esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network Handover Algorithm Based on Location Prediction
### Parameter Definition  
A single moving trajectory point as follows  
\[p=\{p\mid p=(x,y,time,angle)\} \tag{7}\]  
The sequence of moving trajectories, consisting of multiple moving trajectory points in chronological order, as follows  
\[original\,Trail=\{p_{1},\ldots,p_{n}\} \tag{8}\]  
Based on the historical trajectory data, the shortest distance between two neighboring and non-repeating trajectory points in the indoor space is calculated, which is defined as the unit length, as follows  
\[d(p_{i},p_{j})=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{ 2}} \tag{9}\]  
According to the defined unit length, the indoor space model is meshed to generate a meshed indoor space model. The collected trajectory data are projected according to the grid model, and a grid sequence is generated as follows  
\[gridSequence=\{g_{1},g_{2},\ldots,g_{m}\},g_{i}=\{id,stoptime,angle\} \tag{10}\]  
where \(id\) is the grid number, \(stoptime\) is the user dwell time, \(angle\) is user deflection direction.  
User dwell time: In a grid cell, if multiple trajectory points belong to the same grid but at different times, the time difference between these points is called dwell time. If the dwell time exceeds the predefined threshold, the grid cell is considered a dwell state, as shown in Equation (11).  
\[stoptime=\left\{\begin{matrix}0,g_{j}[id]\
eq g_{1}[id]\parallel g_{ik}[time] -g_{i1}[time]<\delta_{t}\\ g_{ik}[time]-g_{i1}[time],g_{ik}[id]=g_{i1}[id]\end{matrix}\right. \tag{11}\]  
Due to the large scale of indoor mobile object trajectories and their redundant characteristics, the original trajectory data include many repetitive and consecutive trajectory points. The large trajectory dataset consumes significant storage space. Therefore, the original trajectory needs to be preprocessed.  
The specific steps are as follows:  
1. The deflection angle is obtained by calculating the angle between two trajectory points, which serves as the basis for analyzing changes in trajectory direction.  
Figure 3: Overall flow chart.  
The user's residence time is calculated based on its definition. Redundancies in the trajectory sequence are supplemented to ensure data integrity, facilitating the analysis of user behavior within the grid area.
3. The deflection angle is standardized. The angle interval is divided into eight non-overlapping sub-intervals, and the number of sub-interval angles is counted to analyze the user's movement distribution and enrich the trajectory prediction.
## 2 The IoV Scenario and Use Cases
### Use Cases
#### 2.2.4 Remote Driving  
This area sets the framework for UAVs and their remote operation. The area presents major differences with the previous ones for multifarious factors. Raw and processed data are gathered locally by the vehicle and are forwarded up to the cloud, where the remote driving platform resides. That is, data are not exchanged with nearby vehicles, but merged in the cloud with other vehicles, infrastructure data, etc., to enable their remote operation. Other aspects such as the calculation of efficient routes can be included into this area. The required reliability in the exchange of information between the vehicle and the V2X application server is 99.999% and the maximum allowed end-to-end latency is estimated around 5 ms.
## 6 Discussion  
IT adoption and diffusion have been extensively studied and continue to be a productive area of organizational-level investigation [21]. As the IoT remains an emerging technology, IoT adoption studies in organizations are largely in the preliminary stage. Remarkably few studies examined IoT adoption models in the MS and did not extend beyond the DOI and TOE framework to identify the determinant factors of IoT adoption [7; 8; 12; 16; 27; 32]. However, IoT adoption factors cannot be captured holistically by TOE and DOI, and some may be overlooked; thus, there are still factors that might influence IoT adoption in organizations has not been investigated yet. Therefore, this study reviewed the literature and existing theories to propose a comprehensive model that can investigate the crucial factors that affect the adoption of the IoT.  
This paper contributes to the existing IoT adoption literature from various points. Firstly, this study constructs a holistic conceptual model of the factors that could affect IoT adoption in the MS at the organizational level and explores the relationships between these factors. The conceptual model was developed from a multi-theoretical perspective that integrated VAM, DOI, and TOE framework with the constructs captured in 12 research propositions. Unlike previous models, the proposed model provides a theoretical insight into the effect of perceived value on IoT organizational adoption and contributes significantly to existing organizational theories. Examining perceived value's influence on IoT adoption in the MS contributes to the academic literature and presents new possibilities for organizational success; furthermore, the developed model contributes to examining the effects of the direct and indirect perceived benefits of IoT adoption and investigates the perceived challenges that can affect IoT adoption in different contexts. The researchers could be used the model to study IoT adoption in other sectors or other technologies adoption in the MS.  
Secondly, this study highlights the alternative way to expand IoT literature by conceptual study. Conceptual studies can open new horizons that help to understand the IoT adoption concept in organizations and suggest practical solutions, which could be a starting point for empirical studies.  
Thirdly, decision-makers can use the proposed model as a strategy to promote wide-scale deployment of the IoT in organizations. The understanding of the factors that affect IoT adoption in the MS will aid informed decision-making about future implementation of the IoT, such as assessing IoT adoption benefits and challenges, and estimating the perceived value of IoT adoption. Finally, the model is considered beneficial to develop Industry 4.0 and promote smart manufacturing and a globally competitive market.
## 2 Experimental Setup, Methods, and Procedures  
We utilized various investigative methods and procedures based on different physical principles to analyze and investigate changes in the mechanical and magnetic properties of ASSs. This knowledge base will be valuable for improving the production processes of implantable biocompatible substitutes and ensuring their functionality and quality. Our experiments included measurements of mechanical parameters (microhardness, hardness, and tear-off force) and measurements of the intrinsic magnetic field of the samples using a suitable sensor for measuring weak magnetic fields. The investigation focused on analyzing material samples made of austenitic stainless steel, specifically grade AISI 304. The samples were originally part of a square bar measuring 10 mm \(\times\) 10 mm \(\times\) 3000 mm, which was subsequently cut into smaller pieces approximately 20 mm in size. Before the cutting process, the bar underwent plastic deformation from longitudinal cold rolling in one direction. The analysis was performed on samples that were annealed at various temperatures ranging from 50 to 1200 \({}^{\circ}\)C in increments of 25 \({}^{\circ}\)C for 30 min each. Each cut sample underwent different heat treatments using the annealing process. In total, 49 samples were prepared (one sample in the initial state and forty-eight samples after heat treatment). Each sample was labeled with a numerical code stamped directly on the sample wall for clear identification at high processing temperatures, as seen in Figure 1. Controlled heating, temperature resistance, and rapid water cooling were used to achieve material structure homogenization and eliminate any influences previously caused by the manufacturing process. During cold rolling, the steel undergoes plastic deformation. Annealing reduces the areas affected by plastic deformation and leads to a more uniform structure by dissolving carbides, oxides, sulfides, and intrusions in the steel. Rapid cooling in water prevents the further exclusion of these substances. Annealing at specific temperatures and holding times brings the steel to a more favorable thermodynamic state, and recrystallization helps reduce intrinsic stresses and eliminate deformation strengthening. Prolonged annealing leads to significant changes in the material's properties due to the growth and coarsening of austenitic grains.  
Kalling's No. 2 Reagent (ES Laboratory LLC, Glendora, CA, USA) was used for sample preparation, which includes 2 g of CuCl\({}_{2}\), 40 mL of HCl (CuCl\({}_{2}\): copper chloride; HCl: hydrochloric acid), and 50 to 80 mL of ethanol in the stainless steel etchant.
# Evaluating the Capacity Coordination in the Urban Multimodal Transport Network
## 2 Methodology
### Model Formulation and Solution Algorithm  
SUE is the most widely used network flow model, which is suitable for describing a user's probabilistic route choice in a congested multimodal network [14]. The objective function is to minimize the generalized cost of travelers, and the flow follows the principle of user equilibrium. The assignment assumes that travelers choose the path with the smallest cost. However, travelers will consider transferring to other network to achieve the whole equilibrium of a multimodal transport network when the users and cost of a network increase. Given the OD pair set \(W\), the SUE model used in the evaluation process was required to have the following characteristics:  
\[\begin{array}{ll}\min Z&(f)=\begin{array}{ll}\frac{1}{b}\sum\limits_{w}\sum \limits_{k}f_{k}^{w}\ln f_{k}^{w}+\sum\limits_{a}\int_{0}^{\tau_{a}}I_{a}(w)dw,\\ s.t.&\sum\limits_{k}f_{k}^{w}=q^{w},\forall k\in K^{w},w\in W\\ &f_{k}^{w}\geq 0,\forall k\in K^{w},w\in W\\ &x_{a}=\sum\limits_{w}\sum\limits_{k}f_{k}^{w}\delta_{a,k}^{w},\forall a\in A \end{array} \tag{17}\]  
where \(f_{k}^{w}\) is the passenger flow on path \(k\) between OD pair \(w\), \(q^{w}\) is the total passenger flow between OD pair \(w\), \(\theta=0\) if users do not have any information on the travel cost in a network and they are obliged to choose routes randomly, the perfect information of a network may be given to users if \(\theta=\infty\); \(\delta_{ak}^{w}\) is the incidence relationship between link and path; if link \(a\) is on path \(k\) between OD pair \(w\), \(\delta_{ak}^{w}=1\) and \(0\) otherwise; \(l_{a}(w)\) is the flow and cost function of link \(a\), and \(K^{w}\) is the set of links.  
A solution algorithm based on the Method of Successive Weighted Averages (MSWA) is proposed for solving the SUE passenger distribution problem in a multimodal transport network, which allocates more weights to the later iterations instead of the equal average in the Method of Successive Averages (MSA) [15]. The detailed steps for the solution algorithm are presented below.  
* Step 1. Set the number of iterations \(n=0\), and the link flow matrix of the urban multimodal transport network \(x^{(0)}=0\).
* Step 2. Calculate the fares of each link \(I_{a}^{n}\), and obtain path flow and the path fares using Depth First Search (DFS) method. Then, get the shortest path and fares \(I_{f,min}\) [16].
* Step3. Get the path flow using the Logit model, yielding auxiliary link flow \(y^{(0)}\).
* Step4. Calculate new link flow matrix using an MSWA scheme, with \(d=1\).  
\[x^{n+1}=x^{n}+\chi^{n}(y^{n}-x^{n}). \tag{18}\]  
\[\chi^{n}=\frac{n^{d}}{1^{d}+2^{d}+\ldots+n^{d}}. \tag{19}\]Step 5. For an acceptable convergence level \(\varepsilon\), if \(\sqrt{\Sigma\big{(}x^{n+1}-x^{n}\big{)}^{2}}\times\left(\Sigma x^{n}\right)^{-1} \leq\varepsilon\), stop; otherwise, set \(n=n+1\), and go to Step 2.
# Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on General Imaging Model
###### Abstract  
Orthogonally splitting imaging pose sensor is a new sensor with two orthogonal line array charge coupled devices (CCDs). Owing to its special structure, there are distortion correction and imaging model problems during the calibration procedure. This paper proposes a calibration method based on the general imaging model to solve these problems. The method introduces Plucker Coordinate to describe the mapping relation between the image coordinate system and the world coordinate system. This paper solves the mapping relation with radial basis function interpolation and adaptively selecting control points with Kmeans clustering method to improve the fitting accuracy. This paper determines the appropriate radial basis function and its shape parameter by experiments. And these parameters are used to calibrate the orthogonally splitting imaging pose sensor. According to the calibration result, the root mean square (RMS)of calibration dataset and the RMS of test dataset are 0.048 mm and 0.049 mm. A comparative experiment is conducted between the pinhole imaging model and the general imaging model. Experimental results show that the calibration method based on general imaging model applies to the orthogonally splitting imaging pose sensor. The calibration method requires only one image corresponding to the target in the world coordinates and distortion correction is not required to be taken into account. Compared with the calibration method based on the pinhole imaging model, the calibration procedure based on the general imaging model is easier and accuracy is greater.  
or 2018 8 1399 19 August 2018
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 3 Test Methods and New Approach
### Selection of Kriging Variogram Parameters  
Initially, only uniform sampling in the group-delay scenario is examined. Kriging, Lagrange, and windowed WSI calculate the target value by a weighted sum of a limited number of input samples. Thus, they can be considered as FIR filter. To compare Kriging with WSI, the frequency response of the filters was evaluated using the following approach: A pulse function with \(T_{S}=1\) s and length of 103 samples was taken as input with \(y_{51}=1\) and otherwise 0. The signal was reconstructed with a resolution of 0.1 s from the input. Its spectrum was calculated with a fast Fourier transform (FFT) of length 1024 (Figure 2).  
WSI without a window function produced a sharp cut at 0.5 Hz, complying with the desired brick-wall characteristics. However, it also showed ripples in the pass band due to signal truncation. Applying a window of \(\pm 50\) samples length significantly reduces the ripples, at the expense of a slightly less steep filter.  
Different variogram types and parameters were tested for Kriging. A Gaussian variogram with \(r=6\cdot T_{S}\), \(s=1\) and \(n_{g}=0\) produced a pulse response and filter characteristics that were nearly identical to those of the windowed WSI. Taking \(\pm 30\) samples on either side of the target point was sufficient as inputs for Kriging.  
When the range was increased to \(r\geq 6.5\ T_{S}\), ripples appeared in the frequency response and the Kriging matrix became ill-conditioned for long input windows. Further testing will be presented in Section 4.  
The standard method for defining the variogram model involves fitting the model to the estimated statistical properties of the input signal obtained experimentally, as we did in earlier publications [23; 26]. The above-described method to tune the variogram model to achieve the desired FIR filter response is less common. Nevertheless, this method yielded accurate reconstructions for a wide range of input signals (Section 4). Notably, the reconstruction accuracy was better than that of WSI, particularly near the edges of time-limited signals.  
The second case considers the extension to non-uniform sampling. It is assumed that the same variogram can be applied to this case because Kriging does not discern between uniform and non-uniform spaced inputs. In Section 4, we will evaluate this assumption and compare the performance of Kriging with Lagrange interpolation through simulation.  
In the third case, which involves the single-delay scenario, the FIR filter is truncated and no longer symmetric. This prevents the use of standard methods for FIR filter design. Simulations show that the filter becomes noise sensitive (Section 4.3). Kriging provides solutions for both problems. The weights of the remaining filter taps are inherently corrected by filling the Kriging matrix with the remaining samples. The nugget value can be adjusted to the noise amplitude for reducing distortions. From the other variogram parameters, only the range was slightly increased.
# A Real Time Algorithm for Versatile Mode Parking System and Its Implementation on FPGA Board
## 5 Conclusions  
This paper has analyzed a novel idea for an Autonomous and Versatile Parking System (AVPS) intended to be implemented for future vehicle generation. The adopted method revolves around the recognition of a free parking space between parked vehicles in perpendicular parking mode, parallel parking mode, head-in angled parking mode and head-out angled parking mode using the data provided by infrared sensors. The detection of the two corners of parked vehicles could efficiently allow the selection of the parking mode. Moreover, the free parking space conditions, which have been checked over by calculating distance \(\mathrm{D_{1}}\), in the reverse of the detected corners, proved to be reliable and robust. The proposed real time algorithm is able to handle each of the presented parking mode situations. Therefore, the proposed AVPS executed the parking operation without any error. To achieve this objective, the software was developed taking into account the condition the distance threshold \(\mathrm{D_{1}}\) and the orientation of the path to track so as to reach the final position of the selected parking mode. The major advantage of the proposed system might be the combination of four parking modes to a single algorithm. This idea could be a good solution to be implemented in the near future of vehicle generation. Furthermore, because of the high price of laser radar, intelligent camera and other intelligent sensors, we have resorted to the infrared sensors in our application. Before proceeding with the experimental tests, a kinematic model of the mobile robot platform was proposed. A real time combinational algorithm was developed using the VHDL language and simulated using the ModelSim simulation tool. These respect our method consisting of a predefined diagram for parking and a tracking control procedure in each of the four presented parking modes. Then, the developed real time algorithm was tested and validated experimentally on a mobile robot system. Other revisions can be proposed in order to improve the system design performances for a variety of industrial applications using mobile robots. The potential future works include creating tests with high performance sensor networks, which may solve exceptional situations, use vehicles as interconnected objects, and conceive a novel system that requires less memory and computation cost than other proposed automatic and semi-automatic parking systems.  
Conceptualization, B.N. and C.A.; methodology, B.N. and C.A.; software, B.N. and C.A.; validation, B.N. and C.A.; formal analysis, B.N. and C.A.; investigation, B.N. and C.A.; resources, B.N., C.A. and M.M.; data curation, B.N. and C.A.; writing--original draft preparation, B.N. and C.A.; writing--review and editing, B.N., C.A. and M.M.; visualization, M.M.; supervision, M.M.; project administration, M.M.; funding acquisition, B.N., C.A. and M.M. 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.
## 3 Multi-Layer Security Approach Based on Vulnerability Indices
### Reinforcement Learning-Based Private Defense
Firstly, the observation signal (\(o^{t+1}\)) is computed regarding the estimate of likelihood \(\varphi_{t}\) and \(X_{t}\) at time \(t+1\). To describe \(o^{t+1}\), the optimal action (\(a^{t+1}\)) is obtained based on the \(\varepsilon\)-greedy policy in order to select the best action. It is important to say that the current \(P\) will be updated with the use of the SARSA algorithm to carry out well over the PODMP problem [11]. Finally, to apply the second phase, there is a need to save \(P\) in the \(Y\) table. After the learning phase that is executed in the pseudo-code of Algorithm 1, the newly recorded data are compared with the same condition in table y. The end of the process will detect false data. This trend is shown in Algorithm 2 as pseudo-code. The second phase is designed based on the \(Y\) table obtained in the previous phase as represented in Algorithm 2. In fact, this phase is aimed at raising alarm about the cases of the data manipulation with the aim of minimizing the stopping time \(t_{s}\). Overall, the proposed method procedure indicates that such a learning agent is able to detect the attack in minimum stopping time. Therefore, considering the RL method based on a smart grid, the relevant devices measure the needed data from the smart grid such as voltage, bus, angle, power generation, load, etc. to analyze energy management of the electrical grid in the first place. Then, these data, in order to detect false data, are assessed by the learning process. Figure 5: The attack detection scheme.
# TSViT: A Time Series Vision Transformer for Fault Diagnosis of Rotating Machinery
## 4 Experimental Results and Evaluation
### Noisy Environment  
In actual industrial scenarios, the collected vibration signals usually contain varying degrees of noise. Therefore, the anti-noise ability of the TSViT is discussed in different noisy environments. We added Gaussian white noise with different signal-to-noise ratios (SNRs) to the samples in the PBR, CWRU, and XJTU datasets to simulate noise disturbance. The SNR is defined as Equation (15) [22].  
\[\text{SNR}_{dB}=10\log_{10}\biggl{(}\frac{P_{signal}}{P_{noise}}\biggr{)} \tag{15}\]  
where \(P_{signal}\) is the power of the signal and \(P_{noise}\) is the power of the noise. If the SNR is 0 dB, the power of the noise is the same as the power of the original signal.  
Noise signals with an SNR ranging from 0 dB to 10 dB were added to the three datasets. The trial was repeated 10 times under each condition to eliminate randomness. Figure 14 presents the average accuracy with different SNRs on the three datasets throughout the 10 trials. Although the accuracy decreases as the noise signal increases, the TSViT still performs well in noisy environments. The larger the dataset, the smaller the impact of the noise signal.  
Figure 14: Accuracies on three noise-added test sets.  
Figure 13: Confusion matrix of the minimum optimal model of the CWRU test set.