esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Existing Pathfinding Techniques
### Uninformed Search Algorithms
#### 3.1.3 Dijkstra's Algorithm  
First introduced in 1956 by [PERSON] and published three years later, this conventional Algorithm is used to find the shortest distance between start node and end node in a graph [26]. It is used in a variety of applications such as digital mapping services such as Google Maps, telephone networks, IP routing for finding Open Shortest Path First, robotics, designate file server, etc. [27]. It was also used in traffic information systems in order to track source and destinations from another specific source and destinations [28]. This Algorithm was preferred earlier in video games for finding the optimum path until the A-star Algorithm which could find the solution more quickly [29]. There is no heuristic function used in this Algorithm, but it is extended to the A-star Algorithm by making use of the heuristic function [30]. This Algorithm requires a lot of memory usage as all nodes are expanded in order to find the destination node unlike the case of A-star which improves its functionality [31]. One of the drawbacks of this Algorithm is that it cannot be used for negative value costs, thereby leading to acyclic graphs and therefore the correct shortest path cannot be found [32; 33]. For each node in a graph, a label is assigned which is used to determine the minimal length from the start point to all the other nodes of the graph [34]. After each step, the value of the label of the graph nodes decreases; that is, the Algorithm runs sequentially [35].  
Figure 6: Depth first search traversal.  
For [PERSON]'s Algorithm, G(n) which is the cost required to move from start node to present node 'n', and heuristic value H(n), which is the acceptable cost to move from present node to target node, are assigned to 0 for [PERSON]'s Algorithm 2. It cannot be overestimated and is given in Equation (5). The total cost required to reach the target node is:  
\[\text{F(n)}=\text{H(n)}+\text{G(n)} \tag{5}\]  
therefore, resulting in F(n) = G(n).  
In Figure 7, the start vertex is taken as A. Vertices D and B are connected to vertex A but minimum distance is from vertex D which is 1. From vertex D, B and E are connected but total distance from A to D to E is 2 which is less than from A to D to B which is 3. Therefore, vertex E is visited. From E, only B or C can be chosen since D is already visited, but total distance from A to D to E to C is 7 which is less than one from A to D to E to B to C which is 9. Therefore, the shortest path will be A to D to E to C with minimum cost of 7.
## 5 Platform Testing and Validation
### Target Infrastructure  
It emulates a water CI composed of a water Dam, pumps, tanks and water treatment labs with sensors that measure the temperature and provides a measurement in several key points from the whole water distribution chain (as depicted in Figure 3). The average temperature is set to 40.0 \({}^{\circ}\)C (default value in this test scenario), with minimum and maximum threshold values set to 40.0 \({}^{\circ}\)C. The measurement of the sensors is performed every hour by the simulated PLCs and the corresponding data are stored in a local database.  
The Water distribution scenario has been developed using Rapid SCADA ([[https://rapidscada.org/](https://rapidscada.org/)]([https://rapidscada.org/](https://rapidscada.org/)), accessed on 5 June 2021), an open-source industrial automation platform that provides tools for rapid creation of monitoring and control systems. The main objective is to simulate SCADA elements from a water CI and perform abnormal/malicious actions against the data generated by the Programmable Logic Controllers (PLCs) that is stored in the local databases. These actions should be detected by security devices, and alarms must be generated accordingly to warn the SCADA operators about the presence of a potential threat or attack.  
The water CI has Ubuntu as the operating system with an IP address 192.168.66.6 and contains a working SCADA system with simulated PLCs and a Modbus ([[http://www.modbus.org/](http://www.modbus.org/)]([http://www.modbus.org/](http://www.modbus.org/)), accessed on 5 June 2021) Slave. Modbus is a SCADA oriented protocol used for transmitting information over serial lines among electronic devices. Unlike conventional IT networks that use as protocols HTTP, FTP, and SNMP, industrial networks use proprietary protocols such as Modbus, Powerlink and DNP3 in their communications. The device requesting information is known as the Modbus Master, whereas the one that provides the information is known as the Modbus Slave [42].  
Figure 3: Water Distribution Scenario.
## 3 Results and Discussion
### Variation of the Energy Consumption in Different Scenarios  
Figure 5 shows a considerable difference between the energy consumption in the SRES scenarios and the RCP scenarios: in the cities of Barcelona and Rome, energy consumption notably increased in the RCP scenarios, whereas in the cities of Berlin, Bern, and Vienna, it notably decreased.  
Regarding the adaptive total energy consumption, in Barcelona and Rome there was a reduction in the SRES scenarios between 59.29 and 99.34, and between 56.59 and 94.55 kWh/year, respectively, whereas in the RCP scenarios, there was an increase between 198.94 and 376.09, and between 356.76 and 445.79 kWh/year. In the cities of Berlin, Bern, and Vienna, this reduction in the energy consumption varied in the SRES scenarios between 311.98 and 513.89, between 114.15 and 136.67, and between 112.01 and 239.03 kWh/year, respectively, whereas this reduction was generally greater in the RCP scenarios, varying between 467.12 and 657.78; 101.17 and 553.17; and 460.90 and 495.85 kWh/year.  
On the other hand, the static total energy consumption in the cities of Barcelona and Rome slightly increased and decreased in the SRES scenarios, whereas there were significant increases in the RCP scenarios, varying between 217.89 and 507.38, and between 607.30 and 770.54 kWh/year. As for the cities of Berlin, Bern, and Vienna, such reduction in the energy consumption varied in the SRES scenarios between 364.43 and 582.12; 7.75 and 47.6; and 162.80 and 245.99 kWh/year, whereas in the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**City**} & \multirow{2}{*}{**Scenario**} & **Static-Adaptive** & **Static-Adaptive** & **Static-Adaptive** & \multirow{2}{*}{**Cooling-Heating** (kWh/year)**} \\ \cline{3-5}  & & **Heating** & **Cooling** & **Total** & & **(kWh/year)** \\ \cline{3-5}  & & **Assumption** & **Assumption** & **Assumption** & **(kWh/year)** \\ \hline \multirow{5}{*}{Barcelona} & Current & 138.07 & 991.49 & 1129.56 & 853.41 \\ \cline{2-5}  & A1B & 75.01 & 1110.45 & 1185.46 & 1035.43 \\ \cline{2-5}  & A2 & 73.56 & 1153.86 & 1227.41 & 1080.30 \\ \cline{2-5  
RCP scenarios the variation was between 484.14 and 573.60, \(-\)72.85 (i.e., increasing consumption) and 267.19, and 232.05 and 296.34 kWh/year.  
Figure 5: Energy consumption difference from current scenario, in which negative and positive values indicate increases and decreases, respectively.  
In B1 and RCP4.5 scenarios, the city of Bern did not have the same tendency of the cities of Berlin and Vienna as there were increases, instead of decreases, in the total consumption.  
In addition, in Barcelona and Rome, the variation in heating prevailed over cooling in the SRES scenarios, whereas in the RCP scenarios the variation in cooling prevailed over heating. However, in Berlin, Bern, and Vienna, the variation in heating prevails both in the SRES scenarios and in the RCP scenarios.
## 6 Conclusions  
In this study, a first-step study in developing a soundproof panel featuring low-frequency sound attenuation and air-transpassing capabilities is presented. The basic size of the HR is derived from the classical theory and then numerical studies using COMSOL Multiphysics are conducted to understand the relationships between the shape and arrangement of the resonators, followed by experimental verification. From the numerical and experimental results and observations, the following conclusions can be drawn:  
* A unit cell for a soundproof panel is conceived from the classical formula and its transmission loss is predicted using COMSOL Multiphysics. As the shape changes, the transmission loss of a single resonator increases up to 232.37% and 64.47%, respectively, at the resonance frequency of 500 Hz and 1000 Hz. Depending on cavity, neck, and duct shapes, the peak frequency shift as well as the magnitude change can occur.
* In different arrays in series and parallel dispositions of HRs, the transmission loss of resonators increased up to 256.3% and 111.27% at resonance frequencies of 500 Hz and 1000 Hz. The serial disposition of two HRs to attain noise attenuation at 500 Hz and 1000 Hz shows that a soundproof panel design for sound attenuation at multiple resonance frequencies is feasible. However, it is also observed that, for more effective traffic noise reduction, the noise attenuation performance between the two target frequencies needs to be improved.
* A prototype of a soundproof panel (1960 mm (W) \(\times\) 127.5 mm (L) \(\times\) 1000 mm (H)) with a resonance frequency of 1000 Hz was built and the transmission loss was measured in a reverberation room for verification. From the comparison result, a similar pattern of transmission loss is observed between numerical and experimental results and apparent transmission loss at the resonance frequency of 1000 Hz, but the discrepancy between two curves, especially near the resonance frequency, can be observed, which should be identified for developing a reliable design procedure via further studies.
* Although this study is limited to providing design methods at an early stage, it may be necessary to derive the optimal dimensions of the soundproof panels for the required transmission loss. Future research should be conducted regarding this topic.  
Methodology, B.K. and S.C.; investigation, B.K., S.K., M.M., Y.P. and H.Y.; writing--original draft preparation, B.K.; writing--review and editing, S.C.; project administration, J.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 2 ICTAP-C152289-03). This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2019R1A5A8083201).  
The authors declare no conflict of interest.
## 5 Discussion  
We presented a shape-from-focus algorithm for multi-wavelength holography using a phase noise measure, based on phase gradients. Firstly, a generic measuring setup for the application of this method was introduced. Two approaches (pixel wise maximum filter and the Gaussian fit on the shape-from-focus stack output) for the all-in-focus measurement data calculation were presented subsequently. The output of the algorithm is further used as a rough height estimation to overcome the phase ambiguities, and allows the extension of the unambiguous measurement range.  
We were able to calculate an all-in-focus measurement and extend the range of unambiguouses beyond 12 mm at the largest synthetic wavelength of only 915 \(\upmu\)m. In this range, we could not observe a decrease in signal quality. To date, no fundamental limit to the possible range are known. Using a setup with collimated illumination, experiments at meter-scale distances would be possible.  
After having explained the algorithm in general, their respective performance at complex free-form surfaces was demonstrated. A gear tooth flank and a step sample at steep viewing angles demonstrate the performance of the algorithm. Both filters are able to reconstruct an all-in-focus phase map. However, only the Gaussian filter approach is able to reconstruct an unambiguity-free height map on top of the synthetic measurement data without combination errors.  
Conceptualization T.S., M.F., T.B., and A.S.; software and investigation, T.S., M.F., and T.B.; writing (original draft preparation), T.S.; writing (review and editing), T.S., T.B.; and supervision and funding acquisition, A.B. and D.C.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Experimental Verification
### Experiment of New GNSS Spoofing Signal Power Control Algorithm
Compared with experiment 1, the change range of positioning results caused by spoofing signal is obviously small, which indicates that the process of spoofing signal taking over receiver is more stable, this is helpful to improve the concealment of spoofing. The time is between 184 s and 200 s. At this time, the corresponding spoofing signal begins to be biased. The positioning result of the receiver is gradually biased. The three-dimensional coordinate range of ECEF is 1.498 m, 12.68 m and 2.747 m, respectively. Because the code phase and carrier phase of six spoofing signals change 16 m during this period, the three-dimensional coordinate range of ECEF in experiment 2 is more reasonable than that in experiment 1. It shows that receiver is taken over by spoofing signal, and spoofing is implemented successfully. Based on the analysis of experiment 1 and experiment 2, compared with the conventional spoofing signal high power control algorithm, the new GNSS spoofing signal power control algorithm can shorten Doppler frequency fluctuation time by 72.2%, reduce the range by 75.9%, and reduce \(C/N_{0}\) of the received signal. When spoofing signal takes over the receiver, the range of the three-dimensional coordinates of ECEF is significantly reduced, which indicates that the newly designed GNSS spoofing signal power control algorithm can make the spoofing behavior more hidden and make it more difficult for the target receiver to detect the spoofing behavior. Figure 16: Changes in \(C/N_{0}\) of SVID29, 30, 31 in experiment 2.
# Metabolic Potential of Halophilic Filamentous Fungi--Current Perspective
## 5 Biomolecules Produced by Halophilic Fungi
### Biosurfactants and Surface-Active Proteins
Their structure contains eight cysteine residues in a specific primary sequence pattern that form four disulfide bonds, stabilizing an amphipathic tertiary structure and driving hydrophobin self-assembly into amphipathic layers at hydrophobic-hydrophilic interfaces [96]. Those layers show good adhesion properties, ability to change the surface wettability, and protein adsorption behaviors [97]. Hydrophobins are divided into two classes. Class I hydrophobins form highly insoluble amyloid-like rodlets at interfaces, often undergoing a conformational change that can only be dissolved with strong acids. Hydrophobins of class II form a highly ordered two-dimensional crystalline monolayer at interfaces that can be easily dissolved with detergents, organic solvent solutions, or under high pressure [96]. Some of these features are exploited by fungi in hypersaline environments, where modulation of cell wall permeability can be of great importance in the presence of toxic salt ions constantly permeating the cell. With changes in the osmolarity of the environment, it is also useful to strengthen and stiffen the cell wall [95]. Comparison of halophilic fungi producing hydrophobins in saline environments with division into classes is summarized in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Halophilic Fungi** & **Hydrophobins Class** & **Salinity Growth Conditions** & **References** \\ \hline _Aspergillus sydowii_ BMH-0004 & I & 2.0 M NaCl & [27] \\ \hline _Penicillium roseoprupureum_ MUT 4892 & I & 0.5 M NaCl & \\ \hline _Acemonium sclerotisenum_ MUT 4872 & I & 0.5 M NaCl & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Halophilic fungi as a source of hydrophobins. Another fungal surface-active biomolecules are cerato-platanins that are small cysteine-containing proteins (CP). They are mostly secreted into the culture filtrate but can also be found in the cell wall of fungal hyphae and spores. Solutions of CP lead to strong foam formation, and they self-organize on hydrophobic: hydrophilic interfaces into ordered, amphipathic layers. Proteins that belong to the cerato-platanins family have been found in some marine fungi such as _Aspergillus terreus_[87], _Trichoderma harzianum_[87], and _Trichoderma atroviride_[98].
## 2 Introduction to Acoustic Feature Extraction Methods
### Mel-Frequency Cepstral Coefficients  
Feature extraction for the recognition of acoustic signals, such as voice and audio, is used by a nonlinear distribution of filter banks that transform basic frequencies into the mel-scale focusing on human hearing ability. Voice vectors obtained by these filter banks are called MFCC [28]. The procedure for extracting the features of MFCC is as follows; a pre-emphasis is performed after extracting the voice signal frame by frame. Then, the frame signal is applied to the fast Fourier transform to obtain a power spectrum. Afterward, this result is passed by the mel-scale filter bank and converted back to the frequency scale again. Finally, the logarithm is taken to reflect the perceived characteristics of the frequency, and MFCC is extracted through both the discrete cosine transform (DCT) and mean normalization process. Figure 1 shows the process of MFCC.  
A brief description of each block in Figure 1 is as follows.  
* Pre-Emphasis: This performs the role of a kind of high-pass filter and emphasizes high frequency components.
* STFT: The length of the frame is divided into about 20 to 40 ms (short time), and a frequency component is obtained by performing Fourier transform for each frame.
* Magnitude Squared: Finds the energy for each frame.
* Triangular Frequency Integration: For each frame, a triangular filter bank is applied.  
Figure 1: Block diagram of the Mel-Frequency Cepstral Coefficients (MFCC).  
* Logarithmic Nonlinearity: This takes a logarithm of the integration result.
* DCT: Performs discrete cosine transform operation to make the cepstral coefficient.
* Mean Normalization: Takes an average to reduce the impact on fast-changing components.
# Fault Tolerant Spectral/Spatial Optical Code Division Multiple Access Passive Optical Network
###### Abstract  
High-capacity communication networks are built to provide high throughput and low latency to accommodate the growing demand for bandwidth. However, the provision of these features is subject to a robust underlying network, which can provide high capacity with maximum reliability in terms of the system's connection availability. This work optimizes an existing 2D spectral-spatial optical code division multiple access (OCDMA) passive optical network (PON) to maximize connection availability while maintaining desirable communication capacity and capital expenditure. Optimization is performed by employing ring topology at the feeder level, which is used to provide a redundant path in case of connection failures. Furthermore, high transmission capacity is ensured by utilizing a pseudo-3D double-weight zero cross-correlation (DW-ZCC) code. The analysis is performed with Optisystem simulations to observe the performance of the system in terms of bit error rate (BER), received power, and eye openings. It is observed that the introduction of ring topology at the feeder level of the PON does not impact the overall transmission capacity of the system. The system can still support maximum transmission capacity at receiver sensitivities of up to \(-19\) dB. Reliability analysis also shows that the optimized ring-based architecture can provide desirable connection availability compared to the existing system.  
spectral-spatial; optical code division multiple access (OCDMA); passive optical network (PON); network optimization; two dimensional (2D) 2024
# A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis
## 3 The Proposed SSGCN Model
### Semantic and Syntactic Information Enhancement Layer  
In this section, we further describe the enhancement of the combination of semantic and grammatical information for aspect words with SSWs. Herein, the mask matrix of SSWs is employed to obtain the features of enhanced semantic and grammatical information of SSWs. Similarly, the mask matrix of aspect words is employed to obtain the features from the enhanced semantic and grammatical information of aspect words. For the semantic and grammatical information enhanced for SSWs, we masked the non-sentiment support words in the weighted representation \(H^{\prime}l\) of the GCN output to obtain an SSW representation, retaining important information in the SSW representation through average pooling, as shown in Equation (8).  
\[H_{SSW}^{\prime}=f(h_{SSW_{1}}^{\prime},h_{SSW_{2}}^{\prime},h_{SSW_{3}}^{ \prime},\dots,h_{SSW_{r}}^{\prime}) \tag{8}\]  
For the semantic and grammatical information enhanced for aspect words, we masked the final output of the GCN to represent non-aspect words in \(H^{\prime}l\) to obtain aspect word representations. Similarly, in Equation (9), we retained important information in each aspect word representation through average pooling.  
\[H_{asp}^{\prime}=f(h_{a_{1}}^{\prime},h_{a_{2}}^{\prime},h_{a_{3}}^{\prime \prime},\dots,h_{a_{r}}^{\prime}) \tag{9}\]  
where \(f\) represents the function of average pooling.  
We then concatenated the SSW representation with the aspect word representation to obtain a more complete semantic and syntactic final feature representation. Finally, we represented the final feature by obtaining the probability distribution via the \(Softmax\) function, as shown in Equations (10) and (11).  
\[H_{con}^{\prime}=Concat(H_{ssW}^{\prime},H_{asp}^{\prime}) \tag{10}\]  
\[y=Softmax(W_{c}H_{con}^{\prime}+b_{c}) \tag{11}\]  
where \(Concat\) represents the concatenation operation, and \(W_{c}\) and \(b_{c}\) are linear transform weights and bias terms, respectively.
## 2 Preparation, Measurements, and Fitting Algorithm
### Fitting Algorithm  
First, the minimum delta (\(\delta\)) in Equation (3) may be used to first determine lambda (\(\lambda\)) by adjusting \(\mathrm{k_{N}}\) and Vth, leaving the locations of kinks at around \(\mathrm{V_{DS}}=(\mathrm{V_{GS}}-\mathrm{Vth})\). Secondly, the discrepancies between the measured data and the fitting data are found to determine (\(\chi\), \(\alpha\)) in Equations (1) and (2). Finally, the width of the kink (associated with \(\beta\)) at different \(\mathrm{V_{GS}}\) is to be determined using Equation (3). In total, six parameters, (\(\mathrm{k_{N}}\), Vth, \(\lambda\), \(\alpha\), \(\beta\), \(\chi\))\({}_{\mathrm{VG}}\) are used to fit a specific characteristic curve well [21]. Different sets of six parameters at various values of \(\mathrm{V_{G}}\) may be applied as beams to determine the different characteristic curves at other different gate bias. Once the fitting for \(\mathrm{I_{DS}}\)-\(\mathrm{V_{DS}}\) is as perfect as possible, the whole electrical performance can be addressed because even \(\mathrm{I_{DS}}\)-\(\mathrm{V_{GS}}\) is also expressed in the same formula.