esa-sceva/satcom-chunk-collection huggingface dataset:
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 8 Conclusions and Future Works  
### Conclusions  
This paper addresses the problems related to resource management and scheduling in the MLSCS, particularly focusing on the non-uniform distribution of users/services and resources, and the rapid changes in network topology caused by the dynamic characteristics of LEO satellites. These factors adversely affect system resource management and scheduling. To address these challenges, we propose a three-level resource management architecture, which includes an NCC, SBSs, and UTs. This architecture decouples resource management from quickly changing network conditions into three levels: global spectrum scheduling and service area planning at the NCC; time slot, beam, and channel resource management and access management at the SBSs; and user service data management and bandwidth requests at the UTs. Hierarchical resource management reduces management difficulty and improves system response speed, providing a guarantee for the resource scheduling of delay-sensitive services. In addition, we present the functions of the three-level management architecture. Based on this architecture, we study the influencing factors, scheduling approaches, and algorithms for global spectrum scheduling and service area planning. Finally, considering the characteristics of the MLSCS, we propose a performance evaluation system that includes several indicators, such as coverage characteristics, network capacity, access control, QoS assurance, and adaptability, and perform several simulations to validate the effectiveness of hierarchical resource management in the MLSCS.
# Error Dynamics Based Dual Heuristic Dynamic Programming for Self-Learning Flight Control
## 4 Numerical Simulation and Discussion  
In this section, the ED-DHP's capability of online self-learning without complete knowledge of system dynamics and reference trajectory dynamics is demonstrated. Two numerical simulations are conducted to verify the design of the control scheme, sinusoidal signal tracking at a fixed operating point and command tracking with a morphing process at variable operating points.
## 2 Simulation Models  
### Medium-Voltage Power Line  
This paper presents a simulated 20 km, 20 kV power line incorporating Distributed Generation (DG), as depicted in Figure 3. The Single-Line Diagram (SLD) illustrates three distinct voltage levels, each one of them denoted by different a color: namely, HV (150 kV), Medium Voltage (20 kV), and Low Voltage (400 V). The distribution network has a three-phase, three-wire configuration, with grounding implemented at the sending end of the HV/MV substation via a 12 Ohm resistance, restricting single-line-to-ground faults to a maximum of 1000 A. Detailed information regarding the key characteristics of the MV line is provided in Tables 1-4.  
The protection scheme for the modeled MV line in Figure 3 comprises several components [21]:  
1. A Current Transformer (CT), an overcurrent relay, and a Circuit Breaker (CB) are included. The CT monitors the main feeder, while the overcurrent relay and CB are designed to promptly identify and safely clear any short-circuits within the distribution network.
2. Branches along the line, where compact substations are installed, are safeguarded by fuse cut-outs (T-Type Fuses).  
For the purposes of this paper, the ABB REF 615 relay's definite time and extremely inverse overcurrent elements (51P, 50P1, 51N, and 50N function blocks) are modeled using the DIgSILENT PowerFactory 2022 software tool. The reclosing operational sequence of the CB involves one instantaneous operation and three time delays (TDs). The instantaneous operation of the CB is set to 0.15 s, and the time delays are determined based on the IEC Extremely Inverse Time-Current Characteristic (TCC) [22; 23; 24; 25]. As it is presented in Figure 4, CB's reclosing operational sequence is set to one instantaneous operation and three TDs.  
The instantaneous operation of the CB is set to 0.15 s and the TDs are set according to IEC 60255 Extremely Inverse Time (EIT)-Current Characteristic by selecting \(\alpha\) = 2, TD = 0.7, and a pickup current of 80 A described by the Equation (1) [22; 23; 24; 25]:  
\[t=\frac{k\times B}{\left(\frac{I}{t_{s}}\right)^{A}-1} \tag{1}\]  
The corresponding EIT-Current Characteristic of the CB and the fuses is depicted in Figure 5. The chosen fuses for branch protection are the 30 T type. To ensure proper coordination between the Circuit Breaker of the main feeder and the fuse cut-outs of the branches, the relevant TCCs should have a minimum separation of 0.4 s. This arrangement guarantees that, in the event of a SC in one of the branches of the MV line, the smallest possible segment of the network will be isolated [26].
# A Novel TLS-Based Fingerprinting Approach That Combines Feature Expansion and Similarity Mapping
## 3 Methodology  
It is clear from the current research that, as technology progresses, TLS fingerprints are becoming less granular, which naturally limits their effectiveness. This presents a significant challenge in malicious domain detection, as actors frequently make minor modifications to their server configurations to evade detection [9; 10]. These small changes--such as adjusting a single cipher suite or TLS extension--can produce entirely different hash-based fingerprints, despite the underlying infrastructure remaining largely unchanged [7]. This fingerprint-evasion technique is particularly effective against traditional hash-based approaches like JARM, where even minimal alterations generate completely different signatures, making it difficult to track malicious infrastructure over time, as operators make small defensive changes.  
This paper proposes a methodology to produce an enhanced fingerprint that combines TLS features with HTTP header data, complemented by a similarity-mapping technique that mitigates the subversion of current hash-based approaches. The methodology is presented in three main sections. Firstly, we detail the selection and breakdown of the datasets used. Secondly, we detail the tooling used throughout the process. Finally, we provide an end-to-end process flow followed by a detailed explanation of how fingerprints are obtained and transformed into fine-grained similarity relationships.
## Appendix A
### Accuracy Analysis  
The consistency or accuracy analysis usually means how accurately the numerical solution approximates the true (or analytical) solution or how accurately the analytical solution satisfies the numerical method [45]. Below, we present the consistency analysis for the Crank-Nicolson scheme, and a similar analysis can also be easily applied to the backward Euler method. The analysis is simply based on the substitution of several Taylor series into the numerical scheme. Let \(P\) and \(P_{k,\,h}\) represent the analytical and numerical operators, respectively, i.e.,  
\[Pc=\frac{\partial c}{\partial t}-\frac{\partial}{\partial x}\left(D(x)\,\frac {\partial c}{\partial x}\right)-\rho c\left(1-\frac{c}{c_{\text{max}}}\right), \tag{117}\]  
\[P_{k,h}c\,=\,\left(\frac{c_{i}^{n+1}-c_{i}^{n}}{k}\right) - \frac{1}{2}\frac{D_{i+1/2}\frac{c_{i+1}^{n+1}-c_{i}^{n+1}}{h}-D_{ i-1/2}\frac{c_{i}^{n+1}-c_{i-1}^{n+1}}{h}}{h} \tag{118}\] \[- \frac{1}{2}\frac{D_{i+1/2}\frac{c_{i+1}^{n}-c_{i}^{n}}{h}-D_{i-1/ 2}\frac{c_{i}^{n}-c_{i-1}^{n}}{h}}{h}\] \[- \frac{1}{2}\rho c_{i}^{n+1}\left(1-\frac{c_{i}^{n+1}}{c_{\text{ max}}}\right)-\frac{1}{2}\rho c_{i}^{n}\left(1-\frac{c_{i}^{n}}{c_{\text{max}}} \right),\]  
where \(k=\Delta t\), \(h=\Delta x\). We say that the numerical operator is consistent with the analytical one or \(P_{k,h}\) is consistent with \(P\) if \(P\phi-P_{k,h}\phi\to 0\) as \(h,k\to 0\) with any smooth function \(\phi\). Here, we also assume that the diffusion coefficient \(D\) is smooth for analysis purposes, otherwise, we had to set \(D\) to a constant maximum value as we did in Section A.1 and our analysis would be much easier. Moreover, since \(D\) is a function of \(x\) only, we use \(D^{\prime}\,=\,\frac{dD}{dx}\) and \(D^{\prime\prime}\,=\,\frac{d^{2}D}{dx^{2}}\).
# Stronger Cumulative than Lagged Effects of Drought on Vegetation in Central Asia
## 4 Discussion  
### Spatial and Temporal Distribution of Vegetation and Drought  
The vegetation GPP was dominated by a slight upward trend from 2000 to 2018 in Central Asia, which corresponded with the findings for the GPP in Central Asian countries estimated based on MODIS products [77]. The lowest value of vegetation GPP occurred in 2008, and this inflection point was due to the severe drought that occurred in 2008 in Central Asia, which limited vegetation growth [78]. Compared with other regions in Central Asia, the Tian Shan mountainous region has a high GPP, high altitude, and a relatively humid climate, which are favorable for vegetation growth [79]. Most Central Asian mountainous regions have snow in winter, which melts in spring to provide a year's supply of moisture for the mountainous vegetation; thus, the vegetation in this region has better growth [80].  
In the context of global warming, the SPEI is more dominant in indicating arid and semi-arid conditions, and the correlation between the SPEI and vegetation productivity is stronger under arid conditions [52; 81; 82]. During the 19-year period, the SPEI showed a trend of decreasing and then increasing, and there was a slight overall decreasing trend, indicating that Central Asia is gradually drying out. This finding is consistent with the composite increase in Central Asian drought and heat wave trends [83]. Some studies have also shown that Central Asia shows a warm and humid trend [84], which may be due to the different years from the present study. The phase comparison in this study found that 2002 and 2003 were wetter years, which corresponds with the conclusions of existing studies showing that extremely wet levels were reached [85]. Northern Kazakhstan, Kyrgyzstan, Tajikistan, and the western part of Xinjiang, China, generally had positive SPEI values, and the high-altitude mountains and plateaus blocked moisture from the westerly airflow, creating a large amount of rainfall as the reason for the relative wetness in the region [86]. In contrast, southern Kazakhstan, Uzbekistan, and Turkmenistan have generally negative SPEI values, and these regions have common perennial droughts. Frequent droughts lead to problems, such as reduced regional crop yield and damaged ecosystems [87; 88]. At the same time, irrational water resource management due to frequent human activities also exacerbates drought [89; 90].
## 5 Summary and Conclusions
Moreover, it can potentially aid civil authorities in determining the proper response to an impending eruption and can be easily implemented by a NVEWS. It should be stressed, however, that this algorithm is meant to assist and not replace the volcanologist in assessing the potential hazard associated with volcanic unrest episodes. Its results should be weighed carefully against a scientist's personal experience and all other available information. It must also be stressed that low probability of occurrence means the event is unlikely, but not impossible. There will be situations where an unlikely event will occur. Future work will focus on expanding the training dataset. Emphasis will be placed on identifying additional explanatory variables that can further enhance the predictive power of the algorithm. The Earth Observatory of Singapore, Nanyang Technological University, is leading a research initiative to develop a global volcanic unrest database referred to as WOVOdat [72]. This database will contain observations from various monitoring technologies and source modeling results from volcanoes all over the world. However, this database is under construction and is not yet open to the public. Once available, each of the logistic models will be redefined to exploit this diverse and comprehensive database. In addition, comparisons with other decision making techniques such as Classification and Regression Tree (CART) analysis will also be performed. This research was funded, in part, by Florida Space Research Program award 66018006-Y2. Seismic event catalogs used in this study were derived from open catalogs posted on the internet by the USGS, University of Alaska Fairbanks, Alaska Volcano Observatory (AVO), Icelandic Meteorological Office, and the INGV. GPS data was acquired from the Icelandic Meteorological Office and Icelandic Institute of Earth Science websites [68,69]. The advice of Dr. [PERSON] is greatly appreciated and was instrumental in the successful completion of the bootstrapping study and derivation of logistic coefficients. The maps shown in this report were created with the Generic Mapping Tool (GMT) [73].
# Identification of Land Use Mix Using Point-Based Geospatial Data in Urban Areas
## 5 Conclusions  
This study presents an effective method for identifying and classifying LUM in urban areas using point-based geospatial data. By employing Voronoi triangulation and an entropy-based LUM formula, we demonstrated that it is possible to dynamically determine LUM without requiring extensive fieldwork or human intervention. The methodology was validated in two distinct urban settings: Ankara and Kadkoy. The results underscore the robustness and adaptability of our approach, achieving up to 78% accuracy and an F1-score of 83% in Kadkoy, where the density of mixed-use developments is higher.  
Crucial findings from this study indicate that the number and density of POI within a given area significantly influence the accuracy of LUM classification. Higher accuracy was observed in fishnets with a POI density ranging between 29 and 60, emphasizing the importance of sufficient and representative data for reliable LUM identification.  
The comparative analysis revealed that different urban environments could yield varying performance levels. Kadikov's slightly higher accuracy rates compared to Ankara highlight the method's effectiveness in densely populated areas with complex urban mixes.  
Our study offers valuable insights for urban planners and policymakers, supporting efficient resource allocation and the planning of sustainable, mixed-use urban environments. Although the focus was on residential and non-residential land use types, future research could expand this approach to include additional land use categories such as recreational, commercial, and transportation. Additionally, integrating social media data could provide further insights into human interactions with urban spaces, enhancing the understanding and prediction of mixed land use dynamics.  
Overall, our study's primary contribution is the development of an efficient, automated method for identifying LUM in urban areas using publicly available point-based data, eliminating the need for fieldwork and human intervention. This methodology represents a significant advancement in urban land use studies, providing a scalable and efficient method for modern city planning.  
Conceptualization, M.A.A.; methodology, M.A.A.; software, M.A.A.; validation, M.A.A.; formal analysis, M.A.A.; investigation, M.A.A.; resources, M.A.A.; data curation, M.A.A.; writing--original draft preparation, M.A.A.; writing--review and editing, M.A.A., T.T.T., S.D. and N.B.; visualization, M.A.A.; supervision, T.T.T., S.D. and N.B.; project administration, M.A.A., T.T.T., S.D. and N.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original data presented in the study are openly available in FigShare at [[https://doi.org/10.6084/m9.figshare.25987963.v1](https://doi.org/10.6084/m9.figshare.25987963.v1)]([https://doi.org/10.6084/m9.figshare.25987963.v1](https://doi.org/10.6084/m9.figshare.25987963.v1)), accessed on 10 June 2024.  
The authors declare no conflicts of interest.
## 4 Hierarchical Cooperative MIMO Clustering
### Design Considerations
#### 4.5.3 Factors Affecting Reclustering  
Topology maintenance is critical for a clustered network. There are certain cases in which an update is required in a cluster structure. This happens in any of the following four scenarios:  
* When a node \(v\) is powered on, it broadcasts a HELLO message to all its neighbors to determine if there is a clusterhead in the neighborhood. If there is a clusterhead, the node joins the clusterhead. Otherwise, it compares its energy with the energies of its neighbors to see if it has the highest energy in its neighborhood. If so it becomes a clusterhead. If not, it asks the node with highest energy to become clusterhead.
* A clustering update is required when a link is created between two nodes.
* When an existing link is broken:
* If one node is a clusterhead and the other is an ordinary sensory node then both will trigger clustering again.
* If both the nodes are ordinary nodes then both nodes announce their new weights to their clusterheads and all the neighbors. This would make the clusterheads update the neighbor list of their neighbors.
* When the residual energy of a clusterhead reaches a threshold value (an energy parameter), clusterhead delegates the responsibility to another node in the cluster who has high enough energy to become a clusterhead. If some of the neighbors of old clusterhead cannot communicate with the new clusterhead then either they re-trigger clustering or join neighboring clusters. This property of our algorithm overcomes the flaw in state-of-the-art CMIMO [3] where in worst case the whole network has to get reclustered. In our setup when energy of a clusterhead reaches one third of its initial energy (when it initially became a CH), it designates the responsibility to another node with a higher amount of energy (subject to availability of such a node in the cluster).  
Figure 2: Hierarchical cooperative routing.
# Experimental Study on Dynamic Effects of a Long-span Railway Continuous Beam Bridge
## 8 Conclusions  
In this study, an evaluation of dynamic effects for a long span high-speed railway continuous beam bridge was carried out using the deflection and strain data from field tests and finite element analysis. The following conclusions are drawn:  
* The IMs from one train running on one track is greater than that from two trains running on two tracks. The average value of IMs of the central span is 0.15 under one running train condition and 0.11 under two running trains condition. A peak IM (0.19) occurred when one train travelled on the bridge at 20 km/h, which suggests that a resonance occurred. The IM is insensitive to the train speed. But from the overall development law, with the increase of train speed, the IM increases. The weight of vehicles has a significant influence on the IMs. The IM decreases with the increase of the weight of vehicles. Meanwhile, the IMs were found to depend strongly on the track irregularity, the track irregularity will greatly increase the IMs. Resonance may occur at multiple speeds, and will be affected by the track surface conditions. The IM calculated from deflections is smaller than the IM calculated from strains. So it is not reasonable to assign the same IM for different load effects. The IM calculated from strains reflects the local dynamic performance of the structure, and the IM calculated from deflections reflects the overall dynamic performance of the structure.
* The IMs obtained by experimental and numerical analysis are greater than the design value specified in the bridge code, which indicates that the bridge code underestimates the impact effect of the train on the bridge. Therefore, the authors recommend the design value of the IM should be properly improved in the bridge design. Because the long-term operation of the bridge will lead to the deterioration of the track surface conditions, and the train-bridge interaction will be more intense, which may seriously aggravate the impact of train on bridges. Thus maintaining good track surface conditions is very important for ensuring the safe operation of bridges. Moreover, when a component of bridge structures needs to be independently designed, the strain IM should be adopted.  
The authors have contributed equally to this work. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
The research described in this paper was financially supported by the National Natural Science Foundation of China (Grant Nos. 51508474 and 51108382), the Sichuan Science and Technology Program (Grant No. 2018Y0294), and the Science and Technology Research and Development Plan of China Railway Construction (Grant No. 2014-C34).  
The authors declare no conflict of interest.  
Figure 18: Impact factors calculated from deflections (track irregularity is considered) under: (**a**) one freight train; (**b**) one passenger train.
# Design and Calibration of a Tri-Directional Contact Force Measurement System
## 4 Static Loading Experiment  
For the validation of the calibration process, the calibrated tri-directional force measurement system was appropriately loaded in three orthogonal directions simultaneously using the wire-pulley setups as seen in Figure 6. Several load cases consisting of different load combinations along x, y and z axis (\(F_{x}\), \(F_{y}\) and \(F_{z}\)) were applied and the output force values read by sensors (\(F_{1}\), \(F_{2}\) and \(F_{3}\)) were logged for each set of simultaneously applied loads. The calibrated tri directional force measurement system was zeroed before commencing the load cases to eliminate the possibility of any offset errors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Averaged Percentage–\% (Standard Deviation)** & \multicolumn{3}{c}{**Applied Loads**} \\ \cline{3-5}
**Lock Nut Collar Link** & \(F_{x}\) & \(F_{y}\) & \(F_{z}\) \\ \hline \multirow{2}{*}{**Measured Force**} & \(F_{1}\) & 98.12 (0.61) & 0.17 (0.17) & 2.61 (0.2) \\  & \(F_{2}\) & 3.89 (0.065) & 99.89 (0.91) & 2.60 (0.15) \\  & \(F_{3}\) & \(-\)3.36 (0.053) & 0.12 (0.15) & 97.16 (0.55) \\ \hline \multirow{2}{*}{**Averaged Percentage–\% (Standard Deviation)** & \multicolumn{3}{c}{**Applied Loads**} \\ \cline{3-5}
**Necked Collar Link** & \(F_{x}\) & \(F_{y}\) & \(F_{z}\) \\ \hline \multirow{2}{*}{**Measured Force**} & \(F_{1}\) & 99.51 (0.23) & \(-\)0.34 (0.065) & \(-\)0.50 (0.28) \\  & \(F_{2}\) & 1.98 (0.26) & 98.76 (0.47) & 2.08 (0.34) \\ \multirow{2}{*}{**Outputs (\%)**} & \(F_{3}\) & \(-\)3.34 (0.10) & 0.11 (0.13) & 99.00 (0.12) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calibration matrices indicating distribution of loads applied in the directions x, y, and z onto sensors 1, 2, and 3. Locknut collar and necked collar cases.  
Figure 5: Calibration plot for system with necked collars.