esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
## 4 Results and Discussion
### Static PPP Results
#### 4.4.2 Convergence Time  
The convergence time performance of GPS, GLONASS, and GPS+GLONASS processing modes was studied. The criterion of convergence is achieving a positioning error of less than 1 decimeter on N, E, and U components. The mean convergence time for each station is shown in Figure 14. The mean convergence time for each network is listed in Table 10. The results clearly show that the convergence time of GLONASS PPP is distinctly longer than that of GPS, and the increased percentages are 51.90%, 45.28%, and 105.30% in the high, middle, and low latitude networks, respectively. Compared with GPS PPP, GPS+GLONASS processing reduces the convergence time, and the shortened percentages are 6.11%, 16.57%, and 14.60% in the high, middle, and low latitude networks, respectively. The convergence time gets longer as the latitude decreases for GPS, GLONASS, and  
Figure 13: The RMSE of coordinates of all the stations estimated with PPP.  
GPS+GLONASS positioning modes, and the convergence time of GLONASS in the low latitude network is obviously longer, up to 49.83 minutes.
## 2 Methodology
### Shape Feature Analysis and Flow of Feature Shape Region Searching Algorithm
#### 2.2.2 Flow of Proposed Feature Shape Region Searching Algorithm for Image Registration  
The proposed feature shape region searching algorithm is shown in Figure 5. Here, \"Capture printed images online\" means that the printed matter for defect detection is captured on an actual printing press in real time. The proposed algorithm mainly includes four main steps.  
Figure 4: Several shape types from printed packaging. (**a**) Shape 1; (**b**) Shape 2; (**c**) Shape 3; (**d**) Shape 4; (**e**) Shape 5.  
1. Prior to the implementation of the good shape searching algorithm, all shapes in the printed image should be extracted. The step is called the preliminary shape extraction. In this step, all the shapes are preprocessed, and the shape with the appropriate size is selected. The shape regions that are too small or too large are removed because excessively small shape feature affects the accuracy of image registration and an oversized shape greatly reduces the speed of the process. During the preliminary shape extraction process, we implement a series of processes on the printed images captured online. First, the adaptive segmentation of the captured image is performed, and a connected region analysis is conducted to remove the regions that are excessively small and large. Second, the shape extraction method similar to Canny edge detection is performed, and a high and low threshold idea similar to the hysteresis threshold method is used to exclude the partially inconspicuous edge contour shape. At the same time, the initially extracted shapes are recorded, and each shape is given a label number for the subsequent steps of further searching for a good shape region.
2. The shape feature region is searched on the basis of the contour point distribution information (Section 2.3). In this step, the shape, including the edge points in several direction bins, is retained, and the shape contours that do not satisfy the judgment condition are eliminated.
3. The shape feature region is searched on the basis of the histogram information of the edge gradient direction (Section 2.4). In this step, the shape that includes several contour edge points in four main gradient directions and contour edge points that are evenly distributed in other  
Figure 5: Flow chart of the proposed feature region searching algorithm.  
gradient directions is retained. The shape contours that do not satisfy the judgment condition are eliminated.
4. The contour point distribution information and the edge gradient histogram information are combined to propose an improved automatic feature region searching algorithm for image registration in printing defect inspection systems. The detailed description is provided in Section 2.5.
###### Abstract  
In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on the intercomparison of soil moisture monitoring from Global Navigation Satellite System Reflectometry (GNSS-R) signals and passive L-band microwave radiometer observations at the Valencia Anchor Station is introduced. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and a dual-pol down-looking antenna for receiving LHCP (left-hand circular polarization) and RHCP (right-hand circular polarization) reflected signals from the soil surface. Data were collected from the three different antennas through the two channels of Oceanpal GNSS-R receiver and, in addition, calibration was performed to reduce the impact from the differing channels. Reflectivity was thus measured, and soil moisture could be retrieved. The ESA (European Space Agency)-funded ELBARA-II (ESA L Band Radiometer II) is an L-band radiometer with two channels with 11 MHz bandwidth and respective center frequencies of 1407.5 MHz and 1419.5 MHz. The ELBARAII antenna is a large dual-mode Picket horn that is 1.4 m wide, with a length of 2.7 m with \(-\)3 dB full beam width of 12\({}^{\circ}\) (\(\pm\)6\({}^{\circ}\) around the antenna main direction) and a gain of 23.5 dB. By comparing GNSS-R and ELBARA-II radiometer data, a high correlation was found between the LHCP reflectivity measured by GNSS-R and the horizontal/vertical reflectivity from the radiometer (with correlation coefficients ranging from 0.83 to 0.91). Neural net fitting was used for GNSS-R soil moisture inversion, and the RMSE (Root Mean Square Error) was 0.014 m\({}^{3}\)/m\({}^{3}\). The determination coefficient between the retrieved soil moisture and in situ measurements was R\({}^{2}\) = 0.90 for Oceanpal and R\({}^{2}\) = 0.65 for Elbara II, and the ubRMSE (Unbiased RMSE) were 0.0128 and 0.0734 respectively. The soil moisture retrievals by both L-band remote sensing methods show good agreement with each other, and their mutual correspondence with in-situ measurements and with rainfall was also good.  
Keywords:ELBARA-II radiometer; GNSS-R; L-band radiometry; Oceanpal; soil moisture; Valencia Anchor Station
## 3 Capacitive Sensing with Electronic Detection
### Electronic Detection System
#### 3.3.2 Generation of Excitation Sensor Signals  
Excitation sensor signals are generated in the Field Programmable Gate Array (FPGA) and transferred to the Application Specific Integrated Circuit (ASIC), where further shaping and amplification takes place. The amplitudes and the DC levels of each excitation signal can be adjusted. They are connected to the corresponding sensor's capacitors and provide the possibility of transferring the information from slowly varying sensor capacitances to the trans-impedance charge amplifier at a high frequency, well above the flicker noise corner frequency. The whole measurement system looks like a modified lock-in amplifier [26, 27], using double-mixing architecture to sense and amplify at high frequency above the 1/_f_ noise corner of the CMOS charge amplifier. Each spectral component is not immediately reduced to the DC because, in that case, the 1/_f_ noise and the offset voltage of the ADC would influence the results. The amplitudes, DC levels, and frequencies of excitation signals are programmable, and we can adapt each excitation generator to a particular sensor. At the moment up to four differential sensors are connected to one measurement channel according to Figure 12. Each sensor is driven with a square-wave excitation signal with slightly different frequency \(f_{\rm av}\), where all the signals are coherent [28]. One measurement channel can process the signals of four differential sensors. The \"DC\" signal-processing happens only in the DSP, and thus all DC drifts and 1/_f_ noise problems of the analogue electronic modules are removed. As a result, the noise properties are improved.
# Integrated Photonics on Glass: A Review of the Ion-Exchange Technology Achivements
## 3 Telecom Devices  
### Context and Historical Overview  
Optical Telecommunications was originally the reason why [PERSON] introduced the concept of integrated optics in 1969 [46]. Therefore, the pioneering work of integrated photonics on glass has been mainly devoted to telecommunication devices pushing steadily towards the development of not only ion-exchange processes but also of a full technology starting from the wafer fabrication and ending with the packaging of the manufactured Planar Lightwave Circuits. Figure 7 shows this evolution by displaying on one side one of the first demonstrations of a 1 to 8 power splitter made by cascading multimode Y-junctions [47] and, on the other side, its 2006 commercially available counterpart, single mode and Telcordia 1209 and 1221 compliant [7; 48].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Glass Type** & **GO14** & **BGG31** & **BF33** \\ \hline Losses & \textless{}0.05 dB/cm [7] & \textless{}0.1 dB/cm & \textless{}1 dB/cm (@7/80 nm) \\ \(\Delta\)n max & \(8\times 10^{-2}\)[7] & 3.2 \(\times\) 10\({}^{-2}\)[38] & 1.8 \(\times\) 10\({}^{-2}\)[35] \\ Birefringence & \textless{}5 \(\times\) 10\({}^{-4}\)[39] & \textless{}2 \(\times\) 10\({}^{-5}\)[40] & N.A. \\ Burying depth & \textless{}10 \(\upmu\)m (50 \(\upmu\)m max) & \textless{}10 \(\upmu\)m [38] & \textless{}5 \(\upmu\)m [35] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main characteristics of single mode waveguides realized on three different glasses.  
Once elementary functions, such as Y-junctions and directional couplers were demonstrated, studies were oriented towards all the functions that could be required for optical fiber communications like thermo-optic switches [49], Mach-Zehnder interferometers [50; 51] and Multimode Mode Interference (MMI) couplers [52; 53; 54; 55]. These buildings blocks have then been optimized and/or combined on a single chip to provide more functionality. In the next sections, we will review some of them and put the emphasis on the specificity brought by the use of ion-exchange on glass.
# Combined Use of LoRaWAN Medium Access Control Protocols for IoT Applications
## 6 Performance Evaluation
### Discussion  
The simulation results show that the PLR values obtained by LBT AFA are lower than those of Pure ALOHA. In particular, the difference increases with the traffic load. The reason for this result is that with Pure ALOHA, the end-devices transmit without performing any CCA; therefore, they may incur a high number of collisions. On the contrary, LBT AFA runs CCA before starting a transmission and uses AFA (and may resort to backoff) whenever the channel is found to be busy. Consequently, with LBT AFA, the packet loss decreases, but the CCA increases the energy consumption. Pure ALOHA outperforms LBT AFA in terms of average e2 eDelay, as the end-devices that use Pure ALOHA do not adopt a backoff algorithm; therefore, they do not delay their transmissions.  
The PLR increases with the number of end-devices that use Pure ALOHA because the end-devices transmit right away whenever they have messages to send (without running CCA). Our results show that the PLR increases with the percentage of mobile end-devices because in our scenario, they generate a higher network traffic than stationary ones. Furthermore, in our scenarios, neither the percentage of stationary and mobile end-devices nor the percentage of end-devices using Pure ALOHA and LBT AFA significantly impact the average e2 eDelay.  
When the messages to be transmitted are of equal importance, LBT AFA can be a good option, as it reduces collisions. However, in some scenarios, e.g., in the industrial communications, different end-devices typically transmit traffic flows with different criticality levels (e.g., alarms, and best-effort traffic). In these cases, the choice of the MAC protocol to be used by each end-device can help to meet the requirements of the relevant traffic flow. For example, the end-devices that can generate high-priority messages that have to be delivered as soon as possible (e.g., alarms) should prefer Pure ALOHA.
# A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras
## 3 The Proposed System Methodology
### Preprocessing Stage  
During video pre-processing, the first step is the conversion of videos into image frames. Thus, the depth frames are extracted from static videos \(V=[F_{1},F_{2},\dots,F_{n}]\) where n is the number of frames. The next step is to remove complex backgrounds from these depth frames using the Kernel Density Function (KDE) technique. KDE is a non-parametric technique for estimating the densities of the pixels. The main idea of KDE is that each frame background is identified by the histogram of the most recent pixels \(N\), where each pixel is smoothed by a kernel (generally a Gaussian kernel). Perhaps, the main objective of using KDE is to detect changes occurring frequently in the background and to accurately detect and identify the target objects in the forground with high sensitivity.This technique works by selecting the most recent frames of a video and updating these frames continuously in order to update changes in the background. These frames are collected as samples and their pixels are further processed to obtain the intensity values of each pixel.  
By using the intensity value samples of the pixels the probability density function of each pixel has the intensity value \(i_{T}\) at time \(T\), and kernel estimator \(E\) can be used to estimate it as [45];  
\[P(i_{T})=\frac{1}{n}\sum_{x=1}^{n}E(i_{T}-i_{x}) \tag{1}\]  
Figure 1: System architecture of the proposed HASTAC system.  
where \(i_{1},i_{2},\ldots,i_{n}\) are the recent intensity value samples of a pixel.  
\(E\) can be calculated using Gaussian distribution for the depth frames as in [45];  
\[P(i_{T})=\frac{1}{n}\sum_{x=1}^{n}\frac{1}{\sqrt{2\pi\sigma_{k}^{2}}}e^{\frac{- 1}{2}\frac{(i_{T_{x}}-i_{x})^{2}}{\sigma_{k}^{2}}} \tag{2}\]  
By calculating the probability estimate, the set of pixels are considered as foreground pixels if \(P(i_{T})<Th_{1}\) where \(Th_{1}\) is the global threshold that can be adjusted for every image frame in order to get a desired false positive rate. Figure 2a,b show the results obtained after background subtraction using the KDE technique over PCDS and GOTPD datasets, respectively.
## 2 Study Area  
The Zhaotong area, situated in the northeastern part of Yunnan Province, China, spans geographical coordinates ranging from 26\({}^{\circ}\)55\({}^{\prime}\) to 28\({}^{\circ}\)36\({}^{\prime}\) N latitude and 102\({}^{\circ}\)5\({}^{\prime}\) to 105\({}^{\circ}\)19\({}^{\prime}\) E longitude (Figure 1a). It is a prefecture-level city located in the northeast corner of Yunnan Province of China, bordering the provinces of Guizhou to the south and southeast and Sichuan to the northeast, north, and west. The Zhaotong area comprises nine counties with a total area of 23,000 km\({}^{2}\). It is inhabited by 6.33 million people from 24 ethnic groups.  
The Zhaotong area is situated in the hinterlands of the Wumeng Mountains at the confluence of the Yunnan, Guizhou, and Sichuan provinces, along the downstream region of the Jinsha River. This location forms a transitional zone where the Sichuan Basin rises to the Yunnan-Guizhou Plateau, characterized by complex geological structures and distinct geomorphological features. The terrain slopes from the southwest to the northeast with an elevation difference of 3773 m, ranging from the highest point at Dayao Mountain in Qiaojia County at 4040 m, to the lowest point at Gunkan Dam in Shuifu County at 267 m, with an average elevation of 1685 m. The annual average precipitation of the study area is between 660 and 1230 mm, concentrated primarily from May to October.  
The strata outcropped in the Zhaotong area range from the Sinian to the Quaternary systems (Figure 1b). The most widely distributed are the Paleozoic, dominated by marine sedimentary limestone and mudstone, with the Permian lithology primarily composed of basalt. The Mesozoic shifted to terrestrial deposits with the Triassic strata characterized mainly by limestone, sandstone, and clastic rocks. The Cenozoic are primarily found near the Zhaotong Basin, consisting mostly of loose clay, loam, and gravel. The region has numerous active faults, including the Zhaotong-Ludian fault zone (late Pleistocene), the Lianfeng fault (middle Pleistocene), and the Xiaojiang fault zone (Holocene), among others. This area is prone to frequent seismic activity, with 32 earthquakes of Ms 5.0 between 1844 and 2023. The largest recorded earthquake was a Ms 7.1 earthquake event in 1974 in Daguan, Yunnan.  
Figure 1: (**a**) Map showing the location of Zhaotong, Yunnan Province; (**b**) the geological unit distribution, earthquakes, and tectonic setting of the Zhaotong area.
# Accident Risk Analysis of Gas Tankers in Maritime Transport Using an Integrated Fuzzy Approach
## 2 Backgrounds
### Fuel Leaks and Safety Precautions  
Fuel leaks are among the most critical incidents that pose hazards to operational safety, personnel well-being, and vessel integrity under the GCT framework. The objective of the studies conducted within this scope is twofold: first, to identify the causes of leaks, and second, to develop effective risk mitigation strategies. In a study evaluating the causes of high-impact events such as Boiling Liquid Expanding Vapour Explosions (BLEVEs), analyses were conducted based on the Zarzalico accident, and strategies for preventing such accidents were discussed [22]. Another study on hazardous material transportation examined the Kannur accident involving an LPG tanker, detailing the extent of casualties, injuries, and environmental damage caused by spills [19]. Research conducted specifically on LNG-fuelled ships has revealed the potential fire risks posed by natural gas leaks through CFD (Computational Fluid Dynamics)-based analyses, highlighting safety vulnerabilities [23].  
Research conducted on the risks posed by LNG leaks, particularly in enclosed spaces such as ship elevators, has proposed various engineering solutions to mitigate these risks [24]. Furthermore, an additional study incorporating simulation-based analysis of accidental fuel releases has identified areas for improvement in the safety of LNG fuel systems [25]. A study addressing fuel leaks in the context of discharge systems evaluated the design of ventilation stacks on LNG ships and presented design recommendations for controlling low-temperature flammable gases [26]. Finally, the effectiveness of plasma-based partial oxidation technologies was analysed using experimental approaches [27]. These technologies have been proposed as an alternative method for the mitigation of LPG leaks.
## 4 A DFN Approach to Analyze Scale Effects in RQD Measurement
### Comparison with Empirical Formulae for the Determination of RQD  
[PERSON] and [PERSON] (2018) defined a relationship between RQD and discontinuity (fracture) spacing measurements made on core or from surface exposures for different rock types (the majority of sedimentary rock types). The relationship is given in the Equation below:  
\[\text{RQD}=100\;\text{e}^{-0.1\lambda}\;(0.1\;\lambda+1), \tag{1}\]  
where \(\lambda\) represents the mean fracture frequency (equivalent to the linear P\({}_{10}\) intensity used in DFN modeling).  
The results are presented in Table 6. There is a good agreement between simulated RQD values and those obtained using the equation for orthogonal boreholes. The difference between the simulated RQD values and those calculated using the equation for inclined boreholes could be explained considering the orientation of the fracture sets and the fact that the equation was primarily developed from data collected for sedimentary rock types. It is safe to assume that for sedimentary rock types, there would be a much better agreement between fracture frequency (spacing) and RQD along core orientations that are normal to the orientations of the main fracture sets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Simulated \(\lambda\)**} \\ \hline Orthogonal boreholes (OB) & 4.0 & 6.4 & 9.2 & 13.3 \\ Inclined boreholes (IB) & 2.7 & 5.0 & 7.0 & 10.1 \\  & \multicolumn{4}{c}{Calculated RQD} \\ Empirical Formulae (OB) & 93.8 & 86.2 & 76.3 & 61.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of simulated vs calculated RQD.  
Figure 19: Room-and-pillar mine DFN model. Calculated RQD vs. core run length for the inclined boreholes.  
Figure 18: Room-and-pillar mine DFN model. Calculated RQD vs. core run length for orthogonal boreholes.
## 3 Data and Methods
### Unsupervised Machine Learning Models  
Unsupervised machine learning is a major part of machine learning. It can study the intrinsic relationship of datasets without data labels when dealing with practical problems. The main applications of unsupervised learning are: segmenting a dataset by some shared attributes; detecting exceptions that are not suitable for any group; and simplifying datasets by aggregating variables with similar properties. Among these, the objective of this study is to explore the susceptibility of submarine landslides. As a consequence, an important unsupervised machine learning class named clustering, which contains k-means, spectral, and hierarchical clustering, was selected as the study method. Each clustering method was built after parameter selection with internal validation measures: the Calinski-Harabasz index [22], silhouette index [23], and Davies-Bouldin index [24]. The more precise the clustering result, the higher the Calinski-Harabasz score and silhouette index. The lower the Davies-Bouldin index, the more accurate the clustering result. The clustering results' performances were validated with external validation measures; for instance, liquefaction distribution, hydrodynamic action, slope angle, etc.