esa-sceva/satcom-chunk-collection huggingface dataset:
# RSB: Robust Successive Binarization for Change Detection in Bitemporal Hyperspectral Images
## 4 Post-Processing
### Morphological Transformations  
Regarding both the _Hermiston_ and the _Bay Area_ dataset, the white regions of the RSB-produced change map (Figure 9, the first picture in the first row and the first picture in the last row) appear very neat and with a precise silhouette; therefore, it is appropriate to use a noise removal filter, in order to eliminate the spurious noisy pixels. To this end, a morphological opening transformation (see, e.g., [46], Chapter 5) with the following kernel is applied:  
\[Kernel=\left(\begin{array}{ccccc}0&0&1&0&0\\ 0&1&1&1&0\\ 1&1&1&1&1\\ 0&1&1&1&0\\ 0&0&1&0&0\end{array}\right).\]  
The obtained results for the _Hermiston_ and the _Bay Area_ datasets are discussed in Section 5 and the produced final binary change maps are shown in Figure 10, in the leftmost sub-figure and in Figure 10, in the rightmost sub-figure, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Thresholding**} & \multicolumn{2}{c}{_Hermiston_} & \multicolumn{2}{c}{_USA_} & \multicolumn{2}{c}{_River_} & \multicolumn{2}{c}{_Bay Area_} \\  & **OA** & **K** & **OA** & **K** & **OA** & **K** & **OA** & **K** \\ \hline RSB & **0.9867** & **0.9410** & **0.9076** & **0.7060** & **0.9624** & **0.7250** & **0.8935** & **0.5873** \\ Otsu & 0.9633 & 0.8522 & 0.7323 & 0.3939 & 0.9061 & 0.5970 & 0.8335 & 0.5134 \\ Mean & 0.7691 & 0.4155 & 0.6939 & 0.3960 & 0.5637 & 0.1595 & 0.6585 & 0.2882 \\ Li & 0.9028 & 0.6710 & 0.7276 & 0.4225 & 0.8091 & 0.3946 & 0.7190 & 0.3525 \\ Sauvola & 0.2029 & 0.0223 & 0.2822 & 0.0302 & 0.1403 & 0.0104 & 0.2833 & 0.0483 \\ Triangle & 0.2543 & 0.0415 & 0.2330 & 0.0043 & 0.8474 & 0.4628 & 0.7427 & 0.3820 \\ Yen & 0.8579 & 0.5663 & 0.7117 & 0.4079 & 0.9409 & 0.6977 & 0.8865 & 0.5962 \\ \hline \end{tabular}
\end{table}
Table 1: Comparisons with other thresholding techniques. RSB achieves the best results (in bold).
## 2 Problem Formulation
### Prescribed Performance Control and Error Transformation  
To guarantee the transient and steady-state performance, the prescribed performance control strategy is adopted here. The objective is to translate the prescribed performance into tracking error constraints and cause the compensated tracking error signals \(z_{1}\)(defined later) to exhibit the desired transient and steady-state performance.  
In order to achieve the desired control objective, the so-called performance function is proposed, which bounds the system's performance. The definition of the performance function is as follows:  
**Definition 1** ([11]).: _A smooth function \(\rho(t):\mathcal{R}^{+}\rightarrow\mathcal{R}^{+}\) will be called as a performance function if it satisfies (1) \(\rho(t)\) is positive and decreasing (2) \(\lim_{t\rightarrow\infty}\rho(t)=\rho_{\infty}>0\)._  
The function \(\rho(t)\) is conventionally selected as  
\[\rho(t)=(\rho_{0}-\rho_{\infty})e^{-lt}+\rho_{\infty} \tag{10}\]  
where \(\rho_{0}\), \(\rho_{\infty}\) and \(l\) are positive scalars. By using the performance function, the compensated tracking error \(z_{1}\) is bounded by the following constraints,  
\[\begin{cases}-M\rho(t)<z_{1}(t)<\rho(t),&z_{1}(0)>0\\ -\rho(t)<\tilde{z}_{1}(t)<M\rho(t),&\tilde{z}_{1}(0)<0\end{cases} \tag{11}\]  
where \(0\leq M\leq 1\). According to the performance function (10) and the performance constraints (11), if the initial value of the compensated tracking error \(z_{1}\) satisfies \(0\leq|z_{1}(0)|<\rho_{0}\), the behavior of \(z_{1}(t)\) will be governed by the performance function \(\rho(t)\) and the convergence speed of the compensated tracking error can be bounded by the performance function as well.  
Figure 1: The structure of the constrained command filter.
## 3 Results
### Structural Equations Model Analysis
In terms of ANX, a negative relationship is observed with TC (\(p\leq\) 0.05; r = \(-\)0.160). On the other hand, a positive relationship is observed between ANX and EC (r = 0.053) and MDA (\(p\leq\) 0.05; r = 0.099). Finally, a negative relationship is shown between EC and TC (\(p\leq\) 0.001; r = \(-\)0.541). Looking at the model developed for participants showing a high level of weekly physical activity, it shows a good fit for each of the indices. The chi-square test showed a non-significant \(p\)-value (X\({}^{2}\) = 43.361; df = 16; pl = 0.000), the CFI, NFI, IFI, and TLI scored above 0.890. In addition, the root mean square error of approximation analysis (RMSEA) showed a value of 0.079. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Associations**}} & \multicolumn{3}{c}{**R.W.**} & \multicolumn{2}{c}{**S.R.W.**} \\ \cline{2-6}  & **Estimations** & **S.E.** & **C.R.** & \(p\) & **Estimations** \\ \hline ANX \(\leftarrow\) TC & \(-\)0.069 & 0.073 & \(-\)0.953 & 0.341 & \(-\)0.068 \\ \hline ANX \(\leftarrow\) EC & 0.177 & 0.081 & 2.191 & ** & 0.303 \\ \hline EC \(\leftarrow\)\(\rightarrow\) TC & \(-\)0.255 & 0.037 & \(-\)6.846 & *** & \(-\)0.342 \\ \hline \hline \end{tabular}
**Note 1:** EC = Ego climate; TC = Task climate; PM = Punishment for mistakes; UR = Unequal recognition; MR = Member rivalry; CL = Cooperative learning; EI = Effort/improvement; IR = Important role; ANX = Anxiety; MDA = Mediterranean diet adherence. **Note 2:**\(p\leq\) 0.05; ***\(p\leq\) 0.001.  
\end{table} 
Table 2: _Cont._  
Figure 3: Theoretical model proposed for participants showing a medium level of weekly PA. **Note:** EC = Ego climate; TC = Task climate; PM = Punishment for mistakes; UR = Unequal recognition; MR = Member rivalry; CL = Cooperative learning; EI = Effort/improvement; IR = Important role; ANX = Anxiety; MDA = Mediterranean diet adherence. Figure 4 and Table 4 show the effects of the variables for participants showing a low level of weekly physical activity. A positive relationship is observed between MDA and TC (\(p\leq 0.05\); r = 0.204), with exactly the same occurring between MDA and EC (r = 0.029). Regarding TC, a positive relationship is obtained with IR (r = 0.938), EI (\(p\leq 0.001\); r = 0.816), and CL (\(p\leq 0.001\); r = 0.848). Continuing with EC, it shows positive relationships with PM (r = 0.826), UR (\(p\leq 0.001\); r = 0.900), and MR (\(p\leq 0.001\); r = 0.488). With regard to ANX, this disruptive state shows positive relationships with MDA (\(p\leq 0.05\); r = 0.038) and EC (\(p\leq 0.05\); r = 0.183), while it shows a negative relationship with TC (r = \(-0.078\)). Finally, a negative relationship is shown between both motivational climates (\(p\leq 0.001\); r = \(-0.574\)). Figure 4: Theoretical model proposed for participants showing a high level of weekly PA. **Note:** EC = Ego climate; TC = Task climate; PM = Punishment for mistakes; UR = Unequal recognition; MR = Member rivalry; CL = Cooperative learning; EI = Effort/improvement; IR = Important role; ANX = Anxiety; MDA = Mediterranean diet adherence.
## 2 Robust Design Optimization Methods and Challenges
### Measures for Evolutionary Algorithms and Uncertainties
This methodology does not need to recalculate these results. Moreover, it was proven mathematically that if the population size is infinite, the proportional selection is not affected by noise [48; 49]. It is always an interesting question whether explicit or implicit averaging can reduce the computational complexity better. Genetic Algorithms (GAs) with a finite population size have been studied in many of the models created, which can optimize the sample size and the population size at the same time [186; 187]. Many researchers suggested modifying the selection operator and using deterministic selection schemes in genetic and evolutionary algorithms to better handle the different types of uncertainties [48; 49; 188]. [PERSON] et al. [189] suggested a threshold during the selection process in an evolutionary strategy. This methodology accepts the newly generated offspring only if its fitness is significantly better than its parent. [PERSON] and [PERSON] proposed a derandomization for better handling of uncertainties in the selection process [190; 191]. [PERSON] et al. [192] proved that simulated annealing does not converge under a specific class of noisy environments. A modified, deterministic selection operator for simulated annealing can improve the performance under noisy optimization tasks [48]. [PERSON] and [PERSON] suggested a bee colony optimization algorithm, which uses the operator mentioned above and the selection and implicit averaging techniques together [188]. In their other paper, they introduced four principles for selecting an evolutionary algorithm in a noisy environment [193]: Firstly, the sample size during the optimization should be adapted in time, increased exponentially with the generation number. Secondly, they proposed to use the above-mentioned modified, deterministic selection schemes [194; 195]. Thirdly, they proposed to use a clustering approach. Finally, they developed a robust crowding distance scheme that can work better in noisy environments [193].
# Electronic Voting System Using an Enterprise Blockchain
###### Abstract  
Conventional electronic voting systems use a centralized scheme. A central administration of these systems manages the entire voting process and has partial or total control over the database and the system itself. This creates some problems, accidental or intentional, such as possible manipulation of the database and double voting. Many of these problems have been solved thanks to permissionless blockchain technologies in new voting systems; however, the classic consensus method of such blockchains requires specific computing power during each voting operation. This has a significant impact on power consumption, compromises the efficiency and increases the system latency. However, using a permissioned blockchain improves efficiency and reduces system energy consumption, mainly due to the elimination of the typical consensus protocols used by public blockchains. The use of smart contracts provides a secure mechanism to guarantee the accuracy of the voting result and make the counting procedure public and protected against fraudulent actions, and contributes to preserving the anonymity of the votes. Its adoption in electronic voting systems can help mitigate part of these problems. Therefore, this paper proposes a system that ensures high reliability by applying enterprise blockchain technology to electronic voting, securing the secret ballot. In addition, a flexible network configuration is presented, discussing how the solution addresses some of the security and reliability issues commonly faced by electronic voting system solutions.  
Hyperledger Fabric; blockchain; election; permissioned; permissionless; non-fungible token (NFT); Hardware Security Module (HSM); SoftHSM; enterprise blockchain +
Footnote †: journal: Applied
## 3 Datasets
### WorldView-2 Stereo Pair  
WorldView-2 (WV2), launched in October 2009, was the first VHR 8-band MS commercial satellite. This satellite provides 0.46 m GSD PAN and 1.85 m GSD MS, resampled for commercial purpose to 0.5 m and 2 m GSD, respectively [37]. The images are distributed as a basic product or projected onto a plane with a constant height [38]. This second product is known as the Ortho Ready Standard Level 2A product (ORS2A). In this study, a WV2 PAN along-track stereo pair covering the study site was acquired on 5 July 2015. This stereo pair has already been used by [PERSON] et al. [19] and [PERSON] et al. [20]. It was collected in Stereo ORS2A format and also counts on the corresponding RPC sensor camera model and metadata file. The delivered products were ordered with a dynamic range of 11 bits (without dynamic range adjustment) and 0.5 m GSD (PAN). The off-nadir angle for the two images that made up the stereo pair turned out to be \(12.6^{\circ}\) and \(24.6^{\circ}\). The WV2 stereo pair presented a convergence angle of \(31.6^{\circ}\).
###### Abstract  
As \"Industry 4.0\" progresses, construction machinery is evolving toward large-scale, automation, and integration, resulting in the equipment becoming increasingly sophisticated, and the designs more difficult. Labor costs, transportation, and time will be huge challenges for construction machinery, and mixed reality technology is one of several possible ways to solve this challenge. The research presented in this paper develops a holographic visual verification platform for a digital prototype of construction machinery based on virtual terminal equipment, through investigating the synchronous remote collaboration of multiple terminal devices in a mixed reality scenario. These included semi-physical virtual-real fusion assembly, multi-person real-time voice communication, dynamic loading of MR model based on a cloud server, virtual imitation control, interface design, and human-computer interaction. The effectiveness of this paper's method is demonstrated through remote collaborative design cases. These included a double drum roller, loader, and milling planer welding production line, as well as tractor modeling review and virtual simulation manipulation of an aerial work platform. The experimental results show that this visual verification platform is a feasible, low-cost and scalable solution, which brings a qualitative breakthrough to the design, research and development, production and other stages in the field of construction machinery.  
Industry 4.0; mixed reality; collaboration; design verification; construction machinery
## 2 Overview of Cyber Threats in EM
### Communication Media
#### 2.1.5 Satellite Communications
They have demonstrated this capability using low-cost commercial hardware and GNUradio-based software for receiving and filtering data. Thus, a second open issue is the development of stronger encryption algorithms that can be retrofitted on current satellite phones.
## 5 Discussion
### Dense Citrus Detection with the Improved YOLOv5  
This experiment aimed to use the dataset of \"Kumquat\" trained at different growth stages to verify whether the improved processing above-mentioned can improve the performance of the YOLOv5 model for dense small target detection. When testing the model, 20 images of growing stage and 20 images of the mature stage were selected. The improved YOLOv5 model was used to detect the dense fruits of the plant canopy. The ground truth was compared with the number of model recognitions. The experimental results showed that the improved version had better recognition performance compared to the original YOLOv5 model to detect citrus fruits at different growth stages, in which the standard YOLOv5l were used as the representative of YOLOv5.  
The quantitative comparison results of the above schemes are shown in Table 4, and the visual effects of model recognition are shown in Figure 9. For growing stage \"Kumquats\" fruits, the improved YOLOv5 network detected more citrus fruits and could more correctly identify citrus fruits, while YOLOv5l detected fewer citrus fruits, and there were more misidentifications. For mature \"Kumquats\" fruits, the improved YOLOv5 method performed better than the original method. During the growing period, the  
Figure 8: The training result: (**a**) mAP; (**b**) loss.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Parameters (M)**} & \multirow{2}{*}{**GFLOPS**} & \multicolumn{2}{c}{**Average**} & \multirow{2}{*}{**\(\mathbf{F_{1}}\) Score**} & \multirow{2}{*}{**mAP**} \\  & & & **Time/ms** & & \\ \hline YOLOv5n & 1.7 & 4.2 & 8 & 0.75 & 80.2 \\ YOLOv5s & 7.0 & 16.3 & 9 & 0.78 & 83.3 \\ YOLOv5m & 20.8 & 48.0 & 14 & 0.80 & 85.8 \\ YOLOv5l & 46.6 & 114.1 & 19 & 0.81 & 85.4 \\ YOLOv5x & 86.1 & 204.0 & 22 & 0.80 & 87.7 \\ Improved YOLOv5(ours) & **50.9** & **116.5** & **19** & 0.82 & 88.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The training results of each version of the YOLOv5 model on the original dataset.  
\"Kumquat\" fruits were small in size, the fruit and leaves were green, and the color features were not obvious. It could be seen that the improved method significantly improved the detection ability of the YOLOv5 model for citrus, and the recognition accuracy was not affected by the color or individual features. \"Kumquats\" at maturity had more obvious color features, with larger individual volumes, and the occlusion was less. After many quantitative analysis and comparison experiments, it was proven that after adding the CA attention mechanism and BiFPN to enhance feature extraction, the improved YOLOv5 method significantly improved the small target fruit.
# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 4 Application and Usage of Brainwave-Based Fine-Tuned Model in Architectural Spaces
### Application of Fine-Tuned Model to Architectural Space: 360\({}^{\circ}\) VR Experience  
This study applied the fine-tuned AI model (Version 8) in a 360\({}^{\circ}\) video-based VR environment to explore users' emotional responses in architectural spaces. EEG data were collected while participants experienced two distinct urban environments: Seoul's Dongdaemun Design Plaza (DDP) and New York's Long Island City. These locations were selected for their differing ratios of artificial and natural elements, allowing for a comparative analysis of how spatial characteristics influence emotional responses. Each VR session lasted three minutes to ensure adequate exposure while maintaining participant focus.
## 5 Conclusions  
In this paper, we proposed a comprehensive cost function for energy efficient computational offloading in MEC. A supervised deep learning approach was used to find the partitioning of a task in \(n\) components along with an offloading policy having minimum cost in terms of energy consumption and time delay. Our cost function takes into account various energy consumptions and delays due to task-division, transmission, execution, and reception. The proposed approach comprehensively models the real environment which is better suited for implementation in practical scenarios. The end-user can use the trained DNN to find the offloading policy and partitioning for \(n\) number of components per task with minimum cost. Since, the cost function depends on both energy consumption and time delay, therefore, the end-user will consume minimum energy with a faster decision process in selecting the best offloading policy for \(n\) number of components per task. The simulation results demonstrate improved energy consumption and service delay with more than 70% accuracy of the DNN. For future work, DNN can be trained to simultaneously optimize the number of components per task, the size of each component, and offloading policy for all types of applications.  
Conceptualization, Z.A., Z.H.A. and L.J.; methodology, Z.A.; software, G.A.; validation, Z.H.A., G.A. and M.B.; formal analysis, G.A., M.B. and Z.H.A.; investigation, Z.A., L.J. and Z.H.A.; resources, G.A., M.B., L.J. and D.-Y.S.; data curation, Z.H.A., Z.A. and G.A.; writing--original draft preparation, Z.A.; writing--review and editing, Z.H.A., G.A., M.B. and D.Y.S.; visualization, Z.H.A. and Z.A.; supervision, L.J., Z.H.A. and G.A.; project administration, M.B. and M.J.P.; funding acquisition, M.B., M.J.P. and D.-Y.S. All authors have read and agreed to the published version of the manuscript  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IIITP-2021-2015-0-00742) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).  
The authors declare no conflict of interest.
## 4 Results  
### Calibration Results  
The number of calibration procedures conducted amounted to 20 in total. Angulation measurements (\(\alpha\)) resulted in a mean of 10.5\({}^{\circ}\) and a standard deviation (SD) of \(\pm\)4.1\({}^{\circ}\) within the range of 4.4-18.8\({}^{\circ}\). The distance between the averaged calibration points and the marker points at each location (\(\Delta_{\text{D}}\)) is shown in Figure 6. The deviation of averaged calibration points from marker points is seen to exponentially increase from Locations 1 to 4, while the distribution of values (interquartile range) in all locations does not change significantly.  
The overall deviation of distance between the calibration and the marker points is 1.97 (\(\pm\)1.17) mm. The precision measurement values for the calibration setup are shown in Figure 7. The average distance between the calibration points and their respective averages (\(\Delta_{\text{K1}\ldots\) 4) and SD values noticeably decrease from Locations 1 to 4. The calibration point average in Location 4 is the closest to the marker point value (\(\Delta\) = 0.14 mm) out of all four locations. The overall precision of the calibration points is 0.88 (1.39) mm.  
In both the precision (\(\Delta_{\text{K1}\ldots\) 4) and the trueness (\(\Delta_{\text{D1}\ldots\) 4) cases, Kruskal-Wallis tests resulted in \(\chi^{2}(3)\) = 23.18, \(p<0.001\) and \(\chi^{2}(3)\) = 228.79, \(p<0.001\), respectively, indicating statistically significant differences between the data sets in each case. Post-hoc tests in the trueness  
Figure 6: Distance values between averaged calibration and marker points in each location. Asterisks imply statistically significant difference between data sets.  
Figure 7: Calibration points and marker point precision values displayed as error bars. Asterisks imply statistically significant difference between data sets.  
case comparisons signified difference only between Location 4 and the others (\(p<0.001\)). In the precision case, post-hoc tests did not show a significant difference only in the \(\Delta_{\text{KJ,2}}\) pair (\(p=0.12\)).
# UCMAC: A Cooperative MAC Protocol for Underwater Wireless Sensor Networks
## 4 Operation of Proposed Protocol
### Waiting Times
#### 4.3.2 Waiting Times at Cooperators  
As shown in Figure 7a, the cooperator \(j\) takes a random backoff (\(\tau_{\text{back}}^{\text{C}_{j}}\)) before transmitting RTC, which is intended to prevent RTC-RTC collisions. The cooperator \(j\) then stays silent to avoid causing any collision with the source-destination communication. In order to estimate the silence duration, the cooperator \(j\) needs to know how many cooperators could be engaged in the communication by reading the \(\mathbb{N}_{\text{coop}}\) carried in DATA, so, the cooperator \(j\) waits to overhear the DATA during the period of:  
\[d_{\text{w}_{\text{-DATA}}}^{\text{C}_{j}}=T^{*}=d_{\text{delay}}^{\text{S}}+ \tau_{\text{S}\text{C}_{j}}+T_{\text{DATA}} \tag{7}\]  
where:  
\[T^{*}=d_{\text{w}_{\text{-CTS}}}^{\text{S}}-\left(\tau_{\text{S}\text{C}_{j}} +\tau_{\text{back}}^{\text{C}_{j}}+T_{\text{RTC}}\right), \tag{8}\]  
and \(d_{\text{delay}}^{\text{S}}\) is a modified value of \(d_{\text{delay}}^{\text{S}}\) which is given in Equation (4). Due to the lack of knowledge of \(\tau_{\text{D,H}}\) at the moment, the cooperator guesses \(d_{\text{delay}}^{\text{S}}\) as the maximum value of \(\tau_{\text{max}}\). Then, according to the number of cooperators engaged, the cooperator extends the silence duration in which the cooperator itself possibly participates in retransmission. Figure 7b shows the extended silence duration of the cooperator \(j\) after the expiration of \(d_{\text{w}_{\text{-DATA}}}^{\text{C}_{j}}\), which is given by:  
\[d_{\text{w}_{\text{-ACK}}}^{\text{C}_{j}}=\left(\tau_{\text{S}\text{D}}-\tau_ {\text{S}\text{C}_{j}}+\tau_{\text{C}_{j}\text{D}}+T_{\text{ACK}}\right)+ \sum_{\text{C}_{j}\in\mathbb{N}_{\text{coop}}}\Delta_{\text{C}_{j}}. \tag{9}\]  
In case the cooperator fails to overhear DATA, the maximum number of cooperators (\(N_{\text{coop}}^{\text{max}}\)) is used to calculate the second term of Equation (9).