esa-sceva/satcom-chunk-collection huggingface dataset:
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 4 Protocol Description
### Network Initialization and Hop Count Setting  
At the commencement of network initialization, the sink node initiates a regular and periodic dissemination of Hello packets across the entire network, adhering to a consistent time interval. The Hello packet format is (forwarding node, hc), including the ID of the forwarding node (initially the sink ID) and the hop count (initiated to 0). Upon receiving a Hello packet, the recipient node (referred to as node x) conducts a comprehensive assessment. It meticulously checks whether its current hop count variable HC(x) exceeds the hop count hc carried by the received Hello packet. If this condition is met, node x proceeds to update its hop count HC(x) by incorporating the value contained within the Hello packet plus 1. Subsequently, it retransmits the Hello packet, ensuring that the propagated packet carries the newly updated hop count HC(x) and forwarding node updates to x. Conversely, in the event that the condition is not satisfied, node x responsibly discards the received Hello packet, thereby preserving the integrity and consistency of the hop count. The above-mentioned process is repeated until the Hello packet goes through all the reachable sensors. A description of the hop count setting algorithm is shown below as Algorithm 1. Upon the culmination of this setup stage, each node within the network acquires precise and up-to-date information pertaining to its hop distance to the sink node.  
```
1: Sensor nodes N = {1, 2, x, y,..., n} with their hop count HC
2: The hop count of the sink is initiated to 0, and other nodes is initiated to infinity
3: Surface sink broadcasts a Hello packet (forwarding node, hc)
4:if node x receives a Hello packet then
5: Compare the node hop count HC(x) and the Hello packet carrying hop count
6:if HC(x) > hc then
7: HC(x)\(\leftarrow\)hc + 1
8: Rebroadcast the Hello packet by setting hc\(\leftarrow\)HC(x) and forwarding node\(\leftarrow\)x
9:else
10: Discard the Hello packet
11:End
12:end
```  
**Algorithm 1**Algorithm for hop count setting
## 4 Simulations  
In this section, two simulation studies are presented to verify the effectiveness of the control algorithm. Although a numerical example is provided suggested control algorithm. These simulations were conducted in the MATLAB-Simulink environment, wherein the fixed-step ODE4 (Runge-Kutta) was chosen to be the solver. The sampling time was determined separately for each case study, and other settings remained as the defaults.
# Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm
## 4 Experimental Simulation and Analysis
### 50 \(\times\) 50 Simulation Results and Analysis  
To evaluate the performance of three algorithms under increased environmental complexity, path planning was conducted in an expanded 50 \(\times\) 50 static grid with randomly distributed obstacles, while maintaining consistent parameters across all tests. The resulting paths and convergence curves are illustrated in Figure 4. The traditional ant colony algorithm required 34 iterations to converge, achieving a path length of 120.25 m with 54 turning points. In contrast, the improved ant colony algorithm from reference [12] converged after 30 iterations, reducing the path length to 65.74 m with 28 turning points.  
The improved ant colony algorithm proposed in this paper, which incorporates artificial potential field attraction into the heuristic function, demonstrated superior performance,  
Figure 4: A 50 \(\times\) 50 algorithm simulation comparison. (**a**) Path planning of the traditional ant colony algorithm. (**b**) Convergence curve of the traditional ant colony algorithm. (**c**) Literature [12] improved the ant colony algorithm for path planning. (**d**) Literature [12] improved the convergence curve of the ant colony algorithm. (**e**) This paper improves the path planning of the ant colony algorithm. (**f**) This paper improves the convergence curve of the ant colony algorithm.  
converging in only 25 iterations. This resulted in an optimal path length of 60.79 m with 20 turning points. These results further confirm the effectiveness and reliability of the proposed algorithm in optimizing path planning in complex environments.  
The data presented in Table 5 highlights the performance improvements achieved by the proposed ant colony algorithm. Compared to the traditional ant colony algorithm, the path length was reduced by 45.33%, while a 49.45% reduction was observed in comparison to the improved algorithm from reference [12]. Additionally, the number of iterations decreased by 11.76% and 26.47%, respectively. The number of turning points was reduced by 48.15% compared to the traditional algorithm and by 62.96% relative to the improved algorithm in reference [12].These results clearly demonstrate the significant advantages of the algorithm proposed in this paper, which is capable of planning optimal paths in complex and diverse environments. Furthermore, the algorithm exhibits excellent stability and adaptability across a range of conditions, outperforming the other two algorithms in terms of both efficiency and reliability.
## 4 Results
The VGG16 model has a recall score of 0.7743, which is higher than the other models, but a lower precision score of 0.7151, indicating that it identifies false positive cases. The accuracy score for the VGG16 model is 0.7177, the lowest among the models evaluated. The InceptionV3 model has the lowest recall score of 0.6739, indicating that it misses more true positive cases. The specificity values are reasonably high, with a score above 0.65 for all models. Overall, the ODIR model performs reasonably well, with all models achieving accuracy scores of at least 0.6723. However, the DenseNet121 and MobileNetV2 models perform best on this task. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Recall** & **Precision** & **Specificity** & **Accuracy** & **Train Loss** & **Valid Loss** \\ \hline MobileNetV2 & 0.9850 & 0.9850 & 0.9850 & 0.9850 & 0.0735 & 0.0949 \\ DenseNet121 & 0.9850 & 0.9850 & 0.9850 & 0.9850 & 0.1927 & 0.1169 \\ InceptionV3 & 0.9830 & 0.9683 & 0.9686 & 0.9757 & 0.0941 & 1.0060 \\ VGG16 & 0.3902 & 0.9952 & 0.9982 & 0.6981 & 0.1231 & 1.6910 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: APTOS model evaluation metrics results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Recall** & **Precision** & **Specificity** & **Accuracy** & **Train Loss** & **Valid Loss** \\ \hline MobileNetV2 & 0.8781 & 0.8781 & 0.8781 & 0.8781 & 1.6679 & 2.1374 \\ DenseNet121 & 0.8910 & 0.8910 & 0.8910 & 0.8910 & 0.1412 & 0.2933 \\ InceptionV3 & 0.8821 & 0.8793 & 0.8723 & 0.8773 & 0.2352 & 0.3494 \\ VGG16 & 0.7154 & 0.9420 & 0.9536 & 0.8313 & 0.2617 & 0.5014 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: EyePACS model evaluation metrics results. Table 7 shows the evaluation metrics results for the model trained on a combined dataset, which includes data from the APTOS, ODIR, and EyePACS datasets. The DenseNet121 and VGG16 models have the highest recall, precision, and accuracy scores, all of which are 0.9897 and 0.9879, respectively. The MobileNetV2 model also has high recall, precision, and accuracy scores, with 0.9851. The InceptionV3 model has lower recall and precision scores than the other models, with scores of 0.9715 and 0.9727, respectively. However, its accuracy score of 0.9721 is still high. The specificity value of all models is also high, meaning the model correctly identifies negative cases and has fewer false positives. Overall, the model trained on the combined dataset performs very well, with all models achieving high accuracy scores of at least 0.9721.
# Facial Expression Recognition Using Computer Vision: A Systematic Review
## 7 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Year** & **Classifier** & **Approach** & **Pre-Processing** & **Features** & **Testing Procedure** & **Databases** & **Accuracy** \\ \hline
[172] 2011 & LSTM/HMM/SVM & Video & - & Landmarks & CV (PI) & SAL & 85.00\% \\ \hline
[173] 2014 & NN & Audiovisual & Geometric/PCA & Landmarks & HO & AVEC2013 & 54.99\% \\ \hline
[174] 2013 & KNN/HMM & Audiovisual & PCA & LBP/AAM & CV (PI) & AVEC2011/\({}_{-}\) & 52.60\% \\ \hline
[175] 2013 & SVR & Audiovisual & - & HOG/Harris3D & HO & AVEC2012 & 41.70\% \\ \hline
[176] 2015 & KNN/SVR & Video & - & LBP/OH/LPQ & CV & AVEC2012/2013 & 14.09\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of reviewed works based on the circumplex model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **Classifiers** & **Pre-Processing** & **Features** & **Testing Procedure** & **Databases** & **Accuracy** \\ \hline
[154] 2015 & Bayes & Geometric & MK & CV & eNTERFACE’05 & 98.00\% \\ \hline
[155] 2013 & SVM & Smooth & Gabor/PCA & CV & eNTERFACE’05 & 80.27\% \\ \hline
[156] 2014 & MLP/RBF & - & ITMI/QIM & HO (PI) & CK/eNTERFACE’05 & 77.78\% \\ \hline
[157] 2010 & NN & - & FAPs/OF & HO (PI) & eNTERFACE’05 & 75.00\% \\ \hline
[158] 2018 & ED/CNN/LSTM (TL) & Geometric/DA & Landmarks & CV & AFEW/STED & 61.87\% \\ \hline
[159] 2008 & SVM & PCA & Landmarks & HO & eNTERFACE’05 & 57.00\% \\ \hline
[160] 2016 & CNN (TL) & - & ROI & HO & FER-2013/AFEW & 53.90\% \\ \hline
[161] 2015 & SVM & DCT/Geometric & AUs & HO & EmotiW & 53.80\% \\ \hline
[162] 2015 & CNN & PCA & SIFT/LBP/\({}_{-}\) & CV & AFEW & 53.62\% \\ \hline
[163] 2015 & CNN/RNN (TL) & HE/DA & ROI & HO & TFD/ER-2013/AFEW & 52.88\% \\ \hline
[164] 2017 & CNN & Geometric & ROI & CV (PI) & AFEW/FER-2013/\({}_{-}\) & 49.92\% \\ \hline
[165] 2016 & SVM/RF & - & LBP_TOP/AUs & CV & AFEW & 46.88\% \\ \hline
[166] 2014 & SVM & Geometric & HOG_TOP & HO & AFEW & 45.21\% \\ \hline
[167] 2009 & SVM/NN & - & FAPs/OF & HO & eNTERFACE’05 & 45.00\% \\ \hline
[168] 2014 & SVM & Geometric & LBP_TOP & HO & AFEW & 41.77\% \\ \hline
[169] 2013 & SVM/CNN (TL) & Smooth/Contrast & ROI & HO & TFD/AFEW & 41.03\% \\ \hline
[170] 2013 & SVM/HMM & - & Gabor/OF & HO & AFEW & 20.51\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of reviewed works for audiovisual approaches. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **Classifier** & **Pre-Processing** & **Features** & **Testing Procedure** & **Databases** & **Accuracy** \\ \hline
[171] 2010 & SVM & LDA & Landmarks & CV & JAFFE & 87.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of a reviewed work with a video/static multimodal approach. remarks made by the authors are that the regions close to the eyes, mouth and eyebrows have dominant influence on FER, and that people tend to avoid eye contact when they do not want to talk about some topics.
# Pharmacokinetics of Marine-Derived Drugs
## 3 Pharmacokinetics Studies in Animals
### Absorption
#### 3.1.2 Crustacean-Derived Compounds  
Chitosan (7) is a cationic polysaccharide, a copolymer of \(N\)-acetyl-n-glucosamine and \(\beta\)-1-4-linked p-glucosamine, which is obtained by the deacetylation of chitin. The core source of chitosan is the exoskeleton of crustaceans. Due to its unique properties, chitosan is extensively used in pharmaceutical formulations and biomedicine [87; 88; 89]. Beside this, chitosan is reported to be useful for body weight reduction, in the treatment of neuronal disorders, and for its anti-bacterial, antihypertensive, etc. activities [90; 91; 92]. The commercial \({}^{125}\)I-labelled chitosan (MW = 5-10 kDa) was found in blood at 5 min after i/v injection in rats [93]. The pharmacokinetics of commercial water-soluble chitosan with MW of 3.8-230 kDa was investigated in vitro and in vivo. In vitro, chitosan penetrates across the Caco-2 cell layer in a dose-dependent manner. The highest permeability was observed for the low-MW (3.8 kDa) form. The penetration rate decreased with an MW increase. Similar results were observed in rats. After the peroral administration of chitosan (20 mg/kg), C\({}_{\text{max}}\) in plasma ranged between 20.23 and 4.32 ug/mL for low MW and high MW at a T\({}_{\text{max}}\) of 30 min. AUC was dose dependent between 24.13 and 0.97 ug/mL-h. The absorption of chitosan with MW of 3.8 kDa was 25-fold higher compared with an MW of 230 kDa [53]. Similar results were obtained in another study. Chitosans with MW of 990 Da to 76 kDa were prepared from commercial chitosan and administered perorally in mice. The best absorption (C\({}_{\text{max}}\) 0.68 mg/g, T\({}_{\text{max}}\) = 30 min in plasma) was observed for chitosan with an MW of 990 Da. The C\({}_{\text{max}}\) for all other chitosan with higher MW was lower and was observed at T\({}_{\text{max}}\) of 1 h. The result indicated that the intestinal absorption of chitosan increases with decreasing molecular weight. The result also confirmed that the intestinal absorption of chitosan with lower MW is more rapid [54].
## 3 Temperature and Humidity Characteristics of [PERSON]'s Tomb and Their Variation Patterns
### Annual Variation Patterns of the Temperature and Humidity in the Burial Chamber
#### 3.1.2 Variations in the Relative Humidity  
Figure 4a shows a comparison of the relative air humidity levels inside and outside [PERSON]'s tomb throughout the year. The figure reveals that the relative humidity outside the chamber varied more widely and was less responsive to seasonal variations than that inside the chamber, and the relative humidity remained high throughout the year. Affected by rainfall, the relative humidity could reach a maximum value of 100% and a minimum value of 33%, which is related to the climatic conditions in the area of the burial chamber location. However, the humidity inside the chamber ranged from 42% to 89% and was highly responsive to seasonal variations, with higher levels in summer and lower levels  
Figure 3: Temperature variations inside and outside the chamber: (**a**) variations throughout the year; (**b**) average monthly variations.  
in winter. The humidity in winter was reduced both inside and outside the chamber. Considering the above temperature variations, the wind speed imposed a similar effect on the temperature and humidity variations in the burial chamber. In summer, the wind speed was lower, and heat and humidity transfer between inside and outside the burial chamber was reduced. Thus, a higher relative humidity could be maintained inside the burial chamber.  
The average monthly relative humidity inside and outside the chamber (Figure 4b) were analyzed. As shown in the above figure, there was a clear seasonal pattern of the relative humidity variation inside the chamber, reaching a higher level in summer and a lower level in winter, while the average monthly relative humidity outside the chamber remained high throughout the year. The relative humidity outside the chamber was lower than inside the chamber in summer, which is due to the higher temperature outside the chamber. The air water vapor content outside the chamber was higher than that in winter, but the saturated water vapor pressure was more notably affected by the temperature and sharply increased, resulting in a lower relative humidity outside the chamber than inside the chamber.  
Combined analysis of Figures 3b and 4b reveals that the temperature inside the burial chamber was lower than that outside the chamber in summer due to water vapor transport to areas with lower temperatures during heat and moisture transfer. Therefore, water vapor was transported into the chamber, and the relative humidity inside the chamber became higher than that outside the chamber. During the other seasons, the temperature inside the chamber was higher than that outside the chamber due to the lower atmospheric temperatures, and water vapor was transported to the outside during heat and humidity transfer, so the relative humidity outside the chamber was higher than that inside the chamber.
# Nonparaxial Propagation Properties of Specially Correlated Radially Polarized Beams in Free Space
## 3 Statistical Properties of a Nonparaxial SCRP Beam
On the other hand, we see clearly in Figure 12 that the evolution propagation of the SDOC for a PCRP beam is in contrast with that of an SCRP beam, i.e., the SDOC evolves from the initial Gaussian distribution into non-Gaussian distribution with two or four sidelobes around the central bright spot, and the evolution behaviors of the SDOC of nonparaxial and paraxial SCRP beams are different and related to \(\sigma_{0}\). The comparison shows that both the initial beam waist width and the coherence function play an important role in the evolution properties of the SDOC. Figure 11: Modulus of the spectral degree of coherence (SDOC) \(|\mu(\rho,-\rho,z)|\) (cross line at \(y=x\)) of a nonparaxial SCRP beam with \(\rho=\sqrt{x^{2}+y^{2}}\) and \(\delta_{0}\)\(=2\lambda\) at propagation distances \(z=0\) (first column), \(z=z_{\rm R}\) (second column), \(z=3z_{\rm R}\) (third column), \(z=10z_{\rm R}\) (fourth column) for the beam waist size \(\sigma_{0}\)\(=10\lambda\) (first row), \(\sigma_{0}\)\(=\lambda\) (second row), \(\sigma_{0}\)\(=0.5\lambda\) (third row).
# Target Detection for Coloring and Ripening Potted Dwarf Apple Fruits Based on Improved YOLOv7-RSEs
## 2 Materials and Methods
### Improvements to YOLOv7  
The network architecture of YOLOv7 consists of three main parts: the input layer, the backbone network, and the detection head [19]. On the input side, YOLOv7 follows the Mosaic data enhancement approach proposed by YOLOv4, which is trained by randomly cropping four images and splicing them into a single image, thus enriching the dataset and improving the training efficiency, while keeping the training and inference costs unchanged.  
To strike a balance between detection accuracy and efficiency, YOLOv7 introduces a series of innovative strategies, including Reparametrized Convolution (RepConv), the Efficient Lightweight Aggregation Network (ELAN), and dynamic label assignment. However, it is noteworthy that YOLOv7 primarily relies on convolutional operations for feature extraction, which are inherently local operations. Convolutional layers often only model relationships between neighboring pixels, making it difficult to capture long-range dependencies. This limitation may result in the loss of features for small objects and missed detections for immature fruits. To address this issue, an attention mechanism is introduced to pay closer attention to the relationships between pixel features. Serving as a global operation, the attention mechanism computes weights between features through matrix operations, enabling the model to better capture long-range dependencies. The image features obtained from self-attention mechanisms complement those obtained from convolutional operations, collectively enhancing the model's performance.  
In addition, the originally utilized CIoU loss function in YOLOv7 only accounts for the scale loss of the bounding boxes, without considering the mismatch between the predicted and true box orientations. Therefore, this paper adopts the SIoU loss function to replace the CIoU loss function, incorporating orientation loss into the model training process, aiming to further enhance the model's performance.
## 4 Performance Evaluation and Discussion
### Performance of UWB Ranging  
Dacawave's MDEK1001 Development Kit was used to evaluate the accuracy of ranging using UWB. The measurement results of 36 nodes when the anchor nodes were placed at the positions of (0.0 (m), 0.0 (m)), (2.4 m, 0.0 m), (0.0 m, 2.4 m), and (2.4 m, 2.4 m) are shown in Figure 9. In Figure 9, the circle marks represent the installation position, and the cross marks represent the measurement position. The error is within 20 cm. Therefore, it is considered that distance information can be shared between terminals by handling the distance information with the unit of at least 20 cm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Detail** \\ \hline Unencoded bit length (\(k\)) & 600 \\ Codeword length (\(n\)) & 1200 \\ Shortened information length (\(m\)) & 0–500 \\ Column weight (\(q\)) & 3 \\ Decoding algorithm & LogMAP \\ Maximum number of repetitions & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of shortened RA code.  
Figure 7: Bit error ratio (BER) characteristics of the receiver whose shortened information is known under the additive white Gaussian noise (AWGN) channel. The shortened information length \(m\) using distance information is varied from 0 to 500.  
Figure 8: Bit error ratio (BER) characteristics of the cyberterrorist whose shortened information is unknown under the additive white Gaussian noise (AWGN) channel. The shortened information length \(m\) using distance information is varied from 0 to 500.  
Figure 9: Evaluation result of measurement accuracy using Dacawave’s MDEK1001 Development Kit. The circle marks and the cross marks represent the installation position and the measurement position, respectively.
## 3 Results and Discussion
### Numerical Simulation Set-Up
#### 3.1.3 Model C: Hexagonal Lattice  
Similarly, consider the transmission of water waves across the hexagonal lattice structure, which is derived by putting cylinders at the vertices of a hexagon, shown in Figure 5. In Figure 5a,b and Figure 5c,d, the incident waves are along the \(\Gamma K\) and \(\Gamma M\) directions, respectively. In both cases, the simulation results indicate effective wave enhancement phenomena. However, the concentration regions are different for different incident directions. Figure 5b,d present the results for the maximum wave amplitude dependence on \(h_{0}/h\) and \(r/a\) in the ranges of 2-16 and 0.1-0.3, respectively. For the \(\Gamma K\) direction, it is clear that the enhancement factor is only higher and limited in regions for \(h_{0}/h\) and \(r/a\), being from 14 to 16 and from 0.15 to 0.24, respectively. On the other hand, for the \(\Gamma M\) direction, the enhancement factor increases smoothly with the values of \(h_{0}/h\) and \(r/a\).  
Figure 4: Square structure: (**a**) The spatial distributions of the wave amplitude in the \(\Gamma M\) direction; (**b**) The maximum wave amplitude as functions of the ratio \(h_{0}/h\) and the ratio \(r/a\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & \\ \hline \(h_{0}/h\) & 0.14 & 0.15 & 0.16 & 0.17 & 0.18 & 0.19 & 0.20 & 0.21 & 0.22 & 0.23 & 0.24 \\
8 & 0 & 0 & 0 & 4.219 & 4.257 & 4.252 & 4.211 & 4.144 & 4.065 & 3.976 & 3.876 \\
9 & 0 & 0 & 4.42 & 4.488 & 4.492 & 4.452 & 4.379 & 4.284 & 4.183 & 4.077 & 3.962 \\
10 & 0 & 4.597 & 4.694 & 4.722 & 4.689 & 4.615 & 4.514 & 4.396 & 4.277 & 4.157 & 4.029 \\
11 & 4.712 & 4.876 & 4.937 & 4.923 & 4.853 & 4.75 & 4.624 & 4.487 & 4.353 & 4.221 & 4.084 \\
12 & 5.001 & 5.129 & 5.149 & 5.092 & 4.99 & 4.86 & 4.713 & 4.56 & 4.414 & 4.273 & 4.128 \\
13 & 5.271 & 5.354 & 5.332 & 5.235 & 5.104 & 4.951 & 4.787 & 4.621 & 4.465 & 4.316 & 4.164 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Enhancement factor in square lattice.