esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Method
### Research Hypothesis Development
#### 3.1.5 Relationship between Trust and Loyalty  
Trust serves as the cornerstone for cultivating strong customer relationships and achieving success [58]. When consumers trust a product or service, they are more likely to become loyal customers, consistently make repeat purchases, and actively promote the product or service [58]. As trust deepens, customer loyalty expands, resulting in increased customer retention and positive brand reputation [10]. Businesses that prioritize trust-building strategies can foster enduring customer loyalty, which is crucial for long-term marketing success and profitability. Researchers such as [PERSON] et al. [54], [PERSON] et al. [56], and [PERSON] et al. [59] provided evidence of the positive impact of trust on loyalty in business-to-business services, online shopping, and mobile payment systems, respectively, thereby reinforcing the integral link between trust and loyalty. The following hypothesis is proposed to understand the relationship between trust and loyalty in entertainment platforms:  
**H11**: _Trust is positively related to loyalty._  
1.6 Mediating Role of Customer Satisfaction and Trust in the Relationship between Perceived Value and Loyalty  
Understanding the mediating role of customer satisfaction and trust in the relationship between perceived value and loyalty is crucial because these intermediaries explain how perceived value influences and shapes loyalty. Several studies have investigated this relationship. [PERSON] et al. [7] and [PERSON] and [PERSON] [60] found that customer satisfaction mediates the relationship between perceived value and loyalty in business-to-business services. [PERSON] et al. [61] demonstrated that in the vacation rental and short-term lodging industry, perceived value influences word-of-mouth through customer satisfaction. [PERSON] et al. [53] found that trust mediates the relationship between perceived value and loyalty in the lumber market. Additionally, [PERSON] and [PERSON] [62] explored how customer satisfaction and trust mediate the relationship between perceived value and loyalty in the telecommunication services sector. Therefore, the following hypotheses are proposed to understand this mediation in entertainment platforms:  
**H12**: _Customer satisfaction mediates between utilitarian value and loyalty._
**H13**: _Customer satisfaction mediates between hedonic value and loyalty._
**H14**: _Customer satisfaction mediates between social value and loyalty._
**H15**: _Trust mediates between utilitarian value and loyalty._
**H16**: _Trust mediates between hedonic value and loyalty._* _Trust mediates between social value and loyalty._  
This study investigates the relationship between perceived value, customer satisfaction, trust, and loyalty. Figure 1 illustrates the research model.
## 3 Results and Discussion  
### Determination of Harvest Parameters and Physicochemical Properties of Kiwifruit and Persimmon  
The properties of investigated fruits, including ripeness indices were evaluated and compared. At commercial maturity stage the following results of harvest parameters were obtained: firmness (Newton (N)) _for Actinidia (A.) deliciosa_ cv.Hayward (ADH) was 40.32 \(\pm\) 0.32; followed by _A. eriantha_ cv. Bidan (AEB) of 38.52 \(\pm\) 0.28; _Diospyros kaki_ Thunb. cv. Fuyu (DKF) of 32.61 \(\pm\) 0.43 and _A. arguta_ Cheongsan (AAC) of 29.64 \(\pm\) 0.21. Total soluble solids (TSS, Brix) showed the following values: DKF (11.94 \(\pm\) 0.15) \(>\) AAC (8.14 \(\pm\) 0.13) \(>\) ADH (7.81 \(\pm\) 0.09) \(>\) AEB (7.34 \(\pm\) 0.04). Acidity (%) was estimated in the following order: ADH (1.47 \(\pm\) 0.07) \(>\) AAC (1.45 \(\pm\) 0.09) \(>\) AEB (1.40 \(\pm\) 0.08) \(>\) DKF (1.21 \(\pm\) 0.04). The measurements of pH were the following: DKF (5.41 \(\pm\) 0.34) \(>\) ADH (3.22 \(\pm\) 0.16) \(>\) AEB (3.20 \(\pm\) 0.12) \(>\) AAC (3.04 \(\pm\) 0.09). In the investigated samples the dry matter (%) was calculated and presented in the following order: DKF (18.69 \(\pm\) 0.94) \(>\) ADH (16.66 \(\pm\) 0.73) \(>\) AEB (16.19 \(\pm\) 0.68) \(>\) AAC (15.72 \(\pm\) 0.49). The most important indices for the ripening of the investigated fruits were firmness and TSS, which showed the ranges of 40-30 N and 12-7 Brix, respectively. Based on these results the harvest was done. The determined ripening data, acidity, pH and dry matter were in the ranges similar to other recent reports [3,19,23,24].
## 1 Introduction
### Main Contributions  
The proposed path-tracking controller for AES is an extension of the previous work ([PERSON] et al., 2019) in which the actuator dynamics were neglected in the vehicle model.  
* The dynamic responses of the vehicle model in the previous work might deviate from those of the actual vehicle model with actuator dynamics for severe maneuvers. The EPS and EHB dynamics are integrated with the four-state bicycle model to enhance the accuracy of the prediction model.
* A diagonal matrix with components of tire workload ratios is used to replace the identity weighting matrix of CA to prevent saturating tire forces while generating the desired DYM.
* Different road friction coefficients are used for sensitivity analysis to evaluate the robustness and handling enhancement of the proposed algorithm at different vehicle velocities via simulation studies.
# Spintronic RF-Direct on-off Keying Modulation Using a Frequency Division Multiplex
## 1 Introduction  
As the use of multimedia based on AI increases in IoT environment, the need to increase transmission speed is rising. In addition, devices used in IoT environments often operate based on battery, so low-power consumption is important, and small-sized implementations are required to be applied anywhere. In this environment, communication technology based on a spin torque nano-oscillator (STNO) technology is becoming an alternative.  
An STNO features an ultra-wide band-tuning range that exceeds nearly 100%, and it has the advantage of being driven by low power consumption operations [1, 2].  
Furthermore, an STNO has a remarkably small size (100 nm or less in diameter) as the unit of STNO itself has an oscillation microwave frequency. An STNO can thus easily be structured with an array, as STNOs are made hundreds of nanometers in size.  
Owing to these characteristics of STNO, it has been reported that an STNO can feasibly be used for wireless communication by using frequency shift keying (FSK) or phase shift keying (PSK) modulation [3, 4, 5, 6, 7, 8]. However, although the transmission rate is announced up to 4 Mbps, it has limitations in improving the transmission rate due to the poor frequency oscillation characteristics, and frequency nonlinearity characteristics of STNO's current.  
A new modulation method is required for the realization of adequate communication using an STNO. We use on-off keying (OOK) modulation [9, 10]. This non-coherent communication method for wireless transmission allows data transmission via air without changing the frequency to transmit wirelessly, even in the presence of STNO non-linearityphenomena. Additionally, as STNO has the setting time in nano-second units of fast oscillation signals [11], it has the advantage of realizing high speed transmission through OOK operation.  
By exploiting nanotechnology, spintronic devices can be easily realized in an STNO array structure, and each STNO can be set to one of the isolated various frequencies among many channels that can be assigned due to wideband operation. Based on this, data are carried at each frequency for every STNO.  
We then propose a new modulation concept that is adaptable to the STNO in the study, and provides frequency and amplitude modulation simultaneously. It facilitates spintronic wireless communication at higher data rates according to the number of STNOs within an array, as OOK is executed with each frequency assigned at each STNO with the targeted frequency. We have previously reported on the need for a new communication concept that can be adapted to STNO characteristics, as well as the result of a modulation technique, in [10]. In this manuscript, we report spintronic communication results between a new spintronic modulator and demodulator developed by the authors as well as a transmitter and receiver considering communication link budgets in a new communications system with spintronic oscillators. Communication performance is also analyzed describing transient characteristics with the Landau-Lifshitz-Gilbert-Slonczewski (LLGS) equation.
## 3 Results and Discussion  
### Differences in the Dyslipidemia Prevalence by Subject Characteristics by Age  
#### 3.1.1 Differences by General Characteristics and Health Status  
The subjects of this study were 12,028 adults who received a diagnosis of dyslipidemia, including 2310 (19.2%) aged 19-34; 3330 (27.7%) aged 35-49; 3533 (29.4%) aged 50-64; and 2855 (23.7%) aged 65 or older. The dyslipidemia prevalence in the subjects was 16.7% for those aged 19-34; 12.8% for those aged 35-49; 18.8% for those aged 50-64; and 32.5% for those aged 65 or older, and there was a significant difference by age (\(\chi^{2}\) = 392.767, \(p\) < 0.001).  
Through analyzing the difference in the dyslipidemia prevalence according to general characteristics and health status by age (Table 1), a significant difference was found by age in general characteristics such as gender (\(\chi^{2}\) = 4.734, \(p\) < 0.004), marital status (\(\chi^{2}\) = 1347.042, \(p\) < 0.001), household income (\(\chi^{2}\) = 158.485, \(p\) < 0.001), and education level (\(\chi^{2}\) = 486.545, \(p\) < 0.001). When examined by age, there was no significant difference between the men and women in the 19-34, 35-49, and 50-64 age groups, but there was a significant difference in the dyslipidemia prevalence in the 65 and older group.  
A \"good\" subjective health status showed the highest rate in the 19-35 age group, at 41.9%, and a \"bad\" health status showed the highest rate in the 65 and older age group, at 26.4%. Regarding weight, underweight or healthy weight occurrence showed the highest rate in the 19-34 age group, and obesity showed a higher rate in the 50-64 age group and the 65 and older age group compared to that in the other groups. There were significant differences by age in terms of the subjective health status (\(\chi^{2}\) = 35.501, \(p\) < 0.001), body mass index (\(\chi^{2}\) = 39.149, \(p\) < 0.001), and family history (\(\chi^{2}\) = 59.417, \(p\) < 0.001).
## 3 HGV Tracking with Multiple Model Algorithms
### IMM Tracking Algorithm  
In this section we establish the HGV IMM tracking algorithms with three-model IMM algorithm and nine-model IMM algorithm based on aerodynamic equations and radar measurement equations. In the three-model IMM algorithm the distinguishing aerodynamic model parameters are combination of three known fixed attack angles and corresponding unknown varying bank angles. The three fixed attack angles are \(6^{\circ}\), \(9^{\circ}\) and \(12^{\circ}\). The process equation of corresponding unknown varying bank angle can be set as  
\[\
u_{k}=\
u_{k-1}+\mathit{T}\mathit{v}_{k-1,\
u} \tag{20}\]  
In the nine-model IMM algorithm, the distinguishing aerodynamic model parameters are combination of three known fixed attack angles and three known fixed bank angles. The three known fixed bank angles are \(-20^{\circ}\), \(0^{\circ}\) and \(20^{\circ}\). The transition between the \(N\) models is governed by a Markov chain, named as the transition probability \(\rho_{ij}\), which is selected at the beginning of the algorithm.  
The IMM algorithm can be divided into four parts namely interacting, Kalman filtering (model updating), model probability calculation and estimate combination. For ease of comparison with other algorithms and considering above discussion about different nonlinear Kalman filters, we use CKF in model updating. Then, the IMM algorithm can be demonstrated as follows:
###### Abstract  
The Internet of Things (IoT) connects massive smart devices to collect big data and carry out the monitoring and control of numerous things in cyber-physical systems (CPS). By leveraging machine learning (ML) and deep learning (DL) techniques to analyze the collected data, physical systems can be monitored and controlled effectively. Along with the development of IoT and data analysis technologies, a number of CPS (smart grid, smart transportation, smart manufacturing, smart cities, etc.) adopt IoT and data analysis technologies to improve their performance and operations. Nonetheless, directly manipulating or updating the real system has inherent risks. Thus, creating a digital clone of a real physical system, denoted as a Digital Twin (DT), is a viable strategy. Generally speaking, a DT is a data-driven software and hardware emulation platform, which is a cyber replica of physical systems. Meanwhile, a DT describes a specific physical system and tends to achieve the functions and use cases of physical systems. Since DT is a complex digital system, finding a way to effectively represent a variety of things in timely and efficient manner poses numerous challenges to the networking, computing, and data analytics for IoT. Furthermore, the design of a DT for IoT systems must consider numerous exceptional requirements (e.g., latency, reliability, safety, scalability, security, and privacy). To address such challenges, the thoughtful design of DTs offers opportunities for novel and interdisciplinary research efforts. To address the aforementioned problems and issues, in this paper, we first review the architectures of DTs, data representation, and communication protocols. We then review existing efforts on applying DT into IoT data-driven smart systems, including the smart grid, smart transportation, smart manufacturing, and smart cities. Further, we summarize the existing challenges from CPS, data science, optimization, and security and privacy perspectives. Finally, we outline possible future research directions from the perspectives of performance, new DT-driven services, model and learning, and security and privacy.  
Digital Twin; Internet of Things (IoT); cyber-physical systems; smart-world applications +
Footnote †: journal:  
_Future Internet_2022, _14_, 64. [[https://doi.org/10.3390/fi14020064](https://doi.org/10.3390/fi14020064)]([https://doi.org/10.3390/fi14020064](https://doi.org/10.3390/fi14020064))
# Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN
## 6 Conclusions  
This study proposes a DRL-based method called D-JERDG for joint data collection and energy replenishment in UAV-WRSN. The main problems D-JERDG addresses are individual nodes' low data collection efficiency and the imbalance in node energy consumption.  
Figure 11: Experimental results of time utilization rate.  
Figure 10: Experimental results of average flight energy consumption.  
D-JERDG optimizes the network to tackle these issues by considering node inefficiency, UAV flight energy consumption, and UAV throughput. The clustering algorithm based on K-means and an improved dynamic routing protocol is used to cluster the nodes in the network. CH nodes are selected based on the remaining energy and geographical locations of the nodes within the clusters, effectively adapting to the dynamic nature of node energy consumption. Furthermore, a simulated annealing algorithm is employed to determine the visiting sequence, and the DRL model MODDPG is introduced to control the UAV for node servicing. Through extensive simulation results, the evaluation metrics of node inefficiency, UAV throughput, and average flight energy consumption are used to assess the performance of D-JERDG. The results demonstrate that D-JERDG achieves joint optimization of multiple objectives. It outperforms the existing MODDPG approach by significantly reducing node inefficiency, saving flight costs, and improving data collection efficiency. Moreover, multiple research studies suggest that multiagent systems have significant advantages in accomplishing complex tasks. Therefore, Multi-UAV-WRSN is expected to be a primary research focus in the future.  
Conceptualization, J.L.; Data curation, J.L. and Z.D.; Funding acquisition, Y.F.; Methodology, J.L. Project administration, Y.F. and N.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant No. 62062047, the major scientific and technological projects in Yunnan Province under Grant No. 202202 AD080006, and the Yuxi Normal University under Grant No. 202105 AG070010.  
Not applicable.  
The data are not publicly available due to it is not permitted.  
The authors declare no conflicts of interest.
## 1 Introduction
Different equalizer design techniques can also be adopted (classified in the following as minimum-phase or mixed-phase). More recently [10], equalization in spatio-temporally transformed domains for the adaptive equalization of massive multichannel sound reproduction systems has been investigated, and is presently a topic of active research. This paper aims to provide an up-to-date review on RRE, discussing the pros and the cons of each technique, following the historical evolution. It is worth underlining that the RRE problem is analyzed from the viewpoint of impulse response analysis. All approaches that are not directly based on RIR analysis (e.g., parametric or graphic equalizers) are not discussed. The reader is referred to [11] for a comprehensive review on this topic. Another research field related to RRE which is not addressed in this paper is sound spatialization. The reader is referred to [12] for a recent review. This review article is organized as follows: Section 2 describes the characteristics of room impulse responses and its perception by the human auditory system. Section 3 introduces the basic concept of RRE, explaining the main challenges in inverting room responses. Section 4 describes the approaches used for equalizer design following their historical evolution. Section 5 discusses pre-processing techniques used to cope for RIR variations by exploiting human perception. Section 6 covers the evolution from single-point to multi-point equalization using multiple microphones placed within the room. Section 7 reports adaptive approaches for RRE in the framework of single-point and multi-point equalization. Section 8 introduces innovative approaches following a wave-theoretical view on the problem. Section 9 describes instrumental and perceptual measures used for state-of-the-art evaluation of RRE approaches. Section 10 reports emerging methods and new trends in the field. Finally, Section 11 concludes this review.
## 3 Results
### Precursor Polysiloxane Microspheres
As the proportion of cross-linking agents increase, the number of SiOH groups and SiOSi bonds decreases, while the number of bridges formed by hydrosilylation increases significantly, Table 1. When the weight/weight ratio (\(w/w\)) of DVB to PHMS approaches 0.48, the content of SiOH groups, and thus SiOSi cross-linking, drops significantly. Under these conditions, the polymer is cross-linked mainly by DVB bridges. The influence of the \(w/w\) ratio of DVB to SiH on the structure of cross-linked microspheres is demonstrated in Figure 1, which compares the \({}^{29}\)Si MAS NMR spectra of the microspheres obtained at low and high DVB content. This phenomenon leads to an increase in the carbon content in the preceramic microspheres from about 30 w% to 53 w% carbon as the DVB / PHMS weight ratio increases from 0.17 to 0.96, Table 1, Figure 2. In contrast, DYTMDS cross-linked microspheres, which do not have aromatic groups, contain less carbon. They are rich in hydroxyl groups and SiOSi bridges Table 1, sample PB-1. These changes in the chemical structure affect the elemental composition of the precursor polysiloxane microspheres (Table 1) and strongly affect their behavior during the ceramization process, which will be discussed in the next section. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Crosslinker** & \begin{tabular}{c} **Crosslinker/PHMS** \\ _uv/uv_ \\ \end{tabular} & **Av. C w\%** & \
\begin{tabular}{c} **Av.H** \\ **w\%** \\ \end{tabular} &
\begin{tabular}{c} **([SiOH] +** \\ **[SiOSiD]/SiCC]** \\ \end{tabular} \\ \hline PA-1 & DVB & 0.17 & 29.8 & 6.03 & 3.67 \\ \hline PA-2 & DVB & 0.33 & 36.1 & 6.77 & 1.69 \\ \hline PA-3 & DVB & 0.48 & 42.49 & 7.11 & 0.59 \\ \hline PA-4 & DVB & 0.65 & 49.12 & 7.34 & 0.1 \\ \hline PA-5 & DVB & 0.80 & 46.98 & 9.83 & 0.12 \\ \hline PA-6 & DVB & 0.96 & 53.0 & 10.1 & 0 \\ \hline PA-7 & DVB & 0.96 & 55.6 & 9.44 & 0 \\ \hline PB-1 & DVTMDS & 0.22 & 22.67 & 6.42 & 5.29 \\ \hline \end{tabular}  
PA 1-7 and PB-1 denote preceramic samples corresponding to samples of ceramic materials A 1–7 and B-1.  
\end{table} 
Table 1: Carbon and hydrogen content and the ratio of ([SiOH] + [SiOSi])/[SiCC] in the precursor polysiloxane microspheres. Figure 1: \({}^{29}\)Si MAS NMR spectra of the precursor polysiloxane microspheres obtained at different DVB/PHMS \(w/w\) ratio: A-1 = 0.17, A-6 = 0.97; X = CH\({}_{2}\)CH\({}_{2}\)
# Probability Density Estimation through Nonparametric Adaptive Partitioning and Stitching
## 2 Nonparametric Adaptive Partitioning and Stitching
### Test Distributions  
Results for any density estimation comparison will inevitably vary from one distribution to the next. Specific features of a data sample may be more difficult for some methods to accurately estimate than others. NAPS has been tested against dozens of known distributions as well as mixtures of these distributions. For most of the results shown here, we have selected the following eight representative distributions: uniform on [0,1], normal (\(\mu=5,\;\sigma=1\)), trimodal Gaussian mixture model; (\(p_{1}=0.33,\;p_{2}=0.33,\;p_{3}=1-p_{1}-p_{2},\;\mu_{1}=4,\;\mu_{2}=5,\;\mu_{3 }=6,\;\sigma_{1}=0.5,\;\sigma_{2}=0.25,\;\sigma_{3}=0.5\)), Beta with three sets of parameters; (\(\alpha=2,\;\beta=0.5\)), (\(\alpha=0.5,\;\beta=1.5\)), (\(\alpha=0.5,\;\beta=0.5\)), Stable (\(\alpha=0.5,\;\beta=0.5,\;\gamma=1,\;\mu=4\)) and generalized Pareto (\(\mu=0,\;\sigma=1,\;\xi=2\)). The specific features, challenges, and performance of each of these distributions will be discussed with examples in the next section. Based on reviewer suggestions, we also considered six additional distributions that include the generalized extreme value (\(k=2,\;\sigma=2,\;\mu=2\)), i.e., GEV(2,2,2), Gumble, i.e., GEV(0,1,5), Fechet, i.e., GEV(1,1,5), Weibull (\(a=5,\;b=0.9\)), uniform mixture model; (\(p_{1}=0.1,\;p_{2}=0.6,\;p_{3}=1-p_{1}-p_{2}\)), and an equal weighted mixture modeling using a Cauchy, i.e., stable (\(\alpha=1,\;\beta=0,\;\gamma=0.2,\;\mu=0.5\)) and a Beta (\(\alpha=0.5,\;\beta=0.5\)) distribution. These additional six distributions are very challenging and are discussed separately as a second test set in Section 3.