esa-sceva/satcom-chunk-collection huggingface dataset:
# Hub Airport End-Around Taxiway Construction Planning Development: A Review
## 2 Research Methodology and Literature Review  
### Research Methodology  
The search strategy and the implementation of the review followed the flow diagram (Figure 1).  
The authors initially surveyed academic literature on Google Scholar, using the keywords \"end-around taxiway\" and \"bypass taxiway\". It was found that English-language research on this topic was limited, with only a handful of studies focusing on specific airports like DFW, ATL, DTW, and Istanbul Airport. This limitation was attributed to the fact that end-around taxiways are typically found at hub airports with multiple runways, and there are only a few such airports globally. To compensate for this, the authors broadened their search on Google using the same keywords and found additional information from guiding documents, such as those from the FAA and ICAO, regarding the construction of end-around taxiways. A survey was also conducted using the Chinese CNKI Scholar search to explore the situation of end-around taxiway construction in China.  
Expert interviews were conducted, and comprehensive information about the 15 existing end-around taxiways worldwide was collected. The layouts of these 15 end-around taxiways varied significantly, resulting in differences in taxiing lengths.  
Based on these resources, the authors carried out the following analysis steps:  
Firstly, we summarized the operational status, advantages, disadvantages, and limitations of existing end-around taxiways based on the available literature. However, these studies focused on already constructed end-around taxiways. It was proposed that the drawbacks of increased taxing time and distance caused by end-around taxiways can be mitigated through proper design.  
We then classified end-around taxiways using two methods. One was based on the spatial relationship with end-around taxiways, terminals, and runways, categorizing them as N-type, M-type, and large N-type. The other method classified them based on operational flow patterns into three modes: BAT, REAT, and SAT.  
Thirdly, reducing the distance between end-around taxiways and runways was identified as a key factor in minimizing taxiing length. The design constraints and considerations for BAT, REAT, and SAT modes were summarized based on FAA and ICAO regulations and relevant studies. For each mode, methods and optimization steps to design the shortest end-around taxiway were proposed. Visual obstructions, a concern for the REAT mode, were also incorporated.  
Finally, the end-around taxiway design case at KWE airport was analyzed, applying and validating the proposed methods and principles.
## 4 Experimental Results
### Test Dataset  
The test dataset is composed of 9 TIR maritime sequences, and each sequence represents a typical scenario in TIR ship detection applications. These TIR ship image sequences are labeled as Seq1-9. Figure 12 shows the representative images of the TIR ship image sequences used for performance evaluation. Figure 12a displays a bright ship target located in a relatively mild sea and sky background. Figure 12b represents a bright ship target under a heterogeneous background with sea, sky, and islands. Figure 12c shows a low-contrast bright ship target submerged in long ribbon-like sea clutter. Figure 12d depicts a dark ship target against strong backlighting sea-sky background. Figure 12e is a dark ship target disturbed by huge rolling ocean waves. Figure 12f depicts a low-contrast dark ship target buried in strongly fluctuating sea clutter and sun glints. Figure 12g includes two fast-moving ships with strong long-tail wave interferences [45]. Figure 12h is the case where both bright and dark ship targets appear under heavy sea fog. Figure 12i represents multiple dim bright point ship targets appearing near the sea-sky line/coastline. Table 3 lists the detailed information about the test image sequences. Accordingly, the test images are variable in scene type, clutter type, target type, target shape, and target quantity. Testing on this dataset proves that the algorithm is suitable for many TIR maritime scenarios.  
Figure 12: The illustration of test images. (**a**) the 25 th frame of Seq1, (**b**) the 21 st frame of Seq2, (**c**) the 450 th frame of Seq3, (**d**) the 12 nd frame of Seq4, (**e**) the 112 nd frame of Seq5, (**f**) the 301 st frame of Seq6, (**g**) the 26 th frame of Seq7, (**h**) the 275 th frame of Seq8, (**i**) the 181 st frame of Seq9.
# The Evolution of Volatile Memory Forensics
## 4 Memory Analysis
### Sandbox Tools  
There are many commercial sandbox systems based on virtualization or emulation, including _AnyRun_[51], _Crowdstrike Falcon Sandbox_[52], _FireEye_[53], _JoeSecurity_[54], _Palo Alto WildFire_[55], and _VirusTotal_[56]. Many such systems would well suit the needs of a corporate computer security team, but are not entirely free or open source, making them less suitable for the research teams focused on memory forensics. Some free, open source sandbox tools include _Cuckoo_[57], _DRAKVULF_[58], _Sandboxie_[59], and _SpeakEasy_[60]. Virtually all behavior-based malware detection research efforts incorporate some sort of sandbox system [61, 62, 63]. Users interface with these systems--providing programs and configurations to test and retrieve data to analyze--in a variety of ways, from command line utilities to web requests (via web GUI or HTTP APIs) and local GUIs. For instance, a typical task-creation request for a _Cuckoo_ instance running at \"<server>\" takes the following form:  
curl -H \"Task Name\" -F file=@/program http://<server>/tasks/create/file  
Despite their ubiquity, sandbox systems are not without limitations. Sandbox systems can be difficult to configure correctly so that they effectively counter anti-analysis techniques. There is a plethora of research regarding the evasive techniques employed by malware authors [64, 65] and countermeasures to combat sandbox evasion [66, 31]. Research has also been conducted to improve the performance and compatibility of sandbox systems. [PERSON] et al. [63] proposed a novel sandbox system which leverages VM introspection and memory forensic techniques. Because most sandbox systems must capture all system call behaviors, they must modify the event monitoring routines of VMExit in the hypervisor kernel, which is highly dependent on the virtualization hypervisor. To decrease this hypervisor dependency and increase the compatibility and scalability, [PERSON] et al. proposed a novel system. The proposed system works by setting up a Xen hypervisor on a system, accessing the virtual machine's memory with _LibVMI_, and then using the _Volatility_ framework to analyze system behavior. A few details were provided about the implementation of the system. The authors claim that this approach could be used to analyze live memory data, but appear to only apply it to memory dumps. The accuracy rates of the system at detecting malicious behavior individually in the process, file, and registry systems were somewhat low, but when combined, yielded accuracies as high as 90%.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Bionic Graphene ECG Electrodes
#### 2.1.1 Gecko-Inspired ECG Electrode  
Conventional dry ECG electrodes usually require auxiliary adhesion in the absence of their adhesive layer, which means contact between electrode and skin is not tight enough, resulting in high electrode-skin impedance. Besides, the auxiliary adhesion may decrease in viscosity over time, leading to the deterioration of ECG signal quality, which is not suitable for long-term ECG monitoring. Gecko-inspired structures consisting of high-aspect-ratio nanopillars have been proven to be able to repeatedly adhere and detach from human skin with the help of van der Waals interactions [35]. By combining high conductance, high flexibility, and excellent adhesion properties, some gecko-inspired graphene ECG electrodes have been developed recently. [PERSON] et al. proposed a concept of conductive dry adhesives (CDA) [36] by using graphene, carbon nanotubes, and other higher dimensional carbon materials to form the conductive network in the polydimethylsiloxane (PDMS) elastomeric matrix, and further inversely replicating the CDA pad by the intaglio Si platform prepared by photolithography and deep reactive ion etching. The acquired CDA-based ECG electrode has mushroom-like pillar arrays with an aspect ratio greater than three over a large area (Figure 1a). When used in ECG signal monitoring, the graphene-based CDA with \(\sim\)100 \(\Omega\)-cm low volume resistance enables clear ECG waveform display (P, QRS, and T waves), and its high, \(\sim\)1.3 N/cm\({}^{2}\) normal adhesion force ensures it can achieve more than 30 repeated adhesions, even on rough of the human body. Most significantly, the CDA electrode is waterproof, dustproof, and reusable, unlike conventional ECG electrodes. Based on its high-aspect-ratio micropillars, the gecko-like surface of the electrode is superhydrophobic (Figure 1b), which means dust on the electrode can simply be washed out without damaging its structure.
## 3 Results  
### Validation and Grid Independence Study  
To validate the _fsiFoam_ solver used in this paper, the motion of the plate is simulated for different non-dimensional frequencies \(f^{*}\) and compared with the experimental results of [PERSON] et al. [25]. The results are compared in terms of trailing edge amplitude and phase lag between the leading and trailing edge motion. The motion of the plate's trailing edge can be expressed by its vertical displacement \(z_{t}\) as;  
\[z_{t}=A_{t}\sin(2\pi\phi_{t}),\ \ \ \phi_{t}=\phi-\phi_{\text{lag}}, \tag{2}\]  
where \(\phi_{\text{lag}}\) is a phase lag between the motion of trailing edge and leading edge of the plate and \(A_{t}\) is the amplitude of trailing edge's motion.  
Simulations are conducted for the non-dimensional frequency in the range of \(0.93<f^{*}<\)1.47, which is around the range of resonant frequency. The three flapping frequencies with the increment of 0.4 Hz, \(f=1.4\), 1.8 and 2.2 Hz, are selected for this non-dimensional frequency range and the amplitude of motion \(A_{t}=3\) mm is kept constant for all cases. The computed trailing edge amplitudes \(A_{t}\) and phase lags are compared with the corresponding experimental data as shown in Figure 3. A reasonable agreement is observed in the trend of amplitude and phase lag despite some discrepancies in the magnitude. The resonance frequency of this plate is observed to be \(f_{n}=1.5\) Hz, which is close to the value of \(f_{n}\) obtained in the experiments of [PERSON] et al. [25]. The experimental and numerical results are also presented in Tables 1 and 2.  
of \(f=1.4\) Hz. The effects of mesh resolution on the displacement of trailing edge are shown in Figure 4. The results show that the difference in the peak value of trailing edge displacement is about 2.65% between the medium and fine mesh. Therefore, the medium mesh is adopted for all simulations in the present study.
# Energy-Efficient Wireless Communication Strategy for Precision Agriculture Irrigation Control
###### Abstract  
In smart farming, precision agriculture irrigation is essential to reduce water consumption and produce higher crop yields. Closed-loop irrigation based on soil moisture measurements has demonstrated the capability to achieve a considerable amount of water savings while growing healthy crops. Automated irrigation systems are typically implemented over wireless sensor networks, where the sensing devices are battery-powered, and thus they have to manage energy constraints by implementing efficient communication schemas. Self-triggered control is an aperiodic sampling strategy capable of reducing the number of networked messages compared to traditional periodical sampling. In this paper, we propose an energy-efficient communication strategy for closed-loop control irrigation, implemented over a wireless sensor network, where event-driven soil moisture measurements are conducted by the sensing devices only when needed. Thereby, the self-triggered algorithm estimates the occurrence of the next sampling period based on the process dynamics. The proposed strategy was evaluated in a pecan crop field and compared with periodical sampling implementations. The experimental results show that the proposed adaptive sampling rate technique decreased the number of communication messages more than 85% and reduced power consumption up to 20%, while still accomplishing the system control objectives in terms of the irrigation efficiency and water consumption.  
energy-efficient communication; wireless sensor network; self-triggered control; precision agriculture +
Footnote †: journal: Physics Letters A  
1
## 2 Transmission Line Ice Cover Monitoring System
### LDKA-NET Algorithm Model  
The detection of insulators on transmission lines presents several challenges: first, the background is complicated, as transmission lines are often located in remote and complex environments, making it difficult for existing visual detection algorithms to distinguish foreground from background [9]; and second, the target is small, as monitoring devices are often installed atop transmission towers, making it difficult to recognize the target from a distance due to its small size [10]. To address these difficulties, this paper makes targeted improvements to the baseline model, the overall structure of which is shown in Figure 2.  
As shown in Figure 2, the algorithm presented in this paper consists of four parts: image preprocessing, Backbone, Neck (feature extraction network), and Head (prediction  
Figure 1: Transmission lines ice cover monitoring system diagram.  
Figure 2: LDKA-NET network structure diagram.  
network), with the red boxes indicating the improved modules. The Wide-Domain Vision Convolution Network (WFVC Net) employs larger convolution kernels to enhance the network's perception and generalization capabilities, making it better suited for the complexities of transmission line scenarios. Secondly, the multi-dimensional dynamic convolution significantly improves the accuracy of the CNN by learning linear combinations of multiple convolution kernels and weighted attention related to their input, demonstrating higher parameter efficiency and feature extraction capability. Finally, through the Expectation Maximization Dynamic Convolutional Attention (EM-DCA) mechanism, which aims to retain information on each channel while reducing computational overhead, some channels are reshaped into batch dimensions, and the channel dimensions are grouped into multiple sub-features, ensuring that spatial semantic features are evenly distributed within each feature group.
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 3 Multi-Objective Optimization Deployment Model
### Network Connectivity Description  
WSNs are task-oriented networks that revolve around data and sense the monitoring area, with nodes transmitting data through single-hop or multi-hop wireless communication. For the sake of computation, it is assumed that \(2R=R_{c}\). A directed graph adjacency matrix \(M\) is established, which is used to store the connectivity status between any two nodes. Connectivity is determined based on Formula (10), where \(M[i][j]=1\) indicates that node \(i\) can transmit information to node \(j\). Subsequently, the overall network connectivity is determined using the matrix \(S_{v}\) calculated according to Formula (11)  
\[M[i][j]=\left\{\begin{array}{ll}1&if\quad d\big{(}s_{i},s_{j}\big{)}\leq R_ {i}^{c}\\ 0&\quad\text{otherwise}\end{array}\right. \tag{10}\]  
\[S_{v}=M+M^{2}+M^{3}+\cdots M^{n-1} \tag{11}\]  
where \(n\) is the number of sensor nodes, if all elements in the vector \(S_{v}\) are 1, it indicates that the network is connected; otherwise, it indicates that the network is disconnected. On the basis of WSNs connectivity, the Kruskal algorithm mentioned in [31] is used to generate the Minimum spanning tree.  
Figure 2: obstacle boundary handling.
## 3 Testing Procedures
### Dynamic Setup
#### 3.2.2 Route  
As observed in Figure 4, a 50 km route was selected to perform the averages and compare the two antenna technologies in as many environments as possible. The route was divided into three sections: highway, city, and forest.  
A highway is an environment with open spaces, without buildings or trees that can interfere with the signal. The city environment runs between wide and narrow streets, and finally, the forest, with trees and irregular topography with mountains.
# Compensation Control Strategy for Orbital Pursuit-Evasion Problem with Imperfect Information
## 3 Construction of Guaranteed Cost Strategy with Perfect Information
### Time-To-Go Calculation  
For the sake of brevity, define the range \(\theta^{P}=\omega t_{go}^{P}\), where \(\omega\) is the orbital angular velocity of the origin of the LVLH coordinate system. The equation \(H_{1}\Big{(}t_{0},t_{f}\Big{)}=m\) is converted to  
\[\|\mathbf{Z}\Big{(}\theta^{P}\Big{)}\|+\frac{1}{\omega}\int_{0}^{\theta^{P}}-\rho _{P}\lambda_{\min}^{1/2}(s)+\rho_{E}\lambda_{\max}^{1/2}(s)\ \mathrm{d}s\ =\ m \tag{17}\]  
Similarly, define \(\theta^{E}=\omega t_{go}^{E}\). The equation \(H_{2}\Big{(}t_{0},t_{f}\Big{)}=m\) is converted to  
\[\|\mathbf{Z}\Big{(}\theta^{E}\Big{)}\|+\frac{1}{\omega}\int_{0}^{\theta^{E}}-\rho _{P}\lambda_{\max}^{1/2}(s)+\rho_{E}\lambda_{\min}^{1/2}(s)\ \mathrm{d}s\ =\ m \tag{18}\]  
We can get the appropriate time-to-go by solving Equations (17) and (18). However, the analytic solution of the equations is difficult to obtain since the equations are nonlinear. Thus, we use the numerical method to solve the equations. The iterative method is an effective numerical method for solving nonlinear equations. However, most iterative methods (such as [PERSON]'s method) require appropriate initial guesses. If the initial value is not selected properly, it is difficult to obtain the solution of the nonlinear equation. Unfortunately, the appropriate initial values for Equations (17) and (18) are difficult to find. Define functions \(F_{P}\) and \(F_{E}\) as follows:  
\[\left\{\begin{array}{l}F_{P}\Big{(}\theta^{P}\Big{)}=\|Z\big{(}\theta^{P} \big{)}\|+\frac{1}{\omega}\int_{0}^{\theta^{P}}-\rho_{P}\lambda_{\min}^{1/2}( s)+\rho_{E}\lambda_{\max}^{1/2}(s)\ \mathrm{d}s-m\\ F_{E}\Big{(}\theta^{E}\Big{)}=\|Z\big{(}\theta^{E}\big{)}\|+\frac{1}{\omega} \int_{0}^{\theta^{E}}-\rho_{P}\lambda_{\max}^{1/2}(s)+\rho_{E}\lambda_{\min}^{ 1/2}(s)\ \mathrm{d}s-m\end{array}\right.. \tag{19}\]  
Figure 2 shows the shapes of functions \(F_{P}\) and \(F_{E}\) in Equation (19) with different current states. The different current relative states for four cases are shown in Table 1. The functions' shapes are highly dependent on the current states, and their uncertainty makes it difficult to find an appropriate initial value. As noted by [PERSON] [28], the homotopy method is good at global convergence. Thus, using the homotopy method to calculate the time-to-go can avoid the convergence problem caused by the improper selection of initial value. However, the homotopy method is not good at convergence speed. Thus, we propose a hybrid method combining the homotopy method and [PERSON]'s method to calculate the time-to-go.
applied sciences Article  
Passive TDOA Emitter Localization Using Fast Hyperbolic Hough Transform  
[PERSON] 1,*(c) and [PERSON] 2  
For position fixing (stage 2), the proposed solutions include iterative techniques [24; 25], closed-form solutions [26; 27; 28; 29; 30], particle filters [31; 32], consistency functions [14; 33], and Hough transform (HT)-based solutions [31; 34]. When outlier measurements are present, the location estimate will be biased, unless the outliers are removed [35; 36]. The voting approach of HT, however, has the advantage of implicitly filtering out outliers; this makes the location estimates robust against bad measurements resulting from sensor faults or non-line-of-sight measurements.  
In this paper, a novel HT-based TDOA emitter localization method is proposed. The location estimate is found at the maximum of the hyperbolic HT (HHT), specially tailored for hyperbolic localization. The proposed method involves a novel hierarchical search to find the maximum of the HHT on a predefined grid. The novelties of the proposed search methods are the following:  
* The speed of the search is higher than those of the earlier HHT solutions [31];
* The search algorithm is guaranteed to find the global maximum on the predefined grid.  
The outline of the paper is as follows: In Section 2, related work is reviewed. In Section 3, the proposed fast hyperbolic Hough transform is introduced. In Section 4, the performance of the proposed method is compared to that of earlier solutions through simulation examples and real measurements. Section 5 concludes the paper.
## Materials and Methods
### Preparation of Cellulose-Polytthiophene Materials
#### 2.2.2 Synthesis of Monomers and Polymerization  
PEDOT:Tosylate and PEDOT:Triflate. Synthesis of PEDOT using Fe(Tos)\({}_{3}\) was synthesized adapting a reported procedure [22]. A mixture of Fe(Tos)\({}_{3}\) (20.47 g) and imidazole (2.14 g) in 55 mL of methanol was added drop by drop to a solution of 2.2 mL of EDOT dissolved in 55 mL of methanol. The polymerization was performed at 50 \({}^{\circ}\)C after 25 min. The product was washed with methanol and then dried under vacuum overnight (99%, 1.9 g). Synthesis of PEDOT:Triflate was carried out following a reported procedure [23] by preparing a solution of 20 wt.% of the surfactant Pluronic(r) F-127 in ethanol (solution 1) and a solution of 20 wt.% of the surfactant in NMP (solution 2). To a mixture of 5.7 g of solution 1 and 0.6 g of solution 2 (0.7 wt.% of surfactant), 1 g of Fe(Trif)\({}_{3}\) was added, and the resulting solution was sonicated during 3 h. Throughout this time, the solution changed from orange to dark red. Subsequently, 0.15 mL of EDOT (0.2 g) was added dropwise into the solution in the ultrasonic agitator with a molar relationship monomer:oxidant of 1:1.27 to obtain PEDOT:Triflate.  
Sonication continued for one more minute, and the dark solution was put in a crystallizer at 70 \({}^{\circ}\)C. The product was thoroughly washed with ethanol to obtain the final black product (99%, 0.584 g).  
PProDOT. Synthesis of 2,2-dibutyl-3,4-propylenedioxithiophene (DBProDOT) was performed as follows based on reported literature [24; 25]. A total of 0.25 g of synthetized monomer was polymerized using a 40 wt.% solution of Fe(Tos)\({}_{3}\) (0.6 mol\(\cdot\)L\({}^{-1}\)) in ethanol with a molar relationship monomer-oxidant of 1:2 to obtain PDBProDOT in 10 min. The polymer was precipitated into methanol and extracted into a Soxhlet thimble for 24 h with methanol and then with hexanes. A subsequent Soxhlet extraction with DCM was used to dissolve the polymer from the thimble and the solvent was removed under vacuum. Poly(3,3-Dibutyl-3,4-dihydro-2H-thieno [3,4-b][1,4]dioxepine) [PDBProDOT]. Yield: 0.29 g of deep purple solid (99%). \({}^{1}\)H-NMR (400 MHz, CDCl\({}_{3}\)): \(\delta\) 7.77 (s, Hterminal), 3.85 (s, 4H), \(1.64-1.0\) (m, 12H), \(0.99-0.69\) (m, 6H). GPC analysis: Mn: 4800 g\(\cdot\)mol\({}^{-1}\); Mw: 15,360 g\(\cdot\)mol\({}^{-1}\); PDI: 3.2.