a focusing mechanism by constructing the gradient gain (focusing coefficient) calculation method.
## 3 Analysis of BER for SISO System  
### Scenario 1  
In this section, the BER performance is evaluated for the SISO system under the presence of jamming or AWGN.
## 2 Overview of Blockchain Technology
### Healthcare  
Blockchain technology has numerous applications in healthcare. One of them is applying blockchain to prevent drug counterfeiting by utilizing the power of an immutable ledger [127]. Blockchain may also be leveraged for exchanging medical information in a safe approach, protecting the integrity and exposure of sensitive medical data. With the incorporation of self-executing contracts, blockchains harness cryptography and accessibility restrictions to avoid the disclosure of confidential information. Medblock [128] and MedShare [129] are examples of frameworks that utilize the power of blockchain technology for secure medical data sharing and management.
## 4 Privacy and Security Enhanced Methods
### Privacy-Preserving Arbitration Mechanism
#### 4.1.1 Exact Voting Arbitration  
Our arbitration mechanism employs deterministic exact voting, where outputs from two executors \(O_{i}\) and \(O_{j}\) are considered consistent if and only if \(O_{i}=O_{j}\). Formally, for a set of executor outputs \(\{O_{1},O_{2},\ldots,O_{n}\}\), we define the consistency function \(\Phi(O_{i},O_{j})\) as  
\[\Phi(O_{i},O_{j})=\begin{cases}1,&\text{if }O_{i}=O_{j}\\ 0,&\text{otherwise}.\end{cases} \tag{1}\]  
For an output to be accepted as valid, a majority of executors must produce identical results. Alternative arbitration approaches, such as inexact voting or approximate matching that accommodate minor variations in outputs, will be explored in future work.
## 3 Archimedean Spiral Antenna
### Circuit Model of the Archimedean Spiral Antenna  
As previously discussed in Figure 9, the most part of the acquired power comes from the mobile frequency bands. In particular, the greatest power contribution comes from 800 MHz and 900 MHz. Thus, this particular distribution of the spectral power makes it possible to focus the design of the rectifying circuit at these particular frequencies. Additionally, the design of a wideband matching circuit is an incredibly complex task.  
A zoom on the spectral power in the 800 and 900 MHz frequency bands is presented in Figure 10. There is no straightforward manner to obtain the analytical expression of both waveforms, due to their intricate shapes. Since the bandwidth of both waveforms is narrow enough, they can be modeled as two delta functions centered at 807 and 942 MHz, respectively. The power of both delta functions is obtained by integrating the spectral power of each frequency band. Hence, the waveform centered at 807 MHz is integrated from 770 to 820 MHz, obtaining a value of \(P_{in1}=-5.80\) dBm indoors and \(P_{in1}=-1.46\) dBm outdoors. Afterwards, the waveform centered at 942 MHz is integrated from 920 to 970 MHz, obtaining a value of \(P_{in2}=~{}-9.20\) dBm and \(P_{in2}=-2.34\) dBm indoors and outdoors, respectively. In short, the spiral antenna is modeled, via the acquisition of the power spectrum in the laboratory, as two delta functions. As depicted in Figure 11, these functions are circuitally represented as two sinusoidal series generators with a source impedance of 188 \(\Omega\) (the spiral impedance).  
Figure 10: Zoom on the 800/900 MHz frequency bands over the spectrum acquired by the Archimedean spiral antenna (resolution bandwidth: 1 MHz).
# Adaptive-Size Dictionary Learning Using Information Theoretic Criteria
## 2 Ingredients
### DL Algorithm  
Many DL algorithms are suited to the framework that we propose. We confine the discussion to standard algorithms, which aim to iteratively improve the dictionary and whose iterations have two stages: (i) sparse coding, in which the representation matrix \(\mathbf{X}\) is computed for fixed dictionary \(\mathbf{D}\); and (ii) dictionary update, in which \(\mathbf{D}\) is improved, possibly together with the nonzero elements of \(\mathbf{X}\), but without changing the nonzero positions. Such algorithms are impervious to dictionary size changes; atoms can be removed or added between iterations without any change in the algorithm. Since they give good results and are fast, we adopt some of the simplest algorithms: Orthogonal Matching Pursuit (OMP) [29] for sparse coding and Approximate K-SVD (AK-SVD) [30] for dictionary update.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 3 Evolution of V2X Technologies
### Evolution of Cellular V2X
* New sub-modes of NR Sidelink Mode 2: unlike C-V2X sidelink mode 4, where there are no sub-modes, the 3 GPP started evaluating four new sub-modes of NR V2X sidelink mode 2, i.e., from mode 2 (a) to mode 2(d) [30]. C-V2X sidelink mode 4, resource reservation algorithm leverages the periodicity and fixed-size data of basic safety messages. In general, this presumption is no longer valid for use in NR V2X, so the resource selection process should be re-engineered.
# Removal of Cu(II) from Aqueous Solutions Using Amine-Doped Polyacrylonitrile Fibers
## 3 Results and Discussion
### Adsorption Isotherms  
The effect of the equilibrium concentrations of Cu(II) ions in the testing solutions on the adsorption capacities of the surface-modified PAN fibers is illustrated in Figure 5. The figure presents the fitting of the equilibrium data, and the obtained parameters of the adsorption isotherms are listed in Table 2. The adsorption capacity of the fibers increased gradually as C\({}_{\text{e}}\) increased. This was attributed to the mass transfer being the driving force of the process at higher C\({}_{\text{i}}\) values [26]. The determination coefficient of the Freundlich model was higher than that of the Langmuir model, which indicated that the Freundlich model fitted the adsorption data better than the Langmuir one. It was inferred that the Cu(II) ions were collected in multiple layers on the heterogeneous surface of the surface-modified PAN fibers [24]. The maximum experimental adsorption capacity on the surface-modified PAN fibers was 112.3 mg/g, which was in the range with the values reported in the literature (45-151 mg/g) for the Cu(II) adsorption capacity on amine-modified PAN fibers (Table 3) [2; 8; 25].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Pseudo-First-Order Kinetic Model**} & \multicolumn{3}{c}{**Pseudo-Second-Order Kinetic Model**} \\ \hline \(\mathbf{k_{1}}\) & \(\mathbf{q_{e}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{k_{2}}\) & \(\mathbf{q_{e}}\) & \(\mathbf{R^{2}}\) \\ **(1/h)** & **(mg/g)** & **(g/(mg h))** & **(mg/g)** & & \\ \hline
0.500 & 80.06 & 0.968 & 0.007 & 89.36 & 0.984 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Kinetics parameters for adsorption of Cu(II) ions onto surface-modified polyacrylamide fibers. Here, k\({}_{1}\), k\({}_{2}\), q\({}_{e}\), and R\({}^{2}\) denote the pseudo-first- and pseudo-second-order rate constants, amount of Cu(II) removed at equilibrium, and determination coefficient, respectively.  
Figure 5: Adsorption isotherms of Cu(II) ions onto surface-modified PAN fibers. Here, q and C\({}_{\text{e}}\) denote the adsorption capacity and Cu(II) equilibrium concentration, respectively.
## 4 Numerical Setup
### Boundary Conditions
For the thermal boundary conditions outside the calculation domain, the convective heat transfer coefficient between the casing wall and the ambient air was set to 50 W/(m\({}^{2}\cdot\)K). The calculations were performed using commercial CFD software--FLUENT. The governing equations were discretized using the finite volume method. The main transport equations were discretized using the second-order upwind scheme. The PRESure STaggering Option (PRESTOI) discretization format was adopted for pressure interpolation under the high-speed vortex. The coupled method was used to couple the pressure and velocity fields. Figure 8 depicts the initial oil distribution of the intermediate gearbox. The initial oil immersion depth of the simulation was set as the same as that of the test intermediate gearbox using the patching oil level operation in FLUENT. The mixed oil--air two-phase flow was formed inside the gearbox after the gear rotates. NATO O-155 oil was used herein. The oil properties at different temperatures, as shown in Table 6, were considered in the simulation. The calculation time with 48 CPU cores was about 2.5 days. Figure 9 shows the flow chart of the coupled thermal-fluid approach. The fluid analysis calculation domain only includes the fluid domain. Considering the boundary \begin{table}
\begin{tabular}{c c c} \hline \hline
**Temperature (\({}^{\circ}\)C)** & **Oil Density (kg/m\({}^{3}\))** & **Oil Kinematic Viscosity (mm\({}^{2}\)/s)** \\ \hline
40 & 879.84 & 66.63 \\
50 & 877.20 & 42.24 \\
60 & 875.15 & 28.71 \\
70 & 873.62 & 20.02 \\
80 & 872.56 & 14.82 \\
90 & 871.96 & 11.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Oil properties at different temperatures. Figure 8: Initial oil distribution. conditions, such as the boundary velocity and the initial oil distribution, the continuity equation, momentum equation, and VOF continuity equation were solved to obtain the flow field of the splash lubrication, including the velocity and oil distribution. The results were transferred to the thermal analysis as the input, which affected the temperature distribution. The thermal analysis calculation domain included both the fluid domain and the solid domain. Considering the thermal boundary conditions, such as the heat generated by the gears and bearings and the convection heat transfer coefficient outside the calculation domain, the fluid/solid energy equations were solved to obtain the fluid and solid temperatures of splash lubrication. The changes in temperature affected the physical parameters of the fluid, which affected the flow field, and the results of the thermal analysis were transferred to the flow field analysis. The above process was repeated until convergence, and the flow field and temperature distribution of the intermediate gearbox with splash lubrication were obtained.
# A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O\({}^{2}\) Facilities
## 3 Methodology
### Hyperparameter Tuning on HDBSCAN
#### 3.5.2 Min_Cluster_Size  
The HDBSCAN introduces _min_cluster_size_ as an additional parameter to DBSCAN. It serves as a critical determinant for the minimum number of points required to form a cluster by establishing the smallest number of log messages that the algorithm will recognize as a valid cluster, thereby influencing the resolution of clustering results. This parameter ensures that clusters are composed of a sufficiently dense group of points, avoiding the identification of noise or outliers as separate clusters. A smaller _min_cluster_size_ allows the algorithm to detect finer-grained, potentially smaller clusters, while a larger value promotes the formation of broader and more robust clusters. However, a larger value also ignores small clusters if it is excessively large. This parameter is particularly important in controlling the granularity and reliability of clustering outcomes.
## 1 Introduction  
With the proliferation of multimedia services and internet applications such as high definition (HD) live streaming, HD video conferencing, voice over IP (VoIP), 4/8 K video transmission, and recently introduced virtual reality technology, i.e., metaverse, contemporary data transmission techniques are experiencing peer pressure due to explosive demands for wide-bandwidth [1]. Radio-frequency communication (RFC) is recognized as a mature through-hole technology for a wide range of terrestrial and space applications [2]. However, RFC does not efficiently support high-speed fifth-generation (5G) and sixth-generation (6G) services due to severe constraints such as bandwidth scarcity, multipath fading, predominant amplitude degradation, vulnerability to security breaches, and the requirement for data transmission licenses [3]. Twisted light-based communication (LBC) has experienced a paradigm shift in data transmission under guided or unguided wireless mediums due to its narrow light beamwidth, unlicensed operations, improved security, high power efficiency, lower amplitude degradations, absence of electromagnetic interference (EMI), and the potential to support high-speed networks [4; 5; 6]. There are three types of LBCapplications such as optical indoor communication (OIC) (short reach up to 5 m) [7], optical terrestrial communication (OTC) (1-30 km) [8], and optical satellite communication (OSC) (ultra long reach up to 80,000 km) [9]. Over the years, there has been a prominent upsurge in the satellite constellations in space, and it is estimated that satellite deployment will grow by 1000% in the next decade. Therefore, OSC is the next frontier and offers rigorous development in broadcasting, navigation, communication, weather forecasting, etc. [10]. The block diagram of the OSC system between two satellites is depicted in Figure 1.  
Satellites can be placed in any of the three available earth orbits depending upon the distance from the earth, such as low satellite orbit (LSO) (\(\leq\)2000 km), medium satellite orbit (MSO) (\(\leq\)20,000 km), and high earth orbit (HSO) (\(\geq\)35,000 km) [11; 12; 13]. The orbiting time of LSO and MSO around the earth is 2-4 and 5-12 h, respectively. Deep space communication refers to interplanetary links that can extend more than 1,500,000 km above the earth. The performance of OSC systems depends on transmitter power, the distances between satellites, transmitter and receiver efficiency, atmospheric losses, line of sight (LOS), pointing and tracking errors (PTE), telescope aperture size, and modulation/line coding/advanced modulation format/multi-level modulations, etc. Background noise, satellite platform vibrations, and PTE are the most dominant impediments in OSC. High-capacity OSC systems can be realized by incorporating multiple wavelengths (WDM), different time slots (time division), diverse codes (optical code division), and distinct modes (mode division multiplexing) (MDM) [14; 15; 16].
# Gyre Precoding and T-Transformation-Based GFDM System for UAV-Aided mMTC Network
## 2 System Model
### Signal Model
#### 2.2.2 UAV Segment  
Due to precoding, signal power varies from rowed to rowed data of \(X_{0}\), and to make identical signal power in every rows of \(X_{0}\), it is to be multiplied by a normalized signal power \(P_{1}\) such that the transmitted power from the ground BS would be \(P_{g^{\prime}}\) and the arrived signal at the main UAV can be represented as:  
\[Y_{0}=H_{0}\sqrt{P_{1}}\left\{W_{\bar{k}}\vec{x}_{\bar{k}}+\sum_{j=1,j\
eq\bar{ k}}^{\bar{j}=8}W_{\bar{j}}\vec{x}_{j}\right\}+n_{m} \tag{21}\]  
where \(n_{m}\sim\mathbb{CN}(0_{N_{T}},\sigma_{m}^{2}I_{N_{T}})\) denotes the additive white Gaussian noise (AWGN). By introducing the ZF signal detection scheme cited at [27], the decoded signal can be represented as:  
\[X_{0}=(H_{0}^{H}H_{0})^{-1}H_{0}^{H}Y_{0}\cong\sqrt{P_{1}}\left\{W_{\bar{k}} \vec{x}_{\bar{k}}+\sum_{j=1,j\
eq\bar{k}}^{\bar{j}=8}W_{\bar{j}}\vec{x}_{j}\right\} \tag{22}\]  
The signal model presented in Equation (22) is rescaled to make it compatible to a desirable UAV transmission power \(P_{uw}\) of matrix size \(N_{T}\times N_{T}\) by multiplying with a normalized signal power \(P_{2}\), and the power scaled signal can be represented as:  
\[\vec{X}_{0}=\sqrt{P_{2}}\sqrt{P_{1}}X_{0}=\sqrt{P_{1}P_{2}}\left\{W_{\bar{k}} \vec{x}_{\bar{k}}+\sum_{j=1,j\
eq\bar{k}}^{\bar{j}=8}W_{\bar{j}}\vec{x}_{j}\right\} \tag{23}\]  
In the first time phase, the main UAV transmits its signal \(\vec{X}_{0}\) to all cooperative UAVs and ground mMTC users. The signal received at the ground mMTC user \(\bar{k}\) can be represented as:  
\[Y_{\bar{k}}^{1}=H_{\bar{k}}\sqrt{P_{1}P_{2}}\left\{W_{\bar{k}}\vec{x}_{\bar{k} }+\sum_{j=1,j\
eq\bar{k}}^{\bar{j}=8}W_{\bar{j}}\vec{x}_{j}\right\}+n_{\bar{k}} ^{1} \tag{24}\]  
where \(n_{\bar{k}}^{1}\sim\mathbb{CN}(0_{N_{R}},\sigma_{\bar{k}}^{2}I_{N_{R}})\) denotes the additive white Gaussian noise (AWGN). The signal received at the two cooperative UAVs are given by:  
\[Y_{01}^{1}=H_{01}\sqrt{P_{1}P_{2}}\left\{W_{\bar{k}}\vec{x}_{\bar{k}}+\sum_{ j=1,j\
eq\bar{k}}^{\bar{j}=8}W_{\bar{j}}\vec{x}_{j}\right\}+n_{01}^{1} \tag{25a}\] \[Y_{02}^{1}=H_{02}\sqrt{P_{1}P_{2}}\left\{W_{\bar{k}}\vec{x}_{\bar{k}}+\sum_ {j=1,j\
eq\bar{k}}^{\bar{j}=8}W_{\bar{j}}\vec{x}_{j}\right\}+n_{02}^{1} \tag{25b}\]  
where \(n_{01}^{1}\sim\mathbb{CN}(0_{N_{T}},\sigma_{01}^{2}I_{N_{T}})\) and \(n_{02}^{1}\sim\mathbb{CN}(0_{N_{T}},\sigma_{02}^{2}I_{N_{T}})\) denote the AWGN, and the superscript \(1\) on different variables indicates the first time phase. The amplifying gain in this system at the two cooperative UAVs can be represented as:  
\[G1=\sqrt{\frac{P_{uw}}{P_{uw}||H_{01}||^{2}+\sigma_{01}^{2}I_{N _{T}}}} \tag{26a}\] \[G2=\sqrt{\frac{P_{s}}{P_{uw}||H_{02}||^{2}+\sigma_{02}^{2}I_{N_ {T}}}} \tag{26b}\]  
In the second time phase, both the cooperative UAVs transmit its amplified signals to the ground mMTC users.