esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### Experiment Results  
#### 5.3.1 Time Synchronization  
For underwater systems, the timing source is usually a crystal oscillator, which has low accuracy and tends to drift over time. However, it is important to maintain the precise reference time for underwater CPNT service. In this part, we simulate the time synchronization using Matlab and assess the performance of localization with and without time service to investigate the influence of the timing service. We evaluated the localization error with varying levels of timing accuracy.  
Figures 14 and 15 present the localization results under timing error standard deviations of \(\sigma_{t}=1\times 10^{-3}\) and \(\sigma_{t}=1\times 10^{-4}\), respectively. The corresponding statistical analysis is provided in Table 4. As evidenced by the tabulated and graphical results, the mean and variance of localization errors exhibit systematic differences between time-synchronized and non-synchronized cases across varying \(\sigma_{t}\) levels. While these discrepancies become less pronounced at lower timing error magnitudes (\(\sigma_{t}=1\times 10^{-4}\)), the statistical analysis still reveals measurable performance gaps.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\sigma_{t}\) & **Mean (w/o Skew)** & **Variance (w/o Skew)** & **Mean (with Skew)** & **Variance (with Skew)** \\ \hline \(1\times 10^{-3}\) & 2.361 & 0.490 & 2.764 & 1.671 \\ \(1\times 10^{-4}\) & 2.361 & 0.490 & 2.362 & 0.501 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statisitcs for localization results.  
Figure 13: The OFDM frame applied in the experiment is showed in the figure, with detailed parameters in one frame. \(D_{H}\) is the duration of HFM, \(G_{H}\) is the guard of HFM, \(D_{C}\) is the duration of CP, \(D_{Z}\) is the duration of ZP, \(G_{Z}\) is the guard of ZP.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency & 24 kHz \\ Bandwidth & 6 kHz \\ Sampling rate & 96 kHz \\ \(D_{H}\) & 37 ms \\ \(G_{H}\) & 5.3 ms \\ \(D_{C}\) & 290.6 ms \\ \(D_{Z}\) & 170.6 ms \\ \(G_{Z}\) & 150 ms \\ (CP-/ZP-OFDM) Number of sub-carriers & 1024/1024 \\ (CP-/ZP-OFDM) Number of data-carriers & 224/672 \\ (CP-/ZP-OFDM) Number of pilots & 672/256 \\ (CP-/ZP-OFDM) Number of null sub-carriers & 128/96 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Signal parameters.  
Since the positioning method we adopted is TDOA, it is reasonable that it is not sensitive to the base station time. However, time synchronization services consistently improve the localization accuracy, even under different timing uncertainty conditions, and we can see that with less clock skew from the positioning result, the estimated trajectory will be more precise and the localization error will be significantly lower.  
Figure 14: Localization results with timing error \(\sigma_{t}=1\times 10^{-3}\). (**a**) shows the comparison of actual and estimated trajectory of beacon with a zoomed-in details. (**b**) shows the localization error distribution with timing error \(\sigma_{t}=1\times 10^{-3}\).
## 1 Introduction
experiment displayed a maximum hardness at 48.72 J/mm\({}^{3}\) E, above which the hardness decreased. For surface roughness, a vice versa trend was found, leading to the conclusion of an optimal laser energy density of 46.15-51.28 J/mm\({}^{3}\). [PERSON] et al. [12] studied the surface quality of AlSi10 Mg LPBF-manufactured parts by evaluating the influence of laser power, scan speed, overlap rate and hatch distance. The results showed that low laser power leads to a smoother surface. Moreover, if high laser power is combined with wide hatch distances, then surface roughness increases, indicating a positive correlation. Finally, optimized values for minimum surface roughness were obtained, in particular 0.32 W laser power, 0.60 m/s scan speed and 88.7 um hatch distance. Some engineering components must possess multiple mechanical properties at different locations in order to fulfil their functionality. Crankshafts, for example, should have higher hardness on their outer surface than in the internal layers to withstand higher and different dynamic loads and stresses. This is usually achieved using processes such as case hardening where the outer layer of the material is exceedingly hardened using conventional case-hardening/treatment techniques. However, due to the thermal nature of the case-hardening process, it is not easy to control the thickness of the layer to be hardened. The AM of functionally graded structures is a popular research topic since AM might provide a precise alternative to unpredictable processes such as case hardening. [PERSON] et al. attempted the printing of functionally graded materials using LPBF and reported that varying process parameters can create a steep microstructural gradient resulting in distinct local mechanical properties [13]. [PERSON] et al. successfully demonstrated that by controlling the process parameters it is possible to control the grain orientation of parts printed using LPBF, thereby achieving tailored magnetic properties in Ni-Fe-based soft magnets [14]. [PERSON] et al. successfully demonstrated that using MAM with steel, it is possible to fabricate components with pre-defined spatial hierarchies that emulate natural bio-inspired structures that provide components with multi-functionality [15]. It can be seen that the layer-wise nature of AM allows the production of parts with customized shapes and functionalities, which enables the integration of multi-mechanical properties in one part. In this context, the aim of this study is to examine the effect of different process parameters, namely scan speed, hatch spacing and laser power, on the output variables of surface roughness and microhardness and compare it to the standard set of parameters, and thus to demonstrate the potential to precisely tailor these parameters to the desired process outcome and produce parts with multi-functionality of varying hardness and surface roughness within a single part.
## 2 Model and Data Description
### SWAN Model  
The SWAN model is the third-generation numerical model for nearshore waves, designed to simulate the wave dynamics in shallow water regions such as coasts and estuaries. Developed and maintained by Delft University of Technology in the Netherlands, the core of the SWAN model is based on the action balance equation. It can incorporate various physical processes, including wave propagation, refraction, shoaling, breaking, and nonlinear wave-wave interactions. Additionally, the SWAN model employs a fully implicit finite difference scheme, which provides greater flexibility in time step selection, allowing the model to remain stable even in shallow water regions.  
The SWAN model employs the action balance equation to describe the governing equation for waves. In Cartesian coordinates, the equation is expressed as follows [38]:  
\[\frac{\partial}{\partial t}N+\frac{\partial}{\partial x}C_{x}N+\frac{\partial }{\partial y}C_{y}N+\frac{\partial}{\partial\sigma}C_{\sigma}N+\frac{\partial }{\partial\theta}C_{\theta}N=\frac{S}{\sigma} \tag{7}\]  
In this equation, \(N\) (m\({}_{2}\)/s) is the evolution of the action density; \(t\) (s) is time; \(C_{x}\) (m/s) and \(C_{y}\) (m/s) are the propagation velocities of wave energy in the x and y directions, respectively; \(C_{\sigma}\) (m/s) and \(C_{\theta}\) (m/s) are the propagation velocities in spectral spaces \(\sigma\) and \(\theta\), respectively; and \(S\) represents the energy source.  
In the SWAN model, the energy source can be represented as follows [38]:  
\[S=S_{in}+S_{ds,w}+S_{ds,b}+S_{ds,br}+S_{nl3}+S_{nl4} \tag{8}\]  
In this equation, \(S_{in}\) represents the wind energy input term, \(S_{ds,w}\) denotes the whitecap dissipation term, \(S_{ds,b}\) is the bottom friction dissipation term, \(S_{ds,br}\) corresponds to the wave-breaking dissipation term, \(S_{nl3}\) signifies the triad wave-wave interaction term, and \(S_{nl4}\) is the quadruplet wave-wave interaction term.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 1 Introduction
### Deep Learning in Remote Sensing
#### 1.2.2 Recurrent Neural Networks  
RNNs are another type of deep learning architecture that are intrinsically designed for sequential data. For this reason, they have been the most studied architecture for SITS classification. They have demonstrated their potential for the classification of optical time series [42; 43] as well as multi-temporal Synthetic Aperture Radar (SAR) [44; 45]. Approaches have been tested with two types of units: Long-Short Term Memory (LSTM) and Gated Recurrent Units (GRUs) showing a small accuracy gain when using GRUs [42; 46]. In addition, these works have shown that RNNs outperform traditional classification algorithms such as RFs or SVMs.  
Some recent works dedicated to SITS classification have also combined RNNs with 2D-CNNs (spatial convolutions) either by merging representations learnt by the two types of networks [47] or by feeding a CNN model with the representation learned by a RNN model [48; 49]. These types of combinations have also been used for land cover change detection task between multi-spectral images [50; 51].  
RNN models are able to explicitly consider the temporal correlation of the data [46] making them particularly well-suited to drawing a prediction at each time point, such as when translating each word in a sentence [22]. In remote sensing, an RNN model based on LSTM units has been proposed to output a land cover prediction at several time steps to detect palm oil plantations [52]. However, land cover mapping usually aims at producing one label for the whole series, where the label holds a temporal meaning (e.g., \"corn crop\"). RNNs might therefore be less suited to this specific classification task. In particular, the number of training steps (i.e., the number of back-propagation steps) is a function of the length of the series ([53] (Section 10.2.2)), while it is only a function of the depth of the network for CNNs. The result is a network that is: (1) harder to train because patterns at the start of the series are many layers away from the classification output, and (2) longer to train because the error has to be back-propagated through each layer in turn.
###### Abstract  
This study introduces a reliability analysis methodology employing Kriging modeling enriched by a hybrid active learning process. Emphasizing noise integration into structural response predictions, this research presents a framework that combines Kriging modeling with regression to handle noisy data. The framework accommodates either constant variance of noise for all observed responses or varying, uncorrelated noise variances. Hyperparameters and the variance of the Kriging model with noisy data are determined through maximum likelihood estimation to address inherent uncertainties in structural predictions. An adaptive hybrid learning function guides design of experiment (DoE) point identification through an iterative enrichment process. This function strategically targets points near the limit-state approximation, farthest from existing training points, and explores candidate points to maximize the probability of misclassification. The framework's application is demonstrated through metamodel-based reliability analysis for continuum and discrete structures with relatively large degrees of freedom, employing subset simulations. Numerical examples validate the framework's effectiveness, highlighting its potential for accurate and efficient reliability assessments in complex structural systems.  
active learning; Kriging; reliability analysis; surrogate model; subset simulation +
Footnote †: journal: Applied Sciences
# MPC and PSO Based Control Methodology for Path Tracking of 4 WS4 WD Vehicles
## 2 Vehicle System Modelling
### Vehicle Body Dynamic Model  
As shown in Figure 2, the vehicle body dynamic model describes the motion of the vehicle by representing each drive unit as a pair of forces, with \(F_{Sl}\) in the longitudinal and \(F_{SL}\) in the lateral direction of the corresponding wheel. The reason for separating the dynamics of the vehicle at the drive unit boundary is to allow force sensors to be incorporated to measure the forces acting on each wheel, instead of relying on the tire model to estimate them. To facilitate the notations, the four wheels are numbered by 1, 2, 3 and 4 in circles. In particular, the force acting at each force sensor is decomposed into two components (i.e., \(F_{Sil}\) and \(F_{SLi}\), \(i=1,\ldots,4\)) normal to each other.  
The equation of the vehicle body dynamics can be expressed as  
\[\mathbf{acc}=\begin{bmatrix}a_{r}\\ a_{l}\\ \gamma\end{bmatrix}=\mathbf{M}^{-1}\mathbf{C}\begin{bmatrix}R_{1}\mathbf{F}_{AS1}\\ R_{2}\mathbf{F}_{AS2}\\ R_{3}\mathbf{F}_{AS3}\\ R_{4}\mathbf{F}_{AS4}\end{bmatrix}, \tag{1}\]  
where the acceleration vector denoted by \(\mathbf{acc}\) consists of longitudinal acceleration \(a_{l}\), radial acceleration \(a_{r}\) and angular acceleration \(\gamma\).  
The mass matrix is represented by \(\mathbf{M}\):  
\[\mathbf{M}=\begin{bmatrix}M-4m_{d}&0&0\\ 0&M-4m_{d}&0\\ 0&0&J_{z}\end{bmatrix}, \tag{2}\]  
where \(M\) is the mass of the vehicle, \(J_{z}\) is the vehicle body inertia, and \(m_{d}\) is the mass of the drive unit.  
The matrix \(\mathbf{C}\) is decided by vehicle dimension, written as  
\[\mathbf{C}=\begin{bmatrix}1&0&1&0&1&0&1&0\\ 0&1&0&1&0&1&0&1\\ -L_{h}&L_{f}&L_{h}&L_{f}&L_{h}&-L_{r}&-L_{h}&-L_{r}\end{bmatrix}. \tag{3}\]  
The force vectors \(\mathbf{F}_{ASi}\) are defined as  
\[F_{ASi}=\begin{bmatrix}F_{Sli}&F_{SLi}\end{bmatrix}^{T},\quad i=1,\ldots,4. \tag{4}\]  
The transfer matrices \(R_{i}\) from wheel frame to vehicle local coordinate frame \(X_{L}-O_{L}-Y_{L}\) are presented in  
\[R_{i}=\begin{bmatrix}\cos\delta_{i}&-\sin\delta_{i}\\ \sin\delta_{i}&\cos\delta_{i}\end{bmatrix},\quad i=1,\ldots,4. \tag{5}\]  
The lateral components \(F_{SLi}\) are determined by the lateral forces acting on tires, while the longitudinal components \(F_{Sli}\) are viewed as intermediate control inputs of vehicle system, which can be achieved by controlling the torques (i.e., \(T_{i}\), \(i=1,\ldots,4\)) applied on wheels. To simplify the coupled issue of left and right wheel steerings, the steering angles are constrained by  
\[\delta_{1}=\delta_{2}=\delta_{f}, \tag{6}\] \[\delta_{3}=\delta_{4}=\delta_{r}.\]  
Therefore, the vehicle system in this work considers six control inputs (i.e., \(\delta_{f}\), \(\delta_{r}\) and \(T_{i}\), \(i=1,\ldots,4\)) in total.
# Security Assessment of Agriculture IoT (AIoT) Applications
## 4 Study Results  
The above results mean that the components in Layer 1 and Layer 2 need additional protective measures. In the AFarCloud project, a conceptual prototype of an improved soil sensor protection concept is demonstrated to show solutions that manipulations are detected immediately, and the theft of field components is made pointless when they are not reusable outside of a defined area by a non-resettable protection measure.  
The conceptual prototype, named SED (Security Evaluation Demonstrator) [24], is built around a standard soil sensor, which utilizes Long Range Wide Area Network (LoRaWAN) communication technology to transfer the field data wireless to a data gateway. From here the data are transported via an Internet connection by using the Message Queuing Telemetry Transport (MQTT) network protocol via the cloud-based AFarCloud Middleware (MW) to the cloud data repository.  
Figure 4 shows the block diagram of the SED, which was built with real hardware to verify the implemented security improvements. If LoRaWAN and MQTT already bring a lot of data security features, the sensor has been upgraded with a Hardware Secure Module (HSM) and additional monitoring sensors, like GPS, which detects unauthorized movements of the device. The HSE is a non-manipulatable cryptographic device which manages cryptographic key storage and protects the sensor firmware. Figure 13 shows the SED sensor hardware without the case; such a sensor represents the most vulnerable part of an outdoor field device.  
The SED prototype is based on a Raspberry-Pi single-board computer with an attached LoRa shield, which also carries the GPS receiver. The HSM is plugged into the expansion bus. In a commercial sensor all components are embedded on a single electronic board, ideally all integrated on a semiconductor chip. The environmental sensors (temperature, humidity, etc.) are also connected to the expansion bus.  
The SED prototype demonstrates extended security functions for future outdoor field devices. It provides security measures for:  
* Detect unauthorized moving of the sensor
* Ensure physical integrity of the sensor, e.g., open the case
* Inhibit unauthorized reuse of manipulated sensor, prevent firmware manipulation
* Prevent sensor communication data manipulation
* Notification on low battery  
Each detected manipulation or irregular operation generates an alarm or a notification messages, which informs immediately selected users.  
The verification results of the above listed security functions are descrip  
Figure 13: SED sensor hardware without case.  
Finally, all employees must be adequately trained in security issues when they operate with field elements which need security authentication for installation and maintenance.
# Optimizing the Scheduling of Teaching Activities in a Faculty
## 3 Model of the Automatic Timetable Generation System
### Enhancing System Integrity
#### 3.5.2 Reliability: The Timefold Solver Library  
Reliability is a paramount aspect of the system, especially in the context of timetable generation. To achieve unparalleled reliability, the project leverages the robust capabilities of the Timefold Solver library. The library ensures this by using advanced algorithms rigorously tested with various datasets. These algorithms generate accurate, conflict-free timetables, contributing to overall system reliability. The library also includes error detection and handling mechanisms, allowing it to quickly identify and mitigate issues, preventing disruptions.  
Figure 2: Proposed workflow architecture.  
3.5.3. Security: Role Guards and JWT Authentication  
Security is important in safeguarding the system against unauthorized access and potential breaches. Security is enhanced through role guards [54], which ensure only users with specific permissions can access parts of the application. This protects critical information and prevents unauthorized tampering. The system also employs JSON Web Token (JWT) [55] authentication to securely verify user identities. This, combined with strong authorization mechanisms, ensures that only authenticated and authorized users can interact with the system, reducing the risk of security breaches.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 3 Resource Management Techniques in C-RAN
### Computational Resource Management Based on RRH Clustering Techniques
#### 3.2.7 Takeaway Points  
The assignment of multiple RRHs to a BBU enhances the transmission power density received by the user. This minimizes the number of resource blocks required to satisfy user demand. Because each BBU can support a larger number of users through one-to-many mapping of BBU-RRH, the number of active BBUs required for providing the demand required in the network is decreased. Moreover, RRH clustering results in reduced interference among the RRHs. This, in turn, results in an increased throughput in the case of exploited interference. This is referred to as inter-cell interference cancellation.  
Frequent re-association between BBUs and RRHs owing to RRH clustering results in the degradation of the QoS of the network. This is because handovers occur when users become connected to different BBUs. Therefore, a few authors considered the network QoS while solving the RRH clustering problem.
# A Guaranteed Approximation Algorithm for QoS Anypath Routing in WMNs
## 2 Problem Description and Related Works
If an algorithm finds an anypath \(P_{s}^{*}\) from \(s\) to \(t\) that satisfies the condition \(\hat{w}(s,P_{s}^{*})\leq\alpha\cdot\hat{w}(s,P_{s}^{opt}),s.t.w_{1}(v,P_{s}^{ opt})\leq L_{1}\), we say it is an approximation algorithm for MCOAP problems._  
**Definition 4**. : _Auxiliary Anypath Weight (AAW) for EWATX [16]. The AAW along anypath routing from node \(v\) to \(t\) is_  
\[w_{m}(v,P_{s})=\frac{w_{m}(v)}{p(v,\mathbf{F}(v,P_{s}))}+w_{m}(\mathbf{F}(v,P_ {s}),P_{s}). \tag{3}\]  
**Remark 1**. : _The OMCAP problem has been proposed in [16], thus the anypath routing of the multi-constrained QoS was studied in an ideal state from an approximate perspective. Differing from previous studies, this paper examines the MCOAP problem: looking for an anypath routing \(P_{s}^{*}\) that satisfies a specific QoS constraint such as tightly limited transmission times, limited transmission power, limited bandwidth, etc., while approximating other QoS constraints._
## 6 Discussion  
In a nutshell, the CFO impact was evaluated in the framework of the DVB-T2 system using three performance evaluation tools: the BER, MER, and EVM. Moreover, the CFO impact was studied when advanced modulation schemes like NUCs and UFMC were applied in the native DVB-T2 system. The CFO values were varied from 0.01 to 0.05. It was demonstrated that the BER of the DVB-T2 system quickly increased with the CFO increase. The main conclusions from the results analysis are summarized as follows.  
Firstly, the higher the velocity of the mobile, the greater the value of the Doppler shift, and the greater the value of the normalized CFO. This value has an impact on the increase in terms of ICI. This becomes more noticeable as the number of subcarriers increases, because the greater the number of subcarriers, the smaller the intercarrier spacing. In our simulations, we used 8192 subcarriers. If we had to increase this number to 32,768, the impact of the CFO would be more considerable. Indeed, the subcarrier spacing used for the case of 32,768 subcarriers is the subcarrier spacing value for 8192 subcarriers divided by four. It is then equal to 279 Hz. This value is not large enough compared to the Doppler shift values. Thus, the impact of ICI should really be noticeable on the OFDM signal.  
Secondly, the lower the carrier frequency, the lower the Doppler shift value, and the lower the normalized CFO value. This means that using high transmission frequencies to transmit audiovisual signals, combined with a high number of subcarriers, could be more challenging for broadcasters when broadcasting signals to mobile receivers. DVB-T2 receivers would need to include CFO compensation or correction methods for these parameters. Indeed, the carrier frequency considered in the simulation was 474 MHz, which  
Figure 27: EVM vs. SNR in the native DVB-T2 system with coding: CFO evaluation in the TU6 and AWGN case.  
Figure 26: EVM vs. SNR in the native DVB-T2 system with coding: CFO evaluation in AWGN case only.  
represents the first channel from the DTT frequency range of 470 to 790 MHz assuming that the channel bandwidth is equal to 8 MHz [38]. When the carrier frequency changes from 474 MHz to 786 MHz (which represents the last carrier frequency available in the band 470-790 MHz), \(\Delta f_{D_{Max}}\) is then equal to 109.16 Hz (Equation (21)) for the mobile speed of 150 km/h using Equation (12).  
\[\Delta f_{D_{Max}}=\frac{150}{3.6}\times\frac{786\times 10^{6}}{3\times 10^{8}} =109.16\ \mathrm{Hz} \tag{21}\]  
The normalized CFO value is equal to 0.09 using Equation (6) when the carrier frequency is 786 MHz, and the mobile speed is 150 km/h. In fact, our simulations focused on the CFO variation between 0.01 and 0.05, because above a CFO value of 0.05, the DVB-T2 system performance is very degraded. In fact, when the CFO increases above 0.05, there is enough binary or symbol errors to prevent the demodulator from performing symbol detection, the results of which would be easily exploited by the LDPC decoder for error detection and correction.