esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
In response to the evolving landscape of CAVs, significant attention has been drawn to the vulnerabilities inherent in their essential communication protocols, including CAN, LIN, FlexRay, MOST, and Ethernet. Initially, automotive cybersecurity guidelines were formalized under SAE J3061 [9], focusing on rigorous risk assessment, threat modeling, and lifecycle validation. These guidelines laid a foundational framework for robust cybersecurity measures in the design and operational phases of vehicle development.  
In recent years, the industry has transitioned to ISO/SAE 21434 [10], a globally recognized standard that builds upon SAE J3061's principles. ISO/SAE 21434 offers a more comprehensive framework for managing cybersecurity risks in automotive systems, emphasizing collaboration among stakeholders and integration with existing engineering processes. This standard ensures that cybersecurity measures are systematically integrated into vehicle development and manufacturing, ensuring alignment with global regulatory standards, including UNECE (WP:29) R155 regulation governing cybersecurity of the road vehicles [11].  
Building upon these challenges and guidelines, the following sections provide a detailed overview of the primary security attacks and critically examine existing literature, highlighting the need for more robust defense mechanisms in CAVs. We present an overview of primary security attacks, for which we propose solutions (Figure 1). Our outline addresses the major surfaces of attack faced by CAVs and delves into the deep learning-based security measures utilized in the literature to defend against such breaches.  
Figure 1: The attack surface for launching security attacks on ECUs.
# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 2 Theoretical Background
### Denoising AutoEncoder
In addition, due to the nature of ocean weather, this residual component always exists. However, when training a data-driven model, this residual component prevents the proper understanding of the remaining components (seasonal component and trend component). Moreover, it causes a significant reduction in training efficiency. Of course, it is best to develop a learning model that can acquire very long periods of data and predict residual components. However, there are limitations in gathering data infinitely. Therefore, we should reduce the noise of the data as one option. Analyzing all the data and removing noise is practically impossible. In this study, the ocean weather is converted into a 2D image and used to train the ConvLSTM model. Therefore, an AutoEncoder that can extract important features and remove noise from 2D data was used. Figure 4: The delayed prediction problem of sea surface temperature (SST). AutoEncoder is a type of ANN used to train efficient data codings in an unsupervised manner [17]. AutoEncoder simply copies input to output, as shown in Figure 6. The process seems simple, but it can have a variety of effects on data by constraining the network in different ways. Among them, Figure 6 shows a type of denoising AutoEncoder that reduces the number of neurons in the hidden layer to be smaller than that of the input layer, encodes the data to take only those characteristics, and then decodes it to remove noise. Figure 5: The decomposed components of SST from 2014 to 2017. Figure 6: Configuration of AutoEncoder for denoising. In this study, the training efficiency of the learning model for ocean weather was maximized by passing data through the denoising AutoEncoder prior to training the data with the ConvLSTM and removing the noise. Of course, if we use the denoising AutoEncoder to remove the residual component (smoothing), the accuracy can be lowered. However, it is considered appropriate to use this method to solve the delay prediction problem using limited training data.
# Cyber Security in a 5G-Based Smart Healthcare Network: A Base Station Case Study +
## 2 Research Background
### 5G and 5G-Based Smart Healthcare Networks  
5G is an advanced mobile network technology that provides a low latency, a high bandwidth, and massive machine-type communications serving large numbers of IoT applications. These applications range from cars and robots to sensors and, in the context of smart healthcare, medical equipment and wearable electronics. A 5G network mainly comprises user equipment (UE), base stations, and core network [3], as follows:  
* The UE is a modem and stores a permanent identifier called International Mobile Subscriber Identity (IMSI) and a key used for mutual authentication between the user and the network.
* The base station (BS) acts as the access point for UEs to attach to the Radio Access Network (RAN), connecting it to the mobile network and, thus, to the Internet.
* The core network (CN) is the backbone of the mobile network; it performs all management tasks and traffic routing.  
Reference [4] described the architecture of 5G and IoT-enabled smart healthcare considering specific key-enabling technologies such as small cells and the software-defined network (SDN). A taxonomy for 5G smart healthcare was also explained, and future research opportunities applied to IoT-based 5G smart healthcare were reviewed. In Taiwan, a 5G-based smart healthcare network has been trialed in hospitals with mobile network operators that deployed 5G private networks. In the network, medical robots were used to help doctors and nurses [5].
# A Hand Motor Skills Rehabilitation for the Injured Implemented on a Social Robot
## 3 Architecture of HaReS, the Hand Rehabilitation System  
As introduced before, HaReS is a system for hand motor rehabilitation. It is composed of pieces of hardware and software. Regarding the hardware, HaReS uses an sEMG sensor in order to track the activation of the muscles during the rehabilitation session. Then, a hand-tracking device is also used to provide the user's hand poses. The rehabilitation exercises are set up on a software app, which record she data of the sessions, including sEMG profiles and hand poses. The records are available for the therapists to review remotely and/or in the future. In addition, some quantitative measurements are given by the system as a potential indicator of the user's performance.  
These functionalities enable a better evaluation of the improvement the user experiences by completing the rehabilitation exercises. In addition, the system is modular, so any of the pieces can be removed at any time. Regardless, it is worth mentioning that the devices we used to create HaReS are fairly low cost. Finally, HaReS can be deployed on a social robot for further improving patients' experience and motivation. A diagram of the complete architecture of HaReS is shown in Figure 1.  
At this point, it is worth noting that the use of a robot is completely optional, as we intended HaReS to be low cost and fully modular. The system can be deployed on a desktop or laptop computer, with no robot involved. We would also like to state that the chosen sensors, such as Leap Motion, are accurate, wrist keeping cost at bay. For instance, hand tracking based on cameras is possible [45; 46], but lacks enough accuracy and requires much more computation power.  
The components of the system are detailed in the following subsections.
## 2 Materials and Methods
### Proposed Solution: Deep Reinforcement Learning
#### 2.2.1 Embedding SFC Deployment Problem into a Markov Decision Process  
Following [14; 32; 48; 51; 52], we propose to serialize the SFC Deployment process into single-VNF assignation actions. In other words, our agent interacts with the Environmenteach time a particular VNF request, \(\hat{f}_{f}^{k}\), has to be assigned to a particular VNF instance, \(f_{i}^{k}\) of some hosting node \(i\) in the vCDN. Consequently, the actions of our agent, denoted by \(a_{\tau}\) are the single-VNF assignation decisions for each VNF request of a SFC.  
Before taking any action, the agent observes the environment's conditions. We propose to embed such conditions onto a vector \(s_{\tau}\) that contains a snapshot of the current incoming request and hosting nodes' conditions. In particular, \(s_{\tau}\) will be formed by the concatenation of three vectors:  
\[s_{\tau}=(\hat{R},\hat{I},\hat{U}) \tag{17}\]  
where:  
* \(\hat{R}=(l_{r},u_{r},s_{r},\hat{f}_{r}^{-})\) is called the request vector, and contains information about the VNF request under assignment. In particular, \(\hat{R}\) codifies the requested CP, \(l_{r}\), the client cluster from which the request was originated, \(u_{r}\), the request's session workload, \(s_{r}\), and the number of pending VNFs to complete the deployment of the current SFC, \(\hat{f}_{r}^{-}\). Notice that \(\hat{f}_{r}^{-}\in\{0,1,2,3,4\}\) and that \(\hat{f}_{r}^{-}\) goes each time from 4 to 0 as our agent performs the assignations actions regarding \(r\). Note that the first three components of \(\hat{R}^{\tau}\) are invariable for the whole set of VNF requests regarding the SFC of \(r\). Notice also that, using a _one-hot_ encoding for \(l_{r}\), \(u_{r}\) and \(\hat{f}_{r}^{-}\), the dimension of \(\hat{R}\) is \(|\hat{R}|=|N_{UC}|+|N_{CP}|+|K|+1\),
* \(\hat{I}=(\hat{i}_{1},\hat{i}_{2},...,\hat{i}_{|N_{|H|}})\) is a binary vector where \(\hat{i}_{j}\) equals 1 if \(f_{j}^{k}\) is ingesting the CP requested by \(r\) and 0 otherwise,
* \(\hat{U}=(\hat{a}_{1},\hat{a}_{2},...,\hat{a}_{|N_{|H|}})\), where \(\hat{a}_{i}\) is the utilization value of the resource that is currently the most utilized resource in \(f_{k}^{i}\).
## 6 Conclusions  
This paper proposes a phasor-based IIDG model for FFC injection, which is suitable for protection studies in large networks. A comparison is made between the power- and current-based FFC concepts for a testing network with three IIDGs and a case study, i.e., the northeast part of the Slovenian transmission system. The obtained results show that both the discussed FFC concepts during network faults increased generated reactive power while decreasing the active power. The responses of generated power were more expressed when using the power-based FFC concept. Furthermore, reactive power generation was not affected considerably by the SSC of an aggregated network supply, and was more significant when using the power-based concept. Moreover, the largest reactive power generation is noticeable at IIDGs electrically close to the fault location and IIDGs distant from the strong network supplies.  
Regarding overall voltage support during network faults, a minor impact of FFC injection was noticed only when the IIDGs' overall capacity was comparable to the SSC of the aggregated network supply. IIDGs might also influence the operation of distance protection in combination with special cases of network topology and the SSC of an aggregated network supply. When the capacity of IIDGs is comparable to the SSC of the aggregated network supply, then an impact could be expected on distance protection operation. However, the most critical situation in radial network topologies will occur when a strong network operates against a weak network during the faults.  
Supervision, B.P. and J.R.; Writing--original draft, B.P. and B.G.; Writing--review and editing, B.P. and J.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by ARRS under Projects P2-0115 and J2-1742.  
Not applicable.  
This study did not report any data.  
The authors would like to thank ELES, Ltd., Electricity Transmission System Operator.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Calculation** & **Unbalanced** & **FFC** & **Max Number** \\  & **Domain** & **FFC** & **Concept** & **of Used IIDGs** \\ \hline Proposed & Phasor & Optional & Current/Power & 13 \\
[26; 27] & Phasor & No & Current & 7 \\
[28] & Phasor & No & Current & 10 \\
[29] & Instantaneous & No & Current & 3 \\
[30] & Instantaneous & Yes & Power & 2 \\
[31] & Instantaneous & No & Power & 1 \\
[32] & Instantaneous & Yes & Power & 5 \\
[33] & Instantaneous & No & Current & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of relevant IIDG models suitable for protection studies.
# Economic Impact Analysis of the Application of Different
## 3 Functions and Calculation Model
#### Limited Conditions of the Computational Model  
The economic impact of the rut depth (_RUT_) parameter was assessed for values according to the operational capability stage classification following the currently valid legislation [32] considered for first-class roads. For each category, a maximal _RUT_ depth was specified with values of 5, 10, 15, and 25 mm. Stage one and two of classification allow a maximal _RUT_ depth after 5 years from construction takeover, which is the warranty period of new constructed or a repaired road (maximal _RUT_ after 5 years from the takeover of new or reconstructed construction is 10 mm). Any first-class road with a _RUT_ depth value above 15 mm has to be assigned to the warning stage and has to be fitted with warning traffic signs (i.e., \"Beware of Surface Roughness\"), and maximum speed needs to be reduced from 90 km/h to 60 km/h. If above 25 mm, the _RUT_ depth parameter of the selected road has to be considered unsuitable for road traffic. Such pavement is considered a repair priority for road administration.  
This classification was chosen to compare the economic indicators of pavement sections at the same level of operational capability. The reference period was considered to be the period at which the pavement is in operation; during this time, the pavement is maintained and produces road user benefits.  
The following two model simulations were used:  
1. Experimental model for section A: - _RUT_ parameter = 25 mm _RUT_ depth (emergency degradation stage); - Required construction costs for repair technology = EUR (euro) 17,000 (milling and replacing with new layer); - Maintenance in the 6 th year of pavement operation = EUR 1700; - Annual average daily traffic = 1250 vehicles in one direction per hour, of which 250 are heavy vehicles, and with annual traffic growth of 2.00%; - Five different performance Equations (1)-(5) were tested on experimental model A.
2. The experimental model for section B: - _RUT_ parameter = 25 mm _RUT_ depth (emergency degradation stage); - Required construction costs for repair technology = EUR 50,845.55 (milling and replacing with new layer); - Maintenance in the 6 th year of pavement operation = EUR 3600.00; - Annual average daily traffic = 13,000 vehicles in one direction per hour, of which 1430 are heavy vehicles, and with annual traffic growth of 1.42%; - Five different performance Equations (1)-(5) were tested on experimental model B.  
A discount rate of 5% and repaired pavement section with a life expectancy of 10 years were applied for both models.  
These models were selected to observe the impacts of rutting evolution functions for pavement sections with different construction under different traffic loads.
## 3 The Working Environment  
In this study, two platforms were used: an AMD Ryzen Threadripper high performance processor [34] and an embedded NXSoC [22]. The architecture of the Ryzen processor is shown in Figure 3. This processor is based on Zen microarchitecture [35], which has a primary building block a core complex (CCX). Ryzen has 4 processing complexes,and each CCX includes 4 cores. Each core provides 2 threads with a base clock speed of 3.4 GHz, and all 32 threads have simultaneous multi-processing power. Each core is called a HGPP in this paper. In addition, all 4 cores share 8 MB of L3 cache memory, and each has its own 512 KB of L2 cache memory. All the L3 cache memories of different CCXs are allowed to be addressed and accessed by all CCX modules [36]. The maximum memory speed was 3.67 GHz.  
The architecture of NXSoC is presented in Figure 4. In summary, it is composed of an EGPP complex and an Embedded GPU (EGPU) [37] complex, where the EGPP complex has 4 clusters and each cluster has 2 ARM cores with a maximum clock frequency of 2.26 GHz. Each of these cores is called an EGPP in this study. In a cluster, 2 EGPP cores share 2 MB L2 cache memory. Furthermore, all the clusters have access to 4 MB L3 cache memory.  
The VTM8.0 reference software was ported over a HGPP and an ARM-based EGPP. Here, the CMake [38] toolchain was adapted for the EGPP. However, no modification was required for the HGPP. Then, \"-O3\" optimization of the GCC7.5 compiler was activated for both platforms. Therefore, profiling was obtained by executing the decoder using only one core of both the HGPP running at 3.40 GHz and EGPP running at 2.26 GHz. Here, only one core was used to fairly compare complexity analysis results obtained for both platforms.
# BeiDou Augmented Navigation from Low Earth Orbit Satellites
## 2 Advantages of Navigation with LEO Constellation
### Positioning Error
For the UEE term, although it varies with different GNSS system and correction model used, the uncertainty is about 1.2 m, 0.2 m, and 1.0 m for ionosphere and troposphere modeling errors as well as Equipment Receiver Noise & Multipath. In total, the UERE is about 1.77 m, 2.51 m, 2.01 m, 1.66 m, and 1.96 m for GPS, GLONASS, Galileo, QZSS, and BeiDou-2. For the LEO navigation constellation, as the same errors are exist in receiver side as well as when the signals pass through the atmosphere, the UEE for LEO constellation may have similar magnitude as that of GNSS. Hence, the clock and orbit accuracy of LEO constellation are essential for SISRE of the LEO navigation constellation. For LEO navigation constellation, three potential data can be used for orbit and clock determination, e.g., ground tracking data, intersat link data, and onboard GNSS tracking data. With ground tracking or intersat link data, as GNSS satellites, the accuracy of orbit and clock are rely on the predicted one. However, as closer to the satellites, the perturbation acting on the LEO are hard to model, resulting in poor prediction ability for longer orbit. Hence, the frequency updating of the broadcast ephemerics of LEO is needed. As to the analysis by [PERSON] (2016), the 3D RMS of orbit error can reach to about 1.5 m. Once the onboard GNSS data available, the real-time onboard solution can be obtained with accuracy about 1.5 m in 3D. For the clock, the worst uncertainty is about 6.0 ns (approximate 1.8 m), that because the LEO use the chip-scale atomic clocks (CSAC) for the restriction of power consumption, size, weight, and cost etc.
# Hybrid Dual-Polarization Synthetic Aperture Radar
## 2 Provenance
### Theoretical Foundation  
The theory behind compact polarimetry is well-known [6; 7]. A quasi-monochromatic partially-polarized electromagnetic field (EM) is represented by the ellipse swept out by its electric potential vector. [PERSON] showed that such a field could be represented by four numbers whose values could be determined from real observable quantities. Given the limited tools then available for observations of an optical field, his results were a major contribution. Although originated in the discipline of classical optics, [PERSON]'s elegant wave equations [8] apply to a wide span of frequencies, hence [PERSON] parametric representation of an EM field are applicable to microwave systems including especially radar.  
There are four essential themes embedded in [PERSON] fundamental formulation: (_i_) observational data are required, collected through two channels each constrained to a polarization that is orthogonal to that of the other channel; (_ii_) relative phase is required in addition to the amplitudes of the dual-polarized images, which implies that signals through the two channels must be mutually coherent; (_iii_) four parameters--the Stokes parameters derived from the data--capture all of the polarimetric information conveyed in the observed field; and (_iv_) for any given EM field, the values of the Stokes parameters are independent of the particular basis vectors in which the coherent dual-polarized measurements are made.  
The backscatter generated by a microwave radar illuminating the Earth's or a planet's surface is quasi-monochromatic and partially polarized, hence a Stokes parameter representation of the received data is appropriate and contains all of the available polarimetric information.
## 3 Performance Analysis of System
### Output of the Correlator in Two Intervals  
The received signals on the reference carrier, active carrier and inactive carrier are expressed as  
\[r_{o}=\sum_{k=1}^{\beta}\left(\sqrt{\frac{P_{s}}{d_{tr}^{m}}}\sum_{l=1}^{L} \alpha_{l}c_{k-_{\eta}}+n_{p,k}^{o}+n_{c,k}^{o}\right), \tag{3}\]  
\[r_{i}=\sum_{k=1}^{\beta}\left(\sqrt{\frac{P_{s}}{d_{tr}^{m}}}\sum_{l=1}^{L} \alpha_{l}c_{k-_{\eta}}v_{i}+n_{p,k}^{i}+n_{c,k}^{i}\right), \tag{4}\]  
\[r_{j}=\sum_{k=1}^{\beta}\left(\sqrt{\frac{uP_{s}}{d_{tr}^{m}}}\sum_{l=1}^{L} \alpha_{l}a_{k-_{\eta}}+n_{p,k}^{j}+n_{c,k}^{j}\right), \tag{5}\]  
where \(n_{p,k}^{o}\), \(n_{p,k}^{i}\) and \(n_{p,k}^{j}\) are three sampled AWGN, which are added to the \(k\)th sampled on the reference carrier, the \(i\)th active carrier and the \(j\)th inactive carrier, respectively. Similarly, \(n_{c,k}^{o}\), \(n_{c,k}^{i}\) and \(n_{c,k}^{j}\) are three sampled conversion noise with zero mean and variance \(\frac{N_{0}c}{2}\) caused by the RF to baseband signal conversion [19]. During the first period, the outputs of the \(i\)th active carrier and the \(j\)th inactive carrier correlators can be formulated as  
\[D_{i}^{1}=\sum_{k=1}^{\beta_{1}}\left(\sqrt{\frac{P_{s}}{d_{tr}^{m}}}\sum_{l=1 }^{L}\alpha_{l}c_{k-_{\eta}}+n_{a,k}^{o}\right)\left(\sqrt{\frac{P_{s}}{d_{tr }^{m}}}\sum_{l=1}^{L}\alpha_{l}c_{k-_{\eta}}v_{i}+n_{a,k}^{i}\right), \tag{6}\]  
\[D_{j}^{1}=\sum_{k=1}^{\beta_{1}}\left(\sqrt{\frac{P_{s}}{d_{tr}^{m}}}\sum_{l=1 }^{L}\alpha_{l}c_{k-_{\eta}}+n_{a,k}^{o}\right)\left(\sqrt{\frac{\mu P_{s}}{d_ {tr}^{m}}}\sum_{l=1}^{L}\alpha_{l}a_{k-_{\eta}}+n_{a,k}^{j}\right), \tag{7}\]  
where \(\beta_{1}\) is the number of chips in the first interval, \(n_{a,k}^{o}=n_{p,k}^{o}+n_{c,k}^{o}\), \(n_{a,k}^{i}=n_{p,k}^{i}+n_{c,k}^{i}\) and \(n_{a,k}^{j}=n_{p,k}^{j}+n_{c,k}^{j}\) present three sampled overall noise including AWGN and conversion noise with zero-mean and variance \(\frac{N_{0}d}{2}\) added to the \(k\)th sampled on the reference carrier, the \(i\)th active carrier and the \(j\)th inactive carrier, respectively. Hence, the means and variances of \(D_{i}^{1}\) and \(D_{j}^{1}\) are given by  
\[E[D_{i}^{1}]\approx\frac{P_{S}}{d_{tr}^{m}}\frac{\beta_{1}}{\beta}\sum_{l=1}^ {L}\alpha_{l}^{2}E_{c}^{o}v_{l}, \tag{8}\]  
\[Var[D_{i}^{1}]\approx\frac{P_{S}}{d_{tr}^{m}}\frac{\beta_{1}}{\beta}\sum_{l=1} ^{L}\alpha_{l}^{2}E_{c}^{o}N_{0,a}+\frac{N_{0,a}^{2}\beta_{1}}{4}, \tag{9}\]\[E[D_{j}^{1}]\approx 0, \tag{10}\]  
\[Var[D_{j}^{1}] \approx\left(\frac{P_{S}}{d_{tr}^{m}}\right)^{2}\frac{\mu\beta_{1}}{ \beta}\sum_{l=1}^{L}\alpha_{l}^{2}E_{c}^{v}N_{s}+\frac{\mu P_{S}}{d_{tr}^{m}} \frac{N_{0,a}N_{s}\beta_{1}}{2}\] \[+\frac{P_{S}}{d_{tr}^{m}}\frac{\beta_{1}}{2\beta}\sum_{l=1}^{L} \alpha_{l}^{2}E_{c}^{v}N_{0,a}+\frac{N_{0,a}^{2}\beta_{1}}{4}. \tag{11}\]  
The number of chips in the second interval is \(\beta-\beta_{1}\).