esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Diffusion Multi-Distribution Filter with IMM for Heavy-Tailed Noise
## 2 Preliminaries and Problem Formulation  
### Modeling  
Consider a sensor network composed of nodes \(\mathcal{N}\) and edges \(\mathcal{A}\). This network can be represented by the two-tuple \((\mathcal{N},\mathcal{A})\), in which the neighborhood of node \(i\in\mathcal{N}\) (including n) is the set \(\mathcal{N}_{i}\), e.t., \(\mathcal{N}_{i}\triangleq\{j:(j,i)\in\mathcal{A}\}\) such that \((i,j)\in\mathcal{A}\) if node i can receive data from node \(j\).  
Similarly, consider a model base of model \(r_{k}\in\mathcal{M}\), which includes a possible dynamical model about the actual motion of the tracking targets. Then, a discrete linear equation of jump Markov process over the sensor network \((\mathcal{N},\mathcal{A})\) and model base \(\mathcal{M}\) can be formulated as follows:  
\[x_{k}=F_{k-1}(r_{k-1})x_{k-1}+G_{k-1}(r_{k-1})w_{k-1}(r_{k-1}) \tag{1}\]  
where \(x_{k}\) denotes the \(n_{x}\)-dimension state vector at instant \(k\), \(F_{k}(r_{k})\) denotes the process transition matrix with a discrete parameter rk which denotes the label of the model in effect at instant k, \(G_{k}(r_{k})\) denotes the control matrix of model rk, and \(w_{k}\) denotes the process noise with the zero mean covariance matrix \(w_{k}(r_{k})\).  
Note that \(F_{k}(r_{k})\) is a finite state Markov chain taking values in \(\mathcal{M}=\{r_{k}|r_{k}=12\cdots m\}\) according to the transition probability matrix \(\Pi=\left[\pi_{ij}\right]_{m\times m}\) with \(\pi_{ij}=\Pr\{r_{k}=j|r_{k}=i\}\) and \(\sum_{j=1}^{m}\pi_{ij}=1\) for any \(i\in\mathcal{M}\). Therefore, the number of possible models will grow exponential as the iteration proceeds if there are not some valid algorithms about choosing represent finitely describe model.  
Meanwhile, there is a set of sensors in the network \(\mathcal{N}\) with a set of measurement equations as follows:  
\[z_{k}^{i}=H_{k}^{i}x_{k}+v_{k}^{i},\quad i\in\mathcal{N} \tag{2}\]  
where \(z_{k}^{i}\) denotes the \(n_{z}\)-dimension measurement vector of node \(i\in\mathcal{N}\) at instant k, \(H_{k}^{i}\) denotes the observation matrix of nodes \(i\in\mathcal{N}\), and \(v_{k}^{i}\) denotes the measurement noise of node \(i\in\mathcal{N}\) with the zero mean measurement noise covariance matrix \(R_{k}^{i}\) at instant k. For the sake of simplicity, the remaining sections of this paper denote model \(r_{k}\in\mathcal{M}\) as a subscript in equations, i.e., \(F_{k}(r_{k})=F_{k}^{r}\), \(G_{k}(r_{k})=G_{k}^{r}\), \(w_{k}(r_{k})=w_{k}^{r}\), \(Q_{k}(r_{k})=Q_{k}^{r}\).
## 4 Discussions and Results
### Applying the Methodology to Real Data and Profile Disaggregation  
The methodology was applied to four transformer centers (CT1, CT2, CT3, CT4) for two significant dates: one in winter and one in summer, when consumption peaked. The results obtained are presented below. So, Figures 11 and 12 show the consumption in winter and summer of considered CTS.  
Figure 11: Energy consumption during the winter for all Cts.  
Figure 10: Cooling usage probability.  
Figure 9: Heating usage probability.  
The RMSE values for the four transformation centers indicate a general trend of higher errors in the winter compared to the summer. Specifically, for CT1, the RMSE is 13.59% in the winter and 7.55% in the summer. Similarly, CT10 exhibits the highest discrepancies, with an RMSE of 24.06% in winter and 12.49% in summer. In contrast, CT19 and CT158 show lower error levels, with RMSE values of 6.56% and 5.10% in the winter and 4.85% and 2.76% in the summer, respectively. These results suggest that the model performs better during the summer, significantly reducing deviations from the actual consumption patterns. This fact could be justified by a more intensive utilization of the air conditioning due to the high temperatures in the summer, reaching usually more than 30 \({}^{\circ}\)C, while softer winters, where temperature rarely goes down below 5 \({}^{\circ}\)C, produce greater variability in the use of heating.  
It should be taken into account that curves shown in Figures 11 and 12 under the label \"Metered profile\" were provided by the electric distribution company, and so they were actually coming from real measurements at the transformation center (CT) level. Measurements at the household level were not available. However, the four transformation centers considered for this study were selected as most of consumers connected (96%) are residential. Validation was performed comparing the measured demand on each CT (assuming that all connected consumers were residential) and the sum of end use profiles modeled according to the proposed methodology, based on probabilities. Therefore, one of the most significant sources of uncertainty is the real power demanded by households compared to the total demand of the CT.  
The disaggregated consumption by end use for the washing machine, dishwasher, and dryer are shown in Figures 13 and 14.  
Previous figures show the load profile for a typical day in winter and summer corresponding to end uses that can provide flexibility to the end-user consumption. Indeed, the obtained profiles present diverse power peaks that represent the possibility to shift power from these periods to others. Therefore, with the appropriate coordination provided by load aggregators, end-users could schedule their activities in some specific periods in order to modify their usual pattern of consumption.  
Figure 12: Energy consumption during the summer for all CTs.
# Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree
## 1 Introduction
In [17], [PERSON] et al. proposed an improved RRT-Connect algorithm, which enhances the efficiency of the traditional RRT algorithm by adopting a rewire-tree method based on the triangle inequality, effectively reducing the redundancy commonly seen in paths generated by the standard RRT algorithm. Similarly, in [18], [PERSON] et al. introduced the improved RRT* algorithm. By updating the tree through reselecting parent nodes, it can find suboptimal paths. However, this approach is characterized by high computational costs and lower efficiency. There are also artificial intelligence-based path planning techniques such as the ant colony algorithm [19], or the use of machine learning [20] and deep learning [21] models to predict and optimize paths. The ant colony algorithm is computationally intensive and has a slow convergence rate, although it can obtain a global optimal solution. Machine learning and deep learning approaches learn strategies for path planning from large amounts of data, and while they perform well in some scenarios, as shown [PERSON] et al. in [22], who proposed a convolutional neural network (CNN)-based path planning algorithm that trains a model of a CNN network capable of non-uniform sampling, they typically require large amounts of training data and have poorly interpreted models.
## 3 Description of the Geometry and Mesh Generation
### The First Model--Shear Model  
An upper sandy soil with the dimensions of (7 m length \(\times\) 3.85 m width \(\times\) 4.5 m depth) and lower sandy soil with the dimensions of (8 m length \(\times\) 3.85 m width \(\times\) 4.5 m depth) mesh was created. Four granular columns in a square pattern with a spacing S = 2.5D (center to center) were used as seen in Figures 2 and 3, where S is the spacing center to center between the columns and D is the diameter of the granular columns. The shear plane was predefined at 4.5 m from the bottom elevation to consider the worst-case scenario where the applied force has pure horizontal effects on the shear plane. By using the symmetry provided by FLAC\({}^{\text{3D}}\), a half of the above-mentioned geometry was created to minimize the time required for solutions. The extensions of the required soil were made by using a brick element. For the geogrids--6336 nodes--have been created in addition to 50,535 nodes for the soil and the granular columns. Moreover, 8736 nodes have been used to construct the interface between the sand and the geogrid. The same properties in Table 1 were used in the current study, however, different values for the angle of friction and dilation angle for the column material and the sand were used; (43, 37) and (13, 7) degrees, receptively.  
Figure 3: Cross section for geometry and mesh of the problem.  
Figure 2: Slope protection by granular columns.
# State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
## 4 Enabling Technologies for Transaction-Based Energy
### Energy Storage Systems  
Apart from DER, ESSs are considered an essential element in balancing micro-generation of energy from renewable resources [22]. Energy storage systems are a great way to support renewable energy generation, by offering their capability to absorb unused or excess energy and release the energy when required. They offer flexibility during periods of high intermittent and fluctuating energy production. The energy system technology can therefore support the grid reliability and electricity supply, as well as help to de-carbonize the energy supply. Therefore, the importance of ESS has been researched in the literature. For instance, [PERSON]. [22] argues that the users with storage systems are able to reduce their monetary expenses to a greater extent than consumers without storage systems. In addition, [PERSON]. [47] highlighted the importance of ESS in shaving peak energy demands and filling valleys in system load. With ESS, a reduction in energy cost is guaranteed.
# Scaling Equipment Effect on Technical-Tactical Actions in U-13 Basketball Players: A Maturity Study
## 3 Results
### Ball Handler Player Finished Actions
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Early (n = 25)**} & \multicolumn{2}{c}{**Late (n = 12)**} & \multicolumn{2}{c}{**Total (n = 37)**} \\ \cline{2-7}  & **FEB** & **Modified** & **FEB** & **Modified** & **FEB** & **Modified** \\ \hline Received Personal & & & & & & \\ Foul & 2.76 \(\pm\) 1.61 & 3.64 \(\pm\) 2.9 & 3.17 \(\pm\) 2.66 & 4.25 \(\pm\) 3.44 & 2.89 \(\pm\) 2.61 & 3.84 \(\pm\) 3.16 \({}^{*}\) \\ Total Turn-Overs & 10.68 \(\pm\) 6.92 & 10.20 \(\pm\) 5.70 & 9.00 \(\pm\) 4.09 & 10.75 \(\pm\) 6.21 & 10.14 \(\pm\) 6.14 & 10.38 \(\pm\) 5.79 \\ Pass & 67.84 \(\pm\) 33.9 & 69.44 \(\pm\) 33.8 & 55.00 \(\pm\) 25.1 & 58.00 \(\pm\) 34.5 & 63.68 \(\pm\) 31.56 & 65.72 \(\pm\) 33.98 \\
1 vs. 0 & 20.28 \(\pm\) 9.86 & 20.80 \(\pm\) 11.38 & 13.58 \(\pm\) 10.18 & 14.83 \(\pm\) 14.1 & 18.11 \(\pm\) 10.33 & 18.86 \(\pm\) 12.45 \\
1 vs. 1 & 67.44 \(\pm\) 63.42 & 66.00 \(\pm\) 26.13 & 56.92 \(\pm\) 21.09 & 66.00 \(\pm\) 26.13 & 64.03 \(\pm\) 32.33 & 66.00 \(\pm\) 3.13 \\
2 vs. 2 & 9.24 \(\pm\) 5.13 & 8.40 \(\pm\) 4.37 & 6.75 \(\pm\) 3.86 & 7.25 \(\pm\) 5.88 & 8.43\(\pm\)4.85 & 8.03 \(\pm\) 4.86 \\
1 vs. 2 & 8.12 \(\pm\) 6.43 & 11.76 \(\pm\) 7.63 & 8.75 \(\pm\) 5.5 & 10.17 \(\pm\) 7.30 & 8.32 \(\pm\) 6.07 & 11.24 \(\pm\) 7.46 \({}^{*}\) \\ Numerical & 24.72 \(\pm\) 11.82 & 23.44 \(\pm\) 12.21 & 16.58 \(\pm\) 10.01 & 16.67 \(\pm\) 15.13 & 22.08 \(\pm\) 11.78 & 21.24 \(\pm\) 13.41 \\ Superiority & 24.72 \(\pm\) 11.82 & 23.44 \(\pm\) 12.21 & 16.58 \(\pm\) 10.01 & 16.67 \(\pm\) 15.13 & 22.08 \(\pm\) 11.78 & 21.24 \(\pm\) 13.41 \\ Numerical Equality & 76.68 \(\pm\) 39.05 & 74.40 \(\pm\) 37.2 & 63.67 \(\pm\) 21.97 & 69.75 \(\pm\) 33.11 & 72.46 \(\pm\) 34.67 & 72.89 \(\pm\) 35.53 \\ Numerical Inferiority & 9.52 \(\pm\) 7.81 & 13.96 \(\pm\) 8.85 & 10.25 \(\pm\) 6.44 & 12.25 \(\pm\) 9.11 & 9.76 \(\pm\) 7.31 & 13.41 \(\pm\) 8.81 \({}^{*}\) \\ High Opposition & 39.76 \(\pm\) 22.91 & 44.56 \(\pm\) 21.07 & 34.33 \(\pm\) 10.88 & 41.92 \(\pm\) 23.57 & 38.00 \(\pm\) 19.82 & 43.70 \(\pm\) 21.62 \({}^{*}\) \\ Medium Opposition & 36.92 \(\pm\) 19.33 & 34.04 \(\pm\) 20.08 & 28.83 \(\pm\) 11.38 & 30.33 \(\pm\) 12.94 & 34.30 \(\pm\) 17.42 & 32.84 \(\pm\) 17.97 \\ Low Opposition & 9.60 \(\pm\) 6.49 & 7.40 \(\pm\) 6.1 & 9.92 \(\pm\) 6.73 & 8.17 \(\pm\) 5.29 & 9.70 \(\pm\) 6.48 \({}^{*}\) & 7.65 \(\pm\) 5.78 \\ Minimum Opposition & 24.84 \(\pm\) 12.87 & 26.08 \(\pm\) 12.98 & 16.92 \(\pm\) 10.94 & 18.83 \(\pm\) 16.94 & 22.27 \(\pm\) 12.69 & 23.73 \(\pm\) 14.55 \\ Opposition & & & & & & \\ \hline \multicolumn{7}{l}{*Statistical significance \(p<0.05\).} \\ \end{tabular}
\end{table} 
Table 3: Mean values and standard deviation of Ball Handler Player Finished Actions, according to maturating timing and tournament.
# The Machine-to-Everything (M2X) Economy: Business Enactments, Collaborations, and e-Goverance
## 2 The M2X Economy
### State of the Art and Related Work  
The idea of the M2X Economy and its ecosystem overlaps with some closely related concepts and applications such as cybernetics, WSNs, CPS, and IoT [1]. This section clarifies the differences and overlaps with those concepts and applications.  
[PERSON] [9] defines the concept of cybernetics as \"the scientific study of control and communication in the animal and the machine\", while WSNs consist of spatially distributed autonomous sensors to monitor physical or environmental conditions and to cooperatively pass their data through a variety of networks to a main location [10].  
CPS are engineered systems that are built from, and depend upon, the seamless integration of computation and physical components. CPS tightly integrate computing devices, actuation, and control, networking infrastructure, and sensing of the physical world [11].  
[PERSON] et al. [12] defines IoT as an \"interconnection of sensing and actuating devices providing the ability to share information across platforms through a unified framework, developing a common operating picture for enabling innovative applications. This is achieved by seamless, large-scale sensing, data analytic and information representation using cutting edge ubiquitous sensing and cloud computing\".  
Robotic Process Automation (RPA) is regarded as one of the most advanced technologies in the area of computers science, electronic and communications, mechanical engineering, and information technology [13]. With software robots autonomously executing their choreography uninterruptedly, quickly, and flawlessly while at the same time being easy to implement at relatively low costs compared to traditional process automation, RPA may automate processes enabling business transactions in the near future [14].  
After clarifying the terms and concepts above, the question remains: Where does the M2X Economy fit in? Several publications list and survey CPS and IoT applications, e.g., [15; 16; 17; 18; 19]), as well as their economic value and impact, e.g., [19; 20; 21]. However, the emerging economy resulting from M2X enactments among humans, smart devices, software agents and physical systems is rarely considered.
## 1 Introduction
### The Departure from Hydrodynamic Regime and the Onset of Relaxation Processes  
Among various phenomena occurring at the departure from the hydrodynamic regime, the coupling of density fluctuations with relaxation phenomena is perhaps the most solidly understood [6; 7; 12; 13; 19]. To gain a basic insight on the physics of a relaxation process, one may start from considering that scattering-generated density waves bring the sample away from its local equilibrium. In response, energy redistributions take place from the density perturbation toward internal degrees of freedom, ultimately driving the sample to a new local equilibrium. The timescale needed by the sample to relax to the equilibrium, \(\tau\), depends both on the wavelength \(\lambda=2\pi/Q\) of the considered density wave and on the sample characteristics. For instance, if \(\lambda\) increases so do both the number of rearranging molecules and the relaxation time.  
Furthermore, the timescale of structural relaxations has a temperature (\(T\)-)dependence roughly as steep as the one of viscosity, due to the circumstance that viscous resistance hampers internal rearrangements.  
These last considerations only apply the so-called structural relaxations, involving collective rearrangements of the structure, which are dominating in highly interconnected many body systems.  
Conversely, for monatomic fluids relaxation phenomena mostly have a collisional nature [7; 20], especially when \(\lambda\) matches first neighboring molecules' separations. Collisional relaxations relate to energy transfers between density fluctuations and mutual inter-particle collisions. Compared to structural relaxations, they are faster (usually spanning the 10\({}^{-13}\) s range) and weakly dependent on thermodynamic conditions [7; 20].  
In general, collisional and structural relaxations coexist in a given fluid and their relative relevance depends on the nature of intermolecular interactions, thermodynamic conditions and probed \(Q\) value.  
Experimental studies of the \(Q\) or \(T\)-transition between structural and collisional relaxation regimes often present major challenges, as the required access to large \(Q\) (or \(T\)) intervals and the high statistical accuracy in the spectral shape measurement. Furthermore, advances in the field are currently held back by instrumental limitations, especially concerning resolution width and spectral contrast. Nonetheless, important advances in the understanding of these phenomena have been made and, most importantly, next generation instruments with the required performance will be soon available for the scientific community.
## 2 Materials and Methods
### Data  
In this paper, 23 Sentinel-1A SAR images were used from October 2016 to June 2017 for deformation monitoring of the high-fill canal in Wenzhuang Village, Ye County of the Middle Route of the South-North Water Diversion Project. The image acquired on 30 January 2017 was selected as the master image, and the rest images were used as slave images for subsequent alignment, interference, and inversion processing with the master image. The image spatial and temporal baseline parameters are shown in Table 1. To improve the accuracy of monitoring results, 30 m SRTM DEM (Shuttle Radar Topography Mission Digital Elevation Model) was selected as the reference data for topographic phase removal in InSAR processing (Figure 2).  
Figure 1: Location of the study area.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Date** & **Vertical Baseline/m** & **Time Baseline/d** \\ \hline
2016-10-02 & 44.6939 & \(-\)120 \\
2016-10-14 & 39.6777 & \(-\)108 \\
2016-10-26 & \(-\)14.6982 & \(-\)96 \\
2016-11-07 & \(-\)45.9389 & \(-\)84 \\
2016-11-19 & \(-\)47.9464 & \(-\)72 \\
2016-12-01 & 59.9997 & \(-\)60 \\
2016-12-13 & 89.2754 & \(-\)48 \\
2016-12-25 & 41.2237 & \(-\)36 \\
2017-01-06 & 24.5749 & \(-\)24 \\
2017-01-18 & 25.7572 & \(-\)12 \\
2017-01-30 & 0 & 0 \\
2017-02-23 & 41.6563 & 24 \\
2017-03-07 & 48.3596 & 36 \\
2017-03-19 & \(-\)17.8744 & 48 \\
2017-03-31 & 6.71604 & 60 \\
2017-04-12 & \(-\)29.0038 & 72 \\
2017-04-24 & 14.7846 & 84 \\
2017-05-06 & \(-\)103.157 & 96 \\
2017-05-18 & \(-\)71.4633 & 108 \\
2017-05-30 & \(-\)5.27029 & 120 \\
2017-06-11 & 12.532 & 132 \\
2017-06-23 & 27.2988 & 144 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Baseline parameter of Sentinel-1A SAR images.  
Figure 2: Study area DEM and coverage of SAR datasets.  
The verification data use high precision second-order leveling measurement results. Figure 3a shows the second-order leveling benchmarks distributed in the road shoulder, wave wall, and other locations on both sides of the canal. There are several monitoring sections, and each section has four benchmarks. The four yellow lines represent the key monitoring sections, which can truly reflect the settlement amount and settlement trend on both sides of the high-fill canal in Wenzhuang Village, Ye County.
###### Abstract  
In unmanned aerial vehicle (UAV) photogrammetry, the qualities of three-dimensional (3D) models, including ground sample distance (GSD) and shaded areas, are strongly affected by flight planning. However, during flight planning, the quality of the output cannot be estimated, as it depends on the experience of the operator. Therefore, to reduce the time and cost incurred by repetitive work required to obtain satisfactory quality, a simulator, which can automatically generate a route, acquire images through simulation, and analyze the shaded areas without real flight, has been required. While some simulators have been developed, there are some limitations. Furthermore, evaluating the performance of the simulator is difficult owing to the lack of a validation method. Therefore, to overcome the limitations, target functions, which can plan flights and can detect shaded areas, were set, developed, and validated in this study. As a result, a simulator successfully planned a flight and detected shaded areas. In this way, the simulator was validated to determine the applicability of its performance. Furthermore, the outputs of this study can be applied to not only UAV photogrammetry simulators but also other 3D modeling simulators.  
s +
Footnote †: journal: Applied sciences
## 4 Numerical Simulation  
### FE Modeling  
Three-dimensional FE models using solid elements were established in ABAQUS to simulate the experimental results of the specimens under tension. The FE models employed the element type C3D8R with a reduced integration scheme for its accuracy and efficiency. The same boundary conditions were applied to the FE models, with the bottom end fixed and enforced displacement applied to the top end. Elements with small sizes were employed in the perforated segment to capture stress and strain concentration there, while the two ends were meshed with relative large sizes. The number of elements for the mesh of the typical specimen H40A85R1 was 22,660, the FE model is shown in Figure 6. The whole failure process was simulated using FE analysis, and both plasticity and fracturing of the specimens were considered through proper mechanical models, which are detailed in the following section. To successfully simulate the whole failure process, an explicit integration scheme was employed to ensure convergence of the simulations until rupture of all the specimens. Meanwhile, a large enough step time was required to ensure that all analyses remained quasi-static, which was the same as the loading condition of the experiments.
## 3 Basic Definitions and System Framework  
In this section, we provide a system model combined with the self-certifying naming ICN structure. User ID is strongly associated with the public key (hash of the public key), so the revocation of the user ID is the revocation of the public key, and the blacklist of the public key equates to the user blacklist. In the following algorithm description, we will use the public key revocation to explain the generation of Public Key Revocation Blacklist (PKBL).  
Meanwhile, we choose Standalone Name Resolution (SNR) approach as the routing approach to design the management model. We distribute different initial voting weights according to different grades of the public key. Voting algorithm rules will be described in the next section.