esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Modeling the Target Tracking Process Using Kalman Filters
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models—Research**} & \multirow{2}{*}{**Research**} & \multirow{2}{*}{
\begin{tabular}{c} **\(\lambda\) = 9** \\ \end{tabular} } & \multicolumn{4}{c}{**GERT-Model**} \\ \cline{3-6}  & & \(\lambda\) = 9 & \(\lambda\) = 5 & \(\lambda\) = 2 & \(\lambda\) = 0.2 \\ \hline \multirow{2}{*}{Markov model} & KL-divergence & 0.0001 & 0.0013 & 1.7714 & 0.2945 \\  & Wasserstein distance & 0.0016 & 0.0066 & 0.1451 & 0.0217 \\ Hidden Markov & KL-divergence & 0.2147 & 0.1413 & 0.0909 & 0.4969 \\ Model & Wasserstein distance & 0.1037 & 0.0832 & 0.1909 & 0.1346 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results of the developed GERT model and prototype models. At \(\lambda=0.2\), the highest values for KL-divergence and Wasserstein distance are observed, suggesting greater complexity and diversity in the data.
# Dynamic Decoupling and Trajectory Tracking for Automated Vehicles Based on the Inverse System
###### Abstract  
A simultaneous trajectory tracking and stability control method is present for the four-wheel independent drive (4 WID) automated vehicles to handle dynamic coupling maneuvers. To conquer the disadvantage that attendant disturbances caused by the dynamic coupling of traditional decentralized control methods degenerate the trajectory tracking accuracy, the proposed method takes advantage of the idea of decoupling to optimize the tracking performance. After establishing the dynamic model of the 4 WID automated vehicles, the coupling mechanism of the vehicle dynamic control and its negative effect on trajectory tracking were studied at first. The inverse system model was then determined by machine learning and connected in series with the controlled object to form a pseudo linear system to realize dynamic decoupling. Finally, differing from previous tracking methods following the apparent lateral position and longitudinal velocity references, the pseudo linear system tracks the ideal intermediate targets transferred from the target trajectory, that is, the accelerations of vehicle in longitudinal, lateral and yaw directions, to indirectly achieve trajectory tracking and validly restrain the vehicle motion. The effectiveness of the proposed method, i.e., the high tracking accuracy and the stable driving performance, is verified through three coupling driving scenarios in the CarSim-Simulink co-simulations platform.  
trajectory tracking; the four-wheel independent drive vehicles; automated vehicles; the inverse system; dynamic decoupling +
Footnote †: journal: Applied Sciences
# Capturing a Space Target Using a Flexible Space Robot
## 4 Controller Design for the Manipulator  
In capturing process, big impacts between the robot and the non-cooperative target may result in the damage of the manipulator or disorder of the robot attitude. In this paper, an active compliance controller is designed to eliminate the impact of collision. Next, the derivation process of the controller is given as follows.  
The dynamic equation of the space robot system (Equation (6)) involving the capturing operation can be written as  
\[\mathbf{Z\ddot{y}}+\mathbf{z}=\mathbf{F}_{c}+\mathbf{u} \tag{13}\]  
where \(\mathbf{u}=\mathbf{f}^{xy}\) is the vector of control force that is generated by electrical motors installed on the joints, and \(\mathbf{F}_{c}=\mathbf{h}\) is the vector of contact force on the end effector. It is reasonable to predict that, in the case \(\mathbf{F}_{c}\
eq 0\), the desired coordinates \(\mathbf{y}_{d}\) of the robot system are no longer ensured. Inspired by the passive compliance control method, the system equation can be written as  
\[\mathbf{Z\ddot{y}}+\mathbf{D}(\dot{\mathbf{y}}-\dot{\mathbf{y}}_{d})+\mathbf{K}(\mathbf{y}-\mathbf{y}_{d} )=\mathbf{F}_{c} \tag{14}\]  
where \(K\) and \(\mathbf{D}\) are the active stiffness weighting and active damping weighting, respectively. To obtain Equation (14), the controller should be  
\[\mathbf{u}=\mathbf{z}-\mathbf{D}(\dot{\mathbf{y}}-\dot{\mathbf{y}}_{d})-\mathbf{K}(\mathbf{y}-\mathbf{y}_{d}) \tag{15}\]  
From Equations (14) and (15) we can find that the response of the robot system to the contact force depends not only on the inertia parameters (\(\mathbf{Z}\)) of the robot system but also on the compliance parameters (\(\mathbf{K}\) and \(\mathbf{D}\)) of the controller. Therefore, the system response can be lowered by choosing suitable compliance parameters of the controller. Thus, the influence of impact is reduced.
## 6 Conclusions, Recommendations, and Limitations
### Policy Recommendations
#### 6.2.3 Emergency Response Enhancement: Establishing an Efficient Crisis Intervention Mechanism  
(1) Set up national or regional rapid-response psychological intervention teams to ensure quick reactions during disasters.  
(2) Regularly conduct crisis intervention drills to ensure emergency personnel are familiar with operational procedures.  
(3) Establish a cross-regional collaboration mechanism for psychological crisis interventions to quickly allocate resources and enhance intervention efficiency.
# A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model
## 2 Heading Estimation Based on an AR Model  
A smartphone contains a variety of sensors, and it is an effective device for pedestrian navigation. When a smartphone is used for navigation, it is held in the hand more than 87% of the time [26], so we assume that the heading of a pedestrian is consistent with the smartphone in this paper. That phone is held in the hand of a pedestrian. The heading is determined by the KF based on AR model in this paper.
## Appendix A Locations Selection, System Design, Dimensioning, and Components
### Technological and Economic Characterization of System Components in Two Scenarios
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Label**} & \multicolumn{3}{c}{**Specific Energy**} \\ \multicolumn{2}{c}{**Energy Conversion Processes**} & \multicolumn{2}{c}{**Consumption/Production (SEC/SEP)**} \\ \multicolumn{2}{c}{**Near Future**} & \multicolumn{2}{c}{**Mid Century**} \\ \multicolumn{2}{c}{**(See Figure 2**} & \multicolumn{2}{c}{**[kWh\({}_{\text{e}}\)/kg H\({}_{2}\)]**} & \multicolumn{2}{c}{**[kWh\({}_{\text{e}}\)/kg H\({}_{2}\)]**} \\ \hline W4 and S4 & Alkaline water electrolysis [246; 255; 256; 257; 258; 259] & 44.4–50.0 1 & 42.6–47.7 1 & 2 \\ S5 and W5 & Hydrogen purification [260; 261] & 1.3 & 1.1 \\ S6 & Compressor at local solar (500 bar) [115; 263; 264; 265; 266] & 3.0 3 & 1.8 3 \\ W6 & Compressor at wind turbine park to SHS (180 bar) & 1.9 3 & 1.0 3 \\ HFS1 & Compressor at HFS ([30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 62; 63; 68; 64; 69; 60; 63; 65; 66; 67; 68; 69; 60; 69; 6  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Label**} & \multirow{2}{*}{**Subsystems and Components**} & \multicolumn{4}{c}{**Near Future**} & \multicolumn{4}{c}{**Middle Century**} \\ \cline{3-10}  & & **IC\({}_{\rm i}\)** & **OM\({}_{\rm i}\)** & **LT\({}_{\rm i}\)** & **IC\({}_{\rm i}\)** & **OM\({}_{\rm i}\)** & **LT\({}_{\rm i}\)** \\  & & **IC\({}_{\rm i}\)** & **[Y/year]** & **[year]** & **[year]** & **[year]** & **[Y/year]** & **[year]** \\ \hline \multicolumn{10}{c}{**Solar and wind electricity production**} \\ S1 & & Star activity system [134,280,281] & 725 EA/GW & 2.8\% & 25 & 440 EA/Wp & 2.3\% & 30 \\ W1 & Wind turbine system [134,132,280,281] & 975 EA/W & 2\% & 25 & 800 EA/W & 1\% & 2
## 3 Local Estimation of the Clay Roof Relief Based on Electrical Resistivity Prospection  
ERI measurements were made with the Terrameter LS apparatus of the Swedish company ABEM, alongside three 70 m long measurement lines with 7 m intervals. The spacing of the electrodes on each of the lines was 1 m. Measurements were made by a gradient array with multiple current-electrode combinations, which yields solid outcomes when recognising/mapping horizontal variability [37]. Apparent resistivity data obtained from field measurements were processed with the Res2 DInv software [38]. Acquired two-dimensional resistivity models of the surface level of the geological medium were used to perform the 3D interpretation in the Voxler program (Figure 3).  
The determination of the top surface of the Neogene clay was the primary task for the ERI measurements. The location of the relief of the Neogene clay formation roof was assumed on the basis of the iso-resistivity line of 30 \(\Omega\)m. Resistivity values below 30 \(\Omega\)m are the most common resistivity values for clays in Poland [39]. In the Warsaw city region, the juxtaposition of borehole profiles with the distribution of low resistivity values shows that Neogene clays are assigned to soils with electrical resistivity value below 30 \(\Omega\)m [9; 13; 40].  
The results of the resistivity distribution in the study area were related to geological data from archive boreholes. Such an approach reduced uncertainty of interpretation and increased confidence in placing borders interpreted on the basis of resistivity distribution. Over the Neogene clays (resistivity below 30 \(\Omega\)m), there is a layer of glacial till, with resistivity from 30 to 80 \(\Omega\)m. Just above tilts, there is a near-surface layer of high resistivity, with resistivity ranging from 80 to over 5000 \(\Omega\)m. It can be identified as fluvioglacial sands or human-made soils. In this case, electrical resistivity tests do not allow for the differentiation of those deposits and the placement of clear borders between them.
## 2 Preliminaries and Problem Statement
### Problem Formulation  
Considering an MAS with \(N\) (\(N\geq 2\)) mobile agents, as shown in Figure 1, the agents are initially located on a predefined circle, and no pair of agents hold the same position at the same time. For simplicity, the agents are labeled counterclockwise and the position of agent \(i\), \(i\in(1,2,...,N)\), is measured by angles \(x_{i}(t)\). Typically, initial locations of all agents are set to satisfy:  
\[0\leq x_{1}(0)<...<x_{i}(0)<x_{i+1}(0)<...<x_{N}(0)<2\pi. \tag{2}\]As we can see in this case, each agent only has two neighbors, i.e., in front of or behind itself. The following rules are given to denote two neighbors of the mobile agent \(i\):  
\[\mathcal{N}_{i}=\{i^{+},i^{-}\}, \tag{3}\]  
where  
\[i^{+}=\begin{cases}i+1,&\text{when}\quad i=1,2,...,N-1,\\ 1,&\text{when}\quad i=N,\end{cases}\]  
and  
\[i^{-}=\begin{cases}i-1,&\text{when}\quad i=2,3,...,N,\\ N,&\text{when}\quad i=1.\end{cases}\]  
The dynamics of agents for circle formation control are described as:  
\[\dot{x}_{i}(t)=u_{i}(t),\quad i\in\mathcal{V}, \tag{4}\]  
where \(x_{i}\in\mathbb{R}\) stands for the scalar state of agent \(i\), and \(u_{i}\in\mathbb{R}\) denotes its control input.  
At time \(t\) in counterclockwise, \(y_{i}(t)\in\mathbb{R}\) are described as the angular distance from agent \(i\) to agent \(i^{+}\) measured by agent \(i\), which yields to:  
\[y_{i}(t)=\begin{cases}x_{i^{+}}(t)-x_{i}(t)+2\pi,&\text{when}\quad i=N,\\ x_{i^{+}}(t)-x_{i}(t),&\text{when}\quad i=1,2,...,N-1,\end{cases} \tag{5}\]  
where \(y(t)=[y_{1}(t),y_{2}(t),...,y_{N}(t)]^{T}\in\mathbb{R}^{N}\), and \(\sum_{i=1}^{N}y_{i}(t)=2\pi\) always holds.  
Then, we define:  
\[d=[d_{1},d_{2},...,d_{i},...,d_{N}]^{T} \tag{6}\]  
determines the desired circle formation for MASs, where \(d_{i}\in\mathbb{R}\) stands for the desired angular distance between agent \(i\) and agent \(i^{+}\). A desired circle formation is achievable if and only if \(d\) satisfies \(d_{i}>0\) and \(\sum_{i=1}^{N}d_{i}=2\pi\), \(i=1,2,...,N\).  
The definition of the circle formation problem for MASs is described as follows:  
**Definition 1**.: _(Circle Formation Problem) Given an admissible circle formation characterized by \(d\), a distributed control law \(u_{i}(t,y_{i}(t)),i=1,2,...,N\) is designed such that the solution to system (4) converges to the equilibrium point \(x^{*}\) under initial condition (2). Namely, \(y^{*}=d\) is satisfied._  
Figure 1: N agents distribute on a circle.
## 4 System Model
### MNs to MNs Delays  
In networks, the delay means how long it takes for data to travel from the source node to the destination node. If the next forwarder is far from the source node, it may create forwarding delays. Due to this delay, there is a high possibility of data packet loss and more energy utilization by the nodes. To eliminate this issue, an algorithm was proposed to reduce the node-to-node delay for selecting the next forwarded ESEDG. All nodes are position aware, and one node deployed at the network's center position plays a major role in choosing the next forwarder. A source node decides the next forwarder node's direction based on the center node's location.  
In Algorithm 1, num represent all-sensor nodes of the network, and dx and dy represent the node center position. The algorithm is divided into four phases. A horizontal ellipse draws at the quadrant with X and Y-axes when a node selects a source node. The node status (\"alive\" or \"dead\") is included in the algorithm and will help us find the next forwarded node. If the node's status is \"alive\", then it will save the numbers of that node, and the same node will be selected as the next forwarded node. The procedure will continue until the all-next forwarder nodes reach the GNs. This can help us increase the network life and lower energy use by the sensors. The dead nodes cannot be considered as the next forward node. The source node has four possible paths to the node because the network region has four quadrants as shown in Figure 3. Initially, the source node compares its distance from the  
```
1:num = network nodes
2:s = nodes status
3:\"arr = new int(n)
4:dx = Central node X - axis
5:dy = Central node Y - axis
6:for\(z=1\) to \(num\)do
7:if s==\"Alive\"then
8:arr[]=z
9:else
10: Discard node z
11:endif
12:endfor
13:arraySize = sizeof(arr)
14:intSize = sizeof(arr[0])
15:length = arraySize / intSize
16:for\(i=1\) to lengthdo
17:\(a=ith\) node X - axis
18:\(b=ith\) node Y - axis
19:if\(a\leq dx\) and \(b\leq dy\)then
20: Next forwarder \((a,b++)\),\((a++,b),(a++,b++)\)
21:elseif\(a\geq dx\) and \(b\leq dy\)then
22: Next forwarder \((a,b++)\), \((a--,b),(a--,b++)\)
23:elseif\(a\leq dx\) and \(b\geq dy\)then
24: Next forwarder \((a,b--)\), \((a++,b),(a++,b--)\)
25:elseif\(a\geq dx\) and \(b\geq dy\)then
26: Next forwarder \((a,dy--)\), \((a--,dy),(a--,b--)\)
27:endif
28:endfor
```  
**Algorithm 1** Selection of next forwarded Node  
By implementing these conditions, each region's node can choose the next forwarder.  
Figure 3: Selection of next forwarded node.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 7 Conclusions  
This work qualitatively reviewed state-of-the-art power optimization strategies in FPGA-based wearable medical devices for physiological monitoring. The benefits and constraints of these techniques were summarized, along with their effects on power, performance, and resource consumption. Potential shortcomings, challenges, and solutions were highlighted, providing guidelines and future research directions for WMD designers.  
Our findings indicate that power optimization strategies often optimize resource consumption at the cost of performance or accuracy. Algorithmic optimizations and transmission load reductions decrease resource and power consumption, potentially improving latency but compromising result accuracy. In contrast, task scheduling approaches combined with dynamic partial reconfiguration and clock and power gating reduce power consumption by delaying component activation until necessary. This approach affects latency but maintains accuracy, which is crucial for physiological monitoring. FPGAs offer significant potential for such deployments due to their customizability and reconfigurability, helping reduce idle power consumption without compromising accuracy. Moreover, the flash and hybrid-based FPGAs, designed for low-power applications, are under-explored.  
Looking forward, several promising areas for future research have been identified. Power-gating, voltage-scaling, and sleep mode techniques remain underutilized in WMDs and present opportunities for more energy-efficient implementations in physiological monitoring devices. Enhancing the capabilities of power gating and voltage scaling at fine-grained levels through FPGA vendors could offer more flexible options. Partial reconfiguration should be further explored in scenarios where algorithms can be decomposed into sequential functional blocks to minimize static leakage. Additionally, the compressed learning approach is underutilized and could be leveraged by exploiting the sparse nature of physiological signals, optimizing power consumption and resource utilization. Furthermore, flash and hybrid-based FPGAs, designed for low-power applications, could be investigated further for power-sensitive WMDs. By addressing these areas, future research can significantly advance the development of more efficient and effective wearable medical devices.  
Conceptualization, B.d.S.; methodology, M.I.K.; databases search, M.I.K.; analysis, M.I.K.; draft preparation, M.I.K.; review, B.d.S.; editing, M.I.K. and B.d.S.; supervision, B.d.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported through a Ph.D. scholarship from Pakistan and Vrije Universiteit Brusssel in Belgium.  
Data Availability StatementThis is a qualitative literature review paper. The outcomes of the analysis, which synthesize and analyze the existing state-of-the-art literature, are presented within this paper. No other data were used or generated.
# Machine Learning Applied to the NOx Prediction of Diesel Vehicle under Real Driving Cycle
###### Abstract
Thus, the \(\mathrm{NO_{x}}\) emissions of Euro 6 diesel vehicles under real driving conditions, considering three types of factors influencing the \(\mathrm{NO_{x}}\) emissions (road environmental factors, atmospheric factors, and after-treatment performance factors) need to be investigated. Accordingly, the objectives of this research were as follows: (1) to test Euro 6 light-duty diesel vehicles using PEMSs under real-world driving conditions in South Korea; (2) to collect the various types of data (road environment, atmospheric, and after-treatment performance factors) from the tested vehicles; (3) to propose an artificial neural network (ANN) approach as an evaluation tool and perform model verifications; (4) to derive the relative importance of \(\mathrm{NO_{x}}\) concentration values and analyze \(\mathrm{NO_{x}}\) concentrations using the proposed method. This study was focused on determining the \(\mathrm{NO_{x}}\) emissions of Euro 6 light-duty diesel engines during real-world driving using the proposed ANN approach as an evaluation tool; the artificial intelligence model is widely used to deal with big data in various research areas. ANN models have been applied to pollutants and meteorological areas as estimation models. This study demonstrates the \(\mathrm{NO_{x}}\) concentrations of Euro 6 diesel vehicles as a function of the road environment, atmospheric conditions, and after-treatment performance factors, all of which were explored separately by previous studies. The proposed ANN model could be applied to road networks in South Korea for a better understanding of \(\mathrm{NO_{x}}\) concentrations during the real driving of Euro-6 diesel vehicles.
# An Efficient Resource Allocation Algorithm for Device-To-Device Communications
## 3 Proposed Algorithm
First initialize the D2D user and channel preference list, set Association, unmatched D2D pair set UNMATCH, transmit power, and other parameters. 2. Lines 3-27 are the core part of the algorithm, and the loop will only terminate if the maximum number of iterations is reached. Lines 7-17 of the algorithm indicate that for each subchannel \(k\), first find a D2D user pair \(d\) that currently wants to multiplex channel \(k\) from the set Cluster(\(k\)), and then check if the user pair satisfies Equations (7)-(11) constraints. If the condition is met, continue to add to the Association(\(k\)) according to the preference list of channel \(k\) D2D users until there are two D2D pairs and channel \(k\) share resources, otherwise update the D2D user preference list, Cluster(\(k\)) and \(Pre\). 3. Lines 18-20 of the algorithm indicate that there are already two D2D pairs on channel \(k\). If there is a D2D pair \(r\) in the set Cluster(\(k\)) that wants to multiplex channel \(k\), then channel \(k\) refuses to allocate resources to the D2D pair \(r\), updating the D2D pair \(r\) preference list. 4. Lines 22-24 of the algorithm indicate that in the final stage of the algorithm, some D2D users cannot find suitable channel resources due to the limitation of the signal-to-noise ratio threshold. This article also assumes that such users have found the right resources to remove these D2D users from the collection UNMATCH.
# A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions
## 4 Challenge: Data Secrecy
### Federated Learning  
Federated learning (FL) was initially proposed to enable information sharing without exchanging raw data between the distributed clients during training [128]. In FL, each client trains a local model and uploads the intermediate parameters, i.e., gradients and weights, to the central server. The central server averages the model parameters and then sends them back to the participating clients for the next iteration.  
Frequent parameter exchange can, however, pose high communication burdens to the system that are even greater than from directly sharing raw data. Meanwhile, infrequent parameter exchange can impede model convergence. The strategies are to maintain the balance between communication frequency and model convergence [129]. Nevertheless, the parameter sharing can bring risks to data secrecy as well. With respect to these, Privacy Protection Federated Learning with encryption approaches has been proposed, in which the parameter aggregation and exchanging are in the ciphertext domain [130]. [PERSON] et al. propose applying homomorphic encryption to federated learning for machinery prognostics and health management to protect the data secrecy of enterprises [131]. In addition, FL can be combined with differential privacy in which the local model parameters are permutated before exchanging to prevent the adversary clients from inferring local data of other clients [132]. [PERSON] et al. propose a data processing framework for multiple robots based on federated learning [133]. In their work, the data generated by each robot is permuted using differential privacy approaches. Their experiments demonstrate that the implementation can be applied to multiple robotics while balancing privacy and accuracy.  
The above-introduced is conventional federated learning in which each client holds the whole model of individual versions locally, and it is called vertical federated as well. In horizontal federated learning, which is also known as split learning, each client or server owns a portion of the DNN layers, and they cooperate together for training and inference [134]. Therefore, only intermediate layer outputs (cut layer) are transferred between the clients and servers instead of raw data. Compared with vertical FL, horizontal FL is still at a very early stage. [PERSON] et al. firstly proposed split learning for medical image processing with CNNs [134]. Afterward, [PERSON] et al. applied split learning to one-dimensional CNNs and imposed DP noise on the cut layer activation functions to protect data privacy [135].