esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of BDS-3 Time Group Delay and Differential Code Bias Corrections on Positioning
## 3 Materials and Methods  
Six stations provided by iGMAS were selected for this experiment, including BJF1, BRCH, GUA1, WUH1, HMNS and KUN1. The data covered the 30-day period of day of year (DOY) 92 to 121, 2020. The broadcast ephemeris of BDS-3 was provided by iGMAS, WUM provides the BDS-3 precise satellite clock and orbit products and DCB products of BDS-3 provided by CAS. Moreover, Table 1 introduces the BDS-3 single-frequency and dual-frequency SPP and PPP data processing strategies in detail.  
To test the influence of the code bias on positioning performance of BDS-3 with a different frequency combination, Table 2 gave the different schemes. Not only a single-frequency based on BDS-3 B1I, B3I, B1C and B2a signals, but also dual-frequency based on recommended B1I/B3I and B1C/B2a dual-frequency, are used for SPP testing. However, only B1I/B3I and B1C/B2a combinations are processed for PPP. Table 3 displays the information about six selected stations, including the type of receiver and antenna. Furthermore, the distribution of the stations as described in Figure 4, which can track BDS-3. Note that the station coordinates given in the SINEX file of iGMAS are sufficient to be used as the reference values for validation [26; 27; 28].  
\begin{table}
\begin{tabular}{c c} \hline
**Items** & **Processing Strategies** \\ \hline Date span & 1–30 April 2020 \\ \hline Cutoff angle & 10\({}^{\circ}\) \\ \hline Ionospheric delay & Single-Frequency: BeiDou Global Ionospheric delay correction Model (BDGIM) \\  & Dual-frequency: Ionosphere-free combined observables \\ \hline Sampling rate & 30 s \\ \hline Phase wind-up & Corrected [23] \\ \hline Tropospheric delay & ZHD: corrected with Saastamoinen [24] \\ \cline{2-2}  & ZWD: estimated [25] \\ \hline Tidal displacement & IERS Conventions 2010 [25] \\ \hline Relativistic effect & Corrected [25] \\ \hline [PERSON] effect & Corrected [25] \\ \hline PCO and PCV & Corrected using “ATX” file \\ \hline Station reference coordinates & iGMAS SINEX solutions \\ \hline \end{tabular}
\end{table}
Table 1: The summary of BDS-3 SPP and PPP processing strategies.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **Single-Frequency** & **Dual-Frequency** & **Schemes** & **Comments** \\ \hline SPP & B1I, B3I, B1C, B2a & B1I/B3I, B1C/B2a & non-corr & \\  & & tgd-corr & non-corr: code without TGD or DCB corrections \\  & & dcb-corr & tgd-corr: TGD corrections \\ \hline PPP & - & B1I/B3I, B1C/B2a & non-corr & dcb-corr: DCB corrections \\ \hline \end{tabular}
\end{table}
Table 2: Summary of the processing strategies.
# A Lunar-Orbiting Satellite Constellation for Wireless Energy Supply
## 8 Discussion and Conclusions
Once the architecture has been validated in all aspects, thanks to the system's scalability, the transmitted energy can be increased by expanding the size of the satellite constellation, as explained earlier. Moreover, one of the strengths of this mode of power generation and transmission is that it can potentially act on the entire lunar surface. This system scalability can have a significant impact for many planned missions and not just for the lunar base energy supply at the lunar south pole. This application may be crucial for the many missions that aim to explore lunar regions never reached by sunlight, and therefore this is interesting for the potential presence of frozen water. Likewise, this may be used for the exploration of natural sites such as lavatubes, caves that in the future may offer natural shelter for establishing new human settlements. Conceptualization, F.L., A.M., G.M., D.E.S. and A.V. ; methodology, F.L. ; software, F.L., D.E.S. and A.V. ; validation, F.L. and D.E.S. ; formal analysis, F.L. ; investigation, A.M., G.M. and A.V. ; data curation, F.L. and D.E.S. ; writing--original draft preparation, F.L., A.M. and D.E.S. ; writing--review and editing, G.M. and A.V. ; visualization, F.L. ; supervision, S.M. ; project administration, S.M. All authors have read and agreed to the published version of the manuscript. The authors would like to thank Fondazione Compagnia di San Paolo with the support of the venture capital holding subsidiary LIFTT SpA, for supporting this research under the program: 'Convezione CSP-POLITO-PoC Instrument Linea 1-PoC Launchpad-Metodo e sistema di trasmissione wireless di energia nello spazio' SIME 2022.2239.
# Enhanced Teaching-Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem
## 5 Experimental Studies
### Results of multimodal functions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **Metric** & **ETLBO** & **TLBO** & **TLBO(S)** & **PSO** & **DE** & **GA** \\ \hline \multirow{4}{*}{F11} & Best & \(\mathbf{0.00\times 10^{00}}\) & \(3.83\times 10^{-09}\) & \(7.28\times 10^{-19}\) & \(3.70\times 10^{-245}\) & \(4.13\times 10^{-101}\) & \(3.20\times 10^{-78}\) \\  & Worst & \(\mathbf{0.00\times 10^{00}}\) & \(9.47\times 10^{-04}\) & \(3.08\times 10^{-12}\) & \(5.38\times 10^{-232}\) & \(1.40\times 10^{-93}\) & \(8.07\times 10^{-06}\) \\  & Median & \(\mathbf{0.00\times 10^{00}}\) & \(7.89\times 10^{-05}\) & \(4.01\times 10^{-14}\) & \(2.71\times 10^{-240}\) & \(1.26\times 10^{-97}\) & \(1.37\times 10^{-21}\) \\  & Average & \(\mathbf{0.00\times 10^{00}}\) & \(2.17\times 10^{-04}\) & \(3.06\times 10^{-13}\) & \(1.13\times 10^{-233}\) & \(4.01\times 10^{-95}\) & \(1.67\times 10^{-07}\) \\  & SD & \(\mathbf{0.00\times 10^{000}}\) & \(2.51\times 10^{-04}\) & \(6.46\times 10^{-13}\) & \(0.00\times 10^{00}\) & \(2.02\times 10^{-94}\) & \(1.17\times 10^{-06}\) \\ \hline \multirow{4}{*}{F12} & Best & \(\mathbf{0.00\times 10^{000}}\) & \(9.30\times 10^{-08}\) & \(\mathbf{0.00\times 10^{000}}\) & \(\mathbf{0.00\times 10^{000}}\) & \(\mathbf{0.00\times 10^{000}}\) & \(\mathbf{0.00\times 10^{000}}\) & \(\mathbf{0.00\times 10^{000}}\) \\  & Worst & \(\mathbf{0.00\times 10^{000}}\) & \(3.44\times 10^{-01}\) & \(2.01\times 10^{-13}\) & \(\mathbf{0.00\times 10^{000}}\) & \(\mathbf{0.00\times 10^{000}}\) & \(2.99\times 10^{-03}\) \\ \cline{1-1}  & Median & \(\mathbf{0.00\times 10^{000}}\) & \(1.07\times 10^{-01}\) & \(6.55\times 10^{-15}\) & \(\mathbf{0.00\times 10^{000}}\) & \(\mathbf{0.00\times 10^{000}}\) & \(0.00\times 10^{000}\) & \(0.00\times 10^{000}\) \\ \cline{1-1}  & Average & \(\mathbf{0.00\times 10^{000}}\) & \(1.19\times 10^{-01}\) & \(2.01\times 10^{-14}\) & \(\mathbf{0.00\times 10^{000}}\) & \(\mathbf{0.00\times 10^{000}}\) & \(1.44\times 10^{-04}\) \\ \cline{1-1}  & SD & \(\mathbf{0.00\times 10^{000}}\) & \(8.05\times 10^{-02}\) & \(3.83\times 10^{-14}\) & \(\mathbf{0.00\times 10^{000}}\) & \(\mathbf{0.00\times 10^{000}}\) & \(5.66\times 10^{-04}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._In summary, the proposed ETLBO performed better and provided more accurate results for most multimodal functions.
# Energy-Efficient RIS-Enabled SISO-OFDMA Communication via Lower Bound Optimization
###### Abstract  
The pursuit of energy-efficient solutions in the context of reconfigurable intelligent surface (RIS)-assisted wireless networks has become imperative and transformative. This paper investigates the integration of RIS into an orthogonal frequency-division multiple access (OFDMA) framework for multi-user downlink communication systems. We address the challenge of jointly optimizing RIS reflection coefficients alongside OFDMA frequency and power allocations, with the aim of maximizing energy efficiency. This optimization is subject to specific quality-of-service (QoS) requirements for each user equipment (UE) and a constraint on transmission power and the RIS phase shift matrix. To address this complex optimization problem, we propose a novel practical and low-complexity approach that is based on optimizing a computationally efficient and numerically tractable lower bound on energy efficiency. The numerical results highlight the effectiveness of our approach, demonstrating a substantial increase in energy efficiency compared to scenarios without RIS, with random RIS integration, and with the scheme using the Genetic Algorithm (GA).  
RIS-assisted network; OFDMA; energy efficiency; RIS phase design +
Footnote †: journal: Electronics Electronics
## 2 Methodology
### Algorithm Improvement
#### 2.3.2 Construction of Initial Population Based on Tent Chaotic Map  
The EM algorithm is a population-based algorithm, so the quality of the initial population has a great impact on the search performance of the algorithm. In the classical EM algorithm for solving the SCP problem, we use the greedy algorithm to obtain a local optimal solution, and then repeat the local search process m times to generate the initial population, which will make it difficult to retain the diversity of the population, resulting in poor optimization results of the algorithm. However, chaotic motion is characterized by randomness, regularity, and ergodicity, which can make the algorithm easily escape from the local optimal solution when solving the SCP, so as to maintain the diversity of the  
Figure 2: Flowchart of EM algorithm.  
population and improve the global search ability at the same time. Therefore, this paper will use the tent chaotic map to construct the initial population.  
Chaotic systems are a class of complex systems that exhibit extremely sensitive initial conditions and nonlinear dynamic behavior, in which even small changes in the initial conditions will lead to the rapid development of completely different trajectories, and the evolution of the system does not exist and does not exhibit significant periodicity [23]. The tent chaotic map is a type of chaotic system, also known as a tent map, and its mapping model is  
\[x_{i+1}=\left\{\begin{array}{l}\frac{x_{i}}{u},0\leq x_{t}<u\\ \frac{1-x_{t}}{1-u},u\leq x_{t}\leq 1\end{array}\right.. \tag{8}\]  
From the perspective of the target space, we take the diversity of generated solutions, i.e., the variability of the objective function values, as a key consideration factor to determine the selection of the control parameter u. In this experiment, the value of u is set to 0.7 and the initial value \(x_{0}\) is randomly generated in the range of [0,1].
## 3 Materials and Methods
### RNA Extraction and RNA-seq  
Cultures of US6-1, \(\Delta\)_rsh_ and \(\Delta\)_rsh_ (pCM62-rsh) after 24 h, 36 h, 48 h and 72 h of static incubation were frozen in liquid nitrogen and stored at \(-\)80 \({}^{\circ}\)C. Total RNA extraction was performed with RNAprep pure Cell/Bacteria Kit (Tiangen biotech, Beijing, China) according to the manufacturer's instructions. RNA concentration and purity were determined at 230 nm using a NanoDrop ND-1000 (Thermo Scientific, Waltham, MA, USA). Genomic DNA contamination was removed and cDNA was synthesized using FastKing gDNA Dispelling RT SuperMix (Tiangen biotech, Beijing, China).  
Samples of cDNA of US6-1 and \(\Delta\)_rsh_ at 36 h of incubation (exponential phase) were chosen for RNA-seq analysis. Library construction and sequencing were performed on a BGISEQ-500 platform by Beijing Genomic Institution (Shenzhen, China). All the raw sequencing reads were filtered to remove low quality reads and reads with adaptors, reads in which unknown bases are more than 10%. Clean reads were then obtained. These reads were stored as FASTQ format and then deposited in the NCBI Sequence Read Archive ([[http://trace.ncbi.nlm.nih.gov/Traces/sra](http://trace.ncbi.nlm.nih.gov/Traces/sra)]([http://trace.ncbi.nlm.nih.gov/Traces/sra](http://trace.ncbi.nlm.nih.gov/Traces/sra)), 22 May 2018) under the accession number SRP148564. HISAT [70] was used to map clean reads to genome of US6-1. For gene expression analysis, the matched reads were calculated and then normalized to reads per kilobaseper million mapped reads using RESM software [71]. The significance of the differential expression of genes was defined according to the combination of the absolute value of the fold change \(\geq\)2 and an adjusted \(p\) value \(\leq\) 0.05 using DESeq2 algorithm [72]. KEGG-based annotation and pathway enrichment analysis was performed using the KEGG pathway database ([[http://www.genome.jp/kegg/](http://www.genome.jp/kegg/)]([http://www.genome.jp/kegg/](http://www.genome.jp/kegg/)), 25 September 2017). All these KEGG terms were decided by a Fisher's exact test with phyper function in R language [42]. An adjust \(p\) value of 0.01 was used as the threshold to obtain significantly over-represented KEGG terms.
# RF Energy Harvesting Wireless Communications:
## 6 Secure Communications  
In this section, we introduce a unique security issue, which is mainly caused by the huge power difference between energy packets and data packets, in RF-EHWC. The potential methods to improve the communication security of RF-EHWC networks are also investigated.
# DYNASTI--Dynamic Multiple RPL Instances for Multiple IoT Applications in Smart City
## 1 Introduction
If the applications could associate to multiple instances, this flexibility would improve the service differentiation for each kind of applications, since each application could be adapted its traffic class. * Sporadic applications that eventually send messages are not supported. It is important to emphasise that the RPL control and applications messages are currently generated and sent at regular intervals throughout the lifetime of the network. The related work implementations do not reflect a network with different traffic patterns. * Multiple instances performing occurs alternately: one single instance each time. When one instance is communicating, the remainder instances are in wait mode in a First In First Out (FIFO) queue. Thus, there is no way to prioritize instances. This aspect makes very difficult to support applications with different levels of critical requirements such as delay. This article proposes DYNAmic multiple RPL instanceS for multiple ioT applicatlons (DYNASTI), a solution that extends the implementation of multiple instances in RPL, in order to provide more dynamicity and flexibility to routing, thus more optimizing QoS and traffic performance. This solution allows that multiple IoT applications with different requirements communicate in the same LLN. Thus, the DYNASTI enables:Support to sporadic applications. * Implementation of concurrency between multiple instances. * Providing more dynamic and flexible applications, allowing each application to have different instances over time. * Adapting to more critical and sporadic applications that eventually can run on the network (service differentiation between applications with critical requirements). * Interruption and adjustment of control and application message transmissions. We implemented DYNASTI on Contiki Operating System (Contiki-OS Version 3.0) [27] and evaluated it using COntiki OS Java (COOJA) simulator [28]. To the best our knowledge to date, there is no work in the literature addressing the shortcomings of existing multi-instance developments allowing them to be performed with multiple applications with different traffic requirements and patterns on the same LLNs through a management dynamic and flexible of instances. This paper is structured as follows. Section 2 includes the main concepts and definitions of RPL protocol. Section 3 describes the main related work focusing on proposals of the multiple RPL instances in the literature. Section 4 presents all the developed mechanisms in the DYNASTI, while Section 5 discusses the the assessment of the proposal through a performance evaluation based on simulation. Conclusions and future work are presented in Section 6.
# Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability
## 3 Inter-Frame Detection Based on Geometrical Invariability
### False Alarm Suppression  
The procedure of track correlation presented above is implemented for each candidate point detected in the first frame and lasts for L frames. However, a formed trajectory would be eliminated on the condition that we cannot find the next correlated point for it in the following \(\xi\) frames continuously. In this paper, we uniformly set L = 20 and \(\xi=3\). For each candidate point in the L-th frame, two maps can be constructed: points in the L\({}^{\prime}\)-th frame, where this candidate point is correlated successfully for the first time, being seen as dots, and the Euclidean distances between each other, being seen as edges, compose the first map; those of the L-th frame (these points should also exist in the L\({}^{\prime}\)-th frame) compose the second map. Here, we demonstrate a fact that the distance between two background points remains constant regardless of the rotation and translation of background. An assumption is made that the rotation angle and the translation distance for the two maps defined above are \(\Delta\Phi\) and \(\Delta\rho\), and the rotation center is \(\mathrm{P}_{*}\). As a result, for the n-th point \(\mathrm{P}_{n}^{\mathrm{f}}\) in the first map and \(\mathrm{P}_{n}^{\mathrm{s}}\) in the second map, there is an equation existing obviously:  
\[\mathrm{P}_{n}^{\mathrm{s}}-\mathrm{P}_{*}=\Re(\Delta\Phi)\cdot(\mathrm{P}_{n }^{\mathrm{f}}-\mathrm{P}_{*}+\Delta\rho) \tag{15}\]  
where \(\Re(\Delta\Phi)\) means the rotation matrix and  
Figure 3: Schematic diagram of track correlation. \[\mathbb{N}(\Delta\Phi)=\left( \begin{array}{cc}\cos\Delta\Phi&-\sin\Delta\Phi\\ \sin\Delta\Phi&\cos\Delta\Phi\end{array} \right) \tag{16}\]  
Based on Equations (15) and (16), the Euclidean distance between two background points, \(\mathrm{P}_{\mathrm{m}}^{\mathrm{s}}\) and \(\mathrm{P}_{\mathrm{n}}^{\mathrm{s}}\) in the second map, can be expressed as  
\[\left\|\mathrm{P}_{\mathrm{n}}^{\mathrm{s}}-\mathrm{P}_{\mathrm{m}}^{\mathrm{s }}\right\|_{2}=\left\|\mathbb{N}(\Delta\Phi)\cdot\left(\mathrm{P}_{\mathrm{n} }^{\mathrm{f}}-\mathrm{P}_{\mathrm{*}}+\Delta\rho\right)-\mathbb{N}(\Delta \Phi)\cdot\left(\mathrm{P}_{\mathrm{m}}^{\mathrm{f}}-\mathrm{P}_{\mathrm{*}}+ \Delta\rho\right)\right\|_{2} \tag{17}\]  
where \(\left\|\cdot\right\|_{2}\) stands for 2-norm. Under the circumstance that \(\mathrm{P}_{\mathrm{m}}^{\mathrm{s}}\) and \(\mathrm{P}_{\mathrm{n}}^{\mathrm{s}}\) are two background points, there are no other displacements for them except for the translation of background \(\Delta\rho\).
## 2 Materials and Methods
### The Proposed Algorithm
#### 2.3.2 Training a Cascade Classifier for ISME  
In this paper, the concept of the region of interest (ROI) was used to improve computation efficiency. Furthermore, we selected the area around the ISME that contained the root part as the ROI and then calculated the distance from the tip of the ISME to the root. In each experiment, there were some differences in every ISME's shape. Additionally, some noise in the microscopic images, such as root hairs, microorganisms and impurities magnified by the microscope, could seriously affect the recognition of the ISME. For micro-object recognition in this work, there was still a challenge to determine the ROI. The traditional template matching method is sensitive to noise and does not have gray scale invariance and rotation invariance. Thus, in a complex environment, recognition accuracy is seriously affected. In addition, the traditional template matching method has a high computational cost, as it takes such a long time to recognize the ISME that cannot meet the real-time requirements well.  
In a microscopic image, if the gray value of a mico-object is lower than that of the background environment, there is much more edge information. The improved Haar-like feature contains more edge information. Therefore, Haar-like features are suitable for ISME detection based on microscopic images. For detailed process, please refer to [22; 23; 24].  
Besides rich local texture features, local binary pattern (LBP) features are also characterized by gray scale invariance and less computation [25]. Therefore, we can use LBP features and Haar-like features combined with an AdaBoost algorithm for rapid object recognition [22; 26; 27; 28; 29].  
In the proposed method, AdaBoost is used to both select a small set of features and train the classifier. First, we extracted Haar-like features and LBP features of the ISME, and then we selected some distinct features for weak classifier training using the AdaBoost algorithm. For the detailed process, please refer to [30; 31].  
With ensemble learning, one can get a strong classifier by linearly combining some weak classifiers, which can greatly improve detection efficiency (Figure 7). Multi-feature fusion can effectively improve the detection rate of a cascade classifier. We can linearly combine both Haar-like classifiers and LBP classifiers to get a multi-feature cascade classifier. In this study, the Haar-like features and LBP features of an ISME were extracted and selected. Many weak classifiers based on Haar-like and LBP features were concatenated into strong cascade classifiers for ISME recognition. Then, we compared the strong cascade classifiers with the traditional template matching method.  
Figure 7: Schematic depiction of the detection cascade.
## 4 Case Study and Discussion
### Coupling Correlation of Complex System
#### 4.1.2 Coupling Degree and Related Factors
Then propagation time from element 1 to element 9 is 0.7 + 0.7 + 0.4 = 1.8(s). Similarly, the whole indirect propagation time matrix \(C_{c}^{T}\) is formed as shown in Matrix (28). \[C_{c}^{T}=(C_{c}^{T})_{9\times 9}=\left[ \begin{array}{cccccccc}0.0&0.5&0.7&0.6&1.4&1.4 &2.0&1.6&1.8\\ 0.5&0.0&0.8&0.4&1.5&1.5&2.1&1.7&1.9\\ 0.7&0.8&0.0&0.6&0.7&0.7&1.3&0.9&1.1\\ 0.6&0.4&0.6&0.0&0.8&0.7&1.4&0.8&1.2\\ 1.4&1.5&0.7&0.8&0.0&0.5&0.6&0.7&0.4\\ 1.4&1.5&0.7&0.7&0.5&0.0&0.7&0.5&0.6\\ 2.0&2.1&1.3&1.4&0.6&0.7&0.0&0.6&0.5\\ 1.6&1.7&0.9&0.8&0.7&0.5&0.6&0.0&0.8\\ 1.8&1.9&1.1&1.2&0.4&0.6&0.5&0.8&0.0\\ \end{array} \right] \tag{28}\]
## 2 Multipath Routing Algorithm for Inter-Satellite Links Based on Load Balancing
### Multipath Backtracking  
All the shortest pathways from the source node to other nodes are traced layer by layer in accordance with the front nodes, starting with the source node as the root node. To determine the number of hops from every node in the entire network to the source node, follow the steps in Figure 3: first, trace back the path of the one-hop node, then trace back the path of the two-hop node, until the shortest path of the farthest node in the network is determined.  
Figure 3: Multipath backtracking.
## 2 Implementation Devices  
### Industrial AR  
Industrial AR enhances productivity and efficiency by combining real-world and virtual information, as shown by the SUS and systematic literature reviews covering AR applications, industry interest, development, benefits, and challenges [30; 31]. These systems primarily deliver necessary data to workers visually through AR glasses or mobile devices. These data serve as advanced instructions and guidance, helping workers perform tasks more efficiently. For instance, during machine assembly, real-time display of installation instructions or work procedures for actual parts can minimize errors and maximize productivity. During maintenance tasks, it can similarly assist in recognizing faulty parts and guiding repair procedures. Industrial AR technology is recognized as a crucial part of digital transformation, and, consequently, the demand for it continues to grow. It is expected that this technology will be more widely adopted across various industries, including manufacturing, automotive, shipbuilding, and energy, in the future. Many domestic and international companies are developing such industrial AR technologies, providing various solutions tailored to the Fourth Industrial Revolution era. These technologies not only contribute to improving companies' productivity and competitiveness but also play a significant role in enhancing the safety and convenience of workers. The air spring AR content developed in this paper was created to provide work guidelines necessary for the maintenance of railway vehicle air springs by applying industrial AR.