esa-sceva/satcom-chunk-collection huggingface dataset:
# Tectonic Subsidence and Its Response to Geological Evolution in the Xisha Area, South China Sea
## 5 Discussion
### The Tectonic Subsidence of Paleocene (53.5-23.3 Ma)
#### 5.1.2 Second Phase--Rapid Subsidence Phase in Oligocene (32-23.3 Ma)  
The rate of tectonic subsidence in the Early Oligocene was higher than that in the Late Oligocene, as shown in Figures 6 and 7. The tectonic subsidence in the Zhongsha Trough was 0.15 m/ky in this phase for the sediment depositing all over the study area. This rapid subsidence was universal in the north margin of the South China Sea [10; 31; 34; 48]. The inherited stretched and thinned lithosphere and crust were considered to be responsible for  
Figure 9: Tectonic subsidence history of the Xisha area coupled with geological evolution. GU: Guangle Uplift, XU: Xisha Uplift, ZT: Zhongsha Trough, ZJD: Zhongjian Depression, CD: Changchang Depression, HD: Huaguang Depression, SCS: South China Sea, RRFZ: Red River Fault Zone.  
the rapid subsidence and the crust-stretching factors of the Qiongdongnan Basin. Spanning from \(\beta\) = 1.2 of uplifts in the basin margin to \(\beta\) = 3.14 in the central basin [42] interpreted the change in tectonic subsidence from uplift to central basin.  
The activities of faults that continued to the end of the Oligocene indicated that the process of stretching and thinning probably terminated at approximately 23.3 Ma (T\({}_{6}\)), as shown in Figures 4 and 5. This couples well with the rapid subsidence occurring during the Oligocene, and the homeochronous high sedimentary rate also matched the rapid subsidence [10; 27]. According to the analysis of seismic data, the process of the Xisha Block splitting from the South China Block initially occurred in the western Qiongdongnan Basin and gradually extended to the east. The homologous extension of the rift zone ranged from 15 km to 35 km, which indicated that the anticlockwise rotation of the Xisha Block was intensified. In the rift zone, there were three echelon fault zones: one of them was being along the No. 1 Fault and the other two were distributed in its eastern portion [47; 49]. Seismic profiles revealing the half graben and titled block of structural styles also concluded that the nearly NE-trending faults around the Xisha area should be under the control of extrusion stress, as shown in Figures 2 and 5, although there have been other arguments on the structural differences between the western and eastern portions of Qiongdongnan Basin, which probably were related to the sinistral movement of the offshore Red River Fault and the subduction of the proto-South China Sea in the North Borneo Trench separately [6; 8]. The characteristics of faults around the Xisha area and the anticlockwise splitting process of the Xisha Block suggest that the extrusion of the Indochina Block and the associated left-lateral slip of the Red River Fault Zone led to the stretching and thinning of the lithosphere and crust in the Xisha area, resulting in the rapid Oligocene subsidence in the Xisha area. This standpoint was also supported by the geophysical imitation of crust deformation of the Qiongdongnan Basin [2; 50; 51].
## 4 Results and Discussion  
### Definition and Concept  
The concept of sequence or \"_staging_\" was first described in the literature by [PERSON] [24] as the \"_collection of stages and procedures used to achieve the position of the teeth using the Treat software_\". Technicians would establish the final target position of the treatment and divide the required dental movement into different stages corresponding to each aligner. The concept of staging evolved into a more \"_biological_\" ideal deeply connected to anchorage, adding the suffix \"_of orthodontic tooth movement_\". Thus, [PERSON] et al. [20] define \"_Staging of orthodontic tooth movement_\" as the \"_breakdown of the predicted movement of the teeth sequentially with aligners_\". The importance of this concept lies in understanding the principles of orthodontic tooth movement and its application with aligners [25].  
Staging should not be confused with \"_stadification_\", which refers to the amount of movement (linear and/or angular) programmed for each tooth in each aligner. \"_Staging_\" is related to the number of teeth in movement and the immobile teeth used as anchorage during the progress of treatment with aligners.  
An approach to this staging-anchorage relationship was described by [PERSON] [24] in 2006, dividing the staging into two categories: (1) a low anchorage pattern or \"X\" pattern, where all the teeth in the arch move simultaneously, and (2) a high anchorage pattern or \"V\" pattern, where some teeth in the arch remain immobile at different stages of treatment to maximize anchorage. The choice between these patterns depended on the case's complexity and anchorage demands, influencing the programmed stadification. In a low anchorage pattern, Invisualign (Align Technology, San Jose, CA, USA) software allowed a maximum linear speed of 0.25 mm and a rotation of 3\({}^{\circ}\) per aligner stage.  
In the high anchorage pattern, however, posterior teeth could move a maximum of 0.33 mm per aligner (since this pattern was used in cases involving distalization) [24]. Currently, the maximum values per stage in the Invisualign system are independent of the proposed staging (0.25 mm for linear movements, 1\({}^{\circ}\) for angular movements, and 2\({}^{\circ}\) for rotations) [26].
# Active Sonar Target Classification Method Based on Fisher's Dictionary Learning
## 4 Method Performance Verification Based on Measured Data
### Overview of the Measured Data  
We used an indoor pool as the test environment, as shown in Figure 2. Figure 3 is the test placement method. The depth of the transceiver transducer was the same as that of the targets. The distance between them was \(4\) m.  
Figure 1: General block diagram of FRFT-DLSRC.  
Figure 2: The indoor pool.  
Test parameters: a chirp signal was the incident signal (LFM), 100-200 kHz was the frequency range, 0.5 ms was the pulse width, and the incident angle was constant.  
Test target: We set four types of targets--hollow aluminum pipes, solid PVC pipes, solid aluminum cylinders, and cylindrical shells.  
The transducer sends the signals, and receives the signals from the objects. Thus, the echo signals are obtained. The four types of target echo signals are shown in Figures 4-6.  
Figure 4: Four types of similar original target echo signal graphs. (**a**) Hollow aluminum tube echo signal; (**b**) solid PVC echo signal; (**c**) solid aluminum cylinder echo signal; (**d**) cylindrical shell echo signal.  
Figure 3: The test placement method.  
Figure 5: Signal diagram of the target echo with a signal mixing ratio SRR = 0 dB. (**a**) Hollow aluminum tube echo signal; (**b**) solid PVC echo signal; (**c**) solid aluminum cylinder echo signal; (**d**) cylindrical shell echo signal.  
Figure 6: Signal diagram of the target echo with a signal mixing ratio SRR = \(-\)3 dB. (**a**) Hollow aluminum tube echo signal; (**b**) solid PVC echo signal; (**c**) solid aluminum cylinder echo signal; (**d**) cylindrical shell echo signal.  
FRFT Optimal-Order Domain Characteristics of an Active Sonar Signal under a Reverberation Background  
Taking the signal-mixing ratio equal to 0 dB as an example, the FRFT diagram of each signal type can be drawn as follows.  
Figure 7 illustrates the two-dimensional distribution of each type of target signal after the FRFT, where a peak is observed in the two-dimensional distribution of the order P and the U-domain values. The peak value of each signal type has a certain difference in amplitude, the position corresponding to the U-domain, and the corresponding optimal order P. We first performed an FRFT on the four types of data, and then used the obtained signal U-domain values as input data to perform dictionary training and reconstruction classification based on these features.  
Figure 7: FRFT of each target signal’s U-domain three-dimensional map. (**a**) U-domain of a hollow aluminum tube signal; (**b**) U-domain of a solid PVC pipe signal; (**c**) U-domain of a solid aluminum cylinder signal; (**d**) U-domain of a ribbed cylindrical shell signal.
## 1 Introduction
Genetic algorithms have also been applied to optimize the tuning of the Kalman filter, exploring an ample space of possible values for \(\mathbf{Q}\) and \(\mathbf{R}\) and selecting the best parameters based on performance criteria [3; 24; 25]. This paper presents a novel approach for tuning the EKF, utilizing Monte Carlo simulations and taking advantage of the Central Limit Theorem (CLT). The Monte Carlo method enables the simulation and statistical analysis of filter tuning parameters across a diverse range of scenarios [26; 27], providing a robust way to estimate uncertainties and complex system behaviors. The CLT ensures that, as the number of samples increases, the distribution of parameter estimates converges to a normal distribution [28; 29], offering a theoretical basis for tuning the process noise covariance matrix \(\mathbf{Q}\) of the EKF. This work proposes using Monte Carlo simulations to calculate the covariance matrix \(\mathbf{Q}\), utilizing ideal data from inertial sensors along a trajectory in conjunction with their error models applied to navigation. This method allows for extracting the variances of the \(\mathbf{Q}\) matrix for both direct and indirect filter implementations. This approach facilitates quicker and more precise filter tuning, resulting in more reliable position and velocity estimates, especially in scenarios where the GNSS signal is degraded. The results are compared with the traditional EKF tuning method and those obtained from the inertial platforms of Castro Leite Consultoria, PINA-F, and PINA-M. The remainder of this paper is organized as follows: Section 2 explains the concepts of INS/GNSS integration. Section 3 details the novel approach for Kalman filter tuning methodology. Section 4 describes the testing procedure. Section 5 presents the simulations and experimental tests. Section 6 discusses the results obtained. Finally, Section 7 provides the concluding remarks.
# Simulation of Test Arch Based on Concrete Damage Plasticity Model and Damage Evolution Analysis
## 6 Conclusions  
Based on the analysis of large-span continuous casting arches and stress calculations, vulnerable positions during construction were identified. Using ABAQUS finite element software, the arch was simulated with a CDP model grounded on experimental data. Displacement changes were observed for various vulnerable positions and damage rates, with both local and overall damage evolution being studied. Key findings include:  
1. Displacement data comparison revealed that top plate damage critically influenced structural failure more than bottom plate damage. The ductile nature of top plate failure contrasted with the brittle bottom plate failure, offering better prospects for engineering monitoring and strengthening. Foot damage notably affected structural failure more than hance damage.
2. Both foot and hance damage in the arch began at the section corners. Initial damage rates primarily influenced early damage evolution, with less impact on subsequent trends. To mitigate damage, a crack prevention mesh could be incorporated at section corners.
3. In the event of substantial local material failure, even if most reinforcing bars remained unyielded, the bottom plate bars faced more challenging conditions. Consequently, the reinforcement design should recalibrate specifications and ratios for both plates based on these conditions.
4. Given laboratory test constraints, this study primarily assessed the eccentric loading of arch ribs. Upcoming research will delve into damage evolution under midspan loading, striving for a comprehensive understanding.  
Conceptualization, Z.T. and Y.C.; methodology, Y.C.; software, Y.C.; validation, Y.C.; formal analysis, Y.C.; investigation, Y.C., H.S., G.W., Z.Z., Y.D. and B.X.; resources, Z.T.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C. and Z.T.; supervision, Z.T.; project administration, Y.C.; funding acquisition, Z.T., H.S., and G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Foundation of China, grant number 51478049; the National Nature Science Foundation of China, grant number 52078058; the Hunan Natural Science Foundation of China, grant number 2022 JJ50323; the Major Technological Achievements Transformation Program of Hunan Strategic emerging industries of China, grant number 2017 GK4034; the Postgraduate Research and Innovation Project of Hunan, grant number CX20200834; Research on Key Technology of Long Span Reinforced Concrete Suspension Arch Construction, grant number DQJ-2021-B02.  
Not applicable.  
The authors declare no conflict of interest.
# Screening of Plants and Indigenous Bacteria to Improve Arsenic Phytoextraction
## 3 Results and Discussion
### Soil Analysis
This behavior may be partially due to the fact that phosphate can bind to Fe oxide on different types of surface sites, with different types of bonds and strengths (e.g., monodentate versus bidentate bond). Based on the modest solubilization of the arsenate, it was decided to proceed with a dissolution of the superficial structural cations of the oxides, with consequent dissolution of the mineral and release of arsenic adsorbed on the surface, according to the following scheme, which can occur with all the iron oxides/hydroxides, although it varies substantially with the mineral phase:  
\[(\mathrm{Fe_{x}O_{y}})\mathrm{AsO_{4}}+\mathrm{C_{2}O_{4}}^{2-}\rightarrow \mathrm{Fe_{2}(C_{2}O_{4})_{3}+\mathrm{AsO_{4}}^{3-}}\]  
These aspects were evaluated by means of As extraction tests from two soils coming from the internal and external areas (PZ and S2, respectively) using agents capable of destroying the iron oxides/hydroxides, such as EDTA, oxalate, and pyrophosphate (the latter is also able to interact with humic substances), as well as testing agents potentially capable of solubilizing arsenic, such as thiosulfate and phosphate at higher concentrations. The results showed that a treatment with oxalate can solubilize considerable quantities of As, supporting the hypothesis that arsenic is essentially retained in these soils by iron oxides. Oxalate reacts with the amorphous iron oxides and, as a result, arsenic is released into the soil solution, from which it can be absorbed by plants. The results of the extraction with EDTA (iron complexing agent) are also in line with this hypothesis. In the light of these data, an extraction with oxalate was carried out for soils S6 (0.3-0.6 m) and SW (1-2 m). The amount of As extracted was 5.99 and 14.1 mg kg\({}^{-1}\), respectively. New microcosm tests (second and third sets) were then prepared to assess whether this potential bioavailability of As could be used by plants.
## 1 Introduction
The main contributions of this paper can be summarized as follows:  
(1) A novel PP-NFTSMC-ESO controller was proposed for high-performance motion control of a deep-sea electric oil-filled joint actuator in the presence of unmeasured system state, dynamic uncertainties, and unknown disturbances, which combines the advantages of NFTSMC control in terms of robustness against uncertainties, chattering phenomenon elimination, fast dynamic response, finite time convergence, and simple design procedure, the restraining action of prescribed performance control for the transient and steady state performance, and the excellent observation ability of the ESO for system states and lumped uncertainties. (2) With the proposed control method, the mechanical configuration of the deep-sea electric oil-filled joint actuator can be simplified with only an angular position sensor, which benefits for the structure design and electrical design, but with no performance deterioration of trajectory tracking control. (3) The stability of the proposed controller was theoretically proven by the Lyapunov stability theory. The excellent trajectory tracking performance was demonstrated with the studies on different working conditions, and the superiority of the proposed controller was illustrated by the comparison with proportional-integral (PI) controller, ESO-based sliding-mode controller (SMC-ESO), and ESO-based non-singular fast-terminal sliding-mode controller (NFTSMC-ESO). The remainder of this paper is organized as follows. Section 2 presents the dynamic modeling and problem formulation. Section 3 introduces the ESO. The design procedure of the PP-NFTSMC-ESO control method and the stability proving process of this controller-observer strategy are described in Section 4. The effectiveness is demonstrated via simulation studies in Section 5, and conclusions are provided in Section 6.
# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
## 5 Conclusions
GraphRAG also produced fewer unsupported claims and halved the rate of answers containing any unsupported information (13.3% vs. 26.7%), underscoring the advantages of structured graph-based retrieval for producing traceable, context-grounded responses in the e-government domain. Distinct agents are responsible for graph queries, dense retrieval, and live search. These outputs are synthesized through an agent reasoning process that ensures source-level traceability, conflict detection, and reduced hallucination risk. The result is a robust and transparent QA system aligned with trustworthy AI and digital governance principles. The proposed architecture emphasizes modularity and ethical design, enabling customization in indexing, retrieval, and generation workflows. With transparency and responsible AI use at its core, this work presents a concrete solution for Q&A on official EC content, demonstrating how such systems can empower public institutions with efficient, explainable, and policy-aware AI tools. This article addresses the requirements of the AI Act by emphasizing the framework's design for transparency, traceability, and auditability, which are crucial for trustworthy AI in e-government. The developed multi-agent GraphRAG system could offer immediate and substantial benefits for e-government scenarios, specifically for institutions seeking explainable, transparent, and policy-aligned AI assistants. By combining structured graph-based retrieval, dense embeddings, and real-time web search in a production environment, the framework could enable public administrators, policy analysts, and citizens to obtain traceable, source-linked answers to complex queries and in our use case about EC activities, news and decisions. The agenti orchestration further supports audit trails aimed at reducing the risk of hallucinated or unsupported claims and enhancing the credibility of automated responses in sensitive public-sector environments. As a result, this system not only streamlines information access but also strengthens trust and accountability in AI-driven digital governance, making it a valuable tool for improved knowledge extraction in a Q&A format. Lastly, to ensure reproducibility and transparency, the source code is openly available, including detailed deployment instructions, system setup documentation, a mock Q&A UI, relationship previews, and graph visualizations. The interface, built with custom CSS and HTML, is deployed on Google Colab to facilitate accessibility and replicability.
# Machine Learning Detection of Cloud Services Abuse as C&C Infrastructure
## 3 Related Works
### ML-Based Detection  
[PERSON] et al. [21] undertook a detailed assessment of several previously studied abusive social bots. The authors incorporate spatial and temporal correlations to identify patterns of the social bots. They gathered source code, builders, and execution patterns from established social botnets, including Twitterbot ([PERSON] [22]), Twebot ([PERSON] et al. [23]), Yazanbot ([PERSON] et al. [24]), Nazbot ([PERSON] et al. [11]), wbbot ([PERSON] et al. [25]), and fbbot. Their objective was to scrutinize the techniques these bots employ to bypass current detection systems. Drawing from their analysis, they proposed a detection strategy using 18 features. This strategy uses spatial correlations to recognize patterns across child processes, like multiple bots on one IP, and temporal correlations to study event sequences for behavior patterns. However, their focus on just six bots could limit the study's applicability to other bots.  
[PERSON] et al. [26] introduced a method designed to detect Android applications that abuse Google Cloud Messaging (GCM) for C&C communication. By adopting the Flow-droid tool [27] to extract GCM flows and identify GCM callbacks, they trained an ML model using features like GCM registration ID, sender ID, and a GCM type of message. Their findings indicate that GCM flow features can effectively distinguish malicious applications. Nonetheless, the method might be vulnerable to evasion tactics like obfuscation, which adversaries might use to mask GCM flows. Additionally, its applicability is constrained, as it only works for Android OS and is not applicable in Windows OS environments.  
The existing literature predominantly focuses on the use of rule-based and behavior tree-based detection techniques within the realm of social networking platforms, while ML techniques for detecting abuse in CLS environments are often neglected. This creates a gap in the detection of C&C abuse within CLS environments. To address this limitation, we introduce a detection technique that employs ML and is specifically designed to identify C&C abuse across diverse CLS environments. Given the lack of prior research applying ML to detect abuse of cloud services as a C&C channel, we have undertaken a comparative analysis of our approach alongside existing studies that leverage ML techniques for general malware detection.