esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Location in Double Circuit Medium Power Distribution Networks Using an Impedance-Based Method
## 4 Simulation Results  
### The Studied Network  
To evaluate the performance of the proposed method, many investigations were done based on different fault situations that can happen on a changed 13 bus IEEE network. This network was simulated in MATLAB and a \(\pi\)-line model was used in different sections. The tested system was a 13-bus system, and the distributed loads are shown in Figure 4. The length of each section is shown in Table 2. Each section of the lines was a \(\pi\)-model circuit. The number of the circuit presents the section of the line which it models. For each fault simulation, the voltage and current were measured and recorded from the local terminal. For this algorithm, the amplitude and phase of the three-phase's voltage and current at the beginning of the feeder is required. These parameters were obtained from Fourier's transform. To find the accuracy of the proposed method, the location's error percentage is obtained by using the equation below:  
\[error\%_{0}=\frac{|x_{actual}-x_{calculated}|}{l_{t}}100, \tag{25}\]  
\[\begin{cases}x_{actual}&\text{\emph{The actual location of the error}}\\ x_{calculated}&\text{\emph{The calculated fault location}}\\ l_{t}&\text{\emph{The total length of the feeder}}\end{cases}.\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bus from** & **Bus to** & **Distance (km)** & **Bus from** & **Bus to** & **Distance (km)** \\ \hline
1 & 2 & 4.6 & 5 & 6 & 3.2 \\
2 & 3 & 4.6 & 5 & 11 & 2.6 \\
3 & 4 & 7.763 & 6 & 7 & 2.6 \\
3 & 9 & 5.1 & 6 & 13 & 3 \\
4 & 5 & 7.763 & 7 & 8 & 4 \\
4 & 10 & 5 & 11 & 12 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Length of the lines.  
Figure 4: The studied distribution feeder (the numbers is nodes number).
## 4 Results  
### Analysis of a Typical Test  
Continuous monitoring makes it possible to obtain the necessary data to determine the variables needed to calculate the radiant system's heat flux absorbed or dissipated. Figure 7 shows experimental results of surface temperature \"\(ST\)\". Likewise, the floor is represented by \"F\" and the ceiling by \"T\" in Figure 7. These data belong to a test carried out for a flow rate of 2 \(\frac{1}{min}\) and combined operation modes 1 and 2 described above.  
Figure 7 shows the evolution of surface temperatures. Each period is linked to mode 1 (first period) and 2 (second period). During the first period, the water temperature did not exceed 15 \({}^{\circ}\)C. Then, the temperature of the surfaces decreased rapidly due to the radiant effect of the ceiling. When the heat pump was off (mode 2), the radiant system's absorption capacity diminished since the temperature difference reduced. These types of tests were repeated with different operating conditions to characterise the solution.  
Figure 8: Thermography of active ceiling: cooling mode (**A**) before the start-up, (**B**) during start-up, (**C**) after 1 h of operation.  
The hydraulic circuit that involves the radiant system was fully monitored, as specified in the previous section. Figure 8 shows different thermographic images. Here, it is possible to visualise the contrast of temperatures throughout the fluid through the modules and even the surface temperature just above the radiant cover.  
The images of Figure 8 correspond to the test the water flow of 8 \(\frac{1}{\min}\) but at different times. The first one (Figure 8A) corresponds to the system completely off (no water circulates). The second one (Figure 8B) took place during the minutes after starting, in which the average water temperature showed the temperature difference between the water and the surroundings. The water temperature was around 19 \({}^{\circ}\)C and the surface temperature and air temperature of the space were 28 \({}^{\circ}\)C-30 \({}^{\circ}\)C. The third image (Figure 8C) shows the temperature after one hour.  
Finally, Figure 9 shows an example of a combination of the operating modes for 8 \(\frac{1}{\min}\). Thanks to the surface temperatures and the different shape factors, it was possible to determine the mean radiant temperature (see Equation (8)).  
Figure 9 is an example of the data treatment during the three periods. First, transition time appears at the beginning, this was period 1 (see Figure 9). The duration of period 1 was around 30 min. Period 2 (mode 1 in Figure 9) began when the heat pump was on. During period 1, it was possible to guarantee a constant temperature difference due to the heat pump's operation. Also, mode 2 is essential to ensure the free running when the water is not controlled (heat pump is off).
## 4 Materials and Methods
### Image Analysis and Data Statistics  
Image analysis was performed using open-source public domain image processing software [42]. Images were captured in RGB format and converted to 8-bit format for analysis according to the specifications of the analysis software. The ROI was extracted for each test condition, with the regions having nearly same luminance tonal frequency. The main image analysis items were projected area \(A\), perimeter length \(L\), number of particles, and circularity \(\gamma_{c}\). Floc diameter \(d\) was a circle equivalent diameter (CED). The diameter \(d\) was converted from the projected area \(A\) as follows:  
\[d=2\sqrt{\frac{A}{\pi}}. \tag{4}\]  
The circularity \(\gamma_{c}\) was a shape indicator, \(\gamma_{c}\):  
\[\gamma_{c}=\frac{4\pi A}{L^{2}}. \tag{5}\]  
and the closer it was to \(1\), the closer it was to being circular. The results of the analysis were assessed statistically, and the representative value for the floc diameter was \(D_{90}\), which is the 90% diameter of the cumulative frequency distribution on a volumetric basis. The structural change of the floc was represented by the relationship between \(D_{90}\) and the \(GT\) value. The \(GT\) value was the product of the agitation intensity \(G\) and the cumulative rotation time \(T\), a dimensionless quantity that normalizes the agitation intensity and time received by the floc. Floc growth and breakup were indicated by the increase or decrease of \(D_{90}\) with the \(GT\) value. In a previous report [36], it was reported that \(D_{90}\) could be used to determine the typical trend of floc particle size change. Because of the background lighting, the flocs in the image appeared as a shadow. Although flocculation began immediately after the start of the test, the flocculation flocs in the very initial stage were so small that they were not clearly detectable by the image sensor.
# Buffer Allocation via Bottleneck-Based Variable Neighborhood Search
## 3 Solution Methodology
### Variable Neighborhood Search (VNS)  
To achieve a balance between computation time and solution quality of buffer allocation, a proper initial neighborhood structure is set to detect the bottleneck of a production line rapidly, where an additional neighborhood structure is set to improve the solution quality in case no satisfactory throughput is generated in the initial neighborhood structure. Here, neighborhood structure refers to candidate bottlenecks.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 6 Approaches and Algorithms
### Beam Scheduling for Beam-Hopping
This algorithm  
```
0: The position \(\mathcal{P}_{s}=\{l_{s},b_{s},h_{s}\}\) (longtitude, latitude, altitude) of SBS, the position \(\mathcal{P}_{u}=\{l_{u},b_{u},l_{u}\}\) of user/grid, user/service distribution \(\mathcal{D}=\{d_{1},d_{2},\ldots,d_{M}\}\), the number of cells is \(M\), available frequency band \(\mathcal{B}_{s}\) of SBS, the maximum number of available beams \(N\), the slot set \(\mathcal{T}\). 0: The slot and beam assignment for cells. 1: Check the table to match the grid with SBS's footprint and the grids with each cell in the service area. 2: Calculate the number of users or service volume of each cell in the SBS's service area and generate the service volume set \(\mathcal{C}_{s}=\{c_{1},c_{2},\ldots,c_{M}\}\) of cells. 3: Calculate the proportion of service volume in each cell to the total service volume in the satellite service area. 4:for\(i\in\mathcal{T}\)do
5:\(\mathcal{C}_{s\_temp}=\mathcal{C}_{s}\);
6:for\(j=1:N\)do
7:while the slots in \(\mathcal{C}_{s\_temp}\) larger than 0 do
8: Select the cell \(c_{max}\) with the largest service volume;
9:if The slots assigned to \(c_{max}\) is not larger than the proportion and the left service volume within it is greater than 0 then
10:if The CCI between \(c_{max}\) and the other cells that have been assigned beams in slot \(i\) is less than interference threshold. then
11: Assign slot \(i\) and beam \(j\) to cell \(c_{max}\). 12: Delete \(c_{max}\) from \(\mathcal{C}_{s\_temp}\) or update its service volume with 0. 13: Update the left service volume of cell \(c_{max}\) in \(\mathcal{C}_{s}\). 14: Break. 15:else
16: Update the service volume of cell \(c_{max}\) in \(\mathcal{C}_{s\_temp}\) with 0. 17:endif
18:else
19: Delete \(c_{max}\) from \(\mathcal{C}_{s\_temp}\) or update its service volume with 0. 20:endif
21:endwhile
22:endfor
23:endfor
```  
**Algorithm 1** Beam Scheduling Algorithm.
# Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions
## 5 Analysis and Comparison of Experimental Findings  
### Configuration of Experiments  
In this study, we utilized the Solomon database as a case study to conduct experiments that evaluate the performance of the algorithms. The Solomon standard test set comprises three types: C-type, R-type, and RC-type. The C-type test set features an aggregated distribution of customer locations; the R-type test set exhibits a random distribution of customer locations; and the RC-type test set incorporates characteristics from both aggregated and random distributions. To effectively assess the performance of the algorithm, six test sets--C101, C201, R104, R204, RC104, and RC204--are selected for analysis in this paper. The number of customers is 100, and their demand, service time, and service time window are predetermined. Based on the literature [37; 39] and current market conditions, the relevant parameters are established as shown in Table 1.
## 3 Hierarchical Classification
### Catalog of Words from a Message  
The catalog we created is a dictionary of words to which we associated the codes of the most specific themes they belong to, as shown in Figure 1.  
To generate the hierarchical codes, our algorithm reads the data.noun file starting from the root node and descending to each child or hyponym successively, creating in this process the hierarchical codes assigned to each node. For each level descended, more digits are added to the hierarchical code being created. As a result, a file is obtained that associates each hierarchical code with an offset code.  
Figure 2 shows a fragment of the generated \"codes\" file where the offset code appears in the first column and the hierarchical code in the second column.  
The algorithm creates the hierarchical codes using character strings grouped into blocks of three digits (from 000 to 999), allowing up to 1000 possibilities per node, reflecting a theoretical maximum of 1000 children per theme. For example, Node Theme 0.1.2.2 has the hierarchical code 000001002002, indicating its position within Theme 0.1.2, which in turn has the code 0000002. The root node is identified with the code 000, simplifying the representation of the thematic tree in data_noun and facilitating future expansions.  
The hierarchical codes we create are not a number of bytes, like the offset codes, but rather codes that represent the meaning of a word and all its hypernyms in the thematic hierarchy, which will be useful in thematic analysis. By looking at Figure 2, we can deduce that the more digits two hierarchical codes have in common starting from the left, the  
Figure 1: Generation of hierarchical codes.  
Figure 2: Fragment of the codes file.  
more similar the themes they represent are, although the length of both codes must also be considered.  
In the process illustrated in Figure 3, the \"generatewords\" algorithm transforms the index.noun file into a new file called namesjer. In this new file, the original offset codes of index.noun are replaced by hierarchical codes. Thus, each line of the namesjer file begins with a word followed by hierarchical codes representing the themes associated with that word, forming a structured catalog of themes and their corresponding hierarchical codes.
## 2 Design of the 1 \(\times\) 4 Channel Wavelength Demultiplexer  
In this section, we first describe the structure and properties of the low-loss 2.8 \(\upmu\)m \(\times\) 72 nm cross-section area Si\({}_{3}\)N\({}_{4}\) waveguide. Next we will present the simulation method and results used to design the MMI couplers and the overall MZI based demultiplexer.
# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 3 Problem Definition  
We use \(U=\{u_{1},u_{2},\ldots,u_{M}\}\) to represent the user set, \(P=\{p_{1},p_{2},\ldots,p_{N}\}\) to represent the POI set, and \(C=\{c_{1},c_{2},\ldots,c_{K}\}\) to represent the attributes which the POIs have, where \(M\), \(N\), and \(K\) are the total number of users, POIs, and attributes, respectively. Each POI \(p\) is denoted by a quadruple \(p=(\textit{id},\textit{lon},\textit{lat},\textit{attr})\), which includes the id, longitude, latitude, and attributes of the POI.  
Check-in. A check-in is represented by a triplet \(q=(u,p,\textit{t})\), which represents that user \(u\) visited POI \(p\) at time \(t\).  
Sequence and check-in history. A sequence is a set of continuous check-ins of a user within a period of time. We use \(s_{m}^{u}=\left\{q_{1}^{u},\ q_{2}^{u},\ldots q_{n}^{u}\right\}\) to represent the \(m\)-th sequence of user \(u\), which includes \(n\) check-ins. \(q_{k}^{u}=\left(u,p_{k}^{u},t_{k}^{u}\right)\) denotes the \(k\)-th check-in of the sequence, where \(k\) is a positive integer. Considering two sequences \(s_{n}^{v}\) and \(s_{m}^{u}\), if \(s_{n}^{v}\) ends before \(s_{m}^{u}\) starts, we denote \(s_{n}^{v}\) as the historical sequences of \(s_{m}^{u}\). The historical sequences of a user includes the sequences belonging to the same user and different users. All the check-in sequences of the same user constitute the check-in history of the user, which is denoted by \(S^{u}=\left\{s_{1}^{u},\ s_{2}^{u},\ldots s_{|S^{u}|}^{u}\right\}\), where \(|\,S^{u}|\) is the number of check-in sequences of user \(u\).  
The next point-of-interest recommendation aims to provide a list of POIs which a user is most likely to visit in the future, based on the current sequence and the historical sequences of all users. This list ranks the POIs in order of the probabilities. Specifically, given the set of all users' historical check-in sequences \(\{S^{u}\}_{u\in U}\) and the current sequence \(s^{u_{i}}=\{q_{1^{u_{i}}}^{u_{i}},\,q_{2}^{u_{i}},\ldots q_{n}^{u_{i}}\}\) of user \(u_{i}\), the next point-of-interest recommendation is to predict where the users will visit at the future time \(t_{n+1}\):  
\[\hat{y}_{i}=f\Big{(}p_{i}\Big{|}\,S^{1},S^{2},\ldots,S^{M},\,s^{u_{i}},\,t_{n +1}\Big{)}\]  
where \(\hat{y}_{i}\) represents the probability of the user visiting POI \(p_{i}\), f represents the model parameters, and \(S^{1},S^{2},\ldots,S^{M}\) represents the historical sequences of the user, including the sequences of the same user and different users.
## 2 Materials and Methods  
### Sampling Site and Data Survey  
The experimental site was located at the Longbao Experimental Station (Longbao Station for short) at the Qinghai China Meteorological Administration cold ecological meteorological field science experimental base (Figure 1). Longbao Station is located in the Longbao Wetland Nature Reserve, which is northwest of Yushu City, Yushu Prefecture, Qinghai Province, China. It is 4167 m above mean sea level and is located at 96.55\({}^{\circ}\) E, 33.2\({}^{\circ}\) N, with an annual mean temperature of \(-0.4^{\circ}\)C and an annual precipitation of 731 mm [15]. The underlying vegetation type is typical alpine wetland. _Kobresia Tibetica, K. pygmaea_, and _K. humilis_ are the main herbage species in the plot. _Kobresia Tibetica_ accounts for 60-70% of forage species [15], and the soil type of the plot is mainly alpine marshland [15].  
The Longbao Experimental Station was built in 2011, and its micrometeorological observation system was put into operation after the station was built in 2011. The vorticity flux observation equipment was built and put into operation in July 2018. A high observation tower of 2.5 m was set up at Longbao Station, and an open-circuit eddy correlation system (Li-7500A, Li-cor, Logan, Lincoln, NE, USA) was installed to measure three-dimensional wind speed and carbon dioxide and water vapor concentrations at 10 Hz. To process raw data collection, the half-hour mean value of NEE and evapotranspiration (ET) were calculated after removing peaks, two-dimensional coordinate rotation, time-lag compensation,  
Figure 1: Site introduction.  
and density fluctuation correction using open-source software Eddypro 6.0 [16; 17]. The observation program also included micrometeorological forcings, including air pressure, four-component radiation (1.5 m), photosynthetic effective radiation (1.5 m), wind speed and wind direction of two layers (1 m/2 m), soil temperature and humidity of five layers (0.05/0.1/0.2/0.3/0.4 m), two soil heat fluxes (8 cm/10 cm), precipitation (0.75 m), and air temperature and humidity of two layers (1 m/2 m). The parameters of the power supply and data acquisition accessories were three 20 W solar panels, a 65 AH colloid cell, a CR1000 data acquisition device, and an AM16/32B Expansion Panel. The eddy correlation observation system was equipped with three 80 W solar panels, one 100 AH colloidal cell, and one 120 AH colloidal cell. The data were CR3000 and CR1000.  
The data from Longbao Station were selected from the original data observed by the vorticity correlation system from January to October in 2019; data quality levels were chosen with QC = 0 and QC = 1 (QC of 0 indicates the highest data quality, 1 is average, and 2 is poor). The data missing from the NEE observations are shown in Table 1, with a missing data rate of 17.92 percent in 2019. Among them, 47.22% were grade 0 quality data, 24.26% were grade 1 quality data, and 10.60% were grade 2 quality data.
# Reliability of Social Networks on Activity-on-Node Binary-State with Uncertainty Environments
## 4 Proposed AON BAT-Based Algorithm
### Preprocessing Operations: Transfer of Uncertainty Components into Crisp Components  
The preprocessing steps of the proposed algorithm include dealing with uncertainty nodes whose reliabilities are unknown. The basic concept of transferring uncertainty nodes into crisp nodes is based on fuzzy set theory [46], as mentioned in Section 3.2. The pseudocode for the proposed preprocessing operation is as following Algorithm 3 [47]:  
\begin{tabular}{l l} \hline \hline
**Input:** & An AON BN \(G(V,E,\mathbf{D})\) with uncertainty nodes. \\
**Output:** & Convert each uncertainty node into a crisp node. \\  & Each expert rates the uncertainty nodes using linguistic variables. \\  & Transfer each linguistic variable into a fuzzy number. \\  & Average experts' fuzzy numbers and calculate their related \(\alpha\)-cuts for each \\  & uncertainty node to have an AFN [47]. \\  & Convert AFN to FPS with defuzzification [55]. \\  & Convert FPS to FFR [56]--which is the unreliability. \\ \hline \hline \end{tabular}  
Taking Table 6 as an example of which nodes 3-5 are uncertainty nodes, in STEP P0, experts first rate these uncertainty nodes using linguistic variables, as shown in Table 4 and the second column of Table 6. Then, STEP P2 calculates the average fuzzy number (AFN) based on Table 4 and STEP P1, as shown in the third column of Table 6.  
STEP P2 mainly focused on aggregating experts' ratings into an AFN. Subsequently, each \(\alpha\)-cut of the related AFN is also required to be calculated, as shown in the last column of Table 6.  
After many fuzzy numbers are aggregated into an AFN and the related \(\alpha\)-cut, the next step is to defuzzify the values. Based on the left and right fuzzy ranking method proposed in [55], STEP P3 defuzzifies AFN to FPS, as shown in columns 2 to 4 of Table 7. The last step, that is, STEP P4, transfers APS to FFR based on Equation (21), as listed in the last two columns of Table 7. Consequently, the FFR is treated as the unreliability of the related uncertainty node.
## 4 Discussion
### Limitations and Further Work  
The 91.56% classification accuracy of the proposed criterion is not yet fully satisfactory, and adopting a simplified \(UTCI\) result as the true values is only applicable under a few surrounding outdoor conditions within the built environment. The accuracy could be improved by further calibrating the weighting factors used by performing in situ measurements and adjusting \(MRT\) for computing \(UTCI\) accordingly. These tests and data elaboration will reduce the number of both \(FP\) and \(FN\), thus increasing the accuracy.  
In addition, a normed value could allow capturing and communicating more information on the intensity of the thermal stress and air pollution hazard; as well as providing a rather continuous function, instead of such a discrete trend when applying Booleans.  
Further work is planned to include also the physiological characteristics of sensitive groups to further understand the thermal stress variability among those sensitive groups at risk. Furthermore, gathering larger yearly databases and handling time-series missing data would allow making more reliable assumptions and conclusions from the results.  
In addition, generating hazard risk frequency daily profiles from environmental data eases future work on the combination of other risk-related analysis, which have a narrower arousal temporal scale (e.g., earthquakes, terrorism attacks), for the superimposing of multiple risk assessment.