esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling and Linearization of the System
### Stator Algebraic Equations
#### 3.3.2 Negative Sequence  
From Figure 2, the following equivalent circuit is obtained, where \(R^{neg}_{i}\) and \(X^{neg}_{i}\) represent the negative sequence impedance parameters of the machine:  
When solving the Kirchoff law:  
\[(V^{neg}_{aR_{i}}+jV^{neg}_{aIm_{i}})+(I^{neg}_{aR_{i}}+jI^{neg}_{aIm_{i}})( R^{neg}_{i}+jX^{neg}_{i})=0 \tag{27}\]  
By separating into the real and imaginary parts, the resulting equations of the machine are:  
\[V^{neg}_{aR_{i}} + I^{neg}_{aR_{i}}R^{neg}_{i}-I^{neg}_{aIm_{i}}X^{neg}_{i}=0 \tag{28}\] \[V^{neg}_{aIm_{i}} + I^{neg}_{aR_{i}}X^{neg}_{i}+I^{neg}_{aIm_{i}}R^{neg}_{i}=0 \tag{29}\]  
where:  
* \(R^{neg}_{i}\) the negative sequence resistance (pu);
* \(X^{neg}_{i}\) the negative sequence reactance (pu);
* \(V^{neg}_{aR_{i}}\) the real part of the negative sequence voltage (pu);
* \(V^{neg}_{aIm_{i}}\) the imaginary part of the negative sequence voltage (pu);  
Figure 2: Circuit of the negative sequence for the multimachine generator case.
# Recent Advances in Efficient Dynamic Graph Processing
## 4 Algorithms
### Path Traversal
#### 4.4.3 Reachability Queries  
The above index maintenance algorithms of shortest distance queries can be easily adapted to answer reachability queries, i.e., if the returned shortest distance is infinity, the reachability query answers false. Otherwise, it answers true. In addition to these algorithms for shortest distance queries, there are also some dedicated index maintenance algorithms for efficiently answering reachability queries.  
To efficiently manage large-scale graphs, Ref. [110] introduces a foundational framework on reachability indices for dynamic graphs. Ref. [110] introduces a total order labeling (TOL) framework that summarizes a family of reachability indices with the best performance among the existing techniques for static graphs. The work in [165] investigates reachability-preserving compression, a technique that produces compact graph representations while retaining all the necessary information for answering reachability queries, without requiring decompression. Notably, incRPCO guarantees optimal compression ratios in its output, whereas incRPCF prioritizes speed by only modifying nodes and edges that impact reachability relationships. Recent research by [166] introduces a specialized indexing structure known as the WTB-index, which is specifically optimized for reachability queries in such temporal bipartite graphs.
## 2 Materials and Methods
### MCDM Methods
#### 2.2.2 COPRAS Method  
The COPRAS (Complex Proportional Assessment) method was first proposed by [PERSON] and [PERSON] as a multi-criteria decision-making method. The COPRAS method uses a procedure of progressive ranking and evaluation of alternatives in terms of importance and utility [29]. COPRAS is a ranking approach that works with the idea of weighted arithmetic operations, taking into account the nature of the criteria. Due to its simplicity, the method is preferred by many researchers for solving decision problems [30]. The COPRAS method consists of 7 stages. The stages of the method are as follows [29]:  
* Step 1: Creating the Decision Matrix As in all multi-criteria decision-making problems, the decision matrix is first created. The decision matrix is as follows: \[D=\begin{array}{cccc}A_{1}\\ \vdots\\ A_{m}\end{array}\begin{bmatrix}x_{11}&\cdots&x_{1n}\\ \vdots&\ddots&\vdots\\ x_{m1}&\cdots&x_{mn}\end{bmatrix}\] (8)
* Step 2: Normalization of the Decision-Making Matrix  
A normalization procedure is used to convert the performances of the considered alternatives into comparable dimensionless values. In the COPRAS method, the formula in Equation (9) is used for normalization:  
\[\widetilde{x}_{ij}=\frac{x_{ij}}{\sum\limits_{i=1}^{m}x_{ij}} \tag{9}\]  
where \(\mathrm{x_{ij}}\) is the performance of the i-th alternative with respect to the j-th criterion, \(\widetilde{x}_{\bar{\mathrm{y}}}\) is its normalized value, and m is the number of alternatives.  
* Step 3: Determination of the Weighted Normalized Decision-Making Matrix After creating the normalized decision-making matrix, the next step is to create the weighted normalized decision-making matrix using the formula in Equation (10): \[D^{{}^{\prime}}=d_{ij}=x_{ij}^{*}.w_{j}\] (10)
* Step 4. Calculation of Maximization and Minimization Index for Each AlternativeAt this stage, each alternative is categorized into maximizing and minimizing indices by Equations (4) and (5):  
\[\text{S}_{i+}=\sum_{j=1}^{k}d_{ij}\;j=1,2,\ldots.,\text{k maximizing index} \tag{11}\]  
\[\text{S}_{i-}=\sum_{j=k+1}^{n}d_{ij}\;j=k+1,k+2\ldots,\text{n minimizing index} \tag{12}\]
* Step 5. Calculating the relative weight of each alternative The relative weight \(Q_{i}\) of the ith alternative is calculated by Equation (13): \[\text{Q}_{i}=S_{++}+\frac{\underset{i}{\text{min}}S_{-i}\sum_{i=1}^{m}S_{-i}}{S _{-i}\sum_{i=1}^{m}\frac{\underset{i}{\text{min}}S_{-i}}{S_{-i}}}\] (13)
* Step 6. Prioritizing the alternatives The priority order of the compared alternatives is determined by their relative weights using Equation (14). The alternative with higher relative weight has higher priority (ranking) and the alternative with the highest weight is the most acceptable alternative. \[A^{*}=A_{i}\;\underset{i}{\text{max}}Q_{i}\] (14)
* Step 7. Calculating the Performance Index (\(P_{i}\)) Value for Each Alternative In the last section, the \(P_{i}\) values are calculated using Equation (15): \[P_{i}=\frac{Q_{i}}{Q_{max}}100\%\] (15)  
The 100 ranked alternatives are the best. These alternatives are ranked from largest to smallest.
## 3 Path Planning Algorithm Based on the Offset Curve
### Generation of the Offset Curve on a Triangular Mesh Surface
#### 3.2.2 Details of CODP
(3) Generation of offset points and the offset curve  
After the judgment of the invalid points, the invalid points are removed, and then the offset points are generated from the remaining discrete points. The details are shown in the above section, \"The calculation of offset points on a triangular mesh surface.\" Since the discrete points are ordered on the original curve, the offset points are also ordered and attached on the triangular mesh surface. In this step, the offset curve can be obtained by sequentially generating connected lines between adjacent offset points. Figure 11 shows the offset segment generation between two adjacent offset points. A connection plane can be established based on the two offset points and the vector \(\overrightarrow{n}\) (\(\overrightarrow{n}\) = \(\overrightarrow{n_{1}}\) + \(\overrightarrow{n_{2}}\), \(\overrightarrow{n_{1}}\), \(\overrightarrow{n_{2}}\), which are the normal vectors of the triangular facets where the offset points are located, and \(\overrightarrow{n}\) lies in the connection plane). In addition, an intersection polyline could be obtained between the triangular mesh surface and the connection plane. As shown in Figure 12, taking a high-heeled shoe model as an example, we set the starting layer height as 5 mm, the offset distance as 2 mm, and the distance between adjacent discrete points as 0.1 times the offset distance. The results show that the stability of the offset algorithm and the filling effect of the theoretical pr  
Figure 11: Offset curve generation between two adjacent offset points. Figure 10: Judgment of the invalid points.
# Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation
## 3 Front-End
### Camera Pose Interpolation  
Camera pose is an important factor that affects the accuracy of the point cloud. Although the Cartographer can estimate the robot pose more accurately, it is difficult to collect synchronized laser and RGB-D sensor data, resulting in the two being not strictly aligned on the timestamp. Interpolation is a method of estimating new data between two discrete points; we thus use it to refine camera poses.  
The robot's pose \(\mathbf{T}\ e\ \mathrm{SE}(3)\) contains a translation vector \(p\in\mathbb{R}^{3}\) and a rotation matrix \(\mathbf{R}\in\mathrm{SO}(3)\). \(\mathrm{SO}(3)\) and \(\mathrm{SE}(3)\) represent the special orthogonal group and the special Euclidean group, respectively. They are defined as follows:  
\[\mathrm{SO}(3)=\Big{\{}\mathbf{R}\in\mathbb{R}^{3\times 3}\Big{|}\mathbf{R} \mathbf{R}^{\mathrm{T}}=\mathbf{I},\ \det(\mathbf{R})=+1\Big{\}}. \tag{1}\]  
\[\mathrm{SE}(3)=\bigg{\{}\mathbf{T}=\left[\begin{array}{cc}\mathbf{R}& \boldsymbol{p}\\ 0^{\mathrm{T}}&1\end{array}\right]\in\mathbb{R}^{4\times 4}\ |\ \mathbf{R}\in\mathrm{SO}(3), \boldsymbol{p}\in\mathbb{R}^{3}\bigg{\}}. \tag{2}\]  
The method for robot pose interpolation mainly includes two paradigms: the split and the joint [37]. The former first interpolates the position and rotation separately and then composes the results of both to generate a new pose. On the contrary, the latter interpolates the position and rotation  
Figure 2: Occupied grid map and robot trajectory generated by Cartographer.  
simultaneously from the perspective of the special Euclidean group. As shown in Figure 3, they have different interpolation trajectories. We use the joint interpolation to generate camera poses, since the robot trajectory is more in-line with it.  
Assuming that the timestamp \(t_{\text{c}}\) of an image is between two laser timestamps \(t_{\text{I0}}\) and \(t_{\text{I1}}\), and the camera poses corresponding to these timestamps are \(\mathbf{T_{c0}}\) and \(\mathbf{T_{c1}}\), then the interpolated camera pose \(\mathbf{T_{c}}\) at the time \(t_{\text{c}}\) is:  
\[\mathbf{T_{c}}=\exp\biggl{(}\frac{t_{c}}{t_{\text{I1}}-t_{\text{I0}}}\log \Bigl{(}\mathbf{T_{c1}}\mathbf{T_{c0}^{-1}}\Bigr{)}\biggr{)}\mathbf{T_{c0}}\, \tag{3}\]  
where \(\exp(\cdot)\) and \(\log(\cdot)\) are the exponential and logarithm maps of \(\text{SE}(3)\).
## 5 Conclusions  
In this study, the temperature distribution and thermal stress of the brake disc were analyzed concerning the positioning of the friction blocks on the brake pad. An optimization process was conducted on the brake pad, composed of circular friction blocks, utilizing the Design of Experiments (DOE) and artificial neural network (ANN) techniques. The optimized brake pad, consisting of nine circular friction blocks, was compared to a conventional brake pad. The following conclusions were drawn.  
1. Smaller temperature deviations lead to lower maximum temperatures on the disc surface, while larger temperature deviations result in higher maximum temperatures. The optimized pad ensures a more even temperature distribution across the disc, which reduces the maximum temperature compared to the original pad.
2. DOE was employed to generate a dataset for the training of the ANN. The ANN model, utilizing the Levenberg-Marquardt backward propagation algorithm, was used to predict the temperature and temperature deviation. The optimization aimed to minimize the temperature deviation of the disc during the braking process, thereby reducing the thermal stress.
3. The optimized pad demonstrated improved performance over the original pad, achieving a significant 17.9% reduction in the maximum temperature and an 11.6% reduction in thermal stress.  
In summary, the use of the DOE and ANN techniques allowed for the development of an optimized brake pad design, leading to enhanced braking performance and reduced thermal stresses on the brake disc.  
Conceptualization, methodology, and writing--original draft preparation, H.R.H.; writing--review and editing and supervision, C.-W.H. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Comparison of temperature at the end of the braking process.  
Figure 13: Comparison of thermal stress in the brake disc.  
**Funding:** This research was funded by the National Research Council of Science & Technology under the project \"Development of Core Machinery Technologies for Autonomous Operation and Manufacturing\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
_Article_  
**A Novel Concentrator Photovoltaic (CPV) System**  
**with the Improvement of Irradiance Uniformity and**  
**the Capturing of Diffuse Solar Radiation**  
**[PERSON] and [PERSON] \({}^{\star}\)**  
Department of Information and Communication Engineering, Myongji University, Yongin 17058, Korea;  
[EMAIL_ADDRESS]  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +82-31-330-6768  
Academic Editors: [PERSON] and [PERSON]  
Received: 16 June 2016; Accepted: 6 September 2016; Published: 8 September 2016  
This paper proposes a novel concentrator photovoltaic (CPV) system with improved irradiation uniformity and system efficiency. CPV technology is very promising its for highly efficient solar energy conversion. A conventional CPV system usually uses only one optical component, such as a refractive Fresnel lens or a reflective parabolic dish, to collect and concentrate solar radiation on the solar cell surface. Such a system creates strongly non-uniform irradiation distribution on the solar cell, which tends to cause hot spots, current mismatch, and degrades the overall efficiency of the system. Additionally, a high-concentration CPV system is unable to collect diffuse solar radiation. In this paper, we propose a novel CPV system with improved irradiation uniformity and collection of diffuse solar radiation. The proposed system uses a Fresnel lens as a primary optical element (POE) to concentrate and focus the sunlight and a plano-concave lens as a secondary optical element (SOE) to uniformly distribute the sunlight over the surface of multi-junction (MJ) solar cells. By using the SOE, the irradiance uniformity is significantly improved in the system. Additionally, the proposed system also captures diffuse solar radiation by using an additional low-cost solar cell surrounding MJ cells. In our system, incident direct solar radiation is captured by MJ solar cells, whereas incident diffuse solar radiation is captured by the low-cost solar cell. Simulation models were developed using a commercial optical simulation tool (LightTools(tm)). The irradiance uniformity and efficiency of the proposed CPV system were analyzed, evaluated, and compared with those of conventional CPV systems. The analyzed and simulated results show that the CPV system significantly improves the irradiance uniformity as well as the system efficiency compared to the conventional CPV systems. Numerically, for our simulation models, the designed CPV with the SOE and low-cost cell provided an optical power ratio increase of about 17.12% compared to the conventional CPV without the low-cost cell, and about 10.26% compared to the conventional CPV without using both the SOE and additional low-cost cell.  
**Keywords:** concentrator photovoltaic (CPV); Fresnel-lens-based CPV; CPV with irradiance uniformity; CPV for capturing diffuse radiation
## 4 Simulation Model Design
### Target System  
The KPIs shown in Table 1 were considered to assess the simulation scenarios.  
For measuring the external system performance, the KPIs with more importance were \(1\) (passengers), \(6\) (the time a passenger waits until his request is accepted), and \(11\) (the extra-time a passenger waits for landing). The first indicator implied how many people (quantity) were provided with the requested travelling service, and the other two indicators reflected the quality of the service in terms of the additional time over the minimum time needed to provide the service. For measuring the internal system performance, the most important KPI was \(2\) (the average aircraft on ground). If minimized, more trips would be made, and more potential passengers would be provided with the requested travelling service.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Key Performance Indicators (KPIs), Units, and Formulas**} \\ \cline{2-3}  & **Indicator** & **Formula** \\ \hline
1 & Passengers (persons) & \(\sum\) Passengers \\
2 & Average aircraft on ground (\%) & \(\frac{\sum\text{Time on ground}}{\text{Simulation time}}\times 100\%\) \\
3 & Average aircraft availability (\%) & \(100\%-\) Average maintenance time (\%) \\
4 & Average aircraft utilization rate (\%) & \(100\%-\) Average aircraft on ground (\%) \\
5 & Average flight request backlog (requests) & (\(\sum\) Request without aircraft unit) /Simulation time \\
6 & Average flight request backlog (minutes) & (\(\sum\) Periods without aircraft unit for a request) /Simulation time \\
7 & Average capacity utilization rate of veritorits (\%) & \(\left(\sum\frac{\sum\text{Arxfernt units in vertipert}}{\text{ Vertiport Capacity}}\times 100\%\right)\)/Simulation time \\
8 & Average veritorports full (\%) & (\(\sum\) Periods with vertiport full)/Simulation time \\
9 & Average veritorports empty (\%) & (\(\sum\) Periods with vertiport empty)/Simulation time \\
10 & Average arrival backlog (aircraft units) & (\(\sum\) Aircraft units waiting for landing)/Simulation time \\
11 & Average arrival backlog (minutes) & (\(\sum\) Waiting time of aircraft units for landing)/Simulation time \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key Performance Indicators.  
Figure 10: Simulation Design Methodology.
## 4 Discussion
These authors reported an increased GPC in drought conditions, but in their study, the drought conditions had no effect on the GSC. The results of other studies on this issue are also different. For example, [PERSON] et al. [81] stated that irrigation and cultivar did not influence the GPC and GOC in either year, which was opposite to our study results. Interesting study results published by [PERSON] et al. [32] showed that the highest maize GY and GOC and the lowest GSC were recorded after a fully irrigated treatment. In our study, the impact of irrigation on the GOC was year dependent, whereby in the extremely wet year (a1 = 3.28%, a3 = 3.38%) and in the average year (a1 = 4.6%, a3 = 4.8%), the irrigation rate increased the GCC, while in the extremely dry year, the irrigation rate decreased the GCC (a1 = 3.35%, a2 = 3.55%). As previously stated, in our study, a higher N rate increased the GPC and reduced the GSC, which was partially in line with [PERSON] et al. [81]. These authors reported an increased GPC with a higher N rate. From the correlation analysis, it is clear that the GPC and GSC had a significant (\(p<0.01\)) negative correlation (Figure 8) in the extremely wet (r = \(-0.48\)) and extremely dry years (r = \(-0.81\)). Different year-to-year study results, as well as the results of other studies, indicate the complexity of the issue and that additional research is needed. As for the soil N level, although the net irrigation rates were different depending on the weather conditions, no statistical significance was observed (Table 6). As expected, the soil N level increased with the increase of N fertilizers (r = 0.40) and decreased toward the end of the study period (r = -0.72), which is in line with the previous study of [PERSON] et al. [49]. Our result confirmed that nitrate leaching due to a higher N rate occurred, especially in growing conditions with a high rainfall amount and a high irrigation rate. The same general pattern of higher leaching losses was found in many previous studies [46; 49; 82; 83].
# GPU Algorithms for Structured Sparse Matrix Multiplication with Diagonal Storage Schemes +
###### Abstract  
Matrix-matrix multiplication is of singular importance in linear algebra operations with a multitude of applications in scientific and engineering computing. Data structures for storing matrix elements are designed to minimize overhead information as well as to optimize the operation count. In this study, we utilize the notion of the compact diagonal storage method (CDM), which builds upon the previously developed diagonal storage--an orientation-independent uniform scheme to store the nonzero elements of a range of matrices. This study exploits both these storage schemes and presents efficient GPU-accelerated parallel implementations of matrix multiplication when the input matrices are banded and/or structured sparse. We exploit the data layouts in the diagonal storage schemes to expose a substantial amount of fine-grained parallelism and effectively utilize the GPU shared memory to improve the locality of data access for numerical calculations. Results from an extensive set of numerical experiments with the aforementioned types of matrices demonstrate orders-of-magnitude speedups compared with the sequential performance.  
matrix multiplication; optimization method; diagonal storage; banded matrix; structured sparse matrix; GPU; massively multithreaded parallelism +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 5 Protocol Stack
### Link Layer
#### 5.2.1 Beaconing System  
The designed beaconing system has a double function: synchronizing the network devices and scheduling the different actions to be performed in a time-division multiplexing scheme. Two types of beacons are used for this purpose: _primary_ and _secondary beacons_ (see Figure 4).  
Both beacons include a timestamp, the time until the next _primary beacon_, and the next action to be taken by the network: for instance, an association phase (_network association primary beacon_), or an uplink data transmission phase (_data primary beacon_). _Secondary beacons_ include the same information as _primary_ ones, and are used to guarantee information redundancy for already associated STAs as well as to accelerate network discovery for non-associated ones. However, no action is performed by STAs after a _secondary beacon_.  
Time between two consecutive _primary beacons_ and two consecutive _secondary beacons_ is defined as \(T_{p}\) and \(T_{s}\), respectively, where \(T_{p}=(k_{s}+1)\cdot T_{s}\), with \(k_{s}\) being the number of _secondary beacons_ transmitted after every _primary beacon_.
# A Novel MPPT Based Reptile Search Algorithm for Photovoltaic System under Various Conditions
## 3 Reptile Search Optimization (RSA) Based MPPT Algorithm
### RSA Algorithm for MPPT  
The proposed modifications to the traditional RSA algorithm make it suitable for use in MPPT of PV systems. The algorithm continuously reads the output voltage and current of the PV array, as depicted in the flowchart of the algorithm in Figure 3. This information is then utilized to calculate the output power, which serves as a validity function that helps to update and determine the next duty cycle of the boost converter until the optimum duty cycle (\(D_{\textit{Best}}\)) is reached. The RSA algorithm employs two main techniques, encircling and  
Figure 1: Block diagram of the grid connected PV system.  
Figure 2: PV characteristic curves (**a**) under uniform solar irradiance and (**b**) under PSC.  
hunting, to update the position of the candidate duty cycle (**D**). The following sub-sections will elaborate on these techniques and provide a detailed mathematical model.