esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Bulk Density  
Prior to heat exposure, the bulk density in hardened state (\(\rho\)) of the various mortars ranged between 432 and 737 kg/m\({}^{3}\) for mortars incorporating insulating aggregates and between 1590 and 1730 kg/m\({}^{3}\) for sand mortars; these significant differences highlight one of the main features of the former mortars--lightweight.  
For the temperature range studied, exposure to elevated temperatures influenced the bulk density of the mortars in the hardened state (Figure 3 and Table 2), with variations around 10%, except for the silica aerogel mortar (C\({}^{\text{AERO}}\)), whose bulk density presented a reduction of about 25% for the temperature of 250 \({}^{\circ}\)C. According to EN 998-1 [7], these thermal mortars can be classified as lightweight mortars (before and after heat exposure), since their bulk density is (considerably) lower than 1300 kg/m\({}^{3}\).  
In sand mortars (D\({}^{\text{ref}}\) and E\({}^{\text{AD}}\)) no significant variations in density were observed up to 100 \({}^{\circ}\)C. Between 100 \({}^{\circ}\)C and 150 \({}^{\circ}\)C there was a reduction in density that can be explained by the evaporation of water from the cement paste [47], chemically combined (starting from 105 \({}^{\circ}\)C) and in small pores (starting from 120 \({}^{\circ}\)C) [48; 49], and by a probable internal expansion of cement paste [50; 51]. Around 150 \({}^{\circ}\)C, the cement paste undergoes a strong shrinkage due to dewatering, which may justify the increase of the density of these mortars beyond 150 \({}^{\circ}\)C [50; 52; 53]. In addition, rising temperatures above 100 \({}^{\circ}\)C may lead to the production of various forms of calcium silicates, which although generally porous and weak, may also justify increased density [48]. Mortars D\({}^{\text{ref}}\) and E\({}^{\text{AD}}\) present  
Figure 3: Bulk density (%) vs. exposure temperature.  
similar variation in density with temperature change (especially after 100 \({}^{\circ}\)C), indicating that the used admixtures do not affect the influence of temperature on mortar bulk density.  
Despite the increase in density of aerogel mortar (C\({}^{\text{AERO}}\)) at 100 \({}^{\circ}\)C, probably related to the initial hydration products formed at elevated temperatures [54], after 150 \({}^{\circ}\)C this mortar showed the same pattern of variation as the other ones. The mortar with cork (B\({}^{\text{GEC}}\)) also followed the trend of C\({}^{\text{AERO}}\) mortar. However, this mortar showed a better density stability with the temperature increase, with a maximum variation of 40 kg/m\({}^{3}\) (\(\approx\)9% of the initial mass).  
For the expanded clay mortar (A\({}^{\text{EC}}\)) the density also increased for exposure temperatures higher than 150 \({}^{\circ}\)C. However, up to 150 \({}^{\circ}\)C, compared to other mortars, there was a more constant decrease in the density of expanded clay mortar, which may be related to the higher porosity of expanded clay--this allows greater water movement than in more compact materials [48], possibly leading to an earlier water evaporation than in the other mortars.
# Traffic Light Detection and Recognition Method Based on YOLOv5s and AlexNet
## 3 Experiment
### Traffic Light Image Processing  
After determining the location of the traffic light, you first need to determine the direction of the traffic light installation. The installation of the traffic light is generally divided into vertical and horizontal installations, as shown in Figure 8.  
Figure 6: YOLOv5s detection night image output results.  
Figure 7: YOLOv5s fog detection image output results.  
Figure 8 shows a vertically mounted traffic light on the left and a horizontally mounted traffic light on the right.  
Based on the detected traffic light area, the upper left coordinate point (\(X_{1}\), \(Y_{1}\)) and the lower right coordinate point (\(X_{2}\), \(Y_{2}\)) of the traffic light area is output, and then whether the traffic light is installed vertically or horizontally is determined according to Equation (1).  
\[F=(X_{2}-X_{1})-(Y_{2}-Y_{1}) \tag{1}\]  
If \(F>0\), the width of the area is greater than the height, which means that the traffic signal is horizontally mounted, and conversely, if \(F<0\), the signal is vertically mounted.  
When the traffic light is mounted horizontally, the image is cut by lines \(L_{1}\) and \(L_{2}\). The expressions for lines \(L_{1}\) and \(L_{2}\) are:  
\[L_{1}=(X_{2}-X_{1})/3+X_{1} \tag{2}\]  
\[L_{2}=2(X_{2}-X_{1})/3+X_{1} \tag{3}\]  
When the traffic light is installed vertically, the image is cut by lines \(L_{3}\) and \(L_{4}\). The expressions for lines \(L_{3}\) and \(L_{4}\) are:  
\[L_{3}=(Y_{2}-Y_{1})/3+Y_{1} \tag{4}\]  
\[L_{4}=2(Y_{2}-Y_{1})/3+Y_{1} \tag{5}\]  
Finally, a single traffic light image is obtained, as shown in Figure 9.  
After processing the image, the original image is divided into three equal parts. Complex traffic signals are also split into individual simple traffic signals. This not only reduces the variety of samples but also reduces the time required to train the network. Finally, the obtained individual traffic light images are sequentially fed into the trained AlexNet for image recognition, and the recognition results are combined to obtain accurate traffic signals.
# Unravelling the Environmental Application of Biochar as Low-Cost Biosorbent: A Review
## 6 Regeneration of Biochar
Similarly, [PERSON] et al. [94] demonstrated the high adsorption capacity of lime fiber biochar by the removal of sulfamethoxazole and methylparaben and its feasible regeneration by several alternative AOPs (Fenton, electro-oxidation-H\({}_{2}\)O\({}_{2}\) and electro-Fenton). To sum up, in this kind of process, biochar is a key factor as an adsorbent and catalyst or activator. This synergic effect increases the efficiency of the degradation process and demonstrates the potential application of biochar as a cost-effective agent to treat organic pollutant wastewater.
## 3 On-Site Testing  
### Modal Testing  
For large-span structures, pulsation tests utilize the small and irregular vibrations induced by various external factors to determine the dynamic characteristics of the structure. The pulsation tests on the bridge employed JM3872G sensors (velocity mode, Yangzhou Jingming, Jiangsu, China) with 4G signal transmission and GPS data synchronization, along with 941-type vibration pick-ups. Velocity data, collected for more than 20 min under environmental random excitation, were used to calculate the natural frequencies. Spectral analysis was performed on the velocity responses of the main girder and the main arch to obtain the bridge's inherent frequencies, mode shapes, and damping ratios. The field-testing setup is shown in Figure 2.  
Figure 1: Schematic diagram of bridge span arrangement (unit: m).  
Velocity measurement points were uniformly distributed on both the main arch and the bridge deck, with a total of 48 points. For the acceleration measurements, the JM3872G sensors (acceleration mode) were also used, and the data were processed with a 20 Hz low-pass filter to distinguish the bridge's natural frequencies from forced vibrations. This instrument is used to measure speed or acceleration, where it can realize ultra-low frequency (as low as 0.072 Hz) and large displacement (1000 mm) vibration measurements. Dynamic deflection testing was conducted using the BJQN-L bridge deflection measurement system from Beijing Optoelectronics Institute(Leitu technology, Beijing, China), which provided precise measurement of dynamic displacement on the Nanning Three Banks Yongjiang Special Bridge. The measuring range of this instrument is 0.01 mm-500 m, the sampling frequency is 100 Hz, and the accuracy is 0.02 mm. A 15 Hz low-pass filter was applied to the data to ensure accuracy. The distribution of the measurement points is shown in Figure 3.  
Figure 3: Pulsation test and traveling test measurement point arrangement.  
Figure 2: On\(-\)site testing: (**a**) JM3872G sensor and 941 vibration sensor; (**b**) waveform recorded during on-site testing; (**c**) dynamic displacement target; (**d**) train crossing bridge.
# A Digital Twin-Based Approach for Detecting Cyber-Physical Attacks in ICS Using Knowledge Discovery
## 2 Background
### Modbus  
Modbus [14] is an industrial protocol used to operate across various communication platforms, including satellite, telephone, and radio technologies, and is not restricted to the proprietary technology of any single manufacturer. It supports two fundamental modes of communication: (i) a query/response mode facilitating interactions between a master and a slave device, and (ii) a broadcast mode enabling a master device to communicate with all slave devices simultaneously. Modbus assigns temporary storage in PLC programs to four distinct register types: discrete output coils, discrete input contacts, analog input registers, and analog output holding registers. Both coils and discrete inputs utilize single-bit registers, whereas input and holding registers employ 16-bit registers. The latter can also be aggregated to form larger memory registers such as 32- or 64-bit registers. Operations involving these registers are controlled by function codes, which define read/write capabilities for coil and holding registers while limiting discrete and input registers to read-only activities. A Modbus operation comprises a single query, response, or broadcast message. The structure of a Modbus message includes the recipient's address, the instructions to be executed, and the necessary data for execution. In the context of Modbus/TCP [15], a Modbus message is encapsulated within a TCP packet, with Modbus TCP/IP devices monitoring and accepting data through port 502. It should be noted that the Modbus/TCP protocol does not include any cybersecurity protection mechanisms.
# Exploring Carbon-Fiber UAV Structures as Communication Antennas for Adaptive Relay Applications
## 2 Materials and Methods
### Experimental Setup for Antenna Characterization
#### 2.1.3 Voltage Standing Wave Ratio (VSWR)  
Based on the reflection coefficient S\({}_{11}\), the Voltage Standing Wave Ratio (VSWR) can be determined in two ways. The first method involves using the conversion Formula (1) below:  
\[VSWR=\frac{1+|S_{11}|}{1-|S_{11}|} \tag{1}\]  
The second method consists of the experimental determination of the \(VSWR\) using the Vector Network Analyzer (VNA). In this approach, the VNA is employed to directly measure and display the system's VSWR characteristics. By adjusting the frequency and observing signal reflections and transmission, the VNA provides empirical data that allow for a clear representation of VSWR variation across the selected frequency range.  
Another key parameter of interest is the bandwidth and the central frequency. The bandwidth of an antenna refers to the range of frequencies over which it operates efficiently, maintaining acceptable values for parameters such as VSWR or gain [18].
## Abbreviations  
* TEG  Thermoelectric Generator; MPPT  Maximum Power Point Tracking; P&O  Perturb and Observe; IC  Incremental Conductance; MRA  Mud Ring Optimizer; GWO  Grey Wolf Optimizer; GM  Global Maxima; PSO  Particle Swarm Optimization; WOA  While Optimization Algorithm; GMPP  Global Maximum Power Point; LMPP  Local Maximum Power Point.
## 2 Theory and Formulation
### Vibration Study
#### 4.2.2 Sensitivity Analysis of Vibration  
In this section, we focus on the sensitivity of the natural frequency with respect to, the nonlinear temperature gradient. The sensitivity of the i_th_ natural frequency of the MHCAP is defined as  
\[S_{i}=100\left(\frac{f_{i}-f_{i}^{0}}{f_{i}^{0}}\right)\ \ \ i=1,2,\ldots \tag{52}\]  
with \(f_{i}\) and \(f_{i}^{0}\) indicating the corresponding natural frequency of the i_th_ mode number with and without including the effect of temperature gradients, respectively. The sensitivity of the fundamental natural  
Figure 11: The effect of different outer radius to inner radius ratios on the nondimensional natural frequency of MHCAP with clamp-clamp boundary conditions for different orientation angles of carbon fibers and h/b = 0.1, STR, T\({}_{1}\) = 273 [K], T\({}_{0}\) = 273 [K], W\({}_{\rm CNT}\) = 0.02, V\({}_{\rm F}\) = 0.2, FG-A, \(\beta\) = 1, K\({}_{\rm p}\) = 10 [MN/m], and K\({}_{\rm w}\) = 100 [MN/m\({}^{3}\)]. (**a**) without elastic medium; (**b**) rested on Winkler medium; (**c**) rested on Winkler-Pasternak medium.  
Figure 12: The effect of applied temperature of the top surface on the nondimensional natural frequency of MHCAP with clamp-clamp boundary conditions for different temperature power \(\beta\) and h/b = 0.1, b = 4 x a, T\({}_{1}\) = 273 [K], W\({}_{\rm CNT}\) = 0.02, V\({}_{\rm F}\) = 0.2, FG-A, K\({}_{\rm p}\) = 10 [MN/m], and K\({}_{\rm w}\) = 100 [MN/m\({}^{3}\)]. (**a**) STR; (**b**) PTR.  
frequency as a function of the temperature gradient and the \(\beta\) parameter (involved in the STR and PTR) is presented in Figure 13. The absolute values of the frequency sensitivity increased with increasing temperature gradient and \(\beta\) parameter. Also, the absolute value of the sensitivity for PTR was higher than STR for a fixed temperature gradient and \(\beta\) parameter.
## 3 Results  
### RABV Infection Activated IFN and IFN-Related Signaling Pathway in MRC-5 Cells  
To determine the difference in RABV production between MRC-5 cells and Vero cells, MRC-5 cells were infected with the PM RABV strains (adapted for replication in MRC-5 cells) at MOI values of 0.05, 0.1, 0.5, and Vero cells were infected with the aG RABV strains (adapted for replication in Vero cells) at MOI values of 0.0001, 0.001, and 0.05. The viral RNA and titer in the culture supernatant were tested by qRT-PCR and FFU assays at 168 h post infection (hpi) (Figure 1A,B). The results showed that MRC-5 cells had a significant reduction in RABV production compared with Vero cells. The viral titer in Vero cells reached its maximum at an MOI of 0.001 and was 10-fold higher than that of MRC-5 cells. Infection in MRC-5 cells at an MOI of 10 showed a significant increase in viral RNA expression but not a significant increase in viral titer. These results indicate that an enhancement of RABV production cannot be achieved by an increase in MOI. RABV MOI of 0.05 in MRC-5 cells and an MOI of 0.001 in Vero cells were determined to be the optimum MOI values for use in the following experiments. Additionally, the expression of IFN-\(\alpha\), IFN-\(\beta\), and IFN signaling pathway endogenous genes, including STAT1, IRF7, and OAS1 in RABV-infected MRC-5 and Vero cells, was examined by qRT-PCR at 8 to 106 hpi, respectively (Figure 1C). The expression of these genes in MRC-5 cells was significantly upregulated compared with that in Vero cells at 24 and 48 hpi. Notably, IFN-\(\beta\) mRNA expression in MRC-5 cells showed a 200% increase compared to that in Vero cells. The IFN-\(\alpha\) mRNA expression was not observed. This may be due to the fact that RABV, as a single-stranded RNA virus, enters cells and is mainly recognized by RIG-I, which can activate the expression of IFN-\(\beta\)[31; 32]. These results suggest that the activation of the IFN signaling pathway genes acts as a crucial limiting factor to affect RABV production in MRC-5 cells. Inhibiting the activation of IFN response may be an effective approach to enhance RABV production in MRC-5 cells.
## 3 Numerical Algorithm
### The Backward Euler (BE) Method  
The first-order backward Euler (BE) time scheme together with the second-order space discretization of Equation (1) can be given in the following form  
\[\frac{c_{i,j}^{n+1}-c_{i,j}^{n}}{\Delta t}-\frac{D_{i+\frac{1}{2} j}c_{xi+\frac{1}{2},j}^{n+1}-D_{i-\frac{1}{2},j}c_{xi-\frac{1}{2},j}^{n+1}}{ \Delta x}-\frac{D_{i,j+\frac{1}{2}}c_{y\,ij,\frac{1}{2}}^{n+1}-D_{i,j-\frac{1 }{2}}c_{y\,ij,\frac{1}{2}}^{n+1}}{\Delta y}\] \[-\rho c_{i,j}^{n+1}\left(1-\frac{c_{i,j}^{n+1}}{c_{\max}}\right) \approx 0, \tag{6}\]  
Figure 1: Left column: _One-_ and _two-_dimensional initial tumor concentrations; Right column: _One-_ and _two-_dimensional discontinuous diffusion coefficients.  
where \(c_{i,j}^{n}\) approximates the true solution at the grid location \((x_{i},\,y_{j})\) and time \(t_{n}\) (e.g., \(c_{i,j}^{n}\approx c(x_{i},\,y_{j},\,t_{n})=c(i\Delta x,\,j\Delta y,\,n\Delta t)\) and the following derivatives are approximated by  
\[c_{x\,i+\frac{1}{2},j}^{n+1}\approx\frac{c_{i+1,j}^{n+1}-c_{i,j}^{n+1}}{\Delta x }\quad c_{x\,i-\frac{1}{2},j}^{n+1}\approx\frac{c_{i,j}^{n+1}-c_{i-1,j}^{n+1}}{ \Delta x}\]  
\[c_{y\,i,j+\frac{1}{2}}^{n+1}\approx\frac{c_{i,j+1}^{n+1}-c_{i,j}^{n+1}}{\Delta y },\quad c_{y\,i,j-\frac{1}{2}}^{n+1}\approx\frac{c_{i,j}^{n+1}-c_{i,j-1}^{n+1}} {\Delta y}.\]  
Here, \(\Delta x=x_{i+\frac{1}{2}}-x_{i-\frac{1}{2}}\) and \(\Delta y=y_{j+\frac{1}{2}}-y_{j-\frac{1}{2}}\) are the size of the \((i,j)\)th cell in \(x\) and \(y\) directions, \((x_{i},\,y_{j})\) represents the center of the \((i,j)\)th cell, and \((x_{i\pm\frac{1}{2}},y_{j\pm\frac{1}{2}})\) denote the left, right, top, and bottom edges of the \((i,j)^{th}\) cell (Figure 2). The spatial domain \([0,50]\times[0,50]\) is divided into \(M_{x}\) and \(M_{y}\) sections in the \(x\) and \(y\) directions. We use the uniform grids in this study, implying that \((\Delta x_{i}\,,\Delta y_{j})=(\Delta x\,,\Delta y)=(\frac{50}{M_{x}},\frac{5 0}{M_{y}})\) where \(i=0,\ldots M_{x}-1\) and \(j=0,\ldots M_{y}-1\).  
We utilize the following discrete Neumann boundary conditions for Equation (4)  
\[c_{x\,-\frac{1}{2},j}^{n+1}\approx 0,\quad\ c_{x\,M_{x}-\frac{1}{2},j}^{n+1} \approx 0,\quad\ c_{y\,i,-\frac{1}{2}}^{n+1}\approx 0,\quad\ c_{y\,i,M_{y}- \frac{1}{2}}^{n+1}\approx 0. \tag{7}\]  
Equation (7) can be used to set the ghost cell values in the \(x\) direction as  
\[0\approx c_{x\,-\frac{1}{2},j}^{n+1}\approx(c_{0,j}^{n+1}-c_{-1,j}^{n+1})/ \Delta x\Rightarrow c_{-1,j}^{n+1}\approx c_{0,j}^{n+1},\]  
\[0\approx c_{x\,M_{x}-\frac{1}{2},j}^{n+1}\approx(c_{M_{x},j}^{n+1}-c_{M_{x}-1,j}^{n+1})/\Delta x\Rightarrow c_{M_{x},j}^{n+1}\approx c_{M_{x}-1,j^{\prime}}^ {n+1}\]  
and, similarly, in the \(y\) direction as  
\[0\approx c_{y\,i,-\frac{1}{2}}^{n+1}\approx(c_{i,0}^{n+1}-c_{i,-1}^{n+1})/ \Delta y\Rightarrow c_{i,-1}^{n+1}\approx c_{i,0}^{n+1},\]  
\[0\approx c_{y\,i,M_{y}-\frac{1}{2}}^{n+1}\approx(c_{i,M_{y}}^{n+1}-c_{i,M_{y}- 1}^{n+1})/\Delta y\Rightarrow c_{i,M_{y}}^{n+1}\approx c_{i,M_{y}-1}^{n+1}.\]
### Motivation of the Study  
The importance of propaganda detection lies in its potential to mitigate the spread of misinformation and its associated societal harm. Despite its critical role, existing DL-based methods, including Bi-directional Encoder Representations from Transformers (BERT), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), exhibit limitations [11; 12]. These approaches often focus on local word associations and sequential patterns, failing to capture long-range and non-adjacent word dependencies critical for identifying nuanced propaganda techniques. Moreover, while fine-tuned models have demonstrated success in multi-label, multi-class (ML-MC) text classification tasks, their application to propaganda detection remains underexplored [13].  
Existing methods also face challenges in detecting specific propaganda techniques such as logical fallacies, sowing doubt, and repetition [14; 15; 16]. These limitations are exacerbated by the weak annotations common in datasets like those derived from social media platforms. To address these issues, we propose a novel DL approach tailored to detect fine-grained propaganda techniques using advanced model architectures like RoBERTa, a robustly optimized BERT pre-training method. This model demonstrates state-of-the-art performance in ML-MC tasks, making it a promising candidate for improving propaganda detection.  
The motivation for this study is further reinforced by the challenges posed during the COVID-19 pandemic. The rapid surge of information, coupled with misinformation and propaganda about the virus, created an urgent need for advanced detection methods to ensure accurate and timely information dissemination. By focusing on a curated dataset of COVID-19-related discussions, annotated with six distinct propaganda techniques and themes, our research addresses gaps in existing models and datasets.  
Additionally, previous studies have highlighted the limitations of annotated datasets, such as the Propaganda Text Corpus (PTC) ([[https://alt.qcri.org/semeval2020/](https://alt.qcri.org/semeval2020/)]([https://alt.qcri.org/semeval2020/](https://alt.qcri.org/semeval2020/)) (accessed on 2 February 2025)), which may not fully capture real-world propaganda techniques [11]. By creating a comprehensive and representative dataset and leveraging advanced DL models, this research aims to overcome these challenges. Our work also aligns with the diffusion of innovations theory, providing a pathway for integrating and scaling these advanced methodologies within the broader IS community. This theoretical underpinning ensures that the proposed methods are not only effective but also widely adopted to counteract the pervasive threat of propaganda on social media.
# A Review of Machine Learning and IoT in Smart Transportation
## 1 Introduction  
Over the last decade, applications based on mobile devices, sensors, and actuators have become smarter, enabling the communication among devices and the execution of more complex tasks. In 2008 the number of connected devices surpassed the global population [1] and the number keeps increasing exponentially until today. Smart phones, embedded systems, wireless sensors, and almost every electronic device are connected to a local network or the internet, leading to the era of the Internet of Things (IoT). With the number of devices increasing, the amount of data collected by those devices is increasing as well. New applications emerge that analyze the collected data to make meaningful correlations and possible decisions, leading to Artificial Intelligence (AI) via Machine Learning (ML) algorithms.
## 1 Introduction
AI has been previously employed in telemedicine applications such as remote image guidance, in training methods for operators with minimal traditional ultrasound training, and in the analysis of the vast amount of data generated by ultrasound imaging [25]. Several reviews have focused on the integration of general ultrasound with AI to expand its applications and utilization [26]. Additionally, these reviews mentioned the benefits of AI in LRS where AI can transform POCUS into a continuous monitoring device [27]. Despite the creation of such algorithms as well as their widespread usage and integration with ultrasound technology, minimal progress has been made in applying them to POCUS due to limitations of increased noise, blur, and distortions found in POCUS images, as well as difficulties in saving images to build a database [27]. [PERSON] et al. introduced the CALPOCUS project as a protocol to collect data and develop machine learning (ML)-based decision-making tools even for non-expert users [25]. Existing reviews such as the ones performed by [PERSON] et al., [PERSON] et al., and [PERSON] et al. do not provide us with a comprehensive understanding of the state of AI-enhanced POCUS in LRS due to the limited scope of the review [28; 29] and limited retrieval of the literature [30]. This study aims to conduct a thorough review of the current state of POCUS-based imaging in LRS,examine the utilization of AI-enhanced POCUS in such settings, and explore the future directions necessary to elevate the standard of care in these areas.