esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reliable Routing Algorithm Based on Path Satisfaction in the Energy Internet
## 4 Solution Approach
### Path Satisfaction-Based Service-Aware Routing Algorithm (PSSRA) for the Energy Internet
(2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Survivability-aware routing restoration mechanism for smart grid communication network in large-scale failures. _EURASIP J. Wirel. Commun. Netw._**2020**, _2020_, 104. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Collaborative Learning of Communication Routes in Edge-enabled Multi-access Vehicular Environment. _IEEE Trans. Cogn. Commun. Netw._**2020**, \(6\), 1155-1165. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON] Estimation and update of betweenness centrality with progressive algorithm and shortest paths approximation. _Sci. Rep._**2023**, _13_, 17110. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Hybrid strategy for routing, modulation and spectrum assignment in elastic optical networks. _Opt. Quantum Electron._**2021**, _53_, 611. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An efficient shortest path algorithm for content-based routing on 2-D mesh accelerator networks. _Future Gener. Comput. Syst._**2021**, _114_, 519-530. [CrossRef]
* Stotts and Andrews (2024) [PERSON]; [PERSON] Probability density function models for adaptive optical systems operating in turbulence. _Opt. Eng._**2024**, _63_, 088101. [CrossRef]
* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON] \(k\)-connected network partitioning under shared risk link groups. _J. Opt. Commun. Netw._**2015**, \(7\), 695-706. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON] Optimized Scheduling Algorithm for Power Demand Response Service Based on Multi-priority Services and SRLG. _Power Syst. Technol._**2019**, _43_, 1000-3673. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON] Optimal planning and design of SRLG-aware survivable LR-PON for wireless and FTTx networks. _Comput. Netw._**2021**, _194_, 108142. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] RL-routing: An SDN routing algorithm based on deep reinforcement learning. _IEEE Trans. Netw. Sci. Eng._**2020**, \(7\), 3185-3199. [CrossRef]

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## 7 Conclusions  
The pervasive use of location-finding services underscores their importance across various sectors, yet their susceptibility to RF interference remains a significant challenge. This paper addresses this concern by mathematically modeling the problem in a 3D space, focusing on GNSS receivers during the landing phase of flying vehicles. Incorporating factors like flight path, antenna radiation patterns, and multipath fading, the analysis employs SPIDEH and OEP graphs to elucidate \(P_{RX}\) dependencies and operational insights. Simulations reveal the spatial arrangements of maximum and minimum \(P_{RX}\) points, emphasizing the impact of distance between the Source and landing site. The results highlight the dynamic nature of \(P_{RX}\) regions and unequal interference biases, suggesting avenues for future research on optimizing flight paths amidst interference.  
All authors have the same level of contribution. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments
## 4 Collision Avoidance System
### Prediction Model of USV  
The prediction model of the USV is described by the standard three-degree of freedom (DOF) planar ship motion model, neglecting the roll, pitch and heave motions [34]. The kinematics of the system can be written as  
\[\dot{\eta}=J(\psi)v \tag{8}\]  
where \(\eta=[x,y,\psi]^{T}\) represents position and course in the earth-fixed frame, \(v=[u,v,r]^{T}\) includes surge and sway velocities and yaw rate in the body-fixed frame, and \(J(\psi)\) is rotation matrix from body-fixed to earth-fixed frame. The \(\eta\) and \(v\) together form the state quantity \(X\) of USV, which is  
\[X=[x,y,\psi,u,v,r]^{T} \tag{9}\]  
Assume that:  
* The environment force can be neglected in the model;
* The inertia, added mass, and damping matrices are diagonal.  
The simplified model of vessel can be written as [35; 36]  
\[\left\{\begin{array}{l}\dot{u}=\frac{m_{21}}{m_{11}}vr-\frac{d_{11}}{m_{11}}u +\frac{1}{m_{11}}\tau_{u}\\ \dot{v}=-\frac{m_{11}}{m_{22}}tr-\frac{d_{22}}{m_{22}}v+\frac{1}{m_{22}}\tau_ {v}\\ \dot{r}=\frac{m_{11}-m_{22}}{m_{33}}uv-\frac{d_{33}}{m_{33}}r+\frac{1}{m_{33}} \tau_{r}\end{array}\right. \tag{10}\]  
with [37; 38]  
\[\left\{\begin{array}{l}\tau_{u}=T\cos(\delta)\\ \tau_{v}=T\sin(\delta)\\ \tau_{r}=T\sin(\delta)L/2\end{array}\right.\]  
\[T=\left(1-t_{p}\right)\rho n^{2}D_{p}^{4}K_{T}\]  
where \(m_{ii}\) values are given by the vessel inertia and the added mass effects, \(d_{ii}\) values are given by the hydrodynamic damping, \(L\) is the USV length, \(T\) is thrust of thruster, and the inputs of model are the propulsion angle \(\delta\) and thruster speed \(n\).  
Based on the servo response characteristics of the actual steering process and the actual data collected from the experimental process, the steering gear response model is processed according to the second-order link [37; 39]  
\[\ddot{\delta}+2\zeta\omega_{n}\delta+\omega_{n}^{2}\delta=K\omega_{n}^{2} \delta_{r} \tag{11}\]  
Figure 9: Block diagram of finite control set model predictive control (FCS-MPC) for the collision avoidance subsystem (CAS). USV: unmanned surface vehicle.  
Here, \(\delta_{r}\) is the command propulsion angle, and \(\omega_{n}\), \(\zeta\), and \(K\) represent the natural frequency, damping ratio, and proportionality coefficient, respectively. \(|\delta|\leq 30^{\circ}\) is the actual propulsion angle range.
# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 2 Materials and Methods
### Pre-Processing  
#### 2.4.1 Data Organization  
The workflow consisted of arranging the initial dataset for the further process and the related works of fisheye detachment, panorama stitching, and LS data preparation in the flowchart (Figure 3). It was necessary to import the laser scanner project file (.rtc360 file) into the Leica commercial software (Cyclone REGISTER 360, V2021.1.2) after data acquisition (Table 3). The significance of this step lies in the creation of an original file that can be converted into various adoptable point cloud formats, such as LAS or LAZ files. It can also be further processed in other software such as Cloud Compare (v2.12 beta). Initially, the accuracy of each scan was controlled by overlapping checks between the stations in Cyclone. Consequently, PTX or FTS files (raw files of LS) were converted into LAS files in Point Zip [28], an open-source program. Hence, the final applicable version of the laser scanner point cloud was generated for further processes in the assessment stage (Figure 6). To stitch panoramas from the raw dataset of the spherical cameras, we utilized the commercial software Insta360 Studio (v5.1.10). This allowed us to obtain both fisheye and panorama images. While fisheye images could be extracted directly from the raw file, the front- and rear-side images of the camera had to be separated and rotated to a realistic viewpoint in Adobe Photoshop (2019, v20.0.10). This was necessary to ensure a suitable input configuration for the subsequent 3D reconstruction software. Therefore, the pictures captured with the X2 and X3 camera were separated into those with a front and rear view. For the camera calibration process (Section 2.5.2), random images with different orientations and positions were captured. The goal was to cover the entire possible chessboard area in the defined FOV (field of view) with respect to the chessboard (for spherical and DSLR cameras).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Laser Scanner Accuracy Settings** & **Values** \\ \hline
3D point accuracy & 1.9 mm in 10 m12.9 mm in 20 m15.3 mm in 40 m \\ \hline Ground/space sampling resolution & 3 mm (highest sampling resolution) \\ \hline Angular accuracy of LS & 18\({}^{\prime\prime}\) \\ \hline Range accuracy & 1 mm in 10 ppm \\ \hline Optical camera for colorized point cloud & 3 cameras, 36 MP resolution \\ \hline Noise distance & 0.4 mm in 10 m10.5 mm in 20 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detailed specifications of the laser scanner and the captured dataset.
# RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System
## 3 Results  
In this section, we set up an experimental environment to evaluate the performance of RSIMS, and provided an example to show the usage of RSIMS from the perspective of users. As described in Section 2, RSIMS consists of three modules: RSIMeta, RSIData, and RSIAPI. In the follow-up experiments, we will first verify the spatiotemporal retrieval performance of RSIMeta, and then evaluate the I/O performance of RSIData under different access concurrency. Finally, we provide an example to show how to make a composite Landsat image of China mainland with RSIAPI.
# Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review
## 3 Inversion Analysis of Initial Ground Stress
### Intelligent Inversion Analysis Method
This method enables the integration of an artificial neural network and an optimization algorithm and effectively reduces the workload of numerical calculation by using the information measured in the field to construct learning samples. [PERSON] et al. [36] proposed a neural network algorithm of GMDH (Group Modeling of Data Handling) for the critical issue of sparse measured sample data of ground stress. This algorithm constructs the boundary condition model under complex geological conditions by combining the nonlinear characteristics of the gravity and tectonic stress fields and fitting nonlinear expressions of the boundary load. The effectiveness of the algorithm was verified by establishing a two-dimensional geological regional model. The author randomly selected 15, 12, 9, and 6 position points as the \"field measured stress\" points. The abscissa 2, 5, 8, 9, 11, and 14 in the Figure 7 are the serial numbers of measuring points under the above four position points. The results show that the GMDH algorithm has better nonlinear data predictability than the BP neural network. [PERSON] et al. [37] simulated the evolution process of the river valley during excavation by establishing a three-dimensional numerical model of the Shuangjiangkou underground hydropower station. They inputted coordinates, current buried depth, and current lateral stress coefficient into the GAN model as training samples. After nonlinear analysis of the model, the optimized lateral stress coefficient was determined and applied to the reconstruction of the paleo-stress field. Figure 7: The contrast of inversion accuracy between GMDH and BP neural network [36]. According to the statistical results of some typical engineering cases and simulation analyses (shown in Tables 1 and 2), the intelligent inversion analysis method is superior to the traditional inversion analysis method in terms of calculation error and accuracy. When conducting inversion analysis of the initial ground stress field in complex geological conditions, such as high ground temperature and high ground stress, the optimization method that uses deep learning network architecture can achieve better results than the single neural network algorithm or numerical analysis method. Moreover, intelligent monitoring equipment and numerical simulation software can improve the applicability and accuracy of inversion results. The artificial intelligence method also allows the existing inversion analysis method to select calculation methods based on different geological characteristics and engineering requirements, significantly enhancing the accuracy of inversion results. However, these methods also have problems. Firstly, the calculation process considers too many influencing factors, resulting in a large amount of calculation. Secondly, the stress function applied to the boundary load condition of the model is relatively simple
# Application of Blockchain Technology in Emergency Management Systems: A Bibliometric Analysis
## 5 Discussion
### Theoretical Insights and Practical Applications
#### 5.1.2 Supply Chain and Emergency Logistics Management  
Clusters #1 and #6 collectively underscore blockchain's transformative role in humanitarian supply chains and emergency logistics. Theoretically, these studies contribute to the development of decentralized models that improve visibility, traceability, and resilience in logistics networks [75; 76; 77; 78; 79; 80; 81; 82; 83; 100; 83; 101; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103]. Game theory, systems modeling, and evolutionary dynamics are some of the different approaches employed to conceptualize decision-making and coordination in complex emergencies [100; 101; 102; 103].  
Practically, blockchains can enhance logistical efficiency by enabling real-time tracking of resources, secure sharing of logistics data, and streamlined collaboration between stakeholders. Use cases include cold-chain medicine tracking, resource allocation during disasters, and coordination among humanitarian actors [79; 101; 102]. These improvements address long-standing challenges such as fraud, lack of transparency, and information asymmetry.
## 6 Performance Analysis
### Network Performance  
Network performance was measured in terms of RSSI (Received Signal Strength Indicator), SNR (Signal to Noise Ratio), and data loss from the SN received by the DS. For the WARM application in some remote locations using a mobile DS radio signal, the quality depends on various environmental parameters along with the distance between the SN and the DS. With varying distances, data loss is the primary performance indicator, and when measured was found to depend on both the RSSI and SNR. Figure 11a shows that data loss becomes significant when the RSSI goes below \(-95\) dBm with the SNR below 0. Increasing data loss was observed with a decrease of the SNR and RSSI, mainly above a 1000 m distance and without a line of sight. Figure 11b, c show the relationship between the RSSI and SNR with the distance between the SN and DS, respectively; the line curve is for the median values, and the box graphs show the ranges of RSSI and SNR values at different distances. These results can also be used by the mobile DS to improve the data delivery rate by using a RN or by changing the transmit power or the travel path.
## 3 Local Differential Privacy
### PrivKV
#### 3.2.1 Sampling  
Suppose that \(n\) users exist and let \(S_{i}\) be a set of key-value tuples \(\langle k,v\rangle\) possessed by the \(i\)-th user. In PrivKV, the set of tuples is transformed into a \(d\)-dimensional vector. Here, \(d\) represents the cardinality of key domain \(K\), and the missing key is denoted as \(\langle k,v\rangle=\langle 0,0\rangle\). For instance, a set of key-values \(S_{i}=\{\langle k_{1},v_{1}\rangle,\langle k_{4},v_{4}\rangle,\langle k_{5},v _{5}\rangle\}\) is converted into a \(d=5\) dimensional vector \(\textbf{S}_{i}=(\langle 1,v_{1}\rangle,\langle 0,0\rangle,\langle 0,0\rangle, \langle 1,v_{4}\rangle,\langle 1,v_{5}\rangle)\). In this case, keys \(k_{1}\), \(k_{4}\), and \(k_{5}\) are implicitly indicated by the presence of 1 in the corresponding position. PrivKV employs a 1-out-of-\(d\) random sampling method to select one element \(\langle k_{a},v_{a}\rangle\) from the \(d\)-dimensional vector \(\textbf{S}_{i}\) of the key-value data.
# Development of an Estimation Formula to Evaluate Mission Motion Suitability of Military Jackets
## 4 Discussion
In this study, four equations were derived through a multiple linear regression analysis in which general motions with a correlation of 0.4 or more served as the independent variable (\(p<0.01\), \(p<0.001\)). However, we suggested a final estimation formula for ammunition stowing I and boarding motions, which had loading factors as high as 0.975 and 0.962, respectively. Furthermore, we used the estimation equation with the highest explanatory power (R\({}^{2}\)) as a representative estimation formula for evaluating the mission motion suitability of the tracked vehicle crew jacket. Furthermore, regarding the results of the final estimation formula application, there was no difference between the values calculated using the estimation formula and the real scores of mission suitability evaluation at the level of \(p<0.05\). Moreover, repeated measures analysis showed that there was no significant difference in the results of the estimation formula at the level of \(p<0.05\). Therefore, the estimation formula was valid and reliable. Since all motions require the performance of several consecutive motions, the development of clothes should be taken into consideration for joint use for a specific motion rather than considering all motions for ease of measurement and accuracy. However, in order to obtain accurate motion suitability evaluation results for clothes jamming due to structures during boarding and entry, it is necessary to reproduce the hatch environment, which consists of a 2 m high deck with steps and a narrow passage that is 0.5 m wide. Nevertheless, the efficiency of clothing development can be improved even when only the general motion is considered, instead of evaluating the mission motion suitability of the tracked vehicle crew jacket.
# Effect of Pore Size Distribution and Amination on Adsorption Capacities of Polymeric Adsorbents
## Materials and Methods
### Characterization  
The BET surface area (S\({}_{\text{BET}}\)) of the resin was calculated using the isothermal adsorption and desorption experiments of N\({}_{2}\) at 77 K. The micropore volume (V\({}_{\text{micro}}\)) and the micropore area (S\({}_{\text{micro}}\)) were determined by the t-plot method [18]. When the relative pressure is close to 1 (0.98 in this experiment), the N\({}_{2}\) adsorption amount (V\({}_{\text{t}}\)) corresponds to the total adsorption amount of micropores and mesopores in the resin. The mesopore volume (V\({}_{\text{meso}}\)) of the resin can be obtained by subtracting V\({}_{\text{micro}}\) from the adsorption amount at this time. The average pore diameter in the mesoporous region was obtained through a BH desorption experiment. The whole process was completed automatically by an accelerated surface area and porosimeter system (ASAP 2010, Micromerities, Georgia USA). The exchange capacity was determined by acid-base titration. The pretreated resin was soaked in the HCl aqueous solution, and the capacity was obtained by NaOH titration of the residual HCl in the solution with phenolphthale [19]. Fourier transform infrared (FTIR) spectroscopy was performed with an FTIR spectrophotometer (Nicolet 170 SX, Georgia USA) via KBr pressed-disk technique.
# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 7 Further Probabilistic Forecasting and Applications
### Probabilistic Forecast of Time and Flexibility of EV Charging  
In [134], the authors employed a quantile forecast to probabilistically predict EVs' parking duration as well as their upcoming trip distance using the forecast framework introduced by [135]. To develop the model, German datasets regarding travel logs were adopted. In this regard, the authors first determined the requirements that are used as inputs of smart charging systems. In the second stage, they extracted features from the travel logs. Then, the paper compared the charging stations' current information with those of historical parking events. If the EV users grant the permission, the travel data are extracted from the smartphone applications. As a result, the authors proposed a forecasting model based on cross-validation performance. In the final stage, the results demonstrate  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Uncertain Parameters**} & **Methods to Capture** & \multirow{2}{*}{**Objective**} \\  & & **Uncertainties** & & \\ \hline \multirow{3}{*}{[127]} & PV generation and ambient & Chance-constrained & & Active distribution network management incorporating office buildings by \\  & temperatures & & & tracking building consumption and PV generation \\  & temperatures & programming & & \\  & & & & Minimizing network losses \\ \hline \multirow{3}{*}{[128]} & Renewable generation & Chance-constrained & & Finding potential self-sufficient sub-networks within the existing electrical \\  & and loads & programming & distribution grid & \\  & & & by maximizing average load served in each sub-network \\ \hline \multirow{3}{*}{[129]} & \multirow{3}{*}{PV generation} & Chance-constrained & & Designing solar-based microgrid and solving related dispatch problem by \\  & & & minimizing the capital costs of PV panels and the capacity costs and \\  & & programming & installation costs of energy storage system, the expected costs of multi-year \\  & & & operation of the microgrid which include load shedding penalty costs and \\  & & & wind micro-turbine generation costs \\ \hline \multirow{3}{*}{[130]} & \multirow{3}{*}{Renewable generation} & Chance-constrained & & \\  & & programming & Minimizing the total cost of generating power and gas \\ \hline \multirow{3}{*}{[131]} & \multirow{3}{*}{Loads and system frequency} & Chance-constrained & & Optimal scheduling of grid-connected batteries providing frequency-related \\  & & programming & services by minimizing the cost of purchasing energy from the grid as well \\  & & & as the system costs \\ \hline \multirow{3}{*}{[132]} & \multirow{3}{*}{Renewable generation} & Mixed integer second & & \\  & & order cone & \\  & & chance-constrained & \\  & & programming & \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._that the charging station operator using the proposed forecasting model can profit by selling flexibility services. The model was also proven to resolve the congestion issue within the station.