esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling the Trip Distributions of Tourists Based on Trip Chain and Entropy-Maximizing Theory
## 4 The Trip Distribution Forecasting Model Based on Trip Chains
### Trip Chain Constraints
#### 4.1.2 Trip Distance Constraint  
The actual trip distance of tourists in a tourism city with a complex traffic network reflects the distance limitation of tourist trip chains. Generally, when calculating trip impedance, the trip distance or time should be calculated according to the actual traffic network. Thus, to obtain the actual trip distance between traffic zones and further calculate trip impedance, it is necessary to establish a tourism traffic network, and take into consideration the length and interaction of public transit roads, railways, and other types of transit adopted by tourists to travel. The needs for data preparation and processing should also be considered.  
The trip distance of a tourist's travel from an origin traffic zone to a destination traffic zone is normally limited within a certain range of tourist destinations. Thus, the constraint of the total trip distance of tourists can be defined as Equation (2):  
\[d=\sum\limits_{m=1}^{M}\sum\limits_{H\in H^{n}}S_{mH}\times d_{mH} \tag{2}\]  
where \(d\) represents the total trip distance, and \(d_{mH}\) is the trip distance of different choice sets in which the trip chains take \(m\) as the origin traffic zone.
# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 1 Introduction
By integrating crowdsourced data and historical signal features, we establish a dynamic signal fingerprint database that continuously optimizes the ranging reference data, significantly enhancing the ranging stability in complex scenarios. Finally, we offload computation tasks such as signal matching and trajectory estimation to the network edge nodes. Load balancing between MEC servers is achieved based on device locations. This strategy effectively reduces the data transmission pressure on the core network while leveraging edge computing resources to respond to terminal ranging requests in real-time. Through the construction of a multi-base station collaborative testing environment, systematic comparative evaluation results show that--in GPS failure scenarios--the proposed solution significantly outperforms traditional static signal methods in terms of ranging accuracy. The dynamic update mechanism allows the system to maintain stable performance over long-term operation. The edge computing architecture demonstrates superior resource utilization efficiency in terminal-dense scenarios. The main contributions of the current article are as follows:  
1. A general 5G wireless signal ranging framework integrating crowdsourced perception and MEC architecture is proposed. For dynamic environmental characteristics, the framework can collaborate with multiple algorithms to achieve continuous optimization of the signal fingerprint database with low computational overhead. Through distributed data collection and edge computing, the framework systematically addresses the three major challenges, namely, data acquisition cost, environmental adaptability, and resource constraints. 2. A progressive signal fingerprint update algorithm is designed, enabling the autonomous evolution of ranging reference data in dynamic environments, overcoming the performance degradation bottleneck of traditional static models. 3. A lightweight edge-ranging service engine is developed to validate the effectiveness of the MEC architecture in reducing network load and improving service response speed, providing a feasible solution for the practical deployment of 5G base station-assisted ranging technology. This paper is structured as follows: Section 2 provides a comprehensive literature review, discussing the latest research achievements in the field and analyzing the existing limitations and research gaps. Section 3 outlines our 5G crowdsourcing-assisted wireless signal ranging framework and its detailed components, highlighting the innovations of our approach and summarizing our original contributions to the field. Section 4 discusses the technical advantages of our solution in detail, showcasing the advantages of our approach. Section 5 presents our experimental validation process, including the experimental setup, configuration, and the results obtained. Section 6 is a detailed discussion of future work. Section 7 presents our conclusion, summarizing the advantages of the proposed solution and pointing out potential directions for future research.
## 2 Fuel Cell
#### Fuel Cell Losses
Figure 1: Block diagram of the overall equivalent circuit of the fuel cell. The model used in this study has some limitations, such as neglecting the influence of temperature and humidity on the internal resistance or ignoring water and gases flow through the membrane [27]. In this detailed model, the following assumptions are made while simulating the PEM fuel cell [28]:  
* The gases are ideal. * The hydrogen and air are fed into the stack. * The cooling system is embedded so that the temperature of the anode and cathode is stable at the stack temperature. * A water management system is designed to adjust the humidity inside the cell to an appropriate degree under various conditions. * Pressure drops across flow channels are ignorable. * The cell resistance under any operation condition is considered to be constant. * Considering that in most cases, the fuel cell does not operate in the mass transport region, the mass transport losses or concentration losses are negligible. All the required parameters were modified based on the 25-kW used fuel cell as below:  
* Nominal voltage: 210 V
* Nominal current: 120 A
* Number of cells: 300
* Nominal stack efficiency: 55%
* Operating temperature: 65 \({}^{\circ}\)C
* Fuel cell resistance: 0.487 \(\Omega\)
* Nominal airflow rate: 2100 L/min
## 3 Advancements in Generative Adversarial Network Research
### Supervised Generative Adversarial Networks
The research presented in [7] introduces a unified framework called Priv-FairGAN (PF-GAN) that aims to enforce image privacy and fairness protections simultaneously. Using contrastive learning, the framework combines state-of-the-art GAN models with de-identification constraints, similarity measurements, data balancing, and fair pre-trained weights. PF-GAN can effectively remove private information, ensure fairness, and address data and model utility concerns by integrating these components. Experimental evaluations conducted on the CelebA-HD dataset demonstrate the effectiveness of the framework in providing fair privacy and fairness protections while achieving high prediction accuracy. Future work in this area involves extending the proposed methods to other computer vision tasks, such as segmentation, and further enhancing fairness protections to minimize performance disparities between generated and real images. Additionally, the authors trained a GAN model using a training set of over 10,000 male or female facial images. The generated facial images were then utilized for training a smile classification task, employing a MobileNet model with a resolution of 224 \(\times\) 224. The trained model was evaluated on a test set of 800 images (400 male and 400 female) from the larger dataset of 8000 images used for training. This research also explores image segmentation as an alternative approach to reducing flaws in each image. The current state of the art in GANs has significantly advanced synthetic image generation, especially with the architecture known as StyleGAN. This architecture has demonstrated its ability to generate high-quality and realistic images, overcoming many previous limitations in image generation. However, despite the impressive results obtained, there are still challenges and limitations in generating images that resemble real people without noticeable flaws and defects. One of the critical areas where these limitations are evident is in the blending of facial features. Despite advances, there are still difficulties in achieving an accurate and realistic representation of the facial features of a person in the generated images. Facial feature blending involves the seamless and coherent combination of facial features like eyes, nose, mouth, and other distinguishing features. The challenge lies in generating images where these features blend naturally and organically without producing noticeable visual artifacts or distortions. Table 3 summarizes various research papers in the field of GANs, including the metrics utilized to estimate the performance of the models, the datasets used for training and testing, and the specific architectures employed. The table illustrates the diverse range of metrics used to assess the quality of GANs, highlighting the wide variation in the field. The results clarify that numerous quality standards exist due to the diversity of datasets and variants in GAN architectures.
## 4 Design and Control of SCIM-FESS
### Control Structure of SCIM-FESS
#### 4.1.1 Machine Side Converter Control  
The FOC controls the torque producing current (_iq_) and magnetic flux producing current (_id_) separately leading to a simple control structure as of a DC machine with less uncertainty of parameter variations [75]. As shown in Figure 4, the feedback loop is formed by measuring three-phase stator currents, which are then transformed into the synchronous _d-q_ reference frame by [PERSON]'s transformation. DC stator currents _i\({}_{ds}\)_ and _i\({}_{qs}\)_ are compared with reference currents _i\({}_{ds}\)_' and _i\({}_{qs}\)_' using PI current controllers which will produce reference voltage signals _v\({}_{ds}\)_' and _v\({}_{qs}\)_' for generating gate pulses for MSC by SVPWM modulator. Flux estimator calculates the actual magnitude of rotor magnetic flux using Equation (31) which is then compared with the reference flux. Reference d-q axis currents _i\({}_{ds}^{*}\)_ and _i\({}_{qs}^{*}\)_ are calculated using Equations (32) and (33) respectively by currents (_i\({}_{d}\) and _i\({}_{q}\)_) calculator blocks. Calculated reference values of stator currents in synchronous _d-q_ reference frame are compared with actual stator currents to produce reference voltages (_V\({}_{dref}\)_ and _V\({}_{qref}\)_). The rotor flux angle is a crucial parameter in this control technique and it is used to determine the position of rotor flux in IM as calculated using Equations (34) and (35).  
\[\lambda^{est}=\frac{L_{m}i_{d}}{1+sT_{r}} \tag{31}\]  
\[\tau_{ds}^{*}=\frac{\lambda_{d}}{L_{m}} \tag{32}\]  
\[i_{qs}^{*}=\frac{2}{3}\frac{2}{P}\frac{L_{r}}{L_{m}}\frac{T_{e}^{*}}{\lambda^ {est}} \tag{33}\]  
Figure 4: Control structure of machine side converter (MSC) and grid side converter (GSC) in squirrel-cage induction machine-based flywheel storage system (SCIM-FESS).  
\[\theta_{e}=\int(\omega_{m}+\omega_{sl})dt \tag{34}\]  
\[\omega_{sl}=\frac{i_{qs}^{*}}{i_{ds}^{*}}.\frac{1}{T_{r}} \tag{35}\]  
where \(\lambda^{est}\) is an estimated flux, \(\lambda_{d}\) is d-axis rotor magnetic flux linkage, \(T_{e}^{*}\) is the reference electromagnetic torque produced by PI controller, \(L_{m}\) and \(L_{r}\) is mutual inductance and rotor inductance respectively, \(\omega_{sl}\) is the slip frequency, \(T_{r}\) is the rotor time constant, \(i_{ds}^{*}\) and \(i_{qs}^{*}\) are the reference stator currents.
# Solar Power System Assessments Using ANN and Hybrid Boost Converter Based MPPT Algorithm
## 1 Introduction
### Major Contribution  
The use of MPPT has increased the performance of solar power system and now the demand of solar plant installation is enhanced. The efficiency of the MPPT based solar system can be further improved applying ANN and HBC. Thus, this paper studies the contributions of ANN and HBC based MPPT in the solar system. The major contributions of this work are discussed as follows.  
1. The outcomes of solar system are enhanced in this model using ANN and HBC based MPPT algorithm;
2. The mathematical model is elaborated in detail to show how the ANN and HBC based MPPT algorithm improve the quality of service of a solar power system;
3. The model is designed for simulation analysis and is compared among various procedures like Elman neural network (ENN), with install HBC and ANN mechanisms and without install HBC and ANN procedures in order to evaluate proposed model executions;
4. The proposed ANN model presents reliable outcomes in view of simple structure, fast training and robust performance. This effectiveness is further modified by applying HBC in the proposed model.
###### Abstract  
Using a single photon avalanche diode (SPAD) as a receiver in an optical wireless communications (OWC) system can effectively expand the transmission distance. However, the performance of the SPAD receiver is usually affected with the bit error rate (BER) lower bound determined by background light and the inter-symbol interference (ISI) distortion caused by dead time. In this paper, external time-gated technology is employed, and the SPAD is only activated within the set gate-ON time to alleviate the influence of background light and ISI distortion. The SPAD photon counting model and the communication BER model are established according to the external time-gated characteristics. Considering the functional relationship among the gate-ON time, signal light flux, background light flux and blocking time, we take the minimum BER as the optimization target, the optimal gate-ON time is derived. The numerical results show that whenever the signal light flux or the background light flux is higher, the BER performance of the time-gated mode is apparently better than the free-running mode. For example, when signal photons and background photons are 30 and 10 per symbol time, respectively, the BER of free-running SPAD converges to 0.1, while the BER of the time-gated scheme is about \(10^{-6}\).  
single photon avalanche diode (SPAD); optical wireless communications (OWC); time-gated SPAD; optimal gate-ON time; background light +
Footnote †: journal: Electronics Letters  
0
Footnote 0: Copyright** © 2022 by the authors. Liconsee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 3 Results
### Limitations of XPS  
X-ray Photoelectron Spectroscopy (XPS) is a powerful surface-sensitive analytical technique that provides valuable insight into the elemental composition and chemical state of materials. However, it is not without its limitations. One significant drawback of XPS is its relatively low spatial resolution, typically on the order of 1-10 \(\upmu\)m. This constraint can be particularly problematic when investigating heterogeneous materials--such as layered structures or materials with fine features--where localized variations in composition must be accurately assessed. Additionally, because XPS only probes the top 1-10 nanometers  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Anion** & **Al 2p** & **Mg 2p** & **O 1s OH** \\ \hline CO32\({}^{-}\) & 74.5 & 50.1 & 531.6 \\ PO43\({}^{-}\) pH 11.9 & 74.4 & 50.1 & 531.7 \\ PO43\({}^{-}\) pH 12.5 & 74.5 & 50.1 & 531.7 \\ SO42\({}^{-}\) & 74.3 & 49.9 & 531.7 \\ MoO42\({}^{-}\) & 74.2 & 49.8 & 531.5 \\ CrO42\({}^{-}\) & 74.3 & 49.9 & 531.6 \\ Fe(CN)64\({}^{-}\) & 74.4 & 50.0 & 531.7 \\ Fe(CN)63\({}^{-}\) & 74.4 & 50.0 & 531.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: BE values of Al 2p, Mg 2p and O 1s for the LDH layer structure as a function of the interlayer anion.  
of a sample, it may not provide a comprehensive understanding of bulk properties or subsurface characteristics.  
In the context of analyzing layered double hydroxides (LDHs), XPS exhibits specific challenges that may affect the reliability of the data obtained. The presence of moisture or atmospheric contamination can introduce artifacts that may skew the results, highlighting the need for stringent sample preparation and vacuum conditions. Additionally, the understanding of the oxidation states and coordination environments of the metal species within LDH structures can be intricate, often requiring complementary techniques to corroborate XPS findings.  
Another noteworthy limitation of XPS is its relative insensitivity to light elements such as hydrogen and lithium, which may be critical for some LDH systems. The inherent constraints of XPS in detecting low atomic number elements can lead to an incomplete picture of the chemical environment, particularly when metal cations or interlayer anions play a significant role in the material's properties and reactivity. Consequently, while XPS remains a valuable tool for elucidating the surface chemistry of LDHs, researchers must acknowledge these drawbacks and adopt a multi-faceted approach--utilizing complementary analytical techniques such as atomic force microscopy (AFM), scanning electron microscopy (SEM), or Fourier-transform infrared spectroscopy (FTIR)--to obtain a more robust understanding of the material's characteristics.
## 4 Discussion  
The objective of this study was to ascertain which loading approach yields superior patient satisfaction and overall oral health outcomes for individuals with two-implant overdentures. The results of this clinical trial suggested no significant differences in patient satisfaction and patient-reported outcomes between the conventional and immediately loaded implant-supported overdentures using magnetic attachments. Hence, the null hypotheses were accepted. This result is in accordance with the study of [PERSON] et al., who reported that in single implant-retained overdentures, the loading strategy, including immediate and delayed loading, had no effect on OHRQoL. Furthermore, a single mandibular implant did not significantly improve OHRQoL when used to stabilize a complete denture [25]. A meta-analysis conducted by [PERSON] et al. revealed a statistical inclination favoring conventional loading. Although all three loading methods demonstrated high survival rates, early and conventional loading protocols have received more comprehensive documentation compared to immediate loading, and they appear to result in fewer implant failures within the initial year [26]. In addition, these findings are consistent with a systematic review by [PERSON] et al., who found no statistically significant difference in satisfaction and quality of life between immediate and conventional loading, despite prosthetic complications and maintenance favoring the conventional loading group [27]. The study identified significant differences in OHIP-EDENT-J, patient satisfaction, and PDA scores between the baseline and yearly evaluations within each group. Regarding the OHIP-EDENT-J score and patient satisfaction within each group, significant differences were found in T1-T3 and the baseline; however, no significant difference was noted in T4, T5, and the baseline. [PERSON] et al. reported similar results, emphasizing that the most significant benefit of immediate loading, particularly during the initial stages of healing, is high patient satisfaction [28]. This could be attributed to the increased desire in patients to expedite the transition from implant placement to functional prosthesis, thereby offering rapid comfort and aesthetic benefits. Such expedited results may also offer economic advantages and decreased discomfort, especially during professional and social engagements [28]. Ideally, implant dentistry success should be measured by the overall implant-prosthetic complex's long-term result [29]. Multiple variables, including oral and dental conditions, personality and mental health, the aging process, and neuromuscular adaptation, may affect patients' appreciation of implant overdenture. Nevertheless, the existing scientific literature does not unequivocally support this assertion [30]. In addition, when it comes to the PDA results of T4-T5, the immediate loading group exhibited significant differences in several items compared to the conventional group. A recently published study
# Experimental Demonstration of Single-Channel EEG Signal Using 32 x 32 Pixel OLED Screen and Camera
## 2 Related Works
### Current EEG Procedure in Hospitals and RF-Based EEG Products
These wireless RF-based EEG products are used in EEG for certain applications, but none of them has been considerably deployed in hospitals due to electromagnetic interference. In order to overcome the above-illustrated problems of current RF-based wireless wearable EEG systems in RF sensitive areas, the proposed EEG systems must be EMI free, unlike the traditional systems which are tedious, wired, have EMI, and provide discomfort to patients [4; 38]. In view of the shortcomings of EEG products based on RF technology, VLC is the promising solution due to low implementation cost, security, no eavesdropping, and being free from EMI [12].
## 2 Data and Method
### Methods  
#### 2.3.1 Scatter Index  
The Scatter Index (SI) is a widely used microwave-based precipitation retrieval algorithm proposed by [PERSON] [20]. Theoretically, the high-frequency microwave radiation emitted by the land surface could be seriously scattered by the rainfall particles (such as big raindrops and ice crystals) in the clouds, so the TBs at high frequency over rain areas are usually lower than that over no rain areas. In contrast, the low-frequency microwave radiation tends to be less scattered by rainfall particles, leading to similar TBs at low frequency over rain and no rain areas. Based on this theory, [PERSON] [20] assumed that the TBs at high frequency over the no rain areas could be simulated by the TBs at low frequency, and the difference between the actual and simulated TBs at high frequency could consequently present the scattering intensity of the rainfall particles in the cloud, which is correlated with the spatial distribution and the physical attributes of the rainfall particles in the clouds. Therefore, the SI is a reasonable indicator of the occurrence and intensity of precipitation. According to the research of [PERSON] [20] and [PERSON] [21], SI could be derived from vertically-polarized brightness temperatures at 21.3, 19.0 and 85.5 GHz (termed TB\({}_{21\text{V}}\), TB\({}_{19\text{V}}\) and TB\({}_{85\text{V}}\)) using a two-step algorithm. First, the brightness temperature at 85 GHz under the no scattering condition at the location k is defined as \(E(k)\) and calculated by Equation (1).  
\[E\left(k\right)=A^{\ast}TB_{21\
u}\left(k  
_Remote Sens._**2014**, \(6\)  
\[SI\left(k\right)=TB_{\text{s}_{SV}}\left(k\right)-E\left(k\right) \tag{2}\]  
After the \(SI\) is calculated, the rain areas were delineated with a threshold, and then, the precipitation of the test dataset was calculated using delineated rain areas according to Equation (3),  
\[P(y)=m\text{*}SI(y)^{n} \tag{3}\]  
\(P(y)\) is the estimated precipitation at the raining location \(y\); the coefficients \(m\) and \(n\) are acquired by regressing the \(SI\) and the precipitation of samples under raining conditions in the training datasets.