esa-sceva/satcom-chunk-collection huggingface dataset:
# Trends and Perspectives of mHealth in Obesity Control
## 3 Results
### Trends in mHealth for Obesity Control
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Items** & **Questions** & **Responses** \\ \hline \multirow{6}{*}{Personalization and tailored interventions:} & What trends are emerging in the development of personalized mHealth interventions for obesity control, such as adaptive algorithms or dynamic feedback mechanisms? & Personalized mHealth interventions for obesity control are advancing with adaptive algorithms, dynamic feedback mechanisms, personalized coaching, multi-modal approaches, and integration of wearable technology to deliver tailored support for individuals managing their weight. \\  & & mHealth platforms utilize user data and behavior analytics to tailor interventions to individual preferences, goals, and needs? \\  & & mHealth platforms utilize user data and behavior analytics to tailor interventions to individual preferences, goals, and needs? \\  & & mHealth platforms utilize user data and behavior analytics to tailor interventions to individual preferences, goals, and needs, employing personalization algorithms and dynamic adaptation to deliver personalized feedback and recommendations for enhanced engagement and effectiveness in obesity control and other health management areas. \\  & & The trends in the use of remote monitoring and support features within mHealth applications for obesity management include integration with wearable devices, real-time data tracking, automated alerts and reminders, remote obesity management, enabling personalized interventions, continuous monitoring, and enhancing user engagement, adherence, and outcomes. \\  & & Advancements in wearable devices, sensors, and mobile connectivity have revolutionized real-time tracking and feedback in obesity management, enabling personalized interventions, continuous monitoring, and data-driven insights to support individuals in achieving and maintaining a healthy weight. \\  & & Behavioral change techniques commonly used in mHealth interventions for obesity control include goal setting, self-monitoring, feedback, social support, cognitive behavioral techniques, and tailored interventions. Emerging trends include the integration of gamification, automated tracking, adaptive algorithms, social networking, interactive tools, and and personalization. \\  & & Behavior change techniques in mHealth interventions address motivation, self-regulation, and social support by providing goal setting, feedback, self-monitoring, cognitive restructuring, social support networks, and tailored interventions, empowering individuals to make sustainable behavior changes for obesity control. \\  & & \\ \hline \end{tabular}
\end{table} 
Table 1: Cont. The trends in mHealth for obesity control generated by ChatGPT include eight items, with two questions for each item. The eight items are as follows: usage patterns and demographics; emerging technologies and platforms; integration with healthcare systems; personalization and tailored interventions; remote monitoring and support; behavioral change techniques; and data analytics and insights.
# MEMS and IoT in HAR: Effective Monitoring for the Health of Older People
## 4 Materials and Methods
### Predictive Models
#### 4.3.2 Logistic Regression  
Logistic regression is a supervised learning algorithm used for binary and multi-class classification tasks. It calculates the probability of belonging to a specific class, producing outputs between 0 and 1. It uses the logistic function (sigmoid) to estimate the probability of an event (Figure 7) [40]. It can be represented by Equation (1),  
\[p=1/1+e^{-y} \tag{1}\]  
where \(e=\) Euler number, and \(y=\) value of the dependent variable. Logistic regression is versatile and can be applied to numerical and categorical data, offering discrete outputs and using the maximum likelihood estimation (MLE) method.  
This study used the Random Forest model to classify human activities. Several reasons justify this choice, highlighting the advantages and unique characteristics of this algorithm. Random Forest demonstrates substantial accuracy in predictions, which is particularly important for recognizing human activities in applications such as physical rehabilitation and health monitoring. Compared to other algorithms, Random Forest is less prone to overfitting by combining multiple decision trees. This allows the model to generalize better to unseen data, enhancing its reliability in real-world scenarios. The algorithm effectively manages complex and noisy data, such as information from accelerometers and gyroscopes. Aggregating decisions from multiple trees helps filter out noise and improve prediction quality. Random Forest measures feature importance, allowing us to identify which sensors or data features are most relevant for classifying activities. This can help optimize the system and reduce model complexity. The algorithm is highly scalable and can be applied  
Figure 7: Logistic function.  
to large datasets without significant performance loss, which is beneficial when working with real-time sensor data collected over extended periods. Random Forest is relatively simple to implement and train, making it easier to develop and maintain the model. The algorithm is resilient to changes in input data, allowing it to adapt well to variations in movement or environmental conditions while maintaining high accuracy.
# An Ensemble Transfer Learning Model for Detecting Stego Images
###### Abstract  
As internet traffic grows daily, so does the need to protect it. Network security protects data from unauthorized access and ensures their confidentiality and integrity. Steganography is the practice and study of concealing communications by inserting them into seemingly unrelated data streams (cover media). Investigating and adapting machine learning models in digital image steganalysis is becoming more popular. It has been demonstrated that steganography techniques used within such a framework perform more securely than do techniques using hand-crafted pieces. This work was carried out to investigate and examine machine learning methods' critical contributions and beneficial roles. Machine learning is a field of artificial intelligence (AI) that provides the ability to learn without being explicitly programmed. Steganalysis is considered a classification problem that can be addressed by employing machine learning techniques and recent deep learning tools. The proposed ensemble model had four models (convolution neural networks (CNNs), Inception, AlexNet, and Resnet50), and after evaluating each model, the system voted on the best model for detecting stego images. Since active steganalysis is a classification problem that may be solved using active deep learning tools and modern machine learning methods, this paper's major goal was to analyze deep learning algorithms' vital roles and main contributions. The evaluation shows how to successfully detect images that contain a steganography algorithm that hides data in images. Thus, it suggests which algorithms work best, which need improvement, and which are easier to identify.  
deep learning; transfer learning; steganography; feature extraction; ensemble model; steganalysis; stego images +
Footnote †: journal: Applied Sciences
## 4 Results
### Phase One Research
#### 4.1.2 Data Collection  
Interview Outline: We designed a series of questions aimed at assisting participants in recalling their experiences using AIVGTs in video production (see Appendix A for the interview outline). These questions were intended to delve deeply into the participants' perceptions of using AIVGTs in the video production process, including comparisons between live-action and AI-generated videos, technological challenges, impacts on the  
Figure 4: AI-generated video vs. video of an actual shooting (production section).  
Figure 5: AI-generated video vs. video of an actual shooting (partial screen comparison).  
creative process, as well as industry application and adoption barriers. The first two questions focused on the participants' initial impression differences when viewing live-action versus AI-generated videos, and their comparison of the two types of videos in terms of visual effects, narrative storytelling, and emotional conveyance (questions 4-7). Subsequent questions explored the technical challenges encountered while using AIVGTs and the areas they believe require the most improvement. Participants were asked about the positive or negative impacts of AIVGTs on the creative process and how these tools could assist during the creative phase. Regarding industry application, we inquired about the main barriers to promoting AIVGTs, as well as the adoption rate of these tools within the participants' fields and the factors influencing their application (questions 8-11). The final questions focused on the potential of AIVGTs to enhance production efficiency, reduce costs, and maintain content originality; participants' expectations for future technological developments; and their advice for peers considering using AIVGTs (questions 11-17).  
Interview Process: Eight participants were interviewed via the internet, and two participants underwent face-to-face interviews. To ensure the freedom and openness of the interviews, we clearly communicated the research objectives at the beginning and emphasized that participants had the right to terminate the interview at any point. We promised confidentiality regarding the interview content and used pseudonyms in the study to protect participants' privacy. Each interview was meticulously recorded, with participants' consent, using a smartphone for subsequent analysis. To gain deeper insights, we flexibly posed supplementary questions based on the progress of the interviews. The interviews continued until the point of theoretical saturation was reached, where no new information emerged. The duration of the interviews ranged from 40 to 60 min, followed by comprehensive transcription and analysis of the interview content.
# Effect of Fiber Wave Structure in Printed Circuit Boards on Signal Transmission Characteristics
## 3 Results and Discussion
### Impact of the Fiber Weave Effect on Impedance Fluctuations
#### 3.2.1 Effect of the Fiber Weave Effect on Single-Ended Impedance Lines  
Single-ended impedance is affected by location distribution. Thus, when the Dk exhibited a cyclical variation with the location distribution, it was reflected on the single-ended impedance TDR curves. The effect of fiber weave style on single-ended impedance fluctuation is shown in Figure 6. It was found that 1035 and 1078 had relatively lower impedance fluctuation amplitudes, whereas 1080, 3313, and 2116 had relatively higher impedance fluctuation amplitudes. Here, 1078 and 1035 had the lowest impedance fluctuation amplitudes, below 1.0 \(\Omega\) for 50 \(\Omega\) designed traces, whereas for other styles of fiber weave, impedance fluctuation amplitudes could reach 2.0-2.5 \(\Omega\). After flattening treatment, the medium homogeneity of 1078 and 1035 was better than the traditional style of fiber weave. The test results were consistent with theoretical calculations.
# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 5 Traffic Flow Models
### Microscopic Models
#### 5.2.1 Car-Following Models
Their work introduced the concept of backward-looking effects and headway changes with memory, which are crucial for understanding the behavior of autonomous vehicles in dynamic traffic conditions. [PERSON] and [PERSON] [171] extended this by proposing an information-sharing traffic flow model that considers time delays within intelligent transportation systems utilizing wireless communication. The intersection of game theory and traffic modeling has also been explored, as demonstrated by [PERSON] et al. [172], who developed a cellular automata model incorporating game theory to simulate mixed traffic flows of human-driven and autonomous vehicles on foggy highways. [PERSON] et al. [173] proposed a car-following model that captures the behaviors of connected and automated vehicles, leveraging graph theory to describe the communication topology among vehicles in both fixed and switching communication environments. Advanced machine learning techniques have further enriched car-following models. [PERSON] et al. [174] combined a convolutional neural network with a long short-term memory network to model and reproduce the hysteresis phenomenon observed in congested traffic flows, particularly in mixed traffic environments with adaptive cruise control vehicles. [PERSON] and [PERSON] [175] developed a car-following model to simulate the behaviors of human-driven, autonomous, and connected automated vehicles within mixed traffic scenarios. Their model provides a comprehensive framework for simulating and analyzing car-following behaviors across different vehicle types. In summary, while car-following models are a central component of microscopic traffic modeling, their evolution has been shaped by a variety of approaches and technologies. From the foundational safe distance models to the latest applications of artificial intelligence and deep learning, these models have continually adapted to meet the challenges posed by increasingly complex traffic systems. The relationships between these studies demonstrate a continuous refinement and integration of new methodologies, making car-following models an essential tool in both theoretical and practical traffic analysis.
# Reduction of Schottky Barrier Height at Graphene/Germanium Interface with Surface Passivation
## 1 Introduction  
Combinations of two-dimensional materials and bulk three-dimensional semiconductors have gained considerable attention lately, owing to potential new device concepts. Most studies focused on the properties of graphene/silicon (G/Si) Schottky junctions [1]. Many different new functionalities were observed such as bias-controlled Schottky barrier height [2, 3] (SBH), photodetectors [4, 5, 6, 7, 8, 9] or solar cells [10, 11, 12]. When covered with a metal, Metal/G/Si can be used as an efficient spin injector [13, 14] or a low-resistance contact [15, 16]. The reduction of the Si SBH is made possible by simultaneous etching of the silicon oxide and hydrogen passivation of the surface with graphene already on top [17]. In that case, the hydrogen passivation of Si is protected during metallization. This results in alleviation of the Fermi-level (FL) pinning and a low SBH. It is, therefore, possible to strongly reduce the SBH and associated contact resistance on moderately doped Si without any thermal treatment, or heavy doping at the interface.  
Because of similar FL pinning issues, it is very challenging to realize low-resistance contacts to n-type Ge. Reduction of the SBH is particularly important for the realization of ohmic contacts or for impedance matching for spin injection [18, 19]. In order to alleviate the FL pinning, different strategies such as Ge surface passivation through wet chemicals [20, 21] or deposition of a thin insulator layer on Ge surfaces have been employed [22, 23, 24, 25, 26, 27].  
The aim of this work is to obtain a low SBH for electrons in graphene/Ge junctions using a similar strategy as for graphene/Si junctions: to associate graphene with surface passivation of the semiconductor in order to alleviate FL pinning and reduce the SBH. For that purpose, we used X-ray Photoemission Spectroscopy (XPS) to study the interface between physico-chemistry and bandalignments of graphene/germanium contacts and to show the possibility to modify the SBH via wet chemical passivation of the underlying germanium. We then observed the effect of the modification of the graphene work function by gold deposition on the SBH of the junction.
## 3 Results
### Use of FFoV Array Synthesis Methods
#### 3.2.2 Example B.2  
In our second example, we use a technique on a square array to produce equal sidelobes in all constant \(\varphi\) cuts around the array.  
The LFoV indices are \(SLL_{\text{L}}<-25\) dB, \(HPBW_{\text{L}}\)-1\({}^{\circ}\), and \(D_{\text{L}}>42\) dBi and the range of view is \(\sim\)10\({}^{\circ}\). Let us now follow the steps of the proposed design procedure:  
Step 1: The scaling factor is found to be approximately equal to 5.76.  
Step 2: The respective FFoV indices are: \(SLL_{\text{F}}<-25\) dB, \(HPBW_{\text{F}}\)-5.76\({}^{\circ}\), and \(D_{\text{F}}>26.8\) dBi.  
Step 3: Next, we apply the FFoV design method. To design the array, we need the interelement distance, the number of the elements, and the excitation.  
Figure 6: The power pattern of the designed array for various \(\varphi\) cuts (red: without element pattern, green: with element pattern).  
The number of elements is determined by [4]:  
\[D_{F}=e_{T}N_{e}D_{e}\Rightarrow N_{e}=D_{F}/(e_{T}D_{e}), \tag{25}\]  
where \(D_{e}\) is the average element directivity (gain divided by loss efficiency) and \(e_{T}\) is the taper efficiency. We assume that \(e_{T}=1/2\) and \(D_{e}\) is equal to the isolated element directivity since we aim at approximately estimating the number of elements of the array. We also assume that the radiating elements are placed in a \(\lambda/2\)\(\times\)\(\lambda/2\) lattice in the FFoV array and also that they are uniformly illuminated squares (\(\lambda/2\times\lambda/2\)). \(D_{e}\) is then equal to \(\pi\). Applying (25), we find that \(N_{e}=17.62\). Thus, since we are going to design a square array, we choose the value of \(18\)\(\times\)\(18\) for the number of the elements.  
The excitation for the \(18\)\(\times\)\(18\) array is computed following [27] and using the \(SLL_{F}\) prescription. For this array, the \(HPBW\) is \(1.14\) times greater than the prescribed FFoV one, which is equal to \(\sim\)\(5.7^{\circ}\). Thus, we set the inter-distance equal to \(d=(1/2)\)\(\times\)\(1.14\).  
Step 4: Using the scaling transformation, the final array produces the power pattern given in Figure 7. The respective directivity is \(D_{L}=44.44\) dBi for elements with side-length equal to \(\mu\times d\).
## 2 Results and Discussion
### Application of the HS-SPME/GC-MS Method to Strawberry Fruit Samples
#### 2.2.3 Lactones  
Lactones are cyclic esters and are structurally related to furanones; however, for the purpose of discussion, they were classified as a separate group. They contribute to fruity and coconut aromas. This chemical class is one of the most intense and important strawberry aroma volatiles. \(\gamma\)-Decalactone represents the major compound, and it can be used for variety classification [25; 26; 28; 32; 37]. It has been reported that the amounts of individual lactones vary according to type of cultivar; for example, Independence, Camarosa, and Hood strawberry cultivars were characterized by higher levels of lactones than other cultivars [25]. Moreover, Hood and Independence strawberry cultivars had higher total odor activity values (OAVs) for lactones, which correlated well with perceived high peach note levels in the sensory study [25]. In this work, six lactones (\(\gamma\)-butylrolactone, \(\gamma\)-caprolactone, \(\gamma\)-octalactone, \(\gamma\)-nonalactone, \(\gamma\)-decalactone, and \(\gamma\)-dodecalactone) were detected, representing 30.05%, 14.82%, 13.83%, 8.62%, 7.18%, and 4.80% of total volatiles in frozen, freeze-dried, shade air-dried, microwave-dried, oven-dried (45 \({}^{\circ}\)C), and oven-dried (60 \({}^{\circ}\)C) strawberry samples, respectively, with respect to 4.07% in fresh sample. \(\gamma\)-Decalactone was the major lactone in frozen (26.05%), freeze-dried (12.14%), shade air-dried (8.60%), and oven-dried (45 \({}^{\circ}\)C) (4.78%) samples, while \(\gamma\)-butylrolactone was the major lactone in microwave-dried (4.72%), oven-dried (60 \({}^{\circ}\)C) (3.15%), and fresh (3.11%) samples. \(\gamma\)-Decalactone possesses a \"peach\", \"strawberry-like\" aroma and is among the key strawberry flavor volatiles in many cultivars [32]. Noteworthily, the levels of lactones were significantly increased in all treatments, particularly frozen, freeze-dried, and shade air-dried samples.
# Optical and SAR Image Registration Based on the Phase Congruency Framework
###### Abstract
Then, the Harris detector is used to extract keypoints in the novel multi-scale space and we name the method PC-Harris. PC-Harris is compatible with large-scale differences between optical and SAR images. 2. In order to solve the problem that most of the descriptor construction methods based on the PC algorithm are not suitable for large-scale and rotation differences between optical and SAR images, we propose a PC-based descriptor (named PCLG), which combines the PC maximum moment and the log-Gabor filter. The remainder of this paper is organized as follows: Section 2 describes the PC-based feature detector and the novel descriptor. Section 3 analyzes the performance of the proposed algorithm. Section 4 presents the conclusion and the recommendations for future work.
# Quality Evaluation of Wasteless Mining in Dongguashan Based on Intuitionistic Fuzzy Set and VIKOR
## 3 Intuitionistic Fuzzy Sets and VIKOR Method Theory
### Intuitive Fuzzy Set Multi-Attribute Decision Making  
For a multi-attribute decision problem, it consists of \(p\) scenarios \(x_{i}(i=1,2,\,\ldots\,\,p)\) composing the set of scenarios X = \(\{x_{1}\),\(x_{2},\,\ldots\,\,x_{p}\}\). Each scenario can be evaluated by q attributes \(o_{j}(j=1,2,\,\ldots\,\,d)\), and the set of attributes is noted as \(O=\{o_{1}\),\(o_{2},\,\ldots\,o_{q}\}\). The evaluation value of scheme \(x_{i}\)\(\in\)\(X\) belonging to \(o_{i}\)\(\in\)\(O\) is written in the form of the intuitionistic fuzzy set as \(F_{ij}=\{<\)(\(o_{i}\),\(x_{i}\)),\(m_{ij}\),\(n_{ij}\)\(>\)(\(i=1,2,\,\ldots\,p\); \(j=1,2,\,\ldots\,d\)), abbreviated as \(F_{ij}=<\)\(m_{ij}\),\(n_{ij}\)\(>\), where \(m_{ij}\in\) [0,1] and \(n_{ij}\in\) [0,1] denote the affiliation degree of \(x_{i}\)\(\in\)\(X\) with respect to \(o_{j}\)\(\in\)O, satisfying \(0\leq m_{ij}+n_{ij}\leq 1\). The matrix form of the multi-attribute decision in the intuitionistic fuzzy sets can be expressed as follows:  
\[F=(<m_{ij},n_{ij}>)_{p\times q}=\begin{bmatrix}<m_{11},n_{11}>&<m_{12},n_{12}>& \cdots&<m_{1q},n_{1q}>\\ <m_{21},n_{21}>&<m_{22},n_{22}>&\cdots&<m_{2q},n_{2q}>\\ \vdots&\vdots&\vdots&\vdots\\ <m_{p1},n_{p1}>&<m_{p2},n_{p2}>&\cdots&<m_{pq},n_{pq}>\end{bmatrix} \tag{4}\]  
Weight is a very important factor in the multi-attribute decision problems, while attribute weight is another fuzzy concept, and different results can be obtained by various weighting methods. As a result, it is difficult to determine the weights accurately in practical decision problems. In this study, the intuitionistic fuzzy set VIKOR is combined with Analytic Hierarchy Process (AHP) to obtain weighted indicators to reflect the importance of the different indicators [16,17,32,33].
## 3 Charrelation Matrix Based Blind Detector for DS-CDMA System
### Blind User Separation and Blind Spreading/Chip Sequence Estimation
Note that there exist a linear transformation relationship between \(\mathbf{U}_{\text{B}}^{T}\) and \(\mathbf{G}\) If the linear transformation is assumed as \(\mathbf{A}\), we can establish the blind separation model:  
\[\mathbf{U}_{\text{B}}^{T}=\mathbf{AG}^{T} \tag{31}\]  
The matrix \(\mathbf{A}\)  denotes the mixing matix in the BSS model, \(\mathbf{G}^{T}\)  is the source matrix, and the matrix \(\mathbf{U}_{\text{B}}^{T}\) is the observation matrix. Therefore, the blind separation can be used to estimate the matrix \(\mathbf{G}^{T}\). Assume that \(\mathbf{Y}\) denotes the separated signal. After the separation is executed, the hard decision is carried out for \(\mathbf{Y}\), namely:  
\[\hat{\mathbf{G}}^{T}=sign\big{(}\mathbf{Y}\big{)} \tag{32}\]  
Then we can estimate the spreading sequence from Equation (32) as \(\hat{\mathbf{G}}=\left[\tilde{\mathbf{c}}_{1},\ldots,\tilde{\mathbf{c}}_{K} \right]_{C\times K}\). The steps of the proposed method can be outlined as shown in Table 2.