esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Tuna Detection and Automated Counting Method Utilizing Improved YOLOv7 and ByteTrack
## 4 Experiment and Results
### Analysis of Experimental Results
#### 4.2.6 Model Validation  
To understand the classification accuracy and counting performance of the proposed counting algorithm, a segment of daytime fishing video and a segment of nighttime fishing video were selected for video detection and counting. The results are shown in Table 6, where \"day\" and \"night\" represent the fishing time of tuna during the day and night, respectively.  
From Table 6, it can be observed that the counting accuracy has reached 96.4%. One albacore tuna caught during daytime fishing was accidentally released back into the sea, resulting in its being counted twice. Due to poor visibility at night, one yellowfin tuna caught during nighttime fishing was incorrectly classified as an albacore tuna. The results indicate that the model proposed in this study is capable of effectively counting multiple categories of tuna.
## 2 Materials and Methods
### Antimicrobial Activity  
Strains used for determining antimicrobial activity included _Bacillus subtilis_ ATCC 6051, _B. alvevi_ ATCC 6344, _B. cereus_ ATCC 21772, _B. megaterium_ ATCC 25848, _B. pumilus_ ATCC 7061, _Staphylococcus aureus_ ATCC 6538, _S. epidermidis_ ATCC 14990, _S. saprophyticus_ ATCC 15305, _Enterococcus faecalis_ ATCC 29212, _Mycobacterium smegmatis_ ATCC 19420, _Escherichia coli_ ATCC 9637, _Proteus mirabilis_ CECT 170, (from Type Culture Spanish Collection), _Pseudomonas aeruginosa_ AK 958 (from the University of British Columbia, Department of Microbiology collection), _Salmonella sp._ CECT 456, _Klebsiella oxytoca_ LMM2 (clinical isolate, University of La Laguna), and _Candida albicans_ CECT 1039. The bacteria cultures were developed in nutrient broth (NB) or brain heart infusion broth (for _E. faecalis_ and _M. smegmatis_ containing 0.06% Tween 80), and the yeast was cultured in Sabouraud liquid medium at 37 \({}^{\circ}\)C. All culture media were purchased from Oxoid. The minimum inhibitory concentrations (MICs) were determined for each compound in triplicate by broth microdilution method (range 0.08-40 \(\underline{\rm u}\)g/mL) in 96 well microtitre plates, according to the M07-A9 of the CLSI (Clinical and Laboratory Institute) [32]. Wells with the same proportions of dimethyl sulfoxide (DMSO) were used as controls and never exceeded 1% (_v_/_v_). The starting microorganism concentration was approximately 1-5 \(\times\) 10\({}^{5}\) CFU/mL, and growth was monitored by measuring the increase in optical density (OD) at 550 nm (OD\({}_{550}\)) with a microplate reader (Multiskan Plus II, Tritertek, Huntsville, AL, USA) and viable count in agar plates. The MIC was defined as the lowest concentration of compound that completely inhibits growth of the organisms compared with that of the control after incubation time.
# Synthesis of Silicon Carbide Powders from Methyl-Modified Silica Aerogels
## 3 Results and Discussion
The oxygen contents in the SiC powder prepared at 1525 \({}^{\circ}\)C for 5 h with C/SiO\({}_{2}\) ratios of 0.5 and 3 were 0.36 and 0.05 wt.% for the samples after the heat treatment in air, respectively. Almost-pure SiC powders were obtained when the C/SiO\({}_{2}\) ratio was 3. Figure 6: XRD patterns of the powder samples synthesized from (**a**) the hydrophobic silica aerogel (C/SiO\({}_{2}\) ratio of 0.5), (**b**) the hydrophobic silica aerogel (C/SiO\({}_{2}\) ratio of 3), (**c**) the hydrophilic silica aerogel (C/SiO\({}_{2}\) ratio of 1), and (**d**) the quartz (C/SiO\({}_{2}\) ratio of 3). Figure 7: Surface structure of the hydrophobic silica aerogel used in this study. The overall carbothermal reduction reaction for silica, which is a solid (SiO\({}_{2}\))-solid (C) reaction, is shown in Equation (1). At low temperature, the carbothermal reduction reaction can be written in as follows [43]:  
Figure 1:0  
Figure 1:1  
At a high temperature and high P\({}_{\text{SiO}}\)/P\({}_{\text{CO}}\) ratio, whisker- or nanofiber-like SiC can be grown on the SiC nuclei based on Equation (4), which is similar to the CVD of SiC [35,44]. The SiC particles obtained through Equation (3) serve as nucleation sites for Equation (4). Figure 1:2  
The morphologies of the SiC particles differ, as shown in Figure 9a,b, which indicates that the formation mechanisms for the SiC particles differ. At a C/SiO\({}_{2}\) ratio of 0.5, the morphologies of the synthesized SiC particles are similar to that of the starting silica aerogel, suggesting that the SiC formation mechanism relies on Equation (1), that is, the solid-solid reaction. The solid SiO\({}_{2}\) particles directly react with carbon from the surface methyl groups, whereby spherical SiC powder was observed at a C/SiO\({}_{2}\) ratio of 0.5. The molar ratio of carbon in the SiO\({}_{2}\)-C mixture is of great importance because the CO gas that is produced by Equations (1) or (2) was sufficiently supplied. The high C/SiO\({}_{2}\) ratio (3) leads to an  
Figure 1:3 SEM images of (**a**) the starting spherical silica aerogel and b-SiC powders synthesized from the SiO\({}_{2}\)–C mixtures at (**b**) 1450 \({}^{\circ}\)C, (**c**) 1475 \({}^{\circ}\)C, (**d**) 1500 \({}^{\circ}\)C, and (**e**) 1525 \({}^{\circ}\)C for 3 h. The C/SiO\({}_{2}\) ratio was 0.5. Figure 1:4 SEM images of the SiC powders synthesized from SiO\({}_{2}\)–C mixtures with C/SiO\({}_{2}\) ratios of (**a**) 0.5 and (**b**) 3 at 1525 \({}^{\circ}\)C for 1 h. The powder samples were heat-treated in air at 1000 \({}^{\circ}\)C for 1 h after reduction. increase in the partial pressure of gaseous species, such as SiO(g) and CO(g); consequently, SiC particles form and grow, as illustrated in Equations (3) and (4). The nanofiber-like SiC observed in Figure 9b might be due to the reaction expressed in Equation (4).
## 1 Introduction
Our primary objective is to mitigate those unexpected artifacts caused by the staircasing effect by incorporating appropriate constraints into the gradient domain within optimization problems. In this paper, we propose effective smoothing methods based on minimizing the \(\ell_{0}\) gradient with novel gradient constraints that suppress those pseudo-edge artifacts. In particular, we introduce two types of gradient constraints: a box-type gradient constraint and a ball-type constraint. We incorporate them in the \(\ell_{0}\) gradient minimization problem. These constraints are imposed using an appropriate reference image (e.g., an input image is used as a reference for image smoothing) to suppress pseudo-edges in homogeneous regions and the blurring effect of strong edges. We find a solution to the proposed minimization problems via the alternating direction method of multipliers (ADMM) [37,60,61] for nonconvex optimization. The contributions of this paper are as follows. 1. Image smoothing while maintaining gradient characteristics of reference image: Existing smoothing methods based on the \(\ell_{0}\) gradient and TV do not explicitly consider any constraints in the gradient domain. In the proposed method, the local smoothness properties of a reference image can be explicitly considered constraints in the gradient domain. Therefore, we can suppress artifacts, including the staircasing effect, through image smoothing. 2. Strict or flexible gradient constraints on the sign of gradients: Since the box-type gradient constraint is strict with respect to the sign of gradients, gradient reversals are well suppressed. In contrast, the ball-type constraint is flexible with respect to the sign of gradients, allowing robust image smoothing, even when a reference image is degraded by noise and has different shading characteristics, including gradient reversals. The remainder of this paper is organized as follows. In Section 2, we present several mathematical preliminaries and the ADMM algorithm. In Section 3, we introduce the novel box- and ball-type gradient constraints and the minimization problems for image smoothing. In Section 4, several examples are shown to confirm the validity of the proposedmethods compared with the existing smoothing methods based on the \(\ell_{0}\) gradient. Finally, Section 5 concludes the paper. In the previous paper [62], we introduced only the box-type gradient constraint to extend the \(\ell_{0}\) gradient minimization. Additionally, robustness to noise and performance when using some reference images with different luminance and colors with/without reversed edges were not sufficiently evaluated. In this paper, we show the limitation of the box-type gradient constraint and propose a novel ball-type gradient constraint that can be applied flexibly, even when using references with noise or edge reversals. We also extend the proposed gradient constraints to the \(\ell_{0}\) gradient projection [37] and the details of the algorithm derivation. Furthermore, we examine an experiment on JPEG artifact removal.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 3 Constructing the Framework for CNN and LSTM Assisted GNSS/INS Navigation System
### Constructing the Framework for CNN- and LSTM-Assisted GNSS/INS Navigation System
#### 3.2.1 CNN Feature Extraction  
The CNN structure consists of an input layer, convolutional layers, pooling layers, a fully connected layer, and an output layer. Among them,  
Input Layer: Takes preprocessed GNSS and INS data as input.  
Convolutional Layer: Utilizes multiple convolutional kernels to perform convolution operations on input data, extracting local features.  
Pooling Layer: Conducts pooling operations on the feature maps output from the convolutional layer, further compressing features and reducing computational complexity.  
Fully Connected Layer: Flattens the output of the pooling layer and integrates and transforms features through fully connected layers.  
The mathematical model expression for the CNN is  
\[y_{i}^{n}=f\Big{(}x^{n-1}*C_{i}^{n}+d_{i}^{n}\Big{)} \tag{34}\]  
where \(y_{i}^{n}\) is the output of the \(i\)th convolution in the \(n\)th convolutional layer; \(x^{n-1}\) is the input of the \(n\)th convolutional layer; \(*\) is the convolution operation; \(C_{i}^{n}\) is the weight of the \(i\)th convolutional kernel in the \(n\)th convolutional layer; and \(d_{i}^{n}\) is the bias parameter of the \(i\)th convolutional layer in the \(n\)th convolutional layer.  
In order to increase the diversity of the learned features, CNN layer feature extraction is performed for 4 convolutional computation layers, where the 1 st convolutional layer is 64 layers, the 2 nd convolutional layer is 126 layers, the 3 rd convolutional layer is 256 layers, and the 4 th convolutional layer is 256 layers. After the convolutional computation layer, the output of the convolutional layer is made nonlinearly mapped by the activation function. Here, the ReLU function is used. The expression is  
\[f(x)=\max(0,x) \tag{35}\]  
It is characterized by fast convergence and simplicity in finding the gradient. Its function image is shown in Figure 5.  
The pooling layer is sandwiched between successive convolutional layers and is used to compress the amount of data and parameters and reduce overfitting. The fully connected layer is at the tail of the convolutional neural network and is connected in the same way as traditional neural network neurons.
## 4 Case Study
### Comparative Analysis of Encoding Scheme Effectiveness  
To validate the effectiveness of the two encoding schemes developed in this paper, we applied both schemes to encode and initialize the test case provided by [PERSON] et al. [13]. Each encoding scheme was executed independently for 30 iterations, generating 300 encodings per iteration, and recording the minimum and maximum values of \(C_{max}\) across all encodings. According to the experimental results, the distribution ranges of the initial individuals generated by the two encoding schemes are shown in Figure 8.  
By comparing the range of \(C_{max}\) values of the initial individuals generated by the two encoding methods, it can be observed that the encoding scheme based on the operation sequence constraint matrix (encoding scheme 2) produces a broader distribution of individual fitness values. This indicates that individuals generated by this method exhibit greater diversity.
# NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms
## 7 Heuristic Solution: FastTs
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Strategy/Technique** & **General Description** & **Features and Challenges** \\ \hline \multirow{6}{*}{Pre-partition Strategy} & Strategy based on a divide-and-conquer method. In this strategy, the original set of \(N\) is divided into subsets. The OptTs strategy is applied to each of these subsets. This operation reduces the complexity by reducing the \(VarSer\) and the \(CombSer\) in each processed subset. This heuristic strategy iteratively processes all sub-problems, and the solution is obtained as a combination of partial solutions. & - Easy implementation from OptTs. \\ \hline \multirow{6}{*}{Greedy Approach} & Iterative and constructive algorithm, in which the \(VarSer\) to be processed, one at time, is selected based on the value of a parameter (e.g., \(P_{L}^{LJ}\), \(T_{L}^{k}\) or the ratio between \(P_{L}^{LJ}\) and \(T_{L}^{k}\)), respecting the \(P_{ES}\) and with the aim of first minimizing \(P_{RES}\) and then increasing \(AR\). & - The strategy must be built from scratch and there are many feasible implementations. \\ \hline \multirow{6}{*}{Genetic Algorithm} & Strategy inspired by the behavior of biological systems. First, an initial population of chromosomes is created, in which each chromosome represents a possible \(CombSer\) (solution) formed by randomly selected \(VarSer\). Later, the population evolves along with generations considering the action of the genomic operators (crossover and mutation) and the selection of fitest individuals (combinations with the best \(P_{RES}\) metric). Finally, in the last generation, the solution with the best fitness function (minimum \(P_{RES}\)) is selected. & - Medium complexity implementation due to features and requirements of the proposal (e.g., time-shifting, priorities, rejection). \\ \hline \multirow{6}{*}{Dynamic Programming Approach} & Strategy that uses a dynamic programming method. The algorithm simplifies the analysis of \(N\) simultaneous services by analyzing and solving them one at a time. Systematically, using a bottom-up approach, the results of a previous set of services (considering their \(VarSer\)) are stored and used for solving a greater \(N\). & - Good running time performance, but difficult implementation due to the two-dimensional behavior of the problem (power and time parameters) and multiple-choice analysis for \(VarSer\) selection. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Heuristics strategies analyzed to solve the \(P_{RES}\) minimization problem efficiently.
## 4 Experiments and Discussion
### Crack Region Detecting Accuracy
From the comparisons of Table 1, the developed MECD method has achieved the most satisfactory result, with a \(FPR\) value within the bounds of 0.025. Through the comparisons, it is clear that the proposed MECD method and DL-based method have smaller \(FPR\) values than SVM-based one. A possible reason is that the MECD model and DL-based one both exploit multi-layer crack region feature representation, thereby addressing the troublesome surrounding noises will. Furthermore, the DL-based crack defect detection had a larger \(FNR\) value than our developed MECD algorithm, which may be due to the over-fitting issue.
## 2 Background Knowledge
### Differentiable Pooling  
As one kind of hierarchical pooling method, differentiable pooling (DIFFPOOL) is a pooling mechanism based on graph collapse, which fuses the nodes in the same cluster to form the signal of a super-node in the next layer. Compared with global pooling methods, the DIFFPOOL method can capture the rich structure information better in various graph architectures. Real-world graphs tend to be more complex ones; hierarchical pooling methods can extract the complex hierarchical structure and generate a better modeling of the entire graph structure for downstream tasks.  
An illustration of a DIFFPOOL module is shown in Figure 1. Nodes with the same color are divided into the same cluster. The graph collapses hierarchically into more and more sparse subgraphs, and eventually one super-node is formed that contains the whole graph information. Given the cluster assignment matrix \(S^{(l)}\), adjacency matrix \(A^{(l)}\) and node embedding matrix \(Z^{(l)}\), a new coarsened adjacency matrix \(A^{(l+1)}\) and a new matrix of embeddings \(H^{(l+1)}\) for each of the nodes/clusters in the coarsened graph is generated as described in Equations (4) and (5), where \(n_{l+1}\) represents the number of nodes (or clusters) in the \(l+1\) layer.  
\[H^{(l+1)}={S^{(l)}}^{T}Z^{(l)}\in R^{n_{l+1}\times d} \tag{4}\]  
\[A^{(l+1)}={S^{(l)}}^{T}A^{(l)}S^{(l)}\in R^{n_{l+1}\times n_{l+1}} \tag{5}\]  
Two independent GNN modules are used to generate \(S^{(l)}\) and \(Z^{(l)}\), as defined in Equations (6) and (7). Notice that two GNN modules input the same graph, but training parameters and execution functions are different. One GNN module is used to generate the node embedding, while the other GNN module is used to generate the probability that the input nodes assign to the \(n_{l+1}\) clusters. In the last DIFFPOOL module of the networks, the number of clusters is fixed as 1, and a final embedding is generated for downstream tasks.  
\[Z^{(l)}=GNN_{l,embol}\Big{(}A^{(l)},H^{(l)}\Big{)} \tag{6}\]  
\[S^{(l)}=softmax\Big{(}GNN_{l,pool}\Big{(}A^{(l)},H^{(l)}\Big{)}\Big{)} \tag{7}\]
# An Efficient Framework Model for Optimizing Routing Performance in VANETs
## 5 Simulation and Performance Analysis
### Results and Discussion
#### 5.3.2 Packet Delivery Ratio  
Figure 7 describes the delivery ratio results using several urban scenarios for different node densities. The proposed BOLSR-PSO performed better than the OLSR RFC-3626, OLSR-PSO, OLSR Gomez and RAND algorithm. The configuration of BOLSR-PSO is suitable for coping with rapidly changing network topology. It gives the ability to deliver packet quite more than other protocols, it also reaches a good results in term of overhead and the End-to-End delay. Table 6 shows the statistical results of BOLSR-PSO and OLSR-Gomez algorithms in which they have optimal packet delivery ratio with a mean (53.3, 53.6), median (53.6, 54.1), and mode (96.9, 96.4) respectively. BOLSR-PSO have a best of 99.7 and worst of 6. Here again, most of the routing demonstrates similar results whereas the  
Figure 6: Packet Loss vs. Number of Nodes.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **BOLSR-PSO** & **OLSR-RFC** & **OLSR-RAND** & **OLSR-Gomez** & **OLSR-PSO** \\ \hline Mean & 82,793.8 & 82,879.6 & 84,344.4 & 83,036.5 & 86,929.6 \\ Median & 61,094.4 & 61,302.6 & 61,581.1 & 61,049.0 & 64,640.4 \\ Mode & 28.03 & 34.7 & 61,580.1 & 100,130.6 & 1704.4 \\ Best & 28.0305 & 34.7051 & 87.8117 & 40.2057 & 1704.3806 \\ Worst & 272,844.5102 & 270,937.9988 & 275,417.8391 & 273,434.5102 & 276,548.6217 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical Result of Packet loss Performance Analysis.  
OLSR-PSO represents the worst PDR performance. However, there is a little difference between the delivery ratios achieved under different scenarios, which can be justified to the routing configuration background of each protocol.
# Kiwifruit Allergy--Molecular Basis, Diagnostics and Treatment
## 3 Protein Families
### Profilin  
The profilin from kiwifruit, Act d 9, is a 14 kDa protein and is classified as a minor kiwifruit allergen. Profilins are panallergens found in all eukaryotic cells. They are cytosolic proteins that regulate actin polymerization [67]. Profilins play a key role in reorganizing plant microfilaments during critical processes like cytokinesis, cytoplasmic streaming, cell elongation, and pollen tube or root hair growth [68; 69; 70]. Profilins exhibit a highly conserved sequence and similar structural folding. These observations are in line with the A-RISC index (Figure 5A) predicting cross-reactivity between Act d 9 and virtually all ingested profilin allergens [71; 72]. Monosensitization to Act d 9 is uncommon; sensitization is rarely linked to severe systemic reactions and usually associated with mild symptoms, PFAS, and latex-food syndrome. Act d 9 may also be responsible for cross-reactivity between kiwifruit and timothy grass, as well as birch pollen [73]. In a study of 30 patients with confirmed kiwifruit allergy, IgE reactivity to Act d 9 was observed predominantly in polysensitized patients with pollen and/or latex allergy, rather than in those monosensitized to kiwifruit [28]. Latex-food syndrome with kiwi-latex cross-reactivity is often caused by similarity between latex profilin Hev b 8 and Act d 9 [74].  
Figure 4: A-RISC index for PR-10 proteins registered as ingestion allergens. Sequences were selected from the WHO/IUIS (proteins with full allergen nomenclature) and AllFam (proteins without numbers) databases. All A-RISC diagrams in this work are scaled against each other, such that red indicates a high likelihood of IgE-mediated cross-reactivity, green indicates a low likelihood, and intermediate colors suggest intermediate cross-reactivity.