esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management
## 3 Research Analysis
### Traffic Management Methodologies Consisiting of Primary Goals
#### Efficiency, Safety, Ecology, and Passenger Comfort  
Future traffic management methods may be most effective if they can simultaneously consider all four categories of goals and strike a reasonable balance between them. [PERSON] et al. [36] suggested a non-linearly constrained programming problem formulation for a centralized cooperative intersection control strategy for unsignalized junctions. The recommended technique may decrease travel time by 88.56-95.38%, increase fuel efficiency by 17.18-37.81%, and enhance traffic flow by 10.49-17.61% compared to actuated junction control. Furthermore, it lowers CO\({}_{2}\) emissions by 61.13 to 67.6%. Reference [49] detailed the advantages of six alternative real-time traffic management techniques, including traffic diversion, ramp metering, changing message signs, dynamic speed restrictions, and lane control systems. These plans are assessed using 13 subcriteria prioritized using fuzzy multi-criteria decision-making and the four core criteria of economic, public, political, environmental, and traffic safety (MCDM). For this purpose, the combined compromise solution (CoCoSo) approach was presented with the distinctive additions of the logarithmic technique and the Power Heronian function.  
A semi-decentralized multi-agent-based vehicle routing system, considering trip time prediction and computing efficiency, was developed in [49] to enhance the likelihood of arriving on time, travel time, driver satisfaction, accident rate, fuel usage, and emissions. The experimental results of [50; 78; 79; 80] showed that it outperforms existing methods in terms of factors such as average total journey time, fuel consumption, and air pollution. For unsignalized intersections, ref. [47] developed a method for multi-objective optimization to coordinate the CAVs to lower fuel consumption, boost traffic efficiency, and improve driving comfort. The recommended solution, which requires low computational work and ensures safety, increases CAV effectiveness, fuel efficiency, and ride ease, according to the simulation results.  
Reference [81] suggested a novel method to steer CAVs across a junction utilizing traffic-light information and infrastructure-to-vehicle communication to save travel time and fuel consumption. According to the simulation, the suggested algorithm surpasses human-driven vehicles in terms of energy usage and trip time. Optimization, rule-based, and machine-learning techniques have been established to increase intersection effectiveness and environmental impact while considering traffic safety and passenger comfort, as shown in Table 3. Overall, the suggested approaches' throughput, fuel use, and trip time are better than the basic scenarios. Most of the tests employed a single intersection with simplified traffic circumstances to validate the suggested methodologies.
## 4 Simulation and Test Verification
### Actual Experimental Verification  
In the above simulation experiments, the SAIOR algorithm's interference suppression performance was validated. To further assess the effectiveness of the algorithm, a hardware platform was set up on the rooftop of the New Main Building at Beihang University to conduct actual signal anti-jamming experiments. This system uses a dedicated Iridium satellite antenna to capture its signals. Gaussian interference is generated by a signal source and combined with the Iridium signal using a combiner. The combined signal was then frequency-shifted to intermediate frequency (IF) through a down-converter. A sampling rate of 25 MHz was used to collect the signal, which was subsequently processed by the signal reception and processing platform to apply the anti-jamming algorithm and evaluate performance. During the test period, a total of four Iridium satellites were visible. The constellation map corresponding to the visible epoch of the Iridium satellites is shown in Figure 9.  
Similarly, two interference scenarios were designed by configuring the signal source, with the interference scenario parameters set as shown in Table 3. The SAIOR algorithm was applied for interference suppression under different interference intensities (with JSR of 15 dB and 25 dB, respectively). The positioning results after interference suppression were then compared with those obtained without applying the interference suppression algorithm and under no interference conditions. The positioning method adopted instantaneous Doppler positioning approach.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Scene One** & **Scene Two** & **Scene Three** \\ \hline Offset between the PFM Interference Carrier & & & \\ Frequency and the Signal Center Frequency & & & \\ \hline Modulation Period & & & \\ \hline JSR & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment parameter setting of the interference detection performance.  
Figure 8: Interference detection ratio (IDR). (**a**) Scene one. (**b**) Scene two. (**c**) Scene three.  
The positioning results indicate that, compared to the positioning results under interference-free conditions, the positioning accuracy after interference suppression in interference scenarios shows a certain degree of decline. However, it still successfully retrieves Doppler information and achieves effective positioning. In contrast to interference scenarios where the interference suppression algorithm is not applied, activating the SAIOR algorithm significantly improves the positioning accuracy. The experimental results further validate the effectiveness of the SAIOR algorithm and its interference suppression performance in LEO satellite PFM interference scenarios.  
Figure 11: Comparison of positioning results of scene one. (**a**) Positioning result after anti-jamming (JSR is 15 dB). (**b**) Positioning result without anti-jamming algorithm (JSR is 15 dB). (**c**) Positioning result after anti-jamming (JSR is 25 dB). (**d**) Positioning result without anti-jamming algorithm (JSR is 25 dB).
# Hypergraph Analysis of Functional Brain Connectivity During Figurative Attention
## 6 Hypergraph Construction
### Dual Clique Expansions  
The high-order interactions shown as the hypergraphs in Figures 11-14 can also be presented in the form of dual clique expansions shown in Figures 17 and 18. As was defined in Section 2.1, a clique expansion is a transformation where each hyperedge is replaced by a clique connecting all vertices within that hyperedge. This is commonly used to approximate hypergraphs using graphs. To construct dual clique expansions of \(\mathcal{H}_{1-4}\), we first created dual hypergraphs \(\mathcal{H}_{1-4}^{*}\) and then performed clique expansions on \(\mathcal{H}_{1-4}^{*}\), meaning that each hyperedge (which corresponds to a vertex in \(\mathcal{H}^{*}\)) is expanded into a clique. As a result, we created new hypergraphs \(\mathcal{G}_{1-4}\) where hyperedges correspond to cliques in \(\mathcal{H}_{1-4}^{*}\).  
The analysis of the dual clique expansions \(\mathcal{G}_{1-4}\), presented in Figures 17 and 18, reveals distinct connectivity patterns among brain regions. Specifically, all connectivity measures--coherence, correlation, and mutual information--are mutually connected through the nodes \(e_{1,3,5}\). Conversely, all disconnectivity measures--anticoherence, anticorrelation, and information loss--also form a mutually connected structure. In other words,  
Figure 16: Four-layer hypergraphs representing brain connectivity (**left layers**) and disconnectivity (**right layers**) associated with right-cube attention. The lines indicate inter-layer connections, with the corresponding channels responsible for these links labeled near the lines.  
Figure 17: Dual clique expansions \(\mathcal{G}_{1,2}\) of hypergraphs \(\mathcal{H}_{1,2}\) associated with left-cube attention at (**left**) \(f_{1}\) and (**right**) \(f_{2}\).  
these results highlight the brain regions (edges in these graphs) that predominantly contribute to both increasing and decreasing connectivity associated with figurative attention.  
For both left- and right-cube attention, the primary contributors to increased connectivity are the parietal region (P3, Pz, P4) at \(f_{1}\) and the refrontal and frontal regions (FP1, FP5, F8) at \(f_{2}\). In contrast, decreased connectivity is primarily driven by the centro-frontal region (Cz, F7, F8) at \(f_{1}\) and the fronto-parietal region (Fz, F3, Cz, P3, Pz, P4) at \(f_{2}\).  
Additionally, an inverse relationship emerges between different frequency bands and cube percepts. The parietal cortex (P3, Pz, P4), which enhances connectivity at \(f_{1}\), becomes disconnected at \(f_{2}\) during right-cube attention. Similarly, the right frontal region (F8), which supports increased connectivity during left-cube attention task (MLV) at \(f_{2}\), disconnects at \(f_{1}\) during the right-cube attention task (MRV). This suggests an antiphase activation-deactivation dynamics, where increased connectivity for one percept at a specific frequency coincides with decreased connectivity for the alternative percept at another frequency.
# An Application of BOTDR to the Measurement of the Curing of a Bored Pile
## 4 Analysis of Monitoring Data  
### Analysis of Pile Strain Data  
The average strain values of both sides of the pile are taken to draw the strain variation with pile depth curve (Figure 8). It can be seen that the strain of the pile keeps increasing; the result of concrete expansion is caused by the temperature rise of the pile due to the heat released from the hydration of concrete. It is also observed that the strain of the pile head is small, because the heat generated by the concrete hydration of the pile head can be easily released into the air. The change of strain tends to be obvious with the increasing pile depth; the constant temperature of underground layer restricts the emission of hydration heat, and the influence of hydration heat on pile deformation tends to be noticeable. However, with the increasing pile depth, the strain changes show a tendency of increasing first and then decreasing; these changes might be related to temperature variation in different soil layers, and they affect the hydration heat emission and the pile deformation. The first day after concrete pouring, the deformation of the fiber sensor (2)-(1) is significantly higher than the deformation of the second and third day (3)-(1, 4)-(1). The main reason for this phenomenon is that after the completion of concrete casting on the first day, the concrete has a more significant impact on the deformation of optical fiber sensor; the deformation on the first day is greater than that of the second and third days.  
Figure 8: Average of strain variation curve of fiber sensor.  
Figure 7: Strain curve of fiber sensor of one side of the pile.  
This experiment uses the distributed fiber optic sensing technology for the monitoring of pile deformation. When BOTDR technology is used to monitor the strain change information of bored pile in the loading test, it can obtain the strain change of pile accurately. Compared with the traditional monitoring results such as axial force meter, it confirmed the reliability of the data [24; 25; 26]. The monitoring data of the static load test on the pile have high accuracy [11], which also strengthens the reliability of the test data.
## 4 Implementation and Testing  
For validation of the RA, a proof of concept is provided, thus, validating the applicability of the designed RA. The proof of concept depicts, in 3D visualization, a virtual art gallery in an imaginary 3D immersive environment. The immersive world is constructed from various digital assets that depict a Greek island in which, at the top of one virtual mountain, our virtual gallery is placed [35]. The digitized physical acrylic artwork created by the main author of the conducted research was imported into the depicted virtual gallery via the developed plug-in Visio Creativia (as documented in [36]). The actual virtual  
Figure 7: Deployment View.  
gallery was created via means of procedural generation. For the needs of the project, an AI NPC educator/art historian metahuman was also inserted in the virtual environment, showcasing the future of AI copilots in the GLAM sector as well as art education [37].  
The virtual copilot was educated with a basic level of art knowledge and given a specific profile to match the environment. Finally, the outfit of the virtual educator was textured with one of the artist's digitized paintings, showcasing the future of virtual e-commerce and digital fashion and education in the metaverse. The unique features of the application are the virtualisation of physical fine-style abstract art in a 3D environment, the presence of an AI-powered NPC educator, and the ability of the end users to guide throughout the whole virtual environment with the companion of the virtual educator.  
This design intends to showcase the applicability of the designed plug-in Visio Creativia [36] to the digital transformation efforts of the GLAM and educational sector. By incorporating realism through realistic movement speed, gravity forces, detection collision, as well as AI-driven agents, the reference architecture is tested for future integrations. The character can move using the keyboard and mouse to the corresponding directions: W: Front, S: Back, D: Right, A: Left, as well as interact with the virtual educator by either typing the message or activating the microphone. The end users can obtain valuable information for the virtualised interactable paintings as well as generic information regarding art from the AI-powered NPC educator.
# Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects
## 4 Case Study
### Generation of the BuildingTree and Visualization Result
#### 4.3.4 BuildingTree Visualization  
BuildingTree combines the generalized model from different levels into a tree structure and can preserve the visualization similarity while reducing the unnecessary details of distant buildings. Figure 15a gives an example of a BuildingTree-based visualization. We can see that the buildings near the user's viewpoint are in LOD3 ES, and the buildings in the middle area and background are in LOD2 and LOD1, respectively. In Figure 15b, a street level visualization is given that indicates the proposed BuildingTree can better preserve the visualization feature in the street level view, in which the building in the distance usually cannot be seen.
# BSTCA-HAR: Human Activity Recognition Model Based on Wearable Mobile Sensors
## 4 Experiment and Results
### Evaluation Metrics  
The performance of classifiers in HAR can be measured by a variety of performance measures; in this paper, we have selected four performance metrics: accuracy, precision, recall, and F\({}_{1}\)-Score. Accuracy is defined as the proportion of correctly predicted samples out of the total number of samples. If the data are balanced, using the accuracy rate to measure the classification performance is recommended. The accuracy rate is defined as follows: \(\frac{TP+TN}{TP+TN+FP+FN}\). However, accuracy metrics do not adequately account for the bias that arises from unbalanced datasets; thus, the F\({}_{1}\)-Score is often more useful as a performance metrics than the accuracy. The F\({}_{1}\)-Score is the weighted average of precision and recall, combining the performance of both. Especially in the case of unbalanced data (for instance, both WISDM and ISLD are unbalanced datasets), the F\({}_{1}\)-Score offers a more balanced evaluation of model performance compared to precision or recall individually. The precision rate is defined as \(\frac{TP}{TP+FP}\), recall is defined as \(\frac{TP}{TP+FN}\), and the F\({}_{1}\)-Score is calculated using the formula  
\[F_{1}=\sum_{i}2\cdot w_{i}\frac{\text{precision}_{i}\cdot\text{recall}_{i}}{ \text{precision}_{i}+\text{recall}_{i}}, \tag{11}\]  
where TP represents positive examples of correct classification, TN indicates cases where negative examples are accurately classified, FP signifies occasions where positive examples are mistakenly labeled, and FN covers situations where negative examples are erroneously mislabeled. \(w_{i}=n_{i}/N\) is the proportion of samples of class \(i\), \(n_{i}\) is the number of samples of class \(i\), and N is the total number of samples. The F1-score is used to assess the effectiveness of the classification model, taking a value in the range between 0 and 1. When the F\({}_{1}\)-Score is 1, this indicates that both the precision and recall rates of the model have reached the optimal level.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **WISDM** & **UCI-HAR** & **ISLD** \\ \hline Training set & 13,654 & 7319 & 600 \\ Test set & 3036 & 2947 & 76 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sample sizes for the three public datasets.
## 3 System Structure
### Robust Denoising Algorithm and IDA Detection Algorithm
#### 3.2.1 Data Washing Algorithm  
In general, denoising autoencoders, which remove the malicious signal added by the attacker, achieve good protection against target attacks, but are less effective against paralysis attacks. By contrast, the Data Washing algorithm proposed in this study provides an effective protection against both types of attacks. The details of the proposed algorithm are described as follows.  
As shown in Figure 1, the data are first passed through a denoising autoencoder. A small amount of Gaussian noise (also called a lightweight Gaussian white noise) is then added to the data and the data are then passed through the autoencoder once again to obtain the restored data. Through denoising autoencoder, the algorithm effectively eliminates the malicious signal added by the attacker.  
Intuitively, the accuracy of the denoising process should improve as the number of washing operations increases. However, the preliminary experimental results showed that the modification effect of the picture data became too large, and some of the pictures became blurred, when the washing process was repeated multiple times. Furthermore, limiting the modification effect to a certain maximum value was also found to reduce the cleaning effect. Hence, in processing the poisoned dataset, the number of washing operations was limited to just one.  
The denoising autoencoder proposed in the present study comprises three encoding layers and one convolutional layer on the encoding side and three decoding layers and one convolutional layer on the decoding side. Although the main aim of the denoising autoencoder is to restore the signal with Gaussian noise to a clean signal, the autoencoder also can eliminate some non-Gaussian noise. Thus, the proposed algorithm provides an effective defense against not only specific attack algorithms, but also general unknown algorithms.
## 3 Global Horizontal Irradiance Models  
In this study, from their accuracy, applicability, and versatility, well-known semi-empirical models were chosen to generate the GHI over the Korea peninsula [1,3,8,11]. The [PERSON], [PERSON], and [PERSON] models originally utilized the Meteosat imagery system and their results were validated through ground measurements located in Europe. [PERSON] et al. [10] modified the original Heliosat-1 model by introducing new variants for geometrical correction, backscatter correction, and clear-sky irradiance computation. It was revealed that the hourly insolation accuracy of the [PERSON] model has an RMSE of 120 Wh/m\({}^{2}\) (rRMSE of 16%). [PERSON] et al. [3] improved the Heliosat-1 method by applying calibrated radiances instead of digital numbers. The application of the calibrated radiances as input opened the opportunity for substituting several empirical components of Heliosat-1 into the physical computation. The results of the Rigollier model revealed RMSEs and rRMSEs of 62 Wh/m\({}^{2}\) (45%), 96 Wh/m\({}^{2}\) (27%), and 103 Wh/m\({}^{2}\) (18%) for January 1995, April 1995, and July 1994, respectively.  
The Hammer model was established to forecast the GHI several hours ahead. A motion vector field from two consecutive satellite images can be generated to determine cloud motion. The vector field was computed with the current images and extrapolated up to 120 min ahead with a 30-min interval. Finally, the GHI could be forecast by applying the extrapolated pixels of the satellite image to the Hammer model. The relative RMSEs  
Figure 1: Example of a satellite image from visible channel provided by COMS (Communication, Ocean, and Meteorological Satellite), stationed at 36,000 km above the equator. This image is considered as level 1B data.  
(rRMSE) were 35% and 40% for 1-h and 2-h forecast horizons, respectively. Contrary to the other three models, the Perez model used the geostationary operational environmental satellite (GOES) system in the United States. Validation of the Perez model was then conducted against ten ground measurements from different climatic types of the American region. The RMSE of the Perez model varied from 44 to 118 \(\mathrm{W/m^{2}}\), depending on the location of the ground measurement. Below are the detailed explanations of the procedures used for each model.
## 3 Proposed RL-Based Real-Time Control Framework
### Implementation of the Proposed RL-Based Control Framework  
The proposed RL-based control framework for multi-objective real-time controls is implemented in Python 3.7. The Tensorflow V1.14 framework trains various RL agents using the soft actor-critic algorithm for volt/var, line flow, and transmission loss controls. The developed RL-based control prototype is deployed on a Linux computing server with 64 cores and 128 GB memory in the control center of a provincial power system that interacts with their live EMS system, capturing system snapshots every 5 min. The specific voltage and line flow control models with or without transmission loss are described in Tables 1 and 2, where AC power flow equations and limits of physical devices are considered.
# Securing the Future of Web-Enabled IoT: A Critical Analysis of Web of Things Security
## 6 Defenses
### Mutual Authentication and Integrity Checks  
MitM attacks can be thwarted by ensuring mutual authentication between communicating devices and performing integrity checks on transmitted data [70; 71]. Mutual authentication ensures that both parties in a communication verify each other's identities, while integrity checks confirm that data have not been altered during transmission. These measures help in maintaining the authenticity and integrity of communications.