esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Challenges in Connection with the Running Reference Method
Some potential users may perceive it as a challenge that the running reference analysis method needs a (semi-)continuous data history to work. The differences in the physical state of the fiber from one measurement to the next must be small enough to not lead to disturbances in the strain data retrieved. This may require taking more measurements than usual. However, this process can be automated, and free software for automated measurements is provided for the reader [53]. The only drawback of the running reference method is the necessity to load not just the new measurement-file, but also the new reference file into the proprietary OBR analysis software that comes with the OBR instrument for each measurement. This considerably increases the work of analyzing the data. However, again, automatization of this task will not require supervision by the user, and free software is provided on GitHub [53] for that purpose. These are the main challenges that may lead engineers and scientists to perceive the running reference method as too cumbersome to use. However, all four of these issues can easily be overcome by automatizing the involved processes: measuring, data analysis, outlier correction and adding up the strain differences to the absolute strain values.
## 4 Engineering Application of Roadway Expansion Using SCA
### Construction Techniques and Effectiveness  
The construction process for static cracking agent at the project site is as follows: Remove external shotcrete and steel mesh--42 mm borehole drilling. Mix static cracking agent dry material with water according to the water-cement ratio. Pour static cracking agent slurry. Wait for the static cracking agent to expand and crack the lane-side rock. Extract stone blocks along the fissures with pneumatic pick and drill. Support with shotcrete. Complete expansion. The on-site drilling construction results of the roadway expansion are shown in Figure 11.  
Figure 10: Layout design of SCA drilling.  
After the static cracking agent is injected into the boreholes, the crack propagation effect of roadway rock mass is as depicted in Figure 12. During roadway expansion, faint rock fracturing sounds can be heard during the reaction period. After 8 h of grouting, cracks begin to emerge around the drill hole and progressively extend outward, with through-cracks forming first around the longitudinal boreholes. As these cracks interconnect, their width increases over time. Finally, the cracks primarily extend longitudinally across the face, accompanied by minor transverse cracks. Considering the coal mine's construction schedule, the roadway expansion work needs to be carried out immediately 12 h after the static cracking agent infusion. The pneumatic pick and pneumatic drill are used to dig out rocks along the cracks, speeding up the roadway expansion process. After the expansion, observing the rock fracture surface, as shown in Figure 13, the cracks induced by the static cracking agent develop vertically along the boreholes into larger surfaces, demonstrating a clear fracturing effect for roadway expansion.  
Because explosive use is restricted at the construction site of this mine, and large rock-breaking equipment could not be used due to space limitations, the use of only pneumatic drills and pickaxes to break the rock and expand the roadway had a low efficiency, with a daily progress of only 0.5 m/day. However, after using static cracking agents, the daily progress increased to at least 1.2 m/day, significantly improving the efficiency of the roadway expansion, reducing the time required for the expansion, saving labor, and generating substantial economic benefits.  
Figure 11: Construction results of SCA drilling.  
Figure 12: Roadway crack propagation after SCA charged.
## 1 Introduction
This strategy can be combined with a sequential convex programming method to develop a closed-loop guidance control scheme that robustly drives a spacecraft maneuvering close to the target [29]. This strategy can also be used to control mobile robots to locate sensor nodes in unknown wireless sensor networks [30]. The strategy has proven to be effective to enhance the scheme quality and reduce the computational burden by dividing the optimization horizon into smaller time windows [31]. The underlying motivation of this paper lies in the need for combining the receding-horizon strategy with Lame-curve blending for vision guidance in the limited field of view of mobile robots. Our main contribution is twofold. On the one hand, a multi-loop receding-horizon control framework is outlined for vision guidance of mobile robots. Herein, cascaded control loops of path tracking, robot control and drive control are developed based on kinematics and dynamics models. On the other hand, Lame-curve blending is proposed for kinematic path tracking to generate a trajectory with \(G^{2}\)-continuity to blend its current posture with a certain point of the guide path. The balance of the paper is organized as follows: the problem description and the receding-horizon control framework for vision guidance of mobile robots are addressed in Section 2; the kinematics and dynamics models are formulated for DWMRs in Section 3; the kinematic path tracking technique based on Lame-curve blending is proposed in Section 4; simulation results are reported in Section 5, while conclusions and recommendations for future research work are given in Section 6.
# Learning State-Specific Action Masks for Reinforcement Learning
## 1 Introduction
In addition, we propose a novel metric known as Bisimulation Metrics on Actions by States (BMAS), which leverages the concept of bisimulation [22] to gauge the behavioral dissimilarity between actions. Our principal contribution lies in the practical learning procedure for updating the mask model, comprising two alternative stages. In the initial stage, we acquire transition and reward models to forecast the consequences of all actions. Subsequently, we refine the mask model with supervised learning while the mask labels are generated through aggregating states and rewards. We also introduce an auxiliary feature vector for simultaneous aggregation, ensuring that the labels inherit both properties. When training the mask model, transition data can be collected by any policy, indicating that the mask model is separated from the learning procedure of an RL policy. Therefore, our framework can be seamlessly integrated into various RL algorithms. For illustration, we apply it to two representative algorithms, namely Deep Q-Network (DQN) and Proximal Policy Optimization (PPO). We conduct extensive experiments in environments with discrete action spaces, including Maze, Atari, and \(\mu\)RTS2. To the best of our knowledge, this work represents the pioneering effort in the automatic learning of action masks for RL, and the key contributions are summarized as:  
* Bisimulation Metrics on Actions by States (BMAS): The paper introduces a novel metric, BMAS, based on the concept of bisimulation, which quantifies the behavioral differences between actions by states. * Automatic Action Masking: The paper introduces a method for automated acquisition of state-specific action masks in RL for environments with discrete action spaces, alleviating the need for expert knowledge or manual design. This contributes to making RLmore applicable in real-world scenarios by accelerating exploration efficiency while preserving strong interpretability. * Experimental Validation: The paper demonstrates the effectiveness of the proposed approach through comprehensive experiments conducted in environments with discrete spaces, including Maze, Atari, and \(\mu\)RTS2. The results showcase compelling performance, complemented by visualizations that underscore the interpretability of the proposed method.
## 4 Experimental Design and Results
### Case 1: The Performance of HEMOGWO on Benchmark Functions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Function** & & **HEMOGWO** & **SMOGWO** & **MOGWO** & **MOEAD\_DE** & **MOPSO** & **NSGA-III** \\ \hline CF1 & Mean & \(\mathbf{3.89\times 10^{-3}}\) & \(6.77\times 10^{-3}\) & \(4.89\times 10^{-3}\) & \(6.86\times 10^{-1}\) & \(3.74\times 10^{-1}\) & \(8.55\times 10^{-2}\) \\  & Std & \(\mathbf{5.68\times 10^{-4}}\) & \(1.19\times 10^{-3}\) & \(1.89\times 10^{-3}\) & \(8.32\times 10^{-2}\) & \(1.44\times 10^{-1}\) & \(7.69\times 10^{-3}\) \\  & Mean & \(3.12\times 10^{-3}\) & \(\mathbf{2.85\times 10^{-3}}\) & \(4.38\times 10^{-3}\) & \(4.01\times 10^{-2}\) & \(1.27\times 10^{-1}\) & \(7.70\times 10^{-2}\) \\ CF2 & Std & \(\mathbf{5.40\times 10^{-4}}\) & \(6.38\times 10^{-4}\) & \(1.56\times 10^{-3}\) & \(1.46\times 10^{-2}\) & \(8.57\times 10^{-2}\) & \(2.47\times 10^{-2}\) \\  & Mean & \(\mathbf{3.27\times 10^{-2}}\) & \(4.08\times 10^{-2}\) & \(3.93\times 10^{-2}\) & \(4.41\times 10^{-1}\) & \(6.92\times 10^{-1}\) & \(4.17\times 10^{-1}\) \\ CF3 & Std & \(1.06\times 10^{-2}\) & \(\mathbf{5.30\times 10^{-3}}\) & \(8.28\times 10^{-3}\) & \(1.06\times 10^{-1}\) & \(3.00\times 10^{-1}\) & \(9.52\times 10^{-2}\) \\ CF4 & Mean & \(\mathbf{3.57\times 10^{-3}}\) & \(4.11\times 10^{-3}\) & \(4.61\times 10^{-3}\) & \(2.08\times 10^{-1}\) & \(2.07\times 10^{-1}\) & \(1.39\times 10^{-1}\) \\  & Std & \(\mathbf{1.57\times 10^{-4}}\) & \(1.61\times 10^{-3}\) & \(1.21\times 10^{-3}\) & \(8.24\times 10^{-2}\) & \(6.75\times 10^{-2}\) & \(8.28\times 10^{-2}\) \\ CF5 & Mean & \(2.70\times 10^{-2}\) & \(\mathbf{2.21\times 10^{-2}}\) & \(2.97\times 10^{-2}\) & \(3.32\times 10^{-1}\) & \(6.34\times 10^{-1}\) & \(3.79\times 10^{-1}\) \\  & Std & \(1.54\times 10^{-2}\) & \(\mathbf{4.90\times 10^{-3}}\) & \(1.30\times 10^{-2}\) & \(1.11\times 10^{-1}\) & \(3.01\times 10^{-1}\) & \(1.10\times 10^{-1}\) \\  & Mean & \(\mathbf{2.75\times 10^{-3}}\) & \(2.97\times 10^{-3}\) & \(3.32\times 10^{-3}\) & \(1.35\times 10^{-1}\) & \(1.74\times 10^{-1}\) & \(1.36\times 10^{-1}\) \\ CF6 & Std & \(\mathbf{3.37\times 10^{-4}}\) & \(9.81\times 10^{-4}\) & \(1.03\times 10^{-3}\) & \(1.74\times 10^{-2}\) & \(6.70\times 10^{-2}\) & \(4.65\times 10^{-2}\) \\  & Mean & \(\mathbf{4.04\times 10^{-2}}\) & \(4.92\times 10^{-2}\) & \(6.64\times 10^{-2}\) & \(3.75\times 10^{-1}\) & \(1.65\times 10^{0}\) & \(3.37\times 10^{-1}\) \\ CF7 & Std & \(\mathbf{1.18\times 10^{-2}}\) & \(1.22\times 10^{-2}\) & \(2.94\times 10^{-2}\) & \(1.47\times 10^{-1}\) & \(2.38\times 10^{0}\) & \(1.39\times 10^{-1}\) \\ \(+/\approx/-\) & & & \(11/3/3\) & \(14/2/1\) & \(17/0/0\) & \(17/0/0\) & \(17/0/0\) \\ \hline \hline \end{tabular} Bold indicates the best result under each indicator.  
\end{table} 
Table 6: _Cont._Figure 2: Results of all algorithms on benchmark function UF2,ZDT3, and CF1. The experimental results show that the HEMOGWO algorithm has better convergence and diversity in solving multi-objective problems, which proves the effectiveness of the improved strategy.
## 6 Enabling Technologies in Healthcare
### Blockchain Technology  
Blockchain technology possesses the potential to revolutionize healthcare by enabling the secure storage and transfer of patient data. This technology is utilized to establish a more secure, accurate, and transparent system for maintaining and sharing medical records and other health-related data. It contributes to the reduction of medical errors, enhances data security, and facilitates the tracking and analysis of patient health trends. By employing blockchain technology, healthcare data can be securely stored and remain immutable. It enhances the precision of medical records and diminishes medical errors. Healthcare data, including patient records, medical bills, clinical trial results, and other pertinent information, can be stored using blockchain technology. Furthermore, it holds the potential to streamline administrative tasks, reduce costs, and improve the quality of care. Additionally, blockchain technology safeguards patient privacy, ensuring that only authorized users can access sensitive health information. A significant advantage of utilizing blockchain technology in healthcare is the elimination of intermediaries such as insurance companies, for mediating transactions between patients and healthcare providers. This reduction in intermediaries decreases costs and enhances the security and transparency of transactions. Moreover, it facilitates more efficient data sharing between healthcare providers, leading to more accurate diagnoses and effective treatments. Data provenance, robustness, decentralized management, security, and privacy are considered vital aspects of blockchain technology in health and medical services [66]. Although blockchain technology has the potential to revolutionize healthcare, its adoption also entails risks and challenges. The technology is still in its early stages of development, and its scalability remains uncertain. Additionally, implementing blockchain technology in healthcare requires significant investment from healthcare organizations, with no guarantee of success.
# Cognitive Interference Cancellation with Digital Channelizer for Satellite Communication
## 4 Cognitive Interference Cancellation
### Simple Interference Detection  
The cognitive interference detection and cancellation scheme is developed with a 1024-subchannel digital channelizer. For the \(m\)-th subchannel, an average signal power is measured as  
\[T(m)=\frac{1}{L}\sum_{i=1}^{L}|\bar{y}(i,m)|^{2}, \tag{3}\]  
where \(L\) is the number of samples for power measurement for each subchannel. In this work, this is denoted by the number of subchannel blocks used for power measurement. The output of the digital channelizer for the the \(n\)-th subchannel, which is assumed to include the tone interference, is given by  
\[\left\{\begin{array}{ll}\bar{y}(i,n)=\bar{s}(i,n)+\bar{x}(i,n)+\bar{w}(i,n),&\text{under }\mathcal{H}_{1},\\ \bar{y}(i,n)=\bar{x}(i,n)+\bar{w}(i,n),&\text{under }\mathcal{H}_{0},\end{array}\right. \tag{4}\]where \(H_{0}\) and \(H_{1}\) denote the null and alternative hypothesis, i.e., under \(H_{0}\) where we assume the tone interference does not exist, and under \(H_{1}\) where the tone interference is assumed to exist. Other subchannels without interference are given by  
\[\tilde{y}(i,m)=\tilde{x}(i,m)+\tilde{w}(i,m),\ \ \text{for}\ m\
eq n. \tag{5}\]  
Detection of tone interference is done by comparing the measured average signal power with a given threshold \(\lambda\). To correctly detect the existence of the interference, the following conditions are satisfied:  
\[\left\{\begin{array}{l}T(n)\geq\lambda,\\ T(m)<\lambda,\ \ \ m\
eq n.\end{array}\right. \tag{6}\]  
For no false alarm, i.e., the correct detection of absence of the interference, the following condition is used:  
\[T(m)<\lambda,\ \ \text{for all}\ m\in\{0,\cdots,1023\}. \tag{7}\]  
The proposed cognitive interference detection scheme is easy to implement because the structure of the digital channelizer is inherently utilized. The required additional complexity is negligible. However, the detection performance of the simple algorithm is a problem. With a given number of blocks \(L\), e.g, \(L\leq 10\) (total number of time domain samples \(\leq\) 10240), the detection probability for a given false alarm rate is too low to cancel the interference. In other words, a large number of signal blocks is needed, i.e., the output of the digital channelizer, to achieve the acceptable detection performance.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 5 Implementation of On-Board Systems
### Active Approach
#### 5.1.2 Defect Detection Principles  
The pair of receivers placed at the two sides of the transmitter are employed in a differential detection scheme; this compares the strength of the signal received at both sides. If an internal flaw is present on one side of the transmitter or on the other one,  
Figure 12: Schematic of the non-contact defect detection system based on ultrasonic guided waves: (**a**) side view and (**b**) top view. The two red lines indicate the possible locations of a _vertical split head_ and a _transverse defect_[46].
# An Improved Algorithm for Detecting Pneumonia Based on YOLOv3
## 2 Materials and Methods
### Methods
#### 2.2.2 Double K-Means  
The anchor box is a preset bounding box size. Regression of the anchor box helps to improve the localization accuracy of the algorithm. K-means is used in Yolov3 to generate the anchor box. In the algorithm proposed in this study, double K-means is used to generate the anchor box for lesions proposed in a specific method for the pneumonia dataset. The method consists of two phases. In the first phase, K-means is used to generate the lung anchor box for the algorithm to locate the lung. In this study, the lung anchor box with three scales of {[78; 136], [129; 207], [163; 256]} were generated. In the second phase, K-means is used again to generate one scale ratio for each lung anchor box; therefore, in PYolo, the anchor box of three scales was obtained for lesions. Figure 5 shows the steps involved in generating the scaling ratio, where the lesion-bounding box and lung-bounding box are clustered into the three clusters shown in the Figure 5 through K-means clustering. The mean IOU for the two kinds of bounding box in each cluster is calculated to obtain the scale ratio. In this study, the scale ratio of the anchor box with the size of {[78; 136], [129; 207], [163; 256]} was {0.7, 0.7, 0.5}.  
Figure 4: Mapping relationship between the image and feature. In the left image, the red and green boxes are ground-truth boxes. In the right feature, the red box is a ground-truth box, and the yellow box is an anchor. The center points of the anchor and ground-truth bounding boxes that fall on the same grid cell were designated as positive samples, and other anchors were designated negative samples. If the Intersection over Union (IOU) of the anchor and ground truth is greater than the threshold but the center points do not fall on the same grid cell, then the anchor it is regarded as a negative sample.
## 3 Results and Discussion
### Effect of Different Refrigerants on Heat Transfer Characteristics  
In order to study the influence of different refrigerant properties on the heat transfer of the system, R134a and R22 refrigerant are selected for comparison in this experiment, and the change of heat transfer with the refrigerant filling rate is shown in Figure 8 below. It can be seen from Figure 8 that the change of heat exchange rate of R134a refrigerant with refrigerant filling rate is basically the same as that of R22, and its optimal refrigerant filling rate range is about 65% to 79%, but the heat exchange rate of R134a is about 5% lower than that of R22 as a whole, because the latent heat of R22 gasification is slightly higher than that of R134a, and under the optimal refrigerant filling rate, the heat exchange of refrigerant is mainly two-phase heat exchange.  
The pressure at the inlet and outlet of R134a and R22 evaporators increase with the increase of refrigerant filling rate, and increase rapidly under the condition of a low refrigerant filling rate. Under the same conditions, the inlet and outlet pressure of R134a are lower than R22. At the optimal filling rate, R134a pressure is only about 580 kPa, while R22 pressure is about 870 kPa. The pressure drop of R134a and R22 in the evaporator is very small, the pressure drop is about 10-15 kPa. Figure 9 shows the temperature of R22 and R134a at the inlet and outlet of the evaporator at different refrigerant filling rates. It can be seen from the figures that the outlet temperature of the evaporator is close to 35 \({}^{\circ}\)C at low refrigerant filling rates, and the refrigerant at the outlet of the evaporator becomes superheated steam at low refrigerant filling rates. The heat exchange capacity is almost 0, and the refrigerant temperature is close to the inlet air temperature. With the increase of refrigerant filling rates, the temperature at the inlet and outlet of the refrigerant in the evaporator is close to the inlet air temperature which the temperature at the outlet is approaching gradually. When the refrigerant filling rate reaches 55%, then the temperature difference between the inlet and outlet of the two refrigerants approaches, and the heat exchange increases gradually. When the refrigerant filling rate reaches 65-79%, the refrigerant in the evaporator is in the state of a two-phase flow, at this time, the heat exchange is mainly latent heat, and the heat exchange is the largest. However, compared with R134a, the temperature difference between the inlet and outlet of R22 is larger. To sum up, in the optimal range of refrigerant filling (65-79%), the system has the largest heat exchange and the smallest temperature  
Figure 8: Heat exchange and refrigerant inlet and outlet pressure in evaporator.  
difference between the inlet and outlet of the refrigerant in the evaporator. It can be seen that different refrigerants operate in different states in the backplane system of the heat pipe, and the influence of different refrigerants should be considered in the actual application of the backplane heat pipe system.
## 5 Results
### Laboratory Tests at PEL  
The first test for the LIBS instrument alongside the LRU rover took place in October 2021. At that time, the instrument box still featured a pin-shaped 3D-printed distance pin. Additionally, the instrument case was covered with a thin yellow fabric that could be quickly removed to facilitate visual checks and hardware adjustments. In the experiments, the LIBS instrument was docked in a standard payload application. Initially, the instrument was placed on the payload carrier of the LRU, and the robotic arm was in its parking position. The LIBS was then manipulated onto a sample rock of convenient height using the manipulator arm. This rock served as a test target, and the arm positioned the instrument so that the distance pin touched the target from above, and the laser aperture faced down on the target. Subsequently, a firing sequence of the laser was automatically initiated. As depicted in Figure 8, the box was still tethered by a power cable, bypassing the power connector of the docking interface. This was necessary, as the power required for firing the laser had to be conveniently buffered by the LIBS instrument's PBIMS. Since the two development tracks were carried out at different locations (DLR-OS in Berlin and DLR-RM in Oberpfaffenhofen), the adjustment of the integrated buffer had to be performed based on measurement data derived from the tests. Throughout the tests, firing sequences were successfully executed, and all data needed for the remaining optimizations to ensure the safe and reliable operation of the instrument were collected. The distance pin was found to be too weak for consecutive measurements and was subsequently redesigned as a cylindrical baffle for the dress rehearsal. Following this dress rehearsal experiment, the LIBS module was returned to DLR-OS in Berlin.  
Figure 8: LRU2 with LIBS module taking a measurement of a rock during testing at the planetary exploration laboratory (PEL) at DLR Oberpfaffenhofen.