esa-sceva/satcom-chunk-collection huggingface dataset:
# An Innovative Construction Technique for Curved Structures
## 4 Prototype  
According to the model proposed in Figure 6, a combined solution between b and c was assumed, as illustrated in Figure 7, in order to build a first experimental prototype. In fact, a total of nine blocks (I-IX) and eight hinges (1-8) were necessary to reach a semi-circle shape. In particular, seven blocks were identical (i.e., II-VIII) with an extrados length equal to 328 mm from \(\alpha\geq 33^{\circ}\) (solution c), while two base blocks (I and IX) were almost double-length (i.e., 657 mm) according to solution b. Both the thickness and the width of the arch were 150 mm, and the total span was 2000 mm long.  
A local stone, namely, Leccese stone, with a mass density equal to \(\sim\)1700 kg/m\({}^{3}\), was used. The trust-line method was used in order to check the stability of the proposed curvature for the prototype, as mentioned in the Introduction section, according to [17; 18]. The shape of the prototype was optimal, considering that the transversal cross-sections were completely compressed (trust-line entirely falls within the central core of inertia of the arch cross-section). It is remarkable to note that the proposed construction method is valid for any curvature of the arch (e.g., round, pointed, elliptical, lancet,segmental, rampant, Tudor, etc.). The stone's compressive strength was estimated experimentally equal to 12.03 \(\pm\) 1.25 MPa, according to ASTM C170/C170M-17 [27]. The blocks were cut by using CAM (Computer-Aided Manufacturing) waterjet technology suitable for stone cutting. As a consequence, the control of the geometrical dimensions, including the design curvature, was very sharp.  
First, the extrados of the blocks was cleaned with compressed air to eliminate surface dust, and then it was prepared with a film of epoxy primer. Afterward, the blocks were joined by gluing a carbon sheet (\(150\times 3600\) mm\({}^{2}\)) on the extrados through an epoxy-based matrix. The main properties of the Carbon-FRP (CFRP) are reported in Table 1, as tested according to ASTM D7565/D7565M-10 [28]. The hinge in between two side-by-side blocks was obtained by using the butyl tape (see Figures 1 and 8) for a length of 10 mm and a width of 150 mm (arch width), avoiding the bond between the FRP and the substrate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Tensile Strength** & **Tensile** & **Young’s Modulus** & **Nominal Thickness** & **Nominal Mass Density** \\  & **(GPa)** & **Strain (\%)** & **(GPa)** & **(mm)** & **(g/cm\({}^{3}\))** \\ \hline Mean & 2721.06 & 1.28 & 210.89 & & \\ Median & 3072.40 & 1.34 & 209.76 & & \\ Standard Deviation & 668.80 & 0.22 & 23.75 & & \\ Coefficient of Variation & 0.25 & 0.17 & 0.11 & & \\ \hline \hline \end{tabular}  
* data from a technical sheet.  
\end{table}
Table 1: Carbon Fiber-Reinforced Polymer (CFRP) characteristics.  
Figure 8: Detail of the hinge achievement: (**a**) butyl tape removal and (**b**) hinge achievement.  
Figure 7: Prototype geometry (_dimensions in mm_).
# Semantic Segmentation Based on Depth Background Blur
## 3 Proposed Method
### Embed into an Existing Network  
According to (8), the selection of fuzzy factor \(\sigma\) is merely related to the background rate(\(R^{B^{*}}\) and \(R^{B}\)). Therefore, for different datasets, we only need to calculate the proportion of each category before using the bokeh method, which can be used as a general method. With the FCN network, the overall network architecture after adding our bokeh module is shown in Figure 5. For each block, the convolution stride is one and the stride of pooling is two. There are two more convolution operations with stride 1 after \"Conv5 + pool\". After each downsampling, the size becomes half of the original. The decoder consists of outputs of three different structures. First, FCN-32s(Output_1) is obtained from the results of \"Conv5 + pool\" through 32x upsampling. The output of Conv5 + pool with a 2x upsampling is added to the output of \"Conv4 + pool\" to obtain \(Add\_1\). Then, FCN-16s(Output_2) is acquired from \(Add\_1\) through 16x of upsampling. Similarly, as shown in the figure above, we can obtain FCN-8s (Output_3). Detailed structure is shown in Table 3. The network backbone can use AlexNet [27], VGGnet [28], and GoogLeNet [29].  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Name** & **Details** & **Kernel Size** & **Stride** & **Output Size** \\ \hline \multirow{2}{*}{Conv1 + pool} & Conv + BN + ReLU \(\times\) 2 & \(3\times 3\times 64\) & 1 & \(64\times 512\times 512\) \\  & MaxPool & \(2\times 2\) & 2 & \(64\times 256\times 256\) \\ \hline \multirow{2}{*}{Conv2 + pool} & Conv + BN + ReLU \(\times\) 2 & \(3\times 3\times 128\) & 1 & \(128\times 256\times 256\) \\  & MaxPool & \(2\times 2\) & 2 & \(128\times 128\times 128\) \\ \hline \multirow{2}{*}{Conv3 + pool} & Conv + BN + ReLU \(\times\) 3 & \(3\times 3\times 256\) & 1 & \(256\times 128\times 128\) \\  & MaxPool & \(2\times 2\) & 2 & \(256\times 64\times 64\) \\ \hline \multirow{2}{*}{Conv4 + pool} & Conv + BN + ReLU \(\times\) 3 & \(3\times 3\times 512\) & 1 & \(512\times 64\times 64\) \\  & MaxPool & \(2\times 2\) & 2 & \(512\times 32\times 32\) \\ \hline \multirow{2}{*}{Conv5 + pool} & Conv + BN + ReLU \(\times\) 3 & \(3\times 3\times 512\) & 1 & \(512\times 32\times 32\) \\  & MaxPool & \(2\times 2\) & 2 & \(512\times 16\times 16\) \\ \hline \multirow{2}{*}{Deconv\_32x} & ConvTranspose & \(32\times 32\times C\) & 32 & \(C\times 512\times 512\) \\ \hline \multirow{2}{*}{Deconv\_16x} & ConvTranspose & \(16\times 16\times C\) & 16 & \(C\times 512\times 512\) \\ \hline \multirow{2}{*}{Deconv\_8x} & ConvTranspose & \(8\times 8\times C\) & 8 & \(C\times 512\times 512\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detailed structure of the network. Backbone: VGG16, Input size: \(512\times 512\), \(C\): the number of object classes.
# Analyzing Zone-Based Registration under 2-Step Paging in Mobile Communication Network
## 2 Zone-Based Registration and 2-Step Paging  
### Zone-Based Registration  
When the UE moves to a new zone, it registers the zone to the network database and stores it in the zone list in the UE. A case in which the UE only maintains one zone is referred to as 1 ZR. As shown in Figure 1, when a UE moves from zone A to zone B, the UE stores zone B in the zone list and registers the location as zone B in the network database. Accordingly, the stored zone list of the UE and the network database happen to be the same. At this time, when a call is made to the UE, the UE connects the call by paging to all cells in the zone where the UE was last registered.  
A UE may store one or more zones in a zone list. The case in which two zones are stored is called 2 ZR. As shown in Figure 2, after the UE registers zone A, it enters register zone B, and when a call to the UE occurs while in zone B, the network pages zone B and connects the call.  
By contrast, if the UE is in zone B and then returns to zone A, it does not register again, since the zone is already in the zone list. Therefore, the number of location registrations of 2 ZR is less than that of 1 ZR; however, the paging cost increases. If an incoming call occurs after the UE returns to zone A, the network first pages zone B--which was registered recently--since the network does not know that the UE is returning to zone A. If there is no response, the call is connected by paging zone A. As such, 2 ZR uses a 2-step paging method. Nevertheless, the paging cost is bound to increase compared to that of 1 ZR. In summary, when 2 ZR is adopted, the location registration cost is reduced compared to 1 ZR, but the paging cost is increased.
# Conjugation of Urokinase to Water-Soluble Magnetic Nanoparticles for Enhanced Thromolysis
## Materials and Methods
### Evaluation of the Amount of Urokinase Bounding on PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs  
The amount of urokinase bound on PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs was determined by using Bicinchoninic Acid (BCA\({}^{\text{TM}}\)) Protein Assay Reagent Kit (P0009, Beyotime, Shanghai, China) through a multi-mode microplate reader (FlexStation3, Molecular Devices, San Jose, CA, USA). FlexStation3 can measure absorbance, fluorescence intensity, fluorescence polarization, luminescence, and time-resolved fluorescence. In this study, a calibration line was established to correlate absorbance and urokinase concentration in solution. We prepared 10 urokinase solutions, with the concentration increasing from 0.1 mg/mL to 1 mg/mL in sequence. Quantities of 200 \(\upmu\)L BCA reagent and 20 \(\upmu\)L of each solution were injected into the sample wells of the 96-well plate. Another solution in a well without urokinase was set as the control. The plate was subsequently preserved at room temperature for 2 h. The absorbance  
Figure 3: Size distribution of **(a)** PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs in water at the beginning of dilution and 100 min later; **(b)** PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs in physiological saline at the beginning of dilution and 100 min later; **(c)** Urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs in water at the beginning of dilution and 100 min later; **(d)** Urokinase@PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs in physiological saline at the beginning of dilution and 100 min later.  
of the solution in each well was detected by FlexStation3 microplate reader at 562 nm. Figure 4a shows that the absorbance OD\({}_{562}\) varies linearly with urokinase concentration. Because the absorbance of the solution can be detected by FlexStation3, the concentration of urokinase in the reaction solution can be directly read by this line. The amount of urokinase attached to NPs was calculated as \(D=W-CV\), where \(W\) is the total amount of urokinase added in the reaction, \(C\) is the measured concentration of urokinase in the supernatant after centrifugation of the NPs solution after completion of the urokinase reaction, and \(V\) is the volume of the supernatant. For simplicity, the test was conducted using 1 mg of PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs. Figure 4b shows that the obtained amount of urokinase bound to magnetic NPs varies with the amount of reactive urokinase. Experiments were repeated five times. The quantity of urokinase immobilized on the 1 mg of magnetic NPs increased from 80 \(\upmu\)g to 756 \(\upmu\)g with the use of urokinase from 0.1 mg to 0.8 mg; it then appears saturated and does not increase even if more urokinase is used. Therefore, the maximum dose of synthetic product (756 \(\upmu\)g of urokinase coated on 1 mg of PMAO-OA-Fe\({}_{3}\)O\({}_{4}\) NPs) was used in subsequent experiments.
## 3 Data Preparation
### Characteristics of Mechanical Components  
As shown in Figure 2, we estimated how many parameters a specific object had in our dataset. Notice that although the dataset had different types of mechanical components, the number of parameters should be roughly the same. We first measured the inner and outer circle radii from the motor image. In addition, we measured the height of the hollow cylinder. The circle's edge is not completely rigid, so we added another curvature parameter. For the motor gear, we noticed different kinds of gear on the side. There were two kinds of gear on the motor: One could be approximated as part of a circle and thus had three parameters: the circle's radius, the angle of the arc, and how much of the arc part is shown. Also, some of the gears on the circle could be modeled as a triangle. Therefore, we obtained the number of triangles, the base length, and the triangle's height. In total, there were around ten parameters for a specific object. To use some abstract variables to capture objects' properties, we propose using a roughly similar number of variables to describe them. The past literature on obtaining the visual features of 3D objects [33] illustrates the importance of eigenvalues in defining corners with corner detectors and on deciding other features of the object. Specifically, after observing the structure of a 3D point cloud of specific mechanical components and looking at the past literature [34; 35; 36; 37], we decided that including linearity \(L\), planarity \(P\), sphericity \(S\), omni-variance \(O\), anisotropy \(A\), the sum of eigenvalues \(\Sigma\), and curvature \(C\) would be necessary.  
We can express the values above using the eigenvalues of the neighborhood's covariance matrix. The dimension of the covariance matrix would be \(3\times 3\), so there would be three eigenvalues. We denote them by \(\lambda_{1}\geq\lambda_{2}\geq\lambda_{3}>0\), as the eigenvalues of the covariance matrix are positive. Then, we have  
\[L =\frac{\lambda_{1}-\lambda_{2}}{\lambda_{1}}\leq 1 \tag{1}\] \[P =\frac{\lambda_{2}-\lambda_{3}}{\lambda_{1}}\leq 1\] (2) \[S =\frac{\lambda_{3}}{\lambda_{1}}\leq 1\] (3) \[O =\sqrt[3]{\lambda_{1}*\lambda_{2}*\lambda_{3}}\] (4) \[A =\frac{\lambda_{1}-\lambda_{3}}{\lambda_{1}}\leq 1\] (5) \[\Sigma =\lambda_{1}+\lambda_{2}+\lambda_{3}\] (6) \[C =\frac{\lambda_{3}}{\lambda_{1}+\lambda_{2}+\lambda_{3}} \tag{7}\]  
Figure 2: Figure of the motor from three different angles and annotations. All the elements that our model takes into account are marked on the figure, including radii and curvatures.  
For instance, for the figure of the point cloud data of one motor above, we should depict the sphericity of the motor and the curvature of the curves.
# Hysteresis Compensation and Trajectory Tracking Control Model for Pneumatic Artificial Muscles
###### Abstract  
The optimum performance position control of pneumatic artificial muscles (PAM) is restricted by their in-built hysteresis and nonlinearity. The hysteresis is usually depicted by a phenomenological model, while the model mentioned above always only describes the hysteresis phenomenon under certain conditions. Thus, the universality of the compensator is due to its weakness in handling disparate outside conditions. Our research employs the FN-QUPI (feedforward neural network-quadratic unparallel Prandtl-Ishlinskii) model to depict the phenomenon of pressure-displacement hysteresis in PAMs. This model has high-precision expression and generalization ability for the PAM hysteresis phenomenon. According to this, an inverse model of the QUPI operator is established as a feedforward control while combining with the feedback control of incremental PID-type iterative learning. The results show that due to the hysteresis of PAM, the compound control of feedforward control and iterative learning has better tracking performance than the ordinary PID compound control in terms of convergence rate and stability. According to the mean absolute error (MAE) and root mean square error (RMSE) of the tracking process, it can be seen that the control model can achieve accurate nonlinear compensation, and the control system shows excellent robustness to different input signals.  
pneumatic artificial muscle (PAM); FN-QUPI model; high precision; iterative learning; hysteresis compensation; tracking control +
Footnote †: journal: Applied sciences
# Isolation and Characterization of Starch from Different Potato Cultivars Grown in Croatia
## 3 Results and Discussion
### Swelling Power and Solubility Index  
The swelling power and the solubility index show the interconnection of starch chains within the crystalline and amorphous regions of the starch granule, which is determined by the ratio of amylopectin and amylose, their conformation and the degree of branching [20].  
Figures 1 and 2 show the swelling power and solubility index of the starches isolated from different potato cultivars at temperatures of between 55 \({}^{\circ}\)C and 85 \({}^{\circ}\)C. As suggested by the results in the figures, there is a distinct tendency to increase the swelling power, as well as the solubility index, with an increase in the treatment temperature. At the highest temperature, the starch of the Sereno cultivar showed the highest, and the starch of the SL 13-25 cultivar showed the lowest value of swelling power, while the highest value of the solubility index was found in the starch of the Stilleto cultivar, and the lowest in the Senata cultivar. The swelling power and solubility index depend on the type of starch and the ratio of amylose and amylopectin. The values of the swelling power decrease with an increase in the amylose content, and as the amylopectin content increases, the swelling power increases too, and such starch is more stable to freeze-thaw processes, which significantly accelerates the processes of retrogradation and syneresis. Potato starch consists of relatively large granules that have relatively high swelling power values [21]. The morphological characteristics of the granules can also influence the swelling power and solubility index [22].  
Figure 1: Swelling power of the isolated potato starches at different temperatures. Different letters above the bars indicate that the values are significantly different at \(p<0.05\).
# Spatial Small Target Detection Method Based on Multi-Scale Feature Fusion Pyramid
## 3 Method
### Improved Spatial Pyramid Pooling (ISPP Module)  
Spatial pyramid pooling [38] was first proposed in 2014 and has been applied to the task of target detection, followed by a large number of research works such as those on ASPP [39], SimSPPF [40], and RFB [41]. Spatial pyramid pooling extracts and splices features together on the same image by convolving the same image with different sizes of nulls, which increases the sensory field and simultaneously improves the performance of target detection. For the task of detecting small spatial targets, we propose improved spatial feature pyramid pooling to improve the detection performance for small targets.  
Our improved spatial feature pyramid pooling is shown in Figure 5. It makes the existing structure more suitable for our spatial small target detection task. Firstly, in order to obtain more information about small targets that exist in the image, we cropped the \(3\times 3\) convolution kernel in the original structure, then the activation function of ReLU was selected to improve the running speed, and finally, the size of the pooling kernel was changed from (5, 5, 5) to (\(3,3,3\)); the sensory field of the pooling kernel of smaller size was more suitable for the small target scale, which is conducive to the extraction of the features of the small targets and further improves the detection of small targets. The ability of small target detection was further improved.  
Figure 4: Improved SE attention mechanisms. (**a**) is a diagram of the residual network structure. (**b**) is the structure of the CSE module.
# Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances
## 3 Materials and Methods
### Data Generation  
Generating synthetic PQDs includes generating voltage and current waveforms with different types of disturbance [7; 21; 22]. The disturbances are introduced by manipulating the waveform parameters based on established models and algorithms. Moreover, the generated PQDs are labeled and annotated according to their type and severity. This annotation process ensures that the generated dataset contains accurate labels for the different types of disturbances, which are essential for training and evaluating deep learning models.  
Two datasets were generated using [7]. The first dataset consists of PQ signals with random Gaussian noise, where the signal-to-noise ratio (SNR) ranges between 20 and 50 dB. The second dataset is generated without any noise. Each dataset contains a total of 50,000 signals, with 5000 signals being dedicated to each disturbance type. The disturbance types included in the datasets are as follows: normal, flicker, harmonics, oscillatory transient, impulsive transient, interruption, notch, sag, spike, and swell.  
The datasets aim to provide a diverse representation of PQDs encountered in real-world scenarios. By including different disturbance types and generating datasets with and without noise, the authors enable the evaluation and comparison of various algorithms and techniques for PQ analysis and classification tasks. Collectively, these datasets facilitate a thorough evaluation of the proposed approach across varied contexts and conditions. To maintain analytical consistency, each dataset is segmented into training (60%), validation (20%), and test (20%) subsets. This partitioning allows for effective model training, unbiased performance assessment during the validation phase, and a final evaluation of the model's generalization capabilities on unseen data. Such a rigorous approach not only strengthens the validity of the findings but also facilitates meaningful comparisons with existing methodologies, ultimately contributing to the advancement of power quality disturbance classification techniques.  
Data augmentation is utilized to expand and diversify the training dataset, enhancing the performance and generalization capabilities of the proposed model. By altering the temporal characteristics of PQD signals through the addition of noise to the time domain data, the proposed model can effectively learn to identify and generalize patterns across varying noise levels and temporal scales, improving its robustness and accuracy in classifying power quality disturbances.
# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
###### Abstract  
Monitoring soil organic carbon (SOC) typically assumes conducting a labor-intensive soil sampling campaign, followed by laboratory testing, which is both expensive and impractical for generating useful, spatially continuous data products. The present study leverages the power of machine learning (ML) and, in particular, deep neural networks (DNNs) for segmentation, as well as satellite imagery, to estimate the SOC remotely. We propose a new two-stage pipeline for remote SOC estimation, which relies on using a DNN trained to classify land cover to perform feature extraction, while the SOC estimation is performed by a different ML model. The first stage is an image segmentation DNN with the U-Net architecture, which is trained to estimate the land cover for an observed geographical region, based on multi-spectral images taken by the Sentinel-2 satellite constellation. This estimator is subsequently used to extract the latent feature vector for each of the output pixels, by rolling back from the output (dense) layer of the U-Net and accessing the last available convolutional layer of the same dimension as our desired output. The second stage is trained on a set of feature vectors extracted at the coordinates for which manual SOC measurements exist. We tested a variety of ML models and report on their performance. Using the best extremely randomized trees model, we generated a spatially continuous map of SOC estimations for the region of Tuscany, in Italy, with a resolution of 10 m, to share with the researchers as a means of validating the results and to demonstrate the efficiency of the proposed approach, which can can easily be scaled to create a global continuous SOC map.  
deep neural networks; land use; image segmentation; U-Net; LUCAS; CHLSOC; CORINE; environment; artificial intelligence +
Footnote †: journal: _remote sensing_  
Article
## 3 Scientometric Analysis: Results and Discussion
### Yearly Publication Trend  
The number of publications and citations of articles in a research field depicts its development patterns. Hence, the yearly publication trend, including the cumulative number of papers during the time horizon for the defined search strings, is included in Figure 2. The first article was found in 2020, coinciding with the start of the COVID-19 pandemic; therefore, this study's time horizon was set to the period from 2020 to 15 June 2022. A preliminary analysis of Figure 2 shows a progressive increase in the number of publications, starting with 83 documents in 2020 and 197 in 2021. If the previous year's trend continues, taking into account the fact that, by mid-2022, the number of published documents had already reached 126, we expect that, by the end of the year, it will exceed the previous year's quantities.