esa-sceva/satcom-chunk-collection huggingface dataset:
# ViT-Based Classification and Self-Supervised 3D Human Mesh Generation from NIR Single-Pixel Imaging
## 6 Experimental Results
### Discussion of the Proposed Method  
For evaluation of the proposed network architecture (see Figure 4), we tested different NIR-SPI-based image reconstruction approaches using the SMPL-X model to reconstruct human positions at night-time from distances 0.5 m and 1 m while taking into consideration the limited field of view of the single-pixel camera, which is \(74^{\circ}\times 57^{\circ}\). We captured NIR-SPI images of human poses. We observed some limitations concerning the reference image in the hand and body positions, particularly in the bending position, due to loss of information  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Human Pose** & **V2V Error \(\downarrow\)** & **MPJPE Error \(\downarrow\)** \\ \hline IPNET [73] & 28.2 & 43.8 \\ \hline PTF [74] & 23.1 & 41.1 \\ \hline VIBE [75] & 57.29 & 53 \\ \hline
**Our method** & **36.1** & **39** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results are Mean Vertex-to-Vertex (V2V) (in mm) and Mean Per-Joint Position Error (MPJPE) (in mm) body for the different human positions.  
in the input NIR-SPI image and points cloud generated resulting from reflection effects and low resolution in the reconstructed NIR-SPI image. The 3D human reconstruction demonstrated improved accuracy regarding the positions of the vertex and joint, as detailed in Table 3. Our model registers a V2V error of 36.1 mm, ranking it second behind PTF and ahead of IPNET and VIBE. This indicates a commendable performance, although there remains a potential to enhance the accuracy of finer geometric details relative to the top-performing model. Regarding MPJPE, our model demonstrates the best performance with the lowest error at 39 mm, showcasing its superior accuracy in joint localization compared to the other models. This achievement is particularly significant, as precise joint prediction is critical for many practical applications involving human body pose estimation.
# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Classification Based on Mode-Based Method
#### 5.2.2 Active Tracking
However, because of underwater noise, the position information obtained from the trilateration is not accurate and even unavailable for the equation of trilateration having no resolution. To this end, the authors in [83] propose a consensus estimation algorithm based on 3 DUTT, which makes use of the fusion concept to improve the tracking accuracy. Compared with the scenario that there is only one designated projector node actively transmitting pulses in [42], each hydrophone sensor in this paper can send acoustic wave after detecting the presence of the target, which means activated sensors can directly obtain relative distances to the target. For the three-dimensional underwater target, every four non-coplanar sensors forming a cluster can determine the position of it. To save energy consumption, the four sensors broadcast and receive the remaining energy from each other. Then the node containing the maximum remaining energy is selected as the cluster head to calculate the rough location information of the target. The consensus estimation algorithm based on 3 DUTT is aimed to fuse this rough information to reduce the negative effect of noise on the final tracking result. References [38; 81] share in common that the active source is stationary. However, this will bring high tracking error owing to the severe propagation loss in an underwater environment, especially in the case that the target is far from the active source. Different from previous works, the transmitter in [82; 83] can be changed with the trajectory of the target. During tracking process, there is only one transmitter in [82], while there are multiple active sources in [83] which will result in extra energy consumption compared with that of [82]. In this section, various algorithms based on tracking mode (passive and active) are introduced in detail. Now, we compare them in the aspect of tracking precision in Table 4. To make readers know the algorithm proposed in each paper quickly, we also list the main tracking technologies and the types of measurements used in each paper.
# Numerical Study on the Bending Resistance of Lightweight Built-Up Steel-Concrete Composite Beams
## 3 Parametric Numerical Analysis Results
### Influence of CW Thickness and Spot Weld Density  
#### 3.7.1 Full Degree of Shear Connection  
The influence of spot weld density in the case of a full degree of shear connection is shown in Figure 14 for models with steel beam heights of 500 mm and 600 mm. Figure 14 shows the results for a steel beam with a height of 500 mm and CW thicknesses of 0.8 mm (500_SP10_CW08) and 1.0 mm (500_SP10_CW10); Figure 14 shows the results for a steel beam with a height of 600 mm and CW thickness of 0.8 mm (600_SP10_CW08) and 1.5 mm (600_SP10_CW15). In Figure 14, when comparing models with a CW thickness of 1.0 mm, a difference of 20 kNm is observed, and for the models with a CW thickness of 0.8 mm, a difference between models with different numbers of spot welds does not exist. In Figure 14, the difference between models with the same CW thickness and different spot weld densities is negligible, but a greater difference is observed in models with the same spot weld density and different CW thicknesses, approximately 80 kNm. This difference can be explained by the fact that models in Figure 14 exhibit greater differences in terms of CW thickness.  
Figure 14: Influence of CW thickness and spot weld density on models with full shear connection: (**a**) steel beam height of 500 mm; (**b**) steel beam height of 600 mm.  
Figure 13: Influence of the number of spot welds between C profiles and other steel elements: (**a**) steel beam height of 400 mm; (**b**) steel beam height of 500 mm.  
Figure 15a,b show the deformed shape and von Mises stress fields of the steel beams of the 600_SP10_CW08_TIE_02 and 600_SP10_CW15_TIE_02 models, respectively. Von Mises stresses are shown in the step when failure is observed. In Figure 15a, it is shown how stresses in the area of the CW are increased compared with the results in Figure 15b. The main reason for this concerns the differences between corrugated web thicknesses.
## 3 Data and Methods  
### Data and Variables  
To achieve the objective of this study, which examines the impact of digital transformation on the environmental performance of emerging economies, a comprehensive panel dataset was constructed. This dataset encompasses four European Union member states--Bulgaria, Hungary, Poland, and Romania--that are classified as emerging economies by the International Monetary Fund (IMF) [2; 3; 4; 5; 41]. The analysis of digital transformation is operationalized through three key dimensions of digitalization: infrastructure, affordability, and user adoption. These dimensions were meticulously selected and calculated based on the rigorous methodology put forth by [PERSON] and [PERSON] [42], incorporating a set of 10 carefully chosen indicators, which are detailed in Table 1.  
The environmental performance of these economies is evaluated using two critical metrics: greenhouse gas emissions and the proportion of energy generated from renewable sources. Greenhouse gas emissions are a direct measure of the environmental impact, reflecting the levels of carbon dioxide and other harmful gases released into the atmosphere. In contrast, the percentage of energy derived from renewable sources serves as an indicator of the sustainable energy practices adopted by these economies. The data employed in this study were meticulously sourced from reputable international databases to ensure accuracy and reliability for the period from 2010 to 2022. Specifically, the digitalization indicators were obtained from the International Telecommunication Union (ITU) and World Bank Group (WBG), which provides comprehensive data on global telecommunication and information technology trends. The environmental performance data, including GHG emissions and renewable energy statistics, were sourced from the International Energy Agency (IEA) and Eurostat, ensuring that the analysis is grounded in the most up-to-date and reliable data available. The digital transformation of the four emerging economies within the European Union--Bulgaria, Hungary, Poland, and Romania--was rigorously assessed in the present study through the calculation of a comprehensive digitalization index (DIGIX) for the period 2010-2022. This index encapsulates the three critical dimensions of digital transformation: infrastructure (INFR), affordability (AFFORD), and user adoption (USAD), as illustrated in Figure 1.
Considering the above-mentioned context and the need for time, we are motivated to develop a solution for predicting diabetics from non-diabetic patients from the electronic health records obtained from local Saudi hospitals. Therefore, the goal of this research paper is to develop predictive classifiers and models to investigate real diabetic patients' data gathered from different Saudi hospitals and regions, utilizing different metrics. Although the obtained records have very few health-related attributes including lab test results such as cholesterol tests (HDL and LDL), and the diabetes-specific tests (FPG and HbA1c), our objective is to identify those factors that can be controlled by the patients-at-risk or non-diabetes as precautions for avoiding diabetes. Previous work in this direction has used a much larger number of variables in different contexts. Thus, the current work presents a novel perspective on diabetes prediction. The insights obtained from this work in the prediction of Diabetes Mellitus (DM) and its associated risk factors can be useful at different levels: To support and strengthen the existing findings of DM medical research, particularly, in the context of Saudi Arabia, to assist the community in understanding the causes and prevention of diabetes, and to help the government to allocate efforts in the right direction to minimize the effect of the growing number of diabetes patients. With these objectives in mind, we developed a model that used five machine learning classifiers to evaluate two datasets; each one containing some basic attributes about the patients (age, gender, weight, height, presence of hypertension, and the level of physical activity), the results of cholesterol laboratory tests (HDL and LDL) and one of these two tests: HbA1c in one dataset and FPG in another dataset. With these limited sets of features, we evaluated each dataset and identified the importance of various features and their role in diabetes prediction. To improve the performance of our classifiers, we also applied feature elimination using feature permutation and hierarchical clustering techniques. Finally, using the rank-based correlation method, we also identified and analyzed correlation among various features and their impact on diabetes risk and prediction. The remainder of this paper is structured as follows. In Section 2, we briefly explain DM followed by the related work done in diabetes classification. In Section 3, we explain our research methodology from data collection and preprocessing to the process of feature engineering and dataset creation. Section 4 explains the development of machine learning models for diabetes prediction and classification with a focus on improving the model performance through feature elimination. The results are then discussed in Section 5. Section 6 discusses the outcome and benefit of our research and Section 7 concludes this article.
# Joint Uplink and Downlink Resource Allocation for D2D Communications System
## 2 System Model and Problem Formulation
### System Capacity
#### 2.2.1. _Uplink_ Phase  
When the \(m\)th CU uses the uplink spectrum, the received signal-to-noise-and-interference ratio (SINR) for the \(m\)th CU can be expressed as  
\[\gamma_{m,up}^{c}=\frac{P_{m}^{c}g_{mB}}{\sigma^{2}+\sum\limits_{k\in K}\rho_ {mk}^{u}P_{k}^{d}h_{kB}} \tag{4}\]  
where \(P_{m}^{c}\) and \(P_{k}^{d}\) are the transmission power of the \(m\)th CU and the transmitter of \(k\)th D2D pair, respectively, \(g_{mB}\) is the channel gain of the signal link between \(m\)th CU and the BS, \(h_{kB}\) is the channel gain of the interference link between the transmitter of \(k\)th D2D pair and the BS, and \(\sigma^{2}\) is the power of additive Gaussian white noise on each channel.  
In the uplink, the CU causes the interference to the D2D pair, thus the SINR of the \(k\)th D2D pair under channel \(m\) is calculated as  
\[\gamma_{k,up}^{d}=\frac{P_{k}^{d}g_{kk}}{\sigma^{2}+\sum\limits_{m\in M}\rho_ {mk}^{u}P_{m}^{c}h_{mk}} \tag{5}\]  
where \(g_{kk}\) is the channel gain of the signal link between the \(k\)th D2D pair, and \(h_{mk}\) is the channel gain of the interference link between the \(m\)th CU and receiver of the \(k\)th D2D pair.  
Then the overall system capacity of CU and D2D pair in uplink phase is written according to Equations (4) and (5)  
\[R_{sim}^{up}=B\log_{2}\Bigl{(}1+\gamma_{m,up}^{c}\Bigr{)}+B\log_{2}\Bigl{(}1 +\gamma_{k,up}^{d}\Bigr{)} \tag{6}\]
## 4 Experiments & Analysis
### Comparative Experiment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Precision** & **Recall** & **F-Measure** & **FPS** \\ \hline TD500 & 0.91 & 0.80 & 0.85 & 40 \\ TD500 trained model in MEND & 0.36 & 0.26 & 0.30 & 28 \\ SynthText pre-trained in MEND & 0.06 & 0.16 & 0.86 & 25 \\ SynthText pre-trained, and & & & & \\ \hline SynthText pre-trained on new pictures in MEND & 0.86 & 0.84 & 0.85 & 36 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison test: DBNet takes ResNet50 as the backbone. Using a deeper neural network as the backbone ResNet50 with slightly reduced prediction speed still achieved significantly better performance than ResNet18. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Precision** & **Recall** & **F-Measure** & **FPS** \\ \hline ICDAR2015 & 0.91 & 0.83 & 0.87 & 12 \\ TD500 pre-trained model in MEND & 0.09 & 0.083 & 0.08 & 10 \\ ICDAR2015 pre-trained, and & & & & \\ trained on new pictures in MEND & 0.72 & 0.45 & 0.55 & 11 \\ TOTALTEXT & 0.86 & 0.84 & 0.85 & 25 \\ TOTALTEXT pre-trained & & & & \\ model in MEND & 0.17 & 0.08 & 0.10 & 25 \\ TOTALTEXT pre-trained and & & & & \\ trained on new pictures in MEND & 0.91 & 0.86 & 0.89 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative test results of the SAST approach using the ResNet50-D network as a backbone for text recognition in complex industrial scenarios. To optimize its performance, the approach is initially trained on the TOTALTEXT dataset and then fine-tuned on new images. The results show that the SAST approach achieves high accuracy while maintaining appropriate recognition speed. The method proposed in this paper is a pre-processing technique for text detection, which does not affect the efficiency of the model's use, but can enhance the inference accuracy. In practical applications, this pre-processing algorithm has a relatively small computational cost and high operating efficiency on the experimental host, with a speed of up to 20 fps. The delay caused by this method during barcode scanning or data collection is negligible for practical applications. Our method has also effectively improved the detection accuracy in experiments with Faster RCNN and YOLOv3; see Table 6. However, since these two detection algorithms are general-purpose and not specifically optimized for text detection, their accuracy is lower than that of other algorithms.
# Lightweight Integrity Preserving Scheme for Secure Data Exchange in Cloud-Based IoT Systems
## 6 Proficiency Evaluation
### Computation Cost of the Uploading Stages  
According to Section 4, the semi-trust server needs to generate a challenge key _Ck_, verification key _Vk_, and metadata \(\sigma\) for each smart device user separately. This study evaluated the generation of challenge key _Ck_, verification key _Vk_, and metadata \(\sigma\) for the system. Figure 3 indicates that the parameter generation cost is proportional to the number of smart device users. Note that the system upload cost is one-time and will not influence the real-time performance of the integrity checking.
## 1 Introduction
However, such a dataset for assistive walkers is unavailable in the literature. In this work, we performed data collection and labeling, followed by the evaluations of various machine learning models to validate their accuracy. Intuitively, a drastic fall-like motion that causes a user to lose balance or collapse to the ground usually results in significant spikes in acceleration on all three axes and noticeable deviations in gyroscopic position [5]. Similarly, acceleration and rotation data can also be used to detect the motion of intervals of walking and stopping. The primary challenge is to separate the motions of walking from falls while eliminating false positives and negatives. Missing a life-threatening fall is clearly critical, but false alarms can also seriously jeopardize the adoption of this new technology. Our labeled dataset is shared publicly on Kaggle [6] for easy access to the research community. The main contributions of this work are the following: (1) We developed an IMU-based fall detection dataset for assistive walkers, which is published online for sharing with the IoT and the healthcare research community; (2) we evaluated different machine learning models and compared their performance using the dataset; (3) we built a low-power, real-time IoT device prototype and validated its functions through experiments. The IoT device can also communicate with others via Bluetooth and Wi-Fi, enabling it to transmit pertinent information to smartphones or a web server, facilitating prompt alerts in the event of falls. Additionally, the device collects and maintains various statistics, including the number of steps the user takes and their duration. These statistics can offer valuable insights to physicians who monitor a patient's progress in recovery. In the following sections of this paper, the design, implementation, and evaluation of the proposed fall detection system will be presented. In Section 2, a review of the related work will be conducted for recognizing existing approaches and identifying gaps. The detailed design and implementation of the novel hardware prototype will be presented in Section 3. Section 4 will describe the procedure for data acquisition and labeling. Section 5 will discuss the machine learning and deep learning models to be used for fall detection. The results and discussion of the SafeStride system evaluation will be included in Section 6. Finally, Section 7 will summarize the project, describe the main findings, and identify potential areas for future work.
# Characteristics of BeiDou-3 Experimental Satellite Clocks
###### Abstract  
The characteristics of the improved Atomic Frequency Standard (AFS) operated on the latest BeiDou-3 experimental satellites are analyzed from day-of-year (DOY) 254 to 281, of the year 2017, considering the following three aspects: stability, periodicity, and prediction precision. The two-step method of Precise Orbit Determination (POD) is used to obtain the precise clock offsets. We presented the stability of such new clocks and studied the influence of the uneven distribution of the ground stations on the stability performance of the clock. The results show that the orbit influence on the Medium Earth Orbit (MEO) clock offsets is the largest of three satellite types, especially from \(3\times 10^{3}\) s to \(8.64\times 10^{4}\) s. Considering this orbit influence, the analysis shows that the Passive Hydrogen Maser (PHM) clock carried on C32 is approximately \(2.6\times 10^{-14}\) at an interval of \(10^{4}\), and has the best stability for any averaging intervals among the BeiDou satellite clocks, which currently achieves a level comparable to that of the PHM clock of Galileo, and the rubidium (Rb) clocks of Global Positioning System (GPS) Block IIF. The stability of the improved Rb AFS on BeiDou-3 is also superior to that of BeiDou-2 from \(3\times 10^{2}\) s to \(3\times 10^{3}\) s, and comparable to that of Rb AFS on the Galileo. Moreover, the periodicity of the PHM clock and the improved Rb clock are presented. For the PHM clock, the amplitudes are obviously reduced, while the new Rb clocks did not show a visible improvement, which will need further analysis in the future. As expected, the precision of the short-term clock prediction is improved because of the better characteristics of AFS. The Root Mean Square (RMS) of 1-h clock prediction is less than 0.16 ns.  
BeiDou-3; clock analysis; Allan variance; harmonic analysis; clock prediction 2018 10.3390/rs10111847; doi:10.3390/rs10111847; doi:10.3390/rs10111847; doi:10.3390/rs10111847
# Performance Prediction and Design of Stratospheric Propeller
## 6 Result and Discussion
### Performance Prediction for the Designed Propeller  
The designed propeller performances calculated using the vortex theory and simulated in CFD are compared in Figure 18. The performance predicted by the vortex theory appeared in good agreement with that determined by CFD simulation. This result indicates that the prediction value of \(C_{T}\) was lower than the simulation value when \(\lambda<0.55\) and started to deviate from the simulation value when \(\lambda>0.75\), when the deviation began to decrease. The prediction value of \(C_{P}\) was higher than the simulation value when \(0.65<\lambda<0.85\). The variation tendency of \(\eta\) between the prediction and the simulation values was the same, and the prediction value was higher than the simulation value in the entire range of \(\lambda\).  
Although the vortex theory can determine the performance of the propeller, it must iteratively make computations for each section of the blade. Using the prediction method mentioned above, the aerodynamic performance of the propeller could be predicted based on the blade element performance at \(0.75R\), thus reducing the number of iterative calculations. The values \(r_{0}=0.15R\), \(0.2R\), and \(0.25R\) were calculated by Equation (22) to predict the propeller performance, which meant the propeller performance was attributed to \(0.6R\)-\(0.9R\), \(0.55R\)-\(0.95R\), and \(0.5R\)-\(R\). These results were expressed as Predict 1, Predict 2, and Predict 3, respectively.  
The propeller performances predicted based on the blade element at \(0.75R\) and simulated in CFD are compared in Figure 19. All three predictors had the same variation  
Figure 18: Comparison of the results from vortex theory with those form CFD simulation: (**a**) thrust coefficient; (**b**) power coefficient; (**c**) efficacy.  
tendency as the simulation in \(C_{T}\), \(C_{P}\), and \(\eta\), but the Predict 1 and Predict 2 values of \(C_{T}\) and \(C_{P}\) were far lower than the simulation value. The \(C_{T}\) value of Predict 3 was higher than the simulation value, and the deviation decreased with increasing \(\lambda\). The \(C_{P}\) value of Predict 3 almost coincided with the simulation value, causing the \(\eta\) value of Predict 3 to be higher than the simulation value. The \(\eta\) values of the three predictions were the same according to Equation (24).  
Table 4 indicates that the prediction accuracy of Predict 3 was the same for the three aerodynamic parameters as that of the vortex theory at the design point. For \(r_{0}=0.25R\), Predict 3 was in accordance with the result in Figure 17. Predict 3 was more similar to the CFD simulation value within a larger \(\lambda\) range, and the tendency was more consistent; a higher \(C_{T}\) predicted value and a lower \(C_{P}\) predicted value were conducive to designing the propeller.