esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 4 Future Trend of ML in IVLC
### Intelligent Network Layer
#### 4.2.3 Security Network  
The characteristics of optical networks and wireless communications are combined in VLC systems. As a result, VLC suffers more complex cyber security challenges. When VLC becomes the infrastructure of future communication services, it will be subjected to additional attacks [103].  
The first is the jamming attack in visible light communication. Since VLC is an LOS channel, it is highly susceptible to interference by external interference sources. Brief jamming attacks can cause enormous volumes of data to be incorrect or leaked due to the high transmission rates. ML can identify the presence and level of interference by learninginterference signals targeting the physical layer in optical communications, as an example of deep Q-learning (DQN) [103].  
The second is the cyber-physical attack in visible light communication. VLC is vulnerable to external intervention because it runs in an open environment. Injecting damaging signals into unprotected visible communication equipment can have a variety of negative consequences for communication [104]. By learning the characteristics of different devices or watermarks carried by the transmission, ML can be used to identify illicit transmission devices in VLC systems, as an example of TF-FSN [105].  
The third is eavesdropping in visible light communication. VLC channels are vulnerable to unauthorized terminals because of their broadcast nature. Eavesdropping in public places compromises the privacy of legitimate users. Eavesdroppers' capacity to infer information over the channel is reduced by a smart beam anti-eavesdropping system based on deep reinforcement learning (DRL) [106]. The suggested intelligent beamforming method based on DRL may also make full use of complicated and high-dimensional structure information, improving network security for users.  
VLC has some fixed security properties; however, entities communicating under the same space are vulnerable to discovery and information theft. Future research will focus on how to successfully use machine learning to counter attacks in the visible light field.
## 3 Results and Discussion
### FT-IR and Deconvolution Study
Indeed, a shift to lower wavenumbers of the Si  
Figure 5: Deconvoluted FT-IR spectrum of (**A**) S; (**B**) SP50; (**C**) SQ20; and (**D**) SP50Q20 in the 1650–750 cm\({}^{-1}\) range. O-Si vibration was also detected (see the absorption band at 1157 cm\({}^{-1}\)). The deconvoluted spectrum showed that other new absorption bands have been found at 1474 cm\({}^{-1}\) and 1399 cm\({}^{-1}\), both related to C-H vibrations. Finally, Figure 5D reports the influence of both organic phases on the silica matrix. Both Q and P affect the main Si-O-Si skeletal absorption band, resulting in a shift at higher wavenumbers (from 1179 and 1058 cm\({}^{-1}\) to 1196 and 1077 cm\({}^{-1}\)). The signals observed, along with the intensified C-O absorption band (which is more prominent compared to the deconvoluted spectrum of the SP50 hybrid in Figure 5B) at 1033 cm\({}^{-1}\) and the enhanced intensity of the C-H absorption (represented by a broad peak detected at 1366 cm\({}^{-1}\)) originating from the organic phases, provide further support for the hypothesis of interactions among all the components within the hybrid materials.
## 2 Methods  
### Traditional RRT Algorithm  
The rapidly exploring random tree algorithm is a global path-planning algorithm based on random sampling, which needs to master all environmental information and belongs to static planning. The RRT algorithm starts to expand the random tree incrementally with the starting point position as the root node while avoiding obstacles. When the nodes of the random tree expand to the area near the end point, the exploration of the state space is ended [16].  
The method of extending nodes in the RRT algorithm is shown in Figure 1. \(x_{start}\) represents the starting point, which is added to the random tree when the algorithm begins. Next, random sampling, \(x_{rand}\), is performed in the map to obtain a random sampling point, and then the point \(x_{nar}\) closest to the random sampling point is searched in the random tree. Next, the set step size is extended in the \(x_{rand}\) direction to generate a new node \(x_{new}\); then, it is detected whether there is an obstacle collision between \(x_{nar}\) and \(x_{new}\). If there is no collision, the new node is added to the random tree, and the process is repeated until the target point is found.  
The RRT algorithm is a probabilistic complete sampling algorithm, which is suitable for path planning in complex unstructured scenes, which is also the biggest feature of the RRT algorithm. However, the RRT algorithm also has some limitations, mainly:  
* Strong randomness: The path generated by the RRT algorithm is based on random sampling, and the randomness is strong. The algorithm randomly scatters points in the environment map. If the scatter point is close to the target point, it can search the end point faster. If the scatter point is far away from the target point, the search efficiency is reduced.
* Non-optimal solution: The path of finding a feasible solution by using the RRT algorithm many times may be different, and it does not guarantee that the optimal solution will be found. The RRT algorithm is affected by the different layout of obstacles between the starting point and the target point, resulting in a large difference in the quality of the planned path.
* Poor smoothness: The random points selected by the RRT algorithm each time may lead to the discontinuity of the path, making the path turn back or turn sharply in space. As a result, the path generated by the RRT algorithm is not smooth enough visually and does not conform to the actual motion law [17].
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
## 1 Introduction  
Despite significant achievements by numerous researchers in the field, the establishment of Bridge Health Monitoring Systems (BHMS) based on Global Navigation Satellite Systems (GNSS) has been limited due to the reliability issues of GNSS positioning solutions. The information provided by GNSS is based on both the received data and the processing of the data and both are critical for obtaining proper information. If any apparent deflection from GNSS position solutions is detected, it is necessary to verify that it is, in fact, a real deflection behavior resulting from external loading, or if it is an outlier from the data itself. This is an important part from the monitoring system operation perspective. For the former case, potential structural defects can be incurred that pose a great threat to bridge users and to the safety of the bridge itself. For the latter case, false information transfer due to outliers cannot be avoided, thereby having an adverse effect on the reliability of the total monitoring system. In particular, the latter case is a potential risk factor for a GNSS-based BHMS. In line with such a trend, the importance of the GNSS-based BHMS has been emphasized more than ever. Hence, it is essential to consider the accuracy and reliability of GNSS positioning solutions to construct GNSS-based monitoring systems. This investigation aims to address these issues byimplementing a quality control algorithm to detect and identify the potential outliers within the data, thereby enabling credible data to be provided to both bridge users and managers.
# A Procedure to Prepare Sand-Clay Mixture Samples for Soil-Structure Interface Direct Shear Tests
## 5 Assessment of Samples' Repeatability  
After checking the water content and density in sand-clay mixture specimens of 75% K clay content, the average water content, void ratio, and density of the whole samples at different K clay contents (from 0% to 100%) were measured to check the repeatability of the sample preparation. The oedometer and interface direct shear tests were selected to further verify the ability of the S1 protocol for preparing identical sand-clay mixture samples. The K clay contents of the specimens for oedometer and interface direct shear tests were 50% and 75%, respectively.
## 2 Materials and Methods
### Structure of the Nondestructive Detection System
#### 2.3.4 Signal Processing Module  
The signal processing module comprises four parts--five switches, a lowpass filter, a signal amplifier and a microcontroller. The switches (Vishay IRF530, Malvern, PA, USA) were connected to a Direct current (DC) power supply (Agilent E3464A, Santa Clara, CA, USA) and the aforementioned light source and disc-shaped bandpass filter holder. A microcontroller (ATMEL MEGA328P, Microchip Corporation, San Jose, CA, USA) was used to control the state of each switch (open or close) and the bandpass filter holder. Once the NIR photodetector receives the light reflected by the fruit's flesh, this light is converted into an electrical voltage signal. A low-pass filter is used to eliminate high-frequency noise while a signal amplifier (LM324, Texas Instruments, Dallas, TX, USA) amplifies the signal. The microcontroller then samples the signal, performs an analog-to-digital conversion and packages the data into a readable format before sending the data to an external computer via a communication interface. LabVIEW is then used to perform the necessary numerical computations to calculate \(r_{\text{P}}^{2}\) and RMSEP and store the data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Wavelength (nm)** & **Part No.** & **Full Width at Half Maximum** & **Manufacturer** \\ \hline
1 & 1050 & FB1050-10 & \(\pm 10\) nm & Thorlabs \\
2 & 1100 & FB1100-10 & \(\pm 10\) nm & Thorlabs \\
3 & 1150 & FB1150-10 & \(\pm 10\) nm & Thorlabs \\
4 & 1200 & FB1200-10 & \(\pm 10\) nm & Thorlabs \\
5 & 1250 & FB1250-10 & \(\pm 10\) nm & Thorlabs \\
6 & 1300 & FB1300-12 & \(\pm 12\) nm & Thorlabs \\
7 & 1350 & FB1350-12 & \(\pm 12\) nm & Thorlabs \\
8 & 1400 & FB1400-12 & \(\pm 12\) nm & Thorlabs \\
9 & 1450 & FB1450-12 & \(\pm 12\) nm & Thorlabs \\
10 & 1500 & FB1500-12 & \(\pm 12\) nm & Thorlabs \\
11 & 1550 & FB1550-12 & \(\pm 12\) nm & Thorlabs \\
12 & 1600 & FB1600-12 & \(\pm 12\) nm & Thorlabs \\
13 & 1650 & FB1650-12 & \(\pm 12\) nm & Thorlabs \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the near infrared (NIR) bandpass filters.
Compared with the existing path planning algorithms, the main contributions of this article are as follows:  
1. In the I-APF (I-APF) method, a heuristic method based on the number of adjacent obstacles to break away from the local minimum is proposed so that the algorithm can quickly eliminate local minima and break away from obstacles. 2. In the I-RRT (I-RRT) algorithm, a triangular nearest neighbor node selection method is proposed, which improves the convergence of the algorithm. 3. Based on the I-APF algorithm and the I-RRT algorithm, a hybrid algorithm is proposed that combines these two improved algorithms to search for the optimal path. First, the distance between the nearest neighbor node and the obstacle is judged. According to the different distances, the I-APF method and the I-RRT algorithm are used to expand the path, which improves the search speed and obstacle avoidance ability of the algorithm. The rest of this article is organized as follows. Section 3 introduces the classic APF method and the classic RRT algorithm. Then, Section 4 introduces the improved APF and RRT hybrid algorithm. In Section 5, to smooth the path, the path is fitted with a Bezier curve. In Section 6, the planned path is verified by an experimental simulation using Python language development tools and robot operating system (ROS) tools.
# Blockchain and IoT Convergence--A Systematic Survey on Technologies, Protocols and Security
## 5 Distributed Ledgers
### Cryptography and Hashing
#### 5.3.2 Asymmetric Encryption Algorithm  
The real novelty of the last century is the invention of a cryptographic technique, called Asymmetric cryptography, that uses different keys to encrypt and decrypt a message, facilitating the task of key distribution. In Asymmetric cryptography the secret key is divided into two parts, a public and a private key. The public part ca be shared, while the private key must be kept secret. The public key can be made public for the sender to encrypt the information to be sent, and the private key can be used for the receiver to decrypt the received encrypted content. Blockchain technology uses cryptography as a means of ensuring transactions are done safely, while securing all information and storages of value. Therefore, anyone using Blockchain can have complete confidence that once something is recorded on a Blockchain, it is done so legitimately and in a manner that preserves security. The most commonly used and secure asymmetric encryption algorithms are Rivest-Shamir-Adleman (RSA [67]) and Elliptic-curve cryptography (ECC [68]).
# Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities
## 5 Case Studies
### Outcomes  
The AI-driven environmental monitoring system at the Port of Rotterdam delivered significant improvements:  
* Enhanced real-time monitoring and management of air and water quality;
* Rapid identification and response to pollution events, minimizing environmental damage;
* Improved compliance with environmental regulations and standards;
* Increased transparency and communication with the public regarding environmental performance.  
The case studies of Maersk Line and the Port of Rotterdam demonstrate the transformative potential of AI technologies in the maritime industry. By optimizing fuel consumption, predictive maintenance, and environmental monitoring, AI contributes to more efficient, sustainable, and responsible maritime operations. These examples highlight the critical role AI can play in advancing maritime sustainability and addressing the industry's environmental challenges.
# Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides
## 3 Methodology
### Confidence Interval  
In the present study, because of the noise in the DEM data reducing the accuracy of data analysis significantly, not all DEM data are reliable. Therefore, the noise needs to be eliminated to determine the reliability of the DEM data. The confidence level of the DEM data is assessed to improve the accuracy of data analysis.  
The confidence interval is a commonly-used interval estimation method used for sample statistics, which shows the confidence probability of the measured parameter value [42,44]. The elevation difference is considered as the sample data in the present study. The average value of elevation difference is \(\mu\), and the standard deviation is \(\sigma\). The confidence probability is obtained as follows:  
\[\Pr(c_{1}\leq\mu<c_{2})=1-\alpha \tag{1}\]  
where \(\alpha\) is the significance level and the interval (\(c_{1}\), \(c_{2}\)) is the confidence interval. Therefore, the confidence interval of the average value is \((\mu-\sigma Z_{\alpha/2},\mu+\sigma Z_{\alpha/2})\), where \(=Z_{\alpha/2}\) is the corresponding standard score. In general, the confidence probability in the literature [42,45,46] is 90% or 95%, and \(Z_{\alpha/2}\) is 1.645 or 1.96, respectively.  
Figure 6: Methodology of research applied in this study. LRM—logistic regression model.
## 5 Discussion  
Some papers mention the possible use of spectral clustering as a coherent receiver when reviewing the different non-supervised learning techniques but without addressing its behavior in the presence of nonlinear phase noise [21]. In this paper, we expand the work presented in [15] to evaluate the spectral clustering algorithm in a channel dominated by nonlinear phase noise and AWGN. The expansion of the work focuses on covering larger modulations, so we add 32-QAM and 64-QAM to observe the effect of nonlinear  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Clustering Algorithm** & **Modulation** & **Complexity** & **Execution TIME [s]** & **BER** \\ \hline \multirow{3}{*}{k-means} & 16 & \multirow{3}{*}{\(\mathcal{O}(knt)\)} & 0.0034 \(\pm\) 0.0004 & 4.60000 \(\times\)\(10^{-4}\) \\ \cline{3-5}  & 32 & & 0.0066 \(\pm\) 0.0005 & 3.508000 \(\times\)\(10^{-4}\) \\ \cline{3-5}  & 64 & & 0.0143 \(\pm\) 0.0052 & 2.188889 \(\times\)\(10^{-4}\) \\ \hline \multirow{3}{*}{OSC} & 16 & \multirow{3}{*}{\(\mathcal{O}(n^{3})\)} & 0.6702 \(\pm\) 0.0323 & 1.250000 \(\times\)\(10^{-6}\) \\ \cline{3-5}  & 32 & & 0.9581 \(\pm\) 0.0119 & 8.000000 \(\times\)\(10^{-7}\) \\ \cline{3-5}  & 64 & & 1.4782 \(\pm\) 0.0714 & 2.500000 \(\times\)\(10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance metrics of the assessed algorithms. The execution time is presented as the average and standard deviation for ten runs.  
Figure 8: SNR vs. BER performance of spectral clustering and k-means algorithms for 64-QAM.  
noise as the format increases. The nonlinear noise characteristics used in this paper were extracted from work such as [22], where the development of optical oscillators with phase noise of \(-100\) dBc/Hz at a frequency offset of 1 MHz is mentioned, and other experimental references such as \"phase noise of \(-90\) dBc/Hz at 1-10 MHz frequency offset will be low enough for transmission with 16-ary QAM\". Also, Ref. [23] mentions characteristics such as \"the minimum requirement for the carrier phase noise is \(-96\) dBc/Hz at 1 MHz offset for 64 QAM\". The mentioned characteristics were used as a guide to define the ranges used in the simulation. Our goal for future work is to implement the algorithm in real time on an FPGA-based architecture.
# Optimization of Multimodal Transport Paths Considering a Low-Carbon Economy Under Uncertain Demand
## 5 Calculation Analysis and Discussion
### Algorithm Comparison  
To increase the diversity problem of solutions, the simulated annealing mechanism is introduced into the improved algorithm. The pheromone volatilization factor is set as a dynamic parameter, which enhances the algorithm's capacity for a worldwide search, due to the larger value in the beginning phases, and quickens the algorithm's convergence, due to the smaller value in the later stage. The results show that the limitations of the traditional ACO algorithm can be solved by these two improvement steps to find more feasible solutions and search for better feasible solutions. The iterations of the two algorithms when searching for optimization are shown in Figure 7:  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Transportation Mode Shift**} & \multicolumn{3}{c}{\(\mathbf{f}_{j(\text{m},\text{m})}/\text{min}\)} & \multicolumn{3}{c}{\(\epsilon_{i}^{m\text{m}}/\text{CNY}\)} & \multicolumn{3}{c}{\(\epsilon_{i}^{m\text{/kg}}/\text{kg}\)} \\ \cline{2-9}  & **Road** & **Railroad** & **Airplane** & **Road** & **Railroad** & **Airplane** & **Road** & **Railroad** & **Airplane** \\ \hline Road & - & 0.268 & 0.2 & - & 8.57 & 11.42 & - & 1.56 & 3.12 \\ Railroad & 0.267 & - & 0.35 & 8.57 & - & 17.14 & 1.56 & - & 6 \\ Airplane & 0.2 & 0.35 & - & 11.42 & 17.14 & - & 3.12 & 6 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Transit parameters by mode of transportation.  
Figure 6: Multimodal transportation network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Mode of Transportation**} & **Road** & **Railroad** & **Airplane** \\ \hline Velocity (km\(\cdot\)h\({}^{-1}\)) & 90 & 60 & 600 \\ \(c_{ij}^{m}\) (CNY\(\cdot\)km\({}^{-1}\)) & 2 & 1 & 4 \\ \(c_{i}^{mm}\) & 0.2 & 0.004 & 0.19 \\  & & & 8:00 & 9:00 \\  & & &  
As can be seen in Figure 7, in the process of calculating the optimal path of the same multimodal transportation network under different policies using the ACO algorithm with identical parameter configurations, including the quantity of iterations and the number of ants, the convergence curve of the algorithm shows several drops and stagnation during the iteration process and finally falls into the best local option, while the improved algorithm can keep searching the unknown solution space due to the acquisition of more choices of different paths. The curves in the figure show that the optimization algorithm keeps searching for different solutions, resulting in an undulating average value for each round. The blue color in the figure symbolizes the mean value of the SAACO algorithm, and the orange color represents the mean value of the ACO. The average value of the SAACO algorithm is located below the ACO, which indicates that the SAACO algorithm solves well and is more stable than the ACO algorithm. In addition, the rate of convergence is quicker than the unimproved ACO algorithm.
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding
### Whisper  
Wideband HF Ionospheric Sounder for Propagation Environment Research (WHISPER) project implements a pulse-compression HF ionospheric sounder using wide bandwidth (i.e., up to 80 kHz) to demonstrate the feasibility and benefits of wideband HF communications [28]. It transmits low power coded signals between remote sites of an oblique mid-latitude ionospheric link. It is a project mainly sponsored by UK Ministry of Defence with the collaboration of the Communications Research Centre in Ottawa, Ontario, Canada, under an UK-Canadian agreement on defence science and technology.
## 3 Parameter and Performance Comparison
### Execution Speed  
The performance speed of an algorithm is evaluated based on the speed of executing three operations: key generation, signing, and signature verification. The execution speed depends on the platform on which a PQC (post-quantum cryptography) algorithm is run, as well as on the implementation of the algorithm. The scientific and professional community continuously proposes new algorithm implementations with the goal of their optimization and achieving better performance.  
Performance speed tests are more detailed for the following platforms:  
* Arm Cortex M4.
* x86/x64 Processors.  
Besides the two mentioned platforms (Arm Cortex M4 and x86/x64 processors), there are implementations of PQ algorithms for other platforms. However, these are individual implementations where a comparative analysis of all three digital signature algorithms is not possible. For example, Dilithium was implemented and tested on various FPGA  
Figure 5: Comparison of public keys (left bars/dots) and signatures (right bars/dots) according to [19; 23; 26] (bars) and [27] (dots).  
platforms [28; 29; 30; 31], on the Cortex M3 platform [21], on ARMv8 platforms (Cortex-A72 and Apple M1) [32], as well as on IBM Z computers [33]. Falcon's implementation was also described on ARMv8 platforms (Jetson Xavier CPU with 8 ARMv8.2 cores) [34], and the implementation of Sphincs+ was reported on FPGA [35] and ARM Cortex M3 platforms [36].