esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Comparation of Three Nanoenhancers  
To investigate and find the optimal nanoenhancer, the enhancement effects of OTR 202 nanosubstrate, silver nanosubstrate, and gold nanosubstrate were compared and analyzed in this study. The structure diameter of OTR 202 nanoparticles, silver nanoparticles, and gold nanoparticles were characterized by Transmission Electron Microscopy (TEM) in Figure 3a-c respectively.  
As can be seen in Figure 3, the nanoparticle sizes of OTR 202, silver substrate, and gold substrate were uniform. Moreover, the gold nanoparticle size was bigger than that of the OTR 202 and the silver nanoparticle, while the nanoparticle size of OTR 202 was the smallest. The SERS values based on the OTR 202 nanosubstrate, the silver nano-substrate, and the gold nanosubstrate are shown in Figure 4a-c respectively. According to these values, the enhancement performance of the Raman peaks of OTR 202 nanosubstrate at 994, 1028, 1370, 1554, and 1636 cm\({}^{-1}\) were superior to that of the gold substrate. In addition, the Raman peaks of 1555 and 1630 cm\({}^{-1}\) appeared when OTR 202 was used while those  
\begin{table}
\begin{tabular}{c c c} \hline
**Solid-RS (cm\({}^{-1}\))** & **SERS (cm\({}^{-1}\))** & **Assignment** \\ \hline
544 (s) & - & \(\delta\)(C–N) \\ - & 995 (m) & \(\upsilon\) (N–H) \\
1008 (vs) & 1027(m) & \(\upsilon\)(C–N) \\
1182 (m) & 1182 (m) & \(\rho\) (NH\({}_{2}\)) \\ - & 1370 (vs) & \(\upsilon\) (N–H) \\ - & - & \(\delta\)(N–H) \\
1540 (m) & 1555 (s) & \(\upsilon\) (C=O) \\ - & 1630 (s) & \(\upsilon\) (N–H) \\
1650 (m) & - & \(\delta\) (NH\({}_{2}\)) \\ \hline \end{tabular} Note: vs = very strong; s = strong; m = medium; w = weak; \(\upsilon\) = stretching; \(\delta\) = in-plane bending; \(\rho\) = in-plane swaying vibration. RS: Raman spectroscopy; SERS: Surface-enhanced Raman spectroscopy.  
\end{table}
Table 1: The proposed assignment of Raman peaks of urea molecules.  
Figure 3: The structure of Opto Trace Raman 202 (OTR 202), silver, and gold substrate were characterized by transmission electron microscopy: (**a**) OTR 202; (**b**) silver nanoparticle; (**c**) gold nanoparticle.  
two Raman peaks did not appear in the SERS combined with the gold substrate, which indicated that the enhancement of OTR 202 was better than that of the gold substrate. The reason might be that the ultraviolet characteristic absorption peaks of urea and OTR 202 nanoparticles were more similar. Thus, the surface plasmon resonance of OTR 202 can be stimulated more easily, resulting in a higher detection sensitivity [32]. As for silver nanosubstrate, although there were seven stronger Raman peaks (465, 616, 939, 1107, 1266, 1410, and 1449 cm\({}^{-1}\)) in the SERS, these peaks also occurred in the SERS of soil extract without urea (Figure 4d).This might be because the peak signals of other substances in the soil extract were amplified, which covered up the molecular vibration of soil water-soluble nitrogen. Therefore, the OTR 202 nanoparticle was selected as the enhancer in the following study.
## 2 Materials and Methods
### Pure Water Flux and Retention Test  
The initial pure water flux of PES ENMs was determined using a dead-end filtration cell (AMICON 8200, Millipore, Burlington, VT, USA). The filtration cell was connected to a 5 L reservoir. The wetted PES ENM with a diameter of 63.5 mm was put in the filtration cell, and de-ionized water (18.2 M\(\Omega\) cm, Milli-Q, Millipore, Burlington, VT, USA) in the reservoir was supplied to the membrane under the feed pressure of 0.2 bar. The mass of water that permeated through the membrane during 5 s was recorded, and the pure water flux was obtained by Equation (4):  
\[\mathrm{J}=\frac{Q}{A\Delta t} \tag{4}\]  
where J is the pure water flux (L/(m\({}^{2}\) h)), \(Q\) is the volume of the permeated water (L), \(A\) is the effective membrane area (m\({}^{2}\)), and \(\Delta t\) is the sampling time (h).  
Retention tests of 0.6 \(\mathrm{\SIUnitSymbolMicro m}\) PS particles were performed at 0.2 bar on the simple dead-end filtration setup. The feed of 400 mL PS suspension (20 mg/L) in the reservoir was passed through the wetted PES ENM. The PS concentration of the permeated solutions was determined by a UV-Vis spectrophotometer (Optizen 2120 UV, Mecasys, Daejeon, Korea) at the wavelength of 450 nm. The separation factor (S.F.) was calculated as follows:  
\[\mathrm{S.F.}=\left(1-\frac{C_{p}}{C_{f}}\right)\times 100\% \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Exp. No.**} & \multicolumn{5}{c}{**Coded Levels**} & \multirow{2}{*}{\(\mathrm{Y_{1}}\) **(kV)**} & \multirow{2}{*}{\(\mathrm{Y_{2}}\) **(nm)**} \\ \cline{2-2} \cline{5-7}  & \(\mathrm{X_{1}}\) & & \(\mathrm{X_{2}}\) & & \(\mathrm{X_{3}}\) & & \(\mathrm{X_{4}}\) & \\  
where \(C_{f}\) and \(C_{p}\) are the PS concentration of the feed solution (mg/L) and permeated solution (mg/L), respectively.
## 4 Analysis Method for Source Appointment and Contribution  
### Previous Methods for Source Appointment  
There are several source analysis methods for environmental pollutants: Chemical Mass Balance (CMB) methods including Positive Matrix Factor Analysis (PMF), as well as Principal Component Analysis (PCA) and Diagnostic Ratios (DR), although the PMF and This has been deletedPCA methods are not necessarily belonging to chemical mass balance category. These methods have been used for source appointment of atmospheric NPAHs in many papers. By using the PMF method, the total contribution of anthropogenic primary and secondary biogenic contributions was 68-71% in the atmospheric organic carbon in Grenoble, France [61]. The PMF method found that 1-NP was a good primary organic molecular marker to trace traffic emissions, notably in diesel exhaust [62]. However, the application of the CMB to source appointment of primary emitted NPAHs is limited because of the necessity of many combustion source data sets of not only NPAHs and PAHs but also other pollutants. The PCA method was used to identify the contribution of vehicles to NPAHs in Kigali, Rwanda, Nanjing and Qingdao, China, where the source information of NPAHs was used to identify contributors [63; 64; 65]. The DR method is the most popular method for source appointment of PAHs. However, this method cannot be applied to NPAHs, due to the lack of NPAHs source information.  
As described above, 2-NFR and 2-NP are formed through secondary processes in the atmosphere [14]. The concentration ratios of 2-NFR to 1-NP ([2-NFR]/[[1-NP] and 2-NFR to 2-NP ([2-NFR]/[2-NP]) are often used to distinguish the two processes in the secondary formation in the atmosphere. A ratio close 10 indicates OH\({}^{-}\) radical-initiated formation, and a ratio over 100 indicates NO\({}_{3}\) -initiated formation [66]. In Table 3, many reports have used these thresholds and most suggest that the OH\({}^{-}\) radical-initiated formation process is the main process except for in Fukue Island, Japan, where NO\({}_{3}\)\({}^{-}\) radical-initiated formation was predominant [67]. A relational expression between NPAH and NO\({}_{2}\) concentrations has been proposed for determining secondary formation [68]. However, the ratios of OH\({}^{-}\) radical-initiated formation and NO\({}_{3}\)\({}^{-}\) initiated formation could not be distinguished. The conversion of gaseous fluoranthene and pyrene into 2-NF and 2-NP in a marine atmosphere has also been studied [69].
# Opportunistic Sensor Data Collection with Bluetooth Low Energy
## 5 Evaluation
### Sensor Node Current Consumption
#### 5.1.3 Current Consumption Comparison of the Advertisement-Based and the Connection-Based Approaches  
In order to ease a comparison between the advertisement- and connection-based approaches, Figure 16 plots the average current consumption of each method, as a function of _advInterval_, and for different \(T_{contact}\) values. For the sake of clarity, only BER = 0 has been considered. Note that, for advertisement-based approaches, the minimum _advInterval_ value is 100 ms.  
For a given value of \(N\), the corresponding advertisement-based approach has a lower current consumption than connection-based approaches. This happens due to the current consumption of  
Figure 16: Average current consumption of the sensor node, for the advertisement-based and connection-based approaches, as a function of _advInterval_ and for different \(N\) and \(T_{contact}\) values, and for BER = 0.  
Figure 15: Average current consumption of the sensor node in the connection-based approach, for a time between contacts of one day, for \(N\) = 1, Tcontact = 45 s, and connInterval = 4 s, as a function of _advInterval_, and for different BER values.  
data exchange in the latter, and becomes more significant as _advInterval_ increases. Note that current consumption of advertisement-based approaches is independent of \(T_{contact}\). For the highest _advInterval_ values, current consumption of advertisement-based approaches is roughly one order of magnitude lower than that of connection-based approaches.
# Model Checking Resiliency and Sustainability of In-Vehicle Network for Real-Time Authenticity
###### Abstract  
The Controller Area Network (CAN) is the most common network system in automotive systems. However, the standardized design of a CAN protocol does not consider security issues, so it is vulnerable to various security attacks from internal and external electronic devices. Recently, in-vehicle network is often connected to external network systems, including the Internet, and can result in an unwarranted third-party application becoming an attack point. Message Authentication CAN (MAuth-CAN) is a new centralized authentication for CAN system, where two dual-CAN controllers are utilized to process message authentication. MAuth-CAN is designed to provide an authentication mechanism as well as provide resilience to a message flooding attack and sustainably protect against a bus-off attack. This paper presents formal techniques to guarantee critical timing properties of MAuth-CAN, based on model checking, which can be also used for safety certificates of vehicle components, such as ISO 26262. Using model checking, we prove sufficient conditions that MAuth-CAN is resilient and sustainable against message flooding and bus-off attacks and provide two formal models of MAuth-CAN in timed automata that are applicable for formal analysis of other applications running on CAN bus. In addition, we discuss that the results of model checking of those properties are consistent with the experiment results of MAuth-CAN implementation.  
controller area network bus; authentication; authenticity; resiliency; sustainability; formal verification; model checking; in-vehicle network Article
# Unraveling Convolution Neural Networks: A Topological Exploration of Kernel Evolution
## 2 Theoretical Background
### Betti Curves of Convolutional Kernels  
Here, we outline the main idea underlying our research. We analyze the evolution of convolutional kernels in CNNs through a topological approach. Initially, we reshape each 3 \(\times\) 3 convolutional kernel into a nine-dimensional vector, effectively representing each kernel as a point within a nine-dimensional Euclidean space. This transformation allows us to create a point cloud representation of the convolutional kernels. Utilizing the concept of persistent homology, we then quantify the topological \"shape\" of these kernels, identifying consistent features and patterns that prevail across different scales. This is achieved by calculating the persistent homology of the point cloud, which elucidates the number and nature of \"holes\" or voids within the topological structure of CNN kernels.  
Figure 2: Three types of representations of persistent homology: persistence diagram, persistence barcode, and Betti curve. These representations show the persistence of the topological features of point clouds in Figure 1.  
Key to our analysis is the use of Betti curves, which provide a graphical representation of these topological features. These curves enable us to track and visualize the changes in the convolutional kernels' topology as the training of the CNN progresses. The dynamic alterations in the weights of the kernels during training lead to changes in their topological structure. By generating and examining Betti curves at each iteration of the training process, we gain deep insights into the evolving patterns and complexities of the kernels' topological landscape. It offers a unique perspective on how convolutional kernels adapt and modify their structures in response to the learning process.
## 4 Test Results and Discussions
### Dynamic Compression Tests
#### 4.2.2 Dynamic Stress-Strain Responses  
Figure 9 compares the dynamic stress-strain curves of BFRP, AFRP, and CFRP-confined and unconfined high-strength concrete specimens under dynamic compression with different impact energies. Note that all the dynamic stress-strain curves (see Figure 9) terminate at the descending portion (at around 80% of the peak stress). The dynamic stress-strain curves of the specimens shown in Figure 9 lead to the following characteristics: (1) The initial straight-line segment of the FCHC specimens is longer than that of the unconfined high-strength concrete specimens, indicating that the deformation resistance of the high-strength concrete is enhanced by the confinement; (2) the effect of the strain rate on the elastic modulus of the FCHC with the same FRP jackets is negligible; (3) the dynamic stress-strain response fluctuates beyond the first linear segment. With an increase in the strain rate, the fluctuated second portion becomes longer and the dynamic peak stress and the ultimate axial strain also increase. At a given number of FRP layers, the ultimate axial strain and dynamic peak stress of the CFRP-confined high-strength concrete are the largest and those of the BFRP-confined high-strength concrete are the weakest (Table 4 and Figure 9) and (4) the stress-strain curve of the FCHC specimens finally decreases sharply due to the sudden loss of the confinement, which is similar to the behavior of CFRP-confined concrete under quasi-static compression.  
Figure 9: _Cont._
## 5 Discussion and Open Research Issues
### Emerging I2O WBAN Issues
#### 5.2.3 I2O WBAN Models Validation  
Owing to technical constraints and legal provisions, practical human I2O radio channel experiments are not possible [48; 49]. Alternative approaches to validate the I2O WBAN communication systems involve advanced computational electromagnetics and biological phantoms [5; 49]. In the future, multi-disciplinary collaboration with clinicians to measure radio channel in animals is one promising technique that can derive statistical I2O body models that could overcome the limitations of performing measurements in humans.
## 3 Parametric Study
### Surface Current Distribution  
This section focuses on the third-generation antenna scheme (i.e., the proposed portable wideband directional antenna scheme) due to its wide impedance bandwidth (Figure 8), high gain (Figure 9), and unidirectional radiation pattern (Figure 10).  
Figure 11a-c illustrates the simulated surface current distribution of the proposed portable wideband directional antenna scheme at 470, 680, and 890 MHz, respectively. At 470 MHz (the first resonant frequency), the currents are concentrated around the feeding point and along the corrugated reflector, as shown in Figure 11a. In Figure 11b, at 680 MHz (the second resonant frequency), the currents are concentrated around the feeding point, the lower part of the balun, and the upper edge of the corrugated reflector. At 890 MHz (the third resonant frequency), the currents are concentrated around the feeding point, the folded bowtie radiator, and the balun, as shown in Figure 11c. Essentially, the third-generation antenna scheme (i.e., the proposed portable wideband directional antenna scheme) covered the target frequency band for the digital terrestrial television reception of 470-890 MHz, as indicated in Figure 8.  
Figure 10: Simulated xz- and yz-plane radiation patterns of the first-, second-, and third-generation antenna schemes.  
Figure 9: Simulated antenna gains of the first-, second-, and third-generation antenna schemes.
## 4 Results and Discussion
### Performance Evaluation
#### 4.4.1 MatchScore  
The MatchScore value gives a quantitative measure of the number of ON pixels in the ground truth line matching the ON pixels in the detected line. A MatchScore table is constructed by computing the MatchScore value of a detected text line with all ground truth text lines of the corresponding document. The MatchScore value between the \(ith\) detected line and \(jth\) ground truth line is calculated using Equation (17):  
\[\text{MatchScore}\ (i,j)=\frac{C\big{(}G_{j}\cap D_{i}\cap I_{b}\big{)}}{C\big{(} \big{(}G_{j}\cup D_{i}\big{)}\cap I_{b}\big{)}} \tag{17}\]  
where \(C(X)\) gives the number of elements in set \(X\), \(I_{b}\) is the preprocessed image or binary image that is used to extract the text lines, \(G_{j}\) is the set of pixels in the \(jth\) ground truth line and \(D_{i}\) is the set of pixels in the \(ith\) detected line. The range of MatchScore values is between 0 and 1, where 1 indicates the best match. The detected text line or the extracted text line is correct if there exists a one-to-one match between the detected text line and ground truth line and the MatchScore value is greater than a threshold value, \(T_{ms}\). The threshold value chosen is 0.9999, compared to the existing works in [31; 32]. It is observed that if the MatchScore is greater than 0.9999, the detected text line and ground truth line are perfectly matched. The total number of ground truth (GT) lines is 7535, and 6482 lines are detected from the 402 document images. The detected lines having a one-to-one match with the ground truth lines, with a MatchScore value greater than 0.9999, amount to 6443. This shows that 85.5% of all the correctly detected text lines are perfectly matched with the corresponding ground truth line in all the documents considered for simulation. The number of ground truth (GT) lines and detected (D) lines and the % of detected lines with a MatchScore higher than \(T_{ms}\) are depicted in Table 2.
# A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
## 5 Performance Evaluation
### Parameter Settings  
The test beds are respectively two wireless sensor networks with 100 nodes and 500 nodes randomly placed on a half sphere of radius \(R=1\) (m), which are shown in Figure 8a,b.  
The sink node is located at the half sphere vertex. Let the interference range \(2r\) of each node is set to \(2\)C/\(5\), where C = \(\pi R/2\). We assume the width of the outmost tier is \(\delta r\) and \(\delta\) = 0.6. The retransmission number \(\mu\) is changed from 0 to 2. All links are assumed to successful transmission with the same probability \(p\). Changing \(p\), the number of time slots required for the sink to receive the information value is counted and the rate of packet lost is measured. Each simulation runs 1000 times and the results are averaged.
# Automated Real-Time Assessment of Stay-Cable Serviceability Using Smart Sensors
## 4 Experiment  
### Experimental Setup  
A laboratory-scale experiment was conducted using a cable to verify the performance of the serviceability assessment system, as presented in Figure 4. The physical and geometric properties of the cable are listed in Table 1. The target cable, an IWRC type, has 6.95 m of length with 43\({}^{\circ}\) inclination, 0.314 kg/m of mass density, 5.46 Ton of the break load, and 10 mm of the diameter. The tension force of the cable was set to be 457.8 N that is measured using the vibration-based tension estimation method proposed by [PERSON] [30]. The leaf node was installed on the center of the cable to measure the vibration and assess the serviceability level (Figure 4). To operate the system, external batteries were used to provide power to the single-board computer with the MEMS sensor. In the experiment, an attempt was made to command the leaf node every 5 s by locating the gateway node 10 m away from the leaf node. Note that the sampling time for serviceability assessment can be adjusted depending on the inspector's requirements or the criticality of maintenance. A fan located under the cable was used to induce vibration in it with ambient motion, as shown in Figure 4.  
Figure 3: Flowchart for automated real-time cable serviceability assessment.
# A Hybrid DSCNN-GRU Based Surrogate Model for Transient Groundwater Flow Prediction
## 3 Results  
The mean squared error (MSE) is employed as the loss function to evaluate the performance of surrogate models. A lower MSE value indicates reduced loss during the training process, signifying a heightened understanding of the relevant parameters and characteristics of the training samples generated using the physics-based model. Furthermore, MSE is used to evaluate the similarity between the surrogate model's predicted outputs and the original outputs from the forward model.  
However, due to the high sensitivity of MSE to outliers and its neglect of structural information, it simply calculates the gap between outputs at each grids. The calculation formula for MSE is given as follows:  
\[\text{MSE}=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}[\text{x}(i,j)-y(i,j)] ^{2};\]  
In the above formula, x represents the predicted water head field data, y represents the input water head field data, and m and n represent the number of predicted and input water head field data, respectively.  
To address this limitation, the coefficient of determination (R\({}^{2}\)) is introduced for a more comprehensive evaluation of prediction accuracy. A higher R\({}^{2}\) value, closer to 1, indicates a stronger correlation between the outputs from the surrogate and the forward model, reflecting superior prediction accuracy. The calculation formula for R\({}^{2}\) is given as follows:  
\[\text{R}^{2}=\frac{\sum_{i=1}^{n}\left(\hat{y}_{i}-\overline{y}\right)^{2}}{ \sum\left(y_{i}-\overline{y}\right)^{2}}\]  
Figure 9: Design flowchart for neural network surrogate model in Case 3.  
In this equation, \(\hat{y}_{i}\) is the predicted value, \(\overline{y}\) is the average of the predicted values, and \(y_{i}\) is the observed value.  
Since CNN models are used for surrogate evaluation experiments in Cases 1, 2, and 3, \(\text{R}^{2}\) is less affected by data size compared to MSE, making it more appropriate for cross-dataset comparisons. To further evaluate the accuracy of the prediction, the structural similarity index (SSIM) is also introduced, which comprehensively evaluates the overall similarity of two images based on brightness, contrast, and structural similarity. The formula for SSIM is as follows:  
\[\text{SSIM}(x,y)=f(l(x,y),c(x,y),s(x,y))=\frac{(2\mu_{x}\mu_{y}+C_{1})(2\sigma _{xy}+C_{2})}{(\mu_{x}^{2}+\mu_{y}^{2}+C_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_ {2})}.\]  
In this formula, \(\mu_{x}\) is the average value of x, \(\mu_{y}\) is the average value of y, \(\sigma_{x}^{2}\) is the variance of \(x\), \(\sigma_{y}^{2}\) is the variance of \(y\), \(\sigma_{xy}\) is the covariance between \(x\) and \(y\), and \(C_{1}\) and \(C_{2}\) are constants used to maintain stability.  
In addition, considering that the generation time for each set of predicted images (six images) by a trained model is less than 5.625 ms, which is negligible, the training time is used to evaluate model efficiency.