esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The interlayer Cu-Cu coupling is relatively weak and short-ranged since it is mediated by a thick pyrophosphate [P\({}_{2}\)O\({}_{7}\)]4- layer composed of [PO\({}_{4}\)] tetrahedral units. Moreover, based on a comparison of \(\alpha\)-Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) and \(\alpha\)-Cu\({}_{2}\)As\({}_{2}\)O\({}_{7}\) with \(\beta\)-Cu\({}_{2}\)V\({}_{2}\)O\({}_{7}\), [PERSON] et al. found that \(p\) cations of P and As atoms donate electrons to the neighboring O anions, leading to more ionic P-O bonds, which confines the interlayer superexchange to the Cu-O-Cu path [12]. However, the electronic band structure calculations employing local density approximation (LDA) wrongly predict a metallic ground state [12], implying that strong electron correlations in Cu \(3d\) states, included in that work via Hubbard-\(U\) corrections (LDA+\(U\)), are critical for obtaining microscopic insight into the electronic and magnetic properties of complex compounds. Previous studies concluded that generalized gradient approximation (GGA) combined with the Hubbard-\(U\) correction (GGA+\(U\)) gives correct results for the electronic and magnetic properties of Cu\({}_{2}\)V\({}_{2}\)O\({}_{7}\)[23; 26]. Thus, the calculations yielded band gap values of 2.85 eV [26] and 3.17 eV [23] and magnetic moments on Cu atoms of 0.84 \(\mu_{\rm B}\)[26] and 0.8 \(\mu_{\rm B}\)[23] in the AFM order, which is compatible with experimental findings [12; 23]. Moreover, a negative thermal expansion behavior of \(\alpha\)-Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) was disclosed recently by the X-ray and neutron diffraction investigations, which is caused by the transverse vibrations of O atoms [13]. Although the Raman spectra of Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) in \(\alpha\) and \(\beta\) phases have been studied experimentally [16] and also theoretically including the full phonon dispersion relations [23], the whole set of elastic, electronic, and magnetic properties of Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) have not been systematically studied. Motivated by the studies mentioned above, here we systematically study the atomic, electronic, and magnetic structure, as well as the elastic properties and lattice dynamics, of \(\alpha\)-Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) using hybrid-functional calculations based on density functional theory (DFT). Importantly, we find that PBE0 and HSE hybrid functionals give more accurate descriptions of the electronic structure and thermodynamic properties of \(\alpha\)-Cu\({}_{2}\)P\({}_{2}\)O\({}_{7}\) relative to the accuracy of semi-local PBE functional calculations, thus demonstrating the usefulness of the hybrid-functional approach in studies of transition metal pyro compounds. The remainder of this article is organized as follows. The first-principles calculation methods are briefly described in Section 2.1. Section 2.2 presents the details of the elastic constant calculations. In Section 3 we report and discuss the crystal structure, thermodynamic stability, and other properties derived from the calculated electronic and phonon spectra. Finally, the conclusions are drawn in Section 4.
# An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles
## 3 Research and Current Status of Energy Management Strategies for HEVs
### Rule-Based Energy Management Strategies
#### 3.1.1 Deterministic Rule-Based Energy Management Strategies  
Deterministic rule-based control determines the operating mode and energy distribution of the vehicle by setting a series of logical judgment rules [62]. In this kind of strategy, rules are determined on the basis of the engine's fuel economy or emission map. Rule-based energy management strategies are generally executed using a pre-calculated look-up table. Such strategies are currently used in thermostat control methods, power-following control methods, and state machine control methods [63].  
The state machine model developed and built by [PERSON] implemented power distribution by executing predefined control rules or logical thresholds [64]. In this strategy, the maximum power was extracted through the electric motor to drive the vehicle with the rest of the power being provided by the engine. [PERSON] et al. demonstrated a cyber-physical energy optimization management system [65]. Deterministic rule-based energy management strategies allocate power tasks for the engine and electric motor based on the battery's state of charge (SOC) and the power demanded by the vehicle. To enhance the performance of energy management systems (EMS), an enhanced firework algorithm  
Figure 5: Classification of energy management strategies for HEVs.  
(EFWA) has been proposed to optimize controller parameters and obtain better parameters more quickly, making it better suited for the complex optimization of EMS. In [66], the rules were extracted from recognized algorithms and their control parameters were optimized offline and corrected online for series-parallel hybrid systems with automatic mechanical transmissions (AMT) to maintain fuel economy and the SOC balance of the battery.
## 6 Conclusion  
Spectral separability analysis was undertaken to examine the ability of Sentinel-2A MSI data to detect fire-induced spectral change, which is both a challenging and an important task for burned area mapping. It is evident that Sentinel-2A MSI data can provide spectral differentiation between unburned and burned classes. The NDVI derived from the 10 m and 20 m red and NIR bands, and the NBR derived from the 20 m NIR and SWIR bands, provided variable discriminative ability. Individual 20 m NIR, red-edge, and SWIR bands were most sensitive to the change in reflectance caused by fire. These findings are similar to other studies undertaken using different sensors and data. To date, burned area mapping has been undertaken over large areas using coarser than 20 m resolution data. The findings reported in this study suggest the potential and need for further research to develop a systematic Sentinel-2A MSI 20 m burned area mapping capability.  
This research was funded by the NASA Land Cover/Land Use Change (LCLUC14-2): Multi-Source Land Imaging Science Program, Grant NNX15 AK94G. We acknowledge [PERSON] for his useful comments on the paper manuscript.  
[PERSON] undertook the data analysis, and processed the data with help from [PERSON], [PERSON], [PERSON], [PERSON], and developed the graphics with assistance from [PERSON] and [PERSON]; [PERSON] structured and drafted the manuscript with assistance from [PERSON], [PERSON], and [PERSON].  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Study Object  
The selection of traditional villages and dwellings located in the Jiangxi-Anhui border area was derived from four primary sources: (1) the Jiangxi-Anhui Junction area included in the six batches of \"Chinese traditional villages\" officially recognized and announced; (2) the Jiangxi-Anhui Junction area in the provincial traditional villages that have been announced; (3) villages designated as provincial-level famous historical and cultural villages or hosting provincial-level and county-level cultural relics protection units; (4) to ensure comprehensive coverage of the Jiangxi-Anhui junction area and avoid \"substituting points for surfaces\", one to three villages with typical geographic environments and representative cultural traits were selected from each township based on on-site census data. The sources were thoroughly screened and organized to remove duplicates and exclude highly urbanized villages, resulting in a total of 260 traditional villages being identified as samples (Figure 2).  
Figure 2: Spatial distribution Map of traditional villages in the border area of Jiangxi and Anhui Province, Source: Drawn by authors based on the satellite base map of Map World GS (2024) No. 0650 Data Sources.
# Stress Dispersion Design in Continuum Compliant Structure toward Multi-DOF Endoluminal Forceps
## 4 Discussions  
The novel design method proposed in this study largely decreased the stress, providing a practical way for designing continuum-compliant structures. Moreover, the proposed method obtained the design parameters at a lower computational cost than FEA. This has been successfully demonstrated in the FEA and the prototype experiment. Nevertheless, some aspects should be discussed.  
First, FEA results showed that the maximum bending stress is smaller than the yield strength of PAI. This is a significant achievement, as designing below yield stress in limited sizes and materials with conventional design methods is challenging. However, the same mesh size was applied to verify the effectiveness of the proposed design method using FEA. The validity of the mesh size should be further investigated by performing a mesh sensitivity analysis.  
Second, stress evaluation was performed only for wire tension without grasping an object. Therefore, the maximum stress is expected to be higher while holding an object. Further, when lifting a part of the mucous membrane, there is a possibility to cause a large deformation. It should be investigated assuming a clinical application. On the other hand, the proposed method could also be applied to the design improvement of the grasping segment. In addition, the stress concentration at the base end of the bending segment was improved. However, it is necessary to consider compressive stress and rigidity as evaluation indexes.  
Third, there is a possibility that an external force may be applied to the forceps in the narrow gastrointestinal tract. This aspect was not considered in this study. Moreover, covering the bending segment with a heat-shrinkable tube might affect the stress dispersion and the radius of curvature.  
Finally, the prototype was developed using rapid prototyping with Verowhite. However, the 3D printing method of PAI is typically a fused deposition modeling (FDM) method; thus, this method is not suitable for building micro-parts. In addition, the FEA results and prototype experiments showed similar tendencies but with some errors between each other. There are differences in material properties such as yield stress and elongation between VeroWhite and PAI. Future work should be conducted in these directions.
## 3 The Time-Varying Global Reliability Analysis Based on the HM-IMEM
### The High-Order Moments-Based Improved Maximum Entropy Method
#### 3.2.2 The Determination of Integral Limits  
It is easy to find that the integrals shown in Equations (6) and (7) are defined over the infinite domain (-\(\infty\), +\(\infty\)) where the integrals are difficult to be calculated directly. In practice, the infinite domain is frequently replaced by the finite domain (\(\mu-10\sigma\), \(\mu+10\sigma\)), where \(\mu\) and \(\sigma\) are the mean and standard deviation of the performance function, respectively. However, the convergence of the integral problem greatly depends on the accuracy of the integral limits. Obviously, the approximation on the finite domain (\(\mu-10\sigma\), \(\mu+10\sigma\)) is subjective and may lead to truncation errors, and even make the MEM not converge. In this regard, to enhance the numerical stability and accuracy, a more rational determination of the integral limits is introduced in this section [31]. Fistly, the linearly shifter moment \(m_{i}\) involving an unknown variable C can be defined [35] as follows:  
\[m_{i}=\sum_{j=0}^{i}\left(\begin{array}{c}i\\ j\end{array}\right)(-C)^{i-j}M_{Z,j} \tag{15}\]  
and then, the following equations containing variable C can be constructed, namely  
\[\tau=\Delta_{1}/\Delta_{2}\] (16a) in which \[\Delta_{1}=\det\begin{bmatrix}m_{0}&m_{1}&\cdots&m_{n^{\prime}}\\ m_{1}&m_{2}&\cdots&m_{n^{\prime}+1}\\ \vdots&\vdots&\ddots&\vdots\\ m_{n^{\prime}}&m_{n^{\prime}+1}&\cdots&m_{2n^{\prime}}\end{bmatrix} \tag{16b}\] \[\Delta_{2}=\det\begin{bmatrix}m_{0}&m_{1}&\cdots&m_{n^{\prime}+1}\\ m_{1}&m_{2}&\cdots&m_{n^{\prime}+2}\\ \vdots&\vdots&\ddots&\vdots\\ m_{n^{\prime}+1}&m_{n^{\prime}+2}&\cdots&m_{2n^{\prime}+2}\end{bmatrix}. \tag{16c}\]  
The solutions about the variable C, noted as \(\varsigma^{\prime}\) and \(\varsigma\), can be obtained by solving the Equation (16a), where \(n^{\prime}\) equals to\(\lfloor n/2\rfloor\) and \(\tau\) equals to \(10^{-(n^{\prime}+1)}\). Finally, \(\varsigma^{\prime}\) and \(\varsigma\) are considered as the upper and lower integral limits, respectively.
## 4 Simulation Results
### Results and Discussion
#### 4.4.4 Communication Performance  
BER performance with the estimated channel (PSNR = 30 dB) and with perfect channel state information at the receiver (CSIR) is provided in Figure 6, showing the BER performance as a function of the SNR/bit in the low- and high-IPT regimes. As expected, the proposed P-IPIC achieves lower bit error rates in both regimes. In addition, BER curves are closer to the ideal curves obtained with perfect channel state information at the receiver.  
Figure 4: Average number of estimated paths as a function of pilot SNR.  
Figure 5: Probability mass function of the number of detected paths.  
Figure 6: Bit error rate as a function of SNR/bit.
## 4 Experiment and analysis
### Simulation Result Analysis  
As demonstrated in Table 3, the average delivery ratio from MSNN increases by 15%, 60%, 7% and 6% as compared with those from spray and wait, Epidemic, ICMT and EIMST, respectively. Alternatively, the average end-to-end delay from MSNN decreases by 90%, 75%, 67% and 50% as compared with those from spray and wait, Epidemic, ICMT and EIMST, respectively. Finally, the average network overhead from MSNN reduces by 68%, 44%, 24% and 13% compared with those from spray and wait, Epidemic, ICMT and EIMST, respectively. Overall, the MSNN algorithm is superior to other four approaches in terms of delivery ratio, average end-to-end delay and average network overhead.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Attributes** & **Classes** & **Nodes** \\ \hline Infocom5 & 6 & 2 & 3276 \\ Infocom6 & 6 & 2 & 680 \\ Cambridge & 6 & 2 & 2310 \\ Intel & 6 & 2 & 837 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the datasets used in the experiment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Environment** & **Description** \\ \hline Simulator & Opportunistic Network Environment (ONE) \\ Mobility model & MGMM \\ Communication area (m\({}^{2}\)) & \(4500\times 3400\) \\ Total simulation time (h) & 2–6 \\ Number of nodes & 100, 200, 300,400, 500,600,700 \\ Cache space of a node (Mb) & 30 \\ Speech of a node (m/s) & 1–20 \\ Initial energy for a node (J) & 100 \\ Number of social attributes & 6 \\ Mobile similarity threshold & \(  
This is because that the MSNN algorithm mainly divided into four continuous phases. First, to achieve the functional integrity of the algorithm, it is necessary for mobile nodes to acquire network routing state. Secondly, the mobile similarity of the nodes is calculated by the relationship among nodes. Thirdly, the retained nodes are selected by the clustering algorithm, and the distance between the Minkowski distance and the MVD distance is calculated, and the distance of the social attribute is calculated according to the social attribute. Finally, the relay node with the highest probability of message forwarding success is selected as suitable relay nodes in data dissemination process.
# Modular Bus Unit Scheduling for an Autonomous Transit System under Range and Charging Constraints
## 3 Solution Algorithm
### Master Problem  
We define the following master problem:  
\[min\sum_{r\in R}C_{r}\cdot Z_{r} \tag{10}\]  
\[\sum_{r\in R}V_{ri}\cdot Z_{r}\geq\lceil d_{i}/M\rceil\ \ \ \ \ \ \forall i\in K \tag{11}\]  
\[\sum_{r\in R}V_{ri}\cdot Z_{r}+\sum_{r\in R}\sum_{a=1}^{a_{max}}V_{r(\gamma_{i }^{a})}\cdot Z_{r}\leq U\ \ \ \ \ \ \ \forall i\in F \tag{12}\]  
\[Z_{r}\in\{0,\ 1,\ 2,\ \ldots,g_{max}\} \tag{13}\]  
where \(C_{r}\) means the cost of the trip sequence \(r\in R\), \(V_{ri}\) is given as a parameter with \(V_{ri}=1\) if node \(i\) is covered by the trip sequence \(r\) provided by the pricing subproblem and 0 otherwise. \(Z_{r}\), an integer variable, reveals the number of MB units assigned to the sequence \(r\).  
Step 1: Initialization. Depending on the characteristics of the actual problem, a greedy algorithm can be employed to construct the columns that could potentially form a feasible solution.  
Step 2: Obtain the dual solution. The initial solution is fed into the relaxed master problem to obtain the dual solutions of the constraints associated with trip nodes and charging nodes and then pass them to the subproblem.  
Step 3: Solving the subproblem. The trip sequence with a negative reduced cost obtained from the subproblem is fed to the set \(R\) of the master problem. The label-setting algorithm is recognized as an effective approach for tackling these subproblems [40]. By leveraging the cost and energy consumption of nodes and arcs in the directed graph, the algorithm dynamically expands from node \(o\) to node \(s\), generating labels that store cost and cumulative energy consumption information. Importantly, after each expansion, the algorithm performs a dominance test to compare the newly generated labels with existing labels in terms of cost and cumulative energy consumption. Labels that are dominated are deemed invalid and pruned, resulting in a streamlined label set and improved computational efficiency.  
Step 4: Termination condition for iteration. The column generation process terminates when the newly generated trip sequence does not contribute to the objective function of the master problem, meaning there is no sequence \(r\) with a negative reduced cost.  
Step 5: Obtaining integer solutions. The optimal solution obtained from the column generation process may be fractional. While the branch-and-price algorithm is commonly used to convert fractional solutions to optimal integer solutions [41], it can be time-consuming. In this study, a diving heuristic is adopted to obtain high-quality integer solutions. Based on the fractional solutions obtained, we selectively fix one or more fractional variables that are very close to integers to positive integer values and then invoke the column generation process until all solutions are integers.  
Figure 3: The flow chart of the column-generation-based heuristic algorithm.
# A Linear-Time Algorithm for the Isometric Reconciliation of Unrooted Trees
## 2 Algorithm
### Tree Primitives  
In our algorithm, we use the following algorithmic building blocks, whose efficient implementation is explained in Section 2.8. We work with points in the trees represented as \(T(u,v,a)\) for some nodes \(u\) and \(v\) in tree \(T\) and distance \(\alpha\) such that \(0\leq\alpha\leq d_{T}(u,v)\). The following statements assume that \(O(N)\)-time preprocessing is done on each input tree \(T\in\{G,S\}\).  
* Given two points \(u\) and \(v\) in tree \(T\), we can compute their distance \(d_{T}(u,v)\) in \(O(1)\) time.  
Given three points \(u\), \(v\) and \(w\) in a tree with strictly positive edge lengths, we can determine if \(v\) is located on the path connecting \(u\) and \(w\) in \(O(1)\) time; we denote this predicate \(\texttt{between}_{G}(u,v,w)\).
* Given two points \(p_{1}=T(u_{1},v_{1},a_{1})\), and \(p_{2}=T(u_{2},v_{2},a_{2})\) in tree \(T\) where \(u_{1}\), \(u_{2}\), \(v_{1}\), \(v_{2}\) are nodes of \(T\), we can represent point \(p=T(p_{1},p_{2},\beta)\) in the form \(p=T(w_{1},w_{2},\gamma)\) where \(w_{1},w_{2}\in\{u_{1},v_{1},u_{2},v_{2}\}\), and this representation can be computed in \(O(1)\) time.
* Given tree \(T\) with \(m\) edges, we can list all its \(2m\) rooted subtrees so that if node \(w\) is a child of \(u\) in a subtree \(\texttt{sub}(T,u,v)\); then subtree \(\texttt{sub}(T,w,u)\) occurs in the list before subtree \(\texttt{sub}(T,u,v)\). We call this ordering bottom-up order of the rooted subtrees, and it can be computed in \(O(N)\) time. If we process rooted subtrees in the bottom-up order, all subtrees of each rooted subtree \(T^{\prime}\) are processed before \(T^{\prime}\) itself.
# Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment
## 4 Observer Design
### Adaptive Noise Matrix Adjustment  
The variance matrix in the UKF is hard to get. The traditional variance matrix in the UKF is a fixed value depending on experience.  
A prominent difference between a skid-steered wheeled vehicle and an Ackerman steered vehicle is that when a skid-steered vehicle is turned, the sideslip angle of the tires is considerably more than that of an Ackerman vehicle. Therefore, the nonlinearity of the skid-steered wheeled vehicle is larger than that of an Ackerman vehicle, especially when the vehicle turns. When the sideslip angle of the tires increases, the inaccuracy of tire model also increases. To decrease the influence of nonlinearity, an adaptive variance matrix algorithm is proposed.  
The sideslip angle of the tires is regarded as the main factor which is an incremental impact of variance matrix for a skid-steered wheeled vehicle. Equation (6) shows the lateral velocity and yaw rate are positively correlated to the sideslip and longitudinal velocity is negatively correlated to the sideslip.  
The authors of [35] and [30] proposed a linear variance matrix adaptive algorithm and based on this algorithm, an adaptive variance matrix algorithm is proposed for a skid-steered wheeled vehicle. In this algorithm, the major factor influent on variance matrix is regarded as the sideslip rate of the tires.  
The variance matrixes are shown as (32):  
\[Q=\left[\begin{array}{ccc}q_{11}&&\\ &q_{22}&\\ &&q_{33}\end{array}\right]R=\left[\begin{array}{ccc}r_{11}&&\\ &r_{22}&\\ &&r_{33}\end{array}\right] \tag{32}\]  
where  
\[\left\{\begin{array}{ccc}q_{11}=&q_{a1}|a_{x}|+q_{a2}|a_{y}|+q_{a3}\biggl{|} \sum_{i,j}S_{ij}\biggr{|}\\ &&+q_{da}\biggl{|}\sum_{i,j}\alpha_{ij}\biggr{|}+q_{a5}|\gamma|+q_{a0}\\ q_{22}=&q_{b1}|a_{x}|+q_{b2}|a_{y}|+q_{b3}\biggl{|}\sum_{i,j}S_{ij}\biggr{|} \\ &&+q_{b4}\biggl{|}\sum_{i,j}\alpha_{ij}\biggr{|}+q_{b5}|\gamma|+q_{b0}\\ q_{33}=&q_{c1}|a_{x}|+q_{c2}|a_{y}|+q_{c3}\biggl{|}\sum_{i,j}S_{ij}\biggr{|} \\ &&+q_{c4}\biggl{|}\sum_{i,j}\alpha_{ij}\biggr{|}+q_{c5}|\gamma|+q_{c0}\\ \left\{\begin{array}{l}r_{11}=K_{r1}[r_{a1}(d_{a1}-|a_{x}|)+r_{a3}(d_{a3}-| \gamma|)]+r_{a}\\ r_{22}=K_{r1}[r_{b2}(d_{b2}-|a_{y}|)+r_{b3}(d_{b3}-|\gamma|)]+r_{b}\\ r_{33}=r_{c}\end{array}\right. \tag{34}\]The parameters \(K\), \(q\), and \(r\) are calibrated by the experimental data. Equation (33) expresses that the wide range maneuver would increase the nonlinearity, which will result in the increasing variance of the system. Equation (34) expresses that the large measurement data would decrease the relative error of measurement.