esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Setup and Results
### Experiment 1--Proposed Communication System Accuracy  
In this experiment, the accuracy of the detection of hand gestures is presented. The formula for accuracy is presented in Equation (1). The accuracy is presented in percentages in Table 3. The accuracy values are also used to determine the performance of the algorithm.  
\[\text{Accuracy}=\frac{\text{Correct Detection}}{\text{Total number of tries}} \tag{1}\]  
In this experiment, the repeatability of the algorithm is evaluated. The system is validated by processing the gestures of all letters and numbers. For validation, the most accurate gesture detected by the system is selected and processed. The results are presented in Table 3. The accuracy is listed for letters and numbers of the three individual sign language datasets. The table also lists the size of subsets used for the training and testing  
Figure 8: A subset of the Pakistani sign language dataset.  
Figure 9: A subset of the Spanish sign language dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **\% Accuracy** \\ **(Letters)** \\ \end{tabular} & \begin{tabular}{c} **\% Accuracy** \\ **(Numbers)** \\ \end{tabular} & \begin{tabular}{c} **Train Dataset** \\ **(70\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Test Dataset** \\ **(30\%)** \\ \end{tabular} \\ \hline \hline
\begin{tabular}{c} \(\Delta\)SL \\ PSL \\ SSL \\ \end{tabular} & 98.5 & 98.9 & 1750 & 750 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of detection.  
of the system. It is also observed that the accuracy of the proposed system depends on the accuracy of the hand gestures, i.e., how accurately the gesture is made.
## 2 Problem Description and System Models
### Constrained Markov Decision Process
#### 2.4.3 Transition Probability  
The transition probability describes the probabilistic relationship between the current state, taken action, and the next state. It is determined by the system dynamics, including the power flow equations and equipment operational characteristics. In power systems, the evolution of the environment and its state transitions are determined by the system model. In this study, the PYPOWER simulation tool is employed to model and analyze the state transition probabilities within the power system.
# Primary User Localization Algorithm Based on Compressive Sensing in Cognitive Radio Networks
## 2 System Model
### Signal Loss Model  
Massive statistical experimental results show that a wireless signal can be easily interfered with by environmental factors such as large scale path loss. In CRNs, the relation between source signal intensity received by SUs and signal transmitting distance is  
\[P_{r}(d)=P_{t}-10\eta\lg(\frac{d}{d_{0}})[\mathrm{dB}] \tag{1}\]  
where \(P_{r}\) denotes the source signal intensity received by SU whose distance to source is \(d\). \(P_{t}\) is the signal intensity, a constant, received by SU whose reference distance is \(d_{0}\). The unit is dBm, \(\eta\) is the path loss index (value 2\(\sim\)4).  
In a cognitive radio network, Transmitter (PU) and Receiver (SU) are mostly in Non Line-of-Sight (NLOS) condition. Based on empirical data, a known model has been developed for NLOS propagation model namely Log distance path loss model. It is a generic model and is used to predict the propagation loss for a wide range of environments. Considering the influence of shadow fading in an actual wireless environment, a coverage model based on shadow fading environment is more helpful to improve the performance of PU localization algorithm. The relationship between the received power and the distance under the shadow fading model can be described as  
Figure 1: spectrum sensing model in cognitive radio networks.  
\[P_{r}(d)=P_{t}-10\eta\lg(\frac{d}{d_{0}})[\mathrm{dB}]+X_{\sigma} \tag{2}\]  
If one is to model an accurate CR environment, the shadowing effects cannot be neglected. To add shadowing effect, a shadow fading factor \(X_{\sigma}\) which is generally approximated as Gaussian distribution random variable with zero mean (regardless of the underlying frequency-selective fading mechanism) can be assumed. \(X_{\sigma}\) is often estimated by empirical measurements and its range of standard deviation is between \(4\sim 10\). The log-normal shadowing model of Equation (2) is a linear model for received power as a function of the log of the path length.
## 3 Current Measurement Techniques
### Other Techniques Applicable to Both Soil Moisture and Snow Property Determination  
#### 3.3.1 Neutron Probes  
Neutron probes can determine water content in a small volume around a measurement tube. A neutron probe consists of a source of fast neutrons, combined with a detector for slow neutrons. Water content is determined by measuring slow neutrons that are scattered back from hydrogen atoms hit by fast neutrons. The most common application is soil moisture in agricultural applications, but neutron probes have also been applied in snow and ice. The footprint is a distance weighted sphere volume, with a radius of around 15-20 cm that varies with the moisture content [49]. Other applications of neutron detectors use above ground measurements of natural slow neutrons emitted upwards from the soil water under influence of cosmic rays. These measurements have a footprint of 10-100 m and are sensitive to moisture at a depth of 10-100 cm [50, 51].
## 5 Results and Perspectives
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Consumption** & **Heating** & **Domestic Hot Water** & **Lighting** & **Total** \\ \hline Solution S1 & & & & \\ \hline Annual consumption [MWh/year] & 575.4 & 1.6 & 50.6 & 627.6 \\ Specific consumption [kWh/m\({}^{2}\)/year] & 263.0 & 0.7 & 23.1 & 286.9 \\ Energy reduction & & & 18.16\% & \\ solution S2 & & & & \\ Annual consumption [MWh/year] & 591.2 & 1.6 & 50.6 & 643.4 \\ Specific consumption [kWh/m\({}^{2}\)/year] & 270.3 & 0.7 & 23.1 & 294.1 \\ Energy reduction & & & 16.1\% & \\ Annual consumption [MWh/year] & 714.7 & 1.6 & 31.9 & 748.1 \\ Specific consumption [kWh/m\({}^{2}\)/year] & 326.7 & 0.7 & 14.6 & 342.0 \\ Energy reduction & & & 2.44\% & \\ solution S4 & & & & \\ Annual consumption [MWh/year] & 664.1 & 1.6 & 50.6 & 716.3 \\ Specific consumption [kWh/m\({}^{2}\)/year] & 303.6 & 0.7 & 23.1 & 327.5 \\ Energy reduction & & & 6.59\% & \\ Annual consumption [MWh/year] & 714.7 & 1.6 & 18.1 & 734.4 \\ Specific consumption [kWh/m\({}^{2}\)/year] & 326.7 & 0.7 & 8.3 & 335.7 \\ Energy reduction & & & 4.24\% & \\ solution S1 + S2 + S3 + S4 & & & & \\ Annual consumption [MWh/year] & 451.9 & 1.6 & 50.6 & 504.1 \\ Specific consumption [kWh/m\({}^{2}\)/year] & 206.6 & 0.7 & 23.1 & 230.4 \\ Energy reduction & & & 43.29\% & \\ solution S1 + S2 + S3 + S4 + S5 & & & & \\ Annual consumption [MWh/year] & 419.9 & 1.6 & 0.0 & 421.5 \\ Specific consumption [kWh/m\({}^{2}\)/year] & 192.0 & 0.7 & 0.0 & 192.7 \\ Energy reduction & & & 47.53\% & \\ Final energy class & D & A & A & B \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of annual consumption and energy reduction for each proposed solution. When these ideas are implemented all together, the outcomes are notably remarkable. The integration of solutions S1 through S4 leads to a significant decrease in energy usage by 43.29%, demonstrating the synergistic impact of comprehensive building retrofits. The addition of solar panels (S1-S5) further amplifies this effect, resulting in a 47.53% reduction in energy use and raising the building's energy rating to \"B\". This holistic strategy not only greatly reduces operational expenses but also establishes the building as an example of sustainable retrofitting in historical structures, highlighting the feasibility of combining contemporary technologies with preservation principles. This technique not only advances environmental objectives but also preserves the cultural and historic integrity of heritage structures.
## 3 Results and Analysis
Similar to the MFI scheme using the Calinski-Harabasz index [26], the proposed MFI scheme also makes use of 4 QAM-BPS to roughly recover the phase of the CMA-equalized signal, only the proposed MFI scheme can still achieve 100% correct identification rates for QPSK, 16 QAM, 32 QAM, and 64 QAM when their corresponding OSNRs are no less than the thresholds corresponding to the 20% FEC. Furthermore, in contrast to the MFI scheme using the Calinski-Harabasz index [26], the proposed MFI scheme is able to identify an additional higher-order modulation format (128 QAM). In order to comprehensively compare these two schemes, the comparison of their average execution times is shown in Figure 14. These two schemes are implemented by _MATLAB R2022b_, which run on a graphics workstation equipped with a Core i9-13900K CPU at 3 GHz and 128 GB RAM. The graphics card is RTX A6000 with 48 GB of memory. It  
Figure 11: The tolerance with respect to the residual CD for the five modulation formats. Figure 12: The tolerance with respect to the DGD for **(a)** QPSK, 16 QAM, 32 QAM, 64 QAM, and **(b)** 128 QAM. should also be noted that, unlike clustering algorithms, the Gaussian weighted KNN is a supervised algorithm. However, without enormous computations in training, the training phase of the Gaussian weighted KNN algorithm consists of storing the training samples only [28]. For identifying a testing sample, the average execution time required for the proposed scheme is only 59.2% of the MFI scheme using the Calinski-Harabasz index.
# Seismic Isolation via I-Shaped and T-Shaped Large-Scale Phononic Metamaterials
## 1 Introduction
For example, seismic metamaterials, which are designed for the foundations of a structure [15; 16; 17], although they could be very effective, can only be implemented in new structures and not in existing ones. Therefore, for the implementation of seismic metamaterials for protecting existing infrastructures, several shapes, geometries, and proposed strategies were studied and are more likely to be implemented in the field. Some of them are considered embedded in the ground surface [22; 23; 25; 30; 31; 32; 33; 35; 38], whereas another strategy of implementation is placing the structures to stand at the ground surface as pillars [34; 36; 37]. It should be noticed at this point that seismic metamaterials that stand at the ground surface as pillars and provide band gaps either based on local resonances or the Bragg scattering, despite their effectiveness on wave attenuation, could have stability issues caused by the strong ground motion of a surface seismic wave. In addition, their formation demands available space in front of or around the infrastructure, which is aimed to be protected. This could limit the free available space around an infrastructure. On the contrary, seismic metamaterials that are embedded into the ground surface, like the ones studied in this work, could be considered more practical and less space-consuming, apart from their effectiveness and applicability for the protection of several kinds of infrastructure. In this work, I-shaped and T-shaped trenches, which are considered embedded in the ground surface, are studied numerically as large-scale phononic metamaterials (LSPMs) for the isolation of surface seismic waves. The dispersion relation of each examined structure is studied by using the finite element method and the Bloch theorem, and the calculated limits of the PBGs of each examined structure were recorded. In addition, for the investigation of the optimal performance of each examined structure, an optimization strategy was followed by studying the effectiveness of each geometric parameter on the PBG width. Moreover, for a more realistic approach to their efficiency, the transmission spectrum of selected structures was also calculated by using a proper numerical half-space scheme.
## 3 Methodology
### The TOPSIS Algorithm  
The TOPSIS algorithm is based on the idea that the best alternative (in this case, the best satellite for handling communication after handover) should be the one closest to the positive ideal solution--which is a combination of optimal values for each criterion--and the farthest from the negative ideal solution which represents the worst performance for each criterion. The steps of the TOPSIS algorithm are shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Methodology Used** & **Advantages** & **Limitations** & **Application Domain** \\ \hline \multirow{3}{*}{10} & Entropy-TOPSIS, & Reduces handovers, & & Limited to stable & Heterogeneous \\  & SD-TOPSIS & improves user & & cellular networks & cellular networks \\ \hline \multirow{3}{*}{6} & MADM algorithms & Reduces ping-pong & & & \\  & with utility functions & & effect and handoff & & \\  & & failures & & & \\ \hline \multirow{3}{*}{12} & M-ANP for & & & & \\  & weighting, TOPSIS & & & & \\  & for ranking & & & & \\ \hline \multirow{3}{*}{13} & TS-REPLICA & Improves load & Not tested in & & \\  & (Entropy-TOPSIS) & balancing and & dynamic LEO & & \\  & & system performance & environments & & \\ \hline \multirow{3}{*}{7} & Importance-TOPSIS & Reduces handovers, & & & \\  & for ranking LEO & decreases forced & & & \\  & Satellites & connection & environments & & \\  & & terminations & & & \\ \hline \multirow{3}{*}{14} & Multi-attribute & Reduces handover & & & \\  & decision handover & frequency, improves & & & \\  & scheme & data stream stability & & & \\ \hline \multirow{3}{*}{4} & PASMAD & Enhances QoS, & & & \\  & (SINR-based MADM) & balances network & & & \\  & for access/handover & loads, outperforms & & & \\  & RSS-based methods & & & & \\ \hline \multirow{3}{*}{5} & User-centric & Improves & & & \\  & handover scheme & throughput, delay, & & & \\  & with data buffering & and latency & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of related work.
## 3 Proposed Fault Diagnosis Method
### Fault Data Recognition Model Based on MSPCA  
In the long-term study and summary of traffic detectors, it is found that, due to the aging of sensors in detectors and the wear and tear of various devices, traffic flow parameters detected during a certain period of time seem to be at a normal level with little fluctuation of data, but the offset of data are more and more significant, along with this kind of gradual cumulative failure. Ultimately, it will cause systematic false alarm and is not easy to detect.  
With the maturity of wavelet technology, the multi-scale characteristics of fault data have made remarkable progress. In order to provide more detailed and high-resolution analysis data and to overcome the shortcomings of customer service PCA, [PERSON] [22] proposed a multi-scale principal component analysis fault diagnosis method. This model does not depend on the analytical model of the target system, but proceeds from the process data, combines wavelet analysis with PCA, and uses  
Figure 3: Geometric schematic of fault detection based on SPE (Squared prediction error) index.  
PCA to process more offline data. The multiscale characteristic of wavelet is used to overcome the shortcomings of conventional PCA in meta-statistical analysis.  
Figure 4 is the flow chart of MSPCA. It can be seen from the graph that MSPCA combines wavelet analysis with PCA organically. The model uses offline normal data to establish principal component subspace, then decomposes the sample data by wavelet, and models the coefficient matrix of wavelet analysis coefficients separately in principal component space. After reconstructing the decomposed signal, the principal component will be modeled again.
## 3 Results
### Analysis of Factors Affecting SOM Content
#### 3.3.5 Difference of the Influence of Topography and Climate on Organic Matter  
As topographic, meteorological, and climatic factors are numerical variables, we conducted correlation analysis between these numerical variables and SOM content. According to the results shown in Table 7, among all topographic variables, elevation and slope had significant positive correlations with SOM, while aspect had no significant correlation with it. This indicates that SOM levels increased in conjunction with elevation and slope, which is consistent with studies from the Guizhou and Hubei Provinces in China, and those conducted in the Spanish Peninsula [18; 26; 27]. Climate variables are positively correlated with SOM, which indicates that SOM content increases with the increase in precipitation and temperature levels [28; 29].
# Evolution of Access Network Sharing and Its Role in 5G Networks
## 2 Access Sharing Models
### Local Loop Unbundling (LLU)  
Local Loop Unbundling (LLU) [3] was one of the first techniques for enabling competition in the fixed access networks, where the incumbent leases to registered competitors the copper line (called the local loop) connecting the end-user to the local CO. There are three types of LLU, namely: line sharing, full unbundling, and legacy bitstream access.
## 3 Engineering Case Study  
### Engineering Overview  
Majiagou landslide is located in Guizhou Town, Zigui County, Hubei Province. Since the initial impoundment of the Three Gorges Reservoir in 2003, the landslide began displaying noticeable deformation, with extensive tension cracks emerging at its rear. The landslide measures 537.9 m in longitudinal length, with a width of 150 m at the front edge and 210 m at the rear edge. The shear outlet of the landslide is situated below 145 m at the front edge, while the elevation at the rear edge reaches 280 m. The overall picture of the landslide is shown in Figure 2a. The landslide's sliding direction is approximately perpendicular to the Zhavi River, a tributary of the Yangtze River. The area of the sliding mass is approximately 96,800 square meters. Sliding surfaces (at depth of about 19 and 30 m) were identified to have developed in the bedrock, so the Majiagou landslide belongs to a multi-layer sliding zone-developed traction-type reservoir landslide [33]. The preliminary estimated volume of the unstable body is approximately 2,516,800 cubic meters.  
The bedrock in the landslide area mainly consists of gray-white feldspar quartz sandstone and fine sandstone, interbedded with purple-red siltstone and mudstone. These rocks are prone to weathering, and soften easily when exposed to water, making them highly susceptible to sliding in the Three Gorges area. The bedrock is characterized by developed fractures intersected by bedding planes, resulting in blocky, fragmented rocks that reduce the slope's strength. Additionally, weak mudstone interlayers within the bedrock create favorable conditions for potential sliding surfaces.  
To study the deformation and evolution characteristics of the Majiagou landslide, various monitoring instruments were installed at the landslide monitoring holes (JC), such as fiber optics, deep displacement, and water level gauges. Figure 2a,b illustrate the monitoring plan and engineering geological profile of the landslide, respectively.  
Infiltration tests were conducted at several test holes (ZK) on the Majiagou landslide (distribution shown in Figure 2a) to obtain the permeability coefficients of the mass of landslide. The infiltration test results were shown in Table 1. However, as previously mentioned, the experimental results exhibit significant randomness and deviations, making them unsuitable for precise theoretical analysis and numerical simulation.
# Plasmonic Tweezers towards Biomolecular and Biomedical Applications
## 2 Optical Forces
### Self-Induced Back-Action Effect
This parameter also can be understood as the capability of the particle in shifting the resonance frequency of the plasmonic cavity. In a different position, the particle will generate different values of \(\delta\omega(\mathbf{r})\) as in Equation (7), where \(0\leq f(\mathbf{r})\leq 1\) is a normalized spatial intensity profile of the empty cavity and it follows the resonance symmetry of the plasmonic cavity. To have a large \(\
u\) parameter, the plasmonic cavity should be designed with a large \(Q\) and a small \(V_{m}\). For a fixed working environment (i.e., background medium, nanoparticle and plasmonic cavity), a proper value of detuning \(\Delta\) should be selected:  
\[\
u\equiv\frac{\alpha(\omega)}{\varepsilon_{0}V_{m}}Q=\frac{\alpha(\omega)}{ \varepsilon_{0}V_{m}}\frac{\omega_{c,0}}{\Gamma}, \tag{6}\]  
\[\frac{2\delta\omega(\mathbf{r})}{\Gamma}=-\
u\cdot f(\mathbf{r}). \tag{7}\]  
To understand the SIBA effect, the number of photons in the cavity can be examined to demonstrate the change in the momentum that transferred to the particle from the photons. In Equation (8), \(p\) is the momentum of the particle, \(\hbar\) is the reduced Planck constant, and \(n(\mathbf{r})\) is the number of photons in the cavity that is decided by the position of the particle and can be expressed in Equation (9), where \(\Gamma_{\alpha}\) denotes the source of the injection of photons into the cavity at an incident intensity \(I_{0}\) at laser frequency \(\omega\) and the decay rate of the cavity into specific external channels. When the particle is located at the resonant position and \(\Delta=\delta\omega(\mathbf{r_{max}})\) is satisfied, then the number of photons in the cavity is also locally maximized. In addition, the strongest gradient force is generated to trap this particle. When the particle is getting closer to a resonant position, a deeper potential will be generated to suppress the Brownian motion of the particle. Compared to a trap (static type) working at the resonance condition, the time averaged intensity experienced by the trapped particle is much lower since, most of the time, the system is not at resonance due to the detuning. This dynamic trapping mechanism protects the trapped particle from possible photo damage:  
\[\frac{dp}{dt}=-n(\mathbf{r})\hbar\delta\omega(\mathbf{r}), \tag{8}\]  
\[n(\mathbf{r})=\frac{4I_{0}\Gamma_{\alpha}}{\Gamma^{2}}\frac{1}{1+\left(\
u\cdot f( \mathbf{r})+\frac{2\Delta}{\Gamma}\right)^{2}}. \tag{9}\]  
Polystyrene particles with 22 nm diameter have been successfully trapped with SIBA effect at 2.5 mW [53]. For smaller biomolecules, e.g., proteins in a few nanometers, advanced designs of plasmonic cavities are needed to provide a large quality factor and a small mode volume to boost the back-action parameter. This novel trapping mechanism is quite important. A slightly red-detunedlaser beam with an extremely low incident power can provide deep potential dynamically to trap and manipulate nanosize bioparticles without thermal nor photonic damage.
# Automated Trucks' Impact on pavement Fatigue Damage
## 3 Results
### Alternative Payement Structures
#### 3.3.1 Layers' Thickness Effect  
As shown in Figure 7, in the zero-wander cases, increasing the thickness of the surface layer decreases the maximum BUDI value for all the penetration rates. It can be seen that the differences are more pronounced for the higher penetration rates. For instance, by increasing the thickness from 10 cm to 20 cm, the maximum BUDI value is decreased by 20.6% in the case of 20% ATs and by 42.9% in the case of 100% ATs. However, in the uniform-wander cases, the differences between BUDI values caused by increased thickness are less than 6.2% for all the penetration rates.  
Figure 7: DI values for the different surface thicknesses for lane widths of (**a**) 3 m and (**b**) 3.5 m.  
The impact of increased base thickness on the BUDI values is presented in Figure 8. In the zero-wander mode, increasing the thickness of the base layer from 20 cm to 40 cm resulted in a decrease of approximately 9.1% in the maximum BUDI values for the case of 20% ATs and 8.5% for the case of 100% ATs. In the uniform cases, the BUDI values have reductions ranging from 9.5% to 9.7% across all penetration rates.  
The results presented in Figures 7 and 8 provide insight into the influence of pavement layer thickness on the fatigue DI. These figures show that increasing surface and base layer thickness could reduce the maximum BUDI values in zero- and uniform-wander modes.  
Figure 8: DI values for the different base thicknesses for lane widths of (a) 3 m and (b) 3.5 m.