esa-sceva/satcom-chunk-collection huggingface dataset:
# Extended Spatial-Index LED Modulation for Optical MIMO-OFDM Wireless Communication
## 2 System Model
### The Maximum a Posteriori Estimator
In the proposed ESI-LED, the power allocation of the OFDM real part competent is exploited (varied) along with the active LED indices to improve the utilization of LED spatial domain. To determine the active channel (LEDs) for both the real component \(i_{R}\) and the imaginary component \(i_{I}\) as well as the estimated \(\tilde{c}_{n_{R}}\) and \(\tilde{c}_{n_{I}}\), all the output (results) from (17) are found for all prospective joint power allocations \(i_{R}\) and \(i_{I}\) combinations (see Tables 1 and 2, for example) as explained in Algorithm 1. In the algorithm, the number of expected LED combinations is equal to \(2^{p+2}\). ```
0: All the expected LED indices \(i_{R}\) and \(i_{I}\), the received vector \(\mathbf{y}\), and \(H\). 0: For each expected LED indices:
1: Find the values of \(\mathcal{A},\ \mathcal{B},\ \mathcal{C},\ \mathcal{D}\), and \(\mathcal{E}\) by applying (16). 2: Find all the positive values for \(\tilde{c}_{n_{R}}\), and \(\tilde{c}_{n_{I}}\) by applying (17). 3: Store the \(\tilde{c}_{n_{R}}\), and \(\tilde{c}_{n_{I}}\) with their corresponding joint power allocations LED indices. 4: Repeat steps 1, 2, and 3 for each expected joint power allocations LED indices. 5: Outputs : All possible estimated values for \(\tilde{c}_{n_{R}}\), and \(\tilde{c}_{n_{I}}\) with their corresponding LED indices \(\tilde{i}_{R}\) and \(\tilde{i}_{I}\). ```  
**Algorithm 1** Estimating the values of \(\tilde{c}_{n_{R}}\), and \(\tilde{c}_{n_{I}}\) for each expected joint power allocations LEDs indices. Finally, the actual estimation of \(\tilde{c}_{n_{R}}\), \(\tilde{c}_{n_{I}}\), \(\tilde{i}_{R}\), and \(\tilde{i}_{I}\) can be found by  
\[\mathbf{y}=\boldsymbol{h}_{i_{R}}\boldsymbol{c}_{n_{R}}+\boldsymbol{h}_{i_{I}} \boldsymbol{c}_{n_{I}}+\boldsymbol{v}, \tag{6}\]5Having obtained these estimates, the transmitted \(p\) are estimated by locating the group \(\mathbb{G}\) in which the joint power allocation \(\tilde{i}_{R}\), and \(\tilde{i}_{I}\) are located. Finally, the OFDM signs information are estimated based on power allocation \(\tilde{i}_{R}\), and \(\tilde{i}_{I}\).
## 2 Materials and Methods  
### Search Strategies  
A systematic review of the literature was performed using guidelines in the Cochrane Handbook for Systematic Reviews of Interventions (version 5.1.0) and following the checklist for the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2015 (PRISMA). Two investigators (M.G. and B.G.) independently searched six electronic databases, namely, the Cochrane Library, PubMed, Google Scholar, Web of Science, ScienceDirect, and Scopus, to avoid a biased literature sample. The searches covered all available literature data up to December 2023. Keywords were collected through expert opinion, a literature review, and controlled vocabulary (e.g., Medical Subject Headings [MeSH]) [34], with all the conceivable combinations utilizing \"OR\" and \"AND\" in and between these categorical terms: category 1--wedge, incline, decline, slope, degree, change, and position; category 2--foot, feet, heel, ankle, and leg; category 3--muscle, muscular, activity, activation, EMG, and electromyography; category 4--thigh, quadriceps, hamstring, lower limb, lower extremity; and category 5--squat and lifting. Non-English publications were excluded, as were review studies, as well as the following terms (using \"NOT\"): elderly, children, teenager, patients, abnormal, malalignment, and disorder. The search results were screened for titles, abstracts, and full texts by two investigators ([PERSON], [PERSON]), and the third investigator ([PERSON]) in the case of disagreement (Figure 1).
# Partial Order Rank Features in Colour Space
## 2 Background  
### Rank Features  
Rank features are a well established technique for dealing with noise in images, enforcing invariance to all sorts of contrast or illumination variations and sensor nonlinearities [7]. Because of their robustness, they were first developed in the context of wide-baseline stereo matching--see for instance the census and rank transforms [8]. More recently, descriptors in the popular Local Binary Pattern (LBP) family, including Texture Spectrum, Binary Gradient Contours, etc. [9,10] have turned rank features into a general purpose tool, with applications--among others--in texture classification, face recognition, surface inspection and content-based image retrieval [11]. The descriptive power of rank features has been expanded to explicitly capture orientation (Ranklets [12]) and second-order stimuli (Variance Ranklets [13]), all types of information that were seen as the preserve of linear filters or ad-hoc algorithms.  
Common to all rank features is the fact that they are defined in terms of ordinal information between pixels only, with the actual pixel values being discarded. This can be done in terms of pairwise pixel comparisons (rank and census transform, LBP), pixel ranks (Ranklets) or a permutation of ranks (Variance Ranklets), but it is easy to see that the two approaches are equivalent [12] and that all descriptors rely on the natural order relation (\(\leq\)) between pixel values.  
Before proceeding to definitions it is worth noting that, notwithstanding the trend towards the use of convolutional neural networks as feature extractors [14], rank features are still competitive in texture applications [15]. In the following section we recall the axioms for an order relation.
## 4 Experimental Design and Data Collection
### Data Collection  
After building the experimental device, the infrared imager is used to collect video images of the gas at different leakage rates. The specific experimental steps are as follows: 1 Set the leakage rate. Combined with the range of the pressure-reducing valve and the volume of natural gas that can be used, 6 levels of leakage rates were designed, as follows: 2.5 L/min, 5.0 L/min, 7.5 L/min, 10 L/min, 12.5 L/min, and 15 L/min, which correspond to levels 1 to 6, respectively. 2 Set up the infrared imager. Combined with the shooting distance of the infrared imager and the limitations of the experimental site, the shooting distance of this experiment is designed to be 1.5 m. The setting height is 2.2 m. 3 Start recording. Switch the infrared imager to video mode and center the leakage point in the picture. 4 Repeat the experimental process. According to the leakage rate, shooting distance, and shooting angle, the shooting point is adjusted continuously, and the experimental process is repeated to obtain leakage data under various working conditions.  
To obtain more realistic experimental data, the data collection of this experiment is carried out in a natural gas plant in Nanjing, China. The experimental device is set up according to the schematic diagram of the experimental device shown in Figure 5. First, holes were drilled on the seamless steel pipe using professional drilling equipment to simulate the gas leakage points in real situations. Then, the liquefied gas tank and the seamless steel pipe were connected with a hose. The connection needed to be sealed and fixed. A gas leakage infrared detector was set up at the pre-designed point, and the tripod height and detector angle were adjusted. The liquefied gas switch was turned on, and the gas flow rate was controlled by the brass pressure-reducing valve. Figure 6 shows the state of the leaking gas at different leakage rates taken at a distance of 1 m, focusing on the shape of the gas column.  
Figure 5: Schematic diagram of the experimental site model.
## 4 Experiments
### Evaluation Metrics  
We employ the mean average precision at an intersection over union (IoU) threshold of 0.5 (mAP50) as our evaluation metric. This is a comprehensive indicator that can fully measure the overall performance in multi-class detection tasks. The prediction is considered correct when the IoU between it and the ground truth (GT) object exceeds 0.5. In FSOD, mAP50 is obtained by calculating the average precision (AP) for each novel class at this threshold and then averaging these AP values. The specific calculation formula is as follows:  
\[P=\frac{TP}{TP+FP} \tag{15}\]  
\[R=\frac{TP}{TP+FN} \tag{16}\]  
\[AP=\int_{0}^{1}P(R)\,dR \tag{17}\]  
\[mAP=\frac{1}{N}\sum_{c=1}^{N}AP_{c} \tag{18}\]  
Here, \(TP\) stands for true positives, \(FP\) for false positives, and \(FN\) for false negatives. The term \(P\) refers to precision, while \(R\) refers to recall. The variable \(c\) indicates a particular class, and N denotes the total number of classes.  
A higher value of mAP50 indicates that the model can more effectively balance precision and recall in the detection task, reflecting improved overall performance. The mAP values are presented in percentage form (mAP%).
## 2 Theoretical Background  
### Message Queue Telemetry Transport (MQTT)  
The Message Queuing Telemetry Transport (MQTT) protocol [5] is a lightweight, open-standard publish-subscribe messaging protocol designed for constrained environments and low-bandwidth, high-latency networks. Standardized by OASIS and ISO/IEC 20922, MQTT is widely adopted in Internet of Things (IoT) applications to facilitate efficientcommunication between distributed devices and services. Figure 1 illustrates a standard MQTT communication model secured by TLS. In this architecture, the MQTT Publisher sends messages on a specific topic (e.g., Topic1) to the MQTT Broker over a TLS-secured session. The MQTT Subscriber, which has previously subscribed to the same topic, also communicates with the broker via another TLS session, ensuring that both data transmission and topic subscriptions are encrypted. This setup ensures the confidentiality and integrity of the messages exchanged over potentially insecure networks.  
MQTT operates [5] using a client-broker-client architecture. Publishers send messages to a centralized broker, which then distributes them to all subscribed clients based on hierarchical topic filters. This model ensures decoupling between data producers and consumers, enhancing scalability, interoperability, and system modularity. However, MQTT inherently lacks direct end-to-end communication between clients. MQTT Version 5.0 [5] introduces several protocol-level enhancements that address limitations in previous versions and improve flexibility, robustness, and extensibility [5]. Notable features include:  
1. Reason Codes: provide explicit acknowledgment feedback, enabling more granular error reporting and diagnostic capability.
2. Shared Subscriptions: allow multiple clients to share a single subscription, supporting distributed processing and load balancing.
3. Message Expiry Intervals: define the validity period of a message, preventing outdated data from being delivered.
4. Topic Aliases: replace long topic strings with shorter aliases to reduce payload size, which is beneficial for low-bandwidth links.
5. User Properties: enable the transmission of custom metadata through MQTT packets for application-specific use cases.  
Security in MQTT is typically implemented through Transport Layer Security (TLS), which ensures confidentiality, integrity, and authentication of transmitted data. However, MQTT itself does not enforce any specific security mechanisms, placing responsibility on the application layer to implement key management, access control, and encryption policies appropriate for the deployment context. For full protocol specification and implementation guidance, the official OASIS documentation of MQTT Version 5.0 serves as the authoritative reference [5].
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 6 Conclusions  
This paper draws on the research approach of Starlink staff in boosting satellite network performance, presenting SatScope--a low-cost, highly flexibility, scalable real-time satellite network emulator with acceptable fidelity. From SatScope's simulation results, we analyze the service performance and topological similarity of single-shell satellite networks. Extensive simulations show their service performance across various scenarios, with key statistics like terrestrial node IP coverage and satellite-served IP counts. In the topological similarity study, we find that each satellite network connection switch changes 38.94% of nodes and 83.66% of links on average. This major finding offers precise indicators for handover and routing strategy research, aiding researchers in optimizing LEO satellite internet communication systems to enhance overall performance and reliability.  
At present, research on terminal user positioning technology for LEO satellite internet is limited. This poses a major challenge for SatScope in achieving high-precision positioning of terminal users in satellite networks. Traditional satellite positioning methods are typically confined to the physical layer. However, from a network layer perspective, correlating logical addresses (IPs) with physical addresses (geographic coordinates) is a novel and challenging topic for satellite networks. To effectively tackle this challenge, we plan to explore a multi-source positioning-based IP positioning technology for satellite networks. By integrating information from multiple data sources, this approach is expected to significantly enhance positioning accuracy and play a crucial role in improving the overall positioning precision of our model.  
Conceptualization, Q.W. and G.Y.; methodology, Q.W.; software, Q.W., Y.L. and C.C.; validation, Q.W. and Q.Z.; writing--original draft preparation, Q.W. and G.Y.; writing--review and editing, Q.W., G.Y. and S.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new dataset was created in this study, and the acquisition methods for all relevant data have been detailed in the paper.  
The authors declare no conflicts of interest.  
Figure 12: Minimum node and edge edit distance. SatScope conducted a continuous simulation for 8 min, with a direct interval of 15 s between adjacent topologies.
## 3 Control of Thermal Deformation Optimization Strategy
### Orbital Temperature Distribution
#### 3.1.2 Analysis of Temperature Distribution
It needs to be coupled with \(\pm\)Y planes to dissipate heat generated by the high-power operation of phased array antennas. With the phased array antenna as the reference feature point, the minimum heat dissipation capacity of +Z and \(\pm\)Y cooperative honeycomb planes was defined as the high temperature condition. Figure 9 shows the corresponding heat dissipation capabilities of the +Z and \(\pm\)Y planes. When the \(\beta\) angle is \(\pm\)60\({}^{\circ}\), the heat dissipation capacity of the +Z and \(\pm\)Y planes is the lowest. Therefore, the external heat flow condition is selected as the analysis input for the high temperature condition when the \(\beta\) angle is \(\pm\)60\({}^{\circ}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Equipment Name**} & \multirow{2}{*}{**Maximum Power/W**} & \multicolumn{2}{c}{**High Temperature**} & \multirow{2}{*}{**Low Temperature**} \\  & & & **Condition/W** & & **Condition/W** \\ \hline
1 & Ka band phased array transmitting antenna & 250 & 250 & 0 \\
2 & Ka band phased array transceiver antenna 1 & 314.3 & 314.3 & 0 \\
3 & Ka band phased array transceiver antenna 2 & 314.3 & 314.3 & 0 \\
4 & Combined processing load & 409 & 409 & 0 \\
5 & Terahertz test load & 184.4 & 0 & 0 \\
6 & Laser communication load 1 & 53 & 53 & 53 \\
7 & Laser communication load 2 & 53 & 53 & 53 \\
8 & Other loads & 423.2 & 392.2 & 7 \\
9 & electric propulsion & 527 & 0 & 0 \\
10 & Platform power consumption & 270.2 & 253.4 & 161.4 \\
11 & Heating belt compensates power & / & 0 & 613.2 \\  & consumption & Total & 2798.4 & 2039.2 & 887.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary statistics of internal heat source power. Figure 10 shows the heat dissipation capacity of the satellite at different \(\beta\) angles. In the range of \(\beta\) angle \(-60\)-\(+60^{\circ}\), the heat dissipation capacity of the satellite is stable. When \(\beta\) angle is greater than \(\pm 60^{\circ}\), the heat dissipation capacity of the satellite is continuously enhanced due to the solar panel blocking the external heat flow of the sun. According to the \(\beta\) angle simulation, when the \(\beta\) angle is \(\pm 90^{\circ}\), the heat dissipation capacity of the satellite is the strongest, so define that condition as the low temperature condition. The temperature distribution under high- and low-temperature conditions is shown in Figure 11.  
Figure 10: The heat dissipation capacity of the satellite. Figure 9: Heat dissipation capability of the +Z and \(\pm\)Y planes.
## 4 Experimental Results
### Parameter Setting  
In this paper, all algorithms are implemented based on MATLAB2021A and PyTorch [36]. Firstly, the DNN runs on PyTorch. The DNN-PLC method uses two deep neural networks with the same structure for learning amplitude spectrum and phase spectrum of speech, respectively. Each DNN contains four hidden layers, and the number of neurons is 2048. Adaptive moment estimation (Adam) [37] is used as the optimization function to update and optimize the network parameters. The DNN used for learning the amplitude spectrum employs log-power spectra of some speech frames as input features and the log-power spectrum of the target speech frame is taken as output feature. According to [21], when log-power spectra of 11 frames before the target frame are used as the input features, the learning effect of the DNN is the best. Thus, in this experiment, \(T\) is chosen as 11. Similarly, the input features of the neural network for learning the phase spectrum come from 11 frames of speech before the target frame, and output feature is the phase spectrum of the target frame.  
Secondly, the parameters of WSOLA in the WSOLA-PLC method and the DNN+WSOLA-PLC method are set as follows: the value of the time scale factor \(\alpha\) is 1.55, the sliding distance \(\Delta z\) between the segments is 10 ms (160 samples) when the segments are superimposed, the search radius \(\Delta_{max}\) is 5 ms (80 samples), the input signal is composed of 6 correct data packets in front of the lost packet, and its length is 960 samples, and the output signal is composed of 7 packets (6 correct packets and 1 concealment packet) with a length of 1120 samples.  
Finally, in all the methods of the experiments, the frame length of the signal is 20 ms (320 samples), and the frame shift is 50% (160 samples). The sinusoidal window with the length of 20 ms is used, 512-point FFT (with 192 points zero-padding) is used on the signal, and 257-dimension log-power spectrum and phase spectrum are used.
# Path Planning of Intelligent Mobile Robots with an Improved RRT Algorithm
## 2 Improved RRT Path Planning Algorithm  
### The Fundamentals of RRT Algorithm  
In the field of autonomous driving, the Rapidly Exploring Random Tree (RRT) algorithm based on sampling planning is widely used for path planning. The idea is to treat the initial node as the root node and, through a finite number of random samples, increase the leaf nodes to generate a randomly expanding tree. When the leaf nodes of the random tree reach the target point or target area, an optimal or suboptimal collision-free path can be found from the initial node to the target point within the tree. As shown in Figure 1, the green area represents the final path points, the red area indicates obstacle collision detection, the gray dashed area is the reachable region, and \(\mu\) represents the step size.  
The RRT algorithm has strong search capabilities, and as long as there is a feasible path in the environment, the RRT algorithm can find a feasible path. However, the traditional RRT algorithm, while exhibiting strong randomness, also introduces several drawbacks. For example, the direction of growth of the random tree during expansion is quite random, leading to low search efficiency, slow convergence speed, and issues such as lack of stability and deviation from the optimal solution in dynamic environments.  
To optimize the traditional RRT algorithm, this paper proposed a variable steering angle strategy that integrates Kalman filtering with the RRT algorithm and incorporates collision detection. This approach filters redundant particles through Kalman filtering, thereby enhancing search efficiency and accuracy.
## 3 Approach
### DDPG with DMP-Based Policy and Adaptive Inverse Controller  
In this subsection, we introduce the proposed method DDPG-DMP as a novel framework to integrate the DMP-based policy into the traditional actor-critic RL-approach DDPG. The principle and the workflow of the proposed method are illustrated in Figure 2. Compared to an NDP, which employs policy networks and value function critic networks, the proposed method is mainly based on the traditional actor-critic framework where the critic networks estimate the q function in order to inherit the off-policy characteristic from DDPG. Given the input state as \(\mathbf{s}_{t}\), the actor networks in DDPG-DMP output two sets of parameters: \(\mathbf{\phi}\) and \(\mathbf{\rho}\). The first set \(\mathbf{\psi}=[\mathbf{g},\mathbf{w}]\) is consistent with the NDP and presents the target position and the weights vector required by the DMP. The second set \(\mathbf{\rho}\) is simultaneously passed to an adaptive inverse controller as its neural networks' parameters.  
Using this setup, the DMP generates an \(m\)-step trajectory \([\mathbf{y}_{j},\mathbf{\dot{y}}_{j},\mathbf{\dot{y}}_{j}]_{j=1}^{m}\) with position, velocity, and acceleration information based on parameter set \(\mathbf{\psi}\) using Equation (14). The adaptive inverse controller then converts it into a discrete control trajectory \(\mathbf{a}=[\mathbf{a}_{t},\mathbf{a}_{t+1},...,\mathbf{a}_{t+K-1}]\) with the consideration of the information related to velocity and acceleration. Compared with the inverse controller in an NDP, the adaptive inverse controller proposed in DDPG-DMP enhances the capability of the RL framework in fine-tuning and translating the trajectories generated by the DMP, especially when the control variables are not included in the observed states (e.g., adaptively mapping from joint angles to torque or end-effector position). It contributes to the superior feasibility of the RL and DMP-based policy in a wide range of robot systems without requiring additional human prior knowledge. In practice, the adaptive inverse controller is implemented as an additional neuron (with a tanh activation function) at the end of the actor networks, with its weights and the bias of this neuron determined by \(\mathbf{\rho}\). Unlike an NDP, where the DMPand inverse controller are external processes of the actor networks, the DDPG-DMP considers \(\pi_{\mathbf{\phi}}\), DMP, and adaptive inverse controller as three parts of the entire actor network, outputting a control action trajectory with \(K=5\) steps based on the input state \(\mathbf{s}_{t}\).