esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application
### Visualization of Traffic Behaviors in Simulation Area \"A\"  
The result of the visual processing of the traffic phenomenon in the target sea by integrating the maritime traffic density, traffic direction, and traffic flow identified and analyzed using AIS data of ships passing through the simulation area \"A\" is shown in Figure 10.  
Additionally, the integrated visual processing results can be overlaid on an electronic nautical chart of the S-57 standard, which is currently used for the navigation and control of most ships (Figure 11). The simulation area \"A\" features three primary shipping lanes, with most ships concentrated along the route through the Mok  
\begin{table}
\begin{tabular}{c c c c} \hline
**No. of Cluster** & **WP** & **Latitude** & **Longitude** \\ \hline \multirow{4}{*}{1 st cluster stream} & 1-1 & N 34.78528 & E 126.3621 \\  & 1-2 & N 34.78962 & E 126.3536 \\  & 1-3 & N 34.78679 & E 126.3454 \\  & 1-4 & N 34.77966 & E 126.3403 \\ \hline \multirow{4}{*}{2 nd cluster stream} & 2-1 & N 34.77850 & E 126.3421 \\  & 2-2 & N 34.78577 & E 126.3473 \\  & 2-3 & N 34.78754 & E 126.3554 \\  & 2-4 & N 34.78225 & E 126.3618 \\ \hline \multirow{4}{*}{3 rd cluster stream} & 3-1 & N 34.77835 & E 126.3418 \\  & 3-2 & N 34.78567 & E 126.3439 \\ \cline{1-1}  & 3-3 & N 34.79333 & E 126.3454 \\ \cline{1-1}  & 3-4 & N 34.79989 & E 126.3490 \\ \hline \end{tabular}
\end{table}
Table 2: Latitude and longitude of the representative routes.  
Figure 10: Visualization of maritime traffic data in the simulation target area.  
azimuth of the vessels is limited owing to fixed obstacles such as surrounding lands and shallow waters.
## 4 Experiments and Results
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{} & \multirow{2}{*}{**Clean**} & \multirow{2}{*}{**Other**} & \multicolumn{3}{c}{**5 dB**} & \multicolumn{3}{c}{**15 dB**} & \multicolumn{3}{c}{**Average**} \\ \cline{4-9}  & & & **Clean** & **Other** & **Clean** & **Other** & **Clean** & **Other** \\ \hline \hline \multicolumn{9}{c}{Baseline\({}_{1}\)} & 6.65 & 21.17 & 7.78 & 24.77 & 6.70 & 21.30 & 7.24 & 23.03 \\ baseline\({}_{2}\)[14] & - & - & 7.79 & 24.44 & 6.67 & 21.21 & 7.23 & 22.83 \\ baseline\({}_{3}\) & - & - & 7.74 & 24.74 & 6.64 & 21.19 & 7.19 & 22. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**WER (\%)**} \\ \cline{3-10}  & \multirow{2}{*}{**Clean**} & \multirow{2}{*}{**Other**} & \multicolumn{2}{c}{**5 dB**} & \multicolumn{2}{c}{**15 dB**} & \multicolumn{2}{c}{**Average**} \\ \cline{3-10}  & & & **Clean** & **Other** & **Clean** & **Other** & **Clean** & **Other** \\ \hline \multicolumn{10}{c}{Baseline} \\ \hline baseline\({}_{1}\) & 6.65 & 21.17 & 13.10 & 37.69 & 7.43 & 23.81 & 10.27 & 30.75 \\ baseline\({}_{2}\)[14] & - & - & 12.43 & 36.58 & 7.41 & 23.65 & 9.92 & 30.12 \\ baseline\({}_{3}\) & - & - & 12.36 & 36.24 & 7.39 & 23.65 & 9.88 & 29.95 \\ \hline \multicolumn{10}{c}{Proposed} \\ \hline bDNN\({}_{senone}\) & - & - & 11.32 & 34.98 & 7.36 & 23.60 & **9.34** & **29.29** \\ bDNN\({}_{MS}\) & - & - & 12.21 & 35.76 & 7.40 & 23.65 & 9.81 & 29.71 \\ bDNN\({}_{M\text{FCC}}\) & - & - & 12.89 & 36.10 & 7.40 & 23.71 & 10.15 & 29.91 \\ \hline \multicolumn{10}{c}{} \\ \hline \end{tabular}
\end{table} 
Table 4: Performance (WER in %) comparison using LibriSpeech database with 4-gram language model when the motor is on. Figure 5: An example of the spectral feature mapping (**a**) clean MFCC feature; (**b**) noisy MFCC feature (SNR 5 dB, “motor off” state) (**c**) noisy MFCC feature (SNR 5 dB, “motor on” state); (**d**) reconstructed MFCC feature (SNR 5 dB, “motor off” state), (**e**) reconstructed MFCC feature (SNR 5 dB, “motor on” state). Figure 6: WER as a function of the position of the bottleneck layer on the “clean” test set (The result is the average for two types of the ego-noise).
## 4 Discussion and Conclusions
A small deviation from this angle can be accommodated; however, significant deviations lead to poorer performance due to increased occlusion and perspective loss, which negatively impact depth estimation accuracy. The current setup is optimized for rehabilitation scenarios, where specialists configure the cameras in controlled environments, and its suitability for these contexts is emphasized. Although our dual-camera system significantly reduces depth prediction errors and improves joint tracking, limitations remain, particularly with the fixed 90-degree angle between cameras. The current setup is optimized for rehabilitation scenarios, where specialists configure the cameras in controlled environments, and its suitability for these contexts is emphasized. Future research should address this limitation by exploring alternative configurations or advanced techniques such as unified latent representations to further enhance accuracy [36]. It would also be interesting to investigate the proposed setup in telemedicine contexts, where users configure the cameras themselves, potentially requiring greater adaptability. In addition, expanding the system to include other key joints, such as the hips and shoulders, should facilitate the integration of more complex rehabilitation exercises. Future work should also focus on developing real-time feedback mechanisms that not only monitor but also analyze movement quality to provide immediate correctionsand optimize rehabilitation outcomes, which would lead to more effective and adaptive rehabilitation practices, ultimately improving patient recovery and engagement. Conceptualization, R.M., A.S. and V.A. ; methodology, V.A. and A.S.; software, E.G. ; validation, E.G., V.A. and K.D. ; formal analysis, R.M. ; investigation, V.A. ; resources, K.D. and J.Z. ; data curation, K.D. and J.Z. ; writing--original draft preparation, V.A., R.M., E.G., and J.Z. ; writing--review and editing, V.A. and R.M. ; visualization, E.G. ; supervision, R.M. ; project administration, R.M. ; funding acquisition, V.A. All authors have read and agreed to the published version of the manuscript. This project has received funding from the Research Council of Lithuania (LMTLT), agreement No S-PD-24-29. The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Vilnius Regional Biomedical Research Ethics committee (2023/6-1439-978). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data presented in this study are available on request from the corresponding author. The authors declare no conflicts of interest.
## 7 Simulation Results
### Performance Evaluation in a MANET Scenario
#### 7.2.3 A Numerical Example  
In this section, we show a numerical example to calculate the gain obtained with our proposal for \(N\) = 2 users using Equation (54), _i.e._, \(G_{1}\) and \(G_{2}\). To do that, we use the values obtained during simulation from the RTCP packets. They are shown in Table 4.  
We calculate the variable \(k_{b/m,1}\) and \(k_{b/m,2}\) for players 1 and 2 using Equation (50) and the simulation output values shown in Table 4. Results are shown in Table 5.  
After that, we simplify \(\left(\frac{n_{rb,i}-n_{sb,i}}{n_{sb,i}}\right)\) and \(\left(\frac{n_{rm,i}-n_{sm,i}}{n_{sm,i}}\right)\) as seen in Equations (55) and (56) in order to be easily calculated later using Table 4.  
\[\left(\frac{n_{rb,i}-n_{sb,i}}{n_{sb,i}}\right)=\frac{n_{rb,i}-n_{s,i}\cdot p _{i}^{*}}{n_{s,i}\cdot p_{i}^{*}}=\frac{\hat{n}_{b,i}}{p_{i}^{*}}-1 \tag{55}\]  
\[\left(\frac{n_{rm,i}-n_{sm,i}}{n_{sm,i}}\right)=\frac{n_{rm,i}-n_{s,i}\cdot(1- p_{i}^{*})}{n_{s,i}\cdot(1-p_{i}^{*})}=\frac{\hat{n}_{m,i}}{(1-p_{i}^{*})}-1 \tag{56}\]  
Next, we calculate \(\phi_{b}\) and \(\phi_{m}\) for each player using Equation (23) so they can be substituted in Equation (17).  
\[\phi_{b,1}=k_{b,1}\cdot MOS_{b,1}=k_{b/m,1}\cdot k_{m,1}\cdot MOS_{b,1}=3.134 \cdot k_{m,1} \tag{57}\]  
\begin{table}
\begin{tabular}{c c} \hline \(J_{b,1}\), \(J_{b,2}\) & (0.021067 s, 0.008867 s) \\ \(J_{m,1}\), \(J_{m,2}\) & (0.011379 s, 0.010136 s) \\ \(\hat{n}_{b,1}\), \(\hat{n}_{b,2}\) & (0.6, 0.45) \\ \(\hat{n}_{m,1}\), \(\hat{n}_{m,2}\) & (0.2, 0.4) \\ \(MOS_{b,1}\), \(MOS_{b,2}\) & (5, 5) \\ \(MOS_{m,1}\), \(MOS_{m,2}\) & (4, 5) \\ \hline \end{tabular}
\end{table}
Table 4: Simulation output values for \(N\) = 2 users.  
\begin{table}
\begin{tabular}{c c} \hline \(k_{b/m,1}\), \(k_{b/m,2}\) & (0.6268, 0.7111) \\ \hline \(p_{1}^{*}\), \(p_{2}^{*}\) & (0.72, 0.54) \\ \hline \end{tabular}
\end{table}
Table 5: Best response probabilities \(p_{i}^{*}\) for \(N\) = 2 users.  
\[\phi_{b,2}=k_{b,2}\cdot MOS_{b,2}=k_{b/m,2}\cdot k_{m,2}\cdot MOS_{b,2}=3.5555 \cdot k_{m,2} \tag{58}\]  
\[\phi_{m,1}=k_{m,1}\cdot MOS_{m,1}=4\cdot k_{m,1} \tag{59}\]  
\[\phi_{m,2}=k_{m,2}\cdot MOS_{m,2}=5\cdot k_{m,2} \tag{60}\]  
Finally, substituting Equations (55) to Equation (60) in Equation (54), we obtain that \(G_{1}\approx 0.65\) and \(G_{2}\approx 0.85\).  
These values mean that for player 1 the gain is 65% more using the game-theoretical model instead of not using it. In the same way, for player 2 the gain is 85%. Figure 15a,b show the utility functions \(U_{1}\) and \(U_{2}\) and their maximum values obtained for \(p_{i}=p_{i}^{*}\) for players 1 and 2, computed with Equation (26).
## 3 Materials and Methods
### Modeling of Feed Forward Neural Network  
The FFNNs are used to model the non-linear input-to-output mapping function between independent variables and the dependent variable, which is the water level of the wetland, as presented in Equation (10).  
\[WL=F_{\theta}\Big{(}RF\,,Evap\,T_{min},T_{max},RH_{day},RH_{night},WS\ \Big{)}+\delta \tag{10}\]  
where \(WL\) is the water level of the wetland. \(RF\), \(Evap\), \(T_{min}\), \(T_{max}\), \(RH_{day}\), \(RH_{night}\), and \(WS\) are daily rainfall to the catchment (in mm), daily evaporation (in mm), minimum daily temperature (in \({}^{\circ}\)C), maximum daily temperature (in \({}^{\circ}\)C), daily relative humidity at daytime (in %), daily relative humidity at nighttime (in %), and daily average wind speed (in km/h). The \(F_{\theta}\) is the mapping function, and \(\delta\) denotes the prediction error. Hence, the loss function of the FFNN for the given application can be presented as Equation (11), where \(\theta_{p}\) represents the trainable parameters of the NN. The architecture of the FFNN used for the prediction is presented in Table 1.  
\[L_{p}=\Big{(}F_{\theta}\Big{(}RF\,,Evap\,,T_{min},T_{max},RH_{day},RH_{night}, WS\,,\,\theta_{p}\Big{)}\,\,-WL\,\Big{)}^{2} \tag{11}\]  
The proposed FFNN accepts seven independent variable readings as inputs mentioned above and tries to predict the water level at the output layer. The input variables may have a varying degree of sensitivity to the water level. Accordingly, the weights and biases of the NN will be adjusted during the training phase. The NNs are very good at mapping such non-linear relationships compared to traditional mathematical models.
# Assessment of Areal Methane Yields from Energy Crops in Ukraine, Best Practices
## 4 Conclusions  
In this study, Ukrainian energy crops were harvested at different harvesting times, related to the different stages of crop phenological development and analysed according to their dry matter content, volatile solids content, dry matter yield, crop nitrogen uptake, specific methane yield, and areal methane yield. Miscanthus, sweet sorghum and maize are, in that order, particularly well suited for use as energy crops in Ukraine. Whereas the AMY of maize and sweet sorghum are mainly influenced by DMY of the crops, the SMY of miscanthus has a great influence on its methane yield per hectare. In relation to the biogas formation potential, miscanthus and silage maize showed the highest nitrogen use efficiency. This means that they have the lowest N requirement relative to biogas formation. For the continental climate of Ukraine, miscanthus appears to be the most interesting energy crop under the aspects of cultivation costs, methane yield per area and nitrogen use efficiency.  
Conceptualization: I.M., A.L., M.R.; data curation: I.M., B.H.; formal analysis: I.M., B.H.; funding acquisition: H.O., A.L., I.M.; investigation: I.M., B.H.; project administration; I.M., H.O., A.L., M.R.; resources: H.O., A.L., M.R.; supervision: [PERSON]; visualization: I.M.; writing--original draft: [PERSON]; writing--review & editing: [PERSON], I.M., B.H. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Correlation between crop nitrogen (N) uptake in kg ha\({}^{-1}\) and areal methane yield (AMY) in m\({}^{3}\) ha\({}^{-1}\) for the total of 22 varieties from 7 crops harvested in three harvesting times (H1, H2 and H3) in 2017 (total amount of samples \(n=66\)).  
**Funding:** This research was supported by the German Academic Exchange Service (Deutscher Akademischer Austauschdienst, DAAD) and the State Institute of Agricultural Engineering and Bioenergy, University of Hohenheim. The APC was partly funded by the German Academic Exchange Service.  
**Acknowledgments:** We would like to thank the Institute of Bioenergy Crops and Sugar Beet of Ukraine for the assistance with the cultivation of the crops. We want to thank [PERSON] for her valuable help with harvesting and pretreatment of the samples. We are grateful [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] for their advisory services and assistance with the laboratory analysis of the crop samples in Ukraine.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Case Study
### The Service of the Trained ANN Model in Actual  
#### 3.5.1 Single Damage  
Normally, to detect and locate the damage of the bridge structure, a comprehensive survey investigation would be carried out. After the survey, experts make a computational model and evaluate. Such work requires a lot of human resources and costs a lot of money. Even when considering only the measurement of the vibration of the entire span structure, the implementation of the setups measurements as described above also requires a lot of work. Instead, using an artificial neural network offers a huge advantage.  
The use of a trained network can be applied to the actual span structure of Chuong Duong bridge. During servicing, there was an accident on Chuong Duong bridge that affected the bridge structure (Figure 22).  
A simple experiment was conducted, since only the structure's natural frequency needs to be ascertained. A single vibration measurement point in three directions was located on Chuong Duong bridge (Figure 22). With only one measuring point, the natural frequency of Chuong Duong bridge was identified. Although it was not possible to identify the mode shapes of the bridge, with the trained ANN it was possible to locate and quantify the damage.  
The results of the natural frequencies of the first seven mode shapes determined through the experiment were [1.229; 2.4167; 4.1954; 4.2623; 4.5925; 6.2147; 7.7865]. These results were used as the input for feeding the trained network. Results after putting data into the network returned [25; 6]. This meant that 25% damage was detected at element 6 (truss number 6). It is easy to see that the result of locating damage at element 6 was correct. According to the report of the UCT company (who performed the calculation of damage assessment), the damage level of truss rod was 30%. Results using the ANN were relatively accurate. This is a case of damage that was easy to identify and recognise in practice. However, for cases where the damage location is difficult to detect (for example, if the damage is located in prohibitively high or difficult to reach locations, such as the middle of the river), the ANN will show its advantages.
# Dichotomy Graph Sketch: Summarizing Graph Streams with High Accuracy Based on Deep Learning +
## 2 Related Work
### Graph Stream Sketch  
In contrast to data stream sketches, graph sketches are specially designed for graph stream summarization, keeping the topology of a graph and thus simultaneously supporting several queries such as edge query and node query. [PERSON] et al. designed gSketch [37], which combined CM-sketch and with a sketch partitioning technique to support edge query and aggregate subgraph query. [PERSON] et al. proposed TCM [1] which adopted several adjacency matrices with irreversible hash functions to store a graph stream. Different from TCM, gMatrix [38] used reversible hash functions to generate graph sketches. [PERSON] et al. proposed GSS [2] which consisted of not only an adjacency matrix but also an adjacency list buffer. Adjacency list buffer was used to store the edge when an edge collision happened to improve the query accuracy. In their follow-up work [6], they proposed an improved version called blocked GSS, and designed two directions of accelerating query: GSS with node bitmaps and GSS implemented with FPGA. In [39], [PERSON] et al. proposed Dynamic Graph Sketch which was able to adaptively extend graph sketch size to mitigate the performance degradation caused by memory overload.  
In summary, all the existing graph sketches use a single matrix to store both heavy edges and light edges and thus suffer from low query accuracy (especially in light edge query task) due to hash collisions.
## 4 Analysis of Results  
In this section, the decoy state protocol's ability to detect PNS attacks is examined. First, the efficiency based method of detecting PNS attacks is explained, including expected operational variations from non-ideal optical components and processes. Next, simulation results for several common decoy state protocol configurations are described. Based on these results, an optimization of the decoy state protocol is presented and demonstrated. Lastly, implementation security guidance is offered for decoy state enabled QKD systems.
## 7 Simulation Results
### Results Obtained for Unknown Parameters \(\epsilon\) and \(\delta\)  
We now consider the fourth design method presented in Section 5.2.4. Theorem 4 addresses the two unknown parameters \(\epsilon\) and \(\delta\), outlining the developed LMI requirements for stabilization and the associated minimization problem. By solving the LMI problem and using the two additional LMI conditions described by (95a) and (95b), we derive the subsequent outcomes for the gain \(\mathcal{K}\) and the minimal value, \(\epsilon_{\min}\) of the parameter \(\epsilon\), and the maximal value, \(\delta_{\max}\), of the parameter \(\delta\):  
\[\mathcal{K}=\left[\begin{array}{cc}-3.2384&-1.1943\\ \end{array}\right],\qquad\epsilon_{\min}=6.0827,\qquad\text{and}\qquad\delta_ {\max}=0.8422 \tag{117}\]  
\begin{table}
\begin{tabular}{c c c} \hline \(\epsilon\) & \(\mathcal{K}\) & \(\delta_{\max}\) \\ \hline
10.00 & \(\left[-2.57\right.\)\(-0.98\) & \(0.83\) \\
5.00 & \(\left[-5.35\right.\)\(-1.60\) & \(0.81\) \\
4.00 & \(\left[-10.08\right.\)\(-2.35\) & \(0.81\) \\
3.70 & \(\left[-14.42\right.\)\(-2.89\) & \(0.81\) \\
3.30 & \(\left[-32.25\right.\)\(-4.52\) & \(0.81\) \\
3.00 & \(\left[-121.85\right.\)\(-9.11\) & \(0.81\) \\
2.80 & \(\left[-3033.50\right.\)\(-46.80\) & \(0.81\) \\
2.75 & \(\left.(\dagger\right)\) & \(\left.(\dagger\right)\) \\ \hline \end{tabular}
\end{table}
Table 5: Numerical results of the feedback gain \(\mathcal{K}\) and the optimal/maximal value \(\delta_{\max}\) obtained for various values of the parameter \(\epsilon\) by resolving the LMI issue in Theorem 3 and considering the two additional LMIs (95a) and (95b).  
Figure 5: Simulation results obtained relying to the third designed method. Temporal variation of: (**a**) the angular position error \(\phi\), (**b**) the angular velocity error \(\phi\), (**c**) the affine state-feedback controller \(u\), and (**d**) the lumped disturbance \(\Delta_{t}\).  
We provide the graphical results presented in Figure 6 by using the gain \(\mathcal{K}\) stated in (117) in the proposed controller (25). The actuated knee exoskeleton robot reaches stability in the intended condition. Furthermore, there is essentially no control effort once the knee exoskeleton system is stabilized.
# Determination of Cyanotoxins and Prymnesins in Water, Fish Tissue, and Other Matrices: A Review
###### Abstract
The surveyed studies herein show that ELISA and LC-MS/MS analysis have most frequently been used for detection/quantifation. While specific extraction procedures used vary considerably, overall method performance metrics, such as matrix spike recoveries and replicate precision, were sometimes poor or unreported. This raises questions about the quality of the data in the case of fish tissue and organs, shellfish and similar matrices exposed to HABs. Most methods developed to date depend on extracting toxins in some form of an aqueous matrix, typically water and one or a combination of organic solvents. For example, ELISA methods should be carried out directly in water with only limited amounts of organic solvent, while LC-MS/MS methods typically require samples to be prepared with significant amounts of organic solvent in water [20]. In addition to the presence of organic solvents, some methods are sensitive to low pH, lipid content, or other matrix effects due to the properties of the target toxins or the nature of the matrix [21]. Hence, analytical methods for cyanotoxins in complex matrices, such as tissue, organs, and plants, frequently require efficient extraction, clean-up, and transfer of the toxin into a suitable solvent for analysis. Use of inefficient extraction methods may lead to poor matrix spike recoveries and poor precision among replicate samples. The prevalence of HAB events globally and the potential for exposure and bioaccumulation of toxins necessitate monitoring and detecting cyanotoxins in tissues associated with HAB-related mortality events. There is a need for the development of reliable extraction and analytical methods capable of analyzing edible fish/shellfish, plant, and animal tissues for multiple toxins and for such methods to be broadly applicable to all inland waters and coastal food webs. The overarching goal of this study is to summarize available methodologies for the measurement of toxins, identify gaps in the detection of toxins in complex matrices, and highlight the complexity of quantifying groups of compounds with diverse chemistries.
# A Sensor for Characterisation of Liquid Materials with High Permittivity and High Dielectric Loss
## 4 Simulation Analysis  
Before extracting the permittivity from measured data, a systematic analysis has to be conducted so as to build a mapping model. This section describes simulation work using full wave simulation to conduct feasibility analysis and establish a mathematical model, so that the complex permittivity of the liquid sample under test can be expressed by the measured \(S_{21}\) (resonant frequency and quality factor). Since the proposed CSRR sensor is designed for high-permittivity and high-loss liquid samples, \(\varepsilon_{\rm r}^{\prime}\) is set to 5-90 during the simulation, with a step size of 5, and the loss tangent is set to 0-1 with a step size of 0.1.  
To ensure that the transmission coefficient \(S_{21}\) is a single value function of \(\varepsilon_{\rm r}^{\prime}\) and \(\tan\delta\), two groups of values \(\varepsilon_{\rm r}^{\prime}=(20,70)\) and \(\tan\delta=(0,0.3,0.6,0.9)\) are chosen as examples. The simulated results are plotted in Figure 7. It can be observed that: (1) higher permittivity creates lower resonant frequency, and (2) higher loss tangent reduces the resonant dips; (3) each pair of \((\varepsilon_{\rm r}^{\prime},\tan\delta)\) only corresponds to a single curve of \(S_{21}\). These facts verify that it is feasible to extract the complex permittivity from \(S_{21}\).  
Figure 6: Variation of quality factor versus loss tangent for \(\varepsilon_{\rm r}^{\prime}=60\).