esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Performance Evaluation Metrics  
In this study, the performance evaluation method is based on accuracy which measures the percentage of the correctly classified instances. The metric values were computed using the statistical values of True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN).  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+ \text{TN}}\times 100\% \tag{1}\]  
To determine the performance stability of prediction models, Co-efficient of Variation (C.V) was applied to the results of the prediction models. C.V which is the percentage ratio of standard deviation (SD) and average (AVE) is used to remove the effect of average difference on the comparison stability [15; 40]. The formula for C.V is given as thus:  
\[\text{C.V}=\frac{\text{SD}}{\text{AVE}}\times 100\% \tag{2}\]  
Prediction models with high C.V values are regarded as unstable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline S/No. & Datasets & Language & Number of Features & Number of Modules \\ \hline
**1.** & CM1 & C & 37 & 327 \\ \hline
**2.** & KC1 & C++ & 21 & 1162 \\ \hline
**3.** & KC3 & Java & 39 & 194 \\ \hline
**4.** & MW1 & C & 37 & 250 \\ \hline
**5.** & PC2 & C & 37 & 722 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Software Defect Datasets.
# A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios
## 3 Related Work
### Location-Aware DTN Work  
Several works focused on location-aware architectures in order to make more efficient the considered application scenarios.  
A first example is AeroRP [14], designed for airborne telemetry applications. Although not directly related to DTN but to classic ad-hoc networks, it is worth mentioning it, since the proposal checks the location and the current trajectory of nodes to take a more precise forwarding decision. AeroRP predicts the available connection with the recipient (base station) using this information, increasing the delivery ratio and reducing the overhead with respect to classic routing protocols, such as AODV and DSDV.  
In Ref. [15], a protocol performance evaluation is performed considering a search and rescue scenario in the simulation configuration. Classic DTN routing protocols are evaluated for the comparison, and the authors conclude that contact opportunities represent useful information.  
The authors in Ref. [16] propose an optimization for the Public Unmanned Aerial Vehicles (UAV-P) trajectory for delivery time minimization. UAV-P act as delivery devices, storing the data and delivering them to a base station, making this system a DTN. The work faces the problem by determining theoptimal route that connects all the coordinates of the information sources. Our proposal is different, since we do not act on the UAVs' trajectory; rather, we analyze the planned trajectory to choose the current best delivery UAV.  
A delay tolerant application platform deployed on the Public Transportation System is proposed in Ref. [17]. The authors analyze the possibility to provide opportunistic connectivity using a carrier-based approach based on bus routes. Simulation outcome shows good performance in several scenarios, making the proposal a practicable non real-time solution. A related and more detailed work is done in Ref. [18].  
Some works focus on routing strategies that merge existing ad-hoc routing protocols with DTN characteristics. It is the case of Ref. [19], which adapts the Ad hoc On Demand Distance Vector (AODV) routing protocol for the DTN architecture. Basically, a new DTN-aware routing protocol is implemented on top of the underlying and unmodified AODV.  
The authors of Ref. [20] present the possibility to make the UAVs' movements dynamic, based on the connectivity level between neighbor nodes. The objective is to change the position of UAVs in a DTN in order to establish the best communication quality in terms of signal-to-noise (SNR) ratio and packet throughput. The optimization algorithm orders the UAVs to move in a specific direction, so that the two metrics are minimized.
## 3 Methodology
### Data Preprocessing  
In the study of pedestrian crossing intention or trajectory recognition and prediction based on an on-board view, the 2D pose point coordinate values of pedestrians can often be obtained by calculating the body pose angles. However, some body pose angles, e.g., the forward tilt angle of the pedestrian torso, cannot be obtained if only 2D pose coordinate values of pedestrians are used in the pedestrian pose calculation based on a road-based view. To solve this problem, we obtain the depth information of pedestrian skeletal points by acquiring the image depth to determine the 3D coordinates of the pedestrian pose points. In the actual data acquisition process, a Kinect camera can obtain the scene depth of field through infrared and using its computing platform to collect the real-time 3D coordinates of skeletal points. However, the process of outdoor data acquisition will be disturbed by lighting and other conditions, resulting in poor recovery of the depth of field. Moreover, since binocular stereo vision is less affected by lighting but is more computationally intensive in the later data processing, we adopt a combination of both methods for the acquisition of data (for more detailed information, refer to Experimental Procedure (2)).  
The 3D feature point calculation of the pedestrian pose based on a ZED binocular RGB camera is described as follows. First, we obtained the coordinates of any skeletal 2D feature point of the pedestrian in the left and right-side cameras based on OpenPose, and obtained the following:  
\[\mathbf{A}_{l}=\begin{pmatrix}x_{0}^{l}&\cdots&x_{4}^{l}\\ \vdots&\ddots&\vdots\\ x_{20}^{l}&\cdots&x_{24}^{l}\end{pmatrix} \tag{8}\]  
\[\mathbf{A}_{r}=\begin{pmatrix}x_{0}^{r}&\cdots&x_{4}^{r}\\ \vdots&\ddots&\vdots\\ x_{20}^{r}&\cdots&x_{24}^{r}\end{pmatrix} \tag{9}\]  
where \(x_{i}^{l}(i=0,1,\cdots,24)\) and \(x_{j}^{r}(j=0,1,\cdots,24)\) denote the horizontal coordinate values of the 25 skeletal points in OpenPose in the left and right cameras, respectively (for more detailed information. Then, the parallax matrix D is obtained:  
\[\mathbf{D} =\mathbf{A}_{l}-\mathbf{A}_{r} \tag{10}\] \[=\begin{pmatrix}x_{0}^{l}-x_{0}^{r}&\cdots&x_{4}^{l}-x_{4}^{r}\\ \vdots&\ddots&\vdots\\ x_{20}^{l}-x_{20}^{r}&\cdots&x_{24}^{l}-x_{24}^{r}\end{pmatrix}\]  
With the camera focal length f and baseline b, the depth matrix Z of each skeletal feature point can be obtained:  
\[\mathbf{Z}=\frac{b\cdot f\cdot\mathbf{D}^{*}}{|\mathbf{D}|}=\begin{pmatrix}z_ {0}&\cdots&z_{4}\\ \vdots&\ddots&\vdots\\ z_{20}&\cdots&z_{24}\end{pmatrix} \tag{11}\]where \(z_{k}(k=0,1,\cdots,24)\) denotes the depth values of each of the 25 skeletal points in OpenPose, i.e., the distance of each skeletal point from the camera. From this, we can obtain the 3D feature point coordinate matrix \(\textbf{Pose}_{t}^{i}(x_{t}^{j},y_{t}^{j},z_{t}^{j})\) of any skeleton of pedestrian j at moment t.
## 2 Materials and Methods
### Data Analysis
#### 2.4.1 Physiological Data Pre-Processing  
To pre-process our data, we used AcqKnowledge software v4.4 (BIOPAC Systems, Inc., Aero Camino, Goleta, CA, USA). To use them as reference points for event-related analysis, we identified glare onsets based on the signal outputted by the light meter sensor. Occasionally, we identified more than the expected four glares per block due to errors in manually operating the carousel. In these cases, we disregarded the glares where the time until the next glare was shorter than 20 s, as these were clearly unintentional (with the experimental protocol demanding 30 s between glares). All blocks of Subject 3 and the first block of Subject 38 were excluded due to technical problems. The remaining 44 subjects produced 32 valid glares each (four glares in each of the eight blocks). Altogether we analyzed 1438 instances of glare.  
As a proxy for sympathetic activation, we analyzed amplitudes of skin conductance responses (SCRs) to glares [38]. Because our original skin conductance recording exhibited gradual baseline shifts and contained a moderate amount of high-frequency noise, we filtered it using a 0.05-1 Hz band-pass filter. We located the SCRs in the first 8 s of each epoch (According to seminal literature, latency and rise time can altogether add up to 6 s after the stimulus presentation [39]; we opted for epochs 8 s long to conservatively account for possible inaccuracies in the process of stimuli presentation). In each epoch, we used the evaluation method B presented by [38] to analyze the amplitude of the first SCR following the stimulus presentation, which was larger than 0.1 microsiemens (\(\upmu\)S; we conservatively chose half of what seminal literature considers minimal response [39]). We excluded 598 epochs due to the absence of any SCR larger than 0.1 \(\upmu\)S and an additional six epochs due to visually identified artifacts. We extracted the SCR amplitude values from the remaining 834 epochs. Because the SCR amplitudes distribution notably deviated from the normal distribution, for the purposes of statistical analysis, we applied logarithmic transformation to the SCR amplitudes' values.  
We further analyzed the number of eye blinks recorded with vertical EOG in 20 s long epochs starting at the glare onset. Due to technical errors, eight epochs were shortened by 125 to 2930 ms. To identify individual blinks, we first suppressed EOG elements caused by eye movements by applying a 0.5 Hz high-pass filter. Subsequently, we identified blinks by visual inspection--assisted by a simple peak-detecting algorithm--and then assessed the relative frequency of blinks in the 20 s intervals. Since the relative frequency depends on the length of the epoch, in the epochs shorter than 20 s, we linearly extrapolated the number of blinks to estimate the expected number of blinks in 20 s. We excluded 61 epochs due to visually identified EOG artifacts.
## 3 Results and Discussion
### Formula Fitting for Stiffness Reduction and Bending Moment Adjustment Coefficients  
According to the above discussions in terms of different influence parameters, only changes in joint stiffness ratio (\(\gamma\)) and aspect ratio (\(\beta\)) had obvious effects on changes in the stiffness reduction coefficient (\(\eta\)) and bending moment adjustment coefficients (\(\xi_{t}\) and \(\xi_{b}\)), while neither the coefficient of subgrade reaction nor the embedment depth had a significant influence on \(\eta\), \(\xi_{t}\), and \(\xi_{b}\). Therefore, a fitting formula was proposed for describing \(\eta\), \(\xi_{t}\), and \(\xi_{b}\) in terms of \(\gamma\) and \(\beta\), as follows:  
\[\chi=a\ln\gamma+b\beta+c \tag{8}\]  
where \(a\), \(b\), and \(c\) are all fitting coefficients to be determined, and \(\chi\) represents \(\eta\), \(\xi_{t}\), or \(\xi_{b}\).  
Origin software was used for the formula fitting. The values of the undetermined coefficients are shown in Table 1, and the fitted surfaces are shown in Figure 9. Figure 9a shows the correlation of \(\eta\), \(\gamma\), and \(\beta\) in a 3D view with \(\eta\) as the ordinate. Figure 9b,\(c\) show correlations of \(\xi_{t}\) or \(\xi_{b}\), \(\gamma\), and \(\beta\) in a 3D view with \(\xi_{t}\) or \(\xi_{b}\) as the ordinate. After calculation, the coefficients of determination of the fitting formulas were all greater than 0.95, indicating good accuracy.  
Figure 9: Surfaces of fitting formulas: (**a**) Stiffness reduction coefficient and (**b**) bending moment adjustment coefficient for upper end of sidewalls; (**c**) bending moment adjustment coefficient for lower end of sidewalls.
# Effects of Wind Barrier Porosity and Inclination on Wind Speed Reduction
## 3 Results
Figure 4: Visualization of the spatial distribution of the average wind velocity according to distance for Case A with a wind barrier porosity of 0% and an inclination of (**a**) 90\({}^{\circ}\), (**b**) 80\({}^{\circ}\), and (**c**) 70\({}^{\circ}\). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Porosity (\%)** & **Inclination (\({}^{\circ}\))** & **Distance (cm)** & **Height (cm)** & **Velocity (m/s)** & **Wind Reduction Ratio (\%)** \\ \hline \multirow{8}{*}{90\({}^{\circ}\)} & \(-10\) (C) & 30, 60, 90 & 5.000 \(\pm\) 0.000 \({}^{\circ}\) & 0 \\ \cline{2-5}  & 50 & 30, 60, 90 & 3.077 \(\pm\) 1.207 \({}^{\rm b}\) & 38 \\ \cline{2-5}  & 100 & 30, 60, 90 & 2.180 \(\pm\) 0.319 \({}^{\rm a}\) & 56 \\ \cline{2-5}  & 200 & 30, 60, 90 & 1.673 \(\pm\) 0.020 \({}^{\rm a}\) & 67 \\ \cline{2-5}  & 300 & 30, 60, 90 & 1.570 \(\pm\) 0.117 \({}^{\rm a}\) & 69 \\ \cline{2-5}  & 400 & 30, 60, 90 & 1.603 \(\pm\) 0.289 \({}^{\rm a}\) & 68 \\ \cline{2-5}  & 500 & 30, 60, 90 & 1.675 \(\pm\) 0.398 \({}^{\rm a}\) & 66 \\ \cline{2-5}  & 600 & 30, 60, 90 & 1.801 \(\pm\) 0.568 \({}^{\rm a}\) & 64 \\ \hline \multirow{8}{*}{25\%} & \(-10\) (C) & 30, 60, 90 & 5.000 \(\pm\) 0.000 \({}^{\rm d}\) & 0 \\ \cline{2-5}  & 50 & 30, 60, 90 & 3.350 \(\pm\) 1.114 \({}^{\rm c}\) & 33 \\ \cline{2-5}  & 100 & 30, 60, 90 & 2.553 \(\pm\) 0.284 \({}^{\rm b}\) & 49 \\ \cline{2-5}  & 200 & 30, 60, 90 & 1.818 \(\pm\) 0.065 \({}^{\rm ab}\) & 64 \\ \cline{2-5}  & 300 & 30, 60, 90 & 1.695 \(\pm\) 0.185 \({}^{\rm a}\) & 66 \\ \cline{2-5}  & 400 & 30, 60, 90 & 1.644 \(\pm\) 0.306 \({}^{\rm a}\) & 67 \\ \cline{2-5}  & 500 & 30, 60, 90 & 1.695 \(\pm\) 0.354 \({}^{\rm a}\) & 66 \\ \cline{2-5}  & 600 & 30, 60, 90 & 1.852 \(\pm\) 0.540 \({}^{\rm ab}\) & 63 \\ \hline \multirow{8}{*}{70\({}^{\circ}\)} & \(-10\) (C) & 30, 60, 90 & 5.000 \(\pm\) 0.000 \({}^{\rm c}\) & 0 \\ \cline{2-5}  & 50 & 30, 60, 90 & 2.409 \(\pm\) 1.101 \({}^{\rm ab}\) & 52 \\ \cline{2-5}  & 100 & 30, 60, 90 & 2.883 \(\pm\) 0.635 \({}^{\rm b}\) & 42 \\ \cline{2-5}  & 200 & 30, 60, 90 & 1.992 \(\pm\) 0.211 \({}^{\rm a}\) & 60 \\ \cline{2-5}  & 300 & 30, 60, 90 & 1.902 \(\pm\) 0.312 \({}^{\rm a}\) & 62 \\ \cline{2-5}  & 400 & 30, 60, 90 & 1.886 \(\pm\) 0.450 \({}^{\rm a}\) & 62 \\ \cline{2-5}  & 500 & 30, 60, 90 & 1.939 \(\pm\) 0.560 \({}^{\rm a}\) & 61 \\ \cline{2-5}  & 600 & 30, 60, 90 & 1.997 \(\pm\) 0.618 \({}^{\rm a}\) & 60 \\ \hline \end{tabular} (C) represents the windward control point of wind speed. Wind velocity (m/s) is given with corresponding standard deviation values. Superscripts with different letters for wind velocity denote identical groups of the average wind velocity for each inclination of the wind barrier (90\({}^{\circ}\), 80\({}^{\circ}\), and 70\({}^{\circ}\)) classified using Duncan analysis (significant at \(p<0.05\)).  
\end{table} 
Table 2: Case B—average wind velocity at 25% porosity and three different inclinations of wind barriers (90\({}^{\circ}\), 80\({}^{\circ}\), and 70\({}^{\circ}\)) for every measuring point.
## 2 Related Works
### Sensors for Distance Measurement  
The camera is a widely used and cost-effective sensor for environmental perception. It mimics the capabilities of the human visual system, excelling in the recognition of shapes and colors of objects. However, it does have limitations, particularly in adverse weather conditions with reduced visibility.  
The radar (radio detection and ranging) is widely used to precisely track the distance, angle, or velocity of objects. Radars can be broken down into a transmitter and receiver. The transmitter sends radio waves in the targeted direction and the waves are reflected when they reach a significant object. The receiver picks up the reflected waves and gives information about the object's location and speed. The greatest advantage of the radar is that it is not affected by visibility, lighting, and noise in the environment. However, compared to a camera, a radar is low-definition modeling and is weak at providing the precise shape of objects and identifying what the object is.  
The mechanism of the LiDAR (light detection and ranging) is similar to the radar but utilize laser light to determine ranges instead of radio wave. The LiDAR is a more advanced version of a radar that can provide extremely low error distance measurement. It is also capable of measuring thousands of points at the same time to model up a precise 3D depiction of an object or surrounding environment [16]. The disadvantages of the LiDAR are its high cost and the requirement of a remarkable amount of computing resources compared to cameras and radars.  
Although the costs of cameras, radar systems, and LiDAR can vary significantly due to factors such as brand, specifications, and quality, a general assessment of equipment costs with comparable capabilities reveals the following: Cameras typically range in price from $100 to several thousand dollars, depending on factors such as resolution, image quality, and additional features. Radar systems used for object detection and tracking start at a few hundred dollars for basic short-range sensors, while more advanced and specialized radar systems can cost several thousand dollars or more. Likewise, LiDAR sensors range in price from a few hundred dollars for entry-level sensors to several thousand dollars for high-end models with extended range, higher resolution, and faster scanning capabilities.  
Considering the pros and cons of the three types of sensors for distance measurement, the camera is the most appropriate sensor to be utilized in the research due to its low cost, being less sophisticated, and its high definition. The 2D information recognized by the camera can be adopted directly by the deep learning algorithms of object detection.
###### Abstract  
The tea plant (_Camelia sinensis_), as a major, global cash crop providing beverages, is facing major challenges from droughts and water shortages due to climate change. The accurate estimation of the actual evapotranspiration (ET\({}_{\text{a}}\)) of tea plants is essential for improving the water management and crop health of tea plantations. However, an accurate quantification of tea plantations' ET\({}_{\text{a}}\) is lacking due to the complex and non-linear process that is difficult to measure and estimate accurately. Ensemble learning (EL) is a promising potential algorithm for accurate evapotranspiration prediction, which solves this complexity through the new field of machine learning. In this study, we investigated the potential of three EL algorithms--random forest (RF), bagging, and adaptive boosting (Ad)--for predicting the daily ET\({}_{\text{a}}\) of tea plants, which were then compared with the commonly used k-nearest neighbor (KNN), support vector machine (SVM), and multilayer perceptron (MLP) algorithms, and the experimental model. We used 36 estimation models with six scenarios from available meteorological and evapotranspiration data collected from tea plantations over a period of 12 years (2010-2021). The results show that the combination of R\({}_{\text{a}}\) (net radiation), T\({}_{\text{mean}}\) (mean air temperature), and RH (relative humidity) achieved reasonable precision in assessing the daily ET\({}_{\text{a}}\) of tea plantations in the absence of climatic datasets. Compared with other advanced models, the RF model demonstrated superior performance (root mean square error (RMSE): 0.41-0.56 mm day\({}^{-1}\), mean absolute error (MAE): 0.32-0.42 mm day\({}^{-1}\), R\({}^{2}\): 0.84-0.91) in predicting the daily ET\({}_{\text{a}}\) of tea plantations, except in Scenario 6, followed by the bagging, SVM, KNN, Ad, and MLP algorithms. In addition, the RF and bagging models exhibited the highest steadiness with low RMSE values increasing (\(-\)15.3\(\div\)18.5%) in the validation phase over the testing phase. Considering the high prediction accuracy and stability of the studied models, the RF and bagging models can be recommended for estimating the daily ET\({}_{\text{a}}\) estimation of tea plantations. The importance analysis from the studied models demonstrated that the R\({}_{\text{a}}\) and T\({}_{\text{mean}}\) are the most critical influential variables that affect the observed and predicted daily ET\({}_{\text{a}}\) dynamics of tea plantations.  
evaportsnigration; tea plantations; machine learning; prediction models +
Footnote †: journal: Applied Sciences
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 8 Discussion and Limitations
### Limitations  
ErrorExplainer is intentionally lightweight. It visits Solidity AST and builds error contexts without fine-grained data-flow analysis. As a result, ErrorExplainer cannot determine whether a try-catch block handles a revert that originates deeper in the call stack. If a catch handler converts the failure into a benign return value, ErrorExplainer still lists the original error, because nothing in the syntax indicates its cancellation.  
At present, ErrorExplainer skips the inline assembly blocks (assembly {}). Thus, any reverts issued inside these blocks remain undetected. Extending coverage would require assembly pattern signatures or a decompiler pass. Both are left for future work.  
ErrorExplainer assumes that the source code has been _verified_ on a block explorer, which means the published hashes exactly match the on-chain bytecode. Verification is a one-time task best performed by the developer. When source code is unavailable, we can fall back to bytecode decompilation. However, the recovered high-level abstractions are coarse, and the resulting error context may be unreliable.
# Innovative Application of Blockchain Technology for Digital Recipe Copyright Protection
## 4 Text Similarity Detection
### PageRank Algorithm
By using this method, we can calculate the PageRank score for each word in the vocabulary, which reflects its importance within the text. This method effectively filters out noise words that make minimal semantic contributions, accurately capturing the semantic information of the text. During the iterative process, we first calculate an initial PageRank score for each word, then iteratively update based on the PageRank formula and the damping factor. After each iteration, the updated PageRank score for each word represents the change in the importance of that word within the sentence, better reflecting the semantic information of the text. Taking the sentence \"the cat is sitting on the mat\" as an example, through several iterations, we continually update the PageRank scores for each word, ultimately obtaining a more precise representation of the sentence's semantics through the weighted words. This text hashing calculation method based on the PageRank algorithm offers a novel and more precise approach for text similarity detection. The computation results are shown in Table 1. * **Initialization of PageRank values:** \[ \begin{split}&\text{PR}(\text{The1})=\text{PR}(\text{cat})=\text{PR}( \text{sitting})=\text{PR}(\text{on})=\text{PR}(\text{mat})=\text{PR}(\text{The2})\\ &=\frac{1-d}{N}=\frac{1-0.85}{6}=0.025\end{split} \] * **First iteration:** \[\begin{split}&\text{PR}(\text{The1})=\frac{1-d}{N}+d\times \frac{\text{PR}(\text{The1})}{\text{C}(\text{The1})}=0.025+0.85\times\frac{0.025}{ 1}=0.025\\ &\text{PR}(\text{cat})=\frac{1-d}{N}+d\times\frac{\text{PR}(\text {The1})}{\text{C}(\text{The1})}=0.025+0.85\times\frac{0.025}{1}=0.056875\\ &\text{PR}(\text{sitting})=\frac{1-d}{N}+d\times\frac{\text{PR}( \text{cat})}{\text{C}(\text{cat})}=0.025+0.85\times\frac{0.056875}{1}=0.097515625 \\ &\text{PR}(\text{on})=\frac{1-d}{N}+d\times\frac{\text{PR}(\text {sitting})}{\text{C}(\text{sitting})}=0.025+0.85\times\frac{0.097515625}{2}=0.0975 15625\\ &\text{PR}(\text{mat})=\frac{1-d}{N}+d\times\left(\frac{\text{PR} (\text{The2})}{\text{C}(\text{The2})}+\frac{\text{PR}(\text{on})}{\text{C}( \text{on})}\right)\\ &=0.025+0.85\times\left(\frac{0.02