esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Methodology of Hybrid Channel Modeling  
This section describes the development of a hybrid channel-modeling scheme by distinctly using the multimode waveguide and GO model for underground mines. The complete flow structure of channel modeling is shown in Figure 3. The multimode waveguide model is used to characterize electromagnetic waves, and the basic modes of this model are the key explanations to Maxwell expressions, which particularize the existence of specific EM signals in the critical environment of mines. However, the major problem is its reliance on mode intensity and estimation from the excitation plane, which cannot be resolved with the multimode model [24; 29]. Therefore, the GO model is used to facilitate the cross-section area of the transmitter, as it is useful to analyze the EM distributed field.  
This research categorizes the areas of the mine into different categories. The tunnel of the underground mine is considered to be the mine gallery, and its branches are represented as sub-galleries, as shown in Figure 4. The interior architecture of the sub-gallery comprises the room-and-pillar structure, which resembles the free-space-congested terrestrial environment of high buildings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{Single mode} & Simple computation & Invalid for high frequencies \\  & Considers Physical factors & Only path-loss evaluation \\  & Frequently used in theoretical models & Invalid for near zone \\ \hline \multirow{3}{*}{GO model} & Simplicity & Less accurate on low frequencies \\  & Considers tunnel branches & Complex computation on high link distance \\  & Considers physical factors & \\ \hline \multirow{3}{*}{Multimode} & Valid for multiple frequencies & Complex \\  & Valid for near \& far zone & Incapable for wall roughness \\  & Valid for RMS delay & Invalid for mine branches \\ \hline \multirow{2}{*}{Full-wave} & High accuracy & Complex \\  & Capable for wall roughness and branches & FDTD computational extension \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of advantages and disadvantages of existing communication channel models for underground mines.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 5 Results and Discussions
### Mining Scalability and Adaptability
To maintain consistency and control in the tests, a 15 m by 15 m arena was chosen as the standard scenario for all models for assessing mining efficiency and robustness. The simulation environment was meticulously designed to replicate diverse mining scenarios, enabling the testing of the optimization and adaptation of the swarm models to these conditions. The results of these scalability and adaptability tests are illustrated in Figure 12.  
This section evaluates the mining performance of the four swarm models in terms of scalability and adaptability, focusing on how these models handle varying piles numbers and block distributions. Figure 12 shows that the honeybee model achieves the highest efficiency, improving by 39%, followed by the firefly, ant, and baseline models. All models take longer to mine with 1 pile compared to 10 piles, as more piles reduce exploration time by increasing the chances of finding minerals. However, as the number of blocks increases, so do the mining time and the workload. The baseline model struggles with multitasking, leading to inefficiency. In contrast, the ant, firefly, and honeybee models use role specialization and coordination to minimize idle time and boost productivity. The honeybee model's performance shows high consistency across different pile configurations, making it adaptable to various mining environments, as can be observed in Figure 12. This model's strategy of searching the entire area before the coordinated extraction allows it to maintain consistent performance regardless of pile numbers or ore volume. The ant model performs better in environments with fewer, concentrated ore piles, like vein mining, where it efficiently reduces operation time through task division. The firefly model is versatile in handling large ore volumes and maintains performance across different pile numbers, making it suitable for scattered resources in open pits or large-scale mining operations. Overall, the honeybee model stands out for its adaptability and scalability in dynamic and heterogeneous environments, such as placer or alluvial mines with varied deposits, and may be particularly effective in strip mining with irregular ore distribution.
## 5 Results and Analysis  
Results for the indoor test site are presented first, then the outdoor LoRaWAN test site and interference measurements results and analysis. For some measures, the movable monitoring station was reported separately due to the different operation of this device compared to the other sensors.
# Quality of Experience in 6G Networks: Outlook and Challenges
## 2 Quality of Experience in the Cellular Evolution
### QoE in the First Four Generations  
#### 2.1.1 First and Second Generation Cellular, and Mean Opinion Score  
The first-generation (IG) cellular networks' essential goal was to enable wireless and mobile voice calls, i.e., establishing a wireless interface to the standard public switched telephone networks. This involved introducing the cellular concept and maintaining an analog basis of over-the-air signals [23]. Digitized cellular communication for voice connection was introduced in second-generation (2G) networks. In the backend, this involved migrating away from a connection-oriented, public-switched telephone network to a packet-oriented network. Other functionalities allowed for structuring the network into a hierarchy that enabled mobility through handovers and roaming. In addition, 2G introduced the first digital services, including short messaging service, multimedia messaging service, and wireless application protocol. In combination, these allow for short text messages, picture-based messages, and accessing the web through mobile interfaces.  
The evolution from the earliest instances of 2G networks, i.e., a global system for mobile communications (GSM), to the later evolutions, i.e., general packet radio service (GPRS) then enhanced data for global evolution (EDGE), exhibited changes in the radio interface and the core. For the radio interface, a greater emphasis was made on capable spread techniques. This allowed for an increase in data rates from 64 kbps to 2 Mbps (in small cell deployments). Meanwhile, remnants of connection-oriented communications at the core gave way to a more packet-oriented operation.  
With voice being the predominant service in the first two generations, capturing user impression of the service relied on mean opinion score (MOS)--a subjective test extensively used in regular phone networks. In 1996, the International Telecommunication Union-Telecommunication (ITU-T) standardization took place for the MOS scoring scheme through the recommendations in [24]. This includes standardizing aspects such as room characteristics of the two subjects and the subject eligibility. It then assigns a score from 1 (lowest or bad) to 5 (highest or excellent). The standard also advises how the aggregate score can be calculated for an individual service. Quality models derived from human-based MOS estimates have also been generated to automate MOS assessment, as seen in [25]. Similar standards, both manual and automated, are set by the ITU in other multimedia communications.
## 2 Materials and Methods
### Assessment of Prediction Models  
The discriminating ability of each model was assessed from the area under the receiver operating characteristic (AUROC) curve as well as that under the precision-recall curve (AUPRC), in addition to the sensitivity and specificity. The AUPRC was calculated because it is an insightful performance metric for imbalanced data such as those for infrequent outcome events [36]. The Youden index method was used to determine the optimal cut-off value that maximizes sensitivity and specificity. The calibration performance of each model was assessed using the calibration slope and intercept. The overall performance was assessed using the Brier score [37]. A stratified five-fold cross-validation was performed for internal validation, and the mean and standard deviation of each metric were calculated.
# Method for Generating Panoramic Textures for 3D Face Reconstruction Based on the 3D Morphable Model
## 1 Introduction
The utility of 3 DMM models can be further enhanced by providing a more realistic and comprehensive texture. This study aimed to generate a panoramic face texture for a 3D face mesh from a single in-the-wild image. To overcome the limitations mentioned above, we developed a low-cost panoramic face texture dataset and panoramic face texture translator network that can be applied to a single-image 3D face reconstruction approach. The face texture dataset is used to train the texture translator network. The texture translator is trained using adversarial training and applied to a 3 DMM-based 3D face reconstruction approach by substituting or supplementing the texture module. Given a face image as the input, the 3D face reconstruction network predicts the 3D mesh and directly unwraps the input image to a texture map. The texture translator supplies an unwrapped texture map to build the relevant panoramic face texture. Finally, the 3D mesh and panoramic face texture constitute a 3D object with a texture. The principal contributions of this study are as follows:  
* We present a cost-effective method for generating panoramic face texture datasets, which included a large number of faces and panoramic face textures corresponding to each identity. * We propose a GAN-based method to produce a panoramic texture from an unconstrained image using image-to-image translation. * The texture translator is applied to the 3 DMM-based 3D face reconstruction method to add panoramic texture to the reconstructed 3D face model. The remainder of this paper is organized as follows. Section 2 examines the 3D morphable models and related studies. Section 3 presents the proposed method for producing a panoramic face texture for a 3D face mesh from a single input image, a low-cost method for generating a synthesized face texture dataset, a GAN-based panoramic texture translator and the application of the proposed method. Section 4 outlines the training procedure and results. The experimental settings are described in Section 4.1. In Section 4.2, the synthesized face texture dataset results and the generation method are described. Section 4.3 provides the results of the panoramic face texture generation and textured 3D mesh visual effects and discusses the sufficient trials conducted to evaluate the quality of the generated textures. Finally, Section 5 presents the conclusion of this study.
# Reliable User Datagram Protocol as a Solution to Latencies in Network Games
## 2 Related Research
Other protocols including the game transport protocol (GTP) [21] have not been successful as their commercialization was always questioned. In the final analysis, it is clear that existing end-to-end QoS approaches for multimedia traffic do not work well with wireless games. As traffic generated during wireless games can be quite heavy depending on the number of participants and indicate an extreme value distribution [22; 23], some new approaches that can effectively and efficiently deal with QoS-related issues should be devised to handle traffic [24].
## 4 Performance Evaluation and Analysis
### Performance Analysis
Then:  
\[E\{\blacklozenge\blacklozenge\blacklozenge\} =E\{[s_{r}(k_{1}T)+n_{r1}(k_{1}T)]\cdot[s_{r}(k_{1}T-\tau_{0}T+ \tau_{1}T)+n_{r2}(k_{1}T+\tau_{1}T)]\] \[\cdot[s_{i}(k_{2}T)+n_{i1}(k_{2}T)]\cdot[s_{i}(k_{2}T-\tau_{0}T+ \tau_{2}T)+n_{i2}(k_{2}T+\tau_{2}T)]\}\] \[\approx E[s_{r}(k_{1}T)\cdot s_{r}(k_{1}T-\tau_{0}T+\tau_{1}T) \cdot s_{i}(k_{2}T)\cdot s_{i}(k_{2}T-\tau_{0}T+\tau_{2}T)] \tag{30}\]  
\[E\{\blacklozenge\blacklozenge\blacklozenge\blacklozenge\} =E\{[s_{r}(k_{1}T)+n_{r1}(k_{1}T)]\cdot[s_{r}(k_{1}T-\tau_{0}T+ \tau_{1}T)+n_{r2}(k_{1}T+\tau_{1}T)]\] \[\cdot[s_{r}(k_{2}T)+n_{r1}(k_{2}T)]\cdot[s_{r}(k_{2}T-\tau_{0}T+ \tau_{2}T)+n_{r2}(k_{2}T+\tau_{2}T)]\}\] \[=E\{s_{r}(k_{1}T)\cdot s_{r}(k_{2}T)\cdot s_{r}(k_{1}T-\tau_{0}T+ \tau_{1}T)\cdot s_{r}(k_{2}T-\tau_{0}T+\tau_{2}T)\] \[+\underbrace{x_{i1}(k_{1}T)\cdot x_{i2}(k_{1}T+\tau_{1}T)}_{ \bigstar}\]0 \[+\underbrace{x_{i1}(k_{1}T)\cdot x_{i2}(k_{1}T+\tau_{1}T)}_{ \bigstar}\]1 \[+\underbrace{x_{i1}(k_{1}T)\cdot x_{i2}(k_{1}T+\tau_{1}T)}_{ \bigstar}\]2  
Carrying out a similar manipulation, one can also obtain \(E\{\blacklozenge\blacklozenge\blacklozenge\blacklozenge\}\) and \(E\{\blacklozenge\blacklozenge\blacklozenge\blacklozenge\blacklozenge\}\). By substituting Equations (30) and (31) into Equation (29), and after performing some algebraic manipulations, we can obtain:  
\[+\underbrace{x_{i1}(k_{1}T)\cdot x_{i2}(k_{1}T+\tau_{1}T)}_{ \bigstar}\]3 \[+\underbrace{x_{i1}(k_{1}T)\cdot x_{i2}(k_{1}T+\tau_{1}T)}_{ \bigstar}\]4 \[+\underbrace{x_{i1}(k_{1}T)\cdot x_{i2}(k_{1}T+\tau_{1}T)}_{ \bigstar}\]5 \[+\underbrace{x_{i1}(k_{1}T)\cdot x_{i2}(k_{1}T+\tau_{1}T)}_{ \bigstar}\]6 \[+\underbrace{x_{i1}(k_{1}T)\cdot x_{i2}(k_{1}T+\tau_{1}T)}_{ \bigstar}\]7 \[+\underbrace{x_{i1}(k_{1}T)\cdot x_{i2}(k_{1}T+\tau_{1}T)}_{ \bigstar}\]8  
By substituting Equation (32) into Equation (28), we can find:  
\[cov(\hat{R}_{x_{1}x_{2}}(\tau_{1}),\hat{R}_{x_{1}x_{2}}(\tau_{2}))=\left\{ \begin{array}{ll}cov(x,y)&\tau_{1}=\theta,\tau_{2}=-\theta\\ cov(x,z)&\tau_{1}=\theta,\tau_{2}=0\\ var(z)&\tau_{1}=\tau_{2}=0\\ var(x)&\tau_{1}=\tau_{2}=\theta\\ \vdots\end{array} \right. \tag{33}\]  
Taking Equation (33) to Equation (27), we solve for \(var(\hat{\tau}_{0})\) (see [11] for details), yielding \[MSE((\tau_{0})\approx\left(\sum_{p=1}^{P}p\times a_{p}E^{p-1}(\tau_{0})\right)^{2} \times var(\tau_{0})+\left(\sum_{p=2}^{P}p(p-1)a_{p}E^{p-2}(\tau_{0})\right)^{2} \times var^{2}(\tau_{0}). \tag{34}\]  
As far as the MSE is concerned, we find that it consists of two contributions, the covariance \(var(\tau_{0})\) and the bias \(E^{2}(\tau_{0}-\tau_{0})\). Referring to Equation (26) for further analysis, \(var(\tau_{0})\) is diverse because \((\partial\tau_{0}/\partial(E(\tau_{0})))^{2}\) varies with the time delay, as will be demonstrated in the next section.
# New Product Short-Term Demands Forecasting with Boxplot-Based Fractional Grey Prediction Model
## 1 Introduction  
In the equipment manufacturing industry of semiconductors and displays, the production environment is rigorous, life cycles are becoming shorter, and once the product has been reviewed and approved by the customer, the production is gradually increased. It is a severe challenge for the company to accurately predict the demand for extremely new products, because there is no large amount of historical sales data [1; 2; 3]. Therefore, how to accurately predict the demand for its products will be a serious challenge. How to find a reasonable demand prediction method is also worthy of further exploration.  
Product diversification usually requires the shortening of the time for new product development (NPD) processes in order to accelerate the launch of prototypes to markets is the key to gaining more market shares. Moreover, data collected from the demand forecasting provides important resources for decision support to plan capacity and allocate the associated capital investments for capacity expansion, requiring a longer lead time. However, how to forecast sales demands for new and extremely new products is the greatest challenge due to lack of data [1; 3].  
The grey model (GM) was first proposed to deal with the issues concerning uncertainty and insufficient information form short-term time-series data [4]. It aims to make predictions based on few-shot datasets and is the foundation of grey system theory. The GM(1,1) model is the most common and basic model. The features include the low requirement of population data, is easily operated, and includes the low loading of calculations [5]. The main concept is to detect the underlying patterns from the collected data with an accumulated generating operation (AGO), which processes sample data indirectly [6]. The grey models based on the fractional order model (FGM) has been proven to be a better method than the traditional GM, while the fractional order-r value is set in a continuous real number space [7; 8; 9; 10; 11]. In the past, much research used heuristic optimization algorithms, such as genetic algorithms and particle swarm optimization, etc., to improve the accuracy of predictions [5; 9; 11; 12; 13; 14]. In this paper, we focus on how to improve the FGM by revealing a new fractional grey prediction model, which uses box-and-whisker plots to estimate the trend of data, known as the box-plot-based fractional scale prediction model (boxplot-based FGM, BP-FGM), to improve the accuracy of predictors by setting the coefficient sets of background value \(\alpha\) in a traditional grey model.  
The following sections are organized as follows, the box-and-whisker plots theorem and grey system model are addressed in Section 2. Section 3 re-examines selected new product forecasting techniques, including fractional scale accumulation and optimization for background values. We also provided the discussion of the models presented in this research as the final section.
## 4 Results
### Preference  
The participants were asked to share their preferences across the three systems. Five participants preferred the Daria system, citing its ability to accurately understand their utterances and the ease of use. One participant specifically appreciated that it did not require pronouncing challenging letters, such as \"R\".  
Two participants favored Alexa, including one who had moderate and one who had mild dysarthria, explaining that they were comfortable with it and capable of articulating words and sentences using this system. However, for some participants, using Alexa was not preferred because continuous speech was found to be tiring, particularly for those who had more severe forms of dysarthria in which prolonged speaking can be physically demanding.  
Meanwhile, one participant (moderate dysarthria) preferred the eye gaze interaction, valuing the option to interact without the need to use their voice. However, two participants (P5 and P1) reported that the eye gaze system was not their preferred choice because of the discomfort caused by the laser from the tracker, which was uncomfortable or even painful for their eyes. In addition, the effort required to accurately control the eye gaze system was mentioned.  
This feedback sheds light on the diverse experiences and preferences of the participants with each interaction modality. A breakdown of the participants' preferences and their respective diagnoses is provided in Table 4.
## 5 Localized Recovery of Query Losses
### The Route Length of Recovery (RLR)  
In the conventional approaches of localized recovery of query loss, the sink node plays a central role to recover from failure of receiving query packets at a sensor node. In [10], the retransmission of query packets to a sensor node that lost the query message can be localized by using unicast from sink to a sensor node, assuming that every query is disseminated to all sensor nodes in the network and each sensor node knows the route to the sink.  
We compare the sink based recovery approach with the proposed approach by analyzing the _route length of recovery_ (RLR), which is defined as the minimum route length (in hops) that is taken in  
Figure 6: (**a**) A detector cluster in loss recovery; (**b**) Collective recovery of query packet loss.  
the recovery operation. RLR reflects the effectiveness of localization in the query loss recovery. In the calculation of route length, we assume that the link between any two nodes in the sensor network is bidirectional.  
As shown in Equation (3), in the proposed recovery scheme, the RLR \(L_{IS}\) consists of one hop for loss notification and retransmission of the recovery packet. While in the conventional sink-based approach, the RLR \(L_{SB}\), as shown in Equation (4), includes the number of hops \(R(\textit{sink},i)\) from sink to a sensor node \(i\) for loss notification and the same number of hops from the sink to the sensor for the retransmission of the recovery packet.  
\[L_{IS} =1 \tag{3}\] \[L_{SB} =2R(\textit{sink},i) \tag{4}\]  
Figure 7 illustrates the ratio of RLR between \(L_{IS}\) and \(L_{SB}\) for varying route length from sink to the sensor where query loss occurs. The RLR of sink-based recovery grows linearly with the distance between the sink to where the loss occurs, while our proposed method is independent of the location of the loss, which gives an exponential decrease in ratio.
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 2 Theoretical Background
### Challenges in Mitigating RFI
#### 2.4.7 GPS Jamming and Spoofing Detectors  
Specialized tools for detecting GPS interference are used to ensure the accuracy of satellite-based navigation systems. These detectors monitor signal quality and identify instances of jamming or spoofing that could disrupt critical navigation functions.
## 4 Model Formulation and Solution
### Stackelberg Equilibrium
Solve Equations (19) and (20) to find the optimal values of \(p\) and \(T_{b}\) (say \(p(\omega)\) and \(T_{b}(\omega)\), which are functions of \(\omega\)), and then substitute their values into Equation (18) to obtain \(TP_{CT_{0}}(n,\omega|p=p(\omega),T_{b}=T_{b}(\omega))\). Step 2. Let \(n=1\). Step 3. Find the value of \(\omega\) (say \(\omega_{(n)}\)) by setting \(\partial TP_{CT_{0}}(n,\omega|p=p(\omega),T_{b}=T_{b}(\omega))/\partial\omega=0\). Step 4. Substitute \(\omega_{(n)}\) into Equation (18) to obtain \(TP_{CT_{0}}(n,\omega_{(n)}|p=p(\omega_{(n)}),T_{b}=T_{b}\Big{(}\omega_{(n)}))\). Step 5. Set \(n=n+1\), and repeat Step 3 to get \(TP_{CT_{0}}(n+1,\omega_{(n+1)}|p=p\Big{(}\omega_{(n+1)}\Big{)}T_{b}=T_{b} \Big{(}\omega_{(n+1)}\Big{)}\). Step 6. If \(TP_{CT_{0}}(n+1,\omega_{(n+1)}|p=p\Big{(}\omega_{(n+1)}\Big{)}T_{b}=T_{b} \Big{(}\omega_{(n+1)}\Big{)}\)\(T_{b}=T_{b}\Big{(}\omega_{(n)}\Big{)}\)\(T_{b}=T_{b}\Big{(}\omega_{(n)}\Big{)}\), then \(TP_{CT_{0}}(n^{*},\omega^{*}|p=p(\omega^{*})\)and \(T_{b}=T_{b}(\omega^{*}))=TP_{CT_{0}}(n,\omega_{(n)}|p=p\Big{(}\omega_{(n)}),T_{b }=T_{b}\Big{(}\omega_{(n)}\Big{)}\). Hence, \((n^{*},\omega^{*})=\Big{(}n,\omega_{(n)})\) is the optimal solution for the manufacturer. Otherwise, return to Step 5. Substitute \((n^{*},\omega^{*})\) into Equations (19) and (20) and solve them to find the optimal equilibrium values of \(p^{**}=p(\omega^{*})\) and \(T_{b}^{*}=T_{b}(\omega^{*})\). ```  
**Algorithm 1. Equilibrium Solution for Stackelberg Game. **  
Once the equilibrium solution \(\left(n^{**},\omega^{**},p^{**},T_{b}^{*}\right)\) is obtained, it becomes easy to find the manufacturer's optimal shipping quantity \(q^{**}=\frac{n^{**}D(p^{**})}{\theta_{2}}\Big{(}e^{\theta_{2}T_{b}^{**}}-1\Big{)}\), the retailer's optimal order quantity \(Q^{**}=n^{**}q^{**}=\frac{n^{**}D(p^{**})}{\theta_{2}}\Big{(}e^{\theta_{2}T_{b}^ {**}}-1\Big{)}\), and the amount of carbon emissions produced by the retailer \(E_{b}\Big{(}p^{**},T_{b}^{*}\Big{)}\) and the manufacturer \(E_{v}(n^{**},\omega^{**})\). Furthermore, the corresponding maximum profits can be calculated as \(TP_{CT_{0}}\Big{(}p^{**},T_{b}^{*}\Big{)}\) and \(TP_{CT_{0}}(n^{**},\omega^{**})\).