esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Due to the proliferation of high-speed railway lines and other improvement projects, electric railway systems in the Korea are being expanded recently [1]. A typical electric railway system consists of a power system and an OCL (Overhead Contact Line) [2]. The OCL transmits electricity to a railway vehicle through contact with the pantograph installed on its roof [3]. In particular, the pantograph transmits electricity from the OCL to the electric machinery through its contact strip [4]. The contact strip of the pantograph is periodically replaced because it wears out owing to frequent contact with the OCL [5]. Deformations such as cracks and chipping often occur on the contact strip owing to friction with fittings on the OCL [6, 7]. These deformations affect quality of the power transmission to the railway vehicle because of arcing due to contact loss with the OCL [8]. Moreover, these deformations also cause economic loss because of the need for frequent replacement of the contact strip and accidents due to interruptions in the electricity supply of the railway vehicle [9, 10]. In Korea, railway technicians decide to replace worn-out contact strip on the railway vehicle based on visual inspection in order to prevent accidents [9]. However, because these visual inspections are performed only when the railway vehicles are not in service, it is difficult to prevent accidents due to strip deformations when they are in operation. Therefore, recently, pantograph monitoring systems based on image processing have been developed to check the condition of the contact strip [11; 12; 13; 14; 15]. This monitoring system composed of a camera and a laser are installed on the OCL. However, change in the thickness of the contact strip due to deformation can only be detected by extracting the shape and edge of the contact strip from the captured images [14; 16]. In addition, the hardware including camera and laser sensor is expensive.  
In this study, we propose a simple algorithm to measure the size of a wear on a deformed contact strip based on deep learning and image processing. The measurement target is a contact strip used in Korean high-speed railways. The image is acquired using a monitoring method developed in the laboratory. The experimental contact strip is physically machined to create wear, which is measured using our proposed approach. The ROI (Regions of Interest) is performed using deep learning and image processing of the deformed contact strip. The laser line acquired from the image of the contact strip is extracted through edge detection. Then, the least squares method is applied on the captured laser line to extract the fitted line of the contact strip. Subsequently, the laser line and the fitted line are compared with each other to determine the pixels indicating wear, in order to estimate the wear size. Finally, the sizes of the deformations are obtained via blob processing, which are then compared with the actual values to prove the accuracy of our proposed approach.
# Optimal Robust LQI Controller Design for Z-Source Inverters
## 2 Circuit Analysis
### Shoot-Through Mode  
In this mode, as shown in Figure 1c, the output terminals of the inverter are shorted by a combination of upper and lower switches. During this mode, the diode is reversely biased, no voltage appears across the load and inductors are charged by the energy already stored in the capacitor. The boosting capability of the inverter is in fact due to this energy transfer according to the  
Figure 2: Three-phase simple boost pulse width modulation (PWM) waveform.  
shoot-through duty ratio. In this mode, voltages across inductors and the dc-link are \(V_{L}=V_{C}\) and \(V_{dc}=0\), respectively. The state space equation of this mode can be written as (2).  
\[\dot{\mathbf{x}}=\underbrace{\left[\begin{array}{ccc}-\frac{r}{L}&\frac{1}{L}& 0\\ -\frac{1}{C}&0&0\\ 0&0&-\frac{R_{a}}{L_{o}}\end{array}\right]}_{A_{sh}}\mathbf{x}. \tag{2}\]  
This equation does not have the term \(B_{sh}\mathbf{u}\) because the inverse bias of the diode in Figure 1c results in \(B_{sh}=0\). In this mode, the duty cycle is equal to \(d\).  
By using the SSA method, the overall state space equation can be computed as given by (3). Then, substituting (1) and (2) in (3) yields (4).  
\[\dot{\mathbf{x}}=\left(A_{n}\times(1-d)+A_{sh}\times d\right) \mathbf{x}+\left(B_{n}\times(1-d)+B_{sh}\times d\right)V_{in} \tag{3}\] \[\dot{\mathbf{x}}=\left[\begin{array}{ccc}-\frac{r}{L}&\frac{2 d-1}{L}&0\\ -\frac{2d-1}{C}&0&-\frac{1-d}{C}\\ 0&\frac{2(1-d)}{C}&-\frac{R_{a}}{L_{o}}\end{array}\right]\mathbf{x}+\left[ \begin{array}{c}\frac{1-d}{L}\\ 0\\ -\frac{1-d}{L_{o}}\end{array}\right]V_{in}. \tag{4}\]  
By ignoring the parasitic resistances of inductors, i.e., \(r=0\), the first row of (4) in the steady-state, \(\dot{\mathbf{x}}=0\), gives (5).  
\[V_{C}=\frac{1-D}{1-2D}V_{in} \tag{5}\]  
where, \(D\) is the steady-state value of \(d\). Therefore, the average voltage across dc-link can be calculated as (6).  
\[V_{dc}=(1-D)\times(2V_{C}-V_{in})+D\times 0=\frac{1-D}{1-2D}V_{in}. \tag{6}\]  
On the other hand, using Equation (5), the peak dc-link voltage can be expressed as (7).  
\[v_{dc}=2V_{C}-V_{in}=\frac{1}{1-2D}V_{in}=BV_{in} \tag{7}\]  
which means that the dc-link voltage is the boosted version of \(V_{in}\) with \(B=1/(1-2D)\) as the boosting factor. On the basis of (7), the peak output voltage of the ZSI can be reached as (8).  
\[v_{ac}=M\frac{v_{dc}}{2}=MB\frac{V_{in}}{2} \tag{8}\]  
where \(M\) denotes the modulation index. Hence, contrary to traditional inverters, the ZSI can buck-boost voltage to a desired level by offering an extra adjustable parameter, i.e., the boosting factor \(B\).
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
###### Abstract  
This paper proposes a MAC protocol for ad hoc networks using In-band Full-duplex (IBFD) wireless communications, which are named as AdHoc-FDMAC. To utilize IBFD communications in ad hoc networks, this protocol modifies a number of control frames in the IEEE 802.11 Distributed Coordination Function (DCF) MAC standard. Here, the detailed time sequences for all types of IBFD communications are shown for the data transmission and routing. In this paper, the probability and throughput equations for IBFD communications in different situations have been derived. The performance of the proposed AdHoc-FDMAC has been analysed in terms of probability, throughput, and routing time. The Maximum throughput of AdHoc-FDMAC has been found to be 48.34 Mbps, and it is compared with a recently published ad hoc MAC as well as with the conventional HD MAC. The AdHoc-FDMAC outperforms the recently published ad hoc MAC and conventional HD MAC by 16.80% and 66.50% throughput gain, respectively. AdHoc-FDMAC incorporates the existing Ad hoc On-demand Distance Vector (AODV) routing protocol, but this AODV routing is utilized here using IBFD communications. This paper also compares the routing time of the IBFD-based AODV with the conventional AODV. The result shows that the IBFD-based AODV requires 33.33% less routing time than that of the conventional AODV for 3-hop distance between the transmitter and receiver. This paper suggests that the AdHoc-FDMAC protocol provides much more throughput in ad hoc networks by utilizing IBFD communications.  
AdHoc MAC; AdHoc-FDMAC; IBFD MAC; FD MAC
## 4 Simulation and Discussion
### Impact of Gate-ON Time on Receiver Performance  
According to the BER performance of SPAD, \(K_{S}\), \(K_{b}\), and \(T_{g}\) are the main factors affecting the time-gated SPAD receiver. Firstly, the influence of different gate-ON time on BER is analyzed.  
The performance of time-gated SPAD is observed by setting different gate-ON times. To explain the results, we set different signal photon flux and background photon flux to verification. The experimental results are shown in Figure 4. When the background photon flux or signal photon flux are weak, increasing the gate-ON time can effectively improve BER performance. Further increasing the background photon flux, the SPAD receiver has an optimal gate-ON time.  
In order to further explore the functional relationship between gate-ON time, background photon, and signal photon, we set the target BER as \(10^{-3}\) and fixed the gate-ON time as 10 ns, 15 ns, and 20 ns. By changing the background photon, we observe the number of minimum signal photons to achieve the target BER. The results are shown in Figure 5. When the background photon is weak, \(T_{g}=20\) ns requires the least signal photons to achieve the target BER. Further increasing the background photon flux, the longer the gate-ON time, the more signal photon flux required to achieve the target BER. The reason for this result is that as the background photon increases, the BER performance of the receiver is mainly affected by the background photon, and the increasing gate time may result in more background photon incidents, making the BER performance decrease. If a shorter gate-ON time is selected, the influence of background photons on BER can be reduced, and fewer signal photons are to achieve the target BER. The SPAD array receiver of \(N_{g}=64\) is selected, and the experimental results are shown in Figure 5. When the background light flux is weak, the gate-ON time \(T_{g}=20\) ns requires the least signal photons to achieve the target BER, but different gate-ON times are less affected by BER performance. With the increase of background photon flux, when the gate-ON time is 20 ns, more signal photons are needed to achieve the target BER, and when the gate-ON time is 10 ns, fewer signal photons are required to achieve the target BER. Therefore, the SPAD's optimal gate-ON time needs to be selected according to the background photons and signal photons.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristics Parameter** & **Value** \\ \hline Photon Detection Efficiency @550 nm & 0.45 \\ Dead time & 25 ns \\ Symbol time & 37.5 ns \\ Gate-On time & 0\(\sim\)37.5 ns \\ Number of SPADs & 16/64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SPAD system parameter table.
## 3 Data Collection  
### Overview of Experiments  
In our experiment, we used the SmartEye Pro eye-tracking system (version 10.2.0, Noldus, Sweden) to capture the visual behavior of drivers interacting with blind spot images during lane changes. The system is equipped with three infrared cameras and an additional supplementary camera. It calculates the line-of-sight direction by measuring the vector between the pupil center and the corneal reflection reference point, which is produced using an active infrared light source. Head rotation angles are obtained from the head pose captured by the cameras, and the height of the eyes is recorded in real time from the camera images. The data are then synthesized using HRT (Human Research Tool) software (version 1.6.0, INFO instrument, Shanghai, China), which includes a foreground wide-angle camera, to merge the driver's foreground video with the line-of-sight direction data and determine the driver's point of gaze coordinates.  
For the first set of experiments (Experiment 1), a total of 15 drivers with experience in smart car driving and the habit of observing their blind zone image during lane changing were recruited and divided into three groups, with each group driving the same vehicle model. Three vehicle types with significant differences in the blind zone image designs chosen are shown in Figure 3. The experimental route (Figure 4) spanned approximately 38.5 km, with each test lasting roughly 90 min. The route was designed with numerous turns to prompt drivers to perform autonomous lane changes.  
Figure 3: Three types of vehicles used in experiment 1 (from **left** to **right**: Type 1, Type 2, Type 3).  
For the second set of experiments (Experiment 2), nine drivers with similar backgrounds were recruited, each of whom completed three driving trials using a vehicle of Type 1 with different blind zone image positions, as the blind zone image of Type 1 can be dragged to three positions as shown in Figure 5. Each trial covered approximately 15 km and lasted about 20 min (Figure 4). To mitigate the influence of experimental sequence on drivers' cognitive load variations, the order of blind zone image positions varied for each driver during the experiments.
# Effects of Imperfect Assembly and Magnetic Properties on the Three-Pole AMB System
## 3 Design of Stable-Levitation Controller  
The stable-levitation controller will be designed using the method of feedback linearization and integral sliding mode control (ISMC) [19]. Note that the system (10) is a non-affine nonlinear system since the state equation depends on the control currents in a quadratic form. Fortunately, it is shown in [18] that the system is feedback linearizable with the control currents designed as  
Figure 1: The 3-pole AMB system.  
Figure 2: The rotor position with assembly error.  
\[\begin{bmatrix}i_{1}\\ i_{2}\end{bmatrix}=\frac{1}{\sqrt{c_{0}}}\begin{bmatrix}-(2l_{0}+y_{r})&x_{r}\\ \frac{1}{\sqrt{3}}x_{r}&\frac{-1}{\sqrt{3}}(2l_{0}-y_{r})\end{bmatrix}K^{-1} \begin{bmatrix}\sqrt{-(\tilde{i}_{2}+g)+\sqrt{\left(\tilde{i}_{2}+g\right)^{2}+ \tilde{i}_{1}}^{2}}\text{sgn}(\tilde{i}_{1})\\ \sqrt{\left(\tilde{i}_{2}+g\right)+\sqrt{\left(\tilde{i}_{2}+g\right)^{2}+ \tilde{i}_{1}}^{2}}\end{bmatrix} \tag{11}\]  
where \(\tilde{i}\) is the new control input to be designed and \(\text{sgn}()\) is the sign function. The linearized system becomes  
\[\begin{array}{c}\dot{\eta}=\xi\\ \dot{\xi}=\tilde{i}+\Delta(\eta,\xi,\tilde{i})\end{array} \tag{12}\]  
where \(\eta=\begin{bmatrix}\eta_{1}&\eta_{2}\end{bmatrix}^{T}=\begin{bmatrix}x_{1}&x_{ 3}\end{bmatrix}^{T}\), \(\xi=\begin{bmatrix}\xi_{1}&\xi_{2}\end{bmatrix}^{T}=\begin{bmatrix}x_{2}&x_{ 4}\end{bmatrix}^{T}\), and \(\Delta\Big{(}\eta,\xi,\tilde{i}\Big{)}=\Delta\Big{(}x,\tilde{i}\Big{)}\) represents the uncertainty. To design ISMC for stabilization and robustness, the integral sliding manifold is first taken as  
\[\begin{array}{c}\sigma=\xi+b_{1}\eta+b_{2}z_{m}\\ \dot{z}_{m}=\eta\end{array} \tag{13}\]  
Then, the new control input is decomposed into two parts: the equivalent control and switching control, i.e., \(\tilde{i}=\tilde{i}_{eq}+\tilde{i}_{s}\); \(\tilde{i}_{eq}=-b_{1}\xi-b_{2}\eta\). With the equivalent control, the dynamics of the sliding variable is given by:  
\[\dot{\sigma}=\tilde{i}_{s}+\delta(\eta,\xi,\tilde{i}_{s});\delta(\eta,\xi, \tilde{i}_{s})=\Delta(\eta,\xi,-b_{1}\xi-b_{2}\eta+\tilde{i}_{s}) \tag{14}\]  
It is assumed that the uncertainty is bounded by:  
\[\left\|\delta(\eta,\xi,\tilde{i}_{s})\right\|_{\infty}\leq\rho+k\left\| \tilde{i}_{s}\right\|_{\infty} \tag{15}\]  
Finally, one can design the switching control using [PERSON]'s analysis and the overall control law will be:  
\[\begin{array}{c}\tilde{i}_{s}=-\frac{\rho+\alpha}{1-k}\text{sat}(\frac{ \sigma}{\varepsilon})\Rightarrow\tilde{i}=-b_{1}\xi-b_{2}\eta-\frac{\rho+ \alpha}{1-k}\text{sat}(\frac{\sigma}{\varepsilon})\end{array} \tag{16}\]  
where \(b_{1},\,b_{2},\,\rho,\,k,\,\alpha\) and \(\varepsilon\) are positive constants. In this paper, the uncertainty is mainly due to the assembly error given by (7) and (8). The upper bounds of its effect can be estimated. The ISMC controller is robust and can easily deal with such uncertainty. On the other hand, the non-uniform flux distribution is directly compensated using feedback linearization, as represented by the matrix \(K\) in Equation (11).
# The Efficiency of Calcium Oxide on Microbial Self-Healing Activity in Alkali-Activated Slag (AAS)
## 3 Results and Analysis
### Mechanical Properties
\[\text{Ca}^{2+}+\text{Cell}\rightarrow\text{Cell-Ca}^{2+}\] (10) \[\text{Cell-Ca}^{2+}\rightarrow\text{Cell-CaCO}_{3}\downarrow\] (11)  
Flexural strength behavior has a similar trend to compressive strength when calcium oxide is incorporated as a partial replacement (7%) of slag. A drop of 21.6% was observed in the value of 28-day flexural strength (4.86 MPa) concerning the control mix, as shown in Figure 4b. This is related to the increased amount of total shrinkage with time as a result of the increase in autogenous shrinkage, which imposes several pore structures inside the matrix. A study carried out by [PERSON] et al. [39] and [PERSON] et al. [40] on adding lime to an AAS composite activated by water glass demonstrated that the magnitude of autogenous shrinkage is directly affected by the percentage of the added lime  
Figure 4: Mechanical properties testing results of AAS specimens after 7 and 28 days: (**a**) compressive strength, and (**b**) flexural strength. (see Figure 4 in [39] and Figure 6 in [40]). Autogenous shrinkage mainly appeared in the fresh state due to chemical shrinkage, which denoted the high reactivity and accelerated setting time of the mixture. Thus, the behavior of the composites varies in the hardened state, where the continuous rearrangement of the AAS matrix induces extra-autogenous shrinkage [42,43]. The remarkable decrease in the flexural strength upon adding CaO to the microbial AAS mix (from 9.65 MPa for C5 to 4.9 for Ca-5), as clear in Figure 4b, showed the possibility of introducing more CaO that helped in reacting with the water glass and inducing more autogenous shrinkage and pores at an early stage. Furthermore, the rapid setting time caused by the excessive amount of calcium led to a quick hydration process and fluidity loss that resulted in defects in the composite. Hexagonal portlandite crystals are formed immediately by incorporating lime into the mixture. The interface and the layered surface of the produced plate crystals are the main factors for the poorly developed mechanical properties [41]. This is explained clearly in Sections 3.3 and 3.4 when studying the microstructural behavior of a matrix.
###### Abstract  
To solve the problem of poor steering consistency for each steering wheel of a four-wheel, independent-steering, high-clearance paddy field management machine, given that the true steering angle of the front wheel cannot be directly obtained through the left and right front wheels steering angle value, a BP (Back Propagation) neural network equivalent steering angle prediction method based on signal feature extraction is proposed in this paper, which can be used to obtain the equivalent steering angle of the front wheel. First, the kinematics model of the paddy field management machine was constructed with the prediction of the steering angle of the management machine as the object. The body was set up in two application environments of cement ground and paddy field, two moving speeds (25 cm/s, 50 cm/s) and three preset steering angles (0\({}^{\circ}\), \(\pm\)10\({}^{\circ}\)) to form six motion modes. The steering angle of the front wheel of the body, the three-axis acceleration of the body, and the angular velocity of the \(z\)-axis under twelve conditions were collected. Combining the collected data with the actual trajectory data of the differential BeiDou, the feasibility analysis of the equivalent steering angle prediction was carried out. With the aim of determining an appropriate combination of input factors for optimal equivalent steering angle prediction by the BP neural network, we used FFT (Fast Fourier Transform) and power spectrum conversion to extract and analyze the signal features of the airframe attitude data and obtain the frequency characteristics of the peak power point of the power spectrum, respectively. Through a factor combination test, the optimal BP neural network training factors were determined and, finally, we confirmed that the rotation angles of the front wheels and the \(z\)-axis angular velocity may be used as effective training factors. In our investigation on the test set, the mean square error of the equivalent steering angle was found to be less than 0.66\({}^{\circ}\), demonstrating that our approach is effective for obtaining the true steering angle of the front wheel.  
paddy field machinery; BP neural network; frequency spectrum analysis; equivalent steering angle +
Footnote †: journal: Applied Sciences
_Applied_  
_sciences_  
_Article_  
**Tunable Temperature Characteristic of Terahertz Bragg Fiber**  
**Filled with Liquid Water**  
[PERSON] 1, [PERSON] 1, [PERSON] 1,2,*(c), [PERSON] 1, [PERSON] 1,2(c), [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2 and [PERSON] 2  
1 Tianjin Key Laboratory of Optoelectronic Detection Technology and System, School of Electrical and Electronic Engineering, Tangong University, Tianjin 300387, China; [EMAIL_ADDRESS] (C.G.); [EMAIL_ADDRESS] (M.S.); [EMAIL_ADDRESS] (L.T.); [EMAIL_ADDRESS] (W.X.); [EMAIL_ADDRESS] (H.C.); [EMAIL_ADDRESS] (H.B.); [EMAIL_ADDRESS] (Z.X.)  
2 Key Laboratory of Opto-Electronic Information Technology, Ministry of Education, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China; [EMAIL_ADDRESS] (D.X.); [EMAIL_ADDRESS] (J.Y.)
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-152-2278-2770  
**Abstract:** Hollow-core terahertz (THz) fibers have attracted a lot of research interest due to the low loss and easy inner modification with functional materials. Liquid water has unique properties in the THz region and has been widely investigated in THz emission, sensing, and devices. In this paper, a hollow-core THz Bragg fiber with a water defect layer is proposed. The finite element method is used to verify and analyze the tunable temperature characteristic of the water-filled THz fiber. The numerical analysis results show that the confinement loss and the low-frequency side of the dip near 0.5 THz can be controlled by the temperature of the liquid water. The temperature sensitivity of the THz fiber is obtained at 0.09614 dB\(\cdot\)m\({}^{-1}\)/K at 0.45 THz with a high core power fraction up to 98%. The proposed THz fiber has potential applications in THz interaction with liquid and THz tunable devices.  
**Keywords:** terahertz; liquid water; temperature; tunable devices; hollow-core Bragg fiber
## 1 Introduction
The delay time generating the highest SNR value was selected as the optimal delay time. However, the SNR method needs a reference signal without any background noise. The SNR value varies significantly according to the value of the noise. The width of the signal intensity requires a correction because these values may change with the movement error of the measurement. In this study, the appropriate delay time was selected based on an estimation of the calorific values obtained from three different data analytical methods, including correlation analysis (CA), the partial least squares (PLS), and the SNR methods. At the determined delay time, the LIBS intensity signals were collected and the calorific value was estimated with the intensity data via the CA, PLS, and SNR methods. The coefficient of determination (\(R^{2}\)) was considered to evaluate the measurement accuracy of each analysis method. The obtained calorific values of the coal samples were compared to those of the chemical analysis method to assess the accuracy of the analysis methods.
## 4 Spatial Variance Analysis and Correction for GTLR-BiSAR
### Azimuth Variance Analysis and Correction Strategy
Through the azimuth variance corrections, the signal in RD domain becomes the the right part of Figure 5, which means that the target clusters with the same \(R_{r0}\) are characterized with the same \(R_{T}\). Referring to the previous Equation (21), we make a conclusion that the conventional monostatic processing strategy of RD and CS algorithms can be modified to handle the range variance problem. In order to avoid the blocking processing of RD algorithm, we prefer to modify CS algorithm to handle the range variance problem in next subsection.