esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Leak Detection Technologies
### Hardware-Based Methods
#### 4.1.1 Non-Acoustic Methods
The transient-based approach exhibits several merits: non-invasiveness, cost-effectiveness, and a broad operational range [40]. Presently, leak detection strategies grounded in transient signals are categorized into two primary domains: time-domain techniques [41] and frequency-domain methods, which can be derived from time-domain data through fast Fourier transforms or wavelet transforms [42; 43]. Despite its advantages, the transient-based approach for leak detection is not without limitations. One notable constraint lies in the necessity for specific boundary conditions and signal characteristics, which may not be applicable to all pipeline systems. Furthermore, the accurate estimation of the leak size and location can be compromised by complexities arising from multi-component pipe networks. Additionally, the reliance on computational methods, such as fast Fourier transforms and wavelet transforms, for signal analysis introduces the need for rigorous calibration and validation. Future research endeavors should prioritize the development of more robust algorithms and models capable of accommodating diverse pipeline configurations. Furthermore, the integration of advanced sensor technologies and data fusion techniques could enhance the precision and reliability of transient-based leak detection.
# Comparison of Functional Connectivity Analysis Methods in Alzheimer's Disease
### Model/Seed Based Methods  
In seed-based methods, seed voxels or regions of interest (ROI) are chosen. Further, their correlation with the rest of the brain's ROIs is calculated by measuring spontaneous BOLD signals. The ROIs that have a strong positive correlation with the seed are referred to as functionally coupled ROIs [7]. A study utilizing two seeds positioned in the posterior cingulate cortex (PCC) and ventromedial prefrontal cortex (vmPFC) demonstrated that age-related alterations in the default mode network (DMN) were observed [8]. A decrease in FC in subjects with AD has been found by placing the seed in bilateral hippocampi [9,10]. However, it is quite usual for multiple seeds to result in different connectivity detection. Furthermore, the prior knowledge criteria constrain the study of possible FC.
## 3 Results
### QPE Improvement
#### 3.2.3 Runway Condition
The traditional approach with no QI was characterized by poorly estimated rainfall amounts that often appeared as evident outliers--in some cases exceeding values of 10 mm. These are striking mostly in the right bottom regions of the plot, where they are related to very low values of the measured water film heights. The large spread of the QPE estimates was, therefore, reflected in a very low coefficient of determination (\(R^{2}=0.18\)). On the other hand, the cloud of points for the _qPrec_ estimates was more compact. This was underpinned by the fact that the estimated 4-min rainfall amounts did not exceed 3 mm. While the coefficient of determination (\(R^{2}=0.36\)) of the _qPrec_ method was still relatively low, it was sufficient to derive the overall conclusion of the experiment, i.e., the added value of the novel, QI-based approach to QPE. The results clearly depended on the settings of the experiment, particularly on the time window of the accumulation of the _qPrec_ amounts. The _qPrec_-based scatter plot indicated that the estimates of the 4-min amounts underestimated the observed heights of the water film. The 5- and 6-min time windows yielded similar scatter plots (with the \(R^{2}\) practically with the same magnitude); however, the corresponding linear regressions generally indicated overestimation of the observed values (not shown). Figure 9: Coverage of the territory of Slovakia by the network of four meteorological radars of SHMU, expressed in terms of the height of the lowest visible radar beam at the given point (in m above the ground). The location of the Poprad Airport is denoted by the icon of a plane. The radar icons denote the locations of Malý Javornik (West), Kubinska hofa (North), Kojsovska hofa (East), and Spani laz (South).
## 3 System Model
### Framework for HNOMA
In each sub-frame, gNB transmits the composite signals of UE\({}_{1}\) and UE\({}_{2}\) when \(i=1\), UE\({}_{3}\) and UE\({}_{4}\) when \(i=2\), UE\({}_{5}\) and UE\({}_{6}\) when \(i=3\), and UE\({}_{7}\) and UE\({}_{8}\) when \(i=4\). This kind of pairing experiences less complexity with the complexity order of \(O(M)\)[21; 28]. * **Odd-odd and even-even (OO-EE) pairing:** In the context of NN-FF pairing, the pairs experience similar channel gains. This configuration is not conducive to the NOMA phenomenon. Consequently, to augment diversity in the channel gains of each pair, devices categorized as odd-odd and even-even are paired according to their respective channel gains. This methodology enhances system performance; however, it also elevates the complexity to a significant degree, resulting in \(O(MlogM)\). The schematic representation for OO-EE pairing is delineated in Figure 3.  
Figure 2: Schematic of the proposed IOS-aided C-HNOMA system with NN-FF pairing. * **Stronger-weak strong-weak (SW-SW) pairing:** Figure 4 illustrates SW-SW pairing. The OO-EE pairing demands the value of \(M\) to be a power of 2 in contrast with other pairing schemes. To address this limitation, the devices are ordered according to their channel gains and divided into two equal groups. The first group consists of the first half of the SUE, and the next group contains the second half of the WUE. The devices within each group are chosen as a pair to ensure a uniform channel distribution in every pairing. This takes the complexity order of \(O(MlogM)\)[28]. * **Strong-weak (SW) pairing:** The SW pairing method is examined, as illustrated in Figure 5. In this pairing approach, the strongest UE is paired with the weakest UE, followed by the next strongest UE being paired with the weakest UE, adhering to the complexity order of \(O(2M)\)[21, 27]. Figure 4: Schematic of the proposed IOS-aided C-HNOMA system with SW-SW pairing. Figure 3: Schematic of the proposed IOS-aided C-HNOMA system with OO-EE pairing. In the context of C-HNOMA, each sub-frame is further divided into two equal slots. The first half is for direct transmission, while the second half employs cooperative decode-and-forward (DF) relaying. During the direct transmission, the composite signal for the \(p\)th pair at the \(i\)th time slot is transmitted. The power fraction allocated to the SUE during the \(i\)th time slot is denoted as \(\mu_{si}\), while the power fraction allocated to the WUE is \(\mu_{wi}\). To ensure fairness, it is required that \(0~{}<~{}\mu_{si}~{}\leq~{}\mu_{wi}<1\) and that \(\mu_{si}+\mu_{wi}=1\). The total transmit power budget is denoted as \(P\). The composite signal transmitted for the \(p\)th pairing during the \(i\)th time slot in the direct transmission is given by the following:
# Knee Loading Asymmetries During Descent and Ascent Phases of Squatting After ACL Reconstruction
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Mean Difference** & **SD** & **SEM** & **95\% CI** & \(p\) \\ \hline \multirow{3}{*}{vGRFi ILR} & Unadjusted & 0.029 & 0.083 & 0.014 & 0.001 to 0.058 & 0.045 * \\ \cline{2-7}  & Adjusted & 0.024 & - & 0.032 & \(-\)0.039 to 0.088 & 0.449 \\ \hline \multirow{3}{*}{KFMi ILR} & Unadjusted & 0.066 & 0.095 & 0.016 & 0.034 to 0.099 & \textless{}0.005 * \\ \cline{2-7}  & Adjusted & 0.071 & - & 0.093 & \(-\)0.115 to 0.257 & 0.449 \\ \hline \multirow{3}{*}{HKRi ILR \({}^{\dagger}\)} & Unadjusted & \(-\)0.218 & 0.418 & 0.074 & \(-\)0.368 to \(-\)0.067 & 0.006 * \\ \cline{2-7}  & Adjusted & \(-\)0.223 & - & 0.260 & \(-\)0.744 to 0.298 & 0.395 \\ \hline Time (s) & - & 0.047 & 0.129 & 0.022 & 0.003 to 0.092 & 0.036 * \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of paired t-tests comparing the vertical ground reaction force impulse, external knee flexion moment impulse, ratio of external hip flexion to knee flexion moment impulse and time in each squat phase during the descent compared to ascent phase of bilateral squats. Figure 2: Interlimb ratios for vertical ground reaction force impulse, external knee flexion moment impulse and ratio of external hip flexion to knee flexion moment impulse during the descent and ascent phases of squats. Error bars represent \(\pm\)1 standard deviation. * \(p\)-value of \(<\)0.05. Note: The dashed line represents an interlimb ratio of 1 and an equal distribution of loading for each variable between the injured and uninjured limb. Abbreviations: ILR, interlimb ratio; vGRFi, vertical ground reaction force impulse; KFMi, external knee flexion moment impulse; HKRi, ratio of external hip flexion to knee flexion moment impulse. After adjusting for time during each phase, there was no longer a statistically significant difference in vGRFi ILR (\(F\)(1, 67) = 0.581, \(p\) = 0.449, partial \(\eta^{2}\) = 0.009), KFMi ILR (\(F\)(1, 67) = 0.579, \(p\) = 0.449, partial \(\eta^{2}\) = 0.009) or HKRi ILR (\(F\)(1, 61) = 0.733, \(p\) = 0.395, partial \(\eta^{2}\) = 0.012). Adjusted means and variability for each of the biomechanical variables are provided in Table 3.
## 6 FEL Commissioning  
After the Injector Test Facility (ITF) had stopped its operation by the end of September 2015, the photocathode RF gun and the two S-band accelerating structures at the ITF were moved to the main PAL-XFEL LINAC. The installation of 51 klystron modulators in the LINAC gallery was finished as of 30 November 2015. Twenty (20) HX undulators for the HX line were installed as of December 2015 in the 250-m long HX undulator tunnel. Since the PAL-XFEL LINAC has all new RF components, an RF aging or conditioning period is required. This RF conditioning had started in November 2015, and continued until the Korean Nuclear Safety and Security Commission (NSSC) issued the operation permission on 12 April 2016. The actual beam commissioning was started on 14 April 2016. Since the ITF parts have already been conditioned during their use from 2012 to 2015, the electron beam emitted from the photocathode gun quickly arrived at the first beam analyzing station (BAS0) on the same day. By 25 April, the 10-GeV electron beam reached BAS3, and the commissioning of the LINAC was completed [8].  
Even though the 10-GeV beam is available, we have decided to reduce the electron beam energy to 4-GeV in order to send the electron beam to the main dump through the 6.7-mm gap undulator chambers. Also, the gaps of all undulators were fully opened. These two actions were intentionally chosen to minimize radiation damages to the permanent magnets of the undulator system. After the 4-GeV electron beam reached the main dump, an effort to lase the photon beam was carried out with several feedback algorithms including the beam-based alignment (BBA) technique. Finally, we lased the photons at 0.5 nm on 06:00 14 April 2016. The third harmonic spectrum of 6.6 keV was measured with a single shot spectrometer located in the HEH1, as shown in Figure 8 [9]. Its width was 30 eV or 0.45%. Figure 9 shows the snapshot of the 0.2-nm lasing. A bunch length of 12.7-fs was measured by S-band Transverse Deflecting Cavity (TCAV) with a peak current of 3.7-kA. Major achievements and unexpected interruptions during the commissioning are summarized in Table 4.  
Figure 8: The third harmonic spectrum of 6.6 keV was measured with a single shot spectrometer located in the HEH1.
# Multi-Constrained Optimization Method of Line Segment Extraction Based on Multi-Scale Image Space
## 2 Methods  
### Model Construction  
#### 2.1.1 Line Segment Detection Method  
At present, [PERSON]'s LSD line segment extraction algorithm is one of the most widely used methods. The principle of several mainstream line segment extraction algorithms is similar to LSD. Figure 2 shows the workflow of the LSD algorithm.  
In the line detection process, the input image was first down-sampled to 80% of the original image by the Gaussian down-sampling method to weaken the sawtooth effect of image, as well as to maintain a balance between the sawtooth effect and image blur. Next, a \(2\times 2\) gradient template was given to calculate the gradient direction (level-line angle (LLA)) and amplitude (intensity) of all pixels after image down-sampling. Depending on the pixel gradient amplitude, 1024 containers were opened and all image pixels were pseudo sorted from high to low order. Simultaneously, a state list was established and the initial state of all pixels was set to UNUSED. The state of pixel labels whose gradient amplitude was smaller than the threshold \"\(\rho\)\" was changed to USED, to represent an area where the image is flat or the gradient changes slowly; this means it did not participate in the line support region (LSR) and rectangle construction. Then, an UNUSED pixel from the sorted list was selected as a seed point. The region growing algorithm was used to generate LSR with the seed point. The seed point and LLA were continuously updated until the current point could not meet the threshold requirement. The final regional direction angle \(\theta_{reg}\) was achieved. The rectangle and the number of false alarms (NFA) was calculated according to \(\theta_{reg}\) and finally, line segments were determined after traversing all UNUSED seed points.  
A point can only belong to certain line segment with a traditional LSD algorithm where extracted line segments cannot intersect with each other. If two line segments intersect, they break and split into four short line segments. Additionally, the algorithm is susceptible to pixel grayscale gradient mutation during the regional growth period, so that the difference between the current point LLA and the regional  
Figure 2: Workflow of LSD line segment extraction algorithm.  
direction angle exceeds the growth threshold, resulting in segmentation fracture of the line segments. The EDLines algorithm uses the Edge Drawing operator for feature extraction. The speed of this method is fast, but the line segmentation fracture problem is not improved. Therefore, traditional line segment extraction algorithms should be further optimized to solve the segmentation fracture effect.
## 2 Materials and Methods
### Connectivity Models
#### 2.2.1 Temporal Causal Discovery Framework  
The Temporal Causal Discovery Framework (TCDF) developed by [14] is based on the concept of a one-dimensional Temporal Convolutional Network (TCN) and is available on Github [27]. Input to the framework consists of an NxL data set consisting of N time series of equal Length L. Within the framework, one depthwise-separable TCN is used to obtain a prediction for a single source (target). The input of the network consists of the history of all time series, including the target time series. The output is the history of the target time series. An attention mechanism is added: each TCNj has its own trainable attention vector Vj = [vX1j, vX2j,..., vij,.... viNj], that learns which of the input time series is correlated with the target by multiplying attention score vij with input time series Xi in TCNj. When the training of the network starts, all attention scores are initialized as 1 and are, as such, adapted during training. The direction of connectivity and significance is determined using a shuffling procedure. For significance determination, one of the time series is shuffled while keeping the other one(s) intact when predicting the target. The runs with shuffled time series did not involve any model retraining. Instead, in the prediction step, the losses obtained when using the \"shuffled\" time series as predictors were compared with the losses obtained when using the non-shuffled time series. Only if the loss of a network increases significantly when a time series is shuffled that time series is considered a cause of the target time series. A time series X1 is only considered to be a significant contributor to another time series X2 if, in the first stage, its attention score is larger than one. Only if, after shuffling the potentially contributing time series X1, the difference between losses obtained by predicting future time steps with the unshuffled time series and losses obtained by predicting using shuffled time series is large enough, using an a priori determined threshold significance value, time series X1 is considered a significant contributor to time series X2. TCDF was run with PyTorch (version 1.4.0, www.pytorch.org, accessed on 17 December 2021).  
Configuration. For TCDF, the chosen parameters were the number of hidden layers = 1, kernel size = dilation coefficient = 4 (a time-dimensional kernel), learning rate = 0.01,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Architecture** & **Self-Causation** & **Connectivity Measure** \\ \hline
**TCDF** & Depthwise Separable 1D Convolutional Network & yes & Attention score \\
**LSTM-NUE** & Long Short-Term Memory Network & no & GC (0 or 1) \\
**Conv2D** & 2D Convolutional Network & yes & R\({}^{2}\)-score \\
**TRGC** & Time-Reversed Granger Causality & no & GC (0 or 1) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Set-up of the compared ANNs and TRGC.  
optimizer = Adam, number of epochs = 1000, significance threshold= 0.9998, seed = 1000. Kernel weights are initialized following a distribution with \(\mu\) = 0, variance = 0.1.
## 5 Conclusions  
In this study, a prescribed performance constraint controller is proposed for electro-hydraulic system to improve the output position accuracy of EHS. Firstly, the EHS model is constructed as a state-space strict-feedback model with uncertain nonlinearities. Secondly, according to the required boundary of the tracking position error, the technique of prescribed performance constraint is used to design the time-varying error boundary to not only regulate the large initial error in satisfactory time but also consider the control capability of EHS. Furthermore, the dynamic surface is adopted to replace the repeatedly calculated differentiations of the virtual control variables in backstepping design. The comparative results with the PI controller verify that the proposed controller has better performance than PI controller.  
**Acknowledgments:** The authors are grateful to the anonymous reviewers and the editor for their constructive comments. This research is supported by National Natural Science Foundation of China (51775089, 61305092, 51205045), the Fundamental Research Funds for the Central Universities, China (ZYGX2016J160), the Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems (GZKF-201515).  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the architecture of this paper; [PERSON] wrote the draft; [PERSON] and [PERSON] performed the simulation; [PERSON] and [PERSON] checked the literature and the typos of this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Notation**  
EHS  Electro-hydraulic system  
BLF  Barrier Lyapunov function  
PPC  Prescribed performance constraint  
\(K_{sp}\) (m/V)  Gain of the servo valve  
\(u\) (V)  control voltage of the servo valve  
\(C_{d}\) (-)  Discharge coefficient  
\(w\) (m)  Area gradient of the servo valve  
\(p_{s}\), \(p_{r}\) (Pa)  Supply pressure and return pressure  
\(p_{L}\) (Pa)  Cylinder load pressure  
Figure 10: The cylinder position responses \(x_{1}\) by prescribed performance constraint controller with hydraulic parametric uncertainties and the external load, the demand \(y_{d}=25(\sin(1.6\pi t)+\sin(0.8\pi t)+\sin(0.4\pi t))\) mm.
## 3 Simulation and Analysis
### Data Input
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Value of**} & \multirow{2}{*}{**Notes**} \\  & **Narrow-Shaped Island** & & **Island** \\ \hline \([X_{\text{min}},X_{\text{max}}]\) & \([-0.8718\) km, \(0.9056\) km] & \([-0.3424\) km, \(0.6201\) km] & The range of MGF along \(X\)-axis \\ \(X_{\text{max}}-X_{\text{min}}\) & \(1.7774\) km & \(0.9625\) km & The length of MGF along \(X\)-axis \\ \([Y_{\text{min}},Y_{\text{max}}]\) & \([-0.5638\) km, \(0.4370\) km] & \([-0.5162\) km, \(0.5892\) km] & The range of MGF along \(Y\)-axis \\ \(Y_{\text{max}}-Y_{\text{min}}\) & \(1.0008\) km & \(1.1054\) km & The length of MGF along \(Y\)-axis \\ \([Z_{\text{min}},Z_{\text{max}}]\) & \([0\) km, \(0.1998\) km] & \([0,0.1971\) km] & The range of MGF along \(Z\)-axis \\ \(Z_{\text{max}}-Z_{\text{min}}\) & \(0.1998\) km & \(0.1971\) km & The length of MGF along \(Z\)-axis \\ Planar area & \(1.1220\) km\({}^{2}\) & \(0.6582\) km\({}^{2}\) & The planar area of the MGF under the current tide \\ Number of facets & \(74\),\(157\) & \(35\),\(080\) & The number of triangle facets that \\ Number of vertices & \(37\),\(988\) & \(17\),\(542\) & The make up the MGF \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of MGFs for experiment. Figure 4: 3D models of MGFs for experiment. (**a**) The narrow-shaped island; (**b**) The triangular-shaped island. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Notes** \\ \hline \(R_{\text{res}}\) & \(0.5\) km & Radar range resolution \\ \(A_{\text{res}}\) & \(1^{\circ}\) & Radar azimuth angle resolution \\ \(E_{\text{res}}\) & \(1^{\circ}\) & Radar elevation angle resolution \\ \(\sigma_{L}\) & \(0.5\) km & Standard deviation of the aircraft launch platform planer \\  & & position error \\ \(\sigma_{SXY}\) & \(0.1\) km & Standard deviation of the radar activation point planer \\  & & position error \\ \(\sigma_{SZ}\) & \(0.01\) km for high-altitude aircraft & Standard deviation of the radar activation point elevation \\  & \(0\) km for sea-skimming aircraft & error \\ \(\sigma_{BXY}\) & \(0.5\) km & Standard deviation of the MGF planer position error \\ \(\sigma_{BZ}\) & \(0.005\) km & Standard deviation of the MGF elevation error \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters. Figure 5: Distribution of \(P_{ident}\) in marine areas under different radar positions for the narrow-shaped island. (**a**) When regarding \(S_{1}\) as radar position (sea-skimming); (**b**) When regarding \(S_{2}\) as radar position (high-altitude); (**c**) When regarding \(S_{3}\) as radar position (sea-skimming); (**d**) When regarding \(S_{4}\) as radar position (high-altitude); (**e**) When regarding \(S_{5}\) as radar position (sea-skimming); (**f**) When regarding \(S_{6}\) as radar position (high-altitude). Figure 6: _Cont_.