esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Evaluation
### Image Size  
The processing of the original images, with the size of 8000 \(\times\) 6000 pixels, is not feasible due to hardware limitations. Thus, two different approaches were evaluated: (i) resize the images for 1333 \(\times\) 800 pixels, which are the Detectron2 default dimensions; and (ii) cropping the images with dimensions of 2000 \(\times\) 2000 pixels.  
The first approach has the advantage of keeping the original contours of the anomalies, but it loses information due to the small size that usually has the corrosion instances. The second approach maintains the image's resolution but takes more training time and the contours of some cases might change because of the cropping edges.  
Table 3 shows the maximum percentage values for the precision metrics for models based on resized and cropped images for the validation dataset and, also, the iteration of occurrence, which represents other states of the model presented. It is possible to notice satisfactory metric values for the cropped images in terms of precision with IoU 50% for the detection and segmentation. These values are about 10% higher than those obtained from the resized images. The iteration number where these maximum values occur indicates the importance of running the algorithm with 40k training cycles.  
Figure 11 shows the examples of three inferences (EX1 to EX3) performed by models trained with resized and cropped images in the validation dataset. Example 1 demonstrates close results between the two models but with one more instance detected by the model using cropped images. In Example 2, the model with resized images detected an instance close to one edge, showing that the shape of the corrosion is a determinant factor since, in this specific case, the cropping/separation of the image changed a larger dimension instance. Example 3 illustrates the substantial improvement that the cropped images can provide in small dimension instances, with 9 out of 12 (75%) annotates detected, while in the resized model, no annotation was detected. This is because the small instances become  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Resized Images**} & \multicolumn{2}{c}{**Cropped Images**} \\ \cline{2-5}  & **Maximum** & **Iteration** & **Maximum** & **Iteration** \\  & **Value (\%)** & **Value (\%)** & **Value (\%)** & **Iteration** \\ \hline \(\text{AP}_{\text{50}}\) Detect & 43.5 & 26,999 & 55.8 & 18,499 \\ \hline \(\text{AP}_{\text{50}}\) Segm & 37.4 & 25,999 & 50.5 & 17,499 \\ \hline \(\text{AP}_{\text{L}}\) Detect & 47.9 & 26,999 & 43.6 & 20,499 \\ \hline \(\text{AP}_{\text{L}}\) Segm & 32.5 & 25,999 & 31.0 & 13,499 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Metrics values for models based on resized and cropped images for the validation dataset.  
imperceptible to the algorithm, resembling only noise since the resized model reduces the image by approximately eight. These results motivated the selection of the strategy where images are cropped before entering the training process.
## 2 Methods and Tools
### Data Processing  
The signal-processing stage is crucial when interpreting GPR data. With an increasing depth, the radar reflection energy diminishes rapidly within the medium, resulting in the  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter Description** & **Eq.** & \begin{tabular}{c} **Parameter** \\ **Symbol** \\ \end{tabular} & \
\begin{tabular}{c} **Drone-Based** \\ **GPR, m** \\ \end{tabular} &
\begin{tabular}{c} **Ground-Based** \\ **GPR, m** \\ \end{tabular} \\ \hline The radius of the maximum exposure area at the depth \(Z=2\) m & 4, 5 & \(R_{Z}^{D}\) & 0.96 & 0.57 \\ \hline Vertical resolution & 6 & \(D_{Z}\) & 0.17 & \\ \hline The minimum size of a single detectable object at the depth \(Z=2\) m & 7 & \(A_{min}\) & 0.3 & 0.2 \\ \hline The diameter of a linear object (wall width) & 8 & \(D\) & (0.09–0.17) \(\approx\) 0.14 & \\ \hline \end{tabular}
\end{table}
Table 1: The values of the estimated GPR parameters.  
Figure 4: Two grids of GPR surveys. The red grid is for ground-based GPR, and the blue grid is for drone-based GPR.  
target's amplitude being smaller than the background signals. The employed processing techniques aimed at accentuating faint signals and discerning deep underground targets.  
Table 2 shows the filters applied to the raw GPR data collected by both instruments to enhance the signal-to-noise (SNR) ratio.  
A comparison of the lists of filters reveals that they were identical except for the data-editing filter. Data editing utilizing GeoHammer Software (v.1.1.10) (SPH Engineering, Riga, Latvia) involves removing the drone trajectory segments between adjacent lines and the flight data from the ground station to the survey's starting point and from the survey's final point to the drone's base station. This procedure enables us to eliminate inaccurate GPR data characterized by a heightened amplitude resulting from drone maneuvers and data captured during flights with higher velocity from and to the ground station. Breaking down a 'large' source file into multiple lines streamlines the data-processing procedure and diminishes the time required for data processing. Figure 5 shows the flight scheme before (a) and after (b) the GeoHammer application.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Filter Type** & **Drone-Based GPR** & **Ground-Based GPR** \\ \hline
1 & Data Editing & + & \\ \hline
2 & ’Dewow’ & + & + \\ \hline
3 & Static Correction & + & + \\ \hline
4 & Background Removal & + & + \\ \hline
5 & X-Interpolation by GPS Coordinates & + & + \\ \hline
6 & Median Filter & + & + \\ \hline
7 & Automatic Gain Control & + & + \\ \hline
8 & Stolt (F-K) Method & + & + \\ \hline \end{tabular}
\end{table}
Table 2: The list of filters applied to the raw GPR data.  
Figure 5: The part of the drone flight program before (**a**) and after (**b**) the application of GeoHammer software.  
The processing of the data collected by drone-based GPR was continued using Prism 2 software (RADAR Systems Inc., Riga, Latvia), while those by ground-based--using the software REFLEXW [47].
# The Influence of Sensor Size on Acoustic Emission Waveforms--A Numerical Study
## 4 Discussion
### Effect of Wavelength over Sensor Size  
The case of surface waves obviously exhibits the strongest aperture effect. To quantify the trends, the amplitude data are presented in terms of the normalized parameter \(D/\lambda\) (sensor size over wavelength) in Figure 9. The individual curves follow a <<master curve>>, stressing that the crucial parameter is the ratio of sensor size/wavelength. This curve can be well fitted by an exponential function. As the sensor size increases away from the ideal case of \"point\" sensor, the relative amplitude sharply decreases until the point \(D/\lambda\approx 1\) when the sensor size is equal to the wavelength. At that point, the relative amplitude has already dropped to 20% of the reference. For larger sensor size, the amplitude decreases further but with a lower rate reaching 4% for \(D/\lambda>8\).  
Figure 8: Relative amplitude of the received signal for point (1 mm, in grey), medium (5 mm, in blue), long (20 mm, in red) along the frequency range, from 50 kHz to 1 MHz, and concerning the three source positions: (**a**) surface; (**b**) diagonal; (**c**) vertical.
## 1 Introduction  
Low-Earth orbit (LEO) satellites have often been used to acquire image data for Earth observations, such as natural disasters and terrain changes, using synthetic aperture radars (SARs) [1, 2]. These LEO satellites have a rapid velocity of around 7.6 km/s at an altitude of 550 km to a fixed point on a ground station and transmit the image data through a downlink which is the X-band (8025 MHz-8400 MHz) for the high-rate data. In order to predict the data transmission situation from LEO satellites in rapidly changing interference situations, many studies have been conducted on the link budget analysis in terms of path loss [3], the elevation angle of the satellite [4], and non-line-of-sight situations [5, 6, 7]. However, link budget analysis studies on misaligned off-axis and interference situations considering antenna radiation patterns and space environment have not yet been sufficiently performed. In particular, there are no in-depth studies of scenarios where power controllable interference sources are exposed to the side lobe of the receiving antenna. In addition, most previous studies have been conducted considering losses in the near-ground atmosphere [8, 9, 10], without accounting for the entire space environment.  
In this paper, we investigate an analysis of a LEO satellite downlink, considering antenna patterns and the space environment in interference situations where the side lobe of a ground station antenna is exposed to a strong interference signal source. To calculate the link budget, considering the bore-sight error loss between the ground station and the satellite when the LEO satellite moves rapidly over the Earth, antenna patterns of the ground station and the LEO satellite are employed. The ground station antenna is assumed to be a parabolic type, and its radiation pattern is calculated by using methods of geometrical optics (GO) and physical optics (PO) [11]. The radiation pattern of a choke ring antenna, which it is assumed is that used in the LEO satellite, is obtained through a full electric magnetic (EM) simulation. The actual radiation patterns of the receiving and transmitting antennas are obtained and applied to the study, in order to more accurately estimate the data link budget when interference is strong in the side lobe of the receiving antenna. Finally, an analysis of the interference situation based on the space environment is calculated when the interference source moves over the ground station in various interference situations. We employ ray tracing techniques and geometrical optics to analyze the interference situation when the LEO satellite is located at a specific low elevation angle. Jamming-to-signal (J/S) ratio results according to the space environments are examined and they differ by about 3 dB at low elevation angles according to the space environments.
# Deep Learning-Based Optical Camera Communication with a 2D MIMO-OOK Scheme for IoT Networks
## 3 System Architecture  
The core principle of OCC technologies is to manipulate the intensity of optical waveforms for data transmission and reception. One of the most widely used for digital communication is the On-Off Keying (OOK), a simple form of amplitude-shift keying that utilizes two signal states--ON and OFF--to represent binary data, with \"1\" corresponding to ON and \"0\" to OFF. In this paper, we present a detailed explanation of our proposed 2D MIMO-OOK, designed for monitoring applications using OCC technology. By strategically positioning each LED within the array according to the spatial frame format of the 2D MIMO-OOK, the receiver can efficiently detect and decode data. In Figure 1, we show the 2D MIMO-OOK architecture, with the functionality of its components.
# Rockburst Hazard Evaluation Using an Extended COPRAS Method with Interval-Valued Fuzzy Information
## 2 Preliminaries  
In this section, the preliminaries of IVFNs are first introduced as follows.  
(1) Definition of IVFNs  
Let \(\widetilde{\gamma}=[\gamma^{L},\gamma^{U}]\), which is called an interval-valued fuzzy number (IVFN). \(\gamma^{L}\) and \(\gamma^{U}\) are the lower and upper limits of \(\widetilde{\gamma}\), respectively. If \(0<\gamma^{L}<\gamma^{U}\), then \(\widetilde{\gamma}\) is called a positive IVFN. The middle point and half-width of \(\widetilde{\gamma}\) are defined as \(\pi(\widetilde{\gamma})=\frac{\gamma^{L}+\gamma^{U}}{2}\) and \(\omega(\widetilde{\gamma})=\frac{\gamma^{U}-\gamma^{L}}{2}\), respectively [47].  
(2) Basic operations of IVFNs  
Supposing \(\widetilde{a}=[a^{L},a^{U}]\) and \(\widetilde{b}=[b^{L},b^{U}]\) are two positive IVFNs, and \(\lambda\) is a positive real number, their basic operations can be performed as follows [44; 48]:  
\[\widetilde{a}+\widetilde{b}=[a^{L}+b^{L},a^{U}+b^{U}], \tag{1}\]  
\[\lambda\widetilde{a}=[\lambda a^{L},\lambda a^{U}], \tag{2}\]  
\[\widetilde{a}^{\lambda}=[{(a^{L})}^{\lambda},{(a^{U})}^{\lambda}], \tag{3}\]  
\[\widetilde{a}\times\widetilde{b}=[\min(a^{L}b^{L},a^{L}b^{U},a^{U}b^{L},a^{U}b ^{U}),\max(a^{L}b^{L},a^{L}b^{U},a^{U}b^{L},a^{U}b^{U})], \tag{4}\]  
\[\frac{\widetilde{a}}{\widetilde{b}}=[\min(\frac{a^{L}}{b^{L}},\frac{a^{L}}{b^{ U}},\frac{a^{U}}{b^{L}},\frac{a^{U}}{b^{U}}),\max(\frac{a^{L}}{b^{L}},\frac{a^{U}}{b^{ U}},\frac{a^{U}}{b^{U}})]. \tag{5}\]  
(3) Comparison method between two IVFNs  
Let \(len(\widetilde{a})=a^{U}-a^{L}\) and \(len(\widetilde{b})=b^{U}-b^{L}\); the possibility degree of \(\widetilde{a}\geq\widetilde{b}\) is defined as follows [45; 49]:  
\[p(\widetilde{a}\geq\widetilde{b})=\max\Biggl{\{}1-\max\Biggl{\{}\frac{b^{U}- a^{L}}{len(\widetilde{a})+len(\widetilde{b})},0\Biggr{\}},0\Biggr{\}}. \tag{6}\]Similarly, the possibility degree of \(\widetilde{b}\geq\widetilde{a}\) is defined as  
\[p(\widetilde{b}\geq\widetilde{a})=\max\Biggl{\{}1-\max\Biggl{\{}\frac{a^{U}-b^{L} }{len(\widetilde{a})+len(\widetilde{b})},0\Biggr{\}},0\Biggr{\}}. \tag{7}\]  
On this basis, there are the following properties:  
\[0<p(\widetilde{a}\geq\widetilde{b})<1,\;0<p(\widetilde{b}\geq\widetilde{a})<1, \tag{8}\]  
\[p(\widetilde{a}\geq\widetilde{b})+p(\widetilde{b}\geq\widetilde{a})=1, \tag{9}\]  
\[p(\widetilde{a}\geq\widetilde{a})=p(\widetilde{b}\geq\widetilde{b})=0.5. \tag{10}\]  
(4) Distances between IVFNs  
The Euclidean distance (\(d_{E}\)) and Hamming distance (\(d_{H}\)) between two IVFNs can be calculated as follows:  
\[d_{E}(\widetilde{a},\widetilde{b})=\sqrt{\left(a^{L}-b^{L}\right)^{2}+\left(a ^{U}-b^{U}\right)^{2}}, \tag{11}\]  
\[d_{H}(\widetilde{a},\widetilde{b})=\left|a^{L}-b^{L}\right|+\left|a^{U}-b^{U} \right|\!. \tag{12}\]
## 4 Finite Element Analysis
### Finite Element Model Validation  
The finite element and test skeleton curves of the specimen are shown in Figure 18, and the comparison between bearing capacity and displacement is shown in Table 10.  
Figure 16: Energy dissipation coefficients of the specimens.  
It can be seen from Figure 18 that the skeleton curve simulated by the finite element software ABAQUS is consistent with the test, which can well reflect the force performance of the coal gangue concrete shear wall specimens. It can be seen from Table 10 that the bearing capacity and displacement of the characteristic points obtained by the finite element simulation have some errors compared with the test, but the relative error is within 10%. It indicates that the established finite element model has achieved certain rationality and accuracy and could simulate the bearing capacity and deformation capacity of the coal gangue concrete shear wall specimens well.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Data Type**} & \multicolumn{2}{c}{**Characteristic Point Bearing Capacity**} & \multicolumn{2}{c}{**Characteristic Point Displacement**} \\  & \multicolumn{2}{c}{**(kN)**} & \multicolumn{2}{c}{**(mm)**} \\ \cline{2-5}  & **Yield** & **Peak** & **Peak** & **Ultimate** \\  & **Load** & **Load** & **Displacement** & **Displacement** \\ \hline test & 93.427 & 120.905 & 12.47 & 23.95 \\ finite element & 101.837 & 113.099 & 12.13 & 22.36 \\ Error (\%) & 9.00 & 6.46 & 2.73 & 6.64 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Characteristic point bearing capacity and displacement of the test and finite element.  
Figure 17: Finite element model (**a**) Loads and boundary conditions (**b**) Meshing.  
Figure 18: Backbone curves of the test and finite element.  
Compared to the experiment, the error obtained from the finite element simulations may be because the concrete defined in the simulations is an isotropic homogeneous material. In contrast, concrete is a mixture of multiple materials, and its nonlinear inhomogeneity cannot be simulated. In addition, the finite element simulation adopts a separate model, which assumes that the steel reinforcement has good adhesion to the concrete and cannot simulate the slip issue of the specimen in the test.
# Microgravity, Bone Homeostasis, and Insulin-Like Growth Factor-1
## 5 Mechanotransduction in Simulated Microgravity (g \(\geq\) 10\({}^{-3}\))
### Osteoblasts  
Using NASA's rotary cell culture system, [PERSON] and associates found that modeled microgravity inhibited osteoblasts and increased adipose differentiation in human stem cells. The transformation involved reductions in RhoA activity and cofilin phosphorylation, disruption of F-actin stress fibers, and decreased integrin signaling through focal adhesion kinase [27]. Employing the same culture system, [PERSON] and [PERSON] reported a 50-80% reduction in alkaline phosphatase, Runx2, osteoclastin expression, and AP-1 transactivation in cultures of rat osteoblasts [28], and [PERSON] and associates noted that 3 days of rotation altered the expression of 61 mechanosensitive genes, including osteomodulin, Runx2, and osteoglycin in cultures of mouse tibial pre-osteoblasts [29].  
Using two-axis random positioning machines, [PERSON] and associates found that simulated microgravity inhibited rat calvarial osteoblast differentiation, maturation, and ciliogenesis and attributed the change to atrophy of osteoblast cilia [30]. [PERSON] and associates found that simulated microgravity reduced intracellular free calcium levels by inhibiting calcium channels in mouse osteoblasts [31]. As previously noted, low levels of intracellular calcium impairs osteogenic gene expression in vitro and abrogates load-induced bone formation in vivo. [PERSON] and associates found that simulated microgravity inhibited the proliferation and osteogenesis of rat bone marrow mesenchymal stem cells [32]. Using cultures of human bone marrow mesenchymal stem cells, [PERSON] and associates found that simulated microgravity inhibited genes regulating proliferation and differentiation while upregulating genes involved in adipogenesis [33]. [PERSON] and associates found that simulated microgravity inhibited osteogenic differentiation of mesenchymal stem cells by inhibiting the expression of transcriptional coactivator with PDZ-binding motif, an important regulator of osteogenesis [34]  
Experiments measuring the metabolomics and proteomics of cultured human osteoblasts found that simulated microgravity caused a decrease in mitochondrial proteins, a reduction in mitochondrial energy potential, and an oxidative stress response (decreases in oxidized glutathione and antioxidant enzymes) [35].
## 4 Simulation Results
### Receiver Size and Design Characteristics
Additionally, since the effect of atmospheric turbulence in the coupling was not present, high SKR values could be achieved, even in low elevation angles. Therefore, it was suggested that free-space SPAD detectors, combined with small- or medium-sized telescope apertures, may be the most cost-effective and practical selection for use cases where the point-to-point link terminates at the telescope location. At this point, it is worth mentioning that tracking LEO satellites with large telescopes, such as those with 2.3 m apertures, poses significant challenges due to their weight and slower movement capabilities. Figure 11: SKR over time for the different \(D_{rx}\) values of (**a**) SNSPD with SMF coupling, **(b)** SPAD with SMF coupling, and (**c**) SPAD without SMF coupling. Figure 10: Contour plots of the SMF coupling loss (dB) over focal length Fr and receiver aperture diameter Dr for (**a**) full-AO correction, (**b**) tip–tilt correction for Cn\({}^{2}=10^{-14}\) m\({}^{-2/3}\), and (**c**) tip–tilt correction for Cn\({}^{2}=10^{-13}\) m\({}^{-2/3}\) with an elevation angle of 90\({}^{\circ}\).
## 4 Simulation and Discussion
### Sensitivity Analysis  
Since the user-side energy storage providing load shaving service cannot reach revenue, it is critical to research its profit condition. Two influence factors, capacity cost of investment (\(c_{e}\)) and peak-valley electricity price difference (d), are sensitively analyzed using NPV index, respectively. What's more, only the NPV of the service can reach a certain, it is worthwhile for user to invest. Thus, the worthwhile investment condition is analyzed by the same factors using IRR for clearly comparison results. Since LFP is widely applied in user-side, LFP is taken as battery type. Two conditions are discussed as follows:  
**Profit condition.** Under different energy storage investment costs and peak-valley electricity price, NPV of this project are displayed in Figures 12 and 13, respectively. As illustrated in Figure 12, the investment starts to make fortune when the unit capacity cost of the energy storage battery drops to 1500 (\(\forall\)/kWh), which is 47% of the current cost. In addition, from Figure 13, it starts to make fortune when peak-to-valley electricity price difference is above 1.08 (\(\forall\)). In other words, peak-to-valley electricity price difference should further extend to the 140% of the current price.  
**Investment condition.** This paper refers to the project investment boundary standard widely recognized by the photovoltaic industry, that is, when the IRR of the full investment is greater than 9%, then the project is considered a worthwhile investment. Under different energy storage investment costs and peak-valley electricity prices, IRR of this project are displayed in Figures 14 and 15, respectively.  
As shown in Figure 14, the LFP battery cost needs to be reduced to 1100 (V/kWh) when the investment project reaches the boundary standard under the same conditions, which is 35% of the current cost. From the analysis of Figure 15, under the current energy storage cost, the yield of the investment project will reach 9%, and the peak- valley electricity price difference will reach 1.68 (V). In other words, the peak-valley electricity price difference should further extend to the 140% of the current price difference.
###### Abstract  
Climate change has contributed substantially to the weather and land characteristic phenomena. Accurate time series forecasting for climate and land parameters is highly essential in the modern era for climatologists. This paper provides a brief introduction to the algorithm and its implementation in Python. The pattern-sequence-based forecasting (PSF) algorithm aims to forecast future values of a univariate time series. The algorithm is divided into two major processes: the clustering of data and prediction. The clustering part includes the selection of an optimum value for the number of clusters and labeling the time series data. The prediction part consists of the selection of a window size and the prediction of future values with reference to past patterns. The package aims to ease the use and implementation of PSF for python users. It provides results similar to the PSF package available in R. Finally, the results of the proposed Python package are compared with results of the PSF and ARIMA methods in R. One of the issues with PSF is that the performance of forecasting result degrades if the time series has positive or negative trends. To overcome this problem difference pattern-sequence-based forecasting (DPSF) was proposed. The Python package also implements the DPSF method. In this method, the time series data are first differenced. Then, the PSF algorithm is applied to this differenced time series. Finally, the original and predicted values are restored by applying the reverse method of the differencing process. The proposed methodology is tested on several complex climate and land processes and its potential is evidenced.  
forecasting; univariate; time series; Python; PSF