esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Domain Access Control Model in Industrial IoT Environment
## 3 Distributed Access Control Model Based on a Master-Slave Chain
### Cross-Domain Access Control Model
#### 3.3.2 Grouping Strategy Retrieval Algorithm  
In the process of attribute-based access control, the resource policy becomes more and more complex with more and more devices with properties. Currently, in the access control process of devices, many scholars compare attributes with policies one by one, so the matching of attributes and resources takes a long time, which greatly increases the delay of access requests. In order to improve the retrieval efficiency of retrieval strategies, the authors of this paper designed a binary encoding of strategies and constructed a grouped strategy retrieval algorithm. The strategy set was organized into a strategy tree. The process of attribute and strategy matching was also changed from the previous one-by-one matching of attributes and strategies in the strategy set so that only traversal access to the strategy tree is required. By accessing the attribute strategy tree, a large number of irrelevant strategies can be filtered out in the attribute matching process, thus reducing the time for strategy retrieval. The rules for constructing the strategy tree are as follows.  
The number of all attributes in a strategy is taken as the total number of bits N of the binary code, each taking 0 or 1, where 0 means that the attribute is not included and 1 means that the attribute is included. Thus, all strategies in the strategy set of each resource can be represented in the form of a binary code, the code is used as a group number, and the strategies with the same code are combined into a group. The strategy set is grouped as shown in Figure 3, and the root node represents all the strategy information in the strategy set. In addition, all strategy sets are grouped by R1 to Rn. There are several sets of strategies in each grouping R. The rules for grouping the strategy sets are as follows: If a strategy in the strategy set of the root node does not contain the first attribute, it is temporarily merged into the group number 011... 111, and if it does not contain the last attribute, it is temporarily merged into the group 111... 110, and so on. The final binary code of the strategy group is determined as the group number. The complexity of the strategy tree is determined by the number of strategies, which can be divided into most \(2^{N}-1\) groups.
## 4 Discussion on the Mechanism of Damage
### Mechanism of Macroscale Damage  
The changes in shear strength result from the combined effects of cohesion and the internal friction angle. The cohesive strength between soil particles is primarily determined by mineral composition and pore structure, while the internal friction angle is influenced by the roughness of particles and their arrangement [43]. Mineral composition plays a crucial role in determining cementation strength, while pore structure is key to determining intergranular attraction strength. A decrease in the abundance of clay minerals leads to a significant reduction in cementing force; an increase in pore size and number results in a marked reduction in intergranular attraction, while the presence of cracks can severely hinder stress propagation. Collectively, these factors lead to a significant reduction in soil cohesion. Although water flow smooths soil particles, reduces granule surface roughness, and alters the arrangement of particles, the impact of short-term water-rock interactions on particle roughness is relatively limited. Consequently, during the wet-dry cycling process, while the internal friction angle generally decreases, the overall change is relatively modest.  
After the initial dry-wet cycle, the enlargement of pore size and the hydrolysis of certain clay minerals contribute to a decrease in cohesion. However, at this stage, the degree of hydrolysis of clay minerals is still relatively limited. As the wet-dry cycles progress, cracks emerge as the primary pathways for water infiltration into the soil, accelerating the hydrolysis and subsequent loss of clay minerals. Concurrently, the presence of cracks disrupts the material's continuity, impeding the transmission of stresses. This results in a significant reduction in cohesion, making cracks the primary factor influencing its magnitude. In the later stages of the wet-dry cycles, the crack rate, pore structure, and mineral composition stabilize, and consequently, cohesion also stabilizes during this period.
# Effective Seed Scheduling for Directed Fuzzing with Function Call Sequence Complexity Estimation
## 3 System Overview
### Complexity Estimation of Function Call Sequence
#### 3.1.2 Reaching Seeds Scheduling  
For DGF, when the seeds have not reached the target site, the purpose of scheduling is to select appropriate seeds for fuzzing so the seeds that can reach the target point can be generated as soon as possible. To achieve this goal, we design the scheduling algorithm shown in Algorithm 1. When a seed \(S_{N}\) that triggers a new coverage enters the scheduling process, we check the sequence of instrumented functions \(Q_{S_{N}}\) that the seed has run through. In order to estimate the complexity of the seed to the target function, we take the last function \(F_{N}\) in the sequence \(Q_{S_{N}}\) as the starting point and the target function \(F_{T}\) as the endpoint to find all the simple paths \(P_{S_{N}}\) in the call graph \(G\) of instrumented functions. After that, the complexity of sequences in \(P_{S_{N}}\) is estimated and we take the average complexity \(C_{EXP}\) as the expected complexity of the seed to reach the target site. In order to approach the target point as soon as possible, we select the seed with the lowest expected complexity to fuzz. Since it is possible to have multiple seeds with the same \(C_{EXP}\), we estimate the complexity \(C_{EXP}\) of the executed sequences of these seeds \(S\_Mint\) and choose the seed with the lowest complexity to make fuzzing towards the goal faster. It is worth noticing that we use the \(allSimplePaths\) method in the Algorithm 1 for two reasons: One is that \(G\) is a directed cyclic graph, where the existence of cycles will lead to infinite paths; simple paths can mitigate this problem. The other is that the computational cost of \(G\) is acceptable [18].
# Framework for Fast Experimental Testing of Autonomous Navigation Algorithms
## 5 New Localization Module: GNSS/SLAM Fusion
### AMCL Subsystem  
One of these subsystems is AMCL described in the previous section. To properly implement the fusion, we need to pay attention to the variances that this subsystem provides, associated to each estimated pose (Section 4.2.1) because they provide information about the confidence of the solution. This information will be used by the Kalman filter in the correction step, and it also will be useful for the integrity monitoring system.
## 5 Experiment  
### Experimental Setup  
#### 5.1.1 Dataset  
The proposed model, RGISQL, was evaluated by using two public datasets: Spider and Spider-Syn. Spider is a large-scale, cross-domain text-to-SQL dataset that includes complex natural language questions and SQL queries from multiple databases. It tests the model's ability to generate accurate SQL across different database schemas, assessing its generalization performance. Spider-Syn is a variant of Spider that contains different formulations of the original questions, examining the model's ability to handle multiple linguistic expressions of the same query, thereby enhancing the model's language robustness.
# Operation Risk Assessment of Urban Dense Cable Passageway Based on Fuzzy-Analytic Hierarchy Process
## 2 Operational Risk Parameters for Urban Dense Cable Corridors
### Cable Body Risk Parameters
#### 2.1.1 Overheating of Cables  
Under normal operation, the cable running load exceeds the rated carrying capacity for 1 h or more in a day, and the measured temperature of the skin of the cable and accessories exceeds 75 \({}^{\circ}\)C.
# Contribution of Atmospheric Fallout to the Soil-Root-Leaf Transfer of PAHs in Higher Plants
## 4 Discussion
### Soil-to-Aerial Parts Pathway
When 5-ring PAH concentrations were compared, differences were significant between root and stem contents (t = 4.5771, df = 4, \(p\)-value = 0.01021), as well as between root and leaves (t = 6.041, df = 4, \(p\)-value = 0.003787). The concentration of 6-ring PAHs was very similar in the soil and in the roots--IP 1.2 \(\mathrm{\SIUnitSymbolMicro g}\)/kg and 1.5 \(\mathrm{\SIUnitSymbolMicro g}\)/kg and B(g,h,i),p 2.1 \(\mathrm{\SIUnitSymbolMicro g}\)/kg and 2.8 \(\mathrm{\SIUnitSymbolMicro g}\)/kg. They were not detected in the above-ground parts of the test plants (Figure 2). These two PAHs were also excluded from maize tissues when uptake of PAHs in wastewater-irrigated soils was assessed [59]. [PERSON] et al. concluded that HMW PAHs were less able to be transported via transpiration stream and showed low accumulative potential in corn grains [60]. A similar tendency was reported by [PERSON] et al. [53]. In experimentally treated soils, the ratio of HMW PAHs was relatively high (5-ring PAHs amounted to 20% and 6-ring PAHs to 85%), but they were not detected in vegetables cultivated in these model soils. Available studies, however, are still contradictory. Most studies agree that high-weight PAHs show strong adsorption upon the root epidermis, which works against translocation to shoots [61; 62]. POPs are generally assumed to bind tightly to the root epidermis [63]. [PERSON] et al. compared PAH distribution in soil, rhizosphere soil, and the roots of different crop species, including one Brassicaceae, broccoli (_Brassica oleracea_ L. var. _botrytis_ L.). The study suggested that uptake can be prevented by root exudates and the root peel [64]. On the other hand, [PERSON] et al. [65] detected HMW PAHs in shoot tissues of _Salicornia fragilis_ (a halophytic plant) which was artificially exposed to petroleum-polluted sediments. In an exceptional case, significant bioconcentration of the 6-ring B(g,h,i,)p was found in maize in Eastern China [59]. The uptake of PAHs in the root zone is suggested to depend on lipophilicity (log Kow, the water-octanol partition coefficient), and as such, HMW molecules are adsorbed on the roots but not absorbed by them [66].
# Fault Prediction of Mechanical Equipment Based on Hilbert-Full-Vector Spectrum and TCDAN
## 2 Fault Feature Extraction  
With the development of equipment precision, to ensure the reliability and integrity of signal acquisition, multiple identical sensors are often used for the signal acquisition of a certain part of the equipment at the same time. In addition, sensors are generally arranged in the horizontal-vertical direction or V-shaped direction in the same plane, as shown in Figure 1.  
The Hilbert-envelope spectrum mainly uses the Hilbert transformation to demodulate the amplitude of the original vibration signal of the equipment, removes the high-frequency vibration component, realizes the envelope demodulation of the signal, and has great advantages in extracting fault features [29, 30]. The processing flo  
Figure 1: Layout of sensors: (**a**) Being installed in the horizontal–vertical direction; (**b**) Being installed in the V-shaped direction.  
The traditional Hilbert-envelope spectrum can only process and analyze the signals collected by multiple sensors separately, and the signals collected by a single sensor are not comprehensive enough, which leads to one-sided analysis results. In this paper, full-vector spectrum technology is introduced based on the traditional Hilbert-envelope spectrum, and information fusion is carried out after envelope processing for the signals collected in the two directions of the device. Finally, FFT (Fast Fourier Transform) operation is performed to obtain more comprehensive spectrum information on the device. The processing flow is shown in Figure 2b.
# Full-Field Strain Determination for Additively Manufactured Parts Using Radial Basis Functions
## 3 Evaluation of Strain Determination Using RBFs
### Real Application Example of a Tube under Tension and Torsion
#### 3.2.1 Comparison of Triangulation Concept and RBF (Torsional Case)  
The torsion sample investigated in the first study was milled off on the outside using a lathe. The maximum torsional angle is \(\vartheta=15^{\circ}\) (torsional couple \(\approx 770\) N m). Figure 11 shows the maximum principal Green strains (\(m=2\) in Equation (11)) for the triangulation concept (lower figure) explained in [4] and the RBF-approach using inverse multi-quadrics functions with monomials (upper figure). To obtain a better impression of the differences, the projection of the 3D data onto the \((Y/Z)\)-plane is shown (for the coordinates, see  
Figure 10: Reconstruction of rough surface of a specimen using GiD: data generated with (**left**) 6 px point distance and 9 px for facet size, (**right**) 15 px point distance and 19 px for facet size.  
Figure 1). The triangulation concept yields a more smeared strain distribution, whereas the RBF-approach yields a more finely resolved picture.  
Apart from the more methodical examination, the notches (transitions of the welding bead stacks) on the inside of the tube can be observed on the externally smooth specimen, as higher strain values occur there. Thus, plastification and failure will occur in these regions. If a deformation of pure torsion were to be assumed according to Equation (34) for \(\lambda=1\), \(R=27.2\,\)mm, and \(D=\theta/L_{0}\) with \(\theta=15^{\circ}\) (transformed in radian) and \(L_{0}=82\,\)mm, a maximum principal Green strain \(\epsilon_{\max}^{(2)}\approx 0.045\) would be obtained. This is not achieved in the thicker parts of the specimen's wall, but the measured strains are larger in the thinner parts. Thus, a localization of the deformation occurs.  
As discussed previously, the number of pixels to build up a facet has an influence on the geometry, see Figure 10. Further, the influence is transferred to the strain analysis as well. In the previous (and all other) investigation, we chose 9 px to form the facets and 6 px for the point distance, to ensure high resolution of the geometry, see Figure 10 (left). If one takes a facet size of 19 px and a point distance of 15 px, both the geometry, Figure 10 (right), and the resulting maximum principal strain distribution \(\varepsilon_{\max}^{(2)}\) are more smeared, see Figure 12.  
This figure represents a comparison to Figure 11 (top). Consequently, the facet size has also a significant influence on the strain analysis. In the following, only evaluations using a 9 px facet size are investigated.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 2 Methodology
### Contour Mission Flight Planning
#### 2.2.2 S2: Calculate the Basic Flight Parameters  
* Import of flight area DEM: DEMs with a particular spatial resolution can be provided worldwide (mostly in low resolution), nationwide (mostly in different resolutions), and locally from previous surveys. Worldwide, the DEM has Shuttle Radar Topographic Mission (SRTM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Due to the SRTM spatial resolution of 3 arc seconds [31], the SRTM DEM must be more accurate for planning a UAV mission in steep hillside terrain [32]. The ASTER (1 arc second) DEM was selected for the study. The ASTER DEM is used to access publicly available data repositories such as the NASA Earth Observing System Data. Users can download the desired DEM files from the platform in the appropriate flight area.
* Flight height: The user provides the aerial photo area, as well as the conditions for capturing images (such as GSD and overlap), the camera parameters (focal length, sensor size, image resolution), according to the GSD and sensor parameters can calculate the AGL, as Equation (1). \[AGL=\frac{f\times GSD\times W}{w}\] (1) where \(AGL\) is the flight altitude above ground level (m), \(f\) is the focal length (mm), \(GSD\) is the ground sample distance (m/pixel), \(W\) is the image width (pixel), and \(w\) is the sensor width (mm). Most topographic surveys have a scale of 1/300-1/500, and the corresponding \(GSD\) is 3-5 cm. Usually, the 3-5 cm \(GSD\) flight altitude is 150-250 m. The lower the flight altitude, the higher the \(GSD\), but the smaller the image coverage, the more images will be taken, and additional flight and image post-processing time will be required. \[B=GSD\times W(1-p)\] (2) where \(B\) is the photo baseline (m), and \(p\) is overlap (%).
## 2 Materials and Methods
### Envelope Detection from Fourier Transform Coefficients  
The envelope of a signal in the time domain represents the instantaneous amplitude of a time-varying signal. In medical imaging applications, this envelope conveys structural or anatomical information along the signal path. In general, an analytic expression of a signal is first derived to obtain the envelope of a time-domain signal. These analytic expressions, a widely used concept in signal processing, are alternative complex-valued representations of real-valued signals that make various processes related to a signal's time dependence conceptually simpler [35]. In the time domain, the real part of the analytic signal, \(x_{h}(t)\), is simply the original signal, \(x(t)\), and the new imaginary part, \(x_{h}(t)\), is calculated using the Hilbert transform (\(x_{h}(t)=HT\{x(t)\}\)), which suppresses the negative frequency components, i.e.,  
\[x_{a}(t)=x(t)+jx_{h}(t). \tag{9}\]  
The mathematical definition of the Hilbert transform of \(x(t)\), \(x_{h}(t)\), is given by:  
\[x_{h}(t)=p.v.\ \int_{-\infty}^{\infty}\frac{x(t)}{\pi(t-\tau)}d\tau, \tag{10}\]  
where p.v. is the Cauchy principal value of the integral. Figure 1 shows the spectral properties of the original and analytic signals in the frequency domain. Then, the envelope of a signal, \(E\), is defined as the magnitude of the analytic signal as below,  
\[E=\sqrt{x(t)^{2}+x_{h}(t)^{2}}. \tag{11}\]  
An alternative approach that is valid for most engineering applications has been proposed for calculating discrete-time domain analytic signals, based on the spectral properties shown in Figure 1[36].  
Figure 1: Frequency spectra of (**a**) an original time-varying signal, (**b**) the Hilbert transform of the original signal, and (**c**) the analytic form of the original signal.  
Since, from the spectrum, an analytic signal has no negative frequency components, and its positive frequency components are double the size of that of the original signal, it can be estimated based on a DFT of the original signal. In this procedure, \(X[m]\) represents the coefficients of the \(N\)-point DFT of the original signal, \(x[n]\), defined as,  
\[X[m]=T\sum\
olimits_{n}^{N-1}x[n]\exp(-j2\pi fnT),\text{ where }x[n]=x(nT). \tag{12}\]  
The coefficients of the DFT of the analytic signal, \(Z[m]\), can thus be obtained from \(X[m]\) as follows:  
\[Z[m]=\left\{\begin{array}{rl}X[0],&\text{for }m=0\\ 2X[m],&\text{for }1\leq m\leq\frac{N}{2}-1\\ X\left[\frac{N}{2}\right],&\text{for }m=\frac{N}{2}\\ 0,&\text{for }\frac{N}{2}+1\leq m\leq N-1\end{array}\right.. \tag{13}\]  
The analytic signal in the discrete-time domain can subsequently be recovered from \(Z[m]\) using the inverse DFT. Finally, the envelope is also directly calculated from the real and imaginary parts of the complex discrete-time analytic signal, \(x[n]\) and \(x_{h}[n]\), respectively, as follows:  
\[E=\sqrt{x[n]^{2}+\ x_{h}[n]^{2}} \tag{14}\]
# Superhydrophobic Surfaces Enabled by Femtosecond Fiber Laser-Written Nanostructures
## 3 Results and Discussion
### Water-Repellent and Self-Cleaning Properties  
The initial concept of ultra-hydrophobicity was inspired by the lotus leaves of Nelumbo [1]. When water droplets encounter the surface of the lotus leaf, they simply bead up and roll off. Studies have shown that the surface of the lotus leaf has a hierarchical structure, which exhibits a Cassie-Baxter state [32]. Water in the Cassie-Baxter state is suspended on the top of the surface, creating air pockets between the solid nanostructures, resulting in a water-repellent surface. Dirt particles on the surface stick to the droplets due to the natural adhesion between water and solids as well as the significantly reduced contact area between the water and leaf.  
Figure 7 depicts water-repellent characteristics through a series of frames extracted from a video clip over the course of eight seconds. The water droplets, upon contact with the laser-textured surface, immediately bounce off. The self-cleaning characteristic is also demonstrated. From a nine second video clip, Figure 8 shows how water droplets can quickly clean up debris by tilting the surface at a 3\({}^{\circ}\) angle.  
Figure 5: The contact angle versus pulse fluence at a scanning speed of 100 mm/s and pitch of 5 \(\upmu\)m (left). The contact angle versus scanning speed at a pulse fluence of 2 J/cm\({}^{2}\) and pitch of 5 \(\upmu\)m (right).  
Figure 6: Varying contact angles as a result of different pitches.
## 2 Related Works
[19] proposed the maximum average coverage deployment (MACD) algorithm, used the grid model to simulate the monitored region, and used greedy iterative strategies to complete the node deployment. As a new type of swarm intelligence optimization algorithm, ant colony algorithm (ACO) has the advantages of positive feedback, being distributed, parallel,self-organizing, etc., and has fewer adjustable parameters, which is suitable for node deployment in large-scale complex environments of UASNs. Based on the ant colony optimization (ACO) algorithm, [PERSON] et al. [20] proposed a sensor node deployment strategy, which can effectively avoid the blindness of node movement. However, there are many disadvantages such as too many nodes participating in the movement and too many redundant nodes. Therefore, [PERSON] et al. [21] proposed a three-dimensional grid minimum cost deployment algorithm based on ant colony optimization. The algorithm modifies the standard ACO algorithm to achieve node sparse deployment, but it is easy to fall into local optimization in the late iteration, while the algorithm execution time is longer. The aforementioned algorithms can achieve high network coverage and connectivity, or even full coverage and full connectivity. However, these algorithms require a high density of nodes, which is not suitable for the application scenario of the sparse deployment of UASNs. Furthermore, these methods do not consider the communication problems between nodes and the effects of the sink location on the deployment strategies. Therefore, these methods have great limitations.