esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Result Analysis and Discussion
### LVRT Enhancement by the Proposed Control Scheme under an Asymmetrical Fault
#### 5.3.1 Single-Line to Ground Fault (1 LG)  
In this section, the effectiveness of the proposed control scheme is tested and compared with and without the proposed control (MPPT control) scheme under asymmetrical fault conditions. For this purpose, a single-line to ground (1 LG) fault (phase A to ground) is applied in line 2 in Figure 7, at t = 0.5 s for 150 ms. The simulation results of the grid-connected SPV system without the proposed control scheme are shown in Figure 12. Observations from the grid voltage (phase 'a') profile Va in Figure 12a indicate that the voltage amplitude drops to Va = 0.71 pu from its nominal value Va = 1.01 pu, corresponding to a voltage sag of 70%. The DCL voltage is unstable with 1050 V over-voltage, which seriously exceeds the DCL over-voltage limitation, as shown in Figure 12b. The SPV system injects an average amount of 80 kW (i.e., 80% of the SPV rated power capacity) into the grid, which means the MPPT control scheme does not effectively reduce the output active power during the fault, as is demonstrated in Figure 12c, so that the SPV inverter experiences an over-current. This case inverter can be disconnected from the grid, which is a negative sign of LVRT enhancement.  
The simulation results of the grid-connected SPV system with the proposed control scheme are shown in Figure 12. As shown in Figure 12a, DCL voltage maintains the maximum value of 580 V with few fluctuations. It is worth pointing out that, when the voltage sag occurs, the DC side input current may not be equal to the output current, leading to flucation of the DCL voltage. Figure 12b shows that, with the proposed control scheme, the SPV system delivers less active power than the MPPT control scheme during the fault. The reason is that with the proposed control scheme the SPV system supplies the grid with reactive current as required in the GC (Figure 12d). This will reduce the active power flowing through the inverter to the grid. Hence, the SPV inverter will not experience any over-current, which is a positive sign of LVRT enhancement. Furthermore, the SPV system is injecting the required amount of reactive power according to the GC into the grid for voltage recovery (Figure 12c), and after the fault is cleared, the system goes back to its normal mode within the time limit mentioned in the GC.  
Figure 11: _Cont._  
Figure 11: Simulation results of the MPPT control scheme under an asymmetrical fault (1 LG). (**a**) Grid voltage at PCC, (**b**) DC-link voltage (DCL), (**c**) active power (P), (**d**) reactive power (Q).  
Figure 12: _Cont._
## 3 Numerical Uncertainty Analysis
### Evaluation of the Results
#### 3.2.5 Comparison between LSST and LSPL  
In comparison with the LSST method, LSPL tends to identify a more sparse model and has a lower overfitting risk according to the analysis of sparsity. The model trained by LSPL generally has a lower divergence ratio, \(\eta_{div}\), than the one trained by LSST, which means LSPL delivers more usable models, especially with a large initial amplitude. In terms of natural frequency, LSPL is advantageous when the initial amplitude is large, which means that LSPL gives more accurate predictions with smaller distributions. The coefficient of determination \(R^{2}\) indicates that LSPL suffers from less noise interference in the training  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **LSSL** & **LSPL** \\ \hline Median & 0.9996 & 0.9997 \\ Upper quartile & 0.9998 & 0.9999 \\ Lower quartile & 0.9992 & 0.9994 \\ Maximum & 1 & 1 \\ Minimum & 0.9936 & 0.9962 \\ Number of points & 10,000 & 10,000 \\ Number of outliers & 474 & 476 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of \(R^{2}\) of LSST and LSPL with \(Ord=3\) and \(\epsilon_{N}=0.005\).  
Figure 10: Median and quantile of \(R^{2}\) with the two methods (**a**) LSST and (**b**) LSPL.  
data. Thus, it can be concluded that the LSPL method is a more suitable regression method for identifying the governing equation of a single-mass oscillator in the SINDy framework.
## 1 Introduction  
Long-term monitoring of highways built on soft clay settlement after the construction of road embankments plays a significant role in preventing the long-term damage and economic losses induced from long-term subsidence and ensuring the project quality of highways [1]. In China, a large number of highways are built on soft clay under complicated geotechnical conditions, such as roads crossing agricultural paddies, lake areas, or valleys. Due to the geotechnical characteristics of large natural moisture content, high compressibility, low intensity, and unconsolidated structure, soft clay is more prone to ground subsidence under the action of traffic load. The stability control of soft clay subgrade has become one of the major technical challenges in the field of subgrade engineering [2]. G1501 and Lungui Highway are two examples of highways built on soft clay located in Fuoshan, Guangdong Province, China. Both highways play an important role in local transportation. Therefore, long-term temporal-spatial deformation monitoring of those highways is imperative and significant to understand the dynamics of ground movements and prevent potential traffic problems.  
Traditional deformation monitoring methods, such as Leveling and GPS, have been proven to be highly precise and have been widely applied in highway deformation monitoring [3]. However, those methods have significant deficiencies of limited spatial coverage and expensive consumption of time and manpower, thus they are avoided in the application of deformation estimation for linear ribbon infrastructures including highways. Small baseline subset synthetic aperture radar interferometry (SBAS-InSAR) is an advanced InSAR technology proposed by [PERSON] and [PERSON] [4; 5], which can obtain time series deformation results through extracting and processing the high coherence points in multi-scene differential interferometric images through a least squares method [6; 7; 8]. It can suppress temporal-spatial decorrelation of the traditional differential InSAR (D-InSAR) technology [9; 10] and has been widely applied in ground movements detection and deformation investigation induced by surface subsidence [11; 12; 13], volcanic eruption [14], glacier drift [15; 16], earthquakes [17], and landslides [18; 19; 20].  
Modeling the functional relationship between the deformation phase components and unknown parameters is a vital step in SBAS processing. Earlier study revealed that an accurate and reliable deformation model can not only improve the accuracy of deformation estimation, but also control the residual phase within an entire phase range, which affects the estimation of deformation parameters [21; 22]. Considering this, in order to obtain more precise and reasonable deformation time series and better interpret the final time series subsidence, we conducted a comparative study based on two groups of analyses using traditional linear velocity model and seasonal model for both highways. SBAS-InSAR processing with 13 TerraSAR X images acquired from October 2014 to November 2015 is applied to obtain the unwrapped interferograms.
# A Multiview Representation Learning Framework for Large-Scale Urban Road Networks
## 4 Experiments with a Real-World Dataset
### Baselines and Downstream Traffic Tasks for Performance Evaluation  
According to the categories of related works, we introduce baselines that are retrieved from general-network representation learning methods and geospatial property-based representation learning methods, as follows:  
**DeepWalk**[14] embeds road segments in latent space via random walks based on only the topology structure of the road network.  
**Node2 vec**[6] is derived from DeepWalk. Node2 vec designs a biased random walk procedure to capture the diversity of connectivity patterns. In road network representation, compared to DeepWalk, this method can learn richer topological features.  
**Road2 vec**[5] learns road segment representations from transportation elements. This method converts transportation elements into several NLP terms and utilizes the word2 vec method to generate segment representations. In accuracy verification with traffic forecasting as the downstream task, this method provides great advantages over traditional methods.  
To demonstrate the effectiveness of the proposed method, we choose a downstream traffic task that is highly dependent on the quality of the input features to assess the performance of these models with different representation inputs that are generated by DeepWalk, Node2 Vec, Road2 Vec and our method. The estimated time of arrival (ETA) as a basic service in intelligent transportation systems is widely deployed in various ride-hailing mobile apps [27]. Recently, outstanding ETA models are route-based models achieving high-precision estimation results by aggregating information of each road segment that is passed by vehicles [27; 28; 29; 30]. Existing works note that the quality of road segment representation is crucial for accurate ETA estimation [3; 4]. Therefore, we select several ETA models (i.e., GRU [31], GRU + ANN, DeepTTE [30], and WDR [28]) combined with road network representations that are retrieved from baseline methods and our framework to verify the advantage of the proposed representation learning framework.  
In a performance evaluation, we use three metrics, i.e., the mean absolute percentage error (_MAPE_), root-mean-square error (_RMSE_), and mean average error (_MAE_):  
\[MAPE(\hat{y}_{i},y_{i})=\frac{100\gamma_{i}}{N_{T}}\sum_{i}^{N_{T}}\left|\frac {\hat{y}_{i}-y_{i}}{y_{i}}\right| \tag{9}\]  
\[RMSE(\hat{y}_{i},y_{i})=\sqrt{\frac{1}{N_{T}}\sum_{i=1}^{N_{T}}(\hat{y}_{i}-y_ {i})^{2}} \tag{10}\]  
Figure 5: The study area and the corresponding road network of (a) Shenzhen dataset and (b) Xi’an dataset.  
\[MAE(\hat{g}_{i},y_{i})=\frac{1}{N_{T}}\sum_{i=1}^{N_{T}}|\hat{g}_{i}-y_{i}| \tag{11}\]  
where \(N_{T}\) is the number of trips for which the ETA is needed, and \(\hat{y}_{i}\) and \(y_{i}\) denote the model output and ground truth, respectively.
# End-to-End Mispronunciation Detection and Diagnosis Using Transfer Learning
## 4 Experiments and Results
### Experimental Results
#### 4.4.3 Using Textual Modulation Gate and Contrastive Learning
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Models** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline GOP [53] & 35.42 & 52.88 & 42.42 \\ CTC-ATT [7] & 46.57 & 70.28 & 56.02 \\ CNN-RNN-CTC+VC & 56.04 & 56.12 & 56.08 \\ XLSR & 63.12 & 56.05 & 59.37 \\ XLSR(+TIMIT) & 62.86 & 58.20 & **60.44** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparison of MDD performances between our proposed methods with other conventional methods. Within the TextGate series, the difference in performance between using sigmoid and softmax activation functions is marginal. However, using tanh activation function obtains a much lower performance, and we also notice that the \"AudioGate\", performing control on the audio branch, reports poor performance. These results suggest that even incorporating the extra reference text, the fusion framework needs to be carefully designed. Based on our experimental results, we also found that better phoneme recognition model implementations cannot always report better results in the context of MDD, which is in line with the result reported in [24]. Figure 6 lists phone error rate (PER) and F1-score of different MDD models. As the PER decreases, the F1-score trend is hard to conclude. The failure is due to the mismatch of learning objectives between MDD and phoneme recognition. Therefore, we further explored the benefit of introducing contrastive learning. Moreover, we tried to utilize a more powerful acoustic model (XLSR) for further improvement. We integrated the proposed _TextGate_ into _XLSR_ model in which the contrastive loss was used. _TextGateXLSRContrast_ obtains a performance gain of 1.5% F1-score compared to the model _TextGateContrast_ and achieves the best performance with an F1-score of 61.75% (see the bottom two rows of Table 4), suggesting the effectiveness of our models by introducing textual modulation gates with a contrastive loss. Moreover, _TextGateXLSRContrast_ reports the lowest false accept rate, which corresponds to our discussion. Given the prior text, the model learns more discriminative features about the reference and annotation phonemes which are hard to distinguish, naturally making the prior transcripts more informative. Figure 5: Learned correlation maps of BaselineConcatenate (**upper**) and TextGate (**lower**). Figure 6: Performances of MDD models with different textual modulation gates in terms of F1-score and phone error rate (PER).
## Materials and Methods
### Adsorption Experiments  
For the biochar Cd\({}^{2+}\) adsorption experiments, a fixed amount of biochar was accurately weighed and placed in a 50-mL centrifuge tube. Cd\({}^{2+}\) (25 mL of a 100 mg/L solution in the form of Cd (NO\({}_{3}\))\({}_{2}\)) was added, and 1 M HCl or NaOH was used to adjust the pH. The centrifuge tubes were capped and then shaken (200 r/min) at 25 \(\pm\) 1 \({}^{\circ}\)C in the dark for a fixed period of time. Next, the tubes were centrifuged for 15 min at 4000 rpm. The supernatant was collected and passed through a 0.45-um Nylon filtration membrane. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to determine the Cd\({}^{2+}\) concentration in the supernatant. The experiments were carried out in triplicate, and blank controls were also used.  
The influencing factors included (1) the effect of the adsorbent loading (various amounts of biochar (25, 50, 100, 150, or 200 mg) were added at pH 5.0); (2) the Cd\({}^{2+}\) adsorption kinetics experiments (50 mg of biochar, pH 5.0, and shaking times of 1/6, 1/3, 0.5, 1, 2, 3, 6, 12, 18, and 24 h); and (3) the effect of pH on the Cd\({}^{2+}\) removal (50 mg of biochar was weighed, and the pH was adjusted to the desired values (pH values of 4, 5, 6, 7, and 8) with 1 M HNO\({}_{3}\) or 1 M NaOH solution). The solutions were agitated on a shaker for 24 h.  
The Cd\({}^{2+}\) removal level and the adsorbed amount were calculated as follows:  
\[\text{Cd}^{2+}\text{ removal (\%)}=(\rho_{0}-\rho_{\text{e}})/\rho_{0}\times 1 00, \tag{1}\]  
\[Q=V(\rho_{0}-\rho_{\text{e}})/m. \tag{2}\]  
In Equations (1) and (2), \(\rho_{0}\) and \(\rho_{\text{e}}\) are the Cd\({}^{2+}\) concentration (mg\(\cdot\)L\({}^{-1}\)) before and after adsorption, \(Q\) is the amount of Cd\({}^{2+}\) adsorbed (mg\(\cdot\)g\({}^{-1}\)), \(V\) is the volume of the solution (L), and \(m\) is the amount of biochar added (g).  
Kinetics equations were obtained from Reference [12]. The pseudo-first-order kinetics Equation (3) and the pseudo-second-order kinetics Equation (4) were employed to describe the kinetics of Cd\({}^{2+}\) adsorption by biochar, and are shown below.  
\[\ln\left(q_{\text{e}}-q_{\text{t}}\right)=\ln q_{\text{e}}-k_{1}t,\text{ i.e., }q_{\text{t}}=q_{\text{e}}\left(1-\text{e}^{k1x}\right), \tag{3}\]  
\[t/q_{\text{t}}=(1/k_{2}q_{\text{e}}^{2})+(t/q_{\text{e}}), \tag{4}\]  
where \(q_{\text{e}}\) is the amount of heavy metal adsorbed at equilibrium (mg\(\cdot\)g\({}^{-1}\)), \(q_{t}\) is the amount of heavy metal adsorbed by biochar at time \(t\) (mg\(\cdot\)g\({}^{-1}\)), \(t\) is the reaction time (min), \(k_{1}\) is the reaction rate constant (min\({}^{-1}\)) of pseudo first-order kinetics, and \(k_{2}\) is the reaction rate constant (g\(\cdot\)mg\({}^{-1}\cdot\)min\({}^{-1}\)) of pseudo second-order kinetics.
## 5 Challenges and Future Research Directions
### Existing Challenges
_Intelligent Energy Management_: UAVs are limited by their size, weight, and power, which influence flight performance, task execution capabilities, and application scenarios. Such issues are similar to resource management but equally important. In energy management, the objective of joint optimization is often to minimize the energy consumption ratio [120]. To address challenges of limited energy, more efficient energy harvesting methods need to be proposed, and energy-saving management strategies should be explored to optimize UAV deployment, power distribution, flight path planning, and other issues. As future network architectures, SAGINs not only impose more requirements on scalability, stability, security, sustainability, and QoS but also need to address the challenges posed by emerging fields such as edge computing, the IoT, blockchain, quantum communication, and cloud computing, while collaborating closely with these domains to develop integrated solutions.
## 3 Experimental Setups
### Anechoic Chamber: 2D System  
The experimental configuration of the 2D array of Helmholtz resonators is characterized under realistic conditions in an anechoic chamber, as shown in Figure 4. The whole structure is surrounded by acoustically rigid wood panels at all ends except the side where the incident wave is impinging on the structure (see Figure 4). Both the Helmholtz resonators and the rigid panels are made of medium density fiber, a material that has an acoustic impedance much greater than the one of air and hence, can be considered as acoustically rigid. The procedure for the experimental characterization is based on a modified version of the Transfer Function Method (TFM), as described in references [46; 47]. The loudspeaker (Genelec 8030A) is controlled by the same sound card mentioned previously and it is placed sufficiently far from the structure to ensure quasi-plane wave propagation at the frequency range of interest (\(D=3\) m). Two B&K 4189 microphones with their respective preamplifiers (B&K 2671) are placed very close to the structure (\(d=1.5\) cm), separated by a distance \(d=2.5\) cm.
# An Assessment of the Structural Performance of
## 3 Test Results and Discussion
### Test Results under Cyclic Load
For localized corrosion in the constant shear area, the average corrosion rate increased in the order of S-2-2 > S-3-2 > S-1-2. Although specimen S-1-2 was corroded at a rate of 7.9%, the ultimate strength was equivalent to the reference specimen. However, specimens S-2-2 and S-3-2 corroded at rates of 14.8% and 12.0%, causing an ultimate strength reduction of 5.1% and 4.9%, respectively. Figure 13 illustrates the crack patterns in the concrete cover and the occurrence of cracks with corroded rebars due to the imposed cyclic loads on the beam specimens. Except for specimen A-3-2, approximately 100 mm crack intervals were observed from the center of all specimens. Similar to the monotonic loading case (see Figure 9), the bottom face clearly shows longitudinal cracks throughout and adjacent to the corrosion areas (see specimens A-1-2, A-2-2, and A-3-2 for rebar corrosion of the entire area; B-3-2 for localized corrosion in the constant shear area; and S-2-2 and S-3-2 for localized corrosion in the constant shear area). Furthermore, specimens A-3-2 and B-3-2 clearly show that the strength reductions were strongly influenced by a couple of main longitudinal cracks at the bottom side. Specim A-3-2 particularly exhibits two main flexural cracks toward the two load points, which might be attributed to the occurrence of very high local corrosion, along with the longitudinal rebars B and C aligning with the load points. No diagonal shear failure occurred in any of the specimens. Figure 11: Corrosion weight loss (%) versus yield and ultimate loads under cyclic loading condition. Figure 12: Corrosion weight loss of rebars measured at 100 mm intervals along the longitudinal direction under the cyclic loading condition.
# RMFNet: Redetection Multimodal Fusion Network for RGBT Tracking
## 2 Related Work
### Multimodal Fusion Network  
A suitable fusion strategy can take full advantage of multimodal information and reinforce the robustness and accuracy of the tracker. Existing multimodal fusion methods can be divided into three fusion methods, pixel level, feature level, and decision level. In early fusion approaches, pixel-level fusion was used by directly connecting the multiple modality data channels and feeding the fused data into a convolutional neural network (CNN) for feature extraction, as shown in Figure 1a. However, with the outstanding performance of feature aggregation in multiple domains, [PERSON] et al. [11] designed a feature fusion network that directly integrates RGB and thermal infrared depth features, as shown in Figure 1b, and fused the two modality features using element-wise addition. [PERSON] et al. [19] designed a fusion network that adds the output classification results, as shown in Figure 1c. [PERSON] et al. [13], as shown in Figure 1d, distinguish the multimodal information into shared and exclusive information extracted by different adapters, proposing a multi-adapter fusion network. [PERSON] et al. [20], as shown in Figure 1e, adaptively fuses modality features with hierarchical features in the form of a recursive fusion chain to achieve a double height of speed and accuracy. However, the above algorithms only use the fused features in the subsequent processing stage, without considering the unreliable fused features caused by noise or failure of one modality. As shown in Figure 1f, it is the RMFNet network proposed in this paper, the parallel fusion branches, can fully exploit the shared information of two modalities, and the three-branch fusion network gives full play to the advantages of commonality and characteristics of multimodal features.
## 4 SDN-NFV Based QoS Management  
In 5G and beyond, SDN-NFV-based transport networks can instantly change the behaviour of the network on a software basis using the SDN controller. This flexibility enables faster adaptation to different QoS requirements in the transport network. In order for the network to be able to quickly adapt to changing requirements and be configured via software, the core network, the radio access network and the NFV-MANO units need to work in an integrated manner.  
In this study, an SDN algorithm is proposed that enables dynamic scaling of network resources to ensure continuity of quality of service to users as traffic density increases in a network that is NFV-MANO-integrated. Open-source tools have been used to test the proposed algorithm.