esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics
## 3 Thermodynamic Formalism in Neuroscience  
In this section, we make the connection between Thermodynamic Formalism and spiking neuronal dynamics. From the standpoint of mathematics, there are at least two ways to consider spiking neuronal networks. First, they can be considered as biological objects whose activity can be experimentally recorded while using Multi-Electrode Arrays (MEA), often requiring sophisticated mathematical methods and algorithms for data analysis [96,97]. Second, neuronal networks are characterised by dynamical models, more or less derived from biophysics [35,36].  
Here, we begin considering the statistical analysis of spike trains recorded from neuronal networks. For this case, Thermodynamic Formalism provides a powerful and insightful method to analyse the spatio-temporal statistics from experimental spike trains. We briefly mention that this formalism has afforded us to develop algorithm for spike train analysis [49,89,98] leading to the software PRANAS [99] freely available at [[https://team.inria.fr/biovision/pranas-software/](https://team.inria.fr/biovision/pranas-software/)]([https://team.inria.fr/biovision/pranas-software/](https://team.inria.fr/biovision/pranas-software/)), although we do not develop along these lines in this paper. We focus then on a specific question. When dealing with a model of spiking neurons, how much of the intrinsic dynamics of neurons, their interaction via synapses, and the influence of stimuli, constrain the collective spatio-temporal spike statistics?  
Neurons are (nonlinear) entities that evolve in a concerted way (as they interact via synapses) and responding to external stimuli. The theoretical analysis of this high dimensional systems can be made thanks to mathematical methods (dynamical systems, bifurcations theory, stochastic processes, partial differential equations) or theoretical physics (statistical physics, nonlinear physics). Here, one might be interested in what Thermodynamic Formalism can contribute when considering neuronal models dynamics. In this spirit, we consider two models, the Integrate and Fire model and the Galves Locherbach model. Most of our presentation focuses on stationary situations that are characterised by equilibrium states. Nevertheless, we consider the extension of Thermodynamic Formalism to non-stationary situations. At the end of the section, we address a couple of open questions.  
1. What is the natural alphabet for spiking neuron dynamics? As we shall see, although the binary representation of spikes is a good candidate, it is too naive, as the relevant alphabet can be constructed on time blocks of spikes. A subsidiary question is about the size (time depth) of these blocks.
2. Under which conditions can Thermodynamic Formalism machinery be faithfully applied to a spiking neuronal network model?
3. What are the limits when the main theorems of Thermodynamic Formalism can and cannot be applied and what are the consequences for neuronal dynamics and spike statistics?
# Image Analysis and Processing for Generating Camouflages from Digital Earth Photographs
## 4 Selected Technical Aspects of the Camouflage Generator Approach
### Logic Layer
#### 4.2.1 ColorExtractor Component  
The _ColorExtractor_ component is responsible for providing the function of extracting colors from an images. The class diagram in Figure 11 shows the relationship between the implemented classes and the classes from external packages. The main class of the component is the _ColorExtractor_ class with a static _GetPallet()_ method that takes a bitmap image, the number of colors the user wants, and BackgroundWorker to monitor and report the progress of the color extraction operation. The _ColorExtractor_ class uses the _kMeans_ external class from the _Accord.MachineLearning_ package. It uses the implemented _Distance()_ method from the _CIELabDistance_ class, which implements the _IDistance_ interface from the _Accord.Math.Distance_ package, to calculate the distance between colors. To calculate the distance between colors, the _CIELabDistance_ class uses the _CnCComparison_ class from the _ColorMine.ColorSpaces_ package.  
The _color extraction algorithm_ is based on the centroid algorithm (_kMeans_), which, thanks to the use of _CIELab_ color space, allows to group colors according to their similarity, similar to the perception of color differences in humans. For the grouped colors, the average color  
Figure 11: Class diagram for _ColorExtractor_ component in logic layer of _CammoGenerator_ application.  
Figure 10: Data layer class diagram of _CammoGenerator_ application.  
for each of the groups obtained is calculated. The obtained average colors after conversion to the RGB color space are the final result of the algorithm.  
The _Kmeans_ class from the _Accord.MachineLearnig_ library contains the implementation of the centroid algorithm. This algorithm groups similar points concentrated around the searched centroids, being the mean value of the value of the points assigned to a given group (centroid). When the image is analyzed, the algorithm obtains a set of colors that are vectors of the three values. Then, the initial values of the centroids are drawn. After they are drawn, the colors are assigned to the centroid to which they are most similar. When all colors are separated, centroids are assigned the average value of the colors in their set. Then, the mean square error is computed. When this error is greater than the assumed tolerance, we repeat all operations, omitting the initial randomization of centroid positions. In the case when the calculated error is lower than the assumed tolerance, the algorithm is interrupted.
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 1 Introduction  
Queueing systems are vital tools for optimizing resource utilization and managing fluctuating service demands across various operational environments. However, traditional queueing models typically rely on static service mechanisms, limiting their ability to respond to dynamic changes in system conditions such as traffic intensity, arrival rates, and resource availability.  
To overcome this limitation, this study proposes a Fuzzy-Based Control System (FBCS) for a Bulk Service Queueing Model with Vacation, designed to enhance system performance through real-time, adaptive parameter adjustments. In this framework, the service process is categorized into three operational levels:  
1. High Bulk Service: Multiple arrivals are processed in batches to increase throughput during peak periods.
2. Medium Single Service: Individual customers are served at a steady, moderate rate under normal loads.
3. Low Vacation: The server takes minimal rest breaks to maintain continuous operation during low-demand periods.  
The decision-making mechanism is driven by a Fuzzy Inference System (FIS), which responds to real-time input variables such as queue length, arrival rate, and server utilization. By adjusting the service rate, batch size, and vacation duration, the fuzzy logic controller enables the system to adapt fluidly to changing conditions, improving responsiveness and overall stability compared to rigid, rule-based queueing models.  
Fuzzy control has a proven track record in engineering systems. In 1985, [PERSON] and [PERSON] demonstrated its effectiveness in railway simulations, leading to successful deployment in the Sendai line by 1987. Around the same time, [PERSON]'s use of dedicated fuzzy logic chips in an \"inverted pendulum\" experiment showcased the potential of fuzzy systems for real-time control in uncertain environments.  
This research evaluates the impact of fuzzy-based control on critical performance metrics, including queue stability, average waiting time, and server utilization. The simulation results highlight the system's ability to optimize service efficiency while maintaining a balanced trade-off between speed and resource allocation. The paper is organized as follows: model formulation, fuzzy logic design, performance evaluation, and comparative analysis, followed by an illustration of the fuzzy inference process and a discussion of its practical implications. An example of the Fuzzy Inference System is as follows (Figure 1):  
Fuzzy logic and queueing models have been extensively studied for their applications in optimizing decision-making and service efficiency.
# The Effect of Non-Transferred Plasma Torch Electrodes on Plasma Jet: A Computational Study
## 3 Results
### Temperature and Current Density Variations  
In Figure 9a, we observe the temperature variation within both the stepped and cylindrical nozzle anodes. Notably, the temperature variation remains consistent across both nozzle types, characterized by an increase at the cathode tip and a subsequent decrease towards the outlet. This thermal behavior in our computational model is regulated by factors such as the surface areas of the cathode tip, inlet velocity, and inlet power. Given that these conditions are uniform for both stepped and cylindrical nozzle configurations, no discernible differences in temperature profiles emerge with respect to anode shapes.  
Figure 9b presents the variations in current density along the inner walls of the anode, extending from the cathode tip to the outlet. The plot distinctly highlights the initial peak occurring at the cathode tip, positioned at 9.1 mm. The second peak, found at 14 mm, designates the location where the arc attaches to the anode surface. Notably, the stepped nozzle anode exhibits a distinctive pattern of increase and subsequent decrease in current density at 19 mm, coinciding with the stepped region. Beyond this point, the current density in the stepped nozzle decreases significantly. In contrast, the cylindrical anode portrays a more stable and uniform current density variation, resulting in a more stable arc.
## 3 Field Testing Results on Performance
### Inspection System  
The inspection robot can capture the details of obstacles inside the pipeline. Besides, the pipeline information, pipeline scratches, and other texture details can also be recorded clearly at a running speed of 0.7 m/s. The inspection results are demonstrated in Figure 10. The testing results show that the inspection system can capture a 0.4-mm pipeline scratch caused by mechanical tools during manufacturing.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Voltage (V)** & **Time (Hour)** & **Mileage (km)** & **Temperature (\({}^{\circ}\)C)** \\ \hline
1 & 54.0 & 0.0 & 0.0 & 20 \\
2 & 52.0 & 6.5 & 17.0 & 20 \\
3 & 50.0 & 15.5 & 33.7 & 16 \\
4 & 48.0 & 20.0 & 44.5 & 16 \\
5 & 46.7 & 24.0 & 53 & 16 \\
6 & 46.0 & 26.5 & 59.5 & 16 \\
7 & 44.0 & 31.5 & 70.5 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Testing results of operation endurance at each timestamp.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Incline Angle (\({}^{\circ}\))** & **Rate Speed (\%)** & **Slip** \\ \hline
1 & 16 & 100 & No \\
2 & 20 & 100 & No \\
3 & 30 & 100 & No \\
4 & 37 & 95 & No \\ \hline \hline \end{tabular}
\end{table}
Table 3: Testing results of gradeability.  
Figure 9: Testing data of gradeability.  
Figure 11 demonstrates that the inspection system can clearly capture the detailed information of each section inside pipelines when the robot is moving, facilitating accurate positioning on the postprocessing of the scratch problems.  
Figure 11: Information captured by inspection system.  
Figure 10: Performance of inspection system when the robot is moving at 0.7 m/s. The left column shows the original objects under normal illumination, and the right column shows the inspection results inside pipelines.
# Assessment of Two Streamline Curvature Correction Methods for an Elliptic Blending Turbulence Model
###### Abstract  
Using two different methods, a previously developed elliptic blending model (the original STT \(k\)-\(\omega\)-\(\sigma\)-\(\alpha\) model) is modified for sensitization to streamline curvature. One method involves modifying the dissipation term in the turbulent dissipation equation, while the other constructs a new formulation for the turbulent kinetic energy production term based on an explicit algebraic stress model. The capabilities of the proposed models are evaluated by applying them to three flows with curved surfaces; namely, the two-dimensional (2D) infinite serpentine passage flow, the 2D U-turn duct flow, and the 2D periodic hill flow. The STT \(k\)-\(\omega\) model with rotation and curvature correction (the STT \(k\)-\(\omega\)-CC model) is also used for comparison. The computed results are compared with the relevant direct numerical simulation, experimental, and large eddy simulation data from the literature. It is found that the two proposed models significantly improve upon the original STT \(k\)-\(\omega\)-\(\sigma\)-\(\alpha\) model. Compared with the STT \(k\)-\(\omega\)-CC model, the two proposed models produce better results in the 2D infinite serpentine passage flow and the 2D periodic hill flow. The proposed models are similarly competitive with the STT \(k\)-\(\omega\)-CC model in the 2D U-turn duct flow.  
turbulence model; elliptic blending; streamline curvature correction; curved boundary Article Assessment of Two Streamline Curvature Correction Methods for an Elliptic Blending Turbulence Model.
## 6 Pharmacological Activities of _Astragalus_ Polysaccharides
### Antiviral Effects  
The _Astragalus_ polysaccharides can protect the body against viruses, induce, to a certain extent, the production of IFN, and inhibit the viral reproduction. It can also induce CD4\({}^{+}\) T cells to produce IL-4, IL-2, and IFN-\(\gamma\), suggesting that APS can be a potent adjuvant for a hepatitis B virus (HBV) DNA vaccine [74]. It has been reported that 0.5 mg of APS can significantly enhance the efficacy of FMDV vaccine by significantly enhancing the phagocytic capacity of peritoneal macrophages, proliferation of splenic lymphocytes, the titer of serum antibodies, and the production of IL-4 and IL-10. The _Astragalus_ polysaccharides can maintain the health of livestock and poultry by inhibiting the propagation of the virus [75]. Moreover, APS could significantly increase the resistance of 15-day-old chickens to H5N1 avian influenza virus [76], and prevent porcine circumovirus infection by decreasing the oxidative stress and activating the NF-\(\kappa\)B signaling pathway [77]. Furthermore, APS at a concentration of 30 \(\mu\)g/mL could inhibit the reproductive capacity of herpes virus, thereby reducing the incidence of tumor [78]. Briefly, the antiviral activity of APS is generally closely related to cytotoxic T lymphocytes, induced T lymphocytes (CD3\({}^{+}\) and CD4\({}^{+}\)), and NF-\(\kappa\)B.
# Blockchain and Smart Contracts for Insurance: Is the Technology Nature Enough?
## 3 Blockchain Applications in Insurance: Selected Use Cases
### Premium Computation/Risk Assessment/Frauds Prevention  
In this scenario, the blockchain is used to let multiple certified intermediaries record information related to a person (by linking them to his/her address).  
Such intermediaries could be insurance companies (e.g., to record previous claims), police officers (e.g., to store criminal acts), medical staffs (e.g., to record a person's injuries and treatments), or even smart wearable devices (which could inject in the blockchain data about one's physical activity).  
A smart contract could read all the information linked to a person and automatically compute the premium and perform risk assessment, based on his/her physical health, driving behaviors, etc. [50].  
Another application scenario is represented by fraud prevention. In this scenario, a smart contract could analyze collected data and identify frauds during claim processing (e.g., by crossing data related to a person's previous claims).  
A scenario such as the one depicted in the above examples, however, could be difficultly realized in the short term. In fact, it implies that each person possesses a unique blockchain address (as presented in Section 3.2), and requires the active involvement of different actors (insurance companies, police officers, medical staff, etc.) as the quality of the results would be a consequence of the quality and quantity of data stored in the blockchain. Privacy is another relevant issue (especially for what it concerns medical records). In this view, in the construction of such a system, a thorough attention should be devoted to let only selected actors link information extracted from the blockchain to a person's identity. Furthermore, particular care should be devoted to the definition of common standards to record the information, in order to enable interoperability.  
The most suitable architecture for this use case is a consortium blockchain. The blockchain would be maintained by selected nodes of the consortium, e.g., belonging to the different actors involved. The limited number of trusted nodes would increase security and privacy. Furthermore, the blockchain would keep track of the sender of each transaction. Finally, being controlled by a small number of nodes, mechanisms to revert blockchain state in case of transactions erroneously made (e.g., a driving infraction notified to the wrong person) could be devised.
## 2 Beidou-Binocular Cooperative Navigation Model
### Binocular Vision Recognition and Perception Module
#### 2.2.3 Local Path Planning of Binocular Vision  
After generating depth information and identifying obstacles, the binocular vision module is responsible for providing obstacle avoidance capabilities through local path planning. Local path planning uses a grid map to represent the environment and employs the A* algorithm to compute the obstacle-free path:  
1. Environment Modeling: A grid map is generated from the depth map, marking obstacle areas as impassable cells.
2. Path Search: Using the A* algorithm, the path from the robot's current position to the target position is searched for the minimum-cost path.
3. Path Optimization: The local path is optimized in combination with the global path provided by Beidou, ensuring smoothness and reachability.  
The core function of the binocular vision recognition and perception module in the BBCN model is to provide high-precision environmental perception and dynamic obstacle avoidance capabilities. Through close collaboration with the Beidou navigation module, this module not only compensates when Beidou signals are interfered with, but also dynamically adjusts paths during robot navigation to ensure efficient operation in complex environments.  
Figure 4: Schematic diagram of YOLO and DeepLab collaboration.
# Person Re-Identification Based on Attention Mechanism and Context Information Fusion
## 3 Experiment
### Experimental Results
#### 3.3.2 Comparison with Mainstream Methods  
In order to verify the superiority of the model in this paper, we also compare with the current mainstream methods on the datasets Market1501 and DukeMTMC-reID. We divide these mainstream person ReID methods into six categories: traditional methods, deep metric learning, local feature-based methods, global-local based feature methods, auxiliary feature-based methods, and global feature-based methods. It can be seen from Tables 4 and 5 that the experimental results of our method show better performance on the two datasets. DSA [10] achieves the surprising performance on Market1501 dataset without using re-ranking. However, this method uses a set of local features of the pedestrian, and multi-branch network structure makes the model too complicated. Without re-ranking, the performance of the ADMS [9] method using global features on the two datasets is slightly higher than our method, but ADMS architecture includes five branches and five loss functions, and our model only has two branches with two loss functions. After adding re-ranking [32] to optimize, the rank-1 and mAP of our model on the Market1501 dataset are increased to 95.9% and 94.5%, respectively. On the DukeMTMC-reID dataset, the rank-1 and mAP are increased to 90.1% and 89.6%, respectively. The results of experiment show that our method outperforms the current mainstream method in case of only using global feature.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Baseline** & **Baseline + L1** & **Baseline + L1 + L2** &
\begin{tabular}{c} **Baseline + L1 +** \\ **L2 + L3** \\ \end{tabular} \\ \hline Rank-1 & 91.8 & 93.1 & 94.5 & 95.2 \\ mAP & 81.8 & 84.7 & 86.1 & 87.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of ablation experiment results (Market1501).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Baseline** & **Baseline + L1** & **Baseline + L1 + L2** &
\begin{tabular}{c} **Baseline + L1 +** \\ **L2 + L3** \\ \end{tabular} \\ \hline Rank-1 & 82.6 & 85.3 & 86.4 & 88.9 \\ mAP & 70.5 & 74.1 & 76.9 & 78.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of ablation experiment results (DukeMTMC-reID).
# Self-Organized Proactive Routing Protocol for Non-Uniformly Deployed Underwater Networks
## Related Work  
### Protocols Based on RSS  
The reliable energy-efficient cross-layer routing protocol (RECRP) [7] uses RSS to estimate the distance between the nodes and choose the shortest path. RECRP selects the forwarding node based on distance, residual energy of the sender, and the potential forwarding node and hop count to the sink node. It is also assumed that the nodes are time synchronized. Once the routing update phase is over, the path information is regularly updated using data packets. To update the routing path information regularly, the data packets contain residual energy transmission power information. The neighbor nodes always update their routing table when they receive the data packet, even though they are not the relay node. This increases the data packet overhead cost and unnecessary processing, which increases the energy consumption.  
In [8], the authors propose a routing protocol which uses the RSS to adapt the transmission power to conserve the node energy. The master node initiates the network configuration process by transmitting a topology discovery message (TDM). The nodes will propagate the packet through all the nodes in the network. This way, each node will make a list of all of its neighbors and forward that list to the gateway to select the most appropriate path for each node. The master node selects the path based on the available capacity across the path and number of hops. This protocol has some serious problems. Firstly, it has centralized the path selection process. All the nodes collect the data and send them back to the gateway node, which establishes a routing path for the nodes. If any node goes down, then the master node will not be able to inform the other nodes of the status before the next periodic transmission of TDM. In order to keep the master node more accurately updated with changes in the topology, the periodic transmission of TDM must be quite frequent. In [1], the RSS measurement for short-distance (0-1000 m) was used for underwater acoustic communication and, once again, the distance measurement was used to determine the location of the nodes. The Lambert W function, also known as product log or omega function, calculates the distance using RSS. Lambert W functions are evaluated by Taylor series or numerical methods like the Newton or Halley methods (this last one was used in [1] to approximate distance in four iterations). However, fast convergence using the Halley method increases computational complexity and requirements in the power supply to the processor, which means a larger delay and energy consumption.