esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methods  
### VIIRS Data  
The VIIRS sensor is currently operational onboard SNPP (since October 2011) and the US Joint Polar Satellite System 1 (JPSS-1 or NOAA-20, since November 2017). In addition, three more VIIRS sensors will fly on the follow-on JPSS2-JPSS4 satellites, planned for launch from 2021 to 2031 [38; 39] assuring an operational continuity like the one already offered by AVHHR and MODIS, namely its predecessors. Thanks to its large swath (i.e., 3060 km), VIIRS has been providing a daily full Earth coverage [40], collecting data in 22 different spectral bands of the electromagnetic spectrum, ranging from 0.412 \(\upmu\)m and 12.01 \(\upmu\)m, with a spatial resolution of 750 m at the nadir for both the Day/Night panchromatic Band (DNB) and the 16 Moderate resolution bands (M-bands) and of 375 m for the 5 high-resolution Imagery bands (I-bands) [40].  
Figure 2: Basilicata river levels measured in the period 20/11/2013–20/12/2013 at the correspondent gauging stations indicated in Figure 1[28].  
In this work, the following I-bands, namely I1 (0.60-0.68 \(\upmu\)m, RED), I2 (0.85-0.88 \(\upmu\)m, Near Infrared -NIR), I3 (1.58-1.64 \(\upmu\)m, Shortwave Infrared-SWIR) and I5 (10.5-12.4 \(\upmu\)m, Thermal Infrared-TIR) acquired in the month of December from 2013 to 2017 (for a total of 171 SNPP orbits) were analysed. In addition, the I-band terrain-corrected geolocation data (GITCO), including longitude, latitude, solar zenith angles, solar azimuth, sensor zenith and sensor azimuth angles, were also exploited. The I1-I3 data were converted in reflectance (R) values, while those related to I5 band in brightness temperature (BT).  
In more detail, SNPP Sensor Data Record (SDR) data, directly acquired at the satellite receiving station of the Institute of Methodology for Environmental Analysis (IMAA) located in Tito Scalo (Basilicata region, southern Italy), have been processed by running the Community Satellite Processing Package (CSPP). Furthermore, SDR data were downloaded from NOAA CLASS archive [41] to fill any gaps within the considered historical series. The Polar2 Grid v2.2 software [42] allowed the SDR spatial sub-setting over the ROI.  
A total of 684 VIIIRS SDR data was collected for the study area of 601 \(\times\) 600 pixels, shown in Figure 1. The cloud-free VIIIRS daytime imagery of the event, acquired on 4,5,6,7 and 8 December 2013, at 12.10 GMT, 11.52 GMT, 11.35 GMT, 11.12 GMT and 12.36 GMT, respectively, have been processed in terms of RST-FLOOD to analyse the above mentioned flooding event.
# A Diagnostics of Conveyor Belt Splices
## 4 Problem Definition  
The analysis and comparison of signals from splices is problematic, as the signals are shifted with respect to each other. Shift values depend on splice geometry, production technology and wear processes. Note that shifts (nearly) do not occur in straight splices, which therefore cannot be compared with bias splices.  
For good condition splices, each signal should have a similar shape, so just finding the phase difference and then shifting all signals with reference to the first channel may fix the problem. For bad conditions, it is very difficult to find a reference point and shifting the signal may provide misleading results.  
From an engineering perspective, the main goal is to shift each signal for bias splices by a certain value (with reference to selected signal) that would allow finding a minimum of their difference defined as simple sample-by-sample residual and RMS errors. The ideal case means no difference between signals, so the RMSE will be zero. For simple (not biased) splices, the shifting procedure is not required; one may simply compare signals as they are. It may be said that we developed a \"synchronization\" procedure for biased splices to convert them to \"straight\" splices and use a simple technique for splice condition evaluation.  
From a practical point of view, the problem can be defined as finding the shift value for each channel, such that some predefined objective function describing error in mutual positioning of all channels is minimized. In the past, several attempts have been made to solve this problem either manually or using simple deterministic algorithms (also presented in this paper for reference). However, the authors propose to define the problem as an optimization problem. We consider the genetic algorithm (GA) as a potential tool, because of its ease of implementation and the problem definition in such a case. GA is also very robust regarding avoiding local minima due to intrinsic solutions such as random mutation.
## 4 Discussion
### Effect on Vegetation Net Primary Productivity (NPP)
The overall NPP has maintained a stable rising level and will continue to rise (Figure 6).In the process of urbanization, the protection of forests and other natural resources in Beijing made the proportion of vegetation coverage increase, and the vegetation NPP also showed an upward trend, which has a certain reference significance for the future urban planning of developing countries. For example, in the development process of some cities, the proportion of vegetation and net ecosystem production decreased [60; 61]. Beijing, which has a relatively rapid urbanization process, can provide important information for land use planning and sustainable urban development in similar urban expansion areas, especially in less developed countries. [PERSON], [PERSON] and others observed mismatches between past/current land cover and the existing land use plan [61]. The mismatch was also between Beijing's regional land use plan and unregulated urban expansion (\"The Master Plan of Beijing (2004-2020)\" requires that the scale of construction land in 2020 be controlled at 1650 square kilometers, but the actual area is 3475.76 square kilometers). Therefore, it is more necessary to carry out multi scenario future land use simulation to provide a scientific basis for urban planning. This paper can provide a way for cities in China and around the world to carry out spatial planning for sustainable development.
###### Abstract  
Due to concerns about radio frequency interference from emerging telecommunications technology, there have been intensive discussions on the changes to the international Radio Regulations around 24 GHz at the recent World Radiocommunication Conference in 2019. Although the sensitivity to total precipitable water (TPW) at 23.8 GHz over land is small, and in some cases close to zero, state-of-the-art retrieval systems with no dependence on real-time ancillary data (e.g., numerical weather prediction (NWP) model forecasts), such as the National Oceanic and Atmospheric Administration (NOAA) operational Microwave Integrated Retrieval System (MiRS), have been producing reliable TPW products over both ocean and land, which implies that the microwave channel at 23.8 GHz is providing valuable information on TPW over land as well as over ocean. The contradiction between the zero or near-zero sensitivity and practical performance over land raises questions for the remote sensing community and public users of such data. In this study, we examine the underlying physics and include mathematical explanations, which address and clarify the apparent contradiction. The channel at 23.8 GHz is a direct measurement and indispensable for its combined use with microwave temperature and moisture sounding channels.  
passive microwave sensors; 5G wireless business; radio frequency interference; total precipitable water 202113489. [[https://doi.org/10.3390/rs13030489](https://doi.org/10.3390/rs13030489)]([https://doi.org/10.3390/rs13030489](https://doi.org/10.3390/rs13030489))  
1
# Forward Fall Detection Using Inertial Data and Machine Learning
## 4 Testing the Developed Fall-Detection Models Using Experimental Measurements  
To test the proposed method of training using digital models for fall simulations and the developed deep learning and classification models, experimental measurements of 6 male volunteers were taken. The developed sensor was strapped tightly using a belt to the pelvis zone of the volunteers, either at the front or back. The physical characteristics of the volunteers, as well as the placement position of the inertial measurement unit (front or back), are shown in Table 3.  
Figure 16: Confusion matrix for the trained Fine KNN.  
For a better understanding of the correlation between the simulated and real fall events, the data was normalized and compared in Figure 19.  
Figure 19 illustrates how the simulated fall event compares with the real recorded data of six different subjects by considering only the actual fall event. The simulated fall (orange line) shows a relatively smooth pattern, with peaks occurring primarily between 0.2 and 1.4 s. Consequently, in the same fashion, Subjects 1, 3, and 4 also exhibit more prominent peaks around 0-0.2 and 0.8-1.4 s. Subject 2 (magenta) has more noise and lower overall values. The peaks for the subjects tend to occur between 0.1 and 1.5 s, having at least two prominent maximum spikes between 0.3 and 0.5 s apart, with some differences in magnitude and timing of acceleration across the subjects, especially for Subject 6.  
The discrepancies between simulated and recorded data could reduce model accuracy. Addressing these differences through model refinement or calibration would likely enhance the robustness and applicability of the predictive model. Additionally, in future studies, considering additional signal metrics for acceleration values, such as the Root Mean Square (RMS) of Acceleration, Peak Acceleration Values, and Frequency Analysis, would be a solid approach to bridge the gap between simulated and recorded data.
## 2 Detailed Model of MMC-Controlled Wind Turbine Generator
### Wind Turbine Model
#### 2.1.3 Pitch Blade Controller  
The power output of the wind turbine generator always fluctuates depending on wind speed variations, and its power output is not allowed to exceed its rating capacity. Therefore, the pitch blade controller works to maintain the rotor speed of the wind turbine so as not to exceed the permissible speed limit. The schematic diagram of the pitch blade controller model is shown in Figure 5[21]. The controller maintains the rotor speed of the wind turbine (\(\omega_{r}\)), ensuring that it does not exceed its reference value (\(\omega_{r}^{*}\)). The control loop of the pitch actuator is represented by a first-order transfer function with time constant \(T\). The PI controller is used to obtain a pitch-angle reference (\(\beta^{*}\)).
## 3 Application to SFRC Post-Tensioned Dry Joints
### Material Characterization of SFRC  
In the first place, we carried out several numerical tests, as presented in Table 1, in order to determine the set of material properties required by the structural model, which are presented in Table 2.  
For the concrete mix, we considered a maximum aggregate size of 8 mm and a Fuller's parabola with exponent 0.5 for the sieve curve. Coarse aggregate distributions with volume content of 40% were generated. The elastic properties used for the concrete matrix were E\({}_{\rm m}\) = 20 GPa and E\({}_{\rm a}\) = 70 GPa, with parameters \(\alpha_{\rm n}\) = 1 and \(\alpha_{\rm s}\) = 0.2. Regarding the fracture properties, the following were used: f\({}_{\rm t,m}\) = 4 MPa, f\({}_{\rm c,m}\) = -48 MPa, c\({}_{\rm m}\) = 8 MPa, \(\sigma_{\rm m}\) = 35\({}^{\circ}\), and G\({}_{\rm F,m}\) = 10 N/m. Smooth steel fibers with L\({}_{\rm f}\) = 35 mm and d\({}_{\rm f}\) = 0.5 mm were randomly placed (\(\theta\) = 60\({}^{\circ}\)) within the specimens, as in [27]. An elastic modulus E\({}_{\rm s}\) = 210 GPa was used. The bond behavior was characterized by an elastic modulus of E\({}_{\rm b}\) = 5 GPa, shear stress pair of \(\tau_{1}\) = 1 MPa and \(\tau_{2}\) = 0.1 MPa, and ratio \(\varepsilon_{\rm f}/\varepsilon_{\rm i}\) = 15. The following values of V\({}_{\rm f}\) were considered: 0.0%, 0.5%, 1.0%, and 1.5%.  
The elastic and inelastic properties determined by through the uniaxial tensile and compressive tests are presented in Table 2. The raw data is fitted using a linear model dependent on V\({}_{\rm f}\), determining the elastic modulus (E), tensile and compressive strength (f\({}_{\rm t}\) and f\({}_{\rm c}\), respectively), and mode I fracture energy (G\({}_{\rm F}\)) of the resulting SFRC material.
## 5 Conclusions
**Data Availability Statement:** The Sentinel-1 A/B precision orbit data were obtained from the European Space Agency ([[https://dataspace.opernicus.eu](https://dataspace.opernicus.eu)]([https://dataspace.opernicus.eu](https://dataspace.opernicus.eu)), accessed on 28 May 2024) and the archived Sentinel-1 A/B observations downloaded from the Alaska Satellite Facility ([[https://search.asf.alsaka.edu/#/](https://search.asf.alsaka.edu/#/)]([https://search.asf.alsaka.edu/#/](https://search.asf.alsaka.edu/#/)), accessed on 28 May 2024). The focal mechanism solutions (Mw \(\geq\) 6) were sourced from the Global Centroid Moment Tensor Project (GCMT, [[https://www.globalcmt.org/CMTsearch.html](https://www.globalcmt.org/CMTsearch.html)]([https://www.globalcmt.org/CMTsearch.html](https://www.globalcmt.org/CMTsearch.html)), accessed on 28 May 2024). **Acknowledgments:** The SDM and PSCMP/PSGRN codes were provided by [PERSON]. The figures in this paper were generated using MATLAB (version 2018a) and GMT (version 6.4) software packages. **Conflicts of Interest:** The authors declare no conflicts of interest. **Abbreviations**  
GNSSGlobal Navigation Satellite System  
SLCSingle Look Complex  
KPJFKunlun Pass-Jiangcuo Fault  
InSARInterferometric Synthetic Aperture Radar  
GCMTGlobal Control Moment Tensor Project  
D-InSARDifferential InSAR  
TOPSTerrain Observation by Progressive Scan  
SRTMDEMShuttle Radar Topography Mission Digital Elevation Model  
LOSLine of sight  
SARSynthetic Aperture Radar  
SNRsignal-to-noise ratio  
StaMPSStanford Method for Persistent Scatterers  
PSPersistent Scatterers  
\(\Delta\)CFSCoulomb Failure Stress changes  
GYXFGarze-Yushu-Xianshuihie Fault  
EKFEast Kunlun Fault  
DFDari Fault  
PS-InSARPersistent Scatterer InSAR
# Dual-Gratings Imaging Spectrometer
## 2 Dual-Gratings Imaging Spectrometer
### Double Images Offset Problem
\begin{aligned} sin(\alpha)\pm sin(\beta)=\frac{m\lambda}{d }\\ \alpha+\gamma=arctan(\frac{h}{s})\\ sin(\alpha-t1)\pm sin(\beta-t2)=\frac{m\lambda}{d}\end{aligned} \right. \tag{7}\]  
Among them, \(h\), \(s\), \(m\), \(\lambda\), \(h\), \(s\), \(\gamma\) are known, and the formula can obtain the relationship between \(t1\) and \(t2\), that is, \(t2\) can be obtained through \(t1\). According to the two effects caused by \(t1\), it can be determined that the relationship between the output light rotation angle \(t3\), \(t2\), and \(t1\). \[t3=t1+t2 \tag{8}\]  
The rotation of the outgoing light causes the movement of the spot on the imaging lens. Since the system is an image-side telecentric system, the optical path from the focusing lens to the image plane does not participate in the bi-image shift. The optical path mainly involved in the dual image shift is the optical path from the grating to the mirror. Assuming that under the influence of \(t1\), the change in the height of the reflected light is \(h1\). Due to the complex calculation process, it is approximated here and deduced from the geometric relationship. The relationship between \(h1\) and \(t3\) is shown in Equation (9). \[h1\approx h\times(\frac{sin(\beta-\gamma+t3)}{sin(\beta-\gamma)}-1) \tag{9}\]  
The effect of \(\beta\) rotation on the double image offset is relatively simple. Due to the distribution of double gratings, \(\beta\) rotation is similar to the rotation of a plane mirror. Assuming that there is \(\beta\) rotation \(k1\), the exit angle increases by \(k2\), and the exit light rotation angle is recorded as \(k3\). Then, there is \(k3=k1+k2=2k1\). Assuming that under the influence of \(k1\), the change in the height of the reflected light is \(h2\). By geometric relations, the relationship between \(h2\) and \(k1\) is shown in Equation (10). \[h2\approx d\times 2 sin(k1) \tag{10}\]  
The effect of \(\gamma\) rotation on the double images offset is also relatively simple. The rotation of \(\gamma\) only affects the direction of dispersion, so the relative rotation of the grating by m1, the rotation of the dispersion direction will also rotate with \(m1\).
## 4 Numerical Examples and Discussion
### Calculation of Long-Term Deflection of Short-Span PSC Beam (Case A)
Thus, higher compressive strain occurs at the top than at the bottom, and the tensile strain and stress occurs at the bottom. This is same in the equivalent load analysis. There must be some differences in the amplitude of strain and stress in the staggered analysis and equivalent load analysis. However, the restraint to the shrinkage strain is small due to relatively small reinforcement ratio. Therefore, tensile stress is compared to the total stress in the beam faces including the self-weight, prestressing force, and applied loads, the stress difference between the staggered analysis and equivalent load analysis is very small and does not make a significant difference. The difference in the tensile stress between the staggered analysis and the equivalent load approach was less than 0.2 MPa and that in the deflection was less than 1.9 mm at the end of the analysis period. Figure 9 shows the variations of the strain and stress of the concrete bottom and mean prestress at the mid-span section with time. As shown in Figure 9a, in all analyses, the tensile strain occurs at the bottom after 84 days. The tensile strains from the staggered analysis and equivalent load analysis (_C&US+NUS+Cracking_) are the largest owing to the non-uniform shrinkage. However, as shown in Figure 9b, the tensile stresses are smaller in the analyses considering cracking because the cracks relieve stress. The tensile stresses from the staggered analysis are the smallest owing to the larger deflection and larger crack strain. In addition, as shown in Figure 9c, the staggered analysis gives the lowest prestress. This suggests that the total shrinkage strain (uniform plus non-uniform shrinkage) is larger at the center of the prestressing tendons in the staggered analysis than in the equivalent load analysis. Figure 5.1 Distribution of crack strain of the short-span PSC beam at 84 th and 1750 th days. (**a**) _Cracking only_; (**b**) _C&US+Cracking_; (**c**) _C&US+NUS+Cracking_.
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 3 Visual-Based Positioning System
### Image Pre-Processing  
Lens distortion and noise directly disturb acquired images, decreasing the detection of power conductors in the images. Therefore, the system must be capable of filtering out these phenomena to measure the separation between conductors in world units and to determine the drone's location in the work-space. With this aim, traditional camera calibration method estimates the intrinsic and extrinsic parameters and distortion coefficients, while the digital signal processing algorithms--presented herein--reduce the noise, correct image defects and remove blurry distortion, guaranteeing the transmission lines detection [51].  
On the other hand, each pre-processed image has several extra objects, which are not related to the transmission grid. These objects from the scene must be attenuated or removed; otherwise, the system must intensify the pixels related to the transmission lines to isolate the three-phase transmission lines from the image background. With this goal, the image is subjected to anisotropic Gaussian filtering [52], which improves the texture quality of transmission lines and removes irrelevant data, regardless of line location in the image, as well as its length and slope.  
The filter bank consists of an edge filter, at 6, 12 or 24 orientations and one scale \(\left(\sigma_{x},\sigma_{y}\right)=\left(1,3\right)\). The orientations are selected based on the computational time and the image size. The resulting image in each filtering process is filter with the Sobel detector to intensify the edges and to obtain a binarized version with the probable power wires. The system delivers a tensor with 6, 12 or 24 binarized images.  
A fixed pattern in the transmission grid, mainly on its conductors, is the symmetric geometric design. The system looks for patterns with similar characteristics to conductor in each binarized image. In this work, the UAV flies over transmission grids with conductors approximating a straight line, without interruption. Hough transform can be successfully used to solve this problem, since this method identifies the section of the binarized image where high probability of finding straight lines exists [53].  
The Hough transform defines a straight line as a co-linear set of points, mapping \(\mathbb{R}^{2}\) into the function space of sinusoidal functions defined by:  
\[f:\left(x,y\right)\rightarrow\rho=x\cos\left(\theta\right)+y\sin\left(\theta\right) \tag{1}\]  
where \(\rho\) and \(\theta\) are the perpendicular distance of the line \(\ell_{i}\) to the center of the coordinates and the angle between the normal of this line and x-axis, respectively.  
In this context, the algorithm returns a dataset with all lines (\(\rho_{i}\) and \(\theta_{i}\)) that meet the previous specifications related to the geometric design of the transmission grid for each binarized image. Then, all measurements related to same line are merged using Fuzzy C-Means algorithm.
## 3 Results and Discussion
### SPME Performance  
The extraction performances of the MIP- and NIP-coated chips were investigated using IBA solutions of different initial concentrations (10-60 mg\(\cdot\)L\({}^{-1}\)) at pH 2.5. Figure 4 shows that the extraction amount on the MIP and NIP coatings increased with the IBA concentration and that the adsorption amounts of the two coatings were almost saturated at 50 mg\(\cdot\)L\({}^{-1}\). The extraction capacities of the MIP- and NIP-coated chips were 152.9 \(\upmu\)g and 60.8 \(\upmu\)g, respectively. Clearly, the extraction amounts of the MIP-coated chips were much higher than those of the NIP-coated chips. This result indicates that the MIP-coated chips exhibit high selectivity and specific adsorption capacity toward the template IBA. The MIP coating exhibited a quantitative response towards IBA (Figure 4) in the buffer, and a quantitative correlation was determined, as described by the equation  
\[\text{FI}=16.80\times\text{[IBA]}-27.09, \tag{1}\]  
where FI denotes the emission intensity of the chip after IBA adsorption, and [IBA] represents the concentration (mg\(\cdot\)L\({}^{-1}\)) of IBA in the buffer solution. It should be noted that the FI value can be linearly (R\({}^{2}\) = 0.9918) plotted against the IBA concentration in solution over the range of 2 to 50 mg\(\cdot\)L\({}^{-1}\), indicating that the MIP-coated chip can be used to quantitatively determine IBA over this concentration range.  
Figure 4: Curve showing the amount of IBA extracted by the polymer coating at pH 2.5.  
Figure 3: Fluorescence intensities of MIP- and NIP-coated quartz chips in different solutions (5 mg\(\cdot\)L\({}^{-1}\) IBA solution, \(\uplambda\)ex/em = 294/370 nm).