esa-sceva/satcom-chunk-collection huggingface dataset:
# Techniques for Calculating Software Product Metrics Threshold Values: A Systematic Mapping Study
## 4 Systematic Mapping Process
### Q2 What Are the Quality Attributes for Threshold Calculation Methods?
[2] suggested some threshold values that determine the problematic classes with respect to design flaws. The work in [41] derives a threshold metrics value against the bad smell using risk analysis at five different levels. The authors in [39] and their tool (RTOOL) presented a way to calculate the metrics thresholds. [PERSON] et al. [42] specify that the test theory is useful to predict fault and the effect of OO measures and show the principles in dealing with metrics. [PERSON] and [PERSON] [43] showed that thresholds according to distribution-based techniques are not practical in identifying fault-prone components. [PERSON] et al. [38] defined metrics thresholds values to detect God classes in software product lines. [PERSON] and [PERSON] [1], [PERSON] et al. [55], and [PERSON] and [PERSON] [56] derived metrics thresholds for predicting faults on Eclipse projects. [PERSON] et al. [44] used threshold values to identify noisy modules in software fault prediction datasets. [PERSON] and [PERSON] [41] aimed to predict faulty classes. Prioritization of classes for refactoring was targeted in the paper by [PERSON] et al. [15]. [PERSON]'s [49] research was prioritized on quality assessment. [PERSON] et al. [50] proposed a practical tool to measure threshold values in software projects. [PERSON] et al. [22; 39] aimed to control and monitor technical depth. [PERSON] and [PERSON] [40] have replicated the derivation of thresholds using ROC curves on the change-proneness of modules. The derivation and validation of thresholds were confirmed on five versions of the Android operating system. [PERSON] et al. [58] used a cumulative relative frequency graph to identify thresholds. The technique was validated on the detection of large classes (one of the code bad smells). [PERSON] [23] selected the mean as the threshold value if the distribution is normal; otherwise, percentiles (70% and 90%) were used to select thresholds of the large corpus of Android applications. [PERSON] et al. [63] proposed a benchmark-based threshold identification technique from a large corpus of systems. The authors identified thresholds at different risk assessment levels (i.e., very low, low, moderate, high, and very high). Thresholds were used to detect two bad smells (i.e., large classes and lazy classes). [PERSON] et al. [60] have derived thresholds from a large benchmark composed of more than 3000 systems. The study derives thresholds by choosing the top ranks, 90%, and 95 modules as problematic. The problematic modules were considered as bad smells. [PERSON] and [PERSON] [64] replicated the use of ROC curves, VARL, and Alves rankings. The authors compared the performance of these techniques in detecting faults in twelve open-source systems. The results have shown that the ROC curve outperforms all others in fault detection. Table 6 summarizes the studies into quality attributes. As shown in Table 6, fault detection was the most studied quality attribute.
## 3 Experiments and Analysis
### Model Evaluation Metrics
#### 3.3.2 Recall, Precision, and F1 Score  
There are three types of damage in this work, including cracks, surface shedding, and normal. Therefore, in the confusion matrix for the surface damage of the wind turbine blade, each column represents a type of damage, each row represents the prediction results of each type, and the sum of the data in each row is the real sample number of this type.  
Figure 8: The pre-processing process.  
Figure 9: Three types of wind turbine blade pictures. (**a**) Normal; (**b**) cracks; (**c**) surface shedding.  
Therefore, the basic form of the surface damage confusion matrix of the wind turbine can be obtained, as shown in Table 1.  
The recall refers to the original sample, which means the probability of being recognized as a positive sample in the actual positive sample. Taking the recall rate of the cracks label as an example, it is shown as:  
\[r=\frac{a}{a+b^{\prime}+c^{\prime}} \tag{10}\]  
The precision refers to the recognition results. It means the probability of actually positive samples among all identified positive samples. Taking the precision of crack label as an example, its calculation is shown as:  
\[p=\frac{a}{a+a^{\prime}+a^{\prime\prime}} \tag{11}\]  
From the standpoint of identification, recall and precision are incompatible. If the ability to identify models remains unchanged, increasing recall will decrease precision and vice versa.  
The F1 score is an evaluation metric that combines the previous two and is used to comprehensively represent the model's overall evaluation metrics. The formula is as follows:  
\[F=\frac{p\times r\times 2}{p+r} \tag{12}\]  
Similarly, the accuracy, recall, and F1 score of the wind turbine blade surface shedding damage and normal identification can be calculated. Finally, using macro-averaging, the overall evaluation index of the network model is derived, as shown in Equations (13)-(15).  
\[p\_Avg= \frac{p_{\textit{cracks}}+p_{\textit{Surface shedding}}+p_{\textit{Normal}}}{3} \tag{13}\] \[r\_Avg= \frac{r_{\textit{cracks}}+r_{\textit{Surface shedding}}+r_{ \textit{Normal}}}{3}\] (14) \[F\_Avg= \frac{F_{\textit{cracks}}+F_{\textit{Surface shedding}}+F_{ \textit{Normal}}}{3} \tag{15}\]
# Extremely Low-Profile Monopolar Microstrip Antenna with Wide Bandwidth
## 2 Antenna Design and Analysis  
### Antenna Configuration  
Figure 1 displays the configuration of the proposed antenna. As addressed in the Introduction section, the whole antenna geometry and denoted antenna design parameters are almost identical compared to [31] in our previous research result. In this study, we only deal with the degenerated TM\({}_{02}\) modes in this work. The variables of the proposed antenna are kept almost equally compared with [31] and its description is shown in Table 1. As shown in Figure 1, the proposed antenna has a gray-color dotted center feeding patch, which is directly connected through a 50-Ohm commercial coaxial line and six black-color dotted radiating patches that are electromagnetically coupled with physical metal to metal gap capacitance. The feeding patch on the top side is hexagonal, and the bottom side has six isosceles trapezoidal ground slots. The isosceles trapezoid not only serves to secure the soldering part, but is also a key part for configuring the degenerated TM\({}_{02}\) mode. Each side of the hexagonal feeding patch consists of six radiators at a distance as far as the coupling gap. It can be seen that each radiator is composed of three equilateral triangle ground slots.
###### Abstract
Numerous studies have demonstrated the utility of NIRS combined with chemometric techniques for predicting the content of key compounds such as tea polyphenols and caffeine [20], conducting rapid assessments of fresh tea leaf quality [21], and distinguishing different tea types [22]. For example, [PERSON] et al. utilized NIRS with chemometric approaches to rapidly assess black tea quality [23; 24], while [PERSON] et al. applied NIRS alongside linear discriminant analysis (LDA) to classify green teas, including Huangshan Maofeng and Emei tea, achieving an impressive accuracy rate of 97% [25]. The aforementioned studies have mainly focused on the rapid estimation of conventional tea components and the non-destructive classification of tea grades and types. However, there is still a scarcity of studies exploring the digital prediction of tea quality scores. Recent research on YCXY tea primarily focuses on its processing techniques [26], aroma component profiling, quality characteristic analysis [27], and amino acid composition [28]. Additionally, some studies have investigated its functional properties [29]. However, there is no existing research on leveraging NIRS for the rapid evaluation of YCXY tea quality. To fill this gap, this study aims to apply NIRS in conjunction with synergy interval partial least squares (siPLS), principal component analysis (PCA), and Jordan-Elman backpropagation artificial neural networks to establish a predictive model to evaluate the YCXY tea quality. This research aspires to lay a theoretical foundation and provide technological support for the rapid, non-invasive, and objective assessment of YCXY tea quality.
## 2 Materials and Methods
### Structure with \(\mu\)Tomography  
Internal structural determinations were conducted on freeze-dried worm samples using the micro CT Skyscan 1272 system (Bruker, Kontich, Belgium) [41]. The system fulfills two main roles: data acquisition and image reconstruction. Throughout the analysis, the X-ray source voltage was configured to 40 kV alongside a current of 193 \(\upmu\)A. Scans were executed using a rotation step of 0.3\({}^{\circ}\) and a resolution of 25 \(\upmu\)m. For the creation of 3D images from the \(\upmu\)CT projections, the NRecon software (version 2.0, Bruker, Kontich, Belgium) was utilized for image reconstruction. In the course of this process, adjustments were applied to rectify beam hardening and ring artifacts, thereby improving image quality.
# Improved Visual SLAM Algorithm Based on Dynamic Scenes
## 3 Method
### Dynamic Object Detection Based on Object Detection and Depth Maps
When removing dynamic points, we first calculated the average depth value and standard deviation of key points within each bounding box. By sorting the depth values to obtain the median, which was then used as a threshold for an initial filtering process that retains points with depth values less than the median, the mean standard deviation of the depth values for all retained points was subsequently calculated. Next, we checked whether each key point lay within the \"person\" bounding box detected by YOLOv5x, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Size** & **COCO mAP** & **Speed** & **Params** & **FLOPs** \\  & **(Pixels)** & **(0.5)** & **(ms)** & **(M)** & **(GFLOPs)** \\ \hline YOLOv5s & 640 \(\times\) 640 & 36.7\% & 6.4 & 7.2 & 17.4 \\ YOLOv5m & 640 \(\times\) 640 & 43.9\% & 8.1 & 21.2 & 49.0 \\ YOLOv5l & 640 \(\times\) 640 & 47.9\% & 10.0 & 46.5 & 109.1 \\ YOLOv5x & 640 \(\times\) 640 & 49.2\% & 12.1 & 86.7 & 205.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: YOLOv5’s versions parameter comparison. compared the key points inside the bounding box with the mean standard deviation, and classified those with no significant difference (as measured by the normalized deviation) as dynamic points, which were then removed from the current list of key points. Finally, the list of key points and descriptors for the current frame was updated with the filtered static key points and descriptors, preparing them for subsequent SLAM processing. This method was employed to generate semantic segmentation masks that outline object contours while minimizing the excessive removal of static feature points caused by detection boxes. Although numerous open-source semantic segmentation algorithms offer the capability, they frequently struggle with real-time performance. By integrating object detection algorithms with depth information, our approach effectively utilized sensor data and enhances algorithm efficiency.
# Energy-Water Management System Based on MPC for a Greenhouse in a Mapuche Indigenous Community
## 7 Simulation Results
### Medium-Term Controller: Implementation of the EWMS  
Implementing previous short-term controllers required the reference value of the water volume to be used for irrigation each day \(\bar{V}\), as provided by the EWMS, which operated as a medium-term controller. As mentioned in Section 6, three scenarios for the medium term were studied. In one scenario, half of the greenhouse was irrigated manually, while in the second case, the irrigation was managed by the EWMS. The third scenario combined both previous cases by having each control scheme irrigate one half of the greenhouse but having only the EWMS control the pump.  
Figure 11: Daily irrigation \(V_{r}\) performed during the simulation. This daily irrigation volume, represented by the blue points, was compared with the values denoted by the red line, indicating the daily irrigation objective \(\bar{V}\) based on medium-term management. (**a**) Daily irrigation \(V_{r}\) in the manual irrigation scenario; (**b**) daily irrigation \(V_{r}\) in the EMS irrigation scenario.  
Figure 10: Energy and water stored in the greenhouse components during the simulations. These resource availability results correspond to the values obtained using climate data from the winter season. Here, the storage limits are represented by black lines. (**a**) State of energy (SoE) of the battery in the manual irrigation scenario; (**b**) water volume stored in the tank \(V_{tank}\) in the manual irrigation scenario; (**c**) state of energy (SoE) of the battery in the EMS irrigation scenario; (**d**) water volume stored in the tank \(V_{tank}\) in the EMS irrigation scenario.  
The results of these simulations are presented in Table 7 and Figures 12 and 13. In both simulations in which the EWMS was used, the system managed to consume less water than manual irrigation while maintaining a high relative yield (over 0.986). Additionally, the EWMS scenarios used significantly less energy than the manual scenario, with the combined scenario consuming slightly less energy than the sum of the two separate scenarios. However, in the case of EWMS irrigation, the system did not achieve all of the daily irrigation objectives. This drawback could have been due to the higher daily objectives compared to those used in the previous test; additionally, the EWMS irrigation case had a comparatively strict minimum tank volume constraint. Still, the system maintained a high relative yield, suggesting that it could adapt to adverse situations. A final observation was that the system did not provide irrigation on some days. This issue may lead to a lack of adoption in communities that usually irrigate daily.
# An Enhanced Distributed Data Aggregation Method in the Internet of Things
## 4 Performance Evaluation
### Control Overhead Evaluation  
According to the assertions in Section 2 of this paper, a relatively high percentage of network activity time is spent on environmental control message exchanges, in as much as each connection or communication in RPL mechanism requires the transmission of control packets. Furthermore, due to the utilization of wireless communication medium, the collision rate (i.e., signal collision) of nodes in a tree-structured network in non-extensive environments is relatively high. This is why the precipitation of reaching the steady state procedure in a network graph, prohibition of Lossy communication, and reduction of network communications through aggregating multiple packets in one packet, will desirably reduce the number of efforts required for accessing the medium. Note that another cause of signal congestion in a RPL tree is the usage of multicast and broadcast messages throughout the network.  
In other words, according to Figure 14, our proposed method has increased the medium access probability for adjacent nodes; in that this method reduces not only the required medium access time but also the number of back-offs occurred in the medium access control layer. The criterion number of control packets in a network is appreciably dependent on the traffic rate (i.e., the number of packets generated in the time unit) as well as on the changes that have taken place in the graph topology structure. In the real world, numerous factors can intensify this procedure; such as noise, signal error in an indoor environment, signal distortion and attenuation, and similar drawbacks preventing the high-quality signal from reaching the intended place. The reduction of RSSI rate is commonly derived from the aforementioned factors, all of which are directly related to the network decisions. Due to the expensive costs of detection, restoration, and recognition of signals in hardware implementations; the message retransmission approach is proposed.
# A Blockchain Network Communication Architecture Based on Information-Centric Networking
## 6 Evaluation
### Broadcast Delay Results
Taking into account the proportion of ICN nodes in the actual relay network in the Bitcoin network and the previous experimental results, we set the range of ICN node proportion from 0% to 25% and also considered the case with a small proportion of ICN nodes (such as 1%). Figure 17 shows the broadcast time of ICN relay nodes with broadcasting coverage of 50% and 90% under different ratios. Figure 18 compares the broadcast time of BLOCK-ICN with a proportion of ICN nodes of 1% and 10% in the relay network (high bandwidth) and the non-relay network (normal bandwidth) under different broadcasting coverage rates. It can be seen from Figure 17 that as the proportion of ICN relay nodes in the blockchain network increased, the block broadcasting time gradually decreased. In addition, it can be observed from Figure 18 that the reduction in broadcast time was more significant with ICN relay nodes under a broadcasting coverage rate of 50%, and when the broadcasting coverage rate was 90%, the broadcast time with the deployment of ICN relay nodes was basically the same as that without ICN relay nodes. We can also see that deploying a small number of ICN nodes in the blockchain network can significantly improve the blockchain broadcasting efficiency. For example, deploying only 1% of ICN relay nodes reduced the block broadcasting time by 26.8% and 37.5%, respectively, at broadcasting coverage rates of 90% and 50%, compared to the Bitcoin network. In addition, deploying only 1% of ICN non-relay nodes also reduced the broadcast time by 25% and 33.2%. In summary, in the BLOCK-ICN network architecture, deploying a small number of ICN nodes in the blockchain network can significantly improve the broadcasting efficiency of blocks. Deploying ICN nodes as high-bandwidth relay nodes can improve the broadcast efficiency under low broadcasting coverage rates, but under high broadcasting coverage rates, there is little difference in broadcast efficiency between deploying ICN nodes as relay nodes and non-relay nodes. Figure 16: Block propagation time in BLOCK-ICN under different ratios of ICN nodes.
## 5 Experimental Verification and Result Analysis
### Policy Evaluation  
According to the planning results of sub-section A, the improved algorithm is smaller than the conventional QL planning algorithm in terms of running step size. In addition, the QL algorithm has a high repetition coverage rate. The target decision algorithm can play a good role in assisting with decision making. To evaluate whether the corresponding parameters of the target decision algorithm can effectively assist in the decision problem when the target position is uncertain, 20 simulation experiments were carried out in the mine SAR scenario in Figure 5. Figure 5 shows that the median set of step size of the guidance scheme is smaller than that of the average scheme. In addition, the step sizes of guidance schemes is concentrated below 200, and the data fluctuation is not significant. In contrast, the step sizes of the average scheme are discretely distributed between 225 and 375, resulting in significant data fluctuation. The above results illustrate that the guided solution has a better adjustment effect on the target decision algorithm.  
The search results under different schemes are shown in Figure 6. In the early and medium stages, all schemes remain explored and developed to obtain the best search path. In the later stage, however, the average scheme will still maintain a high probability of exploration, reducing data use. The above results indicate that improved policy has advantages from the perspective of action selection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Algorithms** & **Step** & **Coverage (\%)** & **Repeated Coverage (\%)** \\ \hline \multirow{4}{*}{Scenario 1} & A\({}^{*}\) & - & 9.81 & - \\  & RRT & 68 & 31.31 & 1.40 \\  & QL & 143 & 57.94 & 9.35 \\  & Ours & 45 & 21.50 & 0 \\ \hline \multirow{4}{*}{Scenario 2} & A\({}^{*}\) & - & 5.96 & - \\  & RRT & 80 & 37.16 & 0 \\  & QL & 66 & 30.73 & 0 \\  & Ours & 40 & 15.60 & 3.21 \\ \hline \multirow{4}{*}{Scenario 3} & A\({}^{*}\) & - & 9.95 & - \\  & RRT & 56 & 27.01 & 0 \\ \cline{1-1}  & QL & 73 & 32.23 & 2.37 \\ \cline{1-1}  & Ours & 42 & 20.38 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative evaluation of planning results for different scenarios.  
Figure 4: Comparison of changes in \(CD\) values between different algorithms. (**a–c**) Numerical statistical results of \(CD\) of four algorithms in Scenario 1, Scenario 2, and Scenario 3, measuring ability to search for target.
## 2 Proposed Method
### Fusion Model  
In the fusion-based approach, LR-HSI and HR-MSI are usually assumed to be the spatial blur downsampling and spectral downsampling of HR-HSI, respectively. Furthermore, the spectral response function can be estimated from the hyperspectral camera and multispectral camera parameters, and the estimation of spatial blur downsampling can be approximated by several spatial blur estimation functions. Thus, the following equation can be obtained:  
\[\mathbf{H}=\mathbf{ZBS} \tag{1}\]  
where \(\mathbf{B}\in\mathbb{R}^{WH\times WH}\) and \(\mathbf{S}\in\mathbb{R}^{WH\times wh}\) represent the convolution blur and the downsampling matrix, respectively. The HR-MSI can be seen as the spectrally downsampled version of HR-HSI, i.e.,  
\[\mathbf{M}=\mathbf{RZ} \tag{2}\]  
where \(\mathbf{R}\in\mathbb{R}^{l\times L}\) denotes the spectral response matrix. As mentioned above, both the spectral response function and spatial fuzzy downsampling can be obtained. Therefore, the matrices \(\mathbf{B}\), \(\mathbf{S}\), and \(\mathbf{R}\) in Equations (1) and (2) are all determined, so solving HR-HSI means solving the unknown matrix \(\mathbf{Z}\) in the above matrix equation. In matrix factorization-based methods, it is usually assumed that the spectral basis exists in a lower subspace [13], i.e.,  
\[\mathbf{Z}=\mathbf{DC} \tag{3}\]  
where \(\mathbf{D}\in\mathbb{R}^{L\times L_{x}}\) and \(\mathbf{C}\in\mathbb{R}^{L_{x}\times WH}\) are the spectral bases and spectral basis coefficient matrix, respectively. Therefore, the following equations can be obtained:  
\[\mathbf{H}=\mathbf{DCBS} \tag{4}\]  
\[\mathbf{M}=\mathbf{RDC} \tag{5}\]