esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest
## 6 Conclusions  
In this study, an automated SA detection method was developed to accurately identify sleep apnea events using ECG and SpO2 signals. The best results in terms of accuracy, sensitivity, specificity, and AUC were obtained using the RF classifier after fusing the features of ECG and SpO2. The model takes full advantage of the complementary information of the two signals and outperforms the model developed based on a single signal in terms of diagnostic performance. In addition, experimental results on the Apnea-ECG database showed that the performance of our method has been further improved compared to previous studies. Although the evaluation results of the model met our expectations, there are still some limitations. The database provided by Dr. [PERSON] does not annotate hypoventilation events. Therefore, in future work, we will combine multiple datasets to distinguish apnea from hypoventilation events and further validate the proposed algorithm.  
Conceptualization and methodology, J.Z. and A.Z.; software, A.Z. and J.H.; validation and formal analysis, Q.G. and Y.Z.; writing--original draft preparation, A.Z.; writing--review and editing, J.Z. and Z.C.; funding acquisition, J.Z. and Z.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangxi Natural Science Foundation of China (grant number 2022 GXNSFAA035507); the Guangxi Science and Technology Major Special Project (grant number 2019 AA12005); the National Key R&D Program of China (grant number 2016 YFC1305703);  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Signal** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **AUC** \\ \hline [PERSON] et al. [36] & ECG & 93.9\% & 92.26\% & 94.92\% & 0.99 \\ [PERSON] et al. [12] & ECG & 90.75\% & 91.91\% & / & 0.97 \\ [PERSON] et al. [31] & ECG & 89.70\% & 85.07\% & 92.42\% & 0.96 \\ [PERSON] et al. [25] & SpO2 & 97.38\% & 84.57\% & 97.28\% & / \\ [PERSON] et al. [37] & SpO2 & 90.20\% & 87.6\% & 94.1\% & / \\ [PERSON] et al. [38] & ECG + SpO2 & 84.40\% & 85.89\% & 86.81\% & / \\ This Study & ECG + SpO2 & 97.5\% & 95.9\% & 98.4\% & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the proposed method with other previous studies.  
the 2019 Guangxi One Thousand Young and Middle-Aged College and University Backbone Teachers Cultivation Program; the Foundation of Guangxi Key Laboratory of Automatic Detection Technology and Instruments (grant number YQ20112); and the National Natural Science Foundation of China (grant number 61627807).  
Not applicable.  
**Data Availability Statement:** [[https://physionet.org/content/apnea-ecg/1.0.0/](https://physionet.org/content/apnea-ecg/1.0.0/)]([https://physionet.org/content/apnea-ecg/1.0.0/](https://physionet.org/content/apnea-ecg/1.0.0/)) (accessed on 11 January 2021).  
The authors declare no conflict of interest.
# A Helping Hand to the Elderly: Securing Their Freedom through the HAIE Framework
### Involvement of IoT and the Cloud in Smart Homes for the Elderly  
The concept of the Internet of Things (IoT) involves the interconnection of various devices to the internet. These devices, such as sensors and actuators, are equipped with a telecommunication interface, a processing unit, limited storage, and software applications [5]. The IoT facilitates the integration of these objects into the internet, allowing interactions between people and devices, as well as between devices themselves [6]. The term IoT refers to the integration of diverse devices capable of sending and receiving signals. IoT deployment can be observed through sensors, near-field communication, guidance systems, DDPG [7], mobile devices, energy-harvesting cognitive networks [8], etc. These devices are connected to the internet to exchange information, significantly impacting social and environmental domains to achieve a shared objective [9].  
The integration of IoT with computer intelligence is likely to be incorporated into the gadgets necessary for managing modern homes [10]. In recent years, several initial efforts have been made to design and implement smart homes using IoT technologies. The unified IoT-based smart home paradigm aims to connect all household devices and appliances [11]. In studies by the authors of [12; 13], a smart home was created by networking smart objects using ZigBee technology and programming sensors and actuators using the Arduino platform. Furthermore, cloud computing has transformed home services and applications  
Figure 1: Life expectancy for the period 1990–2050 [4].  
in the field of home automation. Manufacturers today integrate IoT with the cloud in various devices to meet modern demands. Cloud-based smart homes are technically viable and will have a significant societal impact. The virtualization of services allows customers to access the system and its services from anywhere, at any time, and on any device. This integration offers many benefits, such as security, reliability, scalability, and interoperability. Figure 2 illustrates the various applications made possible through IoT technology.
# A Novel Real-Time Match-Moving Method with HoloLens
## 4 Experimental Results
### Match-Moving with Motion Blur  
As mentioned earlier, factors such as motion blur and occlusion can affect the camera tracking results in the Boujou method. These factors affect the feature point detection process and consequently lose the camera path. Therefore, the virtual-real image synthesis cannot be accurate. This limitation of the Boujou method can be seen in the video recorded in the second scenario when there were no cross patterns in the background and there was motion blur. As the checkerboard pattern is printed on the surface of the cardboard cube, the Boujou method can find feature points from the captured images even when there are no cross patterns in the background. However, the amount of error is increased when compared to the first scenario when cross patterns were added in the background. The difference in the accuracy of virtual-real image synthesis can be seen in Tables 2 and 3.  
Due to the abrupt motion of the camera in the second scenario, the video frames between 479 and 542 have motion blur. As a result, the lowest match rate of the Boujou method was 46.41% and 52.41% at 702 frames and 975 frames, respectively, which is indicated in Table 3. This shows that the  
\begin{table}
\begin{tabular}{c l c c c} \hline  & **Method** & **Number of Unmatched Pixels** & **Specified Rate Resolution** & **Match Rate (\%)** \\ \hline
11 frame & GT-BM & 938 & 60,000 & 98.4 \\  & GT-PM & 500 & 60,000 & 99.1 \\
710 frame & GT-BM & 1199 & 60,000 & 98.0 \\  & GT-PM & 3371 & 60,000 & 94.4 \\
952 frame & GT-BM & 577 & 60,000 & 99.0 \\  & GT-PM & 155 & 60,000 & 99.7 \\
1303 frame & GT-BM & 1936 & 60,000 & 96.8 \\  & GT-PM & 773 & 60,000 & 98.7 \\ \hline \end{tabular}
\end{table}
Table 2: Match rate of different images in match-moving with cross pattern.  
Figure 10: Comparison of different images in match-moving with cross pattern.  
match rate dropped significantly when comparing with the results in Table 2. As seen in Figure 11, the virtual-real image synthesis from 703 frames onwards has completely lost the camera path. On the other hand, the proposed method has a match rate of over 94% for the whole video, as shown in Table 3. This means that the proposed method is not affected by any errors caused by motion blur.  
As a result, when there is motion blur, the Boujou method loses the feature points, which leads to the pausing of camera solve until the feature points are found again. At the point that it finds the feature points again, it generates a new camera solve. As a result, the camera position is lost during that time. Consequently, the new camera solve has a different position and rotation values compared to the existing (first) camera solve, so the virtual-real image synthesis cannot be accurate.
# Additive Manufacturing in Underwater Applications
## 6 Challenges, Limitations, and Predicted Directions of Development  
While AM is developing very rapidly, there are still a number of limitations and challenges connected with 3D printing underwater. Among the main areas that require further research, the most important seems to be the development of machines for totally submerged objects and material issues. The experiment must be conducted in a difficult underwater environment, which creates challenges for their proper design and the appropriate construction of supporting tools. This requires new solutions in the area of construction. Moreover, the usage of the materials creates some limitations, especially in a real environment, where the external conditions can change. Some of the material problems have been recognized and partly solved by using admixtures--for example, for concrete--but other materials, such as metal powders, still need to be isolated from an underwater environment to obtain the desired effect [49].  
In the literature, there are only a few works that mention the further development of 3D printing. This is usually in the general context of the whole marine industry. Most of them confirm that 3D printing technology is one of the most important for the marine industry as a whole [24; 125], but they do not specify the particular application or only mention it briefly. The applications mentioned as being connected with future trends are AM for the production of custom or small-batch components for both yachts and sailboats [125] and 3D-printed spare parts, tools, and accessories in the marine industry [126].  
The presented areas of application illustrate these trends well. They show the great potential of AM for underwater applications. However, there are visible gaps in this area that require further research, and the main motivation for development is often the low cost and accessibility of solutions [127]. The analysis shows that AM is also a technology that has the potential to contribute to the invention of the most advanced and complex solutions. Examples in this area are 4D printing technology and smart devices for underwater applications [13].  
The other, not previously mentioned visible trend in the research is the increasing concerns about the environment and trials to quantify these data. The first research made use of some tools, such as life cycle analysis, providing an example of work connected with the life cycle estimation of an underwater drone hull to study the Arctic [128]. Other analyses are connected with the environmental footprint, including estimations of the possible reduction of CO\({}_{2}\) emissions or its capture by using proper materials for artificial reef structures [129].
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 3 System Model and Problem Formulation  
### System Architecture for UAV-Enabled 6G NTNs  
The proposed system comprises the NTN node, RIS, and a high-mobility user as depicted in the Figure 2. This architecture is designed to enhance connectivity in dynamic environments such as high-speed trains, airplanes, and autonomous vehicles. A control link facilitates logical signaling between the NTN node and the RIS, which is solely responsible for transmitting optimized phase-shift parameters and reconfiguration commands. This enables real-time RIS adjustments to accommodate changes in channel conditions, user mobility, and interference patterns, thereby ensuring coherent signal reflection and seamless handover in high-mobility scenarios.  
An NTN node refers to a satellite or a UAV that transmits with power \(P\). The RIS is modeled as a planar array consisting of \(M\) passive reflecting elements, each capable of imposing a reconfigurable phase shift \(\phi_{m}\in[0,2\pi)\). The user is a single-antenna receiver that operates under high-mobility conditions.  
This hybrid NTN architecture, also known as the HAP model, achieves robust connectivity through mechanisms such as path diversity, redundancy, and extended coverage. Although the HAP offers low-latency localized service, a low-Earth-orbit (LEO) satellite ensures global coverage. The RIS intelligently optimizes the signal reflections from both the HAP and the LEO nodes to counteract the adverse effects of mobility-induced channel degradation, thus achieving effective load balancing and improved link reliability. This work assumes a passive, reflective RIS without embedded baseband signal processing or energy harvesting. Each element applies a discrete phase shift from a finite set (e.g., 4-6 bits of quantization). The RIS is assumed to be a non-amplifying, narrowband, continuous-phase metasurface with a shared controller via a backhaul or satellite/UAV-side interface. This assumption ensures low power consumption but requires accurate external CSI estimation and centralized control. Our model does not consider active RIS or hybrid reflective-transmissive metasurfaces, which are left for future work.
# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 6 Challenges of Deep Learning
### Neural Architecture Search  
Neural architectures are constructed by hand by humans who are experts in the fields of ML and deep learning, and this can be time consumption and possibly allow mistakes [127]. Therefore, there is great interest in neural architecture search (NAS) [127]. NAS is an automating process for building neural architectures [127]. NAS provides better-designed architectures than manually designed architectures--e.g., in object detection [123] and in image classification [123; 128]. NAS methods are categorized regarding to three dimensions: search space, search strategy, and performance estimation strategy [127].  
In search space, the architectures that can be represented are identified. The search space size can be decreased, and the search can be simplified if it can be done using previous knowledge on the characteristics of architectures that are appropriate for the mission. Nevertheless, this leads to human bias, and hence, this inhibits discovering the building blocks of new architectures that surpass the knowledge of present people [127].  
The usually massive search space is discovered by a search strategy. The algorithm faces the classical tradeoff of exploration-exploitation. On the one hand, it can discover satisfactorily performing architectures rapidly. However, on the other hand, it should be averse to early convergence on architectures that are suboptimal [127].  
Discovering architectures with high expected performance upon invisible data is the aim of NAS. The meaning of performance estimation is the procedure of calculating this performance. Applying on data the standard training and validation of architecture is considered the simplest choice. Nonetheless, it presents limitations upon the number of architectures that can be discovered, and their computations are expensive. Based on these, a lot of research is concentrated upon building methods to decrease the performance estimation costs [127]. The following are methods that quicken the performance estimates: lower fidelity estimates [123; 128; 129], learning curve extrapolation [130; 131], weight inheritance/network morphisms [132; 133], and one-shot models/weight sharing [134; 135].
## 1 Introduction
The first approach defines the material's fracture energy \(G_{f}\) as a 3D random field with a Weibull probability distribution, which also defines \(\varepsilon_{p}\) for each \(i\)-th element used in the simulations (see Equation (5)) according to its location (considering the barycenter of the bar) [44]. The correlation lengths \(L_{cx}\), \(L_{cy}\) and \(L_{cz}\) along the corresponding axes \(x\), \(y\) and \(z\) determine the fracture energy's linear spatial correlation. The second is to introduce small mesh perturbations in the regular cubic LDEM arrangement through small initial dislocations of the nodal points. This approach leads to significant improvements in predicting the responses from structures under compressive loading when instability effects are not negligible [45; 46]. By numerical experimentation, considering a perfectly cubic nodal array as the initial state, the coefficient of variation, \(CV_{p}\), for the normally distributed perturbations, was chosen as 2.5%. One should note that the focus of this study is not on the experimental procedure per se, but on the sensitivity of each analysis method on the data sets from the different sources, especially regarding their ability to identify the system's entry into a critical damage state.
# Pore Structure Degradation of Different Cement
## 3 Methods  
### Manufacturing of Specimens, Curing Process, and Subphuric Acid Exposure  
Prismatic specimens, according to the UNE-EN 196-1 standard [23], using standardised moulds for cement mortars (4 \(\times\) 4 \(\times\) 16 cm), were manufactured to carry out the tests. Six specimens were made for each type of mortar (control, NS, ZS, and ES mortars), obtaining a total of 24 specimens. The specimens were removed from the moulds after 24 h and stored in a humid chamber (95 \(\pm\) 2% RH, 20 \(\pm\) 2 \({}^{\circ}\)C) until the curing process (28 days) is completed. The specimens, to which the ES coating had  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Element** & **Percentage (\%)** \\ \hline CaO & 55.36 \\ SiO\({}_{2}\) & 14.89 \\ SO\({}_{3}\) & 4.62 \\ Al\({}_{2}\)O\({}_{3}\) & 3.36 \\ MgO & 3.29 \\ Fe\({}_{2}\)O\({}_{3}\) & 3.06 \\ K\({}_{2}\)O & 1.06 \\ TiO\({}_{2}\) & 0.25 \\ Na\({}_{2}\)O & 0.23 \\ P\({}_{2}\)O\({}_{5}\) & 0.14 \\ SrO & 0.12 \\ Cl & 0.11 \\ MnO & 0.04 \\ Other elements & \(<\)0.30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: X-ray fluorescence of CEM I 52.5R.  
Figure 1: X-ray diffraction (XRD) spectra of cement type CEM I 52.5.  
to be applied, were extracted from the humid chamber at 14 days. The treatment was applied with the aid of a nebuliser in the stipulated time. The specimens were then returned to the humid chamber to complete the curing period. Twelve specimens were then exposed to a sulphuric acid attack and the other 12 were kept under laboratory conditions (24 \(\pm\) 2 \({}^{\circ}\)C) and used as reference.  
There is no standardised European methodology to evaluate the chemical resistance of cementitious materials. The methodology followed in this study for sulphuric acid exposure has already been used in previous studies [3; 24; 25]. The specimens were immersed in a solution (3% w/w) of sulphuric acid (H\({}_{2}\)SO\({}_{4}\)), which was changed weekly. Before the specimens were re-immersed, they were washed with tap water and brushed to remove the layer of adhering material on the mortars' surfaces. In this study, tests of the specimens were conducted after 28 days of acid exposure (i.e., 56 days after their manufacture) and 90 days of exposure (118 days after their manufacture). In each test, three replicates were carried out to obtain average values. Porosimetry was performed for two samples at 90 days of sulphuric acid exposure, due to its repeatability.At the end of the acid exposure, the mortars were washed and dried at 105 \(\pm\) 2 \({}^{\circ}\)C for 24 h. Before starting any test, the specimens were left to cool down at room temperature.
# A Highly Integrated Millimeter-Wave Circularly Polarized Wide-Angle Scanning Antenna Unit
## 4 Conclusions  
The SIW-based antenna unit combines circular polarizers, filters, and matching loads to achieve integrated design, strong out-of-band suppression, circular polarization, and wide-angle scanning capabilities. Utilizing PCB technology enables cost-effective mass production of the entire antenna unit. This innovation represents a new method for integrating millimeter-wave antennas and their components.  
**Author Contributions:** Investigation, G.Y.; methodology, S.G.; validation, K.W.; writing--original draft preparation, G.Y.; writing--review and editing, S.G. and K.W.; supervision, J.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 11: The fabricated antenna array.  
Figure 12: The comparison of simulated and measured results of the scanning pattern at the center frequency.
# The Analysis of WJ Distribution as an Extended Gaussian Function: Case Study
## 3 Results and Discussion
### On Mutual Fitting of WJ Distributions and Arbitrary Gaussian Functions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\mu\) & \(\sigma\) & \(\alpha\) & \(\beta\) & \(\
u\) & \(\mathbf{R^{2}}\) \\ \hline \multirow{4}{*}{WJ fitting GS} & 0 & 1 & 12.52 & 0.07915 & 63.96 & 0.9997 \\  & \(-1\) & 0.5 & 25.01 & 0.1575 & 31.17 & 0.9998 \\  & 1 & 2 & 6.251 & 0.0399 & 127.6 & 0.9998 \\  & 2 & 5 & 2.526 & 0.01584 & 322.1 & 0.9997 \\ \hline \multirow{4}{*}{GS fitting WJ} & \(\alpha\) & \(\beta\) & \(\
u\) & \(\mu\) & \(\sigma\) & \(\mathbf{R^{2}}\) \\ \cline{2-6}  & 12.52 & 0.07915 & 63.96 & 0 & 1 & 0.9997 \\ \cline{1-1}  & 1 & 2 & 3 & 3.632 & 0.8439 & 0.9500 \\ \cline{1-1}  & 2 & 20 & 5 & 5.305 & 0.3055 & 0.8588 \\ \cline{1-1}  & 1 & 15 & 10 & 10.59 & 0.5974 & 0.8347 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter selection and fitting results for mutual fitting between Gaussian functions and WJ distributions. Figure 7: Mutual representation of WJ distributions and Gaussian functions (refer to Table 1 for parameter selection and fitting results). (**a**) Fitting of Gaussian functions (in dots) by WJ distributions (in solid line); (**b**) Fitting of WJ distributions (in hollow squares) by Gaussian functions (in solid line). the peaks of the curves. Thus, it follows that Gaussian functions are not able to capture the main characteristics of the WJ function in general. Statistically, the features of the curves are overall manifested in the change in their correlation coefficients from almost one (0.9997 in actual) for the first panel declining to 0.8347 for the last panel. The explication in the above points to the fact that if a stochastic process is describable by a Gaussian function, it can be at the same time quantified by a WJ distribution almost in the identical way; on the contrary, it may not be the case.