esa-sceva/satcom-chunk-collection huggingface dataset:
# Pressure Fluctuation and Flow-Induced Noise of the Fin and Rudder in a Water Tunnel
## 4 Flow Field and Pressure Fluctuation
### Comparison of Pressure Fluctuation Results
Radiated Noise Level (RNL) is used to render SPL values at different distances from the sound source equivalent to the SPL at a fixed distance so as to compare different computation conditions. RNL is defined as:  
\[RNL=20\cdot\log_{10}\left(\frac{p_{\text{RMS}}}{p_{\text{ref}}}\right)+20\cdot \log_{10}\left(\frac{D}{D_{\text{ref}}}\right) \tag{11}\]  
where \(p_{\text{ref}}\) represents the far-field root-mean-square sound pressure; \(p_{\text{ref}}\) represents the reference sound pressure; \(D\) represents the distance from the monitoring point to the center of the source; and \(D_{\text{ref}}\) represents the reference value for distance, which is 1 m [5]. In addition, the Sound Power Level (SWL) represents the radiated sound power. Its formula is as follows:  
\[\text{SWL}=\text{RNL}+10\log_{10}\left(\pi D_{ref}^{2}\right) \tag{12}\]  
The RNL values at 5 m/s from the monitoring points positioned in a far-field configuration were extracted and depicted as directivity curves in Figure 22. In this way, the noise radiation in various directions surrounding the fin and rudder can be observed directly [24]. In the XY plane, the noise radiation exhibits noticeable directional and symmetrical characteristics, which can also be seen in the literature about hydrofoil noise [18]. It can be observed that the noise values generated on the sides and rear of the fin and rudder are relatively higher compared to those at the leading edge. The noise has high values at around 120\({}^{\circ}\) and 240\({}^{\circ}\). This correlation can be attributed to the presence of significant vortices in the rear region and wake area, as illustrated by the vorticity distribution in Figure 15.  
Figure 21: Sound pressure contours in the YZ plane at different frequencies: (**a**) 100 Hz; (**b**) 500 Hz; (**c**) 1000 Hz. In the XZ plane, the noise level at the top of the fin and rudder is significantly higher than that at the bottom. This phenomenon can be attributed to the complex flow dynamics at the top of the fin and rudder, as it is connected to the top wall of the computational domain, leading to more intricate flow patterns compared to those at the bottom of the fin and rudder. In the YZ plane, it can be observed that the noises radiating in all directions are mostly on the same level.
## 3 Results and Discussion
### Fatty Acid Composition
Moreover, the n-6/n-3 fatty acid ratio is considered an important index for assessing the nutritional quality of fats. In our research, the lowest n-6/n-3 ratios were found for linseed oil (0.33) and chia seed oil (0.39), whereas the highest ratios were observed in poppy seed oil (364), pine cone oil (232), apricot oil (148), pumpkin seed oil (123), hazelnut oil (116), and carrot oil (100). The high proportion of \(\alpha\)-linolenic acid C18:3n3, as the main FA in linseed and chia seed oils, resulted in a low n-6/n-3 ratio, whereas, on the other hand, the n-6/n-3 ratios of poppy seed, pine cone, apricot, pumpkin seed, hazelnut, and carrot oils were attributed to high levels of linoleic acid C18:2n6 and poorer \(\alpha\)-linolenic C18:3n3 and docosahexaenoicenoic C22:6n3 acids. Nutrition interventions often involve specific adjustments, such as controlling caloric intake and managing the distribution of macronutrients, including a balanced n-6 to n-3 PUFA ratio, as recommended for the management of obesity [28; 29]. It has been proposed that an optimal n-6:n-3 ratio ranging from 1:1 to 5:1 may be essential for good health.. However, typical Western diets are characterized by excessive n-6 consumption, leading to n-6/n-3 ratios ranging from 10:1 to 20:1, thus increasing the risk of inflammatory diseases like obesity [30]. Accordingly, if we include the above recommendation for the n-6/n-3 ratio as a selection parameter, linseed, chia seed, macadamia, and canola oils could be considered as potential sources of a balanced intake of n-6 and n-3 PUFAs.
# Concatenated Attention: A Novel Method for Regulating Information Structure Based on Sensors
## 2 Methods
### Neural Network with CA Method  
In underwater real-time monitoring, short time intervals between data acquisitions typically result in a large volume of data being generated within a short period. This high data throughput places significant demands on processing systems, making the computational efficiency of the neural network a critical consideration. Among the various neural networks that incorporate self-attention mechanisms, the Swin Transformer stands out for its ability to achieve high training accuracy while maintaining low computational resource consumption. By leveraging a hierarchical design and localized attention, the Swin Transformer effectively balances accuracy and efficiency, making it highly suitable for real-time monitoring of underwater target objects.  
The modified neural network architecture incorporates the proposed CA method, as shown in Figure 3. The network consists of four stages, each containing a specific number of layers required for training. The layers in each stage are trained repeatedly, with repetition counts of 2, 2, 6, and 2, as indicated below the figure. The structures of different stages vary to accommodate the diversity and complexity of features at different levels. The design of repetition within the same stage balances the number of parameters trained and the final accuracy. Additionally, the consistency within each stage simplifies parameter sharing andreduces redundancy, thereby improving computational efficiency. Similarly, the size of the core matrices applied in the CA method varies across stages, dynamically adjusting to the resolution and complexity of the input data at each level.  
The application of the CA method, combined with the staged design strategy, ensures scalability and adaptability, enabling the network to handle different computational requirements effectively. Early stages focus on extracting localized and fine-grained features, while later stages progressively integrate the information into high-level representations. This hierarchical approach not only enhances the model's ability to capture contextual relationships but also optimizes computational efficiency by allocating resources to where they are most needed. Furthermore, the integration of the CA method within the framework adds flexibility, allowing the network to enrich feature representations without increasing computational overhead. This makes the modified architecture particularly suitable for underwater monitoring, where the ability to process large volumes of data quickly and accurately is crucial.
## 2 Materials and Methods
### Procedures
#### 2.3.2 RTT Protocol  
The _Test VAM-HPSS_ application was used to determine running performance (MAS and VO2 max) following the manufacturer's instructions. First, the RTT protocol was selected based on the subjects' self-reported peak velocity: (i) \(<\)17.0 km\(\cdot\)h\({}^{-1}\) (\(>\)41.0 min in a 10-km race), 17.0-19.0 km\(\cdot\)h\({}^{-1}\) (36.5-41.0 min in a 10-km race), and \(>\)19.0 km\(\cdot\)h\({}^{-1}\) (\(<\)36.0 min in a 10-km race). Second, subjects completed five minutes of running at low intensity, two ten-second progressive runs, and three minutes of walking as part of the specific warm-up. Third, subjects received an auditory _\"ready, set\"_ cue before beginning the RTT protocol with a beep signal. After pressing the start button, the stopwatch, distance, and velocity fields were launched in the _Test VAM-HPSS_ application. Subjects were previously instructed to reach each cone located every 25 m around a running track while they regulated their running pace according to the beep signals. The frequency of the beep signal was automatically set according to the peak velocity selected for each RTT protocol. All auditory cues and beep signals were provided by the _Test VAM-HPSS_ application connected to a loudspeaker. The RTT protocol ended when the subjects were unable to reach the cone at the time of the beep signal on two consecutive occasions, or they voluntarily decided to stop running after perceiving maximal exertion. The peak heart rate (HR) was recorded with a Polar H10 chest strap (Polar Electro Oy, Kempele, Finland) during the RTT, and the Borg's category-ratio 10 scale (CR-10) was reported after the test. The HR and CR-10 were used as maximal effort criteria [23]. The _Test VAM-HPSS_ application automatically estimated the MAS and VO2 max from the peak velocity achieved in each RTT [20].
## 5 Robot Propulsion
### Motion Analysis  
The swimming robot model was implemented in Simulink(r) on the basis of the description given in Section 4. Three equal IPMC links are concatenated and modeled with the same dynamics since they have the same length. Specifically, the link dynamics is defined as \(P_{L_{q}}(s)=H(s)G_{1_{d}}(s)\), where the model parameters of \(G_{1_{d}}(s)\) for each link are contained in Table 2. The total displacement of a link is calculated by means of its relative displacement and the angle of the previous link end.  
Figure 12 shows the displacement of each link tracking its reference for the designed control strategies (see Figure 11(a)) and the comparison between the ideal linear Carangiform motion and the motion resulting from the distributed actuation (see Figure 11(b)). Firstly, let us comment on the results of Figure 11(a). Independently of the flagellum segment, both controllers track closely the references, although the PID controller achieves better tracking as the controller increases the speed of the system. However, an increase in the tracking error is also observed as a consequence of the amplitude increase in the reference signal and the compensation of the dynamic motion of the previous link. In order to evaluate the tracking performance of the controllers, the ITAE and IAE indices were calculated. The results of the performance indexes obtained from the simulation are given in Table 5. It is important to remark that the results of the non-integer order controller correspond to its approximation. The indices validate the previous statement: the best performance is achieved in the first link, where the effort demanded by the reference is lower and the second and third links obtain worse tracking, although the error between them is similar, which shows the robustness of the controller. In addition, the PID controller exhibits a better tracking performance, although its behavior at step response is worse.  
Figure 11: Step response of each robot link when applying: (**a**) PID controller, (**b**) non-integer order integrator and its approximation.  
Figure 12: Swimming robot motion: (**a**) tracking results for the three robot links, (**b**) comparison of the ideal, discretized, and realized linear Carangiform waveform.  
With respect to the results of Figure 12b, the ideal linear waveform, the discretized one obtained from the projection method described in [28] and the realized waveform are illustrated. As can be seen, the swimming robot is able to perform a non-reciprocal motion through a distributed actuation, although there exists a higher amplitude error and lag on the response when applying the fractional integrator, which will affect the robot's propulsion. The non-reciprocal motion for the ideal waveform and the obtained by applying each designed controller can be observed more clearly in Figure 13, which represents a three-dimensional graph showing time, the normal and transverse axis of the swimming robot. For the three cases, the ideal and simulated results show that the flagellum motions perform a wave that travels along the flagellum and over time.
# Response to Variations in River Flowrate by a Spaceborne GNSS-R River Width Estimator
## 4 Discussion  
The monotonic relationship between the AGW and the flowrates for the Pascagoula River case study shown in Figure 6 indicates the presence of a signal that can be exploited for remote-sensing-based river flowrate algorithms. However, further research is required to determine the robustness and limits of this relationship. One important factor is the lack of exact repeat overpass locations by CYGNSS due to the asynchronous relationship between its orbit properties and those of the GPS satellites. Since there are natural variations in width along the river on the order of tens of meters, some of the decorrelation between AWG and flowrate in Figure 6 may be a result of natural variations in width  
Figure 5: Fitted power series for each of the five April 2019 Pascagoula overpasses, using a threshold of 22% of the peak as the cutoff for the portion of the waveform to be fitted.  
Figure 6: Linear regression between the river AGW derived from CYGNSS observations and the river flow rate measured by the USGS stream gauge. The R\({}^{2}\) coefficient is 0.97. Marker colors correspond to the tracks and flowrates shown in Figure 4.  
along the river that have not been accounted for in our analysis. It also remains to be determined over what range of flowrates the AGW metric correlates with the river streamflow. We hypothesize that at flowrates that correspond to stage heights much lower than the flood stage height for the river, the AGW may cease to show as strong a correlation to flowrate due to the river being contained within its banks. At low enough flows, however, sand bars will become exposed and the AGW may again correlate strongly with the flowrate. These limits to the algorithm will be explored in future studies with the additional collection of raw IF data over the Pascagoula River and other rivers.  
The potential utility of the AGW metric is vast; many remote-sensing-based flowrate estimation algorithms require knowledge of river width, which could be derived from the AGW. The river width estimations could provide up-to-date input to remote-sensing-based flowrate retrieval algorithms such as the at-many-stations hydraulic geometry (AMHG) method [(7; 8; 9)], the mean-annual flow and geomorphology (MFG), and mean flow with constant roughness (MFCR) streamflow estimation algorithms, and others that have been proposed in recent years (e.g., [(10; 11)]). Moreover, our results suggest that it may be possible to derive a direct relationship between the flowrate and AGW, bypassing the need for a complicated flowrate estimation algorithm entirely. It should be noted that the question of whether or how best one could expand this method to river width estimation on a global scale is unclear. The correlation between flowrate and AGW demonstrated on the Pascagoula River needs to be tested elsewhere. The relationship between the two may vary, for example, with river bathymetry or the flowrate itself. A comprehensive investigation would likely require ancillary information about both.
# Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks
## 3 Related Work
### Machine Learning
[67] claimed that deep learning approaches are more capable of detecting known and unknown VANET attacks compared to traditional approaches. Their outcomes indicate that CNNs and LSTMs performed better in terms of discovering known attacks with detection rates of 96% and 95%, respectively, while autoencoders are capable of identifying unknown attacks with a detection rate that reached 85%. However, the authors stated that CNNs may struggle with long-term dependencies and LSTM is more complex to train. In addition, an autoencoder may struggle with complex patterns. Based on this thorough review, the authors of this article proposed a lightweight ML model based on an RF classifier with a gain information feature selection method to detect VANET attacks. A balanced version of the well-known and recent dataset CISDS2017 was developed in this study by applying a random oversampling technique. The developed dataset was used to train and test the proposed model. Two layers of enhancement were employed to enhance the model's performance--applying an adequate feature selection method to enhance the model's performance and applying a random oversampling technique to solve the class imbalance problem. Eliminating this problem can greatly improve classification accuracy and avoid bias in prediction caused by the influence of majority classes. Accordingly, the proposed model showed significant performance in terms of classification accuracy, computational cost, and classification error. It achieved an accuracy rate of 99.8%, outperforming all benchmark classifiers--AdaBoost, DT, and MLP. In terms of processing cost, the proposed classifier consumed the least processing time, requiring only 69%, 59%, 35%, and 1.4% of the Ada-Boost, DT, KNN, and MLP processing times, respectively. A summary of the above related work is given in Table 4, which provides key information on each method, including the author, achieved accuracy, dataset used, and limitations. In addition, the model proposed in this article is provided in the last row of the table. This clearly indicates that the proposed model outperforms all relevant and recent methods.
# Characterization of Narrowband Noise and Channel Capacity for Powerline Communication in France
## 3 Main Experimental Results and Discussion
### Noise Cyclostationary Behavior
#### 3.3.1 Summary of the Results for the Sub Rural Site  
Figure 12 gives the pseudo Wigner-Ville transform analysis for the sub rural site in the time-frequency domain. The results exhibit several forms of noise.  
A periodic time-variant noise can be identified. This one is synchronized to half of the mains' frequency (i.e., 10 ms). This, it represents the components of the cyclo-stationary noise.  
The spectrograms also show that periodic noises are typically generated at frequencies lower than 50 kHz.  
Finally, periodic interferences can be identified at frequencies higher than 50 kHz, but the PSD of these disturbances remains at a low level in comparison with the low-frequency disturbances.
## 4 Research Methodology
### Analysis and Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & **Phi Values** & **Indicator** & **Phi Values** \\ \hline L17 & 0.5895 & L27 & 0.0061 \\ L25 & 0.5819 & L11 & \(-\)0.0927 \\ L5 & 0.4957 & L12 & \(-\)0.0927 \\ L15 & 0.4457 & L18 & \(-\)0.1140 \\ L20 & 0.4372 & L13 & \(-\)0.1703 \\ L7 & 0.4098 & L19 & \(-\)0.2056 \\ L26 & 0.3997 & L23 & \(-\)0.2583 \\ L14 & 0.3038 & L21 & \(-\)0.2670 \\ L9 & 0.2702 & L24 & \(-\)0.2677 \\ L6 & 0.2475 & L8 & \(-\)0.4820 \\ L4 & 0.2063 & L1 & \(-\)0.5289 \\ L2 & 0.0924 & L22 & \(-\)0.6342 \\ L3 & 0.0887 & L10 & \(-\)0.6977 \\ L29 & 0.0462 & L16 & \(-\)0.8247 \\ L28 & 0.0148 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 15: PROMETHEE II results for lifecycle cost management in healthcare facilities. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & **Phi Values** & **Indicator** & **Phi Values** \\ \hline P5 & 1.0000 & P6 & \(-\)0.2121 \\ P1 & 0.5873 & P4 & \(-\)0.4978 \\ P3 & 0.3276 & P7 & \(-\)0.7186 \\ P2 & 0.2323 & P8 & \(-\)0.7186 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: PROMETHEE II results for physical aspect of healthcare facilities. This means that delta is between the acceptable ranges. Figure 3: GAIA analysis for (**a**) environmental aspect in healthcare facilities, (**b**) user aspect in healthcare facilities, (**c**) emergency and safety management in healthcare facilities, (**d**) maintenance and repair in healthcare facilities, (**e**) physical aspect of healthcare facilities, and (**f**) lifecycle cost management in healthcare facilities. The GAIA plane shows that pi vector confirms the most important KPIs, which are identified as closeness of action to pi vector, in compliance with PROMETHEE flows. In Figure 3a-f, the convergence of action points to vector pi on the GAIA plane showed that E4, E13, and E1 are the most important KPIs in environmental aspect in healthcare facilities group; U2, U1, and U14 are the most important KPIs in user aspect in healthcare facilities group; S4, S2, and S3 are the most important KPIs in emergency and safety management in healthcare facilities group; M6, M13, and M4 are the most important KPIs in maintenance and repair in healthcare facilities group; P5, P1, and P3 are the most important KPIs in physical aspect of healthcare facilities group; and L17, L25, and L5 are the most important KPIs in lifecycle cost management in healthcare facilities group.
# Temperature Impact on Engineered Cementitious Composite Containing Basalt Fibers
## 2 Materials and Methods
### Mechanical Properties  
Compressive, flexural, and tensile strength tests were carried out on specimens according to ASTM standards. Triplicate specimens for each batch and temperature range were prepared and tested. The average values of the triplicate specimens were taken as the representative results. The compressive strength tests were performed on 50 mm cube ECC specimens conforming to ASTM C109-08. A compression machine with 2000 kN capacity was used to determine the ultimate load-carrying capacity of the ECC specimens under pure compression. An axial compressive load was applied with a rate of 0.4 kN/s according to the ASTM C31 test method. The pressure was continued until the specimen was crushed. The failure point corresponded to the ultimate load- and stress-bearing capacity of specimens in kN and MPa, respectively. The splitting tensile test was carried out to assess the tensile bond strength of the ECC specimens. The same universal testing machine with a rate of 0.4 kN/s was used for splitting tensile tests according to ASTM C496. Prisms specimens with the dimension of 40 mm \(\times\) 40 mm \(\times\) 160 mm and curing age of 28 days were used for the flexural strength test. The test continued within a three-point loading arrangement until an audible and visible crack was observed. The hardened specimens were heated to three different temperatures of 100 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, and 400 \({}^{\circ}\)C for a duration of 10 min after 28 days of curing. In order to prevent cooling shock, samples were left at room temperature to cool down gradually before applying compressive load in the testing machine. Figure 3 shows the testing machines used for flexural and compressive strength.  
Figure 2: Molds after casting for tensile, flexural, and compressive strength tests from top downward, respectively.