esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 2 Materials and Methods
### Impact of Channel Correlation
For example, the assumption of independence between propagation mechanisms at \(\tau_{0}\) and \(\tau_{1}\) is frequently violated, despite the simplicity of this model, which has led to its widespread use. Introducing a more realistic model could significantly increase the complexity. This study investigates DMD-based approaches as a solution to this challenge and proposes a delay-Doppler correlation model. The scattering function \(S_{h}(\
u,\tau)\) represents a combined Doppler-delay spectrum, which characterizes the distribution of power in the received signal concerning both Doppler frequency shift (\(\
u\)) and delay (\(\tau\)). This function is fundamental for understanding the signal's spread across time and frequency resulting from multipath propagation. Mathematically, it is expressed as  
\[S_{h}(\
u,\tau)=\mathbb{E}\Big{[}|\tilde{h}(\
u,\tau)|^{2}\Big{]}, \tag{9}\]  
where \(\tilde{h}(\
u,\tau)\) is the Doppler-delay spreading function representing the channel's response at a given Doppler shift \(\
u\) and delay \(\tau\). The relationship to the time-frequency correlation function \(R_{h}(\Delta t,\Delta f)\) is given by a two-dimensional Fourier transform [24]:  
\[S_{h}(\
u,\tau)=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}R_{h}(\Delta t,\Delta f)e^{-j2\pi(\Delta t\
u-\Delta f\tau)}d\Delta td\Delta f. \tag{10}\]  
This relationship allows the scattering function to encapsulate the statistical properties of the channel in both delay and Doppler domains. A more detailed proof is provided in Appendix A. The Doppler correlation function, denoted as \(R_{\
u}(\Delta\
u)\), quantifies the correlation between the channel's response at different Doppler frequencies and is given by \(R_{\
u}(\Delta\
u)=\mathbb{E}[h(\
u)h^{*}(\
u+\Delta\
u)]\), where \(h(\
u)\) is the channel's response at Doppler frequency \(\
u\), and \(h^{*}(\
u+\Delta\
u)\) is the complex conjugate of the channel's response at \(\
u+\Delta\
u\). The delay correlation function, often denoted as \(R_{h}(\tau)\), quantifies the relationship between the channel's impulse response at different delays and is defined as \(R_{h}(\tau)=\mathbb{E}[h(t,\tau)h^{*}(t,\tau+\Delta\tau)]\), where \(h(t,\tau)\) is the channel's impulse response at time \(t\) with a delay \(\tau\), and \(h^{*}(t,\tau+\Delta\tau)\) is the complex conjugate of the channel's response at a delay offset \(\Delta\tau\).
###### Abstract  
Modern vehicles are rapidly transforming into interconnected cyber-physical systems that rely on advanced sensor technologies and pervasive connectivity to support autonomous functionality. Yet, despite this evolution, standardized methods for quantifying cybersecurity vulnerabilities across critical automotive components remain scarce. This paper introduces a novel hybrid model that integrates expert-driven insights with generative AI tools to adapt and extend the Common Vulnerability Scoring System (CVSS) specifically for autonomous vehicle sensor systems. Following a three-phase methodology, the study conducted a systematic review of 16 peer-reviewed sources (2018-2024), applied CVSS version 4.0 scoring to 15 representative attack types, and evaluated four free source generative AI models--ChatGPT, DeepSeek, Gemini, and Copilot--on a dataset of 117 annotated automotive-related vulnerabilities. Expert validation from 10 domain professionals reveals that Light Detection and Ranging (LiDAR) sensors are the most vulnerable (9 distinct attack types), followed by Radio Detection And Ranging (radar) (8) and ultrasonic (6). Network-based attacks dominate (104 of 117 cases), with 92.3% of the dataset exhibiting low attack complexity and 82.9% requiring no user interaction. The most severe attack vectors, as scored by experts using CVSS, include eavesdropping (7.19), Sybil attacks (6.76), and replay attacks (6.35). Evaluation of large language models (LLMs) showed that DeepSeek achieved an F1 score of 99.07% on network-based attacks, while all models struggled with minority classes such as high complexity (e.g., ChatGPT F1 = 0%, Gemini F1 = 15.38%). The findings highlight the potential of integrating expert insight with AI efficiency to deliver more scalable and accurate vulnerability assessments for modern vehicular systems.This study offers actionable insights for vehicle manufacturers and cyber-security practitioners, aiming to inform strategic efforts to fortify sensor integrity, optimize network resilience, and ultimately enhance the cybersecurity posture of next-generation autonomous vehicles.  
automotive cybersecurity; autonomous vehicles; threat severity assessment; generative AI in cybersecurity; risk prioritization in intelligent vehicles
## 3 Proposed Cumulative QoE Model
### Perceptual Factors  
In video QoE assessment, perceptual factors [38] including video quality, rebuffering frequency, and rebuffering duration are directly perceived by the user. Typically, the user is usually sensitive to the current video segment quality, also known as _short time subjective quality_ (STSQ) [6]. STSQ is defined as the perceptual quality of the video segment being rendered to the user. STSQ can be predicted using any of the robust video quality assessment (VQA) metrics such as Spatio-Temporal Reduced Reference Entropic Differences (STRRED) [39], Multi-Scale Structural Similarity (MS-SSIM) [40], etc.  
In this study, STRRED was utilized to measure STSQ because of the exceptionally robust prediction performance [14; 15]. Rebuffering occurrences also contribute a significant impact to the user's satisfaction [18]. Therefore, rebuffering information such as rebuffering length, rebuffering position and the number of rebuffering events must be investigated. As a result, two rebuffering-related inputs employed are in this method. Firstly, _playback indicator_ (PI) [11; 14; 15] is defined as a binary continuous-time variable, specifying the current playback status: 1 for rebuffering and 0 for normal playback. Secondly, as the user's annoyance increases whenever a rebuffering event occurs [18], _number of rebuffering events_ (NR) happening from the beginning to the current time instant of the session is considered. Intuitively, perceived video quality tends to decrease when distorted events occur, and gradually recovers since the end of those events [13]. This leads to the consideration of the fourth input which refers to the _time elapsed since the last video impairment_ (TR) (i.e., bitrate switch or rebuffering occurrence) is utilized. All considered perceptual factors are fed into an LSTM-QoE model [15] to predict the instantaneous QoE as follows [15]:  
\[q(t)=LSTM^{0}(\mathbf{x}(t),\mathbf{c}(t-1)) \tag{1}\]  
where \(q(t)\) represents the predicted instantaneous QoE at the time instant \(t\), \(\mathbf{x}(t)\) is the input features, and \(\mathbf{c}(t)\) is the memory cells which encode the knowledge of the inputs that have been observed up to the time \(t\). \(LSTM\) provides two functionalities: \(LSTM^{0}\) for output QoE prediction and \(LSTM^{c}\) for memory cells update which is given by [15]:  
\[\mathbf{c}(t)=LSTM^{c}(\mathbf{c}(0:t-1),q(0:t-1)),\forall t\geq 1 \tag{2}\]  
where \(\mathbf{c}(0:t-1)\) and \(q(0:t-1)\), respectively, refer to the past memory cells and the past predicted QoE.  
The examples of four factors (including STSQ, PI, NR, and TR) and the architecture of LSTM-QoE model are illustrated in Figures 1 and 2, respectively.  
Figure 1: Example of rebuffering and bitrate-related features represented by STSQ, PI, NR, and TR.
## 4 Results and Discussion
### Loaded Swelling Deformation
#### 4.2.3 Calculation of Loaded Swelling Strain  
Parameters \(a\) and \(b\) in Equation (9) are different for MB specimens with different initial dry densities. Equations (10) and (11) reflected linear relationships between the parameters and initial dry density of the MB specimen, and the best-fit plots showing the linear relationship between parameters \(a\) and \(b\) and initial dry density are shown in Figure 11.  
\[a=272.25\rho_{d}-266.77 \tag{10}\]  
\[b=54.89\rho_{d}-36.65 \tag{11}\]  
where \(a\) and \(b\) are fitting parameters related to the swelling characteristics of MB, and \(\rho_{d}\) is the initial dry density of the MB specimen (g/cm\({}^{3}\)).  
Figure 11: Fitting plots of relationship between a and b and initial dry density of MB specimens.  
Figure 10: Relationships between the final swelling strains and logarithm of load on MB specimens of different initial dry densities under saline water conditions.  
Based on Equations (9)-(11), a formula for swelling strain, as related to initial dry density and vertical load, was obtained as given by Equation (12):  
\[\epsilon_{P}=-54.89\ln P\rho_{d}+36.65\ln P+272.25\rho_{d}-266.77 \tag{12}\]  
where \(\epsilon_{P}\) is the swelling strain in the MB specimen under vertical pressure \(P\); \(P\) represents the vertical pressure (kPa); and \(\rho_{d}\) is the initial dry density of the MB specimen (g/cm\({}^{3}\)).  
To verify the accuracy of Equation (12), the load swelling strain test of MB specimens with other initial dry densities (1.3, 1.5, 1.6, and 1.8 g/cm\({}^{3}\)) was conducted under different vertical pressures (25, 50, 100, 150, 200, 300, and 400 kPa). The difference between the calculated, and experimental, values is shown to be small (Figure 12) indicating the efficacy of Equation (12) when expressing the load swelling characteristics.
## 1 Introduction  
Stroke is highly prevalent in high-income countries (1015-1184 cases per 100,000 people in 2013) [1] Imbalance and post-stroke depression are common among stroke patients and may severely impair their activities of daily living [2,3]. The majority of stroke patients (around 83%) had a balance disability [4]. In a meta-analysis, the percentage of post-stroke depression was about 31% [5], and depression is associated with falls and negatively correlated with functional status [6]. The risk of falls for stroke patients is 1.77 times compared to healthy adults so post-stroke rehabilitation is essential [7].  
Exercise increases serotonin which works against depression [8]. [PERSON] et al. reported both aerobic and anaerobic exercises are effective [9]. Increased exercise frequency and group therapy seem be beneficial [10]. The practice of exercises promotes improvements in the levels of depression in people who suffered an ischemic stroke [11]. [PERSON] et al. reported yoga practices might be also protective [12]. Yoga is widely integrated into physical therapies [13]. However, few reports have focused on the impact of yoga in stroke patients [14; 15; 16]. Yoga can lower blood pressure, serum cholesterol and increase in internal reasons for exercise [17; 18]. Furthermore, yoga consists of dynamic/static postures to stretch muscles and joints, and improve balance and coordination [19]. [PERSON] and [PERSON] [14] reported an 8-week intervention (1.5-h yoga sessions two times per week) for patients with chronic post-stroke hemiparesis improved balance. Chronic stroke is defined as the condition 90 days after stroke [20]. About 95% of patients achieved their best neurological recovery within 11 weeks, while minor stroke related to faster recovery [21].  
Previous studies enrolled patients who began yoga \"after\" completing the post stroke rehabilitation program. However, no study focused combining yoga into a standard rehabilitation. We compared depression and balance among chronic stroke patients who underwent combining yoga and standard rehabilitation and those with standard rehabilitation alone.
## 2 Materials and Methods
### X-ray Diffraction  
The XRD measurements were conducted in a Bruker (Billerica, MA, USA) DS Discover diffractometer in \(\theta\)-2\(\theta\) configuration. The diffractometer was equipped with a Co K\(\alpha\) source (\(\lambda\) = 1.7889 A) and 192 microstrip channels (1D configuration) detector. The illumination area was fixed to 1 \(\times\) 8 mm\({}^{2}\). XRD data were collected from 2\(\theta\) = 15\({}^{\circ}\) to 95\({}^{\circ}\), with a step size of 0.05\({}^{\circ}\) and a collection time of 1 s per step. The phase identification was performed using ICSD [28] and PDF4+ [29] databases. Rietveld and line profile analysis were performed using the commercial version of TOPAS 6 (Bruker) software [30]. The instrumental profile was retrieved using a LaB\({}_{6}\) (NIST SRM 660c) standard sample [31]. A double-Voigt profile was employed for the microstructural analysis. One of the Voigt functions is attributed to crystallite size, where its integral breadth (\(\beta\)) is related to the mean volumetric crystallite size (Lvol) according to the Scherrer equation (see TOPAS manual for details [30]). The microstrain (\(\epsilon\)) is obtained by the equation \(\epsilon=\beta_{FWHM}\,\tan(\theta)\), where \(\beta_{FWHM}\) is the full width half maximum of the diffraction peak and \(\theta\) is the diffraction angle. A March-Dollase approach was employed for the estimation of preferred orientation in the prepared films [30].
# A Miniaturized and Fast System for Thin Film Thickness Measurement
## 4 Simulation
### Verification of the Thin-Film Interference Model  
We use the thin-film interference model \(I_{r}\) (\(z\), \(\lambda\)) of Equation (1) for simulation, where \(A=R_{01}+T_{01}R_{18}T_{10}\), \(B=2\sqrt{R_{01}T_{01}R_{18}T_{10}}\), \(A\), \(B\)(\(0\),\(1\)), and suppose the light source intensity distribution conforms to the Gaussian distribution \(G(\lambda\); \(\mu\), \(\eta^{2})\) with mathematical expectation \(\mu\) and standard variance \(\eta^{2}\),  
\[G\Big{(}\lambda;\mu,\eta^{2}\Big{)}=\frac{1}{\eta\sqrt{2\pi}}e^{-\frac{(\lambda -\mu)^{2}}{2\eta^{2}}} \tag{5}\]  
The reflected spectrum signal can be abbreviated as below, where \(C\) is the maximum intensity amplitude of the light spectrum,  
\[I_{r}(z,\lambda)=(A+B\text{cos}(2\pi z/\lambda+\phi_{0}))C\cdot G(\lambda) \tag{6}\]  
We take \(A=0.5\), \(B=0.2\), \(C=18000\), \(\mu=660\) nm, \(\eta=80\) nm, \(\phi_{0}=\pi\), and \(z=9\) um. The simulated interference spectrum is shown in Figure 5a and compared with the measured data in Figure 5b. The simulation signal is consistent with the actual measurement signal, which verifies the correctness of the simulation model.
# Endowed Polyphenols in Advanced Delivery Systems for Vaginal Infections
## 6 Polyphenols as Antimicrobial Agents
### Epicatechin  
(-)-Epicatechin ((2R,3R)-2-(3,4-dihydroxyphenyl)-3_A_-dihydro-2H-chromee-3,5,7-triol) is part of the flavonoid subclass of flavon-3-ols [142; 143]; it was discovered in 1948 and it is primarily found in both green and black tea. It is present in lower amounts in berries, common fruits, cocoa, and many non-alcoholic drinks [144]. Its molecule is composed of two benzene rings, with two hydroxyl groups each, and a dihydropyran heterocyclic ring with a hydroxyl group in carbon 3 [144; 145]. The presence of a chiral center in positions two and three of the plant-derived flavon-3-ol led to four diastereoisomers: (+)-epicatechin, (-)-epicatechin, (+)-catechin, and (-)-catechin [142; 146]. The compound has drawn considerable attention, and it has been explored for its biological properties, such as antioxidant, antimicrobial, anti-inflammatory, anti-tumor, anti-diabetic, and cardioprotective activities [4; 142; 144].  
Regarding antimicrobial activity, epicatechins originating in the plant _Saraca indica_ have shown antibacterial activity against a variety of multidrug-resistant pathogens, including _S. aureus_[147]. This polyphenol and its derivates inhibit antibiotic efflux pumps in _S. aureus_, which is resistant to penicillin, erythromycincin, methicillin, and clindamycin, with MIC values between 32 and 512 ug/mL, according to different publications [147; 148; 149]. Another study by [PERSON] et al. (2022) [150] reported the antimicrobial activity of (-)-Epicatechins-3-gallate (a derivate) against _L. monocytogenes_, _P. aeruginosa_, _Bacillus cereus_, and _S. aureus_, with MICs ranging from 375 to 500 ug/mL.
## 3 Results Analysis
### Tolerance of Time Measurement Errors and Radio Range Variations in TTHCA  
The next series of experiments analysed both the accuracy of packet processing time measurement and the maximum radio coverage variance per node necessary to maintain a predefined TTHCA wormhole detection rate of at least 75%. In a real-world MANET, the PTT measurement accuracy can deteriorate for a variety of reasons such as node mobility and limitations in the measurement capability of nodes during the route discovery process. Concomitantly, radio coverage can vary due to different MANET node hardware and variability in the network surroundings. These experiments were specifically performed on a PM O-B wormhole because this type of threat is the most challenging for TTHCA to detect.  
The time measurement error tolerance results are presented in Figure 8. A random error \(\xi=\pm K\%\) was introduced to each node measurement and the equivalent wormhole detection and FP rates analysed. \(K\) was set as a percentage of the median packet processing delay upon a MANET node (4 ms). The results confirm that relaxing the time measurement accuracy produced a correspondingly lower wormhole detection rate and higher FP incidence, since the measurements must be sufficiently accurate if they are to be compared with the median packet processing delay. Despite this constraint, the experiments revealed TTHCA was able to tolerate measurement errors up to \(\xi=800\) ns for all wormhole lengths, while still maintaining a detection rate of greater than 80%. This vindicates the premise in Section 2.2 that achieving satisfactory packet processing delay measurement accuracy on intermediate nodes is feasible without recourse for additional hardware.  
Figure 8: TTHCA time measurement error tolerance per node for a PM O-B wormhole (WH).  
The radio range variation results are displayed in Figure 9. A random variable \(\gamma\) representing the radio range loss per node is introduced, where \(0\leq\gamma\leq\) N% and N is the maximum permissible radio range variation per node, given the peak MANET node coverage is 250 m (see Table 1). Figure 9 reveals that higher variations in node radio coverage lead to a corresponding degradation in wormhole detection rates, though crucially TTHCA is still able to maintain a detection rate for wormholes of 4 or more hops of at least 75% when \(N=20\%\), which corresponds to a radio range between 200 m and 250 m. Similarly for a 6-hop wormhole, the detection rate is at least 80% even for a MANET environment where \(N=40\%\)_i.e._, a nodal radio range between 150 m and 250 m. Interestingly, variations in the radio range of MANET nodes did not lead to FP occurrences because the threshold employed by the TTHCA algorithm in (2) is based upon the maximum radio range per node, so radio coverage losses simply decrease the PTT/HC ratio for these routes.
# Calibrating of a Simulation Model to Predict the Flexural Capacity of Pre-Stressed Concrete Beams
## 1 Introduction
However, in general, material models in finite element calculation tools implement constitutive models of generic materials. Therefore, in order to use them, the different parameters governing the equations must be given values according to the properties of the concrete to be modeled. This entails calibrating the model starting from zero in its different states of behavior, and it is thus sometimes necessary to configure complex curves with some uncertainty, making it difficult to adapt the material to the final outcome, since the knowledge to do so is not always available. This problem has led to the emergence of other tools specifically designed for the modeling of concrete elements. This is the case of the commercially available ATENA tool, developed by Cervenka Consulting (Czech Republic), and designed as a calculation software using the finite element method specifically for the non-linear analysis of reinforced concrete. It uses constitutive models that allow for the auto-completion of the values based on the desired compressive strength, and from which the rest of the values established in the characteristics described in Eurocode 2 [20] are completed. Of these tools, the most noteworthy is the CC3 DNonLinCementitious2 model, which reproduces the behavior of concrete, assuming a hardening regime before the compressive stress is reached [21]. The ATENA tool allows the real behavior of conventional concrete and reinforced concrete structures to be simulated in a simple way, including important parameters, such as concrete cracking, crushing and reinforcement stress. This tool has been widely used with satisfactory results that adequately reproduce those of actual experiments. [PERSON] et al. [22] used the tool to model real flexural tests on beams conducted by [PERSON] and [PERSON] [23] and on elements subjected to compression performed by [PERSON] [24], with results that very closely replicate those of the actual experiments. [PERSON] et al. [25] used the tool to model the behavior of a beam-column element, analyzing the effect of different variables and calibrating the tool with real experiments to model structures with a similar behavior. Other scientific experimentations using the ATENA tool with satisfactory results include the works by [26; 27; 28; 29]. [PERSON] [30] presented multiple validations and real applications of the tool, including a comparison with the experiment conducted by [PERSON] [31], where a simple beam with a shear rupture was analyzed, obtaining load and cracking values similar to those of said experiment. The aim of this work was to develop a finite element simulation model which allows the behavior of different pre-stressed concrete linear structural elements to be simulated, comparing the simulation results with those of experimental tests. This model, once verified and calibrated, can serve as a tool to design, calculate and manufacture new and more complex structural elements. The ATENA tool has been used to achieve this objective.