esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Fog Detection Algorithm Using GK2A/AMI and Ground Data
## 3 Results  
### Frequency Analysis for Setting Threshold Values  
To set the initial thresholds for each test element, the frequency analysis of the test elements over the fog was performed using the 1728 scenes from the training cases. Fog pixels were selected using the co-located visibility meter data and visual inspection. Figure 5 shows the frequency distribution of the three test elements according to day and night on land. The average DCD was \(-\)2.7 K, and most DCDs were less than 0 K (98%) during nighttime (Figure 5a). Pixels above 0 K were mainly found in sub-pixel-sized fog. \(\Delta\)FTs showed a non-negligible difference between day and night (Figure 5b,d). Most \(\Delta\)FTs during the nighttime were greater than 0 K (Figure 5b); however, most \(\Delta\)FTs during daytime were less than 1 K (Figure 5d). This could be due to diurnal variation in the temperature profile at the boundary layer, strong inversion during nighttime, and the lapse rate profile during daytime. Most \(\Delta\)VISs were greater than 3%, with some reaching 25% (Figure 5a). Fog pixels with \(\Delta\)VISs less than 2-3% were the cases with sub-pixel-sized fog or weak fog or limitation of the visibility meter. The distribution of the histogram in each test element appeared broad due to the complexity and diversities in the fog and background. The initial threshold values determined from the frequency analysis were statistically optimized using iterative sensitivity tests. The optimized threshold values were selected when KSS was maximum.  
Figure 5: Frequency distribution of the optical/textural properties of fog pixels in the training cases at nighttime (**a,b**) and daytime (**c,d**). (**a**) Dual Channel Difference (DCD), (**b,d**) Difference between the BT of the fog top and surface temperature (\(\Delta\)FTs), and (**c**) Difference between the reflectance of 0.64 \(\upmu\)m (VIS0.6) and Vis_Comp (\(\Delta\)VIS).  
Since there is no visibility system in the ocean, the threshold was set by visual analysis of the spatial distribution of the test elements along with the frequency analysis using the visibility meter installed on the coast (including the islands) as shown in Figure 6. The final thresholds were set after the sensitivity test from initial thresholds through the visibility meter on the coast and islands.
## 3 Results and Discussion
### Physical Stability
The sedimentation phenomenon was weakened with increasing inulin concentration, inducing the appearance of lower average particle migration rates (\(p<0.05\)). The sedimentation velocity of particles in control yogurt was 7.46 \(\upmu\)m/s, whereas in the sample with 6% addition of inulin it was 5.79 \(\upmu\)m/s. The addition of inulin at a level of 15% decelerated a destabilization by approx. 76%, in comparison to yogurt without prebiotic (Figures 9 and 10). [PERSON] [34], using the dispersion analyser LUMiSizer, indicated that high-fat yogurts (9.4-10% fat) were subjected to the creaming phenomenon (fat droplet flotation). In low-fat yogurts (0.1-3.8% fat) sedimentation mainly occurred. The less fat in the system, the greater is the probability of consolidation of the particle network--this is visible in the chart as larger gaps between two consecutive recorded curves. The presence of fiber in the sample reduced values of its instability index and average sedimentation velocity. [PERSON] [38] undertook research on yogurt-like products based on (milk-like) liquid from tiger nuts (_Cyperus esculentus_ L.), TNM (i.e., 'tiger nuts milk'), with various additives (sodium caseinate, whey protein, carboxymethyl cellulose (CMC), guar gum or xanthan gum). Using the LUMiSizer analytical centrifuge, it was found that TNM-only protein enrichment did not contribute to a decline in the intensity of the creaming process and instability index values. The accretion of the creaming layer followed with an increase in sodium caseinate concentration. The use of hydrocolloids has effectively reduced the occurrence of this phenomenon, and therefore contributed to the increase in the overall stability of the systems, due to the increase in viscosity and stabilizing properties of polysaccharides.
## 3 Observations
### Digisonde Measurements
#### 3.1.3 The Three Identified F-Layer Uplift Phases  
On Figure 4 we are focusing on the ionospheric layer uplift phases during the night of 10/11 May using the B0, B1, the isodensity and the h'F, h'F2 parameter data. At first, the uplift phases were identified in virtual height data (Figure 4e,f). The Digisonde-derived B0 and B1 parameters can be used as indicators for rapid ionospheric changes, which appears as a modification of the shape of the electron density profile. In Figure 4a,b, it seems to be clear that the B0 (thickness parameter) is strongly modified by the geomagnetic storm in the main phase. Our results also strengthen the conclusion of [29; 37], that the B0 parameter is the most affected by space weather effects. The B1 parameter does not show any significant variability during storm conditions. Interestingly the three uplift phases are also nicely seen in the B0 parameter variability (Figure 4a,b).  
The isodensity contours in Figure 4c,d are commonly used to investigate wave-like perturbations in the ionosphere, like large-scale Travelling Ionospheric Disturbances (LSTIDs),  
Figure 4: The Digisonde-derived B0, B1 (**a,b**), isodensity data (**c,d**) and the h’F1 and h’F2 parameter (**e,f**) are displayed for SO and VT stations during a 24 h time interval (from 12:00 UT 10 May 2024 to 12:00 UT 11 May 2024). Light yellow bars (I, II, III) are indicating the three analyzed F-layer uplift phases.  
see e.g., the paper of [54; 55]. LSTIDs mostly occur during geomagnetic storms and appear in the ionosonde data as height variations of the ionospheric layers in time. The second uplift phase identified in Figure 4e,f is such a wave-like variability of the layers, which most probably connected to LSTIDs. Isodensity and B0 data strengthen this possibility at both stations. The third identified uplift phase is the clearest in virtual height data (Figure 4e,f), therefore, those parameters are the most suitable for the investigation of that type of uplift variation during storms.
## 3 Results and Discussion
### Evaluation of Fine Waste-Iron Mobility: Batch Experiments
#### 3.2.1 pH and Conductivity  
The pH variations of the leachate representative of the whole cycles of the test are provided in Figure 4. The pH values recorded for the leachate of the sample FW-ZL show a gradual increase from a minimum of 7.07 to a maximum of 7.73 during the second cycle, followed by a decrease until stabilization during the last three cycles at a value close to 7.55. The increased portion in pH could be explained by the dissolution of CaCO3, the content of which is higher in these wastes (Table 3). However, pH values of the sample FW-R gradually decrease from 7.87, before they stabilize again during the last three cycles at 7.45.  
Results of the EC measured on the leachates in both samples FW-ZL and FW-R are displayed by the two curves provided in Figure 4. This phenomenon shows an increase during the first cycle, then the conductivity decreases during the second cycle, and stabilizes during the subsequent cycles. It is shown, however, that the EC values are high for both samples, but those measured for FW-ZL (10.97 ms/cm to 20 ms/cm) are much larger compared to those of FW-R (3.85 ms/cm to 7.85 ms/cm), indicating a greater ionic charge in the solution for FW-ZL.  
The high values of EC can be explained by the dissolution of the more soluble minerals, such as gypsum and calcite (Table 2). The highest values characterizing the first cycle for the leachates of both samples can be explained by the solubilization of the gypsum contained in the fine wastes [54]. Subsequently, the stable conductivity values during the last four cycles may be attributed to the stability of the leached ion fluxes.
# A Novel Context-Aware Reliable Routing Protocol and SVM Implementation in Vehicular Area Networks
## 3 Approaches behind the Proposed Protocol  
This section discusses the two machine learning techniques, k-means and SVM, which are used in the proposed protocol.
# Crasworthiness Analysis to Evaluate the Performance of TDM-Shielded Street Poles Using FEA
## 2 Literature Review
### Constitutive Equations of Hyperelastic Materials  
To model the hyperelastic TDM, literature sourcing was essential to justify the necessity of such shielding onto a street pole. The selected material for the model is based on the study of the tensile behavior of TDM using ASTM standard [16] conducted by [PERSON] et al. [17] on four different types of TDMs tested: 500, 600, B800 and G800. Due to the additional readiness of data for the model, it was decided to extract the data of the 500 hyperelastic tire and perform a material calibration. The material calibration is based on the robust yet stable Marlow model. The constitutive equation governing the Marlow model, according to Dassault Systems [18], is defined as shown in Equation (3):  
\[U=U_{dev}(\overline{I}_{1})+U_{vol}(J_{el}) \tag{3}\]  
where \(U\) is the strain energy per unit of a reference volume, with \(U_{dev}\) as the deviatoric part and \(U_{vol}\) as the volumetric part. \(\overline{I}_{1}\), which is a first deviatoric strain invariant, is defined using Equation (4):  
\[\overline{I}_{1}=\overline{\lambda}_{1}^{2}+\overline{\lambda}_{2}^{2}+ \overline{\lambda}_{3}^{2} \tag{4}\]  
where \(\overline{\lambda}_{i}=J^{-\frac{1}{3}}\lambda_{i}\), \(J\) is the total volume ratio, \(J_{el}\) is the elastic volume ratio, and \(\lambda_{i}\) are the principal stretches. Furthermore, Figure 1 shows the uniaxial tensile tests performed by [PERSON] et al. [17] on the four TDM models that have been tested. These test data were then extracted using the Marlow Model in the Abaqus Material Calibrator to obtain the Marlow constants for the non-linear hyperelastic model to be declared on the geometry of the TDM shield. The first reason why the hyperelastic TDM 500 was chosen is because of its weight, as the density of the elastomer is only 500 kg/m\({}^{3}\). This makes it easier to transport and less expensive to deploy on many street poles. Another reason is that the composition of TDM 500 contains more discarded tire material in the form of Styrene Butadiene Rubber (SBR) than other discarded industrial rubber in the form of Ethylene-Propylene Diene Monomer (EPDM), as explained by [PERSON] et al.  
Figure 1: Uniaxial tension test of stress vs. strain of the four variants of TDM [17].  
While the B800 variant of the TDM was much stiffer than the 500 variants of the elastomer, it was much heavier with a density of 800 kg/m\({}^{3}\) and required additional manufacturing requirements in comparison to the TDM 500 variant, according to [PERSON] et al. Therefore, the B800 variant might be costly for consideration in this study.
## 2 Method
### Evolution Algorithms
#### 2.2.2 Asynchronous Particle Swarm Optimization  
Asynchronous particle swarm optimization (APSO), proposed by [PERSON], is an alternative to the particle swarm optimization (PSO) algorithm [36]. APSO uses a different velocity update equation, which utilizes a parameter called constriction factor. The purpose of the constriction factor is to improve the convergence. As APSO updates the global best particle position as soon as one is found that is less than the value of the objective function. APSO will converge more quickly than PSO. For this paper, APSO contains six steps in minimizing the value of the objective function, as shown below.  
The number of particles \(X_{i}\) (\(i=1,2,\,\ldots,\,N_{p}\)) with \(D\) dimensional vectors is chosen, where \(D\) is the number of parameters to be optimized, and \(N_{p}\) is the population size:  
Step 1: Randomly set the initial particle position based on the search range.  
Step 2: Evaluate the value of the objective function for each particle and record the particle information, that is, the particle position which has a value less than the current value of the objective function, and the new and smaller value of the objective function.  
Step 3: Update the global best particle position \(x_{gb}\) and best particle position \(x_{pb}\) for the \(g\)th generation based on the data recorded in Step 2.  
Step 4: Ten percent of \(x_{gb}\) will be mutated by the following equation:  
\[x_{mu}=\left\{\begin{array}{ll}x_{gb}-\varphi_{1}\cdot\left[c_{1}-\left(c _{1}-c_{2}\right)\cdot\frac{g}{g_{\max}}\right]\cdot\left(x_{\max}-x_{\min} \right),\ if\ \varphi_{mu}<0.5\\ x_{gb}+\varphi_{1}\cdot\left[c_{1}-\left(c_{1}-c_{2}\right)\cdot\frac{g}{g_{ \max}}\right]\cdot\left(x_{\max}-x_{\min}\right),\ if\ \varphi_{mu}\geq 0.5\end{array}\right., \tag{14}\]  
where \(c_{1}\) and \(c_{2}\) are the scaling parameters. Both \(\varphi_{1}\) and \(\varphi_{2}\) are random real numbers from zero to one. \(g\) (\(g=1,2,3,\,...,\,g_{\max}\)) is the current iteration number. The space limit of the environment is between \(x_{\max}\) and \(x_{\min}\). If the objective function of the mutation particle is the best, update the \(x_{gb}\) with \(x_{min}\).  
Step 5: Calculate the particle velocity using the following equation:  
\[\left[v^{g+1}\right]_{ij}=\Delta\cdot\left[v^{g}+c_{3}\cdot\varphi_{2}\cdot \left(x_{pb}-x^{g}\right)+c_{4}\cdot\varphi_{3}\cdot\left(x_{gb}-x^{g}\right) \right]_{ij^{\prime}} \tag{15}\]  
where \(ij\) is the \(i\)th particle and \(j\)th dimension, \(\Delta=\frac{2}{\left|2-\varphi-\sqrt{\varphi^{2}-4\varphi}\right|}\) is the constriction factor, \(\varphi=c_{3}+c_{4},\ \varphi\geq 4,c_{3}\) is set as 2.8 and \(c_{4}\) is set as 2.8, \(c_{3}\) and \(c_{4}\) are both positive constants, and 1.3 to control the impact of the local and global component in velocity;  
Step 6: Stop the algorithm and output the final data when the termination criteria have been reached.
# Digital-Twin-Based Monitoring System for Slab Production Process
###### Abstract
In summary, the integration of digital twin technology with slab production process detection allows for real-time monitoring and analysis of slab quality during the production process, providing essential production outcomes and associated analytical data. By applying DT technology to quality control and incorporating a defect identification detection network with high stability, this network exhibits outstanding high-precision positioning capabilities. It accurately identifies various defects in slabs, making the production process more controllable and efficient. Additionally, the monitoring system combines various environmental factors in the production environment, including temperature, humidity, and lighting, with quality inspection data. Through the analysis and synthesis of these factors, researchers can more accurately predict the probability of defects in slabs, providing a stronger basis for timely intervention. The application of this integrated analytical approach introduces more intelligence and predictability into the steel production process, with the potential to enhance product quality and improve production efficiency. The monitoring system represents an organic fusion of DT technology with industrial processes, bringing innovative advancements to quality control in the steel industry. The content of this paper is structured as follows. Section 2 provides an overview of intelligent industrial applications, defect detection technologies, and the current state of intelligent applications in the steelmaking process. Section 3 focuses on the introduced digital twin (DT)-based monitoring system for slab production processes. Section 4 conducts experimental validation of the aforementioned concepts. Finally, the concluding section, Section 5, presents a comprehensive summary of the entire manuscript.
## 2 System Model  
### Absorption and Scattering  
The underwater propagation of light is mainly affected by the absorption and scattering effect, which decreases light power greatly and influences the transmission direction of light. Then, the total attenuation coefficient \(c(\lambda)\) of the light beam through the seawater medium can be expressed as [22]  
\[c(\lambda)=a(\lambda)+b(\lambda) \tag{1}\]  
where \(a(\lambda)\) represents the absorption coefficient and \(b(\lambda)\) represents the scattering coefficient. They are related to the wavelength with the unit of m\({}^{-1}\). The widely used absorption and scattering coefficients for three typical seawater environments, including clean seawater, coastal seawater, and harbor seawater, were measured by [PERSON] in [9]. All coefficient parameters are shown in Table 1. As the turbidity of seawater increases, the total attenuation coefficient becomes larger, which means that the transmission distance and quality of the light signal are severely restricted in complex seawater environments.
# Time Management in Wireless Sensor Networks for Industrial Process Control
## 3 Results
### Testing and Validation  
The testing and validation of the time management has been carried out using four development boards from STMicroelectronics: (a) NUCLEO-L476 RG implementing the network coordinator application layer, connected via UART to a NUCLEO-L432 KC implementing the network coordinator data link and physical layer. The ATZB-212B-XPRO extension board featuring the AT86 RF212B transceiver is connected to the NUCLEO-L432 KC development board via an SPI interface and several pins used for interruption management, reset, and setting the transceiver in low-power mode. (b) Two NUCLEO-L152 RE development boards paired to the same ATZB-212B-XPRO extension board, implementing the application, data link, and physical layers of the wireless nodes. The user starts the network by using an application running on a PC which sends commands to the NUCLEO-L476 RG using UART. A diagram of the setup is featured in Figure 7 alongside the actual setup.  
Figure 7: (**a**) The hardware setup diagram showing the hardware components and the connections between them. (**b**) The development boards used for testing and validation, from left to right: two NUCLEO-L152 RE connected to ATZB-212B-XPRO extensions and one NUCLEO-F767 ZI simulating a process.  
Two pins on each of the NUCLEO-L432 KC boards part of the wireless nodes were used as debug pins to signal the user via pulses at the start of the group of timeslots and the start of each timeslot. The pins were then monitored using the logic analyzer to calculate the time drift between the two nodes. The user interface of the logic analyzer shown in Figure 8 displays four channels: Channel 0 monitors the start of each timeslot group and Channel 1 monitors the start of each timeslot for the first wireless node, with Channel 2 and Channel 3 carrying out the same for the second wireless node.  
We have then measured the time drift between the nodes using pairs of markers attached to each two close pulses monitored by Channel 0 and Channel 1 for 1 min after the devices kept running for more than 1 h. The measurements are displayed in Figure 9 below, showing that the time drift is kept below 600 microseconds.  
In comparison, we have stopped the advertisement frames sent by the network coordinator and recorded the time drift between the same two wireless nodes. Figure 10 shows how the time drift increases over time and surpasses the 1 millisecond threshold we initially set.  
Figure 8: The logic analyzer UI showing the timeslot group and timeslot pulses.  
Figure 9: The time drift in milliseconds between two wireless nodes with synchronization.
# Zero-Day Threat Mitigation via Deep Learning in Cloud Environments
## 4 Methodology  
This work proposes the use of a deep learning model called mixed vision transformer (MVT) for detecting zero-day threats in cloud computing environments. The model converts binary files into images using the byteplots technique and then applies deep attention mechanisms to classify between benign and malicious files.  
The choice of using the transformer based architecture mentioned over more conventional architectures, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), is primarily motivated by the nature of the input data and the task requirements. While CNNs excel at extracting spatial features from structured image data and RNNs are effective in modeling temporal dependencies, they have inherent limitations when dealing with high dimensional, sparse, and heterogeneous data typical of malware detection scenarios. In contrast, the transformer architecture offers several advantages: it captures long range dependencies and complex patterns through self attention mechanisms, which are crucial for analyzing byte-level representations and visual transformations of binary files. Additionally, transformers are less prone to losing contextual information compared with CNNs, making them more robust when detecting zero-day threats that lack clear signatures. Consequently, the MVT model is better suited for the challenge of zero-day malware detection in cloud environments (Figure 3), where adaptability and nuanced feature extraction are essential.  
Figure 2: Vision transformer (ViT) architecture used for image-based malware detection.
## 3 Microseismic Evolution Characteristics of Rockburst Formation Process  
### Evolution Stage of Microseismicity  
The formation process of rockburst starts from the change in the stress field due to excavation unloading in the underground cavern to the re-stabilization of the surrounding rock after the rockburst, and the microseismicity in the rock mass is evidently periodic. During the unloading process of the underground cavern excavation, the surrounding rock mass microruptures to release energy under the action of high stress; therefore, the microseismic event can indirectly reflect the stress state of the rock mass. [PERSON] et al. [14] reported statistics on the number of microseismic events occurring in the process of rockburst formation and found that the number of microseismic events has a certain regularity in the rockburst formation process. They divided the formation process of rockburst into three stages: (1) the transition period, which is the period of stress accumulation and when the number of microseismic events is relatively low; (2) the peak period, where large-scale destruction occurs in the rock mass, the high stress accumulated in the rock mass begins to release, and the number of microseismic events increases rapidly; (3) the quiet period, where a large amount of stress in the rock mass is released or transferred because of the occurrence of a rockburst. At this stage, the number of microseismic events in the rock mass is relatively low. However, in the actual monitoring process, not all the rockburst processes conform to the above evolution law. For example, when [PERSON] et al. [17] studied the formation process of three consecutive strong rockbursts along the structural plane. They found that after the rockburst occurred, the microseismic event count continued to rise or remained at a high level, and then the next rockburst occurred. This may be because after a rockburst, the stress in the rock mass is not completely released and transferred to the rock mass in the adjacent area along the structural plane, which continues to accumulate rapidly under the influence of excavation disturbance, resulting in a large-scale failure of the rock mass.
## 3 Results
### Model Performances under Different Weather Conditions
These results demonstrated that although the two-leaf GPP model considered the difference in the photosynthesis of shaded and sunlit leaves and changes in scattering transmission in the canopy, estimation results of \(G\)\(P\)\(P\) in shaded and sunlit leaves remained inconsistent under different weather conditions. On sunny days, the two-leaf GPP model underestimated the \(G\)\(P\)\(P\) of shaded leaves but overestimated that of sunlit leaves, while the simulation results showed the opposite trend on cloudy days. Therefore, the under/overestimation errors were offset, resulting in acceptable total \(G\)\(P\)\(P\) estimates by the two-leaf GPP model under different weather conditions.