esa-sceva/satcom-chunk-collection huggingface dataset:
# Sewater Effect on Fatigue Behaviour of Notched Carbon/Epoxy Laminates
## 2 Materials and Methods  
The material used in this study was a carbon-fibre-reinforced epoxy composite, which was prepared via autoclave/vacuum-bag moulding using a unidirectional high-strength carbon prepreg (Teixpreg HS 160 REM). The plates were produced with 12 layers, arranged in a stacking sequence of (0/45/90/-45/0/90)\({}_{\mathrm{s}}\), corresponding to a thickness of 1.8 mm and V\({}_{\mathrm{f}}\) = 40%. More details about the manufacturing process can be found in [34]. As shown in Figure 1, the specimens were obtained from these plates and consisted of a rectangular cross-section geometry with a 3 mm-diameter central hole. The hole was drilled via twist drill bits for composite materials.  
After production of the samples, they were immersed into natural and artificial seawater for 30 and 60 days. For this purpose, artificial seawater was made following the recommendations outlined in the ASTM D1141 standard [35] with a pH of 8.2 and a NaCl concentration (i.e., weight/volume ratio) equal to 2.45%. The synthetic compounds in the solution are shown in Table 1. To simplify the preparation, compounds whose concentration was less than 0.2 g/L were not included. The natural seawater was collected from the estuary of the port of Figueira da Foz, a coastal village in central Portugal bathed by the Atlantic Ocean. After the exposure time, the samples were superficially dried and submitted to the experimental programme.  
Fatigue tests were carried out in a 100 kN servo-hydraulic tension-compression machine, under uniaxial constant-amplitude loading, using sinusoidal waves, with a stress ratio (R) equal to 0.1 and a frequency of 10 Hz (see Figure 2). The applied normal stress amplitudes (calculated based on the net cross-section, i.e., dividing the applied force by the net cross-section area) and the corresponding fatigue lives for the tested specimens are summarised in Table 2. The stiffness modulus variations were evaluated directly from the  
\begin{table}
\begin{tabular}{c c c c c} \hline
**NaCl** & **MgCl2** & **Na2 SO4** & **CaCl2** & **KCl** \\ \hline
24.53 & 5.20 & 4.09 & 1.16 & 0.695 \\ \hline \end{tabular}
\end{table}
Table 1: Artificial seawater compounds (g/L).  
Figure 1: Specim geometry used in the fatigue tests (units: mm).  
vertical displacement of the crosshead relative to the zero-load displacement. Finally, after the fatigue tests, fracture surfaces were examined by optical microscopy, LEICA MC120 HD DM4000 M LED, to identify the dominant damage mechanisms.
# Quality of Service Criteria in Integrated Passenger Transport Systems: An Overview
## 2 Urban Mobility  
The global trend of urbanisation, coupled with economic growth, has led to increased transport demands on a daily basis. Transport demand is a key factor in developing transport services for specific areas or regions. Demand for transport can be characterised as a variable that expresses the demand for transport between locations [7].  
Over recent decades, car ownership and the use of individual transport have expanded in many urban areas. This growth in individual transport contributes to heightened transport demand, resulting in escalating external costs for communities. External costs are generated by transport users and not paid by them but by society, such as congestion, air pollution, climate change, accidents and noise but also up- and downstream processes, costs to nature and the landscape or additional costs in urban areas [8]. Urban traffic congestion occurs when the volume of vehicles on the roads surpasses the infrastructure's capacity, causing delays and increased fuel consumption and air pollution, particularly during peak hours.  
The concept of sustainable UM has gained widespread recognition in many cities worldwide. It has brought a new understanding of organising UM around the objectives of wider accessibility, reduced car dependence, saving energy and reduced carbon emissions, which increases the liveability of the cities [9].  
The modal shift in UM involves promoting the use of various transport alternatives, thereby reducing the dependence on individual transport in urban areas. The modal split represents the ratio of different transport modes in the total journey from the origin to the destination point [10,11]. A coordinated package of mutually reinforcing transport and land-use policies and measures is the key to a successful modal shift. Measures to encourage a modal shift to public transport can be separated into two main categories [9]:  
1. \"Pull\" measures--persuade the user to use public transport;
2. \"Push\" measures--dissuade the user from using individual transport.  
Over the past 25 years, Vienna succeeded in reducing the car share of trips from 40% to 27%, Munich from 40% to 33%, Berlin from 35% to 30%, Zurich from 39% to 30% and Hamburg from 48% to 42% [12]. The five cities have established a unified framework of complementary transport and land-use policies that, when considered together, have led to a decline in the speed, convenience and affordability of individual transport. At the same time, these policies have endorsed safety, the convenience and practicality of walking, cycling and public transport.  
Modal shift is an important element of sustainable mobility planning, as it helps reduce transport's environmental impact and improve the quality of life for people living in urban areas. PPT systems worldwide are undergoing significant transformations driven by technological advancements, environmental concerns and changing urban mobility policies.
## 4 Experiments
### Ablation Experiments and Result Analysis
#### 4.4.4 Transformer  
Transformer captures long-range dependencies through self-attention mechanisms, constructs global contextual associations, and compensates for the locality limitations of convolutional operations. To verify its impact on model performance, this module was removed through ablation experiments for comparative analysis. The data in Row 5 of Table 1 show that the IoU scores of the model without Transformer decrease by 1%, 1.63%, and 1.82% on LEVIR-CD, WHU-CD, and CLCD datasets, respectively, with F1 values showing a concurrent downward trend. Among them, the impact on LEVIR-CD is minor, but the impact on CLCD is significant because the farmland scene requires global semantic associations to distinguish dispersed changes. The visualization results in Column e of Figure 6 show that large areas of edge defects and locally missed green areas appear in the farmland regions of CLCD, indicating that the lack of global modeling capability causes the model to rely solely on local contextual information, making it difficult to effectively integrate multi-regional structural features and thus affecting change detection accuracy. The above experiments, from both quantitative indicators and visual representations, collectively verify the critical role of Transformer in the change detection network proposed in this paper.
# Experimental Study of Coaxial Jets Mixing Enhancement Using Synthetic Jets
## 2 The Experimental Method
### The Coaxial Jet Nozzles  
Two coaxial jet nozzles were used in the experiment, and the dimensions are shown in Table 1. The central jet was ejected from the inner duct, and the annular jet was ejected from the outer duct. The air of nozzle jet was provided by the wind tunnel. The inner duct outlet diameter of the nozzle was represented by \(\mathrm{D_{i}}\), the outlet velocity was \(\mathrm{U_{i}}\), and the Reynolds number there was 13,000. The outer duct outlet diameter was \(\mathrm{D_{o}}\), and the outlet velocity was \(\mathrm{U_{o}}\). The structure of nozzle A and the combined actuators disk is shown in Figure 1. Nozzle B had a similar structure as nozzle A, except that the outer duct diameter was smaller.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Nozzle**} & \(\mathbf{D_{i}}\) & \(\mathbf{D_{o}}\) & \(\mathbf{U_{i}}\) & \(\mathbf{U_{o}}\) & \(\mathbf{Re_{i}}\) & \(\mathbf{n}\) & \(\mathbf{U_{s}}\) & \(\mathbf{C}\) \\  & & **(mm)** & **(mm)** & **(m/s)** & **(m/s)** & **(m/s)** & **(m/s)** & **(m/s)** & **(\%)** \\ \hline
1 & Nozzle A & 10 & 50 & 20 \(\pm\) 0.21 & 10 \(\pm\) 0.16 & 1.3 & 4 & 5 \(\pm\) 0.11 & 0.45 \\ \hline
2 & Nozzle B & 10 & 25 & 20 \(\pm\) 0.21 & 10 \(\pm\) 0.19 & 1.3 & 4 & 5 \(\pm\) 0.11 & 1.38 \\ \hline
3 & Nozzle A & 10 & 50 & 20 \(\pm\) 0.21 & 10 \(\pm\) 0.16 & 1.3 & 12 & 5 \(\pm\) 0.11 & 1.36 \\ \hline
4 & Nozzle A & 10 & 50 & 20 \(\pm\) 0.21 & 10 \(\pm\) 0.16 & 1.3 & 4 & 9 \(\pm\) 0.14 & 1.47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of nozzles and synthetic jet actuators.  
Figure 1: _Cont._
# Energy-Efficient Subcarrier Pairing and Power Allocation for DF Relay Networks with an Eavesdropper
## 2 System Model and Problem Formulation
### Optimization Problem Formulation  
The key task of this work is to maximize the EE through the joint optimization of the subcarrier pairing and power allocation. From (7) and (8), the objective function is expressed as  
\[\Sigma_{E}\left(\mathbf{P}_{S},\mathbf{P}_{R},\boldsymbol{\rho}\right)=\frac{ \frac{1}{2}\sum\limits_{i=1}^{K}\sum\limits_{j=1}^{K}\rho_{i,j}\log_{2}\left(1 +\min\left\{\Gamma_{SR,i},\Gamma_{RD,j}\right\}\right)-\frac{1}{2}\sum \limits_{j=1}^{K}\log_{2}\left(1+\Gamma_{RE,j}\right)}{\underbrace{\sum \limits_{i=1}^{K}\sum\limits_{j=1}^{K}\rho_{i,j}\left(P_{S,i}+P_{R,j}\right)+P _{\text{SC}}}_{\triangleq P\left(\mathbf{P}_{S},\mathbf{P}_{R},\boldsymbol{ \rho}\right)}} \tag{9}\]  
where \(\mathbf{P}_{S}=[P_{S,1},P_{S,2},\cdots,P_{S,K}]\) and \(\mathbf{P}_{R}=[P_{R,1},P_{R,2},\cdots,P_{R,K}]\) are the transmit power vectors for the source and the relay nodes, respectively, while \(P_{\text{SC}}=P_{SP,S}+P_{SP,R}\). The term \(\sum_{i=1}^{K}\sum_{j=1}^{K}\rho_{i,j}\left(P_{S,i}+P_{R,j}\right)\) serves as the actual transmit power consumption under subcarrier pairing, and \(\boldsymbol{\rho}\in\{0,1\}^{K\times K}\) is a matrix with the entry \(\rho_{i,j}\) which represents a subcarrier pairing indicator to guarantee that each subcarrier in the SR hop is only paired with a single subcarrier in the RD hop, i.e., \(\rho_{i,j}=1\) when the \(i\)th subcarriersubcarrier in the SR hop is paired with the \(j\)th subcarrier in the RD hop and \(\rho_{i,j}=0\) otherwise. With total transmit power and subcarrier pairing constraints, the secure energy efficiency (EE) maximization problem can be formulated as follows:  
\[\begin{split}\text{(P.1)}\max_{\mathbf{P}_{S},\mathbf{P}_{R}, \boldsymbol{\rho}}&\Sigma_{E}\left(\mathbf{P}_{S},\mathbf{P}_{R}, \boldsymbol{\rho}\right)\\ \text{s.t.}&\text{(C.1)}\quad\sum_{i=1}^{K}\sum_{j=1 }^{K}\rho_{i,j}\left(P_{S,i}+P_{R,j}\right)\leq P_{max}\,;\\ &\text{(C.2)}\quad\sum_{j=1}^{K}\rho_{i,j}=1,\;\;\forall i\,;\\ &\text{(C.3)}\quad\rho_{i,j}\in\{0,1\},\;\;\forall i,j\,;\\ &\text{(C.4)}\quad P_{S,i}\geq 0,P_{R,j}\geq 0,\;\;\forall i,j\,. \end{split} \tag{10}\]  
where the objective function \(\Sigma_{E}\left(\mathbf{P}_{S},\mathbf{P}_{R},\boldsymbol{\rho}\right)\) denotes the secure EE and the constraint \(\text{(C.1)}\) presents the entire power consumption under the subcarrier paring condition with a maximum transmit power budget \(P_{max}\) for the source and the relay nodes. Here, the subcarrier pairing constraint \(\text{(C.2)}\) ensures that each subcarrier in the first hop is only paired with a single subcarrier in the second hop, and vice versa, while \(\text{(C.3)}\) stipulates that the subcarrier pairing variables take binary values. The constraint \(\text{(C.4)}\) makes the transmit power values of the source and the relay nodes non-negative.
## 4 Results and Discussion
### Filtering for 1 st De-Noising  
After analyzing the optimal thermal contrast in the 2D image for each moving speed, a process was performed to improve the detectability. For the 1 st de-noising, four types of filtering were applied to the image [37; 38; 39]. Figure 10 shows the non-filtered raw image and the filtered image for 5 mm/s, where the thermal contrast was the largest.  
Figure 10: Image with filtering based on raw image: (**a**) raw image, (**b**) median filtering, (**c**) mean filtering, (**d**) Gaussian filtering, and (**e**) NLmeans filtering.  
Figure 9: Thermal profile graphs of rows for each moving speed: (**a**) 5 mm/s, (**b**) 10 mm/s, (**c**) 15 mm/s, and (**d**) 20 mm/s.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Moving Speed** & **Thermal Contrast** \\ \hline
5 mm/s & 2.41 \({}^{\circ}\)C \\
10 mm/s & 1.52 \({}^{\circ}\)C \\
15 mm/s & 1.04 \({}^{\circ}\)C \\
20 mm/s & 0.89 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 3: The value of the thermal contrast between the defective area and sound area.  
To compare and analyze the detectability of each filtered image, the signal-to-noise ratio (SNR) was calculated. The SNR is defined as the ratio between the signal area and the background noise, where the signal area is defined as a defective part and the noise area is defined as a sound area. The selected ROI area was a defect whose index signature was A4. The SNR equation is as follows [40]:  
\[\text{SNR}=20\log_{10}\left(\frac{|\text{DROI}_{\text{mean}}-\text{SROI}_{ \text{mean}}|}{\sigma}\right) \tag{5}\]  
where \(\text{DROI}_{\text{mean}}\) and \(\text{SROI}_{\text{mean}}\) are the arithmetic means of all the pixels in defective area and sound area, respectively, and \(\sigma\) is the standard deviation of all the pixels the sound area.  
Table 4 shows the SNR values for all filtered images for each moving speed. Overall, the SNR values were improved in the filtered images compared to the raw image (non-filtering). In addition, based on the 5 mm/s image with the highest SNR improvement, it can be seen that the SNR of LIT was significantly improved after the 1 st de-noising presented in [18] was applied. For 5 mm/s and 10 mm/s, median filtering effectively improved the detectability, and for 15 mm/s and 20 mm/s, mean filtering, effectively improved the detectability. Moreover, median filtering and mean filtering can be applied faster than Gaussian filtering and NLmeans filtering because their calculation methods are simple. In fact, although the performance of each PC was different, they were possible to calculate within seconds. After that, binarization was performed by applying the Otsu algorithm to the filtered images selected at each moving speed.
# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 2 Materials and Methods
### Feature Extraction  
It is typical for teacher networks to be pre-trained on standard benchmark datasets, and the knowledge they have acquired can be represented explicitly as parameters or implicitly embedded in features. Building on previous research [39], knowledge transfer is conducted at the feature level, using the features of a pre-trained teacher network to guide the learning of a student network's features hierarchically.  
As illustrated in Figure 1, any given hazy image **I** is simultaneously fed into SCSNet and 1/n-SCSNet for feature learning. For the sake of simplicity, the features produced by the \(i\)-th block of SCSNet are uniformly represented as \(\textbf{t}_{i}\), while the features of the corresponding layer in 1/n-SCSNet are denoted as \(\textbf{s}_{i}\). Note that \(\textbf{t}_{i}\) and \(\textbf{s}_{i}\) have the same resolution, but the number of channels of \(\textbf{s}_{i}\) is 1/n of that of \(\textbf{t}_{i}\). To reduce the computational cost during training, only some representative features are distilled:  
\[\textbf{T}=\{\textbf{t}_{1},\,\textbf{t}_{2},\cdots,\textbf{t}_{8}\}, \tag{1}\]  
\[\textbf{S}=\{\textbf{s}_{1},\,\textbf{s}_{2},\cdots,\textbf{s}_{8}\}, \tag{2}\]  
where **T** and **S** represent the feature sets to be distilled from SCSNet and 1/n-SCSNet, respectively.
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 5 Discussion
### Comparison with Other Methods  
Kriging was the best of the four tested methods in the group-delay scenario, regarding all test cases including jitter, missing samples, noise, and higher SNR. Margolis and Kriging provided similar simulations results for noise-free signals in the single-delay scenario (Figure 10). If noise is added, Margolis is the more accurate method for \(f_{L}>0.18\) Hz. For lower frequencies, the RMSE remained above \(-47\) dB, and Kriging provided more accuracy (Figure 11b).  
The required CPU time was similar for both methods. It remained below 1 ms including additional pre-calculations once per input sample. Both methods are suitable for real-time applications.  
Kriging was the least noise-sensitive method and the only one that could provide an RMSE below \(-50\) dB for the case of missing samples combined with noise. However, Kriging has the disadvantage that two additional parameters must be set correctly. The variogram range can be set to a fixed value of \(r=6.T_{s}\) or \(r=6.5\cdot T_{s}\), depending on the scenario. The nugget value must be adapted to the actual noise of the signal.  
Akima should only be applied if a band limit to \(f_{0}<0.1\) Hz, or an OSR \(>5\) is acceptable and the signal contains high noise above the tested RMSA \(=-60\) dB.  
The comparison of Kriging was limited to three reference methods. Dedicated optimization methods might provide higher accuracy, e.g., compressed sensing assumes that the signal contains only a limited number of spectral components or wavelets. Such methods require high computations resources, mostly beyond the limitations of real-time processing.  
Furthermore, the accuracy of Kriging might be improved by manually fitting of the variogram to a specific input signal. However, such fitting is only feasible, if the statistical properties of the signal do not change over time and if a large set of recorded sample data is available.
## 5 Validation and Results
### Dynamic AR Marker Performance  
The first tests sought to find the optimum blinking frequency of the dynamic AR marker, to maximize data throughput for an acceptable error rate. This frequency was estimated to lie in the interval between 3 and 6 Hz, as shown in Figure 16. Inside this range, the best frequency was \(\sim\)5 Hz.  
Another relevant performance evaluation target was the maximum detection distance, to assess the usability of the system proposed against other technologies that cannot attain both high directionality and relatively long ranges, such as NFC, RFID, WiFi and Bluetooth. This distance is directly proportional to the actual size of the marker. Figure 17 shows the maximum detection distances for our mobile device, for dynamic marker sizes between 2 cm and 10 cm. Smaller sizes would be unfeasible, as the resulting distances would not be valid for AR applications. Nevertheless, bigger sizes would be indeed feasible and even desirable, if longer detection distances are necessary. For example, it would be possible to arrange the marker LEDs around the electrical panel. The prototype presented in this paper used a 6 \(\times\) 6 cm marker yielding a maximum interaction distance of about 160 cm.  
Figure 15: AR application in the testbed deployment showing the status of an electrical panel.  
We also evaluated the effect of the device camera incidence angle in the detection rate of the dynamic marker. We tested this both for detection and tracking. Figure 18 shows the results. They suggest robust detection is feasible for angles between \(-50\) and \(50\) degrees, which is perfectly adequate for the purposes of the system. By adding tracking to the recognition, this range widened to (\(-60\), \(60\)).  
Figure 16: Error rate of the VLC message _versus_ marker blinking frequency.  
Figure 17: Maximum dynamic marker detection distance _versus_ marker size.  
Figure 18: Dynamic marker detection rate _versus_ camera incidence angle.  
The testbed demonstrates the interest and feasibility of the combination of IoT and AR technologies and highlights the advantages in terms of usability and information availability that can be achieved with our proposal.
## 5 Results Analysis  
### Simulation Analysis under Harmonic Excitation  
The wheel without a mass block is called conventional suspension; the vibration-absorbing wheel structure without active control is called passive vibration-absorbing wheel structure (PVAWS); the vibration-absorbing wheel structure with time-delay feedback control is called the time-delay vibration-absorbing wheel structure (TDVAWS). Simulation verification of the vibration reduction efficiency of the absorbing wheel structure is completed near the resonance frequency of the conventional suspension. Take the optimized time-delay control parameters into Equation (1). The SMA, SD, and DTD of the vehicle are simulated under 11.25 Hz, as shown in Figure 7. The performance indexes of the vehicle are given in Table 5.  
Figure 7: Simulation comparison under harmonic excitation. (**a**) sprung mass acceleration comparison chart; (**b**) suspension deflection comparison chart; (**c**) dynamic tire deflection comparison chart.  
As illustrated by the simulation results in Figure 7, it can be seen that SMA, SD, and DTD of the vehicle equipped with the new vibration-absorbing wheel structure under harmonic excitation are better than those of the conventional suspension. From Table 5, it can be obtained that the RMS of SMA, SD and DTD of PVAWS are improved by 43.28%, 43.26%, and 31.54%, respectively. However, the RMS values of SMA, SD and DTD of TDVAWS are improved by 92.41%, 91.87% and 54.13%, respectively. Thus, the vibration reduction effect of TDVAWS is significantly better than that of PVAWS. It can be concluded that TDVAWS has an excellent effect on resonance. From the above data analysis, we can see that the new vibration-absorbing wheel structure plays an effective role in both the vehicle ride comfort and handling stability under resonant frequency band.
## 3 Results and Discussion
### The Antioxidant Activity Changes
#### 3.2.1 DPPH
In a study conducted by [PERSON] and [PERSON], it was also shown that microwave treatment (at a device power of 210 W) increased the antioxidant activity (measured as a percent of the total DPPH free-radical scavenging capacity) of raw red peppers from 48.00% to 68.97% (DPPH Scavenging %). In the same experiment, when a higher microwave treatment power (700 W) was applied, an even greater increase in antioxidant activity was observed (an increase from 48.00% to 74.03% of DPPH radical scavenging (DPPH Scavenging %) was recorded [37]. Also, in the research carried out on field tomato (_Solanum Lycopersicum_ L.), it was shown that the microwave treatment process conducted for 5 min increased the TAA by more than 21% compared to unprocessed raw material [28]. It should be borne in mind that the antioxidant activity of microwave-processed pepper fruits is not only due to the phenolic compounds themselves. Other compounds such as carotenoids, ascorbic acid, and non-enzymatic browning products (e.g., melanoidins) also contribute to the total antioxidant activity [38].
## 3 Performance Analysis of iPyroxy Block
### S11 Reflection Coefficient  
Measuring the reflected signal when an electromagnetic wave strikes a material is necessary to analyze the S11 parameter for the iPyroxy block. A high S11 value indicates significant reflection and possible signal loss, while less reflection and a better impedance match are indicated by a low S11 value.