esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Radio-Frequency Quadrupole Accelerator for the HL-2A/2M Tokamak Diagnostic System
## 3 RF Conditioning
### Measuring \(Q\)-Factor with the Ring-Down Method  
The ring-down method is a way of measuring the resonator \(Q\)-factor. We can obtain the decay time by exponentially fitting the falling edge of the scope amplitude of the cavity power, as shown in Figures 13 and 14.  
\[Q_{L}=\frac{\omega_{0}W}{P} \tag{5}\]  
where \(Q_{L}\) is the loaded quality factor, \(\omega_{0}=2\pi f_{0}\), \(f_{0}\) is the resonance frequency, \(W\) is the stored energy of the cavity, \(P\) is the power loss on the cavity walls.  
Figure 14: Exponential fitting of decay curve.  
Figure 13: Pickup signal of the RFQ cavity.  
Power loss \(P\) also can be written as \(P=-\frac{dW}{dt}=\frac{\omega_{0}W}{Q_{L}}\). By integrating both sides of the equation separately, we can obtain the equation:  
\[W=W_{0}\text{exp}\left(-\frac{\omega_{0}t}{Q_{L}}\right)=W_{0}exp\left(-\frac{t }{\tau_{L}}\right) \tag{6}\]  
where \(\tau_{L}=\frac{Q_{L}}{\omega_{0}}\), and the voltage \(U\propto\sqrt{W}\), then \(U\) can be written in the following form:  
\[U=U_{0}exp\left(-\frac{t}{2\tau_{L}}\right) \tag{7}\]  
According to the exponential fitting of the decay curve in Figure 13, we can obtain \(2\tau_{L}=(11.89\pm 0.17)\) us. The loaded quality factor can be expressed as:  
\[Q_{L}=\tau_{L}\omega_{0}=\tau_{L}\times 2\pi f_{0} \tag{8}\]  
For our experiment, the resonance frequency \(f_{0}\) is 162.6055 MHz. Thus, the \(Q_{L}=6070\pm 87\). Then the intrinsic \(Q\)-value can be calculated to be \(13,780\pm 197\) by:  
\[Q_{0}=(1+\beta)Q_{L} \tag{9}\]  
where \(\beta\) is the coupling factor [Equation (1)], which is equal to 1.27.  
The intrinsic \(Q\)-value after high-power tests is 13,780, which is about 93.6% of the simulated value. The specific shunt impedance by measuring the bremsstrahlung spectrum is 221 k\(\Omega\)-m, which is 92% of the simulated value.  
After the fabrication and assembly of the RFQ cavity, RF measurements were performed to check the RF properties of the cavity and the quality of the electric field. The intrinsic \(Q\)-value after high-power tests is much better than the measurement value of 12,834 in the RF measurements [19], indicating that the high-power tests can improve the conductivity about 6.8% higher of the inner copper surface of the cavity. We compared the measured \(Q_{meas}\) and \(f_{meas}\) with the simulated value, the required power \(P_{req}\) of the cavity is 48.2 kW by:  
\[\frac{P_{req}}{P_{sim}}=\frac{Q_{sim}}{Q_{meas}}.\frac{f_{meas}}{f_{sim}} \tag{10}\]
## 3 Computational Examples and Analysis
### Case Study I: Bi-Objective Optimization
#### 3.1.3 Results of Asphalt Mixture Proportion Optimization Using GPR-AWMOPSO
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Optimization** \\ **Methods** \\ \end{tabular} } & \multicolumn{4}{c}{**Input Variables**} & \multicolumn{4}{c}{**Output Variables**} \\ \cline{2-7}  & \(\mathbf{X_{1}}\) (\%) & \(\mathbf{X_{2}}\) (mm) & \(\mathbf{X_{3}}\) (\%) & \(\mathbf{Y_{1}}\) (kN) & \(\mathbf{Y_{2}}\) (mm) & \(\mathbf{Y_{3}}\) (\%) & \(\mathbf{Y_{4}}\) (\%) & \(\mathbf{Y_{5}}\) (\%) \\ \hline RSM [16] & 0.34 & 6.41 & 6.57 & 10.49 & 3.113 & 4 & 17.7 & 77.43 \\ Traditional MOPSO & 0.3472 & 6 & 6.62 & 10.51 & 3.164 & 4 & 17.9 & 77.67 \\ MADS & 0.3529 & 6 & 6.62 & 10.53 & 3.169 & 4 & 18.1 & 77.88 \\ GPR-AWMOPSO & 0.3547 & 6 & 6.61 & 10.55 & 3.181 & 4 & 18.1 & 77.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of optimization results among RSM [16], traditional multi-objective particle swarm optimization (MOPSO), mesh-adaptive direct search (MADS) and GPR-adaptive weight multi-objective particle swarm optimization (AWMOPSO) algorithm. algorithm has higher mechanical performances. When the main objective is maximizing mechanical performances, the proposed GPR-AWMOPSO algorithm can be considered as the best option. (3) Performance Verification  
To assess the comprehensive pavement performances of the asphalt mixture containing basalt fibre designed using RSM and the proposed GPR-AWMOPSO algorithm, the permanent deformation resistance, crack resistance as well as moisture stability were conducted at high and low temperatures. For the two groups of designed mixture proportions, three replicate specimens were prepared for the rutting experiment at 60 \({}^{\circ}\)C, splitting experiment at \(-\)10 \({}^{\circ}\)C as well as an immersion Marshall experiment and freeze-thaw splitting experiment, and the corresponding results are illustrated in Figure 8. It can be observed that compared with the specimens designed by RSM, the dynamic stability of specimens designed by GPR-AWMOPSO increases by 3.59%, the stiffness modulus increases by 2.43%, and the residual Marshall stability and tensile strength ratio (TSR) increase by 3.48% and 2.16%, respectively. The comparison results verify that the proposed GPR-AWMOPSO algorithm is the best option for maximizing the performances of basalt fibre reinforced asphalt mixture. Figure 7: The proportion optimization results for basalt fibre modified asphalt mixture using GPR-AWMOPSO: (**a**) Pareto front showing two objectives (F\({}_{1}\) and F\({}_{2}\)) and (**b**) the primary objective function value (F\({}_{1}\)) versus iteration.
# Strength Evaluation and Modification of a 3D Printed Anterior Ankle Foot Orthoses
## 1 Introduction  
Neuromuscular and musculoskeletal disorders such as stroke or cerebral palsy can cause great difficulty in ambulation [1]. Lower limb orthoses such as ankle foot orthoses (AFOs) have been used to correct the ankle position, provide stability, and assist limb clearance [2; 3; 4; 5]. Using an AFO can improve an individual's balance, toe clearance during swing phase, gait performance, and mobility, immediately leading to improvement in an individual's activities of daily living [6; 7; 8; 9; 10].  
Anterior type of ankle foot orthoses (AAFOs) are widely used in Asia, because they are suitable for walking [11]. AAFOs are designed in the anterior leaf type and support the foot from the metatarsal head. These types of AFO can be fabricated quickly and easily. However, due to the strength of the material properties, AAFOs can only be used for patients with less plantar spasticity. This material was found to be less durable and only lasted within a few months [12]. Moreover, pre-fabricated AAFOs could also cause some patients to complain about bruises on bony land marks [13].  
Currently, 3D-printing techniques can be applied to patients with different conditions. This technique has been recognized as a feasible way to improve customized AFOs. Common 3D printing techniques including selective laser sintering (SLS) and fused deposition modelling (FDM) have been used to fabricate customized orthoses. These techniques can fabricate not only customized products, but also special designs, revising the product that causes the products to become stiffer [14]. Previous research on 3D printing AFOs (3 DP-AFO) has mainly been based on SLS [15; 16] and fewer AFOs have been produced by FDM. However, the cost of SLS products is relatively high and time consuming; and FDM products are mostly used in manufacturing of posterior-type AFOs (PAFOs). Additionally, the previous study mostly focused on the evaluation of 3D printing PAFOs rather than 3D printing anterior AFOs (3 DP-AAFOs). Therefore, this study conducted FDM to fabricate 3 DP-AAFOs. In order to understand whether the 3 DP-AAFOs were strong enough to support individuals, mechanical tests were applied on the 3 DP-AAFOs. Additionally, a past study implemented finite element (FE) analysis to predict the mechanical behavior of AFOs and found that the FE analysis could predict the weakness of the AFO [17]. [PERSON] et al. [18] addressed that focusing on the design and analysis of AFOs using FE analysis could increase the patient's comfort. [PERSON] et al. [19] reported that the error between the experiment and FE simulation should be controlled within 10% in the FE analysis of AFOs. These studies show the potential to improve the performance of AFOs. Therefore, this study aimed to conduct a FE model to evaluate 3 DP-AAFOs and also enforce its strength based on FE analysis.
# A Data-Driven Model for Predictive Modeling of Vortex-Induced Vibrations of a Long-Span Bridge
## 4 Experimental Results and Analysis
Secondly, a physics-enhanced strategy is applied to construct the proposed model, with which the relationship among responses can be utilized and the physical meaning of the middle output can be complemented. Thirdly, the proposed model employs a modified version of mean square error as a loss function, with which the amplitude of VIV response can be predicted more accurately. By validating the proposed model with the monitored VIV events, some conclusions can be obtained. The proposed model can predict the time history of VIV response accurately, and there are some patterns in the middle output. The WMSE is proven to be effective in training the proposed model. The limitations of applying the used method are as follows. First of all, because of the error accumulation, the effective length of prediction of the proposed model is limited. Therefore, the proposed model can only provide a prediction of VIV response within a small time interval. Secondly, the physical meaning of middle output is not clear because of the lack of enough physical constraints. The proposed model is not fully prepared to uncover the underlying mechanism of VIV events. Therefore, future research should focus on enhancing the capacity for long-term prediction and the interpretability of the proposed model. The numerical cost of the proposed method consists of two parts, which are the training part and the prediction part. In the training part, the numerical cost depends on the training specifics and the amount of data, which can take hours to days. However, the  
Figure 10: Comparison of typical predictions. \begin{table}
\begin{tabular}{c c c} \hline  & **With WMSE** & **With MSE** \\ \hline MAE & 1.567 & 1.688 \\ RMSE & 2.708 & 2.859 \\ WMAPE & 0.144 & 0.155 \\ PCC & 0.982 & 0.980 \\ \hline \end{tabular}
\end{table} 
Table 3: The summary of prediction accuracies. training part needs only one execution. Once the model is trained, in the prediction part, the model can perform prediction within seconds.
# A Multi-Strategy ALNS for the VRP with Flexible Time Windows and Delivery Locations
## 3 Problem Description and Formulation of the VRP-CS
### Assumptions and Decision Variables  
#### 3.2.1 Model Assumptions  
The complex factors in logistics distribution increase the difficulty of solving the VRP-CS, some of which are not necessary to achieve the core optimization goal. By putting forward reasonable assumptions, the problem can be simplified, and the solvability of the model can be improved. Therefore, the VRP-CS makes the following assumptions:  
* The logistics supplier has enough vehicles at the distribution center to complete the delivery task.
* Each customer can only be served in one time window at one location, and the service must be completed within the time window at that location. A vehicle can only be used to serve a customer once.
* All customer services must be completed within the specified scheduled time window.
* Split delivery is not permitted, and the requirements of every single customer do not exceed the maximum load of a single vehicle.
## 2 Materials and Methods
### Testing and Characterization
#### 2.3.5 Compression Testing  
The compressive tests were carried out in accordance with ASTM test method E9-09 on the MTS 810 testing machine at ambient temperature, using a strain rate of 0.01 min\({}^{-1}\).  
The samples with a length to diameter ratio of 1.5 were machined from the extruded rods with a diameter of 7 mm. Key mechanical properties such as 0.2% offset yield strength (0.2% YS), ultimate compressive strength (UCS), failure strain (FS), and energy absorbed (EA) were extracted from the graphs. A minimum of 5 tests were conducted to ensure repeatability.
# The POSEIDON Ocean Observing System: Technological Development and Challenges
## 3 Challenges during Operations
### Vandalism  
Vandalism of ocean data buoys or stations refers to the intentional interference with, damage to or theft of observing platforms by human action [20]. As reported, data buoy vandalism has many forms, including:  
* Fouling of mooring lines or damage to underwater cables or sensors from fishing lines or nets;
* Using the buoy as a temporary anchor which can overstress mooring lines and damage the buoy superstructure;
* Deliberately dragging the buoy from its moored location;
* Cutting mooring lines or cables; and/or
* Removing physical infrastructure, cables or sensors, or other hardware.  
Figure 11: The improvement of the salinity measurements after the replacement of the exposed surface sensor with one equipped with a pump and a flow-through system proven to be more effective against fouling.  
Vandalism of the equipment is a major issue for the long-term operation of the oceanographic buoys and other types of exposed platforms. According to the National Oceanic and Atmospheric Administration (NOAA), annually, about 10 percent of buoy data world-wide are lost due to vandalism [21]. The POSEDION data buoy network has suffered from these forms of vandalism and, on some occasions, they have resulted in a total loss of equipment. Some recent incidents are presented in Table 5.
## 3 5G Technology Enablers for V2X Use Cases
### The Evolution of V2X Communications
#### 3.1.1 WiFi-Based Technologies  
The first dedicated V2X communication solution (released in 2010) was the 802.11p protocol [52], which was developed from the popular 802.11 standard family. The 802.11p is mostly a modified version of the 802.11a standard, tailored to vehicular communication. This means that the channel access model (CSMA/CD) stayed the same as the WiFi technologies. However, certain standard parameters were changed in order to support the high speeds and the fast-changing topology of the nodes, which is specific to V2X communication. Using the WiFi technology also implies that no concurrent transmission is available, OFDM modulation is used, and there is loose asynchronous time synchronization. In the WiFi-based V2X networks, the 802.11 bd standard is actively developed. This standard will be backward compatible with 802.11p and outperforms it in many areas [122].
## 2 Testbed Setup and Measurement Methodology
### Measurement Methodology  
In the first step, the experiment evaluated the cost of setting a timer: the time required to obtain a time value from the timing hardware. We use the term timer cost as a measuring parameter here. Secondly, the measurements of sleeping operations were performed and the difference between the targeted wake-up time and actual times of wake-ups (referred to a _miss time_) was calculated. For measuring both the timer cost and miss time, using the standard Linux _System Call_ was compared with gathering the counter cycles from timer hardware. As an interface to available time counters, the _HighPerTimer_ library was applied. It was used to obtain access to the _TSC_ timer on the current processor from user-space and provides a mechanism of precise sleep operations. The sleep function from _HighPerTimer_ library (_HPTSleep_) allows significantly decreasing the overhead of miss time without starving the CPU. In the present research, the median and MAD (Median Absolute Deviation) were chosen as the main statistical parameters. Since the data follow a non-parametrical distribution, the median value carries more information about distribution than the mean value. The MAD is calculated as half the interquartile dataset range and can be treated as non-parametric analogous to standard deviation. In the next sections, two types of plots are shown: plots with raw data with bold lines that are the medians, and plots with the representation of complementary cumulative distribution function (CCDF) with dashed lines that are the means. CCDF is a distribution function that shows how often the random variable is measured above a particular level. Additionally, the mean values are illustrated within CCDF plots, which allow estimating how mean values deviate from medians. Every experimental result was analyzed with non-parametrical variance analysis Kruskal-Wallis ranking test [25] (or ANOVA on ranks) and posthoc test using ranking analogous of Tukey-HSD [26],with a significance level 0.05, to determine the influence of factors, such as timing operations (_TSC_, _clock_gettime()_, _HPTSleep_, and _uSleep()_), virtual platform and load type.
# Crescent Microstrip Antenna for LTE-U and 5G Systems
## 7 Comparison of the Proposed Antenna with Other Antennas  
The parameter values for the proposed antenna in terms of impedance matching and bandwidth can be compared with other published results in order to obtain a comparative assessment. The frequency response of the measured parameter S\({}_{11}\) for the proposed antenna is not the lowest (especially for comparing the simulation value) compared to the  
Figure 16: The normalized radiation patterns for the proposed antenna model operating in 5G and LTE-U systems for 5.5 GHz (black line—simulation results, red line—measurement results) in polar coordinates for vertical and horizontal planes.  
Figure 14: The 3D view of the radiation pattern for the proposed antenna model at 5.50 GHz frequency.  
Figure 15: The normalized radiation patterns for the proposed antenna model operating in 5G and LTE-U systems for 3.6 GHz (black line—simulation results, red line—measurement results) in polar coordinates for vertical and horizontal planes.  
obtained value of S\({}_{11}\) of the antennas presented in [22; 23; 24; 27; 34], but it is relatively low. Table 2 shows a comparison of the electrical parameters of the proposed antenna with other selected antennas available in the literature. The table shows a comparison of the reflection coefficient, the frequency bandwidth, and the gain of the antennas.  
Based on comparisons of the electrical parameters of the proposed antenna with other antennas, it has been shown that the proposed dual-band antenna has the comparable performance in terms of impedance matching in all cases, especially in cases of stringent matching conditions (S\({}_{11}\leq-10\) dB, S\({}_{11}\leq-13.97\) dB and S\({}_{11}\leq-19.08\) dB) and the values of antenna gain.
## 2 Overview of Recent Radio Trends
### MIMO Systems  
Let us consider the MIMO system model with \(N_{t}\) transmit and \(N_{r}\) receive antennas, as depicted in Figure 4.  
The channel is modeled as an \(N_{t}xN_{r}\) matrix; the received signal \(y\) is expressed as:  
\[y=Cx+n \tag{1}\]  
where \(x\) is the transmit signal vector, and \(C\) and \(n\) are the fading channel matrix and the noise vector, respectively.  
The ergodic channel capacity of the system, in ideal conditions, is:  
\[Capacity_{ideal}=E\Big{[}\max_{Q,\;tr(Q)\leq 1}\log_{2}det\Big{(}I+\rho CQC^{H} \Big{)}\Big{]}=E[\log_{2}det(I+\rho DSD)] \tag{2}\]  
where \(()^{H}\) is the Hermitian transpose and \(\rho\) is the signal to noise power ratio (SNR).  
\(Q\) is the optimal signal covariance, formulated through a singular value decomposition of the matrix \(C\) and an equalization process, as follows:  
\[\left\{\begin{aligned} SVD(C)=UDV^{H}\\ Q=VSV\\ S=[s_{1},\;\ldots,s_{min}(N_{t},N_{r}),0,\ldots,0]\\ s_{i}=\Big{(}\epsilon-\frac{1}{\rho_{i}^{2}}\Big{)}^{+},\;i=1,\ldots, \;min(N_{t},N_{r})\end{aligned}\right. \tag{3}\]  
\(d_{i}\) are the diagonal elements of \(D\), and \(\epsilon\) is chosen \(s_{1}+\ldots+s_{min}(N_{t},N_{r})=N_{t}\).  
We note that if \(s_{i}\leq 0\), it is automatically rounded to zero.  
In the case where the transmitter has only statistical CSI, the ergodic capacity diminishes as the \(Q\) could be optimized from the sense of mutual information:  
\[Capacity_{statistical\;CSI}=\max_{Q}\log_{2}det\Big{(}I+\rho CQC^{H}\Big{)} \tag{4}\]  
If the transmitter has no CSI, the capacity maximization could be on the basis of \(Q\), which is expressed in this case by:  
\[Q=\frac{1}{N_{t}I} \tag{5}\]  
The capacity, in this special case, will be given by:  
\[Capacity_{no\;CSI}=E\Big{[}\log_{2}det\Big{(}I+\rho CQC^{H}\Big{)}\Big{]} \tag{6}\]  
Figure 4: MIMO system Model.
# Target Image Mask Correction Based on Skeleton Divergence
## 2 Materials and Methods
### Mask Optimization Based on Skeleton Divergence
At the same time, the extraction of the skeleton also ensures that the shapes of the source and mask images are not subject to global structural distortion. Regularization constraint verification of the sparse point cloud was also conducted, which reduced error accumulation in the mask correction process without requiring a significant amount of calculation.
## 3 Results and Discussion
### Fabrication and Sensing Experiment of Uniformly Etched TMFBG  
#### 3.3.1 Uniformly Etched TMFBG  
The main parameters of the TFBG used in this study are as follows: the central wavelength is 1544 nm; the grating length is 10 mm; and the grating inclination is 8\({}^{\circ}\) (TFBG8-10, purchased from Shandong NuoGuang Photoelectric Technology Co., Ltd., Jinan, China). The TFBG is processed in a fume hood using the hydrofluoric acid chemical etching technique. The non-grating region of the TFBG is inserted into the quartz capillary and then fixed to the inner walls of the plastic vessels to ensure its structural stability, free from equipment vibration or fluid disturbances. Then, hydrofluoric acid with a volume concentration of 30% is dropped into the plastic container. To ensure a stable etching process, the dropped hydrofluoric acid must cover the entire grating area; meanwhile, a layer of liquid paraffin is used to cover the hydrofluoric acid to prevent its volatilization. The etched TMFBG is repeatedly washed with deionized water and dried to clear away any extra hydrofluoric acid.  
Figure 7 compares the transmission spectra of the TFBG before etching and the etched TMFBG with the diameters of 70 \(\upmu\)m and 30 \(\upmu\)m. When the cladding thickness is thick enough (70 \(\upmu\)m), the complete cladding mode envelope and high-quality transmission spectrum can be observed. At the same time, the core mode does not change, because the cladding is much thicker than the fiber core, inside which most of the energy is still strictly confined. The core mode is not yet affected by the decrease in the cladding thickness. When the diameter of the TMFBG becomes smaller, more energy from the cladding mode will be coupled to the external environment, resulting in a significant decrease in the number of cladding modes (30 \(\upmu\)m).  
Figure 7: Transmission spectra of TMFBGs with different cladding diameters.