esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study of Crash Scenarios
### Evaluation  
We consider two metrics to evaluate the V2P system under different pre-crash scenarios and mechanisms: Available Response Time for the vehicle and number of safety messages received by the vehicle from the VRU. To calculate the Available Response Time for the vehicle before the crash, we consider the time-stamp of first ever beacon received by the vehicle from the corresponding VRU (_detection_ phase). The Available Response Time, for each pre-crash scenario, is give by Equation (1).  
\[ART=CT-FBT \tag{1}\]  
where:  
\(ART=\) Available Response Time  
\(CT=\) Crash Time  
\(FBT=\) First Beacon Time  
The number of safety messages, received from the VRU, gives a measure of reliability for _tracking and prediction_ phase. We consider the total number of safety messages received by the vehicle, from the corresponding VRU in crash, for this purpose. Table 4 shows the Available Response Time and the number of received safety messages for each pre-crash scenario under _Active_ and _Passive_ mechanisms.  
As expected, the Available Response Time is always less in _Passive_ mechanism than in _Active_. This is due to the fact that the VRU device waits for 2 s before the safety message transmission. SAE J2945/9 document indicates that the collision awareness message must be issued 8 s before the crash [49]. Under our simulation scenario, none of the mechanisms can fulfill this condition for  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **Available Response Time (before Crash, in s)** & **Average No. of Received Messages (from VRUs)** \\ \hline
2.a & & \\ Active & 2.13 & 5 \\ Passive & 0.39 & 1 \\ \hline
2.b & & \\ Active & 31.7 & 62 \\ Passive & 29.96 & 13.33 \\ \hline
2.c & & \\ Active & 3.65 & 7.33 \\ Passive & 1.27 & 1 \\ \hline
2.d & & \\ Active & 37.65 & 74 \\ Passive & 35.93 & 16.67 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Warm-Up (in s)** & **Crash Time (in s)** & **Simulation Length** \\ \hline
2.a & 30 & 33.5 & 33.5 \\
2.b & 11 & 44 & 44 \\
2.c & 45 & 49 & 49 \\
2.d & 10 & 48 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scenario-specific Parameters.  
scenarios 2.a and 2.c. Scenario 2.a has a short Available Response Time because the VRU has to travel a short distance (\(<\)4 m) before it crashes with the vehicle. Scenario 2.c has a short Available Response Time because the VRU and the vehicle are unable to communicate with each other due to NLOS. The scenarios 2.b and 2.d show sufficiently long Available Response Times.  
The number of received safety messages are always less in _Passive_ mechanism than in _Active_. Also, in scenario 2.a and 2.c, the vehicle receives only 1 safety message from the VRU under _Passive_ mechanism. This may only be sufficient for the _detection_ phase and no messages are available for _tracking and prediction_ phase. Under scenarios 2.b and 2.d, the vehicle receives a sufficiently large number of messages for both the mechanisms.
# Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes
## 4 Sensitivity Analysis
### Variance-Based Methods  
The variance-based methods measure the sensitivity of the output \(Y=f(\mathbf{\mathsf{X}})\), the outcome of the scenario, to the changes in the model inputs in terms of a reduction in the variance of \(Y\). A review of the variance-based approach can be found in Reference [34]. Two principal measures of the sensitivity of the model output, \(Y\) to an individual input\(X_{i}\) are proposed. The first measure which is given in Equation (14), can be considered as the expected amount by which the uncertainty in \(Y\) will be reduced if we learn the true value of \(X_{i}\). This can be also viewed as the variance of the main effect of \(X_{i}\) (as given in Equation (A2)), \(V_{i}=var(z_{i}(X_{i}))\), or simply represented as,  
\[V_{i}=var\{E(Y\mid X_{i})\}\,. \tag{14}\]  
The second measure, proposed by Reference [38], can be written as:  
\[V_{T_{i}}=var(Y)-var\{E(Y\mid\mathbf{X}_{-i})\}\,, \tag{15}\]  
which is the remaining uncertainty in \(Y\) that is unexplained after everything has been learnt except \(X_{i}\).  
These two measures (Equations (14) and (15)) can be converted into scale invariant measures by normalizing with \(var(Y)\) as follows:  
\[S_{i}=\frac{V_{i}}{var(Y)},\quad S_{T_{i}}=\frac{V_{T_{i}}}{var(Y)}=1-S_{-i} \tag{16}\]  
where \(S_{i}\) is the \(1 st\) order index for \(X_{i}\), and \(S_{T_{i}}\) is the _total effect index_ of \(X_{i}\). The 1 st order indices measure the portion of variability that is due to variation in the main effects for each input variable, while the total effect indices measure the portion of variability that is due to total variation in each input.  
It should be noted that the variance measures are linked to the Sobol decomposition (as discussed in Appendix A), when the parameters are independent, and the total variance of \(f(\cdot)\) can be represented as the sum of the variances for each term given in Equation (14), (see References [36, 37, 39] for further details about the Sobol decomposition and indices).
# Seasonal Monitoring of Volatiles and Antioxidant Activity of Brown Alga _Cladosteplus spongiosus_
## 3 Materials and Methods
### Antioxidant Activity of Extracts  
Three different methods were used to assess in vitro antioxidant activity of crude algal extracts. The oxygen radical absorbance capacity (ORAC) and 2,2-diphenyl-1-picrylhydrazyl radical scavenging ability (DPPH) methods are based on hydrogen atom transfer, while ferric reducing/antioxidant power (FRAP) method is based on electron transfer [11; 65; 66].  
Previously reported methods [11; 65] were used to measure the reducing activity as FRAP. The results were expressed using the Trolox standard calibration curve (\(y=0.0013x\), \(R^{2}=0.99\)) as micromodes of Trolox equivalents (uM TE). The inhibition of the free peroxyl radicals, measured as ORAC [11], was measured for the samples in 1:100 dilution, and the results were expressed using the Trolox standard calibration curve (\(y=22.842x+26.473\), \(R^{2}=0.99\)) as uM TE. DPPH radical inhibition was expressed as a percentage of inhibition and measured according to the previously reported methods [11; 67].
# 400 GbE Technology Demonstration Using CFP8 Pluggable Modules
## 4 Link Performance
### Fiber Transmission  
Furthermore, we measure the performance of the 400 GE CFP8-LR8 module with the single mode fiber. The attenuation of VOA1 is set to zero so that there is minimum error introduced on the forward path from the ONT to the PIC. We adjust the fiber length and measure the BER. The result is shown in Figure 11. As seen, even with 30 km SMF between the transmitter and the receiver, the BER is still below the FEC's UCW threshold of \(2.2\times 10^{-4}\). This clearly demonstrates 400G Ethernet interoperation using two CFP8-LR8 modules.  
Figure 10: Back-to-back performance for interoperation of two CFP8-LR8 modules. The signal is transmitted from the Tx of the second module to the Rx of the first module: (**a**) BER versus VOA2’s attenuation for 16 individual electrical lanes; (**b**) total BER vs. VOA2’s attenuation. The threshold for UCW is shown as the red line.  
Figure 9: Back-to-back performance for interoperation of two CFP8-LR8 modules. The signal is transmitted from the transmitter (Tx) of the first module to the receiver (Rx) of the second module: (**a**) Bit error rate (BER) vs. VOA1’s attenuation for 16 individual electrical lanes; (**b**) total BER vs. VOA1’s attenuation. The threshold for the uncorrected code word (UCW) is shown as the red line.
## 1 Introduction
Consequently, this technology bolsters the algorithm's stability, diminishes sensitivity to environmental and contextual changes, and ultimately augments the algorithm's efficiency and practicality. Thus, utilizing the DQN algorithm to investigate the prioritization of adaptive autonomous driving bus signals holds significant promise and numerous advantages. The remainder of this paper is structured as follows. Section 2 introduces the application of deep reinforcement learning in adaptive signal timing Section 3 begins by introducing the fundamental assumptions of real-time signal priority for self-driving bus lines. Subsequently, a detailed presentation of the DQN algorithm model, designed to control real-time signal priority in self-driving-bus lines, is provided. Section 4 provides an overview of the research site in this study and outlines the parameters employed in the DQN algorithm. In Section 5, the training process and test results of real-time signal prioritization for self-driving-bus arterial lines are presented. The test results include a comparison of real-time signal priority with no-priority and active-priority scenarios, followed by a supplementary discussion. Finally, Section 6 presents the conclusions.
# Analysis of Fire Resistance of Prestressed Concrete T-Beam Based on ABAQUS Numerical Simulation
## 3 Calculation Results and Parameter Analysis of Fire Resistance of Prestressed Concrete T-Beam
### Analysis of Parameters Affecting the Fire Resistance of Prestressed Concrete T-Beams
#### 3.3.4 Effect of Different Concrete Protection Layer Thicknesses on Fire Resistance  
The thickness of the protective layer of concrete can influence the fire resistance of prestressed concrete beams, as shown in Figure 13. The fire resistance grows as the  
Figure 11: Curves of the effect of different ratios of prestressing bar on the fire resistance of T-beams.  
Figure 12: Curves of the effect of different flange plate thicknesses on the fire resistance of T-beams.  
protective layer increases, indicating an approximate linear upward trend. When the thickness of the protective layer is increased from 15 mm to 25 mm and 35 mm, the fire resistance of the prestressed concrete beam increases by about 30% and 40%, respectively. In particular, when the thickness of the protective layer of concrete is increased to 45 mm, the fire resistance of prestressed concrete beams can be increased by a factor of about one. This is due to the fact that increasing the thickness of the concrete protective layer effectively delays the high temperatures to which the steels and prestressing bars in the prestressed concrete T-beam are subjected, resulting in a reduction in the discounted high-temperature performance of the steels and prestressing bars, thus improving the fire resistance of the prestressed concrete T-beam. In prestressed concrete T-beams, whether the performance of prestressing bars can be effectively played directly affects the fire resistance of the structure, so unlike concrete beams, increasing the thickness of the concrete protective layer shows a tendency for the fire resistance to continue to grow.
# Comparative Impact Analysis of Cyclone Ana in the Mozambique Channel Using Satellite Data
## 1 Introduction  
Tropical cyclones are some of the most devastating natural disasters, with far-reaching consequences, such as human displacement, economic loss, and environmental damage and changes [1; 2]. Tropical cyclones are defined as low-pressure systems with organized deep convection that form over warm tropical waters along with maximum sustained surface winds ranging between 63-118 km/h [3]. The Southwestern Indian Ocean is one of the main tropical cyclone areas in the world and the most active one in the Southern Hemisphere [1]. Favorable conditions, such as high sea surface temperature, weak vertical wind shear, and vertical profiles of humidity, lead to the high frequency of depression systems [4]. In the Mozambique Channel specifically, this is reflected in local parameters, such as high sea surface temperature (SSTs) with an average of 29.6 \({}^{\circ}\)C, a soundirly wind shear with height, and below-normal geopotential height anomalies at 500 hPa [5]. In the Mozambique Channel, three to 12 cyclones form every year. Cyclone season in Madagascar typically occurs between November and March, with an average of 1.5 cyclones yearly,the highest number on the African continent [6; 7]. In Mozambique, on the other hand, the cyclone season spans November to April and the area is estimated to suffer from 1.16 cyclones every year on average [5].  
Since mid-March 2022, it has been estimated that around 960,000 people have been affected by the five extreme tropical weather events [6] that occurred in the Mozambique Channel and left displaced people in need of health, water, housing, and alternative livelihoods. Estimating the impacts of tropical weather events in a context of increases in their frequency is therefore crucial for developing climate-adapted solutions.
# A CSAR 3D Imaging Method Suitable for Edge Computation
## 2 BP 3D Imaging
### BP 3D Imaging
#### 2.2.2 IBP 3D Imaging  
The IBP 3D Imaging method transforms the 3D processing of the BP algorithm into 1D phase compensation and searches by constructing a geometric interpolation kernel [4]. The geometric interpolation kernel is as follows:  
\[k_{\text{ref}}=s_{\text{r}}(\tau,\varphi)\cdot\exp\left[j\frac{4\pi}{\lambda} \cdot(r_{\text{ref}}-R_{\text{c}})\right] \tag{4}\]  
where \(r_{\text{ref}}\) is the slant range vector, which can be expressed as follows:  
\[r_{\text{ref}}=\left[R_{\text{min}}\ R_{\text{min}}+\Delta r\ R_{\text{min}}+2 \Delta r\ \ldots\ R_{\text{max}}\right] \tag{5}\]  
where \(R_{\text{min}}\) and \(R_{\text{max}}\) are the minimum and maximum values of the slant range, respectively, and \(\Delta r\) is the slant range interval.  
In the IBP 3D Imaging method, firstly, the slant range \(R_{\text{p}}(\varphi)\) between the coordinate unit \((x,y,z)\) and the platform is calculated. Secondly, the sequence number \(l\) of \(R_{\text{p}}(\varphi)-R_{\text{min}}\) is calculated as the closest element in \(r_{\text{ref}}\). Thirdly, the \(l\) element in the slant range vector \(r_{\text{ref}}\) is searched, and coherent accumulation is achieved.
## 3 Promethe-Sapevo-M1
### Criteria Evaluation
#### 3.5.3 Outranking by Intervals  
The third model of preference analysis, based on the PROMETHE III method, associates for each alternative \(a_{1}\) an interval \([x_{a_{1}},y_{a_{1}}]\), based on a standard error value (16), from the standard deviation of net outranking flows, where each interval is obtained by Equation (17). This model of analysis enables defining the complete ranking, where (18) and (19):  
\[\alpha=\frac{\sigma}{\sqrt{n}} \tag{16}\]  
\[\left\{\begin{array}{l}x_{a_{1}}=\phi(a_{1})-\alpha\\ y_{a_{1}}=\phi(a_{1})+\alpha\end{array}\right. \tag{17}\]  
\[a_{1}\]  
is preferable to  
\[a_{2}(a_{1}Pa_{2})\]  
if  
\[x_{a_{1}}>y_{a_{2}}\]  
(18)  
\[a_{1}\]  
is indifferent to  
\[a_{2}(a_{1}Ia_{2})\]  
if  
\[x_{a_{1}}\leq\ y_{a_{2}}\]  
and  
\[x_{a_{2}}\leq\ y_{a_{1}} \tag{19}\]  
Simplifying, \([x_{a_{1}},y_{a_{1}}]\) is an interval where the center is \(\phi(a_{1})\), and the length is proportional to the standard error, based on the distribution of the values \(\pi(a_{1},a_{2})-\pi(a_{2},a_{1})\)[48]. It is necessary to emphasize that the preference relation \(P\) is transitive while the indifference relation \(I\) remains intransitive. Exemplifying a practical application, Figure 4 exposes the preference relation between three alternatives \(a_{1}\), \(a_{2}\), and \(a_{3}\), where \(a_{1}Ia_{2}\) and \(a_{2}Ia_{3}\), but \(a_{1}\) remains preferable to \(a_{3}\) (\(a_{1}Pa_{3}\)).  
Figure 4: Relation of preference between intervals, adapted from [48].
## 3 Results and Discussion
### Spectral Pre-Processing  
Before filtering the entire spectral data set, we tested the Savitzky-Golay filter on a noisy spectral curve in order to visually inspect its effect on noise (Figure 3). These filter settings (no transformation) were applied to the entire spectral data and their means were compared; their means showed no significant differences. The extremes of the curves were removed due to noise, and the range of 400-924 nm was selected for the analysis.  
After pre-processing, PCA was performed on the filtered data sets to explore their structure. In the most cases, two principal components explained high amount of variability of the spectra (\(>\)95%). However, using first derivative transformation, the explained variability reached 70% with five PCs and this value decreased when derivative order increased. Nonetheless, PCA showed potential for classifying samples from different soil orders from spectral data. A sophisticated method for enhancing the performance of classification of soils from spectral data was proposed by [PERSON] et al. [44].  
Figure 3: Effect of the Savitzky–Golay filter applied to a noisy spectral curve. No derivative transformation was used. Only forty points are displayed in order to appreciate its effect on noise. Inside the parentheses, window size, polynomial order and derivative order are given.  
Figure 2: Carbon content by depth and soil order. (**A**) The full range variation is represented in the box and whisker diagrams. The top of the gray box is the third quartile and the bottom the first quartile. The horizontal line inside the box is the median of the data. Whiskers above and below the box show the minimum and maximum values and outliers are represented by black circles; (**B**) The distribution of the soil total carbon of the whole data set.
# Light-Weighted Password-Based Multi-Group Authenticated Key Agreement for Wireless Sensor Networks
## 4 Security Analysis
### Resistance to Off-Line Password-Guessing Attacks  
In the authentication and key agreement phase of the proposed protocol, only messages \(C_{i}=h(DID_{i}\|Q_{i}\|K_{1}\|X_{i}\|C_{i}\|T_{i})\) in \(M_{i,1}\) and \(CS_{i}=h(K_{1}\|Q_{i}\|Y_{i\cdot 1}\|Y_{i+1}\|GID_{im}\|T_{S})\) in \(M_{i,2}\) contain password \(pw_{i}\), where \(Q_{i}=h(ID_{i}\|pw_{i})\). However, \(pw_{i}\) is protected by \(K_{1}\), and the one-way property of hash functions. Similarly, in the password change phase of the proposed protocol, only messages \(D_{i}=h(K_{1}\|T_{i})\oplus Q_{i\_new}\) and \(E_{i}=h(DID_{i}\|Q_{i}\|Q_{i\_new}\|K_{1}\|X_{i}\|T_{i})\) in \(M_{i,1}\) and \(CS_{i}=h(K_{1}\|Q_{i}\|Y_{i\cdot 1}\|Y_{i+1}\|GID_{im}\|T_{S})\) in \(M_{i,2}\) contain password \(pw_{i}\), where \(K_{1}=T_{r_{i}}(pk_{S})\)\(mod\)\(p\), \(DID_{i}=K_{1}\oplus ID_{i}\), \(Q_{i}=h(ID_{i}\|pw_{i})\) and \(Q_{i\_new}=h(ID_{i}\|pw_{i\_new})\). However, \(pw_{i}\) and \(pw_{i\_new}\) are protected by \(K_{1}\) and the one-way property of hash functions. No information helps to confirm the correctness of the guessed passwords, so off-line password-guessing attacks are unsuccessful against the proposed protocol.
# Concrete Cracks Detection Based on FCN with Dilated Convolution
###### Abstract  
In civil engineering, the stability of concrete is of great significance to safety of people's life and property, so it is necessary to detect concrete damage effectively. In this paper, we treat crack detection on concrete surface as a semantic segmentation task that distinguishes background from crack at the pixel level. Inspired by Fully Convolutional Networks (FCN), we propose a full convolution network based on dilated convolution for concrete crack detection, which consists of an encoder and a decoder. Specifically, we first used the residual network to extract the feature maps of the input image, designed the dilated convolutions with different dilation rates to extract the feature maps of different receptive fields, and fused the extracted features from multiple branches. Then, we exploited the stacked deconvolution to do up-sampling operator in the fused feature maps. Finally, we used the _SoftMax_ function to classify the feature maps at the pixel level. In order to verify the validity of the model, we introduced the commonly used evaluation indicators of semantic segmentation: Pixel Accuracy (PA), Mean Pixel Accuracy (MPA), Mean Intersection over Union (MIoU), and Frequency Weighted Intersection over Union (FWIoU). The experimental results show that the proposed model converges faster and has better generalization performance on the test set by introducing dilated convolutions with different dilation rates and a multi-branch fusion strategy. Our model has a PA of 96.84%, MPA of 92.55%, MIoU of 86.05% and FWIoU of 94.22% on the test set, which is superior to other models.  
FCN; crack detection; residual network; dilated convolution; semantic segmentation +
Footnote †: journal: Medical Image Processing
## 5 Experimental Results  
After defining the main parts of the proposed design system and methodology, along with the main structure of the testbed, this section highlights practical measurements and results.  
This proposed design was deployed and tested in a real scenario on a farm in Medenine, Tunisia in the period of March 2020 to March 2021. This system was deployed and validated in Medenine, Tunisia, due to its varied terrain and options of open farms that covered a large set of different agricultural activities. Indeed, the chosen farm covered both open-field and greenhouse cultivation, together with the production of several different agricultural products. Furthermore, the farms were manually controlled by the farmers without the support of any technology, just based on their experiences.  
Assessing the impact of environmental factors and prototyping outdoor applications as part of live projects provides a better understanding of smart IoT that greatly combined with UAV technology, which helps in precision agriculture, and, in turn, intensely boosts crop productivity, saving natural resources in a real-case scenario.  
Experimental results in this section are discussed from two aspects--first, the results of smart farming's' environmental monitoring of sensing temperature, humidity, soil moisture, rain level, and solar radiation are presented, and second, the results of the Hata propagation model, which include PL and RSS. These parameters show the focal point between the ground station and the drone at the cloud layer.