esa-sceva/satcom-chunk-collection huggingface dataset:
# An Energy-Efficient and Robust Multipath Routing Protocol for Cognitive Radio Ad Hoc Networks
## 4 Energy-Efficient and Robust Multipath Routing
### Qualitative Comparision of Routing Protocols  
A qualitative comparison of different routing protocols is presented in Table 1 with respect to different aspects.  
Each protocol uses a different routing metric to select the best routing path in terms of the metric. AOMDV is a shortest-path routing protocol that uses hop count as a routing metric. L2 ER, on the other hand, has a cumulative routing metric that tends to select the optimal path with the minimal energy consumption and end-to-end delay combined. E-D2 CARP stresses the probability of packet loss and link delay in its routing procedure via its routing metric called expected path delay (EPD). PMRC ensures that the selected routes are not close enough to be interrupted by an active mobile PU. Route closeness is the measure for the closeness of the routing paths in this protocol. MRPC based on MAODV uses hop count to select the shortest routes, whereas channel stability ensures that the channel assigned is the most stable one. The proposed ERMR uses a hybrid metric, a combination of energy consumption and channel stability, to evaluate routing paths and channel assignment. The path with the maximal residual energy is taken as the most preferred routing path. For a link, the channel that ensures the highest stability is chosen.  
AOMDV uses a single channel for both control message and data dissemination. L2 ER considers the channels that are less interfered from PU communication. E-D2 CARP tends to make use of the channels currently not occupied by any PU. The channel assigned to a particular link is the one from which the control packet is received. The same channels are used for control packet exchange among SU nodes when available. In PMRC, there is a channel for each of the data and control messages. The proposed ERMR selects the most stable channel from the set of available channels for data transmission.  
Regarding energy, L2 ER and the proposed ERMR aim at improving energy efficiency. In the proposed ERMR, multiple paths are explored between source and destination. The nodes are examined for their residual energy level while the channels are formed for their free access duration. Eventually, the path with the best routing metrics designed is set up as the primary routing path. The backup or alternate path is the one with the second-best routing metrics.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
## 3 Results and Discussion
### Color Values of Shrimp
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Treatments** & **Heating Time** & \(L^{\star}\) & \(a^{\star}\) & \(b^{\star}\) & \(W\) \\  & **(s)** & & & \\ \hline Peeled raw shrimp & & 25.23 \(\pm\) 0.60 \({}^{\circ}\) & 1.90 \(\pm\) 0.12 \({}^{\mathrm{d}}\) & \(-\)1.06 \(\pm\) 0.04 \({}^{\mathrm{a}}\) & 25.20 \(\pm\) 0.58 \({}^{\mathrm{d}}\) \\
130 \({}^{\circ}\)C & 80 & 51.78 \(\pm\) 0.52 \({}^{\mathrm{d}}\) & 6.79 \(\pm\) 0.22 \({}^{\mathrm{c}}\) & \(-\)1.86 \(\pm\) 0.17 \({}^{\mathrm{b}}\) & 51.51 \(\pm\) 0.38 \({}^{\mathrm{c}}\) \\  & 90 & 52.63 \(\pm\) 0.83 \({}^{\mathrm{d}}\) & 7.96 \(\pm\) 0.32 \({}^{\mathrm{b}}\) & \(-\)1.77 \(\pm\) 0.16 \({}^{\mathrm{b}}\) & 52.22 \(\pm\) 0.63 \({}^{\mathrm{c}}\) \\  & 100 & 52.65 \(\pm\) 0.77 \({}^{\mathrm{d}}\) & 8.53 \(\pm\) 0.25 \({}^{\mathrm{a}}\) & \(-\)1.87 \(\pm\) 0.28 \({}^{\mathrm{b}}\) & 52.17 \(\pm\) 0.52 \({}^{\mathrm{c}}\) \\  & 110 & 56.04 \(\pm\) 0.65 \({}^{\mathrm{b}}\) & 8.62 \(\pm\) 0.40 \({}^{\mathrm{a}}\) & \(-\)2.03 \(\pm\) 0.52 \({}^{\mathrm{b}}\) & 55.50 \(\pm\) 0.55 \({}^{\mathrm{b}}\) \\  & 120 & 58.09 \(\pm\) 0.91 \({}^{\mathrm{a}}\) & 8.82 \(\pm\) 0.39 \({}^{\mathrm{a}}\) & \(-\)2.29 \(\pm\) 0.37 \({}^{\mathrm{cd}}\) & 57.48 \(\pm\) 0.50 \({}^{\mathrm{a}}\) \\
90 \({}^{\circ}\)C & 110 & 52.79 \(\pm\) 0.66 \({}^{\mathrm{d}}\) & 6.86 \(\pm\) 0.18 \({}^{\mathrm{c}}\) & \(-\)1.92 \(\pm\) 0.61 \({}^{\mathrm{bc}}\) & 51.66 \(\pm\) 0.54 \({}^{\mathrm{c}}\) \\  & 120 & 52.39 \(\pm\) 1.01 \({}^{\mathrm{d}}\) & 8.12 \(\pm\) 0.09 \({}^{\mathrm{b}}\) & \(-\)1.99 \(\pm\) 0.25 \({}^{\mathrm{bc}}\) & 52.26 \(\pm\) 0.65 \({}^{\mathrm{c}}\) \\  & 130 & 54.96 \(\pm\) 0.76 \({}^{\mathrm{c}}\) & 8.18 \(\pm\) 0.13 \({}^{\mathrm{b}}\) & \(-\)2.02 \(\pm\) 0.26 \({}^{\mathrm{bc}}\) & 54.18 \(\pm\) 0.40 \({}^{\mathrm{bc}}\) \\  & 140 & 54.06 \(\pm\) 0.96 \({}^{\mathrm{c}}\) & 8.57 \(\pm\) 0.30 \({}^{\mathrm{a}}\) & \(-\)2.12 \(\pm\) 0.17 \({}^{\mathrm{cd}}\) & 53.22 \(\pm\) 0.71 \({}^{\mathrm{bc}}\) \\  & 150 & 55.66 \(\pm\) 0.75 \({}^{\mathrm{b}}\) & 8.69 \(\pm\) 0.23 \({}^{\mathrm{a}}\) & \(-\)2.30 \(\pm\) 0.22 \({}^{\mathrm{cd}}\) & 55.24 \(\pm\) 0.53 \({}^{\mathrm{b}}\) \\ \hline \hline \end{tabular} Each value represents the average \(\pm\) standard deviation of three replicates; different lowercase letters indicate significant differences in the same column (\(p\) < 0.05).  
\end{table} 
Table 3: Values of color (\(L^{\star}\), \(a^{\star}\), \(b^{\star}\), \(W\)) in peeled white shrimp using the scaled-up MAIH system set at 130 \({}^{\circ}\)C and 90 \({}^{\circ}\)C for various heating times. Overall, these results indicate that the color values (\(L^{*}\), \(a^{*}\), and \(W\)) in peeled shrimp meat after processing at the scaled-up MAIH under 130 \({}^{\circ}\)C and 90 \({}^{\circ}\)C increased with increased heating time. Compared to its appearance in Figure 2, when the heating condition of scaled-up MAIH was set at 130 \({}^{\circ}\)C for 100 s or 90 \({}^{\circ}\)C for 130 s, the shrimp was cooked well and had a fully red appearance.
###### Abstract  
In complex scenes, factors such as tree branches and leaves occlusion, dense distribution of tomato fruits, and similarity of fruit color to the background color make it difficult to correctly identify the ripeness of the tomato fruits when harvesting them. Therefore, in this study, an improved YOLOv8 algorithm is proposed to address the problem of tomato fruit ripeness detection in complex scenarios, which is difficult to carry out accurately. The algorithm employs several technical means to improve detection accuracy and efficiency. First, Swin Transformer is used to replace the third C2f in the backbone part. The modeling of global and local information is realized through the self-attention mechanism, which improves the generalization ability and feature extraction ability of the model, thereby bringing higher detection accuracy. Secondly, the C2f convolution in the neck section is replaced with Distribution Shifting Convolution, so that the model can better process spatial information and further improve the object detection accuracy. In addition, by replacing the original CIOU loss function with the Focal-EIOU loss function, the problem of sample imbalance is solved and the detection performance of the model in complex scenarios is improved. After improvement, the mAP of the model increased by 2.3%, and the Recall increased by 6.8% on the basis of YOLOv8s, and the final mAP and Recall reached 86.9% and 82.0%, respectively. The detection speed of the improved model reaches 190.34 FPS, which meets the demand of real-time detection. The results show that the improved YOLOv8 algorithm proposed in this study exhibits excellent performance in the task of tomato ripeness detection in complex scenarios, providing important experience and guidance for tomato ripeness detection.  
YOLOv8; tomato maturity detection; swin transformer; DSConv; deep learning
## 1 Introduction
Most of the heavyweight detectors usually have a high accuracy but require a large computational cost, and thus hinder their real-time response and the deployment on UAVs, whereas lightweight detectors have practical deployability and a fast inference speed but it is difficult for them to achieve as high a competitive accuracy as heavyweight detectors, especially for large multi-category object detection tasks [23; 24; 26]. Therefore, optimizing the structure of heavyweight detectors toward a better balance between accuracy, deployability and inference time is an issue well worth  
Figure 1: Three main differences between RSIs and NSIs. The first and second lines show instances from NSIs and RSIs, respectively. investigating. To establish a detector with a better balance between accuracy, deployability and inference time, a novel detector called YOLO-DSD for real-time optical remote sensing object detection based on YOLOv4 was developed in this study. The main contributions are as follows: (1) a new feature extraction module named a dense residual (DenseRes) Block was designed for better feature extraction and to reduce the computational cost and inference time in the backbone network. (2) Convolution layer-batch normalization layer-leaky ReLu (CBL) \(\times\)5 modules in the neck were improved with a short-cut connection and named S-CBL\(\times\)5 to strengthen the transmission of object features. (3) A novel low-cost attention mechanism called a dual channel attention (DCA) Block was proposed to enhance the representation of the object feature. The experimental results in the DIOR dataset indicate that YOLO-DSD outperforms YOLOv4 by increasing mAP\({}^{0.5}\) from 71.3% to 73.0%, with a 23.9% and 29.7% reduction in Params and Flops, respectively, but a 50.2% improvement in FPS. In the RSDD dataset, the mAP\({}^{0.5}\) of YOLO-DSD is increased from 90.0-94.0% to 92.6-95.5% under different input sizes. Compared with the SOTA detectors, YOLO-DSD achieves a better balance between accuracy, deployability and inference time.
# Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model
## 4 Case Study of Transmission Line Insulator Defect Detection  
### Simulation Environment and Evaluation Indexes  
The transmission line insulator defect detection cases were completed in the software environment of Visual Studio Code 2016, Keras 2.1.5, TensorFlow 1.13.2, CUDA 10.0, cuDNN 7.4.1.5, and the hardware environment with GPU Nvidia GeForce GTX 1660 Super, 6 GB video memory.  
In order to compare the performance of different models and algorithms, the precision (\(P\)), recall (\(R\)), average precision (\(AP\)), and mean average precision (\(mAP\)) are taken as indexes to evaluate model performance. The definitions of precision and recall are  
\[precision=\frac{TP}{TP+FP},\;\;recall=\frac{TP}{TP+FN} \tag{13}\]  
where \(TP\) is the number of correctly positioned targets, \(TP+FP\) is the total number of positioned targets, and \(TP+FN\) is the total number of actual targets. The \(P\)-\(R\) curve can be plotted with the calculated \(P\) and \(R\) as ordinate and abscissa, while the area under the curve is \(AP\), and the mean value of \(AP\) is \(mAP\). For multi-class (\(N\) classes) object detection, the definitions of \(AP\) and \(mAP\) are  
\[AP=\int_{0}^{1}P(R)\mathrm{d}R,\;\;\;mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{14}\]  
Figure 8: Multi-scale prediction principle: (**a**) The third-dimension calculation principle of output feature maps in YOLO-Head, (**b**) Prediction box regression.  
In addition, \(F_{1}\) score is also used to evaluate the performance of detection models, which represents the harmonic average of precision and recall and considers that precision and recall are equally important. The definition of \(F_{1}\) score is  
\[F_{1}=2\cdot\frac{precision\cdot recall}{precision+recall} \tag{15}\]  
In order to compare the detection speeds of different object detection models and reflect the real-time detection ability, this paper also introduces frames per second (\(FPS\)) as an evaluation index, which means the total number of images detected by the model in one second, that is, the time taken by the model to detect one image.
## 4 Empirical Evaluation of Proposed Algorithms
### Experiments with Reference Datasets
#### 4.1.2 Results for Clustering Accuracy  
SSE values after the initialization (initial SSE) and after the Lloyd's iterations (final SSE) are summarized in Table 2. For the initial SSE, we did not include any results from the statistical testing because of differences in variances. Moreover, note that the assumption of equal variances of the final SSEs underlying the Kruskal-Wallis test, as tested with the Brown-Forsythe test, was only satisfied for SVH, RCV, USC, KDD, OXB, and M8M. For most datasets (HAR, ISO, LET, GFE, MNI, BIR, BSM, FCT, and TIN), this assumption was not satisfied.  
Clearly, SK-means\(\parallel\) and SRPK-means\(\parallel\) outperform K-means\(\parallel\) and K-means++ in terms of the initial SSE. SRPK-means\(\parallel\) with \(p=40\) reaches almost the same initialization SSE level as SK-means\(\parallel\). For the six largest datasets, SRPK-means\(\parallel\) with \(p=20\) always had smaller max-value of SSE after initialization than the min-value of K-means\(\parallel\). K-means++ has about two times larger initial SSE than SK-means\(\parallel\) and SRPK-means\(\parallel\) (\(p=40\)).  
For all other datasets than SVH and TIN, the final clustering accuracy (SSE) was statistically significantly different between the four methods. Overall, in terms of the final clustering error, SRPK-means\(\parallel\) achieved better final SSE than K-means\(\parallel\) and K-means++. Moreover, one can note that for 11 out of 15 datasets, the random projection-based initialization was better than the main baseline K-means\(\parallel\). In many cases, SK-means\(\parallel\) gives better final SSE than the baseline methods, but for the high-dimensional datasets, the results are equally good compared to the baseline methods. One can notice, based on the statistical testing, that the final SSE is highly similar for K-means++ and K-means\(\parallel\). Please note that in Table 2 the min-values of all methods for the final SSE are equal for small number of clusters (\(K\leq 10\)). This is probably due to the fact that the smaller number of possible partitions [50] implies a smaller number of local minima compared to higher values of \(K\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Data**} & \multicolumn{1}{c}{**K+\(+\)**} & \multicolumn{1}{c}{**K1**} & \multicolumn{1}{c}{**SK1**} & \multicolumn{1}{c}{**SK1**} & \multicolumn{1}{c}{**SK1**} & \multicolumn{1}{c}{**SK1**} & \multicolumn{1}{c}{**SK1**} & \multicolumn{1}{c}{**SK1**} & \multicolumn{1}{c}{**SK1**} & \multicolumn{1}{c}{**SK1**} & \multicolumn{1}{c}{**SK1**} & \multicolumn{1}{c}{**SK1**} \\ \hline H3D\({}^{+}\) & medium & 2.9881 & 1.3897 & 1.3988 & 1.4454 & 1.4192 & 2.4044 & 1.4033 & 1.3809 &
## 2 Configuration
### Heterogeneous Human Model and Equivalent Tissue Layers  
This configuration is for the validation of the simplified PL model. First, the PL is simulated in a heterogeneous human model in any of the regions specified in [16]. This simulated PL is then compared with the PL calculated using the simplified model in a tissue layer equivalent to the heterogeneous human model (in this case, the human frontal thorax).  
Active implants are positioned at specific locations inside the human body to carry out special tasks, such as drug delivery, transplanted organ monitoring and functional electrical stimulation. In the case of liver transplantation, implants are placed on the liver for constant monitoring for a period of 7-10 days to report circulation deficiency to physicians [9]. Taking such a scenario into consideration, PL is simulated at various positions of the frontal thorax of [PERSON], which is an enhanced anatomical model of a 34 year-old adult, from the Virtual Family [18] at 2.45 GHz in SEMCAD-X. [PERSON] has a height of 1.74 m and a weight of 70 kg. The model consists of more than 80 different tissues. The Tx antenna is placed in front of [PERSON] at 1 m, and PL is extracted from the simulations performed in the area of the human frontal thorax. In order to validate the PL, an equivalent tissue layer of the human frontal thorax is considered. PL is calculated in this equivalent tissue layer using the simplified PL model. The equivalent tissue layer consists of skin (2 mm), SAT (5 mm), BT (1 mm), muscle (10 mm) and liver (10 mm) tissue with dielectric properties as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & \(\mathbf{Skin}\) & \(\mathbf{SAT}\) & \(\mathbf{Breast\ Tissue}\) & \(\mathbf{Muscle}\) & \(\mathbf{Liver}\) \\ \hline \(\epsilon_{r}\) & 38 & 10.8 & 5.15 & 52.7 & 43 \\ \(\sigma[S/m]\) & 1.46 & 0.27 & 0.14 & 1.74 & 1.69 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of \(\epsilon_{r}\) and \(\sigma[S/m]\).  
Figure 2: Air to layers equivalent to human frontal thorax.
# IoT-Based Implementation of Field Area Network Using Smart Grid Communication Infrastructure
## 1 Introduction
An information and communication infrastructure has laid the foundation of \"precision farming\" or \"precision agriculture.\" Precision farming is a technique to gain a higher yield by the complete monitoring and control of agricultural parameters. It is intended for complete monitoring and control of various parameters pertaining to soil, the environment, and the crop for better quality and yield. Wireless sensor networks can fulfill the sensing and measurement requirements of field area networks. Tiny sensor nodes placed at various locations can collect and communicate variousparameters such as current, humidity, and temperature [16]. This data can be used for remote monitoring and the control of field area networks. Moreover, the excess energy can be sold back to utilities for waivers in billing or revenue generation. Monitoring of soil conditions and weather parameters can be used to make decisions that can result into higher yield. This paper describes an experimental investigation of smart farming using a smart grid communication infrastructure.
## 6 Conclusions  
The DOA estimation model in DILL in this paper can not only realize the DOA estimation of a mobile target but also locate and track the targets in real time. In order to eliminate the influence of nonlinearity and noise on the system error, we choose the UKF filtering based on UT transformation to filter the DOA estimated signal to get a tracking waveform with higher accuracy. At the same time, this differs from the traditional DOA estimation algorithm and tracking models in its simple computational complexity and reliable stability. It deserves great attention in the fields of future communication systems.
# Comparing the Performance of Vivotless Tracking and Fixed-Type Floating Solar Power Systems
## 2 Materials and Methods  
Floating photovoltaic power generation facilities are located on the water surface of reservoirs and dams; thus, they need to be stably positioned and oriented so that the  
Figure 1: Layout of the typical floating photovoltaic system.  
floating structures do not shake, even in the event of frequent water level fluctuations; general natural phenomena such as wind, waves, and currents; and adverse weather conditions such as typhoons. Therefore, the floating body should be able to move freely according to water level fluctuations, and be simultaneously fixed to minimize the left and right movements of the floating body due to the wind. Figure 2 shows the layout of the pivotless tracking-type floating solar power system, which consisted of a pontoon to float the floating body on the water and a mooring system device to fix the floating body in response to changes in the water level. The mooring system is connected by forming an inclination with the floating body to prevent the floating body from deviating from the set position.  
Two 310 W inverters were connected [39] to transmit the generated power and connect it to the power grid, and a solar radiation meter [40] was installed to measure and collect the amount of solar radiation. Also, collected energy output was automatically calculated by computer. To compare the output of the pivotless tracking-type and fixed-type solar power systems proposed in this study, a typical floating solar power system was installed in the same place; each system was equipped with five 310 W crystalline silicon photovoltaic modules [41] with an inclination angle of 27\({}^{\circ}\). Each system has five modules, all connected in series to generate power.
# Comparison and Identification of the Aroma-Active Compounds in the Root of _Angelica dahurica_
## 2 Results and Discussion  
### Comparison of Quantity of Aroma Compounds by the Different Extraction Methods  
Three replicate experiments using the same sample were performed on the same day to test the repeatability of each method. The coefficients of variation (CV values) of the total ion counts for SPME, P&T, SBSE, and DHS are 1.40%, 5.65%, 6.76%, and 6.441%, respectively, indicating that SPME was more repeatable than the other three methods. The compounds extracted by the four headspace extraction methods and analyzed by GC-O-MS are shown in Table 1 and Figure 1. A total of 52, 54, and 43 aroma compounds were identified in the samples from Hangbaizhi, Qibaizhi, and Bobaizhi, respectively. These compounds included terpen, aromatic species, alcohols, aldehydes, ketones, acids, and esters. Moreover, a total of 20, 21, and 17 aroma-active compounds were detected in the _A. dahurica_ samples from Hangbaizhi, Qibaizhi and Bobaizhi, respectively, by the sniff test. These compounds contributed aroma characteristics of pine, camphor, woody, herb, citrus, spice, cucumber, sour, and so on. We also found that the most abundant type of compounds in all three types of _A. dahurica_ were terpenes, with 28, 30, and 24 derivatives detected in Hangbaizhi, Qibaizhi, and Bobaizhi, respectively, followed by aldehydes with 9, 8, and 7 derivatives identified in Hangbaizhi, Qibaizhi and Bobaizhi, respectively. Among these, 1,8-cineol, limonene and caryophyllene have been detected previously from the root of _A. dahurica_[7], to use gas chromatography-mass spectrometry (GC-MS) by SPME. The fifty-two aroma compounds detected by GC-O-MS in the sample from Hangbaizhi are listed in Table 1. The compounds represented a variety of smells, such as pine, camphor, woody, spice, herb, citrus, sour, and grass. Among these compounds, the content of terpenes, the main contributors to the fragrance of certain plants, was the highest. Terpenes may substantially contribute to the aroma, and thus the flavor, of Hangbaizhi. The content of aldehydes (9 derivatives) was second highest. The aromas of aldehydes can be described as fatty, cut-grass-like, and citrus. Because aldehydes usually have low odor thresholds, they may contribute to the overall odor of _A. dahurica_. Terpenes (followed by aldehydes) were also the most abundant compounds extracted from Qibaizhi and Bobaizhi. Nineteen terpenes and 6 aldehydes were common aroma components among the three kinds of _A. dahurica_, which may cause the _A. dahurica_ samples from different locations to have similar odor characteristics. Qibaizhi and Bobaizhi contained more kinds of alcohols than Hangbaizhi; however, due to their low contents or high thresholds, few alcohols were detected during the sniff tests. Aromatic species, ketones, acids, and esters detected in the _A. dahurica_ samples may also contribute to their overall odor. \begin{table}