esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] and [PERSON] [36] used [PERSON]'s works and developed an analytical formula to calculate the absorbed fraction of monoenergetic electrons (25 keV-4 MeV) and beta-emitters, which are uniformly distributed in unit density spheres of different sizes (0.13-6.20 cm). The Siegel-Stabin results have been used by [PERSON] and [PERSON] for comparison with EGS4 and MCNP [37]. Later, [PERSON] and co-workers [38] calculated the absorbed fractions for electrons uniformly distributed in ellipsoidal volumes by Geant4 version 9.1. In that study, the absorbed fractions in spheres for monoenergetic electrons were determined for comparison with the recommended values of [PERSON] and [PERSON] [37]. These studies [37; 38] are included in the more recent work of [PERSON] et al. [39], which presented the absorbed fraction and the S factor for targets of different sizes and compositions. Specific absorbed fractions were calculated by the MCNPX code for monoenergetic photons, electrons, and \(\alpha\)-particles in spherical and ellipsoidal tumor volumes, while S factors for 22 radionuclides were also provided. The aim of the present study is (i) the development of an analytic range-energy relation, effective over the energy range from 100 keV to 20 MeV, which covers most applications of radiotherapeutic interest; (ii) the use of the new formula for DPK-based calculations of the absorbed fraction and S factor and their comparison against MC simulations (both published and present work data) as well as the MIRDcell software that uses a similar analytic methodology; and (iii) the study of the influence of simulation parameters (step-size, tracking/production cut-off energies, and ionization model) in Geant4-based calculations of S factors. The motivation for the development of a new analytic formula is to overcome the energy limitations of the existing formulas, thus facilitating the application of DPK-based analytic methods as an alternative to effort- and time-intensive MC simulations in the calculation of the absorbed fraction and S factor in RPT dosimetry. The work focuses on \"intermediate\" size target volumes (-mm to cm), relevant to micrometastasis and disseminated disease, which represent an important field of application of RPT. The validation of the present analytic formulas is limited to the energy range from 100 keV to 4 MeV, which covers the usage of \(\beta\)-emitters in RPT, and published data of the absorbed fraction and S factor are available. However, the proposed parameterization extends up to 20 MeV electron energies, in order to cover potential application to external beam radiotherapy (EBRT).
# Application of Absolute Nodal Coordinate Formulation in Calculation of Space Elevator System
## 5 Discussion
### Lateral Loading  
In the case of lateral loading, the calculation results of the three methods are basically the same; however, since the bending stiffness of the rope is not involved in the LM model,  
Figure 14: Relative value of the calculation residual.  
it cannot be obtained when the flexible deformation contour line is locally required. The beam element of FEM uses an approximate curvature, which causes the overall system stiffness matrix in FEM to remain unchanged. This feature can turn the system's nonlinear equations into a linear equation, while the gravitational and centrifugal force received by each node will change as the coordinates of the node change. Therefore, it is necessary to continuously update the loaded nodal force in the calculation. Under certain conditions, the result of the calculation will not converge.  
For example, when \(m_{c}\) is \(1.96\times 10^{6}\) kg, lateral force is \(1\times 10^{5}\) N, the result of the calculation is shown in the Figure 15.  
In this case, the results of ANCF method and LM model calculations are basically the same, but the FEM calculation failed. The mass of the zenith anchor is reduced, resulting in a drop in the rope tension. When loading laterally, the change in the nodal force caused by the displacement of the previous step leads to a greater nodal displacement, which leads to non-convergence of the calculation results.
# Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks
## 3 The Preliminary Description of the Problem  
### Problem Formulation  
To formulate this energy issue, N number of nodes equipped with the power-receiving batteries distributed throughout the two-dimensional area, a rest station (RS), a base station (BS), and a WPCD are considered. The deployed nodes are considered as static (fixed) nodes. Notations for the entire network model are defined in Table 1.  
The WPCD starts its journey from a RS and travels among all the nodes to transfer power via WPT. After completing the cycle, it recharges its own battery at the RS. In a practical environment, the WPCD can be the vehicle driven by a person on the proposed traveling path. If a person is driving the vehicle, he can also save the WPCD from the hurdles found during its traveling path. The WiTricity Corporation has numerous light weighted, low power and small products that can be installed on the WPCD for wireless power transfer Li et al. (2019). For instance, the WPCD can be equipped with the WiTricity-3300 which weighs 3.6 kg and is 20 cm \(\times\) 28 cm \(\times\) 7 cm in size. In our scenario, energy and maintenance cost required for the moving of the WPCD is not considered as it is not a bottleneck. A set of N nodes deployed in a two-dimensional region is displayed in Figure 1.  
All the nodes in the wireless sensor network are assumed to be facilitated with a battery of maximum energy E\({}_{max}\). The energy of the node decreases with the passage of time and then dies out if the energy reaches below the E\({}_{min}\). Thus, E\({}_{min}\) is the minimum energy required to keep a sensor node functional. Each node senses data and forwards it to the base station. The flow rate is different with time because our energy consumption model is handling the rate of flow of data and routing as well. Further, each node sends data at the rate of R\({}_{i}\) (bps), where \(i\) is any single node in network N.  
[PERSON] et al. described the renewable energy cycle of the WPCD [31]. In the proposed energy cycle, the WPCD starts its journey from a RS and remains engaged for recharging the battery of all sensor nodes that come along its way. We assume that the WPCD has adequate energy stored to charge all the sensor nodes in the network. The WPCD will traverse all the nodes for recharging the power with the velocity of V (m/s). After leaving from the RS, the WPCD starts to charge the first node \(i\). As mentioned
## 2 Remaining Useful Life (RUL) Prognostics Methodologies
### RUL Prognostics Methodologies Based on Artificial Intelligence
Thus, [PERSON] _et al._[32] proposed a training algorithm (Baum-Welch algorithm) based on an improved particle swarm optimization (MPSO)-amended hidden semi-Markov model (HSMM) to produce the RUL reliability function and the system failure rate, and finally acquire the RUL distribution of the equipment. Given that RUL prediction accuracy of the training algorithm is not high and applicability is not perfect, [PERSON] _et al._[33] reduced noises in characteristic signals using wavelet decomposition and estimated the battery RUL, which was based on NASA's lithium-ion battery data and used the RVM degradation model. The prediction methods based on RVM are the main methods for lithium-ion battery RUL estimation. However, the training time increases rapidly with the increase of the training sample. Other methods are also used in the parameter identification of the model. [PERSON] _et al._[34] constructed three kinds of regression models based on the statistical data (N order polynomial regression model [35], bivariate polynomial regression model, and the index regression model), and introduced the particle swarm optimization (PSO) algorithm to optimize the model parameters. Simulations indicate that the regression models using discharged voltage and internal resistance as aging parameters can more accurately build a state of health profile than those using cycle numbers. [PERSON] _et al._[36] proposed a double-index lithium battery degradation model and used the Dempster-Shafer theory (DST) to initialize the model parameters and the Bayesian Monte Carlo (BMC) method to update the model parameters, which are used to predict the battery RUL. [PERSON] _et al._[37] put forward a nonlinear kernel regression model of lithium battery degradation, obtained degradation parameters through the K-nearest neighbor, and used PSO to optimize the weight of the K-nearest neighbor regression model. [PERSON] _et al._[38] developed a quantitative approach for the battery RUL prediction based on an adaptive bathtub-shaped function and used the artificial fish swarm algorithm method to optimize the parameter model. This prognostic model can capture the dynamic behaviors of the battery capacity.
## 3 Load Profiles Model and Assessment of the Solar-Rooftop Energy Potential
### Study Case 1: Load Profiles Model
#### 3.1.2 Outputs of Model  
The model allows us to generate different profiles. These data consider aggregated load profiles by houses and for the whole community. The total electricity consumption  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Characteristics** \\ \hline \multirow{4}{*}{Number of inhabitants} & Single \\  & Couple \\  & Couple with children \\  & Family with grandparents \\ \hline \multirow{4}{*}{Lifestyle} & Full-time employed \\  & Reitred \\  & Homeworker \\ \hline \multirow{4}{*}{Electrical appliances} & Small (type devices) \\  & Medium (type devices) \\  & High (type devices) \\ \hline House size & One to five bedrooms \\ \hline \multirow{2}{*}{Emergency case} & Medical devices \\  & Basic appliances \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of residential load consumption.  
Figure 3: Load profiles model concept.  
can be split by day, month, and year, as well as the overall consumption of each individual appliances. The output data are used to plot and analyze the peaks of energy consumption and the hours of maximum energy consumption, thus identifying the critical case, corresponding to the case with the highest energy consumption.
## 3 Materials and Methods
### Vertical and East-West Deformation Field Estimation  
As increasingly done in the Sentinel-1 InSAR literature (e.g., [24; 61; 79; 80]), the ascending and descending datasets were combined to retrieve the vertical \(V_{U}\) and east-west \(V_{E}\) velocity field. A fishnet of 90 m square elements was used to resample the point-wise datasets to a regular grid, and to link the output datasets into a unique digital layer. When both P-SBAS outputs (ascending and descending) were available at the same location \(i\), the combination was achieved under the assumption of negligible north-south velocity, \(V_{N}\) = 0. This assumption is typically employed in InSAR studies to account for the relatively poor visibility of north-south horizontal movements that the sensor LOS can achieve (e.g., [81; 82]).  
Given the known values of the LOS deformation velocity in the ascending (\(V_{A}\)) and descending (\(V_{D}\)) mode at each location \(i\), the \(V_{U}\) and \(V_{E}\) were estimated as follows (e.g., [65]):  
\[V_{Ui}\ =\frac{E_{Di}*V_{Ai}-E_{Ai}*V_{Di}}{E_{Di}*U_{Ai}-E_{Ai}*U_{Di}} \tag{1}\]  
Figure 4: Google Earth output file obtained after P-SBAS InSAR processing of the Sentinel-1 IW SAR ascending mode stack acquired in 2014–2020 along track T146: (**a**) overview, and (**b**) detail over the area of Crotone and Capo Colonna. As any long-wavelength signals were removed during the processing (see Section 3.2), the absence of regional scale deformation in the map should not be interpreted as such. Background image ©2020 Google.  
\[V_{Ei}\ =\ \frac{U_{Ai}*V_{Di}-U_{Di}*V_{Ai}}{E_{Di}*U_{Ai}-E_{Ai}*U_{Di}} \tag{2}\]  
where \(E\), \(N\), and \(U\) are the east-west, north-south and vertical directional cosines or unit vectors that define the 3D orientation of the Sentinel-1 IW LOS, and depend on the incidence angle \(\theta\), and the ground track or heading angle \(\alpha\) of the satellite flight direction (i.e., \(\sim\)345-350\({}^{\circ}\) and \(\sim\)190-195\({}^{\circ}\) for the ascending and descending modes, respectively). Their values can be estimated at each location as: \(E_{i}=-cos\alpha_{i}*sin\theta_{i}\), \(N_{i}=sin\alpha_{i}*sin\theta_{i}\), and \(U_{i}\) = \(cos\theta_{i}\), and accounting for \(\theta\) variations across the IW swath.  
When only the ascending (or descending) mode dataset was available at location \(i\), the additional assumption of null east-west velocity was required, \(V_{E}\) = 0, and the vertical velocity was then estimated by simply dividing the LOS velocity value by \(U_{i}\).
## Materials and Methods
### Pyrosequencing and Pre-Processing of Sequence Reads  
Purified PCR products (library) were pooled in equimolar ratios and paired-end reads were generated on an Illumina GAIIx sequencer. Image analysis and base calling were done using Illumina Analysis pipeline (Version 2.2). High quality reads with more than 70% of bases with Phred Score greater than 20 were considered for subsequent analysis. Reads with adapter sequences, primer sequences, the barcode and the degenerate bases were removed using an automated Perl code generating processed reads. Duplicates and chimeras were removed using CD-HIT DUP [24].
## 1 Introduction  
With the increasing need for electricity and the problems associated with centralized fossil fuel power plants--such as environmental pollution, exorbitant construction and maintenance costs, etc.--the use of MEMGs is a good solution; they allow the extensive utilization of renewable energies, distributed energy resources (DERs), and participation of consumers in the optimal management of power system operation, and using these systems can play a vital role in optimal energy consumption, system stability, and system reliability [1; 2; 3; 4]. Furthermore, renewable energy sources have high uncertainty and an intermittent nature [5; 6; 7; 8]. One solution to this problem is the micro-grid, which facilitates the response to load demand [9; 10; 11; 12].  
In terms of operation, micro-grid energy management systems (MEMSs) can be divided into centralized and decentralized (distributed) perspectives [13]. In centralized approaches, a central agent, which can process large amounts of data, is needed in order to gather information from other agents [14]. In multi-carrier micro-grids with distributed energy management, the privacy of agents is preserved. Moreover, each independent agent can optimize its costs in a parallel or sequential manner [15]. Providing an optimal approach to planning of system components is very important. The network used in this research consisted of micro-turbines, waste power plants, fuel cells, wind turbines, boilers, anaerobic reactors, inverters, rectifiers, and some energy storage units. It is also possible to exchange information between different levels of the system. This feature increases the reliability of the system and, compared with non-participating systems, achieves a better result. In this research, we sought to find the optimal performance strategy for system components, while meeting the electrical and thermal needs of customers. The micro-grid system is capable of exchanging electricity with high energy levels, as well as daily component performance scheduling.  
The rest of this paper is organized as follows: In Section 2, a review of the related work, along with a description of the proposed methodology of this research, is provided. Section 3 presents the proposed MEMG structure. In Section 4, agents are modeled. In Section 5, the simulation of the proposed method to achieve optimal performance of MEMG agents is performed. Section 6 presents the simulation results, and we review the obtained results with different criteria and compare them with other methods in order to validate the proposed method.
### Our Contributions  
In the present paper, we study the discrete, entropy-regularized MOT problem with a tree-structured [3; 13] or a circle-structured cost function, where all measures are supported on a finite number of points (atoms) in \(\mathbb{R}^{d}\). Then, the computational time of the Sinkhorn algorithm [28; 29; 30], which iteratively determines a sequence converging to the solution, depends linearly on the number \(K\) of input measures. If the numbers of atoms is large, however, the Sinkhorn algorithm still requires considerable computational time and memory, which mainly comes from computing a discrete convolution, i.e., a matrix-vector product with a kernel matrix.  
This is significantly improved by Fourier-based fast summation methods [31; 32]. The key idea is the approximation of the kernel function using a Fourier series, which enables the application of the non-uniform fast Fourier transform (NFFT). Although such fast summation methods are frequently used in different applications such as electrostatic particle interaction [33], tomography [34], image segmentation [35], and, very recently, also OT with two marginals [36], they have not been utilized for MOT so far. Furthermore, a method for accelerating the Sinkhorn algorithm for Wasserstein barycenters via computing the convolution with a different kernel, namely the heat kernel, was discussed in [37].  
Our main contribution is the combination of the fast summation method with the sparsity of the tree- or circle-structured cost function in the MOT problem for accelerating the Sinkhorn algorithm. Each iteration step has a complexity of \(\mathcal{O}(KN)\) for a tree- and \(\mathcal{O}(KN^{2})\) for a circle-structured cost function, compared to \(\mathcal{O}(KN^{2})\) and \(\mathcal{O}(KN^{3})\), respectively, with the straightforward matrix-vector operations, where \(N\) is an upper bound of the number of atoms for each of the \(K\) marginal measures. Our numerical tests with both tree- and circle-structured cost functions confirm a considerable acceleration, while the accuracy stays almost the same. A different acceleration of the Sinkhorn algorithm via low-rank approximation for tree-structured cost yields the same asymptotic complexity [38]. We note that MOT problems with tree-structured cost functions are used for the computation of Wasserstein barycenters [4], and with circle-structured cost for computing Euler flows [39].
# AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things
## 3 Results and Discussion
### TSP-Based Method Comparison  
In this section, we compare the proposed Vol-aware forward DP method with a conventional TSP-based approach. The TSP method solves the Euclidean Traveling Salesman Problem using the horizontal positions of CH centroids to minimize total travel distance. Once the TSP order is determined, a single waypoint per CH is selected, specifically, the one on its horizontal communication circle with the lowest travel time from the previous point. Both trajectories are executed using the proposed Dubins-like path design, and mission time and residual Vol are evaluated.  
Figure 8 illustrates the resulting paths for both methods, highlighting the differences in waypoint selection and routing behavior. As shown in Table 2, the Vol-aware method outperforms the TSP-based approach by effectively prioritizing time-sensitive data. While  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Proposed Method** & **Straight-Line Trajectory** & **Single-Point Design** & **TSP-Based Method** \\ \hline
**Total Residual Vol** & 2.7161 & 2.7080 & 2.0749 & 2.4568 \\
**Total time (s)** & 45.0167 & 45.3374 & 74.1794 & 55.8793 \\
**Preserved Vol Ratio** & 65.95\% & 65.75\% & 50.38\% & 59.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the proposed method with straight-line, single-point, and TSP-based designs.  
Figure 7: Comparison of the proposed (green solid line) and the single-point (red dash-dot line) designs: (**a**) 3D trajectories and USV positions; (**b**) 2D x–y projection.  
the TSP method preserves 59.65% of the total VoI, the proposed method retains 65.95%, representing a 6% increase in VoI preservation. This improvement results from the forward DP method's joint consideration of path feasibility, travel time, and data value, leading to more efficient and information-preserving trajectories. These findings demonstrate that distance-minimizing approaches alone do not necessarily yield the highest residual VoI.
# An Accurate Platform for Investigating TCP Performance in Wi-Fi Networks
## 4 Evaluation  
### Environmental Settings  
The first evaluation aims to demonstrate the effectiveness of the emulator approach by comparing the emulated TCP performance results with those from an actual Wi-Fi network. We have evaluated two scenarios: one Wi-Fi client and two Wi-Fi clients. In the former scenario, a Wi-Fi client communicates with an application server via an AP and its backhaul link in both networks. In the actual network, we use a laptop running Ubuntu with Linux kernel 4.19.97 as the Wi-Fi client. Moreover, we use two Raspberry Pi 4 devices as a server and an AP. The two Raspberry Pi devices run the Raspbian Buster OS (Debian version 10). Additionally, we use hostapd (version v2.8-devel) to implement the Wi-Fi AP, operating with IEEE 802.11g. The Raspberry Pi's Wi-Fi module is BCM4345/6 with the brcmfmac 7.45.154 driver. To set the bandwidth and delay values of the backhaul link, we use tc and netem, respectively. However, we do not use netem to measure the RTT between the client and server; instead, we use ping. The results show that the average RTT value in the actual network is 1.3 ms.  
In the emulator environment, we use the same kernel as the Wi-Fi client's machine on the host computer. In Mininet-WiFi, we have created five nodes, which are the Wi-Fi client, an AP, two switches, and a server, as shown in Figure 3. The first hop from the client is a Wi-Fi access point using IEEE 802.11g. Then, there are two additional switches (S1 and S2), which are Open vSwitches (OVSs), to emulate the backhaul link. An OVS controller is used in the emulator to find routes between devices. However, this occurs only once at the beginning of each experiment and does not affect the network. It is hence omitted. In the comparative environment, we set the bandwidth of the backhaul link to 10 Mbps and the delay to 0.65 ms (to match the value in the actual network). The wired links other than the backhaul have a bandwidth of 1000 Mbps in the emulator environment. Moreover, we investigate queue sizes of 20 and 100 packets (i.e., S1's queue in the emulator and the AP's output queue in the actual network). This queue size setting is based on [48]. In the latter scenario, we have added one more Wi-Fi client to the topology in Figure 3. We have added a similar laptop, one Mininet-WiFi's Wi-Fi client, for the actual and emulated environment, respectively.  
Figure 3: Wi-Fi network topology.  
In the second evaluation, we investigate CCAs with different Wi-Fi network settings, including the Wi-Fi link as the bottleneck and different signal strengths. The third evaluation considers and compares the fairness of multiple TCP flows with each CCA in Wi-Fi networks. The network configuration parameters used in each performance evaluation are summarized in Table 3.