esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment
## 5 Results from on Field Test
### Case Study  
Wireless accelerometers were deployed on a road bridge near in Central Italy to monitor its structural integrity and dynamic behavior under operational conditions. The bridge, which spans 18.2 m, was equipped with six sensors, strategically distributed to ensure comprehensive monitoring, with three placed on each side. One accelerometer was positioned at the central span, while the other two were installed at the quarter-span and three-quarter-span positions. Figure 5 shows the mounting schema of the sensors along the bridge and some photos taken during the mounting operations.  
Figure 5: Mounting schema and photos of sensor installation on the real bridge. Subfigure (**a**) shows an operator during the mounting operations, subfigure (**b**) presents a close-up of the mounted sensors, and subfigure (**c**) illustrates the schematic representation of sensor placement.  
The decision to use wireless accelerometers provided a significant advantage in terms of installation time and system scalability. The entire setup process was completed in just three hours, a stark contrast to the time-consuming and labor-intensive procedures required for traditional wired systems. Wired installations typically involve extensive cable routing, physical connections, and additional infrastructure to support data transmission, often requiring several days to complete. By eliminating the need for cables, the wireless system reduced not only the installation time but also the overall complexity of the process. This efficiency translates directly into cost savings, as it minimizes labor hours and reduces the need for specialized equipment or personnel.
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 3 Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme
### Gateway Maintenance Process  
The IV sets the timer and begins the transmission after deciding on the gateway. The connection maintenance process is managed by the periodic channel quality indicator (CQI) reports, which show the current contact channel quality if there is no gateway between the IV and the infrastructure. If a gateway is chosen, however, the transmitting period will be calculated using Equation (12). Afterward, if there are more packets to be transmitted, the IV will check the existing gateway (using Algorithm 3), if any. Otherwise, it reiterates the process of electing the appropriate gateway for the remaining packets (Algorithm 2).  
\[\mathit{Timer}=\mathit{LLT}_{\mathrm{MGW}}-\gamma \tag{12}\]  
where \(\mathit{LLT}_{\mathrm{MGW}}\) is dependent on the gateway selected and \(\gamma\) is the back value to avoid network disconnection before the handoff procedure occurs.  
```
1:The new IV checks for the existing gateway in the coverage area
2:if MGW is FOUND && \(\mathit{LLT}_{\mathrm{MGM}}\) is not expired then
3:The new IV will transmit the request to the existing gateway
4:else
5:Call Algorithm 2
6:endif
```  
**Algorithm 2** Multimetrics gateway selection
## 5 Experimental Setup and Results  
### Experimental Setup  
As shown in Figure 14, the experimental setup included a computer, signal generator (DG1022Z), data acquisition card (NI USB-6356) and probe. The pulse excitation signal was obtained from the signal generator and applied to the probe. The data acquisition card communicated with computer by USB interface. The data acquisition card was set to trigger on the rising edge, which means that the PEC signal was recorded when the signal rising edge began. The probe signal was processed by low pass filter and was enlarged in LabVIEW software (LabVIEW 2015, NI, United States, 2015). The ferromagnetic material can be seen as many small magnetic domain structures. When the environmental magnetic field changes, the magnetic domain organization is ordered in the direction of the magnetic field. Therefore, a magnet can be used to change the environmental magnetic field so that the permeability of the ferromagnetic material is considered to have changed. In order to change the permeability of ferromagnetic samples, a magnet piece was used to change the magnetic field caused by material permeability. The magnet piece was only used to change the magnetic field in the testing environment, which means that any magnet piece could be used in testing. The distance between magnet and sample was set to change the size of the magnetic field, as shown in Figure 15.
## 4 Simulation and Analysis  
The simulation experiment utilized the Starlink constellation; specifically, the data from the Starlink constellation on 19 July 2024 were employed. The ground station was located in Zhengzhou (34.76\({}^{\circ}\)N, 113.65\({}^{\circ}\)E), with an observation elevation angle of 10 degrees. A total of 6623 Starlink satellites were included in the simulation. Satellite data were generated at one-second intervals, and the observation period spanned one hour (for a total of 3600 s).  
To evaluate the satellite selection performance of the proposed MSFGWO algorithm, a comparative analysis was conducted using the traversal method, GWO algorithm, Strategy Fused Grey Wolf Optimization Algorithm A (SFGWO-A), Strategy Fused Grey Wolf Optimization Algorithm B (SFGWO-B), and the MSFGWO algorithm. Specifically, SFGWO-A enhances the global search capability through integrating the perturbation operation from the VNS algorithm and the mutation strategy from the GA into the traditional GWO framework, while SFGWO-B incorporates the EWM strategy into the GWO algorithm to extend the observation duration of satellites. During the experiment, simulations were performed for three distinct scenarios involving the selection of four, five, or six satellites. Each scenario was repeated 10 times, with the average result serving as the final outcome for comparison against the satellite selection obtained using the traversal method.
## 2 Pretreatment Methods for Solids Reduction
### Biological Pretreatment
#### 2.4.1 Enzyme Addition  
The qddition of enzymes such as cellulase, peptidase, carbohydrates, proteases, and lipase to AD influents have been studied to break down the organic components of the sludges thus making more soluble matter available for digestion along with increasing the rate of solubilization [50]. The dose of enzyme, pH, temperature, and exposure time are the factors that determine the efficiency of this pretreatment method, which has only been studied to date at the laboratory scale and has not been applied at full scale. Few studies have shown that a combination of enzymes tend to improve performance over single enzyme dosing. The primary functioning mechanisms are that the enzymes appear to break-down recalcitrant compounds and cell walls within the sludge making soluble particles readily available for AD. A study by [PERSON] et al. on the effects of fungal mash pretreatment on waste activated sludge co-digested with food waste yielded a 19.9% reduction in the VS present in the sludge. The biogas increased from 0.24 L/g VS for the control to 0.37 L/g VS for the pretreated [51]. The fungal mash was produced in situ with the waste including the cake waste and was inoculated with _Aspergillus awamori_. This enzymatic solution effectively hydrolyzed the sludge mixture without later separation of the enzymes [52]. The fungal mash pretreatment of the mixed sludge projected up to 54.3% total sludge VS concentration reduction. The use of cellulase and Pronase-E on the degradation of recalcitrant sludge components for solids reduction was studied by [PERSON] et al. The study used both the enzymes independently and in combination resulted in enhanced TSS and COD removal in an AD by accelerating the hydrolysis step. Total solids reduction was 80% when the enzymes were used, while the TSS reduction was 36% and 29% for Pronase-E and cellulase, respectively [53]. When applied in an upstream anaerobic sludge reactor, the available carbohydrate was consumed by endogenous microorganism which did not contribute to biogas production, but significantly aided the resulting solids reduction. Similarly, pretreatment using amylase and proteases on WAS at 37 \({}^{\circ}\)C and 28 h increased biogas by 23% [51]. Enzyme mixtures have been proposed for sludge reduction with some success noted, but the actual cost and the optimal dosages need to be investigated much more prior to full-scale implementation/commercialization.
# Deep Neural Network Equalization for Optical Short Reach Communication
## 2 Principles of Nonlinear Equalizer
### Memory Polynomial Volterra Equalizer  
Shrinking the architectural complexity of VNLEs makes sense for two reasons. Less digital signal processing (DSP) resources are needed for deployment in actual transceivers and the LS matrices become less ill-conditioned. Figure 2 illustrates the MP-VNLE structure. MP-VNLEs prune general VNLEs simply by removing all cross terms generated by \(y(n)\) and its delayed versions \(y(n-m_{k})\):  
\[\tilde{y}(n)=\sum_{p=1}^{p}\sum_{m=0}^{M}h_{pm}y(n-m)|y(n-m)|^{p-1} \tag{2}\]  
One may rewrite this as  
\[\tilde{y}(n)=\sum_{m=0}^{M}g_{m}y(n-m)\quad\text{ with }g_{m}=\sum_{p=1}^{p}h_{pm}|y( n-m)|^{p-1} \tag{3}\]  
Given a memory tap \(m\), \(g_{m}\) becomes a constant gain to the memory term \(y(n-m)\). \(g_{m}\), in turn, depends only on the polynomial power terms of \(|y(n)|\). Again, \(y(n)\) can be real- or complex-valued.
# Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain
## 3 Proposed Method
### Algorithm Process  
To better illustrate the algorithm flow, Algorithm 1 shows the watermark embedding process and Algorithm 2 shows the watermark extraction process.  
```
Stage1:Training watermark embedding network \(N_{enc}\), watermark extraction network \(N_{dec}\) Input: Original DWI image \(I_{o}\), Original watermark \(W_{o}\) Output: Well-trained \(N_{enc}\), \(N_{dec}\) Required model: \(N_{enc}\), \(N_{dec}\), attack layer \(Att\) While epoch<maxepochs:
1: Robust watermark embedding \(I_{e}=N_{enc}(I_{o})\)
2: Random attack \(I_{a}=Att(I_{e})\) or \(I_{n}=Att(I_{o})\)
3: Watermark extraction \(W_{d}=N_{dec}(I_{a})\) or \(W_{z}=N_{dec}(I_{n})\)
4: Total loss \(L_{stage1}(I_{o},I_{e},W_{o},W_{d},W_{z})\)
5: Update the parameters for \(N_{enc}\), \(N_{dec}\) Stage2:Training watermark extraction network \(N_{dec}\) Input: Original DWI image \(I_{o}\), Original watermark \(W_{o}\), Pre-training \(N_{enc}\), \(N_{dec}\) Output: Well-trained \(N_{dec}\), Marker Image \(I_{m}\) Required model: \(N_{enc}\), \(N_{dec}\), \(Att\), Compensation information generation \(R_{gen}\), Reversible embedding \(R_{emb}\) While epoch<maxepochs:
1: Robust watermark embedding \(I_{e}=N_{enc}(I_{o})\)
2: Compensation information generation \(R_{inf}=R_{gen}(I_{o},I_{e})\) (Section 3.3)
3: Reversible embedding \(I_{m}=R_{emb}(R_{inf})\)
4: Random attack \(I^{\prime}_{a}=Att(I_{m})\) or \(I_{n}=Att(I_{o})\)
5: Watermark extraction \(W_{d}=N_{dec}(I^{\prime}_{a})\) or \(W_{z}=N_{dec}(I_{n})\)
6: Total loss \(L_{stage2}(W_{o},W_{d},W_{z})\)
7: Update the parameters for \(N_{dec}\)
```  
**Algorithm 1** Watermark embedding algorithm.  
```
Input: Marker Image \(I_{m}\), Original watermark \(W_{o}\) Output: Watermark \(W_{d}\), Original DWI image \(I_{o}\) Required model: \(N_{dec}\), Contains watermark test \(W_{tes}\), Copyright Authentication \(C_{aut}\), Integrity Verification \(A_{tes}\), Image Recovery \(R_{rec}\)
1: Watermark extraction \(W_{d}=N_{dec}(I_{m})\)
2: Contains watermark test \(True|False=W_{tes}(W_{o},W_{d})\) (Section 3.6)
3: Copyright Authentication \(True|False=W_{tes}(W_{o},W_{d})\) (Section 3.6)
4: Integrity Verification \(True|False=A_{tes}(I_{m})\) (Section 3.6)
5: Image Recovery \(I_{o}=R_{rec}(I_{m})\) (Section 3.6)
```  
**Algorithm 2** Watermark extraction algorithm.
# Object/Scene Recognition Based on a Directional Pixel Voting Descriptor
## 2 Related Works  
Several works which addressed both feature-based and CNN-based approaches for object and scene recognition have been presented. Researchers have explored several feature extraction techniques, such as SIFT, SURF, ORB, and their variants, to capture discriminative information from visual data. Furthermore, multiple works have focused on the utilization of deep learning architectures, particularly CNNs. These works have contributed to advancing the state-of-the-art in object and scene recognition, offering insights into effective feature representation, model architecture design, and optimization strategies.  
Figure 2: The scene recognition problem: consist of extracting and matching features from a template to a query. Then, depending on the percentage of “successfully” matched features, the scene can be recognized or not. This approach works with relatively high performance under simple scenarios (low changes in texture, illumination, etc.) (**a**), but presents poor performance under complex scenarios (**b**). In those scenarios, deep learning-based approaches have demonstrated a proper trade-off between robustness and computational resources usage.
## 3 Results
### Mechanisms of Polymer-Induced Saturation Stabilization in RTV  
A radial distribution function (RDF) analysis was used to see how RTV's saturation levels change. RDF is a common method for examining how solvent molecules interact in molecular dynamics simulations. By studying RDF, we can understand the attractive forces between solvent molecules and how they work together in a system [46]. Figure 6 shows that RTV's saturation improves significantly when it forms an inclusion complex with PVP K30. This highlights PVP K30's role in keeping RTV saturated, which is important for effective drug delivery. The RDF results suggest that PVP K30 helps RTV interact more effectively with solvent molecules, potentially leading to faster dissolution and better bioavailability. Overall, these insights from the RDF analysis can help us design more effective pharmaceutical formulations.  
The RDF graph shows that Eudragit L100 helps maintain RTV's saturation well, but the interaction between RTV and Eudragit L100 is relatively weak at certain distances. This means that while Eudragit L100 is effective at maintaining saturation, its interaction with RTV is not as strong as with some other polymers. On the other hand, PVP K30 does an excellent job at maintaining saturation due to its effective interactions at specific distances, creating a stable, water-friendly environment. PVP K30, being a water-soluble polymer, significantly helps keep RTV saturated by forming interactions that boost its dissolution in water. This highlights how important PVP K30 is in pharmaceutical formulations where maintaining saturation is crucial. These findings stress the need to carefully choose polymers to ensure optimal saturation maintenance and drug effectiveness. The stronger interaction between RTV and PVP K30 suggests that PVP K30 might be better for formulations aimed at increasing drug bioavailability. Overall, these results also help us understand how different polymers can affect the physical properties of drug compounds.
## 5 Results and Analysis
### System Optimization and Comparison
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Systems** & \(\eta_{\text{stor}}\)**/\%** & **Systems** & \(\eta_{\text{stor}}\)**/\%** \\ \hline CAES & 60.65 & SC-CCES & 73.02 \\ AA-CAES & 67.22 & SSC-CCES & 67.72 \\ LCAES & 56.64 & STC-CCES & 77.75 \\ TC-CCES & 60.69 & TCES-HP & 66.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Energy storage efficiencies of different energy storage technologies. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Technologies** & **Operation Parameters** & **Value** \\ \hline \multirow{5}{*}{CAES [32]} & Turbine import temperature/K & 823/1098 \\  & Turbine import pressure/MPa & 4.2/1.1 \\  & Compressor import pressure/MPa & 0.1 \\  & Turbine export pressure/MPa & 0.1 \\  & After cooling temperature/K & 313 \\ \hline \multirow{5}{*}{AA-CAES [59]} & Ambient temperature/K & 288.15 \\  & Ambient pressure/kPa & 101.3 \\  & Cavern charge pressure/MPa & 8 \\  & Cavern discharge pressure/MPa & 6 \\ \hline \multirow{5}{*}{LCES [59]} & Compressor export pressure/MPa & 8 \\  & Pump1 export pressure/MPa & 14.13 \\  & Turbine export pressure/MPa & 1 \\  & Evaporation pressure/MPa & 3.415 \\  & Turbine2 import temperature/K & 439.787 \\  & Cold terminal temperature difference/K & 5.034 \\ \hline \multirow{5}{*}{TC-CCES [33]} & LST outlet pressure/bar & 30 \\  & HST inlet pressure/bar & 180 \\  & Turbine import pressure/bar & 150 \\  & Expander import pressure/bar & 35 \\ \hline \multirow{5}{*}{SC-CCES [32]} & Turbine import temperature/K & 873 \\  & Turbine import pressure/MPa & 20 \\  & Compressor import pressure/MPa & 7.5 \\  & Turbine outlet pressure/MPa & 8 \\  & After cooling temperature/K & 313 \\ \hline \multirow{5}{*}{SSC-CCES [60]} & High temperature reheater outlet temperature/K & 668.15 \\  & Pressure of energy storage/MPa & 40 \\  & HSR throttle valve pressure drop/MPa & 20 \\  & Final stage expander outlet pressure/MPa & 8 \\ \hline \multirow{5}{*}{STC-CCES [60]} & Outlet temperature of high temperature reheater/K & 668.15 \\  & Pressure of energy storage/MPa & 40 \\  & HSR throttle valve pressure drop/MPa & 20 \\  & Final stage expander outlet pressure/MPa & 2 \\ \hline \multirow{5}{*}{TCES-HP [61]} & Storage pressure/MPa & 17 \\  & Inlet pressure of first stage turbine/MPa & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Optimal operation parameters of different energy storage technologies.
## 3 Proposed Method
### Global Feature Attention Module  
As aforementioned, many of the existing methods suffer from redundant information or increasing numbers of parameters in the upsampling stage. To address this problem, we introduce a global feature attention module (GFA) that utilizes high-level features to select and fuse relevant low-level features, as shown in Figure 4. The GFA module operates in the following steps: First, a 3 \(\times\) 3 convolution followed by a BN layer adjusts the channel dimension of the high-level feature map to make it the same as the low-level feature map. Then, a 3 \(\times\) 3 convolution followed by a BN is applied to extract low-level information. Secondly, by utilizing the channel attention module, we can obtain the channel attention  
Figure 2: The overview of our proposed AFFNet. MaxPool denotes the maxpool2d layer with a kernel size of 2.  
vector from the processed high-level feature map, and the results are then multiplied with the low-level feature map. By assigning distinct weights to the channels of feature maps, the module's capacity to represent features is improved. After that, the size of the high-level features is doubled by using bilinear upsampling. By incorporating the spatial attention module with the previous high-level feature map, we can obtain a spatial vector that has the same dimensions as the low-level feature map. Then, this spatial vector is multiplied with the low-level feature map to extract more relevant contour features about the target. Finally, the processed high-level features and low-level ones are concatenated as the output result.  
In the GFA module, we have utilized two attention modules, one is the channel attention module, and the other is the spatial attention module. Subsequently, we will present an in-depth exposition of the two modules.
# Investigation of Element Migration from Aluminum Cooking Pots Using ICP-MS
###### Abstract  
The present study examined the migration of elements from aluminum cooking pots to foods after the cooking process. This study investigated the impact of pot quality (manufacturer), pot type (traditional or pressure cooker), water supply (tap water/mineral water), food acidity, salt, spices, temperature, and cooking time on the migration of elements into cooked food. The cooking experiments were conducted to simulate the actual cooking conditions. Standard food simulant B, with 3% (\(w/v\)) acetic acid, was used in subsequent cooking trials to confirm the results. Three methods were employed to analyze the elements in the food: ICP-MS, EDS-SEM, and XPS. The cooking pots used in this investigation were examined using a Spectromaxx metal analyzer to characterize their chemical composition. The concentration of aluminum in cooked food samples increased significantly when using an aluminum pressure cooker. Food acidity, cooking duration, and the type of aluminum pot (traditional/pressure cookers) all affected the concentration of elements that migrated into the food. The aluminum level increased from 80.17 to 133.7 \(\mu\)g/g when tomato sauce was added to the food. Increasing the heating time resulted in an increased aluminum content (157.9 \(\mu\)g/g) in the cooked food. Aluminum pressure cookers exhibited the highest amount of aluminum migration into the food. Foods cooked in a pressure cooker made by manufacturer (3) contained the highest aluminum content (252.7 \(\mu\)g/g), which increased the risk of exceeding the daily intake limit of aluminum. The prepared food samples under all conditions showed a safe health profile for daily intake of all elements (Fe, As, Cd, and Pb), except for Al, which exceeded the daily intake limit when using pressure cookers for extended cooking times. The results of element migration into food simulants were consistent with those of food samples. The results confirmed that SEM-EDS and XPS techniques are not suitable for quantifying the elements that migrated into food samples due to their detection limits.  
aluminum cooking pots; pressure cooker; element migration; ICP-MS +
Footnote †: journal: Applied sciences
# Synthetic Image Generation Using Conditional GAN-Provided Single-Sample Face Image
## 3 Proposed Model
### Proposed Generator and Discriminator Network Architecture  
The proposed Conditional Generative Adversarial Network (CGAN) model is composed of two main components: a generator and a discriminator, as depicted in Figures 2 and 3. The generator network comprises an encoder and a decoder. The encoder begins by accepting an image as input and processing it through a series of convolutional layers that progressively down-sample the image. This transformation is illustrated in Figure 3.  
The skip connection layer bridges the encoder and decoder and passes the convolved features to the decoder. This allows the decoder to upsample the image from the extracted features received from the encoder. The resulting image generated by this process is then fed into the discriminator network. The discriminator evaluates two image pairs as input: the input image and the output image produced by the generator. As depicted in Figure 2, the discriminator network itself functions as an encoder that down-samples the paired images through a series of convolutional layers, each followed by max polling. The stride is set to 1 to slightly reduce the image size. It then assesses the extracted features from both images to determine the authenticity of the generated image. The discriminator updates its weights based on the classification error between the generated and original images. Similarly, the weights of the generator are adjusted according to the disparities between the generated and target images.