esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Related Works and Contemporary VSN Projects
### Sensor Network Virtualization Projects
#### 4.1.9 SenQ  
SenQ is a multi-layered embedded query system for efficient, real-time data extraction from heterogeneous sensors [54, 55]. It consists of nesC and Java implementations, and a lightweight network protocol. It has the capability to co-reside with a graphical library on a MicaZ-based embedded user interface. It enables user-driven and peer-to-peer in-network query issue by wearable interfaces and other resource-constrained devices. Complex virtual sensors and user-created streams can be dynamically discovered and shared, and SenQ is extensible to new sensors and processing algorithms. Efficiency and performance of SenQ's have been evaluated in the AlarmNet test bed. It has shown that on-demand buffering, query caching, efficient restart and other optimizations reduce network overhead and minimize data latency. A key design decision was to layer SenQ so that it could be used in part of all of AlarmNet and other applications. The lowest layers can operate entirely between two sensor devices, such as between a wearable user interface and a body-worn sensor. Upper layers include support for management functions on a gateway, and a restricted query language for users.
# Reinforcement Learning for Semi-Active Vertical Dynamics Control with Real-World Tests
## 5 Control System Verification
### Vertical Dynamics Control System  
As described in Section 4, the RL controller was mainly designed to compensate the excitations induced by the road itself. In addition, there were also parts of vertical dynamics effects in real driving operation that were caused by driver inputs. These were, for example, the chassis rolling during cornering or the pitching during acceleration or braking. In order to take these excitations into account in the vertical dynamics control concept, the overall control system consisted of further modules in addition to the RL controller:  
* Prediction module for vehicle body accelerations:  
Lateral and longitudinal accelerations were calculated based on the brake pedal position and the steering wheel angle. A stationary single-track model was applied to neglect the system dynamics and, thus, enable time prediction.  
* Feed-forward control:  
An inversion-based approach was used to calculate the required damper forces, which would compensate for the resulting roll and pitch angle based on the predicted body accelerations.  
Even though the RL-based controller was tested in simulation, a safe operation of a learning-based controller cannot be ensured a priori. To intercept unsafe driving conditions, we added a safety module for all driving tests conducted on real roads:  
* Safety module:  
As soon as critical roll and pitch rates of the vehicle body were detected or in case of ESC activation, the safety module was activated and switched to a certain constant damper current, which provided safe driving. Details regarding this concept can be found in [20].  
Furthermore, there was an SH/GH controller included in the overall control system, which acted as a benchmark controller. The vehicle software allowed us to switch between the SH/GH and the RL controller. The design and parameterization of this reference controller is presented in the following section.
# Search-Space Reduction for S-Boxes Resilient to Power Attacks
## 3 Our Contributions: Reduction of the S-Boxes Search-Space into a Hamming Weight Class SEARCH-Space
### New Search Strategy for S-Boxes Resistant to Power Attacks Based on HW Classes
In group \(r\), there are \(C(n,r)\) elements (the ways of locating \(r\) ones in a binary vector of length \(n\)), corresponding to outputs of the S-box whose weights are equal to \(r\). It is important to note that the order is not crucial in each group because all the weights are equal to \(r\) (indistinguishable elements). However, the order is essential between groups because they correspond to different weights (distinguishable elements). Therefore, to find the number of HW classes of the S-boxes of \(n\times n\), we directly applied the formula \(PR_{2n}^{C(n,0),\ldots,C(n,n)}\) which calculates the number of permutations with repetition. From here (a) follows. Given that in group \(r\), there are precise \(C(n,r)\) elements equal to \(r\); then, they can be permuted in \((C(n,r))!\) ways. In general, we can permute the elements within the groups in \(\prod_{r=0}^{n}C(n,r)!\), which corresponds to the number of S-boxes within a class, demonstrating the statement in (b). We now dwell in an interpretation of Proposition 5. Notice that the numerator \((2^{n})!\) corresponds to the total number of S-boxes of \(n\times n\), while the denominator \(\prod_{r=0}^{n}C(n,r)!\) is the number of S-boxes within a class and its quotient is exactly the number of classes. Although this is an exact expression very appropriate for theoretical analysis, it should be noted that, in practice, for large values of \((2^{n})\) and \(C(n,r)\), the calculation of their factorials will be approximate, using the Stirling formula. The following Corollary will be very useful to quantify the reduction of the search space. **Corollary 1**. : _The number of HW classes among the number of S-boxes of \(n\times n\) is equal to:_  
\[\frac{Number\ of\ HWclasses}{Number\ of\ S-boxes\ of\ n\times n}=\frac{PR_{2^{n}}^{C(n,0),\ldots,C(n,n)}}{(2^{n})!}=\frac{1}{\prod_{r=0}^{n}C(n,r)!}. \tag{12}\]  
Proof. : Using Proposition 5, \(PR_{2^{n}}^{C(n,0),\ldots,C(n,n)}=\frac{(2^{n})!}{\prod_{r=0}^{n}C(n,r)! }\)  
\[\frac{Number\ of\ HWclasses}{Number\ of\ S-boxes\ of\ n\times n}=\frac{PR_{2^{n}}^{C(n,0),\ldots,C(n,n)}}{(2^{n})!}=\frac{\frac{(2^{n})!}{\prod_{r=0}^{n}C(n,r)!}}{ (2^{n})!}=\frac{1}{\prod_{r=0}^{n}C(n,r)!}. \tag{13}\]
## 4 Experiments and Results
The uncorrected overlap factor leads to the result that the aerosol extinction of the RMRL is smaller than that of the CDWL. The continuous observation results from 16-21 February are shown in Figure 9, including the CNR, aerosol extinction coefficients, horizontal wind speed and direction, and vertical wind speed. When the Raman lidar does not work during the daytime, the boundary value is set as the interpolation of the result during the night. To ensure the accuracy of the results, the retrieval of wind speed and direction with the CNR less than \(-17\) dB is plotted as white. In Figure 9e, the positive value of vertical velocity is defined as downward, and the upward wind speed appearing every afternoon is consistent with the results of the extinction coefficient, which also confirms the vertical transport process of aerosols. The results prove the aerosol retrieval capability of the CDWL system. Figure 7: Comparison of the aerosol extinction coefficients with the two lidars, measured at 0.00, 3:00, and 6:00 local time on the 17, 18, and 20 February, respectively. The red lines show the results of the CDWL at 1548 nm, and the blue dotted lines show the fitting results of the RMRL at 1548 nm. Figure 8: Scatter diagrams of the aerosol extinction coefficients derived from the RMRL and the CDWL. Results ranging from 0.5 to 3 km during the nighttime of the 17 and 20 February are compared in Figure 8**(a,b)**, respectively. In contrast, results ranging from 0 to 0.5 km are shown in Figure 8**(c,d)**. The color-shaded dots denote the normalized density. The black dashed lines denote 1:1 lines, and the red dashed lines show the linear fitting results. The distributions of y-x are inserted in the bottom right corner of each panel. Figure 9: Continuous observation by the CDWL from the 16 to 21 February. **(a)** CNR, **(b)** aerosol extinction coefficient, **(c)** horizontal wind speed, **(d)** horizontal wind direction, and **(e)** vertical wind  
speed. The CNR and extinction coefficient are averaged over a vertical scanning duration of 1 min. The positive value of vertical velocity is defined as downward.
## 3. Sacrificial Anode CP
### Aluminum-Based Alloy Anode Material  
The aluminum-based alloy anode material is a new type of sacrificial anode material that is under rapid development. The surface passivation of pure aluminum produces a dense oxide film, making its electrode potential more positive. The potential range for pitting corrosion of industrial pure aluminum in simulated seawater is \(-0.45\) to \(-0.65\) V (v.s. SCE), while the potential range after passivation reaches \(-0.75\) to \(0.85\) V (v.s. SCE) [21]; thus, pure aluminum cannot be used as a sacrificial anode. By adding trace amounts of highly activating alloying elements or denaturing agents, the formation of passivation films on the aluminum surface can be limited or prevented, and the aluminum alloy can be continuously activated and dissolved. Therefore, aluminum-based sacrificial anode materials are under continuous development to improve their formulation. The commonly used activating elements are Zn, In, Sn, Ga, Si, Ti, Mg, etc. [22]. [PERSON] et al. [23] chose Zn to activate the aluminum alloy. The addition of Zn increased the anodic corrosion rate, and the presence of the intermetallic compound structure in the \(\beta\) phase destroyed the alumina passivation film. It was proved by the planned interval test that the anodic weight loss was not due to galvanic coupling corrosion between these two different metals but due to seawater corrosion. For the corrosion protection of submarine pipelines, Al-Zn-In-Si anodes suitable for working in marine mud environments have been developed, and the most widely used in marine engineering are Al-Zn-In anodes and Al-Zn-In-Mg-Ti high-efficiency anodes [24]. The aluminum alloy anode independently developed by [PERSON] et al. [25] has achieved effective protection of low-alloy steel at a depth of 1200 m, with working potentials of \(-1.098\)\(\sim\)\(-0.911\) V (v.s. Ag/AgCl) and almost no corrosion pits on the anode surface with uniform dissolution.
# Integral Seismic Risk Assessment through Fuzzy Models
## 4 An Integral Seismic Risk Fuzzy Model
### Aggravation Fuzzy Model  
The Aggravation Fuzzy Model is based on the seismic risk conceptualization provided by [33] and includes elements representing the social fragility and lack of resilience capacity of an urban area. The fragility dimension aims to enclose proxy elements describing the different life quality levels and social inequality space distribution. The resilience dimension describes a representative set of available city resources to respond to an emergency (some of the indicators included in this model were developed by city authorities and census open data). Table 3 shows a brief description of the indicators used in the Aggravation model.  
The inner structure of the Aggravation Fuzzy Model was developed following the same nature of the Physical Risk Fuzzy model, so each aggravation dimension constitutes an independent FIS. After Fragility and Resilience levels are estimated through their respective FIS, they are used as the primary input for a third FIS, where a final aggravation level is calculated (see Figure 1). In the same way as previously, inference fuzzy rules were defined among indicators to simulate empirical knowledge regarding fragility and resilience development and dynamics. An example of the fuzzy rules used for estimate aggravation can be seen in Table 4 describing the sub-model called Resilience, where three classes were defined for its antecedent part, while its consequent part was divided into five linguistic classes.  
Figure 2: Simple conceptualization of the membership functions used for Physical Risk and Aggravation showing the input FIS (upper plot) and the output FIS (lower plot). The universe of discourse, and the exact parameters of the membership functions, are not shown in the figure since they depend on the variability and numerical range of each indicator’s numerical value.  
\begin{table}
\begin{tabular}{l} \hline \hline
**Fragility** \\ \hline Marginal Slums. Percentage of: Slum area/Locality area. \\ Social Disparity Index: Index from 0 to 1. \\ Population Density: Inhabitants / km2 of built area. \\ \hline
**Resilience** \\ \hline Human Health Resources: Human resource in health per 1000 inhabitants. \\ Emergency Operability: Index given by City Authorities (from 0 to 2). \\ Development Level: Index given by City Authorities (from 1 to 4). \\ \hline \hline \end{tabular}
\end{table}
Table 3: Indicators used for the Aggravation Fuzzy Model.
# 3D Positioning of Drones through Images
## 2 Related Work
### UL14 plus Dataset
In the test set, each UAV image corresponds to 12 satellite images containing that UAV image. These 12 satellite images are local crops from a satellite image covering the entire school. The UAV image is positioned differently on each of the 12 satellite images. The satellite image crop sizes range from 700 to 1800, corresponding to actual coverage areas with side lengths between 180 m and 463 m. The testing phase involves locating the UAV image on the 12 satellite images. In creating the test set, we considered real-world challenges in cross-view matching geolocation such as scale transformations and position offsets. Therefore, each UAV aerial photo corresponds to 12 satellite images of different spatial scales, and the position of the UAV image within the satellite image is randomly generated (Figure 4). This test set design increases the difficulty of geolocation, better aligning with actual UAV working scenarios.
###### Abstract  
Low-orbit micro-satellite technology has developed rapidly in recent years due to its advantages of low time delay, low cost and short research period. However, among the existing inter-satellite routing algorithms, the classical flooding and greedy algorithms and their derivatives also have some limitations. The path delay calculated by the flooding algorithm is small but the calculation is large, while the greedy algorithm is the opposite. In this paper, a balanced inter-satellite routing algorithm based on partition routing is proposed. This paper presents the simulation experiments for the following indexes of the classic inter-satellite routing algorithms and the balanced partition routing algorithm: computation complexity, single-node computation pressure, routing path delay, path delay variance (data in Topo table satisfy \(\mu=5,\sigma^{2}=10\)). The results reveal that the balanced partition routing algorithm achieves better performance. In this paper, two optimization directions of the balanced partition routing algorithm are simulated under conditions that the data in the Topo table satisfy \(\mu=5,\sigma^{2}=6,\sigma^{2}=10\) and \(\sigma^{2}=15\), respectively, when comparing their performance indicators. The experiments show that these two optimization methods can be adapted to various application scenarios and can further reduce the hardware cost of satellite nodes.  
low orbit; micro-satellite technology; star chain; routing algorithm; partitioning algorithm +
Footnote †: journal:  
_Article_  
Research on Routing Equalization Algorithm of Inter-Satellite Partition for Low-Orbit Micro-Satellites
## 4 Materials and Methods
### Sequence Alignment and Phylogenetic Analysis  
The sequences of one nectary development related gene _CRC_, four other YABBY family genes (_FIL_, _YABBY2_, _YABBY5-1/2_) and 11 floral scent genes in _C. praecox_ were selected from the Illumina deep sequencing [27]. Blastn of these genes were obtained and named as _CpFIL_, _CpCRC_, _CpYABBY2_, _CpYABBY5-1_, _CpYABBY5-2_, _CpSAMT_, _CpDMAPP_, _CpIPP_, _CpGPPS1_, _CpGPPS2_, _CpGPP_, _CpLIS_, _CpMYR1_, _CpFPPS_, _CpTER3_, and _CpTER5_. The sequences included in the analysis were downloaded from the NCBI GenBank ([[http://www.ncbi.nlm.nih.gov](http://www.ncbi.nlm.nih.gov)]([http://www.ncbi.nlm.nih.gov](http://www.ncbi.nlm.nih.gov))). The amino acid sequences of the YABBY family were aligned using ClustalX 1.83 [36]. Neighbor-joining (NJ) bootstrap analysis (1000 replications) with Poisson correction for the amino acids was performed using MEGA 4 [37]. Sequence data for analysis can be found in the GenBank/EMBL databases under the following accession numbers: _CpFIL_, _CpCRC_, _CpYABBY2_, _CpYABBY5-1_ and _CpYABBY5-2_ from _C. praecox_; _AtINO_, _AtCRC_, _AtYABBY1_, _AtYABBY2_, _AtYABBY3_, and _AtYABBY5_ (AAF23754, NP_177078, NP_566037, AF136539, AF136540, NM_17949) from _A. thaliana_; _Am.trCRC_, _Am.trIL_, and _Am.trYAB2_ (AJ877257, AB168113, AB126654) from _Amborella trichropoda_; _AfCRC_ (AY854797) from _Aquilegia formosa_; _CsYAB5_ (BAF65259) from _Chloranthus serratus_; _DIYAB2_ (ACNS9438) from _Dimocarpus longan_; _EsCRC_ (GH62810) from _Epinedium sagittatum_; _EcCRC_ (CAQ17052) from _Eschchsolzia californica_; _NtYAB2_, _NnCRC-1_, and _NnCRC-2_ (XP_010247861, XM_010259669, XM_010259670) from _Nelumbo nucifera_; _NjFIL_ (BAD83708) from _Nuphar japonica_; _Ny.allNO_ (AB092980) from _Nymphaea alba_; _Ny.coFIL_ (BAF65258) from _Nymphaea coloration_; _MgFIL_ (BAF65261) from _Magnolia grandiflora_; and _OsCRC_ (AAR84663) from _Oryza sativa_.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 3 Analysis of ATM Big Data Sharing
### ATM System Concepts
#### 3.1.2 ATM System Structure
Ground layer: Built upon the Aeronautical Telecommunication Network (ATN) and international standardized protocols (e.g., ATN/IPS), it enables air-ground data interaction through VHF, HF, and satellite communications. Aircraft surveillance, identification, and interaction are achieved via ATC automation systems for flight plan processing, conflict detection, and traffic flow prediction, while airport departure sequencing is optimized using CDM tools. This integrated spatial architecture forms a comprehensive framework for modern ATM operations, ensuring seamless coordination across all flight domains.
## 3 Data and Methods
### Susceptibility Assessment Process  
The technical workflow of this study illustrated in Figure 3 comprises four main steps, as follows: (1) preparation of the sample data; (2) construction of the susceptibility evaluation factor dataset; (3) application and evaluation of machine learning models; and (4) generation of the debris flow susceptibility map.
# Use of Foamed Cement Banking for Reducing Expressways Embankment Settlement
## 1 Introduction  
With the rapid development of expressway construction, in China, construction on soft ground is sharply increasing, such as Shen-Jia-Hu Expressway that passes through soft soil [1, 2]. Owing to shortened construction periods and to the rheological properties of soft soil, among other reasons, the embankment after settlement of many highways opened abroad is too high, which does not meet relevant regulatory requirements (i.e., post-construction should be less than 30.0 cm) [3]. The post-construction settlement of expressways has become a major type of expressway damage, which will not only seriously affect the vehicle's road speed, traffic safety, and driving comfort, but also seriously affect the stability and safety of the highway itself [4, 5, 6].  
In order to reduce post-construction settlement, the embankment is replaced with light soil to reduce the overburden load and control the settlement [7, 8]. At present, the widely used light soils mainly include EPS beads-mixed lightweight soil, EPS block, and foamed cement banking, which include good integrity, light mass, and convenient construction. The lightweight soil's cohesion, global strength, bulk density, and other parameters can be adjusted by adjusting the output of cement and foam [9]. The advantages and disadvantages of various methods are shown in Table 1. Foamed cement banking research is already well underway in numerous countries [10, 11, 12], and some results have been achieved in practice. Compared with other materials, the FCB has a shorter construction period, lower curing time, and higher construction quality, thus it has broad application prospects [13, 14]. As FCB's bulk density is only conventional soil bulk density of 1/4 to 1/8, its use as a replacement of embankment material [15, 16, 17], can not only reduce the base load pressure, but can also reduce the total settlement and differential settlement [18]. Replacing the undisturbed soil with lightweight soil to process the post-construction settlement ofexpressway under traffic control seriously affected highway traffic operations. Especially, there are no closed roads (ramps) during construction, the lightweight soil is being put down to achieve subgrade compactness, that causes huge economic losses due to the road closure.  
In this paper, foamed cement replacement is carried out on both sides of the embankment, which does not affect the normal traffic operation during replacement construction, its use is targeted and flexible, and only small settlement occurs after improvement. Combined with Shen-Jia-Hu EKO + 323 highway section, this paper verifies the advantages of FCB in dealing with embankment settlement without road closure. Through the analysis of dynamic monitoring data of Shen-Jia-Hu expressway's subgrade settlement, and according to the proportional relationship between settlement and load, the calculation method of embankment replacement thickness is proposed, which can be applied to complex construction processes, this work can thus provide a reference for similar future projects.