esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations and Future Work  
This study aims to analyze the change characteristics of LDD in China's coastal areas and the corresponding influencing factors using the LDDI index, constructed from NDVI and GPP data, which are openly accessible through existing remote sensing products. However, the study has several limitations and shortcomings that may affect the interpretation of the results. Firstly, analyzing the spatial variability of LDD requires high-resolution datasets, but the length, quality, and accuracy of the data used in this study were limited. Furthermore, the LDDI index may not fully capture the detailed spatial variations in mountainous regions with complex terrains due to the limited resolution and uncertainty of the NDVI and GPP data. Secondly, the dynamic process of LDD is complex and multifaceted, requiring consideration of multiple dimensions such as climate, society, economy, and environment. Understanding the complex relationships between these factors is essential to formulate effective strategies to mitigate land degradation and promote sustainable land use practices. However, in this study, only precipitation and temperature were considered as climate factors, and other climatic elements such as wind and soil moisture that could affect LDD were omitted. Although the impact of temperature, precipitation, and some human factors on LDD was analyzed, it may not fully explain the detailed land degradation and development in coastal China. Despite these limitations, this study enhances our understanding of the factors behind the spatiotemporal changes in LDD in coastal China and sets a foundation for similar research in other coastal regions. Future studies should focus on researching additional factors that may influence the dynamic process of LDD and utilize more accurate, multi-source, high-resolution datasets, and mathematical models  
Figure 8: The proportion of different LDD change types in the study area. Deg and Dev represent land degradation and land development, respectively. Sig, Sli, and Non\({}_{\text{sig}}\) represent LDD changes with significant, slight, and no changes. Values less than 0.00001 are represented as 0.  
that consider physical processes to investigate the response mechanisms of LDD changes to climate and human factors.
## 3 Test Results and Discussion  
### Results of the Single Impact Test  
#### 3.1.1 Typical Stress Waveform Analysis  
In SHPB tests, achieving dynamic stress equilibrium between specimen ends can guarantee the effectiveness of the test. Figure 2 shows the feasibility analysis of the dynamic stress equilibrium for typical specimens during a randomly selected impact test. The sum of the incident and reflected stresses at the front end is coincident with the transmitted stress at the rear end of the specimen. This indicates that the dynamic stress equilibrium is achieved, and thus the axial inertial effect can be avoided.  
Figure 3a shows five curves of the incident-reflected stress-time wave signals of sandstone specimens collected at different impact velocities. The amplitudes of the incident stress waves increased with the increasing impact velocities. The patterns of the incident and reflected stress waves did not change with increasing velocity, indicating that the impact tests have good repeatability. In addition, the stress wave loading duration can be measured as 160.32 us, while the theoretical duration is 157.17 us, with an error of only 2.00%. Figure 3a also shows that the reflected stress wave has a steep protrusion at the descending edge and then tends to be flat. Since the protrusion accounts for a small proportion of the reflected wave and can be ignored, the tests can be regarded as a constant strain rate loading. The corresponding five stress-time curves of the transmitted stress wave signals of the sandstone specimens at different impact velocities are shown in Figure 3b. The result shows that the slope of the rising edge of the transmitted wave gradually increases with the increasing impact velocity, which reflects that the dynamic elastic modulus of the sandstone increases with the increasing velocity to a certain extent, based on Equation (10). The strain rate-time curve is shown in Figure 4.  
Figure 2: Verification of the dynamic stress equilibrium for typical specimens. Note: In, Re and Tr represent incident, reflected and transmitted, respectively.
## 2 Materials and Methods  
To explore the changes in surface debris on the Qiyi Glacier from 1972 to 2021, we used three high-resolution satellite datasets (the 1-m-resolution KeyHole-9 satellite on 30 August 1972, the 1-m-resolution Orbview-3 satellite on 8 October 2005, and the 0.6-m-resolution satellite GeoEye-1 on 23 October 2012), and two high-resolution (0.06 m) digital orthophotograph maps from UAVs taken during the ablation seasons of 2020 and 2021. All these images are cloud-free with no temporary snow. The KH-9 and Orbview-3 data were obtained from the United States Geological Survey, and the GeoEye-1 data are available from the Google Earth Platform. The UVA imagery was captured with a Zenmuse X5S camera and acquired with a commercial drone platform (DJ Matrice 200 RTK) at a height of 400 m above the glacier surface with an identical flight path in both years.  
All the satellite images are registered using the UAV DOM data of 2021, from which boulders and rocky outcrops that have not changed position are identified as control points. The errors of the geo-registration are \(\sim\)1 pixel. All the UVA data and satellite images were re-sampled onto a common 1 m spatial resolution. The border of tongue part of the Qiyi Glacier and its debris cover in 1972, 2005, 2012, and 2020 were retrieved by on-screen digitizing.  
The obtained DEM and DOM data from well-matched UVA images in 2020 and 2021 are the key data sources for our analysis of ice mass loss. We used the registration method of [PERSON] et al. (2021) that focuses on the relative position of control points. Firstly, the actual RTK coordinates were considered to be control points for generating the DOM and DEM data in 2021 using pixel4D software (v4.4.12), and from the 2021 DOM the unmoved rocks were identified as control points for the 2020 DOM and the DEM data. The mean absolute errors of the coordinates of check points were 0.002 m (\(\Delta x\)), 0.008 m (\(\Delta y\)), and -0.021 m (\(\Delta z\)), with root-mean-square errors of 0.054 m (\(\Delta x\)), 0.039 m (\(\Delta y\)), and 0.137 m (\(\Delta z\)), respectively. The well-matched UAV images were used to characterize the glacier surface in 2020 and 2021 using visual interpretation. From this, we analyzed the relationship between the  
Figure 1: Location of the Qiyi Glacier (red star) and the surface changes of its tongue part from 2010 to 2020, showing the exposure of englacial debris. (**a–c**) Photographs were taken in summer 2010, 2015, and 2020, respectively. The red rectangle outlines the area of the most obvious increase in debris cover. (**d**) Orbview-3 satellite image of the Qiyi Glacier on 8 October 2005 (downloaded from the United States Geological Survey).  
glacier surface and its corresponding ice mass change, which had already been obtained by calculating the difference in the DEM between 2020 and 2021.
## 6 Evaluated Parameters
### Parameter 2: Hourglass Control  
For a system that is initially static, the energy balance is expressed by the energy conservation equation as  
\[E_{I}+E_{V}+E_{K}-E_{W}=E_{total}=0 \tag{8}\]  
where \(E_{I}\) is the internal energy, \(E_{V}\) is the energy dissipated by damping mechanisms (including the material damping), \(E_{K}\) is the kinetic energy, and \(E_{W}\) is the work done by external loads. Internal energy \(E_{I}\) is the sum of recoverable elastic strain energy, the energy dissipated through plasticity, the energy dissipated through viscoelasticity or creep of the materials, the energy dissipated through damage, and artificial strain energy [87].  
When linear elements with reduced integration are used, the software introduces artificial forces to counteract the hourglass effect that may arise in certain regions of the model during deformation. These artificial forces are converted into artificial energy, which can progressively accumulate throughout the analysis, eventually reaching levels comparable to physical energies [90]. Consequently, this phenomenon becomes one of the main sources of errors in the results. A commonly used criterion to assess the reliability of the analysis is to ensure that the artificial energy remains below 1% of the internal energy [91]. However, this rule is more appropriate for quasi-static analyses where the loading rate is relatively slow. For dynamic analyses that involve faster deformations and higher loading rates, this criterion may not be suitable. In structural dynamic analyses, some researchers consider more reasonable a range of 5-10% [92; 93].  
To mitigate the formation of the hourglass modes, numerical solvers often adopt viscous damping or elastic stiffness added to the stiffness matrix [93]. Abaqus, in particular, provides several numerical techniques to suppress the initiation and propagation of the hourglass effect [87]. Some of these techniques are the relax stiffness and Kelvin viscoelastic approach. The Kelvin viscoelastic approach is defined by the linear combination of an elastic component (pure stiffness) and a viscous component (pure viscous). Pure stiffness is related to the nodal displacements, while pure viscous introduced an artificial force proportional to the nodal velocity. The amount of artificial force introduced by these techniques is controlled by a scaling factor. Based on preliminary tests, three numerical techniques were applied, namely, relax stiffness, (pure) stiffness, and combined (stiffness plus viscous).
## 3 Results
### Validation
#### 3.2.2 NOAA Aerial Imagery  
Following Hurricane Harvey, the NOAA Remote Sensing Division acquired aerial imagery (30 August-2 September 2017) of the selected sites in the study area [83]. Even though cloud cover partly obscured the clarity of the images over some parts of the study area, they provided a vivid representation of the ground conditions (spatial resolution ranging from 35 to 50 cm [83]) during the severe weather event. We visually compared the images acquired by NOAA to the SAR-derived (Sentinel-1 and UAVSAR) flood detection over three localities in the study area (Figure 6a,e-g). In two of these sites (Sites I and III; Figure 6), covered with dense forest and woody land cover types (Figure 1b), there was an overlap in image acquisition by both the UAVSAR and NOAA missions (maximum gap of one day between image acquisitions). As can be seen in Figure 6, the results of the flood detection based on the two data sources spatially correlated well. Moreover, the floodwaters detected using the two (UAVSAR and NOAA aerial imagery) datasets also correlated well with the flood detection based on the approach using the Sentinel-1 datasets. Site IV (Figure 6) is largely covered with cultivated crops, and to demonstrate the potential of the Sentinel-1-based approach to detect floodwater in similar land cover types, a comparison between the NOAA imagery and the Sentinel-1 data-based flood mapping was undertaken (Figure 6a,f). Similar to the case of the forest and woody land cover types, the proposed floodwater detection based on the Sentinel-1 data was also able to capture the floodwater in such land cover types, as seen in Figure 6f. By and large, the NOAA images correlated well with the SAR-derived flood detection, further confirming the accuracy and reliability of the C-band SAR-based floodwater detection approach.
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 5 Comparison of Simulations with Field Measurements
### Quantitative Validation
In addition, uncertainties in accurately determining notch characteristics, potentially caused by disturbances like commutation swings or unbalanced network conditions, can simultaneously affect both the depth and duration of notches, leading to a positive correlation in the field data. In contrast, the simulation model may not fully capture these unaccounted factors, as it assumes certain parameters to be constant or within specific ranges. In addition, the negative correlation in simulations could be due to the way the parameters were varied independently during the simulations, which might not reflect the exact interdependencies present in the real-world system. Similarly, discrepancies in the correlations involving angle (\(I_{1}\)) are observed. The field measurements show a moderate negative correlation between angle (\(I_{1}\)) and both \(d_{com}\) and \(\Delta t_{com}\), whereas, in the simulations, the correlations are weaker. This could be due to differences in background harmonic distortion or unmodeled reactive power compensation devices in the field measurements, which can affect the phase angle of the current. These discrepancies highlight the limitations of the simulation model in capturing all the complexities of the real-world system. However, overall, the low relative errors in most of the statistical metrics indicate that the simulation model provides a reasonable approximation of the behavior observed in the field measurements.
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 3 Deep Keypoint-Based Monocular SLAM
### Deep Keypoint-Based Loop Closing  
Our DK-SLAM system features a deep-keypoint-based loop closure module that performs both loop closure detection and correction. To improve generalization in unfamiliar environments, we utilize an online learning-based Bag-of-Words (BoW) model for effective loop closure detection. Once loop closure is detected, the system identifies the closed-loop nodes and optimizes the global map using the relative poses from the detected loops.
# Research on Solidification Methods and Stabilization Mechanisms of Sulfate Saline Soils
## 4 Results and Analysis
### Microstructure Analysis of Stabilized Soils
#### 4.3.4 TG Analysis  
The TG\(-\)DSC of L3-stabilized soil cured for 28 d, FA, FAS (FA hydrated in deionized water for 28 d), and FAS1 (FA hydrated in a solution of Na\({}_{2}\)SO\({}_{4}\):H\({}_{2}\)O at a ratio of 1:12.5 for 28 d) is shown in Figure 12.  
Figure 12a shows the TG\(-\)DSC curve of L3-stabilized soil after 28 d of curing. The dehydration of Ca(OH)\({}_{2}\), gypsum (CaSO\({}_{4}\)), CaCO\({}_{3}\), and adsorbed and crystalline water in cementitious materials was responsible for the approximately 1.3% mass loss in the 0-380 \({}^{\circ}\)C range [49]. XRD and FTIR data confirm the existence of Ca(OH)\({}_{2}\) and explain the 1.28% mass loss in the 380-420 \({}^{\circ}\)C range [50]. The breakdown of CaCO\({}_{3}\)[51] causes a mass loss of about 2.33% in the 550-700 \({}^{\circ}\)C range, which was consistent with the XRD data.  
Figure 12b shows the TG\(-\)DSC curve of FA-stabilized saline soils. There was no mass loss between 0 and 120 \({}^{\circ}\)C, meaning no adsorbed water in FA. The oxidation of carbon and organic materials in FA accounts for the 5.3% mass loss in the 400-500 \({}^{\circ}\)C range, as well as  
Figure 12: TG\(-\)DSC curves of stabilized saline soils: (a) L3 stabilized for 28 d; (b) FA (c) FAS (d) FAS1.  
the exothermic peak observed. Carbonate breakdown results in a 0.51% mass loss between 650 and 750 \({}^{\circ}\)C.  
Figure 12c,d show the TG\(-\)DSC curves of FAS and FAS1. The 1.07% mass loss in the 0-120 \({}^{\circ}\)C range can be attributed to the dehydration of the hydration products C\(-\)S\(-\)H, C\(-\)(A)\(-\)S\(-\)H, and AFt [50; 52]. The mass loss in the range of 120-200 \({}^{\circ}\)C was attributable to the dehydration of the bound water in the hydration product C\(-\)S\(-\)H gels and AFt, indicating the generation of hydration products from FA [52], agreeing with the SEM and FTIR results. The mass loss in the 400-500 \({}^{\circ}\)C range was due to carbon oxidation in FA, which contradicts the endothermic dehydromyclation reaction of Ca(OH)\({}_{2}\)[53; 54]. Carbonate breakdown, mostly produced by the interaction of Ca\({}^{2+}\) and Na\({}^{+}\), accounts for the mass loss between 650 and 750 \({}^{\circ}\)C [50]. The hydration products created in the FA-stabilized soil, such as C\(-\)A\(-\)H, C\(-\)S\(-\)H and C\(-\)(A)\(-\)S\(-\)H, were the primary components that contributed to the soil's strength. The strength increases with increasing C\(-\)A\(-\)H, C\(-\)S\(-\)H and AFt content.
# Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms
## 2 Materials and Methods
### Simulating Altimeter-like ADT Maps  
One year (2017) of synthetic altimeter-derived ADT maps was obtained from the outputs of the Copernicus Marine Service MFS hydrodynamic simulation, using the data unification and altimeter combination system (DUACS) mapping method. The different steps are detailed below. Firstly, sea level anomaly (SLA) was computed from model outputs by means of Equation (1):  
\[\text{SLA}=\text{SSH}-(\text{MDT}-0.344) \tag{1}\]  
where the mean dynamic topography (MDT) is provided as a static field together with the model outputs. A 0.344 constant (expressed in m) allows us to adjust the SLA values in the Mediterranean Sea to guarantee that the spatio-temporal average of SLA is zero during 2017. The large-scale, high-frequency variability, usually removed by applying a dynamic atmospheric correction (DAC) [49] is filtered out of these synthetic data by applying a Loess filter. The SLA is then sampled along the actual tracks of a synthetic constellation composed of four radar altimeters: Jason-3, Sentinel-3A, SARAL/Altika and Cryosat-2 missions. This step is achieved by running the SWOT simulator software [50], which allows us to account for the actual orbits, errors and noise that characterize each mission. The chosen four-satellite constellation is representative of the constellation ingested in Copernicus Marine Service processing during 2017. Such along-track synthetic measurements are then ingested by the DUACS processing chain to produce L4 SLA maps. The optimal interpolation (OI) scheme follows the DUACS DT2018 (Delayed Time) configuration for the Mediterranean area, described in [32]. The reconstructed small-scale maps are then recombined with the filtered large-scale maps. Such data are provided on a daily basis and over a regular \(1/8^{\circ}\) grid (more details available in [44]).
# Legal Judgment Prediction via Heterogeneous Graphs and Knowledge of Law Articles
## 3 Our Method
### Fact Description Graph Interaction  
We used a graph convolutional network (GCN) to encode the nodes for each graph. A GCN acts directly on the graph and can obtain the feature vectors of its nodes from the information of the nodes' neighbors. All the nodes in the graph are connected to themselves. Let us assume that matrix X contains the features of all \(n\) nodes in the graph, where \(m\) is the dimension of the feature vector of each node. The matrix \(A\) is the adjacency matrix of \(G\) and \(D\) is the degree matrix of \(G\) and the values on the diagonal of \(A\) are all 1. The GCN can obtain the information of direct neighbor nodes after the first layer of the graph convolution operation. When multiple layers of GCNs are stacked, the information of a more extensive range of neighbors can be learned. When the initial node feature matrix is calculated by one layer of GCN, the \(k\)-dimensional node feature matrix \(L^{(1)}\in\mathbb{R}^{n\times k}\) is obtained, which is calculated as follows:  
\[L^{(1)}=tanh(\bar{A}XW_{0}) \tag{10}\]  
where \(\bar{A}=D^{-1/2}AD^{-1/2}\) is the normalized symmetric adjacency matrix and \(W_{0}\) is the parameter matrix. The hyperbolic tangent function (tanh) is used for the activation function based on experiment. For multilayer GCN calculation,  
\[L^{(p+1)}=tanh(\bar{A}L^{(p)}W_{p}) \tag{11}\]  
where \(p\) is the number of layers, \(L^{(0)}=X\). We chose to use a three-layer GCN as the graph feature extractor. Based on the experiments performed in this paper, we show that the three-layer GCN could make the node information pass well and the one-layer and multi-layer GCNs were worse than the three-layer GCN.  
Then, we used the following readout functions for the six kinds of graph of one document:  
\[L_{v}=sigmoid(W_{1}L_{v}^{l})\odot\tanh(W_{2}L_{v}^{l}) \tag{12}\]  
\[h_{G}=Averagepooling(L_{1}...L_{V})+Maxpooling(L_{1}...L_{V})-Minpooling(L_{1}...L_{V}) \tag{13}\]  
where \(L_{v}\) is the graph node feature calculated by the attention mechanism. We then used Averagepooling to add with Maxpooling and subtract with Minpooling. The idea is that each text word contributes to the task, with essential words contributing more information and unimportant words potentially having a negative effect.
## 5 Conclusions  
Sectionalizing 0.4 kV PTLs allows increasing power supply reliability and reducing the electricity undersupply by preventing disconnection of consumers connected to the PTL in the event of a short circuit in the PTL behind the sectionalizing unit. The proposed methodology allows allocating SU in 0.4 kV PTLs in terms of two criteria: criterion of sensitivity limits against single-phase short circuits and criterion of power supply reliability improvement. The main criterion is one for ensuring the sensitivity of line protection, as it is directly related to electrical safety. It allows determining the maximum distance from TS at which SU can be installed. Further, the second criterion is applied so as to set SU installation location as well as to test the feasibility of SU introduction. The developed methodology was tested in the Mezenka electrical network (Oryol region, Russia) and showed economic effects for SUs both with and without automatic reclosing function. The considered case of SU installation demonstrated the remarkable effectiveness with 2.52 years payback period, whereas our experience of the trial operation of sectionalizing units confirms such payback periods.  
Future research directions are planned to develop the improved designs of sectionalizing units allowing for sectionalizing and reclosing PTLs with branch lines, to develop software products automating the process to determine the quantity and locations of SUs taking into account the methodology discussed in this work, and to adopt the proposed methodology for application in MV and HV electrical networks.  
Conceptualization, A.V. ([PERSON]); methodology, A.V. ([PERSON]); software, A.V. ([PERSON]); validation, A.V. ([PERSON]) and A.D.; formal analysis, A.V. ([PERSON]) and V.B.; investigation, A.V. ([PERSON]), V.B. and A.B.; resources, A.I. and A.D.; data curation, A.V. ([PERSON]) and V.B.; writing--original draft preparation, A.V. ([PERSON]) and A.V. ([PERSON]); writing--review and editing, V.B.; visualization, V.B. and A.B.; supervision, A.I. and A.D.; project administration, A.D.; funding acquisition, A.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.