esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Inversion and Results  
Multiple deep seismic reflection profiles (Figure 1) reveal travel-time information resembling Mesozoic volcanic arc structures [4; 7; 9; 10; 11; 15; 16; 17; 18; 19; 20], which, when converted using an average continental crustal velocity of 6.0 km/s [33], provides depth constraints for this study. During the inversion process, the parameters were selected by integrating depth feature constraints to ensure that the magnetic source depth of the inversion in the region was as consistent as possible with the depth of the Mesozoic volcanic arc structure revealed by the multiple deep seismic reflection data. Finally, by selecting the parameters for the mixed L\({}_{\rm p}\) norm regularization (p = 0, q = 2, v = 9, and \(\alpha\)s = 0.3), a 3-D susceptibility model of the crust in the northern SCS was generated. The model features a lateral grid spacing of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) and a depth range of 0-40 km, with a depth step of 2 km. The results of the inversion are shown in Figure 7.  
The model demonstrates that the planar distribution of the magnetic sources in the northern SCS generally aligns with the high-value regions of the RTP aeromagnetic anomalies, representing an NE-trending belt of high magnetism with a primary depth concentration between 20 and 28 km. Additionally, the model shows isolated magnetic sources scattered to the north of the belt, consistent with the speculation by [PERSON] et al. (2017) [34] regarding the intraplate magmatic sources in the northern SCS. The residuals between the real and calculated anomalies are displayed in Figure 8, where the relatively small residual values suggest a strong fit between the model's calculated anomalies and the real RTP aeromagnetic anomalies data.  
\begin{table}
\begin{tabular}{c c c} \hline
**Geological Unit** & **Magnetic Susceptibility (\(\times 10^{-5}\) SI)** & **Magnetic Characteristics** \\ \hline Seawater Layer & 0 & Non-magnetism \\ Sedimentary Strata & 0–25 & Low Magnetism or Non-magnetism \\ Intermediate to Acidic Intrusions & 200–4000 & Moderately High magnetism \\ Mafic and Ultramafic Intrusions & 2000–8000 & High magnetism \\ Crust & 0 & Non-magnetism \\ \hline \end{tabular}
\end{table}
Table 1: Physical–property table for geological units in the study area.
# Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks
## 4 Lessons Learned
### Implicit Feedback  
Relying on frame loss ratio and probing rates different than the selected one are the common approaches of the implicit feedback algorithms. Probing random rates should be done very carefully when considering the duration of probing, because choosing a rate that is higher than the selected one may result in many frame losses. Random probing results in slow convergence of the algorithm towards the optimal rate.  
Implicit feedback algorithms gave promising results in a mobile collision-free environment, but did not prove to have a better performance than the other categories. This can be improved by trying to detect the degree of mobility and studying the direct effects of mobility on the result of frame transmissions. However, in a dense environment, RAAs should be able to estimate the reason of frame loss and, preferably, should rely on different metrics, which will lead to a more accurate rate decision. Combined with a method for rate changing that ensures increasing the rate to achieve the maximum throughput when channel conditions improve and decreasing the rate when channel conditions deteriorate. This is not an easy task, in order to achieve this, an algorithm should be able to have a real-time estimation of the conditions, such as SINR.
## 3 Controller Design Approach
### Fuzzy Logic Control  
The FLC technique becomes a good replacement for the conventional control methods. It can perform efficiently when the operating conditions change rapidly. Hence, these features make it very effective for power system applications, especially with uncertain, incomplete, and non-linear data from wind turbine systems [44; 45]. The FLC consists of three steps: fuzzification, knowledge base creation, and defuzzification.  
1. Fuzzification is the process of transforming the crisp input signals into fuzzy set variables as FLC cannot handle these input signals.
2. The knowledge base is the heart of fuzzy system, as it determines how fuzzy logic operations are performed. It consists of fuzzy IF-THEN rules. The database contains the membership functions of fuzzy subsets. A fuzzy rule includes fuzzy variables and the fuzzy subset described by membership function. Triangular, trapezoid, and Gaussian functions are the more standard membership functions used in FLC.  
Figure 1: Single-line diagram of the nine-bus system.  
Defuzzification is used to convert the fuzzy output to a crisp value using membership functions like the ones utilized in the process of fuzzification. There are some common defuzzification methods such as center of sums, mean- max, weighted average, and center of gravity [46; 47].
## 5 Implementation and Comparison  
### IEEE CEC-2022 Benchmark Test Functions Description  
The CEC2022 benchmark suite, as detailed in Table A1[31], was used to evaluate our evolutionary computation algorithms. The benchmarks include unimodal, multimodal, hybrid, and composition functions, each selected to challenge different aspects of algorithm performance. Unimodal functions test straightforward convergence, while multimodal functions assess FVMDE's ability to navigate complex landscapes with multiple optima. Hybrid and composition functions combine these challenges, simulating complex real-world problems. All functions were tested within a uniform domain of \([-100,100]\) across 30 dimensions, ensuring consistency and comparability in our evaluations. An illustration of sample objective space from CEC2022 benchmark functions (F1-F4) is shown in Figure 1.  
Figure 1: Illustration of sample objective space from CEC2022 Benchmark Functions (F1–F4).
# Project Robust Scheduling Based on the Scattered Buffer Technology
## 3 Literature Review of the Scattered Buffer Scheduling Algorithms  
### Refdf  
RFDFF in resource flow network was first proposed by [PERSON] et al. This algorithm uses forward scheduling method and backward scheduling method to generate the forward schedule and the backward schedule, respectively, and then calculates the start time of each activity in the buffer schedule based on the start times of activities in the forward schedule. This algorithm considers the float of activities and the activity-dependent float factor in the schedule, as well as the scheduled limited resource and the resource conflicts. The resource flow network \(G^{\prime}\) is added to prevent resource conflicts. The project activity network \(G\) and the resource flow network \(G^{\prime}\) are considered in the calculation of the activity-dependent float factor. The start time \(s_{j}(S)\) of each activity in the buffer schedule is calculated according to the equation below.  
\[s_{j}(S)=s_{j}(B)+\alpha_{j}\times float(j) \tag{7}\]  
where \(s_{j}(S)\) is the start time of activity \(j\) in the buffer schedule, \(s_{j}(B)\) is the start time of activity \(j\) in the forward schedule, \(\alpha_{j}\) is activity-dependent float factor, \(\alpha_{j}=\beta_{j}/\left(\beta_{j}+\delta_{j}\right)\), \(\beta_{j}\) is the sum of the weight of the directly or transitively immediate predecessor activities, \(\delta_{j}\) is the sum of the weight of the directly or transitively immediate successors activities, and \(float(j)\) is the difference between the latest start time of the backward schedule and the earliest start time of the forward schedule. The backward schedule is obtained by the backward schedule mechanism under the deadline of the project. \(s_{j}(S)\) should be an integer.
## 3 Results and Discussion
### Mechanism Discussion
#### 3.4.2 Full Scan Spectra Analysis  
UV-vis absorption spectra of different substances were distinct under specific light sources. To investigate whether other substances were produced during the degradation of TCH, a full wavelength scan of the reaction solution at each stage of the degradation process was performed in this experiment. The positions of the absorption peaks in the full wavelength scan of the initial sample and the sample after the adsorption equilibrium remained basically the same, and the positions of the two absorption peaks were mainly at 275 nm and 357 nm, with only slightly reduced intensity (Figure 11a). This indicates that the adsorption process can only absorb contaminant molecules from the solution to the catalyst surface and does not change the original state of the material, which is one of the disadvantages of the adsorption treatment technique. The peak intensities at 275 nm and 357 nm gradually diminished over time with continued exposure to visible light. It was reported that these two positions corresponded to the chromogenic groups on the benzene ring. This indicates that the photocatalytic process destroys the stable benzene ring structure of TCH. In addition, the gradual increase in UV absorption intensity within the 200-240 nm range over time suggests that the photocatalytic degradation of TCH generates intermediate products. This enhanced absorption likely corresponds to the formation of additional substances, possibly due to oxidation reactions involving amino acids, which produce nitrogen oxides--a phenomenon previously documented in related studies [49]. Therefore, this experiment dynamically monitored the changes of TOC in the system to investigate the level of mineralization of organic pollutants by CdS/g-C\({}_{3}\)N\({}_{4}\)-2, as presented in Figure 11b. The TOC of the reaction system decreased by 5.06% after 60 min, and the TCH removal rate reached 84.52%. The reason is that the reactive groups in the system oxidized TCH into intermediate products instead of mineralizing TCH into small inorganic molecules, so the TOC level of the system did not change much. With the increase in time, the TOC decreased by nearly 20% after three hours. This outcome resulted from the continued oxidation of intermediate products by active species, ultimately breaking them down into smaller molecules, including carbon dioxide and inorganic salts. The relatively large molecular weight of tetracycline and the longer time required for mineralization is consistent with expectations.  
Figure 10: (**a**) Effect of CdS/g-C\({}_{3}\)N\({}_{4}\)-2 on the degradation of TCH after the addition of inhibitors and (**b**) kinetic rate constants under different conditions.
## 1 Introduction
As for these algorithms, the optimization performance depends on their convergence, efficiency, and local optimality prevention. Among them, the hybrid wolf optimization algorithm (HWOA) consisting of the GWOA and COA behaves well in global searching capability and convergence performance, which has been adopted to further improve the performance of control systems significantly [43]. In this paper, the speed controller designed by an advanced TSMC incorporated with HWOA is proposed to improve the comprehensive performance of the DITC system. The TSMC with finite converge time has already proven as an advanced intelligent technology. To enhance dynamic response and suppress torque ripple simultaneously, the TSMC based on advanced reaching law is designed in a speed controller for torque ripple reduction, fast dynamic response, and anti-disturbance ability. Moreover, the selection of complex control parameters has a vital influence on the performance. Thus, HWOA is introduced to tune the coefficients in the speed controller for satisfactory comprehensive performance. Under the premise that the effect is similar, avoiding the use of observers reduces the complexity compared with the method in [25]. Compared with [30], considering the prominent improvements in torque ripple suppression of the proposed method, a lesser sacrifice in dynamic response is acceptable. The remainder of this paper is organized as follows. Section 2 describes the fundamentals of SRM, including the structure and mathematic model. The conventional DITC strategy is analyzed. In Section 3, a speed controller using TSMC according to advanced reaching law is presented. Then, in Section 4, HWOA is utilized to optimize the tunable coefficients in the modified sliding mode speed controller. The experimental results are provided in Section 5, followed by the conclusion in Section 6.
## 2 Preliminaries
### The Discrete-Logarithm Problem  
In this section, we recall the _discrete-logarithm problem_ in a cyclic group \(G\) with order \(q\) and generator \(g\). Given a uniform element \(h\in G\), we are required to compute the unique value \(x\in Z_{q}\) such that \(g^{x}=h\). Such a value is called the _discrete logarithm of \(h\) with respect to \(g\)_ and is denoted by \(x=\log_{g}h\). Let \(\mathcal{G}\) be a _group generation algorithm_ that, on input \(1^{\tau}\), outputs a description of a cyclic group \(G\), its order \(q\), and a generator \(g\in G\). The discrete-logarithm problem is formalized in the next definition.  
Definition 1: Let \(1^{\tau}\) be a security parameter. The discrete-logarithm experiment for an adversary \(A\) is as follows:  
\[\begin{array}{l}\text{Experiment }\mathbf{DLog}_{A,G}(1^{\tau})\\ (G,q,g)\leftarrow\mathcal{G}(1^{\tau})\\ h\gets G\\ x\gets A(1^{\tau},G,q,g,h)\\ \textbf{if }g^{x}=h\\ \textbf{then }output\ 1\\ \textbf{else }output\ 0\end{array}\]  
The advantage of \(A\) is defined as  
\[\mathbf{Adv}_{A,\mathcal{G}}^{\mathbf{DLog}}(1^{\tau})=Pr[\mathbf{DLog}_{A, G}(1^{\tau})=1].\]  
The discrete-logarithm problem is hard relative to \(\mathcal{G}\) if, for each probabilistic polynomial time adversary \(A\), the advantage \(\mathbf{Adv}_{A,\mathcal{G}}^{\mathbf{DLog}}(1^{\tau})\) is negligible in \(\tau\).  
There are various classes of cyclic groups in which the discrete-logarithm problem is believed to be hard. In this paper, we considered cyclic groups \(G\), which are subgroups of \(Z_{p}^{*}\), where \(p\) is a prime and the order of the group is a prime \(q\) such that \(q\) divides \(p-1\).
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
###### Abstract  
Wi-Fi is a popular wireless technology and is continuously extended to keep pace with requirements such as high throughput, real-time communication, dense networks, or resource and energy efficiency. The IEEE 802.11 ax standard, also known as Wi-Fi 6, promises to provide data rates of up to almost 10 Gb/s, lower energy consumption, and higher reliability. Its capabilities go far beyond Wi-Fi 5 (802.11 ac) and novel technical concepts have been introduced for this purpose. As such, the Wi-Fi 6 standard includes Multi-User Orthogonal Frequency Division Multiple Access (MU OFDMA), Multi-User Multiple-Input Multiple-Output (MU MIMO), new mechanisms for Spatial Reuse (SR), new mechanisms for power saving, higher-order modulation, and additional minor improvements. In this paper, we provide a survey of Wi-Fi 6. Initially, we provide a compact technological summary of Wi-Fi 5 and its predecessors. Then, we discuss the potential application domains of Wi-Fi 6, which are enabled through its novel features. Subsequently, we explain these features and review the related works in these areas. Finally, performance evaluation tools for Wi-Fi 6 and future roadmaps are discussed.  
IEEE 802.11 ax; Wi-Fi 6; spatial reuse; OFDMA; MU-MIMO; target wake time (TWT); 1024-QAM +
Footnote †: journal: _Future Internet_  
The IEEE 802.11 ax (Wi-Fi 6) task group started to investigate and design the next generation of WLAN appropriate for dense networks and real-time communications in 2014. The Wi-Fi 6 standard [4] was officially published in May 2021. The task group focused on providing a four times higher throughput per station while keeping the same power consumption as IEEE 802.11 ac or improving it. Since this standard focuses on better spectrum efficiency, it is known as the high-efficiency standard. IEEE 802.11 ax modifies both thePHY and MAC layers and introduces multiple features to enhance Wi-Fi users' satisfaction. It achieves current expectations thanks to wider channels, MU OFDMA for channel access, uplink (UL) MU MIMO to improve capacity, SR for spectral efficiency, Target Wake Time (TWT) to manage power consumption, 1024 Quadrature Amplitude Modulation (QAM) to increase throughput, and other additional improvements [5]. Wi-Fi 6 operates on the 2.4, 5, and 6 GHz frequency bands. Together, these features lead to high data rates of up to 9.6 Gb/s.  
The successor to the standard of Wi-Fi 6, IEEE 802.11 be (Wi-Fi 7), is currently under study and targets ultra-high-throughput networks. However, it is only in its early stages and is not expected to be introduced before 2024. This survey serves as a basis for future improvements to Wi-Fi 7, making it a helpful research tool.
# Assessing the Compaction Quality of Dolomitic Asphalt Pavements Using Ground Penetrating Radar
###### Abstract  
The quality of newly constructed pavement depends mostly on compaction, which is essential for ensuring the pavement's longevity and performance. Traditional methods of evaluating pavement compaction and density, such as core sampling and nuclear gauge measurements, are often time-consuming and invasive and provide only a limited amount of data at a low spatial resolution on the potential air void content of the asphalt layers. The present study aimed to assess the specific gravity (Gmb) of a dolomitic asphalt mixture at different degrees of compaction using GPR techniques. Relative density (RD) maps were generated to visualize the spatial homogeneity of the asphalt density. Nuclear density gauging was applied for the calibration, and cores were used to validate the results. The survey was conducted on two recently paved roads in Szeged, Hungary. After testing various approaches, it was found that applying horn antennas and the surface reflection (SR) method is the most feasible way to obtain reliable and accurate dielectric permittivity (\(\varepsilon\)) data. Based on the measurements, clear relationships were found between dielectric constants, Gmb, and aggregate size. The findings highlight that it is possible to indirectly determine the Gmb of asphalts composed of dolomite and limestone aggregates using GPR, with aggregate sizes ranging from 11 mm to 25 mm and Gmb values between 2.43 and 2.57 g/cm3. Consequently, a robust function was developed, which can be applied to other asphalts with similar compositions.  
Ground Penetrating Radar (GPR); dielectric permittivity; specific gravity; relative density; aggregate size 1
## 5 Connected Vehicles
### Benefits and Business Opportunities
#### 5.2.6 Insurance  
As mentioned in the previous section, driving behavior data can be used to provide targeted reference to vehicle insurance items for specific drivers. Meanwhile, some business chances will be created for insurers, such as Usage-Based Insurance (UBI) in the form of \"Pay-As-You-Drive\" (PAYD) and \"Pay-How-You-Drive\" (PHYD) [98; 99]. Originally designed to capture information to improve insurance security levels and billing profit, the widespread introduction of UBI positively saves lives and protects the environment [100]. The PAYD item evaluates drivers based on the quantity of driving. Similar to the mileage charge of taxicabs, the insurance premium for this variant depends on the amount of driving. The PHYD item, however, evaluates drivers according to the quality of their driving, meaning that the insurance premium is calculated based on the frequency of aggressive behavior and irrational decisions made by the driver, as analyzed by driving behavior data [98]. Unfortunately, when the accident or crash occurs, the damage state and pre-accident manipulating data can also assist the insurance carriers in calculating the proper payout.
# A Blockchain-Based Secure Inter-Hospital EMR Sharing System
## 3 The Proposed Scheme
### Smart Contract Initialization  
In the proposed architecture, blockchain technology is applied. During the medical treatment process, some key information will be saved and verified through the blockchain. The key information in the blockchain is defined in the smart contract. The following is the blockchain smart contract structure for medical treatment information.  
Figure 1: The framework of the blockchain-based secure inter-hospital EMR sharing system.  
In the proposed smart contract, we have developed key information that will be stored in the blockchain. In the structure of the patient information smart contract, we developed the field of patient ID (identification), patient detail, and mapping to the medical record. In the structure of the hospital A smart contract, we developed the fields of hospital A ID and hospital A detail. In the structure of the hospital B smart contract, we developed the fields of hospital B ID and hospital B detail. In the structure of the medical record smart contract, we developed the fields of diagnosis ID and diagnosis detail. In the initialization phase, the blockchain center also issues the public and private key pairs for all roles. Besides this, there will be a variable that records the current blockchain state in the blockchain smart contract architecture.
## 3 Experimental Results
When the object plane is not parallel to the image plane, the distance resolutions of the two sides of the width are different and the far side will cause more error. Therefore, the oblique problem will increase the detection error for measuring the width of an object. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Object** & **Object** & **Object** & **Object** & **Object** & **Overall** & **No.** & **Object** & **Object** & **Object** & **Object** & **Overall** \\  & **Seg.** & **Range** & **Image** & **Info.** & **Acc.** & **Seg.** & **Range** & **Image** & **Info.** & **Acc.** \\ \hline \multirow{2}{*}{\(1\)} & & & & 112 cm & & & & & & & 240 cm & \\  & & & & 7 cm & 0.792 & 8 & & & & & 69 cm & 0.800 \\  & & & & 4 cm & & & & & & 56 cm & \\ \hline \multirow{2}{*}{\(2\)} & & & & 127 cm & & & & & & & 155 cm & \\  & & & & 5 cm & 0.890 & 9 & & & & 16 cm & 0.903 \\  & & & & 13 cm & & & & & & 18 cm & \\ \hline \multirow{2}{*}{\(3\)} & & & & 162 cm & & & & & & & 162 cm & \\  & & & 20 cm & 0.834 & 10 & & & & & 21 cm & 0.925 \\  & & & 17 cm & & & & & & 16 cm & \\ \hline \multirow{2}{*}{\(4\)} & & & & 330 cm & & & & & & 112 cm & \\  & & & 10 cm & 0.929 & 11 & & & & & 11 cm & 0.898 \\  & & & 57 cm & & & & & & 12 cm & \\ \hline \multirow{2}{*}{\(5\)} & & & & 137 cm & & & & & & 126 cm & \\  & & & 7 cm & 0.893 & 12 & & & & & 8 cm & 0.881 \\  & & & 14 cm & & & & & & 16 cm & \\ \hline \multirow{2}{*}{\(6\)} & & & & 123 cm & & & & & & 377 cm & \\  & & & 51 cm & 0.948 & 13 & & & & & 26 cm & 0.841 \\  & & & 33 cm & & & & & & 35 cm & \\ \hline \multirow{2}{*}{\(7\)} & & & & 422 cm & & & & & & & & \\  & & 13 cm & 0.798 & & & & & & & & \\ \hline \end{tabular} (Object Info. in order of Distance, Width, and Height).  
\end{table} 
Table 7: Object segmentation results and 3D information of test image 6. The proposed algorithm has been processed by a computer with an Intel Core i7-6700 CPU, 16 G RAM, and NVIDIA GeForce GTX 1080 GPU with 8 GB memory. The software has not been optimized. The processing times of the test images are shown in Table 9. The processing time will vary according to the image resolution. We believe the proposed algorithm can be used for real-world applications.