esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on a PSO-H-SVM-Based Intrusion Detection Method for Industrial Robotic Arms
## 3 Intrusion Detection Method and Response Mechanism
### Hierarchical Support Vector Machine Based on PSO Algorithm
function (RBF) is chosen as the kernel function in this paper, and the hyperparameters have the penalty coefficient C and the parameter gamma, which comes with the RBF function. The size of the penalty factor C affects the generalization ability of the model, and the size of the gamma affects the number of support vectors, which in turn affects the speed of training and prediction. To further improve the classification accuracy of the h-SVM model, the particle swarm optimization algorithm mentioned in Section 3.1 is used for optimization, and the hyperparameters are optimized using PSO while training for each classifier. The chosen fitness function is the sum of the number of classification errors in each category, and the formula is as follows:  
\[\text{fv}=\sum_{\text{i=0,j=0}}^{\text{i=k-1,j=k-1}}\text{confusion\_matrix}[ \text{i}][\text{j}],\text{i}\
eq\text{j} \tag{4}\]  
where, fv is the fitness value; confusion_matrix[i][j] is the value of the ith row and jth column of the confusion matrix; and k is the number of categories. The pseudo-code for the overall workflow of the hierarchical support vector machines based on PSO (PSO-H-SVM) model is shown in Algorithm 1. Each particle is a two-dimensional data point, respectively C and gamma, and the position of each particle represents a set of optimization parameters. The global optimal solution is used in the iterative process to assign values to the two hyperparameters for model training, and the confusion matrix is calculated after each training session, followed by the calculation of the fitness value. The individual optimal solution and the global optimal solution are updated if the optimization conditions are met. After updating the velocity and position of the particles, the next iteration is performed until the optimization is completed by finding a set of optimal solutions, after which the model is used to predict the test set and calculate the accuracy of the model. ```
procedure optimizing each classifier in H-SVM using PSO algorithm Input: X, Y // X is the eigenvector, Y is the result label for classifier_i in (\(\text{k}-1\)) do  particle_init = [(random(), random()*m)] // m is the number of particles  g = 0 whileg < gmax do for i in m  position = particle_init[i]  SC = SVC(kernel = 'RBF', C = position[0], gamma = position[1]).fit(X, Y) // Create SVM  CM = confusion_matrix(Y_true, SC_predict(X)) // Calculate the confusion matrix  fv = sum(CM[i][j]) (i \(\
eq\) j) // Calculate the fitness value ifpbest_fv > fv and gbest_fv > fv then  Update individual and global optimal solutions  w = wmax - itrea(wmax - wmin)/gmax // Update inertia weight  Update the position and velocity of each particle  C, gamma = gbest_position[0], gbest_position[1]  Combine classifiers and calculate accuracy Output: The values of C, gamma and accuracy
```  
**Algorithm 1** Hierarchical Support Vector Machine Based on PSO Algorithm
## 5 Conclusions  
In this article, we study the self-organizing routing protocol of underwater acoustic wireless sensor networks, focusing on the application of AODV protocol in underwater acoustic networks. Considering the broadcast storm problem of AODV, a route-focusing AODV (RFAODV) routing protocol that is more suitable for underwater wireless sensor networks is proposed based on location and energy information. When route discovery location information is used to define forwarding areas hop by hop, limiting the nodes participating in RREQ forwarding, and adaptively setting forwarding delays for nodes within the forwarding area, the route discovery process focuses on fewer nodes. Therefore, it is energy efficient. In addition, RFAODV uses link layer feedback and energy control to reduce protocol overhead and prolongs the lifetime of network during route maintenance.  
We implemented RFAODV and evaluated its performance in the NS-2. The extensive simulation results show that (1) as the load changes, RFAODV has an increase of at least 18% in the discovery success ratio, an increase of about 5% in the delivery ratio, a decrease at least 15% in the number of overhead and a reduction of at least 10% in the consumption energy compared to AODV and DSAODV; (2) RFAODV has a rise of at least 20% in the discovery success ratio, an increase of at least 5% in the delivery ratio, a decrease at least 40% in the number of overhead and a reduction of at least 15% in the consumption energy in large-scale and high-density scenarios than AODV and DSAODV; and (3) RFAODV has a rise of at least 25% in the discovery success ratio, an increase of at least 4% in the delivery ratio, a decrease at least 20% in the number of overhead and a reduction of at least 5% in the consumption energy in high dynamic scenarios. In summary, RFAODV offers a significant improvement compared with AODV and DSAODV in terms of packet delivery ratio, discovery success ratio, protocol overhead and energy consumption, which proves that RFAODV routing protocol is more suitable for large-scale, high-load and dynamic-underwater wireless sensor networks.  
In following research, we will further study cross-layer optimization design methods. Jointing the physical layer and MAC layers for the cross-layer design can effectively improve the comprehensive performance of the routing protocols. The cross-layer design facilitates the attainment of a globally optimal solution by enabling interactions between layers within the Underwater Wireless Sensor Networks (UWSNs).  
Conceptualization, W.G.; methodology, W.G.; software, W.G.; validation, M.Z., B.Y. and Y.W.; formal analysis, W.G.; investigation, W.G.; data curation, W.G.; writing--original draft preparation, W.G.; writing--review and editing, W.G.; visualization, W.G.; supervision, M.Z. and B.Y.; funding acquisition, M.Z., B.Y. and X.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Project (2021 YFC2801200, 2022 YFC2806001), Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7015).  
Not applicable.
# Application of the Supercapacitor for Energy Storage in China: Role and Strategy
## 2 Technology Characterization
### Hybrid Supercapacitor  
Hybrid supercapacitors are also known as asymmetric capacitors. Transition metal oxides or conducting polymers are used as their positive materials (energy source) and activated carbon (AC) is used as their negative material (power source). Its charge storage mechanism combines the fast and reversible faradic redox reactions with reversible adsorption of ions of active materials. So, hybrid supercapacitors have relatively high energy density in combination with high specific capacitance, compared with that of Faraday pseudocapacitors and double-layer supercapacitors. It has a wide application in portable supplies, memory backup systems, electric vehicles, and emergency backup power because of their long battery life, higher power density, and smaller volume. Most recently, [PERSON] et al. studied a type of tip-open CNT/CNF hierarchical hybrids via KOH activation on the CVD-grown Al/Fe-catalyzed CNT/CNF precursors [17]. It can fully open the CNT hierarchies' tips using KOH activation under 700-750 \({}^{\circ}\)C for 90 min. The tip-open CNT-CNF hybrids show a highly improved specific capacitance, which increases by 3.3 times that of the pristine ones. Sweep analysis indicated that the diffusion-type capacitance increases by 3.7 times, while the Helmholtz-type capacitance increases only by 1.5 times, indicating that the tip-open CNTs contribute to 30% of the increase in the double-layer capacitance. A aqueous hybrid supercapacitor (AHSCs) based on the optimized CoMoO\({}_{4}\)/MnO\({}_{2}\) nanowires/Ni foam (KF-CMNWs) electrode and modified alkali-treated carbon nanotubes film (M-CNTF) with K\({}_{3}\)Fe(CN)\({}_{6}\) in neutral aqueous Na\({}_{2}\)SO\({}_{4}\) electrolyte was introduced by [PERSON] et al. [18]. These AHSC can not only improve the device cycle life by more than 10,000 cycles and 96.8% capacitance retention, but also a high energy density of 62.9 Wh kg\({}^{-1}\) at a power density of 984 W kg\({}^{-1}\) is demonstrated.  
Figure 2: Electrode materials and typical pseudocapacitances. (**a**) the preparation process of pseudocapacitance [15], Open Access, (**b**) the contrast of the specific capacitance properties of various bimetallic or multi-metallic oxides pseudocapacitance electrode materials, (**c**) CV curve, cycling performance, and GCD curves of the Sr-Bi bimetallic oxides pseudocapacitance electrode [16], Open Access (**d**) Micro-model, XRD pattern, SEM, and band structure of Sr-Bi bimetallic oxides [16], Open Access.
# Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer
## 2 Materials and Methods
### Data Preprocessing and Correction  
For the Swarm-C satellite, although ESA will publish the accelerometer data of the satellite every once in a while, due to the delay in the data being published by the ESA, the data published by the ESA remained in 2016, when this study was carried out. As a result, ESA only published the corrected results among the three Swarm satellites [31]. Since the accelerometer data of the Swarm-C satellite after 2017 need to be used in this paper, we try to refer to the correction method of the GRACE-A satellite [25] to correct the original accelerometer data of the Swarm-C satellite. We obtained Swarm-C accelerometer data from ESA's public data platform ([[http://swarm-diss.eo.esa.int/](http://swarm-diss.eo.esa.int/)]([http://swarm-diss.eo.esa.int/](http://swarm-diss.eo.esa.int/))) (accessed on 18 November 2022). These include uncalibrated raw accelerometer data and non-conservative force acceleration data derived from GPS [32].  
There is a detailed description of Swarm satellite data in the link ([[https://earth.esa.int/eogateway/documents/20142/37627/Swarm-PDGS-Data-Access-User-Manual.pdf](https://earth.esa.int/eogateway/documents/20142/37627/Swarm-PDGS-Data-Access-User-Manual.pdf)]([https://earth.esa.int/eogateway/documents/20142/37627/Swarm-PDGS-Data-Access-User-Manual.pdf](https://earth.esa.int/eogateway/documents/20142/37627/Swarm-PDGS-Data-Access-User-Manual.pdf))) (accessed on 18 November 2022).
# Sustainable Parking Space Management Using Machine Learning and Swarm Theory--The SPARK System
## 2 Literature Review
### Parking Spaces: A Growing Urban Challenge
SlotFinder stands out by offering more accurate and reliable solutions compared to traditional parking systems, making it an invaluable tool for modern cities grappling with increasing vehicle numbers and limited parking infrastructures. A representation of the efficiency and usefulness of this system can be seen in the example presented by [PERSON], et al. [16] in one of the most congested places in the world. The article SlotFinder: A Spatio-temporal based Car Parking System [16] addresses the issue of parking shortages in congested cities such as Dhaka, Bangladesh. The authors introduce SlotFinder, a system that optimizes the use of available parking spaces through spatio-temporal analysis. The system leverages data collected from 408 buildings and employs a Long Short-Term Memory (LSTM) model to accurately predict parking space availability. SlotFinder outperforms previous methods, as demonstrated by its lower RMSE (root mean square error) and MAE (mean absolute error) values, which confirm the system's efficiency and usefulness in real-world applications. Additionally, research by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [16] showcases SlotFinder's effectiveness in managing parking in crowded cities such as Dhaka by using LSTM models to predict vacant spaces, achieving lower RMSE and MAE values compared to traditional methods. Further comparative studies by [PERSON], et al. [17] highlight the superior performance of gradient-boosted and deep learning models in predicting parking availability by incorporating spatial and temporal features. The study by [PERSON], et al. [18] introduces a clustering approach to predict real-time parking availability, demonstrating efficient parking management through typical day profiles. Additionally, research by [PERSON], et al. [19] applies deep learning for real-time slot detection, achieving high accuracy in identifying vacant slots for both cars and two-wheelers. SlotFinder stands out as a robust solution for modern urban challenges, offering precise and reliable parking predictions that traditional systems lack, thus significantly contributing to improved urban traffic management. Another significant contribution in this field is by [PERSON] [20], who presents a hybrid machine learning model to predict parking slot availability, enhancing the efficiency and accuracy of parking management systems.
# Performance of Granulated Ti-Dopped Manganese Oxide Oxygen Carriers in Chemical Looping Processes
###### Abstract  
The cornerstone in the development of different applications of the Chemical Looping (CL) technology is the oxygen carrier material that transfers the oxygen. We evaluate the performance of manganese oxide doped with TiO\({}_{2}\) to reinforce the release of molecular oxygen and regeneration at high temperatures (850-950 \({}^{\circ}\)C). Different manganese-titanium mixed oxides with mass fractions of titanium oxide from 0 to 50 wt.% were considered and prepared by granulation. The sample with 20 wt.% TiO\({}_{2}\) (MnTi_20) showed better oxygen release/regeneration capabilities than pure manganese oxide under similar conditions and was selected for further development. To evaluate the performance of MnTi_20, long tests (200 cycles) were carried out in a TGA to evaluate the chemical and mechanical stability of the sample. Additionally, MnTi_20 was tested in a batch fluidized bed reactor to evaluate its oxygen uncoupling capability and its reactivity with the main combustion gases (H\({}_{2}\), CO, and CH\({}_{4}\)) as well as its fluidization properties. MnTi_20 is capable of regenerating Mn\({}_{3}\)O\({}_{4}\) to Mn\({}_{2}\)O\({}_{3}\) at 950 \({}^{\circ}\)C and maintains its reactivity with the redox cycles. No fluidization problems were encountered during almost 60 h of continuous fluidization, and a lifetime of 2500 h was estimated for this oxygen carrier.  
chemical looping combustion; chemical looping air separation; oxygen uncoupling; manganese oxide; titanium oxide +
Footnote †: journal: Applied Sciences  
Academic Editors: [PERSON], [PERSON] and [PERSON]  
Received: 20 November 2024  
Revised: 19 December 2024  
Accepted: 28 December 2024  
Published: 14 January 2025  
**Citation**: [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Performance of Granulated Ti-Dopped Manganese Oxide Oxygen Carriers in Chemical Looping Processes. _Appl. Sci._**2025**, 15, 750.  
[[https://doi.org/10.3390/appl/1502/0750](https://doi.org/10.3390/appl/1502/0750)]([https://doi.org/10.3390/appl/1502/0750](https://doi.org/10.3390/appl/1502/0750))  
**Copyright**: 62025 by the authors.  
Louissee MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Temporal Graphs and Temporal Network Characteristics for Bio-Inspired Networks during Optimization
## 4 Review of Related Work
Choosing an applicable centrality measure largely depends on the specific network being studied, its goals, and the environment it exists in. Among the most frequent centrality measures discussed in literature is the betweenness centrality, a simple measure that identifies nodes working as important corridors for information flow within in the network [67; 68; 69; 77; 79; 80; 81]. This identification allows for increased control over this information flow, as well as increased reinforcement of important nodes. The limitations of this measure lie in its high computational cost, disregard for the global structure of the network, and its inapplicability to choose the shortest path for data transmission due to limited energy resources [10]. Another commonly employed centrality measure is the closeness centrality, which measures the mean distance between a node and other nodes (also referred to as the shortest geodesic distance from a node to all other nodes in the network). Closeness centrality is useful in clustering and assigning energy-saving sleep/wake schedules [67; 68; 69; 77; 78; 79; 80]. However, it does not account for unreachable nodes and performs poorly in large networks, where it cannot point to the main leaders [10]. We considered degree centrality [13; 68; 78; 79; 80; 82] and eigenvector centrality [68; 79; 80; 83] to be used for WSNs when implemented in toxic leak detection as described by Voronoi-type approaches [68]. These centralities cover the two types of classifications: (i) local centrality which is demonstrated through degree centrality by focusing on how nodes are connected to their neighbors, and (ii) the global centrality demonstrated through the eigenvector centrality that reflects how often can a node be effective in transferring the data packets among the network [68]. Moreover, in [80] the analysis of four different centrality measures (Degree, Betweenness, Closeness, and Eigenvector) shows them identical in their analysis of robustness against errors in the measurements (addition or removal of nodes) and so choosing either one is adequate for network analysis. Additional research has shown that eigenvector centrality performs better than other centralities at measuring causal inference [84]. Complementing all of this is its relative ease of computation.
###### Abstract  
The Institute of Space Systems at Technische Universitat Braunschweig has long-term experience in the field of space debris modeling. This article reviews the current state of ongoing research in this area. Extensive activities are currently underway to update the European space debris model MASTER. In addition to updating the historical population, the future evolution of the space debris environment is also being investigated. The competencies developed within these activities are used to address current problems with regard to the possibility of an increasing number of catastrophic collisions. Related research areas include, for example, research in the field of orbit determination and the simulation of sensor systems for the acquisition and cataloging of orbital objects. In particular, the ability to provide simulated measurement data for object populations in almost all size ranges is an important prerequisite for these investigations. Some selected results on the distribution of space debris on Earth orbit are presented in terms of spatial density. Furthermore, specific fragmentation events will be discussed.  
space debris modeling; MASTER; long-term evolution; space situational awareness; orbit determination; active debris removal
# Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application
## 5 Conclusions and Future Work  
This paper presents an HHS algorithm by low-level heuristics based on data mining for task scheduling in an FC architecture. After a study of some meta-heuristic algorithms, the proposed method shows better results than other methods. The HHS algorithm reduces simulation time and increases decision-making power; assigning resources with specific  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**A** & **C** & **GA** & **PSO** & **ACO** & **SA** & **MO** &
\begin{tabular}{c} **NSGA-** \\ **III** \\ \end{tabular} & **Con.** & **FCFS** & **DP** & **HHS** \\ \hline  
\hline Avg. & & 92.07 & 43.11 & 176.50 & 51.50 & 95.13 & 101.00 & 69.45 & 61.71 & 56.35 & 52.25 \\ Max & & 171.48 & 153.59 & 306.1 & 150.31 & 217.20 & 222.09 & 180.77 & 166.02 & 160.05 & 150.62 \\ Min & & 10.04 & 2.09 & 10.01 & 1.15 & 16.04 & 16.48 & 2.57 & 2.38 & 1.28 & 1.36 \\ SD & & 41.06 & 30.82 & 72 constraints to users is increased according to the type of workflow. The delay of the application loop is decreased in HHS compared to MaOPSO by 28.1%, NSGA-III by 40.8%, FCFS by 6%, concurrent by 28%, and DP by 52.14%. The average energy consumption is decreased in HHS compared to FCFS by 7.72%, concurrent by 11.69%, and DP by 3.63%. The average total execution cost is decreased in HHS compared to FCFS by 53.47%, concurrent by 55.90%, and DP by 45.28%. The average energy consumption and total execution cost of HHS are not better than those of MaOPSO or NSGA-III. Of course, MaOPSO and NSGA-III take a great deal of time to reduce energy consumption and total execution cost, which is not a good result for an IoT application. Therefore, reducing the delay is an important advantage over the MaOPSO and NSGA-III algorithms.  
For future work, investigation of classification methods for finding appropriate candidate heuristics by adjusting the parameters of these algorithms in accordance with data and resource streams can be useful. Additionally, research on more IoT case studies with various topologies of FCs for medical care, smart homes, and vehicle transportation systems can be valuable. Latency and low power as two major constraints in the IoT require further research. Additionally, scheduling of application modules according to fault-tolerance, QoS requirements, and security overhead is an important issue in FC research.  
This research received no external funding.
# Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices
## 5 Limitations and Future Work
Hence, future research endeavors should focus on refining the methods to make the wheat yield estimation models more accurate, efficient and practical. This could involve optimizing data collection processes, increasing sample sizes and ensuring a more comprehensive representation of different wheat varieties and geographic regions. By addressing these aspects, we can enhance the precision, efficiency and practicality of the models for estimating wheat yield, making them more suitable for widespread application. R.W. : Methodology, formal analysis, writing--original draft, visualization. Y.F. : Methodology, software, writing--original draft. L.Z. : Methodology, data curation, writing--original draft. G.G. : Data curation, writing--review and editing, formal analysis. D.Y. : Supervision, writing--review and editing, funding acquisition, data curation, supervision, project administration. All authors have read and agreed to the published version of the manuscript. This study was supported by the National Natural Science Foundation of China (No. 52174160). The data presented in this study are avalable on request from the corresponding author. The data are not publicly available due to privacy. Author [PERSON] was employed by the company Nuclear Power Planning and Design Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.