esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Pilot Protection Based on Jensen-Shannon Distance
### Protection Action Flow  
A flowchart of the proposed pilot protection scheme is presented in Figure 7. The specific steps are performed as follows:  
1. Upon meeting the startup criterion, the peak and valley values are extracted from one-cycle fault current data acquired at both terminals.
2. Discrete probability distributions are constructed for samples within the obtained value range at each terminal.
3. The Jensen-Shannon distance is computed using the derived discrete probability distributions.
4. If the result exceeds the setting value \(D_{\text{set}}\), the protection trips for the internal fault phase; otherwise, it resets.  
Figure 7: Flowchart schematic of the proposed pilot protection.  
Below is an example demonstrating the process of converting phase A current samples into probability distributions. Given that the sampling frequency and time window of the protection relay are 10 kHz and 10 ms, respectively, the relays installed on both ends of the transmission line will yield 100 data points per window. First, divide the value ranges of sampled data from both ends into fifteen equally-spaced intervals based on their respective maximum and minimum values; then, count the occurrences of current sample values from each line terminal falling within their corresponding fifteen intervals; and finally, calculate the proportion of data counts in each interval relative to the total number of data points per window, thereby generating the required probability distribution.
# Approximate Image-Space Multidirectional Occlusion Shading Model for Direct Volume Rendering
###### Abstract  
Understanding and perceiving three-dimensional scientific visualizations, such as volume rendering, benefit from visual cues produced by the shading models. The conventional approaches are local shading models since they are computationally inexpensive and straightforward to implement. However, the local shading models do not always provide proper visual cues since non-local information is not sufficiently taken into account for the shading. Global illumination models achieve better visual cues, but they are often computationally expensive. It has been shown that alternative illumination models, such as ambient occlusion, multidirectional shading, and shadows, provide decent perceptual cues. Although these models improve upon local shading models, they still require expensive preprocessing, extra GPU memory, and a high computational cost, which cause a lack of interactivity during the transfer function manipulations and light position changes. In this paper, we proposed an approximate image-space multidirectional occlusion shading model for the volume rendering. Our model was computationally less expensive compared to the global illumination models and did not require preprocessing. Moreover, interactive transfer function manipulations and light position changes were achievable. Our model simulated a wide range of shading behaviors, such as ambient occlusion and soft and hard shadows, and can be effortlessly applied to existing rendering systems such as direct volume rendering. We showed that the suggested model enhanced the visual cues with modest computational costs.  
occlusion model; image-space; direct volume rendering +
Footnote †: : 2021: 11, 5717. [[https://doi.org/10.3390/app11125717](https://doi.org/10.3390/app11125717)]([https://doi.org/10.3390/app11125717](https://doi.org/10.3390/app11125717))
# Quantification of SPECT Concentric Ring Artifacts by Radiomics and Radial Features
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed using MedCalc software [19]. Trends of variables with increasing severity of concentric ring artifacts were quantified by \(\chi^{2}\) rank correlation with Spearman's \(\rho\) on all the 40 SPECT images. The discriminative power of radiomic and radial features was investigated by means of the least absolute shrinkage and selection operator (LASSO) approach [20]. The following nested cross-validation procedure was adopted: the whole data set was randomly split into a training set (70%) and a test set (30%).The LASSO shrinkage penalty parameter was tuned on training data through a fivefold cross validation, selecting the value with one standard deviation of the minimum error, and the resulting logistic model was evaluated on the test set [21]. The process was repeated 30 times and the most frequently selected features across iterations were reported as the most informative features. To estimate LASSO-logistic model performance on radiomic and radial features, accuracy, identified as the mean area under the receiver operating curve (AUC) on the test sets across iterations, was calculated. Ambiguously classified phantom images were classified by each iteration using the Youden index [22] as the threshold, and the most frequent classification over iterations was used to estimate the presence of ring artifacts. The whole procedure was performed by using the R glmnet package and the R pROC package (R software v4.0.4).  
Correlation between visual scores and radiomic and radial features was evaluated by [PERSON]'s coefficient (\(\rho\)) of rank correlation.  
Inter-rater agreement and intra-rater reproducibility of visual scores was determined by the kappa (\(\kappa\)) statistic, for which strength of agreement is considered \"poor\" for \(\kappa\) < 0.20, \"fair\" for \(\kappa\) = 0.21-0.40, \"moderate\" for \(\kappa\) = 0.41-0.60, \"good\" for \(\kappa\) = 0.61-0.80, and \"very good\" for \(\kappa\)\(\geq\) 0.81 [23], with significance determined by [PERSON]'s exact test.  
All tests were two-sided and a probability-value (\(p\)) < 0.05 was considered to be statistically significant.
## 3 Results
### Spatial Analysis Using Grids and Zonal Statistics  
Spatial analysis using grids was used in the study together with zonal statistics [64] to create a comprehensive analysis of all data collected and processed. In the study of [PERSON] et al. [64], they identified the most appropriate grid size for their paper using 500 m, 1 km, 2 km, and 4 km. A 500 m grid size was considered to produce the best representation of spatial data. However, this should still be based on the needs of the specific area under study. In this specific study, since the aim is to create a spatial map where all the mentioned parameters will be compared, a 500 m grid size was used, and properties enclosed in each grid were then extracted using zonal statistics representing the data sets. Grid data has many advantages over point data as grids capture all the data enclosed in each grid, unlike point data that present properties only of a specific point [65]. Shown in Figure 5 is the grid map created in GIS with a size of 500 m covering the flood plain area of Boac and Mogpog rivers. The 599 grids were formed over a 150.25 km\({}^{2}\) area. Spatial grid data were created for the following parameters: pH, temperature, EC, TDS, Mn concentrations of surface water (SW), curve number (CN), flood height, and the Mn content of soils are illustrated in the Figure 6a-h. Data of each parameter per grid were extracted and used as data sets in this study. This method helped the researchers simplify data analysis without compromising data quality.  
Figure 4: The flood hazard map of Boac and Mogpog rivers using LiDAR DEM.  
Figure 5: Spatial Grid map of the floodplains of Boac and Mogpog rivers.  
Figure 6: Cont.
## 4 Solution Approach  
Using BCD, problem \(P1\) in Equation (6) is divided into three subproblems. Subproblem \(SP_{1}\) pertains to optimizing the UAV trajectory; subproblem \(SP_{2}\) pertains to optimizing the available battery power; and \(SP_{3}\) aims to optimize the queue occupancy at the UAV. We also utilize personalized federated learning (PFL) with an attention mechanism to reduce the number of communication rounds between the vehicles and UAV. The reduction in communication overhead is realized by selecting a subset of vehicles for communicating local model hyper-parameters in each TTI. When, instead of all the vehicles, a subset of vehicles communicate their local model hyper-parameters with the UAV, a lesser number of communication rounds can be used to transmit the relevant information to the UAV for further processing. Figure 3 illustrates the PFL approach.
# Chaotic Optical Communication with Wavelength-Hopping Technology Based on Tunable Lasers
## 3 Numerical Simulation and Analyses
In order to further improve the security of the system, we refer to the wavelength-hopping communication and use a pseudo-random sequence generator to control the wavelength selection circuit of the laser. The pseudo-randomness of the wavelength sequence makes it more difficult for [PERSON] to predict to which wavelength the laser will hop. In order to ensure high-quality demodulation of chaotic communication systems, the information should be uploaded to the tunable laser when it is in a stable working state after each laser wavelength switching occurrence. The tuning time of the tunable laser through 100 nm is 1 s, so we set the system to suspend communication for 1 s when switching wavelengths, and the working time of each wavelength is also 1 s. Although it may affect transmission efficiency, we can compensate for this problem by increasing the transmission data rate. In addition, improving the stable switching speed of the tunable laser is also effective. Figure 5b shows the BER of [PERSON] and the BER of [PERSON] as a function of time within 60 s when the wavelength is switched pseudo-randomly. The BER of [PERSON] still remains below \(10^{-7}\), which is the same as the BER when the wavelength is switched periodically, while the BER of [PERSON] hops pseudo-randomly due to the switched wavelengths. In the same way as the wavelength switched periodically, we use the average BER to compare the BER of [PERSON] and the BER of [PERSON] in total when the wavelength switched pseudo-randomly. Although the BER of [PERSON] does not have a periodic rule when the wavelength is switched pseudo-randomly, due to the properties of the pseudo-random sequence, the average BER of [PERSON] should be stable when the test time is long enough. Figure 6 shows the average BER of [PERSON] and the average BER of [PERSON] as the test time changes. Within the first 20 min, since the pseudo-random sequence is still relatively short, the BER of [PERSON] oscillates between \(10^{-2}\) and \(10^{-3}\). After 30 min, with the increase in length of the pseudo-random sequence, the BER of [PERSON] gradually stabilizes around \(2.6\times 10^{-3}\). It also indicates that this method of wavelength hopping pseudo-randomly can improve the security of the system. Figure 5: (**a**) BER in three periods under wavelength switching, in order. (**b**) BER under pseudo-random wavelength switching.
# Enhancement of Underwater Images with Retinex Transmission Map and Adaptive Color Correction
## 4 Results
### Improvements over Multi-Scale Retinex  
The proposed method uses multi-scale Retinex (MSR) in the estimation of transmission map. Since multi-scale Retinex is itself an image enhancement method, we conducted comparison experiments to demonstrate that the proposed method offers improvements over MSR.  
We compared three methods: (1) plain MSR [9] used in Retinex Transmission Map Estimation; (2) MSR with color correction [30]; and (3) the proposed method. Note that plain MSR used in Retinex Transmission Map Estimation does not include the color restoration step as presented in [9], while MSR with color correction carries out the color restoration step as well as a final color balance step. In this comparison, the UIEB [10] test dataset was used again for evaluation. Quantitative results of PSNR, SSIM, UCIQE and UIQM metrics are listed in Table 2. It can be seen that the proposed method surpassed the other two methods in terms of the PSNR and UCIQE metrics.  
To compare these three methods qualitatively, the enhanced outputs on some test samples are given in Figure 4, along with the original images and the corresponding ground truth images. It can be seen that plain MSR tends to produce results with low-contrast appearances. MSR with color correction performs better than plain MSR. However, empirically, we observed that, for several test samples, MSR with color correction would introduce reddish artifacts into the enhanced results. Two such cases are shown in the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **PSNR \(\uparrow\)** & **SSIM [27] \(\uparrow\)** & **UCIQE [28] \(\uparrow\)** & **UIQM [29] \(\uparrow\)** \\ \hline Plain MSR [9] & 14.96 & 0.72 & 0.45 & 2.81 \\ \hline MSR with color correction [30] & 17.40 & **0.80** & 0.60 & **2.87** \\ \hline The proposed method & **19.31** & 0.79 & **0.63** & 2.83 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative comparison of two MSR-based methods and the proposed method. The best results in terms of each metric are shown in bold. The UIEB test dataset was used for evaluation.  
Figure 3: Visual comparison of the enhanced outputs produced by other SOTA underwater image enhancement methods and the proposed method. Original images are from the UIEB test dataset.  
last two rows of Figure 4. Unlike the other two methods, the proposed method performs MSR on the white balanced image rather than directly on the original image, and only uses it to estimate the transmission map. In addition, it is based on the underwater imaging model and utilizes an adaptive color correction and thus is more robust when dealing with complex degradations in underwater scenes.
# Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering
## 6 Conclusions  
We proposed a new indoor positioning method, which we called maximum likelihood particle filtering. Its essential idea consists of combing the prediction and updates steps of a traditional particle filter into a single step, which requires solving a maximum likelihood estimation problem. This results in a better utilization of particles. The method so obtained achieves high accuracy indoor positioning with a drastically smaller number of particles in comparison with particle filtering. This makes it numerically feasible for real-time applications. We validated our claims in a WiFi indoor positioning experiment using real data. Our experiment show that our method leads to great accuracy improvements achieving sub-meter level indoor localization accuracy, which exceeds the requirements of the 5G NR Release 16 standard from 3 GPP [33].  
Conceptualization, D.M. and M.F.; methodology, D.M.; software, W.W.; validation, W.W.; formal analysis, D.M.; investigation, W.W. and D.M.; resources, M.F.; data curation, W.W.; writing--original draft preparation, W.W.; writing--review and editing, D.M. and M.F.; visualization, W.W. and M.F.; supervision, D.M. and M.F.; project administration, M.F.; funding acquisition, M.F. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Mean Squared Error [m\({}^{2}\)]** & **90\% Acc. [m\({}^{2}\)]** \\ \hline Static positioning & 2.3962 & 5.6935 \\ MLKF & 11.7255 & 50.8279 \\ PF-RSS (1000 particles) & 8.2366 & 18.3872 \\ PF-CSI (1000 particles) & 2.9418 & 11.1606 \\ MLPF (10 particles) & 0.7755 & 1.5712 \\ MLPF (50 particles) & 0.4939 & 0.9970 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Positioning Error (office rooms).  
Figure 8: Tracking result of MLPF with 10 and 50 particles in the office rooms.  
**Funding:** This research was funded by the National Natural Science Foundation of China grant numbers 61633014, 61803101 and U1701264, Guangdong Basic and Applied Basic Research Foundation grant number 2020A1515011505, and the Argentinean Agency for Scientific and Technological Promotion grant number PICT- 201-0985.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Soil Hg Contamination Impact on Earthworms'
## Materials and Methods
### Experimental Set-up
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Physice-Chemical Characteristics**} & \multirow{2}{*}{\
\begin{tabular}{c} **Uncontaminated** \\ **Soil** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Hg-Contaminated** \\ **Soil** \\ \end{tabular} } & \multicolumn{2}{c}{**Diff. between Soils/**} \\  & & & **Hg Treatments** \\ \hline Soil THg (\(\upmu\)g g\({}^{-1}\)) & 0.25 \(\pm\) 0.09 & 36.10 \(\pm\) 4.80 & 609.8 & \textless{}0.001 \\ Soil water-extractable Hg (\(\upmu\)kg\({}^{-1}\)) & \textless{}LOD & 13.11 \(\pm\) 4.76 & NA & NA \\ Soil MeHg (\(\upmu\)kg\({}^{-1}\)) & \textless{}LOD & 4.59 \(\pm\) 0.51 & NA & NA \\ pH & 8.30 \(\pm\) 0.20 & 7.98 \(\pm\) 0.08 & 9.6 & 0.021 \\ Total C (\(\upmu\)) & 2.71 \(\pm\) 0.32 & 3.88 \(\pm\) 0.94 & 5.6 & 0.056 \\ Total N (\(\upmu\)) & 0.17 \(\pm\) 0.04 & 0.31 \(\pm\) 0.08 & 8.9 & 0.025 \\ Sand (\(\upmu\)) & 44.40 \(\pm\) 12.44 & 29.35 \(\pm\) 3.20 & 5.5 & 0.057 \\ Silt (\(\upmu\)) & 48.73 \(\pm\) 12.05 & 61.18 \(\pm\) 1.95 & 4.1 & 0.088 \\ Clay (\(\upmu\)) & 7.00 \(\pm\) 1.16 & 9.48 \(\pm\) 1.47 & 7.1 & 0.037 \\ Exp. 1—earthworm biomass difference (g) & +0.67 \(\pm\) 3.13 & +0.64 \(\pm\) 4.56 & 0.0 & 0.975 \\ Exp. 1—earthworm total Hg (\(\upmu\)g g\({}^{-1}\)) & 0.39 \(\pm\) 0.10 & 12.55 \(\pm\) 5.46 & 6.3 & 0.030 \\ Exp. 1—earthworm MeHg (\(\upmu\)kg\({}^{-1}\)) & 6.41 \(\pm\) 5.82 & 172.54 \(\pm\) 42.15 & 4.7 & 0.055 \\ Exp. 2—earthworm biomass difference (g) & \(-\)1.45 \(\pm\) 0.65 & \(-\)1.35 \(\pm\) 0.82 & 0.2 & 0.681 \\ Exp. 2—earthworm total Hg (\(\upmu\)g g\({}^{-1}\)) & 0.46 \(\pm\) 0.09 & 6.88 \(\pm\) 2.91 & 24.7 & 0.003 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Physico-chemical characteristics of the uncontaminated (experiment 2 only) and Hg-contaminated (experiments 1 and 2) soils and the earthworms used for the incubation experiments. The difference between the soils or Hg treatment was tested with an ANOVA. Exp. 1 = Experiment 1, Exp. 2 = Experiment 2, THg = total mercury, MeHg = methyl-mercury. LOD = limit of detection. start of the experiment. The pots were kept in the dark at 20 \({}^{\circ}\)C for two weeks. At the end of the experiment, the earthworms were washed, their gut systems were emptied for two days, and they were then weighed, shock-frozen with liquid nitrogen and stored at \(-\)20 \({}^{\circ}\)C. The gut system of each earthworm was extracted and divided into three parts: stomach, upper gut and lower gut. The upper and lower gut parts were further washed with sterile buffer to separate microbial cells attached to the worm tissue from those in the remaining soil in the gut. The dissection resulted in five parts: stomach (Sto), upper gut tissue (UgT), upper gut soil (UgS), lower gut tissue (LgT), and lower gut soil (LgS).
## 4 Experiment and Results
### Results
#### 4.3.3 Data Classification  
According to the results of the previous experiment, the performance of the SSA technique was evaluated by comparing it with other models, and the learning rate of the Convolutional Auto-encoder was high. Therefore, a distribution graph for the loss rate was drawn to use the SSA-CAE-based model in this proposed architecture. As confirmed in Figure 7, there is a time of 20 s at the start and end of machining. We conducted model learning in a cloud environment and imported the learned model into the EID, and the classification time was measured to be less than 15 s on average.  
As shown in Figure 16, the loss distribution graph has normally distributed around a fifth of 0.1, and the output value is obtained according to the number of decimal places to obtain the threshold.  
Abnormal detection was possible based on the threshold value of 0.02391 in Table 4. By inputting the model generated in the edge device, the classification of normal data on the test data of 6 November 2021 was achieved 100%, and the classification of abnormal data was achieved 99% in Figure 17.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Loss\_mae** & **Threshold** \\ \hline
0.021355 & 0.02391 \\
0.018467 & 0.02391 \\
0.017650 & 0.02391 \\
0.0198808 & 0.02391 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Result of selecting threshold value according to loss value.  
Figure 16: Distribution of loss values by applying the CAE model to SSA data.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Original**} & \multicolumn{3}{c}{**FFT Transform**} & \multicolumn{3}{c}{**Wavelet Transform**} & \multicolumn{3}{c}{**Singular Spectrum Analysis**} \\ \cline{2-13}  & **AE** & **AELSTM** & **CAE** & **AE** & **AELSTM** & **CAE** & **AE** & **AELSTM** & **CAE** & **AE** & **AELSTM** & **CAE** \\ \hline RMSE & 0.303 & 0.294 & 0.17 & 0.35 & 0.23 & 0.07 & 0.291 & 0.290 & 0.173 & 0.283 & 0.27 & 0.122 \\ MAE & 0.095 & 0.092 & 0.03 & 0.116 & 0.066 & 0.0048 & 0.084 & 0.084 & 0.034 & 0.082 & 0.081 & 0.017 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of deep learning model learning according to each reconstruction data.
# SIMECK-T: An Ultra-Lightweight Encryption Scheme for Resource-Constrained Devices
## 2 Methodology
### Implementation
```
Input: Password password Output: Processed values \(psum\) and \(pmul\)
1 PBKDF2(\(passwd\), derived_key,65000) ; // Derive the key with PBKDF2
2 psum \(\leftarrow\) 0 ;
3 pmul \(\leftarrow\) 1 ;
4 for\(i\)\(\leftarrow\) 0 to 3 do
5 psum \(\leftarrow\)psum + derived_key[\(i\)] ; // Addition with overflow by design
6 pmul \(\leftarrow\)pmul \(\oplus\) derived_key[\(i\)] ; // Multiplication with overflow
7 psum \(\leftarrow\)psum mod 10 ;
8 pmul \(\leftarrow\)pmul mod 5 ;
9 IV \(\leftarrow\)fnv1a_hash(file_modification_time_str) ;
10 IV \(\leftarrow\)IV + fsize + psum +pmul + fnv1a_hash(filename) ;
11 srand(IV) ;
12 for\(i\)\(\leftarrow\) 0 to \(\text{psum}+\text{pmul}-1\)do
13 IV \(\leftarrow\)rand () ;
```  
**Algorithm 2**Derived Key Processing with PBKDF2  
A Meyer-Schilling detection code (MDC) using SIMECK-T is shown in Algorithm 3. This code is used to build the PBKDF2 function in Algorithm 4. In resource-constrained environments, it is not always easy to access file modification times and sometimes filenames are not available at all. If this is the case, the IV should simply be a random value, unrelated to the password. If this is manageable, the derived key can also be configured to a random value, without the need for PBKDF2 [70].
# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 2 Background and Related Work
### Advanced Air Mobility and Enhancing Disaster Situation Awareness  
Recently, there has been considerable interest in the concept of _advanced air mobility_ (AAM), which aims to provide safe and reliable on-demand aerial transportation for customers, cargo, and packages [4]. The UASs (unmanned aerial systems) may consist of swarms of systems with multiple UAVs, ground-stations, various data sources, autonomous vehicles, smart mobile devices, etc. [4; 18; 40].  
Research topics include a combination of challenges from many disciplines, such as design of UAVs, situation awareness from a broad perspective, autonomous navigation and coordination, and safety and reliability issues.  
UASs can bring additional opportunities for improving situation awareness for disaster/earthquake management. Firstly, UAVs may not only function as data sources but also as the possible data fusion nodes. Secondly, in addition to ground-based data sources, a swarm of UAVs can dynamically cover a large area for better situation awareness. Thirdly, UAVs can share certain responsibilities among each other in lifting some heavy equipment such as radars and precision optical devices.  
To the best of our knowledge, analysis/synthesis of UAS-based data fusion systems for disasters/earthquakes have not been studied sufficiently in the research media. We will elaborate on these opportunities later in this article as a future work.