esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The WTA model is able to improve the prediction accuracy of unstable samples to 99.26%. In order to implement the proposed machine learning model into the online TSA, the higher the prediction accuracy for the unstable situations, the better. Any missed instability situation may lead to loss of synchronization, cascading failures, or even large-scale power outages. Making fast and accurate online transient stability predictions is not enough in transient operation of a power system appropriate online emergency control measures will be of interest in future research.  
Another concern in practically implementing the proposed machine learning model into large-scale power systems is that the feature reduction should be further studied, because too many input features mean a large amount of measurement investment and a huge computational burden in real-time.  
In addition, the main focus of this study is to perform machine learning-based transient stability predictions that only deal with the rotor angle stability of power systems. If appropriate data are available, the transient stability prediction modeling method based on machine learning can also be extended to small-disturbance stability, voltage stability, and frequency stability issues.
## 2 Problem Description  
Consider an MISO-OE system  
\[y(t)=\sum_{i=1}^{r}z^{-d_{i}}\frac{B_{i}(z)}{A_{i}(z)}u_{i}(t)+v(t), \tag{1}\]  
where \(u_{i}(t)\) and \(d_{i}\) are the input and time-delay of the \(i\)th input channel, \(y(t)\) is the output, \(v(t)\) is a white noise vector with zero mean and variance \(\sigma^{2}\), and \(A_{i}(z)\) and \(B_{i}(z)\) are the time-invariant polynomials with constant coefficients in the unit backward shift operator \(z^{-1}\) [i.e., \(u(t)z^{-1}=u(t-1)\)],  
\[A_{i}(z) :=1+a_{11}z^{-1}+a_{22}z^{-2}+\cdots+a_{in_{ai}}z^{-n_{ai}},\] \[B_{i}(z) :=b_{i1}z^{-1}+b_{i2}z^{-2}+\cdots+b_{in_{i2}}z^{-n_{ai}}.\]Assume that the orders \(n_{ai}\) and \(n_{bi}\) are known, \(y(t)=0\), \(u_{i}(t)=0\) and \(v(t)=0\) for \(t<0\).  
To form the identification model, an intermediate variable is introduced [24],  
\[x_{i}(t):=z^{-d_{i}}\frac{B_{i}(z)}{A_{i}(z)}u_{i}(t)=[1-A_{i}(z)]x_{i}(t)+z^{-d _{i}}B_{i}(z)u_{i}(t).\]  
Since the time-delay \(d_{i}\) of each input channel is unknown, an overparameterization method is applied by setting a maximum input regression length \(l\) which satisfies \(l\geq\max(d_{i}+n_{bi})\)[20]. Then, \(x_{i}(t)\) can be written in an impact form  
\[x_{i}(t)=\mathbf{\varphi}_{i}^{T}(t)\mathbf{\theta}_{i},\]  
where  
\[\mathbf{\varphi}_{i}(t) :=[-x_{i}(t-1),-x_{i}(t-2),\cdots,-x_{i}(t-n_{ai}),u_{i}(t-1), \cdots,u_{i}(t-d_{i}),\] \[\quad\quad u_{i}(t-d_{i}-1),\cdots,u_{i}(t-d_{i}-n_{bi}),\cdots,u_ {i}(t-l)]^{T}\in\mathbb{R}^{n_{ai}+l}, \tag{2}\] \[\mathbf{\theta}_{i} :=[a_{i1},a_{i2},\cdots,a_{in_{ai}},\underbrace{0,\cdots,0}_{d_{i }},b_{i1},b_{i2},\cdots,b_{in_{bi}},\underbrace{0,\cdots,0}_{l-d_{i}-n_{bi}}]^ {T}\in\mathbb{R}^{n_{ai}+l}. \tag{3}\]  
The identification model of the system in Equation (1) can be formed as  
\[y(t)=\sum_{i=1}^{r}x_{i}(t)+v(t)=\mathbf{\varphi}^{T}(t)\mathbf{\theta}+v(t), \tag{4}\]  
where  
\[\mathbf{\varphi}(t) :=[\mathbf{\varphi}_{1}^{T}(t),\mathbf{\varphi}_{2}^{T}(t),\cdots,\mathbf{ \varphi}_{r}^{T}(t)]^{T}\in\mathbb{R}^{n}, \tag{5}\] \[\mathbf{\theta} :=[\mathbf{\theta}_{1}^{T},\mathbf{\theta}_{2}^{T},\cdots,\mathbf{\theta}_{r} ^{T}]^{T}\in\mathbb{R}^{n},\] (6) \[n :=\sum_{i=1}^{r}(n_{ai}+l). \tag{7}\]  
It can be seen from Equations (3) and (6) that the parameter vector \(\mathbf{\theta}\) contains many zeros, therefore \(\mathbf{\theta}\) is a sparse vector and the system in Equation (4) is a sparse system. The sparsity level can be measured by \(K:=\sum\limits_{i=1}^{r}(n_{ai}+n_{bi})\), which denotes the number of non-zero elements in \(\mathbf{\theta}\). The identification objective is to estimate the unknown parameters \(a_{ij}\), \(b_{ij}\) as well as the time-delays \(d_{i}\) from observations.
## 3 Results and Discussion  
In this study, a PV-SPP was added to the two area multi source interconnected power system. The LFC of the system was carried out to compare a PID, a GA-PID, a FLC, and the new proposed GA-FLC results. The results obtained from these comparisons are given below. When the control process is performed, a model that gives a power increase value of 0.001 \(P_{u}\) in area 2 for the demanded power at the 5 th s. and a power increase value of 0.01 \(P_{u}\) in area 1 for the demanded power at the 140 th s., were formed. In addition, the fluctuations of the generated power from the solar energy plants inserted in both areas depending on the climatic conditions, starting from the 20 th s., were modelled. In Figure 15, these fluctuations of solar energy plants are depicted.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Fuzzy Logic Rules for GA-FLC (5)**} \\ \hline \multirow{2}{*}{\(ACE\)} & \multicolumn{5}{c}{\(\Delta ACE\)**(k)**} \\ \cline{2-7}  & \(N_{L}\) & \(N_{S}\) & \(\mathbf{Z}\) & \(N_{S}\) & \(N_{L}\) \\ \hline \(N_{L}\) & \(N_{L}\) & \(N_{S}\) & \(N_{S}\) & \(N_{S}\) & \(Z\) \\ \hline \(N_{S}\) & \(N_{S}\) & \(N_{S}\) & \(N_{S}\) & \(Z\) & \(P_{S}\) \\ \hline \(Z\) & \(N_{S}\) & \(N_{S}\) & \(Z\) & \(P_{S}\) & \(P_{S}\) \\ \hline \(P_{S}\) & \(N_{S}\) & \(Z\) & \(P_{S}\) & \(P_{S}\) & \(P_{S}\) \\ \hline \(P_{L}\) & \(Z\) & \(P_{S}\) & \(P_{S}\) & \(P_{S}\) & \(P_{L}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The five fuzzy logic rules of the new GA-FLC.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Controller** & **Controller Parameters** & **Min.** & **Mean** & **Median** & **Max.** & **Standard Deviations** \\ \hline \multirow{3}{*}{GA-PID} & \(K_{p}\) & 6.4295 & 13.6868 & 15.4255 & 15.9988 & 2.8714 \\  & \(K_{i}\) & 3.8590 & 7.5896 & 7.8589 & 7.9999 & 0.7833 \\  & \(K_{d}\) & 0.4094 & 2.8252 & 2.4297 & 6.4565 & 1.2078 \\ \hline \multirow{3}{*}{GA-FLC} & \(W_{ref-DE}\) & 0.1369 & 0.4931 & 0.4588 & 0.8591 & 0.1391 \\  & \(W_{ref-E}\) & 0.0086 & 0.1761 & 0.1262 & 0.9371 & 0.1986 \\ \cline{1-1}  & \(W_{ref-DU}\) & 0.1000 & 1.2374 & 0.7393 & 5.3660 & 1.4304 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimization results for over 30 independent runs of GA-PID and GA-FLC.
# Game Analysis of Different Transportation Modes in a Corridor Considering Carbon Emission Costs
## 4 Model Application
### Equilibrium Solution Analysis  
The analysis of the calculation results presented in Table 9 reveals that the game equilibrium freight rates for the conventional railway, high-speed rail (HSR), air transport, and highways are set at CNY235, 1965, 2591, and 1395, respectively. These rates ensure the highest utility for the consignor. Correspondingly, the equilibrium distribution of market shares among these four transportation modes is established at 4%, 48%, 42%, and 6%. To validate the equilibrium solution, the range of fluctuation for the initial pricing strategy is widened to 50% of the current actual rates, as detailed in Table 10.  
In Figure 5, after 63 iterations, the equilibrium solution was achieved, yielding a calculation result identical to the one before the adjustment of the initial tariff strategy. This outcome signifies that no participant can gain additional benefits by altering their tariff strategy, confirming that the game has indeed reached equilibrium. Compared to current pricing, the attributes of high speed, punctuality, stability, and low emissions of HSR offer it a significantly strong competitive edge. The study suggests reducing the freight rate from CNY2000/ton to CNY1965/ton to capture more traffic volume and generate increased revenue. Historically, air transport accounted for over 80% of freight volume. However, faced with HSR's robust competition, airlines may minimize freight source loss by lowering prices. Meanwhile, conventional railways, though less competitive in fast freight services, can leverage their high-volume capacity by reducing prices during peak periods (such as around the Double Eleven Global Carnival) or for long distances. Despite its lower share rate, the highway's flexibility remains unmatched by other modes. The findings indicate that highways could boost revenue from the current rate of CNY1200/ton to CNY1395/ton.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Transportation Modes** & **Initial Tariff Strategy Set (CNY/ton)** \\ \hline High-speed railway & [1000,1200,1400,1600,1800,2000,2200, 2400,2600,2800,3000] \\ Expressway & [600,720,840,960,1080,1200,1320,1440,1560,1680,1800] \\ Transport aviation & [1400,1680,1960,2240,2520,2800,3080,3360,3640,3920,4200] \\ General railway & [125,150,175,200,225,250,275,300,325,350,375] \\ \hline \hline \end{tabular}
\end{table}
Table 10: Adjusted initial tariff strategy set.  
Figure 4: Convergence chart.  
Transport mode operators can use these game equilibrium results as a theoretical basis to efficiently allocate resources, engage in healthy competition, better fulfill consignors' needs, increase their income, and contribute to economic growth.
## 3 Results and Discussion
### General Characteristics
#### 3.1.1 The Degree of Compression and Flow Rate  
One of the typical characteristics of heat pump compressors is the dependence of the degree of compression (the ratio of the refrigerant pressure upstream and downstream of the compressor) on the mass flow rate of the refrigerant. Figure 6 shows the operating points obtained during the experiment and Figure 7 shows the operating points of different compressors as declared by the equipment manufacturers.  
As mentioned above, Figure 6 shows, in circles, the points of dependence of the refrigerant compression ratio on the flow rate for heat pump compressors from different manufacturers, and the dotted line shows the regression equation for these compressor-specific points, which is generated by a second-degree polynomial.  
It can be seen that the trend of the compression ratio reported by the compressor manufacturers (Figure 7) is in line with the values obtained during the experiment (Figure 6).  
Figure 6: The relationship between the compressor pressure ratio \(\mathrm{P_{1}}/\mathrm{P_{5}}\) and the flow rate (experimental data).  
Figure 7: The relationship between the compressor pressure ratio and the flow rate (manufacturers’ data).  
Observably, as the refrigerant flow rate increases, the degree of compression generated decreases. A similar trend was observed during the experiment.
# Smart-Median: A New Real-Time Algorithm for Smoothing Singing Pitch Contours
###### Abstract  
Pitch detection is usually one of the fundamental steps in audio signal processing. However, it is common for pitch detectors to estimate a portion of the fundamental frequencies incorrectly, especially in real-time environments and when applied to singing. Therefore, the estimated pitch contour usually has errors. To remove these errors, a contour smoother algorithm should be employed. However, because none of the current contour-smoother algorithms has been explicitly designed to be applied to contours generated from singing, they are often unsuitable for this purpose. Therefore, this article aims to introduce a new smoother algorithm that rectifies this. The proposed smoother algorithm is compared with 15 other smoother algorithms over approximately 2700 pitch contours. Four metrics were used for the comparison. According to all the metrics, the proposed algorithm could smooth the contours more accurately than other algorithms. A distinct conclusion is that smoother algorithms should be designed according to the contour type and the result's final applications.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Family** & **Prec.** & **Rec.** & **F1** & **Family** & **Prec.** & **Rec.** & **F1** \\ \hline bamital & 98.95\% & 96.29\% & 97.60\% & orchard & 94.39\% & 62.04\% & 74.47\% \\ banjori & 96.41\% & 99.75\% & 98.05\% & padcrypt & 87.22\% & 78.24\% & 82.40\% \\ bazarloader & 100.00\% & 100.00\% & 100.00\% & phorpiex & 85.13\% & 31.99\% & 46.31\% \\ chinad & 81.41\% & 84.78\% & 83.03\% & pitou & 89.46\% & 80.30\% & 84.61\% \\ conficker & 41.25\% & 22.89\% & 29.39\% & proslifean & 91.65\% & 7.81\% & 14.37\% \\ corebot & 94.92\% & 84.38\% & 89.23\% & pseudodem & 59.10\% & 30.52\% & 39.91\% \\ cryptolocker & 68.29\% & 18.66\% & 29.11\% & pushdo & 82.52\% & 90.42\% & 86.26\% \\ darkshell & 97.73\% & 98.01\% & 97.85\% & pushdotid & 76.74\% & 77.74\% & 77.17\% \\ darkwatchman & 99.57\% & 100.00\% & 99.79\% & pykspa2s & 81.52\% & 83.22\% & 82.35\% \\ dircrypt & 0.00\% & 0.00\% & 0.00\% & qadars & 94.25\% & 88.29\% & 91.15\% \\ dyre & 99.89\% & 100.00\% & 99.94\% & qakbot & 63.71\% & 63.25\% & 63.47\% \\ ebury & 94.24\% & 81.40\% & 87.19\% & qsnatch & 96.85\% & 98.02\% & 97.43\% \\ dyre & 61.90\% & 87.81\% & 72.47\% & ramdo & 97.76\% & 99.91\% & 98.82\% \\ flubot & 68.14\% & 81.04\% & 74.01\% & ramnit & 37.28\% & 24.96\% & 29.87\% \\ fobber & 0.00\% & 0.00\% & 0.00\% & ranbyus & 63.88\% & 58.33\% & 60.91\% \\ gamoover & 98.49\% & 99.49\% & 98.99\% & rovnix & 44.44\% & 0.12\% & 0.23\% \\ gameover\_p2p & 86.51\% & 15.51\% & 26.17\% & shifu & 56.35\% & 76.53\% & 64.76\% \\ gozi & 77.20\% & 37.79\% & 50.42\% & simda & 92.11\% & 98.86\% & 95.36\% \\ grandoreiro & 95.42\% & 89.93\% & 92.47\% & sphinx & 0.00\% & 0.00\% \\ locky & 73.41\% & 33.14\% & 45.59\% & suppobox & 84.70\% & 95.31\% & 89.66\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: CNN on classic training method precision, recall, and F1 score of each of the 61 DGA family.
# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
## 4 Experiments and Results
### Experiments of State-of-the-Art Algorithms on WAID  
#### 4.2.1 Experimental Setups  
We conducted performance comparisons by training and testing the current state-of-the-art object detection algorithms, including Faster R-CNN, SSD, YOLOv5, YOLOv7, and YOLOv8 on the WAID dataset. Additionally, we evaluated the effectiveness of the attention mechanism in small object detection tasks using the SE-YOLO model with an embedded SEBlock module. Among the total 14,375 images in the dataset, we utilized 11,118 images for training, 2054 images for validation, and the remaining 1203 images for testing the six object classes. Random selection was used for distributing images among the training, validation, and test sets, ensuring that various species categories were represented across multiple scenes. During the experiments, we set the input image size to 640 \(\times\) 640 pixels for the models. The initial learning rate was set to 0.01, the learning rate momentum to 0.937, and the weight decay to 0.0005. Additionally, we configured the batch size for model training to be 32, the IoU threshold for evaluation to be 0.5, and conducted training for a total of 100 epochs. Furthermore, to investigate how small of a target SE-YOLO can actually recognize and its performance, we implemented a case study. We first set data with less than 50 pixels in horizontal as minimal targets, as used in [80], then explored exactly how small targets can be recognized accurately by SE-YOLO and compared the performance of SE-YOLO with YOLOv7 on these data.
# Potassium Recovery from Potassium Solution and Seawater Using Different Adsorbents
## 2 Materials and Methods  
### Preparation of Adsorbents  
In this study, three types of adsorbents were used--natural zeolite (i.e., clinoptiolite), ammonium acetate-treated natural zeolite, and manganese nodule. Clinoptiolite (1-3 mm diameter), a natural zeolite, was purchased from the Handu Trade (Duksan, South Korea), and the major components of the natural zeolite include SiO\({}_{2}\) (69.8%), Al\({}_{2}\)O\({}_{3}\) (13.2%), CaO (2.31%), Na\({}_{2}\)O (2.18%), and K\({}_{2}\)O (3.10%). Prior to sorption experiments, the natural zeolite was ground (<0.5 mm diameter), washed with deionized water, and dried at 105 \({}^{\circ}\)C for 24 h. To prepare the ammonium acetate-treated zeolite, the natural zeolite (40 g) was placed and stirred in 1 M ammonium acetate (NH\({}_{4}\)C\({}_{2}\)H\({}_{3}\)O\({}_{2}\); Reagents grade, Duksan, South Korea) solution for 24 h [10]. The treated natural zeolite was rinsed three times with deionized water. The liquid phase and solid phase were separated by filtration and the solid phase was dried at 105 \({}^{\circ}\)C for 24 h before use. Ocean manganese nodules, sampled from the Clarion-Clipperton Zone (i.e., CC zone) of the Pacific Ocean, were provided by the Korea Institute of Geoscience and Mineral Resources. The manganese nodules consisted of 8.85% Fe, 0.73% Cu, 0.97% Ni, 0.18% Co, and 22.35% Mn [15]. Prior to sorption experiments, the manganese nodule was ground to <2 mm. The specific surface areas and porosities of the natural zeolite, ammonium acetate-treated zeolite, and manganese nodule were analyzed using the Brunauer-Emmett-Teller (BET) surface area and porosimetry analyzer (ASAP2420, Micromerics Instrument Corporation, Norcross, GA, USA). The SEM images of the three adsorbents used can be found in the supplementary material.
## 4 Experiments
### Fundamental Matrix Estimation  
We follow D-RANSAC [20] and use the eight-point algorithm on four preferences sampling distributions. As shown in Table 1, SA-Net outperforms existing traditional and deep learning methods in most scenes, and the average F1 score of our method surpasses the second-best method by 2.19 points. In some challenging scenes, such as Buckingham Palace and the Palace of Westminster, our method significantly improves accuracy compared to other methods. Furthermore, we show the cumulative distribution functions (CDFs) of epipolar errors. The epipolar errors are calculated from the 6K test image pairs in PhotoTourism. As Figure 9 shown, the curve of our method is the highest in the figure, and our advantage over other methods is more pronounced at lower epipolar errors. This demonstrates that our method excels at selecting the relatively more accurate model from a set of high-precision models.
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 2 Materials and Methods
### Change Detection  
In this study, we implemented the post-classification change detection exercise: a method mostly used to quantify LULC changes. It involves two images from different years of the same location, where changes are computed on a pixel-by-pixel basis to determine similarities and differences in pixel values [14]. We performed this exercise using the MOLUSCE plugin tool in the QGIS Desktop v.2.18.10 software. The MOLUSCE model conducts an analysis of LULC changes between the two time periods, examines the LULC transition potential and simulates future urban growth. The LULC change between two time periods can be calculated using the following equation:  
\[\text{LULC change}=t_{2}-t_{1} \tag{5}\]  
where \(t_{2}\) is the final period, whilst \(t_{1}\) stands for the initial period [42]. The changes were calculated between 1988 and 1993; 1993 and 1998; 1998 and 2003; 2003 and 2008; 2008 and 2013; 2013 and 2019 for all the classes used in this study.
## 1 Introduction  
Current performance shortcomings in the transport infrastructure formations induced by extreme climatic events cause excessive maintenance requirements with increased costs and disruptions to commuters and loss of productivity in the freight services. Most transport infrastructure formations are essentially compacted, hence remain unsaturated under capillarity stresses or matric suction [1], which influences their hydro-geomechanical response, e.g., water retention behaviour and unsaturated permeability [2], microstructure and stress-strain behaviour [3]. Their post-compaction behaviour in cycles of wetting and drying caused by rainfall and drought periods, respectively, is also strongly influenced by the initial compacted state [4]. Past studies showed that the behaviour of compacted soil is governed by the initial compacted state ([5, 6, 7]). This is particularly important in the areas where local conditions can be unfavourable due to the existence of expansive soils. In these locations, the assessment of the in-service performance of the subgrade soils under notable variations in subsurface moisture caused by climate change; i.e., intense periods of rainfall and droughts (e.g., wetting and drying) becomes critical in design. For instance, [8] reported that the volumetric changes associated with shrink and swell processes in expansive soil subgrades upon seasonal wetting and drying causes the onset of cracking in pavements, which is then further exacerbated as moisture ingress increases. The rising cost of pavement rehabilitation continues to drive engineers to investigate more economically solutions to improve the performance of high plasticity clays when exposed to seasonal changes in moisture.  
In this paper, the role of soil stabilisation in halting volumetric deformation of expansive soils is examined and associated reduction in shear strength derived from the wetting processes (e.g., rainfall periods).
## 4 Results and Discussion
### A Case Study for the Mw6.3 Juzhaigou Earthquake  
Two stations, SCJZ and GSZQ, close to the epicenter were selected to carry out the experiments, and the corresponding results are shown in Figure 12; Figure 13. The final coseismic displacements for these two stations were calculated using 15 days of GPS data before and 4 days after the event from [37]. For station SCJZ, although the data were interrupted after 48,032 s, the seismic waveforms were well recorded. The largest amplitudes of about 4 cm occurred in the north component for all the types of solutions. Compared with the north component for GPS, the seismic signal in the east displacement was indistinguishable from the noise, which might mislead preseismic analysis. In contrast, for BDS,  
Figure 11: 12 h displacements (cm) with respect to daily solutions for the up component at station GSWX on DOY 219, 2018 and averaged power spectral density (PSD) (in dB) on a frequency band from 2 to 100,000 s over all 12 stations. (**a**) shows the solutions without sidereal filtering; (**b**) shows the solutions for which all satellites except for the GEO satellites are sidereally filtered; (**c**) shows the sidereally filtered solutions; (**d**) shows the sidereally filtered solutions excluding the GEO satellites from data processing. The yellow dashed-dotted horizontal lines denote the reference values.  
the signal-to-noise ratio improved with the smaller fluctuations ahead of the event. The GRC solution without sidereal filtering was biased by about -1.30 cm in the east component, which was reduced by 89.2% to -0.14 cm after filtering. For station GSZQ, the largest fluctuation happened in the east component with a peak value of about -2.3 cm. The seismic waveforms embedded in the vertical direction were seemingly overwhelmed by high-level noise. Affected by the remaining systematic errors, some fluctuations of 2 to 3 cm spanning over several minutes could still be found in the east component for the BDS solutions. Because of the data interruption at station SCJZ, 2 h displacements of the sidereally filtered GRC solution before and after the arrival time of seismic waves were used to estimate the static offsets of station GSZQ, which were 1.9 mm and 5.4 mm with respect to the references \(0.4\pm 1.2\) mm and \(3.6\pm 0.8\) mm, in the east and north components, respectively. Overall, the superiority of the GRC solution with sidereal filtering over a single-system or unfiltered solution in alienating low-frequency errors on tens of seconds to minutes is clearly demonstrated.  
Figure 12: Displacements at station SCJZ during the Jiuzhaigou earthquake. The lines are shifted vertically to avoid overlap. The yellow dashed horizontal lines denote the mean of displacements. The black, blue, and purple lines refer to the sidereally filtered G, C, and GRC solutions, respectively, while the green lines refer to the GRC solution without sidereal filtering.