esa-sceva/satcom-chunk-collection huggingface dataset:
# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
## 3 Method  
The workflow of the proposed method is presented in Figure 4.  
Figure 4: Method used to analyze root zone soil moisture (RZSM) by assimilating PROBA-V LAI and S1 SSM satellite observations into the ISBA land surface model. In situ SSM and RZSM observations were used to evaluate S1 SSM and the analyzed RZSM, respectively.
## 2 RIS-Assisted System and Channel Model
### Proposed Deep Learning Model
#### 2.4.1 Dataset Preparation  
In this paper, 128 OFDM subcarriers are taken into consideration for the transmission of data, and quadrature phase shift-keying (QPSK) modulation is used to map the bits belonging to symbols. The data are transmitted over a RIS-assisted cascaded and direct channel. The BS sends the whole OFDM packet to the UE along with the additional AWGN noise. The generated feature vector \(\mathbf{V_{ce}}\) and matching label are used to store the received OFDM packet as training data samples. The number of labels is created by the combination of data from BS antennas where 4 unique symbols can be generated by each antenna; in this way, \(4^{2}=16\) labels are generated where 2 is the BS antennas number. The feature vector \(\mathbf{V_{ce}}\) holds the complex signal of each symbol in the OFDM packet. To train the suggested model, the complex signal is first divided into real \(\mathfrak{R}_{\epsilon}\) and imaginary \(\mathfrak{I_{m}}\) components. The generated transmission and reception data are divided into training \(\xi\) and validation \(\tau\)data, respectively. A total of 256,000 data samples are generated where 80% are used for training \(\xi\) and 20% for validation \(\tau\). Finally, the dataset and label samples are stored for the training and validation process. The example datasets of the real and imaginary parts of symbols with corresponding labels are shown in Figure 3.
# Low-Power Wireless Data Transfer System for Stimulation in an Intracritical Visual Prosthesis
## 3 System Architecture
### Implanted Receiver
#### 3.2.1 The 1-Bit ADC  
From link circuit-model simulations, the received signal voltage level is expected to be in the range of 0.5 to 3 V. This may sometimes be lower than the digital logic level of the digital receiver, for example, if 1.8 V is used. Traditionally, low noise amplifiers are usually used as the first stage of an RF receiver. These may consume up to several milliwatts of power. However, in the context of the 5-10 mm inductive link, we propose a dynamic latch comparator as a low-power solution while delivering sufficient signal level during digitization.
# Preprocessing Acoustic Emission Signal of Broken Wires in Bridge Cables
## 2 Materials and Methods  
### Basic Theory of Acoustic Emission Technology  
#### 2.1.1 Generation and Propagation of Acoustic Emission Signals  
The nature of the acoustic emission phenomenon lies in the reason that the material is subjected to internal or external forces. In the first stage of deformation, the elastic deformation energy has been stored in the local location of the material. As the material continues to be stressed, the material cracks or even fractures when the deformation reaches a certain level. This is due to the fact that the local stress concentration of the material leads to a rapid concentration of local energy. When the energy reaches the threshold, AE may be generated and release elastic waves. This phenomenon is also known as stress wave micro-vibration [19]. The technique of detecting, recording and analyzing the collected AE signals with special instruments and determining the source of acoustic emission is called the acoustic emission technique [20].  
As a physical mechanical wave, the AE waveform has all the properties of sound. Its propagation, reception and radiation properties of the wave are in accordance with the laws of acoustic waves. The acoustic emission signal propagates in solid metal components in transverse, longitudinal, surface and plate waves, etc. The AE wave is a mixed state of the above-mentioned modes of waves [21].
## 4 Results and Discussion
### Method 1: The Crisp Boundary Approach
#### 4.1.2 Faulty Case  
In an ideal testing scenario, only a single fault scenario is tested. However, for the purposes of demonstration, the same faults from Table 2 (for 0.25 s each) are tested in this case. It can be observed in Figure 10b that the ANFIS monitor has not classified the regions correctly. For example, fault F7 (t = 3.5 s to t = 3.75 s) is classified as 'Region 4' at t = 3.75 s instead of 'Region 3' and fault F5 (t = 2.5 s to t = 2.75 s) is classified as 'Region 3' at t = 2.75 s instead of 'Region 2', as assigned in Figure 10b.  
This inaccuracy in the detection of regions is due to the error in rounding up the values of 'z' while assigning the regions to each fault. The misclassification rate is high, at 67%, as seen in Figure 11, due to the inaccurate region assignments using crisp boundaries. Applying any additional approach like causal reasoning to this method will not be beneficial since the regions are not classified appropriately. In order to reduce the effect of this misclassification problem and to narrow down the fault to the concerned LRU, a fuzzy boundary approach is followed along with causal reasoning in Method 2.
# Quality Monitoring for Laser Welding Based on High-Speed Photography and Support Vector Machine
## 2 Experimental Method
### Features of the Welding Sample
Pearson correlation coefficients between each feature and Feature 1 are shown in Table 4. The features of the table were sorted by the absolute value of \(r\) in ascending order. The absolute value of the correlation coefficient between Feature 4 and Feature 1 was about 0.0665, which indicated that the correlation between them was very weak and thus adding Feature 4 to the subset would provide lots of new information. The second lowest correlation coefficient was between Feature 3 and Feature 1, and the value was close to 0.2210, which meant the new information it could provide was still considerable. The coefficient \(r\) between other features and Feature 1, such as 0.4497, 0.5225 and 0.5603, indicated that they had much stronger correlations with Feature 1, and therefore adding them to the subset should not be able to improve the classification accuracy as much as Feature 4 and Feature 3. Scatter plots of three selected features are shown in Figure 8. As can be observed in the figure, most of the data from different classes were distributed in different areas, which could not be easily separated by linear or quadratic models. More complex and nonlinear models, such as SVM, were needed. The classification results using the Feature 1, Feature 4, and Feature 3 above are shown in Table 5, and the confusion matrices for each experiment are shown in Tables 6-8. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Feature** & \(\left|\mathbf{r}\right|\) & \(\mathbf{r}\) \\ \hline
4 & number of spatters & 0.0665 & \(-\)0.0665 \\
3 & horizontal coordinate of plume centroid & 0.2210 & \(-\)0.2210 \\
6 & average intensity of plume & 0.4497 & 0.4497 \\
2 & height of plume & 0.5225 & \(-\)0.5225 \\
5 & vertical coordinate of plume centroid & 0.5603 & \(-\)0.5603 \\
1 & area of plume & 1.0000 & 1.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pearson correlation coefficients \(r\) between each feature and Feature 1. Figure 8: Scatter plots of selected features: (**a**) Feature 4 vs. Feature 1; (**b**) Feature 3 vs. Feature 1; (**c**) Feature 3 vs. Feature 4. From Tables 5-8, it was noticed that the performance of SVM was improved with the introduction of features. Using only Feature 1, the accuracy rate was only 83.04%. As Feature 4 was added to the feature subset, the accuracy rate increased to 91.55%, especially the accuracy rate of Class E had a sharp increase from 68.65% to 81.92%. With all three selected features, the accuracy rate reached 93.58%. Consequently, SVM was able to achieve satisfactory classification performance with new information provided by the two additional features
## 4 Privacy and Security Enhanced Methods
### Privacy-Preserving Arbitration Mechanism
#### 4.1.2 Basic Homomorphic Encryption-Based Scheme  
To protect output confidentiality while enabling effective arbitration, we propose a homomorphic encryption-based arbitration scheme that decouples the arbitration process from direct access to executor outputs. This approach provides strong privacy guarantees at the cost of increased communication overhead and arbitration delay.  
The protocol operates as follows:  
1. Each online heterogeneous executor \(e_{i}\) computes its output \(O_{i}\) and encrypts it using the public key \(pk\) of the intended recipient (such as the cloud platform), producing ciphertext \(C_{i}=Enc_{pk}(O_{i})\).
2. The executors transmit their encrypted outputs \(C_{i}\) to the arbitration and scheduling module.
3. The arbitration and scheduling module performs homomorphic subtraction operations between each pair of encrypted outputs, generating difference ciphertexts: \[D_{ij}=C_{i}\ominus C_{j}=Enc_{pk}(O_{i}-O_{j}),\] (2) where \(\ominus\) represents the homomorphic subtraction operation.
4. The arbitration and scheduling module transmits these difference ciphertexts \(D_{ij}\) to the cloud platform.
5. The cloud platform decrypts each difference ciphertext using its private key \(sk\): \[\Delta_{ij}=Dec_{sk}(D_{ij})=O_{i}-O_{j}.\] (3)The cloud platform evaluates the consistency of executor outputs:  
\[\Phi(O_{i},O_{j})=\begin{cases}1,&\text{if }\Delta_{ij}=0\\ 0,&\text{otherwise}\end{cases}. \tag{4}\]  
The cloud platform sends the consistency evaluation results back to the arbitration and scheduling module.  
For consistent outputs identified by the cloud platform, the arbitration and scheduling module performs homomorphic addition of the corresponding ciphertexts to generate an aggregated result:  
\[C_{agg}=\bigoplus_{i\in\mathcal{C}}C_{i}=Enc_{pk}\left(\sum_{i\in\mathcal{C}} O_{i}\right), \tag{5}\]  
where \(\mathcal{C}\) is the set of executors with consistent outputs and \(\bigoplus\) represents the homomorphic addition operation.  
The cloud platform decrypts the aggregated result and computes the final output:  
\[O_{final}=\frac{Dec_{sk}(C_{agg})}{|\mathcal{C}|}=\frac{\sum_{i\in\mathcal{C }}O_{i}}{|\mathcal{C}|}. \tag{6}\]  
This scheme leverages probabilistic encryption, a feature of modern homomorphic encryption algorithms that ensures identical plaintext messages encrypt to different ciphertexts, thus preventing the arbitration and scheduling module from determining consistency by direct ciphertext comparison. Consequently, the module must rely on homomorphic operations and cloud-based decryption to complete the arbitration process.
# Simulation Tests of the Passing Distance of Ships on a Two-Way Fairway
## Materials and Methods
### Simulation Experiment of Passing Ships on Waterways  
The simulation study procedure used in the design of marine waterways and the determination of conditions for their safe operation is carried out in the following order [9; 10]:  
* Formulation of the research problem, including identification of the design goal, the simulation methods used and the type of simulators;
* Construction of ship movement models on a selected simulator and their verification, as well as construction of a basin model;
* Designing the experimental setup and conducting the experiment;
* Development and statistical analysis of research results.  
The purpose of the simulation study was to determine the distance between passing vessels of equal size on different straight sections of the Swinoujscie-Szczecin waterway with existing navigation markings. The research was conducted on a Kongsberg Polaris-FMBS type simulator at the Maritime University of Szczecin, where the above model of ship-ship interaction was also implemented.  
The distance was determined for passing vessels of equal size with the parameters presented in Table 2:  
Simulation models of the motion of these ships were built and verified. The verification of the motion models was based on the maneuvering characteristics of existing ships. The simulation models consider the ship-to-ship interaction described in the previous chapter (Section 2.1).  
The simulation experiment consisted of performing four series of passing maneuvers of identical ships (12 passing maneuvers in a series). One of the passing ships (a target ship) moved on an assumed course parallel to the track axis. On the second ship (own-ship), the passing maneuver was performed by navigators with maneuvering experience on specific types of ships (captains, including sea ferry captains) each with one passing maneuver in the series.  
Maneuvers were performed on the Swinoujscie-Szczecin waterway on the following straight sections (Figure 6)  
1. [wide=0.5 em,width=0.  
Navigators performing simulated passing maneuvers used visual navigation methods, using existing navigation markings. The Kanal Piastowski is an artificial inland canal, bounded by banks approx. 200 m wide. The fairway in the Zalew Szczecinski has a lateral navigation marking system: paired buoys and beacons. At the same time, in the simulation studies, the buoys were treated as the beacons due to the lack of drift of the buoys on their anchor chains. The spacing of the buoys between Brama Torowa I and II is r = 150 m, while between Brama Torowa II and III it is r = 250 m. In the Port of Swinoujscie, the Kanal Zbiorczyk is the part of Odra River bound by built-up banks with a width 275 m--295 m. The speed of the ships when passing was about 8 knots.
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 4 Cooperative Relative Positioning
### GNSS-Based Relative Localization
#### 4.2.1 Absolute Positioning
An enhancement of GNSS positioning with maps has been developed in [81; 82; 83]. Last, but not least, cooperative approaches for deriving absolute positions have been proposed in [84; 85; 86; 87; 88]. In all of this work, the objective is to estimate the position of the ego vehicle in absolute Earth coordinates.
# Strength Correlation and Prediction of Engineered Cementitious Composites with Microwave Properties
## 2 Methodologies
### Microwave Near-Field Detecting Technique
#### 2.2.2 Microwave Measurement Setup  
The schematic of the measurement setup is illustrated in Figure 2; an Agilent N5225A performance network analyzer (PNA) capable of generating continuous wave signals from 100 MHz to 50 GHz was used. The monitoring of electrical properties of ECC specimens was undertaken by using an R-band RWG probe (WR-430) that has an aperture size of 4.30 inches (109.22 mm) \(\times\) 2.15 inches (54.61 mm). The RWG probe was connected to a single port of PNA that supplied a constant output power of \(-\)5 dBm via a flexible microwave cable. At the beginning of daily measurement, a calibration was the crucial procedure in order to achieve accurate measurement results. When calibrating the system with RWG probe, the impedance of a'matched load' was used as the impedance reference. The load impedance was matched to the waveguide characteristic impedance across the frequency bandwidth of R-band. Normalized impedance was then attained by setting OFFSET \(Z_{0}\) to 1 ohm for each standard and setting system \(Z_{0}\) (SET \(Z_{0}\)) of the PNA to 1 ohm [30].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Constituent** & **PC** & **FS** & **FA-F** \\ \hline
**CaO** & 58.1 & - & - \\
**SiO2** & 24.7 & 95.6 & 76.3 \\
**Al2O3** & 6.4 & 1.9 & 20.2 \\
**SO3** & 3.5 & 0.3 & - \\
**MgO** & 2.4 & - & - \\
**FeO** & 2.2 & 0.3 & 0.4 \\
**TiO2** & - & 0.9 & 0.9 \\
**K2O** & 0.5 & - & 0.4 \\
**LOI** & 2.2 & 1.0 & 1.8 \\
**Total** & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Chemical composition by weight of Portland cement (PC), fine sand (FS) and fly ash (FA-F).
## 3 Methodology
### Dry Beach Length Measurement
#### 3.2.1 Boundary Identification  
The traditional region growth algorithm is a classical image segmentation algorithm that segments images by expanding regions based on pixel similarity. The basic idea is that the surrounding pixels are gradually expanded according to gray scale and color differences, starting from one or more seed points until the predetermined conditions are met [38; 39]. Based on the traditional region growth algorithm, this paper proposes a region growth algorithm that automatically selects seed points by using superpixel segmentation and combines edge extraction operators to identify the beach top and waterline. This method can effectively identify the boundaries of dry beaches and extract pixel coordinates. In this study, boundary identification can be carried out according to the following steps, as shown in Figure 6. The following procedure is written and executed with MATLAB version R2021b.  
Image preprocessing: Firstly, the input image contrast is enhanced, and the image is smoothed using the Gaussian filter. To reduce the processing time of region growth, the high resolution of the original image (8192 \(\times\) 5460) is downsampled to 800 \(\times\) 600, and the image is restored to the original resolution after processing. In addition, due to the reflective phenomenon of the water surface, it is necessary to remove the reflective spot of the water surface before identifying the waterline as shown in Figure 6b.  
located in the dry beach area were selected as the seed points for regional growth, as shown in Figure 6e,l.  
Region growth segmentation and selection mask: The seed point of the dry beach area is used as the starting point of the region growth, expanding the neighborhood pixels. At each extension, the Euclidean distance between the pixel value of the neighborhood and the current region is calculated to characterize the similarity between the neighborhood and the current region. If the difference between the adjacent pixel values and the current region is less than the set threshold, the adjacent pixel is added to the current region (white mask, as shown in Figure 6f,m). Repeat the region growth process until all pixels have been checked. After the regional growth is completed, some small areas of the dry beach are not grown and are considered non-dry beach areas (black mask with small connection area), as shown in the red box in Figure 6f,m. The black masks with a small connection area were removed, and the black mask with the largest connection area was selected as the non-dry beach area, as shown in Figure 6g,n.  
Boundary extraction: The edge-detection operator was performed to extract the edge pixel from the selected mask, as shown in Figure 6h,o. At present, widely used edge detection methods, such as the Sobel operator and Canny operator, can effectively detect the edge information that represents the boundary and extract the edge pixels by calculating the gradient or second derivative of the image [40; 41]. In this paper, edge pixels are extracted using the Canny operator.
## 3 Energy Consumption of Distributed Solutions under Different Scenarios  
### Distributed Computing on WWPM Systems  
In our previous study [36], we compared the lifetime of a WWPM system for both a centralized solution and a fully distributed solution. In this study, we focus on the fully distributed solution, which implements distributed computing, and investigate further ways to improve the WSN lifetime by reducing the energy consumption at the node level. We first evaluate the energy performance of the distributed solution by implementing three variants of Distributed Kalman Filter (DKF) and then proceed further to improve the node's lifetime by using duty cycling and hierarchical sensing techniques. Thus, we study the energy consumption of the fully distributed WWPM solution under two main scenarios. In scenario 1, we assess the energy consumption of three DKFs implemented in the context of WWPM systems. In this scenario, we implement distributed computing by implementing a DKF on the sensor nodes as the sole energy reduction technique. In scenario 2, we employ a hybrid energy reduction technique that combines distributed computing, hierarchical sensing, and duty cycling. Distributed computing is achieved by implementing DKFs on the sensor nodes, hierarchical sensing is achieved by using both a high-power and high-accuracy accelerometer (LSM9 DS1) and a low-power and low-accuracy accelerometer (ADXL344), and duty cycling is implemented via an interrupt-driven sleep/wakeup mechanism. We then analyze the contribution of each energy conservation technique on the energy reduction achieved by the hybrid technique. Table 2 presents the main rationale behind each energy conservation technique and the components of the sensor node that it affects to reduce energy consumption.  
Detailed explanations of scenarios 1 and 2 are provided in the following Sections 3.2 and 3.3, respectively.
## 2 Materials and Methods
### Antioxidant Activity  
#### 2.16.1 ABTS Radical-Scavenging Activity  
The ABTS radical-scavenging assay was assessed following a previously outlined method [19], with some modifications. The ABTS radical cation decolorization assay evaluated the elderberry hydrolate's free-radical-scavenging activity. To initiate the formation of the ABTS** cation radical, 7 mM ABTS was mixed with 2.5 mM potassium persulfate (1:1 \(w/w\)) in water and left to incubate in the dark at room temperature for 12-16 h before utilization. The resulting ABTS** solution was diluted with methanol until reaching an absorbance of 0.700 at 734 nm. After adding four different concentrations of the hydrolate (1%, 5% and 15%, \(w/v\)) to the diluted ABTS** solution, the absorbance was measured 30 min after thorough mixing. Each assay included an appropriate solvent blank, and all the measurements were performed in triplicate. The percent inhibition of absorbance at 734 nm was calculated using the formula:  
\[\text{ABTS}^{\bullet+}\text{ scavenging effect}(\%)=\frac{(A_{B}-A_{A})}{A_{B}} \times 100\ (2)\]  
where A\({}_{\text{B}}\) represents the absorbance of the ABTS radical mixed with methanol. A\({}_{\text{A}}\) represents the absorbance of the ABTS radical mixed with the elderberry hydrolate or standard. The Trolox equivalent antioxidant capacity (TEAC) was calculated by preparing a standard Trolox curve of a standard Trolox solution.