esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Attack Test
### Sensitivity Analysis
#### 4.4.2 Differential Attack  
For many attackers, in order to find the breakpoint of the encryption scheme, they might change a pixel of the plaintext image slightly and analyze the difference between the original cipher image and the newly encrypted one. To prevent such situations that the attackers can find some interesting relations among the cipher image and the encrypted modified images, it is necessary for an image encryption algorithm to perform a sensitive reaction even if there only one single bit has changed. Various methods for testing this type of attribute has been proposed, and in these methods, the number of pixel change rate (NPCR), unified average changing intensity (UACI) [36], and the block average changing intensity (BACI) are applied. The theoretical values of these evaluating indicators are 99.6094%, 33.4635%, and 26.7712%, respectively.  
For the calculating process of these three indicators mentioned above, we first normally encrypted the plaintext image, the cipher image after encryption would be named C. After that, we just change a facultative pixel of the plaintext image and execute the encryption operation again to acquire the cipher image \(C^{{}^{\prime}}\). Finally, these two images will be the input of the formulas shown in (6)-(8) to get the experimental results of the three indicators. The whole process will be executed 15 times, where the changed pixels are randomly different each time. After 15 rounds of process, the average results for three criteria of the four images are shown in Table 3. Compared with the theoretical values, our experimental results illustrate the excellent strength in resisting the differential attack.  
\[\mathrm{NPCR}(\mathrm{P_{1}},\mathrm{P_{2}})=\frac{1}{MN}\sum_{i=1}^{M}\sum_{ j=1}^{N}|Sign(P_{1}(i,j)-P_{2}(i,j))|\times 100\% \tag{6}\]  
\[\mathrm{UACI}(\mathrm{P_{1}},\mathrm{P_{2}})=\frac{1}{MN}\sum_{i=1}^{M}\sum_{ j=1}^{N}\frac{|P_{1}(i,j)-P_{2}(i,j)|}{255-0}\times 100\% \tag{7}\]  
Figure 13: Testing results for key sensitivity. (**a**) [PERSON], (**b**) the encrypted image of [PERSON], (**c**) the decrypted image of [PERSON], (**d–f**) three examples of the decryption result when a random bit of the hash is changed randomly.  
\[\text{BACI}(\text{P}_{1},\text{P}_{2})=\frac{1}{(M-1)(N-1)}\sum_{i=1}^{(M-1)(N-1)} \frac{m_{i}}{255}\times 100\% \tag{8}\]  
In these formulas, \(M\times N\) is the size of the image, Sign(x) is the symbol function, and the \(m_{i}\) shows the average of the absolute values of the difference between the two elements.  
Moreover, Table 4 displays the results of the number of pixel change rate (NPCR) and unified average changing intensity (UACI) values in this paper and other references. It is evident to see that from the comparison result, the NPCR results performs better than [20; 34; 37; 38], and inferior to [1; 33], but for the UACI results, the value in our algorithm is closer to the theoretical value than for any other algorithms. Considering all these three implications, the algorithm proposed here performs well in resisting the differential attacks.
## 5 Simulation Results and Analysis
### Performance of Fragmentation Ratio  
In this section, we compare our proposed algorithm with benchmarks in terms of fragmentation. The fragmentation is calculated as given in Equation (22):  
\[\text{Fragmentation}=1-\frac{N_{\text{FS,largest}}}{N_{\text{FS,total}}} \tag{22}\]  
where \(N_{\text{FS,largest}}\) represents the number of FSs in the largest free block.  
The results presented in Figure 6 demonstrate that the proposed algorithm outperforms benchmark algorithms in terms of the fragmentation ratio. Specifically, in the NSFNET network topology, under a traffic load of 400 Erlang, the fragmentation ratio of the proposed algorithm is reduced by approximately 21.3% compared to the LC(L)-DP(C)-EFM algorithm. In the COST239 network, under a traffic load of 3800 Erlang, the fragmentation ratio of the proposed algorithm is reduced by approximately 21.5% compared to the LC(L)-DP(C)-EFM algorithm. The backup path of the LC(L)-DP(C)-EFM algorithm employs the DPP routing method. Compared to algorithms that utilize the SBPP routing approach, the backup path resources of the LC(L)-DP(C)-EFM algorithm cannot be shared, which increases resource consumption and also elevates the probability of encountering unusable frequency slots on the link, thereby increasing spectral fragmentation in the network. The primary path of the KSP(L)-MSP(C)-EFM algorithm utilizes the KSP routing method. Compared to the minimum congestion routing approach, the algorithm based on KSP routing is more prone to traffic aggregation, which increases the probability of encountering unusable frequency slots on the main path. Therefore, compared to the minimum congestion algorithm, the KSP(L)-MSP(C)-EFM algorithm has a higher fragmentation rate. The primary and backup paths of the LC(L)-MSP(C)-EFM algorithm are the same as the proposed  
Figure 5: The OSNR in (**a**) the NSFNET network and (**b**) the COST239 network.  
algorithm. However, in the crucial aspect of the fragmentation measurement, the LC(L)-MSP(C)-EFM algorithm utilizes an entropy-based approach, which can only measure external fragments. The proposed algorithm can measure the overall situation of the link more accurately. Therefore, the proposed algorithm exhibits superior performance over the LC(L)-MSP(C)-EFM algorithm. The proposed algorithm not only computes primary and backup paths based on the principles of least congestion and maximum shareability, respectively, but also takes into account fragmentation to select optimal paths. Furthermore, the proposed algorithm comprehensively considers both free fragmentation in the primary path and free as well as shareable fragmentation in the backup path, leading to improved network resource utilization and reduced fragmentation overall.
## 3 Results and Discussions  
### In-Situ Test Results  
Table 1 shows the in-situ test results of 35 reinforced concrete columns where the samples of concrete cover were drilled, including the column cross-sections, depth of cover, depth of carbonation, maximum crack width (\(w_{\text{max}}\)) induced by corrosion, and corrosion status of embedded rebars.  
The in-situ test results indicated that there are four typical states in reinforced concrete columns. The first status, shown in Figure 5a, has no corrosion-induced cracks and the rebar is uncorroded as well; thus, this status is uncracked and uncorroded. Figure 5b reveals no corrosion-induced cracks with only the transverse ribs and longitudinal ribs corroded; this status is uncracked and minor corrosion. Figure 5c shows that both the transverse/longitudinal ribs to be corroded. The rebar surfaces between the ribs are also corroded; thus, this status is uncracked with moderate corrosion. Figure 5d shows corrosion-induced cracks and the surface of the rebar being completely exfoliated due to corrosion. This status is cracked and severe corrosion.  
Figure 4: pH measurement procedure of cover concrete pore solutions: (**a**) sample of cover concrete after cutting; (**b**) concrete slices after drying; (**c**) water spraying; (**d**) concrete pore solutions expression; (**e**) pH measurement.  
Figure 5: _Cont._
## 1 Introduction
Additionally, a system based on weighted segmentation of the red channel is proposed to control background noise in which feature selection is performed by averaging and ranking the correlation coefficients of background and foreground weights [25]. Moreover, deep learning methods, especially long short-term memory (LSTM) networks, are widely used in human activity recognition [26; 27; 28; 29]. However, they require big training data and high-dimensional feature vectors to perform well in classification tasks. In previous study [10], LSTM showed dramatically the worst performance with low dimensional feature vectors among the other machine learning classifiers, which are not deep learning-based methods. In order to boost the performance of LSTMs in low dimensional feature space, in this paper, a novel scale and rotation invariant human activity recognition system, which employs LSTM network with low-dimensional 3D posture data, is presented. Since angles are used, proposed system is already scale invariant. In order to provide rotation invariance, body relative direction in egocentric coordinates is calculated. Different from the previous study [10], 3D joint angles are not employed as the feature vector. Instead, the angle of each limb vector with \(X\), \(Y\), and \(Z\) axes of the proposed egocentric coordinate system is employed as the feature vector. Additionally, several compression methods such as averaging filter, Haar wavelet transform (HWT), and discrete cosine transform (DCT) are employed to reduce dimension in feature vectors. This is an essential operation to attain real-time processing speed. Finally, RNN-LSTM network is employed to recognize five classes of human activities, namely, walking, standing, clapping, punching, and lifting. Experimental and benchmarking results show that proposed method dramatically (around 30%) increases the accuracy of LSTM classification in low -dimensional feature space compared to the previous method. The rest of the paper is organized as follows: Section 2 describes the methodology, Section 3 presents the experiment, experiment results, and evaluation; and conclusions are presented in Section 4.
## 2 Materials and Methods
### Data Processing Sequence  
The decision support system for vessel traffic surveillance and management follows the process illustrated in Figure 2 to obtain the priority pair for the vessel. The process includes pre-processing, location-based clustering, priority extraction using fuzzy logic, and overall final priority extraction for the entire target sea area to store the navigation information of the vessel through the AIS and configure the training dataset. First, ship navigation data are collected and stored using the AIS receiving equipment. In the pre-processing process, the received data are extracted according to the target area, where only the vessels in navigation are extracted, excluding any anchoring, mooring, or drifting vessels, based on the allowable speed range. Thereafter, data classification is performed according to the maritime mobile service identity (MMSI) number of the vessel, and position interpolation is performed according to the time interval by standardizing the location-data reception time of the vessel. Subsequently, the data after position interpolation according to the standard time are again classified and stored according to the MMSI number, and the adjusted time is assigned such that it can be called according to the reference time. The data classified according to the standard time constitute a sub-dataset formed on location-based clustering. However, as each sub-dataset reflects the environment according to the location, the proximity distance is different, and a cluster ID is assigned according to the clustering result, followed by the CPA and TCPA calculations. Consequently, a fuzzy membership function that reflects the navigational characteristics of a region can be extracted. Therefore, a fuzzy inference system (FIS) for each region can be configured and a VTS priority index (Pindex) can be obtained, which reflects each clustering characteristic, and the final overall priority can be obtained by comparing the obtained Pindex with the entire target sea area. Finally, this result is displayed on the electronic chart display and information system (ECDIS) to allow the VTSO to intuitively understand the situation, forming a prioritization system for VTS decision support.
## 3 Experimental Results and Discussion
### Sensor Data Analysis  
#### 3.5.1 Air Temperature and Humidity  
Figure 16 shows the recorded air temperature and humidity data graphs over five days (from 7 June 2023 to 11 June 2023). The air temperature profile (blue line) shows a similar daily pattern, where the maximum and minimum temperatures occur at noon around 12:00 h and in the morning around 05:00 h, respectively. The air humidity profile (red line) shows the opposite, where the maximum and minimum humidities happen in the morning and the noon, respectively. These profiles show a climate pattern typical for a tropical area.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Leaf Temperature Measurement**} \\ \cline{2-3}  & **Error (\%)** & **Standard Deviation (\%)** \\ \hline Plant-1 & 1.41 & 0.96 \\ \hline Plant-2 & 1.53 & 1.29 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Average leaf temperature measurement error of Plant-1 and Plant-2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Leaf Temperature (\({}^{\circ}\)C)**} \\ \cline{2-3}  & **Proposed Method** & **Manually** & **Error (\%)** \\ \hline Plant-1 (Figure 14—21:00 h) & 26.19 & 26.11 & 0.31 \\ \hline Plant-1 (Figure 14—11:00 h) & 32.05 & 31.69 & 1.14 \\ \hline Plant-1 (Figure 14—15:00 h) & 31.94 & 31.46 & 1.53 \\ \hline Plant-2 (Figure 15—21:00 h) & 25.71 & 25.78 & 0.27 \\ \hline Plant-2 (Figure 15—11:00 h) & 31.57 & 32.38 & 2.50 \\ \hline Plant-2 (Figure 15—15:00 h) & 30.91 & 30.92 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Leaf temperature measurement error of Plant-1 and Plant-2 in Figures 14 and 15.
# Graph-Driven Exploration of Issue Handling Schemes in Software Projects
## 4 Exploring and Assessing Issue Handling Processes
### IHM Analysis Algorithms
#### 4.3.1 Issue Preprocessing Algorithm (A1)  
Algorithm 1 is focused on processing single issues considering the attributes and history of reports. It derives issue handling paths (state sequences with relevant timestamps) and basic issue statistical features. Here, we can process selected issues according to specified restrictions (R), e.g., issue type, priority. For each issue I\({}_{i}\) in the considered issue set (IS) within the repository, Algorithm 1 extracts relevant data and performs two actions:  
* The creation of the issue handling path P\({}_{i}\) complemented by the handling time T(P\({}_{i}\)), state set S\({}_{i}\), and relevant processing times within states (T(S\({}_{i}\)));
* The creation of a comment statistics set C\({}_{i}\) (the number of involved actors, timing, comment size, etc.) attributed to the considered issue.  
Algorithm 1 uses two auxiliary lists: a list of created issue states (CNL) and a list of state change timestamps (CTL). The list of state changes (_I.history_) is sorted by time stamp (line 10). State changes are detected by comparing the field of relevant state modification with the contents of the field \"status\" (lines 23-31). For each issue, the initial handling state is derived (line 12-19). The lists CNL and CTL are created in lines 21-31. The list of subsequent issue processing states, SL (with appended processing times, TS), is created in lines 33-39 and it constitutes the derived issue handling path object P.SL (line 40). The handling time of the last state in the path is assumed to be 0 (lines 34-38). Statistics on comments correlated with the considered issue are derived in lines 41-46 (word count, the number of unique words, etc.). The issue list I comprises all issue attributes, while the relevant history and comments are stored as a separate list (compatible with the Jira scheme). The history list comprises the name of the relevant issue field (attribute) which was changed, the previous value of the attribute in string format (_fromString_), the new value (_toString_), the timestamp of the performed change (_time_), and the author of the change. In the case of the state attribute assuming the value _resolved_, there is an additional substate specifying the resolution type (e.g., fixed, rejected). Hence, in lines 23-24, we check for the existence of the resolution state (_i.resolution_). It is concatenated with the resolution state (operator + in line 25). The comment list comprises the author, the time stamp of adding the comment, and the comment text. The set of created paths for all considered issues (relevant to assumed restrictions R) are submitted for further processing in Algorithm 2.  
```
input: List of JIRA issues with comments and history [I] output: List of paths objects [P]
1 function translate_to_path([I]):
2 result = new empty list
3 FOR I in [I] DO
4 P = new empty Path
```  
**Algorithm 1**Deriving issue handling paths
# BBSF: Blockchain-Based Secure Weather Forecasting Information through Routing Protocol in Vanet
## 4 Discussion and Results
### Network Overhead  
Figure 8 shows the routing overhead by comparing BBSF to AODV and CBDRP techniques. In this approach, the overhead is involved in sending and receiving packets for the packet transfer rates inside the network. We compared the number of overhead packets with the distance for routing in the proposed technique. The distance was taken among vehicles used as intermediate nodes in the network. In the proposed BBSF approach, the overhead packets increased slowly with the increasing distance. This shows that the packet overhead according to distance is better in the proposed approach than the AODV and CBDRP approach. With distance increase, the overhead in packet delivery increased in AODV and CBRP techniques.
# Implant Model Generation Method for Mandibular Defect Based on Improved 3D Unet
## 2 Materials and Methods  
### Methods  
In fact, the generation of the mandibular defect repair model is a 3D volume shape completion problem from the perspective of modeling. To assist surgeons in achieving an efficient mandibular defect repair model, an automatic generation method of the mandibular defect repair model based on an improved 3D Unet is proposed, as shown in Figure 1.  
Let the volume of the mandible with a defect be \(S_{d}\), the volume of the repaired complete mandible be \(S_{c}\), and the real volume of the complete mandible be \(S_{g}\). The implant of the predicted defect part is \(I_{p}\), and the real implant is \(I_{g}\).  
1. The 3D model dataset for human mandibular defect repair was constructed using the GRD algorithm. First, the mandible 3D model composed of triangular patches was obtained from CT images, then it was converted into a binary voxel grid model (\(S_{g}\)). Based on the 3D model of the complete mandible, a generate random defect (GRD) algorithm was designed to generate a mandibular voxel grid (\(S_{d}\)) with defects. Finally, a dataset containing inputs and labels was constructed. In this dataset, the input of the network is the defective mandible 3D model (\(S_{d}\)), and the label is the real and complete mandible 3D model (\(S_{g}\)).  
Figure 1: Automatic generation method of mandibular defect repair model based on improved 3D Unet.  
2. Based on the 3D Unet network, an improved 3D Unet network (3D RDUnet) was proposed by fusing the residual structure and dilated convolution, the network model was trained with \(S_{d}\) as the input and \(S_{g}\) as the label, and the output \(S_{c}\) was obtained. Mandible evaluation indices were calculated from \(S_{c}\) and label \(S_{g}\). Then, the reconstruction-subtraction strategy was used to calculate the predicted implant \(I_{p}\) and the real implant \(I_{g}\) according to Formulas (1) and (2), and the implant evaluation index was used to evaluate the network's performance. Finally, a mandibular defect implant meeting the surgical requirements was obtained. \[I_{p}=S_{c}-S_{d}\] (1) \[I_{g}=S_{g}-S_{d}\] (2)
## 2 Method
#### Case Study  
The method has been applied to a case study in the agricultural field. The final objective is to design an e-learning path for farmers so that they could embed recent technologies in their daily  
Figure 6: Method for defining the list of needs and respective weights via FER, relying on the interview.  
farming practices. The QFD adoption appeared suitable for understanding the current needs in the field and for correlating needs with services.  
Thirteen people coming from the field of interest have been interviewed with an interview form, which has been developed in the European project \"Farmer 4.0\". The interview, reported in Appendix A, is approximately 40 min long and is composed of 74 questions. All the interviews were audio-recorded; one interviewed person was recorded with the depth camera Intel RealSense SR300. Thus, the 13 audio recordings supported the conceptualization of the needs list and the 3D video-recording was adopted for assigning weights. The person who was recorded with the depth camera was the most representative of the whole group, as he was used to interviews for agriculture projects, to public speaking, and in general with social events and media. He was evaluated as the most suitable candidate for emotion involvement analysis.  
Figure 7 shows the unprocessed depth map given by the camera and a picture taken during the interview.  
\(138\) frames and subsequent facial depth maps were selected and post-processed on MATLAB, relying on the identification of the key moments of the interview between every question and every answer. They have been \"need labelled\" so that the results of the FER method could support the assignment of weights accordingly. Figure 8 shows some processed depth maps in which the face has been framed.  
Figure 8: _Cont._  
The landmark localization algorithm was then run on the faces (Figure 9) and Euclidean distances were computed.  
According to the type of data and the emotions displayed by the interviewee's face, the complete model with weights 1, 2, 3, 4, and 5 was chosen in this case. Thus, five clusters were adopted. Clustering results allowed us to \"cluster label\" every facial frame so that the median was computed among the obtained weights for every need. The resulting QFD matrix is shown in Figure 10.  
Figure 8: Examples of post-processed facial depth maps coming from the interview. The yellow parts correspond to the highest Z coordinates of the matrix, i.e., the most prominent facial parts; the blueish parts are the lowest.  
Figure 9: Landmark localization on two frames. Landmarks are displayed with blue asterisks.
## 2 Antenna Design
### Patch Array Feed
The cross-polarization level remains under 25.5 dB in both the E-plane and H-plane. While the co-polarization outcomes closely align with the simulation results in the E-plane and H-plane, there exists a minor deviation in cross-polarization. This incongruity may stem from factors like manufacturing tolerances, cable losses, and feed misalignment within the anechoic chamber. The measured gain of the feed at 17.2 GHz is 18.16 dBi, indicating a gain loss of \(-\)0.24 dB compared to the simulated gain of 18.4 dBi. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \(L\) & \(W\) & \(L_{P}\) & \(W_{P}\) & \(W_{1}\) & \(W_{2}\) & \(W_{3}\) & \(d\) & \(d_{1}\) & \(d_{2}\) & \(L_{1}\) & \(L_{2}\) & \(L_{3}\) & \(L_{4}\) & \(L_{5}\) \\ \hline
88 & 29 & 5.3 & 5.1 & 1.5 & 0.95 & 0.47 & 45 & 23.6 & 11.5 & 3.1 & 2.9 & 3.1 & 2.5 & 3.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Array antenna fed key parameters. Figure 2: The configuration of the 2 \(\times\) 8 array feed. Figure 4: The simulated and measured reflection coefficients of the feed. Figure 5: Radiation pattern measurement setup. Figure 3: The fabricated array feed. Figure 6: The simulated and measured radiation patterns of the feed at 17.2 GHz: (**a**) E-plane and (**b**) H-plane.