esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Comparison of Some Heuristic Pathfinding Algorithms  
Tables 3 and 4 show the performance analysis of implemented heuristic pathfinding Algorithms without and with obstacles respectively. The parameters taken were grid size, total iteration blocks, total computed blocks without shortest path length, execution time in milliseconds, and shortest path length. Grid size is the size of the grid environment used for implementing the Algorithm. The Algorithms are implemented on \(4\times 4\), \(8\times 8\), \(16\times 16\), and \(32\times 32\) grid systems. Total iteration blocks refer to the total number of blocks the Algorithm has traversed in order to reach destination node. Total computed blocks without SPL refers to the total blocks traversed by Algorithm excluding the shortest path length blocks. Execution time is the time required by Algorithm to reach the goal node from start node. It is measured in milliseconds. Finally, the shortest path length is the minimum number of blocks taken by the Algorithm to reach the destination node.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Grid Size** & **Grid Model** & **Algorithm** & **Total Iterations** & **Total Computed** & **Execution** & **Shortest Path** \\  & & **(Blocks)** & **Blocks without SPL** & **Time (ms)** & **Length (Blocks)** \\ \hline \hline \multirow{2}{*}{Dijkstra’s} & Dijkstra’s & 14 & 9 & 39.70 & 5 \\  & & & & & \\ \hline \hline \multirow{2}{*}{A-star} & A-star & 10 & 5 & 27.95 & 5 \\  & & & & & \\ \hline \hline \multirow{2}{*}{Breadth First Search} & Breadth First Search & 14 & 9 & 33.76 & 5 \\  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance analysis of pathfinding Algorithms without obstacles.  
_Appl. Sci._**2022**, _12_, 5499  
\begin{table}
\begin{tabular}{c c c c c c c} \hline Grid Size & Grid Model & Algorithm & Total Iterations (Blocks) & Total Computed Blocks without SPL & Execution Time (ms) & Shortest Path Length (Blocks) \\ \hline  & [PERSON]’s & 62 & 49 & 177.08 & 13 \\ \hline  & A-star & 50 & 37 & 140.5 & 13 \\ \(8\times 8\) & & Breadth First Search & 62 & 49 & 164.49 & 13 \\ \hline  & Greedy Best First & 14 & 1 & 36.01 & 13 \\ \hline  & Dijkstra’s & 254 & 225 & 899.27 & 29 \\ \hline  & A-star & 226 & 197 & 787.43 & 29 \\ \(16\times 16\) & & Breadth First Search & 254 & 225 & 868.56 & 29 \\ \hline  & Greedy Best First & 30 & 1 & 97.90 & 29 \\ \end{tabular}
\end{table}
Table 3: _Cont._  
+++ ==ERROR: No output for this page==
\begin{table}
\begin{tabular}{c c c c c c c} \hline Grid Size & Grid Model & Algorithm & Total Iterations (Blocks) & Total Computed Blocks without SPL & Execution Time (ms) & Shortest Path Length (Blocks) \\ \hline \multirow{4}{*}{\(8\times 8\)} & \multirow{4}{*}{[PERSON]’s} & \multirow{4}{*}{30} & \multirow{4}{*}{15} & \multirow{4}{*}{102.60} & \multirow{4}{*}{15} \\  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{
## 4 Results
### Detection Quality  
To contrast the performances of the optimized detection methods introduced in this paper, we applied both methods to the evaluation dataset described in Section 2.1. For the comparison, we adduced the particular precision and recall of each method on this dataset. Since tracking the workers in a centimeter-perfect manner is usually not required, we defined an Intersection over Union (IoU) value of at least 0.6 to be sufficient to indicate a true positive detection. On our evaluation data, the classical approach exhibits a recall of 96.2% with 99.8% precision. Contrarily, YOLO only achieves a recall of 88.2% with a similar precision of 99.2% using a confidence threshold of 0.9. Even reducing the threshold to 0.5 results in a recall of only 93.5% while precision drops to 97.0%.
## 2 Materials and Methods
### Construction of the B-Camera
#### 2.2.4 PDU (Processing and Display Unit)  
The PDU can be implemented on either a computer or a smartphone/tablet. Present-day smartphones, despite their affordability, have a sufficient computing power and display resolution to be used for this purpose. The main task of the computing part is to interpolate the two-dimensional magnetic field values into a larger size. We implemented two interpolation algorithms in our device; i.e., bilinear and bicubic [1,17]. These were coded in Java using Android Studio for the smartphone-based PDU and by using Matlab and Python for the computer-based PDU.  
The bilinear method requires four known values obtained by measurements to estimate a field value at a particular point. Based on these known values, the values for desired positions are derived by using linear interpolation. The drawback of the bilinear method is that when the sampling points are sparsely distributed, the constructed field cannot be smoothly interpolated. To obtain a better result, we can use the bicubic method, which uses 16 neighboring values to estimate unknown values. The interpolation is done by a nonlinear function; therefore, a smoother result can be obtained.  
The display unit shows the magnetic field distribution for each component and the total magnitude. For a given measured field at a discrete point \((m,n)\) within the surveyed area, where \(m\) and \(n\) indicate the row and column number, respectively, the vector of magnetic field is given by  
\[\vec{B}(m,n)=B_{x}(m,n)\vec{i}+B_{y}(m,n)\vec{j}+B_{z}(m,n)\vec{k} \tag{1}\]  
where \(\vec{i},\ j,\ k\) are the unit vectors to the direction of \(x,y\) and \(z\), respectively. The magnitude of the magnetic flux density is given by  
\[|\vec{B}(m,n)|=\sqrt{B_{x}^{2}(m,n)+B_{y}^{2}(m,n)+B_{z}^{2}(m,n)} \tag{2}\]  
Each of the components \(\{B_{x},\ B_{y},\ B_{z}\}\) and the magnitude \(|B|\) are displayed separately by the PDU system. The observation of directional field components is hardly possible in other imaging modalities such as optical imaging, ultrasound and non-polarimetric radar. This enriches the features, which can increase the recognition capability of the system.
# Demodulation Technique Based on Laser Interference for Weak Photo-Acoustic Signals on Water Surface
## 3 Results
### Results of Tracking Demodulation of Amplitude Modulation Signals  
The signal generator creates 1 kHz signals with an amplitude modulated by 1 Hz sine waves. The modulation results at a water surface vibration sampling rate of 100 kS/s are given in Figure 8. Apparent periodical changes of the amplitude can be seen from the time domain signals after demodulation, and the correct 1 kHz demodulation result can be found in the spectrum analysis as well. The envelope of the time domain signals was extracted and fitted as a curve, resulting in the amplitude curve shown in Figure 10. The fitting curve equation is expressed as \(A_{s}=-26.39\) sin(\(2\pi\times 1.03t-3.59\)) + 65.28. The fact that the fitting curve frequency 1.03 Hz exhibits the same as the amplitude-modulating frequency 1 Hz of the signal generator within a small tolerance proves the ability of the method used in our study to accurately track signal amplitude changes.
# Activation of Persulfate for Groundwater Remediation:
## 5 Impact of Environmental Application
### Microbial Communities  
The microbial community is an essential sign of the health of the soil and can be closely related to soil remediation equality [143]. The use of persulfate may significantly affect the population and diversity of microbial communities [43]. [PERSON] et al. (2014) pointed out that the injection of persulfate may decrease the number of cultivable heterotrophic bacteria populations [144]. [PERSON] (2015) also observed a significant reduction of microbial communities in all three wells after 21 days persulfate remediation. The possible reason for the decrease in microbial communities appeared to be positively correlated with the negative effects of persulfate on soil acidification [144]. [PERSON] (2022) pointed out that the sulfate radicals (SO4-) and the hydroxyl radical (OH-) generated by persulfate had strong oxidative potential in activated persulfate systems, which would cause harm to anaerobic microbes to some extents and reduce fragile microbes in the soil systems [145]. Even more, after persulfate oxidation, the high sulfate concentrations could encourage sulfate-reducing bacteria, which produces hazardous H2S, which is harmful to public health [146].
# Silk Protein-Based Membrane for Guided Bone Regeneration
## 2 Silk Material Proteins  
Silk, as a protein polymer containing repetitive amino acids, is spun into fiber from the secretory gland of Lepidoptera larvae, such as silkworms, spiders, bees, and flies [27]. The composition and structure of silk differs depending on the species [28]. Silk derived from silkworms has been popularly used in the textile industry for centuries and in biomedical suture material for decades due to its excellent mechanical properties and biocompatibility [28]. Silk derived from silkworms consists of two major proteins, fibroin (72-81%) and sericin (19-38%) [14]. Both proteins contain the same 18 amino acids, including glycine, alanine, and serine in different ratios [14]. Fibroin composes the core of silk filament, and sericin coats the fibroin fiber (Figure 1). Sericin is a glue-like protein that makes two fibroin fibers bind together [29]. Sericin is a water-soluble protein, and generally, it is removed by the thermochemical treatment known as the degumming process [15]. Silk-based biomaterial has been used as a suture material for wound treatment, and recently, it has been widely used as the major component of biomaterials, including bone tissue scaffolding, wound dressing materials, drug delivery, and vascular patches [27; 30]. Silk material has suitable properties for use as bone scaffolding material, and it can be easily obtained and processed in various forms of scaffolding [29].
# Backstepping Based Formation Control of Quadrotors with the State Transformation Technique
## 1 Introduction
The SAA assumes that the ranges of the quadrotor attitude angles are small or zero, which works well when a quadrotor operates with small attitude angles. However, a quadrotor cannot maintain good tracking performance when the attitude angles vary in a broad range. In this paper, we present the backstepping-based quadrotor formation control with the state transformation technique. We can derive the reference attitude angles for the formation control without the use of simple position quadrotor dynamics and the special assumption such as SAA. By using the state transformation technique for both small and large variations in attitude angles, improved performance is expected not only in the position control of a single quadrotor, but also in the formation control of multiple quadrotors. We first derive the quadrotor dynamics using the Newton-Euler formulation. Next, we formulate the formation control problem based on the leader-follower method of quadrotors. Then, we present the backstepping-based quadrotor formation control with the state transformation technique. Stability analysis based on the Lyapunov theorem shows that the proposed formation control method results in a quadrotor formation error system that is asymptotically stabilized. Finally, using comparison simulations with other control methods, we verify the effectiveness of the proposed formation control method. The remainder of this paper is as follows: In Section 2, we describe the dynamics of a quadrotor. In Section 3, we formulate the leader-follower formation control problem and present the backstepping-based quadrotor formation control using the state transformation technique. We also show the stability of the proposed quadrotor formation control. In Section 4, we present the simulation results and conclude the paper in Section 5.
# Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs
## 1 Introduction
A mobile and energy-rich sink node or nodes have been introduced to solve this issue. In this method, the mobile sink moves randomly or follows a predetermined mobility model to gather data from stationary sensor nodes and transmit it to the base station. Additionally, using a mobile sink increases the performance of W.S.N.s in various ways, including decreasing the distance needed for a stationary node to transmit data, lowering the number of intermediary nodes, increasing network throughput, and providing coverage for remote areas [3]. Other researchers have proposed improving the energy efficiency of wireless sensor networks by presenting techniques other than using a mobile sink or sinks. For example, the research proposed in [21] presented a clustering routing protocol where a cluster head is selected based on the centroid position, and the gateway selection is accomplished within the cluster. This paper proposes a sink mobility model using a single, energy-rich mobile sink node to collect data from stationary sensor nodes. The proposed model is based on creating a Bipartite graph of the W.S.N. After that; a sink node mobility path is acquired from the properties of the Bipartite graph. This work also proposes combining single-hop and multi-hop routing to achieve good network performance. The following will be the order of this paper's remaining sections. A concise explanation of the bipartite graph is provided in Section 2. Section 3 follows with a literature review of existing models. In Section 4, the proposed mobility model is explained. Section 5 then discusses the performance indicators and simulated scenarios to investigate the proposed work's effectiveness. Section 6 discusses the simulation's outcomes. Section 7 compares the performance between the proposed model and another three models. Finally, final observations and ideas for further research are discussed in Section 8.
## 2 Results
### Antifungal Activity  
The extracts obtained from _H. odoratissimum_ and _H. pathum_ using acetone and ethanol showed the highest percentage inhibition (\(p=0.019\), F = 8.50, DF = 8) against _B. cinerea_ (Figure 1). The percentage inhibition of 65% and 51%, obtained by ethanol extracts of _H. pathum_ and _H. odoratissimum_, respectively, were higher in comparison with the inhibition observed by acetone extracts (35% and 53%). The antifungal effects produced by the positive control Rovral(r) WP (91%) against the _B. cinerea_ was significantly higher than all extracts (\(p<0.05\)) while no growth inhibition was observed for the negative controls (i.e., extraction solvents). These observations indicated that the extraction solvents alone did not play a role in the inhibitory effects of the crude extracts (Figure 1). The observed high antifungal effects from the ethanol and acetone extracts of _H. pathum_ could be described by the abundant content of methyl cinnamate (cinnamic acid), as fatty acid methyl esters have been evidenced to poses high microbial effects [18]. In addition to that, high levels of \(\delta\)-cadinene detected in acetone and ethanol extracts of _H. pathum_ (20.4 \(\pm\) 1.30% and \(16.1\pm 0.49\)%, respectively) could also be implicated to these high antimicrobial effects observed. This compound has been included as a major compound of numerous plant extracts that showed high antimicrobial effects.
# Traffic Sign Detection Based on the Improved YOLOv5
## 2 Improved YOLOv5 Algorithm
### Attention Mechanism  
To tackle the issue of excessive image information in traffic sign detection, as well as the challenge of selecting useful information for the current task objective from a multitude of data, this paper introduces a convolutional attention mechanism into the YOLOv5 network. The addition of the convolutional block attention mechanism can enhance the specific target region of interest, effectively improve the ability of the network to extract features, and weaken irrelevant background regions. The convolutional block attention structure is shown in Figure 1. The convolutional block attention model combines the spatial attention mechanism (SAM) with the channel attention mechanism (CAM). This combination not only reduces the number of network parameters and the computational power needed for network training but also allows for the network's integration into most network architectures [18].  
\[M(\mathrm{F_{C}})=\sigma(\mathrm{MLP(F_{\mathit{Arg}}^{\mathit{Pool}})+MLP(F_{ \mathit{Max}}^{\mathit{Pool}})}) \tag{1}\]  
In Equation (1) above, \(\sigma\) is the sigmoid activation function, MLP is the multilayer perceptron, \(F_{\mathit{Max}}^{\mathit{Pool}}\) is the input image \(F\) after the MaxPool process, and \(F_{\mathit{Arg}}^{\mathit{Pool}}\) is the input image \(F\) after the average AvgPool.  
The SAM mainly focuses on the position information of the target. The specific process is to obtain two spatial feature maps through the output results of the CAM through AvgPool and MaxPool, splice them according to the channel to obtain an effective feature map, and then perform convolution with a \(7\times 7\) convolution kernel and obtain the spatial attention feature map after Sigmoid activation, which is obtained by the spatial attention formula in Equation (2):  
\[M(F_{S})=\sigma(f^{7\times 7}([(F_{\mathit{Arg}}^{\mathit{Pool}});\ (F_{ \mathit{Max}}^{\mathit{Pool}})])\otimes F_{n} \tag{2}\]  
where \(\sigma\) is the sigmoid activation function, \(f^{7\times 7}\) is a \(7\times 7\) convolution kernel for convolution, and \(\otimes\) is element-by-element multiplication.  
The CSP1_3 model in YOLOv5 can allow the model to learn more image information, but the model's ability to extract effective information from an input image is weak, and it cannot focus on effective information and suppress invalid information. Therefore, the CBAM is fused with the CSP1_3 model in YOLOv5 to form a new CSP1_3 CBAM model.  
Figure 1: CBAM network structure diagram.  
The CSP1_3 CBAM model allows the model to learn more features while inferring the attention map along two different dimensions from the channel and space. Then, the attention map is multiplied with the input feature for self-adaptive feature refinement, so as to highlight the essential information in the input features to achieve the purpose of enhancing the algorithm's ability to extract features and finally raising the detection accuracy for the target. The improved network framework and flowchart are shown in Figures 2 and 3 below.
# Unraveling Functional Dysphagia: A Game-Changing Automated Machine-Learning Diagnostic Approach
###### Abstract  
(1) Background: Dysphagia affects around 16% of the US population. Diagnostic tests like X-ray barium swallow and endoscopy are used initially to diagnose the cause of dysphagia, followed by high-resolution esophageal manometry (HRM). If the above tests are normal, the patient is classified as functional dysphagia (FD), suggesting esophageal sensory dysfunction. HRM records only the contraction phase of peristalsis, not the distension phase. We investigated the utilization of esophageal distension-contraction patterns for the automatic classification of FD, using artificial intelligent shallow learners. (2) Methods: Studies were performed in 30 healthy subjects and 30 patients with FD. Custom-built software (Dplots 1.0) was used to extract relevant esophageal distension-contraction features. Next, we used multiple shallow learners, namely support vector machines, random forest, K-nearest neighbors, and logistic regression, to determine which had the best performance in terms of accuracy, precision, and recall. (3) Results: In the proximal segment, LR produced the best results, with accuracy of 91.7% and precision of 92.86%, using only distension features. In the distal segment, random forest produced accuracy of 90.5% and precision of 91.1% using both pressure and distension features. (4) Conclusions: Findings emphasize the crucial role of abnormality in the distension phase of peristalsis in FD patients.  
functional dysphagia; shallow learners; distension-contraction features +
Footnote †: journal: Applied sciences
# Electronic Nose Analysis to Detect Milk Obtained from Pasture-Raised Goats
## 3 Results and Discussion
### Analysis of Volatile Compounds
The authors also reported higher amounts of acids in the milk of grazing goats. The higher quantity of acids in the pasture milk samples could be due to a higher quantity of aldehydes found in P, since the acids can also derive from the oxidation of aldehydes [52]. However, the results reported in literature on the quantity of acids in milk from animals fed with the addition of fresh fodder show a contrasting trend [36; 50]. In these studies, the fresh fodder (monoculture) was fed in the stable and the animals did not graze. The amount of free fatty acids in milk does not appear to be related to the concentration of total fat in milk [36] but to a post milking lipolytic activity. Higher post-milking lipolysis activity has been reported in the milk of grazing animals compared to those fed a hay-based diet in the stable [53], which could explain the higher amounts of free fatty acids in pasture milk. These findings have led us to hypothesize a greater lipolytic activity in the milk of grazing goats. The distinctive presence of terpene compounds in milk can be due to a directly oral or respiratory transfer from forage plants when are eaten by ruminants [46; 54]. They can be also related to the botanical composition and the geographical origin of pasture. During the summer season the terpene content varied qualitatively and quantitatively in different pastures [55]. [PERSON] et al. [56] reported that in the summer season, goat's milk reached a higher amount of sesquiterpenes than in the other seasons. Recently, [PERSON] et al. [57] have reported that the profile of terpenes in milk varied with pasture quality and may help authenticate milk based on the animal diet. Furthermore, the high biodiversity and phenology of the pasture helped in the discrimination of milk [58]. Sesquiterpenes have only been identified in grazing goat [35] and sheep [46] milk.