esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Method to Test the Stability of High Bedding Rock Slope Subjected to Atomized Rain
## 3 Theoretical Research on the Comprehensive Method
### Failure Definition in the Comprehensive Method  
In the comprehensive method, a key problem is to determine the failure criterion. Formula (10) shows that \(K_{sc}\) depends on \(K_{ss}\) and \(K_{sp}\). For the strength reduction range of structural planes, [PERSON] [20] conducted triaxial compression tests and the water-rock coupling triaxial compression test in MTS815 rock mechanics testing system, and the weakening rates of weak rock and structural planes were obtained. According to substantial engineering experience and test data [20; 21; 22], the strength reduction range of the structural planes is normally 15-30%, which means the strength reserve factor \(K_{ss}\) is approximately 1.15-1.3. For the uplift stage, the overload factor \(K_{sp}\) can be obtain by formula 8. Under the co-action of the increasing sliding force and inducing mechanical parameters of the structural planes, the slope failure modes are uncertain to some extent. Therefore, it is a challenge to accurately assess the slope stability using any routine analysis method.  
With respect to the definition of failure, the mutation of the characteristic point displacement combined with the continuum of cracks can be regarded as a reliable definition of failure, and it can be widely used to analyze slope stability problems [23; 24].  
The typical relationship curve of slope displacement vs. \(K_{sc}\) for the comprehensive method is shown in Figure 3. From this figure, two feature points of slope deformation can be obtained. One is the initial crack feature point, and the other is the penetrating crack feature point. Thus, the basis for judging the safety factor of the comprehensive method is defined as follows: the \(K_{c}\) (\(f^{\prime}\)\({}_{1}\), \(c^{\prime}\)\({}_{1}\), and \(\phi_{1}\)) corresponding to the initial crack feature point is the initial safety factor \(K_{\text{Initial}}\), and the \(K_{c}\) (\(f^{\prime}\)\({}_{2}\)\(c^{\prime}\)\({}_{2}\), and \(\phi\)2) corresponding to the penetrating feature point is the ultimate safety factor \(K_{\text{Ultimate}}\).
## 2 Nonlinear-Trajectory SAR Deception Jamming Algorithm Based on HDE
### Nonlinear-Trajectory SAR System Model
The slant range \(R_{P}\) can be separated as follows [29; 33]:  
\[R_{P}^{\prime} =R_{P}+\delta R\big{(}x^{\prime},x_{P},r_{P}\big{)} \tag{6}\] \[=r_{P}+\Delta R\big{(}x^{\prime}-x_{P},r_{P}\big{)}+\delta r\big{(} x^{\prime}\big{)}+\psi\big{(}x^{\prime},r_{P}\big{)}+\varphi\big{(}x^{\prime},x_{P},r_{P}\big{)},\]  
where \(\delta r(x^{\prime})\) is the space-invariant trajectory deviation of the scene center, represents the projection of the trajectory deviation on the scene center, and depends only on the SAR radar azimuth coordinate \(x^{\prime}\). \(\psi(x^{\prime},r_{P})\) is the range space-variant trajectory deviation, represents the range space-variant behavior of the line-of-sight direction trajectory deviation, and depends on the SAR radar azimuth coordinate \(x^{\prime}\) and the local look angles of points \((x_{P},r_{P})\):  
Figure 1:0  
\(\varphi(x^{\prime},x_{P},r_{P})\) is the azimuth space-variant trajectory deviation, which depends on the SAR radar azimuth coordinate \(x^{\prime}\), the range and azimuth coordinate of point \((x_{P},r_{P})\):  
Figure 1:1  
The three trajectory deviation terms above can be calculated by Equation (5).
## 3 Results and Discussion
### Most Relevant Countries  
The 20 most contributing countries by corresponding authors in this research field are listed in Table 7. Of the top 20 countries listed in Table 7, three were from the North American continent, another three were from South America, five were from Asia, eight were from Europe, and the last continent was Australia. This shows that European countries were more relevant in the field, although with lesser publications as compared to the USA, which demonstrated a high capacity for both single country publications (SCPs) and multiple country publications (MCPs), which were 122 and 38, respectively. Whereas, all the European countries had 65 articles, 40 SCP, and 25 MCP, which is somewhat below the contributions from the USA only as a country, which indicate their giant role in the field is understudied. Contributions from other countries, such as China (Articles: 20; SCP: 11; MCP: 9), Germany (Articles: 19; SCP: 12; MCP: 7), Italy (Articles: 16; SCP: 10; MCP: 6), and Brazil (Articles: 15; SCP: 10; MCP: 5), are also substantial. The ratio of MCP to the number of articles termed the \"MCP ratio\" was high for Australia (0.667), Colombia (0.5), Kuwait (0.5), and Mexico (0.5) compared to others. However, Canada, Denmark, and Poland had no MCP and it is noteworthy that the results of the contributing countries represented in this section were based on the corresponding authors' countries analysis, and in a situation where authors have multiple affiliations with two or more countries, only the primary affiliation is represented and this is the reason why countries, such as Canada, Denmark, and Poland, with no MCP were seen in the countries collaboration network showing connections with some other countries in Section 3.8 below.
## 4 \(V_{oc}\) Loss Due to Rear Surface Metallization  
### \(V_{oc}\) Losses Calculation  
The reverse saturation current density (\(J_{0e}\)) of the emitter is composed of recombination both at the passivated surface (\(J_{0 es}\)) and at the point contact region (\(J_{0e-pc}\)). Therefore, \(J_{0e}=J_{0 es}(1-f)+J_{0e-pc}\) where \(J_{0e}=J_{100}f(1+\dfrac{4\ln 2\times W}{\pi r}+\dfrac{W^{2}}{8r^{2}})\) was introduced when the passivated emitter and rear cell (PERC ) solar cell was invented [14], \(J_{100}\) is the saturation current density with 100% rear contact coverage, \(f\) is the contact ratio 0.56%, \(r\) is the radius of the contact points and \(W\) is the cell thickness. The SRV of the point contact region is \(10^{7}\) cm/s [15]. Table 6 summarizes the calculated \(J_{0}\) and \(V_{oc}\) values.
# High-Performance Computing and Artificial Intelligence for Geosciences
## 1 Introduction  
Geoscience, as an interdisciplinary field, is dedicated to revealing the operational mechanisms and evolutionary patterns of the Earth system. Geoscience is very important to enhancing our understanding of the Earth, including its life, resources, and environment. In recent years, the rapid development of high-performance computing and artificial intelligence technologies has presented unparalleled opportunities and challenges to geoscience research. This book aims to explore the significance of high-performance computing and artificial intelligence research in advancing our understanding of geosciences.  
High-performance computing provides powerful computational capabilities for geoscience research. The Earth's system encompasses vast amounts of data and intricate interrelationships, including atmospheric dynamics, seismic activities, and ocean circulation, among others. Traditional data processing and modeling methods often struggle to meet the demands of these complex systems. However, the development of high-performance computing technologies has enabled scientists to leverage tools such as parallel computing and supercomputers to process large-scale datasets and conduct more precise simulations and predictions. Through high-performance computing, scientists can enhance their understanding of the dynamical processes within the Earth system, resulting in the improved accuracy and timeliness of weather forecasting, climate simulations, earthquake early warning systems, and more.  
Artificial intelligence technology has introduced novel ideas and methodologies into geoscience research. The investigation of geosciences often necessitates the extraction of valuable information from extensive datasets to show the inherent patterns within the Earth system. Machine learning and deep learning algorithms in artificial intelligence are able to process intricate data and discern patterns. They autonomously learn and extract features from massive datasets, aiding scientists in uncovering hidden patterns and trends embedded within the data. For instance, in climate change research, artificial intelligence can analyze climate models and observational data to offer more precise predictions and evaluations. Moreover, artificial intelligence has practical applications in geological exploration and resource surveys, thereby enhancing the efficiency and accuracy of resource exploration.
## 2 Optical Cone and Pendulum Scanning Imaging Method
Then design the ground cover width \(2r_{N}\) according to the triangular relationship as  
\[\frac{\sin\bigl{(}\frac{r_{N}}{R}\bigr{)}}{SB_{N}}=\frac{\sin\Bigl{(}\gamma_{N}+ \frac{FOV}{2}\Bigr{)}}{R} \tag{3}\]  
where the angle between the optical axis of the camera and the nadir axis is \(\gamma_{N}=\alpha+\beta_{max}\), \(R\) is the radius of the Earth, and \(SB_{N}\) is expressed by  
\[SB_{N}=(R+H)\cos(\gamma)-\sqrt{R^{2}-\left[(R+H)\sin(\gamma)\right]^{2}} \tag{4}\]  
At the same time, \(r_{1}\) can also be calculated by the law of sine as  
\[\frac{\sin\bigl{(}\frac{r_{1}}{R}\bigr{)}}{SA_{1}}=\frac{\sin(\gamma_{1})}{R} \tag{5}\]  
It can be further rewritten by substituting Equation (4) into Equation (3) as  
\[r_{N}=Rarcsin\left(\frac{\left\{(R+H)\ cos\Bigl{(}\gamma_{N}+\frac{FOV}{2} \Bigr{)}-\sqrt{R^{2}-\left[(R+H)\ sin\Bigl{(}\gamma_{N}+\frac{FOV}{2}\Bigr{)} \right]^{2}}\right\}*sin\Bigl{(}\gamma_{N}+\frac{FOV}{2}\Bigr{)}}{R}\right) \tag{6}\]  
Similarly, Equation (5) can also be rewritten as  
\[r_{1}=Rarcsin\left(\frac{\left\{(R+H)\cos\Bigl{(}\gamma_{1}-\frac{FOV}{2} \Bigr{)}-\sqrt{R^{2}-\left[(R+H)\sin\Bigl{(}\gamma_{1}-\frac{FOV}{2}\Bigr{)} \right]^{2}}\right\}*\sin\Bigl{(}\gamma_{1}-\frac{FOV}{2}\Bigr{)}}{R}\right) \tag{7}\]  
Geometrically, \(r_{N}\) is the radius from the satellite sub-satellite point to the outer boundary of the image, \(r_{1}\) is the radius from the satellite sub-satellite point to the inner boundary of the image, and the ring formed by the image is filled by the single-camera swing imaging. In order to make the ground coverage complete and leak-free in the OCPSI mode, it is necessary to ensure that the three parts overlap each other. The three parts are the overlap between two frames, the overlap between two pendulum scanning cycles, and the overlap between two cone scanning cycles. The three parameters are related to each other with the sub-satellite velocity (\(V_{s}\)), the satellite cone scanning velocity (\(\omega_{s}\)) and the camera pendulum scanning velocity (\(\omega_{c}\)), as shown in Figure 7. The satellite sub-satellite velocity (\(V_{s}\)) can be determined from the orbital altitude. The satellite sub-satellite velocity (\(V_{s}\)) can be combined with the overlap between the two cone sweep periods to obtain the satellite cone scanning velocity (\(\omega_{s}\)). Then, according to the overlap between the two pendulum scanning periods, the sub-satellite speed (\(V_{s}\)) and the satellite cone scanning velocity (\(\omega_{s}\)), the camera scanning velocity (\(\omega_{c}\)) can be calculated. The corresponding flow chart is shown in Figure 8. In the next section, we will design reasonable parameters for the OCPSI mode according to this process.
# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 5 Framework Assessment for Deep Learning  
The framework presented in this work consists of a graph with a single flow direction from input to output that supports the types of problems discussed in this paper. The problems of cell outage detection, intrusion detection, and mobility load balancing typically require the input to be provided as KPIs, and the method determines whether there is cell outage or intrusion happening or whether load balancing activation is required.  
The current data source used to obtain the KPIs is cell level data which consists of the measurement time, the unique ID of the user, the location co-ordinates of the user in the 2-dimensional plane, the reference signal received power, and the reference signal received quality. With 5G, other sources of data will become available that will be suitable for deep learning. The data/KPI module could obtain data from different types of sources, for example, subscriber level data, such as the call drop ratio, session drop rate, and throughput, and cell level data, such as thermal noise power, channel power, channel quality indicator (CQI), and core network level and App-based data.  
The pre-processing step transforms data into useful attributes. In the current framework, the KPIs are checked for missing values and proper imputation. The KPIs are also mean normalized or scaled between standard 0/1 values. This is usually what is required for a deep learning setup. The KPIs in the cell outage detection framework are also feature engineered. However, with 5G, the possibility of high dimensional data is more likely, which the current framework supports, since it does not require any low dimensional techniques, such as Principle Component Analysis (PCA) or Singular Vector Decomposition (SVD), while other frameworks usually require such methods. Autoencoder-type models or feed-forward neural networks become more powerful tools with the increase in dimensionality, especially in case of deep learning techniques.  
The other nearest-neighbor-based approaches require a score to be computed, which is often a computationally expensive task. The autoencoder-based framework does not need to compute an explicit score; rather, the reconstruction error that is minimized for training the model acts as the score itself. In this way, computational time and resources are minimized by the proposed framework. The test framework also consists of a forward flow graph. The test phase of the proposed framework is fast, since it does not need to train or compute any scores. The test KPIs immediately obtain the score in terms of the reconstruction error. The threshold has to be selected manually, and the test KPIs are then classified as normal or abnormal user/cells, intrusion or non-intrusive cases, and cell loaded or cell non-loaded scenarios.
## 1 Introduction  
Enumerating a large number of sets and finding useful information from them have recently attracted the attention of many researchers. The data structure called a zero-suppressed binary decision diagram [1], ZDD for short, is known to be useful for compactly representing collections of sets and for efficiently manipulating them. ZDDs have been applied to various problems. In the analysis of transaction databases, [PERSON] and [PERSON] [2,3] invented ZDD-based techniques for frequent itemset mining. [PERSON] [4] introduced a ZDD-based approach to solve many graph and set optimization problems. [PERSON] and [PERSON] [5] developed a new paradigm of the exact computation for network reliability by means of binary decision diagrams (see [6,7]), BDDs for short. Recently, for multi-terminal binary decision diagrams, which are a well accepted technique for the state graph based quantitative analysis of large and complex systems, a zero-suppressed version has been studied by [PERSON] _et al_. [8]. Roughly speaking, an idea common to these is to compress a large number of sets into a ZDD (BDD) and manipulate them without decompression.  
In this paper, we study the following basic problem of ZDDs: Given a ZDD representing a set family, extract a hidden structure, called a co-occurrence relation, over the ground set. This computation can be done in time complexity that is related not to the number of sets, but to some feature values of the ZDD representing the sets. Thus it is effective especially when a large number of sets are compressed into a small ZDD. Since we do not put any domain-specific assumption on the sets represented by a ZDD, our algorithm is widely applicable to ZDDs obtained from real-life data.  
The co-occurrence relation is defined as follows: given a set \(S\) and a collection \(\mathcal{C}\) of subsets of \(S\), two elements \(a,b\in S\) co-occur with each other for \(\mathcal{C}\) if it holds that for all \(T\in\mathcal{C}\), \(a\in T\) if and only if \(b\in T\). In a series of work for finding various useful information from databases [9; 10; 11], the co-occurrence relation was introduced, although an efficient extraction algorithm is not known. Clearly the co-occurrence relation is an equivalence relation and it induces the partition consisting of equivalence classes, called a co-occurrence partition. Since ZDDs represent collections of sets, co-occurrence relations and partitions are similarly defined for ZDDs. Since elements in the same block of a co-occurrence partition have the same behavior, when we want to find useful information from a ZDD, we need not distinguish between them and the ZDD can be compressed further.  
This paper is organized as follows. In Section 2 we introduce some basic notions on ZDDs. We present algorithms in Section 3 and provide examples in Section 4. Concluding remarks are given in Section 5.
# Matching the Ideal Pruning Method with Knowledge Distillation for Optimal Compression
## 3 Materials and Methods
### Channel Pruning  
Channel L1 pruning stands as a transformative technique within the realm of neural network compression, enabling the refinement of models through structured sparsity while preserving architectural integrity. This method, rooted in the concept of the L1 norm, empowers practitioners to create leaner and more resource-efficient networks. At its core, channel L1 pruning operates by selectively pruning entire channels or feature maps within the convolutional layers of a neural network. This approach relies on the L1 norm of the weights associated with each channel, allowing for the identification of less influential channels that can be safely removed. Channel L1 pruning hinges on the notion that certain channels contribute minimally to the network's overall performance. By quantifying the importance of each channel through its L1 norm, the technique sorts and prunes channels with lower L1 norm values [23]. This meticulous approach maintains a delicate balance between structural preservation and efficiency gain. Mathematically, the L1 norm of the weights within a channel is determined by summing the absolute values of its constituent weights for a channel, as follows:  
\[\text{L1}_{\text{norm}}(\text{C})=|\text{W}_{1}|+|\text{W}_{2}|+\ldots+|\text{W} _{\text{n}}| \tag{1}\]  
During channel L1 pruning, channels exhibiting the smallest L1 norm values are pruned. Channel L1 pruning is a process that selectively removes entire feature maps from a neural network, reducing its complexity while retaining important information. This pruning technique offers several benefits that reshape the design of neural networks:  
* Architectural Integrity: By focusing on entire channels, channel L1 pruning preserves the underlying architecture of the network. This ensures that critical information flow patterns, which contribute to the model's effectiveness, are maintained.
* Resource Efficiency: Removing less influential channels results in a leaner model, leading to improved resource efficiency. This reduces the computational resources and memory required during both training and inference, making the model more efficient.
* Regularization and Generalization: Channel L1 pruning encourages the network to rely on essential features while diminishing reliance on redundant or less informative channels. This regularization process helps improve the generalization capabilities of the model and reduces overfitting, resulting in better performance on unseen data.
# Aluminum-Based Plasmonic Photodetector for Sensing Applications
## 3 Simulation of the Proposed Structure and Parameter Optimization
### Parameter Estimation from Dispersion Curves  
The grating period can be estimated from the phase-matching conditions for SPP excitation for a given angle of incidence and wavelength. If the simulated PPPD is intended for use in an optical smoke detector, then due to the design of this detector, the resonant angle of incidence of the laser beam of the most available laser on the market with a wavelength of 808 nm on the grating (the angle corresponding to the excitation of the SPP) should be equal to \(\theta_{r}=15^{\circ}\). Taking into account the dispersion relations for SPP plasmons, the phase-matching condition for p-polarized laser radiation, the incidence plane of which on the grating is perpendicular to its grooves, can be written as follows:  
\[Re\left[k_{SPP}\right]=k_{inc}+G_{m}, \tag{1}\]  
where \(k_{SPP}\) is the SPP wavevector, \(k_{inc}=\left(\frac{\sqrt{\varepsilon_{d}}2\pi}{\lambda}\right)\sin\theta\) is the surface component of the incident light wavevector, \(\varepsilon_{d}\) is the dielectric constant of the environment, \(\theta\) is the incidence angle, \(G_{m}\) is the grating vector, \(G_{m}=2\pi m/a\), and m is an integer (\(m\
eq 0\)). Using the approximation of a flat interface between the metal and the dielectric (valid for gratings with shallow relief) for \(k_{SPP}\): \(k_{SPP}=\left(\frac{2\pi}{\lambda}\right)\sqrt{\varepsilon_{m}\varepsilon_{d} /(\varepsilon_{m}+\varepsilon_{d})}\), where \(\varepsilon_{m}\) is the dielectric constant of the metal and \(\varepsilon_{d}=1\), Expression (1) can be rewritten as follows:  
\[\pm\frac{|m|}{a}=\frac{1}{\lambda}Re\sqrt{\frac{\varepsilon_{m}}{\varepsilon_ {m}+1}}\pm\frac{1}{\lambda}sin|\theta|, \tag{2}\]  
Figure 4: A schematic sketch of the structure, where a is the grating period, w\({}_{1}\) and w\({}_{2}\) are the base lengths of the trapezoidal profile, d is the thickness of the intermediate metal layer, and D is the depth of the grating grooves.  
The expression for the grating period is finally obtained as  
\[a=\frac{|m|\lambda}{Re\sqrt{\frac{\varepsilon_{m}}{\varepsilon_{m}+1}}\pm\sin| \theta|}, \tag{3}\]  
For the first-order SPP resonance \(|\,m\,|=1\), Expression (3) provides the values \(a-\approx 1.1\)\(\upmu\)m and \(a+\approx 0.63\)\(\upmu\)m. For the grating with \(a=1.1\)\(\upmu\)m, there is an additional (second) maximum on the angle and spectral dependences of photocurrent, which can be significantly wider than the first one and cause occlusion and blurring of the main maximum. Therefore, it is more convenient to have only one SPP resonance for registering external influences without any interference from the second one. Thus, we chose the grating period \(a=0.63\)\(\upmu\)m.  
The sensitivity of the PPPD is primarily affected by the number of photons absorbed in the semiconductor (taking into account the electromagnetic field intensity distribution). To determine this distribution, we carried out a rigorous electrodynamics simulation of the plasmonic structure under consideration.