esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Model
### PV System Model  
Figure 6 illustrates the model of the 250 MW PV system made up of an inverter and a PV module. The voltage source converter is used as a PV inverter. The active power and the reactive power injected to the grid are controlled by the inverter at the reference set points (Cases 1 and 2: \(P_{ref}=0.225\,\mathrm{pu}\), \(Q_{ref}=0\); Case 3: \(P_{ref}=\sqrt{0.225^{2}-Q_{ref}^{2}}\), \(Q_{ref}=Equation\ (1):VA\) in the equation is the rated apparent power of the PV system). We assumed that the active and reactive power are constant under the steady state, based on the assumption that irradiation and PV cell temperature do not change dramatically within the simulation period. We also assumed that the PV inverter has FRT capability and hence that the PV inverter maintains connection to the grid after the fault.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & & **SG1** & **SG2, 3** & & **SG1** & **SG2, 3** \\ \hline \(R_{a}\) & (pu) & 0.003 & 0.004 & \(X_{d}\)\({}^{\prime\prime}\) & (pu) & 0.171 & 0.134 \\ \(X_{l}\) & (pu) & 0.102 & 0.078 & \(X_{q}\)\({}^{\prime\prime}\) & (pu) & 0.171 & 0.134 \\ \(X_{d}\) & (pu) & 1.651 & 1.22 & \(T_{d0}\)\({}^{\prime}\) & (s) & 5.9 & 8.97 \\ \(X_{q}\) & (pu) & 1.59 & 1.16 & \(T_{q0}\)\({}^{\prime}\) & (s) & 0.535 & 1.5 \\ \(X_{d}\)\({}^{\prime}\) & (pu) & 0.232 & 0.174 & \(T_{d0}\)\({}^{\prime\prime}\) & (s) & 0.033 & 0.033 \\ \(X_{q}\)\({}^{\prime}\) & (pu) & 0.38 & 0.25 & \(T_{q0}\)\({}^{\prime\prime}\) & (s) & 0.078 & 0.141 \\ \(H\) & (s) & 3.0 & 3.0 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: SG parameters.  
Figure 5: Generator control model: (**a**) automatic voltage regulator (AVR); (**b**) governor. EA: terminal voltage, EAS: terminal voltage reference; EF: field voltage; EFS: field voltage reference; Sg: generator speed; SgS: generator speed reference; TQT: turbine torque.  
The time constants of the control system are set to slightly larger values than those of the control system of the VSWG in order to avoid the instability (hunting) of the reactive power injected to the grid [15; 16].
# ORC Optimal Design through Clusterization for Waste Heat Recovery in Anaerobic Digestion Plants
## 3 Methodology
### Off-Design
#### 3.2.2 Off-Design Analysis Results  
During the year, the thermal energy available for the ORC varies due to turbine exhaust gas flow rate and outlet temperature fluctuations, and due to variations of the heat flow required by the digesters. These parameters are the ORC design boundary conditions. Since both the ORC and the gas turbine are very quick to adjust their output according to ambient temperature and the digesters' heat requirement, the dynamic system behavior is not a limiting factor. Therefore, the analysis was carried out in the steady-state conditions, and hour time intervals were considered for estimating off-design system operation.  
According to sliding pressure control, the ORC adapts to load changes by adjusting the evaporation pressure. The resulting superheating grade and working fluid mass flow rate were determined to maximize the cycle power output. In each operating condition, the result is an optimized cycle, like those reported in Figure 6. The ORC is considered turned off when the net power output is less than 20% of its nominal value.  
A grid of operating conditions was mapped and interpolated to define a complete performance operating map such as those reported in Figure 7.  
Figure 6: Off-design ORC cycles for different part-load conditions.  
Figure 7: Off-design ORC maps. ORC power output W\({}_{\text{net,orc}}\) was mapped for several representative values of the heat flow rate required by the digester \(\dot{\Omega}_{dig}\) (\(\dot{\Omega}_{dig}\) = 100 kW (**a**), \(\dot{\Omega}_{dig}\) = 150 kW, (**b**), \(\dot{\Omega}_{dig}\) = 200 kW (**c**) and \(\dot{\Omega}_{dig}\) = 250 kW (**d**)), and of the mGT exhaust mass flow rate \(m_{f}\) and temperature \(T_{f}\).
## 3 Methodology
### SVM Algorithm for Data Mining
Further, in order to estimate \(\omega\) and b, Equation (4) is transformed into the original objective function that has the following form:  
Minimize \(\frac{1}{2}\ \omega^{2}+C\frac{1}{l}\sum_{i=1}^{l}L_{\varepsilon}\left(y_{i},f(x_{i})\right)\)  
Figure 1: Illustration of the main SVM principle for a linear problem (modified) [73]. Figure 2: Illustration of the SVM principle for a non-linear problem (modified) [75]. Subject to \(y_{i}-\omega\cdot\mathcal{O}(x_{i})-b\ \leq\ \varepsilon+\xi_{i}\)  
\[\omega\cdot\omega(x_{i})+b-y_{i}\ \leq\ \varepsilon+\xi_{i}^{*},\ i=1,\ \ldots 1\]  
\[\xi_{i}^{*}\geq 0 \tag{6}\]  
Equation (6) is hard to solve; thus, the four Lagrange multipliers are introduced, which results in the following equation:  
\[\mathrm{f(x)}=\sum_{i=1}^{l}(a_{i}-a_{i}^{*})\omega(x_{i})\cdot\omega(x)+b \tag{7}\]  
Further, the kernel function \(K(x_{i},y_{j})\) for the higher-dimensional space is introduced into Equation (7):  
\[\mathrm{f(x)}=\sum_{i=1}^{l}(a_{i}-a_{i}^{*})K(x_{i},x)+b \tag{8}\]  
\(K(x_{i},x_{j})\) is equal to the inner product of vector \(x_{i}\) and vector \(x_{j}\) within the \(\mathcal{O}(x_{i})\) and \(\mathcal{O}(x_{j})\) places. The use of kernels makes it possible to conduct all calculations in the input space. The pseudocode for the SVM is presented in Algorithm 2. ```
1. # Load the dataset
2.  data = pd_read_csv('enterprisesict_data.csv')
3. # Select the features and target variable
4. X = data[['impic', 'gend', 'age', 'edu', 'ensi', 'fig', 'pros', 'gros', 'ictim', 'ictco', 'ictex', 'ictex', 'ictsol', 'icen', 'icsd', 'icrep', 'iccb', 'inndent', 'indcomp','marcomp', 'findchal', 'covmp']]
5.  y = data['target_variable']
6. # Split the data into training and test sets
7. X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)
8. # Normalize the features
9.  scaler = StandardScaler()
10. X_train = scaler.fit_transform(X_train)
11. X_test = scaler.transform(X_test)
12. # Train a support vector machine
13.  svm = SVC()
14.  svm.fit(X_train, y_train)
15. # Test the model on the test set
16.  accuracy = svm.score(X_test, y_test)
17.  print('Test accuracy:', accuracy)
```  
**Algorithm 2**_Pseudocode for the SVM_. The response variable is _impact_, which refers to the intentions to implement an ICT solution within the enterprise. For validation, 50 observations were taken randomly from the sample, and linear kernel function was used for preprocessing.
# Impact of Chitosan-Genipin Films on Volatile Profile of Wine along Storage
## 4 Conclusions  
Chitosan crosslinked with genipin (Ch-Ge) films, when immersed in wines, modify their volatile composition through the formation and retention of compounds. Ch-Ge films, through chitosan primary amino groups, can react with reducing sugars present in wine, through Amadori reactions, allowing the formation of furan derivatives. It was shown in this study that pentoses, in presence of Ch-Ge films, formed furfural in proportions able to provide toasted aroma notes to the wines. In addition, Ch-Ge films, through free aldehydes available in genipin and reducing terminal of chitosan, can form Strecker aldehydes by reaction with amino acids. Phenylacetaldehyde, benzaldehyde, and 3-methylbutanal may provide positive distinctive fruity, green, floral, honey, and bitter almond aroma notes to these wines. Ch-Ge films retention capacity increases with the hydrophobicity of the compound for the same chemical family. For the same carbon chain of volatile compounds, the acids were more retained by Ch-Ge films than alcohols. This work showed that the use of Ch-Ge films opens new opportunities for winemaking industries as technological adjuvant, acting as preservative and also positively modulating wine volatile compounds without changing varietal aroma.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11146294/s1](https://www.mdpi.com/article/10.3390/app11146294/s1)]([https://www.mdpi.com/article/10.3390/app11146294/s1](https://www.mdpi.com/article/10.3390/app11146294/s1)), Table S1: Area of volatile compounds identified by HS-SPME/ GC \(\times\) GC -ToFMS in white and red wines in contact with chitosan-genipin films during 2 and 8 months.  
Conceptualisation, M.A.C. and C.N.; methodology, M.A.M.R. and S.M.R.; formal analysis, M.A.M.R.; writing--original draft preparation, M.A.M.R.; writing--review and editing, S.M.R., M.A.C. and C.N.; supervision, M.A.C. and C.N. All authors have read and agreed to the published version of the manuscript.  
This research was also developed within the scope of the project CICECO-Aveiro Institute of Materials (UIDB/50011/2020 and UIDP/50011/2020), QOPNA (UID/QUI/00062/2019) and LAQV-REQUIMUTE (UIDB/50006/2020), financed by national funds through the FCT/MEC and when appropriate co-financed by FEDER under the PT2020 Partnership Agreement. [PERSON] and [PERSON] thank FCT for the Doctoral (SFRH/BD/98015/2013) and Post-Doctoral fellowships (SFRH/BPD/100627/2014), respectively. This work was also funded by national funds (OE), through FCT, I.P., within the scope of the framework contract foreseen in the numbers 4, 5, and 6 of the article 23, of the Decree-Law 57/2016, of 29 August, changed by Law 57/2017, of 19 July.  
Not applicable.  
The authors declare no conflict of interest.
## 6 Experimental Results and Analysis
### Receiver Sensitivity  
Since there are many models and evaluations of the propagation of LoRa radio signals in various environments [28], this experiment focuses on testing the performance of LoRa receivers in the Chirinche Bajo community, as there are no studies conducted in these areas. The received signal strength indicators (RSSI) of the packets and the signal-to-noise ratio (SNR) were recorded at each of the positions.  
Figure 13a,b show the RSSI values received at the gateway from node 1 and 2 with different SFs. RSSI values around the \(-111\) dBm and \(-122\) dBm mark are observed at positions D, F and G. The configuration with SF equal to nine emerges as the best option to achieve the highest throughput with the lowest packet on-air time at locations D, F and G. However, the alternative (SF at 12) could also be used. This would depend on the application's tolerance to packet loss.  
The signal-to-noise ratio (SNR) with the RSSI are related, since, in positions D, F and G, there is more interference in the signal flow with SNR values in a range of \(-2.55\) and \(-15.14\), as can be seen in Figure 14a,b. It should be noted that the signal sensitivity is largely influenced by the evaluation environment, which, in the case of positions D, F and G, was located on a 15% slope surrounded by eucalyptus trees and without a line of sight (NLoS).  
Figure 12: PDR Nodes 3.  
Figure 14: _Cont._  
Figure 13: RSSI of the radio (dBm): (**a**) Node 1, (**b**) Node 2.  
The RSSI was evaluated in downlink communication, with a payload of 4 bytes, the sensitivity was in the range of \(-122\) dBm and \(-125\) dBm with SNR in a range of \(-2.51\) and \(-6.27\) in positions D, F and G; although, they are lower than in the uplinks, the PDR had higher efficiency. Figure 15 illustrates the RSSI of node 3 and Figure 16 shows the SNR with SF of 7, 9 and 12.
# A Novel Method for Risk Assessment and Simulation of Collision Avoidance for Vessels based on AIS
## 2 A Theory of a Vessels Collision Warning System  
The collision area is determined by the size of two ships, the position accuracy, and the deviation of the ship's movement in the operating space of each ship. There will be a collision when the distance of two ships is less than the radius of the collision area that is determined by taking a central ship. The location that is used for calculation is the location of the AIS antenna, and the radius of the collision area depends on the size of ship. Thus, we assume this is the application conditions are on the ocean surface. The length of the ship is more than 50 m, and the AIS equipment is located less than 50 m above sea level. The radius of the collision area is calculated as follows [23; 31]:  
\[R_{1}=(L_{0}+L_{1})\times\left(1+\sin 6^{\circ}\right)+(W_{0}+W_{1})/\cos 6^{ \circ}+\ P_{a} \tag{1}\]  
where \(L_{0}\), \(L_{1}\) represent the length of the ship and the target ship, respectively. \(W_{0}\), \(W_{1}\) are the width of the ship and the target ship, respectively. \(P_{a}\) is the location accuracy of the ships. During the crash avoidance study, the size and maneuverability of the vessel will be negligible when two vessels are far away. In this case, two ships will be considered as two moving points with a certain velocity vector. Thus, it can be placed in the geometric coordinate system, as shown in Figure 1[12; 28]:  
In Figure 1, the GPS position of the vessel is (\(\lambda_{0}\), \(\varphi_{0}\)). The speed over ground (SOG), and the course over ground (COG) are \(V_{\rm o}\) and \(C_{o}\), respectively. The GPS position of the target vessel is (\(\lambda_{\rm T}\), \(\varphi_{\rm T}\)). The SOG and COG of the target vessel are \(V_{t}\) and \(C_{t}\), respectively. According to calculating the information of two ships, we can obtain the following results:  
Figure 1: The moving vector diagram of encounter ships.  
1. (RX, RY) in the Descartes coordinate system.
2. The relative distance, R.
3. The relative speed with the target vessel, \(V_{ot}\).
4. The relative course with the target vessel, \(C_{ot}\).
5. The azimuth of the target vessel, \(B_{t}\).
6. The relative azimuth with this vessel--\(B_{ot}\), 0\({}^{\circ}\)\(\leq\)\(B_{ot}\)\(\leq\) 180\({}^{\circ}\).  
Then, the DCPA, and the time to the closest point of approach (TCPA) of encounter ships can be obtained. The magnitude value is expressed as follows:  
\[DCPA=|R\sin(C_{ot}-B_{t})| \tag{2}\]  
\[TCPA=R\cos(C_{ot}-B_{t})/V_{ot} \tag{3}\]
## 3 Results and Discussion
### NACA0012 Models
#### 3.1.1 Results of the Blunt Trailing Edge  
Figure 5 shows the distributions of P/P\({}_{\rm t}\) and T/T\({}_{\rm t}\) of reference data and simulation data, where the \(x\)-axis is the x/L location and the \(y\)-axis is the result. Figure 6 provides the distributions of U/U\({}_{\rm t}\) at location of x/L = 0.95, where the \(x\)-axis is the result and the \(y\)-axis is the y/L location. The corresponding mean error ratios are shown in Table 12 and we could confirm the optimal results under different configurations, as shown in Figure 7, which are highlighted in red. As to the far-field distance, the minimums of three distances are 2.70%, 2.74%, and 3.44%. As to the turbulence model, the best result of SST is 2.70% and that of SA is 3.29%. As to the flux type, the best performance of ROE is 2.70% and that of AUSM is 3.44%. In conclusion, the minimum total mean error ratio is 3.25%, which is achieved by SST + 12 m (ROE). Compared with optimal results of other simulation configurations, the corresponding accuracy gains are 21.64%, 1.53%, 18.09%, 21.54%, and 27.15% respectively.  
Figure 5: (a) Distributions of P/P\({}_{\rm t}\) of the blunt trailing edge; (b) Distributions of T/T\({}_{\rm t}\) of the blunt trailing edge.  
Figure 6: Distributions of \(\mathrm{U/U_{t}}\) of the blunt trailing edge at location of \(\mathrm{x/L}=0.95\).  
Figure 7: Comparison of the simulation performances of the blunt trailing edge under different sim-ulation configurations.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 2 Particularities and Constraints
### Physical Vulnerability  
In general, UWSNs nodes are waterproof, compact and sophisticated in nature. As mentioned above, sensor nodes may be deployed at harsh and unattended sea regions. They could be physically damaged if struck, making them invalid, and are also vulnerable to marine organisms. It is impossible to guard each node from potential physical damage.
# Heterodyne Angle Deviation Interferometry in Vibration and Bubble Measurements
## 5 Conclusions  
HADI is based on CPHI, with an angular sensor for angle deviation measurements in industrial environment. The angular sensor senses the angle deviation according to its phase change. The angle deviation of the test beam due to sample deformation or a particle passing through the beam will induce a phase change of the angular sensor. The phase change can be measured by CPHI in real time. For example, small vibration, water flow, and refractive index change measurements, etc. Due to its anti-influence effect, air disturbance is eliminated. The phase change of the interference signal is only determined by the angle deviation. In the speaker's vibration measurement, the spectrum of the phase can be represented as vibration situation, including the vibration level and frequency response. The better speaker response is within the frequency range of 70-200 Hz. In the water flow measurement, we could measure the bubble size and the speed of water flow in real time. The measurable minimum size of a bubble is 0.2 mm and the maximum speed of water flow is 0.6 m/s. We should increase the beat frequency to expand the measurement range in the future.  
This study was supported in part by the MOST in Taiwan under contract number: MOST 104-2221-E-150-047.  
[PERSON], he contributed the idea, measurement method, and optical system design in this study; [PERSON] and [PERSON] contributed the experimental results and all of figures in the speaker's vibration and water flow measurements, respectively.  
The authors declare no conflict of interest.
# A New Multi-Scale Convolutional Model Based on Multiple Attention for Image Classification  
[PERSON]  
1 College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China; [EMAIL_ADDRESS] (Y.Y.); [EMAIL_ADDRESS] (C.X.); [EMAIL_ADDRESS] (F.D.)  
1  
[PERSON]  
1 College of Information Engineering, Shaoyang University, Shaoyang 422000, China
bottom-up models [16; 17; 18] and task-driven top-down models [19; 20; 21]. The research on the combination of deep learning and attention mechanisms has received considerable attention [22; 23; 24]. The goal of this study was to select information from the input that was relatively important to the current task. In deep neural networks, researchers often use masks to achieve attention. They demarcate key features in the data by training additional multi-layer weights. This approach naturally embeds the attention mechanism into the deep network structure and participates in end-to-end training. Attention models are well suited for solving computer vision tasks such as image classification, saliency analysis, and object detection.  
The same object will show different shapes in different natural scenes. When a computer vision system senses an unfamiliar scene, it cannot predict the scale of the object in the image in advance. Therefore, it is necessary to observe image information at different scales. The multi-scale representation of images can be divided into two types: Multi-scale space and multi-resolution pyramid. The difference between them is that multi-scale space has the same resolution at diverse scales. In the visual tasks, the multi-scale approach of multi-resolution pyramid processing targets can be separated into two categories: Image pyramid and feature pyramid. The image pyramid works best but the time and space complexity is high, and the feature pyramid is widely used because of its ease of implementation. In addition, the sizes of the receptive field and the number of network layers of CNN can also obtain multi-scale features in nature. The smaller receptive field and shallower network layers can focus on local features, the larger receptive field and deeper network layers can perceive global information.  
CNN has a natural advantage for image processing. At the same time, the attention mechanisms and multi-scale features are effective means of image processing. Therefore, this paper combines attention mechanisms and multi-scale features to create a new convolution model for image classification. Res2 Net [25] groups single residual block and constructs hierarchical residual connection between different groups to achieve multi-scale representation of features. However, the randomness of its grouping method affects the robustness and intuitive interpretability of the network. We first sort channels of a single residual block by the attention mechanism, and then group the sorted residual blocks. This can achieve multi-scale representation of features, highlight important features and suppress interference information. Further, we conduct an attentive analysis for each group to improve the accuracy of image classification.
## 3 Materials and Methods
### Dataset, Acquisition Device, and Implementation Details
Figure 1: Myo armband from Thalmic Labs (**left**) and its location for data acquisition (**right**) [24]. time. These discrepancies were corrected using movement detection algorithms, such as the generalized likelihood ratio algorithm or the Lidierth threshold-based algorithm. From the NinaPro DB5 dataset, only a subset of movements was selected for classification. The goal was to classify movements as described by [PERSON] et al. [6], so a comparison was made between the hand postures in NinaPro DB5 and those described by [PERSON]. Based on this comparison, nine hand postures were chosen as classes for the SVM to predict: cylindrical grasp (Cyl), pinch grasp (Pinch), lateral pinch (LatP), oblique palmar grasp (Obl), lumbrical grasp (Lum), hook grasp (Hook), intermediate power-precision grasp (intPP), special pinch/tripod (SpP), and pointing index (nonP). The non-prehensile grasp in [6] was not present in the NinaPro DB5 dataset, and thus it was replaced with the \"pointing index\" posture. These postures are described and represented in Table 2.  
The frequencies of use of each grasp type during the day are shown in Table 3, where it can be seen that the most used grasp is the Pinch grasp (grasping with the fingertips, at least the thumb and the index fingers) with a 36.9% frequency. The next most used grasps have frequencies around 10% and are the Cylindrical grasp (grasping with the whole hand in contact, including the palm, and with the thumb in opposition to the fingers), the Oblique grasp (similar to the Cylindrical grasp, but with adduction of the thumb), and the Lumbrical grasp (grasping with all the fingers contacting the object but not the palm). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Grasp Type** & **Contact with** & **Image** & **Grasp Type** & **Contact with** & **Image** \\ **(NinaPro Posture)** & **Object** & **(NinaPro Posture)** & **Object** & **Object** & **Image** \\ \hline Cyl & Palm and fingers & & Hook & & \\ (C1) & Palm and fingers & & (C3) & Fingers & \\ Pinch & Thumb and some & & intPP & Thumb, fingers, and part of the palm & \\ (C15) & fingertips & & (C4) & Thumb and some & \\ LatP & Thumb and lateral & & SpP/Tripod & Thumb and some & \\ (C14) & part of some & & (C9) & tips/lateral part of the fingers & \\ Obl & Palm and fingers & & Pointing & & \\ (C8) & Palm and fingers & & Index & No contact \\ \hline Lum & Thumb and & & & \\ (C18) & proximal part of & & & \\ (C18) & the fingers & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of grasping postures. All the implementations were carried out offline in MATLAB, for the following reasons:  
* The NinaPro dataset is provided in MATLAB format, ready to be imported. * It has many toolboxes for implementing machine-learning techniques and for fast training classifiers. In this work, the Statistics and Machine Learning Toolbox was employed. * It allows fast prototyping and easy code debugging.