esa-sceva/satcom-chunk-collection huggingface dataset:
# Robots in Partially Known and Unknown Environments:
## 3 Problem Description
### Environment  
In our framework, we use a discrete environment representation. A grid box can have an obstacle or be empty, as shown in Figure 1. In this figure, the starting point is the bottom left corner \((0,0)\) and the ending point is the top right corner \((l-1,m-1)\), where \(l\) is the width and \(m\) is the height of the grid.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Year** & **Approach** & **Nature** & **Offline/Online** \\ \hline
[8] & 1985 & Visibility graphs & Roadmap & Offline \\
[9] & 1987 & Voronoi diagrams & Roadmap & Offline \\
[19] & 1997 & Genetic algorithm & Heuristic & Offline \\
[13] & 2001 & Rapidly exploring Random Trees & Roadmap & Offline \\
[12] & 2004 & Probabilistic Roadmap & Roadmap & Offline \\
[20] & 2006 & Simulated annealing + Potential fields & Heuristic & Offline \\
[7] & 2007 & Cell decomposition & Roadmap & Offline \\
[2] & 2009 & Ant colony & Heuristic & Offline/Online \\
[10] & 2012 & Potential fields & Roadmap & Offline \\
[21] & 2013 & Two-phase ant colony optimization & Heuristic & Offline \\
[3] & 2017 & Surrounding search+ Particle Swarm Optimization & Heuristic & Offline \\
[16] & 2022 & Multi-tree guided RMP & Roadmap & Offline \\
[18] & 2020 & Spatio-temporal RRT* & Roadmap & Offline \\
[22] & 2021 & RRT*-CFS & Roadmap + Heuristic & Offline \\
[17] & 2021 & Trajectory estimation + A* & Heuristic & Offline \\
[25] & 1988 & Neural networks & Heuristic & Online \\
[27] & 2005 & Evolutionary algorithm & Heuristic & Online \\
[28] & 2008 & Evolutionary algorithm & Heuristic & Online \\
[33] & 2018 & Firefly algorithm & Heuristic & Online \\
[34] & 2019 & Surrounding search & Heuristic & Online \\
[4] & 2020 & Machine learning + case-based reasoning & Heuristic & Online \\
[30] & 2022 & Convolutionally evaluated gradient first search & Heuristic & Online \\
**Our approach** & & **Surrounding search + Simulated Annealing** & **Heuristic** & **Offline/Online** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Literature summary.  
From any point on the grid, the robot can move in any of the eight adjacent directions, as shown in Figure 2. Our goal is to find a minimal-distance collision-free path or sequence of movements.
# Experimental Validation of a Gyroscope Wave Energy Converter for Autonomous Underwater Vehicles
## 1 Introduction
Additionally, the effect of different input parameters on the power output of GWEC was simulated and analyzed. In order to reduce the time and economic cost of actual maritime experiments, a hardware experimental platform that simulates wave movement is developed in this paper. Finally, the ability of the GWEC to transform the energy of regular waves was tested and the validity of the mathematical model was verified.
# An Algorithm to Generate a Weighted Network Voronoi Diagram Based on Improved PCNN
## 4 Network Voronoi Diagram Construction Based on the Improved PCNN  
The construction of the planar Voronoi diagram can be described as follows: each entity within the Voronoi diagram expands outward to gain growth area and finally encounters the expansion from neighboring entities [16; 17]. Similarly, in the process of generating the network Voronoi diagram, each initial neuron generates an auto-wave simultaneously, and the auto-waves compete the growth area by expanding along the shortest path between neurons. The competition will not stop until all the neurons are fired and the transmitting auto-wave stops. As a result, the routes and the area taken by generators delineate their impacting range in network space, which can be considered as network Voronoi diagram of the certain point set.  
The concrete steps of the construction can be concluded as: Firstly, the initialization is down so that all facility points can be projected onto the related road segments. Secondly, the projected points act as initial neurons in the improved PCNN, and generate auto-wave  
Figure 3: Structure of the improved PCNN.  
simultaneously. The speed of auto-wave is calculated and then the auto-wave transmits along the shortest path between neurons. Finally, the road segments conquered by a specific auto-wave and their related space are identified as its initial neuron's impacting region. Until all the nodes are fired, the network Voronoi diagram of corresponding facility points is successfully generated. The flowchart of the algorithm is shown in Figure 4, and the following describes the procedure in detail.  
As shown in Figure 4, the new algorithm includes three procedures:  
* Initialization;
* Speed calculation;
* Transmission of the auto-wave.
## 7 Dynamic Routing Mechanism  
Dynamic routing mechanism is another important issue in this work. Our proposal does not present a simulated or implemented routing mechanism for mobile ad hoc nodes, wireless sensor networks or vehicular ad hoc networks such as the PANDORA [36] or LORA-CBF [37,38] protocols.  
The PANDORA protocol possesses two distinct layers: (1) An _ad hoc_ network which is composed of Wireless Mesh Clients (WMC), and (2) Wireless Mesh Routers (WMRs) with a backbone connection between the WMRs. It is important to note that the two types of nodes of a Wireless Mesh Network (WMN) suffer different constraints. WMCs located at the end points have limited power resources and may be mobile, while WMRs possess minimum mobility, but do not suffer from power constraints.  
LORA-CBF has been simulated and implemented in vehicular ad hoc networks and wireless sensor networks. It has also been implemented in a precision agriculture scenario. The implementation of either PANDORA or LORA-CBF will depend of the specific scenario. As we mentioned previously, PANDORA has been implemented for fixed and mobile nodes in home, urban and rural environments. On the other hand, LORA-CBF has been implemented using fixed nodes (agriculture) and highly mobile nodes (vehicles). The results simulated or implemented previously can be applied to this scenario in future work.
# Research on Permanent Magnet Synchronous Motor Control System Based on Adaptive Kalman Filter
## 1 Introduction
Authors in [22] gave the definition and selection method of EKF parameters, and proposed a parameter adaptive law for speed estimation of PMSM by using the hyperbolic tangent function. Authors in [23] proposed a new noise model identification method for the sensorless control system, which optimized the acquisition of covariance matrix in EKF based on the ant colony algorithm. The above-mentioned methods both depend on the accurate selection of process noise parameters, which needs many experiments to obtain an excellent value and increases more workload in application [24; 25]. To address the above problems, this paper proposes a new linear time-invariant parameter adaptive Kalman Filter (PAEKF) for the PMSM control system. The main innovation of this method is that the priori error covariance is adjusted on-line by feedback mining of a posteriori variance instead of using traditional method to estimate the process noise covariance. This new adaptive Kalman filter has multiple advantages. Firstly, the filtering performance of PAEKF does not depend on the accurate process noise covariance. Compared with the EKF, the proposed method reduces the work of obtaining the optimal noise parameters. Secondly, the amount of calculation required by PAEKF is equivalent to the EKF. Finally, the stability and accuracy of PAEKF in velocity estimation are better than the EKF. The remainder of this paper is organized as follows. In Section 2, the PAEKF algorithm is proposed, and the mathematical model of PMSM under EKF and PAEKF algorithm is given. In Section 3, the PAEKF algorithm proposed in this article is compared with EKF and existing adaptive methods, respectively. The proposed method is applied to practice to verify effectiveness. Finally, in Section 4, the research contents are summarized and the future work plan is given.
# Software Design and Experimental Evaluation of a Reduced AES for IoT Applications
## 3 Materials and Methods
### Mix Column Permutation  
The AES constant matrix was selected based on factors that provide the encryption and decryption process with less computation and higher diffusion power [38]. These factors include the linearity over GF(2) and the invertibility of the constant matrix. The matrix should be invertible to perform the decryption process and support a quick handing process. As per AES, an entry of 0 or 1 requires no multiplication process [38]. In LAES, the choice of the coefficients of C(x) are 3, 2, 1, and 0, which allows easier implementation for software and hardware multiplication process. Columns of the state matrix are considered as a polynomial over GF(2\({}^{4}\)) modulo \(x^{4}+x+1\), with a fixed polynomial C(x) = 3x\({}^{3}\) + 1x\({}^{2}\) + 1x + 2. Although the polynomial \(x^{4}+x+1\) is not irreducible, the operation is invertible, since the polynomial C(x) is co-prime to x + + 1.  
The coefficients of the polynomial C(x) and the polynomial of state column matrix are in themselves polynomials in GF(2\({}^{4}\)). The inverse of C(x) is C-1 (x) = 9x\({}^{3}\) + Ex\({}^{2}\) + Bx + 4, which is needed to construct the matrix in the decryption process.  
The 0 th column of the constant matrix can be represented by s3,0x\({}^{3}\) + s2,0x + s1,0x + s0,0, where the coefficient 3 in C(x) is actually the polynomial x + 1 in GF(2\({}^{4}\)), the coefficient 1 is the polynomial 1, and the coefficient 2 is the polynomial x in GF(2\({}^{4}\)).  
The multiplication by C(x) modulo \(x^{4}+x+1\) can also be represented as a matrix transformation. For example, if we take the 0 th column, which has four elements (s0,0, s1, 0, s2, 0, s3, 0), the mix columns component produces the 0 th column of the result (s'0,0, s'1, 0, s'2, 0, s'3, 0), in the manner seen below. Similarly, the operation is repeated for the other columns.
# Rapid Estimation of TVWS: A Probabilistic Approach Based on Sensed Signal Parameters
## 5 Results and Discussion
### Final Results  
In this subsection, we include the information of points P1 to P10 analyzed using the REM. The results are listed in Table 8, where we present the probability of TVWS (\(P_{(TVWS)}\)) calculated with the REM. These results are compared with the real number of unused channels detected in each point (\(R_{(TVWS)}\)). The variation column indicates the difference between the calculated and the detected \(TVWS\). As we can observe, in the worst case, this variation reaches a 23.57% and, in the best case, the variation reaches a value of 4.79%. The average variation error found is 15.24%. The maximum number of Detectable TVWS (\(D_{TVWS}\)) is also presented, and finally, we can assign TVWS according to the preferred order mentioned in Table 7 by applying the PU DM.
# Power-Efficient Resource Allocation for Active STAR-RIS-Aided SWIPT Communication Systems
## 3 Optimization of Algorithm Design  
In this paper, alternating optimization method is used to address joint beamforming optimization problem. That is, problem (5) is split into two sub-problems: For the first, SDR method is used to optimize \(\{w_{i},v_{j}\}\) given \(Q_{k}=P\Theta_{k}^{\rm ES},k\in\{t,r\}\), and for the second, the penalty-based algorithm is used to optimize \(\{w_{i},v_{j}\}\) given \(Q_{k}\).
# HyperDE: An Adaptive Hyper-Heuristic for Global Optimization
## 1 Introduction
### Our Contribution  
Our plan is to define an adaptive hyper-heuristic [35] for adjusting the hyper-parameters of a set of low-level heuristics, in an online manner, such as to maximize the performance in finding the global minimum.  
A hyper-heuristic is a methodology that automates the selection, generation and adaptation of lower level heuristics in order to solve difficult search problems. Therefore, instead of exploring the problem space directly, it explores the space of possible heuristics. Accordingly, our proposed method has two optimization layers:  
* The high-level adaptive algorithm, which is variant of the genetic algorithm (GA), is responsible for the process of online learning of the hyper-parameters of the basic heuristics.
* The low-level optimization algorithm can be any optimization method characterized by a set of hyper-parameters that are tuned by the high-level algorithm. The low-level algorithm acts through agents that directly attempt to solve the optimization problem by exploring the solution space.  
After an analysis that consisted of the evaluation of various heuristics, DE was selected as the main low-level agent in the context of the proposed optimization method. The combination of our variant of GA at the high-level and DE at the low-level is called HyperDE. Two other algorithms, SSA and BES, were also considered appropriate to serve as agents. The resulting methods are called HyperSSA and HyperBES, respectively. The three methods are similar in approach, sharing the same high-level procedure, but differing by which algorithms are adopted as agents.  
Each of the above optimization algorithms that are used as agents presents a set of hyper-parameters that influence the performance of their search. The most important in this paper, DE, has the following hyper-parameters: \(\mathrm{F}\in[0,1]\), weighting factor; \(\mathrm{CR}\in[0,1]\), crossover rate; \(\mathrm{S}\in[0,5]\) (integer), index of the mutation strategy (See Section 2 for details). In the HyperDE algorithm, a population of DE instances is managed by our GA, which tunes the triplets (\(\mathrm{F}\), \(\mathrm{CR}\), \(\mathrm{S}\)) in the attempt to improve the search compared to the case where a single triplet of hyper-parameters is used.  
By this endeavor, it is expected to obtain a methodology that is more general in its applicability, adapting without intervention to the given problem, resulting in a superior average performance given a set of problems without additional effort from the human agent. We consider it to be one possible approach to deal with the \"No Free Lunch\" theorem [36] successfully.  
The evaluation and analysis of the proposed hyper-heuristics in comparison with the other enumerated algorithms was conducted on 12 benchmark functions provided by CEC 2022 competition. We show that HyperDE has indeed the best average performance and Friedman rank, followed by L-SHADE, while HyperSSA and HyperBES demonstrate to be significant improvements over their counterparts.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Random Access Protocols
#### 3.1.3 Adaptive Power-Controlled MAC Protocols  
In [68], the authors present an optimal centralized algorithm based on the bipartite matching technique for ideal channel assignment and control provision problems. The key objective is to maximize capacity as seen by simultaneous CR transmissions and to maximize throughput. Access Windows MAC (AW-MAC) is a centralized polynomial-time algorithm, which is a CSMA-based MAC protocol proposed for single-hop CRN.  
An efficient circulated channel task for a multi-hop CRN has been proposed by the authors that depends just on data given by the two imparting CR users. Utilizing an agreeable task among neighboring CR users, the proposed distributed scheme improves the CR throughput. Figure 4 shows AW-MAC basic operation framework. The Worst Feasible Channel MAC (WFC-MAC) is a protocol based on CSMA for multi-hop CRNs, which is based on the distributed scheme. Every node has a solitary half-duplex radio that has a capacity of \"dual-receive single-transmit\"; therefore, each radio transmits over one channel at a time, but can get two autonomous bits over any two of the accessible channels, simultaneously.
###### Abstract  
Large-scale terminals' various QoS requirements are key challenges confronting the resource allocation of Satellite Internet of Things (S-IoT). This paper presents a deep reinforcement learning-based online channel allocation and power control algorithm in an S-IoT uplink scenario. The intelligent agent determines the transmission channel and power simultaneously based on contextual information. Furthermore, the weighted normalized reward concerning success rate, power efficiency, and QoS requirement is adopted to balance the performance between increasing resource efficiency and meeting QoS requirements. Finally, a practical deployment mechanism based on transfer learning is proposed to promote onboard training efficiency and to reduce computation consumption of the training process. The simulation demonstrates that the proposed method can balance the success rate and power efficiency with QoS requirement guaranteed. For S-IoT's normal operation condition, the proposed method can improve the power efficiency by 60.91% and 144.44% compared with GA and DRL_RA, while its power efficiency is only 4.55% lower than that of DRL-EERA. In addition, this method can be transferred and deployed to a space environment by merely 100 onboard training steps.  
channel allocation; deep reinforcement learning; power control; various QoS; Satellite Internet of Things; transfer learning +
Footnote †: journal: _Sensors_**2022**, 22, 2979. [[https://doi.org/10.3390/s22082979](https://doi.org/10.3390/s22082979)]([https://doi.org/10.3390/s22082979](https://doi.org/10.3390/s22082979))
# Estimating Optical Flow with Streaming Perception and Changing Trend Aiming to Complex Scenarios
## 6 Conclusions  
The two-frame input strategy has long been considered the default input mode for optical flow estimation. In this paper, we introduce a novel input structure that allows more frames to be processed. Our proposed C-MFF module is inspired by streaming perception, so it can fuse continuous and momentary feature maps and help compute more dynamic motion features. We also design a new loss function for our method, making it more sensitive to angle error and trend change errors. Our method has been validated by experiments, as shown in Section 4, and it has a moderately better effect on the Sintel dataset, especially on the Clean version. In Section 5, by applying optical flow estimation models in real-world intersection and parking lot scenarios, we demonstrate our SPCT has relatively better abilities in predicting fast-moving targets, resisting jitter interference, and being robust in the low-brightness environment than other listed models. We also expect further development, such as introducing our method into other optical flow estimation networks and applying them to the autonomous driving scenario, especially when the vehicles have insufficient performance for precepting some unexpected events or under some extreme environments.  
Conceptualization, Z.W. and W.Z.; Methodology, Z.W.; Software, Z.W.; Validation, W.Z. and B.Z.; Formal analysis, Z.W.; Investigation, Z.W. and W.Z.; Resources, W.Z.; Data curation, Z.W.; Writing--original draft, Z.W.; Writing--review & editing, W.Z. and B.Z.; Visualization, Z.W.; Supervision, W.Z. and B.Z.; Project administration, W.Z. and B.Z.; Funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by National Natural Science Foundation of China (No. 51805312, 52172388) and the APC was funded by [PERSON].  
The data that support this research are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.  
Figure 7: Applying optical flow estimation model for parking lot scenario at night. This experiment is almost identical to the previous one in Figure 6 except for the deploying time. The additional low brightness conditions plague the optical flow estimation performances of these models, so there are different levels of noise on the optical flow map. The results presented here once again demonstrate that our SPCT excels in this scenario.
## 2 Materials and Methods  
### Tested Samples  
Three samples of lactose powder produced from lactose monohydrate (SuperTab(r) 30 GR, DFE pharma, Goch, Germany) of the same batch were tested in the present study:(1) Lactose (lactose monohydrate) saturated with a solution of affinity purified rabbit polyclonal antibodies (Abs) to interferon gamma (IFN-\(\gamma\)) which had previously undergone a process of gradual reduction of their initial concentration. Briefly, Abs to IFN-\(\gamma\) (2.5 mg/mL) were diluted in water-ethanol solutions under specific conditions [43] with several steps of centesimal dilution. The first centesimal dilution was prepared by mixing 1 part of the original substance and 99 parts of the solvent. All subsequent dilutions contained 1 part of the previous dilution and 99 parts of the solvent. The final solution contained a mixture of 12, 30, and 50 centesimal dilutions of antibodies to IFN-\(\gamma\) (highly diluted antibodies). The estimated theoretical Abs concentration in the final solution was no more than \(2.5\times 10^{-24}\) mg/mL, or \(2.5\times 10^{-18}\) ppb (w/w). However, it should be noted that the physico-chemical studies showed that for the samples manufactured using the technology of high dilutions such estimation might not be correct [44]. The original substance of the antibodies was produced in accordance with the current EU requirements of Good Manufacturing Practice for starting materials [45] by AB Biotechnology (Edinburgh, UK).  
(2) Lactose saturated with water-ethanol solution free from highly diluted Abs.  
(3) Lactose not subjected to any treatment.  
All the lactose powders tested were provided by OOO \"NPF \"Materia Medica Holding\" (Russia) in an encoded form (the samples were decoded after analysis of all the results obtained). The sampling of powders for the study was carried out in accordance with the internal sampling procedure that meets the pharmacopoeial requirements for sampling of crude drugs and medicinal products, ensuring uniformity of samples and excluding their contamination.  
For experiments, 10% w/w aqueous solutions of the three abovementioned types of lactose powders were used. The solutions were prepared using deionized water obtained using a Milli-Q water purification system (Merck Millipore, Germany) with a conductivity of 18.3 M\(\Omega\)-cm. Dissolution was carried out at room temperature.  
The solution of lactose saturated with a solution of highly diluted antibodies to IFN-\(\gamma\) is hereinafter referred to as \"HD Abs to IFN-\(\gamma\)\"; the solution of lactose saturated with water-ethanol solution free from highly diluted Abs is hereinafter referred to as \"Saturation control\"; the solution of lactose not subjected to any treatment is hereinafter referred to as \"Intact lactose\".
## 2 Analysis of the Macroscopic FP Principle
### Far-Field Fraunhofer Approximation  
On the basis of the macroscopic far-field FP imaging model shown in Figure 1 and assuming that the distance between the object and camera sensor \(z_{2}\) satisfies the far-field Fraunhofer approximation, we obtained the following relationship [6; 8; 9]:  
\[z_{2}>\frac{2d^{2}}{\lambda} \tag{1}\]  
where \(z_{2}\) is the distance between the object and the pupil plane, \(d\) is the diameter of the entrance pupil of the photographic lens, and \(\lambda\) is the wavelength of the laser source.  
The laser point source emits coherent light with a wavelength of \(\lambda=808\) nm, and the focal length of the focusing lens is \(f=180\) nm with a \(f\)-number of \(F/22\), creating the entrance pupil of \(d=8.2\) mm. The Fraunhofer far-field distance calculated is defined as  
\[z_{2}=(2d^{2})/\lambda=166\text{ m} \tag{2}\]  
Thus, for Equation (2) to hold, the camera should be placed at least 166 m away from the object. With such a large distance between the object and the photographic lens, the Fourier transform of the object will always appear on the image plane of the laser point light source. To avoid this, we used a focusing lens to converge the light emitted by the point source to the pupil plane of the photographic lens [7].