esa-sceva/satcom-chunk-collection huggingface dataset:
# Inspection of Semi-Elliptical Defects in a Steel Pipe Using the Metal Magnetic Memory Method
## 2 Materials and Methods  
The measurement system for the ferromagnetic pipe sample is shown in Figure 2. An ATmega2560 microcontroller board (Arduino, Turin, Italy) is used for the data acquisition of the magnetic field sensor, actuator control, and data transmission to the computer. The data processing and MMM signals are plotted using MATLAB software (version 2022).  
The MMM signals are measured with the three-axis magnetoresistive sensor MAG3110 (Freescale Semiconductor Inc., Austin, TX, USA) that is placed at a fixed distance of 2 mm from the pipe surface. The proposed system can measure the MMM signals of defects on the surface of ferromagnetic pipes in rotation using the one-step motor NEMA-17 (Leadshine Technology Co., Shenzhen, China) driven by the DVR8825 (Texas Instruments, Dallas, TX, USA) shield board. The data of the magnetoresistive sensor are transferred to a microcontroller board using I2C. Next, the data of this microcontroller board are transferred to a computer using USB. The structure of the measurement system was fabricated with non-magnetic materials, such as nylamid, copper, and aluminum. This system can be used for pipes with different diameters.  
The pipe is collocated on the nylamid rotary disk that is driven by the x-axis step motor using a speed-reduction copper gear. This speed-reduction gear has internal and external diameters of 78.5 mm and 89 mm, respectively, a width of 5.5 mm, and a total of 102 teeth. A speed reduction from 1.8 degrees/step to 0.00227 degrees/step is achieved with the gear system, as shown in Figure 3.  
Figure 1: Theoretical results of the tangential and normal MMM signals around a defect or SCZ of a ferromagnetic sample.  
The control of the step motors for pipe movement and sensor data acquisition/transmission is achieved by the microcontroller program that is developed in the Arduino programming language. The rotational and longitudinal movements of the pipe are driven by the \(x\)-axis and \(y\)-axis step motors, respectively. Figure 4 depicts the program flowchart used to measure the MMM signals of the defects on the pipe surface. The main program is constantly pooling a command, which is serially received from the computer, to set a counterclockwise or clockwise pipe rotational movement. The pipe rotational angle is computed by an encoder value reading within an interrupt service routine (ISR). A counterclockwise command (N_Clk) is first received at the start of the scan. The sensor data, current degree, and arc length are serially sent to a computer within the counterclockwise scan until a reference angle is reached. Then, a clockwise scanning command is received (Clk), and the same information is serially sent until the initial position is reached. The data are serially received by the computer using the MATLAB software, in which the MMM signals are processed.  
Figure 3: The gear system of the measurement system.  
Figure 2: Measurement system of the MMM signals around semi-elliptical defects on the surface of a ferromagnetic pipe.
# Independent System Calibration of Sentinel-1B
## 9 Interferometric Verification  
In this section, the performance of Sentinel-1B repeat-pass SAR interferometry (InSAR) consistency is analyzed and derived results are discussed, focusing on the quality of the InSAR compatibility. The following parameters give information on the quality of the Interferometric SAR (InSAR) compatibility:  
* **Burst mis-synchronization**, i.e., the ability to start interferometric acquisitions at orbital positions with the same argument of latitude, thus observing targets on the ground under the same Doppler centroid.
* **Mean Doppler centroid frequency**, related to the attitude stability of the satellite for different passes.
* **Resulting common Doppler bandwidth**, coming from the combination of the two previous points.
* **Baseline in the zero Doppler plane**, in order to check whether the satellite is kept within the orbital tube and also relevant in terms of common bandwidth range.
* **Residual azimuth coregistration error consistency** (using SAR Data) for the exploitation of the azimuth shifts for accurate coregistration or geophysical measurements.  
Section 9.1 provides the evaluation of these parameters, made for several passes by systematically analyzing pairs of acquisitions. Only annotation parameters, precise orbit information and a DEM are needed to retrieve the burst mis-synchronization, the mean Doppler centroid frequency and the baseline information. Annotation files of Sentinel-1A (S1A) and Sentinel-1B (S1B) products acquired during the commissioning phase of Sentinel-1B have been employed. The residual azimuth coregistration error analysis has been performed employing SLC data, which achieves a very accurate measurement by exploiting the overlapping areas between bursts. These results are summarized in Section 9.2.  
Figure 27: Cross-talk derived from DLR corner reflectors for different modes (blue: SM, red: IW, green: EW).
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 16 Hierarchical Clustering in a Heterogeneous Network  
### System Model and Design Objectives  
Clustering in a network of heterogeneous nodes which contains both sensors and actuators was considered in [11]. Since the actuator nodes can be deployed with external power sources, they were chosen to be the cluster heads. The cluster head role is not passed to other nodes and therefore some general sensor nodes in the cluster could consume more energy to transmit directly to the cluster head than others. This leads to imbalance in the energy consumption in the clusters which would shorten the lifetime of the network. A multilevel hierarchical structure was proposed in [11] to solve this problem by selecting \"intermediate nodes\" that could relay the data from the general sensors in the clusters to the cluster head. The scheme assigns general nodes to intermediate nodes in order to minimize the maximum energy consumption across all the nodes in the cluster. The following are the main elements of the system model in [11]:* A single cluster was considered in which there are three types of nodes, general (non-relaying) nodes, intermediate (relaying) nodes and a single cluster head node.
* A general node can either transmit to the cluster head directly (single hop) or via intermediate nodes (multihop).
* Each general node can forward data to only one intermediate node.
* The amount of packets that each intermediate node can receive and aggregate, \(k_{i}\), depends on the remaining energy of intermediate node \(i\).
* The problem was considered on a packet by packet basis. So in the model, each general node can transmit up to one packet to an intermediate node. Therefore, each intermediate node \(i\) can be assigned at most to \(k_{i}\) general nodes.
* The node positions were fixed and known to each other.
## 2 Materials and Methods
### Composite Preparation and Experimental Design  
The graphical description of the research method is shown in Figure 1.  
In the production of the composites, 2 mL catalyst and 1 mL accelerator were utilized for each 100 g of polyester resin. The choice of 1 mL accelerator is to avoid a sudden reaction which could affect composite mechanical properties. The measured quantity of CPH was mixed with the UPT at room temperature and stirred continuously for 3 min after which MEKP was added using a syringe and stirred continuously for another 2 min to obtain a homogenous mix. The accelerator was added and stirred for another 1 min before pouring the mix in a mould of dimension 180 \(\times\) 160 \(\times\) 5 mm smeared with a releasing agent for easy removal of composite (Figure 1). The composite was left to cure at room temperature for 24 h as shown in Figure 1 before demoulding and manually cutting off overflow flakes. This was repeated for varying weight fractions of cowpea husk particles which ranged from 10% and so on. A hybrid composite of 3 wt.% clay and varying weight fraction of CPH was produced to check the effect of clay addition on the CPH/UPT. Sample description and composition for the production of the particle boards are shown in Table 1. The amount of clay particles used was fixed at 3 wt.% in line with previous works from the literature [46]. An unreinforced UPT sample was fabricated to serve as control for comparison in studying the effect of the CPH reinforcement.  
Mechanical behaviour of the composites was analysed by carrying out tensile, flexural, and hardness test. These properties of particulate reinforced polymers are dependent on factors such as weight fraction, extent of distribution, particle size, interfacial adhesion, presence of agglomerates, and voids among others.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**Designation**} & \multicolumn{3}{c}{**Composition (wt.\%)**} \\ \cline{3-5}  & & **Polyester** & **Clay** & **CPH** \\ \hline
100\% UPT & Control & 100 & 0 & 0 \\
10\% CPH & A1 & 90 & 0 & 10 \\
12\% CPH & A2 & 88 & 0 & 12 \\
14\% CPH & B1 & 86 & 0 & 14 \\
16\% CPH & B2 & 84 & 0 & 16 \\
18\% CPH & C1 & 82 & 0 & 18 \\
10\% CPH + 3\% clay & C2 & 87 & 3 & 10 \\
12\% CPH + 3\% clay & D1 & 85 & 3 & 12 \\
14\% CPH + 3\% clay & D2 & 83 & 3 & 14 \\
16\% CPH + 3\% clay & E1 & 81 & 3 & 16 \\
18\% CPH + 3\% clay & E2 & 79 & 3 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental design of composite.  
Figure 1: Graphical description of the research method.
## 2 Materials and Methods
### Fuzzy Vector Spaces and the Object-Oriented Model (FVSOOM)
#### 2.2.1 Normalization Function  
We must use a normalization function \(H:[Min(x_{i}),Max(x_{i})]\rightarrow[-1,1],H(VC)=0\), where VC corresponds to the null vector. The normalization function H is described by the following Equation (6). It is used to adapt the definition domain of attribute values to fit in the \([-1,1]\) interval:  
\[H:\mathbb{R}\rightarrow[-1,1],\ H(x)=\begin{cases}-1&if\ x=min,\\ (x-cv)/(max-cv)&if\ x\geq cv,\\ (x-cv)/(cv-min)&if\ x=cv,\\ 1&if\ x=max\end{cases} \tag{6}\]  
An example of the use of H(x) for the attribute Age is given in Table 1, for the pulmonary embolism example application. Two methods of calculating FVS are shown below. The first is expressed by Equations (7)-(10).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Age** & **H(x)** & \(\mu\)**Age(H(x))** & **Age** & **H(x)** & \(\mu\)**Age(H(x))** \\ \hline
35 & \(-1.00\) & \(-1.00\) & 51 & 0.07 & 0.17 \\
36 & \(
## 3 Results and Discussion
### Volatile Composition
#### 3.3.1 Free Volatile Compounds
& 3931.17 \(\pm\) 777.40 & 2h85.84 \(\pm\) 2441.86 b & 1830.33 \(\pm\) 1899.09 b & 137.49 \(\pm\) 196.5 b \\ \hline \multirow{3}{*}{CRL} & 2015 & 1584.69 & 9901.53 & 307.94 & 1298.48 & 2945.80 & 1643.00 & 41.58 \\  & 2016 & 750.43 & 1875.65 & 644.13 & 1284.78 & 2986.58 & 1143.00 & n.d. \\  & 187.85 & 1405.03 & 664.33 & 892.51 & 2918.33 & 663.31 & 14.46 \(\pm\) 142.38 \(\pm\) 142.39 \(\pm\) 142.38 & \\  & 9934.33 \(\pm\) 638.41 & 142.88 \(\pm\) 970.00 a & 5188.83 \(\pm\) 180.30 a & 1155.39 \(\pm\) 2274.8 a & 2718.16 \(\pm\) 360.67 b & 1133.46 \(\pm\) 475.4 a & 138.35 \(\pm\) 239.9 \\ \hline \multirow{3}{*}{LOAR} & 2015 & 2519.85 & 3012.166 & n.d. & 2707.04 & 4310.74 & 780.03 & 208.36 \\  & 2016 & 592.58 & 14915.65 & 927.05 & 742.75 & 656.50 & 2907.47 & n.d. \\  & 311.25 & 1905.05 & n.d. & 1006.72 & 1123.28 & 156.78 & 32.30 \\  & 2918.23 \(\pm\) 2827.22 & 2126.66 \(\pm\) 8819.154.40 & 309.02 \(\pm\) 825.32 a & 1060.70 \(\pm\) 1102.53 a & 4003.50 \(\pm\) 274.64 b & 1723.43 \(\pm\) 1057.52 a & 802.26 \(\pm\) 1121.44 b \\ \hline \multirow{3}{*}{VB} & 2015 & 266.09 & 203,335.81 & n.d. & 2448.60 & 1093.66 & 13,129.63 & n.d. \\  & 2016 & 705.56 & 1434.42 & n.d. & 4034.46 & 3948.07 & 1146.312 & n.d. \\  & 2017 & 802.13 & 7499.42 & n.d. & 998.07 & 1623.99 & 7980.99 & n.d. \\  & 528.93 \(\pm\) 290.37 a & 1408.05 \(\pm\) 5644.74 b & n.d. a & 2483.38 \(\pm\) 1503.00 a & 2221.91 \(\pm\) 1518.24 b & 1087.22 \(\pm\) 2596.11 a & n.d. a. \\ \hline \multicolumn{3}{c}{Significance} & \multicolumn{3}{c}{ns} & \multicolumn{3}{c}{ns} & \multicolumn{3}{c}{ns} & \multicolumn{3}{c}{ns} & \multicolumn{3}{c}{ns} & \multicolumn{3}{c}{ns} \\ \hline \multirow{3}{*}{AL} & 2015 & 118.48 & 32.88 & n.d. & 64.78 & n.d. & 8823.94 & 27,001.00 \\  & 2016 & 341.65 & 95.50 & n.d. & n.d. & 53. & 4661.76 & 82,003.07 \\  & 2017 & 1436.86 & 128.37 & n.d. & 219.57 \(\pm\) 37.0 a & 17.12 \(\pm\) 296.76 \(\pm\) 360.39 & 6609.37 \\  & 2015 & 3149.46 & 2.72 \(\pm\) 187.4 & n  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Variley** & **Year** & **ACIDS** & **ALCOROIDS** & **ALDEHYDES** & **Cs** & **ESTERS** & **PHENOLS** & **THIDICS** \\ \hline  & 2015 & 30.51 & 32.90 & n.d. & n.d. & 23.38 & 200,08.14 & 57,400.21 \\ VB & 2016 & 123.86 & 66.07 & n.d. & n.d. & 10.65 & 5672.47 & 40,500.18 \\
2017 & 135.99 & 52.02 & n.d. & n.d. & 12.03 & 5816.19 & 24,235.38 \\  & 98.29 & 98.82.4 & 43.66 + 97.9 a & n.d. & n.d. & 153.58 + 6.98 & 10,589.39 + 8252.84 & 40,8182.1 + 16,455.7 a \\ \hline   Significance & \multicolumn{1}{c}{ms} & \multicolumn{1}{c}{ms} & \multicolumn{1}{c}{ms} & \multicolumn{1}{c}{ms} & \multicolumn{1}{c}{ms} \\ \hline  & \multicolumn{1}{c}{PAHs: polycyclic aromatic hydrocarbons: All: \"Abrupt: BB: \"Runcen Blancov: CRL: \"Cint: Latino\": \"Courier: \"W: \"Veridello Blancov: \"d.-a: more detected,\" and n indicate a significant or \(\geq\) 0.05 and no non-significant difference, respectively. Internal standards: \"metal-2-2-
## 2 Related Work
### Latent Space
In this context, latent space serves to map random noise to a meaningful space, enabling the generated samples to possess structure and regularity. The application of latent space is not limited to generative models; it also extends to tasks such as classification and regression [26; 27]. For instance, in classification tasks, the latent space transforms input data into a more compact representation, enabling the classifier to distinguish between different data classes more effectively. In regression tasks, the latent space captures underlying patterns in a lower-dimensional representation, thereby enhancing the model's regression performance.
## 5 Applications and Challenges  
### Applications  
#### 5.1.1 Auto-Zoom Imaging  
Auto-zoom systems can sense changes in the environment in real time, intelligently analyse the position and movement of the target object, and adjust the focal length quickly and accurately to ensure that the image remains clear and stable at all times. This system has become a key technology component in many important industries such as consumer electronics, industrial automation, medical equipment, and defence. While traditional autofocus systems mainly rely on complex mechanical structures and motor drives for focus adjustment, the emergence of DETL provides a new solution for the miniaturisation, low power consumption, and high flexibility of autofocus systems. [PERSON] et al. [85,86] developed a dielectric elastomer-driven passive autofocus system with liquid, as shown in Figure 13a, which identifies the in-focus and out-ofocus images by calculating the standard deviation of the image and then adjusts the voltage to achieve autofocus. The voltage can be adjusted to achieve autofocus. [PERSON] et al. [66] developed a dielectric elastomer-driven eye movement lens, which can control the change in the field of view and focal length of the lens by using the electrical signal of eye movement, as shown in Figure 13b, and the zoom ratio can reach 132.6%. [PERSON] et al. [56] developed a dielectric elastomer-driven liquid droplet lens and integrated it into the camera lens of a smartphone. Due to its
## 4 Methods (ASMAG Algorithm)
### Data Pre-Processing
#### 4.1.3 Cloud Cover Mapping  
Although the satellite scenes selected for this study are mostly cloud-free, a cloud detection algorithm is applied to detect potential clouds over glaciers. Here we follow the approach of Irish (2000) [53] that detects clouds in two stages. At first, clouds are classified by excluding dark surfaces, snow is identified and removed with the NDSI (reflectance of snow and clouds is similarly high in the red part of the spectrum), and warm surfaces with high reflectance, vegetation, soil, etc., are identified using 5 out of 7 Landsat bands. In the second stage, a different threshold is applied to the thermal band to discriminate cold clouds from snow surfaces that usually have a higher temperature (at or closer to 0 \({}^{\circ}\)C). The cloud cover mask is calculated for the entire Landsat scene and intersected with the glacier mask to determine the cloud cover percentage for each glacier. If cloud cover over individual glaciers is less than 10%, the glacier is included in the processing of the related scene. The method by [53] works for all Landsat sensors, except the (cloud-free) MSS scene because MSS has no thermal band.
# Landslide-Tunnel Interactions and Control Countermeasures under an Orthogonal System
## 4 Research on the Application Effect of Anti-Slide Piles
### Anti-Slide Pile Form  
Figure 14 shows that anti-slide piles are divided into three types according to their role in the tunnel-landslide systems: the upper blocking (U-b) type, the lower supporting (L-s) type, and the upper block lower support (U-b and L-s) type.  
Figure 12: Displacement distribution of surrounding rock surface. (**a**) Before reinforcement. (**b**) After reinforcement (unit: mm).  
Figure 13: Distribution characteristics of tunnel lining and anti-slide piles.  
An upper blocking anti-slide pile can stabilize the upper side sliding mass adjacent to the tunnel, ensuring that tunnel deformation does not occur. A lower supporting anti-slide pile can prevent the soil under the tunnel from sliding to ensure the stability of the landslide and the safety of the tunnel. When there are no signs of landslides, laying anti-slide piles under the tunnel can stabilize the mountain. The upper blocking and lower supporting anti-slide pile approach is a combination of the former two. The anti-slide pile on the upper side of the tunnel bears most of the landslide pressure to prevent the tunnel from being squeezed. The anti-slide pile under the tunnel prevents the landslide from sliding between the two rows of piles, thus eliminating the landslide's impact on the tunnel and ensuring its safety.  
Figure 15 shows tunnel deformation in its natural state and with the three anti-slide pile forms. Under the same conditions, the upper blocking and lower supporting anti-slide pile approach minimizes lining deformation in the tunnel and has the best reinforcement effect. For the arch shoulder and arch waist of the tunnel near the mountain, the control effect of the upper blocking anti-slide pile on tunnel deformation is \(1.58\) times that of the lower supporting anti-slide pile. The lower supporting anti-slide pile exhibits better control over the deformation of the arch shoulder and the tunnel crown on the far-mountainside than the upper blocking anti-slide pile. Unlike the natural state without anti-slide piles, the three forms reduce the deformation value of the tunnel's lining.
# Removal of Methylene Blue by Metal Oxides Supported by Oily Sludge Pyrolysis Residues
## 2 Materials and Methods
### Regeneration Performance  
The composite materials recovered after adsorption equilibrium in batch experiments were rinsed with distilled water to remove the residual methylene blue dye solution, and then dried. The dried composite material was placed in a stripping solution contained in a 250 mL Erlenmeyer flask. The solution contained 200 mL distilled water adjusted to pH 7 using 0.1 M HCl and NaOH. The flask was then placed in a thermostatic oscillator with settings as given in Section 2.3 to attain adsorption equilibrium. The composite materials were recovered, dried, and placed in a flask containing 200 mL of 5 mg/L initial solution of methylene blue for the next equilibrium adsorption experiment. This cycle was repeated 4 times. The regeneration performance of carbon materials was evaluated by regeneration efficiency and loss rate [25].  
\[R=\frac{Q_{t}}{Q_{0}}*100\% \tag{8}\]  
where \(R\) is the regeneration efficiency of the carbon material, %; \(Q_{t}\) is the adsorption capacity of the composite material after the \(t\)-th regeneration, mg/g; and \(Q_{0}\) is the adsorption capacity of the carbon material before regeneration, mg/g.  
\[L=\frac{m_{0}-m_{e}}{m_{0}}*100\% \tag{9}\]  
where \(L\) is the regeneration loss rate of the carbon material, %; \(m_{e}\) is the mass of the carbon material after the \(e\)-th regeneration, g; and \(m_{0}\) is the mass of the carbon material before regeneration, g.
## 2 Data and Segmentation Methods
### Dataset
#### 2.1.4 Denoising Data Preparation  
After initial segmentation experiments with the spine dataset we found it is a much more challenging task than the brain volumetric segmentation and training the network only on the dataset was not sufficient for highly precise segmentation. Instead of data augmentation, which for 3D algorithms greatly expands computation time, we used denoising autoencoder pre-training. Principle with this method lies in the fact that we add noise to the training data and let the neural network to learn the representation between noised and denoised data and therefore extracting features of the input image and proper learning of deep layers. An example of original training image, image with added noise and denoised image by our network can be seen in Figure 4.  
Training dataset consisted of normal training data with added linear 30 percent noise with normal distribution. The network was trained to denoise data into its original form. As seen in Figure 4 the network learned the representation properly, denoised image only lacks higher level of details [20].
## 8 Retrieval Performance
### Sea Surface Height  
The retrieval performance of sea surface height could be characterized by the standard deviation of the estimated height. The delays of retracked and non-retracked waveform are obtained through determining the AToLE. The standard deviation of sea surface height error could be expressed as  
\[\sigma_{h}=\frac{\sigma_{\text{M}}}{2\cdot\cos\theta} \tag{22}\]  
where \(\sigma_{\text{M}}\) is the standard derivation of the AToLE. Table 3 gives the comparison of the standard deviation of the estimated height using retracked and non-retracked waveform, from which it is seen that (1) when the correlation windows are refreshed on each coherent integration time period, the best retrieval performance with the standard derivation of 4.71 m is obtained; (2) for the waveform with the TRP of 1 s and 3 s, the standard derivation of the estimated delay is 6.19 and 11.49 m, which are improved over 6 and 10 times than the non-retracked waveforms. Moreover, it is also noted that the  
Figure 13: MSE between the retracked waveform and the one compensated on each correlation integration time period as the function of the DCR for (**a**) UK-DMC and (**b**) UK-TDS.  
precision of the estimated height for retracked waveform is decreases as the TRP increasing. Retracking method proposed above significantly improves the precision of the estimated height.