esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Examples of New Empirical Laws from \(y_{2\text{A}}(x)\)
### Porosity/Permeability Relationship in Granular Porous Media  
The relationship between permeability (\(k\)) and porosity (\(\O\)) in natural porous media is an important foundation in petrophysics. For granular media, in particular, porosity values mostly range between 0% and 50%, with \(\sim\)25% representing a very significant structural difference between cemented and unconsolidated media. Prior to 2015, there were two ways to understand the \(k(\O)\) relationship. In the petroleum field, the general idea was that porosity and permeability are positively correlated in granular media, which was reflected in a large number of publications on cemented rocks. However, in the field of hydrogeology, it was widely known that the correlation is negative in unconsolidated granular media. This disagreement was not resolved even though one of the most accepted relationships for estimating permeability is the equation of [PERSON] (2015), according to which the permeability of a medium of smaller grain size (clay-silt) is lower than those in media with larger grain sizes (sand-gravel). It should be noted that both cemented media with low \(\O\) and unconsolidated media with high \(\O\) have smaller grain sizes.  
A continuous relationship was presented in [PERSON] et al. (2016) that encompasses cemented to unconsolidated formations (Figure 6) and is a function of a well-known parameter in petrophysics: the exponent \(m\) of the porosity in [PERSON]'s first law (\(F=1/\O^{m}\)). This exponent is referred to as the cementation coefficient owing to the large number of publications in the petroleum literature, even though it also applies to unconsolidated formations. In this case, the developed fitting function has vertical asymptotes for \(\O=0\) and \(\O=1\) and is thus a modified \(y_{\rm 2A}(\O)\) formula.  
Figure 5: Radioactivity build-up factor for a mass attenuation coefficient \(\mu\) and effective distance \(\chi\)([PERSON], 2019).  
This is a good example to understand to what extent an existing expression, a model based on ideal configurations, or laboratory measurements limited to specific lithologies may or may not be useful to cover a geological reality. That would be the case where the variety of behaviours can only be expressed by relationships from a completely empirical point of view. It should be noted that some lithologies have characteristics that strongly differ from those of most granular formations, such as certain volcanic rocks (e.g., pumice) and karst formations (e.g., fractured limestone).
## 1 Introduction
### Different Types of Chronic Rhinitis and LAR Definition  
While AR can be definitively diagnosed with skin or serum tests (specifically, IgE) and has a clearly understood physiopathology, NAR lacks available diagnostic tools and clear therapeutic protocols. In addition, patients diagnosed with AR can show a concomitant NAR [1].  
Evidence in the literature indicates the existence of a local nasal response in patients suffering from typical AR symptoms, but without a history of atopy, defined as a genetic tendency to develop allergic diseases [2; 3; 4; 5; 6].  
This condition is supposed to be a new endotype called local allergic rhinitis (LAR) or \"entropy\" [1; 2; 3; 4; 5]. LAR is characterized by the production of specific nasal IgE antibodies.  
The inflammatory response in the nasal secretions of LAR is a type 2 (T2) inflammation, with the release of typical mediators such as mast cells, eosinophils, IgE, B cells, and T cells, as well as a positive response to nasal allergen challenge (NAC) with the nasal production of IgE, tryptase, and eosinophil cationic protein (ECP) after exposure to aeroallergens but in the absence of systemic atopy [7].  
According to recent research, LAR might affect more than 45% of patients previously given a diagnosis of NAR [8]. In addition to AR, LAR can be classified as seasonal-perennial, intermittent-persistent, and mild-moderate-severe. Moreover, LAR worsens over time, with impairment in quality of life (QoL), persistent and increasing severity of rhinitis, and associations with conjunctivitis and asthma [9].  
Interestingly, recent reports in the literature state that LAR could be defined as a different endotype, affecting an underestimated number of chronic rhinitis (CR) patients.  
In brief, LAR is characterized by symptoms suggestive of AR (nasal obstruction, rhinorrhea, sneezing, itching) negative skin prick test (SPT) and/or serum IgE tests, the presence of local IgE and local ECP following exposure to aeroallergens, and positive NAC (Figure 1).  
In addition, other clinical entities have been described within the LAR spectrum: local allergic asthma (LAA) in non-atopic patients who have positive bronchial provocation tests and dual allergic rhinitis (DAR) in atopic patients (coexistence of LAR and allergic rhinitis) [3; 10; 11; 12]. Furthermore, although the existence of local allergic conjunctivitis (LAC) has not been widely confirmed in the ophthalmologic community, it would represent a different ophthalmic phenotype [13].  
The aim of this review was to update the literature on LAR. In addition, we wanted to focus on the definition of LAR as an endotype independent of AR and NAR.
## 4 Analysis of the Existing Datasets
### Comparison of Surveillance Datasets
#### 4.8.3 Based on Ground Truth Labeling  
The process of labeling datasets can take various forms, with different approaches being employed depending on the specific context and requirements of the study. However, it is generally acknowledged that there are better methods than labeling at the level of entire clips. This is due to the difficulty of explaining such labeling using real-world examples and the likelihood of inaccuracies in the resulting data.  
A more widely accepted method is frame-based labeling, which provides a more accurate result by reducing the error rate. In certain cases, anomalies may occur at specific positions within the analyzed scene. In these instances, labeling at the pixel level may be employed to capture the anomaly in a particular spatial area of the scene. Table 13 illustrates the various labeling types commonly employed in the field.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Indoor** & **Outdoor** \\ \hline UCF-Crime & Avenue \\ UMN & UCF-Crime \\ Subway Entrance & UMN \\ Subway Exit & ShanghaiTech \\  & UCSD Ped1 \\  & UCSD Ped2 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Based on the recording environment.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Grayscale** & **RGB** \\ \hline UCSD Ped1 & Avenue \\ UCSD Ped2 & ShanghaiTech \\ Subway Entrance & UMN \\ Subway Exit & UCF-Crime \\ \hline \hline \end{tabular}
\end{table}
Table 12: Based on the color space.  
All these different forms of labeling are called the \"ground truth\", which refers to information that is assumed to be true or correct. The various types of datasets that are labeled in this way include clip-level (such as UCF-Crime), frame-level (such as UMN, Avenue, UCF-Crime, ShanghaiTech, UCSD, and Subway), and pixel-level (such as Avenue, ShanghaiTech, and UCSD).
# Modeling of Fuzzy Systems Based on the Competitive Neural Network
###### Abstract  
This paper presents a method to dynamically model Type-1 fuzzy inference systems using a Competitive Neural Network. The aim is to exploit the potential of Competitive Neural Networks and fuzzy logic systems to generate an intelligent hybrid model with the ability to group and classify any dataset. The approach uses the Competitive Neural Network to cluster the dataset and the fuzzy model to perform the classification. It is important to note that the fuzzy inference system is generated automatically from the classes and centroids obtained with the Competitive Neural Network, namely, all the parameters of the membership functions are adapted according to the values of the input data. In the approach, two fuzzy inference systems, Sugeno and Mamdani, are proposed. Additionally, variations of these models are presented using three types of membership functions, including Trapezoidal, Triangular, and Gaussian functions. The proposed models are applied to three classification datasets: Wine, Iris, and Wisconsin Breast Cancer (WDBC). The simulations and results present higher classification accuracy when implementing the Sugeno fuzzy inference system compared to the Mamdani system, and in both models (Mamdani and Sugeno), better results are obtained when the Gaussian membership function is used.  
competitive neural network; fuzzy Mamdani; fuzzy Sugeno; fuzzy logic; fuzzy classification
# Research on Map-SLAM Fusion Localization Algorithm for Unmanned Vehicle
## 2 Structured Representation of Lane Lines and Map Information
### Map Information Processing  
Usually, the latitude and longitude of a road centerline, the width of the road, and the slope of the road can be obtained on the map, but this result is not easy to use directly, so this article first processes the data by straight curve fitting. Use the outlier elimination strategy based on RAndom SAmple Consensus(RANSAC) [23] to represent the longitude and latitude of the lane centerline to fit the representation, fit the two lane lines through the lane centerline and the road width, and calculate the structured representation of the two-lane lines, respectively.  
As show in Figures 2 and 3.This paper adds road width information \(d\) based on road information, obtains the lane line information after translation, translates the distance between the lane and the lane line to the left and right, and obtains the left lane line \(l_{left}\) and the right lane line \(l_{right}\), as well as performs the position-preserving sequence KD-Tree, which is used for storage and is more convenient for a subsequent search.
# Tracking and Monitoring System Based on LoRa Technology for Lightweight Boats
## 4 IoT-Based Communication Technologies in Coastal Scenarios
### Proposed Architecture  
Henceforth, an LP-WAN-based end-to-end architecture is proposed in order to (i) retrieve data from ships and (ii) present them through some kind of data visualization platform or control system. This architecture is shown in Figure 4. It has been divided in three different areas, namely, Radio Access Network (RAN), cloud, and user domain. As explained in the previous section, the RAN is enabled by the use of an LP-WAN solution that permits the communication between ships and the base station/gateway in land. This is the most challenging part of the architecture due to the adversity that may be introduced by propagation and scenario conditions. The gateway is the contact point that forwards the received data to the cloud. This intermediate section of the architecture includes a distribution network, e.g., the Internet, that delivers these data to a central server in charge of their aggregation. At this point, several services may be implemented from a parameters' visualizer to more complex applications based on data analytics or alert systems. In this part of the architecture is where the system intelligence resides. Finally, the processed data is presented as useful information to the users, enabling its visualization or even interaction depending on the service under consideration. This way, the end-users of this architecture are able to accurately monitor the maritime activities, including ships' positions and other sensed parameters.
## 3 Results
### Electrical Characterization  
In order to minimize undesired complications due to ionic conduction associated with performing electrical measurements on nanocircuits embedded within hydrogels, we performed two-dimensional measurements of dehydrated hydrogels instead. We used a two-terminal current-voltage (IV) characterization setup with parallel line-shaped gold electrodes. As shown in Figure S5, the gold microelectrodes were fabricated with 200 \(\upmu\)m spacing and were wire-bonded to a commercial ball grid array (BGA) board connected to the setup. The hydrogel was placed across the gap between microelectrodes and then dried completely before IV curves were recorded. IV characterization allows the measurement of small conductivity as a response to an applied voltage. During the test, the current was measured during 10 consecutive pulses of 10 V. The goal of IV characterization is to investigate whether or not DNA crosslinking creates more organized or clumpy networks based on the changes in conductivity after adding spacers to the conjugates.  
As shown in Figure 5a, dehydrated samples of DNA-SWNT conjugates and DNA-SWNT hydrogel both showed non-linear behaviors. With the same applied voltage pulses, the measured current increased greatly in the hydrogel samples with spacers compared to DNA-SWNT conjugates--over a 650-fold increase. Since MWNTs are highly conductive, they showed a wire-like behavior with a much higher conductivity compared to the SWNT samples. In the MWNT case, adding DNA spacers also increased the conductivity of DNA-MWNT conjugates by 45-fold--see Figure 5b. When testing with DNA-AuNP samples, the current increased four-fold after adding spacers to the conjugates and forming gel-like networks, as shown in Figure 5c.  
These electrical measurements demonstrate that modification and organization of nanomaterials using DNA strands can be used to control the electrical behavior of percolating networks and can change the conductivity of composites by using DNA self-assembly to connect the nanomaterials.
## 5 Modeling and Analysis in Ansys
### Boundary Conditions  
A boundary condition is a set of constraints or conditions applied to a model or system that defines how it interacts with its environment during a simulation or analysis. In the context of structural analysis, boundary conditions include supports that restrict movement or rotation, such as fixed supports or displacement constraints, and loads like forces, pressures, or moments that are applied to the system. These conditions ensure that the model behaves realistically by mimicking real-world physical limitations and external influences, and their proper definition is critical for obtaining accurate and reliable results [41].  
In a modal analysis, as mentioned earlier, the objective is to determine the natural frequencies and mode shapes of a structure, which are critical for understanding its vibrational behavior. To set up a modal analysis in ANSYS, the key boundary conditions typically include the following:  
* **Standard Earth gravity:** This is used to account for the gravitational forces acting on the model. While gravity doesn't directly affect the natural frequencies in a typical modal analysis, it is important for some structures, particularly large or complex ones, where gravity might influence the stiffness or mass distribution, potentially affecting the modal characteristics. In this research, gravity is applied in the negative z-direction to simulate the effect of Earth's gravitational force. The value of gravitational acceleration is set to 9.8 m/s\({}^{2}\), which is the standard value used to represent gravity at the Earth's surface.
* **Fixed support:** A fixed support is crucial in modal analysis as it restricts all translations and rotations at specific points or surfaces of the structure. It serves as the foundation or anchor for the model, preventing rigid body motion and ensuring that the structure is held in place during the analysis.  
Both these boundary conditions help in defining a realistic scenario for the modal analysis, ensuring that the vibrational behavior of the structure is accurately captured and that the model is appropriately constrained for analysis.  
Figure 6 illustrates the mounting platform where the IGBT is positioned on the fixed support section, represented in dark blue. The 3D axis in the figure indicates that gravity acts in the negative z-direction, aligning with the simulation setup.
## 5 Experimental Results
### Experiments with CrimeDetection Dataset
The results consistently demonstrate improvements in model precision and accuracy. The overall performance of the model, as measured by AUC-ROC metrics and the low incidence of classification errors, suggests that the model is sufficiently robust for deployment in real-world applications. This makes it a promising tool for security and surveillance environments, where the ability to respond promptly to critical situations and accurately detect criminal activity in real time is fundamental for operational success.
# Blockchain-Based Resource Allocation Model in Fog Computing
## 1 Introduction
### Motivations and Contributions  
The rise of blockchain technology provides the possibility to solve the resource management problem of fog computing. At the same time, it cryptographically guarantees the data's irreversible and unforgeable characteristics and also protects the data security of users in the fog computing environment [43]. Besides, cloud service providers, fog service providers, and users in the fog computing environment also correspond to the identity characteristics of the alliance chain participants.  
Therefore, the primary goal of this paper is to propose a fog computing resource contribution model based on an alliance chain and advances the blockchain system into the fog computing network architecture. The reward and punishment mechanism of blockchain is used to boost fog computing nodes to contribute resources actively and to solve the resource management problem of fog computing. The resource contribution behavior of fog nodes and the satisfaction degree of task completion are recorded in the blockchain form, producing an untamable evaluation system, which can solve problems such as malicious bad review and brushing in practical applications.  
The primary contributions of this paper can be summarized as follows:  
* A blockchain-based fog computing resource contribution model is proposed, which considers a satisfaction degree (task completion degree) as an evaluation index for service provided by fog computing service providers.
* Using differential game theory to solve the proposed model, the numerical simulation is used to discuss the interaction between the optimal resource contribution strategy of the fog node and the optimal benefit under the optimal strategy.  
The rest of the paper is structured as follows: In Section 2, the construction process of the proposed model is presented and explained by a differential game method. The validity of the experimental results is verified by the numerical simulation experiment in Section 3. Finally, in Section 4, the work carried out in this paper is sorted out, and the future research direction is discussed.
## 3 Energy Management Strategy
### Cooperative Eco-Driving (CED) Guidance  
In previous research [22], only traffic light speed assistance (TSA) using V2I communication has been applied. V2I communication information is provided in the form of SpaT (signal phase and timing) messages. It consists of the signal phase, the signal's remaining time, and the traffic light ID/location. TSA provides the possibility of passing a traffic light and the speed required to do so by considering the remaining time of the light and the distance from the light. Therefore, it was possible to reduce fuel consumption by reducing the stopping time in driving conditions with traffic lights or guiding the driver to know where to stop in advance. In this paper, curve speed assistance (CSA) and slope speed assistance (SSA) based on road information are added. In addition, speed information of vehicles ahead is acquired through V2V communication. Therefore, traffic jams can be recognized and prepared for in advance. CSA and traffic jam recognition can not only prevent accidents in real-world driving but can also be a way to improve fuel economy. Utilizing a variety of information, CED guidance not only provides eco-driving speeds, but also improves the timing of coasting driving to prevent unnecessary accelerator pedal use.  
Figure 6: Simulation scenario with the driving path and traffic elements.
## 4 Experiments and Analysis
### ARDU Model Training and Test Analysis  
Model training used the Adam random optimization algorithm to optimize parameters, the default learning rate was 0.001, _batch_size_ was set to 20, and NVIDIA GEFORCE RTX3090 was used to train, validate, and test the model. During training, the model used the feedback of the value of the cross-entropy loss function to continuously adjust the hyperparameters, such as the weight and bias of each layer, to further reduce the difference between the predicted value of the current network and the label value in order to minimize the difference. The cross-entropy loss function is shown in Formula (12), and the Adam algorithm was used as the optimizer for the iterative solution. The function of the Adam algorithm is to optimize the calculation process of the loss function to accelerate the convergence of the model. The decline curve of the training loss function is shown in Figure 6. About 65 batches (Epoch) were trained over 25 h, then the value of the loss function tended to be stable, and the model training was completed by default.  
\[Loss=-\sum_{i=1}^{2}\sum_{j=1}^{n}M(t,f)\cdot\log(\widehat{M}(t,f)) \tag{12}\]  
To preliminarily validate the feasibility of the denoising of the ARDU model, the model was tested on the prepared test set, and the test results are shown in Figure 7. The orange bars in the figure represent the seismic signal with noise before denoising, and the blue bars represent the seismic signal after denoising. According to the calculation in Figure 7a, the average SNR of the signal in the test set before denoising was 3.77 dB and the average SNR of the signal after denoising was 11.59 dB. The improvement in the SNR shows that the ARDU model can effectively remove the noise in seismic signals and enhance signal quality. According to Figure 7b, the correlation coefficient of the signal in the test set before  
Figure 6: Decline curve of the loss function.  
denoising was mainly in the interval [0.69, 0.87] and the average correlation coefficient value was 0.8024. After denoising with the ARDU model, the correlation coefficient interval is increased to [0.87, 0.99], the average correlation coefficient value is 0.9484, and the correlation coefficient is close to 1. This shows that the ARDU model can protect effective signals and reduce waveform distortion. To sum up, the ARDU model has the feasibility of denoising seismic noise in the co-frequency band and it can significantly improve the quality of seismic signals.