esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Distributed Method for NLOS Cooperative Localization in WSNs
## 5 Numerical Simulations
### ADMM_P Properties in Different Noisy Environments  
In this scenario, we fixed the number of anchors and the distribution parameter of NLOS errors to show localization accuracy and convergence property in different noisy environments. The mean parameter of NLOS error was fixed as \(\alpha_{\text{NLOS}}=3\). Ten anchors were randomly deployed in the region of \(100\times 100\) m. As is shown in Figure 4(a),b, we present the simulation results with \(\sigma_{n}^{2}=0.01\times 100\) and \(\sigma_{n}^{2}=0.1\times 100\). We found that ADMM_P mitigates the effect of NLOS range measurements and provides better estimation accuracy. SDPH is solved with the interior-point method under a centralized framework, so its simulation results are only provided as accuracy references in Figure 5. From the figure, we can see that ADMM_P has good convergence performance in different noisy environments and higher accuracy than SDPH. In Figure 4(b), we further analyzed the convergence performance of ADMM_P. Given that the local objective functions are based on heuristic solutions,the function value cannot converge to an extremely low level. Hence, the curve tendencies in Figure 4(b) still demonstrate a stable convergence of ADMM_P.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **SDPH [35]** & **SDP [36]** & **EDM [37]** & **ADMM_P** \\ \hline Convex problem size & \(dN+1/2N(N+1)\) & \(dN+1/2N(N+1)+N^{2}\) & \(N+M(M-1)/2+N_{c}\) & \(d(\hat{N}_{i}+1)\) \\ Computational complexity & \(O(N^{3})\) & \(O((2N)^{3})\) & \(O(N^{3}+N^{2})\) & \(O(d^{3}+(d\hat{N}_{i})^{2})\) \\ Type of framework & Centralized SDP & Centralized SDP & Three-block ADMM & Parallel ADMM \\ Communication cost & \(N\) & \(N\) & \(N\) & \(d(\hat{N}_{i}+1)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Complexity comparison of available algorithms.
## 3 Packet Delays within a Router
### Delay Caused by Packet Routing  
Packets which enter a router via its network interface card are filtered by their destination network address using its routing table. The delay of this process is dependent on the software to setup the process and the hardware components e.g., memory access time. Performance of router hardware is highly variable since it is dependent on its underlying technology, including its processing power and memory capacity. Additionally, high throughput hardware can be purchased which exhibits performance improvements due to its specification. Networks typically contain equipment of varying ages which results in performance variations. In this work, to enable other factors to be compared, consistent typical performance hardware has been used.  
Router operating systems (OS) are optimized for routing of packets. Routers are also required to perform many other tasks which will be dependent on its feature set. The Basic OS has all the normal routing functionality but the Advanced OS has an additional feature set that includes more than 950 additional functions which include support for VoIP, SNMP, SIP, AAA Server, PIM MIB Extension for IP Multicast, Netflow, IPSec Network Security, TCP-TCP Congestion Avoidance _etc._[31]. This functionality can be invoked by applying the appropriate configuration to the router. A comparison of OS size and number of supported/running processes was undertaken using an OS with basic functionality and another with advanced functionality. For all the measurements reported the hardware used was a Cisco 2600 series routers, basic OS was c2600-js2-mz.122-13.T5 and the advanced OS c2600-adventerprise89-mz.124-19. Applying a configuration that only enabled the interfaces and OSPF routing protocol to the router, the show processes command provided the information shown in Table 1. The third column indicates the total number of processes that the router had run from startup. Many of the processes are only run once or twice and so do not take part in the normal packet handling so the fourth column was introduced to indicate the processes that were being run continually to give a better understanding of the overhead associated with the routing process. Clearly the advanced OS runs many more processes for a given configuration compared to a basic OS which will have an effect on the responsiveness of the CPU and the amount of memory required.  
If a core part of the OS is enhanced with additional functionality e.g., HTTP or DHCP Servers it can have an additional adverse effect on the size of the OS and its performance to that seen in Table1.
## 1 Introduction
Finally, the model with the lowest error and with high accuracy was optimized by two new algorithms, namely, the whale optimization algorithm (WOA) and Harris hawks optimization (HHO). This problem was optimized using a combination of SVR and HHO-WOA models. The \begin{table}
\begin{tabular}{c c c} \hline
**Author (Reference)** & **Soft Computing Technique** & **Aim** \\ \hline [PERSON] et al. [42] & ANN & Provide a hybrid ANN-FEM model for slope stability analysis and FOS \\ [PERSON] et al. [43] & PSO-ANN & Development of a hybrid model for FOS evaluation and comparison with numerical methods \\ [PERSON] et al. [44] & ANN & Applying multiple linear regression and ANN models for 200 cases and comparison with analytical methods \\  & Imperialist competitive algorithm & Development of various ANN models using \\ [PERSON] et al. [41] & (ICA)-ANN, GA-ANN, PSO-ANN and & 4 optimization algorithms and evaluation of FOS with different conditions \\  & artificial bee colony (ABC)-ANN & different conditions \\ [PERSON] et al. [45] & SVR & FOS forecasting using SVR method and testing of \\  & & real cases \\ [PERSON] et al. [46] & ANN & Prediction of minimum FOS against slope failure in \\  & & clayey soils using ANN \\ [PERSON] et al. [47] & ANN & Calculate the factor of safety of dump slope of a coal \\  & & mine using developed ANNs \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of some studies that applied soft computing techniques to evaluate the factor of safety (FOS). results show that a new methodology can be proposed for FOS parameter analysis. This article has the following sections: The second section discusses the analysis methods for FOS. Then, a review of the data conditions is performed, and simulation and optimization models are implemented. Finally, the article continues with discussion and conclusions about the applications of this research.
## 4 Operation of Proposed Scheme
### Data Gathering Phase  
After completing the NPT, the AUV stays at the ending point \(\mathbf{P_{2}}\) in Figure 4, until the last CH notifies the end of clustering. Then, the AUV returns to point \(\mathbf{P_{1}}\) to start a DGT. The AUV selects the nearest cluster as a first visit based on the information on generator points. Then, the AUV finds the associated CH in order to obtain the information required for data gathering, such as the list of PNs to visit and CDMA code being used on the MN\(\rightarrow\)PN links. In most cases, the CH is located near the generator point. Since the AUV has knowledge of the generator points, it approaches each generator point as shown in Figure 5 and takes the well-known neighbor discovery procedure as proposed in [31] to search for the CH. Then, communicating with the CH through a control channel, the AUV acquires the aforementioned information.  
Figure 5: AUV data-gathering tour in UWSN.  
Figure 6: MN\(\rightarrow\)PN and PN \(\rightarrow\)AUV (**a**) communication related event (**b**) TDMA frame structure.  
Figure 6 shows the frame structure of the MN\(\rightarrow\)PN TDMA channel and the relevant events on the time axis. The TDMA channel is switched from MN\(\rightarrow\)PN mode (PN in reception) to PN\(\rightarrow\)AUV mode (PN in transmission) when the AUV arrives. The first time slot, named \(S_{{}_{AUV}}\) in every TDMA frame, is reserved for broadcasting of AUV-related signaling. Overhearing the TDMA channel to find out the \(S_{{}_{AUV}}\) slot, the AUV announces its arrival via \(S_{{}_{AUV}}\).  
Then, on the next \(S_{{}_{AUV}}\) slot, the PN also announces a mode change of the TDMA channel from MN\(\rightarrow\)PN to PN\(\rightarrow\)AUV and starts transmitting the data it has held for the AUV. The PN\(\rightarrow\)AUV mode continues until the PN sends out all data. After that, the PN announces a mode change back from PN\(\rightarrow\)AUV to MN\(\rightarrow\)PN and also the residual energy via the \(S_{{}_{AUV}}\) slot. For the rest of the frame, a new PN could be selected by the CH if the residual energy of the current PN \(E_{{}_{res}}\) is less than a certain threshold level \(E_{th}\). During PN\(\rightarrow\)AUV, MNs may go into sleep mode to save energy.  
After completing the entire PN\(\rightarrow\)AUV gathering for a given cluster, the AUV moves to the next cluster and follows the same procedure as described above until it reaches the last cluster. Then, the AUV returns back to location \(\mathbf{P_{1}}\) to execute the final step, AUV\(\rightarrow\)sink, where a dedicated CDMA code is used to avoid interference to ongoing MN\(\rightarrow\)PN communication in those surroundings. Completing AUV\(\rightarrow\)sink, the AUV starts the next round of data gathering for the DG_Phase.
## 1 Introduction
The designed structure is composed of three layers, where the organic solar cell is placed on top of the antenna in order to obtain 100% insolation. As such, the OSC is fully exposed to sunlight energy, thereby improving the antenna's performance. The novelty of this work is the proposition of the utilization of a lightweight, nanostructured, and radiation tolerated solar cell that is integrated with patch antennas for satellite applications. These unique features can only be found in the third-generation solar cells, known as organic solar cell (OSC). The designed structure can be specifically useful for fixed-satellite-services (FSS) operating over the frequency range of the 11.7 to 12.22 GHz band. The remainder of this paper is organized as follows: Details on the design structure and OSC incorporated assembly is given in Section 2, while the achieved results are analyzed and discussed in Section 3, followed by the main conclusions, which are drawn in Section 4.
## 5 Performance Evaluation and Results Discussion
### Comparison in Scalability  
ME-CBCCP is scalable so when the number of nodes and dimensions of the area are increased, even then it shows its improvement over other protocols. The comparison of ME-CBCCP with a number of nodes (1500) with same numbers of cluster heads in the area of 300 m\({}^{2}\) is performed. It is observed that it outperforms LEACH, SEP, genetic HCR, ERP, MODELACH, T-DEEC in terms of network lifetime and stability and LEACH, SEP, genetic HCR, ERP, EESAA in terms of time. Genetic HCR and ERP are not suitable for the dense network as around 500 nodes are dead after 450 rounds and they again took 26 to 29 h to execute. Both of these protocols are not comparable in this simulation so we excluded them in this comparison. In the parameter of network lifetime the result of EESAA and ME-CBCCP is  
Figure 8: Transmission delay (II) (1000 nodes, 200 m\({}^{2}\)).  
Figure 9: Transmission delay (1500 nodes, 300 m\({}^{2}\)).  
comparable as EESAA lost 456 nodes in 500 rounds but ME-CBCCP lost only 123 nodes. In 1000 rounds ME-CBCCP lost 162 nodes, but EESAA lost 776 nodes. However, after 5000 rounds no node is alive in ME-CBCCP but 25 nodes are still alive in EESAA. This happens because the subarea which is assumed as one cluster in ME-CBCCP has dimensions of 200 m by 20 m, but in the simulation of 1500 nodes in the area of 300 m\({}^{2}\)the number of clusters are kept same, that is only 10 clusters and one cluster has dimensions of 300 m by 30 m, so the distance of transmission is increased for the nodes which results in the death of the number of nodes after 2500 transmissions. Before that it works better than EESAA in terms of network lifetime. This can be solved by getting back to Step 2 of Algorithm 1. In terms of the time parameter ME-CBCCP again outperforms EESAA.
# FANT: Flexible Attention-Shifting Network Telemetry
## 4 Metrics Categorization Strategy
### Implementation  
Algorithm 1 outlines the pseudocode of the metrics categorization strategy.  
```
Input:\(E\) Output:\(E_{b},E_{d}\)
1\(E_{b}\leftarrow\emptyset,E_{d}\leftarrow\emptyset\)
2 for\(e\in E\)do
3 fore\(e_{i}\in E\setminus\{e\}\)do
4 if\(e_{i}\) is not available without \(e\)then
5\(T(e_{i}).\mathrm{append}(e)\);
6
7 if\(e_{i}\) have similar performance with \(e\)then
8\(S(e).\mathrm{append}(e_{i})\);
9
10 elseif\(e_{i}\) can be inferred by \(e\)then
11\(E_{b}.\mathrm{append}(e)\);
12
13 if\(e\) measures delay or influences applicationthen
14\(E_{b}.\mathrm{append}(e)\);
15
16 for\(e\in E_{b}\)do
17 for\(e_{j}\in T(e)\)do
18 if\(e_{j}\
otin E_{b}\)then
19\(E_{b}.\mathrm{append}(e_{k})\);
20
21 for\(e\in E_{b}\)do
22 if\(S(e)\cap E_{b}=\emptyset\)then
23\(E_{b}.\mathrm{append}(min\_length(S(e)))\); \(\triangleright\) Return the minimal length elements
24
25\(E_{d}\gets E\setminus E_{b}\) return\(E_{b}\),\(E_{d}\)
```  
**Algorithm 1**Metrics Categorization Strategy  
**Initialization:** The input to the algorithm is a set \(E\) (\(e\in E\)) that consists of all metrics \(e\) and can be monitored by the FANT system. The output of the algorithm includes a set \(e\in E_{b}\) of basic metrics and a set \(e\in E_{d}\) of detailed metrics. Initially, both \(E_{b}\) and \(E_{d}\) are empty sets.  
**Calculation:** Lines 2-7: Analyze any two distinct metrics in \(E\) to determine if there is a dependency or similarity relationship. If a dependency exists between \(e\) and \(e_{i}\), add \(e\) to set \(T(e_{i})\). If a similarity exists between \(e\) and \(e_{i}\), add \(e\) to set \(S(e_{i})\).  
Check any two distinct metrics in \(E\) for an inference relationship. If any other metric can be inferred by \(e\), classify \(e\) as a basic metric (lines 8-9).  
As described in Lines 10-11, Algorithm 1 classifies metrics in \(E\) based on the metric's attributes. If a metric cannot tolerate delay (impact on functionality) or requires continuous monitoring by some applications, add the metric to \(E_{b}\).  
Lines 12-18: Use the relationships between metrics to further identify which should be classified as basic metrics: First, iterate through the current \(E_{b}\). If e is in \(E_{b}\) but the metrics e depends on are not, then add all its dependent metrics to \(E_{b}\). Next, iterate through \(E_{b}\) again. During this iteration, if a metric and its similar metrics are not in \(E_{b}\), add \(e\) whose report overhead is smallest to \(E_{b}\).  
As reported from Line 19 to Line 20, detailed metrics are obtained by subtracting the basic metrics from the complete set of metrics.
# Optimization of Laser Fixed Contact Process for the Formation of an Al-BSF Layer
## 4 Conclusions  
In this work, we studied laser frequencies, power, and speed, to optimize the LFC development process. The HAZ, which inevitably occurs as a result of the laser process, aggravates cell characteristics, and so the LFC process must be performed using a laser process optimized to create a minimal HAZ. Nanosecond green lasers were used to obtain the thickest BSF layer through the LFC process, and the testing described herein showed that the best LFC procedure was achieved with a 1.2 \(\upmu\)m-thick Al-BSF layer. The HAZ size was minimized at a frequency of 400 kHz, using 5 W laser power, and a laser speed of 100 mm/s. For their electrical analysis, LFC solar cells were fabricated by adjusting the laser pitch and the number of application repeats. The resulting EQE analysis showed that the best cell characteristics were obtained using a laser pitch of 500 \(\upmu\)m and three times of laser process. Overall, the experimental results allowed us to conclude that the LFC technology was appropriate for use in improving cell characteristics.  
[PERSON] was the main author, carried out the experiments, and analyzed the data. [PERSON] reviewed and analyzed the data and formatting. [PERSON] conceived the ideas of the analyzed results. All authors participated in discussing the results, reviewing the manuscript, and providing input for editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Electric Power Corporation (Grant number: R17 XA05-1), the Korea Institute of Energy Technology Evaluation and Planning (KETEP), and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (Grant number: 20193020010650).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: Quantum efficiency graph measured after fabricating the solar cell using: (**a**) one, three, and five treatment repeat numbers; and (**b**) four different laser pitches.
# A Coupled Darcy-Forchheimer Flow Model in Fractured Porous Media
## 3 Numerical Solutions
### Solution Strategy  
According to the solution steps of the finite volume method, a large sparse equation group \(T\cdot x=A\) was obtained by assembling the pressure discrete equations of each element, including the seepage equations in porous media and fractured media. The coefficient matrix \(T\) is,  
\[T=\begin{pmatrix}T^{mm}&T^{mf}\\ T^{fm}&T^{ff}\end{pmatrix} \tag{18}\]where \(T^{mm}\) is the conduction matrix in porous media, \(T\!\!f\) is the conduction matrix of fractures, including the conduction coefficient of fractures and their intersections. \(T^{mf}\) and \(T^{fm}\) comprise the pore-fracture exchange conduction matrix and the fracture-pore exchange conduction matrix. It is worth noting that the fracture conduction matrix \(T\!\!f\) is a function of velocity, so the assembled equations are nonlinear. To solve the equations, a fixed-point iterative algorithm is used [26; 27]. The method is described as follows: Based on the above calculation method, the MATLAB programming function is used to design the nonlinear seepage program in the dual media. The program flow chart is shown in Figure 2. As seen in Figure 2, the connectivity index \(CI\) and \(\Psi\) function are first calculated. At the same time, the fracture velocity is initiated. The conduction matrix of porous and fractured media is then determined. Considering pressure and velocity boundaries, the pressure matrix is assembled and solved. When pore and fracture pressure tolerance is smaller than the allowable value, the calculation finishes. If not, the calculation returns back to the conduction matrix calculation.
# Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases
## 2 Materials and Methods  
### Clustering  
Clustering is an iterative procedure that groups similar data, primarily concerned with distance measures, and is a fundamental method in data mining. Clustering methods are classified as partition-based, hierarchy-based, density-based, grid-based and model-based. Moving object activity pattern analysis (i.e., similar motion patterns) and activity prediction are some typical application scenarios of trajectory clustering [3]. In our case, clustering is used to organize moving objects into groups so that the members of a group are similar, with great compactness, according to a similarity criterion, based on spatiotemporal data. Specifically, for a group of mobile objects, we apply clustering of their current trajectory point attributes (spatial coordinates, angle, and velocity) in specific time-stamps. In other words, we apply on-line clustering.  
K-means (Algorithm 1) [6], used in this work, belongs to partitioning clustering methods and is popular due to its simplicity. It is based on the squared error minimization method and the main advantage of K-means is that, in each iteration, only the distance between a point and the K cluster centers is computed. Its time complexity is \(O(NKt)\), where \(N\), K and \(t\) are the number of data objects, clusters and iterations, respectively. However, K-means clustering suffers in some points, e.g., the number of clusters K must be known in advance, and its computational cost with respect to the number of data observations, clusters and iterations. K-means and other clustering algorithms use the compactness criterion to assign clusters, which is our concern, in contrast with spectral clustering in [7], which makes use of the spectrum (or eigenvalues) of the similarity matrix of the data and examines the connectedness of the data. It is expected that K-means algorithm may be a good option for exclusive clustering (which we considered in this study) against Fuzzy C-Means, which assigns each mobile object to different clusters with varying degrees of membership. Therefore, it may give good results for overlapping clusters. Furthermore, it has much higher time complexity than K-means [8].  
```
1:Input:number of clusters K and training data \(\mathcal{P}\)
2:Output:ast of K clusters
3:Method:Arbitrarily choose K objects from \(\mathcal{P}\) as the initial cluster centers
4:repeat

1. assign each object to the cluster to which the object is the most similar based on the mean value of the objects in the cluster
2. Update the cluster means, i.e., calculate the mean value of the objects for each cluster
5:until no change
```  
**Algorithm 1** K-means.
## 3 Results and Discussion
### PM\({}_{10}\) Calibration  
Figure 5 displays an example of a PM\({}_{10}\) calibration of the low-cost sensors as a function of time. A summary of all PM\({}_{10}\) calibrations in the mass concentration range 20-150 \(\upmu\)g/m\({}^{3}\) is provided in Figure 6, and the data are listed in Table 2. A very good agreement between the AirVisual sensor and the gravimetric reference method was observed (\(E_{\text{DUT}}\) close to 1), while the SDS011 drastically underestimated the mass concentrations. For the PM\({}_{10}\) readings of the SDS011, [PERSON] et al., reported that particles larger than 5 \(\upmu\)m were severely underestimated, resulting in an average \(E_{\text{DUT}}\) value of 0.65 [31], which is consistent with the trends reported here.  
Note that sensors measuring PM mass concentration based on light scattering are very sensitive to the chemical composition and size distribution of the test aerosol. Since the properties of ambient air vary by time and location, it is impossible to define a single test aerosol in the laboratory that would sufficiently simulate all real environmental scenarios. In principle, the generators described in Section 2.2 are versatile, and can easily be used to produce different kinds of inorganic species (e.g., sodium chloride) or mineral dust particles (Arizona test dust, dolomite dust, kaolinite, etc.) to further refine the composition and scattering properties of the test aerosols. However, for simulating remote aerosols, marine aerosols, or specific pollution events (such as wildfires), more dedicated experiments must be carried out, and additional generators (e.g., a stove) would be required. A rigorous calibration of low-cost sensors in the laboratory, akin to that described in this study, would increase the price of the sensors. A more pragmatic approach would be to provide sensor manufacturers, air quality monitoring stations, and other end users with a limited set of calibrated sensors, which could then serve as (mobile) \"reference\" set in the laboratory or field [33; 34; 35].