esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effects of Nanoemulsions System Applied on Liquid-Smoked Salmon Fillets  
Viscosity is considered to be an important indicator to show the properties of NE, indirectly reflecting the physical stability of NE. The blank SPI solution was used as a control. The viscosity versus and shear rate curves revealed that all of the NE samples exhibited non-Newtonian shear-thinning behavior, in which the viscosity decreased with the increasing shear rate (Figure 2). After the addition of ENT and CEO (single or mixed), the viscosity of SPI decreased significantly, and the viscosity of CO-NE was lowest among all of the treatments. The single or mixed addition of ENT and CEO led to an interfacial rheology variation in the NE, then unstable interfacial layers formed, which caused a lower viscosity. Moreover, the formation of the unstable interfacial layer may be due to the accumulation of soybean protein on the droplet surface, which decreases the effective content of soybean protein in the solution. The lower viscosity of CO-NE may be caused by the fact that more soy protein was adsorbed to the droplet surface due to the presence of more ENT and CEO in the NE system. The release rate of ENT and CEO across 50 h was tested, and the release rate of ENT and CEO in the SPI-based NE revealed a continuously increasing trend across 0-50 h, reaching a maximum of 94.11 \(\pm\) 4.62% and 96.30 \(\pm\) 5.01%, respectively. These results demonstrated that a significant, sustained release effect was achieved by containing the ENT and CEO within an SPI-based NE (Supplementary Figure S3).  
Figure 1: The encapsulation efficiency (EE) of enterocin Gr17 (ENT)/cinnamaldehyde (CEO) in nanoemulsions produced by using soybean protein isolate (SPI), whey protein isolate (WPI), sodium caseinate (SCN), hydroxypropyl methyl-cellulose (HPMC), konjac gum (KGM) and xanthan gum (XGM). Means with no letter in common are significantly different (\(p<0.05\)).
## 2 Results and Discussion
### Method Validation  
#### 2.3.1 Linearity Range  
Respecting the FDA validation criteria given in Section 3.4.1., the linear regression coefficient (R2) for the calibration curve for piperacillin was 0.9916, and for impenem it was 0.9934 (Figure 4). For impenem, all back-calculated values were within the accepted \(\pm\)15% (\(\pm\)20% for LLOQ) of the theoretical value. For piperacillin, a single measurement exceeded the \(\pm\)15% accuracy that is allowed at four QC levels, but the calibration curve still met the acceptance criteria (\(\geq\)75% of samples at each level).
They created rainfall data using kriging and proposed an optimal network by calculating entropy. Rain gauge stations have been evaluated using various methods, such as kriging, regression analysis, correlation analysis, and entropy. Entropy-based studies used conditional and joint entropies to select the optimal stations. In addition, several studies focused on the spatiality of stations because the evaluation of stations using entropy is performed through the empirical probabilities of observation data and does not reflect the spatial distribution of stations. However, no quantitative comparison was conducted to assess how much performance the optimal rain gauge network actually has based on entropy. In statistics and meteorology, prediction models are developed and evaluated through differences between the observed and predicted values. In the case of continuous data, the root mean square error (RMSE) is mainly used as a model selection criterion. In this study, several estimators of entropy and weighted entropy were compared. A quantitative comparison of the rain gauge networks selected by various entropy methods allows the design of appropriate rain gauge networks. An entropy-based rain gauge network was quantitatively evaluated by predicting the precipitation values of unselected rain gauge stations based on the stations selected as the optimal rain gauge network using entropy. In addition, the performance of a rain gauge network selected as entropy was evaluated using the RMSEs of all rain gauge network combinations. Additionally, a method of determining the number of rain gauge stations using RMSE was proposed. The number of rain gauge stations that can yield the maximum efficiency was determined by calculating the RMSE reduction and the reduction ratio according to the number of rain gauge stations. The structure of this paper is as follows. Section 2 presents the kriging theory for considering entropy and spatial distribution and discusses the methods of selecting the optimal rain gauge networks and determining the optimal number of rain gauge stations using RMSE. Entropy theory presents several estimators and weighted entropy. In Section 3, the optimal rain gauge network for 11 rain gauge stations in the Daegu and Gyeongbuk area is evaluated based on entropy, and the determination of the number of rain gauge stations for efficient operation is described. Conclusions are presented in Section 4.
# Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
## 4 EEDCF Algorithm
### Message Complexity  
At the beginning of every CH election, we assume the number of live nodes in current round of the network is \(N\), and the number of Head_compete data package should be \(N\) too. After the comparison of the outputs between its neighbor nodes, we suppose \(M\) nodes are elected as CHs, they broadcast CH_Message to their neighbor nodes, and the number of these messages is \(xM\). In the cluster build phase, the number of nodes which are not elected to be CH is (\(N\)-\(M\)), so accordingly the number of Node_JOIN message needed to be sent to the elected CH within the communication radius \"R\" is (\(N\)-\(M\)) as well. Meanwhile, the number of CH_ACCEPT messages which are responded from the corresponding CHs when the CHs receive the request of build cluster is also (\(N\)-\(M\)). The message complexity can be figured out with Equation (16), which calculates the total number of message transmissions (\(N_{MT}\)).  
\[N_{MT}=N+xM+2(N-M) \tag{16}\]  
where \(x\) is the average node degree of all the CH nodes in current round. Thus, the overall message complexity of EEDCF is \(O(N)\).
# COVID-19 Outbreak Prediction with Machine Learning
## 2 Materials and Methods
### Evolutionary Algorithms
#### 2.1.1 Genetic Algorithm (GA)  
GAs are considered a subset of \"computational models\" inspired by the concept of evolution [50]. These algorithms use \"Potential Solutions\", \"Candidate Solutions\", or \"Possible Hypotheses\" for a specific problem in a \"chromosome-like\" data structure. GA maintains vital information stored in these chromosome data structures by applying \"Recombination Operators\" to chromosome-like data structures [51; 52; 53; 54]. In many cases, GAs are employed as \"Function Optimizer\" algorithms, which are algorithms used to optimize \"Objective Functions\". Of course, the range of applications that use the GA to solve problems is very wide [55; 53]. The implementation of the GA usually begins with the production of a population of chromosomes generated randomly, and bound up and down by the variables of the problem. In the next step, the generated data structures (chromosomes) are evaluated, and chromosomes that can better display the optimal solution of the problem are more likely to be used to produce new chromosomes. The degree of \"goodness\" of an answer is usually measured by the population of the current candidate's answers [56; 57; 58; 59; 60]. The main algorithm of a GA process is demonstrated in Figure 3.  
In the present study, GA [60] was employed for estimation of the parameters of Equations (6) to (13). The population number was selected to be 300 and the maximum generation (as iteration number) was determined to be 500 according to different trial and error processes to reduce the cost function value. The cost function was defined as the mean square error between the target and estimated values according to Equation (14):  
\[MSE=\sqrt{\frac{(Es-T)^{2}}{N}} \tag{14}\]  
Figure 3: Genetic algorithm (GA).  
where \(Es\) refers to estimated values, \(T\) refers to the target values, and \(N\) refers to the number of data.
# Staining-Independent Malaria Parasite Detection and Life Stage Classification in Blood Smear Images
## 3 Proposed Methods
### Malaria Parasite Life Stage Classification  
For malaria parasite life stage classification, the convolutional neural network (CNN) algorithm is utilized. Like that of the YOLO, the descriptions of CNN are widely available in the literature [46]. Hence, only brief descriptions are given here. The CNN algorithm used in this research is LeNet-5. LeNet-5 is a pioneering CNN architecture. It is considered a fundamental model in the field of deep learning and computer vision. It has also been adapted for various other image classification tasks, including cell classification in biomedical image processing [46]. To prepare input images for CNN LeNet-5, the malaria parasite life stage classification encompasses 3 distinct cell cropping methods, including (1) direct cropping of a single cell, (2) zero padding method [47], and (3) cell cropping with background. All of these methods are applied on both datasets. Sample cell images cropped by the aforementioned 3 methods are shown in Figure 6.  
According to the composition of different datasets shown in Table 1, both of the 2 datasets have the same heavily imbalanced class towards red blood cell class. This is not surprising because the normal red blood cells are much more abundant in nature than the infected cells. This issue of imbalanced classes poses a significant challenge in this research, particularly when applying machine learning algorithms to detect these infrequent occurrences within sizable datasets. As a result of the unequal distribution of classes, the algorithms tend to favor categorization into the more prevalent class, the majority class. This inclination can misleadingly convey the impression of a highly accurate weight, ultimately resulting in poor classification performance [48].  
Thus, instead of reducing the number of cells in red blood cell groups, the numbers of other infected cell classes are increased to the same level as that of red blood cells. This can be done via image augmentation, i.e., top-down, left-right translation, vertical, horizontal reflection, and rotation [49]. The comparison of the numbers of images before and after data augmentation is shown in Table 1. Each single-cell image is resized to 250 \(\times\) 250 pixels. The images are randomly divided into training and test sets with a ratio of 4:1.  
Figure 6: Sample cropped single cell images to prepare inputs to CNN-based malaria parasite classification.
## 4 Conclusions  
In this investigation, an 8 \(\times\) 10 Gbps OCDMA-based hybrid MDM-FSO-MMF system was designed by incorporating MNZCC code. To design the MDM scheme, modal multiplexing of Donut modes 0 and 1 was used. Users that were transmitted on Donut mode 0 performed slightly better compared to users that were transmitted on Donut mode 1, as the latter suffered more from attenuation. For an acceptable/required BER, the maximum achieved MMF was 385 m with a 100 m FSO range under clear air climate conditions. Moreover, when the MMF was fixed at 100 m, the FSO range could be extended up to 2.07 km. Furthermore, the effect of scintillations, particularly weak and strong turbulences, on the performance of the proposed OCDMA-based MDM-FSO-MMF model was investigated. The results show that for both gamma-gamma and log-normal modeling, all the users transmitted on Donut mode 0 and Donut mode 1 achieved acceptable BER values up to 1150 m. Therefore, the OCDMA-based hybrid MDM-FSO-MMF system can improve the usage of an FSO-fiber link under different atmospheric conditions. The proposed system  
Figure 5: Measured BER under gamma-gamma turbulence modeling. (**a**) Users 1 to 4 operating at Donut mode 0, (**b**) users 5 to 8 operating at Donut mode 1.  
Figure 6: Measured BER under log-normal turbulence modeling. (**a**) Users 1 to 4 operating at Donut mode 0, (**b**) users 5 to 8 operating at Donut mode 1.  
can offer high data-rate and long-range hybrid wired-wireless link services under adverse environment conditions in the future.  
All authors (M.K., A.S. and S.C.) contributed equally. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This project is supported by the Second Century Fund (C2F), Chulalongkorn University, Bangkok, Thailand.  
The authors declare no conflict of interest.
# Optimization of Effective Throughput in NOMA-Based Cognitive UAV Short-Packet Communication
###### Abstract  
Unmanned aerial vehicles (UAVs) are considered an important component of 6G wireless technology. However, there are many challenges to the employment of UAVs, one of which is spectrum scarcity. To address this challenge, non-orthogonal multiple access (NOMA) and cognitive radio (CR) techniques are employed in UAV short-packet communication systems. In this paper, we consider a NOMA-based cognitive UAV short-packet communication system. Firstly, a mathematical expression for the effective throughput of the secondary users is derived. Then, we aim to maximize the effective throughput of the far secondary user by optimizing the sensing time, power allocation, and information bits under the constraints of the transmission power and effective decoding error probability. A joint optimization algorithm is used to solve this problem, where the bisection method and the one-dimensional linear search algorithm are used to solve the subproblem. The simulation results show that the proposed algorithm has low complexity and similar performance compared to the exhaustive method. In addition, the necessity of joint optimization is shown in the simulation results.  
U  
Footnote †: journal: 2023, 13, 599. [[https://doi.org/10.3390/app13010599](https://doi.org/10.3390/app13010599)]([https://doi.org/10.3390/app13010599](https://doi.org/10.3390/app13010599))
# Assessing the Security and Privacy of Baby Monitor Apps
## 5 Mobile App Analysis
### Deployed Network Security Measures  
As detailed in Section 5.3, we studied a scenario in which baby monitoring apps exchange video footage over untrusted networks, which makes ensuring the confidentiality of the transmitted data significantly relevant. In untrusted networks, ensuring the confidentiality of sensitive data is usually achieved by using transport encryption. Given the sensitive nature of baby monitoring video, video streams should be protected by the approach promising the highest level of confidentiality, which we consider to be end-to-end encryption. End-to-end encryption allows only the intended parties to decrypt the transmitted data, effectively protecting confidentiality against any potential eavesdroppers on the communication path. We aimed to investigate in which way the chosen baby monitoring apps fulfill the demands on video footage protection. To identify the network security measures set up to protect the monitoring video streams, we performed a baby monitoring session using each of the investigated applications and evaluated the created network traffic, supported by insights from manual security analysis. Surprisingly, the BabyCam app does not use any encryption at all, leaving the transferred video footage unprotected against potential eavesdroppers. The Dormi and Saby applications rely on encrypted SRTP data streams to securely transfer video streams, for which an encryption key must be exchanged between the communication parties. While the Saby app utilizes the DTLS protocol mechanisms for key exchange, the Dormi app relies on a proprietary key exchange protocol, which is also based on asymmetric cryptography and public keys. For both key exchange protocols, the identity of the remote peer must be verified to protect it from MitM attacks. The Dormi app uses additional anti-tamper codes that are exchanged during pairing, which the app user verifies in case of a valid connection request (Figure 6), so the remote device can get correctly authenticated.  
The Saby app exchanges a hash of the remote peer's certificate over a secured communication path to a signaling server, in this way effectively verifying the identity of the remote peer. Using TLS-secured connections to its relay, the Babyphone Mobile app protects video footage against potential eavesdroppers on the communication path, but not the relay server operators. To hide video footage from server operators, Babyphone Mobile offers optional password-based end-to-end encryption of video data, which requires the user to enter the same password in both app instances. However, the use of encryption is not mandatory. Table 9 summarizes the described network security measures of the applications.  
\begin{table}
\begin{tabular}{l
## 1 Introduction
The decay of this voltage provides information on the distribution of the subsoil conductivity, which can be used for its characterization [11; 12]. This geophysical prospecting technique is used for many tasks. Although this method was originally used in the prospecting of deep mineral deposits [12], its ability to detect changes in conductivity associated with variations in the salinity of aquifers was later confirmed. This led to its widespread use in the investigation of marine intrusions in coastal areas [13; 14; 15; 16] or in the detection of pollution plumes in environmental studies [17]. It has also been used in the analysis of sedimentary basins, in particular, to detect the depth of the base under sedimentary coverings [3; 4; 18; 19]. In addition, the information that magnetic techniques can provide in this geological context is analysed and compared with the above-mentioned methods. Magnetic prospecting is based on the detection of anomalies in the Earth's magnetic field. These anomalies are caused by materials that present high magnetic susceptibility; that is, they can be magnetically polarized in the presence of an inductive field [1]. Before these measurements can be interpreted, the influence of external fields (basically the diurnal variations in the magnetic field) must be considered. This methodology has been extensively described in the literature [1; 6; 20] and it is widely used in different fields of Earth science. In mining, this method has proven useful in prospecting for ferromagnetic materials [21; 22; 23; 24; 25] and in hydrocarbon prospecting, providing information on deep structures that can function as traps [26]. It has also been used as a complementary tool for geological mapping [27; 28], locating faults in covered areas [29; 30]. In this study, the information provided by the surface geological survey and the data from boreholes is compared with the results obtained through geophysical prospecting. This will allow, on the one hand, to complement the geological information of this sedimentary basin, as well as the characterization of the related aquifers. On the other hand, the comparison of the results obtained with the different indirect techniques used (seismic reflection, TDEM and magnetic) allow us to analyse the effectiveness of each of them in this geological context.
## 4 Tests and Results
### Parameters of the Bio-Inspired Model
#### 4.1.4 Global Results  
As a summary of the experiment, Figure 15 illustrated the results obtained for the first 20 samples, while Figure 16 depicts the last 20 case studies. In these figures, a comparison of the results obtained by the three applied techniques is presented, with the error obtained by the OCR technique in the absence of the retinal model in blue and the OCR technique with the implementation of the bio-inspired retinal model in orange. Following the analogy of the OCR technique, the error rate obtained in the absence and presence of the bio-inspired retinal model is shown in gray and yellow, respectively. Finally, the error obtained by using  
Figure 14: Example of sample dataset used and converted to spikes. (**a**) Corresponds to the original input of the dataset, the letter “Y”. (**b**) Corresponds to the image transformed into spikes from the input image.  
the SNN technique in the absence and presence of the bio-inspired retinal model is shown in light blue and green, respectively.  
As the literature indicates, the deep learning method shows better accuracy when recognizing an alphanumeric character, at least for this specific task. This performance can be attributed to the data used for training the deep learning network. However, it is important to note that OCR is a powerful tool in environments with sharp images and known typography, such as advertisements, books, documents, signs, etc. When focusing attention on the comparison between SNN and CNN, it can be stated that the two of them have similar performance, and they differ in processing times, with SNN being faster than CNN as it requires less computing capacity.  
Although the systems perform well in the character recognition task, it is crucial to emphasize that the introduction of bio-inspired retina preprocessing tends to improve performance in the three studied systems. Demonstrating favorable behavior for SNN, since the performance of the SNN in the absence of preprocessing is slightly lower than that of CNN, but after applying the preprocessing, it presents better performance than  
Figure 16: Percentage of error obtained by the OCR, CNN, and SNN techniques in the presence and absence of preprocessing by a bio-inspired retina model, for the last 20 samples.  
Figure 15: Percentage of error obtained by the OCR, CNN, and SNN techniques in the presence and absence of preprocessing by a bio-inspired retina model for the first 20 samples.  
the CNN with preprocessing. It should be noted that the retina model worsened the performance of the techniques in situations with very stained or dirty license plates and in the presence of very large shadows that crossed over the characters. This is because the retinal model highlights the contours of the existing elements in the scene, also highlighting undesired information.
## 5 Power Optimization
### Experimental Optimization Results  
When the forward speed was 800-1400 m/h, the cutter roll speed was 100-140 r/min, and the bending angle was 110-130\({}^{\circ}\). In order to obtain the optimal parameter solution, the optimal protection design was carried out. The objective the boundary condition parameter model of optimization and experimental factors was established as follows:  
\[\text{minY}\left\{\begin{array}{l}800\leq\text{X}_{1}\leq 1400\\ 100\leq\text{X}_{2}\leq 140\\ 110\leq\text{X}_{3}\leq 130\\ 1.24\leq\text{Y}\left(\text{X}_{1},\text{X}_{2},\text{X}_{3}\right)\leq 4.763 \end{array}\right. \tag{7}\]  
In the prospect of low energy consumption and high efficiency in agricultural machinery operation [28, 29], considering the influence of finite-element simulation structure, performance, and practical application production, the optimal factor parameter combination was finally determined as follows: forward speed 900 m/h, rotation speed 100 rad/min, bending angle 115\({}^{\circ}\), and power consumption 1.458 kW. The simulation experiment was carried out when the optimal combination parameters were set. The calculated working resistance was 1.64 kW, which was basically consistent with the optimization results. Meanwhile, the finite-element evaluation was carried out and the calculated results met the mechanical requirements of rotary blade.  
Finally, a field test was carried out to verify the working resistance of 1.83 kW. Compared with the rotary tillage power under the initial parameter setting, the power consumption was reduced by 10%, which proves that the optimization parameters reduce the actual cutting power consumption of the cutter roll, which has reference value for power optimization.  
Figure 13: Comparison of power consumption in field trials and simulation trials.