esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Metrics and Application Scenarios in PLS  
Physical layer secrecy ensures information confidentiality by enhancing the performance difference between the main transmission and the wiretap channels. The selection of appropriate metrics is essential for this approach to secure transmission design. One of the critical steps that must be executed after the design of secure physical layer transmission is the proper measurement of the level of security using the right metric. Therefore, it is essential to select suitable performance metrics that can quantify the relevant aspects. The performance evaluation must indicate the level of security that the wireless schemes or techniques can provide. However, various challenges are commonly being brought up by different performance requirements, especially when the sequence of channel coding has a finite block length [65].  
In this section, we present the basic secrecy metrics that are usually adopted in the design of efficient secure transmission strategies. It needs to be noted that the performance metrics that are used in the literature mainly focus on keyless PLS technologies, which are termed SINR-based performance metrics. SINR-based metrics are used to measure the achievable performance of a secure design under confidentiality constraints, namely the metrics that are related to the secrecy capacity/rate, secrecy throughput, power/energy consumption, secrecy outage probability (SOP) and quality of service (QoS). To be more specific, security metrics, as mentioned above, are commonly taken as the design metrics (criteria) for the design and optimization of PLS paradigms.
# A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training
## 4 Intelligent Data Methods Used in Studies  
The objective of this section is to present the intelligent data analysis methods that were used by the researchers in the domain of smart sport training. Following the recent practice of intelligent method taxonomies proposals on other highly domain specific fields, such as intrusion detection [12], very large-scale integrated circuits and systems [13], program binaries [14], diabetes management [15], the same practice, and establishment of a novel taxonomy of intelligent methods is proposed in the domain of SST. The proposed taxonomy is based on the methods identified and currently used in the domain and may be extended as the domain grows and matures in the future.  
According to Figure 4, our taxonomy comprises of five main groups (some algorithms can be counted in more than one group e.g., Artificial Neural Networks. In our case, we put artificial neural networks in the machine learning group, since most of the studies that reported the use of Artificial Neural Networks also used the other machine learning methods, e.g., Decision Trees in the same study), from which the used algorithms were identified:  
* **Computational Intelligence** methods [16]: -Evolutionary Algorithms: Differential Evolution (DE) [17]. -Swarm Intelligence Algorithms: Bat Algorithm (BA) [18], and Particle Swarm Optimization (PSO) [19]). -Fuzzy systems [20]. -Simulated annealing [21].
* **Data Mining**: -conventional Data Mining methods, i.e., Apriori [22]. -Machine Learning: Conventional machine learning methods, i.e., Decision Trees (DT) [23], adaptive boosting [24], Random Forests (RF) [25], Gradient Boosting (GB) [26], K-Nearest Neighbors [27] (k-NN), Support Vector Machine (SVM) [28], Artificial Neural Networks [29] (ANN), hierarchical clustering [30], and k-means clustering [31].
* **Deep learning**[32]: Recurrent Neural Networks (RNN) [33], Long Short-Term Memory (LSTM) [34], Convolutional Neural Networks [35] (CNN).
* **Other methods**: Case-Based Reasoning (CBR) [36], Dynamic Time Warping [37] (DTW), Bayesian Networks (BN) [38], Naive Bayes (NB) [38], Markov chain [39], generalized additive  
Figure 3: Number of studies published per year included in the literature review.  
models [40], Gaussian process [41], Linear Regression [42] (LR), regularized logistic regression [43], linear discriminant analysis [44], and spline interpolation [45].  
Some research did not define the algorithms used clearly, just the field from which they were (e.g., [46], or used custom non-conventional algorithms (e.g., [47]), in such cases the method used was identified as _custom_ and _data analysis field from which the method was_ (e.g., [47] custom data mining algorithm). In cases where the data analysis method used was not clearly visible and an accurate determination was not possible, the slash sign (\({}^{\prime}\)/\({}^{\prime}\)) was used in the table.
## 3 Ground Motion Selection Techniques
### Selection Based on Causal Parameters and Spectral Shape  
Ground motion databases around the world are increasing rapidly, with NGA West2 being six times larger than NGA West1. Hence, [PERSON] and [PERSON] [90] have argued for screening candidate ground motions according to \(M_{w}\), \(R\) and \(Vs30\) (causal parameters) prior to applying the selection based on spectral shape. The reason is that applying traditional CS and GCIM techniques to a very large database will result in the selection of records that the structure may never encounter. To perform this screening, \(M_{w}\), \(R\) and \(Vs30\) can be bound using deaggregation results from PSHA. If the bounds are too narrow, there would be few records left for selection, and if they are too wide, then incompatible records would be left. As a result, it is necessary to carefully select the bounding values. The following bounding limits for \(M_{w}\) and \(R\) (Table 1) were proposed by [PERSON] and [PERSON] [90], where \(M_{w}\) 1% represents the first percentiles of marginal deaggregation distribution results of \(M_{w}\).  
A different method of analyzing this problem is proposed by [PERSON] et al. [91]; they advocate that records should be screened after the spectral shape selection has been completed. In their approach (referred to as CS-MR), records selected based on their spectral shape are discarded based on the desired \(M_{w}\) and \(R\) bins. Records that do not fit into desired \(M_{w}\)-\(R\) bins or those that exceed each bin's contribution to a particular hazard level are removed. This is followed by the addition of desired ground motions from underrepresented \(M_{w}\)-\(R\) bins, resulting in a set of results that are consistent with both the target spectrum and the deaggregation results.  
The previous sections illustrate that ground motion record selection has become an increasingly complicated process that includes consideration for a variety of seismological properties. Therefore, it is necessary to investigate the relationship between IM-EDP in relation to various seismological parameters. Let us therefore discuss in detail how different  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{**Magnitude (\(M_{w}\))**} & \multicolumn{2}{c}{**Source-to-Site Distance (\(R_{np}\))**} \\ \hline Upper Limit & Lower Limit & Lower Limit \\ \(min\left(\begin{smallmatrix}M_{w}^{1\%}\,,\,M_{w}^{10\%}\,-0.5\end{smallmatrix}\right)\) & \(max\left(\begin{smallmatrix}M_{w}^{99\%}\,,\,M_{w}^{90\%}\,+0.5\end{smallmatrix}\right)\) & \(min\left(\begin{smallmatrix}R_{np}^{1\%}\,,\,0.5\,R_{np}^{10\%}\end{smallmatrix}\right)\) & \(max\left(\begin{smallmatrix}R_{np}^{99\%}\,,\,1.5\,R_{np}^{90\%}\end{smallmatrix}\right)\) \\ \hline \end{tabular}
\end{table}
Table 1: Bounding limits proposed by [PERSON] and [PERSON] (2016) [90].  
parameters (structural and seismological) affect the relationship between IM and EDP. We also provide recommendations for future improvements.
# Quality Assessment of Banana Ripening Stages by Combining Analytical Methods and Image Analysis
## 3 Results and Discussion
Based on the above findings, [PERSON] and [PERSON] [23] reported that the extracted features from the bananas' images could be directly or indirectly related to the ripening process. Furthermore, [PERSON] et al. [24] reported that as the brown spots had extended all over the banana peel, the peel homogeneity had decreased. The scatter diagram (Figure 3) shows the discrimination among banana peel samples on days 2, 7, 11, and 21, with a 96.0% overall discrimination accuracy, according to the features of contrast, energy, short-run emphasis and run-length non-uniformity. The banana peel samples on day 2 (blue circles) (first cluster) occupied the center left zone of the scatter diagram, while the second cluster, which included banana peel samples from day 7 (green squares), occupied the bottom left area of the scatter diagram. The third cluster, which contained banana peel samples from day 11 (red triangles) formed a cluster surrounding the upper right zone of the scatter diagram, and finally, the fourth cluster of the banana peel samples from day 21 (pine green pentagons) formed a cluster surrounding the bottom right zone of the scatter diagram. This four-feature combination was used in the design of a high-performance machine-learning system, which could aptly classified 43 out of the 48 banana peel samples from day 2 (of these, 1 and 4 samples were wrongly assigned to days 7 and 11, respectively), 47 out of the 48 samples from day 7 (1 sample was wrongly assigned to day 21), 46 out of the 48 samples from day 11 (2 samples were wrongly assigned to day 2), and 48 out of the 48 samples from day 21 (all correctly classified). Interestingly, a similar ripening pattern in bananas, activated by the phytohormone ethylene through metabolite profiling, had been observed by [PERSON] et al. [25]. The latter studied banana ripening for seven days and recorded two peaks in respiration rate (ripening index). Specifically, the first peak was on the second day, followed by a downward trend until the fifth day and a new rise and peak on the sixth day. The same results were also reported by [PERSON] et al. [26]. After iodine testing, the banana-flesh slices switched progressively from a dark iodine stain to a lighter one (Figure 4) from the center of the slices and outwards during the 21-day storage period. Simultaneously, a significant increase in ripening was observed in the feature mean, which expressed the mean value of the intensities of all the image pixels (Table 1, Figure S1) derived from the grayscale images of the iodine-stained banana flesh. Specifically, during the 21-day storage period, the intensity of the image pixels presented a significant (\(p<0.05\)) yet gradual increase, 17.8%, in the mean rate of change, which was directly related to the destruction of the amylose-iodine complex. This destruction was directly related to the flesh softening and sweetening during ripening and was ascribed to the activity of the cell-wall enzymes, including the hydrolases as well as to the starch-to-sugar metabolism [27].
# Implementation of Autonomous Mobile Robot in SmartFactory
## 4 Experimental Setup
### Cooperation with SmartFactory
#### 4.2.2 Implementation  
The program for MiR mobile robots was created using individual commands, such as logic functions, move functions, docking functions, and more. These functions were then grouped into so-called \"missions\". The robot's mission was therefore a grouping of commands that determined what the robot should do in a particular mission. Within the mission, the individual commands were executed sequentially and if the robot had already fulfilled all commands, the mission was terminated. Missions were launched to the robot via a web interface, and it was possible to add more missions to the queue for the robot to complete. If the mobile robot had no mission in the queue, nor did it perform any active mission, it stood still and waited for the mission to be assigned.  
* Movement and positioning
* Move: The mobile robot moves to a position on map;
* Docking: The mobile robot moves to a marker or charging station;
* Rel. move: The mobile robot moves to a relative position.
* Logic functions
* Charging: The mobile robot starts charging;
* If: Conditioning decision function;
* Loop: Cycle repeated execution of commands.
* WISE I/O module control
* Set output: Switch on/off the output on a WISE module;
* Read input: Read the actual value on the digital input of a WISE module;
* Wait for input: Detection of the input on a WISE module.
* Other functions
* Mission call: Perform a mission in another mission;
* Light: Sets the style and color of the robot lights;
* Sound: The ability for the mobile robot to play a sound.
The mobile robot MiR100 had the task of communicating with the production line in SmartFactory and taking the manufactured products. According to the proposed implementation, the mobile robot took a pair of products from the line. Each pair of products had a different final delivery position, and the mobile robot had to recognize which product was currently being delivered. It received this information from the line. The mobile robot also monitored the level of the battery so that it could operate continuously and was always ready to take the product. The entire programming code was divided into five missions, where the first was the main mission, and the rest were submissions for specific actions; see Figure 8.
# Application of Convolutional Neural Networks in Visual Feedback of Movable Camera Mounting Control
## 2 Materials and Methods
### CNN Estimator  
#### 2.2.1 Coarse Estimation  
The main goal of coarse head estimation is to detect the position of the head in the image and estimate its bounding box. In this project, four different solutions of head detection were evaluated, namely, the Haar cascade face detector [22], multitask cascaded convolutional networks (MTCNN) [23], histogram of oriented gradients (HOG) face detector with the support-vector machine (SVM) [24], and the DNN module in OpenCV [25]. Each detector was evaluated in terms of the processing time of a single image frame and accuracy. The results are collected in Table 3.  
The tests have shown that an OpenCV DNN module acts superior in terms of processing time. There were additional problems with alternative solutions. The Haar-based detector resulted in many false predictions, whereas the HOG-based detector did not detect faces that covered a small region of an image. Both acted po  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training Set** & **Development Set** & **Test Set** \\ \hline Original sets & 1586 & 283 & 153 \\ \hline Augmented sets & 15,860 & 2830 & 153 \\ \hline Number of people & 51 & 15 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of image frames in each learning set.  
Figure 3: Data flow of CNN valid image dataset preparation. The original image data are fed to a coarse estimation, which results in the square head bounding box. The cropped image is unified in size, normalized, and preprocessed for training purposes.  
For further work, the detector that gave the shortest processing time was chosen, resulting in an operation close to online. The estimation of the head bounding box was carried out with an OpenCV DNN module with a pre-trained CNN model [26]. Since no prior modifications were made to the proposed CNN architecture, a detailed review will be omitted in this work.  
In particular, the neural network selected for the coarse estimation resulted in regression of the bounding box; that is, the coordinates of the left top corner of the bounding boxes and its height. Different bounding boxes' sizes could be estimated depending on the dimensions of the head present on a particular image frame. Subsequently, the square image given by the bounding box was cropped from the original image data, as presented in Figure 3. The size was then unified as \(128~{}\times~{}128\) px to constitute a valid fine estimation input, as described in the previous section.
## 5 Simulation Results and Analysis
### Practical Implications and Recommended Use Cases  
Table 6 summarizes the key advantages, drawbacks, and ideal mission scenarios for each algorithm evaluated. For example, classical population-based methods (GA, PSO) excel in static environments with sufficient offline computation times, whereas off-policy RL algorithms (SAC, TD3, and TQC) are preferable in complex conditions requiring adaptive decision-making.
## 2 Related Work
### Energy Efficient Routing
#### 2.1.3 Geographic Routing Protocols  
They are based on the fact that if the destination of a query is known (sensors locations are assumed to be known, e.g., GPS), the query can be broadcast only to the appropriate geographical region without flooding. Hence, the amount of broadcast data is reduced. In GEAR [16] for instance, a node forwards apacket to its neighbor that is the closest one to the destination. If all neighbors are at the same distance, some neighbor is randomly selected. GAF [17] is another example of energy aware geographical routing protocol. Indeed, each node uses the knowledge of its location provided by its GPS to determine its region in a virtual grid. The size of each cell in the grid is set such that any two nodes belonging to contiguous cells can communicate directly. Nodes located in the same cell are considered equivalent from the routing point of view. Furthermore, GAF allows nodes to sleep as long as the existence of an active node per cell in the virtual grid is achieved.  
There are different ways to compute energy efficient routes. Routing protocols differ by:
## 3 Results and Discussion
### Effects of Pre-Synthesized BYS(\(x\)) Content on the Densities of PZT-BYS(\(x\)) Ceramics
From Figure 5b, it can be seen that the microstructure of the specimen doped with 0.1 moles of Sb\({}_{2}\)O\({}_{3}\) shows higher uniformity with fine grain size, and an average grain size of 2.2 \(\upmu\)m was observed. On further addition of Sb\({}_{2}\)O\({}_{3}\) content, as shown in Figure 5c-g, the average grain size abruptly decreased to approximately \(\sim\)1.7 \(\upmu\)m at \(x\) = 0.2-0.3 moles and approximately 1.4 \(\upmu\)m for \(x\) = 0.4-0.6 moles. This reduction of grain size can be interpreted as a pinning effect of the secondary phases (Y\({}_{3}\)Sb\({}_{5}\)O\({}_{12}\), Bi\({}_{2}\)O\({}_{3}\), Y\({}_{2}\)O\({}_{3}\), and BiSbO\({}_{4}\)) as indicated in XRD patterns in Figure 1a. Figure 4: (**a**) X-ray diffraction patterns of Sb\({}_{2}\)O\({}_{3}\)-doped PZT–BYS(\(x\)) (\(0.1\leq x\leq 0.6\)) ceramics sintered at optimum temperature for 2 h, and (**b**) enlarged portion of XRD at a selected 2\(\theta\) angle. The optimized density of sintered PZT-BYS(\(x\)) ceramics is listed in Table 2. The relative densities of the Sb\({}_{2}\)O\({}_{3}\)-doped specimens were higher than 98% of the theoretical density, indicating the development of dense microstructure in all of these compositions. However, it can be observed that the compositions with \(x=0.1\) show relatively higher density than other compositions. This may be attributed to the relatively uniform grain size with scarcer porosity at \(x=0.1\), which promotes densification. Further addition of Sb\({}_{2}\)O\({}_{3}\) into the compositions decreases the density again. The SEM investigations, as in Figure 5, revealed supporting evidence that the ceramics with higher Sb\({}_{2}\)O\({}_{3}\) (\(x>0.1\) moles) compositions contain small and loosely bonded grains. Evidently, this could be a reason for the much lower density of these compositions. Among all the compositions, a peak density of 7.96 g/cm\({}^{3}\) (99.1% of the theoretical density) was obtained at \(x=0.1\).
## 4 Our Proposed ADDGCN System  
The ADDGCN model's architecture as depicted in Figure 2 comprises three modules: a diffuse graph convolution module, a down-sampling dynamic graph convolution module based on feature augmentation, and a multi-head attention module. The core ideas can be summarized as (1) the fusion of adaptive adjacency matrix and learnable adjacency matrix to construct a dynamic graph structure; (2) using a down-sampling dynamic graph convolution module to simultaneously learn spatio-temporal relationships while modeling the spatial heterogeneity of spatio-temporal sequences; and (3) using a multi-head attention machine to record traffic data dependencies over lengthy periods of time.
## 5 Experiment and Results
### Evaluation Metrics  
This experiment uses Precision, Recall, Mean Average Precision (mAP), and Model Parameters as evaluation indicators to evaluate the performance of different models comprehensively. The calculation formulas for Precision, Recall, AP, and mAP are as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{7}\] \[Recall=\frac{TP}{TP+FN}\] (8) \[AP=\int_{r=0}^{1}P(r)dr\] (9) \[mAP=\frac{1}{K}\sum_{i=1}^{K}AP_{i} \tag{10}\]  
In the formula, \(TP\) is the number of positive samples that are correctly identified, \(TN\) is the number of negative samples that are correctly identified, \(FP\) is the number of negative samples that are false positives, and \(FN\) is the number of positive samples that are missed. The curve, for which the ordinate is the precision, and the abscissa is the recall, is denoted by \(P(r)\). \(K\), and this is the total number of categories, and \(AP_{i}\) is the average precision of the i-th category.
## 4 Discussion
### Impact of Spatial Heterogeneity on Modelling Results  
The simulated carbon stock for FSC using remote-sensing-based forest age data is significantly improved compared to the results based on FSC forest age data. However, some FSC still deviate greatly from the observed data, which is probably caused by the spatial heterogeneity of the FSC. At the beginning, the boundary of FSC was determined by the homogeneity within the forest stand, and similar management was performed. As time goes on, the same FSC might experience different management measures (thinning, fertilization, tree species, etc.) and disturbances (fires, diseases, typhoons), which causes the FSC to be more heterogeneous (for example in Figure 9). Both the CHM and aerial maps (Figure 9a1,a2) show that H in the northeast of the FSC is high, up to 30 m, but is low in the northwest of the FSC, and the maximum difference reaches 20 m. Obviously, the ACS also shows high spatial heterogeneity in this FSC. In Figure 9b, the H of the FSC is generally high, but the heterogeneity within the FSC is more obvious, and the difference between high and low trees is close to 25 m. This spatial heterogeneity could easily lead to overestimation or underestimation of the simulation results in the simulation process. Therefore, it is necessary to redraw the FSC and determine new boundaries to reduce the heterogeneity in future study, which will, potentially, significantly improve the accuracy of simulation results.
## 4 Simulation and Experimental Results
### Simulation Results
#### 4.1.2 Position Trajectory Tracking  
For the second test, we consider the position tracking case with the profile given in Figure 6 as a reference trajectory. Figure 6a shows the position tracking performance; the desired trajectory is in the dotted line, while in the solid line is the angular position of the rotor of the PMSM. The tracking error is presented in Figure 6b. Trajectory tracking of the currents \(i_{d}\) and \(i_{q}\) are shown in Figures 7 and 8. The position tracking error does not exceed 0.5% of the reference maximum value.  
Figure 4: (**a**) Current \(i_{d}(t)\) and reference. (**b**) Trajectory tracking error.  
Figure 5: (**a**) Current \(i_{q}(t)\) and reference. (**b**) Trajectory tracking error.  
Figure 3: (**a**) Speed \(\omega(t)\) and reference. (**b**) Trajectory tracking error.