esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 New Classification of Methods for the Detection of Water Network Operating Conditions
### Software Methods (Indirect)
#### 4.2.2 Acoustic Signal Processing  
There are many modern methods of acoustic signal interpretation in the context of detecting failures of technical networks using the phenomenon of sound wave propagation. They require the additional use of bandpass filters, which remove the noise generated by the external environment surrounding the pipes.  
According to the methods based on acoustic signal interpretation, the aim is to learn the spatial distribution of the sound effect accompanying the failure by rendering it using modern signal processing techniques. The main parameter subjected to analysis is the amplitude of the sound wave, defined by sound pressure (expressed in Pa) [46].  
Depending on the acoustic signal processing and interpretation techniques used, the related methods are divided into three subcategories. The first one, and the most comprehensive, uses wireless sensor networks (WNSs) along with built-in algorithms for signal processing at the site of registration. The second type of method that interprets acoustic signals is based on the wavelet transform (WT) of the signal recorded by traditional acoustic sensors acoustic emission (AE). The last type of method has implemented algorithms for the cross-correlation of two time series recorded by AE before and after the leakage site.  
The application of acoustic sensors is easy to use, operate, and locate, and is flexible, efficient, and relatively cheap [47]. In addition, the sensors can be placed at quite considerable distances from each other outside or inside of the pipeline. Acoustic sensors combined with machine learning are currently state-of-the-art in detecting water supply network operating conditions [48].
# The Effects of Topology on Throughput Capacity of Large Scale Wireless Networks
## 3 The Intra-Cluster Transmission
### The Spheroidal Percolation Model for Each Cluster
According to the node's distribution in each cluster, we have the following equation:  
\[\left(r_{i}\frac{2\pi}{y}\right)^{3}\times\frac{n}{m}r_{i}^{-\delta}=c_{0}, \tag{10}\]  
where \(c_{0}\) is a constant which denotes the number of nodes distributed in quasi-cubelets, and it is independent of \(n\). By simple transformation of (10), the maximum radius \(\rho_{\max}\) of quasi-cubelets that guarantees it contains at least one node is  
\[\rho_{\max}=r_{z}=\Theta\left(\left(\left(1-v\right)\log n\right)^{\frac{1}{ 2-3}}\right) \tag{11}\]  
and the maximum number of concentric sphere is  
\[i_{\max}\equiv z= \Theta\left(\frac{\log\log n}{\delta-3}\left(\frac{n^{1-v}}{ \log n}\right)^{\frac{1}{3}}\right). \tag{12}\]  
It indicates that there is w.h.p. at least one node in quasi-cubelet \(s_{i}\), if \(s_{i}\) is within the distance of \(r_{z}\) away from the cluster center. Until now, each cluster is divided into a \(c_{1}\left(\frac{n^{1-v}}{\left(1-v\right)\log n}\right)^{\frac{1}{3}}\times c _{2}\left(\frac{n^{1-v}}{\left(1-v\right)\log n}\right)^{\frac{1}{3}}\times c _{3}\frac{\log\log n}{\delta-3}\left(\frac{n^{1-v}}{\log n}\right)^{\frac{1}{ 3}}\) cubelets. We consider that a path of cubelets is open if any two adjacent cubelets are open. Based on Appendix I in [2], we can obtain that there are \(\left[\eta\log m\right]\) disjoint paths crossing a rectangle of size \(m\times\left(\kappa\log m-\epsilon_{m}\right)\). Therefore, within the radius of \(\rho_{\max}\), there exists \(\Theta\left(\left(\frac{n^{1-v}}{\left(1-v\right)\log n}\right)^{\frac{2}{3}}\right)\) disjoint paths in the direction of the radius. Similarly, the paths in the directions of south-north and east-west are both \(\Theta\left(\left(\left(\frac{n^{1-y}}{(1-y)\log n}\right)^{\frac{1}{3}}\right)\right)\). In our proposed model, we call the transmission path highway systems. The highway system contains two parts. One is along the direction of the radius, and we call this radial highways (RH). The other is around the sphere, which is known as surrounding highways (SH). As shown in Figure 3, the routing strategy is divided into five consecutive phases. In the first phase, the transmitter drains the packets to the closest RH; in the second phase, the information is carried along the RH until it arrives at the quasi-cubelet, which is located at the same concentric sphere as the receiver. Then, the information will be transmitted around the sphere in which we take four directions (east, west, south, north) into account. That is, the information is transmitted in the direction of south-north, and then it is delivered in the direction of east-west in the fourth phase. Finally, in the last phase, the information is left from the highway to the receiver.
## 2 Literature Review and Hypotheses
### Research Hypotheses of Media Richness and Creativity.
#### 2.3.5 Hypotheses  
Based on the above discussion and the research from [PERSON], [PERSON], [PERSON], et. al., [PERSON], et. al. and [PERSON] [37; 57], this research posits the following hypotheses to be investigated:  
**Hypothesis 1 (H1):**_The potential creativity cognition performance of the CMC learners in the experimental group is better than the Ftf learners in the control group._  
**Hypothesis 1a (H1a):**_The fluency performance of the CMC learners in the experimental group is better than the Ftf learners in the control group._  
**Hypothesis 1b (H1b):**_The flexibility performance of the CMC learners in the experimental group is better than the Ftf learners in the control group._  
Figure 1: Research hypothetical model (e.g., media richness vs. creative thinking fluency) of the hypontized impact on creativity potential after creative training by media richness.  
**Hypothesis 1c (H1c):**_The originality performance of the CMC learners in the experimental group is better than the FtF learners in the control group._  
**Hypothesis 1d (H1d):**_The elaboration performance of the CMC learners in the experimental group is better than the FtF learners in the control group._  
**Hypothesis 2 (H2):**_The overall media richness perception of the CMC learners of the experimental group is better than the FtF learners of the control group._  
**Hypothesis 2a (H2a):**_The perception of the availability of immediate feedback by the CMC learners in the experimental group is better than the FtF learners in the control group._  
**Hypothesis 2b (H2b):**_The perception of the use of multiple cues by the CMC learners in the experimental group is better than the FtF learners in the control group._  
**Hypothesis 2c (H2c):**_The perception of language variety by the CMC learners in the experimental group is better than the FtF learners in the control group._  
**Hypothesis 2d (H2d):**_The perception of personal focus of the medium by the CMC learners in the experimental group is better than the FtF learners in the control group._  
**Hypothesis 3 (H3):**_The perception of media richness is significantly correlated to potential creative thinking._
###### Abstract  
Loads and strains in critical areas play a crucial role in aircraft structural health monitoring, the tracking of individual aircraft lifespans, and the compilation of load spectra. Direct measurement of actual flight loads presents challenges. This process typically involves using load-strain stiffness matrices, derived from ground calibration tests, to map measured flight parameters to loads at critical locations. Presently, deep learning neural network methods are rapidly developing, offering new perspectives for this task. This paper explores the potential of deep learning models in predicting flight parameter loads and strains, integrating the methods of flight parameter preprocessing techniques, flight maneuver recognition (FMR), virtual ground calibration tests for wings, dimensionality reduction of flight data through Autoencoder (AE) network models, and the application of Long Short-Term Memory (LSTM) network models to predict strains. These efforts contribute to the prediction of strains in critical areas based on flight parameters, thereby enabling real-time assessment of aircraft damage.  
m pacs: **Keywords:** maneuver flight recognition (MFR); autoencoder (AE) neural network; flight parameter dimensionality reduction; long short-term memory neural network (LSTM); strain prediction
## 3 Results
When observing solely helmet users, there was a statistically significant difference between conditions 4 and 5 (\(p<0.01\)). Furthermore, a significant difference was also found when comparing the results for helmet users between conditions 5 and 6 (\(p<0.01\)) and 4 and 6 (\(p<0.01\)). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Left Side\_Rt**} \\ \hline
**Interaction** & **Helmet** & **Condition** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline
1 & 0 & 1 & & 0.74 & \textless{0.01 } \({}^{*}\) & 0.59 & 0.80 & \textless{0.01 } \({}^{*}\) \\ \hline
2 & 0 & 2 & 0.74 & & \textless{0.01 } \({}^{*}\) & 1.00 & 1.00 & \textless{0.01 } \({}^{*}\) \\ \hline
3 & 0 & 3 & \textless{0.01 } \({}^{*}\) & \textless{0.01 } \({}^{*}\) & & \textless{0.01 } \({}^{*}\) & \textless{0.01 } \({}^{*}\) & \textless{0.01 } \({}^{*}\) \\ \hline
4 & 1 & 1 & 0.59 & 1.00 & 0.00 \({}^{*}\) & & 1.00 & \textless{0.01 } \({}^{*}\) \\ \hline
5 & 1 & 2 & 0.80 & 1.00 & \textless{0.01 } \({}^{*}\) & 1.00 & & \textless{0.01 } \({}^{*}\) \\ \hline
6 & 1 & 3 & \textless{0.01 } \({}^{*}\) & \textless{0.01 } \({}^{*}\) & 0.01 \({}^{*}\) & \textless{0.01 } \({}^{*}\) & \textless{0.01 } \({}^{*}\) \\ \hline \hline \end{tabular} Legend: \({}^{*}\)\(p<0.05\); Helmet 0—Helmet non-users; Helmet 1—Helmet users; Condition 1—Ski cap; Condition 2—Ski cap and sunglasses; Condition 3—Ski helmet and ski goggles.  
\end{table} 
Table 2: Tukey post hoc results for reaction time when the skier was approaching from the left side. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Right Side\_Rt**} \\ \hline
**Interaction** & **Helmet** & **Condition** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline
1 & 0 & 1 & & 0.45 & \textless{0.01 } \({}^{*}\) & 1.00 & 0.01 \({}^{*}\) & \textless{0.01 } \({}^{*}\) \\ \hline
2 & 0 & 2 & 0.45 & & \textless{0.01 } \({}^{*}\) & 0.71 & 0.46 & \textless{0.01 } \({}^{*}\) \\ \hline  
When comparing the results of helmet users and helmet non-users in the same tested conditions, it is possible to conclude that there was no statistically significant difference in interactions when comparing interactions 1 and 4 (\(p\) = 1.00) and 2 and 5 (\(p\) = 0.46). However, a statistically significant difference was found between interactions 3 and 6 (\(p\) < 0.01), which means that there was a difference in the time required to react to a visual stimulus when it appeared on the right side of the participant between interaction helmet non-users*ski helmet + goggles and interaction helmet users*ski helmet + goggles. Helmet users had a faster response to the visual stimuli when wearing a ski helmet and goggles compared to helmet non-users (0.51 s vs. 0.55 s).
# V2X Communication over Cellular Networks: Capabilities and Challenges
## 4 Delay Analysis for V2X Communications  
Latency is of pivotal importance for V2X systems given its relevance to the very fast reactions to potential hazards that are required. As shown in Table 2 some UCs require extremely low latencies (e.g., 3 ms). This is of course a very challenging goal for a cellular network. The latency that can be supported by Long-Term Evolution Advanced (LTE-A) and 5G-NR systems relates to inherent characteristics of the networks for specific control and user plane functionalities. Delay evaluation has been performed by 3 GPP [27; 28], 5 GAA [22], and 5G Infrastructure Association (5G-IA) [29] for both LTE-A and 5G-NR. Moreover, the authors in [30] evaluated the IMT-2020 KPIs (including user and control plane delay) for 5G-NR following the assumptions provided by 3 GPP [28] and 5G-IA [29]. In this paper, we provide a thorough analysis, with additional delay components that have not been presented in the aforementioned evaluations, such as handover, paging, and core network delay for 5G. The following sections provide detailed information for both the Uu and PC5 mode of communication in LTE-A and 5G-NR. For simplification, we only consider the Frequency Division Duplex (FDD) mode.
## 5 Iterative NSGA-II  
The structure of the iterative NSGA-II algorithm is shown in Figure 7. The initial search space is defined as the region centered on the initial solution obtained from the offline simulation. Within a small search area, NSGA-II can find the regional optimal Pareto front quickly with a small population size in only a few generations. However, when looking into the distribution of the decision variables, there may be variables that are saturated over the Pareto front on the boundary of the search space. This indicates that the objective function costs can be further improved if the search space can be shifted towards the boundary direction (second search space); see the blue arrow shown in Figure 7. Following the NSGA-II search over the second search space, the solved Pareto front from the first and second search spaces is combined and sorted to generate a new Pareto front. Distribution evaluation is applied again to determine whether or not another search space shift is necessary. The search is stopped when the distribution indicates that the optimal solution has been found or the pre-defined maximum shift limit has been reached. As a result, this iterative NSGA-II method can improve optimization performance with a relatively small search space, making it feasible for real-time implementation. During online bench optimization, the decision variables (PID control gains) are saturated by predefining the search space in each iteration of NSGA-II optimization; the details are stated in Section 7.
## 2 Dataset and Feature Extraction
### Preprocessing Steps for Feature Extraction  
PPG techniques are commonly used in biomedical and related research fields for RR, HR estimation, SpO2, and blood pressure prediction. As shown in Figure 2, two PPG waveforms were acquired from the RRSYNTH dataset [37]. The upper panel (a) shows an example of breaths/minute (10 bpm), and the lower panel (b) shows an example of a high bpm (50 bpm). The dashed line of the PPG waveform signal represents the envelope between the PPG peak and trough. We can find fiducial points using these PPG peak and trough information for the resampled wave signals. Next, we used steps 7 to 11 to estimate the RR, as shown in Figure 1. Compared with the reference RRs (10 bpm and 50 bpm), error of the estimated RR in Figure 2a represents (\(\leq\)\(\pm\)1.0 bpm), and error of the estimated RR in Figure 2b illustrates the accuracy of (\(\leq\)\(\pm\)2.0 bpm). First, various high-frequency (HF) signals were reduced using a low-pass filter (Kaiser window function) with a 3 dB cutoff of 35 Hz, as shown in Figure 1. Next, the PPG signal was separated into pulses using the incremental merge segmentation technique [40], which is an adaptive pulse segmentation algorithm [40] for changing the PPG waveform into pulses and the automatic separation of artifacts, as shown in the third box in Figure 1. Next, fiducial points were identified from the PPG peaks and troughs, as shown in the fourth box on the left side of Figure 1. The irregular PPG signals were resampled to 5 Hz by using the linear interpolation method [40] as shown in the fifth box on the left side of Figure 1. Subsequently, the low-frequency (LF)  
Figure 1: Block diagram of the proposed methodology using CAGBA methodology.  
signals were eliminated using a high-pass filter (Kaiser window function) with a 3 dB cutoff frequency. Finally, resampled waveform signals were acquired from PPG signals.  
The RRSYNTH dataset of breathing signals was acquired from PPG and RR target data obtained from 192 records (participants). Each signal had a length of 210 s, and the sampling frequency (Fs) was 500 Hz. RR estimates were determined using the mean second between successive respirations in the Hamming window. Iterative tests were performed to predict the actual RR value as window size was changed to 16, 32, and 64 s. Finally, the proposed technology exhibited better performance at a window size of 32 s than at window sizes of 16 and 64 s [37; 39]. The BIDMC dataset consists of ECG, PPG, and IP respiratory signals obtained from intensive care patients. The dataset consists of 53 recordings of PPG, ECG, and IP signals (Fs = 125 Hz) obtained from adult patients aged 19-90 years for 8 min. The preprocessing of the BIDMC dataset was performed the same way as the RRSYNTH dataset.
## 4 Spectrum Sensing in Maritime Cognitive Radio Networks  
### System Model  
Here, we assumed the UHF band to be the PU's band. It is broad and can offer a bandwidth of more than 100 MHz opportunistically in maritime networks [3]. The communication range for thesefrequencies is up to 10 km. Ships satisfy all the requirements for acting as the SU, which includes spectrum sensing capability and the reconfiguration of operating parameters. The base station at the shore acts as the fusion center. The system model for the performance analysis is shown in Figure 1 in which the maritime wireless network with \(n\) SUs is considered. Let \(s(t)\) be the transmitted signal of the PU:  
\[s(t)=\Re\left\{a(t)e^{(j2\pi f_{c}t+\varphi)}\right\} \tag{4}\]  
where \(a(t)=s_{I}(t)+js_{Q}(t)\) is a complex base band signal with bandwidth \(B\), \(f_{c}\) is the carrier frequency and \(\varphi\) is the initial phase. It is assumed for simplicity that \(a(t)\) is real for propagation model analysis.  
The received signal, \(x(t)\), can be written as:  
\[x(t)=\Re\left\{a(t)*h_{c}(t)e^{(j2\pi f_{c}t+\varphi)}\right\} \tag{5}\]  
where \(h_{c}(t)\) is the base band channel model.  
The received signal after experiencing path loss channel model given in Equation (2) can be written as:  
\[x(t)=\Re\left\{a(t)*\sqrt{\frac{G}{PL}}e^{(j2\pi f_{c}t+\varphi)}\right\} \tag{6}\]  
where \(G\) is the antenna gain.  
The ultimate goal of spectrum sensing is to determine the presence of a PU using a binary hypothesis model, _i.e._, the basic model for spectrum sensing by the SU, which is defined as:  
\[r(t)=\left\{\begin{array}{ll}w(t)&\text{in case of }H_{0},\\ x(t)+w(t)&\text{in case of }H_{1}\end{array}\right. \tag{7}\]  
where \(r(t)\) is the signal received by the SU, \(w(t)\) is the additive white Gaussian noise (AWGN), \(H_{0}\) indicates only noise and the presence of a PU is \(H_{1}\).
# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
## 6 Results and Evaluation
### 3D Space Localization Accuracy  
After validating performance in 2D plane, we test ANTSpin in a 3D space, as shown in Figure 15b,c. We separately test the three methods and, as we expected, the third method performs best, as it makes more use of the positioning information. Later, we mainly test the third method. We move the tags to be located and then collected about 100 test cases. Figure 17 plots the CDF of localization error in 3D. The mean error of relative incident angle is 0.07 in ANTSA, 0.0733 in ANTSB, 80% of the errors are less than 0.10 with minimal error of 0.0006 in ANTSA, 0.0025 in ANTSB and maximal error of 0.28 in ANTSA, 0.29 in ANTSB. The mean error of distance calculation is 5.79 cm in ANTSA, 7.34 cm in ANTSB, 90% of the errors are less than 15 cm with minimal error of 0.08 cm in ANTSA, 0.03 cm in ANTSB, and maximal error of 27.17 cm in ANTSA, 16.46 cm in ANTSB. Errors over 15 cm are also individual phenomena. The mean distance error of ANTSpin under the 3D plane scenario is 3.79 cm in X-axis, 8.37 cm in Y-axis, 6.70 cm in Z-axis, and 13.00 cm in combined dimension; 90% of the errors are less than 20 cm, with a minimal error of 2.37 cm and maximal error of 31.9 cm. We tested some existing schemes in  
Figure 16: Localization error in 2D. (**a**) Relative incident angle error in 2D; (**b**) Distance calculation error in 2D; and, (**c**) Localization error in 2D.  
a similar experimental environment and the experimental results show that ANTspin performed better than APM (30 cm), APAA (45 cm), RW (16 cm), WPM (32 cm), and RWD (22 cm) [13; 15]. Ref. [15] is indeed a good solution. When compared to Ref. [15], ANTspin requires fewer antennas and lower equipment costs. Further, ANTspin is simpler to deploy, which makes it more portable. In addition, the analysis of various parameters in ANTspin shows that it is very robust.
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 5 Design Guidelines
### Step 4--Circuit Design  
Intuitively, it makes sense to start a new radenv-ADC from a previous unhardened design with good electrical performance. However, the hardening task may be impractical if important radiation hardening considerations were overlooked in the original design. For example, for technologies requiring the replacement of the planar transistors by ELTs [86; 87] (to avoid excessive performance degradation caused by TID), it must be checked that the original design does not have planar transistors narrower than the minimum width required by ELTs (\(W_{min,ELT}\approx 3\cdot W_{min,planar}\)). Something similar occurs for the node splitting technique [88] against SET (transistor width should allow being divided at least by two or four in the dual- and quad-paths hardening, respectively). The comparison between radenv-ADCs and scientific ADCs (cf. Sections 4.1 and 4.2) gives an order of magnitude of what is reasonable to increase the power consumption to mitigate radiation effects (for a given \(f_{s}\) between 10 kHz and 100 MHz, collected radenv-ADCs require at least 200 times more power to achieve an ENOB at least 2 bits lower than scientific ADCs). In any case, a new radenv-ADC can be designed from scratch, considering all the applicable hardening-by-design techniques from the beginning. To avoid unnecessary design effort, the unavailable rad-hard cells identified in Step 4 are designed with advanced TCAD (Technology Computer-Aided Design) tools once it is required to integrate them in the circuit. Furthermore, to avoid unnecessary power consumption, it is strongly recommendable to tailor the hardening effort for each particular design case: not all nodes are critical, and some of them can be SET hardened with a capacitance to ground (DC biased nodes, for example). A good approach to detect SET sensitive nodes is by analyzing the effects of injecting calibrated current pulses [89] throughout the circuit in different moments of a transient simulation [90; 91].