esa-sceva/satcom-chunk-collection huggingface dataset:
# From 5G to 6G--Challenges, Technologies, and Applications
## 3 Sixth-Generation Network's Aspects
### Network Requirements
#### 3.2.2 Technologies
For example, the work in [66] proposed dividing the physical layer and creating new logical layers to enhance the user experience in massive multi-player online gaming sessions by enhancing the connection between the server and the client. The work in [67] proposed creating new hybrid protocols based on combining the long-range (LoRa) and IEEE 802.11s protocols for the purpose of enhancing data exchange in UAV groups. It is also worth discussing an interesting new technology that is expected to play a main role in 6G networks, i.e., digital twins [68]. These work by creating an identical version of a physical system with all the real-life constraints, parameters, variables, objects, conditions, etc., in such a way that the digital version behaves identically to the physical version. This concept can be applied in multiple scenarios, such as healthcare systems, manufacturing plants, and smart cities. Through digital twins, we will be able to enhance the user experience by gathering all the possible data of a system that otherwise would be extremely difficult or impossible to collect in real life, as well as allow upgrading physical systems with minimal cost and the greatest efficiency by first implementing the upgrade on the digital twin. We will also be able to create a digital biological version of all humans on Earth for Metaverse application [69].
# An Emergency Message Routing Protocol for Improved Congestion Management in Hybrid RF/VLC VANETs
## 1 Introduction  
Several radio frequency (RF) technologies, such as Dedicated Short-Range Communication (DSRC) or IEEE 802.11p and Wireless Access in Vehicular Environments (WAVE), have been leveraged for inter-vehicular connectivity [1]. Vehicles may broadcast a WAVE Short Message (WSM) to neighboring vehicles when they encounter events on the road [2]. On the other hand, the rapidly emerging Cellular Vehicular-to-Everything (C-V2X) standard defines various communication modes between vehicles and the surrounding environment, including other vehicles over 5G+ cellular networks.  
Although RF communication systems should ideally provide up to 1000 m coverage in open highway scenarios and up to 300 m in urban scenarios, their practical ranges are much lower, especially in tunnels, under bridges, and in downtown areas with many tall buildings [3]. The RF transmission may also encounter disruptions in secure or restricted areas where outside signals are limited [4].  
Furthermore, crowded urban streets significantly increase the mutual interference among RF communication links, and this is especially true during congestion. This elevated interference contributes to high packet collision rates, significantly reducing the throughput of RF-based vehicular communications [5]. This is ironic because it is during congestion when real-time information exchanges among the vehicles and the cloud become so important to overcome [6; 7; 8].  
Therefore, maintaining robust and reliable network connectivity is a major issue in VANETs during emergency situations when timely transmission of safety messages is essential. However, this is exactly when latency and packet losses of RF communication systems tend to rise markedly due to congestion. Even the sheer volume of critical alerts generated during incidents can overburden and degrade network performance [9]. This is where the rapidly emerging Visible Light Communication (VLC) can play an important role in easing the situation because VLC systems have much less mutual interference. The focus of this paper is to design a hybrid RF/VLC system for efficient congestion management.
## 4 Results and Discussions
### Fairness Comparison  
In order to provide a full picture, this section presents a comparison of the fairness of the two algorithms. To that end, we employ the fairness index of R. [PERSON] et al. [PERSON] et al. (2016), which is defined as  
\[F=\lim_{s\to\infty}\frac{\left(\sum\limits_{k=1}^{n}\overline{R}_{k}(s) \right)^{2}}{n\sum\limits_{k=1}^{n}\left(\overline{R}_{k}(s)\right)^{2}} \tag{6}\]  
in order to quantify the fairness among all users. It should be noted that in Equation (6) the value of \(\overline{R}_{k}(s)\) is updated to the long-term stationary value. Furthermore, all packets violating \(\delta_{\text{RT}}\) are excluded if users in question are included as part of the RT traffic.  
In Figure 7, a plot of the fairness index versus the system load is shown. As can be seen, since both the proposed algorithm and the baseline algorithm were designed on the basis of PF, the fairness indexes for both were high. However, the fairness index provided by the baseline algorithm was still slightly lower than that provided by the proposed algorithm. This was presumably because, with the proposed algorithm, each RT user could use all the resources made available by the aggregation of two CCs for transmission, whereas they could only use a single CC under the independent CC mechanism used by the baseline algorithm. In other words, with the independent CC mechanism, the probability of a delay violation with respect to \(\delta_{\text{RT}}\) was higher, which in turn indicated that the variation in \(\overline{R}_{k}(s)\) was larger. As a result, \(F\) was smaller. In addition, Figure 7 also indicates that the fairness indexes for both algorithms were almost equal to 1 when the system load was low.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mean Delay (in Seconds)**} & \multicolumn{4}{c}{**System Load**} \\ \cline{2-6}  & & **0.3** & **0.5** & **0.7** & **0.9** \\ \hline \multirow{3}{*}{RT packets} & \(\eta=1/4\) & 0.010 & 0.016 & 0.032 & 0.1 \\  & \(\eta=1/2\) & 0.010 & 0.017 & 0.037 & 0.1 \\  & \(\eta=3/4\) & 0.012 & 0.020 & 0.045 & 0.1 \\ \hline \multirow{3}{*}{NRT packets} & \(\eta=1/4\) & 0.0624 & 0.1295 & 0.3572 & 1.9830 \\  & \(\eta=1/2\) & 0.0512 & 0.0849 & 0.1826 & 0.9487 \\  & \(\eta=3/4\) & 0.0274 & 0.0337 & 0.1095 & 0.7152 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The mean RT packet delay and mean NRT packet delay when using the proposed algorithm given three different values of \(\eta\) and four different system loads.
###### Abstract  
(1) Background: This study examines the association between asymmetrical movements of an assembly line and machining workers and their overall well-being. The primary aim is to quantify the extent to which asymmetrical movements serve as predictors of work-related musculoskeletal disorders (WMSDs) among these workers and their overall well-being. The study emphasises the predictive relationships between asymmetry metrics and health outcomes. (2) Methods: The study included 86 employees from an automotive manufacturing plant, categorised into machining workers (MWEs) and assembly workers (AWEs). The employment duration spanned from 6 months to 40 years. Inertial motion capture technology was employed alongside the Goldberg 28-item General Health Questionnaire for a retrospective observational analysis and assessment of worker well-being. Movement dynamics were evaluated using a Motion Activity Index (MAI) to measure movement intensity, asymmetry, and quality. (3) Results: The machining group demonstrated nearly double the range of motion (median ROM: 36.6\({}^{\circ}\) vs. 25.5\({}^{\circ}\), \(p=0.019\)) and peak angular velocities up to eight times higher (median: 40\({}^{\circ}\)/s vs. 5\({}^{\circ}\)/s) in lumbar and thoracic rotations compared to the assembly group. Significant differences in ROM and movement speeds were observed (\(p<0.001\)). The MAI showed higher dynamic and symmetrical movements in the machining group (36.6% vs. 25.5%, \(p=0.019\)). No significant mental health issues were identified, aside from complaints related to somatic symptoms. (4) Conclusions: This study highlights significant occupational risks due to movement asymmetry in industrial settings, revealing substantial differences in joint angular displacements, velocities, and accelerations between machining and assembly workers. The findings emphasise the importance of targeted ergonomic interventions to enhance worker well-being and advocate for preventive health measures in occupational environments.  
ergonomics; musculoskeletal diseases; prevention and control; biomechanical phenomena; occupational health; motion analysis 2025
## 3 Motivation
### Challenge 1: Resource Fitting  
Resource fitting refers to the process of mapping the available resource pool to the application services so that the use of the available resources meets the specific application demands as expressed by the user requirements. This process requires evaluating resource characteristics, capabilities, and availability, and assigning them to appropriate tasks [31]. To achieve effective resource fitting in FL, users must undergo an extensive and rigorous process that includes training with different properties, evaluating their impact on resource utilization and training performance, and revealing the inter-component dependencies.  
_Defining Application Requirements._ While orchestrators like Kubernetes can automate the deployment of containerized FL workflows, they are generally agnostic to the specific requirements of each application. Users are responsible for specifying the desired amount of resources for their application to run smoothly. However, different FL workflows have varying computational requirements. For example, a time series prediction model may require significant memory but fewer computations, while a cancer image detection model relies heavily on computational power. Furthermore, ML models can have diverse parameters, structures, and libraries such as TensorFlow [21] and PyTorch [20]. To finely tune the resource requests, researchers need to extensively profile each component of the FL workflows and identify the application's actual needs.  
_Increasing Resource Utilization And Minimizing Wastage._ Even when users understand the workflow profile and resource requirements, they may face limitations in allocating the desired resources due to quotas or allocation requests from other users in a multi-tenant environment. It is therefore important to consider the effects of resource saturation and starvation on submitted FL workflows. Such effects may also be caused by opportunistic schedulers who allocate fewer resources than is necessary, thus causing bottlenecks. Oppositely, conservative schedulers tend to allocate more resources than is necessary (over-provision), thus wasting precious resources and minimizing the potential for multi-tenancy.  
_Revealing Inter-Component Dependencies._ Despite the decentralized nature of FL, the training is still a synchronous process that requires all clients to synchronize with the server at the end of each training round. This synchronization introduces a dependency among participating clusters, as rigorous clients have to wait for latent ones, introducing straggling task effects. To address this, users need to consider the computation- and network-aware placement of FL clients across managed nodes. By optimizing the placement of clients, the system can mitigate the impact of stragglers and improve overall efficiency, even with varying numbers of clients and data sizes.  
Figure 2: Healthcare provider use-case with containerized FL (Flower server and clients) employed over Kubernetes managed cluster administering common control plane and resource pool.
# Constant Envelope Modulation Techniques for Limited Power Millimeter Wave Links
## 3 Error Rate Degradation in the Presence of Co-Channel Interference
\(\mathbf{S}[k]\) is the signal component, as follows:  
\[\mathbf{S}[k]=\sum\limits_{n=1}^{N_{chip}}\phi[n]q_{k}[n]=\sum\limits_{n=1}^{N_{ chip}}2\pi hCx[n]q_{k}[n]=2\pi hC\left(\sum\limits_{n=1}^{N_{chip}}{q_{k}}^{2}[n] \right)I_{k}=2\pi hC\hat{q}I_{k}=dl_{k} \tag{11}\]  
where \(d=2\pi hC\hat{q}\), and for the CE-MTO case, \(C=\sqrt{3/N(M^{2}-1)}\) and \(\hat{q}=N_{chip}\), where \(N_{chip}\) is the number of chips contained in the orthogonal waveforms, \(q_{k}[n]\). For the CE-OFDM case, \(C=\sqrt{6/N(M^{2}-1)}\) and \(\hat{q}=\frac{N_{chip}}{2}\), where \(N_{chip}=N_{DFT}\) is the number of points in the inverse discrete Fourier transformation (IDFT). The interference component in Equation (10) is as follows:  
\[\mathrm{I}[k]=\sum\limits_{n=1}^{N_{at}}\xi[n]q_{k}[n]. \tag{12}\]  
As \(\xi[n]\) is the independent random values, the sum of Equation (12) makes \(\mathrm{I}[k]\) approximately Gaussian, having \(\mathrm{I}[k]\sim N\left(0,\sigma_{1}^{2}\right)\). Based on the literature [25], we may approximate SER as follows:  
\[SER\approx 2\left(\frac{M-1}{M}\right)Q\left(\frac{d}{\sigma_{1}}\right) \tag{13}\]  
where in our case, we have the following:\[\sigma_{I}=\sqrt{\hat{q}E[\xi^{2}[n]]}\approx\sqrt{\hat{q}\frac{1}{2A_{c}^{2}}}E \Big{[}t^{2}[n]\Big{]}. \tag{14}\]  
The substitution of Equation (6) in Equation (14), and \(\sigma_{1}\) and \(d\) in Equation (13), results, for both CE-MTO and CE-OFDM, in the following:  
\[SER\approx 2\Big{(}\frac{M-1}{M}\Big{)}Q\Bigg{(}2\pi h\sqrt{\frac{6N_{chip} SIR}{N(M^{2}-1)}}\Bigg{)}. \tag{15}\]  
A comparison is made between the analytical results obtained using Equation (15) and those obtained from simulations of the CE-MTO and the CE-OFDM demodulation in the presence of external interference. Figure 4 shows the graphs of the SER as a function of the SIR. The correlations between the graphs are revealed. The graphs were drawn assuming \(N_{chip}=16\) chips for each of the N = 14 orthogonal waveforms. Several PAM constellation orders, M = 4, 8, and 16, were considered. In order to keep the bandwidth restrained, the modulation index is set to \(2\pi h=0.4\), 0.5, and 0.6. Figure 4: _Cont._  
\[\hat{\phi}(t)=\arctan\left[\frac{Q(t)}{I(t)}\right]=\arctan\left\{\frac{\sin[ \phi(t)]+\frac{1}{\sqrt{SIR}}\sin[\theta(t)]}{\cos[\phi(t)]+\frac{1}{\sqrt{SIR}} \cos[\theta(t)]}\right\}. \tag{8}\]0 Constant envelope M-ary time orthogonal (CE-MTO) block diagram.
## 6 Simulation Results
The iteration numbers reduce as more areas have been explored because fewer nodes can be reached when the total energy for moving is bounded. Those conclusions can be derived from the analysis. On one hand, the algorithm is too conservative and searches few areas when \(K_{est}\) is too large or \(K_{sd}\) is too small. On the other hand, the algorithm is too aggressive and skips exploiting when \(K_{est}\) is too small or \(K_{sd}\) is too large. Both cases will make the node not end up with the global minimum. Therefore, a moderate combination of \(K_{est}\) and \(K_{sd}\) is preferred. Future research can focus on the development of adaptive algorithms that tune \(K_{est}\) and \(K_{sd}\) over iterations based on errors between real measurements and estimations. Figure 22: Plots of sequential exploration with different \(E_{T}\). In addition, the numerical values of \(E_{T}\) were also picked randomly for testing. In real applications, a fixed \(E_{T}\) should be selected before the operation of this algorithm. The selection of \(E_{T}\) is related to the specific application scenario, size of the camera's operational space, and total energy available \(E_{bound}\) at the beginning. However, the general rule of thumb is the following: choose small values of \(E_{T}\) when both \(E_{bound}\) and operational space is large to encourage exploring over exploiting; otherwise, choose a large \(E_{T}\).
## 4 Evaluation of Quality and Efficiency
### Reduction in the Number of Load Steps to Be Simulated  
In this section, the methods for reducing the number of load steps to be simulated from Section 3.4 are compared with each other. For this purpose, load-notch-strain curves for the hysteresis branch with different numbers of load steps \(\mathrm{n_{H}}=2\), \(3\), \(4\), \(5\), \(7\), \(10\), and \(20\) are determined in the simulation, while the values at the class limits are determined using one of the approaches described in Section 3.4. A reference load-notch-strain curve is calculated with \(\mathrm{n_{H}}=200\) load steps, whereby the simulation directly calculates the values at the class limits of the load-notch-strain curve for the hysteresis branch.  
For the evaluation of the node results, the previously recommended shell elements are used on the surface. However, to be able to exclude deviations resulting from a too-coarse mesh, a finer mesh than the one recommended is used. For example, for the flat sample used in Section 4.5, instead of the recommended number \(\mathrm{n_{E}}=5\), the number \(\mathrm{n_{E}}=20\) is used.  
However, the results shown in this section are equally valid with and without the use of shell elements on the surface for the evaluation of the node results. In both cases, the meshing is chosen in such a way that converged stresses and strains can be assumed.
# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 3 Proposed RF Fingerprinting-Based Emitter Identification Method
### Time-Frequency Feature Extraction  
The next step is to design a feature from the SF. The purpose of this step is to transform the SF domain into a specific feature domain in which the physical measurements between different emitters could be well distinguished. In conventional approaches [4; 5; 6], the designed handcrafted features are calculated from signal characteristics of the SFs. In this  
Figure 4: Examples of the SFs: (**a**) RT, (**b**) SS, and (**c**) FT signals.  
case, the goal is to obtain a feature domain that can ensure robust classification results. However, in more recent approaches [7, 8], the purpose of this step is slightly modified. The SFs are transformed into domains that can express the signal characteristics of the SFs, and the identification of a feature domain that can ensure robust classification is entrusted to the classification step based on a deep learning-based classifier. The relevant procedure is expressed as follows  
\[\mathbf{s}_{Feature}=\mathrm{q}_{\mathrm{SF}}(\mathbf{s}_{\mathrm{SF}}) \tag{12}\]  
where \(\mathrm{q}_{\mathrm{SF}}\) is the transform function for the designed feature domain, \(\mathbf{s}_{Feature}\in\mathbb{R}^{N_{\mathrm{SF}}^{f}\times N_{\mathrm{SF}}^{ f}}\), where \(N_{\mathrm{SF}}^{f}\) and \(N_{\mathrm{SF}}^{f}\) are the sizes of the frequency and time indices, respectively, of the spectrogram transformed from the SF.  
In this study, the time-frequency distribution of the FH signals, that is, the spectrogram, was analyzed. The spectrogram is a well-known time-frequency analysis method used to visualize the variation of the frequency components calculated from nonstationary signals [20]. The feature design strategy used in this study requires analysis of the power density behavior of the SFs in the time-frequency domain. The key idea of the FHSS system is that the carrier frequency of the FH signal hops within a predefined frequency range. Therefore, the signal characteristics must be implied in the distribution of the time-frequency domains.  
A discrete-time short-time Fourier transform (STFT) is applied to compute the spectrogram of the SFs. With the sliding window \(w[n]\) with a size of \(W_{\mathrm{STFT}}\), the STFT of the SFs can be calculated as follows  
\[\mathrm{STFT}_{\mathbf{s}_{\mathrm{SF}}}[m,p]=\sum_{n=-N_{\mathrm{SF}}}^{N_{ \mathrm{SF}}}s_{\mathrm{SF}}[n]w[n-m]e^{-j2\pi pm} \tag{13}\]  
where \(m=1,2,...,K_{\mathrm{SF}}^{t}\) is the time sampling point along the time axis and \(p=1,2,...,K_{\mathrm{SF}}^{f}\) is the frequency sampling point along the frequency axis. We set \(N_{\mathrm{SF}}\) as a sufficiently large value.  
Next, the power density behavior of the spectrogram can be represented as the magnitude squared of the STFT such that  
\[\mathrm{spectrogram}\left\{\mathbf{s}_{SF}\right\}=\left|\mathrm{STFT}_{ \mathbf{s}_{SF}}[m,p]\right|^{2}. \tag{14}\]  
The spectrogram results are presented in Figure 5.
## 5 Overview of Odour Analytical Measurement Methods
### Immission Measurements  
A fairly common procedure is the measurement of immissions by a team of inspectors in the field, in a \"grid\" or in a \"patch\" [116]. The measurements are carried out by a group of inspectors equipped, for example, with field olfactometers, or without equipment (studies based on individual assessment cards--measurement protocols). Various measurement techniques are also used. The measurement results make it possible to characterise the frequency and duration of the selected odour [116]. Before carrying out immission measurements, inspectors are selected on the basis of their odour sensitivity. The team should then be trained in the method of carrying out the measurement and completing measurement reports. To this end, they should also be familiarised with the type of odour that forms the basis for the investigation [116].  
Immission measurements can be carried out \"in a grid\", i.e., in a planned study area divided into squares. The grid is usually defined on the area of a circle in the centre of which the emission source is located. The area defined in this way is covered with a grid of squares, the corners of which are measurement points. The size of the individual squares depends on the type, height and distance of the emission sources. The disadvantage of the grid measurement method is the duration of the study (at least six months) and the fact that the representativeness of the results depends on the meteorological conditions and the changes in emissions during this period [118].  
Measurement \"in the plume\" is an alternative way of determining the study area. It requires the continuous monitoring of atmospheric equilibrium, wind direction and speed, temperature and other atmospheric factors (e.g., precipitation, fog) [119]. The designated area must be corrected ad hoc if the meteorological conditions change during the investigation. The testers move away from the system in the direction of the wind until they are outside the odour plume. Based on the assumed minimum percentage of odour occurrence, the main and side lines of the odour plume and then the measurement points are determined.