esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Network Model
### Path Loss  
Channel measurements such as in [30; 31] demonstrate that mmWave is sensitive to blockages, with different path loss between LOS and NLOS transmission. These signals have high penetration loss through solid materials and high rain attenuation due to scattering [26]. Therefore, LOS transmission is limited by the existence of blockage. Furthermore, NLOS transmission as a result of reflected LOS from blockage surface are generally week but usually can contribute to cover the shadowed areas caused by blockages [31]. In general, LOS propagation is similar to the free space, with a path loss exponent of 2 [32]. While the path loss exponent of NLOS link is shown in [30; 32] to be larger than the LOS exponent, of around 3.86 at UT Austin campus and 5.76 in New York city downtown. Recent path loss model for mmWave above 6 GHz has been released by 3 GPP in [33; 34].  
The Macroscopic path loss between a BS and MTC device is represented by the propagation path loss as shown in Figure 5. where:  
\[d_{\rm 3D}=\sqrt{\left(d_{\rm 2 Dt}\right)^{2}+\left(h_{\rm BS}\right)^{2}} \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Parameter** & **Value** \\ \hline Waveform & OFDM \\ Communication & MTC \\ Tx Power & 10 W \\ Tx antenna gain & 15 dB \\ Tx pattern & As in Equation (1) \\ Electrical tilt & \(-6\) degree (down tilt) \\ Carrier frequency & 26 GHz \\ Inter-Site-Distance (ISD) & 150 m \\ Bandwidth & (1.4, 3, 5, 10, 20) MHz \\ Tx antenna elements & 1, 2 and 4 \\ Tx Antenna height & 10 m \\ Modulation & Adaptive (QPSK, 16 QAM, 64 QAM) \\ Region of interest (ROI) & ROI = 600 \(\times\) 600 m\({}^{2}\) \\ MTC devices & Up to 500 MTC devices per ROI \\ Noise Figure & 10 dB \\ Noise Density & \(-174\) dBm/Hz \\ Traffic Model & FTP \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network model parameters.  
with  
\[\mathrm{PL}_{1}=32.4+20\log_{10}(f_{c})+21\log_{10}(d_{\mathrm{3D}}) \tag{3}\]  
for LOS transmission, and:  
\[\mathrm{PL}_{2}=32.4+20\log_{10}(f_{c})+31.9\log_{10}(d_{\mathrm{3D}}) \tag{4}\]  
for NLOS transmission [34],\(f\) is the carrier frequency in GHz, \(d_{\mathrm{3D}}\) is the distance in metres between BS and MTC device as described in Figure 5 and \(h_{\mathrm{BS}}\) is the height of the BS. The distribution of the shadow fading is log-normal, with different standard deviation for each scenario, outdoor, indoor, office layout, LOS/NLOS. All these details are given in the 3 rd Generation Partnership Project (3 GPP) documentations in [34] in Table 7.4.1-1.
## 2 Numerical Modeling
### Materials and Parameters
Figure 3: Stress–strain relationships of the lining material. presence of a waterproof plate between the primary support and the secondary lining, the dynamic analysis must consider sliding, compression, and separation between them. Consequently, the contact algorithm in ABAQUS is employed to define the interaction between the primary support and the secondary lining. The normal contact behavior is defined as \"hard\" contact, as illustrated in Figure 4a. When a gap exists between the primary support and the secondary lining (h < 0), no contact pressure is generated. When the gap is zero (h = 0), as shown in Figure 4b, a constraint is applied at the contact surface, and only normal contact pressure is transmitted, with no limitation on its magnitude. When the primary support and secondary lining are in contact, a tangential force is generated at the contact surface due to the combined effects of fault dislocation and seismic forces. The classical isotropic Coulomb friction model, based on the penalty function method, is used to describe the tangential mechanical behavior of the contact surface. The red solid line in Figure 4c represents this mechanical behavior. Ideally, relative sliding between the two contact surfaces occurs when the tangential shear stress reaches the critical frictional shear stress \(\tau_{\text{crit}}\). However, simulating the ideal frictional behavior often presents challenges in numerical calculations. As a result, ABAQUS typically defaults to an adaptive penalty function approach that allows for \"elastic slip deformation\", as shown by the blue dashed line in Figure 4c. Equation (3) provides the formula for the critical friction shear stress, where \(\mu\) is the friction coefficient (set to 0.6), and \(p\) is the normal contact pressure [28; 29; 30]:  
\[\tau_{crit}=\mu\ p \tag{3}\]
# Onboard Centralized ISL-Building Planning for LEO Satellite Constellation Networks
###### Abstract  
Large-scale low earth orbit (LEO) satellite constellation projects are increasingly adopting inter-satellite links (ISLs) to enable their autonomous and collaborative operation. Due to the large number of satellite constellation network nodes and their continuous movement in orbit, the network nodes may not remain visible to each other at all times, so the ISL-building choices among satellites are diverse and vary with time. As a result, maintaining a network topology requires onboard planning and management. In this paper, we creatively propose an onboard centralized ISL-building planning scheme with the goal of autonomous topology management. A multi-antenna visibility calculation method that takes the antenna installation angle and the turntable rotation threshold into account is provided for the visibility calculation procedure. Additionally, the link-building planning process is modeled using integer linear programming (ILP); however, to tackle the computational complexity problem of ILP, a link-building planning method based on topology stability optimization is presented. The simulation results show that the proposed onboard centralized ISL-building planning scheme can operate among satellites to successfully realize network status collection, visibility calculation, link-building planning, and planning result distribution, as conducted by the dynamic primary satellite. Moreover, the inter-plane link-building planning method based on topology stability optimization improves the network topology stability on the basis of reducing the network delay.  
inter-satellite links; satellite network; ISL-building planning +
Footnote †: journal: Electronics Electronics
## 3 Simulation Analysis  
In order to testify the PDF derived in Section 2, we simulated the model numerically by MATLAB.  
Based on Equation (17), we notice that the PDF of the CE is related to its mean value and parameter c, both of which are the functions of \(\overline{\chi_{r}}\), \(\overline{\chi_{i}}\), \(\sigma_{r}^{2}\) and \(\sigma_{i}^{2}\). It is necessary to calculate \(\overline{\chi_{r}}\), \(\overline{\chi_{i}}\), \(\sigma_{r}^{2}\) and \(\sigma_{i}^{2}\) before we simulate the PDF. According to Appendix A and ref. [32], the wavelength of the signal light (\(\lambda\)), the pupil aperture of the receiving telescope (\(D\)), the focal length of the coupling system (\(F\)) and the model field diameter (MFD) of the SMF (\(d\)) are the system parameters, while the atmospheric coherence length (\(r_{0}\)) and \(\beta\) represent the turbulence conditions. All the parameters are necessary to simulate the statistical distribution of the CE.  
The hierarchical map is shown in Figure 2. Firstly, one of the turbulence conditions should be decided as the variable, and the range should be chosen properly. With all the system parameters, the input of the simulation is prepared. Then, we calculate \(\overline{\chi_{r}}\), \(\overline{\chi_{i}}\), \(\sigma_{r}^{2}\) and \(\sigma_{i}^{2}\) for next processing. Based on the results, c and \(\overline{\eta}\) are calculated, and the PDF of the CE is derived. We can analyze the distribution of the CE within the range chosen before at last.  
The pseudo-code is listed in Algorithm 1.  
```
0: System parameters and turbulence conditions (\(\lambda,\ D,\ F,\ d,r_{0},\ \beta\)).
0: Distribution of CE
1: Choose variable parameter \(r_{0}\) (or \(\beta\))
2: Initialize \(\lambda,\ D,\ F,\ d\ \beta\) (or \(r_{0}\)) with solid value.
3: Initialize \(r_{0}\) (or \(\beta\)) with the range chosen properly
4:for episode = 1, 2...do as follows
5: Calculate the intermediate variable \(\overline{\chi_{r}}\), \(\overline{\chi_{i}}\), \(\sigma_{r}^{2}\), and \(\sigma_{i}^{2}\)
6: Calculate the intermediate variable c and \(\overline{\eta}\)
7: Analyze the PDF based the results of step 6
8:endfor
9: Analyze the distribution of CE
```  
**Algorithm 1**The pseudo-code of the simulation progress  
The distribution of the CE was simulated in the conditions of the solid \(\beta\) with variable \(r_{0}\) and solid \(r_{0}\) with variable \(\beta\). Here are the results.
# Isolation and Characterization of Starch from Different Potato Cultivars Grown in Croatia
## 3 Results and Discussion
### Clarity of Starch Paste  
Starch paste clarity is affected by the amylose content, molecular weight and starch granule structure, which directly affects starch granule swelling [23]. The transparency data, expressed as % of transmission at 650 nm, are shown in Figure 3. The starch of the Dartiest cultivar had the highest clarity value, followed by the Senata cultivar, while the other cultivars had lower and similar clarity values. The Dartiest cultivar had the lowest proportion of mineral substances and the highest proportion of starch, which is a possible reason for the high clarity.  
Figure 3: Paste clarity (% T) of the isolated potato starches. Different letters above the bars indicate that the values are significantly different at \(p<0.05\).  
Figure 2: Solubility index of the isolated potato starches at different temperatures. Different letters above the bars indicate that the values are significantly different at \(p<0.05\).  
With gelatinization, starch granules swell and dissociate and let more light through--i.e., their clarity increases [24]. The differences in the starch clarity values may be due to several factors such as the amylose content, lipid and protein content, granule particle size, amylose and amylopectin chain length [22] and phosphate monoester content [25; 26].
## 2 Materials and Methods
### Procedure
#### 2.5.2 Computational Fluid Dynamics (CFD) Analysis  
CFD calculations on the 3D models were conducted (Star CCM+, Siemens Industry Software Inc., Plano, TX, USA) in a domain that consisted of a wind tunnel of 6.5 m \(\times\) 2.5 m \(\times\) 2.5 m (L \(\times\) B \(\times\) H), modeled after a wind tunnel facility [30]. Cyclist models were positioned at 2 m from the inlet of the test section, with the models 'facing' the opposite direction of the wind. The surface of the objects was assumed to be uniform and smooth. The bicycle was not included in the CFD domain. We adopted parameters based on previous literature [1], which are summarized in Table 2.  
The size of individual cells in the domain determines total cell count, accuracy, and computation time. For a given domain, a lower base cell size implies higher mesh resolution and accuracy, but implies longer computational time. To obtain the least base cell size for which reliable drag values can be calculated, a mesh convergence study was conducted. We investigated drag for one cyclist model in the domain over 700 iterations each with base cell sizes of 0.1500 m, 0.1000 m, 0.080 m, 0.060 m, 0.050 m, and 0.0039 m. The results of the mesh convergence study revealed that the average drag for the last 50 iterations for the model in each of these domains was 32.75 N, 33.53 N, 31.96 N, 32.23 N, 33.97 N, and 32.35 N. It was concluded that the drag force obtained from the calculations at a base cell size of 0.15 m (\(\sim\)200,000 cells) can provide reliable results for the sake of obtaining trends in drag area at a significantly lower computational time (an average duration of 90 min per simulation, which is roughly 70% faster than the same for the finest mesh). To obtain a high degree of absolute accuracy of modeling the air flow (e.g., a detailed visualization of the wake of the cyclist), we recommend a higher resolution in the domain. However, the aim of the present study was to estimate drag area and compare trends therein with values reported in the literature.
# Topology Optimization and Testing of Connecting Rod Based on Static and Dynamic Analyses
## 3 Computational Analysis  
In this study, we conducted various analyses to evaluate the performance of the connection rods, including static and dynamic analyses. These analyses helped to verify the structural integrity, load capacity, and possible failure modes under different operating conditions.
## 4 Semi-Automated Procedure to Estimate the NKH Model Parameters for Soils and Rocks
### Application of the NKH Model and the Semi-Automated Procedure for Rocks  
Although the application of the nonlinear model for rock material during dynamic analyses has recently been seen frequently with the increase in the structure's size and weight, such as in nuclear power plants (NPPs), high-rise buildings, and gravity dams, a few nonlinear models did exist for rocks in ABAQUS. Drucker-Prager, an isotropic  
Figure 10: G/Gmax and damping curves with parameters of the NKH model (weighting factor, \(w=0.95\)) for (**a**) silica sand, (**b**) mixture soil, and (**c**) Toyoura sand.  
hardening model recommended for rock materials, requires yield stress and plastic strain data, which have to calibrated with complicated techniques [25]. Moreover, in rock engineering, the Young's modulus, shear wave velocity, Poisson's ratio, and rock quality are the common properties provided during the geotechnical survey, whereas the cohesion and friction angle are not very common. Nonlinear dynamic properties for intact or jointed rocks have been proposed in several reports [26; 27; 28; 29]. EPRI [30] proposed referent G/Gmax and damping curves for rock material, depending on its depth. Therefore, by using the semi-automated procedure proposed above, the parameters for the NKH model can be found for rock based on common fundamental properties and referent dynamic curves.  
Table 2 shows the ground profile for Wolsung NPP located in Korea. There were 11 rock layers with shear wave velocities varying from about 549 m/s at the surface to around 2804 m/s at a depth of 100 m. The semi-automated procedure was applied to define the NKH model parameters for layers 3 and 5 at depths of 6 m and 17 m, respectively; based on these depths, the referent G/Gmax and damping curves are presented in EPRI [30]. Figure 11 shows the G/Gmax and damping curves for finding the NKH model for the two rocks, considering only the shear modulus (i.e., the weighting factor, \(w\), is 1). Good results were obtained for G/Gmax for the two rocks, proving the application of the NKH model with the semi-automated procedure for both soil and rock materials.
# Optical Beamforming Networks for Millimeter-Wave Wireless Communications
## 4 OBFNs with Phase Shifter Architectures
### Integrated Phase Shifter Array Architectures  
To miniaturize the OBFNs, multiple phase shifter arrays integrated on one substrate can be employed for phase shifter architectures, owing to the fact that the optical phase shifter has a small footprint for a phase shift in the optical domain. As depicted in Figure 19a, four phase shifters on a silica platform are manufactured for 1 \(\times\) 4 OBFN [22]. This architecture mainly consists of two lasers, an integrated phase shifter array and four optic/millimeter-wave converters (OMC), namely optic-electric converters. The integrated phase shifter array occupies an area of 2 \(\times\) 30 mm\({}^{2}\). Two lasers are coupled to high  
Figure 18: Schematic diagrams of OBFNs based on modulation-induced phase shifter consisting of dual-drive MZM (reprinted with permission from [100] © The Optical Society).  
modulation sidebands of a master laser (LDM) and have a frequency spacing of 60.8 GHz (19 \(\times\) 3.2 GHz). Therefore, except for the phase shift, the frequency up-conversion is also realized in the optical domain, which may enhance the value of the OBFNs applied in PAAs. One disadvantage of this architecture is the slow tuning speed of phase shifters using a thermo-optic effect, which is typically tens of or hundreds of microseconds. This issue can be solved by integrating electro-optic phase shifters with a much higher tuning speed. Figure 19b shows the implementation of a phase shifter array architecture using the electro-optic effect, composed of a continuous work (CW) laser, an electro-optic modulator (EOM) with the ability of single-sideband (SSB) modulation, and four beamforming network elements (BFN-E) fabricated on a silicon platform [28; 102]. The BFN-E includes an optical filter, an electro-optic phase shifter, and a BPD, which occupies an area of \(\sim\)4.5 mm\({}^{2}\). The optical filter consists of a MRR and MZI structure and separates the optical carrier and the sideband. The electro-optic phase shifter has a tuning speed of 5 ns, which is at least three orders of magnitude faster than that of the thermo-optic phase shifter. Therefore, the integrated phase shifter array architectures is promising for realizing small-scale OBFNs with a beam sweeping speed as fast as hundreds of megahertz. Note that, the loss of the electro-optic phase shifter is larger than that of the thermo-optic phase shifter due to the carrier injection in the waveguide region.
# A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques
## 6 Discussion  
The main concerns of this paper are to evaluate opportunities for designing an effective smart building monitoring system and explore different approaches for evaluating smart building parameters. The domains of lighting, HVAC systems, energy, and water consumption, as well as occupancy evaluation, are covered. This study has led to the following conclusions:  
* The effectiveness of lighting management is highly influenced by the type of room and its occupancy pattern. The maximum energy savings can be obtained by applying daylight harvesting and occupancy control strategies.
* Monitoring the temperature throughout the building helps to optimize HVAC systems and avoid energy wastage. This can be achieved by time scheduling and occupancy-based methods; however, one of the challenging aspects of HVAC monitoring is the determination of an optimal sensor placement since the temperature distribution profile is not uniform within one location.
* Energy management systems control and optimize the current energy consumption of buildings by monitoring the related energy-consuming resources. This is achieved by measuring both the static parameters (building materials, orientation, placement) and dynamic parameters (weather data, occupancy characteristics, energy use, water use). There are intrusive and non-intrusive energy monitoring techniques. Although the non-intrusive method is a more advantageous and effective way of gathering load data, it has a notable drawback, such as the inability to monitor all types of loads. One of the advanced methods of energy optimization is connecting HVAC systems to the same interface for a direct fine-tuning of temperature and comfort levels.
* Aggregating the data from different IoT sensors via data fusion is remarkably important for decision-making systems. However, major challenges, such as the imperfection and correlation of data, inconsistencies, and the heterogeneity of the datasets, require the development of advanced predictive analytics algorithms. The accuracy of gathered data can be optimized by combining sensor fusion methods, based on data association, state estimation, or decision fusion.
* The convergence of BIM with an IoT-enabled BACS promotes an informed, data-driven approach to building management. It automates complex operational tasks based on the analysis of IoT-generated data, enhancing system responsiveness to occupant demands while advancing sustainability objectives by optimizing resource allocation.
# A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks
## 3 Proposed Methodology  
The rapid development of 5G technology and the widespread use of IoT devices have greatly impacted various aspects of daily life, offering enhanced connectivity, faster speeds, and a wider frequency range for mobile networks. However, these advancements have also introduced significant security challenges, with Cross-Site Scripting (XSS) attacks being among the most concerning. XSS attacks exploit weaknesses in network components and web applications, leading to serious threats to user privacy, data security, and system integrity.  
The primary goal of this study is to develop a method for detecting XSS attacks on IoT systems that operate over 5G networks. Achieving this would play a crucial role in protecting these systems from security breaches, thereby ensuring their reliability and integrity. To accomplish this, we intend to utilize machine learning techniques to create a model that can accurately identify XSS attacks by analyzing the data packets transmitted within the system.  
The Suggested Approach Figure 1 shows the approach for identifying XSS attacks using the ToN-IoT-v2 dataset. Selection of pertinent data is the first step in the procedure, with an emphasis on categories designated as \"XSS\" and \"benign.\" The data then goes through preprocessing procedures like labeling, normalization, and cleaning. SMOTE is used to solve data imbalance. Both wrapper and filtering techniques are used in the process of selecting features. The dataset is then divided, with 20% going toward testing and the remaining 80% going toward training. For detection, machine learning classifiers like Random Forest, Decision Tree, and Naive Bayes are used. The approach effectively strengthens the security of IoT systems while reducing possible dangers, as demonstrated by its high degree of accuracy in spotting XSS assaults.