esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Prediction of Bifacial Si PV Module with Different Reflection Conditions on Rooftop
## 1 Introduction
### Concept of Experiments  
The variation of the output power could be obtained depending on the amount of irradiance on the rear surface. Bifacial crystalline silicon photovoltaic modules are structured to add electrodes to the back side of the existing mono-facial crystalline photovoltaic module manufacturing process [17]. The passivated emitter and rear cell (PERC) is of very high interest for both research institutes and industry [18]. The solar modules used in this study were therefore fabricated using n-PERC cells.  
In this paper, a module using n-PERC cells fabricated for research purpose was used for the experiment in Figure 1. The main content is that the modeling that predicts the effect of irradiance on the rear side is presented by analyzing the outputs from the front side and the rear side separately. The output is confirmed by predicting the irradiance characteristics according to Albedo, which affects the amount of irradiance on the rear side of the module. Based on the results of various studies, the solar power generation system in Korea is constructed at an installation angle of 30\({}^{\circ}\) for optimal output conditions, so the bifacial crystalline silicon solar module also analyzes the output characteristics at an angle of 30\({}^{\circ}\). In this paper, the direct solar irradiance is estimated by considering the inclination angle of the earth, the latitude, the angle of incidence of solar irradiance, and the installation angle of the solar module, and the total irradiance affecting the rear is calculated using the direct and scattered amount of irradiance. It is explained that the output power varies depending on the albedo of the rear surface. In addition, comparisons were made for different environments to compare the output depending on the amount of irradiance on the rear side.  
The goal of this study is to use the collected data in a preliminary step to improve Rooftop Photovoltaic system and other PV environments with bifacial crystalline Si solar modules.  
Figure 1: n-PERC Cell Structure and Module used in the experiment. PERC: The passivated emitter and rear contact. P-BSF: Phosphorus Back Surface Field. B-Emitter: Boron Emitter.
# Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
## 4 Loss Function and Optimization
### Cosine Annealing Scheduler
Feature Extraction:**  
**For** each sample \(x\)in \(D\):  
\(x^{\prime}\gets A(x)\)  
\(x_{img}\gets CNN(x^{\prime})\)  
\(x_{hist}\gets Hist(x)\)  
\(F\gets x_{img}\oplus x_{hist}\)  
**2. Graph Construction:**  
**For** each mini-batch (size N):  
\(k^{\prime}\leftarrow\min(k,N-1)\)  
\(E\leftarrow DynamicKNN(F,k^{\prime})\)  
\(G\leftarrow(F,E,y)\)  
**3. Graph Classification:**  
\(\hat{y}\gets GNN(G)\) (via global pooling and FC layer with log-softmax)  
**4. Training And Evaluation:**  
\(A_{best}\gets 0\)  
**For**\(t=1\)**to**\(T\)**do**  
**Training:**  
**For** each mini-batch in \(D_{train}\):  
(i) Extract \(F\) and construct \(G\)  
(ii) \(\hat{y}\gets GNN(G)\)  
(iii) \(L\gets CE(\hat{y},y)\)  
(iv) Update parameters (AdamW and gradient clipping)  
**Testing:**  
**For** each mini-batch in \(D_{test}\):  
Repeat (i)-(ii), accumulate accuracy \(A_{test}\)  
**If**\(A_{test}>A_{best}\) then  
Set \(A_{best}\gets A_{test}\) and save the model  
**Update**\(\alpha\leftarrow\text{CosineAnnealing}(\alpha)\)  
**endfor**  
**Return:**\(A_{best}\)
## 4 Routing in 3D Sensor Network without Internal Holes
### Theoretical Insights
#### 4.1.1 Volumetric Embedding  
The volumetric embedding is the process of computing a map between the original volumetric data and a canonical domain in \(\mathbb{R}^{3}\).  
For the purpose of computation, a volume is usually modeled as point clouds or a piecewise linear tetrahedral mesh:  
\[\mathbb{M}=(\mathbb{T},\mathbb{F},\mathbb{E},\mathbb{V},\mathbb{C}) \tag{1}\]  
where \(\mathbb{T}\), \(\mathbb{F}\), \(\mathbb{E}\) and \(\mathbb{V}\) are the sets of tetrahedra, triangular faces, edges and vertices in the mesh, while \(\mathbb{C}\) describes the connectivity among them.  
Volumetric embedding is to assign a set of 3D coordinates to every vertex in the volumetric data. Note that although the mapping function is by definition restricted on vertices, it can be extended throughout the whole tetrahedral mesh in a piecewise manner. More specifically, the function value for an arbitrary point in the volume is defined as the interpolation of the values on the four vertices of the enclosing tetrahedron, inducing a piecewise-linear map from the original volumetric mesh, \(\mathbb{M}\), to a canonical domain, \(\mathbb{N}\). The domain, \(\mathbb{N}\), is a subset of \(\mathbb{R}^{3}\) and should ideally have a regular shape. In our case, the canonical domain is a solid ball in order to support greedy routing.
# Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links +
## 3 Simulation Results
### Water Types  
The next analysis consisted of the calculation of the MALO parameter for different water types using the parameters from Table 1. The distance between the emitter and receiver was 1 m, and a seven-lens scheme was used.  
The results are presented in Figure 11, showing a characteristic curve for each of them. These curves show the optimal divergence that maximized the tolerance to the position misalignment of the system. While this work is a comprehensive analysis of the sensor-lens pair, these results confirm the conclusions of a previously published work [14]. An evident difference between different water types was that the MALO was non-zero at a beam divergence of zero for more turbid waters and increased with the water turbidity. This outcome was expected due to increased scattering, which acted as a diverging factor, maximizing the MALO.
## 3 Proposed VANET
### VANET Features and Interfaces
Vehicles can propagate beneficial data like braking for emergencies, detecting accidents, or traffic situations between vehicles. V2I works mainly to send data among vehicles and the network's infrastructure. Then, the vehicles improve the link with RSUs to interchange data with different networks, such as the Internet. Moreover, the V2I demands a wider bandwidth than V2V, making V2I more protected against any risks or attacks. C-V2X significantly improves vehicular communications, providing reliable, robust, and low-latency communication capabilities. C-V2X increases road safety, traffic efficiency, and a variety of ITS applications by utilizing both direct and network-based communication. C-V2X is a communication technology specifically developed to offer connection for ITS. C-V2X, a 3 GPP standard, enables direct and network-based communication across cellular networks. Using C-V2X, vehicles can communicate directly with each other, infrastructure, and pedestrians through the PC5 interface. This direct communication interface supports the frequency band of 5.9 GHz ITS allocated mainly for vehicular communications. C-V2X enables vehicles for cellular communication using the Uu interface, which was developed for next-generation NodeB (gNB) [37]. This interface provides access to cloud services, traffic information, and broader network resources. Integrating fog and MEC computing with SDN brings control facilities to the network's edge. Instead of delivering data to a central controller, fog computing processes services and data at the network's edge. Low latency, location awareness, and enhanced QoS are just some advantages. We considered the MEC platform introduced in [28] for the proposed VANET. We considered a distributed control scheme, in addition to the centralized controller, to provide global intelligence. Each RSU deploys an SDN RSU controller (RSUC), which has a direct interface to the centralized scheme. This distributed scheme supports distributed edge computing and assists the MEC controller deployed in the considered MEC platform. The SDN controller controls the RSUC, which is also an SDN-enabled device. On the other hand, the SDN controller does not take full control of the network; instead, it gives policy information to the RSUC. The RSUC decides on the exact conduct based on the policy information received and their local understanding. A fog orchestration layer is placed at the SDN architecture, and the operation is located at the SDN controller coordinates between RSUCs. The RSUC sends data to the SDN controller while also maintaining local storage. In the event of an emergency, RSUC serves the requests rather than sending them to the controller, resulting in a significant reduction in latency. This has raised the same worry about latency control, emphasizing the significance of an edge-up design with hierarchically distributed control and an edge layer. This is rather than a cloud-down approach (Internet-based SDVN) in terms of the use of radio resources.
# Review of Indoor Positioning: Radio Wave Technology
## 2 Parameter Based Positioning
### Rssi
The propagation model involves building a map associated with the physical regulations of the wireless signal. The precision and flexibility of the environment are poorer in the range-based method, which can locate the position of the object by using trilateration, min-max, and maximum likelihood algorithms. The latter approach generates the use of a fingerprinting database (radio map) for localization [55]. A range-free method does not require angle or distance measurements among nodes. The fingerprinting technique is higher in accuracy and can be used for various indoor environments. However, RSSI measurement can cause an error due to environmental effects. The real indoor environment consists of multiple obstacles that affect radio signal propagation [1]. RSSI is susceptible to noise and multipath effects which significantly decreases its localization accuracy [56]. In addition, there is the LoS problem between the two nodes, which can significantly influence error. However, RSSI calculation accuracy is increased by calibrating and analysing the radio signal propagation.
# On the Impact of Wavelength Dependency on Supercontinuum Generation in Photonic Crystal Fibers
## 3 Results  
In this section, once the object of the study (PCF) and the methods to be used have been stated, we will present the most-relevant results obtained for the generation of supercontinuum when our model is used.  
The structure in this part is as follows. We will start by explaining the use of the wavelength dependence of the dispersion propagation parameters, i.e., \(\beta(\lambda)\), to solve the problem. Subsequently, the results in both propagation schemes: the constant parameters and the wavelength-dependent scheme, with different pulse widths and peak powers, will be compared, and we will finish by quantitatively defining a parameter to characterize the differences between them.
# Enhancing Security in Smart Robot Digital Twins Through Intrusion Detection Systems
## 2 Security of Digital Twin Systems in Smart Robotics
For instance, an employee of the cloud service provider may introduce a security configuration error, thereby allowing an attacker to access the data or system. Additionally, cloud service providers utilize software that may contain vulnerabilities, which presents another means for attackers to penetrate the cloud infrastructure. Furthermore, it is important to note that cloud service providers implement standard security measures that may not be tailored to the specific needs of their clients, thus posing a risk that these measures may not be sufficient to protect data and systems from specific attacks [11]. Moreover, attackers may employ sophisticated techniques such as zero-day attacks to exploit vulnerabilities in cloud infrastructure, which can be challenging to detect and prevent, and the consequences can be severe. * The vulnerabilities in cloud applications refer to security vulnerabilities in the software used in the cloud, which can be exploited by attackers to gain unauthorized access to data or cause other types of damage. Cloud applications are essential for using cloud services and, therefore, must be protected against cyberattacks. One example of a vulnerability in cloud applications is Denial-of-Service (DoS) attacks [12], which in volve overwhelming the application or its supporting infrastructure with an excessive amount of traffic, effectively rendering the service unavailable. This type of attack can lead to the compromise of users' personal or financial data and material damage to the company. * Vulnerabilities in application programming interfaces (APIs) can be critical to the security of cloud applications and DT systems. These vulnerabilities include authentication and authorization errors, inadequate exposure of sensitive data, and exposing APIs to attacks such as overloading or Denial-of-Service attacks. API vulnerabilities can also be caused by improper use of request methods and parameters, creating vulnerabilities in authentication and authorization security. It is important to adopt appropriate security measures to protect APIs and ensure secure communication between applications and cloud services. Considering the vulnerabilities highlighted above in DT systems, it can be concluded that security is a critical aspect in these systems. The DT system involves a close interconnection between the virtual and physical environment, which means that a vulnerability in one of these environments can have an impact on the other. In addition, sensitive data and critical information are stored and processed in these systems, which means that a cyber-attack can lead to significant losses and compromise the reputation of the affected company or organization. Therefore, ensuring security in DT systems is crucial to avoid financial losses and protect the integrity of the data and associated physical assets.
# Volterra Black-Box Models Identification Methods: Direct Collocation vs. Least Squares
## 3 Collocation Method  
Collocation-type methods are widely used in the discretization of various kinds of integro-functional equations [15]. With sufficiently good accuracy and stability, they are also computationally less expensive in comparison with projection methods of the Galerkin type requiring additional integration [16].  
In order to determine the unknown coefficients \(A_{i}\) and \(C_{ij}\), we introduce a uniform grid of nodes  
\[t_{k}\in[0,T],\ k=0,1,\dots,N, \tag{6}\]  
where \(N+1\) is number of nodes.  
Substitute (5) in (3) and then demand that the equalities be fulfilled at the points (6)  
\[y(t_{k})=\int\limits_{0}^{t_{k}}\widetilde{K}_{1,m}(s)x(t_{k}-s)\,ds+\int \limits_{0}^{t_{k}}\int\limits_{0}^{t_{k}}\widetilde{K}_{2,m_{1},m_{2}}(s_{1 },s_{2})x(t_{k}-s_{1})x(t_{k}-s_{2})\,ds_{1}ds_{2},\ k=\overline{0,N}. \tag{7}\]  
Denote for a simplicity \(y(t_{k})=y_{k}\), and transform the last equalities as follows  
\[y_{k}=\sum\limits_{i=0}^{m-1}A_{i}\int\limits_{0}^{t_{k}}B_{i}(s)x(t_{k}-s)\, ds+\sum\limits_{i=0}^{m_{1}-1}\sum\limits_{j=0}^{m_{2}-1}C_{ij}\int\limits_{0}^{t_ {k}}\int\limits_{0}^{t_{k}}B_{i}(s_{1})B_{j}(s_{2})x(t_{k}-s_{1})x(t_{k}-s_{2} )\,ds_{1}ds_{2}. \tag{8}\]  
As a system of basis functions \(B_{i}(t),\ i=0,1,\dots\), we choose Chebyshev polynomials of the first kind  
\[T_{0}(t)=1,\ T_{1}(t)=t,\ T_{i+1}(t)=2 tT_{i}(t)-T_{i-1}(t),\ i=1,2,\dots. \tag{9}\]  
Sufficient conditions for the applicability of Chebyshev polynomial expansions of the form (5) are the limitation of the first derivatives of the approximated kernels. For more detailed information about convergence, we refer, for example, to the book [17].  
Since these polynomials are orthogonal on the segment \([-1,1]\), we apply a linear mapping to the segment \([0,T]\).  
The controlled norm of the residual corresponding to the selected values of \(m,m_{1}\) and \(m_{2}\) takes the form  
\[\begin{gathered}\varepsilon_{N}=\max_{t\in[0,T]}\left|y(t)-\sum \limits_{i=0}^{m-1}A_{i}\int\limits_{0}^{t}B_{i}(s)x(t-s)\,ds-\\ \sum\limits_{i=0}^{m_{1}-1}\sum\limits_{j=0}^{m_{2}-1}C_{ij}\int \limits_{0}^{t}\int\limits_{0}^{t}B_{i}(s_{1})B_{j}(s_{2})x(t-s_{1})x(t-s_{2} )\,ds_{1}ds_{2}\right|.\end{gathered} \tag{10}\]Let us denote \(N=m+m_{1}m_{2}-1\). The number of equalities (number of nodes in the grid) equals the number of unknown coefficients.  
Thus, we have the following system of linear algebraic equations  
\[y_{k}=\sum\limits_{i=0}^{m-1}A_{i}\beta_{ik}+\sum\limits_{i=0}^{m_{1}-1}\sum \limits_{j=0}^{m_{2}-1}C_{ij}\gamma_{ijk},\quad k=\overline{0,m+m_{1}m_{2}-1}, \tag{11}\]  
with respect to the unknown coefficients \(A_{i},\ i=0,1,\ldots,m-1\) and \(C_{ij},\ i=0,1,\ldots,\)\(m_{1}-1,\ j=0,1,\ldots,m_{2}-1\). Here,  
\[\beta_{ik}=\int\limits_{0}^{t_{k}}B_{i}(s)\,x(t_{k}-s)\,ds,\quad\gamma_{ijk}= \int\limits_{0}^{t_{k}}\int\limits_{0}^{t_{k}}B_{i}(s_{1})B_{j}(s_{2})\,x(t_{k }-s_{1})\,x(t_{k}-s_{2})\,ds_{1}ds_{2}. \tag{12}\]
## 2 Materials and Methods
### Experimental Setup and Procedure
#### 2.3.2 Electrokinetic Percolation Test (EKP)  
Figure 1 (option 2) depicts the layout of the bench scale setup utilized for the electrokinetic soil flushing tests. The EK cell had five sections and was constructed from transparent methacrylate. The previously moistened contaminated soil (tailings) was placed into the larger central compartment (20 cm \(\times\) 10 cm \(\times\) 10 cm), which was divided by a 0.5 mm nylon mesh from the neighboring anode and cathode compartments, respectively. In the event that the electroosmosis caused an overflow, the final two external compartments served as collectors. Graphite electrodes of 12 cm by 10 cm by 1 cm were utilized, and they were linked to the power source. The studies were conducted at 22 \(\pm\) 2 \({}^{\circ}\)C room temperature.  
The electrokinetic percolation tests were carried out with a gradient voltage of 1.5 V cm\({}^{-1}\) and lasted for 4, 7, 10, and 15 days. There were two sets of EK cells for each treatment time: one  
Figure 1: Schematic experimental setup and procedure.  
set had the BL medium put in its anode, while the other set had the iron-free TK medium (as a reference test) filled in its anode. As the liquid was transferred to the cathode zone by the electroosmotic flow, the anode's liquid level needed to be controlled and was always regulated by anolyte replacement. A 1 M H\({}_{2}\)SO\({}_{4}\) solution was used to maintain a pH of 2.0 in the cathode in order to prevent excessive metal precipitation and to neutralize and halt the basic front's advancement into the soil.  
The pH, temperature, and current intensity were measured during the course of the studies. During the experiments, liquid samples were collected from three separate locations: the middle of the soil, the cathode compartment, and the anode compartment. At the start of the experiment, a plastic cylinder equipped with a 0.5 mm nylon mesh and several perforations was inserted into the soil to allow liquid samples to be taken from the water soil pore of the soil. A multiparameter probe (SENSLON, HACH) was used to measure the pH and temperature of all liquid samples twice a day along with the electrical current intensity.  
Soil samples were only taken at the start and finish of the tests in order to quantify the total metal concentration in the tailings and avoid making any disturbances in the permeability of the tailings that could result in preferential flow channels. From the cathode to the anode, five distinct longitudinal sections were used to collect the solid samples. Following sample collection, moisture content was determined using ISO 11465 methodology. Ultimately, the EPA 3051A procedure (already reported) was used to digest the solid samples, and an inductively coupled plasma optical emission spectrometer (ICP-OES 720 ES Varian, Australia) was used to quantify the amounts of metals. Once more, in order to guarantee the method's faithfulness, the digestion procedure was completed in triplicate, with one certified reference material (SQC001 from Sigma-Aldrich) used for every ten samples.
# Comparison of DFN Modelled Microfracture Systems with Petrophysical Data in Excavation Damaged Zone
## 2 Materials and Methods
### Petrographic Description  
To get an idea of the typical compositions of the studied granite and gneiss, petrographic analysis was performed [5]. At the gneiss specimen, biotite defines the foliation of the rock. The idiomorphic biotite grains contain elongated isomorphic opaque minerals and zircon in a large number. Besides biotite, the gneiss contains feldspars and quartz as major components. The plagioclase feldspar grains have polysynthetic twins. Alkali feldspars often show tartan twins, characteristic of microcline and a few myrmekitic grains can be observed in the thin sections. Quartz grains display signs of dynamic recrystallization, developed probably due to grain boundary migration. The potassium feldspar, microcline grains show tartan twinning and also show perthitic features in the thin sections. The metamorphic index minerals in the studied gneiss are kyanite sillimanite and garnet.  
In the granite specimen, feldspars and quartz are also dominant. Plagioclase feldspar, which is sometimes sericitic, microcline, and also perthite, can be obtained in the thin sections. It seems like grain-boundary migration occurred in the quartz clasts. Rarely, myrmekite can be found in the thin sections. The granite contains idiomorphic biotite, but its quantity is not enough to define foliation. Muscovite also occurs in the granite specimen.
## 3 Best Relay Selection Design and Transmission Rate Analysis
### Transmission Rate Analysis
 \tag{5}\]  
where \(\widehat{y}_{b}=\beta(\sqrt{1-\rho}y_{b}+n_{b})\), \(n_{1}\) and \(n_{2}\) are respectively the noises at SU\({}_{1}\) and SU\({}_{2}\) with \(n_{1}\sim CN\big{(}0,\sigma_{1}^{2}\big{)}\) and \(n_{2}\sim CN\big{(}0,\sigma_{2}^{2}\big{)}\), and \(\beta\) is the amplification factor with \(\beta=\sqrt{\frac{P_{R}}{(1-\rho)\big{(}P_{1}f_{b}^{2}+P_{2}g_{b}^{2}+P_{p}|h_ {r}|^{2}+\sigma_{r}^{2}\big{)}}}\). According to (3), \(\beta\) can be further given by \(\beta=\sqrt{\frac{\rho\eta}{1-\rho}}\)[30]. For the nodes with the perfect CSI, the self-signals of SU\({}_{1}\) and SU\({}_{2}\) can be cancelled [9]. After cancelling self-signals, the signals received at SU\({}_{1}\) and SU\({}_{2}\) can be rewritten as  
\[\widehat{y_{1}}= \beta\sqrt{1-\rho}\sqrt{P_{2}}f_{b}g_{b}x_{2}+\beta\sqrt{1-\rho }\sqrt{P_{p}}f_{b}l_{r}x_{p}+\beta\sqrt{P_{p}}l_{x}+\beta\sqrt{1-\rho}f_{b}n_ {r}+\beta f_{b}n_{b}+n_{1} \tag{6}\] \[\widehat{y_{2}}= \beta\sqrt{1-\rho}\sqrt{P_{1}}f_{b}g_{b}x_{1}+\beta\sqrt{1-\rho} \sqrt{P_{p}}g_{b}l_{r}x_{p}+\sqrt{P_{p}}l_{2}x_{p}+\beta\sqrt{1-\rho}g_{b}n_ {r}+\beta g_{b}n_{b}+n_{2} \tag{7}\]  
Based on (6) and (7), the transmission rates of SU\({}_{1}\) and SU\({}_{2}\) can be expressed as  
\[R_{1}^{\text{s}}=\frac{1}{2}\text{log}_{2}\Bigg{(}1+\frac{(1-\rho)\rho\eta P_{2} |f_{b}|^{2}|g_{b}|^{2}}{(1-\rho)\rho\eta P_{p}|f_{b}|^{2}|l_{r}|^{2}+(1-\rho)P_ {p}|l_{1}|^{2}+N_{1}}\Bigg{)} \tag{8}\]  
\[R_{2}^{\text{s}}=\frac{1}{2}\text{log}_{2}\Bigg{(}1+\frac{(1-\rho)\rho\eta P_{1} |f_{b}|^{2}|g_{b}|^{2}}{(1-\rho)\rho\eta P_{p}|g_{b}|^{2}|l_{r}|^{2}+(1-\rho)P_ {p}|l_{2}|^{2}+N_{2}}\Bigg{)} \tag{9}\]where \(N_{1}=\left[(2-\rho)\rho\eta|f_{b}|^{2}+(1-\rho)\right]\sigma^{2}\) and \(N_{2}=\left[(2-\rho)\rho\eta|g_{b}|^{2}+(1-\rho)\right]\sigma^{2}\). With (8) and (9), the \(R_{T}^{s}\) can be obtained. 2. When \(f(P_{T})\leq 0\), from Figures 1 and 2, we can know that the signals received by SU\({}_{1}\) and SU\({}_{2}\) can be expressed by  
\[\widehat{z_{i}}=\sqrt{P_{j}}hx_{j}+\sqrt{P_{P}}l_{i}x_{p}+n_{i},\{i,j\}=\{1,2 \},i\
eq j \tag{10}\]  
Based on (10), the transmission rates of SU\({}_{1}\) and SU\({}_{2}\) can be expressed as  
\[R_{i}^{d}=\text{log}_{2}\Bigg{(}1+\frac{P_{j}h^{2}}{P_{P}l_{i}^{2}+\sigma_{i} ^{2}}\Bigg{)},\{i,j\}=\{1,2\},i\
eq j \tag{11}\]  
With (11), \(R_{T}^{d}\) can be obtained.