esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results, Discussions and Achievements
### Estimation Accuracy
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Actual Angles (Degrees)** & & \(\mathbf{\Phi_{1}}\)= 20 & \(\mathbf{\Phi_{2}}\)= 35 & \(\mathbf{\Phi_{3}}\)= 50 & \(\mathbf{\Phi_{4}}\)= 65 & \(\mathbf{\Phi_{5}}\)= 80 & \(\mathbf{\Phi_{6}}\)= 95 & \(\mathbf{\Phi_{7}}\)= 110 & \(\mathbf{\Phi_{8}}\)= 125 & \(\mathbf{\Phi_{9}}\)= 140 \\ \hline \multirow{4}{*}{**CS**} & Best & 19.593 & 34.793 & 50.403 & 65.458 & 80.113 & 95.054 & 110.006 & 124.515 & 140.290 \\  & Mean & 21.243 & 33.810 & 51.328 & 64.016 & 76.648 & 91.598 & 106.852 & 123.380 & 140.346 \\  & Worst & 29.671 & 44.863 & 62.467 & 78.987 & 95.309 & 112.152 & 127.871 & 138.777 & 180.000 \\ \hline \multirow{4}{*}{**PSO**} & Best & 21.756 & 34.090 & 50.015 & 61.225 & 74.220 & 88.506 & 100.388 & 109.193 & 120.000 \\  & Mean & 21.368 & 32.547 & 48.221 & 58.335 & 68.618 & 80.331 & 93.256 & 107.042 & 120.000 \\  & Worst & 03.442 & 33.559 & 34.329 & 52.492 & 59.308 & 74.175 & 90.715 & 106.549 & 120.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calculation of accuracy for nine targets at 0 dB SNR. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Actual Angles (Degrees)** & & \(\mathbf{\Phi_{1}}\)= 20 & \(\mathbf{\Phi_{2}}\)= 35 & \(\mathbf{\Phi_{3}}\)= 50 & \(\mathbf{\Phi_{4}}\)= 65 & \(\mathbf{\Phi_{5}}\)= 80 & \(\mathbf{\Phi_{6}}\)= 95 & \(\mathbf{\[PERSON]_{7}}\)= 110 & \(\mathbf{\Phi_{8}}\)= 125 & \(\mathbf{\[PERSON]_{9}}\)= 140 \\ \hline \multirow{4}{*}{**CS**} & Best & 19.644 & 36.090 & 50.107 & 64.055 & 79.008 & 94.836 & 110.112 & 125.026 & 140.428 \\  & Mean & 19.403 & 34.089 & 48.515 & 61.256 & 76.077 & 91.722 & 105.888 & 122.097 & 138.261 \\  & Worst & 34.872 & 47.166 & 62.034 & 79.183 & 95.973 & 112.682 & 127.446 & 137.827 & 179.951 \\ \hline \multirow{4}{*}{**PSO**} & Best & 22.095 & 35.295 & 50.271 & 60.816 & 73.875 & 87.558 & 98.122 & 108.159 & 120.000 \\  & Mean & 21.727 & 34.366 & 47.335 & 57.730 & 70.796 & 80.923 & 93.077 & 106.002 & 120.000 \\ \cline{1-1}  & Worst & 02.153 & 32.069 &  
Figure 4: Estimation of robustness using six sources. Figure 5: Robustness against noise for nine sources.
## 4 Selected Use Cases and Opportunities
### Military  
In this scenario, ground troops operating in a hostile environment could establish a secure communication link with the command centre using a relay station deployed in an intermediate location using a triple-hop DF and AF protocol-based THz-VLC system, as  
Figure 4: This diagram illustrates the use of THz and VLC in an underwater wireless communications application. The system employs a floating buoy to facilitate dual-hop communication between a ship and an AUV, enhancing data transfer capabilities for underwater applications. THz communication is utilized for ship-to-buoy communication, while laser-based VLC is utilized for underwater communication.  
Figure 3: This diagram illustrates the use of THz and VLC in a healthcare setting. The system employs a UAV to facilitate dual-hop communication between a hospital and a remote clinic, enhancing data transfer capabilities for medical applications. THz communication is utilized for outdoor communication, while indoor communication utilizes VLC.  
depicted in Figure 5. The first hop of the communication link would utilize THz uplink communication between the ground troops and the relay station. THz waves offer high bandwidth and can penetrate various obstacles encountered in the battlefield, allowing for reliable communication over short to medium distances. The relay station will amplify and forward the information to the relay at the command center again through THz communication. In the third and final hop, the relay at the command center will decode the information and retransmit it to the team inside the command center using a VLC downlink. The use of VLC ensures that the communication remains covert and immune to detection by adversaries.
## 1 Introduction
### Demographic Characteristics  
Demographic characteristics influence people's visual impact assessment [67; 68; 69; 70]. In their studies, [PERSON], [PERSON] [71], [PERSON] [72], and [PERSON] [73] all reported that age affected respondents' visual impact assessment. [PERSON] and [PERSON] [74], [PERSON] [75], and [PERSON] and [PERSON] [76] stated that people's visual impact assessment of the landscape changes based on age. [PERSON], [PERSON] [35] found that men had higher visual impact assessments than women. [PERSON] [77] reported that education level is a key factor affecting people's landscape preferences. [PERSON] and [PERSON] [78] verified that there was a negative correlation between education and visual impact assessment. [PERSON], [PERSON] [79] found that expertise in architecture and design influenced people's responses and preferences regarding the built environment. According to a study by [PERSON], [PERSON] [80], professional knowledge influences respondents' visual impact assessment. [PERSON], [PERSON] [81] found that occupation and design experience influenced respondents' visual impact assessment of the same building exterior. Based on previous studies, demographic characteristics, education level [82], and gender [83; 84] strongly influence people's visual impact assessment. Consequently, the impact of demographic variables on visual impact assessment cannot be disregarded.  
Therefore, in this study, we selected four demographic characteristics as the research dimensions: age, gender, education level, and design experience.  
This study addressed the following questions:  
1. What physical features influence people's assessment of the visual impact of subway station entrance pavilions?;
2. Would respondents with different demographic characteristics assess the visual impact of subway station entrance pavilions differently?;
3. If the answer to the second question is yes, what physical features lead to this difference?  
**2. Research Method**  
_2.1. Research Plan_  
The investigation sites of this study were set in 17 cities across China, including Beijing, Shanghai, and Suzhou (Table 1). The 17 cities cover 23 provinces, 5 autonomous regions, and 4 municipalities directly under the Central Government. Their radiation coverage basically includes the whole country and also includes megacities, large cities, and large and medium-sized cities. The study was highly representative of the numerous subway projects built in these areas. In total, 42 entrance pavilions of different types were investigated, and their images were analyzed. Five physical features were selected as the research objects: the exterior contour of subway station entrance pavilions, the permeability of the facade, the cultural characteristics, the decorative materials, and the number of exterior colors (Table 2).
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 2 Materials and Methods
### UAV Experimental Environment Selection and Radar Parameter Settings
#### 2.2.4 Determination of the Radar Measurement Parameters  
Based on the maximum constant velocity of the UAV, parameters such as radar PRF and pulse width are determined. The PRF setting increases with the maximum unambiguous velocity value. Therefore, based on the selected UAV velocity and the maximum unambiguous velocity calculation method of the radar (Equation (4)), the minimum PRF (\(F_{\mathrm{min}}\)) corresponding to the UAV velocity is determined. The PRF of the radar setting needs to be greater than \(F_{\mathrm{min}}\). This experiment set the maximum velocity of the UAV as 10 m/s, which corresponds to the minimum PRF of about 1253 Hz. Taking into consideration the instability of the UAV velocity when affected by power, environment, etc., in this experiment the PRF was set to 1600 Hz, which corresponds to the maximum unambiguous velocity of 12.8 m/s.  
Range resolution is the ability of a radar system to distinguish between two or more targets with the same orientation but at different ranges. Its size depends on the pulse width of the radar system, i.e., a smaller pulse width results in higher resolution. Radar in different bands has varying range resolutions, as shown in Table 1, and thus requires different pulse widths to be set.  
This experiment was conducted using an X-band radar system, and the pulse width required for radar observation was calculated to be 0.5 \(\upmu\)s.  
\[S_{r}=\frac{c\cdot\tau}{2} \tag{6}\]  
where \(S_{r}\) is the range resolution, \(c\) is the velocity of light, and \(\tau\) is the pulse width.
## 3 Results and Discussion
### AE Method Application for Bridge Monitoring  
#### 3.2.1 Structure Description  
Experimental studies were conducted on a highway bridge across a river. It is a 7-span metal bridge with reinforced concrete supports. The foundations for pylons are piled. The length of the bridge is about 700 m. The bridge has 2 traffic lanes. As a result of the abnormal operating impact, the supports #1 and #5 sank in by 112 mm and 44 mm, respectively. After that it was decided to install an SHM-system on this bridge.  
Preliminary studies consisted of 3 main stages, the objectives of which were: determination of the AE parameters of noise signals, determination of propagation parameters of the AE signals and determination of the optimal arrangement of the sensors on the bridge construction elements [12,13]. Investigation of the possibility of AE signals detection used Hsu-Nielsen source for location and filtration.
# Experimental Design of a Mobile Landing Platform to Assist Aerial Surveys in Fluvial Environments
## 3 Materials and Methods
### Chassis Construction  
For the prototype design and construction, several requirements had to be considered. First, a wide platform surface was needed that offers a large margin of error at the time of landing, as well as good compatibility with different UAV models. This way, the device would have a high versatility. The use of PVC as material for the vehicle structure, was chosen for its optimal resistance in relation to volume and weight. For the shape of the base, PVC elbows and Ts were put together with slow drying glue. Through this method, an easy assembly was achieved. Furthermore, remote-controlled hydroplane skates with a high flotation capacity for contact with water were placed as the key element for the platform. A light surface with wide landing area was essential, thus a polyurethane foam, a low-density material that has good resistance, was used. A 90\({}^{\text{o}}\) aluminum plate to carry the rudder was placed in the vessel, connecting the two rear legs of the base, and allowing its movement. Finally, a waterproof electrical box under the landing surface was needed to include the necessary electronics.  
Figure 2 shows the USV 3D CAD design and construction stages, and Figure 3 the materials employed in the structure construction.
# Novel Common-Mode Current Suppression Method in Transformerless PV Grid-Connected System
## 3 Proposed Novel CM Internal Loop Method to Suppression CM Current  
### Traditional Construction of Single CM Internal Loop to Suppress CM Current  
To suppress CM current, a grid connected PV system with single CM inner loop, as shown in Figure 5, is constructed. The EMI filter is LCL. However, to form the CM inner loop, the original Y capacitor of the filter is replaced by the RC branch of \(R_{1}\)-\(C_{1}\) and \(R_{2}\)-\(C_{2}\) so as to be connected to the dcnegative bus. Figure 6 is the CM equivalent circuit with a single CM internal loop, where \(C_{1\text{-}2}\) and \(R_{1\text{-}2}\) are, respectively, given as:  
\[C_{1\text{-}2}=C_{1}+C_{2} \tag{15}\]  
\[R_{1\text{-}2}=R_{1}R_{2}/(R_{1}+R_{2}) \tag{16}\]
# The Influence of Substrate and Strain on Protein Quality of _Pleurotus ostreatus_
###### Abstract  
Background: The effect of substrate and strain on the nutritional and functional properties of mushroom flours and protein concentrates (PCs) has not thoroughly been investigated. Methods: The proteins of _P. ostreatus_ flours (strains AMRL144 and 150) were isolated following alkaline extraction (pH 12) and isoelectric precipitation (pH 4) as it was determined by the solubility curves. The protein quality of the flours and PCs were evaluated by determining the protein solubility index (PSI) and their functional properties, such as water (WAC) and oil absorption capacity (OAC), foam capacity (FC), foam stability (FS) and emulsion stability (ES). The amino acid (AA) composition of the flours was determined by reversed-phase liquid chromatography after protein hydrolysis and o-phthalaldehyde derivatization. Results: The PSIs of the flours and PCs of _P. ostreatus_ were comparable to those of soybean flours and protein concentrates. The highest AA concentration was found for both strains when cultivated on the barley and oat straw (BOS) substrate, showing a similar trend as the protein content. A principal component analysis (PCA) indicated an impact of the strain on the functional properties. Conclusions: Both strains can produce high quality proteins especially when cultivated on wheat straw (WS). The FS was positively correlated with the _P. ostreatus_ strain AMRL150 whereas the FC was positively correlated with AMRL144.  
P. ostreatus; protein extraction; protein concentrate; amino acids; protein solubility index; functional properties
## 4 Experimental Results and Analysis
### Results of Top-Performing CNN Model  
The best-performing network model with the best combination of hyperparameter values was selected and tested on an independent test set. These findings demonstrated that the proposed CNN model is capable of accurately predicting breast cancer. As a result, the suggested convolutional neural networks are ideal for replacing time-consuming traditional ML models. The model training results are illustrated in Figure 5, which shoes the training accuracy and training loss values.  
Figure 4: Accuracy (%) vs. Classifier.  
The Precision and Recall Curve and Receiver Operating Characteristic (ROC) are presented in Figure 6. The show that the best ANN model has achieved the mean precision of 0.99 and the AUC of 1.00.  
The confusion matrices of the best CNN and ANN models are presented in Figure 7. The ANN achieved slightly better accuracy 99.2% vs. 98.5% of the CNN model. The difference was evaluated using a statistical Mann-Whitley test and was found to be significant at \(p=0.018\).  
Figure 5: Illustration of model training results: (**a**) model accuracy, and (**b**) model loss.  
Figure 6: The (**a**) Precision and Recall Curve and (**b**) Receiver Operating Characteristic (ROC) of the best performing model.  
Finally, we explored the influence of the training/testing ratio on the accuracy of the classification. The results are presented in Figure 8. It is predictable that better accuracy was achieved with the larger number of data samples available for training. The highest accuracy of 0.98 for the CNN model was achieved, when 90% of the data was used for training and 10% for testing.
# Trade-offs between Risk and Operational Cost in SDN Failure Recovery Plan
## 4 Suboptimal Algorithms
### Iterative Risk Reduction (IRR)  
In this section, we introduce an algorithm that gradually decreases the risk of an initial guess. The initial guess, in this case, is the original at-risk path that requires rerouting. It gradually modifies the initial guess by small corrections and discovers paths with lower risk. This algorithm is presented in Algorithm 1. The IRR algorithm first discovers paths with the lowest operational cost that does not traverse \(e_{ik,k}\), where \(k\geq 1\) and equal to or smaller than the path length. Among the discovered paths, it then selects the one with the lowest possible risk. If necessary, it uses the operational cost as a tie-breaker. In other words, it iteratively finds a link whose removal from the network will render a path with a lower risk (compared to that of the path discovered at the previous iteration). In this fashion, the risk gradually decreases. We use three exit conditions in Algorithm 1, namely:  
* If a discovered path achieves a risk of lower than \(\tau\);
* If the number of iterations exceeds \(N\);
* If the risk of the discovered path is larger than that of the path discovered in the previous iteration.  
The value of \(\varepsilon\) must be set so that only the risk is used as the main criterion and the operational cost is only a tiebreaker. To this end, one can choose a very conservative value for \(\varepsilon\) so that \(0<\varepsilon<\frac{min_{i}p_{ij}}{N}\), where \(N\) is the number of links in the network.  
Figure 7: Average operation cost with adaptive risk threshold.  
```
Input: The original path \(R\) Initialize, \(N\), \(\tau\), \(\varepsilon\) \(n\gets 0\), \(\beta_{-1}=\infty\) \(m\gets 0\), \(\beta_{0}\leftarrow\beta(R)\), \(\mathcal{Q}_{0}\leftarrow\mathcal{Q}(R)\) while\(n\leq N\) or \(\beta_{m}>\tau\) or \(\beta_{m}<\beta_{m-1}\)do for all\(e_{\tilde{x},\tilde{x}}\) along \(R\)do  Find the lowest-operational cost path in \(E\backslash\{e_{\tilde{x},\tilde{x}}\}\) \(\beta_{k}\leftarrow\) path risk \(\mathcal{Q}_{k}\leftarrow\) path operational cost end for \(m\gets argmin_{k}(\beta_{k}+\varepsilon\mathcal{Q}_{k})\) \(E=E\backslash\{e_{im,jm}\}\) endwhile
```  
**Algorithm 1**Iterative Risk Reduction (IRR), suboptimal solution to P4.
# Prediction Interval Adjustment for Load-Forecasting using Machine Learning
## 3 Materials and Methods  
In this section, all the concepts of the developed methodology are described. First, there is a data preprocessing stage in which the raw data is transformed to be used by machine learning algorithms. Specifically, in this step time series data is transformed into a tabular form in which every element of the table is a segment of the original time series. Also, every element of the table is paired with its correspondent time period. Then, point forecast and prediction interval estimation are performed using artificial neural network (ANN) models. Specifically, the ANN models perform point forecasts in a test database and every error is stored. The stored errors are used to estimate the prediction intervals. At the same time, association-rule mining is performed to extract significant rules by means of the _a priori_ algorithm. Then, the prediction intervals estimated with the Artificial Neural Networks models are adjusted by means of the obtained rules. Finally, the prediction intervals and its adjusted versions are evaluated. In Figure 1, the overarching methodology is shown.
## 4 Conclusions  
In this work, we comparatively analyzed the membrane interactions of L-Phes and D-Phes antimicrobial peptides with DMPC model membrane, experimentally and computationally. From the different HPLC retention times, we found D-Phes to have slightly higher hydrophobicity than L-Phes. Our experiments and calculations agreed that the two peptide isomers adopted \(\alpha\)-helical secondary structures. MD simulations showed that the second position of L-Phes and D-Phes mostly formed \(\alpha\)-helical and random coil secondary structures, respectively. The MD simulations found a mutually stable contact between d-Phe2 and l-Phe3 in D-Phes peptide in the DMPC bilayer. The contacts could influence the membrane interactions and explain its stronger antimicrobial activity compared to L-Phes.  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] prepared peptide and performed Far-UV CD and QCM experiments. [PERSON] and [PERSON] performed \({}^{31}\)P solid-state NMR experiment. [PERSON] performed VCD experiments. [PERSON] and [PERSON] performed MD simulations. [PERSON] and [PERSON] wrote the paper.  
This work was partly supported by JSPS KAKENHI, Grant Number to [PERSON]. K. (JP16H00828 and JP18H02387), to [PERSON] (JP16H00840 and JP17H03044), and JST MIRAI grants to [PERSON] and [PERSON]. K. (JPMJMI18 CC).  
MD simulations were performed on computer clusters or supercomputers of the Research Center for Computational Science, Okazaki, Japan. The authors thank [PERSON] at Yokohama National University for the valuable discussions. The authors thank [PERSON] at Graduate School of Engineering Science, Yokohama National University for rechecking the sample. [PERSON] acknowledges the scholarship of Mongolian-Japan Engineering Education Development program (M-JEED project, J11B16) and the financial support of \"The Global Doctoral Program for Academic Career Support\" program of Yokohama National University.  
The authors declare no conflict of interest.
Figure A2: MALDI-TOF MS spectra of the isotopic patterns of (**a**) L-Phes and (**b**) D-Phes ([M+H]\({}^{+}\) = 1953.2, [M+Na]\({}^{+}\) = 1976.2).
# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 6 Performance Evaluation Results
### Results of Congestion Use Case Evaluation
#### 6.1.2 Re-Routing
The goal of this calculation is to have the diagrams pre-computed with multiple model incidents and to select the most similar one. Figure 9 displays the graphs obtained for incident A, located at the end of section S_6. In the bottom left graph, it can be observed that, as the scaling factor increases, the number of vehicles entering the section also increases, while the average vehicle speed decreases. However, at factor 5, the number of vehicles reaches a maximum and then decreases slightly for larger factors, along with a decrease in average speed. This peculiar behavior is observed because, for the largest factors, there is significant congestion, causing the vehicles to be practically at a standstill. As a result, the number of vehicles entering the section decreases since they hardly move at all. Figure 7: Fundamental traffic diagram model. To calculate the re-routing percentage, two pieces of data are required: the real number of vehicles entering section S_6 and the desired speed. For instance, in a real scenario with the same characteristics (incident in section S_6) and factor 4.75, the number of vehicles entering the section is 2680.4 (cf. Figure 9), and the average speed desired is 80 km/h  
Figure 8: Fundamental traffic diagram without incident. Figure 9: Fundamental traffic diagram with incident A. (corresponding to 2472.1 vehicles, depicted in Figure 9). The re-routing percentage is computed as follows:  
\[Re-routing\ percentage=\frac{2680.4-2472.1}{2680.4}=7.5\% \tag{3}\]  
Thus, to achieve an average speed of 80 km/h, 7.5% of vehicles should be re-routed. Using the same method, in the scenario where the scaling factor is 5, the corresponding re-routing percentage to achieve an average speed of 80 km/h would be 12.25%. However, for factor 4.5, the average speed is already high enough, so re-routing is not necessary. The impact of applying the previously calculated re-routing percentages has been studied. This was done using a simulation that replicates traffic conditions over a 2-h period, where the traffic data for the second hour is the same as for the first hour, maintaining the same traffic pattern throughout the whole simulation. The results, illustrated in Figure 10, show the speed achieved at each 10-min interval of the simulation. It can be observed that the 80 km/h speed average is achieved at different time intervals for each scaling factor and then maintained for the remaining part of the simulation, proving that the fundamental traffic diagram was calculated correctly. For factor 5, the target speed is reached after 100 min, and for factor 4.75, after 60 min. Studying other scenarios, it has been concluded that the time required to reach the target speed depends on the number of vehicles on the highway. With fewer vehicles, the target speed is achieved more rapidly, compared to highly congested scenarios with larger number of vehicles.