esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 FGI Challenges and NGN Solutions for Future Networks
### End-to-End QoS/QoE  
Quality of Service (QoS) and Quality of Experience (QoE) are two terms, which are related to the perception of the services by end-users. The two terms are closely related. The substantial difference is in the application, where human senses are clearly subjective.  
As the NGN has shown in the telco world, it has stressed the QoS from the beginning. There have been standardized and implemented mechanisms allowing the packet marking in the access to the transport networks and QoS management in the core network. However, the QoS management is done per class of application to minimize the quantity of the Class of Services (CoS) in the core network.
## 1 Introduction
### Our Findings  
Through experimentation, all algorithms perform better than anticipated in theory, particularly with respect to the performance ratios, measured as the value of solution divided by the computed LP objective lower bound. However, each may offer advantages over the others depending on the nature of the data.  
Figure 1: Minimum dominating set for \(Q_{3}\).  
LP rounding does well on sparse real-world graphs, consistent with theory, and normally outperforms the other algorithms. On a graph with 400,000+ vertices, LP rounding took less than 15 s of CPU time to generate a solution with performance ratio 1.011, while the greedy and hybrid algorithms generated solutions of performance ratio 1.12 in similar time. It is remarkable that the hybrid algorithm can solve the problem for very large sparse graphs, where the LP formulation becomes formidable in practice. For instance, it solved a real-world graph with 7.7 million+ vertices in 106 s of CPU time with a performance ratio of 2.0075. The LP solver crashed on this problem. We indicate that the simplex-based LP package used in our experiments performed very fast, although, in theory, simplex is not necessarily polynomially time bounded.  
For hypercubes and k-Queens graphs (which are not sparse) greedy outperforms the rest, consistent with theory, both in terms of speed and performance ratio. In particular, on the 12-dimensional hypercube, greedy finds a solution with performance ratio 1.7 in 0.01 s. On the other hand, the LP rounding and hybrid algorithms produce solutions with performance ratio 13 and 3.3 using 7.5 and 0.08 s of CPU time, respectively. It is notable that greedy gives optimal results in some cases where the domination number is known. Specifically, the greedy algorithm produces an optimal solution on hypercubes with dimensions \(d=2^{k}-1\) where \(k=1,2,3\), and 4. For synthetic graphs - generated k-trees (\(G\) is a \(k-\)tree if it has tree width \(k\) and the addition of any edge increases the tree width by one), and k-planar graphs (\(G\) is \(k-\)planar if it can be drawn in the plane with no edge crossed by more than \(k\) other edges) - the LP rounding is outperformed by the other two algorithms, and the hybrid algorithm outperforms greedy.  
This paper is organized as follows. In Section 2, we set our notations and summarize related materials for the greedy algorithm. The LP rounding and hybrid algorithms are explained in sections three and four, respectively. Section 5 (Environment, implementations and datasets), contains our materials and methods. Section 6 contains the results for synthetic graphs (k-planar graphs and k-trees). Sections 7 and 8 contain the results for hypercubes and k-Queens graphs, and real-world graphs, respectively. In Section 9, we exclusively describe how the hybrid algorithm can be applied to very large sparse graphs, where the LP formulation becomes formidable. We present our conclusions in Section 10.
## 3 Proposal for Smart Energy Management
### Software Description
#### 3.3.5 Human Machine Interface (HMI)  
In addition to collecting data or performing the decision-making, the interaction with users is a fundamental part of the system that makes possible the management of user roles, machines or energy providers. In this sense, an HMI has been developed that is supported by the above described modules. This high-level application is composed of different parts (Figure 9) that provide users with the required tools to manage and operate the system.  
The _configuration part_ is responsible for the general management of users, machinery and energy providers, as well as of the information related with energy contracts, consumption or other. Thanks to the _plants description part_, users can create tree diagrams of components including buildings, rooms, processes, machines, sensors and actuators, and can describe relations between them, such as assigning sensors and actuators to a machine. Later, these relations will be used in the energy design rules module. The _database part_ supports the viewing and managing of all collected data from the WSAN and the Internet, as well as data generated by all software parts and modules. The _energy design rules part_ makes possible for users the introduction all energetic preferences and specifications of machines and task scheduling. It is directly related with the specifications design module for system operation.  
Figure 8: Logical architecture of the system.  
The _optimization of the power consumption part_ is supported by all software modules (with data being constantly updated by the WSN) to carry out simulations and show graphical representation of their results. Finally, the _dashboard part_ shows numeric and graphical representations of actual consumption, costs and alarms, and enables users to monitor and check the proper operation of the plant.
# Wind-Induced Dynamic Performance Evaluation of Tall Buildings Considering Future Wind Climate
## 2 Basic Settings for the Simulations  
### Building Information and Prevailing Wind Conditions  
The case study building is a 49-story residential and office building having a prismatic floor plan up to the higher residential floor, with lateral frames serving as the main wind-force-resisting system. The overall dimensions of the building are about 17 m (depth) \(\times\) 56 m (width) \(\times\) 152 m (height). Figure 1 displays the finite element model (FEM) of the case study building. This model primarily includes beams, columns, and shear walls. The beams and columns were modeled using frame sections, while the wall elements were modeled using shell sections. In total, the model consists of 6715 rectangular beam members of various sizes, 210 column members (13 rectangular and 110 circular), and 5862 shear wall elements of thickness ranging from 400 mm to 1000 mm. The structural system incorporates 187 distinct member classifications, with each group defined by unique cross-sectional properties and material strength characteristics, as detailed in Table 1. The foundation is modeled with fixed support conditions. All structural elements are interconnected with rigid joints for design purposes [27]. Table 2 displays the structural dynamic characteristics of the case study building. The frequencies of the first three modes of the structure are 0.295 Hz, 0.310 Hz, and 0.360 Hz, respectively. For serviceability assessment under wind loading, a 2% damping ratio has been adopted for reinforced concrete buildings [27; 28; 29].  
The case study building is located in Suzhou, a coastal city in China, which is particularly vulnerable to the strong winds associated with typhoons, as tropical cyclones originating from the WNP Ocean frequently impact southeast China [30]. [PERSON] et al. [15] examined the effects of climate change on wind speed, using historical data alongside SSP245 and SSP585 scenario analyses. They performed full-track typhoon simulations to examine wind hazards in coastal areas under changing climatic conditions. They developed a custom random forest typhoon track model to generate future typhoon scenarios in the Northwest Pacific, using the sea surface temperature (SST) changes projected by CMIP6 under the SSP245 emission model. This research utilizes the estimated wind speed data for Suzhou by the same model, as shown in Table 3[15].
## 3 Results
### Comparison between Sensor Grid Maps (R1) and Corresponding Satellite Maps (R2)  
Using the MCARI2 index (Figure 6), greater correlation values were identified when the soil was not cultivated (correlation values close to 1) and when the corn was cultivated (correlations close to \(-1\)). The use of the NDWI index has the opposite behavior, with many values close to 1 in the corn cultivation period and \(-1\) in the period in which the soil was without the crop. These results highlight that the comparison between grid and satellite maps delivers high correlation values during corn cultivation, with a negative direction for  
Figure 4: Distribution of soil water content (%) and precipitation (mm, green line box) in the period of observation.  
MCARI2 and a positive one for NDWI. During the cultivation of triticale, the correlation values were very variable, and this did not define any trends.
## 3 Simulations
### Configuration for the Simulator  
Simulations of the complete system, including the cable, buoy-ballast system, ROV (which is speed-controlled in open-loop), and cable feeder, were carried out using the Vortex(r) simulator, version 2021a (2021.1.0.66). This software integrates the modeling of cables from a mass-spring model, taking into account their physical parameters for realistic rendering. The tether is a complex non-homogeneous object comprising several twisted strands and different coating layers. Some of its physical parameters are assumed to  
Figure 3: Snapshot of the numerical model obtained for the buoy-ballast system using Matlab-Simulink(tm) ([[https://www.youtube.com/watch?v=PtQigrKUr9E](https://www.youtube.com/watch?v=PtQigrKUr9E)]([https://www.youtube.com/watch?v=PtQigrKUr9E](https://www.youtube.com/watch?v=PtQigrKUr9E)) (accessed on 1 December 2023)).  
be constant and are directly provided by the manufacturers or easy to measure directly through experimental setups. Other parameters are more difficult to determine, requiring the use of abacuses. Table 1 summarizes the parameters used for simulation. The simulated tether mimics the Fathom Slim tether. Breaking strength, radius, and linear density were obtained from datasheets. Bending stiffness, torsional stiffness, axial stiffness, damping, and drag coefficients were determined through experiments [6].  
The simulated ROV is adapted from the BlueROV2-Heavy from BlueRoboticsTM, which has four vertical thrusters and four thrusters for the horizontal movements arranged in vectorial mode.
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 5 Results and Analysis
### Image Data  
To verify the storage and access performance of the MongoDB database for large binary files (namely, the remote sensing image data in this experiment), the relational database PostgreSQL is selected for comparison. PostgreSQL is recognized as the most powerful open source object-relational database management system; it supports abundant data types and provides rich interfaces. In this experiment, \"files_id\" and \"n\" are selected as the combined shard key to reduce the load pressure of a single shard in the MongoDB cluster, while PostgreSQL uses \"_id\" as the index, as there are only two fields, and the other is applied to store the binary data.
## 2 Enzymatic Cascade Reactions to Produce Natural and Modified 5\({}^{\prime}\)-NTPs
### Cascades Starting from Nucleosides and Using Nucleoside and NMP Kinases  
Our group has recently explored the biocatalytic production of a variety of natural and modified 5\({}^{\prime}\)-NTPs using pyrimidine or purine nucleosides as substrates in one-pot enzyme cascades involving NKs, NMPKs, and NDPKs [90] (Figure 3). By utilizing a minimal set of enzymes, we successfully synthesized 5\({}^{\prime}\)-NTPs with conversion rates ranging from 27% to over 99% (Figure 3) within 19 h in a modular approach (Figure 3C). Another research group applied a NK-NMPK cascade for ATP production from adenosine, achieving 94% conversion [91] (Figure 3B). However, this process required several days, with the addition of AMP and ADP as intermediates, and some degradation of the product was observed. Furthermore, 5F-CTP was synthesized from 5F-cytidine through a comparable kinase cascade, resulting in a 78% yield after 72 h [92] (Figure 3B).  
While the methods above all utilized NKs and NMPKs for the first and the second phosphorylation step, the third step varied (Figure 3A). Ack was used in ATP synthesis [91], PK in 5F-CTP synthesis [92], and NDPK in our modular synthesis approach [90]. This highlights the flexibility in enzyme choice for the third reaction step. Moreover, Ack or PK not only catalyze 5\({}^{\prime}\)-NTP synthesis but also enables the regeneration of ATP, which was shown to have a positive impact on final product yields.  
The 5\({}^{\prime}\)-NTP synthesis pathway utilizing NKs and NMPKs offers several advantages over alternative methods. It supports the production of a broad range of products, including \(N\)- and \(C\)-glycosidic nucleotides, as well as sugar- and base-modified variants (Figure 3). This approach requires only three enzymes and a cost-effective and easy-to-access nucleoside substrate, making it an efficient and versatile option for 5\({}^{\prime}\)-NTP synthesis. When a phosphate donor regeneration system is incorporated, high conversions are achieved [90; 91; 92].  
Figure 3: Enzymatic cascade reactions to produce 5\({}^{\prime}\)-NTPs starting from nucleosides. Nucleoside and NMP kinases have been applied as biocatalysts. (**A**) Reaction scheme. (**B**) Isolated yields described by [PERSON] et al. [91] and [PERSON] et al. [92]. (**C**) Formation of 5’-NTPs as observed by [PERSON] et al. [90]. ADK: adenosine kinase, UK: uridine kinase, dNK: deoxynucleoside kinase, AMPK: AMP kinase, NMPK: NMP kinase, GMPK: GMP kinase, UMP-CMPK: UMP-CMP kinase, AcK: acetate kinase, PK: pyruvate kinase, NDPK: NDP kinase.
# Photonic Crystal Cavity with a Thin Low-Index Layer for Silicon-Compatible Nanolight Source
## 2 Thin Low-Index Layered Photonic Crystal Cavity Structure  
We propose a photonic crystal (PhC) cavity structure consisting of square lattices of dielectric rods, in which a thin SiO\({}_{2}\) layer with thickness of t is sandwiched between the top and bottom silicon layers, as shown in Figure 1a,b. The total height and radius of each cylinder, and the lattice constant are denoted by H, r, and a, respectively. The central defect cylinder has radius of \(r_{d}\), and the nearest four cylinders are shifted into the center of the cavity with a distance of d. The refractive indices are 3.48 and 1.47 for Si and SiO\({}_{2}\), respectively, which are the indices for the telecommunication wavelength of 1550 nm. There is a total of 19 \(\times\) 19 cylinders. For this structure, we calculated the optical properties of the cavity mode and the horizontal SiO\({}_{2}\) slot slab waveguide mode, as well as the quality (Q) factor, wavelength, mode volumes, and mode profiles using a home-made three-dimensional (3D) finite-difference time-domain (FDTD) code. Perfectly matched layers are used as boundary conditions for all of the directions in the simulations of the cavity mode, and a periodic boundary condition considering the wavevector is used for the y direction, which is the propagation direction in the simulations of the waveguide mode. A grid of 5 nm is used. The proposed rod PhC cavity structure can be fabricated from silicon on insulator substrate using a standard CMOS fabrication process. In addition, by using glutinous substances (e.g., polydimethylsiloxane) [6; 17], the whole PhC cavity can be detached from the Si wafer and transferred into other photonic structures such as waveguides.
## 4 Methods
### Stopping and Convergence Criteria  
The current section aims to define convergence criteria that is suitable for stopping the search sufficiently close to the optimum without knowing its values. Let \(E(\mathbf{d}_{k})\) be the \(k\)-th function value accepted by the Metropolis criterion, and its relative change compared to the \((k-1)\)-th accepted step is  
\[RE_{E(\mathbf{d}_{k})}=\frac{|E(\mathbf{d}_{k})-E(\mathbf{d}_{k-1})|}{|E(\mathbf{d}_{k-1})|}. \tag{17}\]  
The step can be relatively small at any stage of the search process thanks to the stochastically generated new candidate, and thus the convergence condition can be met. The early stop can be avoided by averaging the absolute relative errors \(\overline{RE}\) for the steps accepted in the last \(m_{C}\) iteration. Let \(\mathbf{\varepsilon}=(\mathbf{\varepsilon}_{d},\mathbf{\varepsilon}_{E})\) be the vector of the convergence tolerances with \(\mathbf{\varepsilon}_{d}\) representing the design variables limit, while \(\mathbf{\varepsilon}_{E}\) represents the objective function limit. Thus, the condition for fulfilling the convergence criterion is  
\[cc_{1}=1,\ \text{if}\ \overline{RE}_{E(\mathbf{d}_{k})}<\mathbf{\varepsilon}_{E}. \tag{18}\]  
Let \(\mathbf{d}_{k}\) be the vector of the \(k\)-th accepted design variables, and its element relative change compared to the \((k-1)\)-th accepted step is  
\[RE_{d_{i,k}}=\frac{\left|d_{i,k}-d_{i,k-1}\right|}{\left|d_{i,k-1}\right|},\ i=1, \ldots,n \tag{19}\]  
Thus the condition for fulfilling the convergence criterion is  
\[cc_{2}=1,\ \text{if}\ \overline{RE}_{d_{i,k}}<\mathbf{\varepsilon}_{d,i}\ \text{for every}\ i=1,\ldots,n. \tag{20}\]In the last case, when the maximum iteration number \(m_{max}\) is reached, the process will be terminated by the stopping criterion  
\[sc=1,\text{ if }m\geq m_{max}=3000\cdot n. \tag{21}\]  
Let \(C(cc_{1},cc_{2},sc)\) denote the stopping/convergence criterion, which is activated if any of the previously mentioned conditions are met:  
\[C(cc_{1},cc_{2},sc)=1,\text{ if }cc_{1}=1\mid\mid cc_{2}=1\mid\mid sc=1. \tag{22}\]
## 5 InSAR's Role in Landslide Susceptibility Mapping
### Factors and Their Utilization Frequency

Table 1: _Cont._  
Figure 9: Utilization of evaluation factors in LSM research within the selected literature collection. (**a**) Proportions of different categories of evaluation factors. (**b**) Application of different evaluation factors (only main evaluation factors that have a percentage more significant than 1% are shown). OT: other topographic factors; OGG: other geological and geomorphological factors; OHY: other hydrological factors; OHU: other human factors; OS: other soil factors; OV: other vegetation factors. (Software: Origin 2022). Notably, some evaluation factors used in prior studies do not have predisposing or triggering attributes, such as the InSAR-derived deformation velocity that we will focus on in this review, and cannot be classified into the other six categories, thus being labeled as 'others'.
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
## 7 Experimental Evaluation and Results
Achieved performance is independent of the number of adopted bits for the input sequence quantization that has been tested for values ranging from 1 to 52. Figure 18: Spectra comparison between Matlab double precision FFT and the proposed approach.
# Research on Influence of Switching Angle on the Vibration of Switched Reluctance Motor
## 2 Principle of Switching Angle control Method
At this time, the phase current is:  
\[i(\theta)=0 \tag{11}\]  
In the interval \(\theta_{on}\leq\theta<\theta_{2}\), the voltage at both ends of the winding is \(U_{s}\), and the inductance is the minimum value \(L_{\text{min}}\). And after the winding is excited, both the current and the flux linkage start to rise. Hence, the phase current is obtained by Formula (9):  
\[i(\theta)=\frac{U_{s}}{\omega}\cdot\frac{\theta-\theta_{on}}{L_{\text{min}}+ \frac{L_{\text{max}}-L_{\text{min}}}{\beta_{r}}\left(\theta-\theta_{2}\right)} \tag{12}\]  
In the interval \(\theta_{off}\leq\theta<\theta_{3}\), the voltage across the winding is -\(U_{s}\), and the phase current decreases rapidly. Substitute inductance Equation (7) into Equation (9) to get:  
\[i(\theta)=\frac{U_{s}}{\omega}\cdot\frac{2\theta_{off}-\theta-\theta_{on}}{L_{ \text{min}}+\frac{L_{\text{max}}-L_{\text{min}}}{\beta_{r}}\left(\theta- \theta_{2}\right)} \tag{13}\]  
In the interval \(\theta_{3}\leq\theta<2\theta_{off}-\theta_{on}\), the inductance maintains the maximum value \(L_{\text{max}}\), and there are:  
\[i(\theta)=\frac{U_{s}}{\omega}\cdot\frac{2\theta_{off}-\theta-\theta_{on}}{L _{\text{max}}} \tag{14}\]  
At this time, the current decreases linearly, and the slope is \(-U_{s}/(\omega L_{\text{max}})=\text{const}\), which is a constant. In the interval of \(2\theta_{off}-\theta_{on}\leq\theta<\theta_{5}\), the flux linkage is 0 and the current is also 0:  
\[i(\theta)=0 \tag{15}\]  
Figure 3: Current curve of one electric period. To sum up, in one cycle, the expression of current \(i(\theta)\) is:  
\[i(\theta)=\left\{ \begin{array}{ll}0,&\theta_{1}\leq\theta<\theta_{on}\text{ or }2\theta_{off}\ -\ \theta_{on}\leq\theta<\theta_{5}\\ \frac{U_{s}}{\omega},&\frac{\theta-\theta_{on}}{L_{\min}},&\theta_{on}\leq \theta<\theta_{2}\\ \frac{U_{s}}{\omega}\cdot\frac{1}{L_{\min}+\frac{t_{\max}-t_{\min}}{\beta_{f }}-\frac{t_{\min}}{\beta_{f}}(\theta-\theta_{e})},&\theta_{2}\leq\theta< \theta_{off}\\ \frac{U_{s}}{\omega}\cdot\frac{2\theta_{off}-\theta_{on}-\theta}{L_{\min}+ \frac{t_{\max}-t_{\min}}{\beta_{f}}(\theta-\theta_{e})},&\theta_{off}\leq \theta<\theta_{3}\\ \frac{U_{s}}{\omega}\cdot\frac{2\theta_{off}-\theta_{on}-\theta}{L_{\max}},& \theta_{3}\leq\theta<2\theta_{off}\ -\ \theta_{on}\end{array} \right. \tag{16}\]
## 3 Proposed Method
### Automatic Signal Transformation  
In this paper, automatic signal transformation involves two signal processing methods, one is the real-value equivalent representation of the raw complex baseband signal, and the other is based on the original signal amplitude and phase extraction representation. These two transformations are based on signal modulation characteristics. We apply these two transformations to the raw baseband signal \(r_{raw}\in\mathbb{C}^{N}\) for better extraction of features. The transformations are as follows:  
Transformation (IQ vector): the \(k\)-th point raw signal data \(r_{raw}\in\mathbb{C}^{N}\) can be translated to the \(k\)-th feature vector \(x_{k}\in\mathbb{R}^{N}\). Then, the in-phase and quadrature component parts of the complex baseband signal \(r_{raw}\in\mathbb{C}^{N}\) are represented as two real-valued matrices \(x_{I}\), \(x_{Q}\), that is:  
\[x_{k}{}^{IQ}=\left[\begin{array}{c}x_{I}{}^{T}\\ x_{Q}{}^{T}\end{array}\right]^{T} \tag{2}\]  
For \(x_{k}{}^{IQ}\in\mathbb{R}^{2\times N}\), mathematically, the transformation can be written as  
\[f_{Tan1}: \mathbb{C}^{N}\rightarrow\mathbb{R}^{N\times 2} \tag{3}\] \[r_{raw}\ \rightarrow\ x_{k}{}^{IQ}\]  
Figure 2: The framework of the proposed method with SSTM for directly using raw in-phase and quadrature (IQ) samples.  
Transformation (A / / / / / vector): the k point raw signal data \(r_{raw}\) is mapped into the \(k\)-th feature vector \(x_{k}{}^{IQ}\). We use the phase vector \(x_{\text{A}}\) and magnitude vector \(x_{\text{\tiny\yng(1)}}\) to represents \(x_{k}{}^{IQ}\), that is:  
\[x_{k}{}^{\text{A/\yng(1)}}=\left[\begin{array}{c}x_{\text{A}}{}^{\text{T}}\\ x_{\text{\tiny\yng(1)}}\end{array}\right]^{\text{T}} \tag{4}\]  
where, the \(x_{\text{A}}\) and \(x_{\text{\tiny\yng(1)}}\) is calculated with  
\[x_{\text{\tiny\yng(1)}}(n) =(x_{I}(n)) \tag{5}\] \[x_{\text{A}}(n) =\text{arctan2}\Big{(}\frac{x_{I}(n)}{x_{Q}(n)}\Big{)},\,n=0,1, \ldots,N-1\]  
In this transformation, we use the atan2 function to obtain continuous phase changes. The ordinary atan function causes the sign of the phase to change, losing the phase information of the second and third quadrants. Then, a real-valued convolution layer is used to augment the feature map. For \(x_{k}{}^{\text{A/\yng(1)}}\), we use M convolution kernels with \(2\times 8\) receptive fields to convolve and get the transformed output as  
\[f_{\text{\tiny\yng(1)}}: \mathbb{R}^{N\times 2}\rightarrow\mathbb{R}^{N\times 2} \rightarrow\mathbb{R}^{N\times M\times 2} \tag{6}\] \[x_{k}{}^{IQ}\rightarrow\ x_{k}{}^{\text{A/\yng(1)}}\rightarrow\ x _{k}{}^{\text{\tiny\yng(1)}}\]