esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Prescribed burning is a major source of a fine particular matter, especially in the south-eastern United States, and quantifying emissions from burning operations accurately is an integral part of ascertaining air quality impacts. For instance, a critical factor in calculating fire emissions is identifying fire activity information (e.g., location, date/time, fire type, and area burned) and prior estimations of prescribed fire activity used for calculating emissions have either used burn permit records or satellite-based remote sensing products. While burn permit records kept by state agencies are a reliable source, they are not always available or readily accessible. Satellite-based remote sensing products are currently used to fill the data gaps, especially in regional studies; however, they cannot differentiate prescribed burns from the other types of fires. In this study, we developed novel algorithms to distinguish prescribed burns from wildfires and agricultural burns in a satellite-derived product, Fire INventory from NCAR (FINN). We matched and compared the burned areas from permit records and FINN at various spatial scales: individual fire level, 4 km grid level, and state level. The methods developed in this study are readily usable for differentiating burn type, matching and comparing the burned area between two datasets at various resolutions, and estimating prescribed burn emissions. The results showed that burned areas from permits and FINN have a weak correlation at the individual fire level, while the correlation is much higher for the 4 km grid and state levels. Since matching at the 4 km grid level showed a relatively higher correlation and chemical transport models typically use grid-based emissions, we used the linear regression relationship between FINN and permit burned areas at the grid level to adjust FINN burned areas. This adjustment resulted in a reduction in FINN-burned areas by 34%. The adjusted burned area was then used as input to the BlueSky Smoke Modeling Framework to provide long-term, three-dimensional prescribed burning emissions for the southeastern United States. In this study, we also compared emissions from different methods (FINN or BlueSky) and different data sources (adjusted FINN or permits) to evaluate uncertainties of our emission estimation. The comparison results showed the impacts of the burned area, method, and data source on prescribed burning emission estimations.  
biomass burning; wildland fire; prescribed burning emission; BlueSky; FINN Article An Analysis of Prescribed Fire Activities and Emissions in the Southeastern United States from 2013 to 2020
## 4 Classifier Algorithm
### K-Nearest Neighbor  
\(K\)-nearest neighbor is a target classification method based on the closest training instance in feature space. The function can only be locally approximated, and all calculations are deferred to the classification. The KNN algorithm is divided into two phases: the training phase and the classification phase. The training examples are vectors in the multidimensional feature space. Each vector has a class label. The training phase of the algorithm only stores the feature vector (reference vector library) and class label of the training samples. In the classification phase, K is a user-defined constant, and an unlabeled vector (a query or test point) is classified by assigning the label which is most frequent among the K training samples nearest to that query point. In other words, the KNN method compares the query point or an input feature vector with a library of reference vectors and the query point is identified to be of the class of library feature vector to which it has the closest distance. The calculation process is shown as follows:
# A Novel Perturbation Consistency Framework in Semi-Supervised Medical Image Segmentation
## 4 Experimental Results and Analysis
### Comparison with Other Models
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Method**}} & \multicolumn{3}{c}{**3\% Labeled**} & \multicolumn{3}{c}{**7\% Labeled**} & \multicolumn{3}{c}{**20\% Labeled**} & \multicolumn{3}{c}{**40\% Labeled**} \\ \cline{2-13}  & **Avg.** & **RV** & **MY.** & **LV** & **Avg.** & **RV** & **MY.** & **LV** & **Avg.** & **RV** & **MY.** & **LV** & **Avg.** & **RV** & **MY.** & **LV** \\ \hline BCP [35] & 88.0 & 85.9 & 85.7 & 92.4 & 88.8 & 86.0 & 87.2 & 93.1 & 89.5 & 86.8 & 87.9 & 93.9 & 91.6 & 91.4 & 89.6 & 93.7 \\ ICTMSeg [36] & 86.6 & 83.4 & 84.6 & 91.7 & 88.9 & 87.9 & 87.2 & 91.5 & 91.1 & 89.5 & 90.0 & 93.7 & 91.6 & 91.4 & 89.6 & 93.7 \\ SLC-NET [37] & 86.2 & 84.8 & 82.8 & 91.1 & 89.2 & 86.3 & 87.7 & 93.5 & 91.2 & 90.8 & 89.2 & 93.5 & 92.1 & 91.3 & 90.8 & 94.2 \\ MCF [38] & 73.5 & 72.2 & 74.0 & 75.5 & 82.0 & 79.8 & 81.5 & 85.0 & 85.7 & 83.9 & 85.7 & 83.8 & 88.2 & 85.2 & 86.8 & 92.5 \\ UniMatch & 86.6 & 85.2 & 83.2 & 91.5 & 89.3 & 86.3 & 87.8 & 93.6 & 90.6 & 87.3 & 89.6 & 94.8 & 91.5 & 89.7 & 85.3 & 89.4 \\ UNet & 57.8 & 55.9 & 57.8 & 59.6 & 79.4 & 76.2 & 79.0 & 82.9 & 84.1 & 79.9 & 81.3 & 88.2 & 87.4 & 81.9 & 84.5 & 90.2 \\ DTC [19] & 81.4 & 76.8 & 79.9 & 86.3 & 84.2 & 79.8 & 82.1 & 88.5 & 87.7 & 82.2 & 85.9 & 90.5 & 89.2 & 87.6 & 86.4 & 93.4 \\ ACT-Net & 80.2 & 74.7 & 78.3 & 84.8 & 84.5 & 82.1 & 78.6 & 88.9 & 87.4 & 82.0 & 84.4 & 90.9 & 89.9 & 84.3 & 87.6 & 93.2 \\ MCCauSSL [39] & 81.6 & 75.4 & 79.2 & 86.3 & 85.8 & 80.3 & 83.7 & 89.6 & 87.4 & 81.6 & 84.5 & 90.2 & 89.9 & 83.4 & 86.7 & 92.5 \\ ARCO [9] & 88.3 & 86.9 & 86.2 & 91.9 & 89.8 & 88.5 & 88.2 & 92.7 & 90.1 & 88.9 & 88.4 & 93.1 & 91.1 & 90.4 & 88.9 & 94.0 \\ Ours & **89.2** & 86.3 & 87.8 & 93.6 & **90.6** & 87.3 & 89.6 & 94.8 & **91.5** & 91.1 & 89.9 & 93.5 & **92.6** & 92.1 & 90.7 & 94.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison between HCLM and other state-of-the-art methods on ACDC dataset. The presented table displays the average DICE score for each segmentation category and the overall average DICE score. Bold values indicate the superior performance. Figure 4: Qualitative comparison of segmentation results on the ACDC dataset between our method (HCLM) and other state-of-the-art models. The figure highlights the segmentation performance for various cardiac structures such as the right ventricle (RV), myocardium (MYO), and left ventricle (LV). Figure 5: Training performance of the HCLM model under different label ratios on the ACDC dataset, demonstrating improvements in segmentation accuracy as the labeling ratio increases. Figure 6: Box plots of DICE, HD95, and ASD for different models at 3% labelling rate.
# Urban Transportation Network Design for Fresh Fruit and Vegetables Using GIS-The Case of Bangkok
## 1 Introduction
According to the development of the fresh fruit and vegetables distribution, the design of the transportation network should take into account the location and distribution from a wholesaler to DCs, and then deliver products from DCs to retailers. Therefore, the location-routing problem (LRP) model, which integrates the location-allocation problem (LAP) and vehicle routing problem (VRP), is used to design the network transportation for fresh fruits and vegetables in Bangkok. The main contribution of this research can be categorized five-fold. First, we analyzed the current distribution network for fresh fruit and vegetables from the existing DC to retailers. We provided managerial insights for helping decision makers in charge of designing the distribution network to solve the major problems of the existing network. Second, we have suggested a framework of the two-phase sequential approach that consists of very well-known research problems, LAP, and vehicle routing problems with time window (VRPTW), using Geographic Information systems (GIS). Third, we have devised a systematic approach for designing transportation networks in the public sector by applying the closest facility, minimizing the number of locations, and maximizing coverage based on the exact location and the practical road conditions for urban transportation. Fourth, we compared the performance of eight different scenarios based on total travel time, total distance, number of required trucks, number of DCs, and fairness among drivers. The result of comparing analysis may help decision makers to design a new distribution network in order to support the increasing demand of fresh fruit and vegetables. Finally, the proposed network can improve the efficiency by reducing the travel distance and enhancing the fairness among drivers while satisfying several constraints. The remainder of this research is organized as follows. Section 2 provides a review of relevant literature. Then, in Section 3, the proposed research background is introduced. In Section 4, we describe the research methodology used in this research. Scenario analysis and sensitivity analysis are presented and discussed in Section 5. Finally, conclusions, limitations, and directions of future research are discussed in Section 6.
## 2 Materials and Methods
### Electrochemical Characterization  
The PEC measurements (Ivium Technologies, Nstat, Netherlands) were carried out with a three-electrode system with a Pt mesh as a counter electrode and an Ag/AgCl (sat. KCl) reference electrode in a CO2 saturated KHCO3 electrolyte. All measurements are conducted in the two-compartment H-type electrochemical cell with a piece of proton exchange membrane (Nafion(r) 117, Sigma-Aldrich) as the separator. Before measurement, the electrolytes in the H-type cell were purged with CO2 gas for at least 30 min. A Xe arc lamp used as a light source was calibrated to 1 sun (100 mW/cm\({}^{2}\), AM 1.5 G) using a reference photodiode. For the linear sweep with a cathodic direction, a scan rate of 20 mV/s was used. The measured current density was normalized to the electrodegeometrical area. The measured potential vs. Ag/AgCl was converted to the RHE scale according to the [PERSON] equation:  
\[E_{RHE}=E_{Ag/AgCl}+0.059\times pH+E_{Ag/AgCl}^{0} \tag{1}\]  
where \(E_{RHE}\) is the potential vs. RHE, \(E^{0}\)\({}_{Ag/AgCl}\)= 0.198 V at 25 \({}^{\circ}\)C, and \(E_{Ag/AgCl}\) is the measured potential vs. the Ag/AgCl reference electrode.  
The evolved gas was analyzed using gas chromatography (Agilent GC 7890B, Agilent Technologies, Inc., United States) connected to the cathodic compartment of an H-type electrochemical cell while a constant potential was applied. A 20.0 sccm rate of CO\({}_{2}\) gas was delivered into the cathodic compartment and vented into the gas sampling loop of the GC. The GC was equipped with a micropacked column (ShinCarbon ST 100/120), a thermal conductivity detector (TCD), and a flame ionization detector (FID), with argon as the carrier gas. The faradaic efficiency was calculated according to the following equations:  
\[j_{CO}=\frac{peak\ area\ of\ CO}{\alpha}\times flow\ rate\times\frac{2 Fp_{0}}{RT} \times\left(electrode\ area\right)^{-1} \tag{2}\]  
\[j_{H_{2}}=\frac{peak\ area\ of\ CO}{\beta}\times flow\ rate\times\frac{2 Fp_{0}}{RT} \times\left(electrode\ area\right)^{-1} \tag{3}\]  
\[F.E_{\text{-CO}}=j_{CO}/j_{mussured} \tag{4}\]  
\[F.E_{\text{-H_{2}}}=j_{H_{2}}/j_{mussured} \tag{5}\]  
where \(\alpha\) and \(\beta\) are conversion factors for CO and H\({}_{2}\), respectively, based on calibrations of the GC with a standard sample, \(p_{0}\) = 101.325 kPa, and \(T\) = 298.15 K.  
To compare the electrochemical surface area, double layer capacitance of R-Ag and Ag catalysts was measured on TiO\({}_{2}\)/_p_-Si substrate in 0.1 M KHCO\({}_{3}\) electrolytes according to previously published method [39]. The charging current was measured from the CVs at different scan rates (10-100 mV s\({}^{-1}\)). The relation between the double layer capacitance, the charging current, and the scan rate is given Equation (6).  
\[i_{c}=vC_{DL} \tag{6}\]  
where i\({}_{c}\), \(v\), and C\({}_{\text{DL}}\) are charging current, scan rate, and double layer capacitance, respectively.
# An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials
## 5 Implementation Results and Analysis
### Performance Analysis
#### 5.1.2 Encryption and Decryption  
From the encryption and decryption time trends shown in Figures 11 and 12, the standard CEPKC has the lowest execution time for encryption and decryption, especially after 1024 bits, making it the best in terms of encryption and decryption time. Since the CMPKC-\(k_{i}\) scheme involves additional computational steps for coefficient selection during the encryption and decryption phases, increasing the overall complexity of the algorithm, attackers are unable to guess the private key or plaintext by multiplying the public key and prime numbers. Furthermore, until the typical size of 1024 bits, our proposed CMPKC-\(k_{i}\) scheme performs comparably to CEPKC in terms of encryption and decryption times. For higher bit sizes, CMPKC-\(k_{i}\) demonstrates better efficiency in both encryption and decryption processes compared to CMPKC, slightly lagging behind the standard CEPKC scheme. However, it is worth noting that our proposed encryption scheme is generally applied in lightweight encryption environments, with the recommended modulus \(N\) size being 1024 bits. Therefore, our CMPKC-\(k_{i}\) scheme stands out as efficient and high-performing among all the discussed algorithms. In an overall performance evaluation, among the three algorithms discussed, the CMPKC-\(k_{i}\) algorithm proves to be the most efficient and effective, positioning it as the best algorithm after the standard CEPKC, while CMPKC emerges as the least efficient variant. The reasonable increase in key generation time in CMPKC-\(k_{i}\), compared to standard CEPKC, is justified as the introduction of \(k_{i}\) adds complexity, making the CMPKC-\(k_{i}\) system more time-consuming to break and enhancing overall security.  
Figure 10: Key Generation Time Comparison.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **The Length of Module N** \\ **(in bits)** \\ \end{tabular} } & **Key Generation Time** & **Encryption Time** & **Decryption Time** \\  & & **(in ms)** & **(in ms)** & **(in ms)** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} CMPKC \\ \end{tabular} } & 128 & 68.54 & 1.62 & 1.36 \\  & 256 & 134.02 & 3.93 & 3.89 \\  & 512 & 224.14 & 12.02 & 11.92 \\  & 1024 & 1482.36 & 47.14 & 45.24 \\  & 2048 & 12,696.48 & 318.22 & 297.67 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} CMPKC- \(k_{i}\) \\ \end{tabular} } & 128 & 62.31 & 1.46 & 1.44 \\  & 256 & 121.89 & 3.52 & 3.43 \\ \cline{1-1}  & 512 & 203.76 & 10.96 & 10.46 \\ \cline{1-1}  & 1024 & 1347.6 & 43.61 & 42.13 \\ \cline{1-1}  & 2048 & 11,542.25 & 290.33 & 287.29 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._
# Research on the Reliability of Bridge Structure Construction Process System Based on Copula Theory
## 3 Engineering Applications
### Calculation of System Reliability Indicators
#### 3.2.3 Selection of Copula Function and Calculation of Related Parameters  
Based on the review of a large amount of references, four Copula functions commonly used in engineering were selected in this paper to carry out the analysis of failure mode correlation. These four functions are Gaussian Copula function, Clayton Copula function, Frank Copula function, and Gumbel Copula function.  
We used Equation (8) to calculate the values of the relevant parameters for each failure mode in the case of four different combinations of Copula functions separately, and the results are listed at Table 5.
## 2 Operation Principle of VAPP  
Considering variable control, the variable-displacement mechanism of VAPP includes manual control, mechanical control, electric control, hydraulic control and electro-hydraulic control for it to achieve displacement control, pressure control, flow control, power control and some composite control. As per the AAPP's control differential cylinder hydraulic system requirements, the variable-displacement control unit is composed of a servo-proportional valve and symmetric hydraulic control cylinder (SHCC) (as shown in Figure 1a). The servo proportional valve and the angular displacement sensor, steadily connected with the swash plate, are used to compose the closed-loop control mode. By controlling the size and direction of swash plate angle of the asymmetric pump, the control of VAPP bi-directional variable displacement is attained. Thus, it meets the condition of quick dynamic response and optimal energy efficiency. Figure 1b illustrates the loop principle to control differential cylinder. In Figure 1, Port A, Port B and Port T are the inlet/outlet openings in the valve plate.
# Multi-Module Combination for Underwater Image Enhancement
## 4 Experiment
### Parameters and FLOPs  
In this study, the computational performance of each method was systematically evaluated. The experimental configurations are as follows: the hardware platform consists of an Intel Core i7-13620H processor (4.9 GHz) with 16 GB of RAM. Fifty test images, each measuring 256 \(\times\) 256 pixels, were randomly selected from the EUVP dataset. All methods were tested on the same computer, which was equipped with an Intel(r) Core(tm) i7-13620H CPU operating at 4.9 GHz and 16 GB of RAM. EUICCCLF, MLLE, PCDE, BRUIE, and our method were implemented in MATLAB (R2023b), while the UIESS and UWCNN methods were developed using PyTorch (1.12.0), and the UW-AEE method was implemented in TensorFlow (1.14.0). Table 5 presents a comparison of the average runtimes for the different UIE methods. The experimental results indicate that our method demonstrates superior performance in UIE experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Metric**} \\  & **NIQE** & **UCIQE** & **UIQM** \\ \hline WBVR & 15.254 & 0.782 & 104.244 \\ WBCE & 14.332 & 0.633 & 104.375 \\ WB & 16.417 & 0.703 & 100.291 \\ CE & 12.979 & 1.757 & 113.581 \\ VR & 12.778 & 0.784 & 104.663 \\ UMMC & 12.612 & 2.532 & 124.220 \\ \hline \hline \end{tabular}
\end{table}
Table 4: An underwater image quality evaluation of different variants of the presented method. The best result is in red, whereas the second-best result is in green.  
Figure 8: Ablation results for each key component of our method.
## 3 SSMA Broadcast Protocol
### Broadcast Approaches  
In this section, we describe some methods to reduce message collisions within BSS. Three progressively improved broadcast methods include a contention-based transmission (CBT), a slotted contention-based transmission (SCBT), and a slot-scheduled contention-based transmission (SSCBT).
# Hardware Model Checking Algorithms and Techniques
## 2 Background and Notation
### Propositional Logic
#### 2.4.1 Syntax of Propositional Logic  
From the syntactic standpoint, the language of propositional logic consists of a set of non-logical symbols called _propositional variables_ and a set of logical symbols, comprising _logical connectives_ (\(\
eg\), \(\wedge\) and \(\vee\)) and _logical constants_ (\(\top\) and \(\bot\)).  
**Definition 9** (Syntax of Propositional Formulas).: _Given a set of propositional variables \(AP\), the syntax of propositional formulas is defined inductively as follows:_  
* \(\top\) _and_ \(\bot\) _are propositional formulas._
* _Each_ \(a\in AP\) _is a propositional formula._
* _Given_ \(F\)_, a propositional formula,_ \(\
eg F\) _is a propositional formula._
* _Given_ \(F_{1}\) _and_ \(F_{2}\)_, propositional formula_ \(F_{1}\wedge F_{2}\) _is a propositional formula._
* _Given_ \(F_{1}\) _and_ \(F_{2}\)_, propositional formula_ \(F_{1}\lor F_{2}\) _is a propositional formula._  
Note that \(\bot\) and \(\vee\) can be omitted from Definition 9 without losing expressiveness as they can be easily derived from \(\top\), \(\
eg\) and \(\wedge\). Parentheses \"(\" and \")\" can be used to improve the readability of formulas. We also provide the following definitions about propositional formulas.  
**Definition 10** (Atomic Formulas).: _An atomic formula is a propositional formula consisting of a propositional variable only._  
**Definition 11** (Support).: _Given a propositional formula \(F\), we call support of \(F\), denoted by \(\operatorname{Vars}(F)\), the set of propositional variables occurring in \(F\). Accordingly, given a set of propositional formulas \(\Phi\), we call support of \(\Phi\), denoted by \(\operatorname{Vars}(\Phi)\), the set of propositional variables occurring in at least one formula of \(\Phi\):_\[\text{Vars}(\Phi)=\bigcup_{F_{i}\in\Phi}\text{Vars}(F_{i}) \tag{1}\]
# Steering Pull Model and Its Sensitivity Analysis
## 4 Steering Pull Model  
### Integration of Acting Forces on Tires  
The steering pull distance of a vehicle is determined by the force acting on the tires. When a vehicle goes on the road with a bank angle, the force acting on the tires is shown Figure 7. However, as mentioned in Chapter 2 and 3, there are several forces that hinder the tires from turning and therefore, Equation (1) is modified and the tire turning force can be represented as Equation (17).  
\[Ma_{n}\cos\theta_{BA}=M\frac{V^{2}}{r}\cos\theta_{BA}=Mg\sin\theta_{BA}-F_{tire} \tag{17}\]  
where \(F_{tire}=\) force that hinders tire turning.  
For the calculation of \(F_{tire}\), the bicycle model is employed. As shown in Figure 7, when a vehicle moves, the longitudinal and lateral forces acting on the tires are represented as the following Equation (18).  
\[M\frac{V^{2}}{r}\cos\theta_{BA}=Mg\sin\theta_{BA}-\left(F_{xf}\sin\theta_{WA}+ F_{yf}\cos\theta_{WA}\right) \tag{18}\]  
where \(\theta_{WA}=\) heading angle of wheel, \(F_{xf}=\) longitudinal force acting on front tire, \(F_{yf}=\) lateral force acting on front tire.  
In the case of steering pull in which the vehicle has a small wheel angle and a constant vehicle speed, the longitudinal force can be neglected because it hardly has an influence on the steering pull. On the other hand, polyester force, lateral force due to slip angle, lifting force of cast & kingpin, camber force, and friction force transferred from the steering system should be considered. When all factors are considered, Equation (18) can be rewritten as Equation (19).  
\[M\frac{V^{2}}{r}\cos\theta_{BA}=Mg\sin\theta_{BA}+F_{y,ply}-F_{y}-F_{y,c}-F_{ lift}-F_{fric} \tag{19}\]  
Figure 6: Steering system model.  
Figure 7: Bicycle model.  
where \(F_{y,ply}\) = polyester force, \(F_{y}\) = lateral force due to slip angle, \(F_{y,c}\) = lateral force due to camber, \(F_{lift}\) = lifting force due to caster and kingpin, \(F_{fric}\) = transferred friction force of steering system.