esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
GaAs photoconductive semiconductor switches (PCSSs) have many advantages, including fast response times, high repetition rates (in the order of GHz), simple mechanical structure and low jitter [1; 2; 3; 4]. Thus, GaAs PCSSs have been used in numerous applications, such as ultra-wideband radiation sources, injection wave generators, and precise synchronization control systems [5; 6; 7]. The GaAs PCSSs also have been widely used in THz fields as an emitter and detector. When femtosecond laser pulses are focused on the gap of GaAs PCSSs, photo-excited carries are generated at the semiconductor's conduction band, and are subsequently accelerated by a bias electrical field, and terahertz waves are radiated at the same time [8; 9; 10]. In some special applications, positive and negative symmetric pulses with a fast rising edge are necessary. For example, in a streak camera, the performance of the sweep circuit affects its overall performance profoundly. Firstly, the slope of sweep voltage decides the sweep speed of stripe tube, thus dictating the stripe tube's temporal resolution. Secondly, the stripe tube's temporal resolution in integral mode rests with the jitter time of the sweep circuit. Finally, the linearity of scanning voltage signal has an effect on the sweep linearity of stripe tube, and thus influences the imaging quality of the camera [11; 12; 13].  
In this article, a sweep circuit was designed, in which a GaAs PCSS was used as an ultrafast pulse generator, positive and negative symmetric pulses output was realized, and the performance was tested. The device can be applied in femtosecond streak cameras, and the scanning speed and temporal resolution can be expected to be improved.
## 4 Test Results and Analysis
### Effect of A/A\({}_{g}\) Ratio (A12h-1-5)
#### 4.1.2 Ultimate Bearing Strength
} & \multirow{2}{*}{\(f_{lat,st}\) (MPa)} & \multirow{2}{*}{\(f_{lat,eq}\) (MPa)} & \multirow{2}{*}{\(\begin{subarray}{c}\text{Effective}\,f_{lat,eq}\) (\textless{}13 MPa) \\ \end{tabular}} & \multirow{2}{*}{\(\begin{subarray}{c}\text{Confinement by}\,\\ \textless{  
The effect of the surrounding concrete was confirmed again by the comparison between A12H-4 and -5. However, the increase in strength between A12H-4 and -5 was less than that between A12H-1 and -3. This is reasonable because more cracks were seen in A12H-5 even though those cracks were very narrow, and the micro-cracks weakened the confinement by the surrounding concrete. Thus, in contrast to the FEM-based results by [PERSON] and [PERSON] [8], the use of a concrete bearing area larger than the specified minimum spacing should be considered in the prediction of ultimate bearing strength. In addition, the effect of the additional bearing area is also influenced by lateral confinement conditions. A full section efficiency of over 100% was only seen in A12H-1, where \(A/A_{g}=1.82\), and the lateral pressure reached the maximum effective value of 8.3 MPa for \(f_{lat,sp}\) and 13 MPa for \(f_{lat,eq}\). In the specimens with identical spiral diameters (295 mm) but different rebar diameters and concrete block sizes, as \(A/A_{g}\) increased the sectional efficiency decreased, as shown in Figure 5b. The efficiencies for A12H-1 through -5 were 107%, 44%, 25.3%, 85.1%, and 19.1%, respectively. These results imply that the behavior of the anchorage zone is highly affected by the local behavior near the anchorage bearing plate, and the sectional efficiency is mostly determined by \(A/A_{g}\). In sum, the following conclusions can be drawn for A12H-1-5, which are confined with a small-diameter spiral for minimum anchorage spacing: (1) Strength varies by about 30% depending on \(A/A_{g}\); (2) The effect of additional bearing area on the ultimate bearing strength is influenced by lateral confinement; (3) The sectional efficiency varies from about 20% to 110%, mostly depending on \(A/A_{g}\). The net maximum bearing stress (\(P_{u}/A_{b}\)) on the concrete in direct contact with the bearing plate is presented in Figure 5c, where \(A_{b}\) was determined from the intact bearing plate in Figure 2a. The maximum stress of A12H-1 was computed as 65.8 MPa; it increased to 84.1 MPa in A12H-3. This reconfirms that the surrounding concrete increased the confinement and resulted in an increase in the net maximum bearing stress under the ultimate state. The 84.1 MPa seen in A12H-3 is high enough to be achieved by confinement. Therefore, even if the bearing plate had not been fractured in A12H-3, the net maximum bearing stress may not have exceeded approximately 100 MPa. To be conservative, the current test results were used for further analysis.
# Superior Mechanical Behavior and Fretting Wear Resistance of 3D-Printed Inconel 625 Superalloy
## 2 Experiments
### Mechanical and Tribological Tests  
The Vicker's hardness of AM-processed specimens was measured using a HVS-1000 Vicker's hardness instrument with a load of 1 kg and a dwell time of 10 s. Five random positions for hardness testing are selected on the surfaces of samples and the mean value was obtained. The density was determined based on the Archimedes principle. Uniaxial tension specimens were extracted from the AM materials by wire electro discharge machining with a gauge dimension of 5.3 mm (length) \(\times\) 2.9 mm (width) \(\times\) 1.8 mm (thickness) on this basis of methods in Reference [17]. Samples were clamped by the ends of the plate-type samples. Mechanical properties were assessed by rate-controlled tensile tests at nominal strain rates of 1 \(\times\) 10\({}^{-3}\) s\({}^{-1}\) using a Shimadzu AG-100 kN equipment. All tensile tests were repeated five times.  
To evaluate the fretting wear resistance of AM-prepared Inconel 625 alloys, the wear tests were carried out using a fretting test system with a flat-on-flat configuration at contact loads of 40-106 N and a frequency of 2 Hz for 20,000 cycles. Wear test specimens were extracted from the fabricated parts by wire electro discharge machining with a dimension of 30 mm (length) \(\times\) 30 mm (width) \(\times\) 7 mm (thickness). Details about wear test configuration can be found in our previous works [18; 19; 20; 21]. The experimental parameters were set based on the practical operating conditions of the blade bearing [16]. The contact area of the fretting surface was 6.75 mm\({}^{2}\). The angular displacement amplitude (\(\theta\)) of the fretting test was 1.5\({}^{\circ}\). The 42 CrMo4 stainless steel with a hardness of 25 HRC and a roughness of \(R_{a}\) = 0.2 \(\upmu\)m which is generally used for blade bearing in a propeller was used as a mating material. Tribological experiments were performed at a relative humidity of about 60%. The wear volume was determined by a 3D digital microscope. All the tests were carried out at the  
Figure 1: Size distribution of initial particles fabricated by the gas atomization.  
Figure 2: Overview of the AM processing strategy. The build (BD) and scan directions (SD\({}_{x}\) and SD\({}_{y}\)) are indicated with respect to the sample coordinates: (**a**) the different views of the sample; (**b**) top view of the sample with the bidirectional scan vectors.  
same condition for three times to make sure the repeatability of the experimental results at the same conditions and the average results were recorded.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 6 Challenges
### Data Challenges  
Transportation systems produce massive quantities of data through different sources, such as sensors, GPS devices, and connected vehicles. Effective use of this data is vital for enhancing safety, efficiency, and overall system performance. Nevertheless, one of the main challenges is dealing with real-time data. [PERSON] et al. [103] pointed out that it is crucial to minimize the time gap between the occurrence of an event (such as a traffic issue) and its detection and analysis to improve the performance of ITSs. However, delays in this process can negatively impact the system's effectiveness. Traditional sensors like CCTV cameras and loop detectors have a fixed range of coverage that limits their ability to capture real-time data comprehensively. In addition, existing traffic management systems often do not share data outside their agency's domain, leading to inaccuracy in the real-time data analysis and identifying the underlying causes of traffic issues.  
Furthermore, [PERSON] et al. [104] mentioned that multihop connections are necessary for V2V communication in VFC. However, an excessive hop count on a network might result in a low message delivery ratio, high latency, and data redundancy. The authors in [105] argued that in situations with many subscribers (i.e., receivers like cars, motorcycles, and bikes) needing real-time information. Then, each subscriber can take part in delivering the content to make the distribution process more efficient and reduce redundancy. This process does not put much strain on the subscribers because the real-time information is usually transmitted in a single packet, incentivizing them to forward the content to others. This collaborative approach helps to improve the overall efficiency of real-time content distribution, especially in scenarios with a high density of interested recipients.  
Fault tolerance is also crucial for some critical situations in transportation. [PERSON] et al. [106] demonstrated a publish/subscribe model that enables a group of loosely connected objects to exchange data within a single process. However, in a complex system, relying on a single process to handle all the functionalities is not ideal, as it creates a single point of failure that can be hard to identify and fix.  
Data structure and format heterogeneity is an issue in Smart Cities. [PERSON] et al. [107] pointed out that the data collected from sensors in Smart Cities have diverse formats, and the systems currently in use have varying structures. This diversity makes Smart City system integration or expansion with third-party components or services challenging.
# A Review of Blast Loading in the Urban Environment
## 4 Experimental Methods and Instrumentation
### Pressure Measurements
#### 4.2.2 Optical Pressure Sensors  
[PERSON] et al. [131] explore the use of fibre-optic pressure sensors compared to electrical sensors. The experimental setup consisted of a 2 \(\times\) 2 m table where a hemispherical charge was placed in the centre. Two steel structures were placed at 0.654 m and 1.010 m away from the explosive. The furthest structure was instrumented with both pressure sensors, the closest only containing electrical sensors to ensure the optical sensors functioned within their maximum design range (up to 900 kPa).  
Over 40 tests were conducted with varying explosive masses between 30-80 g. It is stated that _\"favourable performance of the optical sensors has been established against electrical sensors in a series of experimental tests measuring blast waves from small explosive charges\"_. The main reason for this was attributed to the optical sensors' low susceptibility to acceleration, leading to less variation in overpressure readings. Figure 13 details the pressure-time measurements from the two types of sensors [131].  
Figure 12: PILS imaging configuration to obtain a side view and plan view of the study area, adapted from [PERSON] et al. [33].  
An overview covering optical fibre technology conducted by [PERSON] [132] states that \"_single-point fibre optic sensor technologies have been proposed as solutions for pressure sensing for many years\"_. The conventional optical-fibre pressure sensors are Fiber Bragg Gratings and interferometric-based ones. Due to their reliability, multiple variations of pressure sensors of these types have been released as commercial products. Some advanced features include minimal invasiveness, refined measurement accuracy, immunity to electromagnetic interference and resistance to noise from acceleration.
## 1 Introduction
They are applied to a wide range of tasks related to signal, image, and information processing in the artificial intelligence (AI) domain [30]. Deep learning algorithms have the powerful capability of learning optimal features from raw input data automatically through using multiple levels of abstraction and representation of the signal and image data. Furthermore, big data and availability of large-scale computing power by using graphical processing units (GPUs) provide great support for deep learning techniques. In the literature, several methodologies based on deep learning are presented to identify the types of PQDs. The estimation of voltage sag in sparsely monitored power systems by using the CNN model is adopted in [32]. In [33], the CNN combined with a long short-term memory (LSTM) method is used for PQDs classification. The PQDs are estimated based on a deep belief network (DBF) in [34]. Although these methods obtain good performance for PQDs analysis, some limitations can be identified. This paper proposes a novel method combining phase space reconstruction (PSR) with CNN for PQD detection and classification. The PSR method is firstly used to transform 1D raw data of PQD into a 2D image file. Then, a CNN model is developed for the image classification. The novelty of this proposed method is that the traditional PQDs classification problem has been mapped into an image classification issue, which is the expertise of CNNs. The performance of the proposed method is tested and validated by ten types of synthetic PQDs as well as operational signals from the IEEE Working Group on Power Quality Data Analytics. This paper is organized as follows: Section 2 presents the proposed algorithms based on the PSR and CNN models. Section 3 introduces the implementation of the PSR-CNN method for PQDs classification. Section 4 presents the discussion and classification results using different types of synthetic cases and operational signals. Finally, concluding remarks are made in Section 5.
## 3 Wave Propagation in Non-Linear Dispersive Media  
As previously stated, non-linear interaction takes place when a wave that propagates in a medium have a very high intensity. This degree of non-linearity inducing high intensities is usually possible with ultra-short duration pulses and can be practically generated from a mode locked laser or a Q-switched laser, which have durations on the scale of picoseconds or femtoseconds. Since such high-intensity pulses have ultra-short durations, we must perform a dispersion analysis. Impulse response of the polarization density of many dielectric media last much longer in duration than the pulse durations of such high intensity pulses [8,9]. The inclusion of dispersion in the analysis means that we need to solve for the polarization density of the interaction medium separately. In fact, it is much more accurate to solve the wave equation in parallel with the non-linear electron cloud motion equation for a non-linear dispersive medium, since parameters such as the resonance frequency, damping rate, atom density, and atomic diameter have typical values for most solid media, which makes the simulation results more realistic. To determine the time variation of the electric field in a non-linear dispersive medium, we need to solve the following equations (see Figure 1) [1]:  
\[\
abla^{2}E-\mu_{0}\varepsilon_{\infty}\frac{\partial^{2}E}{\partial t^{2}}= \mu_{0}\sigma\frac{\partial E}{\partial t}+\mu_{0}\frac{\partial^{2}P}{ \partial t^{2}}. \tag{3}\]  
\[\frac{\partial^{2}P}{\partial t^{2}}+\gamma\frac{\partial P}{\partial t}+{ \omega_{0}}^{2}P-\frac{{\omega_{0}}^{2}}{Ned}P^{2}-\frac{{\omega_{0}}^{2}}{N^{ 2}e^{2}d^{2}}P^{3}=\frac{Ne^{2}}{m}E. \tag{4}\]  
\(P:\) Polarization density, \(e:\) Electron charge, \(m:\) Electron mass, \(E:\) Electric field \((V/m)\)  
Figure 1: An interaction medium placed inside a cavity. In Equation (4) we have accounted up to the third order of the polarization density as higher order terms are negligibly small. For a dielectric medium, the electric conductivity is almost zero (\(\sigma\approx 0\)). Some typical values for solid dielectric media are [1]; _Resonance frequency_ : \(f_{0}=1.1\times 10^{15}Hz\), _Damping rate_ : \(\gamma=1\times 10^{9}\ Hz\), _Electron density_ : \(N=3.5\times 10^{28}/m^{3}\), _Atomic diameter_ : \(d=0.3\)_nanometer_  
Electromagnetic wave amplification by non-linear wave mixing involves two waves, namely the low intensity stimulus (input) wave to be amplified, and the high-intensity pump wave. Let us first consider the high-intensity pump wave \(E_{2}\) without the presence of the low intensity stimulus wave \(E_{1}\).
# Analysis of the Impact of Multipath on Galileo System Measurements
## 5 Discussion
The multipath effect causes disturbances in the time series of this component in the range of 21 mm for the Galileo system and 17 mm for the GPS system, for the whole range of frequency of changes typical of the multipath effect. The results allow for the assumption that the replacement of observations of the P(Y) code (L2W) by modernized civilian observations of this system, especially at frequency L5, will result in a significant improvement in the accuracy of precise positioning using dual-frequency GPS observations. Similarly, reaching full operational capability by Galileo as a result of the increasing number of available satellites is likely to result in more precise positioning with the use of Galileo-only observations than with the use of GPS-only measurements.
# One-Photon-Interference Quantum Secure Direct Communication
## Appendix B Details of Security Analysis
\begin{split}|\phi_{ABCD}\rangle=&\ \frac{1}{2}\!\left[-| \Psi_{AB}^{-}\rangle|0\rangle_{C}\!\left(\sum_{n=0}^{\infty}C_{2n+1}|2n+1 \rangle_{D}\right)+|\Psi_{AB}^{+}\rangle\!\left(\sum_{n=0}^{\infty}C_{2n+1}|2n +1\rangle_{C}\right)|0\rangle_{D}\right.\\ &\left.+|\Phi_{AB}^{-}\rangle|0\rangle_{C}\!\left(\sum_{n=0}^{ \infty}C_{2n}|2n\rangle_{D}\right)+|\Phi_{AB}^{+}\rangle\!\left(\sum_{n=0}^{ \infty}C_{2n}|2n\rangle_{C}\right)|0\rangle_{D}\right].\end{split}  \tag{105}\]We consider the case where only detector D\({}_{0}\) click and define the measurement operator of the detectors at this moment as \(\hat{M}_{C}\), where  
\[\hat{M}_{C}\equiv\sum_{n}\sqrt{\gamma_{n}^{C}}|n0\rangle_{CD}\langle n0|, \tag{108}\]  
where \(\gamma_{n}^{C}=\langle n0_{CD}|\hat{M}_{C}^{\dagger}\hat{M}_{C}|n0_{CD}\rangle\) is the yield of the \(n\)-photon state, i.e., the conditional probability of only detector D\({}_{0}\) click when there are \(n\) photons in the channel. Tracing out system C and D from system ABCD after [PERSON]'s measurement, we obtain the joint state \(\rho_{AB}^{C}\) of [PERSON] and [PERSON] in the **Step 3**, which is  
\[ \begin{split}\rho_{AB}^{C}&=Tr_{CD}\Bigg{(}\frac{ \hat{M}_{C}}{\sqrt{Q_{u}^{C}}}|\phi_{ABCD}\rangle\langle\phi_{ABCD}|\frac{ \hat{M}_{C}^{\dagger}}{\sqrt{Q_{u}^{C}}}\Bigg{)}\\ &=\frac{1}{Q_{ui}^{C}}\Bigg{(}\sum_{n=0}^{\infty}\sqrt{|C_{2n+1}| ^{2}\gamma_{2n+1}^{C}})^{2}|\Psi_{AB}^{+}\rangle\langle\Psi_{AB}^{+}|+\frac{ 1}{Q_{ui}^{C}}(\sum_{n=0}^{\infty}\sqrt{|C_{2n}|^{2}\gamma_{2n}^{C}})^{2}| \phi_{AB}^{+}\rangle\langle\phi_{AB}^{+}|\Bigg{)}.\end{split}  \tag{109}\]  
where \(Q_{ui}^{C}=\langle\phi_{ABCD}|\hat{M}_{C}^{\dagger}\hat{M}_{C}|\phi_{ABCD}\rangle\) is the total gain, i.e., the conditional probability of only detector D\({}_{0}\) click when [PERSON] and [PERSON] send pulses with the intensity of \(u\). Therefore, we have  
\[ \begin{split} E_{u}^{Z,C}&=\lambda_{3}+\lambda_{4} \\ &=\frac{1}{Q_{u}^{C}}\Bigg{(}\sum_{n=0}^{\infty}\sqrt{|C_{2n}|^{ 2}\gamma_{2n}^{C}}\Bigg{)}^{2}.\end{split}  \tag{110}\]  
Hence, the achievable rate is  
\[ \begin{split} R^{C}=& I^{C}(A:B)-I^{C}(A:E)\\ \geq& q\cdot Q_{u}^{C}\cdot[1-fh(E_{u}^{X,C})-h(E_{ u}^{Z,C})],\end{split}  \tag{111}\]  
where \(q=1-2p(1-p)\) is the mode matching rate, and \(u=|\alpha|^{2}\) is the mean photon number or the light intensity. \(f\geq 1\) is an inefficiency function for forward coding. We skip the discussion for only D\({}_{1}\) click; however, it holds in a similar manner, which is  
\[R^{D}\geq q\cdot Q_{u}^{D}\cdot[1-fh(E_{u}^{X,D})-h(E_{u}^{Z,D})]. \tag{112}\]  
Finally, the total secrecy rate formula is given by  
\[R=\ \max\{R^{C},0\}+\max\{R^{D},0\}. \tag{113}\]
## 2 Materials and Methods
### VOC Analysis Using SPME-GC-MS  
The VOCs in whole and flesh juices were analyzed using solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) [16]. Briefly, 2 mL of juice, 1 mL of EDTA (0.1 M, pH 7.5), and 10 \(\mu\)L of internal standard 1-hexanol (0.5 mg/mL) were placed in a 20 mL glass vial. The mixture was heated at 40 \({}^{\circ}\)C for 5 min, and the volatiles were extracted using a divinylbenzene/carboxen/polydimethylsiloxane 50/30 \(\mu\)m SPME fiber (Supelco, Bellefonte, PA, USA) at 40 \({}^{\circ}\)C for 10 min on a CombiPAL autosampler (CTC Analytics, Zwingen, Switzerland). The analysis was performed using a 7890 B GC-5977A MS system (Agilent Technologies, Santa Clara, CA, USA) at a split ratio of 10:1 for 2 min. The VOCs were separated using a DB-5 MS capillary column (30 m \(\times\) 0.25 mm, 0.25 \(\mu\)m; Agilent Technologies). The GC oven temperature was initially set to 40 \({}^{\circ}\)C for 1 min and then increased to 200 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min. The injection and ion source temperatures were set at 250 and 230 \({}^{\circ}\)C, respectively. The MS electron energy was 70 eV, and the scan range and rate were set to \(m/z\) 33-350 and 1.77 scans/s, respectively. The VOCs were annotated by comparing their mass spectra with those in the NIST-MS library Version 2014 and the retention index upon measurement of the homologous series of \(n\)-alkanes (C\({}_{6}\)-C\({}_{30}\)). The relative concentrations of the VOCs were normalized to that of 1-hexanol as an internal standard using MS-DIAL Version 4.80 (RIKEN Center for Sustainable Resource Science, Kanagawa, Japan).
# Prediction of Wind Power with Machine Learning Models
## 3 Results and Discussion
LSTM is an RNN model utilised in sequential or time series analysis. Particularly in sequential data sets, it demonstrates superior performance due to the self-healing capability of LSTMs. As a result, it achieves higher prediction success compared to other models. In contrast to [PERSON] et al. 's study [20], which employed 3200 data points, this study utilised 8760 data points. They have proposed various prediction models. R\({}^{2}\) metric was used to compare these models. They employed a hybrid approach by combining three methods, achieving the highest R\({}^{2}\) value of 0.98. However, they obtained lower R\({}^{2}\) values with three other hybrid methods, specifically 0.48, 0.82, and 0.80. Their study involved sequential variation mode decomposition (SVMD) to parse pre-processed wind speed data, then optimised the LSTM algorithm through PSO, TSO, and MTSO optimisation techniques. This implies that their approach involved a more complex model and additional data processing stages. In contrast, our study proposed a simpler model and achieved a high R\({}^{2}\) score of 0.95. Furthermore, we incorporated real climatic conditions and turbine regime periods from 2018, making our method more suitable for real-world applications. This demonstrates that our work has a broader range of applications, and the results we obtained can be readily applied in real-world conditions.
## 3 Results and Discussion
### Partial Least Square Regression Prediction Analysis of SSCFC's Fluidity and Compressive Strength
#### 3.5.2 Principal Component Analysis of PLSR Model
The integrated analysis of Qh\({}^{2}\), RMSEP, and VIP metrics collectively confirms that the model with two principal components achieves the optimal balance between predictive accuracy and model simplicity, while effectively reducing overfitting risks.