esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results: Publication Analysis
### Set-Ups (Support and Format)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Times Used** & **Ref.** & **Main Subscales** & **Times Used** & **Ref.** & **Main Complementary Determinants** \\ \hline
12 & [9, 19, 20, 22, 24, 26, 31, 32, 69, 70] & Spatial Presence (SP) & 5 & [5, 12, 20, 24, 60] & Emotional and affective states \\ \hline
7 & [19, 20, 21, 23, 24, 32, 70] & Attention/Involvement & 4 & [12, 20, 22, 32] & Cybersickness \\ \hline
6 & [1, 5, 12, 23, 32, 70] & Realism & 2 & [19, 23] & Spatial Memory \\ \hline
5 & [9, 12, 22, 23, 59] & General Presence & 2 & [9, 22] & Realism \\ \hline
5 & [1, 5, 24, 26, 69] & Commitment/Engagement & 2 & [21, 22] & Attentional Tendency \\ \hline
4 & [1, 5, 26, 69] & Cybersickness & 2 & [12, 20] & Immersion \\ \hline
4 & [12, 20, 21, 60] & Sensory Fidelity & 1 & [26] & (Physiological) Heart rate \\ \hline
4 & [1, 5, 26, 69] & Ecological validity/naturalness & 1 & [26] & (Physiological) Electrodermal activity (EDA) \\ \hline
4 & [20, 21, 23, 60] & Immersion/Adaptation & 1 & [26] & (Physiological) Body temperature \\ \hline
3 & [1, 5, 19] & Sense of Reality & 1 & [31] & (Physiological) Electroencephalogram (EEG) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Top 10 subscales and main complementary determinants. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Times Used** & **Ref.** & **Format** & **Times Used** & **Ref.** & **Support** \\ \hline
7 & [1, 9, 19, 20, 23, 69, 70] & VE [1, 2] & (Traditional) & 5 & [9, 22, 23, 26, 32] & HMD 3 + Accessories \\ \hline
3 & [31, 59, 60] & AVE & (Auditory) & 4 & [5, 19, 20, 24] & PC/Projection (No HMD) \\ \hline
5 & [5, 12, 22, 24, 26] & VE-MIP & (Mood Induction Procedure) & 3 & [1, 12, 70] & HMD/Other supports \\ \hline
2 & [12, 21] & VE/PE & (vs. Physical Environment) & 3 & [31, 59, 60] & Only audio \\ \hline
2 & [12, 21] & VE-DT & (Digital Twins) & 2 & [21, 69] & Only HMD \\ \hline \hline \end{tabular} \({}^{1}\) References in these categories include only those results that differ from their specific subcategories (VE-MIP, VE-DT, VE/PE) \({}^{2}\) VE: Virtual Environment, \({}^{5}\) HMD: Head-Mounted Display.  
\end{table} 
Table 9: Main set-ups (formats and supports). surround-sound systems, fans and heat lamps, with the aim of enhancing multisensory immersion. These results indicate that the HMD is the reference technology for the simulation of virtual environments in the study of presence levels, environmental variables and behavioral responses. However, many of the studies recognized the importance of complementing HMDs with other devices that stimulate sensory channels other than the visual.
# Automated Bone Age Assessment with Image Registration Using Hand X-ray Images
###### Abstract
Note that the proposed method does not utilize gender information in predicting the bone age, so that a more general prediction network can be produced. In some cases, gender information is not available to health practitioners due to privacy reasons. Therefore, a general network that is solely based on the X-ray image alone is more desirable. According to the report in [22], the best-performed methods have used both the X-ray images and gender information in designing their regression networks, which has also been validated on a small testing dataset of just 200 images. Contrary to this approach, this work divides the large dataset of 12,811 X-ray images into a training, validation, and testing set according to the ratio of 8:1:1, which is the standard data division strategy [23], which results in sizable testing data of 1281 images. This work is organized into five sections. Section 2 discusses some conventional machine learning networks that have been applied to bone age assessment, followed by the convolutional neural networks approach. The radiography X-ray dataset of the hand used for training and testing the algorithm is described in Section 3. Subsequently, Section 4 explains the proposed image registration used to standardized and normalized the input X-ray images. Section 5 explains the applied Xception network regressor, which uses residual separable convolution to make an accurate prediction of the bone age. After that, Section 6 is dedicated to results discussion, where the proposed method performance is compared to the other state-of-the-art deep learning models. Concise conclusions and suggestions for future work are given in the last section.
# Space Efficiency of Tall Buildings in Singapore
## 2 Literature Survey
For example, consider a building where the owner wants to implement both a demand-controlled ventilation system and an occupancy-responsive lighting system. The ventilation system might include an economizer with modulating dampers, occupancy sensors (e.g., cameras), and a controller to manage airflow. Meanwhile, the lighting system might use wireless passive infrared sensors for occupancy detection and daylight sensors to adjust lighting levels based on natural light. Although both systems rely on occupancy data, integration between them is often difficult due to the proprietary nature of the technologies involved. There are studies on HVAC systems [71; 72; 73], which are focused on lighting control, often provide standalone solutions that do not easily communicate with each other. The traditional \"one-function-one-box\" approach, where each system operates independently, optimizes individual applications but limits data sharing and cost savings due to the lack of component reuse. This inefficiency arises from the complexity of smart building technologies and a fragmented supply chain, where different vendors produce specialized, often incompatible equipment. As a result, smart building implementations are typically more expensive and less efficient. An emerging alternative is the \"application stack paradigm\" [74], which, through innovations in building operating systems (BOSs) like XBOS [75], allows multiple functions to share components and data. By centralizing control and data management, this approach enhances efficiency, reduces redundancy, and enables flexibility, scalability, and cost savings through shared resources. The shift toward the application stack paradigm represents a significant advancement in smart building design, moving away from siloed systems toward more holistic, interoperable solutions. This enables building owners and operators to achieve higher levels of space and energy efficiency while also improving the occupant experience and reducing environmental impact. Through the seamless integration of components and functions, smart buildings can deliver a more connected, responsive, and sustainable built environment.
# Hybrid Forecasting Models Based on the Neural Networks for the Volatility of Bitcoin
## 1 Introduction and Review of Models
### Review of Models
#### 1.2.2 EGARCH Model  
The exponential GARCH (EGARCH) model proposed by [PERSON] [37] allows negative parameters unlike the GARCH model. That is, the parameters of the model have no restrictions to ensure the non-negativity of the volatility. This model can describe the volatility leverage effect which reflects the asymmetric impacts and captures asymmetric behavior of the time series.  
The EGARCH \((p,q)\) model is defined as follows.  
\[\log y_{t}^{2}=w+\sum_{i=1}^{q}\alpha_{i}\left[\frac{|\varepsilon_{t-i}|}{y_{t -i}}-\sqrt{\frac{2}{\pi}}+\gamma\frac{\varepsilon_{t-i}}{y_{t-i}}\right]+\sum _{i=1}^{p}\beta_{i}\log y_{t-i}^{2}, \tag{2}\]  
where \(\alpha_{i}\) with no restrictions captures the volatility clustering effect, \(\beta_{i}\) measures the persistence in conditional volatility irrespective of the events in the market and \(\gamma\) measures the asymmetric leverage coefficient to describe the leverage effect of volatility. \(\alpha_{i}\), \(\beta_{i}\) and \(\gamma\) are parameters to be estimated.
## 3 Results
### Compression Ratio  
The \(COP_{h}\) simulation and comparison were based on the isentropic efficiency of compression being the same for different refrigerants. However, different refrigerants could lead to large differences of compression ratio, which can affect the clearance volume and isentropic efficiency of the compressor. Compression ratio is thus an important factor in refrigeration and heat pump cycle. The high compression ratio would result in low isentropic efficiency for single stage compression or the requirement of two-stage compression. The compression ratio of ASHP using different refrigerants for space heating under the designed cases were thus simulated, as shown in Figure 10.  
Figure 9: The \(COP_{h}\) of ASHP with different refrigerants to supply water at 35 \({}^{\circ}\)C under varied thermal environments.  
In Figure 10, the compression ratio of ASHP using R134a is highest, and R410A has the lowest compression ratio among the refrigerant candidates. The compression ratio of proposed refrigerant RHR-1 is lower than R134a, but higher than R22. High compression ratio would lead to low isentropic efficiency and result in low refrigeration efficiency. Under the extreme design case in which supply water temperature is 50 \({}^{\circ}\)C and outdoor air temperature is \(-\)12.5 \({}^{\circ}\)C, the maximum compression ratio requirements are 12.35, 11.89, and 9.66 for R134a, RHR-1 and R22, respectively.
# Predicting Phubbing Through Machine Learning: A Study of Internet Usage and Health Risks
## 3 Materials and Methods
### Artificial Neural Networks  
Artificial neural networks (ANNs) are computational frameworks designed to emulate the cognitive functions of the human brain. The development of AI stems from efforts to mathematically model the brain's mechanisms of processing information, thereby contributing to advancements in artificial intelligence applications [29].  
Artificial nerve cells closely resemble their biological counterparts. These artificial neurons connect to create an ANN, functioning similarly to biological neurons. They have components dedicated to receiving input signals, processing them, and then transmitting outputs. An artificial neural cell comprises five essential parts: inputs, weights, a summation function, an activation function, and an output. The inputs symbolise values or signals obtained from other neural cells or the external environment. Weight values are coefficients that determine the effect of the inputs received by the neural cell on the neural cell. Each input has its own weight value. The summation function computes the total input to a neural cell by adding together the inputs, each multiplied by its corresponding weight. The activation function then processes this net input, deciding on the output that the cell will generate in response [30; 31]. The output of each neuron is determined using the following equation:  
\[\mathrm{f(x)}=\mathrm{f}\!\left(\sum_{i=1}\mathrm{w_{i}x_{i}+b}\right) \tag{1}\]  
In the equation, \(x_{i}\) and \(w_{i}\) represent the input values of the neuron and their weights, respectively. \(f\) is the non-linear activation function used throughout the network. The bias value \(b\) represents the activation threshold of the neuron.  
ANNs can be categorised into feedforward and feedback networks based on the configuration of the interconnections among the neurons. In feedforward networks, neurons are systematically organised in successive layers, beginning with the input and culminating at the output layer. Conversely, feedback neural networks exhibit a more complex interconnection scheme, where the output of a neuron can serve as input not only to neurons in subsequent layers but also to neurons within the same layer or in preceding layers [32; 33].
## 4 Discussion
### Three-Dimensional-Printing Gastrointestinal Organs and Their Applications  
The most common 3D-printed gastrointestinal organs are the stomach, colon, and esophagus. Most studies have focused on specific training and preoperative evaluation for surgeries and other procedures related to the gastrointestinal tract. The current 3D gastrointestinal models are patient-specific ones derived from real patient imaging data. Only six studies mentioned the dimensions of these models [21,24,32,35,39,40]. Figure 6 shows the process of generating 3D-printed models from image post-processing and the segmentation of original CT images to a 3D model's development.  
The endoscopic procedure is the primary treatment for gastric diseases. Therefore, 3D-printed stomach models have predominantly been applied for endoscopic procedure training including endoscopic ampullectomy, biopsy, endoscopic hemostasis, intragastric balloon, and stent placement [18; 21; 24; 27; 29; 38].  
Unlike the gastric model, the 3D-printed colon model focuses on preoperative planning in addition to endoscopic training, which is associated with a high incidence of colon tumors. Three-dimensional-printed models have provided patient-specific anatomy and more accurate identification of tumor locations for surgeons under challenging conditions [26; 31]. Moreover, studies have analyzed the colon morphology and designed models based on three common morphological patterns to improve their realism [22].  
The use of 3D esophageal models has increased in recent years. [PERSON] et al. reported the first application of 3D-printed esophageal models to complex esophageal cases [20], and [PERSON] et al. and [PERSON] et al. explored the use of 3D esophageal models in pediatric surgery. The researchers have also developed 3D-printed esophageal models for esophageal atresia with tracheeesophageal fistula repair [36; 39]. Most participants opened that the 3D model was a suitable training tool. The 3D-printed model can potentially be used as a training tool by pediatric surgeons for esophageal procedures.  
Figure 5: Gastric rubber-like models with silicone coatings: (**A**) 3D stereolithographic (STL) gastric models, derived from patient CT images; (**B**) model fabricated using a 3D printer; (**C**) CT scans of the gastric models enable comparison with the corresponding STL models. Reprinted with permission under open access from [PERSON] et al. [32].
# A W-Band Communication and Sensing Convergence System Enabled by Single OFDM Waveform
## 5 Experimental Setup and Results
### Comparison with Existing Works  
A performance comparison of the proposed system is compared with the existing OFDM-based works in Table 4. In most of the experimental demonstrations for the convergence of communications and sensing, the OFDM waveform is evaluated for the sensing performance only. Since the speed resolution is linked with the observation time, higher speed resolution can be realized with longer observation intervals, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **Carrier Frequency** & **Bandwidth** & **Range Resolution** & **Observation Time** & **Speed Resolution** & **Data Rate** \\ \hline
[34] & 24 GHz & 93 MHz & 1.6 m & 3.168 ms & 1.97 m/s & 20 Mbit/s \\ \hline
[31] & 2.4 GHz & 98.28 MHz & 1.5 m & 20 ms & 4.2 m/s & Not evaluated \\ \hline
[32] & 28 GHz & 400 MHz & 0.4 m & 0.25 ms & N.A & Not evaluated \\ \hline
[28] & 77 GHz & 200 MHz & 0.75 m & 8 ms & 0.48 m/s & Not evaluated \\ \hline
[29] & 77 GHz & 1.024 GHz & 0.14 m & 4.4 ms & 0.38 m/s & Not evaluated \\ \hline
[30] & 5.2 GHz & 80 MHz & 1.87 m & 128 ms & 0.22 m/s & Not evaluated \\ \hline Proposed & 97 GHz & 3.9 GHz & 0.04 m & 1.9 ms & 0.97 m/s & 8.08 Gbit/s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of the proposed converged system with the existing works.  
Figure 11: Data transmission results. The baseband spectrum and 16-QAM constellation, and red color for a LOS link of 0.6 m with background reflections. Blue color is the corresponding simulation result.
# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 5 Experimental Results
### Results of Federated Learning with Cyclical Learning Rate  
#### 5.2.1 CLR Results on MNIST  
Starting with the MNIST dataset, we observe the optimization of the federated training done by using a cyclical learning rate under non-IID conditions. First, we run the learning rate range test (LRRT) with SGD as the optimizer.  
The learning rate range test identified 0.007 as the minimum learning rate, where the loss began to decrease, and 0.1 as the maximum, where the loss started to slow down, as demonstrated in Figure 6. These learning rate values were used as bounds for the CLR strategy during federated training.  
Next, we proceeded to the performance comparison between FedAvg with a fixed learning rate and FedAvg with CLR on the MNIST non-IID dataset.  
Figure 7 indicates the evaluation accuracy over the training rounds for the FedAvg with a fixed learning rate set at 0.01 and FedAvg with a cyclical learning rate policy, where the learning rate fluctuates between a minimum of 0.007 and a maximum of 0.1.  
Table 4 illustrates the differences in evaluation accuracy between these two strategies per 10 rounds of training.  
Figure 6: Learning rate range test for MNIST.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Distribution** & **Maximum Accuracy (\%)** & **Rounds to 37\%** \\ \hline CIFAR-10 & 52.2 & 43 \\ \hline CIFAR-10 Non-IID & 37.8 & 176 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maximum accuracy and rounds to 37%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Communication Rounds** & **SGD with Fixed Lr** & **SGD with Cyclical Lr** \\ \hline  
\hline \end{tabular}
\end{table}
Table 4: Evaluation accuracy differences per 10 training rounds.  
Figure 7: MNIST non-IID with fixed learning rate vs. MNIST non-IID with cyclical learning rate.  
The use of the cyclical learning rate strategy helped the model to achieve a higher accuracy of 97.01% in 200 rounds, compared to 94.04% with a fixed learning rate.  
Additionally, as shown in Table 5, CLR enabled the model to achieve the target test accuracy in significantly fewer rounds compared to using a fixed learning rate, demonstrating a notable speedup.
## 2 Case Study Area  
The case study area is located on the western part of Cyprus, at the Paphos district (Lat: 34.76\({}^{\circ}\), Long: 32.41\({}^{\circ}\), WGS 84 projection). This area holds important open-air archaeological sites and monuments like the UNESCO enlisted archaeological sites of \"Nea Paphos\" and \"Tombs of the Kings\" while several historical buildings are found at the Centre of Paphos town. More information regarding the archaeological context of the area can be found in [29]. Figure 1 indicates the area of interest (zones a and b), while a land-use/land-cover map from the CORINE 2018 program [30] is displayed in the background. The CORINE Land Cover inventory was initiated in 1985 and regular updates have been produced in 2000, 2006, 2012, and 2018. It consists of an inventory of land cover with 44 classes [30].  
The landscape of Paphos is threatened by various natural and anthropogenic hazards such as soil erosion [31], urban expansion [32] and fires [33]. Several studies have been performed in the past on these hazards. For instance, in [32], [PERSON] et al. reported an increase of more than 300% in the urban areas in a period of 30 years, indicating the dramatic and sudden landscape changes in the surroundings of these important archaeological sites. Moreover, [PERSON] et al. [34] reported this area to have high erodibility due to water.
###### Abstract  
Existing biodynamic models adopt apparent mass and seat-to-head transmissibility to predict the response of seated humans to whole-body vibration, limiting their ability to capture the actual response of distinct body segments in different excitation conditions. This study systematically develops a 7-DOF seated human model, a vibration experiment, and a novel hybrid optimization to estimate unknown mechanical parameters and predict the response of different human body segments to vertical vibrations. Experimental results showed that the upper trunk and head were most susceptible to transmitted vibrations. Combining the 7-DOF model and HOM resulted in accelerated optimization, improved numerical stability, and significant minimization of the objective function value compared to conventional algorithms. Notably, the estimated parameters, particularly stiffness, remained consistent regardless of increasing excitation magnitude or change in the body segment data used. Additionally, the model captured the non-linearity in human biodynamics through stiffness softening. These findings are applicable in seating systems optimization for comfort and safety.  
seated human model; hybrid optimization methodology; vertical vibrations; parameter estimation; whole-body vibration +
Footnote †: journal: Applied Sciences
## 1 Introduction
Additionally, vibration is a contributing factor to many problems, such as workpiece surface quality, noise, tool wear, and chatter in machinery. Therefore, it is urgent to establish a more accurate dynamic response model, which comprehensively takes into account the flexible boundary conditions and forces, especially for a slender workpiece. Figure 1: Turning a slender shaft fixed to a three-jaw chuck and supported by a tailstock. The finite element method (FEM) is an effective and widely used numerical method that yields results very close to the experimental values [19; 20; 21; 22]. [PERSON] et al. [23] presented an enriched finite element formulation (more efficient than conventional FEA) for analysing the dynamics of one-dimensional bars and Euler-Bernoulli beams. [PERSON] and [PERSON] designed an FEA system using MATLAB to conduct modal analysis of cantilever beams [24]. In their analysis of the effects of various parameters on diametral error during turning with a follower rest, the authors of [25] used a beam element to create a mesh for a slender bar. This approach yielded both a stiffness matrix and a mass matrix. However, they simplified the model by representing the chuck, tailstock, acting point, and follower rest as four nodes, treating them as hinges. The remainder of this paper is organised as follows: Section 2 presents the dynamic model of a flexible workpiece with a tailstock. Section 3 establishes the numerical matrix of the dynamic model using FEA. Section 4 presents the effects of the axial force and supporting stiffness on the natural frequency of the shaft. Section 5 presents the experimental results obtained from a general lathe and shows that they are consistent with the theoretical model. Finally, Section 6 describes our conclusions.
# An Optimization Routing Algorithm for Green Communication in Underground Mines
## 5 GA-Based Network Clustering Model
### Establishment of a Fitness Function  
Based on the DCMST algorithm, the optimal cluster structure required to minimize the weights W of all the routing paths from a chromosome perspective is as follows:  
\[W^{*}=\sum_{i=1}^{C}\sum_{j=1}^{M_{\texttt{D}_{\texttt{D}_{\texttt{D}_{\texttt{i}} }}}}W\left(v,v_{j}\right) \tag{9}\]  
where \(C\) is the number of clusters in the GCUM and \(M_{\texttt{D}_{\texttt{D}_{\texttt{i}}}}\) is the number of member nodes in a cluster with cluster head node \(v_{i}\).  
Therefore, the residual energy, weight, and density of each node will be employed in the fitness function as follows:  
\[f=\sum_{s}\frac{\widehat{E}_{s}(t)}{E_{s}(0)}+\frac{\widehat{E}_{s}(t)}{ \widehat{E}_{s^{\prime}}+\widehat{E}_{s}}+\frac{1}{W^{*}} \tag{10}\]  
This fitness function is used to evaluate the fitness of chromosomes. A medium library that can be customized depending on the specific GCUM is established to control the individuals created in a generation. When the calculations reach the maximum number of steps, or the fitness function converges, the cluster structure of the GCUM is established.