esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Motivated by [PERSON]'s concept, model-assisted active disturbance rejection control (MADRC) is proposed in the work. An auxiliary model of friction torque is established, by which partial friction information is introduced into ESO. Moreover, to decrease ESO parameters, the order of ESO is reduced and a model-assisted reduced-order extended state observer (MRESO) is designed. In addition, the nonlinear state error feedback rate (NLSEF) as a component of the ADRC algorithm has higher response efficiency than the linear control rate. However, when the feedback error suddenly increases due to the influence of friction, the advantage of nonlinear control over linear control is less prominent. To improve the adaptability and rapidity of the control rate, some research on real-time neural network control has been carried out. In [25], the adaptive radial basis function (RBF) was adopted in ADRC to reduce the influence of unknown disturbances. In [26], a new dual-channel composite controller scheme was developed, in which RBF neural network approximation was utilized to tackle system uncertainties and ADRC was designed to real-time estimate and compensate disturbances. In [27], RBF neural network was introduced to supervise proportional differential (PD) control. RBF as a real-time neural network possesses fine generalization ability and simple structure and has been applied in many nonlinear control systems. In [28], the controller utilized ADRC to the robust control of a multi-rotor vehicle and a spatio-temporal RBF neural network to estimate fusion parameters containing actuator faults and model uncertainties. Motivated by the above analysis, a supervised radial basis function (SRBF) is employed to supervise the feedback error. When the feedback error suddenly increases, the SRBF quickly adjusts the gain matrix and raises the control quantity, by which the response speed of the NLSEF is enhanced. To sum up, the SRBF-MADRC strategy is established. The novel and critical contributions of this work are summarized as follows:  
1. By introducing an auxiliary model of friction, the MADRC has a more accurate compensation of disturbances than the general ADRC. 2. The MRESO has lower order and fewer parameters than standard ESO, which is more conducive to improving the efficiency of state tracking and parameter tuning. 3. The SRBF is combined with the MADRC, which can quickly respond to the abrupt feedback error so as to further promote the anti-disturbance ability of the servo system. The outline of this exposition is as follows: The mathematical model of a PMSM with friction disturbance is analyzed in Section 2. An auxiliary model is introduced, and the SRBF-MADRC strategy is proposed in Section 3. In Section 4, the simulation results are shown to verify the prominent characteristics of the proposed strategy. Section 5 shows the experimental results. Finally, conclusions are presented in Section 6.
## 4 Parameters Affecting Nano-EOR
### Salinity  
The salinity of the displacing nanofluids and the reservoir fluids has a significant effect on the stability of the dispersion. Increased salinity reduces the zeta potential of the particles, which leads to easy agglomeration [149]. High ionic strength in the fluid due to the presence of salt will lead to lower electrical repulsion between particles and allows the vdW attraction forces to dominate. As most of the rock surface is charged, it is expected that the attraction and collision will happen for particle-particle but not particle-surface [153]. Thus, in a high-salinity environment, modification to the NPs is necessary to maintain the stability which can be achieved by surface modification, ionic control via a surfactant, or a combination of both [89]. [PERSON] et al. [154] reported the stabilization of NPs by anchoring low molecular weight ligands to the NP surface in a high-salinity environment. The authors claimed that the NP stabilization stems from the steric hindrance.  
On the other hand, by using high-stability silica NPs, [PERSON] et al. [109] proved that high salinity nanofluid injection could alter the rock wettability to be more water-wet. At high salinity, the adsorption of NPs is improved due to increasing physicochemical interactions [155]. Similarly, [PERSON] et al. [156] concluded from their research that increasing the salinity of the fluid did not hinder the transport of NPs but did increase the adsorption on the grain surface. Increasing salinity seems to increase the adsorption of NPs and improve the recovery of the oil. However, at the same time,the stability of NPs will be reduced in a high-salinity environment. Therefore, the appropriate salinity level and surface modification are important aspects to be considered to prevent the agglomeration of NPs.
# Unbiased 3D Semantic Scene Graph Prediction in Point Cloud Using Deep Learning
## 3 Methods
### Scene Graph Relational Reasoning  
In the relational inference process of the scene graph, as shown in a part of Figure 2, after the initialization of the node and edge features, the initial potential features are obtained. This is then fused with the label category's prior knowledge, i.e., unbiased meta-embedding prior knowledge (UME), and the relational inference is performed by the ENA-GNN network to obtain the object and relational feature encoding, respectively. Finally, the category is obtained using fully connected layer (FC) prediction.  
The knowledge fusion process, divided into multiple iterative loop processes, can be carried out several times using the knowledge fusion process. Firstly, for the knowledge selection, the object and predicate category probability vectors can be obtained after the initial prediction of the scene graph. The corresponding features of the predicted category with a top-five likelihood are selected for fusion. The category selection is carried out for the meta-embedding encoding, as seen in Figure 7, and the category's prior feature vector after the screening is obtained. The selected prior embedding UME is fused with the extracted point features. Since they are in different feature spaces, they are transformed into approximate feature spaces using two layers of encoded feed-forward neural networks, \(f\left(\cdot\right)\) and \(g\left(\cdot\right)\)[14]:  
\[z=LN\bigg{(}f(\mathbf{x})+\alpha_{group}\sum\limits_{i=1}^{k}g\big{(}e_{meta}^{i }\big{)}\bigg{)},\]  
\[\hat{\mathbf{x}}=LN(\mathbf{z}+\psi(\mathbf{z})). \tag{11}\]  
Figure 6: Grouping relationship categories by frequency and reducing the weight of common relationship category meta-embedding in the feature embedding fusion process to obtain unbiased meta-embedding.  
We used \(e_{meta}^{i}\) to denote the selected meta-embeddings, \(\alpha_{group}\) to denote the grouping of relationship categories with weights, and \(\psi\) is a two-level MLP.
## 6 Conclusions  
Currently, the new remote sensing technology of GNSS-reflectometry has shown benefits for specific targeted measurements. Hence, the reflected signal information for ocean wind speed was studied. The regression technique is useful and easier for wind speed estimation by fitting in situ wind speed measurements. On the other hand, it does not learn the characteristics of variables. The main idea of this research was to apply the artificial neural network combined with the particle filter for wind speed estimation based on the reflected signals from the BeiDou G4 satellite. Moreover, particle swarm optimization was implemented to solve for the weights and biases of the artificial neural network. From the two periods of dataset observation, 3 to 8 and 12 to 14 August 2013, several available signals of BeiDou GEO were used. In particular, for the period from 12 to 14 August 2013, the data was collected during the typhoon Utor. The experimental results show the effectiveness of the proposed technique by comparing it with the in situ wind speed measurements from a buoy. The overall RMSEs of ANN combined with PF were 2.4997 and 1.9758 m/s for the 30% and 70% training samples, respectively. The results of the ANN based PSO approach shows high efficiency for learning characteristics of the reflected signal for wind speed estimation. Using the performance of the PF approach with ANN enhances the effectiveness of estimating wind speed especially for time series observation. Future work will involve the integration of the reflected signal information from other satellite systems, such as GPS, GLONASS and Galileo, in order to achieve greater efficiency.  
[PERSON] and [PERSON] provided the initial idea for this research; [PERSON] designed the algorithm. [PERSON] and [PERSON] contributed to the analyses of the results. [PERSON] contributed to the collection of field observation data. [PERSON], [PERSON], and [PERSON] contributed to the writing, review, and editing of the paper.  
This research was funded by the National High Technology Research and Development Program of China (2013 AA12A201).  
The authors would like to thank the scholarship from Asia-Pacific Space Cooperation Organization (APSCO) and Beihang University. We are also grateful to the reviewers for their detailed reviews and constructive comments.  
The authors declare no conflict of interest.  
Figure 15: The relationship of in situ wind speed measurement and mean square slope. The up and cross wind speed are represented in blue and red solid line, respectively. The colored points represent up and cross wind speed result from our case study.
# Spherical Planting Inversion of GRAIL Data
###### Abstract  
In large-scale potential field data inversion, constructing the kernel matrix is a time-consuming problem with large memory requirements. Therefore, a spherical planting inversion of Gravity Recovery and Interior Laboratory (GRAIL) data is proposed using the L1-norm in conjunction with tesseroids. Spherical planting inversion, however, is strongly dependent on the correct seeds' density contrast, location, and number; otherwise, it can cause mutual intrusion of anomalous sources produced by different seeds. Hence, a weighting function was introduced to limit the influence area of the seeds for yielding robust solutions; moreover, it is challenging to set customized parameters for each seed, especially for the large number of seeds used or complex gravity anomalies data. Hence, we employed the \"shape-of-anomaly\" data-misfit function in conjunction with a new seed weighting function to improve the spherical planting inversion. The proposed seed weighting function is constructed based on the covariance matrix for given gravity data and can avoid manually setting customized parameters for each seed. The results of synthetic tests and field data show that spherical planting inversion requires less computer memory than traditional inversion. Furthermore, the proposed seed weighting function can effectively limit the seed influence area. The result of spherical planting inversion indicates that the crustal thickness of Mare Crisium is about 0 km because the Crisium impact may have removed all crust from parts of the basin.  
spherical planting inversion; tesseroids; GRAIL data; Mare Crisium; seed weighting function
## 4 Sub-Problem 2
### The Batch Algorithm  
We define the gravity-field coefficients to be determined as \(C\), and the error of the coefficients as \(\hat{c}\). Considering errors in other coefficients, Equation (7) can be reformulated as  
\[y_{i}=H_{x}\hat{x_{0}}+H_{c}\hat{c}+\epsilon \tag{13}\]  
where  
\[H_{c}=\frac{\partial Y_{i}}{\partial X_{i}}\frac{\partial X_{i}}{\partial C}= \bar{H_{x}}\Phi_{c}\ ;\ \Phi_{c}=\frac{\partial X_{i}}{\partial C} \tag{14}\]  
\(\Phi_{c}\) is the sensitivity matrix, which can be calculated by integrating the following equation  
\[\Phi_{c}(X,t)=\frac{\partial\hat{X}}{\partial X}\Phi_{c}(X,t)+\frac{\partial \hat{X}}{\partial C} \tag{15}\]  
Similar to Equation (10), \(\hat{c}\) can be estimated by the following formula if we assume no error to the initial state vector \(\mathbf{X}_{0}\).  
\[\hat{c}=(H_{c}^{T}H_{c})^{-1}H_{c}^{T}y \tag{16}\]  
Same as the orbit determination process, the process to determine the gravity-field coefficients should be repeated until the correction \(\hat{c}\) is small enough.
# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 5 Numerical Results and Analysis
### The Effect of the Number of Reflectors  
In this section, the proposed TD3-based RIS-assisted multi-antenna AmBC signal detection method (denoted as RIS-TD3) is simulated to evaluate the effect of the receiving antennas number and the RIS reflective elements on the maximum energy ratio. The comparison benchmark algorithms include: (1) the RIS-assisted DDPG method, denoted as RIS-DDPG; (2) the RIS-assisted conventional method to optimize the phase shifts at the RIS, where the successive convex appropriation (SCA) method is used [26] denoted as SCA; (3) the semi-define relaxation (SDR) method that is compared [27] denoted as SDR; (4) the random RIS phase shift and receiver combiner coefficients, denoted as RIS-RND. The ambient RF source's transmit power and the ambient noise variance are set as 20 and \(-95\) dBm, respectively. The simulation results are illustrated in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & _Description_ & _Value_ \\ \hline \(\mu_{c}\) & learning rate for q\_net & 0.00003 \\ \hline \(\mu_{a}\) & learning rate for policy\_net & 0.0003 \\ \hline \(Intl_{update}\) & delayed steps for updating the policy network and target networks & 3.0 \\ \hline \(n_{explore}\) & range of action noise for exploration & 1.0 \\ \hline \(n_{eval}\) & range of action noise for evaluation of action value & 0.5 \\ \hline \(D\) & buffer size for experience replay & 500,000 \\ \hline \(I\) & the number of episodes & 5000 \\ \hline \(T\) & the number of steps in each episode & 20,000 \\ \hline \(W\) & the number of experiences in the mini-batch & 64 \\ \hline \(\gamma\) & discounted rate for future reward & 0.99 \\ \hline \(k\) & Rician factor & 3 \\ \hline \(d_{h}\) & the distance from the BS to the IR & 202 m \\ \hline \(d_{g}\) & the distance from the RIS to the IR & 30 m \\ \hline \(d_{H}\) & the distance from the BS to the RIS & 200 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 3 shows the median GRCD versus the number of antennas at the reader with different algorithms. It is observed from Figure 3 that the proposed RIS-TD3 algorithm significantly outperforms the DDPG and the conventional methods. Particularly, with the increased antenna number, the three algorithms' system performance can be enhanced, while the benefit of the proposed TD3 algorithm is the most important. This is because both the phase shift of RIS and the beamforming at the receiver are optimized by TD3, hence achieving the overall optimization, which obtains better median GRCD.
# A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models +
## 4 Real-Time Experiments and Analysis
### Evaluation of ADRC1  
As the first moment of the evaluation of transients in Figure 5, it should be noted that the action variable \(u\) is, under the strong influence of noise, a large part of the transients and also in the vicinity of steady states at the limit. Nevertheless, there is no permanent error in the system as when using PID controllers with anti-windup [51].  
Figure 6: ADRC\({}_{1}\): Performance measures corresponding to the controller in Figure 2 and transients in Figure 5 with the parameter \(T_{c}\), \(T_{dm}=\overline{T}_{d}=0\) and \(T_{dm}=\overline{T}_{d}=T_{d}\).  
Performance measures in Figure 6 quantify the visual experience available from Figure 5. The most significant deviations correspond to the ADRC\({}_{1}\) not considering \(T_{d}\) (\(\overline{T}_{d}=0\)) which occur with a relatively fast disturbance observer tuned for \(T_{c}=T_{d}/2=0.15\). For this tuning, the output wobbling around required states leads already to a slight increase of \(IAE_{s}\). However, it is much more visible by significantly increased \(TV_{1}(y_{s})\) and \(TV_{1}(u_{s})\) values.  
Other found differences show slightly higher values of \(IAE_{s}\) of controllers with the disturbance observer input delayed by \(\overline{T}_{d}=T_{d}\) compared to similar values of the first type of controllers with \(\overline{T}_{d}=0\). This result could lead to the hypothesis that the \(\overline{T}_{dm}\) value used is too large, so it already degrades performance. However, for the disturbance steps, lower \(IAE_{d}\) values of the controller with \(\overline{T}_{d}=T_{d}\) contradict this hypothesis.  
The comparison can be finished by concluding that the controllers with \(\overline{T}_{d}=T_{d}\) depend on the tuning parameter \(T_{c}\) determining the speed of the disturbance observer less than controllers setting with \(T_{c}\) also \(K_{p}\). This result can also be interpreted as an increased sensitivity (reduced robustness) of the first set of controllers at \(T_{c}=T_{d}/2\), signalized by increased shape deviations at the input and output. For the second set of controllers, the sensitivity is roughly uniform and without significant peaks.  
**Remark 2** (An ambiguous benefit of dead-time compensation).: _Experiments have shown that by compensating for \(\overline{T}_{d}=T_{dm}\) at the ESO input, as suggested in [33], the performance of disturbance step responses improves, but worsens for setpoint step responses. For these, a more effective way to reduce the excessive input and output increments is to increase the \(T_{c}\) value. This raises the question of whether it would not be better to compensate only part of the identified dead-time in ESO. In the next we will solve the given problem by including some time-constant delays as part of the identified dead-time. In this way, we also obtain additional state variables suitable for the use of higher-order controllers._
# Variable Delayed Dual-Core Lockstep (VDCLS) Processor for Safety and Security Applications
## 5 Failure Analysis
### Common Mode Failures  
Common mode failure (CMF) is the result of an event or events located close in time that cause a failure in both redundant blocks [36]. If both redundant cores experience the same malfunction at the same time, the checker module will not be able to detect such a condition, as the outputs from both cores will match. The most effective way to mitigate CMF is to introduce diversity in both redundant cores. In the classic DCLS and the derivative VDCLS technique, the delay between master and slave cores adds temporal diversity to mitigate CMF. With the delay between the cores, errors are serialized and reach the checker module one after another manifesting themselves as two SEEs that can be easily detected. On the other hand, the CMF can occur naturally, but can also be injected intentionally by the attacker. If the attacker is aware of the underlying hardware and constant delay in DCLS processor, he or she may potentially conduct a set of events located in time in such a way that will cause same malfunction in both delayed cores and prevent the checker module from detecting it. As shown previously in the execution correlation results, with VDCLS, the attacker does not know the current delay between the cores, which makes the fault injection process much more difficult.  
Figure 19: Comparison of the execution correlation between master and slave CPUs using VDCLS with dummy cycles insertion and legacy DCLS technique while executing the ECDSA verification algorithm.
## 4 Discussion
### Future Thoughts and Ideas  
As a future work, many improvements could be included such as (a) digitizing new biopsy samples and increasing the dataset in more than 1000 liver structures, (b) enhancing the neural network discrimination experience by applying transfer learning updates and (c) parameterizing the CNN architecture to adapt to the imminent amount of data. The first step can be done in parallel with data augmentation techniques, which refer to classic 2D image transformations, including random rotations, random shearing-zooming, horizontal and vertical flips, etc. Applying more optimizers, including RMSprop and Adadelta, could also prove to be a good alternative to the proposed backpropagation algorithms.  
Thereafter, autoencoder neural networks could make a significant contribution to the data preprocessing stage, further limiting the overfitting effect in the training set. Autoencoders (ex. stacked, variational) refer to a sophisticated technique for learning efficient representations of input data, without any supervision [14]. Typically, the autoencoders' output is a reconstruction of the input data in its most efficient form [16]. The current unsupervised model will be employed to reduce the dataset's dimensionality, by preserving the most informative elements that compose a liver disease structure and eliminating the background pixels activations as much as possible.  
Future projects will include an accurate method that could involve real-time histological classifications through a digital microscope. All current study implementations, as well as future improvements, will also be included for disease quantification purposes. During the scan of biopsy specimens, the learned feature weights will lead detection windows (as bounding boxes with active contour lines) to structures identified with the four liver classes (Figure 9). Typical examples of such detectors are (a) region-based convolutional neural networks (R-CNNs) [31; 32], (b) you only look once (YOLO) [33; 34] and (c) single shot detector (SSD) [35]. An interesting choice is also the U-Net architecture [36], which includes a variant of CNNs, for pixel-wise segmentation.  
Figure 9: A blueprint of the object detection method.  
Following the extraction of the histological sample area (e.g. with K-means) and the discrimination of structures of interest, the exclusion of anatomic features not related to pathological findings will be executed, performing an objective assessment of the liver diseases. As a result, clinicians will have at their disposal a quick and accurate diagnostic tool for support, which will compute the percentages of ballooning degeneration and fat accumulation ratio. The quantification of the two conditions will be carried out using the following formula:  
\[\mathrm{P_{LD}}=\frac{1}{n_{t}}\sum_{i=1}^{n}(b_{i}||\mathrm{f_{i}})\times 100, \tag{3}\]  
where \(b_{i}\) and \(f_{i}\) are the total count of pixels that form multiple classified balloon cells or fat droplets, respectively, eventually divided by the total area of tissue pixels \(n_{t}\).