esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Therefore, if the relations between the inspection results and evaluation objective (transverse connectivity of the hinge joints or durability of the hollow slab girders) can be found using an ANN, the bridge condition assessment will become more convenient with a much lower inspection cost. This study focuses on the field test investigation of the transverse connectivity and durability evaluation of the hollow slab bridges using the surface damage and neural networks. A total of 13 hollow slab bridges in the Wu-He highway system were taken as the background bridge. Surface damage was visually checked and statistically analyzed. Static load test was conducted to evaluate the transverse connectivity of the hinge joints based on the girder responses. The hollow slab bridges were then demolished, and a total of 75 concrete girder segments were cut off. Durability of each hollow slab girder was evaluated based on the analytic hierarchy process (AHP) along with the fuzzy comprehensive evaluation (FCE) method. An artificial neutral network (ANN) method was finally employed to investigate the relationship between the surface damage and rating results. The applicability of the method was verified by the field test data of the hollow slab bridges.
## 1 Introduction
As a result, this article attempts to build an improved system with a high detection accuracy rather than the ones proposed in the literature. Machine and deep learning modalities have recently been involved in several applications [26; 27], such as emotion recognition [28], speech recognition [29], and medical diagnosis [30; 31; 32; 33]. In this paper, we propose a drowsiness detection system to determine the level of fatigue based on EEG, EOG, and EMG signals. The objective is to develop a system that can detect a driver's fatigue with a high detection rate and a fast testing time. For this purpose, we employed wavelet transform for the feature extraction task with PCA preprocessing. In addition, machine and deep learning models are deployed on the extracted features to detect fatigue states. The following are some examples of how the proposed work will advance the field:  
1. Propose a feature extraction method based on trigonometric transformations for EEG signals. 2. Medical signal preprocessing using PCA and rescaling techniques. 3. Investigate an enhanced method for fatigue detection from EEG signals based on machine and deep learning models. 4. Evaluate the methods, discuss the results and highlight their advantages and disadvantages. 5. Compare the accomplished performance with the works in the literature. The remainder of this paper is divided into four sections. Section two describes the materials and methods proposed for this work. The third section discusses the outcomes of the proposed methods. Section four briefly compares the proposed methods to works in the literature. Finally, section five brings the paper to a close.
## 2 Materials and Methods
### Measurements  
#### 2.4.1 Ultrasound Shear Wave Elastography (SWE) Measurement  
SWE measurements were carried out using the Verasonics research system (Vantage 128 TM, Verasonics Inc., Kirkland, WA, USA) and linear array transducer with a central frequency of 5.2 MHz (L7-4, Philips Healthcare, Andover, MA, USA). The focused comb-push ultrasound shear elastography (F-CUSE) approach proposed by [PERSON] et al. (2018) was applied in this study [58]. The Verasonics research system and L7-4 transducer were used to generate comb-push beams and track shear wave motion. The setup script for the data acquisition was modified in accordance with the F-CUSE principle. A single push-detection data acquisition triggered multiple sources of distribution of shear waves in tissue concurrently. Therefore, a total of 128 elements of the transducer were divided equally into four subgroups. A 4-tooth focused comb-push (32 elements for each push beam) transmitted focused ultrasound push beams simultaneously. The push duration for each beam was 600 \(\upmu\)s. Multiple laterally distributed sources of the shear wave were then developed. Each push beam created two shear waves propagating in opposing directions, far away from the push beam, and then the waves from the different push beams interfered with each other and eventually filled the entire full field of view.  
Once the transmission was completed, the system instantaneously switched to plane wave imaging mode with all transducer elements. The plane-wave imaging compounding method was used to improve the signal-to-noise ratio (SNR) of shear wave tracking. Three frames at three different steering angles (\(-4^{\circ}\), \(0^{\circ}\), \(4^{\circ}\)) were used to achieve one imaging frame. Shear wave velocity was calculated from axial particle velocity (Vz) induced by the propagation of the shear wave. The Vz was computed from the in-phase/quadrature (IQ) data of consecutive frames using a 1D autocorrelation method. A directional filter was used to eliminate destructive shear wave interferences. Local shear wave velocity was recovered by the time-of-flight algorithm by cross-correlating particle velocity profiles along the lateral direction. The elasticity map was then reconstructed [59; 60; 61].
# Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments
## 3 Key Transmission and Distribution System Components
### Distribution System Switchgears & Protective Devices
This is because in SF6 breakers, liquefaction of SF6 gas occurs at extremely low temperatures, affecting the arc interruptions. For this, temperature sensors mounted on the circuit breaker are used to indicate this drop off in temperature below a threshold value. Intelligent temperature control systems [103] with wireless temperature control units and heating units, lined with temperature sensors to sense the drop in temperatures and automatically start the heating unit to keep the gas from liquefaction, have also been developed for faster response. Furthermore, surface acoustic wave-based online temperature monitoring systems [2] have been developed to be mounted on the circuit breakers. This system consists of a piezoelectric substrate with an interdigital transducer and acoustic reflection grating. A change in temperature or pressure in the windings will be detected since it affects the transmission of frequency of acoustics on the piezoelectric substrate.
# Enhanced Teaching-Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem
## 5 Experimental Studies
### Statistical comparison
In respect to the comparison between ETLBO and ACO(L), the following comments can be made:  
* For the 50 \(\times\) 50 instance, the associated rank sum of ETLBO was 1275, and the resulting \(p\) value was very close to zero. As is stated in previous subsections, the significance level was fixed at 0.05 for this study. Therefore, it can be deduced that ETLBO dominates the ACO(L) in all executions when they are adopted to this instance. * For the instances 20 \(\times\) 20, 30 \(\times\) 30, and 40 \(\times\) 40, the rank sum of ETLBO lay between the maximum and minimum rank threshold values, indicating that ETLBO and ACO(L) produced a mixed ranking. Nonetheless, the associated \(p\) values were less than the predefined significance level. In other words, ETLBO performs equally or even better when compared to ACO(L). * Despite the increasing problem dimensionality raising the optimization difficulty, the resulting \(p\) values decreased with the enlargement of the problem scale. Thus, it can be deduced that ETLBO is more competitive than ACO(L) for a real-world-scale problem. A similar comparison between ETLBO and ACO(G) was conducted and illustrated. In summary, the proposed ETLBO outperforms the compared algorithms in a statistical manner and provides consistent and more accurate solutions for MRPP problems. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multicolumn{3}{c}{**ACO(L)**} & \multicolumn{3}{c}{**ACO(G)**} \\ \cline{2-7}  & \(\mathbf{w_{ETLBO}}\) & \(\mathbf{p}\) & **Win** & \(\mathbf{w_{ETLBO}}\) & \(\mathbf{p}\) & **Win** \\ \hline
20 \(\times\) 20 & 1590 & \(5.75\times 10^{-11}\) & \(\surd\) & 1631 & \(3.56\times 10^{-10}\) & \(\surd\) \\
30 \(\times\) 30 & 1399 & \(4.16\times 10^{-15}\) & \(\surd\) & 1575 & \(2.89\times 10^{-11}\) & \(\surd\) \\
40 \(\times\) 40 & 1300 & \(1.52\times 10^{-17}\) & \(\surd\) & 1438 & \(3.35\times 10^{-14}\) & \(\surd\) \\
50 \(\times\) 50 & 1275 & \(3.42\times 10^{-18}\) & \(\surd\) & 1342 & \(1.74\times 10^{-16}\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Wilcoxon rank-sum test for MRPP problems.
# Recent Trends on Liquid Air Energy Storage: A Bibliometric Analysis
## 3 Results and Discussion
### Countries Distribution  
Figure 8 shows a world map highlighting the countries that published more studies on LAES in the period related to 1974-2020 (updated to November 2019) using the Scopus database. The results showed that only a few countries are involved in the topic selected. The United Kingdom had the strongest output of publications, highlighted by the darker color in the map. Indeed, in recent years, the United Kingdom has been identified as the core of the LAES technology, and hosts the first 350 kW/2.5 MWh pilot plant in the world, developed first by Highview Power Storage at Slough and now relocated to the University of Birmingham [32; 33]. Furthermore, another grid-scale 5 MW/15 MWh plant was launched in 2018 in Bury (Manchester) by Highview Power and Viridor [36]. The second country with the highest number of publications highlighted by the map in Figure 8 was China, followed by Italy and Germany.  
Figure 9 shows the number of publications for each country, also highlighting the number of publications per millions of inhabitants. It is interesting to note that, although it was not the strongest country in the world map in terms of the number of publications, Singapore had the highest density of documents published considering the population of the country. The figure also shows that the United Kingdom was the second country to have the highest number of publications per millions of inhabitants, while China had one of the lowest values due to the high density of population.  
Figure 8: World map of the publications distributed for each country.  
Figure 9: Number of publications for each country and ratio of the number of publications per millions of inhabitants.  
Figure 10 shows the results obtained by elaborating on the data from Scopus with the software VOSviewer. In particular, the figure shows the links between the different countries and their relation in terms of co-authorship on the work published. This figure shows that there were a lot of countries that were not linked to each other. In the center of the figure, two main clusters can be noted. The first cluster (in red) includes the United Kingdom and China, who were connected with a strong relationship marked with a thick line. The second cluster (in green) included the main countries of Italy and Singapore that, also in this case, they were strongly linked together. It is interesting to note that these two clusters were linked together, connecting the United Kingdom with both Italy and Singapore. A third smaller cluster can be detected at the bottom of the figure in blue, linking Germany, Greece, and Poland.
## 1 Introduction
It is also worth mentioning that fabrication of the PS microsphere 2D photonic crystals is simple, this proposed method does not require high-quality 2D photonic samples, and microsphere sizes can be estimated using a simple formula instead of curve fitting. This simplicity makes the method very practical. The experimental results were compared with simulations via the software CST- a finite-difference time-domain (FDTD) electromagnetic field solver. Good agreement between the experimental and simulated transmission spectra further confirmed both the experiment and the adequacy of the theoretical model. The influence of the substrate refractive indexes on the peakpositions and its consequences on PS bead size estimation were also studied. The analysis showed that common transparent substrates could cause minor shifts and broadening of the peak.
# A Simple Method to Convert Cellular Polymers into Auxetic Metamaterials
## 1 Introduction
[24] discussed the influence of the compressive strain rate (0.005 and 200 s\({}^{-1}\)) on auxetic foams based on PU open-foam and LDPE closed-cell foams produced via thermo-mechanical compaction in three orthogonal directions. The minimum NPR values ranged from \(-\)0.4 (at 0.005 s\({}^{-1}\)) to \(-\)0.3 (at 200 s\({}^{-1}\)) for the open-cell foams, while a narrower range of \(-\)0.1 (at 0.005 s\({}^{-1}\)) to \(-\)0.05 (at 200 s\({}^{-1}\)) was observed for the closed-cell foams at a 2% compressive strain. However, the values of Young's modulus increased by almost 100% (between 1 MPa and 1.8 MPa). In our previous work, a combination of solvent (ethanol) evaporation-condensation and mechanical compression (SECC) led to foams with negative Poisson's ratios (NPR) in tension [6]. Different series based on recycled LDPE foams (16, 21 and 36 kg/m\({}^{3}\)) were optimized using a range of time, temperature and pressure. Based on the samples prepared, the minimum NPR values obtained were \(-\)3.5, \(-\)2.6 and \(-\)1.5 for D16, D21 and D36, respectively. The results also showed that ethanol was better than water as the solvent in this technique due to its higher volatility [6; 25]. In the current work, a method for the fabrication of auxetic foams based on recycled LDPE foams was presented. The method was based on vacuum combined with mechanical compression (VMC). The main advantage over the previous SECC method was that no solvent was used, leading to a simpler and greener method by eliminating the need to add and recycle the solvent. The mechanisms to convert the conventional PE foams into NPR materials were discussed, especially in terms of the vacuum level and mechanical stress (weight) imposed on the sample during conversion. For the samples produced, the NPR value was reported for both tensile and compressive deformations. Finally, the foam morphologies were reported and related to the mechanical behavior measured.
# Reliable and Energy-Efficient LEO Satellite Communication with IR-HARQ via Power Allocation
## 1 Introduction
### Contributions  
As the IR-HARQ has the highest diversity gain [24], we adopt it to ensure the reliable transmission of the LEO communication system in this paper. Considering the delay requirements of users, we adopt the truncated HARQ transmission strategy with a maximum of L transmission rounds. In addition, a variable-power allocation scheme is proposed to improve the energy efficiency of the LEO satellite communication system. The main contributions are summarized as follows.  
* The link transmission characteristics between the LEO satellite and the ground user in higher frequency bands are analyzed. We model the transmission link as a combination of large-scale fading and small-scale fading. In large-scale fading, the attenuation caused by different weather is calculated by referring to the recommendation of ITU-R [25; 26; 27; 28]. Moreover, the carrier to noise ratio (\(C/N\)) between the LEO satellite and the ground user is simulated by the Satellite Tool Kit (STK) [29].
* In IR-HARQ, since the outage probability after each round cannot be expressed by an exact closed-form analytical expression, a numerical integration method based on the fast Fourier transform (NI-FFT) is proposed to calculate it quickly. The calculation results obtained by this method are more accurate than the previous method based on approximation.
* An IR-HARQ with a variable-power allocation (IR-HARQ-VPA) scheme is proposed. In this scheme, the variable-power allocation method based on a genetic algorithm (VPA-GA) is presented to optimize the transmission power in each IR-HARQ round. The proposed IR-HARQ-VPA scheme has higher energy efficiency than the IR-HARQ with equal power (IR-HARQ-EP) scheme and achieves reliable transmission.  
In addition, numerical simulation results are presented to demonstrate the improved performance achieved by the proposed IR-HARQ-VPA scheme in a LEO satellite communication window.
## 4 Loose Specimens  
The isotropic compression can only prepare dense specimens; another method (varying-friction method) can be used to generate both dense and loose specimens. The protocol is:  
(1) Similarly, grains are randomly inserted within a cuboidal domain until a density \(\phi_{0l}\) is reached.  
(2) Set the friction coefficient to an initial value \(\mu_{0}\) and grains are left to reach equilibrium.  
(3) Set the friction coefficient back to 0.5 (i.e., the friction coefficient of the studied gains) until equilibrium is reached again, and the assembly may enter a state with positive pressure (\(p^{\prime}>0\)).  
Figure 2: Loosest jammed states and possible jammed states.  
Some simulations are conducted for \(\phi_{0l}=0.59\), 0.60, 0.61, and 0.63. When the initial friction \(\mu_{0}\) ranges from 0.001 to 1000, the equilibrium normalised pressure \(p^{\prime}\) after step (3) is shown in Figure 2(a). When the initial friction is small, the equilibrium pressure is zero. After a critical value, the pressure starts to increase with greater initial friction. Peak pressure is achieved at \(\mu_{0}=0.5\). When the initial friction \(\mu_{0}\) is larger than 0.5, the equilibrium pressure \(p^{\prime}\) is the same. The achieved \(p^{\prime}\)-\(\phi\) combinations for these specimens are shown in Figure 2(b), from which, this varying-friction method can generate specimens falling within the possible jammed states. Additionally, loose specimens (greater pressure at the same density) can also be generated.  
From Figure 2(a), it is seen that at a fixed \(\phi_{0l}\), the maximum possible pressure is obtained with \(\mu_{0}=0.5\) and is related to \(\phi_{0l}\). So the loosest possible specimens that can be generated with this varying-friction method are around the black dotted line in Figure 2(b) (with cross markers). This dotted line is the unloading curve (isotropic expansion) for the specimen obtained after step (3) with \(\phi_{0l}=0.63\) and \(\mu_{0}=0.5\). The red dotted line is the unloading curve for the specimen with \(\phi_{0l}=0.63\) and \(\mu_{0}=0.25\). Therefore, to only generate loose specimens, the following varying-friction unloading method is used in this study:  
(1) Grains are randomly inserted within a cuboidal domain until the density of \(\phi_{0l}=0.63\) is reached.  
(2) Set the friction coefficient to an initial value \(\mu_{0}\) (\(0.25\leq\mu_{0}\leq 0.5\)) and grains are left to reach equilibrium.  
(3) Set the friction coefficient back to 0.5 and the assembly is left to reach equilibrium.  
(4) The periodic domain expands isotopically until a target normalised pressure \(p^{\prime}\) (\(0.0001\leq p^{\prime}\leq 0.02\)) is achieved.  
A total of 100 loose specimens are generated by this method by randomly choosing \(\mu_{0}\) between 0.25 and 0.5, and randomly choosing \(p^{\prime}\) between 0.0001 and 0.02. The \(p^{\prime}\)-\(\phi\) combinations for these specimens are shown in Figure 4 along with the specimens at jammed states.  
Figure 3: Varying-friction method.
# A Technical Survey on Delay Defects in Nanoscale Digital VLSI Circuits
## 10 Test Quality Metrics for SDDs and HDDs
### Quadratic Small Delay Defect Coverage (\(SDDC_{q}\))  
This non-statistical quality metric estimates the SDD coverage by taking into account the test clock period (\(T_{test}\)) and the system clock period (\(T_{sys}\)) information. The \(SDDC_{q}\) value differs for different delay defect sizes. Hence, it is more suitable to find SDD coverage when the delay defect distribution is not available [82]. The major disadvantage of this method is that it may produce some irrelevant results (greater than 100 percent) when the test clock is not the same as the at-speed clock [84]. This is clearly visible in Figure 12, which makes it difficult to predict the coverage of SDDs. Also, there are no discussions about validating FAST for detecting SDDs and HDDs through this quality metric. The \(SDDC_{q}\) formula is mentioned in Equation (4).  
\[SDDCq=\frac{1}{N}\sum_{i=1}^{N}\frac{\left(PD_{LA}+T_{sys}-T_{test}\right)^{2} }{\left(PD_{LT}\right)^{2}}\times 100 \tag{4}\]  
Figure 11: \(DTC\) percentage for different benchmark circuits tested at Slow, Fast and At-speed test clock period [84].
# Enhancing User Experience with Visual Controls for Local Differential Privacy
## 4 Proposed Privacy Mechanism
### Privacy Disclosure Attacks  
To address the privacy disclosure threat, we model a re-identification attack in which an attacker tries to infer a user's original voice command by observing the privatized version shown in the interface. For instance, if a user issues the command \"play jazz music\", and, under minimal privacy, the transformed command remains close to the original (e.g., \"play music\"), an attacker with prior knowledge of the user's behavior might guess the true command. In contrast, under enhanced privacy, the transformed command may become unrelated (e.g., \"turn off light\"), reducing attacker confidence.  
To assess the effectiveness of our approach against potential adversarial inference attacks, we interpret the category change rate under each privacy level as the inverse likelihood of successful re-identification. A higher transformation rate implies that an attacker has less chance to infer the true command, thereby validating the privacy-preserving strength of the Gaussian mechanism. As shown in Table 1, increasing the privacy level from 1 (\(\varepsilon\) = 1.000) to 7 (\(\varepsilon\) = 0.143) leads to a significant increase in transformation rate from 33.2% to 73.6%, indicating reduced inference accuracy and stronger privacy protection.  
We interpret the observed category transformation rates under varying privacy levels as a proxy for evaluating adversarial inference risk. In a re-identification scenario, an attacker aims to guess the original command from the privatized output. Higher transformation rates reduce this likelihood. Table 1 shows that at \(\varepsilon\) = 0.143 (Enhanced Privacy), 73.6% of the categories change after noise is applied, meaning the attacker has only a maximum of 26.4% chance of correctly inferring the original input. In contrast, at \(\varepsilon\) = 1.000, more than 66% of the categories remain unchanged, increasing the risk of re-identification. This supports our claim that our user-controlled Gaussian mechanism effectively resists disclosure attacks, especially at higher privacy levels.
## 2 Materials and Methods
### MODIS Products  
MODIS VIs and land cover type products were used in this study. These data were freely provided from ([https://e4 ftl01.cr.usgs.gov](https://e4 ftl01.cr.usgs.gov)).  
The NDVI and EVI are two commonly-used VIs for monitoring vegetation conditions. These VIs have significant relationships with crop production [38]. We used monthly NDVI and EVI derived from MOD13A3 data, with 1 km spatial resolution. The algorithm ingests all the 16-day 1 km products that overlap the month and employ a weighted temporal average.  
\[NDVI=\frac{NIR-Red}{NIR+Red} \tag{1}\]  
\[EVI=G\frac{NIR-Red}{NIR+C1 Red-C2 Blue+L} \tag{2}\]  
where _Red_ (620-670 nm), _NIR_ (841-876 nm), and _Blue_ (459-479 nm) are the surface bidirectional reflectance factors for MODIS bands 1, 2, and 3, respectively; \(L\) is the canopy background adjustment for correcting the nonlinear, differential NIR, and red radiant transfer through a canopy; C1 and C2 are the coefficients of the aerosol resistance term (that uses the blue band to correct for aerosol influences in the red band); and, \(G\) is a gain or scaling factor. The coefficients adopted for the MODIS EVI algorithm are, \(L=1\), \(C1=6\), \(C2=7.5\), and \(G=2.5\)[39].  
The MODIS land cover type product (MCD12Q1) provides annual data that characterize five global land cover classification systems from 2001 to present, with the spatial resolution of 500 m. The land cover product that we used is based on the IGBP global vegetation classification scheme.