esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 2 CDRs and Development
### Value-Added ICDR and CIR  
The succession of satellites, with different designs and changing performance qualities, makes combining all past and current observations into a consistent long-term record a major challenge. In order to homogenize the satellite measurements across decades and multiple platforms, all the knowledge captured about the sensor characteristics, calibration, and performance must be applied retrospectively through reprocessing, which can be a time consuming task. Thus, the production and delivery of CDRs may not be able to meet the needs of the users who have requirements for near-real time or shortly delayed products. Considering that such users are more concerned with data timeliness than the maturity of the product, an ICDR can be produced by forward processing with the near-real time observations without retrospective calibration and cross-platform homogenization. This ICDR can be later re-processed to improve it to the full CDR maturity.  
For tailored use of CDRs in various user sectors (such as agriculture, fishery, forestry, energy, health, water, transportation, tourism, _etc._), CIRs are derived from CDRs to provide authoritative climate reference datasets for use by decision makers to devise strategies to respond and adapt to, as well as mitigate climate change impacts and trends. For example, various climate and environmental indices/indicators (or CIRs) have been developed by combining satellite CDRs with related _in-situ_and/or model data in the extreme context [26; 27; 28], which have significant economic and societal implications [29].  
To produce a suite of CDRs (FCDR, TCDR, ICDR, and CIR) for sustaining climate information (see Figure 1) as well as serve the diverse needs of users (including academy, industry, government, and the general public), an architecture for CDR development and production is critical, such as the one illustrated in [PERSON] _et al._[3]. This architecture should be defined and developed using a systems engineering methodology that considers requirements definition, design, implementation, test, sustained operation and maintenance, and periodic improvements. At the same time, the development and production of a CDR requires the collaboration between experts in the climate community and experts in data management. The process is also informed by scientific application and associated user feedback on the accessibility and usability of the CDRs. Figure 2 provides a practical example of end-to-end production chain from FCDR through CIR. The Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres-Extended (PATMOS-x) reflectance FCDR at the top of the atmosphere (TOA) was derived from cross- and inter-satellite calibrations [7; 30] and used to generate the cloud fraction CDR [31; 32]. Then, both AVHRR PATMOS-x reflectance FCDR and cloud fraction CDR were used to produce the aerosol optical thickness (AOT) TCDR [33], from which a CIR of long-term AOT trend was derived [34].
# Influence of Uneven Subgrade Frost Heave on Deformation and Damage of CRTSIII Slab Track
## 4 Results and Discussion
### Influence of the Subgrade Frost Heaving Geometric Parameters
#### 4.3.2 Influence on the Track Structure Stress and Damage  
According to the above analysis, under the identical frost heaving condition, the degree of damage degree to the concrete base of the track structure was higher than that in other parts. Therefore, this section mainly analyzes the stress and damage to the base.  
Figure 20 shows the variations in the longitudinal tensile stress peak of the concrete base with the frost heaving geometric parameters when the frost heaving wave peak acted on (b). When the frost heaving wave amplitude was 15 mm or less, the relationship between the maximum tensile stress and the frost heaving amplitude was basically linear, and the damage to the base had not occurred or was less. When the frost heave wave amplitude exceeded 15 mm and the frost heave wavelength was 5 or 10 m, the tensile stress peak reached the concrete tensile strength standard value of 2.39 MPa. The base was significantly damaged, and it entered the softening stage. The tensile stress began to decrease. When the  
Figure 18: Interlayer seam: (**a**) Self-compacting base interlayer seam; (**b**) base subgrade surface interlayer seam.  
frost heating wave amplitude exceeded 25 mm, the tensile stress began to decrease when the frost heaving wavelength was 15 or 20 m. When the frost heaving wave amplitude approached 30 mm and the frost heaving wavelength was 25 and 30 m, the tensile stress began to decrease.  
Figure 21 shows the variations in the average damage factor for the base, when the frost heaving wave peak acted on (b). The average damage factor of the base decreased with an increase in frost heaving wavelength and increased with an increase in frost heaving wave amplitude. In order to facilitate the analysis, the concept of the wave/amplitude ratio was put forward, whose value was equal to the ratio of wavelength to wave amplitude. As can be seen from Figure 22, when the wave/amplitude ratio was high (green bottom condition), there was no damage to the concrete base. When the wave/amplitude ratio decreased (white bottom condition), the upper surface of the base began to show bending damage. When the wave/amplitude ratio was further reduced (yellow bottom condition), there was  
Figure 19: Difference between the two seams.  
Figure 20: Longitudinal tensile stress peak for concrete base.  
significant bend observed in the base, resulting in the reverse bending phenomenon at the frost heave edge, resulting in new and significant reverse bending damage, as shown in Figure 23. During routine maintenance, the frost heaving geometric parameters should be limited to the green area as far as possible to reduce damage to the base and to prolong the service life of the track structure.  
Figure 21: Average damage factor for the concrete base.  
Figure 22: Partition diagram of average damage factor of concrete base.
# Correlation of Microstructure and Mechanical Properties of Metal Big Area Additive Manufacturing
## 3 Results
### Charpy Impact Toughness  
Impact toughness is an important dynamic property for structural materials. However, the impact toughness of carbon steel built by the wire-arc AM techniques is not known. The results of Charpy V-notch impact testing of the MBAAM builds is depicted in Figure 6. The samples with the long axis parallel to the build direction are labelled Z1, Z2, and Z3, where Z1 is near the bottom of the build, and Z3 is at the top of the build. The samples with the long axis perpendicular to the build direction are labelled Y1, Y2, Y3, and Y4, where Y1 is nearest the interface and increases with build height (see Figure 4). The solid black curve shows the curve fitting of all the samples representing the entire build and shows the Ductile to Brittle Transition Temperature (DBTT) to be approximately \(-\)25 \({}^{\circ}\)C. The DBTT was determined at the temperature intersection of the midpoint between the upper and lower impact energy shelf. The upper impact energy shelf is approximately 200 J and the lower impact energy shelf is approximately 20 J. The colored dashed lines represent the curves fit to data from specific locations, i.e., Z1, Z2, etc. The results show that the DBTT increases with build height from \(-\)36 \({}^{\circ}\)C in the bottom of the build to \(-\)17 \({}^{\circ}\)C in the upper build (denoted with the vertical black lines). Although there is a general scatter of the data points over the entire build, each individual location, i.e., bottom, middle, or top, maintains a lower amount of scatter. Figure 7 shows the fractography of the samples from the top and bottom of the build after testing at \(-\)20 \({}^{\circ}\)C in the Z direction. The fracture surfaces exhibit a mixture of cleavage and ductile regions. The regions of cleavage fracture are marked by flat surfaces (white arrow) and the ductile regions are marked by dimple features caused by micro void coalescence (black arrow). Micro cracks can also be observed in both samples. The fractography examination is consistent from sample to sample with no discernible difference between samples. However, the lack of differences is not surprising because the difference in impact energy from the top and bottom is small (a maximum range of 90 J) when compared to literature values (ranges larger than 200 J) [17].  
Figure 6: Charpy v-notch impact toughness curves. The figure shows a Ductile to Brittle Transition Temperature (DBTT) increase from \(-\)36 \({}^{\circ}\)C to \(-\)17 \({}^{\circ}\)C with build height.
## 3 Proposed System
### Knee Joint Angle Estimation Method  
In the proposed system, features are extracted from the values obtained from the three stretch sensors placed on the supporter device, and machine learning is used to estimate the knee flexion angle and knee valgus angle. The features are the same for both the left and right sides, so a machine-learning model is created regardless of the side. The features used are the instantaneous values of the three stretch sensors on the outer, central, and inner sides of the knee, normalized, along with the past instantaneous values obtained at 50 Hz. Since the central sensor value becomes larger compared to the other two sensors, the three stretch sensor values are normalized. The maximum and minimum values of the stretch sensors used for normalization are calculated from the stretch sensor values during flexion at the start of system use. The machine learning model uses a regressor, and the data analyses, including feature calculation, are programmed in Python. The combination of machine learning models and features used for estimation will be compared and investigated in the evaluation experiments in Section 4.
# Multi-Means Comprehensive Forecasting Technology for Water Inrush in Long Tunnels
## 3 Comprehensive Geophysical Exploration Techniques
### Transient Electromagnetic Method  
The transient electromagnetic method functions through the emission of pulsed electromagnetic fields and subsequent measurement of secondary field decay patterns following power termination. This technique analyzes subsurface electrical properties by interpreting the temporal characteristics of induced eddy current dissipation. As a time-domain electromagnetic induction approach, its operational principle involves applying a pulsed square-wave current to a transmitting loop, generating a primary magnetic field perpendicular to the loop plane during current termination.  
This primary field induces eddy currents within conductive geological formations (Figure 3), whose magnitude correlates directly with formation conductivity. Following primary field cessation, these eddy currents persist transiently, undergoing exponential decay while generating a time-variant secondary magnetic field. The receiving loop detects this decaying secondary field (Figure 4), with its spatiotemporal variations providing critical data for reconstructing subsurface electrical conductivity profiles through inverse modeling of electromagnetic induction responses.
## 3 Results and Discussion
### The Firing Temperature Analysis Using SEM-EDS
Researchers claim that the total flux concentration within potsherds provides valuable insights into the thermal behavior of bricks, contributing to the formation of a vitreous or glassy phase [79; 80]. When multiple components like Na\({}_{2}\)O, TiO\({}_{2}\), Fe\({}_{2}\)O\({}_{3}\), CaO, and MgO are present along with SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\), a large portion of the liquid is generated, which is referred to as a glassy or vitreous phase [81]. The total flux content of potsherds was determined using the formula Total Flux Content = RO + R\({}_{2}\)O + Fe\({}_{2}\)O\({}_{3}\) + TiO\({}_{2}\), where RO = CaO + MgO and R\({}_{2}\)O = K\({}_{2}\)O + Na\({}_{2}\)O [81; 82]. In the present study, the total flux content was calculated, with values ranging from 8.91 to 25.77 wt.%, with samples from Mouramora-3, Padum Pukhuri, Pratima Garh, and Rukmini Garh showing higher values, whereas Mouramora-1 and Mouramora-2 bricks showed lower values. If the flux concentration (K\({}_{2}\)O, Fe\({}_{2}\)O\({}_{3}\), CaO, MgO, and TiO\({}_{2}\)) is more than 9%, the clays are classified as low refractory, and they are classified as high refractory if the fluxes in the sample are less than 9% [29]. On the other hand, the clays containing CaO greater than 6% are known as calcareous clays, and those containing CaO less than 6% are known as non-calcareous clays [63]. Based on the above statement and EDS results, it is inferred that all the baring Pratima Garh bricks were made from non-calcareous clay. Moreover, Mouramora-1, Mouramora-2, and Padum Pukhuri showed high refractory clay characteristics, and Mouramora-3, Pratima Garh, and Rukmini Garh reflected low refractory characteristics. Figure 7: Representative EDS pattern of studied fired brick samples: (**a**–**c**) Mouramora-1, Mouramora-2, and Mouramora-3, respectively; (**d**) PadumPukhuri; (**e**) Rukmini Garh; and (**f**) Pratima Garh. From the analysis, silica was the major element in all the bricks, with a percentage that varied from 66.81% to 82.10%. All the bricks with low refractory clay contained high iron oxide concentrations, ranging from 10.88% to 12.84%. The chromium concentrations were noticed in two of the studied samples: Mouramora-1 (0.53%) and Rukmini Garh (0.47%). It should be noted, however, that the pottery composition depends both on the clay source and the recipe used to prepare the clay paste [12]. The variation of the chemical composition in bricks may imply that the pottery comes from different production sites or reflects the natural inhomogeneity of local clay deposits and the application of different manufacturing processes in local workshops. The SEM-EDS results for the firing temperature analysis are in good agreement with the FT-IR and XRD results.
# Automatic Identification of Landslides Based on Deep Learning
## 4 Results and Discussion
### Experimental Results and Analysis
Compared with PSPNet (MobileNet), PSPNet (ResNet50) is less likely to mistakenly identify content in the background (such as roads, green vegetation, bare land) as landslides. At the same time, the precision and recall of ResNet50 are higher than MobileNet. In identifying landslide pixels, when ResNet50 is used as the backbone network, the IoU value is 84.6%, which is 3.45% higher than when MobileNet is used as the backbone network. At the same time, the precision is 5.35% higher than that of PSPNet (MobileNet), and the recall is lower than 1.9%. Therefore, its landslide recognition effect is better. Figure 6: The comparison results of landslide identification of PSPNet using MobileNet and ResNet50 as the backbone network, respectively. Behaviors 1, 3 and 5 use MobileNet as the identification result of the backbone network, and behaviors 2, 4 and 6 use ResNet50 as the identification result of the backbone network. The blue box marks the background pixels that the model misidentified as part of the landslide.
## 2 The Proposed Method  
### Overview of the Proposed Method and Image Preprocessing  
According to the characteristics of production technology, flat tubes are often narrow and long. For example, the whole image size of Figure 3 is 600 \(\times\) 23,000, and the scratches are often longitudinal, and there will be a small deflection angle at the same time. In order to detect the scratches effectively, appropriate image preprocessing methods are needed. In this work, the input images are segmented into ten equal length sections (as seen in Figure 3), and in every section, the scratch detection algorithm is applied, and then all the ten results are fused together to yield the final result.  
For every section of the input image, OTSU thresholding [20] was used at the first step, and then morphological filtering was applied to reduce the background clutters, as seen (b) in Figure 4. After that, a novel method combing the cubic Bezier curve fitting and Monte Carlo random sample was employed for scratch detection. The image after preprocessing then used to construct a vertical projection histogram, as seen (c) in Figure 4. Since it is still hard to locate the defect position in the histogram curve due to background noise, the curvature of every point in the histogram curve was calculated by the Bezier curve fitting and the construction of a curvature curve, as seen in (d) in Figure 4, which is much easier for detecting the scratches. Figure 4 shows the overview of the proposed method in processing the fifth section of the input image.  
Figure 4: Overview of the proposed method. (**a**) The section 5 of input image. (**b**) The result of preprocessing. (**c**) The vertical projection histogram curve. (**d**) The curvature curve.  
Figure 3: Segmentation of the Input Image.
## Materials and Methods
### Data
#### 2.2.2. GIMP DEM and Global TanDEM-X DEM  
To calibrate and validate the constructed DEM, we collected the GIMP DEM and global TanDEM-X DEM. Although the collected GIMP DEM might be outdated, we assumed that the general topography over the earth's surface did not change significantly. The GIMP DEM was constructed by merging the ASTER GDEM [36] and SPOT-5 DEM of the SPOT-5 stereoscopic survey of Polar Ice. The Reference Images and Topographies (SPIRIT) program was used, as described in Reference [37], for the peripheral ice sheet and the photo-enhanced Bamber (PEB) AVHRR DEM was utilized [38] for the interior ice sheet [39]. The temporal coverage of the GIMP DEM ranged from February 20, 2003 to October 11, 2009. A comparison to the ICESat measurement showed \(\pm\)9.1 m of overall root mean square validation error (RMSE), \(\pm\)8.5 m over the ice-covered terrain, and \(\pm\)18.3 m in the ice-free high relief region. The GIMP DEM data was composed of 36 geotiff format tiles with a ESPG 3413 projection and an WGS4 ellipsoid, as in Reference [39].  
In addition, the global TanDEM-X DEM was also used for accuracy assessment of the constructed DEM. Single-pass radar interferometry with a close flight formation of TerraSAR-X and TanDEM-X enabled the development of a world-wide DEM with a 12 m horizontal resolution and a 2 m relative height accuracy for flat terrain [23]. The imaging radar observations to generate the global TanDEM-X DEM were collected from January 2010 to December 2015. A comparison to Global Positioning System (GPS) observations revealed a small absolute vertical mean error for the global TanDEM-X DEM (\(<\)\(\pm\)0.20 m) and a small RMSE (\(<\)1.4 m) [40]. We obtained 12 m resolution in the DEM over the Petermann Glacier through a science proposal from the TanDEM-X Science Coordination.
# An Analysis of Satellite Multichannel Differential Code Bias for BeiDou SPP and PPP
## 2 BDS Satellite DCB and OSB Parameters
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System** & **OBS1-OBS2** & **Signal 1** & **Signal 2** & **The Number of Satellites** \\ \hline \multirow{3}{*}{BDS-2} & C2I–C7I & B1I & B2b & C01–C14\(\backslash\)C16(15) \\  & C2I–C6I & B1I & B3I & C01–C14\(\backslash\)C16 \\ \hline \multirow{6}{*}{BDS-3} & C1X–C5X & B1C & B2a & C19–C30\(\backslash\)C32–C46(27) \\  & C1P–C5P & B1C & B2a & C19–C30\(\backslash\)C32–C46 \\  & C1D–C5D & B1C & B2a & Temporarily no data \\  & C1X–C6I & B1C & B3I & C19–C30\(\backslash\)C32–C46 \\  & C1P–C6I & B1C & B3I & C19–C30\(\backslash\)C32–C46 \\  & C1D–C6I & B1C & B3I & Temporarily no data \\  & C2I–C6I & B1I & B3I & C19–C30\(\backslash\)C32–C46 \\  & C1X–C7Z & B1C & B2b & C19–C30\(\backslash\)C32–C46 \\  & C1X–C8X & B1C & B2I & C19–C30\(\backslash\)C32–C46 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of observed values and display satellites in DCB products of the CAS. bias between any two observations can be obtained by the linear combination of the above defined OSB. OSB is more flexible since it is not concerned with the reference frame of the code bias and is directly confronted with the raw data [24]. The main OSB products provided by the CAS and corresponding satellites are shown in Table 3.  
In addition to DCB products, BDS navigation messages also contain group delay correction parameters to compensate for the influence of hardware delays. The navigation message types, modulation modes, and symbol rates for different types of satellites and signals are shown in Table 4. Attention should be paid to the different types of BDS navigation messages when correcting the group delay.
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 4 AI-Assisted Sensing and Survivability Techniques
### AI-Assisted QoT Estimation  
AI-Assisted QoT estimation not only guarantees the QoS for unicast and multicast services but also helps to improve the survivability of optical sensing and optical communication in optical data center networks. In [142], the authors used machine learning techniques to estimate the QoT of light paths in coherent uncompensated WDM links. In [143], the authors developed a machine learning classifier that predicted whether the bit error rate of unestablished light paths met the required system threshold based on traffic volume, desired route, and modulation format. In [144], the authors evaluated the effectiveness of various machine learning models used to predict the QoT of an unestablished light path. The considered models are: K-nearest neighbor, logistic regression, support vector machines, and artificial neural networks. In [145], the authors realized QoT prediction using machine learning for dynamic operation of optical WDM networks. The AI-assisted failure localization and anomaly detection can quickly and accurately identify the number of failures and the location of each failure. The QoT estimation can help to accurately determine if a failure occurs along a light path or not. These technologies provide prerequisites for the following protection and restoration schemes. In the future, AI can be used to search the optimal protection structure and recovery path, and conduct spectrum-efficient resource allocation. When a large number of working flows are interrupted by disasters, AI should help sort all interrupted working flows and give a reasonable recovery order. At the same time, the transmission capacity of optical fiber is improved gradually. Multiplexing technologies such as time division multiplexing TDM, WDM, and space division multiplexing (SDM) are applied to optical communication to extend transmission capacity [146; 147]. AI can help improve the survivability of optical sensing in large-capacity OSNs.