esa-sceva/satcom-chunk-collection huggingface dataset:
# Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods
## 2 Materials and Methods
### Combination of Multi-Weight Methods
#### 2.3.1 Entropy Weighting Method to Determine Objective Weights  
The concept of \"entropy\" was first proposed by [PERSON], a German physicist, and in 1948, [PERSON] integrated entropy with information theory and called it \"information entropy\". Using information entropy, entropy can be calculated, and the entropy law was born as a result. The entropy weight method is a multi-indicator system of objective assignment methods. The basic idea is to use the concept of information entropy to measure the uncertainty of the indicators and determine their weights. In the entropy weight method, the relationship between information entropy and weight is usually used to calculate the weight.  
The greater the entropy of the indicator, the smaller the degree of change in the indicator value. The less information available, the smaller the role and the lower the weight of the comprehensive evaluation [45]. The smaller the entropy of the indicator, the greater the degree of change in the indicator value. The more information available, the greater the role and the higher the weight in the comprehensive evaluation. The entropy value reaches its maximum when the indicator values of the selected indicators are exactly the same, which means that the indicator does not contain valid information, and deleting the indicator from the evaluation indicator system has no effect on the final evaluation [46]. The use of the entropy weighting method to determine the weights can maximize the elimination of the interference of subjective human factors, enabling more objective indicator weights to be obtained. The specific calculation method is as follows:  
1. Determination of entropy value The entropy value was calculated as follows: \[e_{j}=-k\sum\limits_{i=1}^{m}p_{ij}\cdot\ln p_{ij}\] (4) where \(k=1/\ln m\), \(p_{ij}=r_{ij}/\sum\limits_{i=1}^{m}r_{ij}\), \(i=1\), \(2\), \(\ldots\), \(m\); \(j=1\), \(2\), \(\ldots\), \(n\), \(m\) is the number of indicators in each column; and \(r_{ij}\) is the standardized value of the \(i\)th indicator in the evaluation metrics.
2. Calculation of entropy weights based on entropy values The information entropy redundancy of the \(j\)th indicator was calculated as follows: \[d_{j}=1-e_{j}\] (5) The weights of the indicators were calculated as follows: \[w_{Ej}=\frac{d_{j}}{\sum\limits_{j=1}^{n}d_{j}}\] (6) where \(j=1\), \(2\), \(\ldots\), \(n\), \(e_{j}\) is the entropy value under the \(j\)th indicator.
## 3 Experiments
### Model Evaluation Indicators  
These parameters, precision, recall, mean average precision (mAP), and FPS parameters are used as evaluation indexes, where mAP includes mAP50 and mAP50:95 [23].  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{11}\]  
In the above equation, \(TP\) (true positive) represents the number of detected frames that satisfy the IoU (intersection over union) ratio of predicted and labeled frames greater than 0.5. \(FP\) (false positive) represents the number of detected frames that satisfy the IoU \(\leq\) 0.5. \(FN\) (false negative) represents the number of undetected labeled frames. \(AP\) represents the area of the precision-recall curve calculated by interpolation. \(AP\) represents the area of the precision-recall curve calculated by interpolation. mAP50 is the \(m\)\(AP\) of all of the images when the IoU threshold is 0.5, and mAP50:95 is the \(m\)\(AP\) of all of the images under different IoU thresholds (IoU thresholds ranging from 0.5 to 0.95 with a step size of 0.05).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Epochs & 300 \\ Batch-Size & 16 \\ Optimizer & Adam \\ Learning Rate & 0.01 \\ Mosaic & 1.0 \\ Momentum & 0.937 \\ Weight-Decay & 0.0005 \\ \hline \hline \end{tabular}
\end{table}
Table 2: YOLOv5 training parameters.  
Figure 6: Image samples from the training set.
## 3 Pseudolite-Based Positioning System for Legacy GNSS Receivers
### Pseudolite-Based Positioning Algorithm for Legacy GNSS Receivers
#### 3.3.1 Positioning Process of GPS Receiver  
The legacy GPS receivers in the user terminal estimates the user position using pseudolite signals. The pseudorange measurement for the \(j-th\) pseudolite can be expressed as Equation (1):  
\[\rho^{j}=d_{r}^{{}^{\prime}}+d_{u}^{{}^{\prime}}+T_{r}^{{}^{\prime}}+I_{r}^{{}^{ \prime}}+T_{u}^{{}^{\prime}}+B+\varepsilon_{\rho^{{}^{\prime}}} \tag{1}\]  
where \(T_{r}^{{}^{\prime}}\) is the simulated tropospheric delay of the \(j-th\) GPS signal, \(I_{r}^{{}^{\prime}}\) is the simulated ionospheric delay of the \(j-th\) GPS signal, and \(T_{u}^{{}^{\prime}}\) is the tropospheric delay of the \(j-th\) pseudolite signal. \(B\) is the clock bias of the GPS receiver, and \(\varepsilon_{\rho^{{}^{\prime}}}\) is the thermal noise. This study assumes that \(T_{r}^{{}^{\prime}}\), \(I_{r}^{{}^{\prime}}\), and \(T_{u}^{{}^{\prime}}\) are either negligible or are eliminated by the model. The pseudorange measurement model is thus simplified into Equation (2):  
\[\rho^{j}=d_{r}^{{}^{\prime}}+d_{u}^{{}^{\prime}}+B+\varepsilon_{\rho^{{}^{ \prime}}} \tag{2}\]  
Equation (2) is the measurement model for true situation. However, the GPS receiver does not know the existence of pseudolite. Therefore the measurement model used by the GPS receiver will follow the general pseudorange measurement model as Equation (3):  
\[\rho_{rcv}^{j}=d_{u,rcv}^{{}^{\prime}}+B_{rcv}^{{}^{\prime}}+\varepsilon_{\rho _{rcv}^{{}^{\prime}}}=\left|R^{j}-R_{u}\right|+B_{rcv}+\varepsilon_{\rho_{rcv}^ {{}^{\prime}}} \tag{3}\]  
Figure 7: Illustration of \(j-th\) pseudolite and user terminal (Simulated and Real Simuation).  
GPS receivers does not provide these measurements to the user. In these cases, the original measurements must be estimated from the output position of the GPS receiver.  
As described in Section 3.2, though the GPS receiver output calculated in Section 3.3.1 does not give the correct position, it contains some information about the original measurements. The measurements from the position output can be estimated via Equation (10). Satellite position \(R^{j}\) is provided by the data server:  
\[\rho^{j}=\left|R^{j}-\hat{R}_{u,rv,cv,cg}\right|+\hat{B}_{rv,cvg} \tag{10}\]
# Human Activity Recognition Using CSI Information with Nexmon
## 6 CSI Data Collection
### Challenges Faced for the CSI Based HAR Techniques  
There are several challenges to using the collected CSI information for the classification of activities. Firstly, CSI data provides the Wi-Fi signal variations caused by human activities, white noise, and variability in the surrounding events other than unplanned human activities. These factors were responsible for making CSI raw data inappropriate to use directly for classification using machine learning. Numerous precondition and denoising techniques were used to make the data fit for use. These techniques are discussed in detail in Section 7. Secondly, the presence of pilot and null carriers in the channel, which gives the trend of constant slope and a common characteristic to all the collected data irrespective of the activities, make it difficult for machine learning and classification. These outliers in data are eliminated by identifying and removing such data points. Third, the transitioning of the activity states and sudden peaks due to abnormal change in human actions gives poor segmentation to the activities' classification. This difficulty arose due to the close resemblance between CSI signatures for different human activities, such as walking vs. standing and sitting vs. standing action. The transition states lead to a high number of false positives. Fast Fourier Transform (FFT) was used to transform CSI data for power spectral density and extract its magnitude.  
To denoise the data, Hample filtering and Wavelet signal methods were used. The principal component analysis was applied to extract the feature matrix using moments of order up to fourth. This data was then used for SVM with time-efficient training and classifying the activities. Besides, denoised raw data were used to train the LSTM algorithm with rigorous and deep learning to classify.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Literature Gap and Future Research  
Discrete event simulation offers a wide range of benefits for enhancing hospital processes, encompassing patient care and logistics.  
This systematic literature review aimed to identify knowledge gaps in the existing global research on DES. Data were extracted from PubMed, Scopus, and WOS databases using the PRISMA search method. The analysis revealed that the reviewed systematic reviews lacked specific information on the following:  
* The software utilized in the analyzed case studies.
* The medical services where discrete events were applied.
* The statistical distributions employed for modeling discrete event times.  
This article addresses these knowledge gaps by exploring these aspects, contributing to a more comprehensive understanding of DES implementation in healthcare settings.  
As a suggestion for future research, it is recommended to continue collecting such information on a biannual basis to support scientific studies on the implementation of DES within the hospital sector. This ongoing data collection can further enhance and enrich research related to DES in healthcare settings.
# Performance Comparison of Machine Learning Disruption Predictors at JET
## 3 Models Implementation and Results
### CNN Disruption Prediction Model  
The architecture of the CNN disruption predictor is reported in Figure 7. Due to the ability of the CNN to process images, the plasma profiles, which are 1-D signals, have been treated as a single image, as previously described. The other 0-D signals are fed in the CNN downstream of the first filter block. Note that the first filter block has been previously trained only with the 1-D diagnostic data, and its weights have been frozen. Then, in a second training phase, the CU\({}_{2}\) and FC blocks were trained using all diagnostic signals.  
The free parameters of the training process are reported in Table 7.  
\begin{table}
\begin{tabular}{l|c|c|c} \hline \hline  & & \multicolumn{2}{c}{**Predicted**} \\ \cline{3-4}  & P \(\ast\)\(\cdot\)N = 257 & P = 133 & N = 124 \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & P = 108 & TP = 105 & FN = 3 \\ \cline{2-4}  & N = 149 & FP = 28 & TN = 121 \\ \cline{2-4}  & PRECISION = 0.789 & RECALL = 0.972 & SPECIFICITY = 0.812 \\ \cline{2-4}  & ACCURACY = 0.879 & F1 = 0.871 & F2 = 0.929 \\ \hline Train & SP\% = 100\% & MA/\% = 0\% & FA\% = 0\% \\ \hline Test & SP\% = 97.22\% & MA\% =1.85\% & FA\% = 18.79\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix and performance indices of the GTM prediction model evaluated on the test set.  
Figure 7: Architecture of the CNN disruption predictor.  
Figure 8a reports the image of the plasma profiles of the disrupted discharge #94218. By feeding the CNN with a sliding window of 200 ms on the test discharge, the corresponding disruptive likelihood outcomes are as reported in Figure 8b. The vertical dashed line identifies the alarm time \(t_{alarm}\). The CNN is able to correctly predict the disruption with a warning time \(\Delta t_{warning}\) = 372 ms.  
Table 8 reports the confusion matrix (green: successful results; red: mistakes) and the values of the prediction performance indices of the CNN model. As in the case of the MLP-NN model, all the indices have high values obtaining a tradeoff between successful predictions and false alarms. Note that the plasma profiles have been directly used to feed the CNN model without the feature engineering process implemented for the MLP-NN and GTM.  
Figure 8: (**a**) CNN input image of the disrupted discharge #94218; (**b**) disruptive likelihood of the disrupted discharge #94218. The dashed black line identifies the alarm time.
## 4 Conclusions and Future Work  
In this paper, a new crackle event detection method based on the combination of an autoregressive model, and a support vector machine classification model is proposed. We conclude that the proposed method is a suitable model for detection of the existence or non-existence of crackle events within a dataset with a significant high success rate. The proposed method has achieved highly competitive results in the detection of crackle events in spite of the environmental factors, such as type of crackle (fine or coarse) or very low signal-to-noise ratio. Future work will focus on combining recurrent and convolutional neural networks approaches using different time-frequency representations in order to develop novel criteria to determine the most reliable and discriminant feature map in terms of the abnormal respiratory sound to be detected. Conceptualization, L.D.M., J.J.C.-O. and F.J.C.-Q. ; methodology, L.D.M., J.J.C.-O and F.J.C.-Q. ; software, L.D.M., J.d.I.T.-C., A.M.-M. and P.R.-S.; validation, L.D.M. and P.R.-S.; formal analysis, L.D.M., J.J.C.-O., F.J.C.-Q. and E.F.C. ; investigation, L.D.M., J.J.C.-O., F.J.C.-Q. and E.F.C. ; resources, L.D.M., J.J.C.-O. and E.F.C. ; data curation, L.D.M., J.J.C.-O., J.d.I.T.-C., A.M.-M. and P.R.-S.; writing--original draft preparation, L.D.M., J.J.C.-O. and F.J.C.-Q. ; writing--review and editing, L.D.M., J.J.C.-O., F.J.C.-Q., J.d.I.T.-C., A.M.-M., P.R.-S. and E.F.C. ; visualization, L.D.M., J.d.I.T.-C. and A.M.-M.; supervision, J.J.C.-O. and F.J.C.-Q. ; project administration, F.J.C.-Q. ; funding acquisition, F.J.C.-Q. and E.F.C. All authors have read and agreed to the published version of the manuscript. This work was supported in part under grant PID2020-119082 RB-[C21,C22] funded by MCIN/AEI/10.13039/501100011033, grant 1257914 funded by Programa Operativo FEDER Andalucia 2014-2020, grant P18-RT-1994 funded by the Ministry of Economy, Knowledge and University, Junta de Andalucia, Spain, grant AYUD/2021/50994 funded by Gobierno del Principado de Asturias, Spain and the QUANTUM SPAIN project funded by the Ministry of Economic Affairs and Digital Transformation of the Spanish Government and the European Union through the Recovery, Transformation and Resilience Plan--NextGenerationEU. Not applicable. **Data Availability Statement:**[[https://github.com/loredanadariamang/Applied_sciences_crackle_detection_localization.git](https://github.com/loredanadariamang/Applied_sciences_crackle_detection_localization.git)]([https://github.com/loredanadariamang/Applied_sciences_crackle_detection_localization.git](https://github.com/loredanadariamang/Applied_sciences_crackle_detection_localization.git)) (accessed on 21 September 2023). **Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
Several in vitro and in vivo studies have revealed that meat and dry-cured meat products are excellent sources of biologically active peptides. Bioactive peptides could be generated from meat proteins through the action of endogenous muscular enzymes during processing, during gastrointestinal digestion or by using commercial enzymes in the laboratory or industry under controlled conditions. Peptides with established biologic activity can also be obtained by chemical synthesis and included in drugs or nutraceutical food. The current research studies focus on profiling enzymatic proteolysis to obtain the highest number of peptides with high biologic activity [1; 2; 3; 4]. In some sectors of the food industry, ultrasonic technology has been used [5; 6; 7]. Ultrasound may affect some physical and chemical properties of food when used at frequencies in the range of 20-100 kHz (intensity ultrasound power level in the range of 10-1000 W cm\({}^{2}\)) [6; 7]. In this range, ultrasound mainly works by producing cavitation of bubbles in the biologic matrix, sonochemistry, sonolysis of water or intracellular micromechanical shocks [8]. For dry-cured meat products, the course of proteolysis is influenced by many variables, including the formulation of the product, the processing conditions or the used starter culture. [PERSON] et al. [9] reported that sonication significantly affected the proteolysis pattern, providing a higher content of nonprotein nitrogen and proteolysis index in the pork loin after 21 days of aging. Innovative applications of ultrasound in the production of protein hydrolyses were also mentioned, pointing to their use in achieving changes in enzyme activity. Various techniques used for investigating the conformational changes occurring in fruit juice enzymes or dairy product enzymes after sonication have been summarized [8; 9; 10; 11]. The use of ultrasound treatment at appropriate frequencies and intensity levels can lead to increased enzymatic activity due to conformational changes in the molecules of protein without altering its structural integrity [11]. In addition, the biologic activity of peptides, such as the antioxidant or angiotensin-converting enzyme (ACE-I) inhibitory activity, can be enhanced by ultrasound. Most often this is achieved by pretreatment of protein isolates or the use of sonication during enzymatic hydrolysis. To date, limited research has been performed to evaluate the impact posed by the initial treatment of the raw materials on the final activity of peptides obtained from the final meat product.  
This study aims to assess the impact of sonication and acid whey maceration on organic pork loin without nitrate in order to obtain peptides with high activity against oxidative changes (ABTS, reducing power (RP), thiobarbituric acid reactive substances (TBARS) and oxidation-reduction potential (ORP)) and with high ACE-inhibitory potential.
# Public-Key Cryptography Based on Tropical Circular Matrices
###### Abstract  
Some public-key cryptosystems based on the tropical semiring have been proposed in recent years because of their increased efficiency, since the multiplication is actually an ordinary addition of numbers and there is no ordinary multiplication of numbers in the tropical semiring. However, most of these tropical cryptosystems have security defects because they adopt a public matrix to construct commutative semirings. This paper proposes new public-key cryptosystems based on tropical circular matrices. The security of the cryptosystems relies on the NP-hard problem of solving tropical nonlinear systems of integers. Since the used commutative semiring of circular matrices cannot be expressed by a known matrix, the cryptosystems can resist KU attacks. There is no tropical matrix addition operation in the cryptosystem, and it can resist RM attacks. The new cryptosystems can be considered as a potential post-quantum cryptosystem.  
c 2022 2020  
usual addition. However, \"tropical powers\" of an element exhibit some patterns, even if such an element is a matrix over a tropical algebra. This weakness was exploited by [PERSON] and [PERSON] to propose a fairly successful attack on the public-key cryptosystem in [25]. Then, [PERSON] and [PERSON] improved the original scheme and proposed the public-key cryptosystems based on the semi-direct product of the tropical matrix semiring [26]. However, some attacks on the improved public-key cryptosystem have been suggested by [PERSON] and [PERSON] [27] and [PERSON] and [PERSON] [28]. As we know, most of these tropical public-key cryptosystems have security defects because they adopt a public matrix to construct commutative semirings or there is a tropical matrix addition operation in the cryptosystems. A review of the tropical approach in cryptography was carried out by [PERSON], [PERSON] and [PERSON] [29].  
**Our contribution:** This paper provides new public-key cryptosystems based on tropical \(t\)-circular matrices. The security of the cryptosystem relies on the NP-hard problem of solving tropical nonlinear systems of integers. Since the used commutative semirings of circular matrices cannot be represented by a known matrix and there is no tropical matrix addition operation in the cryptosystem, these cryptosystems can resist all known attacks such as KU attacks and RM attacks. Our results show that these cryptosystems are secure when the computational two-side tropical circular matrices action problem (CTCMAP) and the decisional two-side tropical circular matrices action problem (DTCMAP) are hard. It seems that our cryptosystems based on tropical circular matrices can be considered as potential post-quantum cryptosystems.  
The rest of the paper is organized as follows: We focus on some definitions as fundamental key notions of tropical matrix algebra in Section 2. In Section 3, we present the new public-key cryptosystems based on tropical circular matrices. Then, in Section 4, parameter selection and efficiency of the cryptosystems are discussed. Finally, the conclusion and further research are given in Section 5.
## 4 Identity Management and Access Control Based on Blockchain under Edge Computing for IIoT
### Performance Evaluation  
In this section, we evaluate the performance of the proposed schemes. We use Ubuntu 16.04 (Canonical Ltd., London, UK) and C language to construct the schemes. The device's identity of IIoT in our scheme is underpinned by the interactions between Ethereum smart contracts. Symmetric encryption and message authentication coding are 128 bits. The energy consumption of nodes sending information is 1.62 mJ/bit, and the energy consumption of receiving information is 2.025 mJ/bit. So, the sending message and received message of a terminal are one pass and 367 bits. The energy of communication is 1337.715 mJ.  
The smart contracts of identity management and access control are published in the edge center. Because of the strong capabilities of the edge center, the experiment used a 2.3 GHz Intel Core i7 (8 core) (i7 9900t; Intel, Santa Clara, CA, USA) and 16 GB of RAM memory capacity (DDR4; SAMSUNG, Gyeonggi Province, Korea). Raspberry Pi 3 (raspberry pi foundation, Cambridge, UK) is utilized as terminals of IIoT. We use the edge terminal to issue 50 requests per second to test whether the system can achieve stable throughput. All concurrent clients request access to data resources from the edge  
Figure 6: Flow diagram of the access control based on bloom filter.  
center node. The latency between the edge center and a terminal limits the overall performance of the network.  
The results show that the edge center with identity management and access control performs best under up to 100 concurrent terminals, with 90 requests per second without timeout. The Fairaccess solution pays more attention to privacy and integrity, while our solution focuses more on sensor capacity and unit time to get more effective data. So, we use a lightweight encryption mechanism. Compared with the ordinary edge center, the edge center with identity management and access control can more efficiently acquire the effective data needed by the system and filter some unnecessary data, which can improve the node capacity of the sensor network and process more valid data in unit time. We believe that the performance of the edge center is acceptable because the node has various resources and powerful computing, storage, and communication capabilities. Despite the limited capabilities of the terminal, bottlenecks still occur in the case of an excessive number of nodes, but experiments have shown that terminals with identity management and access control are more scalable than terminals without these features.