esa-sceva/satcom-chunk-collection huggingface dataset:
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Renewable Energy Sources
#### 4.6.4 E-Harbours  
Integration of renewable energy sources has also been the topic of the project \"E-harbours\", which aims at making energy systems more adaptable to renewables. It has focused especially on harbors and their wider industrial area. The goal has been to find flexibility in terms of energy in this environment,  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Project** & **Aim** & **Characteristics** & **SM Technologies Used** & **Other Technologies** \\ \hline NINES & Improve and stabilize the electricity grid & HV network is monitored & \\ \hline ELECTRA & Network management & Control schemes for smart grids that control frequency and voltage & \\ \hline Smart Grid Vendee & Network management, integration of RES & 6 wind farms, 30 PV sites, 350 EV, 6 primary substations, hundreds of secondary substations, 500 smart meters & G3-PLC technology (first link) \\ \hline Model City Manheim & Grid management, absorb energy from RES & Customer side: remote operation of electrical devices is managed through an intelligent controller & IP for remote command of the controller, BB-PLC for controller communication with devices \\ \hline Smart Grid Hyllie & Optimising energy utilisation & Integration of RES via monitoring of power output & \\ \hline Nice Grid & Integration of RES, Grid management & Utilisation of photovoltaic arrays, storage units, smart meters, smart components & G3-PLC (first link) \\ \hline e-GOTHAM & Grid Management, Integration of RES & Dividing the grid into microgrids, algorithms for coordinating central and local controllers & Communication between Communication between Communication between Contlers \\ \hline Arrowhead & Grid management & Collaborative Automation for production, smart buildings, electromobility, end-user services, virtual market of energy & WiFi, Ethernet (Lab environment) \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._maximize the integration of renewable energy sources and exploit electric vehicles or other units for energy storage. Grid stability and exceed grid capacity are also issues, that have been examined [115].
# Visible Light Communication: An Investigation of LED Non-Linearity Effects on VLC Utilising C-OFDM
## 2 Test Setup
### Walsh-Hadamard Transform  
The WHT decomposes a signal into a series of orthogonal, rectangular waveforms known as Walsh functions using a real, nonsinusoidal transformation. Rearranging the rows of a Hadamard matrix into a sequence order yields the Walsh-Hadamard matrix. By rearranging the rows of an order eight Hadamard matrix, an order eight Walsh matrix can be created as follows:  
Figure 3: Hermitian symmetry (HS).  
\[\text{H}_{8\times 8}=\left[\begin{array}{ccccccccc}1&1&1&1&1&1&1&1\\ 1&-1&1&-1&1&-1&1&-1\\ 1&1&-1&-1&1&1&-1&-1\\ 1&-1&-1&1&1&-1&-1&1\\ 1&1&1&1&-1&-1&-1&-1\\ 1&-1&1&-1&-1&1&-1&1\\ 1&1&-1&-1&-1&-1&1&1\\ 1&-1&-1&1&-1&1&1&-1\\ 1&-1&1&-1&1&1&-1\\ 1&-1&1&-1&1&-1&1&-1\\ 1&-1&1&-1&1&-1&1&-1\\ \end{array}\right]\begin{array}{c}\text{S}_{\text{ch}}(0)\\ \text{S}_{\text{ch}}(7)\\ \text{S}_{\text{ch}}(3)\\ \text{S}_{\text{ch}}(4)\\ \text{S}_{\text{ch}}(1)\\ \text{S}_{\text{ch}}(6)\\ \text{S}_{\text{ch}}(2)\\ \text{S}_{\text{ch}}(5)\\ \end{array}\]  
\[\text{W}_{8\times 8}=\left[\begin{array}{ccccccccc}1&1&1&1&1&1&1&1\\ 1&1&1&1&-1&-1&-1&-1\\ 1&1&-1&-1&-1&-1&1\\ 1&1&-1&-1&1&1&-1&-1\\ 1&-1&-1&1&-1&-1&1\\ 1&-1&-1&1&-1&1&-1\\ 1&-1&1&-1&-1&1&-1\\ 1&-1&1&-1&1&-1&1&-1\\ 1&-1&1&-1&1&-1&1&-1\\ \end{array}\right]\begin{array}{c}\text{S}_{\text{ch}}(0)\\ \text{S}_{\text{ch}}(1)\\ \text{S}_{\text{ch}}(2)\\ \text{S}_{\text{ch}}(3)\\ \text{S}_{\text{ch}}(4)\\ \text{S}_{\text{ch}}(5)\\ \text{S}_{\text{ch}}(6)\\ \text{S}_{\text{ch}}(7)\\ \end{array}\right]\]  
where H is a Hadamard matrix and W is a Walsh matrix and S\({}_{\text{ch}}(7)\) illustrates that the sign was changed seven times in this row. The first element in the last row was 1 and then \(-1\), which was the first change; then, \(-1\) changed to 1 as the second change in the sign; then, 1 changed to \(-1\), which was the third change; this changed seven times. Additionally, S\({}_{\text{ch}}(0)\) designated the signs of the matrix element and remained identical.  
Moreover, the WHT matrix's elements could be written as:  
\[W_{x,u}=(-1)^{\frac{n-1}{l}}x_{i}u_{i}. \tag{2}\]  
where \(n=\log_{2}(N)\), where \(x\) and \(u\) are the rows and columns positions in the WHT matrix and \(x_{i}*u_{i}\) is the \(i\)th product operation of bit-by-bit for the binary number which represents the integer numbers \(x\), \(u\) and \(n\); \(-1\) is the number of binary digits in each index. To clarify Equation (2), let us assume that W(6,5) where \(x=6\) and \(u=5\) where \(x\) is the sixth row and \(u\) is the 5 th column in W\({}_{8\times 8}\), are presented in binary as 110 and 101, respectively. Appling these numbers in Equation (2) would result in:  
\[W_{x,u}=(-1)^{g(x_{i}u_{i})}\]  
\[g(6,5)=\sum_{j=~{}o}^{3-1}6_{j}*~{}5_{j}\]  
\[g(6,5)=b_{0}(6)*b_{0}(5)+b_{1}(6)*b_{1}(5)+b_{2}(6)*b_{2}(5)\]  
\[g(6,5)=0*1+1*0+1*1=1\]  
\[\text{W}(6,5)=(-1)^{1}=-1\]
## 2 Measurement of Damping Parameters  
### Parameters Describing the Damping  
Damping reflects the energy dissipation characteristics of vibration systems in the vibration process, where its essence is the conversion of the vibration system energy, namely from mechanical energy to thermal energy or other forms of energy conversion process [17]. There are many parameters used to describe damping, such as phase difference angle \(\varphi\), logarithmic attenuation coefficient \(\delta\), quality factor \(Q\), loss factor \(\eta\), damping ratio \(\zeta\), etc. In the case of small damping, the relationship between the various coefficients is as [18] follows:  
\[\varphi=\frac{\delta}{\pi}=Q^{-1}=\eta=2\zeta \tag{1}\]  
In dynamic analysis and vibration performance evaluation, the damping ratio \(\zeta\) or loss factor \(\eta\) is usually used to describe the damping of structural systems. Because of its convenient application and clear mathematical concepts, this paper adopts the damping ratio \(\zeta\) to describe the damping parameters of high damping alloy materials.
# A New SORM Method for Structural Reliability with Hybrid Uncertain Variables
## 4 Numerical Examples  
### A Toy Example  
Assume that there is a tension \(P\) that forces on a round bar. The diameter of the bar \(D\) is a random variable, obeying the normal distribution with the mean \(\mu_{D}=29.4\) m and variance \(\sigma_{D}=3\). The tension \(P\) is an interval variable with \(P\in[30 KN,90 KN]\), \(P\) and \(D\) are independent and the material strength \(R\) = 170 MPa, is a constant. In the original space, the state function \(G(P,D)=170-\frac{4P}{\pi D^{2}}\), and in standard normal space, the function is written as:  
\[F(\delta,u)=170-\frac{120000\times\left(\delta+2\right)}{\pi\left(3u+29.4 \right)^{2}} \tag{33}\]  
where \(P\) is standard interval transformed into \(\delta\) in which \(\delta\in[-1,1]\), and \(D\) is standard Gaussian transformed into \(u\), which obeys standard Gaussian distribution \(N(0,1)\).  
In this paper, the accuracy of the UPSORM is compared with the new FORM by [PERSON] [22] and [PERSON] (10\({}^{7}\) samples). The comparison results are summarized in Table 1.  
It is found that the proposed method produces better estimation results. In general, it outperforms [PERSON]'s method [22] both in accuracy and efficiency, though the improvement is not significant since the limit-state function is relatively simple.
## 2 System and Channel Model  
### System Model  
The FSO system under consideration consists of a laser diode transmitter, a receiver, and \(N-1\) serially connected DF nodes along the propagation path, which create \(N\) individual intermediate optical links (hops). We first investigate the performance of any of these links, including a link without relays. Such a link assumes SIM with M-ary PSK signals transmitted over turbulent channels characterized by a \(M\)(alaga) distribution. It is also assumed that the M-ary PSK symbols, \(x\), are normalized to unit magnitude, i.e., \(x\in\left\{e^{j2\pi k/M}\middle|k=0,\,1,\,\ldots,\,M-1\right\}\), and that the received baseband signal, \(y\), is distorted by zero mean white Gaussian noise, \(n\), where both the in-phase and the quadrature-phase components exhibit the same variance, which is specified by \(\sigma_{n}^{2}\). The contribution of the fading amplitude, which depends on the path losses, the scintillation, and the pointing error effects, is determined through the fading parameter \(I\). Furthermore, the influence of phase noise is denoted by \(\theta\in[-\pi,\,\pi)\). Hence, the optical signal \(y\) at the receiver input, or at any DF relay input, can be written as [5]:  
\[y=e^{i\theta}\,I\,x+n\,. \tag{1}\]  
The fading amplitude \(I\) consists of the path loss parameter \(I_{I}\), the factor \(I_{a}\) related to the atmospheric fluctuations, and the parameter \(I_{p}\), which describes the misalignment-induced fading. Thus, the fading amplitude is given by the product \(I=I_{I}I_{a}I_{p}\). In the context of this paper, we assume without loss of generality that \(I_{I}=1\)[5].
## 2 Related Works
### Deep Learning-Based Remote Sensing Imagery Methods
#### 2.2.2 BiSeNet  
[PERSON] et al. [25] proposed an effective water segmentation method called the bilateral segmentation network (BiSeNet) for synthetic aperture radar (SAR) satellite images and established SAR water segmentation data based on GF-3 satellite images.  
The BiSeNet architecture first divides the large-scale SAR images into smaller patches of size \(512\times 512\). These patches are then categorized into four types based on the pixel intensity ranges: 0-25, 25-50, 50-75, and 75-100. Each patch from the four types is randomly sampled and generated as input with a size of \(1024\times 1024\). These input patches serve as the training data for the segmentation network.  
BiSeNet consists of two main branches: the spatial path and the context path. The spatial path is responsible for extracting spatial information from the source image and retains the spatial details. The context path, on the other hand, focuses on capturing contextual information and utilizes a lightweight model along with global average pooling to efficiently extract features. The features extracted from the spatial and context paths are fused together. These fused features are passed through a \(1\times 1\) convolutional layer, followed by a sigmoid activation function. The sigmoid function maps the output values to the range \([0,1]\), representing the probability of each pixel being classified as water. After the  
Figure 1: Illustration of the architecture of ResU-Net.The orange part is the encoder, and the blue part is the decoder.  
sigmoid activation, upsampling operations are performed to increase the spatial resolution and generate the final predicted segmentation result.
## 1 Introduction
### Novelty  
The splitting of input polynomials for multiplication is not a new idea and is commonly used in hierarchical/bit-parallel polynomial multipliers [32; 33]. For example, the Karatsuba multiplier splits each input polynomial into two smaller polynomials. Then, the smaller polynomials are further split into two smaller polynomials. This process could be repeated to obtain enough smaller polynomials to apply the schoolbook multiplication method. To generate the resultant polynomial for the Karatsuba method, the multiplication over smaller polynomials is needed to perform in reverse/recursive order (i.e., from smaller polynomials to larger). Variants of Toom-Cook multipliers are another example where the splitting of input polynomials is performed in three (three-way Toom-Cook multiplier) and four (four-way Toom-Cook multiplier) smaller polynomials. The ASIC implementations of Karatsuba and Toom-Cook multipliers are described in [31]. These multiplication approaches (i.e., Karatsuba and Toom-Cook) decrease the computational time for the polynomial multiplication but with hardware resources overhead.  
Therefore, the novelty of this work is splitting only the one input polynomial into a smaller portion of 32-bit lengths. Moreover, to reduce the hardware resources, we exploit the hardware components for a higher 571-bit binary field to compute the multiplications for smaller 163-, 233-, 283- and 409-bit fields using a schoolbook method. Although this is not very new, it is still relevant (from the perspective of NIST-standardized binary elliptic curve fields). Moreover, the computation of a constant-time polynomial multiplication in **four clock cycles** for each binary field (i.e., 163, 233, 283, 409 and 571) further highlights the novelty of this work.
## 2 Analytical Two-Link Model and Equations of Motion
### Equations of Motion of the Model
If the attack angle is zero, Equation (2) can be deformed as follows:  
\[N= \frac{\mathrm{d}^{2}\theta}{\mathrm{d}t^{2}}\big{[}-(m_{0}+m_{1}) l_{1}\sin\theta+m_{1}l_{g1}\sin\theta\big{]}+\frac{\mathrm{d}^{2}\varphi}{ \mathrm{d}t^{2}}\big{[}-(m_{0}+m_{1}+m_{2})l_{2}\sin\varphi+m_{2}l_{g2}\sin \varphi\big{]} \tag{5}\] \[\frac{\mathrm{d}^{2}\theta}{\mathrm{d}t^{2}}\bigg{[}\frac{-m_{1}l_{1}l_{g1 }\sin\theta}{\cos\theta_{0}}\sin(\theta+\theta_{0})+\frac{m_{2}l_{1}^{2}\cos \theta}{\cos\theta_{0}}\cos(\theta+\theta_{0})+m_{1}l_{g1}^{2}+I_{1}\bigg{]}\]0 \[\frac{\mathrm{d}^{2}\theta}{\mathrm{d}t^{2}}\bigg{[}\frac{-m_{1}l_{1}l_{g1 }\sin\theta}{\cos\theta_{0}}\sin(\theta+\theta_{0})+\frac{m_{2}l_{1}^{2}\cos \theta}{\cos\theta_{0}}\cos(\theta+\theta_{0})+m_{1}l_{g1}^{2}+I_{1}\bigg{]}\]1  
Equation (5) is an independent equation that determines the normal force \(N\). The friction force \(f\) can also be derived from the relationship with \(N\). We can then obtain the solution by substituting \(N\) and \(f\) into Equations (3) and (4). The normal force \(N\) has no effect on the horizontal restraining force on the head, because its direction is always perpendicular to this force. However, in this study (\(\theta_{0}\
eq 0\)), the restraining force in the horizontal direction of the head must be balanced in the \(x\)- and \(y\)-directions. This force is simultaneously influenced by the normal force \(N\) and the friction force \(f\) in the \(x\)- and \(y\)-directions. As a result, we obtain an equation that appears to show that the normal force \(N\) is determined by the friction force \(f\). We numerically calculate these equations in a unique way.
## 3 Results
### Study on Permeability of Steel Slag-Cement-Bentonite Barrier Material  
#### 3.3.1 Effect of Steel Slag Substitution Rate on the Permeability of Barrier Materials  
Figure 7 indicates that the permeability coefficient of the barrier material changes with the steel slag substitution rate, respectively. It can be seen from the Figure that with the same bentonite dosage and age, the permeability coefficient of the material with the increase in the steel slag substitution rate shows a decreasing trend. Taking the bentonite dosage at 46% and age 7d as an example, the permeability coefficients of steel slag substitution rates of 0, 12.5%, 25%, 37.5%, and 50% corresponding to age 7d  
Figure 5: Change of cohesion of steel slag–cement–bentonite material with replacement rate of steel slag under different bentonite dosages.  
Figure 6: Change of cohesive force of steel slag–cement–bentonite material with bentonite dosage under different steel slag replacement rates.  
were \(8.21\times 10^{-5}\) cm\(\cdot\)s\({}^{-1}\), \(5.96\times 10^{-5}\) cm\(\cdot\)s\({}^{-1}\), \(3.99\times 10^{-5}\) cm\(\cdot\)s\({}^{-1}\), \(1.61\times 10^{-5}\) cm\(\cdot\)s\({}^{-1}\), and \(2.30\times 10^{-6}\) cm\(\cdot\)s\({}^{-1}\), respectively. The permeability coefficient decreased from \(8.21\times 10^{-5}\) cm\(\cdot\)s\({}^{-1}\) to \(5.96\times 10^{-5}\) cm\(\cdot\)s\({}^{-1}\) when the steel slag substitution rate was increased from 0 to 12.5%. When the steel slag substitution rate was increased from 37.5% to 50%, the permeability coefficient decreased from \(1.61\times 10^{-5}\) cm\(\cdot\)s\({}^{-1}\) to \(2.30\times 10^{-6}\) cm\(\cdot\)s\({}^{-1}\). For the same age and bentonite dosage at 14d and 28d, the permeability coefficient of the material varied with the steel slag substitution rate with a similar magnitude as this pattern. For the same age and bentonite dosage of the material, with the increase in the steel slag substitution rate in the material, the permeability coefficient decreases more and more, indicating that the high dosage of steel slag reduces the permeability coefficient of the material significantly.
# Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks +
## 3 Extended Hybrid Message Passing Algorithm
```
Input: a Hybrid BN net, a maximum execution time max_time, a maximum number of iterations max_iteration, a maximum allowable number of components max_nc, a maximum precision max_pres Output: a set of belief functions \(bel_{ij}\)
1 FunctionHMP-GMR (net, max_time, max_iteration, max_nc, max_pres)
2 for\(i\gets 1\) to max_iterationdo
3 for\(j\gets 1\) to the number of nodes in netdo
4\(\pi_{i}\leftarrow\) ComputeAllPiMsgs(\(j\), max_nc, max_time)
5\(\lambda_{i}\leftarrow\) ComputeAllLambdaMsgs (\(j\), max_nc, max_time)
6 SendPiMsg(\(j\), max_nc, max_time)
7 SendLambdaMsg(\(j\), max_nc, max_time)
8 for\(j\gets 1\) to the number of nodes in netdo
9\(bel_{ij}\leftarrow\) compute belief function using \(\lambda_{j}\) and \(\pi_{i}\) (2)
10\(diff_{ij}\leftarrow\) compare distribution difference between \(bel_{ij}\) and \(bel_{(i\)-1)}\)
11\(max\_diff\leftarrow\) get maximum difference between \(diff_{ij}\) and max_diff
12 if\(max\_diff\) < max_pres then break
13 return a set of \(bel_{ij}\)
14 FunctionComputeAllPiMsgs (\(j\), max_time, max_nc)
15 if\(j\) is discretethendo (3)
16 elseif\(j\) is continuousthen
17 ifparent\(\
ot\) if\(j\) is discretethen do (11)
18 elseifparent\(\
ot\) if\(\
ot\) is continuousthen do (17) with \(M=max\_nc\)
19 elseifparent\(\
ot\) if\(\
ot\) is hybridthen do (18) with \(M=max\_nc\)
20\(ex\_time\leftarrow\) get a current execution time
21\(max\_time\) < exe_time then do not update \(\pi_{i}\)
22 return\(\pi_{i}\)
23 FunctionComputeAllLambdaMsgs (\(j\), max_time, max_nc)
24 if\(j\) is discretethen do (4)
25 elseif\(j\) is continuousthen do (20) for \(k\) with \(M=max\_nc\)
26\(ex\_time\leftarrow\) get a current execution time
27 if\(max\_time\) < exe_time then do not update \(\lambda_{j}\)
28 return\(\lambda_{j}\)
29 FunctionSendPiMsg (\(j\), max_time, max_nc)
30 for\(k\gets 1\) to number of children of \(j\)do
31 if\(j\) is discretethen do (5) for \(k\)
32 elseif\(j\) is continuousthen do (20) for \(k\) with \(M=max\_nc\)
33\(ex\_time\leftarrow\) get a current execution time
34 if\(max\_time\) < exe_time then do not update the Pi message from one of (5) and (20)
35 FunctionSendLambdaMsg (\(j\), max_time, max_nc)
36 for\(k\gets 1\) to number of parents of \(j\)do
37 if\(j\) is discretethen do (6) for \(k\)
38 elseif\(j\) is continuousthen do (15) for \(k\)
39 if\(j\) is hybridthen do (22) and (23) for \(k\) with \(M=max\_nc\)
40 elseif\(j\) is hybridthen do (22) and (23) for \(k\) with \(M=max\_nc\)
41\(ex\_time\leftarrow\) get a current execution time
42
43\(ex\_time\leftarrow\) get a current execution time
44 if\(max\_time\) < exe_time then do not update the Lambda message from one of (6), (15), (21), (22) and (23)
```  
**Algorithm 1**Hybrid Message Passing (HMP) with Gaussian Mixture Reduction (GMR) Algorithm.
## 4 Results  
### Experimental Data and Parameter Setting  
In order to verify the effectiveness of the proposed method, we used trajectory data from Changsha City, Hunan Province, collected in October 2018 for comprehensive analysis. The used trajectory data covers an area of about 7.6 \(\times\) 7.0 km, which is characterized by heavy traffic flow, rich commercial facilities and a variety of road intersections with different shapes and complex structures. The experimental dataset contains a total of 14,839,457 trajectory points collected by 5439 vehicles. The positional precision is about 5-10 m and the most-frequent sampling interval is about 30 s. The experimental parameters for density homogenization, hierarchical trajectory segmentation and road structure delineation are listed in Table 2. \(N_{grid}\) is the proportion threshold for identifying high-density trajectory cells (Section 3.1.1). \(N_{circle}\) is the proportion threshold for calculating the neighborhood circles of high-density cells (Section 3.1.2). \(T_{v}\), \(T_{a}\) and \(T_{dr}\) are the thresholds of speed change, direction change and turning distance ratio for identifying road-intersection trajectory points (Section 3.2.1). _Eps_ and _Mimpts_ are the clustering parameters of DBSCAN for detecting object-level road intersections (Section 3.2.2). _Ratio_ is the proportion threshold for determining the turning modes within road intersection (Section 3.2.3). \(\varepsilon\) is used to terminate the iterative piecewise fitting process for delineating road intersection structures (Section 3.3). These parameters are mostly determined through multiple experiments, of which some parameters are empirically predefined according to the actual road width (10-70 m) and trajectory data quality (positional precision: 5-10 m, time sampling: 30 s). These used parameters could be resized for different data sources or study areas.  
Figure 8: Delineating road intersection structures by iterative piecewise fitting algorithm.
## 4 LoRaWAN Downlink Routing Control Strategy
### Transmission Delay of Downlink Path  
The data transmission delay of the downlink communication service of the LoRaWAN network is limited by the length of the downlink route and the routing control strategy. The sum of the transmission delay is shown in Equation (13):  
\[T_{n}=\sum_{j=1}^{m-1}d_{j}/v_{data}+mT_{switch}+T_{jitter} \tag{13}\]  
where \(T_{n}\) is defined as the total time that the LoRaWAN downlink communication data is transmitted from the root node of route \(R_{n}\) to the destination node, \(d_{j}\) is the length of link \(l_{j}\), \(v_{data}\) represents the transmission speed of data flow between nodes, \(T_{switch}\) represents the interaction delay between different nodes, \(m\) is the total number of all nodes in the routing set \(R_{n}\), and \(T_{jitter}\) is defined as the random jitter delay.