esa-sceva/satcom-chunk-collection huggingface dataset:
# RJ-RRT: Improved RRT for Path Planning in Narrow Passages
## 4 Simulation and Result
### Path Length  
During the experiment, there was an additional discussion, due to the improvement of RJ-RRT's two strategies of greedy space reduction and dependent subtrees exploration, the length of the final planned path is also reduced to a certain extent. Since the main content discussed in this paper is to improve the adaptability of the RRT algorithm in complex environments, especially in the narrow passage, rather than the path length, so only the path length is briefly discussed in this experiment. Figure 7a is used for the map in this experiment, and the rest of the settings are the same as those described above. RRT, RRV and RJ-RRT were tested 50 times, respectively, and the difference between the average of the planned path length of the three algorithms and the shortest path was compared. Figure 11 shows the comparison results of different algorithms on path length. It can be  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Average Time (s)** & **Min Time (s)** & **Max Time (s)** & **Average Number of Collisions Detections** & **Average Number of Nodes** \\ \hline RRT & 7.912 & 5.457 & 14.126 & 9829 & 2361 \\ RRV & 4.587 & 3.746 & 10.578 & 5124 & 1741 \\ RJ-RRT & 3.348 & 1.841 & 6.432 & 2546 & 967 \\ \hline \hline \end{tabular}
\end{table}
Table 4: When extended size is 0.3, the results of three algorithms in the bug trap.  
Figure 10: The performance of the three algorithms in bug trap. (**a**) RRT; (**b**) RRV; (**c**) RJ-RRT.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Average Time (s)** & **Min Time (s)** & **Max Time (s)** & **Average Number of Collisions Detections** & **Average Number of Nodes** \\ \hline RRT & 9.846 & 6.959 & 17.707 & 14596 & 5579 \\ RRV & 5.575 & 4.945 & 13.689 & 5920 & 2861 \\ RJ-RRT & 7.194 & 5.746 & 15.145 & 6218 & 3347 \\ \hline \hline \end{tabular}
\end{table}
Table 3: When extended size is 0.1, the results of three algorithms in the bug trap.  
seen that the difference in length between RRV and RJ-RRT is not obvious, about 82%, 87%, different from the shortest path and they both save much more distance than RRT.
## 3 Proposed Online Fault Diagnosis Model  
The proposed model for online fault diagnosis consists of three processes, as shown in Figure 5. An initialization process builds the initial DSK with a heterogeneous feature vector corresponding to a healthy bearing signal (FFB). An online bearing fault detection and diagnosis process detect new fault modes in unknown signals using \(k\)-means clustering and the proposed MPDFCDF cluster evaluation method. If new fault modes are detected, an updating process incorporates the newly detected fault class into the DSK, and the updated DSK is then used for fault diagnosis
# Integrating a Blockchain-Based Governance Framework for Responsible AI
## 2 Programming Smart Contracts for Responsible AI
### Network-Driven Energy Efficiency  
Energy efficiency is dependent on the frameworks and the hardware on which it is integrated [85]. In general, the energy efficiency of an algorithm is related to the amount of computational power required to run it [86]. Distributed computing and cloud-based resources can be used to manage the hardware complexity of these algorithms in several ways, such as the following:  
* Scalability: Distributed computing allows for the distribution of computations across multiple machines, which can significantly increase the overall computational power available. This can be especially useful when working with large data sets or complex models. Cloud-based resources provide access to a large pool of computational resources that can be easily scaled up or down as needed.
* High Availability: Distributed computing and cloud-based resources can provide high availability of computational resources, ensuring that algorithms can be run continuously without interruption. This can be especially important for real-time or time-critical applications.
* Resource Optimization: Distributed computing and cloud-based resources can be used to optimize the use of resources. For example, it is possible to run algorithmson specialized hardware such as GPUs or TPUs, which can significantly speed up processing times.
* Cost-effectiveness: Using cloud-based resources can be cost-effective, as it eliminates the need to invest in and maintain expensive hardware. Additionally, cloud providers typically offer pay-as-you-go pricing models, which can help organizations to manage costs by only paying for the resources they use.
* Easy Access: Cloud-based resources can be easily accessed from anywhere with an internet connection, making it easy for organizations to collaborate and share resources. Additionally, many cloud providers offer pre-configured machine images with popular deep learning libraries pre-installed, making it easy to set up and run algorithms. It is important to note that while distributed computing and cloud-based resources can greatly increase the overall computational power available, they may not be suitable for every use case, and their appropriateness depends on the specific requirements of the AI system in question [87]. Additionally, the security of data and the cost of using these resources have to be considered.
# 6G Cognitive Information Theory: A Mailbox Perspective
## 11 A Deep Learning-Assisted Cognitive Information Communication System
### Military Applications  
Thus far, data fusion has been widely used by the military, where it originated [91]. Military applications require dynamic target detection, identification, and tracking with the properties of high accuracy, large scale, and automation. Due to the complexity and uncertainty in scenes and military operations, it is difficult to accurately judge and respond to real scenes or entities from a human perspective. Therefore, a large-scale, multi-source data fusion [92] using sensors and computing nodes deployed by the military for applications, such as battlefield situation assessment, combined heaven and earth surveillance, target acquisition, strategic defense, early warning, battle-damage quantitative measurement [93], army management and military decision making, and modernized military information systems, has been used [94].
## 5 Experiments and Results  
### Training  
For the first stage, we train on an input resolution of 512 \(\times\) 512, which yields an output resolution of 128 \(\times\) 128 for the modified hourglass network of stride 4. Data augmentation was not used since the rotations and scaling were performed while generating the synthetic dataset. The Adam [58] optimizer was used to optimize the overall loss function. A batch size of 32 with an NVIDIA GeForce RTX 3090 GPU and a learning rate of 2.5 \(\times\) 10\({}^{-4}\) is used for 150 epochs and decreased in steps of 0.1 if there is no improvement in the validation set for 5 epochs.  
For the second stage, we train on an input resolution of 128 \(\times\) 32, which yields an output resolution of 32 \(\times\) 1648 for the Conv + LSTM network. Data augmentation of scaling and rotation of 10 degrees in both directions was performed. The Adam optimizer was used to optimize the CTC loss. A batch size of 256 with an NVIDIA GeForce RTX 3090 GPU (NVIDIA, Santa Clara, CA, USA) and a learning rate of 1 \(\times\) 10\({}^{-4}\) is used for 50 epochs and decreased in steps of 0.1 if there is no improvement in the validation set for 10 epochs.  
The depth map prediction head is predicted using Mean Absolute Error (MAE) or L1 distance regression loss, while the Gaussian heatmap, width-height, and offset prediction heads are predicted using Mean Squared Error (MSE) or L2 distance regression loss, according to the equation. To update the model weights according to Equations (10) and (11), where \(y_{i}\) is the ground truth, \(\hat{y}_{i}\) is the prediction, and is the total number of samples for a given batch, one can find the mean square error (MSE) by calculating the squared pixel-wise difference between the predicted heatmap and the ground-truth heatmap, and the mean absolute pixel-wise difference between the predicted output and the ground-truth.  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}{(y_{i}-\hat{y}_{i})^{2}} \tag{10}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}{|y_{i}-\hat{y}_{i}|} \tag{11}\]  
The total loss \(L_{T}\) of the object detection model is shown in Equation (12). Here, \(L_{hm}\) represents the heatmap loss, \(L_{wh}\) and \(\lambda_{wh}\) represents the width-height loss and weights, \(L_{off}\) and \(\lambda_{off}\) represents the x-y offset loss and weights, \(L_{dm}\) and \(\lambda_{dm}\) represents the depth-map loss and weights.  
\[L_{T}=L_{hm}+L_{wh}\lambda_{wh}+L_{off}\lambda_{off}+L_{dm}\lambda_{dm} \tag{12}\]  
The loss weight values for \(\lambda_{wh}\), \(\lambda_{off}\), \(\lambda_{dm}\) are 5, 5, and 0.5, respectively. These values are decided based on the maximum model performance for the text class heatmap regression.
## 3 Types of Scanning Patterns  
The uncertainty area can be scanned using various scanning methods, including spiral, raster, square spiral, hexagonal, rose, and Lissajous patterns [29; 34; 35; 36; 41]. Each pattern follows a specific path to acquire the receiver satellite. In this study, spiral, raster, square spiral, and hexagonal scanning methods are analyzed, and analytical expressions are derived for their mean acquisition time.
# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
## 2 Background and Related Work
### Commercial ECG Devices  
ECG devices must be approved by respective health authorities in countries to be used on patients or spread commercially. Currently, there are many devices available in the market that perform remote ECG monitoring. However, most of them have not been approved for medical use by respective authorities in targeted countries, like the Medical Device Bureau in Canada or the Food and Drug Administration (FDA) in America. AliveCor is one of the commercial devices that the FDA has cleared as a clinical-grade consumer product. The device offers real-time diagnoses of heart conditions. It is used to detect recurrent atrial fibrillation only and provides real-time readings for a short time. Also, patients have to stand still and limit their movement during the data acquisition period. The SEEQTM sensor by Medtronic Inc. [25], the ZIOXT Patch by iRhythm Technologies Inc. [26], and the wearable biosensor by Philips [27] all share the same features of continuous ECG data collection from 7 up to 14 days of recorded data. However, the collected data is only for one ECG lead that can only detect limited heart diseases. Additionally, the SEEQTM and the ZIOXT are \"single-use\" devices. While Savvy ECG [28] addresses the shortcomings of the previously mentioned devices like reusability (rechargeable battery) and real-time streaming to a mobile device, the solution does not offer diagnostic information about the heart conditions of the patient. Moreover, the device cannot determine various heart diseases since it only supports single-lead ECG acquisition. Similarly, BioTelemetry [29] introduced the MCOT wireless ECG that provides two channels of ECG data acquisition with up to 30 days of data storage. The device allows for wireless data transmission on demand. Besides, the device can't be used multiple times. Once the battery is exhausted, it can't be used again. Moreover, the battery life lasts for one year, and once the expiration date has expired, the device is no longer usable and needs to be replaced with a new device. On the other hand, [30] delivers continuous wireless ECG monitoring. However, the device captures only one single ECG channel. Moreover, the device is not designed for long-term cardiac diagnoses since the device working time lasts for up to a day, then the device would need to be recharged.
# Re-Optimized Design for the ADR-Based Dual-Loop Controller of 100 m Class Aperture Radio Telescope
###### Abstract  
Pointing accuracy is a crucial performance index for a fully steerable large-aperture radio telescope and is subject to external and internal disturbances. Improvement of the antenna's servo control performance is meaningful for pointing, especially for blind pointing of the antenna. This paper firstly establishes a multi-motor-driving four-degrees-of-freedom (DOF) model of an antenna servo system, then optimizes the original design consisting of the active disturbance rejection (ADR)-based velocity controller and lead-lag-based position controller, making the velocity controller a novel linear ADR-based controller coupled with a disturbance-velocity compensation (DLADRC) and the position controller the simplest proportional (P) controller. Simulation results based on a 100 m class radio telescope servo model indicate that the new control system's velocity-loop response is smoother, the position-loop peak disturbance response is reduced by 17.8%, the position-loop dynamic performance with the short settling time and slight overshoot remains the same as the former PD-LADRC system, and the RMS pointing/tracking error is 2.16 arcsec under a mean wind of 3 m/s, less than the design specification of 2.5 arcsec RMS required.  
large-aperture radio telescope; pointing accuracy; anti-disturbance; dynamic performance
## 3 Experimental Setup  
As shown in Figure 1, the cryostat for superconducting magnet is composed of a liquid helium vessel, liquid nitrogen vessel, and vacuum vessel. In general, the liquid nitrogen vessel surrounds the liquid helium vessel and the space between the liquid nitrogen vessel and liquid helium vessel is maintained in a vacuum condition to minimize conduction heat transfer by residual gas. The liquid nitrogen vessel has a capacity of about 70 L, and thirty layers of multilayer insulation (MLI) [28] is applied to minimize the radiant heat transfer and to maintain a temperature of 77 K. The liquid helium vessel has a capacity of about 76 L. A superconducting magnet, current leads, baffles, and liquid helium level sensor were installed inside the liquid helium vessel. In this experiment, we used the standard liquid helium level sensor from AMI [29], and it has an active length of 20 inches. Figure 8a shows the installation of the vapor-cooled current lead. Several baffles were installed in the superconducting magnet system for the minimization of the radiant heat leakage and the current lead was installed through the baffles. To prevent electrical contact between the baffles and the current lead, an insulation jig made of Teflon was installed, as shown in Figure 8a. There were four supports for installing the superconducting magnet in the system, which were made of glass fiber reinforced plastic with lower thermal conductivity than metal material, because they were directly connected to a superconducting magnet from room temperature like current leads. Figure 8b shows the experimental setup of a 5 T superconducting magnet system for the performance test of the current lead with data acquisition device. The data acquisition device consists of a computer for collection of experimental data, temperature monitor (model Lakeshore 218, Lakeshore Cryotronics Inc., Westerville, OH, USA) for temperature measurement, gauss meter (model Lakeshore 475, Lakeshore Cryotronics Inc., Westerville, OH, USA) for magnetic field energy measurements, and level meter (model AMI 1700, American Magnetics Inc., Oak Ridge, TN, USA) for liquid helium level measurements. The electrical current was supplied via a magnet  
Figure 7: Heat leakages of vapor-cooled and hybrid current leads.  
power supply (model Cryomagnetics 4G-200, Cryomagnetics Inc., Oak Ridge, TN, USA) equipped with a quench protection module and a free ramping rate control. All instruments were controlled with the Labview(tm) software (Labview 2017, National Instruments, Austin, TX, USA), and the experimental data were saved automatically every second through the GPIB communication module.
# Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
## 5 Conclusions and Prospects  
This paper introduces a proposed Multi-channel Residual Attention Network with Efficient Channel Attention (ECA-MRANet). The effectiveness of the method is validated through experiments, yielding the following conclusions:  
1. The network is capable of simultaneously taking in original data, wavelet-transformed time-frequency diagrams, and the optimal Intrinsic Mode Function (IMF) of Variational Mode Decomposition (VMD). This enriched input ensures that the network processes a diverse set of fault information. T-SNE visualization of features extracted by ECA-MRANet demonstrates its ability to significantly enhance feature distinctiveness, effectively improving the model's feature learning and diagnostic accuracy.
2. Under identical working conditions, the diagnostic accuracy of the ECA-MRANet model shows notable improvement compared with other traditional network models, achieving an average accuracy of 96.96%. This underscores the importance of enhancing the input of fault diagnosis network models.
3. Utilizing ECA-MRANet for transfer learning on fault data across different working conditions yields an accuracy rate exceeding 90%, indicating robust generalization capabilities.
## 2 Related Work
### Type of Data and Predictive Output  
Two-dimensional images of chest X-rays and CT scans were classified by the COVID-Con algorithm [11] as COVID-19, pneumonia, or normal cases. The \"MultiCon\" algorithm, proposed by [12], classified the two-dimensional images of the drug chemical structure into 12 categories based on their therapeutic applications. MCoM, proposed by [53], classified numerical tabular security data as either positive (vulnerable case) or negative (nonvulnerable case). An evolutionary multi-objective algorithm, the SPEA2 proposed in [54], was tested in a set of toy problems to evaluate its suitability for performing classification tasks. The DTGMO-SSC method, proposed by [55], was applied on 55 semi-supervised datasets from machine learning databases KEEL and UCI to perform classification. The effectiveness of the semi-supervised regression of the EOPL and EnEOPL methods, proposed by [56], was verified through the Tennessee Eastman (TE) chemical process and an industrial fed-batch chlortetracycline (CTC) fermentation process. In [57], numerical experiments were performed on two Multi-Objective Multi-Tasking Optimization (MO-MTO) benchmarks and a case study on a stationary gas turbine's combustion processes to validate the performance of the proposed EMT-SSC algorithm. The effectiveness of the semi-supervised framework, proposed by [58], was demonstrated by testing it on real-world data gathered from shoe manufacturing enterprises. For 130 out of 140 workers, this framework predicted both risk perception and caution level with good accuracy. We illustrate the categorization of all of the aforementioned semi-supervised prediction models, with regard to their specifications and limitations, in Table 1.
# Three Steps towards Better Forecasting for Streamflow Deep Learning
## 2 Methodology
### Model Performance Evaluation
#### 2.6.4 Mean Absolute Percentage Error (MAPE)  
MAPE is an error metric used to measure the accuracy of forecasting values. It denotes the average absolute percentage deviation of each dataset entry between actual and forecast values [51]. As absolute values are applied, the possibility of negative and positive errors canceling each other out can be avoided. The lower the value of MAPE, the better the model will forecast.  
\[\text{MAPE}=\frac{100\%}{n}\sum_{t=1}^{n}\left|\frac{A_{t}-F_{t}}{A_{t}}\right| \tag{13}\]  
where:  
\(A_{t}\) is the actual value;  
\(F_{t}\) is the forecast value;  
\(n\) = sample size.
## Materials and Methods  
### Materials  
AM was purchased from Jinan Jinrihe Chemical Co., Ltd. (Jinan, China); AMPS was purchased from Zhengzhou Dingshengxin Chemicals (Zhengzhou, China); DMC was purchased from Jinan Duoxin Co., Ltd. (Jinan, China). Sodium hydrogen sulfite, ammonium persulfate and mercaptoacetic acid were purchased from Sinopharm Chemicals (Shanghai, China), and all these materials were analytically pure. All the drilling fluid additives used for preparing the testing WBDFs were provided by China Oilfield Services (Sanhe, China) and Shida Chuangxin technology Co., Ltd. (Dongying, China); sodium bentonite with a cation exchange capacity of 74 mmol/g was purchased from Boyou Bentonite Group Co. Ltd. (Weifang, China), and it consisted of 57.6% montmorillonite, 32% quartz, 8% potassium feldspar, 0.6% kaolinite, 1.2% illite, and 0.6% chlorite. Drilling fluid bentonite was purchased from Weifang Huawei Bentonite Group Co., Ltd. (Weifang, China), following the American Petroleum Institute (API) standard. For the shale recovery rate tests, the shale sample #1 was obtained from the deepwater wells in the South China Sea, and it consisted of 23% clay, 45% quartz, 13% calcite, 10% anorthose, 4% dolomite, 4% halite, and 1% gypsum; the shale sample #2 was obtained from wells in Bohai Bay, and it consisted of 25% clay, 43% quartz, 20% anorthose, 9% potassium feldspar, 2% dolomite, and 1% calcite.
# Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
## 4 Experimentation and Analysis
### ECA-MRANet Model Performance Analysis
#### 4.4.3 Small-Sample Transfer Learning under Different Working Conditions  
In order to verify the effectiveness of the method proposed in this paper, the pre-training model ECA-MRANet is built for Data Sets A, B, and C in Table 4, and the transfer effect of ECA-MRANet is investigated under different working conditions. Figure 18 shows the transfer tasks under six different working conditions. Taking \"A \(\rightarrow\) B\" as an example, this indicates that the pre-training model is built using Data Set A under working condition I while the transfer learning is performed on Data Set B under working condition II. The small-sample transfer in the figure is to use 30% of the target domain Data Set B to train the pre-trained model of Data Set A, and the remaining 70% as a test set. This process is applied to directly assess the pre-trained model of Data Set A,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **X1** & **X2** & **X3** \\ \hline Training set & Data Sets A and B & Data Sets A and C & Data Sets B and C \\ Test set & Data Set C & Data Set B & Data Set A \\ \hline \hline \end{tabular}
\end{table}
Table 7: Working conditions using different combinations of data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **X1** & **X2** & **X3** \\ \hline ECA-MRANet & 96.75\% & 97.14\% & 96.98\% \\ MRANet & 96.26\% & 95.58\% & 95.34\% \\ RANet & 94.23\% & 95.19\% & 94.32\% \\ CNN & 92.76\% & 93.14\% & 92.46\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Diagnostic accuracy of four models under different working conditions.  
utilizing Data Set B to validate the model's diagnostic performance under different working conditions. As depicted in Figure 18, the method proposed in this paper demonstrates strong performance in migration learning across different working conditions, achieving an accuracy exceeding 90%. Notably, the effect of small-sample transfer is better compared with the direct application of pre-trained models, especially in the task of transfer from Data Set A to C. The small-sample transfer can significantly improve the accuracy of the model, and it can also make the pre-trained model adapt to the data under different working conditions to improve the model's generalization ability.