esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, a new human subject detection technique was presented which is based on the windowed Fourier transform (WFT) and a frequency accumulator. The range and respiration frequency of the subject were determined using UWB radar data. This provides a means of detecting trapped victims after natural disasters. The detection performance was evaluated and compared with three well-known methods. The results obtained show excellent suppression of clutter, linear trend, harmonics, and high and low-frequency interference. Further, the proposed method has low complexity. In summary, the developed detection algorithm of life signs provides a novel scheme for human subject detection via employing an UWB radar. With this motivation, the developed UWB radar can show better efficiency and effectiveness than other UWB radar systems. However, it is more challenging to acquire human respiration frequency when the subjects are moving or walking due to being in the same band as human breath with larger amplitudes. Thus, future work will focus on obtaining respiration signals when the subject is in motion such as walking and running. Further, techniques will be developed to identify human subjects from other living animals.  
Figure 24: (**a**) Histogram of the range errors with the proposed method, and (**b**) the probability of error distribution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency Accumulation Method (m)** & **WFT Method (m)** & **Error (m)** \\ \hline
13.70 & 7.82 & 5.88 \\
10.24 & 6.89 & 3.35 \\
9.45 & 6.74 & 2.71 \\
10.54 & 14.36 & 3.82 \\
12.95 & 3.66 & 9.29 \\
13.43 & 5.36 & 8.07 \\
12.12 & 9.39 & 2.73 \\
11.42 & 15.29 & 3.87 \\
13.11 & 9.34 & 3.78 \\
14.56 & 6.89 & 7.67 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Range estimates and errors without a human subject.  
**Author Contributions:** Conceptualization, X.L.; methodology, X.L.; validation, X.L.; formal analysis, X.L.; investigation, X.L.; funding acquisition, S.Y. and H.Q.; writing-original draft preparation, X.L.; writing-review and editing, T.A.G.  
**Funding:** This work was funded by the Nature Science Foundation of China under grant number 41527901, 61501424 and 61701462.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Fog-Computing-Based Heartbeat Detection and Arrhythmia Classification Using Machine Learning
## 2 Preliminaries and Previous Work
The design of portable health-monitoring devices has closely followed the electronics miniaturization evolution over the years [PERSON] et al. (2019). Wearable monitoring devices that are able to provide accurate and precise measurements are gaining support from physicians and healthcare units. Existing solutions _lack computational power to locally process the ECG recordings and to detect abnormal behavior_. With the use of such devices, the recorded signal is transmitted to a nearby gateway device, which in turn relays the recording over to the cloud, where advanced analysis algorithms are executed [PERSON] et al. (2018); [PERSON] et al. (2019). Given the nature of the heart activity, the large majority of the trace periods correspond to normal heart activity. Therefore, in most cases, large amounts of data transmitted over the network infrastructure and processed at the cloud level are simply dropped, leading to a significant waste, not only of the battery resources of the wearable device, but also of network and cloud resources Li et al. (2019); [PERSON] et al. (2019). It is evident that minimal possible latency, network bandwidth preservation, and efficient data-storage resource utilization are elements of paramount importance for any IoT-related application Li et al. (2019); Li et al. (2019). The design of efficient health-monitoring systems that are capable of locally processing the ECG recordings has been a topic of active research over the last few years. Most researchers have tried to merge wireless sensor networks with smart gateways Li et al. (2019); Li et al. (2019), while others have proposed the use of smartphones as gateways for developing personal health monitoring systems Li et al. (2019); Li et al. (2019). More recently, researchers have developed specialized diagnosis techniques for specific types of health-monitoring applications Li et al. (2019) and platforms that use dedicated IoT communication protocols Li et al. (2019). However, all the aforementioned solutions somehow fail to harness the full extent of capabilities that fog computing offers towards providing a holistic solution that will be applicable to a larger number of cases, involving multiple actors.
# Joint Uplink and Downlink Resource Allocation for D2D Communications System
## 4 Numerical Analysis
### Simulation Results
Figure 5: The system capacity versus different maximum transmission power with different cell radius. Figure 6: The system capacity with different distance of D2D pairs, comparison of the JUAD/OU/OD resource allocation schemes. The system capacity of the proposed JUAD algorithm versus different maximum transmission power is shown in Figure 7. It is seen that the performance of the proposed JUAD algorithm decreases with the decreasing of the maximum transmission power of the D2D pairs. In addition, with the increasing of the distance of D2D pairs, the improvement of system performance is limited. As the distance of D2D pairs is 60 m and the maximum transmission power \(P_{\max}=21\) dBm, the system capacity is about 226 bps/Hz, which is close to that of \(P_{\max}=27\) dBm. Figure 8 displays the output performance of the proposed JUAD algorithm versus different cell radius. The same user density is assumed to be for both \(R=500\) m, \(R=800\) m, and \(R=1000\) m, respectively, in the cell. From Figure 8, the system capacity of the proposed JUAD scheme can be improved with the increasing cell radius. Moreover, the system capacity of the JUAD algorithm under the radius \(R=1000\) m is better than that under the radius \(R=500\) m and radius \(R=800\) m.  
Figure 8: The system capacity versus different distance of D2D pairs with different cell radius. Figure 2:0 The system capacity with different distance of D2D pairs in terms of different maximum transmission power.
## 2 Materials and Methods
### From Euler-Bernoulli to Timoshenko
By substituting the term \(\frac{12E_{s}I}{l^{3}}\) with \(\left(\frac{1}{\frac{\beta}{lE_{s}I}+\frac{l}{eAG_{s}}}\right)\), the relationships for elastic modulus and Poisson's ratio based on [PERSON] beam theory were obtained. (c). Hexagonal packing  
For this unit cell, since the analytical relationships for mechanical properties of structure have not been derived based on Euler-Bernoulli theory in the literature [35], the analytical relationships for Euler-Bernoulli and [PERSON] beam theories have both been derived in this study. The detailed derivations can be found in Appendix C.  
(d). Rhombicuboctahedron  
Since the final relationships for elastic modulus and Poisson's ratio of this unit cell have been presented in \(\gamma\) terms, and the stiffness matrix of unit cell contains \(\frac{AE_{s}}{l}\), \(\frac{12E_{s}I}{l^{3}}\), \(\frac{G_{s}I}{l}\), and \(\frac{6E_{s}I}{l^{2}}\), recognition of terms provided in Table 1 in the final Equations (A24) and (A25) of Appendix B (extracted from [30]) is not possible. Therefore, by substituting the term \(\frac{12E_{s}I}{l^{3}}\) with \(\left(\frac{1}{\frac{\beta}{lE_{s}I}+\frac{l}{eAG_{s}}}\right)\) and \(\frac{6E_{s}I}{l^{2}}\) with \(\left(\frac{1}{\frac{\beta}{lE_{s}I}+\frac{l}{eAG_{s}}}\right)\) in the stiffness matrix (Equation (A26) in Appendix B (which is extracted from [30]) and solving the system of equations, the relationships based on Timoshenko theory were obtained. (e). Truncated cube  
The procedure for truncated cube unit cell is very similar to what was described above for the rhombicuboctahedron unit cell. The final stiffness matrix of the unit cell in Equation (A27) of Appendix B (extracted from [32]) contains \(\frac{AE_{s}}{l}\) and \(\frac{12E_{s}I}{l^{3}}\) terms. Therefore, by substituting the term \(\frac{12E_{s}I}{l^{3}}\) with \(\left(\frac{1}{\frac{\beta}{lE_{s}I}+\frac{l}{eAG_{s}}}\right)\), the stiffness matrix based on Timoshenko beam theory can be derived. Afterwards, by solving the system of equations, the mechanical properties relationships based on Timoshenko beam theory were obtained. (f). Truncated octahedron  
The relationships for elastic modulus and Poisson's ratio of truncated octahedron unit cell have been presented in Equation (A28) and (A29) of Appendix B (extracted from [39]). Since the deformation of this unit cell includes axial extension and lateral bending, the terms \(\frac{AE_{s}}{l}\) and \(\frac{12E_{s}I}{l^{3}}\) can be extracted from these equations. By substituting the term \(\frac{12 EI}{l^{3}}\) with \(\left(\frac{1}{\frac{\beta}{lE_{s}I}+\frac{l}{eAG_{s}}}\right)\), the relationships presented for elastic modulus and Poisson's ratio can be transformed into corresponding Timoshenko ones. It is worth noting that the relationships for normalized yield stress for five of the six noted unit cells (BCC, hexagonal packing, rhombicuboctahedron, diamond, and truncated octahedron) based on Euler-Bernoulli and Timoshenko beam theories have been obtained separately in another study [40].
# Data Fusion of Multivariate Time Series: Application to Noisy 12-Lead ECG Signals
## 4 Application of NDFA in 12-Lead ECG Signals  
In this section, for the sake of assessing the performance of NDFA, synthetic ECG signals and realistic ECG signals are applied in the experiments. In Section 4.1, we evaluate the validity of NDFA by synthetic ECG signals. Then based on three types of noises from the MIT-BIH Noise Stress Test Database (NSTDB) [27; 28], the noise tolerance of the algorithm will be analyzed in detail. In Section 4.2, NDFA is executed on the database of PhysioNet/Computing in Cardiology Challenge 2011 [29], and the MIT-BIH database to further illustrate the performance of the algorithm. It is worth mentioning that, in this study, the False Nearest Neighbors (FNN)and the Average Displacement (AD) algorithms were adopted to determine the optimal embedding dimension \(m_{s}\) and delay time \(\tau_{s}\).
## 5 Experiment Procedure
### RVQA Procedures for Detecting Data Confidentiality Violations  
The data confidentiality violation detections were performed for each executed single agent at the individual, social, and content levels using the proposed RVQA. The RVQA procedures kept track of the number of violations detected at each level and calculated the total number of all detected violations as follows:1. For a single agent at the individual level, the RVQA obtained the number of individual data confidentiality violations, RVQASingLEAgentIndividualDCV, by keeping track of the number of the agent accounts that disclosed its identity and location to the public, as shown in Equation (1): RVQASingLEAgentIndividualDCV = noDisclosedIdentity + noDisclosedLocation (1)
2. For a single agent at the social level, the RVQA calculated the number of social data confidentiality violations, RVQASingLEAgentSocialDCV, by including the number of established connections to the online social network. Equation (2) was used for the calculation: RVQASingLEAgentSocialDCV = noDisclosedSocialInformation (2)
3. For a single agent at the content level, the RVQA calculated the number of content data confidentiality violations, RVQASingLEAgentContentDCV, by including the amount of contents disclosed to the public. Equation (3) was used for the calculation: RVQASingLEAgentContentDCV = noDisclosedContent (3)
4. The total number of data confidentiality violations detected by the RVQA from individual, social, and content levels for all agents in the executed CNS model, RVQATotalDetectedDCV, was calculated using Equation (4). In this experiment, x is the agent identification and \(n\) is the number of agents executed in CNS. RVQATotalDetectedDCV = \(\sum\limits_{x=1}^{n}(RVQASingLEAgentIndividualDCVx\ +\)  
\[RVQASingLEAgentSocialDCVx\ +\ RVQASingLEAgentContentDCVx)\]
# An Automatic Geometric Registration Method for Multi Temporal 3D Models
## 5 Conclusions  
In this paper, we propose a fully automatic 3D model registration method, which converts 2D feature points extracted from the highest resolution texture image of the model into 3D spatial-feature points, and solves the model-transformation parameters of the 3D model to be aligned with respect to the base 3D model, and then establishes a unified spatial reference for multi-temporal 3D models. The experimental results show that the multi-temporal 3D models generated by high-precision PPK data combined with GPS-support method can locate ground object displacement that is more than three times that of the GSD of the UAV image. In comparison, the average value of the difference between the measured ground object displacement and the actual object displacement was \(0.01\) m for the 3D model generated by the method in this paper. This method does not need to measure GCPs at each data acquisition to make a multi-time series 3D model with a uniform spatial reference. For periodic dynamic inspection tasks, it has the great advantage of saving project cost and economic cost.  
Conceptualization, H.S.; methodology, H.S. and C.R.; software, C.R.; validation, G.L. and Z.L.; formal analysis, G.J.; investigation, G.L.; resources, G.L.; data curation, H.S.; writing--original draft preparation, C.R.; writing--review and editing, C.R.; visualization, H.S.; supervision, Z.L.; project administration, H.S.; funding acquisition, C.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Northwest Engineering Corporation Limited Major Science and Technology Projects, grant number XBY-ZDKJ-2020-08, and the National Natural Science Foundation of China, grant number (41801383). The APC was funded by 41801383.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multicolumn{4}{c}{**Displacement (m)**} & \multicolumn{4}{c}{**Difference (m)**} \\ \cline{2-7}  & **GPS** & **POS** & **PPK** & **REG** & **POS** & **PPK** & **REG** \\ \hline C1 & \(0.49\) & \(3.13\) & \(0.41\) & \(0.49\) & \(-2.64\) & \(0.07\) & \(-0.01\) \\ C2 & \(0.62\) & \(2.87\) & \(0.58\) & \(0.61\) & \(-2.25\) & \(0.04\) & \(0.01\) \\ C3 & \(0.88\) & \(3.03\) & \(0.83\) & \(0.87\) & \(-2.15\) & \(0.06\) & \(0.01\) \\ C4 & \(0.63\) & \(3.14\) & \(0.53\) & \(0.61\) & \(-2.51\) & \(0.10\) & \(0.02\) \\ C5 & \(0.37\) & \(2.97\) & \(0.24\) & \(0.33\) & \(-2.61\) & \(0.13\) & \(0.04\) \\ C6 & \(0.73\) & \(3.20\) & \(0.69\) & \(0.77\) & \(-2.46\) & \(0.04\) & \(-0.04\) \\ C7 & \(0.47\) & \(3.14\) & \(0.34\) & \(0.45\) & \(-2.67\) & \(0.13\) & \(0.02\) \\ AVG & & - & & & \(-2.47\) & \(0.08\) & \(0.01\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The displacement and difference of the sprayed stones in the two sequential 3D models.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# YOLOv5-Sewer: Lightweight Sewer Defect Detection Model
## 2 YOLOv5-Sewer Network Design
### YOLOv5-Sewer Network Architecture
#### 2.2.3 Convolutional Block Attention Module  
The convolutional block attention module (CBAM) is incorporated into the proposed model to address challenges posed by dim backgrounds and low-resolution images. CBAM enables the model to focus more on learning and extracting crucial features relevant to sewer defects. This becomes particularly crucial under low-resolution conditions, as CBAM is adept in capturing the features of sewer defects at lower resolutions. It takes into account differences between pixel categories, channel features, and contextual correlations. The structure of this module is illustrated in Figure 5.  
The channel attention mechanism of CBAM can be expressed using the following Equation (1):  
\[M_{\text{c}}(F)=\sigma(\text{MLP}(avgpool(F))+\text{MLP}(maxpool(F))) \tag{1}\]  
where \(F\) represents the input feature map; \(avgpool\) and \(maxpool\) denote average pooling and max pooling operations, respectively. MLP refers to a multi-layer perceptron, and \(\sigma\) represents the sigmoid function.  
In the channel attention module, the input feature map undergoes both \(avgpool\) and \(maxpool\) operations based on width and height, resulting in two feature maps. These feature maps are then fed into a shared multi-layer perceptron (MLP), and the generated features are passed through the spatial attention module. Weighting is applied to the  
Figure 4: The structure of the C3 and C3-Faster blocks.  
Figure 5: The structure of the convolutional block attention module.  
feature maps to emphasize crucial features. The spatial attention can be expressed using the following Equation (2):  
\[M_{S}(F)=\sigma(f^{7\times 7}([avgpool(F);maxpool(F)])) \tag{2}\]  
The spatial attention module takes the output feature map from the channel attention module as input and performs channel-wise max-pooling and average-pooling operations to obtain two feature maps. These feature maps are then concatenated along the channel dimension and reduced to a single channel through a convolution operation. Subsequently, the spatial attention feature is generated using the sigmoid activation function. The final output feature map of the CBAM attention mechanism is obtained by the element-wise multiplication of the spatial attention module output feature map with the input feature map, as described by the following Equation (3):  
\[Y=F\otimes M_{\text{C}}(F)\otimes M_{S}(F) \tag{3}\]  
The integration of CBAM into the sewer defect detection model based on YOLOv5s serves to enhance the attention to crucial channels. This improvement results in the enhanced ability of the model to focus on defect features. The heightened attention provided by CBAM contributes to more accurate detection results in practical applications, especially in challenging scenarios with dim backgrounds and low-resolution images. The refined feature extraction facilitated by CBAM enhances the model's discriminative power, making it well suited for sewer defect detection in real-world settings.
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 4 Multiple-Observations Helper Data Scheme
### Uniformity and Zero Leakage  
First, by definition of the MO helper data scheme, the secret key is generated uniformly at random and therefore the uniformity condition of Definition 1, \(\frac{1}{n}H(S)=\frac{1}{n}\log_{2}|\mathcal{S}|\), is satisfied. Second, it has been shown in [17] that, for symmetric SRAM PUFs, zero leakage occurs by all the helper data after repeated enrollments, i.e., \(I(S;W_{1}^{n},\ldots,W_{t}^{n})=0\). It follows that  
\[I(S;M_{t}^{n})\overset{(a)}{\leq}I(C^{n};W_{1}^{n},W_{2}^{n},\ldots,W_{t}^{n} )=0, \tag{19}\]  
where \((a)\) follows from the data processing inequality (see [19] Chapter 2) for the Markov Chain  
\[S\leftrightarrow C^{n}\leftrightarrow(W_{1}^{n},W_{2}^{n},\ldots,W_{t}^{n}) \leftrightarrow M_{t}^{n}. \tag{20}\]  
By (19) the MO helper data scheme achieves _strong secrecy_, i.e., \(I(S;M_{t}^{n})\leq\delta\).
## 4 A Two-Stage Optimal Scheduling Model for Wind-PV-Thermal-Storage System
### Intraday Optimal Dispatching Model  
According to the ultra-short-term forecast data of wind power and photovoltaic power, the adjustment of the day-ahead scheduling plan is divided into two steps in the intraday dispatching stage. First, the thermal units' output power is corrected by minimizing average adjustment cost based on the determined start-stop combination of thermal units. Secondly, the intraday dispatching scheme of each unit is obtained by aiming at low-carbon economic dispatching.
# A Combined Approach of Field Data and Earth Observation for Coastal Risk Assessment
## 5 Discussion
Instead, tidal oscillations, wind and wave climate conditions are significantly milder in the areas selected for the present study (i.e., Torre Canne and Porto Cesareo), thus reducing the uncertainty in the still sea level. By selecting only images acquired in the most favorable weather conditions, wind and wave setup and also wave run-up have been neglected in the present study. Moreover, even by assuming a comparable uncertainty in the measurement of the water level, the errors in the shoreline detection are expected to be significantly affected by the local slope of the shoreline: the higher the local slope, the better the accuracy of the tidal correction. The slope of the Dutch test site is much lower than the slope of the two Apulian coastal areas. Finally, SAR backscattering should be further investigated both in the intertidal zone and in areas covered by only a few centimeters of water. This further highlights the influence that environmental parameters play in the coastline extraction. The main limitation of the presented integrated approach is related to the nature of the radar data. Unlike optical images, SAR images are not straightforward to be visually interpreted. As an example, the algorithm cannot recognize objects along the coast, as in the case of _Posidonia oceanica_ blocks, because as external elements they could not be masked out in the segmentation process. Since they can affect the backscattering values in SAR images, their occurrence can hinder the accurate detection of the shoreline through SAR data segmentation analysis. COSMO-Beach could be successfully applied to other coastal areas because it does not depend on the location of coastal areas, but it has to be highlighted that the performance in shoreline detection is strongly linked to the quality and quantity of available field and remotely sensed data, so the \"one-pixel\" potential accuracy can be achieved as long as all the image quality checks have been satisfied and high accuracy in situ observed data are available. The proposed procedure for beach monitoring could be also effectively extended to SAR data provided by other European Space Agency missions released in the framework of the Copernicus Earth Observation programme. Interestingly, Sentinel-1 interferometric data can reach a revisit time of one image every six days, thus allowing a quasi real-time monitoring but their use is limited by spatial resolution (\(5\times 25\) m\({}^{2}\) in Interferometric Wide Swath mode). The use of these low-resolution images would likely allow the monitoring of beaches presenting relevant shoreline evolution rates (sand spits, river deltas) rather than beach environments having a small dynamic range.