esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Solving Method
### Two-Phase Hybrid Optimization
```
0: Determined task \(a\), feasible time windows in satellite \(j\)\(ftw_{a}^{j}\), remaining capacity of satellite \(j\)\(rc^{j}\), initialize \(rc=0\)
0: Number of the determined satellite \(j_{t}\)
1:for number of satellites \(j=1\), \(2\), \(\ldots\), \(N\)do
2:if\(ftw_{a}^{j}=\varnothing\)do
3:continue
4:else if\(rc^{j}>rc\)and\(rc^{j}>s_{i}\)do
5:\(rc=rc^{j}\)
6:\(j_{t}=j\)
7:endif
8:endfor
```  
**Algorithm 1** Heuristic rule MRC  
In accordance with previous studies [26; 30; 42; 48], the beginning execution time of the determined task is set to the earliest of its feasible time windows in the determined satellite. We also employ a heuristic rule as a comparison, denoted as EFT, indicating that a feasible satellite with the earliest feasible time windows is determined. The heuristic rule EFT is illustrated in Algorithm 2, where Max_v represents a big enough number, and \(min\Big{\{}ftw_{a}^{j}\Big{\}}\) represents the earliest feasible time windows in satellite. ```
0: Input: Determined task \(a\), feasible time windows in satellite \(j\)\(ftw_{a}^{j}\), initialize \(et=\) Max_v
0: Number of the determined satellite \(j_{t}\)
1:for number of satellites \(j=1\), \(2\), \(\ldots\), \(N\)do
2:if\(ftw_{a}^{j}=\varnothing\)do
3:continue
4:else if\(min\Big{\{}ftw_{a}^{j}\Big{\}}<et\)and\(rc^{j}>s_{i}\)do
5:\(\text{et}=min\Big{\{}ftw_{a}^{j}\Big{\}}\)
6:\(j_{t}=j\)
7:endif
8:endfor
```  
**Algorithm 2** Heuristic rule EFT  
The task action and resource action, as determined by the encoder-decoder network and the heuristic rule, interact with the environment resulting in experience samples for training. The trained encoder-decoder network and heuristic rule form the proposed TPHO framework.
# Blockchain-Enabled NextGen Service Architecture for Mobile Internet Offload
## 2 Related Work
The effectiveness of the algorithm is tested based on parameters such as pricing, cellular offered throughput and temperature coefficient. The researchers in [15] proposed a blockchain-based roaming and offloading mechanism for local 5G operators. This proposed solution uses blockchain for agreementsbetween MNOs for the smooth execution of handover and offloads among the subscribers of various MNOs. A WiFi offloading platform is proposed by researchers in [16]. In this platform, researchers proposed offloading based on the WiFi provider's previous ratings, Internet speed and signal strength. This offloading is carried out at the mobile network's software-defined network (MN-SDN) and initiated automatically without the intervention of the user. Unintentional offloading may result in frequent offloading without the consent of MNOs and increase MNO costs manyfold. The difference between this platform and the proposed framework is that the offloading is carried out after verifying the user's authenticity, requiring the intention of all parties, such as the user, WiFi provider and MNO. A scheme based on blockchain is proposed by the authors of [17] for offloading among various base stations. This scheme utilizes a directed acyclic graph (DAG) and uses game theory for bargaining cost and time. SDBlockEdge is a token-based resource management scheme, proposed in [18], which can be used in collaborative edge computing for the offloading of multiple tasks and is based on blockchain technology. Researchers in [19] also developed a scheme for mobile edge computing to optimize task offloading. The review of related work concludes that researchers have worked on various techniques for data offloading with the aims of enhancing the performance of bandwidth allocation and providing an optimized pricing mechanism. It was found from the literature review that researchers worked on offloading data from one MNO to another MNO or from one base station to another base station. Blockchain-based offloading from an MNO to a WiFi provider is discussed by one researcher [16], and SDN level reprogramming is proposed to facilitate automatic offloading, but our proposed framework suggests consensus-based agreement and can work within the existing network protocols. The proposed technique supplements the data offloading research by providing efficient offloading management and providing an efficient and secure authentication mechanism.
## 5 Performance Evaluation of Classification Algorithms
### How Sensitive are Learning Machines to the Size of the Feature Vector?  
The performance of machine learning systems, which includes classification accuracy, generalization ability, computational efficiency and learning convergence, can be negatively affected by a failure of the system to discard irrelevant or redundant features [43]. In practice, every classifier deals differently with the dimensionality of the feature space and the decision as to which features to keep and which to discard may vary drastically, depending on the choice of the classifier and the structure of the input data. Therefore, the following experiment aims at choosing for any given classifier and image type a reduced number of features that are relevant and concise enough to achieve high classification accuracies while keeping the computation times short. Given are ten unique (stratified randomly sampled) training datasets for each image type with a size of 1400 (QB), 1200 (TM) or 720 (MSS) instances. Each instance has a maximum dimensionality of 72 (QB), 109 (TM) or 73 (MSS) features and is labeled with one out of five (QB) or three (TM and MSS) classes. For the classification accuracy assessment, the classes are clustered into two groups (\"built-up areas\" and \"not built-up areas\"). The feature vector for each image type has been ordered following the outcomes of the Relieff feature importance ranking. The goal is to test, in a cross-validation on ten unique and stratified randomly sampled testing datasets for each image type (with 350 instances for QB, 300 instances for TM and 280 instances for MSS), the performance and consistency of a classifier while the number of features is iteratively increased along the ranked feature vector. For each classifier, the feature subset that produces the highest average accuracies with the least number of features is selected.  
From  
For the other classifiers, the optimal dimensionality of the feature space could be identified as 37 (QB), 40 (TM) and 33 (MSS) for RT; 33 (QB), 18 (TM) and 3 (MSS) for KNN; and 6 (QB), 26 (TM) and 2 (MSS) for NB. Averaged over all image types, NB (11) requires the least number of input features to achieve high classification accuracies. KNN (18), SVM (23) and RT (36) need more features to achieve similarly high accuracy values. Even though KNN and NB outperform SVM and RT in terms of the minimum size of the feature vector needed to achieve maximum accuracy values, the undesirable Hughes phenomenon becomes evident for KNN and NB on all image types. With respect to KNN and NB, SVM and RT show stable classification performance with increasing dimensionality of the feature space once the optimal operating point is reached.  
Figure 4: Influence of the size of the feature vector on the classification accuracy for QB. Mean and range of accuracy measures from the ten-fold cross-validation are shown for each iteration.  
Figure 5: Influence of the size of the feature vector on the classification accuracy for TM. Mean and range of accuracy measures from the ten-fold cross-validation are shown for each iteration.
# Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer
## 3 Weather Data
### Backscatter Data from Lidar Ceilometer  
Backscatter data were collected by a lidar ceilometer installed in Boseong, South Korea, from 1 January 2015 to 31 May 2016. CL51 C51 C51 (2016) is a ceilometer manufactured by Vaisala. The CL51 ceilometer can provide backscatter profile and detect clouds up to 13 km, which is twice the range of the previous model CL31. Table 1 gives the basic information on backscatter data. Missing backscatter data were not used. The measurable range of CL51 is 15 km and the vertical resolution is 10 m. Therefore,15,000 backscatter data are recorded in each observation. However, only the bottom 450 data were used considering that the planetary boundary layer height is generally formed within 3 km. The CL51 provided input to our scheme by calculating the cloud height and volume using the _sky-condition algorithm_. The sky-condition algorithm is used to construct an image of the entire sky based on the ceilometer measurements (raw backscatter data) only from one single point. No more details of this algorithm have been released. The mechanical characteristics of ceilometer CL51 are outlined in Table 2.  
The BL-View software [26] estimates the PBLH from two types of ceilometer data: levels 2 and 3. In level 2, backscatter data are stored at intervals of 16 s after post-processing of cloud-and-rain filter, moving average, application of threshold values, and removal of abnormal values. In level 3, the PBLHs calculated using the level 2 data are stored. As the raw backscatter data and the level-2 data of BL-View have different measurement intervals, the data of the nearest time slot were matched and used.  
For the raw backscatter data, the denoising method [27] was applied. The noise was eliminated through linear interpolation and denoising autoencoder [28]. Considering that the backscatter signals by aerosol particles are mostly similar, the relatively larger backscatter signals were removed through linear interpolation. The backscatter data to which linear interpolation was applied was used as input data of the denoising autoencoder. The moving average of backscatter data was calculated and used as input data to denoise the backscatter data. We used the denoised backscatter data in our experiments. More details about the used backscatter data related to denoising and weather phenomenon are given in Appendix A.
# Design Criteria for High-Gradient Radio-Frequency Linacs
## 1 Introduction  
The accelerating gradient is one of the crucial parameters affecting the design, construction, and cost of the next-generation linear accelerators. The challenge is to develop reliable and cost-effective high-gradient linacs or, in the case of radio-frequency (rf) beam diagnostic devices, to increase the measurement resolution.  
The typical working frequencies of normal conducting linear accelerators are from about 1 GHz to 12 GHz. The accelerating gradient of the long-lived SLAC S-band linac is about 17 MV/m [1]. During the development of the Next Linear Collider (NLC)/Japan Linear Collider (ILC), an X-band test accelerator operated at a 65 MV/m unloaded gradient [2; 3]. The CERN-based linear collider design CLIC requires a 100 MV/m loaded gradient at 12 GHz in accelerating structures with heavy wakefield damping [4]. Future accelerators, like compact synchrotron light sources or inverse Compton scattering gamma ray sources [5; 6], may need even higher gradients. High field gradients are required in rf devices for beam manipulation and beam diagnostics where short rf wavelengths are beneficial for microwave undulattors [7; 8] and rf deflectors [9; 10; 11; 12].  
The history of this topic goes back many decades. However, much of the investment in its systematic research began with the development of normal conducting linear colliders, for which operating gradients had to be close to 100 MV/m to be feasible. This article summarizes the major results of this work.  
One of the main goals of this article is to provide practical recipes for the development of state-of-the-art normal conducting high-gradient rf linacs. To achieve this goal, I first review previous research and then discuss the physics of the main obstacles to high-gradient operation: rf breakdown, pulsed surface heating, and field emission. I base this physics discussion on my 2018 review paper [13], to which I have added the results of experiments carried out since, for example, high-gradient tests of C-band cavities [14] and multicell cryogenic accelerating structures [15]. I continue with a description of practical design approaches and end with a brief summary.
## 3 Simulation and Experimental Analysis
### Simulation Results and Analysis
In addition, the currents in Figure 13b have a faster convergence speed and a smaller jump amplitude. Figure 14 shows the speed-tracking waveforms of the two schemes under load startup. As shown in Figure 14a, the rotor speed reaches the set value of 0.4 s. The rotor can converge to the target speed after 0.2 s under the new system. Since high-frequency harmonics and other internal disturbances are filtered out, the speed can be stabilized at 1000 r/min thereafter. Therefore, the new system has good robustness. Figure 11: PMSM rotor position tracking under load startup of the (**a**) Control scheme of combining the backstepping controller with the ESO and (**b**) Control Scheme of combining the integral backstepping controller with the ELESO. Figure 12: PMSM rotor position tracking error under load startup of the (**a**) Control scheme of combining the backstepping controller with the ESO and (**b**) Control Scheme of combining the integral backstepping controller with the ELESO. According to the above experimental data, when the motor starts with load, the control scheme based on the combination of the backstepping controller and the ESO, as well as the control scheme based on the combination of the integral backstepping controller and the ELESO, will eventually produce a stable waveform. However, the control scheme based on the integral backstepping controller and the ELESO can accelerate to the rated speed faster in the startup phase, which is 0.2 s faster than the traditional control scheme. The control scheme combining the backstepping controller with the ESO still has a rotor estimation error of 0.2 rad when the system is stable. Due to the estimation of the internal disturbance of the system and the addition of integral terms to the backstepping controller, the control scheme based on the integral backstepping controller and the ELESO can obtain better static stability. Through no-load acceleration and on-load starting experiments, it is verified that the PMSM sensorless control system based on the integral backstepping controller and the ELESO has better performance and stronger robustness. The simulation results realize the PMSM high-precision control without a position sensor.
## 2 Literature Review
Most of them first computed the lateral distances of trajectory points to road central lines and inferred the lane number or location from the distance distributions based on the Gaussian Mixture Model (GMM), Bayesian Classifier and Kernel Density Estimation (KDE) [1; 43; 44; 45]. Particularly, complete road intersection information is very important for route planning and traffic management. Hence, more and more attention has been paid to extracting road intersections from crowdsourced trajectory data in recent years [4; 46]. The related methods often supposed road intersections as the convergent places of two or more roadways, where vehicle trajectories probably manifest some specific driving behaviors, such as changing lane, changing speed and steering off [7]. Based on such driving characteristics, the general workflow of road intersection extraction consisted of three main steps, i.e., road intersections detection, turning modes identification and road geometries derivation [47; 48]. [PERSON] and [PERSON] (2010) developed a trace classifier trained by localized shape descriptors to distinguish road intersections from non-intersections [49]. [PERSON] et al. (2015) introduced G-statistics hotspot analysis to mine the agglomeration patterns of large turning behaviors at road intersections [3]. [PERSON] et al. (2017) computed the longest common subsequence between two GNSS traces to detect the connecting points where the candidates of road intersections [50]. Recently, [PERSON] et al. (2021) applied a mask region convolutional neural network for automatically detecting road intersections [51]. The turning modes within road intersections can be further identified based on trajectory clustering [48] or entrances/exits matching [46; 52]. Based on turning modes identification, two widely-used strategies of curve fitting [47] and piecewise approximation [1] are adopted to derive the road central lines of different turning modes. Although previous methods [44; 45; 46; 47; 48; 49; 50; 51] made much beneficial work to obtain road intersection information, there are still some improvement spacesin the robustness to approach heterogeneous trajectory data and the reliability to extract road intersections with different shapes and structures. The paper thus implements a density homogenization process for the purposes of alleviating raw trajectories' density unevenness, and meanwhile, decreases huge computational cost of processing all raw data. Particularly, in light of road hierarchies, a hierarchical segmentation strategy is proposed to extract multi-level road intersection elements from the heterogeneous trajectory data.
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 6 Security Assurances and IoT Threat Model
### Definitions of Security Assurances
#### 6.1.3 Integrity  
The property of message (or data) integrity [38] ensures that a message has not been tampered with or altered on its storage site or in the transfer's transit. The requirement of message integrity is essential to a wide array of digital communications, such as emails, and instant messaging, among others. For example, if [PERSON] sends a message \(m\) to [PERSON], the message integrity ensures that [PERSON] receives a message \(m^{\prime}\) such that \(m=m^{\prime}\). In the context of IoT, data integrity ensures that the data exchanged between any two devices in an IoT network do not suffer unintentional modifications. Alteration of position, navigation, and timing of IoT sensors can severely hamper the effectiveness and trustworthiness of IoT systems.
## 4 Monitoring and Analysis of Ground Deformation Based on SBAS-InSAR Technology  
### Monitoring Data Preparation  
The 45-scene C-band, interferometric wide swath (IW), VV-polarized Sentinel-IA orbit ascent data covering the Ehuobulake Coal Mine area were adopted, with the time span from January 2019 to September 2022. Detailed image information is shown in Table 2. SRTM DEM data with a resolution of 30 m were used to remove the terrain phase.  
Figure 5: Vertical displacement curve for different working conditions.  
Figure 6: Ground-based vertical displacement increment curve.
### Assessment of the Suitability of MCDM Methods  
The variability of available MCDM methods increases the complexity of their selection for specific tasks. Therefore, scientists recently presented different tools to assess the suitability of different MCDM methods for the specific tasks. For example, [PERSON] et al. [36] proposed a methodology that uses a suitability index to determine the most appropriate multi-criteria decision-making method for real estate and land management processes. [PERSON] et al. [37] focused on measuring the level of agreement between different MCDM methods (AHP, TOPSIS, SAW, WPM, VIKOR, ELECTRE III, and SHARE MCA), employing nonparametric correlation tests (such as Kendall's tau and Spearman's rho). [PERSON] and [PERSON] [38] proposed a dual verification mechanism that involves the calculation of standard deviation (SD) and Spearman's rho coefficient to compare the ranking results of different MCDM approaches.  
In 2019, [PERSON] [39] proposed the MCDM stability index, determined by applying principles of sensitivity analysis. Sensitivity analysis is an important procedure that allows for the testing of the degree of change in the overall ranking of alternatives when the input data is slightly modified [40]. The stability index proposed by [PERSON] [39] was calculated based on the recurrence rate of the highest-ranking alternative (rank = 1). However, ignoring other ranks might increase uncertainties in decision making. It may not be precise in reflecting the preferences of the decision-maker, and it may not be effective for decision-making problems where the alternatives have similar rankings. Therefore, in this paper, the new method stability coefficient, which describes stability and reliability of the specific MCDM method by analysing the rank recurrence rate for all ranks of alternatives will be presented. Its implementation for the novel MCDM decision-making approach COST (abbreviation for COmbining MCDM approaches using method stability coefficient) will also be presented.  
The remainder of this paper is structured as follows. In Section 2, criteria affecting stair safety and sustainability in a single-family house will be identified and described. The new hybrid decision-making methodology CORST, employing the method stability coefficient to combine multiple MCDM approaches into a single value will be explained in Section 3. It application to the study case, presenting how to select both a safe and sustainable staircase project, will be presented in Section 4. Finally, discussion and conclusions will be presented in Section 5.
# An Early Investigation of the HHL Quantum Linear Solver for Scientific Applications
## 5 Scientific Applications and Evaluation
### Heat Transfer Problem in Climate Projection  
Linear solvers are deeply embedded in linear or non-linear differential equation solving through numerical methods such as the Carleman linearization and the finite difference method [69]. Such methods discretize the domain of the problems into grids, and the dimension of the formed linear system scales as the size of discretization. The number of grid points scales polynomially with system size, while the demands for solving such differential equations (DEs) are ubiquitous in science and engineering. Due to the exponential speedup in problem dimension, the combination of quantum linear solvers and these numerical methods has become an attractive direction [69; 70; 71; 72]. For example, accurate climate projection, one of the most scientifically challenging and socially urgent problems, is cursed by high dimension and could be revolutionized by quantum computing. In this  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Error Budget** & \(\mathbf{n}_{c}\) & **Physical Qubits** & **Logical Qubits** & **Min. Logical Qubit** & **Min. T State** \\  & **After Layout** & **Pre- and After Layout** & **Error Rate** & **Error Rate** \\ \hline  & 4 & 32,144 & 9 to 28 & \(3.977\times 10^{-10}\) & \(9.831\times 10^{-9}\) \\ \(0.01\) & 5 & 28,380 & \(10\) to 30 & \(1.797\times 10^{-10}\) & \(4.762\times 10^{-9}\) \\  & 6 & 28,866 & \(11\) to 33 & \(7.700\times 10^{-11}\) & \(2.236\times 10^{-9}\) \\ \hline  & 4 & 32,144 & 9 to 28 & \(4.406\times 10^{-9}\) & \(1.098\times 10^{-7}\) \\ \(0.1\) & 5 & 32,340 & \(10\) to 30 & \(1.990\times 10^{-9}\) & \(5.319\times 10^{-8}\) \\  & 6 & 32,634 & \(11\) to 33 & \(8.487\times 10^{-10}\) & \(2.483\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Factors of interest for fault-tolerant HHL circuits in power flow problems.  
Figure 5: The number of (**a**) logical cycles for the algorithm, (**b**) \(T\) states, (**c**) physical qubits for the algorithm after layout, and (**d**) physical qubits for the \(T\) factories as functions of the number of clock qubits in OPE, respectively. The estimated circuits are HHL circuits for power flow problems. Both qubit parameter sets (ns, \(10^{-4}\)) and (us, \(10^{-4}\)) have the same values under the same error budget for all four factors in the plots.  
section, we explore the application of a quantum linear system solver to the heat transfer equation that is important for atmospheric processes related to climate projection.