esa-sceva/satcom-chunk-collection huggingface dataset:
# Bearing Estimation for Indoor Localization Systems Using Planar Circular Photodiode Arrays
## 4 Measurements and Evaluation
### Bearing Estimation
#### 4.3.1 Effects of Reflective Surfaces  
Measurements with reflective surfaces were conducted utilizing a measurement setup similar to the one shown in Figure 7, but in this case six beacons were used at the angular positions of \(\pm 15^{\circ}\), \(\pm 45^{\circ}\), and \(\pm 75^{\circ}\). During the measurements full rotations were made with resolution of \(2^{\circ}\). As a reflective surface, white paper and aluminum foil were assembled on an \(80\times 80\) cm board. The distances of the reflective materials in the measurements were set to 0.5, 1.0, and 1.5 m.  
The measured angle estimation errors are shown in Figure 12, while the numerical values can be seen in Table 1.  
Figure 12: Bearing estimation errors with different reflective materials, different distances, and both with the least squares (LS) and frequency domain (FD) angle estimation method. The whiskers correspond to \(\pm 3\sigma\).  
As the results show, by decreasing the distance between the PCPA and the reflective material the error of the estimated angle increases. The proximity of the reflective surface increases the bias of the calculated angle, the direction of the error also depending on the angle between the beacon and the reflective material. The FD estimator clearly shows higher bias compared to the LS estimator, while the standard deviation of the measurements are similar. In worst case (aluminum foil at 0.5 m), the mean error was as much as 4.3\({}^{\circ}\) for the LS, and 9.3\({}^{\circ}\) for the FD estimator. For realistic cases (distance 1.5 m) the error is around 1\({}^{\circ}\) and 2\({}^{\circ}\) for the LS and FD estimators, respectively.
# An Optimized Hierarchical Cluster Formation Approach for Management of Smart Cities
## 3 Proposed Approach  
The setup phase of LEACH is very important for controlling power consumption. This phase consists of two steps: the CH election step, where the CHs are randomly elected; and the cluster formation step, where every node selects the closest CH. Most equivalent studies that recommend LEACH to prolong the network lifetime have exclusively focused on the CH election step of the setup phase, disregarding the other steps.
# Cytoprotective Effect of Acid-Hydrolyzed Sesame Leaves against Oxidative Stress in HepG2 Cells
## 1 Introduction  
Excessive reactive oxygen species (ROS) exposure can drive the progression of metabolic disorders including diabetes, obesity, hypertension, and atherosclerosis [1]. Metabolic diseases have become an important global health problem because of their increasing incidence. In response to oxidative stress, antioxidant enzymes, including glutathione reductase (GR), superoxide dismutase (SOD), glutathione peroxidase (GPx), and catalase (CAT), are induced and may play a pivotal role in endogenous defensive metabolism in the liver [2]. Glutathione (GSH) is a well-known potential antioxidant molecule that can neutralize ROS. Oxidative stress depletes cellular GSH [3]. In addition, excessive ROS generation leads to liver damage by inducing pro-inflammatory cytokine production [4]. Based on numerous studies, the endogenous antioxidant defense system is generally considered to be protective against oxidative stress-induced damage [5, 6]. Reducing ROS levels could thus serve as an effective approach to managing and preventing liver diseases.  
Sesame (_Sesamum indicum_ L.) is an important oilseed crop that is widely cultivated in Asia and Africa [7]. Sesame oil is used in cooking and in folk medicine. Sesame seeds contain polyphen compounds, lignans, minerals, and omega-3 fatty acids, which are responsible for their health benefits. In addition, sesame seeds possess hepato-protective properties against oxidative stress in rat hepatocytes [8]. To date, limited information is available on sesame leaf contents and their functions. Previous studies have shown that sesame leaves contain abundant nutrients and phytochemicals [7, 9]. Verbascoside,also known as acetoside, is one of the main bioactive compounds in sesame leaves. This compound belongs to the family of phenylethanol glycosides, which are commonly found throughout the plant kingdom and extracted from medicinal plants [10]. Studies have demonstrated that verbascoside possesses a range of biological functions including antioxidant, anti-inflammatory, hepatoprotective, and neuroprotective effects [11; 12; 13]. It is widely recognized that aglycones exhibit greater activity within the intestine than their glycoside counterparts, leading to increased bioavailability [14]. Our previous studies have demonstrated that subjecting glycosidic flavonoids to acid treatment enhanced their bioactivity [15; 16]. Acid hydrolysis thus has the potential to enhance the bioactivity of phenolic compounds through deglycosylation and the release of bound phenolic compounds. Here, we investigated the cytoprotective effects of acid-treated sesame leaves, including their ability to modulate antioxidant enzyme activity against oxidative stress, using human HepG2 cells.
# A High Thermal Conductivity of MgO-H\({}_{2}\)O Nanofluid Prepared by Two-Step Technique
## 4 Conclusions  
A novel two-step technique was employed for preparing efficient MgO-DW nanofluid, at room temperature, without using surfactants and/or organic base fluids. Based on the experimental valuation, the following conclusions can be drawn:  
* The co-precipitation method was followed to make crystalline and pure MgO nanopowder with an average particle size of 33 nm.
* SEM images revealed unique feathery or fluffy-like nanostructures of the prepared MgO nanopowder.
* MgO-DW nanofluid was synthesized with volume concentrations ranging from 0.05 to 0.25 vol.%, and ultrasonication treatment times ranging from 45 to 180 min, at room temperature.
* Zeta potential results showed that good stability was obtained with nano MgO content of 0.15 vol.% and a higher ultrasonication time of 180 min.
* The addition of MgO nanoparticles to traditional fluid as DW improved its thermal conductivity, where the highest value of thermal conductivity enhancement of 25.08% was found at 0.25 vol.% concentration and 180 min ultrasonication time.
* The effect of ultrasonication time on thermal conductivity improvement was similar to the impact of the nanoparticles volume concentration. However, this enhancement in the conductivity became limited after an ultrasonication time of 135 min.
* The dynamic viscosity measurements were revealed as directly proportional to the volume concentration of MgO-DW nanofluids, until recording the highest value of 0.0052 (Pa-Sec) at a higher solid content of 0.25 vol.% MgO-DW nanofluid. On the contrary, an increase in ultrasonication time resulted in a sharp decrease in the viscosity of the nanofluid samples.
* An MgO-DW nanofluid with good dispersion stability, high thermal conductivity, and low viscosity was created by controlling ultrasonication time and/or volume concentration.
* The produced MgO-DW nanofluid has the potential for exploitation in various implementations such as working fluid in cooling systems and heat exchangers. Therefore, it can be used as a working fluid in a hybrid solar collector, to study the effect of different flow rates on electrical and thermal efficiency.  
Methodology, H.K.J., A.G.T.A.-H. and F.N.A.Z.; investigation, H.K.J., A.G.T.A.-H., F.N.A.Z. and W.A.K.A.-M.; writing--original draft preparation, H.K.J., A.G.T.A.-H. and F.N.A.Z.; writing--review and editing, H.K.J., W.A.K.A.-M. and F.A.; supervision, B.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We acknowledge support by the Deutsche Forschungsgemeinschaft (DFG) German Research Foundation and the Open Access Publishing Fund of the Technical University of Darmstadt. The authors would like to also thank the University of Technology-Iraq.  
The authors declare no conflict of interest.
## 4 Experiments
### Robustness Testing of the Algorithms
The robustness test results for random modification of a small number of pixels are shown in Table 8. The robustness of our algorithm was basically the same as that of the Swin-Unet-based algorithm, and both of them were stronger than existing algorithms. A comprehensive analysis of our results (Tables 6-8) demonstrated that two Transformer-based subject-sensitive hash algorithms, that is, our algorithm and the Swin-Unet-based algorithm, greatly improved in robustness compared with existing subject-sensitive hash algorithms, especially in robustness against JPEG compression. Of course, our improved Swin-Unet-based algorithm as slightly inferior to the Swin-Unet-based algorithm because of the sacrifices that our algorithm had to make to enhance tampering sensitivity. In addition, the robustness of the Attention R2U-Net-based algorithm was the worst and would not be recommended for practical use. Figure 6: Comparison of images before and after random modification of a small number of pixels (4 pixels randomly selected and set to 0): (**a**) original HRRS images, (**b**) modified image. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Each Algorithm** & \(\textit{T=0.02}\) & \(\textit{T=0.03}\) & \(\textit{T=0.05}\) & \(\textit{T=0.1}\) & \(\textit{T=0.2}\) \\ \hline MUM-Net & 42.05\% & 52.27\% & 71.09\% & 90.38\% & 98.26\% \\ MultiResUnet & 79.69\% & 88.45\% & 95.98\% & 98.68\% & 99.82\% \\ U-net & 51.99\% & 64.22\% & 90.61\% & 93.36\% & 98.57\% \\ M-net & 53.52\% & 63.39\% & 78.54\% & 92.77\% & 98.64\% \\ Attention U-Net & 52.03\% & 64.24\% & 80.58\% & 93.39\% & 98.56\% \\ Attention ResU-Net & 81.49\% & 86.61\% & 92.07\% & 96.43\% & 98.88\% \\ Attention R2U-Net & 19.54\% & 27.02\% & 41.27\% & 64.24\% & 86.12\% \\ AAU-Net & 90.91\% & 93.65\% & 97.38\% & 99.44\% & 100\% \\ Swin-Unet & 94.55\% & 97.09\% & 99.36\% & 100\% & 100\% \\ Improved Swin-Unet & 92.82\% & 97.25\% & 99.54\% & 100\% & 100\% \\ (Our algorithm) & \multicolumn{1}{c}{} & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Each algorithm’s robustness against modification to small number of pixels (4 pixels).
# An Improved Adaptive Iterative Extended Kalman Filter Based on Variational Bayesian
###### Abstract  
The presence of unknown heavy-tailed noise can lead to inaccuracies in measurements and processes, resulting in instability in nonlinear systems. Various estimation methods for heavy-tailed noise exist. However, these methods often trade estimation accuracy for algorithm complexity and parameter sensitivity. To tackle this challenge, we introduced an improved variational Bayesian (VB)-based adaptive iterative extended Kalman filter. In this VB framework, the inverse Wishart distributions used as the prior for the state prediction covariance matrix. The system state and noise parameter posterior distributions are then iteratively updated for adaptive estimation. Furthermore, we make adaptive adjustments to the IEKF filter parameters to enhance sensitivity and filtering accuracy, thus ensuring robust prediction estimation. A two-dimensional target tracking and nonlinear numerical UNGM simulation validated our algorithm. Compared to existing algorithms RKF-ML and GA-VB, our method showed significant improvements in \(RMSE_{\text{pos}}\) and \(RMSE_{\text{vel}}\), with increases of 21.81% and 22.11% respectively, and a 49.04% faster convergence speed. These results highlight the method's reliability and adaptability.  
variational Bayesian (VB); extended Kalman filter (EKF); inverse Wishart distribution (IV); nonlinear; heavy-tailed noise
## 4 Experiment and Application: Lane-Level Trajectory Matching
### Lane-Level Trajectory Matching  
To further examine the feasibility of the constructed map in the applications of intelligent transportation services, a lane-level trajectory matching test is then conducted on the network. With the map sample constructed in the present work, a driving trajectory matching test was conducted. In the test, vehicle trajectory data and lateral driving be  
Figure 7: Diagram of lane line extraction.  
havior information are collected. Combined with the constructed high-definition map, the lane-level trajectory of the probe vehicle on the map is identified. Specifically, a u-blox F9 GNSS receiver and a front camera were adopted to receive IMU data and coordinate data and wheel speed information from the probe vehicle. Meanwhile, the lane-level trajectory of the probe vehicle was deduced using the detection results of lane-changing behavior via the smart vehicle driving process, which provides the platform for high-definition traffic monitoring, lane-level traffic control, and lane-level path planning of smart vehicles.  
Lane-level trajectory matching consists of trajectory matching of road segments and road intersections. Specifically, trajectory matching in the road segment domain involves lane change trajectory smoothing with third-order Bezier curves and lane-keeping trajectory matching with orthogonal projections, and trajectory matching in the intersection domain includes turning trajectory matching with a lane-level topological map matching algorithm. The process of map matching and the specific experimental results are described in detail next.
## 3 Related Works
[PERSON] & [PERSON]'s (BSJC) scheme does not meet the requirements of accuracy, scalability, and variability by voters while the counting method is from a third party [42]. The anti-quantum scheme, similar to the BSJC scheme, does not meet the requirements for accuracy, scalability, or voter variability, but the counting mechanism is self-tallying [40]. Although the open vote network (OVN) [38] does not meet the auditing, accuracy, scalability, or integrity requirements, the counting mechanism is self-tally. The other scheme, DATE, does not meet the auditing, accuracy, or integrity requirements, but it does meet voter scalability and variability [39]. BES, unlike BEA, achieves accuracy, integrity, and scalability, but not anonymity and voter variability [43], which BEA does [44]. Agora, a company based in Lausanne, Switzerland [45], has analyzed and developed a token electoral process mechanism based on Blockchain technology. They point out that current systems do not meet key voting features such as transparency, privacy, and integrity that can be achieved with new technologies. The Australian company, based in Brisbane, Horizon State [46] presents a voting application of Blockchain technology and addresses issues that need to be resolved, such as transparency, anonymity, and voter trust. The American company Voatz, in Boston, MA, USA, has created a Blockchain-based voting system that was approved in the U.S. presidential election. In their technical report [47], this company highlighted the challenges of identity, auditing, and protection against DoS attacks. Zcash is a decentralized payment scheme [25] that aims to provide anonymity, and unlike Bitcoin, proof-of-work in Zcash relies on an optimized form of zero-knowledge proofs called zk-SNARK. Double voting is a concern in Zcash since the same granted vote token is used to vote for several candidates [48]. A zero-knowledge proof refers to a cryptographic approach by which a party, referred to as \"the prover\", can prove to another party, referred to as \"the verifier\", that particular statements are true without giving any other information. Because a malicious user could gain unauthorized access to the Blockchain due to its open nature, the zero-knowledge proof can be used to validate if the prover has sufficient transactions in the Blockchain environment without exposing any data [49]. One of the simplest and most often-used proofs of knowledge is the Schnorr algorithm, also known as the proof of knowledge of a discrete logarithm [50]. Different analyses and approaches have been made based on research and evaluation of related work on blockchain-based electronic voting systems, but there are still gaps in the implementation of security requirements. Security requirements for voting schemes, with an emphasis on anonymity and privacy, need to be addressed in future studies.
# A Geospatial Framework of Food Demand Mapping
## 2 Materials and Methods
### Instruments  
The study was structured into separate sections for a detailed examination of dietary habits and the adoption of online grocery shopping across different demographics. The first part, Demographic Profiling, collected basic demographic information such as age, gender, occupation, education, location, income, and BMI for contextual analysis. The second part, E-Grocery Shopping Preferences, delved into online grocery shopping habits, including preferences for delivery times, responses to delays, expectations for perishable items, shopping frequency, and handling of missed deliveries. The Healthy Eating Assessment questionnaire, developed by the Government of Northwest Territories, was utilized to assess eating habits. This approach involved participants rating their consumption of various food categories, such as fast/fried foods, fruits, vegetables, sugary beverages, snack chips, sweet foods, dairy products, and meat/fish/beans. The assessment utilizes a scoring system, where respondents circle their answers for each question, and the total score is used to determine their health benefit zone. This standardized tool consists of 10 questions focused on dietary behaviors. The outcomes of the questionnaire are compiled into a scoring system, where a total score of 10-19 indicates a need for improvement, 20-29 is considered fair, 30-39 is good, and a score between 40 and 50 reflects excellent eating habits [26].  
Figure 1: Framework for food demand mapping.
# A Lightweight Fire Detection Algorithm Based on the Improved YOLOv8 Model
## 3 Experiment and Discussion
### Evaluation Indicators  
In order to assess the detection performance of the GCM-YOLO model, two evaluation indexes, namely recall and mean average precision (mAP), are utilized.  
The recall rate, expressed as a proportion, indicates the proportion of samples that are correctly recognized as positive cases among all samples that are actually positive cases. This metric is employed to evaluate the model's ability to recognize positive cases. The calculation formula for this metric is presented in Equation (1).  
\[Recall=\frac{TP}{TP+FN} \tag{1}\]  
In the above formula, \(TP\) represents True Positive and \(FN\) stands for False Negative.  
Average precision (\(AP\)) is the area under the precision-recall curve (PR curve) and reflects the average level of precision of the model under different recall rates. The precision-recall curve of each category is calculated based on the prediction results of the model. The  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter** & **Learning Rate** & **Optimizer** & **Batch Size** & **Image Size** & **Epochs** & **Momentum Factor** & **Weight Decay** \\ \hline Value & 0.01 & SGD & 64 & 640 \(\times\) 640 & 150 & 0.93 & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameters.  
Figure 6: Visualization of fire datasets. (**a**) Indoor Scene; (**b**) Forest Scene; (**c**) Residential Building Scene; (**d**) Dark Scene.  
area under the curve is then calculated to obtain the average precision of each category. Subsequently, these averages are combined to obtain an overall \(mAP\) value using the equations shown in Equations (2) and (3).  
\[AP=\int_{0}^{1}P(R)dR \tag{2}\]  
\[mAP=\frac{\sum\limits_{i=1}^{n}AP(i)}{n} \tag{3}\]  
The evaluation metrics also include measures such as the amount of model computations (FLOPs), the number of model parameters (parameters), and model size. The amount of computation refers to the floating-point operations required for the forward propagation process within a given model; it serves as an indicator for assessing computational complexity. The number of parameters denotes the total trainable parameters within a given model, while model size refers to the storage space required for storing said models, typically measured in megabytes (MB).
# A Robust and Effective Two-Factor Authentication (2 FA) Protocol Based on ECC for Mobile Computing
## 3 Proposed Protocol
### Registration Phase  
To obtain authentication from \(S_{j}\), \(U_{i}\) needs to carry out the following registration steps (R. 1-3) and complete the registration in the terminal of \(S_{j}\):  
R. 1 The user \(U_{i}\) chooses an \(ID_{i}\) and, using the secure channel, \(U_{i}\) transmits it to the service provider \(S_{j}\);  
R. 2 Upon receiving \(\{ID_{i}\}\), \(S_{j}\) picks a random number \(r_{i}eZ_{p}^{*}\) and computes \(V_{i}=H(ID_{i}\parallel Y_{i})\). \(S_{j}\) stores \(\{ID_{i},r_{i},Sum=0\}\) in its database, where the parameter \(Sum\) represents the number of login failures allowed for the user, and the smart card is revoked once the user fails more than \(Sum\) times. Finally, \(S_{j}\) adds \(\{X,P,V_{i}\}\) to a fresh smart card \(SC_{i}\) and, using the secure channel, transmits \(SC_{i}\) to \(U_{i}\);  
R. 3 When the user \(U_{i}\) obtains the smart card \(SC_{i}\) from \(S_{j}\), \(SC_{i}\) selects \(a_{i}\ eZ_{p}^{*}\) and randomly generates a number \(2^{4}\leq n_{0}\leq 2^{8}\). Then, \(SC_{i}\) calculates the following parameters: \(RPW_{i}=H(ID_{i}\parallel PW_{i})\) mod \(n_{0}\), \(B_{i}=H(RPW_{i}\parallel a_{i})\bigoplus V_{i}\), \(A_{i}=H(ID_{i}\parallel PW_{i}\parallel a_{i})\) mod \(n_{0}\). Finally, \(SC_{i}\) contains the parameters \(\{a_{i},A_{i},B_{i},X,P,n_{0}\}\).  
The operations are also summarized in Table 3 to provide researchers with a quick understanding of the registration phase.
## 6 Security Analysis
### Informal Analysis
#### 6.3.4 Man-in-the Middle Attack and Replay Attack  
The adversary \(\mathcal{A}\) can obtain the transmitted messages over an open channel and stored parameters in the OBU according to the threat model. However, we show that \(\mathcal{A}\) cannot generate valid vehicle's messages as mentioned above. Furthermore, \(\mathcal{A}\) also cannot generate the \(RSU_{i}\)'s message, because \(\mathcal{A}\) does not know secret random numbers \(r_{i}\), \(r_{j}\) and secret parameter \(RB_{j}\). Thus, \(\mathcal{A}\) cannot impersonate \(v_{i}\) or \(RSU_{j}\) by replaying intercepted messages as all messages are dynamic with random numbers \(r_{i}\) and \(r_{j}\). Therefore, the proposed protocol prevents man-in-the-middle and replay attacks.