esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Acoustic Analysis  
A microphone was positioned 5 cm from the subject's mouth, and the subject was instructed to produce and sustain the vowel /a/ at their most comfortable pitch and volume [18]. The digitally recorded data were then transferred to a computer, with a sampling frequency of 44.1 kHz, for subsequent analysis using the multidimensional voice program (MDVP) software version 2.3 from Kay Elemetrics [18; 19]. Then, measurements such as jitter (%), shimmer (%), the noise-harmony ratio (NHR, dB), fundamental frequency (F0, Hz), speaking fundamental frequency (SFF, Hz), and maximum phonation time (MPT, s) were obtained [9; 20]. For this experiment, the measurements were taken before and after the voice therapy process, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Female** & **Male** \\ \hline Number of samples & 55 (27 Voice users) & 26 (12 voice users) \\ \hline Average age & 51 & 48 \\ \hline \multirow{4}{*}{Types of voice disorders (numbers)} & Vocal fold polyp (15), vocal nodule (16), thyroid nodule (2), hoarseness (1), muscle tension dysphonia (5), sulcus vocals (1), dysphonia (8), presbypmonia (3), vocal cyst (4) & Vocal fold polyp (11), vocal cord paralysis (2), mutational dysphonia (1), vocal nodule (1), vocal cyst (2), leukoplakia (1), dysphonia (8), presbypmonia (3), vocal cyst (4) &
\begin{tabular}{c} Vocal fold polyp (11), vocal cord paralysis (2), mutational dysphonia (1), vocal nodule (1), vocal cyst (2), leukoplakia (1), dysphonia (2), sulcus vocals (1), muscle tension dysphonia (2), presbypmonia (2), vallecular cyst (1) \\ \hline Number of responsive samples (effectiveness, +) & 41 & 18 \\ \hline \multirow{4}{*}{Variables} & Smoking status, alcohol status, voice user status, coffee status, fundamental frequency before and after treatment (Hz), jitter before and after treatment (\%), shimmer before and after treatment (\%), noise to harmonic ratio before and after treatment (NIR, dB), speaking fundamental frequency before and after treatment (SFF, Hz), maximum phonation time before and after treatment (MPT, s) \\ \hline \end{tabular}
\end{table}
Table 1: Information related to the samples in the experimental dataset.
## 5 Case Studies: Validating the CILS Framework
### Case Study Two: HelloTalk  
#### 5.2.1 Detailed Description of HelloTalk  
HelloTalk is an innovative language learning app designed for language exchange among users of diverse cultural backgrounds. It uses the force of social networking to enable real-time conversation between native speakers and learners. Consequently, it provides the learner with an immersive learning environment that goes beyond the confines of a conventional classroom. HelloTalk provides a medium for those users who desire to chat through text, voice, and video calls [40]. Thus, language practice is easier and in real time.  
The application is designed to support more than 150 languages and has features such as translation, phonetics, and grammar corrections that are directly integrated into the chat interface. This feature enables learners to receive immediate responses to their language use, which is another vital element for successful learning. Furthermore, HelloTalk also offers an option for text correction by native speakers, giving genuine examples of language usage that are priceless for learners.  
HelloTalk's space of creativity is its opportunity for cultural exchange. Every interaction is a lesson in language and the cultural context in which the language is found. The double concentration on language and culture enables learners to see the concrete utilization of language in real life. It creates a broader respect for the cultural pervasiveness in language use [37].
# The Optimization of Covert Communication in Asymmetric Jammer - Assist Systems
## 6 Conclusions  
In this work, we investigate covert communication in a complex Gaussian channel with a partially coordinate jammer. Specifically, [PERSON] aims to transmit covert information to [PERSON] using BPSK codebook, while remaining undetected by [PERSON] throughout the covert communication process. A jammer is present to assist [PERSON] in covert communication, but [PERSON] has limited control over the jammer, with adjustments constrained to the power allocation ratio along the real and imaginary axes in the complex Gaussian channel.  
We employ KL divergence as a metric for covertness and mutual information as a metric for transmission rate. The optimization task involves adjusting both the amplitude of [PERSON]'s transmitted signal and the power allocation ratio of the jammer. Leveraging Taylor series expansion techniques, we approximate KL divergence and mutual information. Combining our approximation results, we degrade the complex dual-parameter optimization problem into a single-parameter traversal problem, simplifying the optimization complexity.  
Numerical simulations validate the effectiveness of our proposed theory. In future work, we plan to extend the study to include different modulation schemes adopted by [PERSON], such as 16-QAM, 64-QAM, and other scenarios.  
Figure 5: Performance comparison of difference jammer noise ratio. (**a**) KL divergence versus amplitude gain. (**b**) Transmission rate versus amplitude gain.  
Figure 6: Covert transmission rate versus covertness constraint.  
**Author Contributions:** Conceptualization, S.Q. and R.Z.; methodology, S.Q. and R.Z.; software, J.Z. and X.J.; validation, S.Q. and X.J.; writing--original draft preparation, H.D. and R.Z.; writing--review and editing, J.Z. and H.D.; visualization, H.D. and X.J.; supervision, S.Q. and J.Z.; funding acquisition, R.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China grant number U1836104, 61801073, 61931004, 62072250, National Key Research and Development Program of China grant number 2021 QY0700, and The Startup Foundation for Introducing Talent of NUIST grant number 2021r039.  
**Instructional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 4 Simulation and Numerical Results
Since the algorithm complexity of the SAODV routing protocol is higher than the ISAODV routing protocol based on the elliptic curve cryptosystem, the delay of SAODV is higher than that of ISAODV. Figure 10 shows the routing overhead under normal conditions. It can be seen from the figure that when the UAV moves at a low speed, there is less route breakage, while when the speed increases,  
Figure 8: The delay under normal conditions. Figure 9: The delay after adding malicious nodes. the protocol needs to maintain the route frequently, resulting in a sharp increase in routing overhead. The performance of ISAODV routing protocol is very close to AODV routing protocol in terms of routing overhead, which shows that the improved protocol inherits the characteristics of the original protocol and maintains the routing discovery and maintenance capabilities of the original protocol to the greatest extent. Figure 11 shows the routing overhead after adding malicious nodes. It can be seen from the figure that with the addition of malicious nodes, the routing overhead of AODV routing protocol increases, and the link stability becomes worse, while the routing overhead of ISAODV and SAODV does not change significantly compared with that under normal conditions, which indicates that the secure routing protocol maintains the link stability well. Moreover, the routing overhead of ISAODV is the smallest among the three protocols, and the link stability is the best.
# Application of Artificial Intelligence in Wind Power Systems
## 3 Results
### Application of Artificial Intelligence in the Operation of Wind Turbines
#### 3.2.2 Dynamic Optimization of Turbine Performance  
AI facilitates real-time adjustment of turbine settings, such as blade pitch and yaw angle, in response to changing wind conditions. As mentioned earlier, this dynamic optimization can increase energy yield by up to 20% by ensuring that turbines operate at their highest efficiency under changing environmental conditions [47].  
Maximum power point tracking (MPPT) is an essential step in the operation of wind turbines to ensure efficient power generation. [PERSON] et al. [56] have given an overview of the existing techniques, explaining their benefits and providing a basis for future developments. Two intelligent control strategies are presented in more detail: neural networks and fuzzy logic controllers.  
Artificial intelligence (AI) plays an important role in stabilizing the output voltage of a wind turbine in the grid, especially when a doubly fed induction generator (DFIG) is used. However, the dynamic characteristics of such generators depend on non-linear parameters such as stator flux, stator current and rotor current, which increases the overall complexity of the system. Therefore, to ensure system stability, robust controllers capable of supporting dynamic wind energy frequencies must be implemented. By applying AI, fuzzy logic (FL), fuzzy PI, artificial neuro-fuzzy inference system (ANFIS), fuzzy, fuzzy-PI, and hybrid controller (ANFIS-PI), controllers are developed which have an advantage over the classical proportional-integral controller as described in the work of [PERSON] et al. [57] and [PERSON] [58].
## 1 Introduction
The grinding treatment effectively increases the transitional radius of the welds and reduces the stress concentration without changing the local microstructure, which improves the fatigue life of deck-rib welding details. Furthermore, the influence of the grinding radius and depth of the local stress field at the weld toe have been studied [33; 34]. In the present study, a new grinding type is proposed, and the influence of the grinding radius on the fatigue life is discussed based on the stress integration approach. Scholars have different opinions on the effect of the weld penetration rate on the fatigue life of weld joints. Plotting fatigue test results in an S-N diagram showed that specimens with weld melt-through (WMT) seem to have slightly lower fatigue strengths than 80% partial joint penetration (80% PJP) specimens, but the difference is more likely to be within the usual scatter of test data, which means that both details have comparable fatigue strength [2]. [PERSON] and [PERSON] [35] suggested that a shallower weld penetration (for example, an 80% PJP) appears to have a slightly higher fatigue resistance than a deeper one (for example, a 100% weld penetration). [PERSON] et al. pointed out that an increased penetration rate can decrease the crack-propagation rate and enhance fatigue life [36]. [PERSON] et al. showed that fatigue cracks initiate from the weld toe inside the rib in 100% penetration specimens, but from the weld root inside the rib in 75% penetration specimens [27]. Therefore, based on the stress integration approach, the fatigue life of the deck-rib weld details under 80% partial penetration and 100% full penetration were compared in the present study. In this paper, two parameters and the mesh sensitivity of the stress integration approach for 3D solid elements were first analyzed. Then, the applicability of a master S-N curve of the equivalent structural stress approach was verified based on the experimental data of the deck-rib weld details in the OSD. Finally, the multi-scale finite element model (FEM) of Jiangyin Bridge was established, and the bridge fatigue life calculation steps based on the stress integration approach were given. The influence of the slope of the master S-N curve at high cycles on the bridge fatigue life is discussed. Further, the influence of the weld parameters on the fatigue life of deck-rib welding details in the OSD of Jiangyin Bridge was analyzed based on the stress integration approach, including: (1) The influence of the weld size change caused by weld manufacturing errors on the bridge fatigue life is analyzed based on the measured weld size in the experiment; (2) a new grinding treatment type is proposed, and the influence of the grinding radius on fatigue life is analyzed; and (3) the fatigue life of deck-rib welding details under 80% partial penetration and 100% full penetration are compared.
## 3 Results and Discussions  
For the proposed internal model, the values of the path coefficients were positive (Figure 2) except for those from Perceived Competence (PC) to Intrinsic Motivation (IM) and from Perceived Privacy (PP) to Extrinsic Motivation (EM) whose weights reflected negative paths of \(-0.010\) and \(-0.004\), respectively.  
Being always standardized, the weights of the coefficients register variations between \(-1\) and \(+1\), their weights reflecting weaker or stronger ways [98]. Three strong effects are visible among the paths from Perceived Risk (PS) to Extrinsic Motivation (EM) (0.590), from Extrinsic Motivation (EM) to Intrinsic Motivation (IM) (0.344) and from Intrinsic Motivation (IM), respectively, to the Behavioral  
Figure 2: Path coefficients for the inner model.  
In Figure 3 along with the standardized path coefficients, placed on the corresponding paths, the values of the R\({}^{2}\) value for the three endogenous latent variables (factors) were displayed in the blue ellipses. The general measure of the effect size for the structural model, the R\({}^{2}\) can register values \"high\" (R \(>\) 0.5), \"moderate\" (R \(>\) 0.30) or \"weak\" (R \(>\) 0.1) [98]. For the endogenous variable Behavioral Intention to Use Solutions IoT the value of the R\({}^{2}\) indicates a high size (0.452), which means that almost 45.2% of its variation is explained by the model, by the common action of two factors (IM and EM). Five exogenous variables (EE, AT, PA, PC and PR) jointly explain approximately 53.8% of the variation of the endogenous Intrinsic Motivation (IM) factor. In the case of the Extrinsic Motivation (EM) factor, the value of the R\({}^{2}\) was more substantial, with 63% of the variation of the endogenous variable being explained by five other exogenous variables (PE, SI, FC, PP and PS).  
Measurement loads linking factors to indicator variables should generally be significant and range from 0 to 1 [99]. The proposed reflective model is strong and reliable, confirmed by the size of the standardized track loads which are greater than 0.70 and by their reliability which is over 0.50. [100]. Calculating the square of the incarnation of the measurements, it is observed that the reliability of all indicators connected to the latent variables EE, AT, PA, PC and PS registers values between 0.52 (0.721 ^ 2) and 0.86 (0.928 ^ 2). At the same time, the absolute contribution of the indicators used to define the latent variables PE, SI, FC, PP and PP indicates high loads, ranging from 0.50 (0.704 ^ 2) to 0.76 (0.874 ^ 2) [101].
# Experimental Study on Shear Mechanical Properties of Pile-Soil Interface Under Freezing Conditions
## 5 Conclusions  
Based on the experimental studies on the freezing strength at the interface, the distribution and evolution of the interface shear strength along the pile were studied, and then the influence of temperature and moisture on the interface shear strength was elucidated. The main conclusions are summarized as follows:  
(1) The shear stress-shear displacement curves at the lower part of the pile had obvious strain-softening characteristics, while the shear stress-shear displacement curves at the upper part of the pile had significant strain-hardening characteristics. The peak and residual shear strength decreased with the increase in depth.  
(2) The temperature change had a more significant impact on interface shear strength, compared with the moisture content. Under the test condition of a 15% moisture content, the interface shear strength reached its maximum value of 65.17 kPa at the temperature of \(-2\)\({}^{\circ}\)C, while this increased to 145.88 kPa when the temperature dropped to \(-5\)\({}^{\circ}\)C, with an increasing rate of 124%.  
(3) The cohesion and internal friction angle of the interface at the upper part of the pile were greater than those at the lower part. The interface strength parameters increased with the decreasing temperature. Moreover, the interface strength parameters increased with the increasing moisture content while under the same temperature.  
In this study, only four parameters for moisture and temperature were controlled during the experiments; the preliminary experimental results indicate the shear mechanical properties of the pile-soil interface and the variation in the strength parameters, and a  
Figure 7: The variation in interface strength parameters with depth.  
broader parametric study including the parameter sensitivity analysis would more firmly support conclusions that generalize.  
Conceptualization, T.C.; methodology, T.C. and J.W.; validation, Q.S.; formal analysis, Z.W.; writing--review and editing, Q.S.; funding acquisition, T.C. All authors have read and agreed to the published version of the manuscript.  
This project is financially supported by the National Natural Science Foundation of China (No. 42371134), and the Open Fund of State Key Laboratory of Frozen Soil Engineering (No. SKLFSE201704).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
We would like to express our sincere thanks to the anonymous reviewers for their valuable suggestions and constructive comments which greatly improved the presentation of this paper.  
The authors declare no conflict of interest.
## 3 Research Methods and Processes  
The construction of 3D geological models is a complex endeavor that necessitates a plethora of foundational data, including well position coordinate information, well inclination data, well logging data, seismic data, and drilling-related data. The data loading process is fundamental to establishing a comprehensive database. Petrel(tm) (2018), Schlumberger's software (version 2018), is an integrated tool for exploration and development, featuring the ability to quickly build geological models, providing a shared information platform for geologists, geophysicists, petrophysicists, and reservoir engineers, and offering free versions for academic purposes. It amalgamates geological analysis with seismic, well logging, and drilling data, creating an integrated geoscience platform. This platform facilitates in-depth research on geological elements, enabling the execution of formation correlation and data analysis. It allows for the creation of well models and planar models, culminating in the development of three-dimensional geological models. These models provide insights into the planar distribution laws of lithofacies, the direction of provenance, and the planar distribution patterns of various reservoir parameters (2019; 2020; 2021; 2022; 2023). In the study area, more than 20 wells have been drilled into the target layer. Utilizing the Petrel(tm) modeling software (version 2018), the primary data for this modeling exercise encompass well location coordinate data, well logging data, well inclination data, stratification data, log interpretation conclusions, and lithology data. Upon meticulous sorting and organization of these data, a three-dimensional geological model of the target layer for the study area was constructed. The structural model of the target layer was established using well stratification data and structural maps. Subsequently, the lithofacies model was formulated  
Figure 1: Structure location in the Heshui area of the Ordos Basin.  
based on the distribution maps of sand and mudstone, as well as lithology data. With the lithofacies model as a foundation, the attribute model was developed, incorporating data from logging interpretation, curves, porosity, and permeability distribution maps. The detailed methodology is depicted in the subsequent figure (Figure 2).
## 2 Methodology  
### Experimental Configuration  
The experimental configuration used in this work is shown in Figure 1a. A planar reactor is designed with parallel glass plates as transparent electrodes for generating APHeDBDs. One of the surfaces of each glass plate is coated with indium tin oxide as the conductive layer. The reactive area is a circle with a diameter of 30 mm and the discharge gap is fixed at 5 mm. The sinusoidal external power source is supplied with the voltage frequency and amplitude controlled at \(16.5\pm 0.1\) kHz and \(3.5\pm 0.1\) kV, respectively. The helium flowrate is controlled at 2 slm and a small amount of N\({}_{2}\) admixture (0.1%) is added for measuring the rotational temperature. The discharge voltages and currents are measured by the high-voltage probe (Tektronix P6015A) and current probe (Magnelab CT-C1), respectively, with data stored by the oscilloscope (Keysight Technologies DSOX3054T, 500 MHz and 5.0 GSa s\({}^{-1}\)). A monitor capacitor with the capacitance of 230 pF is installed at the location after the reactor to measure charge transferred for determining the power consumption by integrating the Lissajous curve [23]. To confirm the discharge uniformity, an ICCD camera (Princeton Instruments PI-MAX4) with an ultraviolet (UV) lens (Nikon PF10545 MF-UV) is applied to take photographs from the reactor outlet during the breakdown phase. An OES (AvaSpec ULS2048 LUSB2) with the optical fiber located at the lateral side of the reactor as shown in Figure 1b is employed for measuring the average emission spectrum to determine the rotational temperature. The infrared thermal imager (AVIO F50A) is applied to detect the surface temperature of the reactor from the lateral side of the reactor (similar to that shown in Figure 1b) for validating the temperature distribution simulated by the 3D GFM. The gas temperature is measured after the system reaches its thermal equilibrium as the steady-state gas temperature.