esa-sceva/satcom-chunk-collection huggingface dataset:
# Clinical, Research, and Educational Applications of ChatGPT in Dentistry: A Narrative Review
## 4 Discussion
### Educational Enhancements
#### 4.4.3 Mastery and Expertise Tests  
Several authors tested LLMs' capabilities against licensing exams and specialist boards. Their good proficiency was underlined in multiple=choice questions and short answers rather than thematic, open ones or mathematical analysis. The results exceeded the cut-off scores and showed they performed exceptionally well in specific subjects, in one case performing better than licensed dentists, as described by [PERSON] et Al., with the exception of the Iranian Endodontics Specialist Board, where the tool employed obtained ascore of 40/100 [90; 91; 92; 93; 95; 96; 97; 98; 99; 100; 101]. Another underwhelming result was achieved in oral and maxillofacial surgery. Specifically, in [PERSON] et al.'s study, the students' overall accuracy rate was 81.2%, while that of the chatbots varied from 50.0% to 65.4%. ChatGPT Plus achieved a higher accuracy rate for basic knowledge than the students (93.8% vs. 78.7%). However, all chatbots performed poorly in image interpretation, with accuracy rates below 35.0% [94]. On the topic of image recognition, [PERSON] et al. showed how the new ChatGPT-4V's image recognition feature exhibited limitations, especially in handling image-intensive and complex clinical practical questions, and is not yet fully suitable as an educational support tool for dental students at its current stage [97].  
Tests were also performed to assess the proficiency against patients' oral health-related queries, in-service exam questions, oral lesion diagnosis, and recognized assessments in several dental subjects with good outcomes [78; 79; 80; 81; 82; 83; 84; 85; 87]. Regarding oral and maxillofacial surgery, [PERSON] et al. highlighted that classic diseases are underrepresented, and the current literature on large language models in this field lacks sufficient evidence [86].  
In the dispute between GPT use vs. literature research methodology, [PERSON] et al. showed how students using ChatGPT to compose a learning assignment performed significantly better than their fellow students who used the literature research methodology, while in [PERSON] et al.'s work, higher values through the traditional method than with ChatGPT were reached regarding final scores and scores for the criteria of utilization of evidence, evaluation of arguments, and generation of alternatives. Nonetheless, the students highly valued the experience of using ChatGPT for academic tasks [88; 89].  
Shortcomings of this technology in the educational field have been stressed by authors regarding cognitive and general bias, inaccuracies, privacy issues, and the risk of overreliance [76; 77].
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 4 Development Trend and Key Technology of ISAC towards 6G ISTNs
### Critical Techniques in Integrated Design
#### 4.2.4 Waveform Design of Satellite-Terrestrial ISAC
In [126], the authors came up with a NOMA strategy based on multitype service coexistence handover to ensure the key requirements for the coexistence of user equipment (UE) in a dual-layer LEO high-throughput satellite constellation. The design of the novel satellite-terrestrial ISAC waveform will greatly facilitate the multi-level transmission of information. Using the same waveform and access can reduce computational complexity, simplify signal processing, and achieve wide-area coverage of system signals and high-precision detection capabilities.
## 2 Materials and Methods
### Application of the Proposed Method in Practice
* Calculation of Category _CI_--the sum of the degrees of factors _Ex_, _Pa_, _Pna_, \(M\), Table 8.
* Determination of the degree of severity \(S_{e}\) based on interviews with employees, Table 8.
* Calculation of acoustic risk at low frequencies (Figure 1) according to parameter _Se_ and category _CI_. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **WA1** & **WA2** & **WA3** & **WA4** & **WA5** & **WA6** & **WA7** \\ \hline \(L_{A}\) at 16 Hz [dB] & 1.54 & \(-9.29\) & \(-4.07\) & 10.22 & 10.77 & \(-0.61\) & 8.99 \\ Point rating & 0 & 0 & 0 & 0 & 0 & 0 \\ \(L_{A}\) at 25 Hz [dB] & 2.49 & 7.15 & 8.01 & 17.22 & 17.29 & 15.35 & 7.40 \\ Point rating & 0 & 0 & 0 & 0 & 0 & 0 \\ \(L_{A}\) at 40 Hz [dB] & 14.80 & 21.86 & 21.84 & 31.43 & 30.61 & 31.72 & 9.58 \\ Point rating & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \(L_{A}\) at 63 Hz [dB] & 30.93 & 30.15 & 27.70 & 35.09 & 39.04 & 37.28 & 21.60 \\ Point rating & 1 & 1 & 0 & 1 & 2 & 1 & 0 \\ \(L_{A}\) at 80 Hz [dB] & 49.97 & 32.72 & 33.05 & 42.26 & 42.95 & 46.78 & 27.91 \\ Point rating & 2 & 2 & 2 & 2 & 2 & 2 & 1 \\ \(L_{A}\) at 100 Hz [dB] & 50.63 & 38.02 & 39.93 & 44.37 & 46.43 & 51.16 & 34.08 \\ Point rating & 2 & 2 & 2 & 2 & 2 & 2 & 2 \\ Sum of the point ratings & 5 & 5 & 4 & 5 & 6 & 5 & 3 \\ \hline Degree factor \(P_{a}\) & 3 (\(M\)) & 3 (\(M\)) & 3 (\(M\)) & 3 (\(M\)) & 3 (\(M\)) & 3 (\(M\)) & 1 (\(L\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Determination of the factor _Pa_. Figure 4: Levels \(L_{Z}\) during WA1-WA7 at the considered frequencies. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Work Activities** & **Duration of Work (Hours)** & **Exposure Factor Level _Ex_** \\ \hline W1 & WA1 & 2 & 1 \\ W2 & WA2 & 7.5 & 4 \\ WA3 & 7.5 & 4 \\ WA4 & 3 & 2 \\ W3 & WA5 & 3 & 2 \\ WA6 & 3 & 2 \\ W4 & WA7 & 3 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Determination of the factor _Ex_. Source: own research.
## 2 Related Work  
The concept of SDVS was first introduced by [PERSON] et al. [7], and formalized by [PERSON] et al. [21]. Since then, some efficient SDVS schemes were proposed [22; 23; 24; 25; 26; 27], but the security of those schemes is based on the ideal random oracle. To deal with this problem, [PERSON] et al. [17] designed a SDVS scheme in the standard model. However, its security is highly dependent on the security of the pseudo-random function. If the pseudo-random function leaks the associated index, the attacker can easily generate legitimate signatures for arbitrary messages on behalf of the signer or the designated verifier. Based on the \(q\)-Strong Diffie-Hellman assumption, [PERSON] et al. [16] constructed another SDVS scheme without random oracles. However, their SDVS scheme could not protect the privacy of the signer's identity and did not give formal security proof. Besides, [PERSON] et al. [18] presented a secure SDVS scheme based on [PERSON] scheme [28], but their scheme is malleable. [PERSON] et al. [20] showed that the above three SDVS schemes [16; 17; 18]. do not satisfy strong unforgeability. Later, [PERSON] et al. [20] used the OR proof [29] and [PERSON] et al.'s scheme [30] to design a basic signature scheme with existential unforgeability. Then, [PERSON] et al. [20] constructed a SDVS scheme using their basic scheme and the Cramer-Shoup scheme [31]. To shorten the signature length, [PERSON] et al. [20] proposed another SDVS scheme based on their basic signature scheme and [PERSON] et al.'s encryption scheme [32]. Although [PERSON] et al.'s two SDVS schemes [20] satisfy strong unforgeability, neither provides undeniability. To overcome this shortcoming, [PERSON] et al. [12] designed an undeniable SDVS scheme using chameleon hash function [33]. However, the signer needs to store all previous signature data to identify the real generator in a signature, and the judgment process needs the help of the signer. To improve the fairness of the judgment, [PERSON] et al. [14] designed two undeniable SDVS schemes in which the arbiter can independently identify the real signer in a disputed signature. However, [PERSON] et al.'s scheme [12] and [PERSON] et al.'s schemes [14] were provably secure in the random oracle model. Unfortunately, there is no strongly unforgeable SDVS scheme with the undeniable property in the standard model. Thus, in this paper, we put forward such construction for SDVS.
###### Abstract  
We investigated the structures of the wintertime lower troposphere in Seoul, South Korea on 17 and 18 January 2017 by performing 1 h interval radiosonde observation and numerical simulations. In the daytime on 17 January, the height of the convective boundary layer (CBL) sharply and quickly increased when the residual layer became a part of the CBL. From the afternoon on 17 January, moist air with clouds began to substantially intrude in the lower troposphere in Seoul, and radiative heating/cooling weakened. As a result, the mixing of air in the lower troposphere was inhibited and the vertical gradients of potential temperature and water vapor mixing ratio changed little on 18 January. We evaluated the performance of four planetary boundary layer (PBL) parameterization schemes (the Yonsei University (YSU), Mellor-Yamada-Janjic (MYJ), Mellor-Yamada-Nakanishi-Nino (MYNN), and Asymmetric Convective Model version 2 (ACM2) schemes) coupled with the Weather Research and Forecasting model in simulating the structures of the lower troposphere against 1 h interval radiosonde observation. The general tendencies of the air temperature and wind speed in the lower troposphere were well-reproduced in the four simulations. However, the sharp increase in the CBL height did not appear in the four simulations, implying that the process of the residual layer becoming a part of the CBL in the daytime is not well-parameterized. Additionally, the simulated water vapor mixing ratio near the surface was smaller compared with the observation. We found that small-scale turbulence in the CBL, which mixes advected air and pre-existing air, was not reproduced well by the PBL parameterization schemes. Compared with the other simulations, the most accurate air temperature and wind speed were reproduced in the simulation with the MYJ scheme, while the CBL development and moisture advection were reproduced relatively well in the simulation with the MYNN scheme.  
1h interval radiosonde observation; wintertime lower troposphere; WRF model; PBL scheme +
Footnote †: journal: Applied Sciences
# Spectral Domain-Based Data-Embedding Mechanisms for Display-to-Camera Communication
###### Abstract
To consider the actual implementation of the proposed scheme, we assessed the effects of the optical wireless channel on the communication performance of the D2C system by conducting various experiments according to multiple parameters. Using software simulations and several real-world experiments, we compare and analyze the performance of our proposed data-embedding schemes in terms of the peak signal-to-noise ratio (PSNR), bit error rate (BER), and achievable data rate (ADR). Our work demonstrates successful data embedding and extraction via a D2C link, which facilitates robust data communication without significantly compromising the visual quality of the display. The main contributions of this research can be summarized as follows:  
* Three types of spectral data-embedding mechanisms are presented, and their performance in real-world environments are analyzed. * The effect of the wireless optical channel on the system performance is assessed under various capture orientations of the receiver camera. * The performance of the proposed system is assessed according to the data embedding in various positions within the mid-frequency (MF) region of a DCT image. The remaining of the paper is organized as follows. In Section 2, we provide detailed explanation the D2C architecture, with an emphasis on frequency coefficient selection, encoding, and decoding, where the various types of DCT-based data-embedding mechanisms are discussed. Then, Section 3 presents the performance analysis and evaluation of the proposed model based on both simulations and real-world experiments. Finally, the conclusions are reported in Section 4.
## 2 Data and Methods Used
### Geological Setting and Seismotectonic Aspect  
From a geological point of view, the Durres Bay, and the weathered hills of Tortonian age, lay on poor, thick and unconsolidated Quaternary sediments [24; 25; 26; 27; 28]. The corresponding surface geology (Figure 1a) show that the western part of the hilly chain is composed of Miocene molassic formations comprising clay, siltstone, sandstone, and gypsum, while on the central and eastern parts a composition of Pliocene sandstones and conglomerates is found [29]. The Durres basin is filled with Pliocene and Quaternary deposits. The Pliocene deposits constitute the basement of the overlying Quaternary ones, including sandstones and conglomerates found in the central and eastern flanks of the Durres hills. In the Durres basin, the Quaternary deposits include alluvial, lagoon, marshy and marine deposits, reaching an overall thickness of nearly 130 m [26; 27; 28; 30].  
From a tectonics point of view, Durres and its surroundings belong to the Peri-Adriatic Foredeep. It constitutes a northern segment along the southern convergent margin of the Eurasia Plate, where the Albanian orogenic front is thrusting over the Adria microplate, partly over the Apulian platform and the southern Adriatic basin [25; 26; 31]. Consequently, it is strongly influenced by the Adria-Eurasia continental collision [27; 28; 31; 32; 33]. As a result, the Durres region is affected by a compressional stress regime, acting along a series of thrust and back-thrust faults (Figure 1b). The back-thrust faults have been active since the Pliocene-Quaternary period, namely the Durres and Preza ones, while thrusts along the Durres and Tierana syncline are traced to earlier periods [27; 31; 33; 34].  
Active faults of Durres and the surrounding regions (Figure 1b) have contributed to both historical and present seismic activity [25; 26; 31; 34; 35]. This notion is reinforced by the latest strong earthquakes that struck Durres, namely the September 21 (M5.8) and November 26 (M6.4), 2019. Both events showed the same type of reverse-faulting kinematics (Figure 2), based on their focal mechanism (FM) solutions, supporting the idea of a blind reverse causative fault, NW-SE striking (140\({}^{\circ}\)-155\({}^{\circ}\)) and ENE dipping (65\({}^{\circ}\)-72\({}^{\circ}\)), rooted at the basal thrust front [32; 36; 37; 38; 39]. The results also agree with the aforementioned regional tectonics (Figure 1b), showing significant consistency between observed data andthe Durres thrust fault, whose ramp dip-angle tends to decrease at the hypocenter depth of these strong earthquake [32; 37; 38].  
Figure 1: (**a**): Geological map of Durres (section from the Geological Map of Albania 1:200,000, from [29]); the profile trace line (A—B), oriented WSW—ENS, crosses the main geological units of Durres, through Porto Romano Bay; (**b**): Schematic geologic section, along the A—B profile, trending NE, from the Adriatic Sea to Kruja-Tirana region, [27]. The red star indicates the location the of 26 November 2019 mainshock’s hypocenter (M6.4) [39].
# Cooperative Power-Domain NOMA Systems: An Overview
## 2 Non-Orthogonal Multiple Access
### Advanced Channel Coding and Modulation  
Channel coding and modulation, which are essential physical layer technologies, offer effective means for a radio link that must work close to its channel capacity while maintaining appropriate signal waveforms of baseband and RF at transmitters and/or receivers. In general, transmit waveforms, multiple accesses, and ans similar techniques are all included in channel coding and modulation.
## 2 Materials and Methods
### Data
#### 2.2.2 Training and Validation Samples  
The study areas were classified into distinct land cover classes. Manaus was assigned eight classes: (1) Terra-Firme Forest, (2) Floodplain Forest, (3) Secondary Forest, (4) Agriculture/Pastureland, (5) Burned Areas, (6) Barren Land, (7) Development Areas, and (8) Water Bodies. Porto Velho included all these classes with the addition of (9) Savanna areas, which are found in small patches throughout the region. Detailed descriptions of these land use and land cover classes can be found in Table 2.  
Training and validation samples were collected through a combination of methods to ensure accuracy and representativeness. The primary method involved the manual visual assessment of high-resolution imagery from Google Earth Pro and Planet Labs,  
\begin{table}
\begin{tabular}{c c} \hline
**LULC Class** & **Description** \\ \hline Terra-Firme Forest & Upland forests not subject to seasonal flooding. This is the dominant \\  & forest type in the Amazon, covering most of the non-flooded areas. \\ Floodplain Forest & Forests that are seasonally flooded by river overflow. Also \\  & known as vázzea or igapo forests depending on the water type. \\ Secondary Forest & Regrowth forest on previously deforested areas. Often \\ Agriculture/Pastureland & younger and less diverse than Primary Forests. \\  & Areas used for crops or livestock grazing. \\ Burned Areas & Recently burned or fire-affected areas, which may be due to \\  & natural causes or human activities like slash-and-burn agriculture. \\  & Areas with little or no vegetation cover, such as exposed soil or rock. \\  & Urban or built-up areas, including cities, towns, roads, and other infrastructure. \\ Water Bodies & Rivers, lakes, and other permanent water features. \\  & Open woodland ecosystems with grass understory, found in small patches \\  & within the predominantly forested Amazon region. These are more common in \\  & transitional zones between the Amazon and neighboring biomes. \\ \hline \end{tabular}
\end{table}
Table 2: Land use and land cover classes and their description.  
supplemented by spectral and vegetation indices analysis. This approach allowed for a comprehensive evaluation of land cover types across the study areas. Additionally, to enhance the ground-truth data, 23 sample points were collected in 2022 using a Garmin GPS (Garmin Ltd., Olathe, KS, USA) instrument in both Manaus and Porto Velho.  
To mitigate the impact of spatial autocorrelation while still capturing the gradient of each land cover type, all samples were collected as points rather than polygons. Sample locations were chosen arbitrarily, with the constraint that they be at least 10 m apart from the nearest sample point. This strategy helps to ensure independence between samples and improves the robustness of the classification model.  
A total of approximately 3000 sample points were collected for each year of the study period. This substantial dataset was then divided into two subsets: 70% of the samples were used to train the classification model, while the remaining 30% were reserved for validation and accuracy assessment.
# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 3 Networking Technologies toward Collaborative Intelligence
* **From the perspective of data dissemination and protocol** It is also possible to classify the network technologies of IoV into three categories: unicast communication, geocast, and broadcast communication, which respectively represent from one vehicle to another, from a vehicle to a group of vehicles, and from one vehicle to all other vehicles in the specified range. Important data can be transmitted in a dedicated resource pool using unicast transmission to avoid data transmission, such as platooning and advanced driving. For sensor data sharing or geographical location demanding scenarios, the geocast or broadcast transmission were carried out through the shared resource pool. Broadcast communications are used when collision warnings and traffic jam messages are disseminated [35]. From the perspective of routing, there are single-hop and multi-hop communication methods [36]. Collaborative communication and networking technology is the key to achieving CI tasks between heterogeneous agents in IoV. This section mainly focuses on three different broad topics, namely Radio Access Technology (RAT) selection, routing protocol, and authentication and secure communications in IoV.
# A Task Allocation Strategy of the UAV Swarm Based on Multi-Discrete Wolf Pack Algorithm
## 3 The Wolf Pack Algorithm
### Performance of MDWPA
#### 4.3.3 Comprehensive Analysis of MDWPA Performance  
In the previous two subsections, the efficiency and stability of the proposed algorithm are compared, and it can be seen that MDWPA has obvious performance advantages in the UAV task assignment model. However, in order to more fully prove the practicability of the algorithm, the average flight distance is further used as an evaluation index, and the MDWPA algorithm is compared and analyzed with other intelligent algorithms. Figure 10 shows the average flight distance of the UAV of each algorithm in the three example scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Average** & **Std** & **Max** & **Time (Avg)** \\ \hline GPWPA & 0.162847772 & 0.007316736 & 0.178401271 & 5900.154593 \\ CJADE & 0.101951109 & 0.006901708 & 0.115765696 & 649.6378847 \\ EBOCMAR & 0.11767223 & 0.006358509 & 0.138806691 & 161.0460503 \\ GA & 0.218305764 & 0.024635596 & 0.282582955 & 61.89460519 \\ MDWPA & 0.263548569 & 0.01590524 & 0.290838572 & 2827.711819 \\ PSO & 0.122822677 & 0.007341032 & 0.146461445 & 72.66523946 \\ WPA & 0.093725006 & 0.003100226 & 0.101597478 & 397.2063249 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The experimental results of variance analysis.  
In Figure 10, the abscissa is the five intelligent algorithms, and the ordinate is the average flight distance of the UAV. The red (set1), blue (set2), and green rectangles (set3) represent the aforementioned Example 1, Example 2, and Example 3, respectively. Each intelligent algorithm has three bar graphs to represent the average flight distance of the UAV under the three mission scenarios. It can be clearly seen from Figure 10 that the MDWPA has obvious advantages compared to the other four intelligent algorithms. Regardless of the mission scenario, its average flying distance is the shortest, which proves that in the actual scenario, it can always find the best and shortest flight path for the UAV.  
The above experimental results show that the efficiency and stability of the MDWPA in solving the UAV task allocation problem has considerable advantages compared with other algorithms. And it solves the shortcomings of the traditional wolf pack algorithm that can easily fall into the local optimum. Therefore, the improved wolf pack algorithm is suitable for large-scale and complex UAV task assignment and has relatively large performance advantages.