esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper proposes a distributed algorithm in the cell clustering for downlink joint processing coordinated multipoint. This proposed algorithm tackles the nonlinear sum capacity optimization problem through approximation into distributed linear-form message passing. This linear approximation scheme is interpreted into multi-layer message passing as an algorithm foundation for dynamic cell clustering.  
The extensive simulation result confirms that the proposed algorithm provides considerable performance improvements in JP-CoMP clustering. The proposed algorithm provides higher performance consistently compared to the conventional methods in terms of both average UE throughput and average edge user throughput. The proposed algorithm enables the distributed control of the network, thus allowing for adaptive properties for dynamically changing networks. Accordingly, the proposed algorithm guarantees high throughput performance for different cell sizes and different number of users. In addition, the message update among neighbor base stations in the proposed algorithm only incurs low computational complexity and consumes a small amount of transmission bandwidth. Despite all of the advantages, the message update procedure in the proposed algorithm causes the latency issues. However, for most typical applications of JP-CoMP, the latency issue would not be critical.  
Future research directions can include more practical technical issues to further improve JP-CoMP performance outcomes. The imbalance conditions, i.e., the UE imbalance in each cell and the power transmitting imbalance from each base station become interesting topics in this field. Another aspect that should be exploited is the implementation of the algorithm in coordinated beamforming CoMP.  
Conceptualization, G.R.R.D. and I.S.; Methodology, I.S. and C.P.; Software, G.R.R.D.; Validation, G.D.; Formal Analysis, G.R.R.D. and I.S.; Writing--Original Draft Preparation, G.R.R.D. and I.S.; Writing--Review & Editing, I.S. and C.P.; Funding Acquisition, I.S. and C.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2017R1A5A1015596) and in part by (NRF-2019R1A2C1090650).  
The authors declares no conflict interest.  
Figure 8: Convergence properties of iterative algorithms.
# 5G V2X System-Level Architecture of 5 GCAR Project
## 4 Detailed Analysis of Selected Features
### Location-Aware Scheduling
These latency budgets might represent an additional source of information for the network to be exploited for optimizing the message delivery. From Figure 8, the location-aware scheduling receives the following inputs from the V2X service:  
* Message information: relevance area (or time deadline), message size. * Vehicle information: location, speed (or planned trajectory with associated timestamps). The above information is exploited by the location-aware scheduling to provide the following outputs:  
* Scheduling information: transfer planning, etc. * QoS requirements: QoS Profile, etc. The outputs of location-aware scheduling could be delivered to the service of relevance, e.g., the service might be informed about the transfer planning to be aware of when to trigger the message transfer. On the other hand, some parts of the output could be delivered to the network for different purposes. For instance, information about the transfer planning can be used by network nodes (e.g., RAN or UP nodes) as additional information regarding the expected traffic load. Furthermore, information about the associated QoS requirements can be then used by the network to enforce the correct QoS for the message transfer (e.g., increase priority of transfers for which vehicles are closer to the relevance area). From Figure 8, considering the information about the vehicle's location and associated speed and trajectory, the location-aware scheduling can schedule in an efficient way the delivery of the layers 1-2 and layer 3. Considering the larger size of layers 1-2 compared to layer 3, together with the information that the relevant area of layers 1-2 is closer to the vehicle with respect to the one of layer 3, the location-aware scheduling schedules the transfer of layers 1-2 and layer 3 in two different windows, in order to guarantee the overall transfer of all layers before the vehicle approaches the associated relevance areas, also guaranteeing that the network load is kept low by splitting and spreading the transfer of the two messages into two different time windows. Figure 8: Example of the utilization of location-aware scheduling applied to high definition (HD) map acquisition use case.
## 2 Methodology of the Analysis Method  
### Tip Timing Basic Principles  
BTT is a non-intrusive measurement technique, very promising for vibration monitoring of turbomachinery rotor blades. The method is based on the concept of the time of arrival of a vibrating blade. When the blades are vibrating, they pass in front of each sensor at a different time (time of arrival), compared to not vibrating blades. Actually, compared to a not vibrating blade, a vibrating blade will arrive at each sensor slightly earlier or later. By calculating the difference of time of arrival for vibrating and non-vibrating blades, the blade tip deflection can be evaluated. The blade deflection signal is the basis of the rest of the analysis. By post-processing this signal, the vibration properties can be determined.  
A typical scheme of a BTT measurement system for blade vibration detection is depicted in Figure 1. As shown, BTT sensors are mounted on the stationary casing and arranged circumferentially around the rotor blades. The number of sensors and their arrangement are chosen based on the values of the natural frequencies, the number of modes and the EO of excitation.
## 3 Results and Discussion
### Phase 1: Perform CFD Simulations
For this reason, the location of the sensors in Scenario 4 was the most demanding case for monitoring CO\({}_{2}\). Figure 13 shows the three points selected among suitable ones for the CO\({}_{2}\) concentration measurement. They were placed in positions that do not correspond to the vortices nor currents of air, avoiding distorting measurement points. The red colour shows areas with high renewal, so that placing the sensors in these areas would distort the average level in the space. Despite the statements of previous research (to the best of our knowledge) and current legislation [103; 104] which establish that measurements should be taken at 1.2 metres above the ground, equivalent measurements can be taken at higher or lower points, as they are not modified either by renovations or by CO\({}_{2}\) concentration in the most unfavourable scenarios. They are as follows:  
* Location 1: right side wall at a height of 1.2 m above floor and a separation of 2.5 m from the wall of the door. * Location 2: exterior wall (corresponding to the window) at a height of 2 m and centered in the gap between the left wall and the window. * Location 3: left side wall at a height of 2 m above the floor and a separation of 1.5 m from the window. Figure 11: Process for determining vortices with ANSYS Discovery 2021 R2 Student Version. Figure 12: CO\({}_{2}\) concentration in a room with no-ventilation (closed window and closed door). As Location 1 complies with current legislation for all kind of activities that can be undertaken in the living space, this is the one selected for the series of measurements.
## 3 D2 HCP (Distributed Dynamic Host Configuration Protocol)
### _Timers_
* _INIT_ASSIGN_TIMER_: This timer is used as much by the node client as by the server when they have received or assigned an IP address block, respectively. That is to say, the server initiates it, on having verified the node entry with the first IP address of the block offered in the IP_ASSIGNED message, and the client initiates it after receiving the IP_ASSIGNED message and to configure its address. Thus there has been time for the whole network to update its Free_IP_Blocks table before more changes take place. During that time, they will ignore SERVER_POLL or IP_RANGE_REQUEST messages, although they will reply to the SERVER_DISCOVERY. * _NODE_DOWN_ASSIGN_TIMER_: When an already configured node detects the departure of another one, and it verifies that it is its turn to gather the IP address that remains free, it starts its timer. More concretely, the timer will be activated when the elimination of the OLSR routing table is detected, that is to say, it will be activated at the same time as the NODE_DOWN_TIMER timer. Until it does not expire, the node will ignore the SERVER_POLL and IP_RANGE_REQ requests. This way, a margin of time will happen to ensure that all the nodes in the network detect the mentioned departure and update their \(Free\_IP\_Blocks\) table, before assigning them to some another new node. Therefore, the duration of this timer must be greater than that of the NODE_DOWN_TIMER to make sure that the rest of nodes in the network not only detect the elimination of a route but they have eliminated it from the _Free_IP_Blocks_ table. The node will keep on replying to the SERVER_DISCOVERY messages fixing the value 0 in the R field from the SERVER_OFFER message. * _SLEEP_TIMER_: Timer used by a client node when it detects nearby nodes belonging to some network, but that are not in a position to assign IP addresses at this moment. This way, it gives a margin of time to allow the processes to end preventing them from assigning address.
## 4 Reinforcement Learning Combined with ANN
### Q learning Combined with ANN Application in Energy Routing Design
#### 4.3.2 Action Space  
According to the structure proposed in Figure 1, an energy routing center provides its redundant energy to other ERCs if necessary. The action space of Q learning can be depicted as follows:  
\[A=\left\{P_{PV},V_{ix},P_{CHP},P_{bat},P_{grid},H_{GB},H_{CHP},H_{i-j}\right\}, \tag{38}\]where, \(P_{CHP}\), \(P_{bat}\) and \(P_{grid}\) are the output electrical power of photovoltaic, CHP, battery, and main grid, respectively. \(H_{GB}\) and \(H_{CHP}\) are the output heat of GB and CHP. \(P_{i-j}\) and \(H_{i-j}\) are the electrical power and thermal power transmitted on connection lines. \(V_{ix}\) is the voltage of the ports which are connected to the connection line.
# Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality
## 2 Materials and Methods
### Noise
#### 2.3.1 Power Line Interference (PLI)  
The harmonics of the electrical power line, typically at frequencies of 50 Hz or 60 Hz, are a significant source of contamination for electrophysiological signals. The nominal power supply is 230 V/50 Hz or 120 V/60 Hz, with fluctuations of 10% in voltage and 1% in frequency. However, it often contains harmonics and interharmonics, albeit with limited relative power content. PLI affects many physiological recordings, including bipolar and unipolar EGMs, often exhibiting amplitude and frequency variations, as well as harmonic content beyond the limits established by its bandwidth. In this study, these characteristics of PLI are considered to adequately approximate the real effects of these perturbations. Random variations within the intervals mentioned above were applied to the amplitude and frequency of the main 60 Hz component, as well as to its first four harmonics. The components were also frequency-modulated with a maximum deviation of 0.5 Hz to introduce low-amplitude interharmonics. The resulting signal, referred to as common PLI, was used to obtain noisy EGM recordings with signal-to-interference ratios (SIRs) of 25, 20, 15, 10, and 5 dB.
# Multilayer Model in Soil Moisture Content Retrieval Using GNSS Interferometric Reflectometry
## 1 Introduction
The obtained antenna RMSE height is 1.9 m, and the results demonstrate that the GNSS interference signal captured by linear polarization may persist for a longer satellite elevation angle range [19]. Two smartphones were connected to the upper and lower sides of the plane, allowing for the isolation and recording of the direct and reflected GNSS carrier noise density ratios as well as the remote sensing of land surface data. The results revealed that the reflected signals exhibited geographical correlations in line with surface properties, including the pond's high reflectivity, susceptibility to different types of land crops, and soil wetness [20]. When an electromagnetic wave shines on a dielectric material--that is, when it passes through the upper dielectric and is reflected by the lower dielectric--it exhibits the phenomena of the complete transmission. Moreover, this phenomenon has something to do with how electromagnetic waves are polarized. The GNSS signal's vertical polarization will result in the complete transmission phenomena. The inaccuracy was less than 8% when [PERSON] et al. utilized this characteristic to invert soil moisture under foliage cover [21]. The theoretical approaches and simulation outcomes of SMC multi-layer measurements in the sink and reverse osmosis modes for the asymmetric water distribution in the soil are presented in this study to retrieve SMC at various depths. The complete transmission of the electromagnetic wave incident on the medium's surface--vertically linearly polarized--was used to capture the GNSS signals reflected by soil moisture in various layers. First, a noise-free simulation of the soil multilayer interference signal was performed, and Lomb-Scargle periodogram (LSP) calculations were used to determine the spectrum features of the reflected signal. The results demonstrate that the soil moisture beneath can be reflected by the distinctive features of the GNSS interference signal \"notch\" with vertical linear polarization. When compared to the reverse osmosis model, the sink model's reflectivity is more significantly impacted by the soil moisture's interval change than the reverse osmosis model. In addition, the mode layer number essentially has no impact on the power reflectivity, regardless of the setting. Finally, we examine the noise-layered model's properties. In this investigation, the envelope of the reflected signal is extracted using the Hilbert transform. The final spectrum of the reflected signal agrees with the noise-free modeling results. This paper is based on simulation, and is organized as follows: the Section 2 presents the theoretical model, the Section 3 presents the simulation study, and the Sections 4 and 5 describe the results and conclusions.
## 3 Results and Discussion  
Figure 3 shows the keyword network diagram of the retrieved papers, which reveals the subtopics related to this study. The bigger the dot, the more frequently the keyword appeared in publications from the literature search. We further noticed that connections are focused on \"machine learning\" and \"plant\". Connections that centered around machine learning models focused on the development of different methods (e.g., the extreme learning method, the support vector method, neural networks, etc.) to predict and optimize the monitoring of CO2/NOx emissions. The key facilities of power plants, such as the boiler, are of particular attention to researchers as their design and process are directly related to CO2/NOx emission levels from power plants.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criterion** & **Inclusion** & **Exclusion** \\ \hline Year & 2008–2023 & \(<\)2008, \(\geq\)2024 \\ Peer review & Peer-reviewed & Non-peer-reviewed \\ Language & English & Non-English \\ Type of article & Journal, proceedings & Book, book chapter, review \\ Topics & Integration of machine learning with CO2/NOx emissions management in fossil fuel power plants & Only related to 1–2 aspects including machine \\  & emissions management in fossil fuel power plants & learning, CO2/NOx emissions management, fossil \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inclusion and Exclusion Criteria.  
The following section presents the discussion of machine learning model applications for evaluating and potentially reducing CO2/NOx emissions from power plants based on different types of machine learning techniques. The presented machine learning techniques mostly include reinforcement learning, neural networks, support vector regression, and the extreme learning method. The characteristics, as well as the advantages and disadvantages, of each machine learning technique are also discussed.
# Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems
## 5 5G Open Challenges
### Autonomous Boat Platoons  
Finding the right position for this use case in 3 GPP standardization is not straightforward. In principle, we can make the assumption that autonomous shipping are enabled by a joint initiative of satellite operators and space agencies, where 5G satellite-related technology drive the autonomous shipping vision [100]. Enabling network availability to moving platforms such as passenger vehicles, aircraft ships, trains, buses, etc., is a requirement for the new radio interface for non-terrestrial networks (NTN) [101]. In [102], the authors discuss a sort of ship-mega constellation, which is similar to boat platooning, based on multi-hop communications enabled by both 5G and satellite communications. A clear vision towards autonomous shipping is presented in [100], based on satellite communications,  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Use Case** & **4G** & **5G** \\ \hline Heathcare & Energy efficiency issue on radio interfaces & Femtocell solution for indoor comms Support scalability \\ \hline Maritime & Radio signal degradation & Satellite transmission but latency concerns \\ \hline Vehicle control loss warning & ProSe to be upgraded & natively supported \\ \hline Vehicles and roadside units interaction & ProSe to be upgraded & natively supported \\ \hline V2I cooperation for traffic management & latency concerns & natively supported \\ \hline \hline \end{tabular}
\end{table}
Table 1: 4G and 5G technology challenges in SafeCOP.  
linked to the joint industry project called Advanced Autonomous Waterborne Applications (AAWA) led by Rolls Royce. Unfortunately, 5G is not included.
# Classification of Shoulder X-ray Images with Deep Learning Ensemble Models
## 3 Methods  
A number of different deep-learning-based methods have been used and developed to classify X-ray images of the shoulder bone in png format and with three channels as normal and abnormal in this study. Firstly, classification was performed with ResNet (34,50,101,152), ResNeXt (50,101), DenseNet (169,201), VGG (13,16,19), InceptionV3, and MobileNetV2, which are CNN-based deep learning models with different structures and layers that are publicly available. Subsequently, new classification networks were established by replacement of the classification layer of each model used herein with the SpinalNet FC layer. Based on the results obtained herein, new ensemble learning models specific to this study were developed in order to further increase the classification accuracy. Built deep learning models, newly developed models with SpinalNet, and details of the ensemble learning models used for classification are described in the following subsections.
## 2 Study Area and Data Sets
### Data Sets
#### 2.2.3 Temperature, Precipitation, and Water Level  
The temperature and precipitation data of the study area were acquired from the National Meteorological Information Center [42]. A temperature data set was based on the latest temperature data of China's ground high-density stations (about 2400 national-level meteorological observatories), which were newly compiled by the National Meteorological Information Center. Spatial interpolation was performed using the Thin Plate Spline (TPS) method of ANUSPLIN software (Australian National University, Canberra, Australia) to generate the monthly temperature and precipitation grid data with a spatial resolution of \(0.5^{\circ}\times 0.5^{\circ}\). The aim of ANUSPLIN software is to provide a facility for the transparent analysis and interpolation of noisy multivariate data, in particular climate data, using the TPS method that can be viewed as a generalization of standard multivariate linear regression, in  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Date** & **Flight** & **Orbit** & **Path/Frame** & \
\begin{tabular}{c} **Date** \\ **(y/m/d)** \\ \end{tabular} & **
\begin{tabular}{c} **Flight** \\ **Direction** \\ \end{tabular} & **Orbit** & **Path/Frame** \\ \hline  
2018/11/10 & Descending & 24532 & 135/508 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sentinel-1 Data Used in This Study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date(y/m/d)** & **Data Type** & **Band Use** & **Resolution** \\ \hline
2014/10/30 & OLI & 4,3,2 & 30 m \\
2015/10/17 & OLI & 4,3,2 & 30 m \\
2016/12/06 & OLI & 4,3,2 & 30 m \\
2017/12/25 & OLI & 4,3,2 & 30 m \\
2018/12/11 & OLI & 4,3,2 & 30 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Landsat 8 Data Used in This Study. OLI: Operational Land Imager.  
which the parametric model is replaced by a suitably smooth non-parametric function [43]. The degree of smoothness, or inversely the degree of complexity, of the fitted function is usually determined automatically from the data by minimizing a measure of the predictive error of the fitted surface given by the generalized cross-validation.  
The precipitation data set was based on the latest reorganization of the 2472 stations on the ground of China, which were based on the National Meteorological Information Center, using the same method as above to generate Chinese precipitation monthly value point data.  
The water-level data were acquired from the monitoring site established in the Erhai region. Daily water level data were re-sampled to get the monthly average water level value. In addition, the official statistics data were obtained from the National Bureau of Statistics [44], including the population (urban population and rural population) and building area of Dali city, which are used to assess the degree of urbanization in the study region.
# Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification
## 3 Computational Experiments
### Feature Importance Analysis  
Relying on the SHAP method, presented in Section 2.7, feature importance scores were calculated for each ML model during the classification analysis on the employed dataset. The average importance scores were summarized in Figure 10. Such a figure presents the variable importance graphs for the MLP, SVC, and XGB ML models, all applied to classify voice registers. The latter graphics emphasize the most influential variables, consistent with the fundamental behavior. Indeed, \(X_{14}\) (the signal zero-crossing rate) was the variable that significantly impacted the classification for all methods. For SVC and XGB, \(X_{11}\) (the signal traveled distance) and \(X_{9}\) (no. peaks from a defined signal neighborhood) came in the second and third positions. Finally, the other features had lower values for all the employed ML methods.  
The zero-crossing rate (\(X_{14}\)) is closely tied to the periodicity and noise characteristics of the human voice, reflecting the distinction between voiced sounds (low \(X_{14}\)) and unvoiced sounds (high \(X_{14}\)) [39]. The distance a signal travels can affect the human voice by introducing attenuation, delays, and environmental noise, which alter its amplitude and clarity. The voice quality degrades over distance due to energy loss and interference, impacting communication effectiveness [40]. The number of peaks in a defined signal neighborhood reflects the harmonic structure and periodicity of the human voice, which are key aspects of the source-filter model of speech production. This feature helps distinguish voiced sounds (with regular peaks) from unvoiced sounds (with irregular or fewer peaks), aiding in pitch detection and speech analysis [41].  
Figure 9: Distribution of internal parameters for the XGB model: (a) parameter \(\alpha\), (b) parameter \(\lambda\), (c) learning rate, and (d) number of estimators.