esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Machine Learning and 5G for People with Disabilities
## 4 Application Scenarios and Benefits of Machine Learning and 5G for People with Disabilities
### Smart Homes  
5G real-time services allow people with disabilities to live independently in connected smart homes since 5G networks transmit a significant amount of data at very high speeds. Neural networks can improve home automation, comfort, security [54], and health care for people with disabilities in their smart homes [55]. Neural networks can implement virtual sensors to replace temporarily unavailable physical sensors for indoor localization systems [56]. These systems are helpful for monitoring and assisting people with disabilities during critical situations, such as wandering or falling. Neural networks [57] can learn the users' habits and activities; this way, their next moves are predicted, and alerts are sent (e.g., time to take a given medicine, fire alarm). The faster speed and lower latency of 5G improves security camera systems significantly and provides enough capacity to connect faster and more efficient Internet of Things (IoT) devices.
## 3 Architecture and Problem Formulation
### System Model
#### 3.2.3 Battery Lifetime and Energy Efficiency  
There exists a direct relation between the battery lifetime and the achievable energy efficiency of wireless devices shown in detail by [PERSON] [36]. The energy efficiency of any \(k_{th}\). MTCD during the transmission is defined as:  
\[U_{k}(R_{k})=\frac{R_{k}}{p_{e}+\begin{array}{c}\eta p_{k}(R_{k})\end{array}}. \tag{7}\]  
Figure 2: MTC energy consumption cycle.  
It is shown that when \(p_{k}(R_{k})\) is strictly convex in \(R_{k}\), with \(U_{k}(R_{k})\) being strictly quasi-concave. This shows that an optimal \(R_{k}\) can be found that maximizes the energy efficiency. The battery lifetime in (5) can now be rewritten as:  
\[L_{k}(t^{\rho})\ =\ \frac{E_{k}(t^{\rho})T_{k}}{D_{k}}\ \frac{R_{k}}{p_{e}+\ \eta \bigg{[}p_{k}+\hat{E}_{s}^{k}\ \frac{R_{k}}{D_{k}\eta}\bigg{]}}. \tag{8}\]  
Now, define \(p_{k}(R_{k})\) as \(p_{k}(R_{k})+\hat{E}_{s}^{k}\ R_{k}/\eta D_{k}\), which will be strictly convex in \(R_{k}\) given \(p_{k}(R_{k})\) remains strictly convex. Hence, (8) can be written in terms of energy efficiency as follows:  
\[L_{k}(t^{\rho})\ =\ \frac{E_{k}(t^{\rho})T_{k}}{D_{k}}\ \frac{R_{k}}{p_{e}+\ \hat{p}_{k}}\ =\ \frac{E_{k}(t^{\rho})T_{k}}{D_{k}}\ \hat{U}_{k}(R_{k}). \tag{9}\]  
The direct dependence of lifetime over energy efficiency \(\hat{U}_{k}(R_{k})\) is evident from the mentioned expression. Hence, maximization of the battery life is equivalent to energy efficiency maximization and the proposed game takes advantage of this relation.
## 1 Introduction  
Non-thermal atmospheric pressure plasma (NTAPP) involves partially ionized gas containing electrically charged particles at atmospheric pressure. These include reactive oxygen and nitrogen species that induce various physiological effects, with promising clinical and preclinical applications in plasma medicine. Recent developments include direct and indirect applications of NTAPP for microbial biofilm eradication, tissue disinfection, wound healing, tissue rejuvenation, and surface modification of biological scaffolds. In dentistry, NTAPP has been studied for periodontal pocket decontamination, adhesion, caries treatment, root canal disinfection, implant surface treatment, and tooth bleaching [1, 2].  
In regenerative medicine, NTAPP may allow a versatile range of applications involving the manipulation of various cell types and processes, including stem cell attachment, proliferation, differentiation, and even apoptosis [3]. [PERSON] et al. reported evidence of NTAPP improving stem cell attachment, proliferation, and differentiation [3]. It activated proliferation of various mesoderm-derived human stem cells [4], and enhanced proliferation of adipose tissue-derived stem cells without affecting their stem cell properties [5]. Yet, direct and indirect effects of NTAPP applications on human dental pulp stem cells (hDPSCs) have not been elucidated. These well characterized cell population with mesenchymal marker expression and potent mulilineage differentiation are essential for tooth pulp-dentin regeneration in dentistry [6; 7; 8; 9].  
Pulp-dentin regeneration is both conceptually and clinically based on a cell homing approach to recruit endogenous stem cells into root canal system. It begins with cell recruitment, attachment, proliferation, and differentiation on root dentin surfaces, for which dentin conditioning is important. Conditioning with ethylenediamininetetraacetic acid (EDTA) alters chemical composition, topography, and wettability of root dentin [10; 11], and thereby affects stem cell adhesion and proliferation on dentin surfaces [12; 13; 14]. Similarly, NTAPP was found to enhance dentin wettability and surface energy, significantly improving protein adsorption and cellular attachment [15]. However, the effects of NTAPP treatment on root dentin as cellular niche in regenerative endodontics are not fully understood.  
The purpose of this study was to investigate the effects of NTAPP treatment of human root dentin surfaces in the recruitment, attachment and growth of hDPSCs. Cellular attachment and growth of primary hDPSCs on NTAPP-treated root dentin surface was quantitatively and qualitatively investigated, and initial recruitment and attachment events were monitored in real-time to find out subcellular changes.
# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
## 2 Related Work
### Vision Transformer  
The transformer block is composed of a multi-head self-attention (MSA), multiple-layer perceptron (MLP), and layer normalization (LN) module [33]. Recent research has been trying to investigate the benefits of transformers in computer vision. In [17], the transformer first applied to image classification for replacing the CNN model was proposed. [PERSON] et al. [34] presented an ImageNet benchmark, and maximally excavated the capability of the transformer. [PERSON] et al. [35] conducted image recognition with a transformer structure. [PERSON] et al. [36] dynamically extracted visual tokens, and used visual transformers to perform the visual tokens. [PERSON] et al. [37] conducted the first pilot study in building a generative adversarial network model using only pure transformer-based architecture. [PERSON] et al. [38] designed a novel vision transformer, which reduced the parameter count. [PERSON] et al. [39] produced the transformer structure and recognition accuracy achieved 83.1% on the ImageNet dataset. [PERSON] et al. [40] introduced the pyramid vision transformer. [PERSON] et al. [19] designed a swin transformer based on the shifted-window strategy. Following that, [PERSON] et al. [20] proposed the swin transformer and multi-layer perceptron (ST-MLP) to classify the strawberry appearance, and the results demonstrated superior recognition performance.  
The CNN-based methods have improved radar-signal modulation recognition performance. Inspired by these excellent works, we adopt the swin transformer blocks to obtain the global context information for the CNN model. In this paper, a new framework including CNN and the swin transformer (CNN-ST) is proposed. CNN-ST first applies the swin transformer to the radar-signal modulation recognition field, which increases the recognition performance, especially for dual-component radar-signal modulation recognition under different SNRs.
## 2 Materials and Methods
### Data
#### 1. MOD16  
MOD16 calculates ET using a semi-empirical method based on the PM algorithm [11; 26]. During the calculation, ET is divided into wet-canopy evaporation, vegetation transpiration and soil evaporation. The process of interception is actually not included in MOD16 directly as it is calculated only under wet canopy evaporation conditions. Compared with previous algorithms, the latest MOD16 algorithm improves the calculati  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Land Cover Used in This Study** & **IGBP Land Cover** & **Area Ratio** \\ \hline Evergreen Broadleaf Forests & Evergreen Broadleaf Forests & 24.83\% \\ Deciduous Broadleaf Forests & Deciduous Broadleaf Forests & 1.63\% \\ Evergreen Needleleaf Forests & 0.59\% \\ Needleleaf Forests & Deciduous Needleleaf Forests & 0.00\% \\ Mixed Forests & Mixed Forests & 1.76\% \\ Closed Shrublands & 0.01\% \\ Shrublands & 0.00\% \\ Shrubls & Woody Savannas & 13.31\% \\ Savannas & 12.54\% \\ Grasslands & Grasslands & 17.69\% \\ Permanent Wetlands & Permanent Wetlands & 1.51\% \\ Croplands & Croplands & 22.49\% \\ Cropland/Natural Vegetation Mosaics & 1.90\% \\ Urban and Built-up Lands & Urban and Built-up Lands & 0.40\% \\ Permanent Snow and Ice & Permanent Snow and Ice & 0.03\% \\ Barren Lands & Barren & 0.52\% \\ Water Bodies & Water Bodies & 0.80\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Land cover types and percentage coverage in LMRB in 2019.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Product** & **Temporal** & **Estimation** & **Spatial** & **Period** & **References** \\ \cline{2-5}  & **Resolution** & **Method** & **Resolution** & **Period** & **References** \\ \hline MOD16 & 8-day/year & PM \({}^{1}\) & 500 m & 2000-Present & [11; 26] \\ PML-V2 & 8-day/year & PM \({}^{1}\) & 500 m/0.01 & 2000–2020 & [7; 9; 25] \\ BESS & 8-day/month & PM \({}^{1}\) & 1 km & 2001–2015 & [27] \\ GLASS & 8-day & BMA \({}^{2}\) & 1 km/0.01 & 2000–2018 & [28] \\ \hline \hline \end{tabular} \({}^{\dagger}\) PM: Penman–Monteith algorithm;\({}^{2}\) BMA: Bayesian Multimodel Average algorithm.  
\end{table}
Table 2: Characteristics of the remote sensing data and methods used in this study.  
fraction, stomatal conductance, soil heat flux and night temperature [11]. In addition, during the calculation of soil evaporation, the soil surface is divided into wet and moist soil surfaces. However, MOD16 only considers the vegetation-covered area, and ignores barren or sparsely vegetated areas and non-vegetated areas.
# Orbit Determination Method for BDS-3 MEO Satellites Based on Multi-Source Observation Links
## 2 Orbit Determination Method  
### Design of the Multi-Source Observation Network  
The satellite-to-ground observation network of the BDS-3 mainly only includes monitoring stations located in China, which cannot track the entire arc of the BDS-3 MEO satellites. The tracking rate is no more than 40% with only the support of satellite-to-ground observation links. If a satellite is not in the covering range of the ground monitoring stations, no observation data can be gained by the main control station. With the application of inter-satellite links, inter-satellite ranging data can be gained by the main control station. Nevertheless, the navigation messages broadcast by the satellites cannot be transmitted between the satellites and returned to the main control station. When LEO satellites are added, more observation data and navigation messages can be gained and returned to the main control station.  
As shown in Figure 1, multi-source observation links consist of satellite-ground, inter-satellite and satellite-based observation links. Inter-satellite and satellite-based observation links can effectively increase the tracking and monitoring arcs of navigation satellites and make up for the distribution limitations of ground monitoring stations.  
The satellite-to-ground observation links refer to observation links between the ground monitoring stations and the satellites. The ground monitoring stations mainly consist of the main control station, upload stations and monitoring stations. The main control station should collect and process returned observation data and generate corresponding navigation messages. The upload stations should receive and inject navigation messages generated by the main control station and return the observed ranging data and navigation messages to the main control station. The monitoring stations should send the observed ranging data and navigation messages back to the main control station.  
The inter-satellite observation links refer to observation links between navigation satellites. The inter-satellite pseudo ranging data can be transported to the main control station.  
The satellite-based observation links refer to observation links between navigation satellites and LEO satellites. The satellite-based ranging data and received navigation messages can be transported to the main control station.
# UAV Image Small Object Detection Based on RSAD Algorithm
## 2 Related Work
### SSD Object Detection Framework  
SSD algorithm, which combines the anchor mechanism from Faster R-CNN and the regression concept from YOLO, is a suggested object detection technique. According to [15], in the common public datasets VOC and COCO, SSD not only outperforms Faster R-CNN in detection accuracy, but also outperforms YOLO in detection speed. It is an end-to-end single-point multi-frame one-stage object detection algorithm, which classifies and regresses the predictions of objects on different scales of feature layers through the anchor frame mechanism, and finally excludes low-scoring prediction frames through the Non-Maximum Suppression (NMS) algorithm to obtain the detection results [30]. The specific detection process is as follows: the backbone network VGG-16 extracts image features to create six feature maps at different scales for simultaneous detection of objects atdifferent scales; then, a set of anchor boxes with different aspect ratios and sizes are tiled by pixels on the feature maps at different scales, and scores are generated for the presence of each object category in each default box as well as adjusting the shape. Finally, using NMS to output the location and category of the object box, the algorithm structure is shown in Figure 1.  
The SSD object detection algorithm has poor accuracy in detecting small objects in UAV images [31], mainly due to the following reasons: The layers of the backbone network VGG-16 are too shallow for feature extraction of small objects [32], and more advanced semantic features cannot be obtained; the algorithm's six feature maps for classification and regression are independent of one another, and the shallow feature maps used for small object recognition cannot fully utilize the deeper feature maps' semantic features [33]. Small objects, which are hard samples, contribute less to the SSD loss function during model training, causing the model to pay them less attention and resulting in lower detection accuracy. In summary, the network's structure of the SSD algorithm needs to be further modified in order to increase its detection performance for small objects in UAV images.
# Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization
## 3 Proposed Methods
### Multi-Start with PR for Firefly Swarm Rebuilding  
Multi-start is a mechanism for reinitiating the search with an under-explored region when the trajectory search loses its efficacy. The multi-start search strategy thus works with two functions. The function for _critical event detection_ emits a signal upon the moment when the search stagnation behavior is observed. The _diversification-generation_ function identifies an under-explored region in the solution space to generate a starting solution for the new trajectory search. Our CFA approach facilitates the critical event detection by monitoring the number of enduring iterations since the last improvement of the best-so-far firefly. If the number of no-improvement iterations for the best-so-far firefly has exceeded a parameter \(t_{2}\), a signal for detection of a critical event is returned. The parameter \(t_{2}\) is also made adaptive by the landscape analysis approach.  
The diversification-generation function of the CFA approach is implemented by using the path-relinking technique, which has been found very useful in identifying a promising solution within an under-explored region and has been adopted as a diversification strategy such as in [PERSON] et al. [6]. When a critical event signal is activated, a ratio \(\delta\) of the swarm fireflies are rebuilt and each new firefly is placed on the best solution along a relinked path connecting a random solution and the best-so-far firefly. The path-relinking technique creates the path by dividing the subspace between the two end points of the path into \(n\) equal-size hyper-grids where \(n\) is the number of decision variables for the addressed optimization problem. A solution is sampled within each hyper-grid, so in total \(n\) solutions will be obtained along the relinked path. The best of the \(n\) sampled solutions is designated as the rebuilt firefly.
## 6 Conclusions  
The Circular Scanning Ground-Based Synthetic Aperture Radar (CS-GBSAR ) has the unique advantage of 3D imaging due to the nonlinear observation geometry. However, such nonlinear observation geometry causes strong sidelobes in SAR image, which make it a difficult task to extract point cloud data. The CA-CFAR algorithm can extract 3D point cloud data layer-by-layer at different heights, which is time consuming and is easily influenced by strong sidelobes to obtain inaccurate results. This paper proposes a new two-step CFAR-based 3D point cloud extraction method for CS-GBSAR, which can extract accurate 3D point cloud data under the influence of strong sidelobes. It first utilizes maximum projection to obtain three-view images from 3D image data. Then, the first step CA-CFAR is applied to obtain the coarse masks of three-views. Then, the volume mask in the original 3D image is obtained via inverse projection. This can remove strong sidelobes outside the potential target region and obtain potential target area data by intersecting it with the SAR 3D image. Then, the second step CA-CFAR is applied to potential target area data to obtain coarse 3D point clouds. Finally, to further eliminate the residual strong sidelobes and output accurate 3D point clouds, the modified DBSCAN clustering algorithm is applied. The modified DBSCAN clusters with a cylindrical template to accommodate the data's features rather than the original spherical template, which can reduce false clustering. The acquired real data using the North China University of Technology (NCUT)-developed CS-GBSAR system validates the proposed method. The comparison with the conventional method shows that the proposed method has better performance.  
In the future, we will conduct a quantitative analysis on setting the parameters of the proposed method, and study the data-driven CFAR to further improve the proposed method to suit the different data's statistical distribution. Additionally, we will collect a large amount of new data in different scenarios to test the method and continue to improve it to handle all possible scenarios.  
[PERSON] and [PERSON] performed the experiments and method. [PERSON] and [PERSON] wrote the manuscript. [PERSON], [PERSON], [PERSON] ([PERSON]), [PERSON] ([PERSON]) and [PERSON] gave valuable advice on manuscript writing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant 62201011, 62131001 and the R&D Program of the Beijing Municipal Education Commission grant KM202210009004 and North China University of Technology Research funds 110051360023 XN224-8, 110051360023 XN211.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.
# Modeling and Optimization of NO2 Stations in the Smart City of Barcelona
## 5 Computational Results
### Prediction of NO\({}_{2}\) Concentration per Station  
Next, we present the results obtained with the different prediction models for our dataset. Table 4 demonstrates that Random Forest outperforms other methodologies regarding prediction accuracy for NO\({}_{2}\) concentrations at each station. The lower MAE and RMSE values indicate a closer approximation to the observed data, while the higher R\({}^{2}\) suggests a better fit to the data variability. These findings underscore the efficacy of Random Forest for predicting NO\({}_{2}\) concentrations in diverse locations, and they explain the specific characteristics influencing the predictive performance of each method.  
When applying the Random Forest algorithm to predict NO\({}_{2}\) concentration in different stations, it is observed that the evaluation metrics showed similar and close values across all stations. However, the question arises as to whether the model's effectiveness in predictions varies depending on the concentration of NO\({}_{2}\) concentrations at each station. To address this question, a correlation analysis is conducted between the normalized evaluation metrics and the annual average concentration of NO\({}_{2}\) at each station. The results indicate that in stations with lower NO\({}_{2}\) concentrations, the MAE is lower than those with higher NO\({}_{2}\) concentrations (Figure 9a). This relationship between NO\({}_{2}\) concentration and MAE yields a correlation coefficient of 0.82. To ensure robustness, station 50 is excluded from the analysis, as it exhibits poor fit and appears as an outlier (Figure 9b).  
The study finds a strong positive correlation between NO\({}_{2}\) concentration and prediction error (RMSE), indicating that stations with higher NO\({}_{2}\) concentrations also experience higher prediction errors. Additionally, a moderate negative correlation is observed between the NO\({}_{2}\) concentration and the coefficient of determination (R\({}^{2}\)), suggesting that the model is less effective in stations with high NO\({}_{2}\) concentrations, where it fails to adequately explain the variability of the observed data. These findings reveal that the performance of the Random Forest model in predicting NO\({}_{2}\) concentrations varies according to the concentration level at each station; it is more effective in stations with low NO\({}_{2}\) concentrations and less accurate in stations with high concentrations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Air Quality Station** & **Meteorological Station** \\ \hline
50 & X4 \\
43 & X4 \\
44 & X4 \\
57 & X8 \\
4 & X4 \\
42 & X8 \\
54 & D5 \\
58 & D5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Application of the KNN algorithm to the stations.