esa-sceva/satcom-chunk-collection huggingface dataset:
# HCLE: Hybrid Clustering Approach for Extending WBAN Lifetime
## 3 Preliminaries
### Performance Metrics
#### 3.4.6 Communication Costs  
Communication costs encompass both overhead and payload costs. Payload represents the data transmitted between the sensors and the sink or among different nodes. When the payload size increases, the associated payload cost also rises, demanding more energy for transmission than smaller packets. The packet size is typically predeterminedduring network initialization, making it challenging to exert direct control over it. Although compression techniques can be used to reduce the payload size, their practicality is limited due to the resource-constrained nature of sensor nodes. Overhead, which includes control information and protocol-related data, can consume a significant portion of the available energy, leaving less energy for actual data transmission and sensing tasks. Minimizing overhead is essential when designing efficient WBAN communication protocols, as it does not directly contribute to the end application.
# Electrically Tunable Propagation Properties of the Liquid Crystal-Filled Terahertz Fiber
## 3 Results and Discussion
### Dispersion Properties of the Designed THz LC-MOF  
For THz systems that use broadband-pulsed THz sources, it is necessary to adopt low group velocity-dispersion waveguides in order to prevent the degradation of the pulse shape. In the THz region, group velocity dispersion (GVD) could be quantified as follows:  
\[\beta_{2}=\frac{1}{2\pi c}\left(2\frac{\partial n_{\text{eff}}}{\partial f}+f \frac{\partial^{2}n_{\text{eff}}}{\partial f^{2}}\right) \tag{2}\]  
in ps/THz/cm. The GVD curves of the x- and the y-polarized fundamental modes are shown in Figure 6. It can be seen that the dispersion parameters change rapidly with the guided frequency approaching the bandgap edge, and the x-polarized fundamental mode has zero dispersion around normalized frequency F = 1.75 and F = 3 within the first and second x-polarization PBGs, respectively. Flattened near-zero GVD of 0 \(\pm\) 1 ps/THz/cm was obtained for the y-polarized fundamental mode within a broad normalized frequency range of 2.1-4.6.  
Figure 5: Confinement losses for degenerate fundamental mode HE\({}_{11}\) (without an external electric field across the fiber) and polarized fundamental modes HE\({}_{11\text{x}}\) and HE\({}_{11\text{y}}\) (with an external electric field applied along the x-axis).  
Figure 6: Dispersion parameter as a function of normalized frequency.
###### Abstract  
As urban traffic density and congestion increase, effective urban traffic management becomes increasingly challenging, negatively impacting travel times and the overall efficiency of transportation systems. In this paper, a hierarchical Stackelberg model is presented to address both priority for emergency vehicles (EVs) and fairness for other vehicles. This model involves the Traffic Management Center (TMC) as the top-level authority, with emergency vehicles as the first-level leaders and regular vehicles (RVs) as the second-level followers. The multilevel decision-making structure enables real-time adjustments to prioritize critical traffic and ensure equitable treatment for regular traffic. Simulations were conducted under various traffic scenarios, including normal conditions, emergency vehicle priority, and peak traffic congestion. According to the results, the hierarchical Stackelberg model outperforms traditional models in terms of reducing average travel time, waiting time, and congestion. The model also incorporates fairness metrics such as Gini coefficients and skewness to ensure that regular vehicles are not disproportionately affected by emergency vehicle priority. According to these findings, the hierarchical Stackelberg model improves both traffic efficiency and fairness in complex urban environments, positioning it as a promising solution.  
hierarchical Stackelberg model; fairness; multi-agent systems; Optimizing Autonomous Traffic Management
## 4 Simulation Results and Discussion
### Sensitivity Analysis of Input Power  
Component tolerances and sensitivities play an important role in practical implementations. For example, variations in fiber length and dispersion characteristics can affect system performance. Similarly, fluctuations in signal and pump powers can also influence the efficiency of nonlinear processes. Here, we take the variation in signal power as an example to discuss the system's sensitivity to changes in signal power. To evaluate the influence of input power on system performance, we conduct simulations under different average powers of input data while keeping other parameters fixed. Similarly, the output performance is evaluated when recognizing 4-bit bit-flip BPSK targets in the input signal with frequencies of 192.8 THz and 193.4 THz, respectively. Both the input data and target remain the same as those specified in Section 4.1.  
Figure 13 shows the simulation results when the average power of input data with frequencies of 192.8 THz and 193.4 THz varies from 0.1 mW to 0.9 mW. It should be noted that in Figure 13, the simulation output aligns with those in Figure 6 only when the input power is 0.5 mW, as the input power in Figure 6 is also set to 0.5 mW. However, when the input power deviates from 0.5 mW, the power of the logic \"1\" in the output exhibits a significant decrease. Specifically, when the input power is 0.5 mW, the power of the logic \"1\" in the output is around 6 mW. However, in Figure 13a, when the input power is 0.3 mW or 0.7 mW, the power of the logic \"1\" in the output drops from around 6 mW to approximately 3.5 mW. In Figure 13b, when the input power is 0.3 mW or 0.7 mW, the power of the logic \"1\" in the output decreases from around 6 mW to approximately 1.5 mW. The simulation results indicate that when the input power is below 0.3 mW, the XPM-induced phase shift is insufficient to reach \(\pi\); when the input power is above 0.7 mW, the XPM-induced phase shift exceeds \(\pi\). Both scenarios degrade the coherent interference results after the optical coupler. If we set the output ER of 9 dB as the threshold, the system can achieve correct results when the output ER exceeds 9 dB; otherwise, it may fail to recognize the results accurately. Then, according to the simulation results, the proposed system still achieves correct results within the input power range of 0.3-0.7 mW (i.e., \(\pm\)0.2 mW deviation from the nominal 0.5 mW). Thus, we conclude that the proposed system can tolerate input power fluctuations of approximately \(\pm\)0.2 mW.
# A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion
## 2 Methods for Ground-Motion Simulation
### Optimization of Coefficients Using NSGA-II  
A genetic algorithm is applied because it is necessary to determine the combination coefficients in Equation (3). The simulating method employed in this procedure takes into account both the matching of the target response spectrum and the target duration, which are obtained from the ground-motion-duration model. This approach differs from the traditional method that only considers matching the target response spectrum based on the prediction equation of the ground-motion-response spectrum. Since it involves multiple constraints and requires solving multiple equations, the conventional single-objective optimization algorithm is no longer applicable, and the computational effort becomes significant. Therefore, the elitist nondominated sorting genetic algorithm NSGA-II is utilized [42]. This algorithm effectively balances the relationship between each objective function, resulting in an optimal solution set that maximizes the performance of each subobjective function. This approach differs from single-objective optimization and offers a unique solution. Consequently, the solution to the multi-objective optimization problem comprises a set of equilibrium solution sets.  
The algorithm uses fast dominant sorting to minimize the complexity of the algorithm and volume of operations. Furthermore, it replaces the fitness-sharing strategy via crowding and crowding comparison operators to perform a peer comparison of the results after fast sorting such that the individuals in the Pareto solution can be evenly extended to the whole domain, and the diversity of the population is preserved. Among them, the banded elite strategy expands the sample by combining the parent and child populations to ensure that better individuals can be retained.
# A Siphon Drainage Method for Consolidation of Soft Soil Foundation
## 4 Field Test of Siphon Drainage in Soft Soil Consolidation  
### Engineering Geological Condition  
The field test is located in the reclamation area in Zhoushan, which has been blown into the land. The quaternary soil layers in the reclamation area are mainly muddy silty clay and muddy silty clay mixed with silty fine sand. The soft soil layer is relatively thick, with an average depth of more than 30 m. The soil has high water content, large void ratio, high compression properties, low strength, and low permeability, which all belong to under-consolidated soil with extremely poor engineering properties.  
In order to have a better description of the engineering geological conditions of the test area, a supplementary investigation was carried out on the siphon drainage test area. According to engineering geological drilling, the main formations are distributed from the top to bottom: flushing soil, grayish-yellow silty clay, gray silty clay, silty clay, and strongly weathered tuff. The details are as follows:  
Flush fill (Q4): artificial fill, gray, grayish yellow, fluid plastic. Contains a small amount of shell fragments and partially contains gravel. The main components are muddy silty clay and muddy, formed by later filling, and the soil quality is uneven. The thickness of the soil layer is 8.7-10.2 m.  
Muddy silty clay (Q4): marine sedimentary layer, gray-yellow, fluid plastic, saturated, rich in various organic matter such as shell fragments and animal carrion, with rancid smell, high dry strength, high toughness, smooth cut surface, uneven soil quality. The thickness of the soil layer is 6.1-7.3 m.  
Muddy silty clay (Q4): marine sedimentary layer, gray, fluid plastic, saturated, containing a small amount of shell fragments and saprophytes, with a fishy smell, partially sandwiched with a thin layer of silt, high dry strength, high toughness, smooth cut surface, uneven soil quality. The thickness of the soil layer is 9.4-11 m.  
Silty clay (Q3): alluvial layer, gray-yellow, brown-yellow, containing a small amount of iron and manganese spots and oxide nodules, partly sandwiched with silt agglomerates, high dry strength, uneven soil quality. The thickness of the soil layer is 0-2.8 m.  
Strongly weathered tuff (J3): gray-yellow, brown-yellow, tuffaceous structure, massive structure, main mineral components are quartz, feldspar, etc. The original rock structure has suffered some damage. Joints and fissures are well developed, and the fissure surface is filled with ferromganese, oxidation, and other secondary minerals. The core is sandy and fragmented.  
The main physical parameters based on the borehole geotechnical tests are shown in Table 1. It can be seen that the permeability coefficient of the site soil is between \(1\times 10^{-7}\) cm/s and 2 \(\times\) 10\({}^{-7}\) cm/s. The permeability coefficient of a few places can reach \(5\times 10^{-7}\) cm/s.
# A Review of Image Inpainting Methods Based on Deep Learning
## 4 Deep-Learning-Based Image Inpainting Algorithm
### Computational Component Optimization
#### 4.2.1 Convolution Method  
Compared to traditional image inpainting algorithms, a major advantage of deep-learning-based image inpainting algorithms is the semantic understanding of images. [PERSON] et al. (2014) made significant contributions to the field of convolutional neural networks in computer vision. The convolution operation is a key operation when neural networks process images, allowing the network to develop its own understanding of related pixel values in an image. In order to improve the model's cognitive ability, enhance the abstraction of features, enhance cognitive logic, and improve the model's semantic understanding ability, some work has improved the convolution calculation method for image inpainting tasks.
# RM-Line: A Ray-Model-Based Straight-Line Extraction Method for the Grid Map of Mobile Robot
## 1 Introduction
### The Hough Transform Approach  
The Hough transform (HT) approach accumulate edges over the entire image into a histogram of potential line positions and orientations [10]. Accuracy can be improved by modeling uncertainty in local edges and propagating that uncertainty to the Hough map [11].  
HT uses global information, allowing it to get a more comprehensive line, but it has some problems in identifying the endpoints of the line segment. Some methods look for the largest chain connecting or near connecting edges by analyzing the obtained straight lines [12, 13]. There has also been some work to detect endpoints by analyzing features near peaks in the Hough map [14, 15, 16, 17, 18]. The main disadvantage of these methods is that, when there are multiple line segments on the line, only one segment will be detected.  
On the other hand, the HT method is computationally intensive and prone to false positives in complex regions with dense edge responses [19]. To overcome the problem of false positives, the progressive probabilistic Hough transform (PPHT) [20] maps pixels to a parameter space with greater probability, which reduces false positives to a certain extent. [PERSON] et al. introduced orthogonal image scanning to reduce the computational complexity of HT [21]. [PERSON] proposed the Markov chain marginal line segment detector. In the first stage, straight lines are detected using the global probabilistic Hough method. In thesecond stage, the points on the line segments are modeled as Markov chains, and then the optimal probabilities are calculated using standard dynamic programming algorithms. This method improves the accuracy and completeness of line segments [22]. However, these methods have not yet completely solved the problem of HT's large computational load and easy false positives in complex regions.
## 6 Challenges and Future Trends in LEV Charging
### Fast Charging and High-Current Converter Topologies  
One way to achieve fast charging times is to use high current DC-DC converter topologies. For high current applications, converters with low/high step-up/step-down voltage gain range have been reported numerous times in recent years. One straightforward way to achieve this goal is to use isolated topologies, which leverage the transformer turns ratio to improve the gain range. Works such as [89; 90; 91] use similar techniques for low voltage high current applications. The authors of [89] used a phase-shifted full bridge converter followed by a current doubler rectifier to achieve this objective. On the other hand, [90] demonstrated a low voltage soft-switched converter which utilized series-connected half-bridge cells on the primary side--operated in an interleaved manner--and parallel-connected single-diode rectifiers on the secondary side. However, one significant limitation of such implementations is the effect of leakage inductance stemming from the high frequency transformer. This inductance can contribute to voltage spikes across the switches, resulting in damage. To overcome this, additional circuitry--active and passive clamping circuits--may be used, which may increase the cost. Owing to this glaring disadvantage of isolated converter topologies, non-isolated DC-DC converters have also been widely reported for high-current applications [92; 93; 94; 95]. The authors of [92] demonstrated high gain of step-down DC-DC converter for high current applications such as voltage regulators for microprocessors, power supply for telecommunications, etc. To achieve this high gain at moderate duty cycle, the authors used transfer capacitors and a built-in transformer in this topology. The sizes of these inductances are further reduced due to the use of interleaving technique for two main switches and two synchronous switches. Furthermore, the extent of coupling of inductors also influences the current ripples. The step-down gain of this converter is augmented on account of the transformer turns ratio. While the peak efficiency of the proposed topology is observed to be around 95%, there is room for improvement by reduction in freewheeling currents, which may lead to duty loss. [PERSON] et al. [94] and [PERSON] et al. [95] both use the interleaving technique in their presented topologies. While [94] uses auxiliary circuits with active switch to achieve soft switching, [95] achieves the same result without the use of active switches. In the case of [92; 95], high gain on account of turns ratio of coupled inductors, in contrast to [94] is achieved. Other wide-gain topologies have been reported by [96; 97], which also achieve inherent current sharing among switches. Converters with quadratic gains are also viable candidates for high current applications [73]. Another way of achieving fast charging in LEVs is through battery swapping. This practice involves the removal of discharged battery and its replacement with a fully charged
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 5 Energy and Delay Aware Data Aggregation in RPL
### Data Reconstruction Using Matching Pursuit Algorithm  
Data reconstruction is a recovery technique, which is used to recover the compressed data over the network nodes. The DODAG root receives the compressed data in the form of observation matrix from M paths. Also, the DODAG root recovers the sparse matrix accurately using matching pursuit algorithm. Finally, the DODAG root rebuilds the compressed data and it matches with original data [36].  
In matching pursuit algorithm, the EDADA-RPL recovers the data d from the input \(y=r\times d\). The residual error (\(r_{error}\)) is initialized as y. The sparse matrix in each parent node PN is generated and it is given in Equation (13)  
\[PN=\overline{d_{i-1}}+r^{T}\times r_{error} \tag{13}\]  
where \(r^{T}\) indicates the random number in each parent node (PN).  
The data recovery process will be continued until both original and recovered data are the same. The pseudo-code of the matching pursuit algorithm is given in Algorithm 3.  
```
Input: compressed data \(y=r\times d\) Output: Reconstructed data \(\overline{d}\)
1:Initialize: \(\overline{d_{0}}=0\), \(r_{error}=0\) and i = 0
2:iteration:  i = i + 1
3: \(\text{P}N=\overline{d_{i-1}}+r^{T}\times r_{error}\)
5: \(\overline{d_{i}}=argmax_{i=[1,2,\_N]}|PN|\)
6:Compute residual error \(r_{error}=y-r\times\overline{d_{i}}\)
7:If there is no error in \(r_{error}\)then
8:return d
9:End
10:End
```  
**Algorithm 3**Data Reconstruction Using Matching Pursuit Algorithm  
We perform the reconstruction at the DODAG root and receive the original data \(d\) of N nodes. Finally, we compute the mean square error and its value is very low. So, it proves that EDADA-RPL provides less data loss when compared to other routing protocols.
## 2 Methods and Model
### Model
#### 2.2.2 Modeling of Red Team Defensive Agents
**(B)** Relationship of the rain rate and specific attenuation in horizontal polarization. **(C)** Relationship of the air and specific attenuation. Figure 2C shows the relationship between specific attenuation and dry and water vapor at different frequencies. Dry air has a greater impact on specific attenuation than water vapor, and specific attenuation increases with frequency. At 3 GHz, the specific attenuation due to dry air reaches its maximum value of only 0.0071 dB/km. By combining Figure 2, we can observe that the impact of atmospheric gases and rainfall on propagation attenuation is minimal in the frequency range of 30 MHz to 3000 MHz. Since we are primarily focused on UAV communication with relatively short transmission distances, in order to reduce algorithm complexity, we adopt the method suggested in ITU-R P.525 Recommendation [28] to calculate \(L\). Among them, the calculation of \(L\) in ITU-R P.525 is shown in the following formula. \[L=32.4+20\cdot\lg(f)+20\cdot\lg(d) \tag{3}\]  
where,\(f\) is the frequency (in MHz); \(d\) is the distance (in km). (1) Determination of communication link interference  
Compare the interference received power calculated by the interference source (as returned by the game program) with the received power of the communication link to determine whether the link is interfered. (2) Punish the agent  
If the communication link is determined to be interfered, penalize the Red Team defensive agent. (3) Reward the agent  
If the communication link is determined to be free of interference, reward the Red Team defensive agent. (4) Calculate Regret Values and Update Regret and Strategy Values Based on CFR  
Update the regret values and frequency selection strategy for the candidate frequency points based on the current interference situation of the selected frequency band, using the CFR algorithm. This will serve as a reference for future frequency selection.
# An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning
## 4 The Proposed Model
### Sample Data Analysis  
In the smart home environment, the likely categories of privacy protection include scenarios of the user's going to work, coming back home, or going to sleep, etc. If fake data messages are only sent when the user is at home, to protect the user's behavior, then the behavior during the time at work, sleep, and other privacy-related periods, can still be leaked. We therefore design an algorithm that produces a transmission sequence which resembles that of a large amount of real data, to achieve better privacy protection. The adversary can only attain a general pattern of the people through monitoring the frequency of radio signals, thus protecting the privacy of individual residents. Consequently, we use FDR to describe the send frequency of the sensor network, which consists of one or more elements in the form [(start time, end time), average frequency].  
Taking into account the different habits among people in different regions, the provider should include sample data from different regions in the analysis, and the procedure is shown in Figure 4. Firstly, we should count the number, as well as the time of message sending per minute, for which the answer is denoted as \(F=\{(t_{1},f_{1}),(t_{2},f_{2}),(t_{1},f_{3})\ldots,(t_{n},f_{n})\}\). Then, we will use formula (1) to standardize \(f\), for which the answer is denoted as \(F^{*}=\left\{(t_{1},f_{1}^{\prime}),(t_{2},f_{2}^{\prime}),\ldots,(t_{n},f_{ n}^{\prime}))\right\}\). Secondly, we use the \(K\)-means clustering algorithm to cluster the elements in \(F^{\prime}\), producing a group of periods over the 24-hour interval. Finally, we calculate the average frequency of sending in each of the periods and update the average frequency of sending, as well as the period \(p\) to the FDR. After the analysis, we will ascertain the FDR that will be sent to the smart home devices in every family.  
\[f_{i}^{\prime}=\frac{f_{i}-\min(F)}{\max(F)-\min(F)} \tag{1}\]