esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Packet Duplication for Industrial URLLC
## 2 Background  
### Industrial Networks  
#### 2.1.1 Wireless Connectivity in Industry  
Wired connections have been widely used in industrial networks, such as ProfiNET, EtherCAT and the set of Time Sensitive Networks (TSN) protocols. Nevertheless, wired infrastructures are costly in terms of installation and maintenance. These are also not suitable for novel Industry 4.0 use cases, such as mobile robots.  
In wireless technologies, there is a division between two types of networks that enables different applications. The division is regarding Local Area Networks (LAN) and Wide Area Networks (WAN). LANs have a coverage range of up to 100 m; covering areas, such as rooms or even full factories. The main wireless LAN technologies are based on the IEEE 802.11 family--commonly named WiFi. Moreover, there exist customized solutions for factories, based on IEEE 802.15.1 and 802.15.4, such as Wireless Interface to Sensors and Actuators (WISA) and WirelessHART. These technologies operate in an unlicensed spectrum and suffer from poor scalability [21].  
On the other hand, WANs provide a higher coverage range, from distances of a few kilometers up to whole countries. The most extended and known wireless WANs are the 3 GPP-based technologies (GSM, GPRS, EDGE, UMTS, LTE and 5G). As cellular networks operate at a licensed spectrum, a better performance can be obtained. Cellular networks provide ubiquitous connectivity beyond the limits of the factory.  
This means that production that is geographically distributed can be monitored and managed using a single network. As WANs may be provided as a service by cellular network operators, they do not imply the acquisition, installation and maintenance of infrastructure, allowing multi-tenancy and, therefore, resulting in lower costs [22] and improved connectivity.  
Wireless networks generally suffer from a higher latency and packet loss probability than wired networks. For this reason, the recent generations of cellular networks have introduced different optimizations for reducing the latency and also improving reliability.  
For 5G, Industry 4.0 is one of the main development verticals, where its applications have been explored for a special network design. One of the main novelties in 5G is the introduction of MEC [20]. MEC consists of moving the application servers to the network edge, thus, reducing the path that a packet must travel. This approach reduces the latency for the devices and the network load beyond the RAN.
## Appendix A EPIC Aerosol Analysis  
The aerosol optical centroid height (AOCH) retrieved from the EPIC (Earth Polychromatic Imaging Camera) observation adopted the oxygen absorption spectroscopy method [63; 64; 68]. We assume the aerosol vertical extinction profile follows a quasi-Gaussian distribution characterized by the AOD, extinction peak height, and half-width parameter. The extinction peak height retrieved by our algorithm represents the altitude where the aerosol extinction has the maximum value, so it is named as the optical centroid height. The half-width parameter is assumed as 1 km. The optical properties of the aerosol follow the smoke aerosol model in [PERSON] et al. (2021) [69]. The algorithm first retrieves the AOD using the 443 nm EPIC channel, then derives the AOCH using the ratio of O2 A and B bands to their neighboring continuum bands (688/680 and 764/780 nm). The cloud screening is conducted through the spatial homogeneity tests at 443 and 551 nm, together with the brightness tests at 443 and 680 nm over land surface and 443, 680 and 780 nm for water surface. The retrievals with a sun glint angle lower than 30\({}^{\circ}\) over water surface are also screened [70]. The surface reflectance over land comes from the 10-year climatology of Lambertian surface reflectance data of the MODIS MCD43 product [71]. For over water surface, the GOME-2 Lambert-equivalent reflectivity product [72] is applied. The Unified and Linearized Vector Radiative Transfer Model (UNL-VRTM) [73] is used to generate the lookup table for the retrieval. We retrieved the EPIC AOCH over the research domain (105\({}^{\circ}\) E-120\({}^{\circ}\) E, 5\({}^{\circ}\) S-10\({}^{\circ}\) N) for July through November of 2016-2018 and calculated the hourly mean of the EPIC AOCH.
# Soft Iterative Decoding Algorithms for Rateless Codes in Satellite Systems
## 2 HARQ Transmissions with Rateless Codes  
### Rateless Codes  
In a transmission system using rateless codes, parities for a given piece of information can be continuously received until the perfect recovery of the original information is achieved. This way, quality of service (QoS) can be guaranteed independent of the channel condition. A block diagram for a transmission system using a rateless code is represented in Figure 1. The encoder at the transmitter continuously generates parity bits for the information word, \(\mathbf{u}\), with a length of \(k\) and forms a codeword, \(\mathbf{c}\). The encoding process is performed using a predetermined degree distribution, and it can be continued infinitely, resulting in infinite length of \(\mathbf{c}\).  
The encoding process is first started by generating the degree \(d\) of the encoding symbol, which is randomly chosen from the given degree distribution. The encoder, then estimates the encoding symbol by selecting \(d\) neighbouring variable nodes and taking the exclusive--or operation of the values in the variable nodes. The degree distribution is critical to the performance of the rateless code. The robust soliton distribution (RSD) is first proposed for the LT code [1], and later the Poisson robust soliton distribution (PRSD) was proposed to improve the decoding efficiency and the symbol-recovery probability [9]. More detailed information on the encoding of the rateless code can be found in [10].  
After this encoding process, the transmitter maps a part of the codeword \(\mathbf{c}\) to modulation symbols and sends them through the channel, and this mapping and transmission processes also continue as more parities are generated. The decoder at the receiver starts its process if a sufficient number of received signals are collected. In this case, the demodulator produces the bitwise estimation vector, \(\mathbf{r}\), for decoding. If the decoding is successfully completed with the given collected signal set, then the ACK signal is sent to the transmitter. Otherwise, the receiver collects more signals for increased parities, and the decoding is performed with increased length of the codeword.  
A hard decision decoding method was originally utilized for rateless codes, because they were first proposed for erasure channels. Recently, soft iterative decoding algorithms were applied to rateless codes in order to use them in wireless systems where a large amount of coding gain was required to combat harsh channel conditions [6; 10]. Since the encoding process of a rateless code is performed over a graph, an iterative decoding method based on belief propagation could easily be adopted.
# Edge-Nodes Representation Neural Machine for Link Prediction
## 1 Introduction
After generating the node's embedding, researchers combine the two nodes' representations in different manners such as summing to represent link for the link prediction task. However, the method loses the link's surrounding environment information which can be considered as edge's representation in this paper. To get better performance, its necessary to represent each target link entirely, including edge's representation and nodes'. Therefore, we propose a new link prediction method Edge-Nodes Representation Neural Machine (ENRNM) which learns each target link's representation fully. For each target link, ENRNM learns the edge's representation through the WLNM [16] and two nodes' representations which can be learned in the network embedding models [20] simultaneously. Then  
Figure 1: A link’s formation. ENRNM joints edge and two nodes' representations vertically as link's representation. Since the neural network has powerful ability to learn patterns automatically from the input sequent data, we train a fully connected neural network where the input is the target link's representation and the output is a probability indicating the possibility of the target link's existence. We show the framework of ENRNM in Figure 2. For an observed or unobserved link, we learn the edge and two nodes' representations, then link's representation and label are fed into the neural network together. The contributions of our paper are summarized as follows. * We propose Edge-Nodes Representation Neural Machine (ENRNM), a novel link prediction framework to learn the link's representation from the given network automatically. * We propose a new method to represent the link fully by combining the edge's representation with the two nodes' representations on the two sides of the edge, so that the neural network can learn abundant, meaningful patterns and link formation mechanism. The rest of the paper is organized as follows. In Section 2, we discuss the related work, and in Section 3, we introduce our framework ENRNM in detail. The experiments and the results are presented in Section 4. Finally, we draw a conclusion and discuss the future work in Section 5.
## 2 Materials and Methods
### Experimental Set-Up and Protocol  
The study protocol involved two sessions of assessment, prior to and following a 4-week training protocol. Both the assessment and training involved the use of a robotic device. Before starting the first session, participants completed a familiarization session where they became accustomed to the robotic apparatus, performing as many practice trials as needed until the tasks were fully understood. During robotic sessions, participants were seated in an upright neutral position in front of a monitor and rested their forearm (with a consistent elbow flexion of 135\({}^{\circ}\)\(\pm\) 3.67\({}^{\circ}\)) on the robotic device, called WristBot (Istituto Italiano di Tecnologia, Genova, Italy) [13; 15], grasping the handle with their hand (Figure 1). WristBot is a custom-built manipulandum with a Range Of Motion (ROM) that replicates typical human wrist motion in three Degrees of Freedom (DoF): flexion/extension, \(\pm\)62\({}^{\circ}\); radial/ulnar deviation, +40\({}^{\circ}\)/ \(-\)45\({}^{\circ}\); pronation/supination, \(\pm\)60\({}^{\circ}\). Wrist angular rotations on each DoF are measured by high-resolution incremental encoders. Additionally, four brushless motors deliver torques necessary to compensate for the weight and inertia of the device and to manipulate the wrist joint. This allows for force feedback to participants during sensorimotor training programs or therapy sessions. An integrated virtual reality environment provides visual feedback to participants and allows to perform eye-hand coordination tasks.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Age**} & \multirow{2}{*}{**MSE**} & **MS** & **Dominant** & **Trained** & **Years since** & **EDSS** & \multirow{2}{*}{**Sex**} \\  & & **Phenotype** & **Limb** & **Limb** & **Diagnosis** & **Score** & \\ \hline S01 & 36 & SPMS & R & L & 14 & 7 & Female \\ S02 & 60 & PPMS & R & L & 20 & 7 & Female \\ S03 & 71 & SPMS & R & L & 20 & 3 & Male \\ S04 & 43 & RRMS & R & R & 6 & 6.5 & Female \\ S05 & 61 & SPMS & L & R & 34 & 6 & Female \\ S06 & 27 & RRMS & R & R & 1 \(\frac{1}{2}\) & 4 & Male \\ S07 & 30 & RRMS & R & L & 7 & 2 & Female \\ \hline \hline \end{tabular} Note: SPMS = Secondary progressive, PPMS = primary progressive, RRMS = relapsing remittent, R = right hand, L = left hand.  
\end{table}
Table 1: Participant Demographics.  
Figure 1: Experimental set-up with participant positioned at the WristBot, grasping its handle to perform the tracking task with visual feedback.
## 6 Conclusions and Prospects  
In our research, a new iterative pruning merging algorithm was proposed to simplify the architecture of DeepESN. As to which neurons should be pruned out, four different similarity estimation criteria were attempted. The unpruned DeepESNs is a benchmark that identifies the evolutionary characteristic of network performance by reducing network size, and the effectiveness of the proposed method was experimentally verified by comparing pruned DeepESNs with unpruned DeepESNs in the same network size. The results showed that these NS-IPMA methods have good network structure adaptability, and the DeepESNs pruned by the NS-IPMA method have better generalization performance and better robustness than unpruned DeepESNs, indicating that the NS-IPMA method is a feasible and superior approach to improving the generalization performance of DeepESN. The NS-IPMA method provides a novel approach for choosing the appropriate network size of DeepESN. One could start with a larger model than necessary for reservoir size and then prune or merge some similar neurons to obtain a better DeepESN model. One could select a simple architecture with small computation requirements while keeping the testing error acceptable. In many tasks, low computational cost and reliable performance cannot be achieved at the same time. The experimental results showed that the newly proposed method could better balance the computational cost and performance; therefore, it has broad application prospects in real-time (RT) systems, including RT control, RT forecast, RT decision, etc., for example, RT control for modernized microgrids [29], intelligent Internet of Things automatic irrigation control system [30], lower generation data prediction for wind power plant [31], trading prediction and dynamic decisions for online trading systems [32], model predictive control [33; 34], predictive feedforward control for servo systems [35; 36]. However, there are still some shortcomings in our work. First, the problem of how to choose the redundant neurons to be pruned out or what the best neuronal similarity estimation criterion should be, remains unsolved. Second, only the hierarchical structure, the reservoir diversity, and the overall error performance are investigated, and more evolutionary characteristics of different reservoirs, such as their spectral radius, resulting from the NS-IPMA method, are not analyzed. Third, the effects of pruning and merging are not clearly distinguished. Future research on the NS-IPMA method could focus on the theoretical analysis of the above-unsolved problem or on practical applications where high-performance computing is required.
# Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images
## 4 Experimental Procedure
### Performance Evaluation Indicators  
In this paper, the performance index of the proposed algorithm operation was scientifically evaluated using the CLEAR-MOT index [25,26]. The algorithm was compared with seven algorithms that are currently popular and have high-performance evaluation indexes for data comparison and analysis. The target data were randomly selected from the 18,586 image sequences described in Section 4.2, and the experimental comparison data are shown in Table 4, where MOTA represents the tracking accuracy and IDF1 integrates ID accuracy and recall. ML represents the ratio of lost true tracks to the number of all tracks; MT represents the ratio of the number of tracked true tracks to the total number of true tracks; IDs represent the number of ID switches in multi-target tracking; FPS represents the processing frame rate per second; and the seven multi-target tracking algorithms are CenterTrack, CSTrack, MOT, SiamFC, MDNef, TrSiam, and TRDiMP [27; 28; 29].  
Through the analysis of the evaluation index pairs, it was determined that the comprehensive performance of the algorithm proposed in this paper was better than that of the target popular optimal algorithm, and the evaluation performance index will continue to improve if the network is optimized and the parameters are rectified, which is the goal and direction of our subsequent research.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **MOTA** & **IDF1** & **ML** & **MT** & **IDs** & **FPS** \\ \hline Ours & 79.7 & 77.5 & 25.4\% & 51.6\% & 1876 & 42.3 \\ CenterTrack & 66.7 & 63.7 & 25.5\% & 34.7\% & 3042 & 22.7 \\ CSTrack & 65.4 & 66.5 & 25.1\% & 33.1\% & 2998 & 20.6 \\ MOT & 75.4 & 73.2 & 19.2\% & 49.2\% & 2082 & 30.4 \\ SiamFC & 77.1 & 74.8 & 25.1\% & 49.8\% & 3845 & 38.5 \\ MDNef & 63.4 & 61.7 & 18.4\% & 37.1\% & 2978 & 30.7 \\ TrSiam & 70.2 & 72.4 & 19.7\% & 42.5\% & 3058 & 28.5 \\ TrDiMP & 76.3 & 71.9 & 21.3\% & 36.7\% & 3176 & 25.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of the results of different methods with the infrared weak target detection dataset.  
Figure 11: Comparison of experimental results with multiple frames. (**a**) Infrared original image. (**b**) The algorithm in reference [5]. (**c**) The algorithm in reference [6]. (**d**) The algorithm proposed in this paper.
## 3 Interaction Technique Design
### Gesture and Voice  
Different interaction methods present distinct advantages and limitations. Eye tracking offers precision; yet, executing actions like confirmations solely through eye tracking poses challenges. The prolonged use of gesture-based interactions may induce fatigue. While voice input facilitates the input of complex content, discerning and isolating explicit  
Figure 1: An illustration of the Bimanual Scaling technique.  
commands from lengthy sentences remains challenging. Hence, this study seeks to utilize the strengths while mitigating the weaknesses of each method by proposing a multimodal interaction technique named \"Gesture and Voice\". This method employs eye tracking for preliminary input selection, pinch gestures for confirmations, and voice commands for entering floating-point data. It was anticipated that merging these interaction strengths would enhance the overall user experience.  
The Gesture and Voice method is illustrated in Figure 2. Interaction steps include the following: (1) The user looks at the target panel to pre-select an item. (2) Confirmation is initiated by the user through a pinch gesture, triggering the voice input process. While maintaining the pinch gesture, the system records the user's voice. Simultaneously, a visual prompt on the data panel indicates ongoing recording. (3) Upon releasing the pinch gesture, the recording stops, and the system converts the voice input into a floating-point number for display.
# Research on a Novel Nanosecond Marx Generator and Its Efficiency Analysis
## 6 Conclusions  
A novel nanosecond Marx generator based on avalanche BJTs is proposed in this paper. No triggering signal is required since avalanche BJTs breakdown automatically as long as the Collector-Emitter voltage over BJTs exceeds the breakdown voltage. The frequency  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Reference [10]** & **Reference [19]** & **Reference [20]** & **Reference [21]** & **Circuit I** & **Circuit II** \\ \hline Amplitude/kV & 4.0 & 6.5 & 4.0 & 3.9 & 2.9 & 4.0 \\ Front edge time/ns & 6 & 3.65 & 0.24 & 0.3 & 7.7 & 5.7 \\ Pulse width/ns & 20 & / & 5.2 & 1.8 & 12.68 & 8.45 \\ Repetition & 2 & 1 & 0.01 & 30 & 10–60 & 10–60 \\ frequency/kHz & 36 & 60 & 24 & 80 & 15 & 16 \\ Load/\(\Omega\) & 50 & 75 & 50 & 50 & 50 & 50 \\ Voltage efficiency/\% & 37 & 56.5 & 55.6 & 16.3 & 77.33 & 94.12 \\ \hline \end{tabular}
\end{table}
Table 2: Parameter comparison of nanosecond Marx generators based on avalanche BJTs.  
Figure 14: The energy efficiency of the circuit at different output pulse frequencies.  
of the output pulses can be adjusted by controlling the average charging current of the full-bridge circuit. The output voltage can be increased by connecting more avalanche BJTs in series or increasing the number of stages in this nanosecond Marx generator. A full-bridge series-resonant charging power supply is used to charge the energy stored capacitors instead of the DC voltage source, which considerably improves the energy efficiency. Replacing the resistors with isolation inductors can further improve efficiency. A 3 \(\times\) 5-stage nanosecond Marx generator was built, and the nanosecond output pulses with a pulse width of 12.68 ns, a voltage amplitude of 2.9 kV, and a repetitive frequency 50 kHz were obtained over a 50 \(\Omega\) load. The charging energy efficiency of the capacitor \(\eta_{\text{c}}\) and the energy efficiency of the circuit \(\eta\) improved to 66.94% and 76.42%, respectively. When isolation inductors are used to replace the resistors, the peak voltage of the output pulses increased to 4 kV, and the energy efficiency \(\eta\) was increased to 94%. From the results, the frequency of the output pulses is adjustable from 10 to 60 kHz.  
Data curation, Yuan Chai; Funding acquisition, J.R.; Investigation, Y.C.; Methodology, Z.L.; Validation, S.J.; Writing--original draft, Z.L.; Writing--review & editing, J.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Ministry of Science and Technology: National Key R&D Program of China] grant number [2019 YFC0119102]. The APC was funded by [MDPI].  
The authors declare no conflict of interest.