esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Gain in Sensing Response Enhancement  
With increased surface-to-volume ratio, the sensing response of our novel nested ZnO nanorod/nanotube gas sensor design is improved compared to the original ZnO nanorod gas sensors. Figure 13 shows the additional enhancement gain of the novel nested ZnO nanorod/nanotube gas sensor when the sensor was operated under saturation-level concentration of ethanol target vapor at different temperatures. The maximum enhancement gain reached approximately 150% at 25 \({}^{\circ}\)C. The calculation formula is shown in Equation (6).  
\[\textit{Gained Enhancement}=\frac{R_{ZnO\;nested}-R_{ZnO\;nanorods}}{R_{ZnO\;nanorods}} \times 100\% \tag{6}\]  
where \(R_{ZnO\;nested}\) is the sensing response of the nested ZnO nanorod/nanotube gas sensors at saturation-level concentration of ethanol vapor, while \(R_{ZnO\;nanorods}\) is the sensing response of the original ZnO nanorod gas sensors at saturation-level concentration of ethanol vapor at the same temperature.
# The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges
## 4 WSN Systems in Different Applications: Aerospace, Bridge Structures, and Wind Turbines
### WSN in Aerospace
#### 4.1.2 AWSN Networks Structure  
The desired parameters are acquired and communicated by various sensors. Due to the fact that current sensors are hardwired, and the fact that the number of sensors is growing due to automation, the accuracy of intended aircraft functions is increased (Figure 5).  
The sensors are distributed throughout the aircraft and are connected by wired networks for signal processing and communication. Line-replaceable units (LRUs), which integrate various sensors, also use a wired approach for communication. Signal integrity and fault detection methods are the key features of signal processing in aviation applications. The application of wireless sensors in aircraft results in enormous benefits in the aircraft's design optimization, flexibility in sensor configuration, as well as its weight optimization. What remains to be solved is the proper placement of wireless sensors and networks in the plane in accordance with protocols, standards, and certification.  
According to [31], the network should be divided into four main areas. The first and most important part is a set of sensors (e.g., pressure, temperature, vibration, etc.) and access points that are part of the so-called internal AWSN network. The second part of the network is all network switches that connect wireless and wired devices, i.e., aircraft computers or internal systems. The third part of the AWSN network includes all portable devices (sensors are not included here), onboard computers, and other network equipment. The last part of the network is the Internet connection, e.g., by means of ATG (Air To Ground) and satellite connections.
## 4 Discussion
### Contribution Analysis  
Based on the sum of squares (_ssq_) analysis achieved from the ANOVA results, presented in Table 4, a contribution analysis of the significant factors is established in Table 5. The sum of squares represents the total variability of each factor in the gathered experimental data. The contribution is calculated as  
\[Contribution=\frac{ssq_{i}}{\sum{ssq_{i}}+\epsilon}\times 100, \tag{2}\]  
where \(i\) represents each factor and \(\epsilon\) is the residual _ssq_ from the ANOVA analysis.  
Based on the contribution analysis presented in Table 5 and the ANOVA analysis and the supporting Figures 6 and 7, the following statements are derived:  
* Locating schemes: Adopting optimal locating schemes can substantially reduce displacement, highlighting the importance of fixture design and workpiece stability in welding operations.
* Welding sequences: Strategic sequencing of welding paths can enhance displacement control.
* Directional control: Precise control over welding path directions is essential in minimizing displacement.
* Combined factors: The significant interactions underscore the necessity of a holistic approach in parameter optimization. The adjustment of one factor should consider the levels of interacting factors to enhance displacement minimization.  
Despite the robust findings, it needs to be considered that the inclusion of all the interactions for combinatorial problems enhances the need for full factorial DOE plans. This complexity can both be computationally infeasible in simulation experiments and obscure the interpretability of individual predictor effects in the ANOVA context. Furthermore, while FEM simulations provide valuable insights, physical welding conditions may introduce additional variables and uncertainties not captured in the simulations. The inherent simulation inaccuracies also need to be included in the analysis of the results.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Predictor** & **Contribution** \\ \hline Assembly I & \\ \hline Locating scheme & 98.45 \% \\ Sequence & 2.19 \% \\ Locating scheme and sequence & 1.82 \% \\ Main weld path (F) & 0.07 \% \\ Secondary weld path (G) & 0.07 \% \\ Residuals & 0.39 \% \\ \hline Assembly II & \\ \hline Locating scheme & 71.80 \% \\ Main weld path (F) & 14.11 \% \\ Sequence & 14.04 \% \\ Tertiary weld path (H) & 0.03 \% \\ Secondary weld path (G) & 0.01 \% \\ Residuals & 8.51 \% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Contribution analysis.
## 6 Security Analysis
### Experiment with Benchmark Datasets  
To validate the reliability and adaptability of the HMPS scheme, experiments were conducted using benchmark datasets representative of real-world IoT environments. These datasets included:  
* Synthetic data simulating vehicle-to-infrastructure communication in intelligent transportation systems.
* Anomalous event datasets are designed to test the system's capability for secure issue detection.  
In these experiments, the hierarchical signature mechanism demonstrated robust performance, successfully validating message authenticity and maintaining data integrity across various conditions. The use of hierarchical aggregation further enhanced the efficiency of the system, allowing it to process large volumes of data in real-time.
## 6 Material and Method  
### Calculating the Location of the Download Points  
The first step of the proposed algorithm is to calculate the \(x\), \(y\), \(z\) position of the DPs where the UAVs will position themselves to download the data from the wireless sensors. This is done by using an iterative approach that relies on the k-means clustering algorithm. The pseudocode of the procedure is given in Algorithm 1. It starts by running the k-means with a single cluster. Although the sensors have a 3D position (they are located on the ground), the k-means is run in 2D, this is to avoid finding a centroid below the elevation of the ground. Once the centroid of the cluster has been found, its elevation is set based on the elevation of the terrain and the default flying altitude of the UAVs. Then, for the cluster found, the distance between the centroid and each sensor is verified. If this distance is longer than the range of the wireless sensors, a new centroid is positioned at the location of the sensor and the algorithm goes immediately back to running the k-means without checking the other sensors. Similarly, it checks to see if the centroid and the sensor are in line of sight and if they are not, a centroid is added at the sensor and the k-means is called again. This process is repeated until all the wireless sensors are in range and in the line of sight of their centroid.  
```
1:Initialize the k-means to use one centroid at the center of the map
2:Run k-means in 2D
3:Add elevation to the centroid(s) based on the terrain elevation
4:for each clusterdo
5:for each sensor in clusterdo
6:if distance between centroid and sensor is longer than wireless
7::range or centroid and sensor are not in line of sight
8: Add a new centroid at \((x,y)\) location of the sensor
9:goto line 2
10:Return the location \((x,y,z)\) of the centroids
```  
**Algorithm 1** Calculation of the positions of the download points  
The iterative k-means approach used is efficient, but may add more clusters than necessary. To account for this, a function was added which is described at Algorithm 2 to remove unnecessary clusters. This function checks each cluster and each sensor within each cluster. If all the sensors in a given cluster are within the range and in the line of sight of another cluster, the cluster can be removed. To demonstrate the functioning of the calculation of the DPs, 70 sensors that have been strategically positioned on a 1700 by 1000-m map are shown in Figure 4. This artificial map represents a canyon with a river, an island and some mountains. The altitude varies from 0 to 250 m. The map also contains three no-fly zones represented using red polygons. The sensors have a wireless transmission range of 170 m. When running Algorithm 1 to calculate the location of the DPs, 22 DPs are calculated. Following the reduction function at Algorithm 2, 15 DPs are remaining clearly showing the need for the removal of extra DPs.
# Exploring Gait Recognition in Wild Nighttime Scenes
## 3 GaitSAT Framework  
In this section, we comprehensively illustrate the proposed method, GaitSAT. GaitSAT initially employs a self-attention module to extract spatial features, aiming to thoroughly learn the intrinsic relationships between various joints of the human body in each frame. Subsequently, to fully capture the walking patterns of pedestrians in outdoor environments along the temporal dimension, we input the extracted spatial vectors into a multi-scale temporal rhythm perception module. This module learns the rich periodic features contained within the gait sequence. In the following subsections, we will provide detailed descriptions of the aforementioned modules.
## 2 Materials and Methods
### Feature Extraction  
The proposed feature extraction technique is presented in Figure 2, which provides an overview of the process. To begin, the source dataset was used to train the CNN1 network. The starting weights used to train CNN2 (which has the same structure as CNN1) on the target dataset were obtained from CNN1. After training both networks, each CNN was utilized to extract high-level features, creating target and source features.  
The utilization of a TL-based EA method (Euclidean alignment block) facilitated the alignment of these feature blocks, thereby ensuring that they had similar distributions. Further details regarding the technique are explained in the following sections.
## 5 Conclusions and Future Work  
### Conclusions  
This paper proposes an intelligent fault diagnosis model for rolling bearings, named IGTO-VMD-PE-IGTO-LSTM, which integrates the IGTO algorithm and VMD, PE, and LSTM networks. The model employs IGTO to optimize both VMD and LSTM parameters, enhancing signal decomposition and feature extraction and ultimately improving fault classification accuracy.  
The experimental results of the Case Western Reserve University dataset and the Jiangnan University dataset demonstrate the excellent performance of the proposed model. Specifically, the model achieves an average testing accuracy of 96.67% and a maximum testing accuracy of 98.96% in the Case Western Reserve University dataset. In the Jiangnan University dataset, the model reaches an average testing accuracy of 98.85%, with the highest accuracy reaching 99.48%. Furthermore, the model exhibits high stability, with low standard deviations of 1.2148 and 1.3217, and narrow 95% confidence intervals of [95.75%, 97.58%] and [96.73%, 97.49%] for the two datasets. These results confirm the model's robustness and adaptability across different datasets and fault types. In terms of computational efficiency, while the proposed model has a slightly longer average runtime of 13.88 s per sample, the trade-off is justified by its superior diagnostic accuracy, making it suitable for high-precision fault detection tasks.
## 3 Results
### Effects of the Intervention Program on Players' Health and Physical Fitness
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multicolumn{2}{c}{**Pre**} & \multicolumn{2}{c}{**Post**} & \multirow{2}{*}{**ES**} \\
**Characteristics** & **M** & **SD** & **M** & **SD** & **t** & \(p\)** & **ES** \\ \hline SMS Intrinsic Scale & 46.20 & 13.01 & 48.00 & 16.43 & \(-0.739\) & 0.501 & 0.330 \\ SMS Integrated Scale & 27.00 & 10.90 & 25.60 & 11.08 & 1.581 & 0.189 & 0.707 \\ SMS Identified Scale & 14.60 & 2.61 & 14.40 & 3.05 & 0.250 & 0.815 & 0.112 \\ SMS Introjected Scale & 15.80 & 3.70 & 15.60 & 3.97 & 0.408 & 0.704 & 0.183 \\ SMS External Scale & 16.00 & 8.60 & 12.40 & 6.69 & 1.857 & 0.137 & 0.830 \\ SMS A-motivated Scale & 18.60 & 4.93 & 17.40 & 3.91 & 0.590 & 0.587 & 0.264 \\ SMS RAI & 38.00 & 35.23 & 42.60 & 34.73 & \(-1.197\) & 0.297 & 0.536 \\ PMCSQ 2 T & 2.02 & 0.34 & 2.43 & 0.58 & \(-2.362\) & 0.077 & 1.056 \\ PMCSQ 2 E & 4.37 & 0.23 & 3.70 & 0.93 & 1.621 & 0.180 & 0.725 \\ TEOSQ T & 4.37 & 0.43 & 4.32 & 0.57 & 0.423 & 0.694 & 0.189 \\ TEOSQ E & 3.23 & 0.54 & 3.27 & 0.64 & \(-0.145\) & 0.892 & 0.065 \\ Perceived Stress Scale & 21.20 & 4.97 & 28.60 & 4.98 & \(-2.607\) & 0.060 & 1.166 \\ \hline \hline \end{tabular} Note: SMS = Sport Motivation Scale-II; RAI = Relative Autonomy Index; PMCSQ 2 = Perceived Motivational Climate in Sport Questionnaire; TEOSQ = Task and Ego Orientation in Sport Questionnaire; T = task-oriented; E = ego-oriented; M = mean; SD = standard deviation.  
\end{table} 
Table 4: Comparison of players’ perception of motivation and stress pre- and post-intervention program. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multicolumn{2}{c}{**Pre-intervention**} & \multicolumn{2}{c}{**Post-intervention**} & \multirow{2}{*}{**t**} & \multirow{2}{*}{**_p_**} & **ES** \\
**CMJ**PreIGT\_1 & & **M** & **SD** & **M** & **SD** & **t** & \(p\)** & **ES** \\ \hline CMJ**PreIGT\_1 & 30.90 & 1.95 & 35.63 & 5.61 & \(-2.662\) & 0.056 & - \\ CMJ**PreIGT\_2 & 32.18 & 3.87 & 35.34 & 5.19 & \(-4.708\) & 0.009 \({}^{**}\) & - \\ CMJ**PreIGT\_3 & 32.85 & 3.81 & 37.00 & 5.98 & \(-2.962\) & 0.041 \({}^{*}\) & - \\ CMJ**PreIGT\_M & 31.98 & 3.13 & 35.99 & 5.56 & \(-3.257\) & 0.031 \({}^{*}\) & 1.457 \\ CMJ**PostIGT\_1 & 29.21 & 4.54 & 34.09 & 6.50 & \(-4.701\) & 0.009 \({}^{**}\) & - \\ CMJ**PostIGT\_2 & 29.82 & 4.66 & 33.48 & 4.51 & \(-7.482\) & 0.002
## 4 Results  
### Expansion of Aquaculture Regions  
From the perspective of spatial expansion, layer aquaculture regions were distributed only in the Jiangjiasha sandbank and the southern coast in 1999, which covered only 11.99 km\({}^{2}\) initially calculated from satellite images. With the continuous development of cultivation technology, the aquaculture regions gradually expanded to the north and peripheral regions of the sand ridges. In 2006, the entirety of the aquaculture regions were distributed in the seaward margins of the sand ridges but since 2007, these regions were detected in the landward margins as well. However, landward aquaculture regions tended to be quite disperse and the quantity began decreasing after 2011. Until 2013, the periphery of the Liangyuesha sandbank, the seaward margins of the Dongsha sandbank, the seaward margins of the Gaoni sandbank, the Zhugensha and the Jiangjiasha sandbanks became serried aquaculture regions (Figure 4a-e). While from the view of area extension, the aquaculture regions has been increasing since 1999 and reached 298.28 km\({}^{2}\) by 2013. In 1999-008, the area grew rapidly at an average rate of 28.21 km\({}^{2}\)/y. The growth rate reached a crest value of 76.75 km\({}^{2}\) within one year during 2007 to 2008. During 2008-2013, the rate increase diminished to an average of only 6.48 km\({}^{2}\)/y (Figure 4f).
## 5 Experimentation
### Results
#### Prediction Time Factor (\(\phi\))  
As shown in Figure 16, a minor factor, i.e., predicting the future congestion in the short term, gives the best performance for all the scenarios. For all of the scenarios, the factor is 2 except for that of Info5, which is 5. Hence, we can determine that we can predict the near future more accurately than the far future. This assertion implies that we need a small factor combined with a small aggregation interval (\(\hat{t}\)). This combination is feasible as, in the previous section, we have seen that a small \(\hat{t}\) leads to better performance.
# A Wideband Power Amplifier in 65 nm CMOS Covering 25.8 GHz-36.9 GHz by Staggering Tuned MCRs
## 1 Introduction
To further decrease the gain ripple, a Gm compensator-based negative feedback chain is also applied. [PERSON] et al. presented a wideband LNA by adding the peaks and valleys of the different resonators [18]. [PERSON] et al. [20] proposed two W-Band wideband power amplifiers by staggering the transformers' resonating frequency using an in situ transformer-coupled resonating peak control simulation methodology to realize 9/16 GHz 3 dB small-signal bandwidth. In reference [21], the transformer-based resonating peak tuning and staggering method was also used to achieve two wideband power amplifiers covering the 16/14 GHz 3 dB small-signal bandwidth. Several studies [18; 20; 21] have adopted the transformer-based staggering method and only small-signal wideband performance was achieved. Meanwhile, the design of the transformer-based staggering method lacks detailed elaboration. For example, reference [20] used a staggering method to flatten the bandwidth but without detailed method processes and particulars. In our work, we give not only the detailed design process but also the detailed whole logical thinking perspectives and design method for a wideband power amplifier. In this work, a symmetrical transformer-based output MCR4 is used to provide wideband optimal load impedance for the power stage. At the interstage between the power stage and driver stage 2, an asymmetrical MCR3 is used to realize wideband matching by making it satisfy both the conditions of \(\omega_{1}=\omega_{2}\) and a good matching at 35 GHz. At the interstage between driver stage 1 and driver stage 2, an asymmetrical MCR2 to form a high-pass form Z21 for stagger tuning is realized by selecting the transformer parameters under the condition of matching at 33 GHz. The input asymmetrical MCR1 is used to achieve wideband response and to compensate for the gain decline at the band edge (\(f_{L}\) and \(f_{H}\)). The symmetrical output MCR4 for wideband optimal \(Z_{opt}\) (leading to wideband large-signal performance) and stagger-tuned interstage and input MCRs for flattened small-signal bandwidth are combined for a better performance. The framework of the paper is organized as follows: A detailed analysis of the design of the four MCRs for input, output, and interstage matching is introduced in Section 2. Section 3 presents the whole circuit design consideration of the power amplifier. Section 4 shows the simulation results, and Section 5 concludes the entire work.
# Numerical Study on the Bending Performance of Steel-Ribbed Composite Slabs for Substations
## 3 Finite Element Modeling
### Validation of Finite Element Models  
The experimental data from previous studies [14; 24] were utilized to validate the aforementioned FE modeling approach. The comparison between test and FE results is presented in Figure 11, which also includes the key parameters of the specimens. Figure 11a demonstrates the unidirectional bending test of a simply supported precast base slab subjected to uniform loading. The FE model demonstrated excellent agreement with experimental results in terms of pre-cracking stiffness and bearing capacity, showing an average error of only 3%. In the post-cracking phase, the FE model exhibited slightly higher stiffness compared with the test results, with a maximum deviation of approximately 10%. The FE model exhibited a slightly higher stiffness compared with the test results, with a maximum deviation of approximately 10%.  
8%, leading to a smaller ultimate deflection. This discrepancy may be attributed to the assumption of perfect bonding between concrete and steel in the FE model, which is acceptable in simulation analysis. Figure 11b presents the load-deflection curves of the composite slab with a post-cast layer under four-point bending conditions. Also, the shape of the load-deflection curves obtained from the FE model was similar to the test result. The error of the cracking load or bearing capacity between the FE model and test was generally within 5%. Overall, the experimental data showed strong correlation with the finite element analysis, thereby validating the FE modeling methodology for subsequent numerical investigations.  
It is noteworthy that both the precast base slab and the composite slab exhibited typical flexural failure modes under unidirectional loading. The ultimate load-bearing capacity closely aligns with the theoretical calculations based on the Technical Specification for Prestressed Concrete Composite Slab with Steel Ribs (T/CECS 1289-2023) [22] and the Chinese Code for Design of Concrete Structures (GB 50010-2010) [25]. As a result, the code-based theoretical calculations of the flexural capacity can be employed as an alternative means for rapid verification of the FE model's rationality, as demonstrated in the following section.
## 3 Short-Reach Systems Classification and Enabling Technologies
### Optoelectronic Oscillator  
Oscillators are normally used for the conversion of energy from a continuous source to a signal that varies periodically. A wide range of electromagnetic, mechanical, and atomic oscillators offers a varied range in the approximation to the achievement of an ideal harmonic oscillator. It is noteworthy that for a given oscillator, the accuracy of this approximation is determined by the degree of output signal stability and spectral purity. This is primarily contingent on the oscillator's energy storage capability, which is based on the resistive loss (normally frequency-dependent) of its different elements [104]. Furthermore, an extensively used oscillator is the electronic oscillator. An electronic oscillator can either be based on a vacuum tube or solid-state technology. Both solutions are not only noisy, but lack sufficient stability for applications that demand very high stability and spectral purity. This performance limitation is due to the ohmic and dispersive losses of the various elements of the oscillator. In light of this, to address the associated challenges, different efforts have been presented by combining a high-quality (Q)-factor resonator with the electronic oscillator. The Q represents a figure of merit for the resonator. Furthermore, different high-Q resonators such as electromagnetic, mechanical, and acoustic can be employed for electronic oscillator stabilization, resulting in hybrid oscillators known as electromagnetic, electromechanical, or electro-acoustic. It should be noted that the resonator selection is normally determined by a range of factors; however, for the maximum achievable Q at room temperatures, the crystal quartz (mechanical) resonator is the best choice for the electronic oscillator stabilization. Nonetheless, owing to their limited high-Q resonant modes at low frequencies (\(\sim\)10 to 100 MHz), crystal quartz resonators offer a restricted range of frequency tenability, making them unsuitable for the direct generation of high-frequency signals. This issue can be addressed by the implementation of an optoelectronic oscillator (OEO) [104; 105].