esa-sceva/satcom-chunk-collection huggingface dataset:
# ConceptVAE: Self-Supervised Fine-Grained Concept Disentanglement from 2D Echocardiographies
## 1 Introduction
Although traditional deep learning (DL) models are capable of performing the aforementioned tasks with good performance, they lack such properties since they are black-box solutions (regardless whether pretraining was used or not in their development). Disentanglement can also be used as a tool to explore the underlying structure of data, through the explicit decomposition into observed local concepts and their style properties. Briefly, ConceptVAE extends the Variational Autoencoder (VAE) framework to encode a 2D input image into a latent space using a 2D grid of concept probability distributions (one \(p_{ij}(c)\) for each image region, where \(c\) is a concept and \(i,j\) are spatial indexes) and their associated style vectors (\(s_{ij}=f(c_{ij},x)\), where \(s_{ij}\) is the style property vector of concept \(c_{ij}\) that is present at location \(i,j\) in input image \(x\)). We find that even a modest number of discrete concepts and styles (e.g., 16 concepts and 8 style components) are sufficient to model 2D echocardiographies. We design a series of loss functions that guide a neural network to detect underlying concepts from an input image and identify particular styles for each concept. We validate the effectiveness of the embeddings learnt via ConceptVAE through distinct tasks including region-based instance retrieval, semantic segmentation, object detection, and OOD detection, demonstrating consistent improvements over more traditional SSL methods. In summary, our work's key contributions are the following:  
* We introduce ConceptVAE, a novel SSL training framework that yields models capable to fine-grained disentangle concepts and styles from medical images. We evaluate the model using 2D cardiac echocardiographies, given the accessibility of datasets for pre-training and validation. Nevertheless, ConceptVAE is designed to be versatile and can potentially be applied to all 2D image modalities. * We qualitatively validate ConceptVAE and demonstrate its ability to identify concepts specialised for anatomical structures, such as blood pools or septum walls. * We quantitatively validate ConceptVAE and show consistent improvements over traditional SSL methods across various tasks, including instance retrieval, semantic segmentation, object detection, and OOD detection. * We assess ConceptVAE's ability to generate data conditioned on concept semantics and discuss its potential to enhance robustness in dense prediction tasks. The remainder of this article is organised as follows. We start by discussing background information and related work (Section 2), followed by a detailed overview of ConceptVAE (Section 3), an analysis of the pre-trained model's ability to disentangle concepts and styles (Section 4), and a quantitative evaluation of the model for multiple tasks (Section 5). The paper ends with conclusions and future work (Section 6).
## 2 Study Area and Data
### Data
#### 2.2.4 Supplementary Data  
The temperature data [55] and precipitation data [56] of MERRA2 were also selected. The produce is reanalysis data obtained by the assimilation of site data and remote sensing data with a resolution of \(0.625^{\circ}\times 0.5^{\circ}\)[57], which is resampled to \(0.05^{\circ}\).  
The night light dataset of DMSP-OLS [58; 59], which is based on the nighttime light signals detected by remote sensing satellites, is from the Defense Meteorological Satellite Program (DMSP). The nighttime lighting data comes from the DMSP/OLS night lighting products available in its visible and near-infrared sensors (DMSP/OLS). It is used to extract city categories with very good results [60]. Its spatial resolution is 30 arc second, which was resampled to \(0.05^{\circ}\).  
For classification, elevation, latitude and longitude information are also necessary, and different land cover types are related to the elevation, latitude and longitude [61]. In this study, the DEM data of ASTER GDEM [62] was obtained, which was also resampled to \(0.05^{\circ}\), and the latitude and longitude information of the study area was extracted and input into the classification model. All data used in this study is shown in Table 2.
# Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids
## 1 Introduction
The inputs, which included historical GHI and meteorological data from the preceding 24 h, were utilized to forecast the GHI for the next hour [44]. The results revealed that the LSTM-based model outperformed the other models, such as auto-regressive integrated moving average (ARIMA) and CNN [44]. Ref. [45] investigated the performance of LSTM and GRU. Refs. [46; 47] published hybrid CNN-LSTM models for hour-ahead GHI forecasting. Their study showed that incorporating external weather information considerably increased the prediction accuracy. Unlike day-ahead irradiance forecasting methods, hour-ahead forecasting algorithms create projections for the following hour only using historical data. On the other hand, RNNs are a form of ML technology widely employed for time series prediction and the modeling of dynamic systems [48; 49]. RNNs are artificial neural networks (ANN) that are particularly useful for modeling time-series data and may be used to anticipate future MG behavior [50; 51]. RNNs may learn and adapt to system dynamics by learning the temporal dependencies in data. RNNs have been used to solve various MG control challenges, including load forecasting, renewable energy integration, and demand response management [52; 53; 54]. RNNs have been applied to various systems, including power systems [55; 56], with promising prediction accuracy and flexibility results. In summary, both control families have benefits and drawbacks, and their complementarity is evident. On the one hand, S-MPC struggles with system complexity and long-term prediction horizons, whereas the combination of AR and LSTM (AR-LSTM) can deal with complex systems and infinite prediction horizons. AR-LSTM, conversely, has difficulty in satisfying constraints and lacks interpretability, whereas S-MPC can provide safety guarantees and understandability. Although there is a clear potential for a synergy between the two families of methods, there have been few attempts to combine their relative advantages. This research deficiency is not limited to applying EM for MG. The control and ML communities evolve independently, adopting radically different notations to formulate the same problem. In spite of the parallel developments, several authors [57; 58; 59] have suggested that a collaboration between the two groups could result in potential advantages. Combining these methodologies represents a powerful method for integrating robust control theory methods with ML approaches, to exploit additional information from real-time data [60; 61]. As shown in Table 1, each control method has its strengths and limitations. MPC and S-MPC offer robust optimality and constraint handling but may have computational challenges. AR and RNN-LSTM are efficient in computation but may not manage complex constraints effectively. S-ANC combines AR models with neural control, balancing optimality and computational efficiency. The choice of control method depends on the specific application and trade-offs between these criteria.
## 3 Quantitative Assessment of Parkinsonian Tremor
### Parkinsonian Tremor Assessment Methods  
The algorithm used to quantify the severity of parkinsonian tremor is very important. Tremor Quantification has been performed by signal processing methods such as time-domain analysis, time-frequency analysis, spectral analysis, and nonlinear analysis [31; 32]. Currently, spectral analysis is used for the majority of these studies [33; 34].  
In this study, signal processing involves FIR (finite impulse response) filters and IIR (infinite impulse response) filters as well as other special algorithms such as power spectral density (PSD) analysis. To detect tremors, the signals are then processed with PSD calculation (auto power spectrum) with a certain time interval (3-10 s).  
For range (displacement) analysis, the angular velocity obtained from the gyroscope needs to be integrated over time only once, but the integration of the acceleration signals requires double integration. However, the sensor bias and drifts are integrated as well. Thus, the IMU raw data were used for the tremor quantification.  
The three-axis accelerometer outputs include gravitational acceleration, which equals 9.81 m/s\({}^{2}\) in vector product, and linear acceleration. Gravitational acceleration can be removed from the  
Figure 2: System diagram of the tremor assessment system. This system consists of two parts: glove part (**left**) and computer part (**right**).  
accelerometer outputs with high-pass filters. Thus, there are only linear acceleration data for the following signal processing.  
Quadratic mean (RMS) interprets actual vibration levels, while PSD results show dominant frequency that contribute the most to the tremor. Because the tremors are based on a dominant frequency, the advantages of PSD compared to a statistical measure (quadratic mean) are that it highlights the tremor signals from noise and other movement with analysis in the frequency dimension and the squared value of the signals.
# Prototype Design and Performance Tests of Beijing Astronaut Robot
## 2 The Mechanical Structure and Test System Design of the Robot  
### Design Indicators  
It is very difficult to transport large volume or heavy weight objects from the ground to a space station. The design of the space robot needs to meet the requirements of the operations on the space station and, at the same time, the mass and the volume should be as small as possible [11]. Parameters and functional indicators of the Beijing Astronaut Robot are as follows:  
* The total mass of the robot is less than 30 kg;
* The total degrees of freedom (DOF) of the robot is no less than 12;
* The robot has at least three arms, each arm has no less than 4-DOF;
* The robot arm length is not more than 700 mm.  
According to the structural environment outside the space station and the characteristics of the robot itself, the robot has three kinds of motion modes: walking, rolling and sliding. The motion index in each motion mode is as follows:  
* Walking mode, the stride of the robot is not less than 30 cm, the robot movement speed is not less than 0.1 m/s;  
Figure 1: Handrails on extravehicular surface with appearance and dimensions.  
* Rolling mode, the robot's stride is not less than 60 cm, the robot movement speed of not less than 0.2 m/s; and
* Sliding mode, the moving speed of the robot is not less than 0.5 m/s.  
When the robot performs the motion and operation tasks outside the space station, the robot's visual measurement, end positioning and force control are supposed to have high precision. The specific indicators are as follows:  
* Visual measurement accuracy not less than 1 mm in the range of 200 mm;
* End effector's positioning accuracy within 2 mm;
* End effector's force control accuracy within 2 N.
## 4 Model to Minimize Cost Dynamically
### Remote Computing in the Cloud  
The CPU cores in CC are not distinguished because they are a single shared resource comparable to a CPU processor. It is not really a device. The CC equations are analogous to those of the local MEC server. Data transference between MEC and CC is composed of both the elapsed time and consumed energy to produce a total cost. The elapsed time is expressed by (Equations (15) and (16)), while consumed energy is expressed in Equations (17) and (18).  
\[T_{i,cloud-up}=\frac{s_{i}+d_{i}}{r} \tag{15}\]  
\[T_{i,cloud-down}=\frac{d_{i}^{\prime}}{r} \tag{16}\]  
\[E_{i,cloud-up}=p_{wireless}*T_{i,cloud-up} \tag{17}\]  
\[E_{i,cloud-down}=p_{wireless}*T_{i,cloud-down} \tag{18}\]  
Note that in Equations (15) and (16), \(r\) is not dependent on \(h_{i}\), because transmissions between MEC and CC are done on fiber optic cables, and there is no mutual interference effect attenuating the data transmission rate. CC processing time (Equation (19)) and dynamic energy consumed (Equation (20)) are calculated the same way for MEC servers. The total elapsed time and total energy consumption for CC are as follows.  
\[T_{i,cloud,total}=T_{i,mec-up}(h_{i})+T_{i,cloud-up}+T_{i,cloud}+ \tag{19}\] \[T_{i,cloud-down}+T_{i,mec-down}(h_{i})\]\[\begin{split} E_{i,cloud\_total}=E_{i,mce-up}(h_{i})+E_{i,cloud-up}+E_{i, cloud}+\\ E_{i,cloud-down}+E_{i,mce-down}(h_{i})\end{split} \tag{20}\]  
Finally, the cost to run a single task \(i\) in the cloud is given in Equation (21).  
\[\begin{split} Cost_{i,cloud}=u_{cloudT}*T_{i,cloud,total}+\\ u_{cloudE}*E_{i,cloud,total}\end{split} \tag{21}\]  
The idle energy cost of CC is not considered, since the CPU offer is virtually infinite. Therefore, it does not make sense to account for this cost, which would cause the system to have equally infinite cost.  
For every task \(i\), the minimum cost is chosen between all three allocation policies, one from each layer, as in Equation (22).  
\[Cost_{i}=min(Cost_{i,local},Cost_{i,mce},Cost_{i,cloud}) \tag{22}\]  
The total system cost, represented by Equation (23), is equal to the sum of all task costs plus the sum of idle energy for IoT devices and MEC servers.  
\[Cost_{system}=\sum_{i=1}^{A}Cost_{i}+\sum_{i=1}^{A}E_{local_{i},idle}+\sum_{i=1 }^{A}E_{mce_{i},idle} \tag{23}\]
## 5 Results and Discussion
### Results
#### 5.1.1 Flat Road Dataset  
First, we applied the supervoxel segmentation method to perform coarse segmentation on the MLS point cloud data of a flat road. Then, using supervoxels as segmentation primitives, we employed a region growing algorithm for iterative clustering to extract the road surface point cloud. Finally, we compared the extracted road surface point cloud with the results obtained from the other three segmentation algorithms, as shown in Figure 14.  
In comparing the results in Figure 14, it can be visually observed that all four methods achieved good segmentation results for the flat road scenario and successfully extracted the road surface. However, as shown in Figure 14d, the extracted road surface exhibits a clearer and more complete structure. In Figure 14a, the edges of the extracted road result are relatively rough, whereas in Figure 14b, the extracted road surface is incomplete. In Figure 14c, the result is relatively continuous, but there are instances of over-segmentation at the edges of the road, leading to insufficient details.  
Figure 14: Extraction results of MLS point cloud road surface for flat road: (**a**) Euclidean clustering-based segmentation; (**b**) RANSAC-based segmentation; (**c**) traditional region growing-based segmentation; (**d**) improved region growing algorithm.
## 2 Direct Under-Sampling Compressive Sensing Method
### Direct Under-Sampling Compressive Sensing Method (DUS-CS)
#### 2.2.2 Direct Under-Sampling Compressive Sensing (DUS-CS) Method
Acoustic echoes are simulated and shown in Figure 2.  
Figure 4: Reconstruction signal at a compression ratio of 50%. (**a**) SNR = 3 dB; (**b**) SNR = 0 dB; (**c**) SNR = -3 dB; (**d**) SNR = -8 dB. Figure 5: Reconstruction signals at a compression ratio of 20%. (**a**) SNR = 3 dB; (**b**) SNR = 0 dB; (**c**) SNR = -3 dB; (**d**) SNR = -5 dB. From Figure 3a, we can see that when the compression ratio is 100% and SNR = 3 dB, both methods can reconstruct the original signal with a noise reduction effect. When the signal-to-noise ratio is further reduced to 0 dB, the DCT-OMP method does not reconstruct the original signal as well as before, while the DUS-CS method still reconstructs the signal well. When the SNR continues to decrease, the DCT-OMP method can hardly reconstruct the original signal, but the DUS-CS method still performs well. The correctness and the engineering achievability of the under-sampled measurement matrix construction have been verified in Figure 3. Figures 4 and 5 are the results of the two methods when the compression ratios are 50% and 20%, respectively. From the results, the DCT-OMP method is not ideal for signal reconstruction, but the DUS-CS method reconstructs the original signal well. To compare the two methods quantitatively, we use the concept of matching rate [34]  
\[\text{Matching\_rate}=1-\left(\|\hat{X}^{\prime}-X^{\prime}\|_{2}/\|\hat{X}^{ \prime}+X^{\prime}\|_{2}\right) \tag{18}\]  
In which \(X\) is the original signal, \(\hat{X}\) is the reconstruction result, and \(X^{\prime}\) and \(\hat{X}^{\prime}\), respectively, represent the absolute values of \(X\) and \(\hat{X}\). We give the variation of the matching rate with the signal-to-noise ratio when the compression ratio is 100% and 50%, respectively (Figure 6a,b). It can be seen that the DUS-CS method has obvious advantages. Therefore, the direct under-sampling structure can be used to observe underwater echo signals. The framework of compressive sensing can effectively recover the original signals, which confirms the correctness and effectiveness of the direct under-sampling observation method.
# A Survey of Planning and Learning in Games
## Planning and Learning in Games
### Planning and Learning
#### 5.3.1 Frameworks for Planning and Learning
The core idea is to extend the usual notion of action in RL to include options, a generalization of primitive actions that includes temporally extended courses of action (i.e., actions that might result in state transitions of extended and variable duration). An option \((\mathcal{J},\pi,\beta)\) consists of three components: an initiation set \(\mathcal{J}\subseteq S\), a policy \(\pi:S\times A\rightarrow[0,1]\), and a termination condition \(\beta:S^{+}\rightarrow[0,1]\). An option is available in state \(s_{t}\) if \(s_{t}\in\mathcal{J}\). If the option is taken, actions are selected according to \(\pi\) until the option terminates according to \(\beta\), in which case the agent has the opportunity to select another option. Examples of options include high level actions such as picking an object, as well as primitive actions such as joint torques. By integrating temporal abstraction into the MDP framework (via options), both the planning and learning processes may take advantage of the simplifications and efficiency gains potentially found when working at higher levels of temporal abstraction. The option-critic architecture [272] proposes an approach to automatically learn options without the need to provide additional rewards or subgoals. More specifically, option-critic learns the internal policies \(\pi\) and the termination functions \(\beta\) of the options simultaneously with the policy over options (the policy used to choose the options).
# Safe Driving Distance and Speed for Collision Avoidance in Connected Vehicles
## 4 Stopping Distance and Braking Dynamics
### Required Braking Force Given the Mass and the Speed to Yield the Same Braking Distance  
The effect of braking force in the determination of braking distance is discussed in this section. For a moving vehicle to come to rest, there must be work done sufficient to remove all its kinetic energy. Work done = Kinetic Energy, where work done is the work needed (in Joule) to remove all vehicle's kinetic energy.  
Kinetic Energy = 0-5 \(\times\) mass \(\times\) velocity\({}^{2}\) measured in Joule, and the work done is the product of braking force and distance. Thus:  
\[F\times D=\frac{m\times V^{2}}{2} \tag{12}\]  
\[D=\frac{m\times V^{2}}{2\times F} \tag{13}\]  
\[V=\sqrt{\left(\frac{2\times F\times D}{m}\right)} \tag{14}\]  
Figure 22: Braking Distance given the mass of a vehicle at different speeds.  
Therefore, for a fixed maximum braking force, the braking distance is proportional to the square of the vehicle's speed. In Figure 24, the required braking force (N) as a function of the vehicle's mass (Kg) for different speeds (km/h) and different braking distances (m) are presented. For example, at 30 km/h, a 500 Kg vehicle needs around 1900 N to stop at a braking distance of 10 m, around 900 N to stop at 20 m, and 500 N to stop at 30 m. At a speed of 60 km/h, a 750 Kg vehicle needs 5000 N to stop at 20 m and 2500 N to stop at 40 m, and 1000 N to stop at 100 m, whereas at a speed of 90 km/h, a 2000 Kg vehicle needs a force of 20,000 N to stop at 30 m and around 8000 N for the same vehicle to stop at 100 m. We can deduce that at high speed, the required baking force is proportional to the mass of the vehicle and the square of the speed but inversely proportional to the braking distance.  
Figure 23: Maximum speed of a Vehicle given the braking distance with different masses of the vehicle.  
Figure 24: _Cont._
## 1 Introduction
### Related Works  
Recently, neural networks have been successfully applied to predict electric and magnetic field levels, as well as signal power loss. In [22], a novel artificial neural network (ANN)-based indoor electric field strength prediction model was presented, while an out  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Frequency (MHz)**} & \multicolumn{2}{c}{**E (V/m)**} \\ \cline{2-3}  & **ICNIRP** & **ICTA** \\ \hline
0.010–0.15 & 87 & 65.25 \\ \hline
0.15–1 & 87 & 65.25 \\ \hline
1–10 & 87/f1/2 & 65.25/f1/2 \\ \hline
10–400 & 28 & 21 \\ \hline
400–789 & 1.375f1/2 & 1.03f1/2 \\ \hline
790–2000 & 1.375f1/2 & 0.96f1/2 \\ \hline
2000–94000 & 61 & 42.93 \\ \hline \multirow{4}{*}{Base station using specific frequency} & 900 & 41.25 & 28.80 \\ \cline{2-3}  & 1800 & 58.33 & 40.72 \\ \cline{1-1} \cline{2-3}  & 2100 & 61 & 42.93 \\ \cline{1-1} \cline{2-3}  & 2600 & 61 & 42.93 \\ \hline \multicolumn{3}{c}{\(f\) is frequency in MHz} \\ \hline \end{tabular}
\end{table}
Table 1: Limit E levels.  
door prediction model for both a macrocell [23] and microcell [24] mobile radio environment was proposed. A spatial electric field strength prediction using a hybrid wavelet-neural model was presented in [25]. Electric field strengths were obtained through drive-test measurements, and a new generalized regression neural network model was created to predict power loss. In [26], a hybrid autoregressive integrated moving average and neural network model were used to predict electric field levels along with the buildings close to digital television transmitters. Computed and measured received signal strengths were compared, which were shown to be reasonably close. Electric [27] and magnetic field [28] estimation was performed under/near high voltage transmission and distribution lines using ANN, and the electric and magnetic field levels were predicted accurately. Indoor wideband electric field radiation was predicted in [29] using the combined wavelet transform and time series analysis-based method. The better prediction accuracy performance of the proposed method was proved by the comparison of the results obtained by non-hybrid methods. In [30], RF-EMF values were assessed with the use of a four-frequency-bands RF sensor. Potential RF-EMF exposure hotspots were determined in a very large geographical area and time-series clustering, and spatiotemporal interpolation techniques were proposed to improve the spatiotemporal assessment of RF-EMF exposure. RF-EMF exposure levels in an outdoor urban environment were predicted in [31; 32] using drive test measurement results and both fully connected and hybrid-connected ANN models. In [33], electric field levels were measured in the dense areas, and ANN models were created to predict electric field values in random locations of a university campus; the prediction accuracy of models was compared.