esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy-Preserving E-Voting System Supporting Score Voting Using Blockchain
## 4 System and Security Models
### Threat Model  
We follow the standard Blockchain threat model presented in Ref. [32], where the Blockchain is trusted for execution correctness and availability, but not for privacy. Two types of adverse attackers are considered: passive attackers and active attackers. The passive adversaries are not actively engaged in the process of voting, but instead are trying to gain knowledge of the election proceedings by eavesdropping on communication channels and the Blockchain. Active adversaries, on the other hand, are actively involved in the proceedings, attempting to manipulate the election process. The smart-contract code is visible and checkable by anyone once it is deployed and is guaranteed to work as specified, free from tampering. Likewise, any data submitted and stored to the contract can be directly read by all parties of the system as well as any external curious users. In addition, the following threats are also considered:  
* Global eavesdroppers can read all transactions recorded on the Blockchain to learn their voting records;
* A voter may cheat by using a score value that is not included in the ballot to increase the chances for one candidate to win the elections. A self-tallying score voting system must satisfy the following security requirements. All four requirements are assumed to be fulfilled for partial collusion only, meaning that in a case of full collusion against one voter, that voter's privacy will be breached.
* _Maximum ballot secrecy_. Any colluding attackers can only learn the partial tally with the consent of remaining voters;
* _Self-tallying_. The final tally can be computed once the voting phase is finished. Any voter or third-party auditor/observer may compute the election results;
* _Fairness_. Strongly related to the first requirement, fairness states that no single user should have a priority to obtain a partial tally before the results are ready to be computed. Fairness issues stem from active adversaries who carry out attacks by exploiting abortive or adaptive issues. An abortive voter refuses to reveal their vote  
Figure 2: System architecture: (1) An organization publishes an election contract to the Blockchain. (2) The voter casts his/her vote to the Blockchain. (3) The Blockchain computes the results of the election and publishes the results to the candidates and voters.  
and hence obstructs the tally; an adaptive exploiter uses the fact they are the last voter to obtain a final tally before casting their ballot, leading to interference or abortive measures.
# Drought Propagation in Brazilian Biomes Revealed by Remote Sensing
## 4 Discussion  
Regarding the Amazon biome, different drought events present distinct effects on intensity and duration. Our results indicated that the 2015 event affected a larger area and had higher intensity than the 2010 drought, as also indicated by [PERSON] et al. [77]. [PERSON] et al. [78] showed that the 2010 drought was well characterized by _TWSA_ in northern Brazil; however, \(P\) did not present significant changes, which could possibly imply that the drought affected mainly surface water and soil moisture. The Cerrado biome presented a long period with areas affected by negative anomalies. [PERSON] [20] described that extreme drought scenarios with a lack of available _SM_ and wilting point can limit \(ET\), which further limits soil moisture depletion but can also limit locally generated \(P\), thereby exacerbating drought conditions. This may have occurred in the Cerrado, considering the event duration. For the Pampa, our results agreed with those of [PERSON] et al. [11], who found that soil moisture declined until its lowest point in the beginning of 2012. Moreover, [PERSON] et al. [43] indicated the highest intensity of drought in August 2012, and our study showed SON-2012 with the highest percentage of area affected by _TWSA_ (78%). In 2013, the biome succeeded in overcoming the drought effects, showing a great capacity to recover soil moisture after periods of drought [11]. \(ET\) and \(EVI\) variables reached anomalous values of z-score \(<\)\(-2\) between January and February 2012. Drought is usually associated with the summer season in this biome, with great influence from \(ET\) due to the greater energy availability for this process as net radiation also increases in this period, which is typically used for growth of crops and harvesting. Hence, agriculture in this region suffers from drought events, as it is a climate-sensitive sector [6]. Regarding drought propagation, a pattern of the drought advancing from anomalies in \(P\) to those in _SM_, _SSM_, \(ET\), \(EVI\), and \(GPP\) was observed, reaching _TWSA_ last. Evaluating the studied variables, our results indicated high negative anomalous values for _SM_ and _SSM_ in all analyzed droughts, while _LST_ presented positive anomalies for all events as well. Furthermore, _TWSA_ were able to represent the hydrological drought duration patterns from the point where _TWSA_ had very negative values until average values were reached again. [PERSON] et al. [33] also showed the relevance of _TWSA_ deficit analysis on the evaluation
# Correction Compensation and Adaptive Cost Aggregation for Deep Laparoscopic Stereo Matching
## 4 Experiments and Results
### Ablation Study
#### 4.3.1 Correction Compensation Module  
The correction compensation module was designed to cope with the correction error of practical images caused by imprecise camera parameters and intraoperative focusing. The experimental results are presented in Table 2. The first row of Table 2 presents the evaluation results of the baseline model. We integrated the correction compensation module on the baseline model without applying correction loss, and the corresponding evaluation results are shown in the second row of Table 2. It can be observed that various metrics exhibited significantly abnormal values, indicating that the network fails to converge. Subsequently, we progressively incorporated correction constraints into the network. The evaluation results are presented in rows 3 to 5 of Table 2, where these metrics are within the same order of magnitude as those of the baseline model. This suggests that the network reconverges effectively with the introduction of correction loss, demonstrating its pivotal role in guiding the training process. We could also find that when all three correction constraints were applied, the network's EPE metric decreases from 2.616 to 1.951, and the MAE metric falls from 2.767 to 1.897, thereby demonstrating the effectiveness of the correction compensation module. Moreover, based on the data presented in the final two rows of Table 2, compensating solely for the correction error on the \(y\) axis, as opposed to simultaneous compensation on both the \(x\) and \(y\) axes, resulted in an increase in the EPE from 1.951 to 2.163 and in the MAE from 1.897 to 2.142. These results underscore the necessity of compensating for the correction error on the \(x\) axis as well.
# Optimization of a Permanent Magnet Synchronous Motor for e-Mobility Using Metamodels
###### Abstract  
Permanent magnet synchronous motors (PMSMs) with rectangular coils in hairpin windings exhibit improved fill factor and reduced end turn of the coils, which in turn improve the efficiency and power density of PMSMs, making them ideal for e-mobility applications. Herein, the shape of a PMSM was optimized for torque ripple reduction using metamodels to improve the noise and vibrational performance of the motor. The objective function of the optimal design aimed to minimize the torque ripple, and the average torque and efficiency were set as constraints. The notch width and depth and barrier length were selected as the design variables to satisfy the objective function and constraints. Using the optimal Latin hypercube design technique, 27 experimental points were selected, and a finite element analysis (FEA) was performed for each point. Furthermore, a function approximation was performed using six metamodels, and the best metamodel was selected using the root mean square error test. Moreover, the optimization was performed by combining the best metamodels for each variable with a sequential two-point diagonal quadratic approximation optimization algorithm. The torque ripple was improved by approximately 1.63% compared with the initial model, whereas the constraint values remained constant. Finally, an FEA was performed on the optimal point, and the FEA results matched with those of the optimal method.  
optimization; PMSM; hairpin winding; e-Mobility; metamodel +
Footnote †: journal: Applied Sciences
# A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
## 5 System Model
### Performance Analysis
#### 5.4.2 Mean Absolute Percentage Error (MAPE)  
In percentage terms, MAE is equivalent to mean absolute percentage error (MAPE). Like MAE, which represents the mean absolute error generated by your model, MAPE indicates the average deviation between the model's predictions and their corresponding outputs. A popular metric for assessing forecast accuracy in time series data is called MAPE, which is computed as the average of the absolute percentage (%) errors (APEs) within the predicted and actual values of time series data. The formal equation of MAE is shown below:  
\[\text{MAPE}=\left(\frac{1}{n}\right)*\sum_{t=1}^{n}\left(\frac{|y_{t}-F_{t}|}{ |y_{t}|}\right)*100\%. \tag{17}\]
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 4 Proposed Schemes
### C-Dbr  
In C-DBR, we have formed clusters to restrict the access of the wireless channel by network nodes to avoid collisions. To select the CH, a node with maximum residual energy is nominated to avoid the immutable CH selection, which leads to high network lifetime. Additionally, it is assumed that the sink node has the knowledge of all the sensor locations. Further, the CHs are found by the source node based on the maximum residual energy. In C-DBR, node \(S\) forwards the sensed information towards the CH, which aggregates the neighbor packets to transmit the packet towards the immediate cluster's CH near the destination. This process continues until it reaches the sink node, as shown in Figure 3. Below are the steps for selecting the CHs of C-DBR:In this scheme, when a node receives a packet, it manages the data packet as follows:  
* If the node is not within the effective forwarding range of a preceding hop, it solely updates its neighbor table without forwarding the packet. In any other case, it enqueues information if there is no report about the packet in the packet queue.
* If the node is not within the forwarding area, it will wait for the subsequent data packet; this means that the packet is inside the suppression area. Otherwise, the node searches for neighboring nodes in the neighbor table.
* Initially, there are no clusters in the network.
* The network is then divided into clusters using the k-means clustering approach.
* The source node broadcasts the message in the cluster.
* The sensor node then compares its own energy with the source node energy.
* If the sensor node energy is greater than the source node energy, then the sensor node sends a reply message; else, the source node waits for another reply from the neighbor node that has the maximum residual energy.
* Once the CHs are selected, clusters are formed using the neighbor nodes within the communication range.
* CH then broadcasts the message to the member nodes along with its ID to receive the data packets.
* CHs aggregates data to transmit a single data packet towards the sink directly or using the multi-hop forwarding approach.
* The neighbor table and packet queue are updated repeatedly till the death of all the network nodes.
## 4 Artificial Neural Network
### ANN-Based Formula  
To facilitate the researchers/practitioners, the developed model was also translated into a trackable mathematical formulation. Based on the developed ANN, the procedure for calculating the \(R_{a}\) is given as follows:  
**Step 1:** Normalise the input parameters according to Equation (1). The minimum and maximum ranges of all the parameters are given in Table 4.  
**Step 2:** Calculate the normalised \(R_{a}\) as follows:  
\[n_{net}=w_{ij}x_{n}^{\prime}+\theta_{hj} \tag{4}\]  
\[n_{out}=\frac{2}{1+e^{-2n_{net}}}-1 \tag{5}\]  
\[R_{a(n)}=Vjn_{out}+\theta_{o} \tag{6}\]  
where \(n_{net}\) is the net input to the hidden layer neurons, \(n_{out}\) is the output of each hidden layer neuron, and \(x_{n}^{\prime}\) is the normalized values of the input parameters.  
The values of \(w_{ij}\), \(\theta_{hj}\), \(V_{j}\) and \(\theta_{o}\) are given as follows:  
\[w_{ij}=\left\{\begin{array}{ll}2.2306&0.3088&-0.3095\\ 0.3609&0.4736&0.6529\\ 3.0656&-2.2762&-10.5984\\ 0.5709&0.6985&0.3968\end{array}\right\} \tag{7}\]  
\[\theta_{hj}=\left\{\begin{array}{ll}-2.5736\\ -1.0580\\ 5.5033\\ 1.4934\end{array}\right\} \tag{8}\]  
\[V_{j}=\left\{\begin{array}{ll}0.5424&0.5781&0.2267&0.7662\end{array}\right\} \tag{9}\]  
\[\theta_{o}=\{0.0734\} \tag{10}\]  
**Step 3:** Calculate the \(R_{a}\) as follows:  
\[R_{a}=0.5\Big{(}R_{a(n)}+1\Big{)}\Big{(}R_{a(max)}-R_{a(min)}\Big{)}+R_{a(min)} \tag{11}\]  
For more comprehension, a design example is presented in the Appendix A section.
# Advances in the Application of Microcapsules as Carriers of Functional Compounds for Food Products
## 8 Conclusions and Future Perspectives  
An overview of the latest studies regarding the application of microcapsules in the stabilization of functional compounds for food applications is presented. There has been growing interest in the development of microcapsules loaded with probiotic bacteria, phenolic compounds, and carotenoids from natural sources. The main objectives of this strategy are to improve the stability of bioactive compounds against adverse environmental conditions, their incorporation into food matrices conferring functional properties to food products, and to enable their controlled release at a specific target of the gastrointestinal tract after food ingestion. The most widely used encapsulating processes are spray-drying followed by complex coacervation. A fine-tuned optimization of process conditions is required, including the selection of the wall material for a specific core material, to produce loaded microcapsules with suitable characteristics for a specific end application.  
Although several functional compounds have already been successfully stabilized by microencapsulation, there is still a lack of knowledge regarding how loaded microcapsules behave in terms of release rate when added to complex food matrices. Additionally, further studies are needed to access the effective bioavailability of released functional compounds after ingestion, which is correlated to the health benefits claimed for most of those compounds.  
**Author Contributions:** [PERSON] collected the data and wrote the manuscript and [PERSON] and [PERSON] supervised and revised the manuscript. All authors read and contributed to the final version with suggestions and critical comments.  
**Funding:** The scholarship of the first author was funded by the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), grant number 206268/2014-9, and this research was funded by the Fundacao para a Ciencia e a Tecnologia (UID/AGR/04129/2013).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# State-Sensitive Black-Box Web Application Scanning for Cross-Site Scripting Vulnerability Detection
## 4 Evaluation
### Case Studies  
The results in the Section 4.3 indicate that SSBW has good code coverage and excellent XSS vulnerability detection capabilities. This section provides an in-depth analysis of some surprising experimental results.  
**Vulnerabilities Missed by SSBW:** Throughout the entire experiment, we found that SSBW misses a total of three XSS vulnerabilities detected by S1 or Black Widow on Vanilla and osCommerce. This is caused by our heuristic filtering method. S1 and Black Widow  
\begin{table}
\begin{tabular}{c|c c|c c|c c} \hline \hline
**Set** & \multicolumn{2}{c|}{**B-A**} & \multicolumn{2}{c|}{**C-B**} & \multicolumn{2}{c}{**C-A**} \\
**Type** & **R-XSS** & **S-XSS** & **R-XSS** & **S-XSS** & **R-XSS** & **S-XSS** \\ \hline WackoPicko & - & - & - & - & - & - \\ Vanilla & - & 1 & - & - & - & - \\ osCommerce & - & 2 & - & - & - & 2 \\ Joomla & - & - & - & - & - & - \\ WordPress & - & - & - & - & - & - \\ ImpressCMS & - & - & - & - & - & - \\ phpBB & - & - & - & - & - & - \\ PrestaShop & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of XSS vulnerabilities missed by SSBW compared to S1, S1 compared to Black Widow and SSBW compared to Black Widow.  
perform fuzzing according to the order in which the form is discovered. Our ranking method modifies the fuzzing order of the form. In the scenario where it is not feasible to perform fuzzing on all forms, this will inevitably lead to some forms not being tested. Therefore, SSBW misses the three vulnerabilities. This is also a limitation of our method.  
**Crash on Web Applications:** We also found that both Black Widow and SSBW may cause server crashes in some applications during the scanning process. The above situation has occurred in Vanilla, osCommerce, phpBB, and ImpressCMS. Unfortunately, we were unable to successfully analyze the more detailed reasons for the server crash. We speculate that the partial payload injected during the fuzzing process has affected the successful parsing of the web page, or that operations during the crawling process have incorrectly configured the website's parsing method. We believe that this is not a flaw only found in SSBW and Black Widow, but rather a common problem with black-box scanners. Therefore, in order to avoid unfair comparisons caused by accidental factors such as website crashes as much as possible, we repeated all experiments three times.  
Surprisingly, the scanning process for phpBB always ended within eight hours; however, SSBW can detect XSS vulnerabilities that Black Widow cannot. In theory, the end of the scan represents that the scanner has fuzzed all the forms that have been crawled. Black Widow should be able to detect all vulnerabilities detected by SSBW. We further analyze the execution path of phpBB and the forms that have been fuzzed. The conclusion is that SSBW's ranking method enables some forms to be fuzzed before the server crashes. The lack of ranking method in Black Widow resulted in some forms not being fuzzed before the server crashed.
# A Study on the Essential and Parkinson's Arm Tremor Classification +
###### Abstract  
In this article, the challenge of discriminating between essential and Parkinson's tremor is addressed. Although a variety of methods have been proposed for diagnosing the severity of these highly occurring tremor types, their rapid and effective identification, especially in their early stages, proves particularly difficult and complicated due to their wide range of causes and similarity of symptoms. To this goal, a clinical analysis was performed, where a number of volunteers including essential and Parkinson's tremor-diagnosed patients underwent a series of pre-defined motion patterns, during which a wearable sensing setup was used to measure their lower arm tremor characteristics from multiple selected points. Extracted from the acquired accelerometer signals were used to train classification algorithms, including decision trees, discriminant analysis, support vector machine (SVM), K-nearest neighbor (KNN) and ensemble learning algorithms, for providing a comparative study and evaluating the potential of utilizing machine learning to accurately discriminate among different tremor types. Overall, SVM related classifiers proved to be the most successful in terms of classifying between Parkinson's, essential and no tremor diagnosed with percentages reaching up to 100% for a single accelerometer measurement at the metacarpal area. In general and in motion while holding an object position, Coarse Gaussian SVM classifier reached 82.62% accuracy.  
Keywords:essential tremor; Parkinson's tremor; classification; machine learning +
Footnote †: This paper is an extended version of our paper published in 28 th Mediterranean Conference on Control and Automation (MED 2020), Saint-Raphael, France, 15-18 September 2020.
# High-Resolution Digital-to-Time Converter Implemented in an FPGA Chip
## 4 Experiments
### Analysis of Clock jitter and Phase Noise  
In this paper, CLKA and CLKB are generated by the MMCMs. The input clock is from an oven controlled crystal oscillator (OCXO) with a frequency stability of \(\pm 0.3\) ppb. In the realization, the MMCM (mixed-mode clock managers) parameter is enabled to minimize output jitter. Experiments are conducted to measure the jitter of CLKA and CLKB. Figure 9 shows the Random Jitter, Period Jitter (RJ, PJ) Histogram of CLKA and CLKB, which is obtained above \(6\times 10^{6}\) transitions. The RJ of CLKA is \(8.52\) ps, and the PJ of CLKA is \(9.79\) ps. The RJ of CLKB is \(8.92\) ps, and the PJ of CLKB is \(7.60\) ps. Here, all the values are root mean square (RMS) values.  
The influence of the jitter on the performance of the proposed DTC is analyzed in two aspects. First, the difference in RJ between CLKA and CLKB is \(-0.4\) ps, and that of PJ is \(2.39\) ps. The proposed DTC is based on the Vernier delay line structure, which is a symmetrical structure. The clock jitter exists on both of the delay lines. Then, the influence of jitter of CLKA and CLKB on the DTC can be offset by the two delay lines to some degree. Second, the control module of the PDL is a close-loop structure, which can reduce the jitter of the clocks.  
Figure 8: Test system of the DTC.  
The period jitter of CLKA and CLKB is related to the phase noise. The relation between the period jitter and the phase noise can be calculated from Equation (9) [14]. In Equation (9), \(J_{\text{PER}}\) is the period jitter, \(f_{c}\) is the clock frequency, and \(L\)(\(f\)) is the phase noise spectrum. The period jitter of CLKA and CLKB are shown in Figure 9, and both of them are \(<\)10 ps. The phase noise and jitter of CLKA and CLKB can transmit to the control modules of PDL and affect the time delay of delay taps. The largest jitter on tap delay of PDL is \(\pm\)5 ps [13]. Though the theoretical resolution of the proposed DTC is 1 ps, and the measurement accuracy is lower, which is about 10 ps (RMS value).  
\[J_{\text{PER}}=\frac{1}{2\pi f_{c}}\sqrt{2\int\limits_{0}^{\infty}10^{\frac{L \left(f\right)}{10}}}df. \tag{9}\]