esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Through the large-scale shaking table tests, a detailed comparison of the seismic response between the jointed rock mass-station model and the homogeneous rock mass-station model was conducted. Different frequencies of harmonics were used to excite along the transverse and longitudinal directions, respectively.  
The following conclusions could be drawn.  
1. The adverse effects of joints and discontinuities on the site were quantified according to the elastic modulus formula of the jointed rock mass. Compared to the HRM model, the JRM model had a 22% reduction in the transverse fundamental frequency due to the effect of joints, but enhanced the dynamic response of the ground surface.
2. The presence of buried metro stations reduces the dynamic response of the ground surface. The closer the input frequency is to the model's fundamental frequency, the more energy the underground structure consumes.
3. The HRM model and the JRM model produce different rock-structure dynamic interactions. Under a harmonic excitation of the same intensity, the JRM model produced a greater energy response to the station structure and reduced the acceleration response of the platform in the high-frequency region. The HRM model exhibited a greater seismic response under transverse excitation, while the JRM model showed a greater seismic response under longitudinal excitation.
4. For both models, greater tensile strains were generated at the connections of the main and subsidiary structures. The JRM model produced a peak tensile strain at the connections of the main and subsidiary structures that was 31% larger than that of the HRM model. In the JRM model, the range of tensile strains at the platform connecting the horizontal passage was approximately three times its span of the horizontal passage. Meanwhile, in the HRM model, the range of tensile strains at the platform connecting the horizontal passage was about twice its span of the horizontal passage.  
This paper provides experimental insights into the seismic response of the jointed rock mass site-station structure. However, many factors may affect the dynamic response of jointed rock mass sites, including the inclination of the joints, the scale effect of the rock mass, and the oblique incidence of seismic waves. After being calibrated with the experiments, further numerical analysis is needed to reveal the seismic response of a jointed rock mass.  
Conceptualization, R.L. and Y.Y.; methodology, Y.Y.; software, R.L.; validation, Y.Y. and R.L.; formal analysis, R.L.; investigation, R.L.; resources, Y.Y.; data curation, R.L.; writing--original draft preparation, R.L.; writing--review and editing, Y.Y.; supervision, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 52061135112).  
Not applicable.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization Model
## 3 Materials and Methods
### Viewport Pixel Quadtree Index Structure  
In VGVM, the issue of plotting geographic vector features is transformed to the issue of querying hit vector features in the spatial range corresponding to display pixels; then, the display values of pixels are calculated according to the query results. The time-consuming part mainly comes from the process of querying vector features according to the spatial range of the pixel. To accelerate this process, we design a viewport pixel quadtree (VPQ) for display pixels.
# Modeling of Historical Marine Casualty on S-100 Electronic Navigational Charts
## 4 Case Study
### Summary of Results  
The case study demonstrated the technical applicability and structural integrity of the proposed data model when applied to a real-world dataset of 21,539 marine casualty records. Through a multi-step process involving manual mapping, GML encoding, and S-100-based visualization, the model was verified to be compliant with the S-100 framework and capable of supporting digital representation and portrayal of casualty data. Visualization tests showed that while the model can handle a high volume of data, practical use cases benefit from context-aware display filtering to prevent symbol overcrowding. These findings support the feasibility of the proposed model as a foundation for future implementation in navigational decision-support systems, and they underscore the need for additional work on real-time integration and user-centered display strategies.
# A SqueezeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment
## 3 Experimental Results and Analysis  
### Overview of the Experimental Area  
Kunming is located in the Yunnan-Guizhou plateau region in southwestern China. The region suffers from inherited subsidence tilt and, in recent years, a large number of projects have been constructed around Dianchi in Kunming, which are large in scale and cover a wide area, resulting in the compression of the soft soil substratum. Second, a large amount of groundwater has been extracted because of the development of industry, leading to subsidence around Dianchi and the city center [50; 51]. For the phenomenon of decoherence and atmospheric delay effects caused by high altitude, complex topography, and large surface vegetation and water coverage in Kunming and its surrounding areas, the distributed scatterer InSAR can obtain more surface deformation information than the PS-InSAR technique [52]. The main experimental area of this study is located in the main urban area of Kunming, Yunnan Province, and the surrounding areas, which mainly involve  
Figure 6: Spark-based parallelization algorithm for spatial adaptive filtering (red represents the different operators provided by spark).  
the Kunming Wuhua, Xishan, Guandu, Chenggong, and Jinning districts. The specific experimental area range is shown in Figure 7. The test dataset is 32-view descending track Sentinel-1A data provided by the ESA covering the Kunming area, and the entire dataset has 139 GB of raw files, collected between May 2020 and May 2021.
# Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia
## 7 Geomorphological Changes
### Opportunities for Near-Real-Time Applications with New SAR Satellites  
What is most noticeably lacking from the case studies presented here are examples of SAR being used for near-real-time hazard response. During unfolding disasters that demand an emergency response, immediate access to EO data is required if it is to be used to build situational awareness, aid in the effective deployment of resources, and inform damage assessments within a useful time frame. The benefit of using SAR in these instances is its ability to operate day and night, in all weathers, with radar capable of penetrating cloud, fog, rainfall, aerosols, and smoke. However, the time of image acquisition and subsequent latency between image acquisition, delivery, and processing, have been major limitations to applications of these data in response to rapidly advancing hazards, such as flash floods and uncontrolled fires (e.g., [7; 22]). Some improvements have been found in the use of commercial data (e.g., by the Western Australia state geodetic agency during cyclone Bianca, 2011, in Western Australia 2011 [90]), but this has been found to be cost-prohibitive [22] unless the International Charter Space and Major Disasters is initiated. A new generation of commercial SAR sensors may offer improvements with lower image costs and sub-hourly orbital repeat (e.g., ICEYE: see Table S2 for details).  
For Australia, the availability of SAR data for hazard response closer to near-real-time will be boosted by the country's first activities in the direct tasking and operation of a civilian EO satellite as part of the NovaSAR-1 mission [8]. This satellite is expected to provide expedited observations over Australia, with data downlinked directly in-country for processing and distribution in closer to near-real-time than has previously been possible [91]. This will improve hazard response efforts through the use of SAR backscatter for damage identification, monitoring and assessment after fires, cyclones, floods, and earthquakes [8; 91].
# Research on Interference Mechanism of 25 Hz Phase Sensitive Track Equipment from Unbalanced Current
## 2 Cause Analysis of Abnormal Red Ribbon
### Influence of Unbalance Current Intrusion on Cloke Transformer
#### 2.3.1 Analysis of Unbalanced Impulse Current  
Unbalanced current includes steady-state unbalanced current and unbalanced impulse current. Due to the great influence of DC bias caused by unbalanced impulse current, the unbalanced impulse current is analyzed.  
When unbalanced current invades the receiving end circuit, the choke transformer is the first equipment to be affected. The transient process of choke transformer can be equivalent to the transition from no-load state to load state, which will produce impulse current. Its equivalent circuit can be represented by RL zero state response, as shown in Figure 4 [19].  
Figure 3: Signal current and traction current.  
The switching K closure is equivalent to the intrusion process of the unbalanced impulse current, which will cause the transition current \(i\) to be generated in the equivalent circuit. The transition current consists of a forced component (AC component) \(i_{Q}\) and a free component (DC component) \(i_{Z}\), so the current can be expressed as Equation (4):  
\[i=i_{Q}+i_{Z}=\frac{E\cdot\sin(100\pi t+\theta-\beta)}{\sqrt{R^{2}+\left(100 \pi L\right)^{2}}}+Ae^{-\frac{\beta}{2}t} \tag{4}\]  
where \(R\) is the sum of copper loss and external resistance, \(L\) is the excitation inductance, \(E\) is the 50 Hz power supply, whose instantaneous expression is \(E\sin(100\pi t+\theta)\), \(\theta\) is the initial phase angle, \(\beta=tg^{-1}(100\pi L/R)\).  
Using the initial state current as zero, that is, \(i_{(0)}=0\), the value of parameter \(A\) can be solved, which is expressed as Equation (5).  
\[A=-\frac{E\cdot\sin(\theta-\beta)}{\sqrt{R^{2}+\left(100\pi L\right)^{2}}} \tag{5}\]  
Let \(I_{M}=E/\sqrt{R^{2}+\left(100\pi L\right)^{2}}\); the final total current Equation (6) is obtained.  
\[i=I_{M}\Big{[}\sin(100\pi t+\theta-\beta)-\sin(\theta-\beta)\cdot e^{-\frac{ \beta}{2}t}\Big{]} \tag{6}\]  
According to Equation (6), when \(|\theta-\beta|=n\pi+\pi/2\) (\(n\) is a positive integer), the amplitude of the free component \(i_{Z}\) is the largest, and the amplitude is \(I_{M}\). Therefore, when the inductance in the equivalent circuit is linear, the peak value of the transition current \(i\) is twice the forced component. At this time, the transformer core will be seriously saturated, which will further reduce the excitation inductance, resulting in an increase in the current in the transformer, and the saturation degree of the core will continue to increase, which will continue to affect the excitation inductance, forming a \"vicious circle\". This process will eventually seriously increase the core loss of the transformer and ultimately, affect the signal circuit.
## 4 Meta-Learning-Based INDI Control and Convergence Analysis
### Meta-Learning-Based INDI Control with Wind Disturbances  
In this subsection we introduce how to use meta-learning combined with adaptive control to obtain the control matrix to resist the model uncertainties caused by wind disturbances.  
One important issue is how to learn from the previous environments and behave better in the next environment. Motivated by the idea of OMAC [12], at the beginning of getting into the environment \(i\), the controller estimates \(\hat{L}\) first, which is an estimation of \(L_{t}^{(i)}\). And then the controller takes action \(u_{t}^{(i)}\), premised on the assumption that the learned model faithfully represents the true system dynamics. \(i\) is incremented to \(i+1\) and this process continues.  
We define a function to describe \(\hat{L}\) as follows:  
\[\hat{L}=\phi(x_{t}^{(i)},\hat{\Theta})\hat{\epsilon}^{(i)}, \tag{23}\]  
where \(\phi\in\mathbb{R}^{4\times h}\) is a state-related variable with weight \(\hat{\Theta}\) shared by all environments, and \(\hat{\epsilon}^{(i)}\in\mathbb{R}^{h\times 4}\) corresponds to a specific environmental condition. Here we represent \(\phi\) by a Deep Neural Network (DNN) to obtain the shared environmental representation, learning and retaining the distrubitions in various conditions. Spectral normalization [16] is integrated to stabilize the training process and enhance the performance of the models. We use Adam Optimizer [17] to update the network weights to minimize the prediction error [18]. This strategy embodies the fundamental concept of meta-learning.  
In each specific environment, we keep the learned environmental representation \(\phi(\cdot)\) fixed, and apply the online gradient descent method for adaptive control to adjust \(\hat{\epsilon}\) as follows:  
\[\hat{\epsilon}^{(i+1)}=\hat{\epsilon}^{(i)}-2\eta_{i}(\hat{L}-B_{d})^{T}\phi, \tag{24}\]  
where \(\eta_{i}\) is the learning rate and \(B_{d}\in\mathbb{R}^{4\times 4}\) is a measurement of the residual control matrix obtained from the reference dynamics. The learning rate \(\eta_{i}\) decreases as the iteration proceeds, which leads to a convergence result. This is one of the defining characteristics of adaptive control. The adaptive approach attempts to update the model parameters by minimizing the prediction error. This rule is online, meaning that the controller parameters are updated immediately each time new data arrives.  
Adaptive control focuses on adjusting controller parameters based on real-time performance, while meta-learning aims to improve the model's generalization ability by learning from a set of tasks. Thus, the system state can iterate on each environmental condition and update the parameters to enhance the system performance as the number of environments increases.
## 5 Discussion
### How Should the Development of UUS Be Carried out Scientifically?  
The suitability evaluation model for UUS development established in this study must take into account the climatic conditions, ecosystems, and environmental sensitivities of different cities. For instance, in arid regions, the development of underground spaces may necessitate enhanced water resource management measures. In contrast, high humidity areas require considerations for moisture-proofing and ventilation design. Geological conditions are a critical factor in underground space development, and the design and construction techniques of underground projects should be tailored according to the geological reports and data of each city. For example, blasting techniques may be required in rocky areas, while reinforcement techniques are needed in soft soil areas. In regions with abundant groundwater, the development of underground spaces requires special attention to waterproofing and drainage issues.  
The development of UUS should be a multidimensional process involving multiple stakeholders. Firstly, the development process must consider the needs and opinions of various stakeholders, including local communities, private sector participants, and urban planners. Such multi-party participation not only enhances the practical applicability of the recommendations but also strengthens the public's acceptance and social support for the project.  
Secondly, the development of UUS faces social, economic, and environmental challenges, such as funding procurement and cost control, market risks, and uncertainties. To overcome these challenges, it is crucial to explore the socio-political and financial feasibility of implementing underground space projects. This includes assessing the environmental impact of the project, ensuring its economic benefits, and addressing funding issues through diversified investment and financing models, such as public-private partnerships. In urban planning, the development of underground spaces can be integrated with urban green spaces to form a green ecosystem that combines underground and aboveground areas. For example, constructing underground parking lots and commercial facilities beneath parks and squares can effectively utilize land resources while minimizing disruption to the ground landscape.  
Lastly, the development of UUS should adhere to the principles of public priority and composite utilization, ensuring that public interests are paramount and promoting composite utilization, while also strengthening the combination of government guidance and market operation.
## 2 Materials and Methods
### Shear-Wave Splitting (SWS) Analysis
This method compares the polarization direction of teleseismic P-waves with the respective back-azimuth of the epicenters to determine the orientation of the positive direction of the instrument's N-S component, which should be pointing towards the North. The results were compared with the orientations documented in the metadata of the stations supplied by the FDSN web services. Significant differences were identified at stations MG06 (N-S azimuth = N83\({}^{\circ}\)E) and the temporary station HELI (N-S azimuth = N340\({}^{\circ}\)E). The method of [47] was not applied to station ALIK, as it could not properly record the teleseismic waveforms. In that case, an orientation correction of N299\({}^{\circ}\)E was adopted after [48], who followed a similar approach, but using the P-wave polarization determined by local earthquakes [35]. For other stations, especially for borehole instruments, the nominal orientations supplied in the stations' metadata were verified. Out of the 14,825 candidate events from both catalogues, a total of 37,263 initial SWS results (15,083 from Cat1 and 22,180 from Cat2) were acquired, with 10,456 events providing a result from at least one station. Other events were discarded due to low SNR or errors during processing, in particular at the clustering stage. In the combined Cat3, 25,266 measurements with grade E and 4223 with grade D were rejected due to low quality. In addition, 3298 measurements were considered as null, having \(\varphi\) values sub-parallel or sub-perpendicular to the corrected source polarization, \(p\). Finally, in Cat3, 192 valid measurements are with grade A, 1299 with grade B and 2985 with grade C, summing up to 4476 (Table S3 in the Supplementary Material).
# Automated Hybrid Model for Detecting Perineural Invasion in the Histology of Colorectal Cancer
## 2 Materials and Methods  
### Data Acquisition  
A total of 77 whole-slide images (WSIs) of 63 patients with CRC who underwent surgical resection at International St. Mary's Hospital, Catholic Kwandong University in Incheon Metropolitan City, Republic of Korea, were selected for the study. The specimens were formalin-fixed, paraffin-embedded, and stained with hematoxylin and eosin. An Aperio AT2 slide scanner (Leica Biosystems, Buffalo Grove, IL, USA) was used to scan the WSIs at 40\(\times\) magnification. From the 77 WSIs, 530 regions were selected, including PNI with tumor and nerve tissue, non-PNI with tumor, non-PNI with nerve tissue, and normal tissue excluding neural tissue, respectively, denoted as \"PNI\", \"tumor\", \"nerve\", and \"normal\", (Table 1). All PNIs inside the region were annotated; non-PNI with tumor tissues, and non-PNI with nerve tissues were randomly extracted and annotated. Tumor, nerve, and normal tissues were annotated by board-certified pathologists ([PERSON] and S.A.) using the automated slide analysis platform (ASAP). All results in this study have undergone two rounds of reviews. Inconsistencies were discussed with another board-certified pathologist ([PERSON]). A total of 490 regions were used for training and validation, and the remaining 40 were used to test the model.
## 6 ML and the Security of Blockchain
### Blockchain Forensics  
In the event of a security breach, ML-based forensic tools can analyze blockchain data to trace the origin and flow of malicious activities. Supervised learning algorithms can identify suspicious addresses and flag those involved in known attack patterns. These ML techniques facilitate comprehensive blockchain forensics by tracking stolen assets and mapping the methods used by attackers, thereby providing critical insights for preventing future incidents.
## 5 Conclusions
The volume estimation method using point clouds obtained from UAV photogrammetry and alpha shape examined in this study is expected to be useful, especially when access is impossible due to spatial constraints or when there are temporal and economic limitations due to a large scale. Although the technology for estimating volume based on drone measurements and that for estimating volume based on the alpha shape have both been introduced previously, they target standardized objects in some cases. Additionally, the method of estimation through drone surveying is required for non-standard objects, such as river vegetation. Calculating river vegetation amount, which requires the exact area and height of the object, is bound to vary depending on the field survey basis. Therefore, the method of using point clouds and alpha shapes based on drone surveying in calculating river vegetation amount, as achieved in this study, is an improved result for irregular objects. In addition, the two techniques used in the existing study can be fully applied. Also, improvements and alternatives are suggested, noting that caution is needed in alpha value selection and preprocessing. Advancements in technology to accurately estimate the volume of vegetation will be used to determine how much vegetation needs to be removed for flood safety and can be used to manage spaces blocked by vegetation for flood level management and to determine the adhesion scale of different types of vegetation. Conceptualization, E.J. ; Formal analysis, writing--review and editing, Investigation, Data Curation, W.K. and E.J. All authors have read and agreed to the published version of the manuscript. This research was carried out under the KICT Research Program (project no. 20230115-001, Development of IWRM-Korea Technical Convergence Platform Based on Digital New Deal), funded by the Ministry of Science and ICT. This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2019R1C1C1009719). Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to security reasons from research funding agencies. The authors declare no conflict of interest.