esa-sceva/satcom-chunk-collection huggingface dataset:
# Severity Prediction of Traffic Accidents with Recurrent Neural Networks
## 4 Experimental Results and Discussion
### Applicability and Limitations of the Proposed Method  
The proposed RNN model is a promising traffic accident forecasting tool that has several applications in practice. First, identifying the most risky road sections (i.e., site ranking) is a daily practice of transportation agencies in many cities around the world. Accident prediction models, such as the one we proposed in this research, are often an effective solution for identifying risky road sections and helping to conduct investigations into methods for improving the safety performance of the road systems. Second, the RNN model is able to explain the underlying relationships between several accidents' related factors, such as accident time and collision type, and the injury severity outcomes. Information about the effects of accident factors on the injury severity outcomes provides huge amount of information to the transportation agencies and stakeholders. Finally, estimating the expected number of traffic accidents in a road section can help road designers to optimize the geometric alignments of the roads based on the accident scenarios.  
However, the proposed RNN model has some constraints and limitations. The major limitation of the model is that the input factors are prerequisite and if any of them is missing, the output probabilities cannot be accurately estimated. Another constraint of the model is the sequence length of the RNN model, which mainly depends on the number of accident records in the training dataset. To handle this limitation, the future works should develop RNN models that operate on input sequences of variable lengths via Tensorflow dynamic calculation.
# DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
## 3 Methodology
### People Detection
#### 3.1.4 Head Module
The diagonal length of the smallest bounding box enclosing both boxes \(B\) and \(B^{gt}\) is represented by \(c\); and \(\alpha\) is a positive trade-off parameter:  
\[\alpha=\frac{v}{(1-IoU)+v} \tag{8}\]  
and \(v\) measures the consistency of the aspect ratio, as follows:  
\[v=\frac{4}{\pi^{2}}\left(\arctan\frac{w^{gt}}{h^{gt}}-\arctan\frac{w}{h} \right)^{2} \tag{9}\]  
Even in case of zero-percent overlapping, the loss functions still gives us an indication on how to adjust the weights to firstly converge the aspect size towards 1, and secondly, how to reduce the error distance of candidate bounding boxes to the centre of the ground-truth bounding box. A similar approach called Distance-IoU is used in [100] for another application. To prevent the overfitting issue we evaluated some common regularisation techniques as shown in the Table 1. Similar to the outlined results in [101], we found the DropBlock (DB) as one of the most effective regularisation methods in comparison with the other options. In this regard, class label smoothing [102] also helps to prevent over-fitting by reducing the model's confidence in the training phase. Figure 6 summarises the three-level structure of our human detection module in a sequence of interconnected components. In the input part, the Mosaic data augmentation (MDA), class label smoothing (CLS) and DropBlock (DB) regularisation are applied to the input image. In the detector part, the Mish activation function has been used, and CIoU metric is considered as the loss function. In the prediction part, for each cell at each level, the anchor boxes contain the information required to locate the bounding box, confidence ratio of the object, and the corresponding class of the object. In total, we have nine anchor boxes.
## 3 Results and Discussion
### Volatile Composition  
In the analysed poppy varieties' oils, 28 different headspace volatile components were detected, and the main ones are shown in Table 3.  
Hexanol, n-hexanal, pentanol and acetic acid were detected in all samples which is in accordance with the previous literature references [12; 13; 14; 34]. However, [PERSON] et al. [15] detected capric acid as the dominant volatile in 12 out of 13 samples, which is not a main component in our samples. The highest proportion of it was detected in 'Zeno Plus' in year 2021 (13.88%). Interestingly, in case of 'Morgana', this compound was absent in 2021 but present at a proportion 5.17% in 2022. Thus, the significant difference in the measured values of volatile compounds can be observed not only among the varieties, but also between the years of cultivation. A further example for this phenomenon is acetic acid, which reached 68.17% in 'Korona' 2022 and 25.11% in 'Snow White' 2022 samples, while it was only 8.91% and 4.64%, respectively, in the former year.  
Pentanol was also present in larger quantities in the plants grown in 2022, except for the noscapine-containing 'Korona' variety. On the other hand, a typical genotype-dependent characteristic seems to be the presence of 2 and 3 methyl-butanal and 3 methyl-butanol only detected in both years of the 'Morgana' industrial variety. Finally, the proportions of pentane and 2-pentylfuran showed large fluctuations among varieties.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Component** & **RT** & **LRI** & **Albakomp** & **Korona** & **Korona** & **Morgana** & **Morgana** & **Orel** & **Snow** & **Snow** & **Zeno** & **Zeno** \\  & & **2021** & **2021** & **2022** & **2021** & **2022** & **2021** & **2021** & **White** & **White** & **Plus** & **Plus** \\ \hline pentane & 1.5 & 520 & 0 & 3.15 & 0 & 0 & 0 & 11.29 & 2.12 & 3.11 & 1.12 & 0.29 \\ acetic acid & 1.77 & 646 & 6.51 & 8.91 & 68.17 & 6.21 & 7.12 & 3.91 & 4.64 & 25.11 & 9.66 & 0.95 \\ 3-methyl-butanal & 2 & 652 & 1.62 & 0 & 0 & 19.97 & 1.17 & 0.11 & 0 & 0 & 0.12 & 0 \\ 2-methyl-butanal & 2.04 & 661 & 0 & 0 & 0 & 7.66 & 0.86 & 0 & 0 & 0.11 & 0 \\ 3-methyl-butanol & 2.48 & 685 & 0 & 0 & 0 & 29.75 & 1.05 & 0.33 & 0 & 0 & 2.61 & 0 \\ 2-pentanone & 2.54 & 688 & 5.01 & 0 & 0 & 0 & 0 & 0.36 & 0 & 0 & 49.06 \\ pentanol & 2.78 & 763 & 3.84 & 7.09 & 3.06 & 5.72 & 8.44 & 6.97 & 1.37 & 10.38 & 2.87 & 3.67 \\ n-hexanal & 3.1 & 797 & 51.75 & 54.55 & 15.34 & 17.01 & 57.7 & 34.95 & 67.37 & 52.41 & 52.38 & 15.51 \\ hexanol & 4.21 & 865 & 18.21 & 6.39 & 0.73 & 1.17 & 8.66 & 6.25 & 13.86 & 1.26 & 5.54 & 12.35 \\ heptan-2-one & 4.6 & 888 & 0 & 0 & 0 & 0 & 2.5 & 7.61 & 0 & 0.83 & 1.09 & 8.52 \\ 2-pentylfuran & 7.07 & 997 & 0 & 2.48 & 0 & 0.79 & 3.78 & 13 & 0.56 & 0 & 3.4 & 2.11 \\ capric acid & 7.29 & 1002 & 0 & 4.48 & 2.2 & 0 & 5.17 & 1.8 & 0 & 0 & 13.88 & 4.66 \\ gamma-n-caprolactone & 9.55 & 1066 & 0 & 0.88 & 0 & 0 & 0 & 1.29 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The main volatile compounds (% of GC area) of the seed oils.
## 4 Analysis Using Experimental Data  
### The Setup for Measurements and Experiments  
To study the signal acquisition performance of the ring vector sensor receiver employing the selection method or the combining method, underwater experiments were conducted. More specifically, one hundred LFM signals were transmitted in a large pool. Experiments were conducted along the length of the pool, which was about 23 \(\times\) 13 m in size, and 1-3 m in depth. The transmitter and receiver were submerged in water and were separated by 20 m. They were 0.6 m below the water surface. During the experiments, there were some swimming activities in some other lanes. The LFM signal was generated in complex baseband, that is, \(s[i]=\exp[j2\pi R_{0}(2T_{0})^{-1}(i/f_{s})^{2}]\), where \(B_{0}\) represents the bandwidth, \(T_{0}\) represents the signal duration, and \(f_{s}\) is the sampling rate. Then it was converted to a real passband waveform before the transmission. The duration and bandwidth of each transmitted LFM signal were \(T_{0}=0.2\) s and 8 kHz, centered at 20 kHz. The spacing between each two consecutive LFM signals was also \(T_{0}\). An omnidirectional transducer was used as the transmitter. On the receiver side, the ring vector sensor receiver that was used simultaneously measured the \(x\) and \(y\) components of the acoustic vector field, i.e., the acoustic particle velocity, and the \(p\) scalar component of the acoustic field, i.e., the acoustic pressure.  
The data measured by all the channels of the ring vector receiver were analog real passband waveforms that were fed into an analog-to-digital converter (ADC) with the sampling rate of \(f_{s}=100\),000 samples per second. The ADC was connected to a laptop in which the collected data were stored. To prepare the data for the analysis conducted in the next subsection and using the methods and equations presented in Section 3, the data were converted to the complex baseband format.
## 2 Related Work
### Path Planning Algorithm  
The shortest path problem in pipeline transportation systems has long been a central topic in academic research, and is widely applied in Pipe Routing Design (PRD). Classical algorithms, such as [PERSON]'s algorithm Dijkstra (1988), the A* algorithm Dijkstra (1988), and maze-solving algorithms [PERSON] (1988), have been extensively studied. These methods predominantly adopt greedy strategies to solve shortest-path problems on graph-based structures. Specifically, [PERSON]'s algorithm systematically explores all possible paths from a source node to determine the global shortest path, making it particularly suitable for accurate searches in static environments. The A* algorithm improves search efficiency by incorporating heuristic functions while maintaining solution optimality. Maze-solving algorithms, on the other hand, emulate human exploratory behavior in complex environments and are especially effective for spatial navigation involving obstacles. In static environments, the [PERSON] algorithm demonstrates both high computational efficiency and accuracy [PERSON] (1988). This characteristic makes it particularly suitable for solving logistics route optimization problems, effectively addressing various challenges in transportation networks [PERSON] (1988). Furthermore, by incorporating real-time traffic flow data as edge weights, the algorithm can adapt to dynamic road conditions, such as traffic congestion and road closures, resulting in improved routing performance [PERSON] (1988).  
In this context, the A* algorithm plays a central role in the present study. For example, [PERSON] (1988) proposed a routing methodology for automated ship pipeline configurations by integrating an enhanced A* algorithm with genetic algorithms, significantly improving pipe layout efficiency and system automation. Similarly, [PERSON] (1988) refined the heuristic function of the conventional A* algorithm to accommodate cleaning and disinfection service robots, thereby ensuring path continuity and operational safety. With ongoing advances in smart manufacturing and digital factories, recent studies have extended shortest-path techniques to complex scenarios, such as multi-layer spaces, dynamic obstacles, and time window constraints. These developments not only enhance the robustness and adaptability of pathfinding algorithms in real-world applications but strengthen their capabilities in intelligent decision-making and real-time scheduling.
## 3 The Proposed Method
### The Combination of WVD and Hough Transform  
WVD produces an energy distribution concentrated along a straight line. As a result, the problem of detecting the chirp interference can be turned into an issue of detecting the TF plane line and performed by Hough transform, which has been widely used for detecting chirp signal combined with the TF distribution [22; 23]. The combination of WVD and Hough transform can be defined as follows  
\[WH_{x}(f_{0},k)=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}X_{a}\bigg{(}t+ \frac{\tau}{2}\bigg{)}X_{a}^{*}\bigg{(}t-\frac{\tau}{2}\bigg{)}e^{-j2\pi(f_{0} +kt)\tau}d\tau dt \tag{11}\]  
where \(X_{a}(t)\) is an analytical signal and \(X_{a}^{*}(t)\) is a complex conjugate of \(X_{a}(t)\), \(f_{0}\) is the initial frequency, \(k\) is the chirp rate. Both \(k\) and \(f_{0}\) can be deduced from Figure 2 by a geometric relationship, the results can be written  
\[f_{0}=\bigg{[}\frac{L}{2}-\frac{M}{2}\tan\theta+\frac{\rho}{\cos\theta}\bigg{]} \Delta f \tag{12}\]  
\[k=\tan\theta\frac{\Delta f}{\Delta t} \tag{13}\]  
where \(\Delta f\) is the frequency resolution, which equals \(\frac{F_{s}}{2M}\) while \(\Delta t\) is the time resolution and equal to \(\frac{1}{F_{s}}\), \(F_{s}\) is the sampling rate. The Hough transform of WVD maps the point \((t,f)\) of the TF plane line onto the point \((\rho,\theta)\) in the parameter plane. As a consequence, a peak is formed at the point \((\rho,\theta)\) of the parameter plane. Once the peak value exceeds a predetermined threshold, it can be determined that there is a chirp interference.
## 3 Results and Discussion
### Characterization of Starch/MCC Films
#### 3.2.4 Mechanical Properties
Moreover, the starch/3% MCC film displayed greater flexibility than commercial co-polyester/polymactic acid film. However, they exhibited lower mechanical strength compared to commercial co-polyester/polymactic acid film. Importantly, the mechanical properties of the starch films with 3% MCC fall within the range observed for both commercial packaging films. These findings suggest that the starch/3%MCC film holds promise as a viable option for biodegradable packaging solutions, offering a balance of elasticity, strength, and flexibility. Figure 6: Mechanical properties, (**a**) tensile stress–strain curve, (**b**) tensile strength, (**c**) young’s modulus, and (**d**) elongation at break of films (\(n=5\)). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Film Formulations** & **Young’s Modulus (MPa)** & **Tensile Strength (MPa)** & **Elongation at Break (\%)** & **References** \\ \hline Starch & 83.85 \(\pm\) 18.9 & 2.85 \(\pm\) 0.27 & 89.94 \(\pm\) 16.00 & \\ Starch/1\%MCC & 112.12 \(\pm\) 25.8 & 3.21 \(\pm\) 0.52 & 44.74 \(\pm\) 15.89 & \\ Starch/3\%MCC & 248.63 \(\pm\) 123.3 & 6.06 \(\pm\) 2.13 & 32.54 \(\pm\) 17.23 & This work \\ Starch/5\%MCC & 488.89 \(\pm\) 105.6 & 11.18 \(\pm\) 1.46 & 4.15 \(\pm\) 0.18 & \\ \hline Commercial & & & & \\ starch/polybutylene adipate & / & 5.6 & 137 & [41] \\  & & & & \\  & & & & \\  & & & & \\ \hline Commercial & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline Cassava starch/ & / & 4.20 & 10.30 & [6] \\ bamboo cellulose nanocrystals & & 216.09 \(\pm\) 34.86 & 5.07 \(\pm\) 0.15 & 22 23 \(\pm\) 8.02 & [41] \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparison of different starch film materials in mechanical properties (n = 5). This indicates that the addition of MCC at this concentration effectively improves the mechanical performance of the films, potentially making them suitable for medical packaging applications. Furthermore, numerous investigations have reported the mechanical properties of various starch film materials; however, their mechanical characteristics were less than the starch/3%MCC film (Table 4). This suggests that the incorporation of MCC enhances the mechanical properties beyond what can be achieved with starch alone or with lower contents of MCC.
###### Abstract  
To accommodate the requirements of extensive coverage and ubiquitous connectivity in 6G communications, satellite plays a more significant role in it. As users and devices explosively grow, new multiple access technologies are called for. Among the new candidates, rate splitting multiple access (RSMA) shows great potential. Since satellites are power-limited, we investigate the energy-efficient resource allocation in the integrated satellite terrestrial network (ISTN)-adopting RSMA scheme in this paper. However, this non-convex problem is challenging to solve using conventional model-based methods. Because this optimization task has a quality of service (QoS) requirement and continuous action/state space, we propose to use constrained soft actor-critic (SAC) to tackle it. This policy-gradient algorithm incorporates the Lagrangian relaxation technique to convert the original constrained problem into a penalized unconstrained one. The reward is maximized while the requirements are satisfied. Moreover, the learning process is time-consuming and unnecessary when little changes in the network. So, an on-off mechanism is introduced to avoid this situation. By calculating the difference between the current state and the last one, the system will decide to learn a new action or take the last one. The simulation results show that the proposed algorithm can outperform other benchmark algorithms in terms of energy efficiency while satisfying the QoS constraint. In addition, the time consumption is lowered because of the on-off design.  
integrated satellite terrestrial network; rate splitting multiple access; energy efficiency; constrained deep reinforcement learning; soft actor-critic 2023237859. [[https://doi.org/10.3390/s23187859](https://doi.org/10.3390/s23187859)]([https://doi.org/10.3390/s23187859](https://doi.org/10.3390/s23187859))
# Real-Time Information Fusion System Implementation Based on ARM-Based FPGA
###### Abstract  
In this study, an information fusion system displayed fusion information on a transparent display by considering the relationships among the display, background exhibit, and user's gaze direction. We used an ARM-based field-programmable gate array (FPGA) to perform virtual-real fusion of this system as well as evaluated the virtual-real fusion execution speed. The ARM-based FPGA used Intel(r) Realsense(tm) D435i depth cameras to capture depth and color images of an observer and exhibit. The image data was received by the ARM side and fed to the FPGA side for real-time object detection. The FPGA accelerated the computation of the convolution neural networks to recognize observers and exhibits. In addition, a module performed by the FPGA was developed for rapid registration between the color and depth images. The module calculated the size and position of the information displayed on a transparent display according to the pixel coordinates and depth values of the human eye and exhibit. A personal computer with GPU RTX2060 performed information fusion in ~47 ms, whereas the ARM-based FPGA accomplished it in 25 ms. Thus, the fusion speed of the ARM-based FPGA was 1.8 times faster than on the computer.  
augmented reality; real-time information fusion; arm-based FPGA +
Footnote †: journal: Applied sciences
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 4 The Anticipated Cloud Storage Structure
### Design Requirements  
In recent years, a diverse range of privacy-preserving techniques has emerged, each incorporating its unique privacy features and enhancements. However, it is essential to construct privacy-preserving techniques in alignment with design principles and privacy-preserving attributes, as outlined in Section 3. Given that many organizations and online services, including CSPs, heavily depend on these attributes, designing privacy defenses becomes a critical endeavor [68]. Prior to developing a new privacy-preserving framework, it is necessary to provide a concise description of the overall system. The following sections will delve into these elements, drawing as follows from sources such as [66; 67; 69]:1. _Technical expertise:_ The developer of the privacy-protecting approach should possess a comprehensive understanding of cloud-computing technologies. This ensures that designers can effectively address any design or security issues that may arise.
2. _Privacy policies and regulations:_ It is essential to establish a set of standards and regulations governing the usage of cloud services during the development of the privacy-preserving technique. Network participants must adhere to these requirements before establishing any connections within the cloud.
3. _Auditing mechanisms:_ Implementing monitoring mechanisms for network traffic is highly recommended. Auditing procedures serve as a vigilant checkpoint and a continuously observing system to identify any doubtful or abnormal activities.
4. _Data segmentation:_ Data partitioning into smaller segments has become a very basic-level approach for concealing sensitive information. Leveraging data segments and cloud technology can be outsourced to a multi-cloud-based storage approach for further enhancing privacy protection.
5. _Access control and authentication:_ Robust approaches for controlling and managing access to cloud services must be employed. Implementing reliable access control measures is paramount in meeting privacy standards and ensuring authorized usage.
6. _Data encryption:_ Data transferred inside the cloud surroundings should be safeguarded against cryptographic threats. It is advisable to encrypt data using appropriate encryption protocols at all levels of the cloud network to ensure data privacy and integrity.
## 3 Wind Farm Controller  
Aerodynamic interactions due to the wake effect make controlling wind farms a difficult task. The upstream turbine's wake increases turbulence intensity while decreasing wind velocity. The downstream turbine power production is reduced as wind velocity slows, and turbine structural loading may increase as turbulence intensity rises [29]. Thus, wind farm controllers are designed to maximize the total wind farm power output by bringing down or redirecting the wake. WFSim allows the incorporation of wind farm  
Figure 6: Global Wind Atlas: Wind Speed Rose at site.  
Figure 7: Global Wind Atlas: Wind speed variability.  
control with a power production optimizer that permits the yaw angle and axial induction factor to be adjusted to optimize the output by ameliorating the wake effect.  
The flowchart of the implementation of a dynamic wind farm controller is shown in Figure 8. In addition to ambient flow conditions and control mechanisms, the Wind Farm Layout block contains information on turbine position, turbine type, and so on. The details regarding wind direction, speed, and turbulence intensity are included in the Ambient Flow Condition block. The block Control Methods allows the wind farm control method, i.e., WRC, AIC, or a combination of the two, to be selected. Wake sub-models, wind direction probability information and an optimizer that optimizes the yaw angle, axial induction factor, or both are included in the Power Production Optimizer block. These optimized values are then forwarded to WFSim.  
In Section 3.1, wake control strategies, i.e., AIC and WRC, are discussed. Section 3.2 discusses the power production optimizer, which optimizes the yaw misalignment angle or the axial induction factor in order to maximize the power production.