esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Level Optimization Method for Elastic Constants Identification of Composite Laminates
## 3 Numerical Examples of Global Minimum Determination
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Function**} & \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Identified Design Variable**} \\ \cline{3-4}  & & \(x\) & \(y\) \\ \hline \multirow{4}{*}{\begin{tabular}{c} Schaffer function \\ (\(-100\leq\text{x}\leq 100\), \(-100\leq\text{y}\leq 100\)) \\ \end{tabular} } & Actual & \(0.00\) & \(0.00\) \\ \cline{2-4}  & MSGO & \(9.98\times 10^{-5}\) & \(1.42\times 10^{-5}\) \\ \cline{2-4}  & PS & \(-8.85\times 10^{-1}\) & \(-6.33\times 10^{-1}\) \\ \cline{2-4}  & GA & \(-1.90\times 10^{-3}\) & \(-2.09\times 10^{-2}\) \\ \cline{2-4}  & MLOM (Present) & \(-7.95\times 10^{-8}\) & \(3.29\times 10^{-7}\) \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Bukin function (I) \\ (\(-15\leq\text{x}\leq-5\), \(-3\leq\text{y}\leq 3\)) \\ \end{tabular} } & Actual & \(-10.00\) & \(1.00\) \\ \cline{2-4}  & \multirow{2}{*}{2 nd} & MSGO & \(-9.97\) & \(0.99\) \\ \cline{2-4}  & PS & \(-10.00\) & \(1.00\) \\ \cline{2-4}  & GA & \(-8.13\) & \(0.66\) \\ \cline{2-4}  & MLOM (Present) & \(-10.0\) & \(1.00\) \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Bukin function (II) \\ (\(-100\leq\text{x}\leq 100\), \(-100\leq\text{y}\leq 100\)) \\ \end{tabular} } & Actual & \(-10.00\) & \(1.00\) \\ \cline{2-4}  & MSGO & \(-9.27\) & \(0.86\) \\ \cline{1-1} \cline{2-4}  & PS & \(-9.76\) & \(0.95\) \\ \cline{1-1} \cline{2-4}  & GA & \(0.86\) & \(0.01\) \\ \cline{1-1} \cline{2-4}  & MLOM (Present) & \(-10.0\) & \(1.00\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Global minimum of function predicted using different optimization methods.
## 4 Experimental Results and Analysis
### Security Analysis  
A good encryption algorithm should be capable of resisting all attacks. In this section, we perform a security analysis on the proposed encryption algorithm.  
1. Key space and sensitivity analysis  
A good encryption approach should be sensitive to the secret keys. In this study, the iteration times, (i.e., \(m\) and \(n\)) can be used as keys as well as the parameters \(r_{0}\) and \(x_{0}\) of a logistic map. Moreover, the precision of parameters of the logistic map can be used as keys as it can influence the performance of chaotic sequences. The key space is proportional to the parameter precision: \(m\) (\(\geq\)1) and \(n\) (\(\geq\)1). If the precision is \(10^{-20}\), the key space size can be at least \(m\)\(\times\)\(10^{40}\). Hence, the key space is big enough to resist brute-force attacks. Moreover, using keys (\(r_{0}\), \(x_{0}\)) only to recover the original image is very difficult as the range of pixel values is changed after using the proposed encryption algorithm. Figure 13 shows two decrypted DEM A with wrong keys.  
2. Classical attacks  
Attackers have many methods of attack. Four classical types of attacks [7] are listed as follows:  
Figure 12: Scatter plots of \(m\) rounds compared to the \(S_{A}\) value of the encrypted DEM B and that of \(C_{R}\) compared to the \(S_{A}\) values.  
Figure 13: Three-dimensional images of decrypted DEM A: (**a**) with keys \(r_{0}\) = 3.7004182, \(x_{0}\) = 0.28, \(r_{0}\) = 3.8994119, and \(y_{0}\) = 0.86; (**b**) with keys \(r_{0}\) = 3.8777651, \(x_{0}\) = 0.21, \(r_{0}\) = 3.7276262, and \(y_{0}\) = 0.27.  
Selected plaintext: The opponent chooses a plaintext string and constructs the ciphertext string when temporary access to the encryption machine is granted.
* Selected cipher text: The opponent obtains a ciphertext string and constructs the corresponding plaintext string when temporary access to the encryption machine is granted.
* Known plaintext: The opponent owes a plaintext string and its corresponding ciphertext.
* Ciphertext only: The opponent owes a ciphertext string The selected plaintext attack is considered the most powerful one. The proposed encryption approach is highly sensitive to the initial parameters for a logistic map. Moreover, at the fusion phase, the encryption data are related to not only the one in the confusion phase but also the one predicted by the three-point prediction technique used at the diffusion stage. Moreover, different encrypted numerical raster data are derived from various former ones because \(m\) and \(n\) are variable. This means that the encrypted data are able to resist the chosen plaintext attack, indicating that it can resist the remaining attacks.
# Secure and Efficient Authentication Scheme in IoT Environments
## 5 Evaluation
### Comparison by Functional Requirement
It is very difficult to extract the password from the password generator because of the difficulty of ECDLP. Even identity \(I_{i}=h(ID_{i}\ ||Psw_{i}\ )\) contains a hash of the concatenation of device identity and the password generator, which is stored in the server. It is very difficult to extract a \(Psw_{i}\) from \(I_{i}\) because of the property of the collision-resistant one-way hash function (\(h(l)\)). * Session-key agreement: The proposed scheme justifies the session-key agreement in the session-key-computation subsection, ensuring secure sessions with highly confidential data exchange between the embedded devices (\(ED_{i}\)) and the server (\(TS\)). In addition, a session key \(\big{(}\ S_{K}=x_{il}.X_{j}\). \(Psw_{i}.P_{bs}=x_{jl}.X_{i}.S.P_{ke}\ \big{)}\) is protected by the concept of ECDLP and CDHP. The result from the Proverif tool in Section 4.3 also verifies its session key security. * Secure password update: The proposed scheme supports password updates for embedded devices. The password update helps ensure the security of the proposed protocol (i.e., it prevents DoS attacks on the scheme). Legitimate embedded devices can change their passwords any time after registration. Finally, as depicted in Table 5, we compared the proposed scheme with the existing schemes on functional requirements. In the comparison, we observed that the scheme proposed by [PERSON] et al., [PERSON] et al., [PERSON] et al., [PERSON] et al., and [PERSON] et al. support all crucial functional requirements. However, the scheme proposed by [PERSON] et al., [PERSON] et al., and [PERSON] et al. failed to support friendly password selection and its security and to secure password updates. The scheme proposed by [PERSON] et al. failed to support a secure password update policy. However, the proposed scheme removed those limitations and reinforced security requirements.
## 3 Hardware Design
### Data Acquisition Module Design  
#### 3.2.1. Barotmetric Acquisition Module Design  
The chip used in the Barotmetric Pressure Acquisition Module is BMP280, which can simultaneously acquire barometric pressure data and temperature data, and communicate data with STM32 through the IIC communication protocol. Due to its small size and low power consumption, it is suitable for application in watches, cell phones, and embedded devices, with a relative accuracy of \(\pm\)0.12 hPa and an absolute accuracy of up to \(\pm\)1 hPa. When the barometric sensor collects barometric data, it needs to be converted to altitude by the relevant code. The peripheral circuit design of the BMP280 is shown in Figure 3. The following notations are used: SDI--data signal; SCK--clock signal; C1 and C2 are filter capacitors; R1, R2, and R3 are pull-up resistors; R4 is a pull-down resistor.  
Due to the barometric pressure being greatly affected by the environment, the size of the barometric pressure value is related to the height, temperature, and other conditions; with a change in temperature, the transformation of the barometric pressure will have a large gap. The barometric pressure value measured under the same height at different times of the day is converted to obtain the height difference, which can be up to hundreds of meters, and the difference in the barometric pressure values measured in different seasons will be even bigger, so it is necessary to introduce temperature data to correct the barometric pressure values. In this thesis, the hypsometric formula is used to correct the barometric pressure values by temperature, and compared with the barometric pressure-height formula, the measured altitude values are more accurate and less affected by temperature changes. The barometric pressure to altitude conversion formula is given below:  
\[h=\frac{[(P_{0}/P)^{(1/5.257)}-1]\times(T+273.15)}{0.0065} \tag{1}\]  
where \(P_{0}\) is the standard atmospheric pressure, \(P_{0}\) = 101.325 kPa, and \(T\) is the current air temperature.  
A comparison of the calculated values of altitude without introducing temperature compensation and with temperature compensation added for barometric pressure sensors running for a long period of time is shown in Figure 4. When the barometric pressure sensor is operated for one thousand seconds without introducing temperature compensation, the barometric pressure value will change dramatically with the change in temperature at a constant height, which, in turn, will cause errors in height readings, as shown in Table 1. In the actual use of the elevator, the standard deviation and variance of the altitude sensor readings without temperature compensation are much larger than when temperature compensation is used, so the use of temperature compensation can effectively reduce the reading error when the elevator is running for a long time.  
Figure 3: BMP280 peripheral circuit design.
## 4 Numerical Results
### One-Way FSI Analysis
#### 4.3.1 Sloshing Analysis  
There are two types of anti-wave plates, 50%H and 70%H, considered in Section 3.1. The detailed settings of the flow analysis were the same as those mentioned in Section 3.4.2.  
Figure 14 illustrates the change in the free surface of the tank under seismic loads with and without the anti-wave plate. A change in the free surface in the presence of the anti-wave plate began at 15 s and became significant by 35 s, when the seismic loading ended.  
Figure 15 compares the forces acting on the tank wall under seismic loads with and without the anti-wave plate. It was confirmed that the force acting on the tank wall started to decrease at approximately 20 s. The effectiveness of the anti-wave plate was evident after 30 s, when the seismic loading ended. However, the difference between CASE III-2 and CASE III-3 was not significant. This indicates that the anti-wave plate mitigated both  
Figure 14: Comparison of free surface motion amongst CASE III-1, -2, and -3. (**a**) CASE III-1, no plate; (**b**) CASE III-2, 50%H; (**c**) CASE III-3, 70%H.  
the free surface motion and the forces generated by the sloshing, but it is not recommended to design a greater height of the plate.
## 3 Results and Discussion  
The chemical composition of the olive-tree pruning raw biomass (% of dry matter) was cellulose 37.9 \(\pm\) 2.6, hemicellulose 18.3 \(\pm\) 1.9, lignin 23.1 \(\pm\) 2.2, acetyl group 2.3 \(\pm\) 0.3, extractives 16.3 \(\pm\) 1.6, and ash 2.7 \(\pm\) 0.6. According to previous works [26; 27; 43; 60], OTP biomass has some general characteristics comparable to other agro-crops and woody species that are widespread industrially. In the batch of original biomass used in this work, hemicellulose represented 18.3% on a dry basis, which is a lower content than the values shown for other agricultural biomasses such as oak (22-36%), corn (26-31%), wheat straw (45-50%), poplar (23-25%), or pine (35%) [61].  
The major component is cellulose, which accounts for almost 38%, a value that is in the range of other biomasses widely used to obtain biofuels by means of hydrolytic and fermentative processes. Therefore, taking into account the potential recovery of this fraction, it can be deduced that OTP biomass is postulated as a suitable source for cellulose production and, from this, the subsequent generation of fermentable glucose for the production of bioethanol in an easy way. Total lignin (23.1% of dry weight) showed similar values compared to corn stover (18-22%) or hardwood stems (18-25%) [62]. In this sense, the total or partial removal of this fraction would facilitate the subsequent treatments of the material obtained, since on the one hand it would improve the purity of the resulting biomass and on the other hand it would reduce the problems associated with the appearance of lignin degradation compounds that would hinder the fermentation processes.  
Regarding the composition of the discarded extraction fraction, it is mainly a lignin-rich stream, which may have great applications if its purification and precipitation as nanolignin could be achieved. This line of research, with enormous potential, should be developed in the future.  
Finally, it should be pointed out that the composition of this type of agricultural biomass is greatly influenced by determining factors based on its geographical origin, the type of cultivation, or the collection process carried out for subsequent use in the laboratory.
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 4 Hansel Services
### Snapshot Service (SNAP)  
The framework implemented in the HANSEL _testbed_ to support snapshot navigation applications envisages a near-real-time gathering and processing of raw GNSS measurements among a sensor network. These measurements are collected and sent asynchronously to a central computational facility built in Amazon Web Services (AWS), where the raw measurements are processed in order to obtain the desired information, typically the sensor position.  
The SNAP service implemented in the HANSEL _testbed_ provides the means to compute the sensor position by using three different technologies, namely GNSS, Cellular, and Hybrid (which combines GNSS and Cellular measurements) and to detect and locate present GNSS interferences within the sensor network. The central computational facility, aside from providing sensor/interference locations is also capable of supplying other valuable information such as the computed observables or the interference source relative emitting power.  
The functional operation of the SNAP service relies on a master-slave approach, where the server collects all the requests generated by the users and stores them in a queue-like system. The built SNAP sensors contain a series of routines (SNAPp) that allow them to autonomously collect the requests from the server and perform the required tasks. Being all the computational consuming processes performed in an independent cloud facility, added to the fact that the sensor-platform communication has been optimised to minimise bandwidth occupancy, the sensors have been proved to be low-power consuming devices with a long-lasting lifetime. A high-level service description can be seen in Figure 4.  
The preliminary tests of the service have demonstrated that the platform is able to support hundreds of simultaneous connections, concurrently storing and processing requests. This was validated by means of a load test using an automated script that emulated virtual users logging into the system and launching requests/executions to the SNAP service. Users with a rate of one request/execution per minute were considered, to emulate a real-life sensor periodically requesting its position to the system. The CPU and memory load of the system was then monitored to make sure that no bottle necks were experienced as the number of users grew, and that the overall load did not degrade the performance of the rest of services running in the system. All the generated information  
Figure 4: High-level Snapshot Service (SNAP) service description, showing the interactions between the inner service components with the front-end.  
is stored in a centralised database and easily accessible via a user-friendly interface, which allows the user to have complete knowledge about the network active devices and keep track of all the generated information.
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 3 Methodology
### CNN-Based Tokenizer for Transformer Input (CCTTokenizer)  
Rather than employing standard patch extraction, the transformer branch uses a CNN-based tokenizer. This approach yields richer and more informative tokens by learning spatial features before sending sequences into the transformer, thus improving the quality of tokens for subsequent attention mechanisms. The CCTTokenizer layer acts as the CNN tokenizer, converting the image into a sequence of patches through a succession of convolutional and pooling layers. Initially, the convolutional layers, which use 3 \(\times\) 3 kernels, capture low-level features, followed by max-pooling layers that decrease spatial dimensions. Finally, the output from this segment is reshaped into a sequence of tokens for processing by the Transformer. The architecture of this module is presented in Figure 6.  
The CCT tokenizer performs convolution followed by max-pooling:  
\[X\_prime=conv2d(X,W,f)\text{ and }X\_prime=maxpooling2d(X\_prime,f) \tag{4}\]  
where \(f\) represents the filter size.  
Spatial information is integrated into the tokenized patches by applying positional embeddings to the CNN tokenizer's output. These embeddings are trained and then merged with the tokenized patches. This allows the Transformer to understand the relative positions of patches in the image.  
The composite raster image (pre- and post-flood) is divided into 2D image patches. These patches are passed through two Conv2D and Pooling layers to learn the spatial and semantic features of the image data. After feature extraction, the resulting feature maps are flattened into 1D feature vectors. To preserve the spatial relationships of these features, positional embeddings are applied. This allows the integration of the learned feature vectors from the Conv2D/Pooling layers with positional vectors, resulting in enriched spatial feature vectors that maintain the spatial information across the image.
## 2 Materials and Methods
### Data Analysis  
All statistical analyses were conducted using JASP (version 0.19.1), and figures were created in GraphPad Prism (version 10.3.1). Data are presented as mean \(\pm\) standard deviation (SD). Normality was tested with the Shapiro-Wilk test. A repeated-measures ANOVA was used to evaluate within-subject differences across conditions (0%, 40%, 80% LOP) and across time (PRE, POST, 5, 10, and 15 min). RFD data were analyzed by time window (0-50 ms, 51-150 ms, and 151-250 ms). Post hoc comparisons were adjusted using [PERSON]'s correction. Statistical significance was set at \(p\leq 0.05\). Effect sizes were classified as trivial (d \(<\) 0.2), small (0.2 \(\leq\) d \(<\) 0.5), moderate (0.5 \(\leq\) d \(<\) 0.8), or large (d \(\geq\) 0.8).
# V2X Communications in Highway Environments: Scheduling Challenges and Solutions for 6G Networks
## 2 Scheduling Algorithms and V2X in Highway Environments
The results demonstrate that, at a similar bandwidth of 10 MHz, C-V2X outperforms DSRC in both inter-packet gap and reliability at distances greater than 100 m. Both technologies, however, show good performance at shorter distances. Furthermore, when using [17]-based one-shot transmissions for critical events, C-V2X's performance at shorter distances is further enhanced. Additionally, with a bandwidth of 20 MHz, C-V2X further improves its performance. The authors in [19] conduct a comparative performance analysis of 5G and 4G networks across highway operational scenarios. This study diverges from earlier research, which primarily focused on urban or controlled settings, and instead explores the less examined area of high-speed highway travel conditions. Employing advanced measurement techniques through portable smartphone devices, this research assesses critical performance parameters, including Reference Signal Received Power (RSRP), Signal to Interference plus Noise Ratio (SINR), and Reference Signal Received Quality (RSRQ), as well as latency and data throughput. Their findings contribute to the existing body of knowledge by revealing the distinct performance patterns and challenges inherent to 4G and 5G networks in these scenarios while suggesting ways for optimization and strategic enhancement by network operators. The authors in [20] examine the performance of C-V2X Mode 4 (sidelink) in high-density traffic scenarios on highways. They use the OpenCV2X framework where vehicles communicate directly in peer-to-peer mode using a distributed resource allocation algorithm, compliant with existing service and application layers defined by the automotive and standards communities. However, it only enables the simulation of LTE vehicular networks and traffic scenarios that require C-V2X Mode 4 communications. The authors in [21] investigate the potential of V2V/V2I connectivity using C-V2X sidelink (Mode 4) in a specific cross-border highway context, focusing on a Cooperative Lane Merging UC that primarily relies on cooperative position awareness. Connectivity and application KPIs are evaluated through simulations, under various infrastructure deployment assumptions. The results show that service availability is high from a pure connectivity perspective, benefiting from multiple RSUs and short-range communication links. However, the cross-border nature of the selected environment has only a limited effect on this initial evaluation of V2V/V2I performance. Aiming to address the gap and analyze V2N scenarios in highway environments, the SOVANET+ scheduler introduced in the following chapter is assessed in a demanding congested highway scenario with high-speed moving vehicles that require delay-critical services.
## 3 Results and Discussion
### System Suitability and Method Validation of the Developed HPLC Analytical Method
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Analyte** & **Spiked Conc. (\(\mu\)g/mL)** & **Measured Conc. (\(\mu\)g/mL)** & **Recovery (\%) a** & **SD** & **RSD (\%) b** \\ \hline  & 1.00 & 1.00 & 100.20 & 2.80 & 2.79 \\
9 & 2.00 & 2.00 & 99.77 & 0.81 & 0.81 \\  & 4.00 & 4.05 & 101.34 & 0.96 & 0.95 \\  & 1.00 & 0.98 & 98.30 & 1.58 & 1.61 \\
**10** & 2.00 & 2.02 & 101.24 & 2.36 & 2.34 \\  & 4.00 & 3.99 & 99.78 & 0.80 & 0.80 \\  & 1.00 & 0.99 & 99.17 & 0.73 & 0.74 \\
**11** & 2.00 & 1.93 & 96.42 & 0.52 & 0.54 \\  & 4.00 & 4.03 & 100.75 & 1.24 & 1.23 \\  & 1.00 & 1.00 & 99.75 & 0.78 & 0.78 \\
**12** & 2.00 & 2.02 & 100.75 & 1.00 & 0.99 \\  & 4.00 & 4.05 & 101.22 & 0.88 & 0.87 \\  & 1.00 & 1.03 & 102.81 & 0.75 & 0.73 \\
**13** & 2.00 & 1.96 & 97.79 & 0.50 & 0.51 \\  & 4.00 & 3.90 & 97.55 & 0.84 & 0.86 \\  & 2.00 & 2.02 & 100.97 & 1.17 & 1.16 \\
**14** & 5.00 & 5.05 & 100.95 & 1.46 & 1.44 \\  & 10.00 & 10.15 & 101.53 & 0.82 & 0.81 \\  & 1.00 & 0.99 & 99.32 & 2.67 & 2.69 \\
**15** & 2.00 & 1.99 & 99.27 & 1.24 & 1.25 \\  & 4.00 & 4.01 & 100.20 & 1.05 & 1.05 \\  & 1.00 & 0.96 & 95.92 & 0.79 & 0.83 \\
**16** & 2.00 & 1.92 & 96.04 & 0.96 & 0.99 \\  & 4.00 & 3.86 & 96.57 & 1.31 & 1.35 \\  & 1.00 & 0.96 & 96.49 & 0.81 & 0.84 \\
**17** & 2.00 & 1.92 & 96.03 & 0.54 & 0.56 \\  & 4.00 & 3.98 & 99.43 & 0.36 & 0.36 \\  & 1.00 & 0.96 & 96.28 & 0.34 & 0.36 \\
**18** & 2.00 & 1.91 & 95.68 & 0.71 & 0.75 \\  & 4.00 & 3.84 & 95.90 & 0.24 & 0.25 \\  & 1.00 & 1.02 & 101.96 & 0.10 & 0.10 \\
**19** & 2.00 & 1.92 & 96.01 & 0.45 & 0.47 \\  & 4.00 & 3.90 & 97.55 & 0.46 & 0.47 \\  & 1.00 & 1.02 & 101.91 & 0.78 & 0.77 \\
**20** & 2.00 & 2.00 & 100.05 & 0.66 & 0.66 \\  & 4.00 & 4.01 & 100.35 & 1.47 & 1.47 \\  & 1.00 & 0.96 & 95.68 & 0.49 & 0.51 \\
**21** & 2.00 & 1.97 & 98.62 & 0.33 & 0.34 \\  & 4.00 & 3.95 & 98.70 & 0.25 & 0.26 \\  & 1.00 & 0.96 & 95.77 & 1.01 & 1.05 \\
**22** & 2.00 & 1.91 & 95.47 & 0.54 & 0.56 \\  & 4.00 & 3.87 & 96.83 & 0.41 & 0.42 \\  & 1.00 & 0.98 & 98.10 & 1.93 & 1.97 \\
**23** & 2.00 & 1.97 & 98.32 & 0.32 & 0.32 \\  & 4.00 & 4.09 & 102.16 & 0.22 & 0.21 \\  & 1.00 & 0.98 & 97.75 & 0.95 & 0.97 \\
**24** & 2.00 & 1.95 & 97.69 & 0.41 & 0.42 \\  & 4.00 & 4.02 & 100.52 & 0.29 & 0.29 \\ \hline \end{tabular} Recovery (\%) a = measured concentration/spiked concentration \(\times\) 100%; RSD (%) b = standard deviation (SD)/mean \(\times\) 100%.  
\end{table} 
Table 2: _Cont._  
\begin{table} \begin{tabular}{c} **Accuracy (\%)** \\ **(\(\mu\)g/mL)** \\ \end{tabular}