esa-sceva/satcom-chunk-collection huggingface dataset:
# Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction
## 5 Conclusions  
In this paper, based on an LSTM neural network, a data-driven method is developed to build the train dynamic model and predict the train speed. Different from the parametric approaches, the proposed method is more adaptive to the real environment due to its strong knowledge expression ability and learning ability. To make fair comparisons with other models, the same inputs as the parametric approaches (analog output, traction brake, rail slope and train speed) were fed to the LSTM based algorithm, after data preprocessing and feature engineering. The hyperparameters of the proposed algorithm were carefully tuned to improve the output accuracy. The performance of the proposed method was evaluated in terms of three measurement criteria: MAE, MSE, and R2. Using the actual data collected from Shenzhen Metro Line No. 7 and Beijing Yanfang Metro Line in China, the proposed model was compared with two traditional parametric approaches: PTDM-I and PTDM-II. The comparison results demonstrate that the model performance was remarkably enhanced compared to these algorithms. By combining lagged features and statistical features, the LSTM based method was then extended to predict train speed for a longer period of time. Experimental results suggest that the extended version can be applied to solve the train speed prediction problem since the prediction errors are well controlled.  
However, there are still some shortcomings in the proposed method. Firstly, since lagged features are employed in the proposed algorithm, high prediction errors may be produced in the beginning phase. It would be useful to improve the proposed algorithm by solving the initialization problem. Secondly, the on-board devices in urban rail transit have the characteristics of limited hardware and low-complexity. How to reduce the complexity of the proposed algorithm is a major concern for future studies.  
Figure 10: Comparison of the original data and the output of 50-step-ahead prediction.  
**Supplementary Materials:** The open code file is available online at [[https://drive.google.com/open?id=lamXoTR6](https://drive.google.com/open?id=lamXoTR6) Tr3 bdq1 khkU-B6_fhjl0p0 gpp]([https://drive.google.com/open?id=lamXoTR6](https://drive.google.com/open?id=lamXoTR6) Tr3 bdq1 khkU-B6_fhjl0p0 gpp).  
**Author Contributions:** Conceptualization, T.T. and C.G.; formal analysis, T.T. and Z.L.; methodology, T.T. and Z.L.; writing--original draft, Z.L.; validation, Z.L.; resources, C.G.; supervision, C.G.  
**Funding:** This research was funded by Beijing Postdoctoral Research Foundation.  
**Conflicts of Interest:** We declare that we have no financial or personal relationships with other people or organizations that could inappropriately influence our work. There is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.
## 1 Introduction
It is rarely addressed how to introduce one or more transmission zeros (TZs) at the cross-polarized frequency to improve polarization isolation, particularly when the two orthogonal polarizations operate at close frequencies. This article describes a dual-band frequency selective surface with polarization selectivity, whose two passbands transmit orthogonally polarized incident waves, respectively. The proposed design consists of two identical U-shaped resonators, a common ground with two coupling apertures, and two dielectric substrates to isolate the U-shaped resonators from the ground. Importantly, each U-shaped resonator is connected to the ground via a metallic via, allowing for multiple modes of resonance with different polarizations. Two transmission zeros are introduced at the lower and upper band edges of the two passbands to significantly improve the frequency selectivity and cross-polarization suppression. Finally, a prototype with an operating frequency of approximately 10 GHz is fabricated and tested to validate the design methods discussed. The following is a summary of this article's main contributions:  
* By grounding the U-shaped resonant structure, a small frequency ratio between the two orthogonal polarizations can be achieved. By adjusting the substrate thickness, the radius of the grounded via, and the dimensions of the U-shaped resonator, the frequency ratio achievable at X-band ranges between 1.05 and 1.4;
* A general framework is provided. In addition, full-wave simulations and equivalent circuits are analyzed and were found to agree well, which would be of great assistance in comprehending the mechanism of our structure and optimizing its parameters during design. Followed by step-by-step explanations and design examples for the S-, X-, and K-bands, the most significant contribution is a summary of the general design flow diagram. The proposed designs have significant application potential in modern wireless communication and multiple-input, multiple-output (MIMO) systems due to their high selectivity, small frequency ratio, low profile, and stable performance under oblique incidence.
## 5 Discussion
### Network Design  
The strengths of blockchain technology, such as visibility, traceability, compliance, and resiliency, have been summarized in previous work [73]. In our simulated environment, the centralized databases we used were susceptible to cyberattacks, posing a risk to the entire system. To address this, blockchain utilizes distributed ledger technology (DLT), which enhances cyber resiliency by eliminating single points of contact. In a DLT system, an attack on one or a few participants does not impact other nodes, ensuring transparency, availability, and uninterrupted transactions.  
To maintain data security, encrypted channels must be used for data transmission. The configuration of these channels has a significant impact on factors such as data volume over the network, workload distribution, network resilience, and visibility. While most data reside in other ERP systems and are accessible only to authorized employees, our research did not have the necessary authorization to access them.  
In a case study in which multiple parties collaborate on a business process, there are various ways to configure the blockchain channels [74]. The channel configuration determines the flow of data within the network. With a single channel, all transactions are posted to that channel, and unless the data payload is encrypted, all parties typically have access to them. Alternatively, multiple channels can be created to separate interactions among subsets of parties. However, managing multiple channels can be complex, as each channel has its own database and cryptographic keys. Additionally, there are alternative configurations and complexities to consider, especially when secrecy and privacy concerns arise with respect to the channel configuration.  
When developing the model, two crucial aspects to consider are security and delay. Regarding security, confidential employee information such as Tax File Numbers (TFNs), wages, and super payment amounts must be protected from potential attack vectors. Blockchain can withstand conventional cyberattacks quite well, but cybercriminals are developing new techniques expressly with the aim of compromising blockchain technology. Additionally, the time consumption associated with this method should be considered (Figure 10).  
[PERSON] and [PERSON] [75] suggest that blockchain technology has the potential to enhance the security of payroll systems and reduce fraud, while improving efficiency. However, it is important to acknowledge that implementing blockchain technology can be costly, particularly for smaller organizations or those with limited resources. The expenses include significant investments in hardware, software, and ongoing maintenance and support. Furthermore, consensus implementation, which is a crucial aspect of blockchain, is both time consuming and costly, requiring the replication of several servers and databases in an integrated approach.
## 7 Conclusions  
We studied absorption phenomena in a hollow waveguide with an inserted graded dielectric layer for the case of TE-wave propagation. The proposed waveguide model is developed to be used in a study of a potential cancer treatment by heating of GNPs inside the cancer cells. A scaling factor was introduced to the exact analytical formulas for transmission and reflection coefficients, allowing the study of absorption only within the central dielectric layer of the waveguide, the region most relevant for the intended medical application. The scaling factor was further generalized to handle losses in a layer of arbitrary thickness. Thus, it can be used to study absorption in a finite waveguide, as is required by experimental setups and many numerical methods, being by necessity of finite length. The fomulas were validated in accordance with a numerical graded waveguide model developed in a commercial finite element software. The analytical solutions and the finite element model were compared for both non-dispersive and dispersive materials and found to be in excellent agreement. In the latter case, dispersion models describing GNPs in electrophoretic motion submerged in human tissue were used for the thin dielectric layer, surrounded by lossy human tissue. The S-parameter results were applied with a relative scaling factor as a function of the waveguide length, thin layer thickness, and material permittivities, and successfully demonstrated the usefulness of the scaling factor in distinguishing between absorption within the thin layer and the lossy ambient. Thus, we conclude that the proposed model provides a valuable analytical and numerical tool for future research of RF absorption phenomena in GNP-fed cancer tissue.  
Conceptualization, M.D.; methodology, M.D. and B.B.S.; software, B.B.S., M.S. and H.C.; validation, B.B.S., M.S. and H.C.; formal analysis, M.D., B.B.S., M.S. and H.C.; investigation, M.D., B.B.S., M.S. and H.C.; resources, M.D.; data curation, M.S. and H.C.; writing--original draft preparation, M.D. and B.B.S.; writing--review and editing, M.D. and B.B.S.; visualization, B.B.S., M.S. and H.C.; supervision, M.D. and B.B.S.; project administration, M.D.; funding acquisition, M.D. All authors have read and agreed to the published version of the manuscript.  
The work of M.D. was supported by the Swedish Research Council (VR) under project number 2018-05001.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
GNP Gold nanoparticle  
RF Radiofrequency  
TE Transverse Electric  
TM Transverse Magnetic
# Hydrological Drought Frequency Analysis in Water Management Using Univariate Distributions
## 2 Methodology
### Probability Distributions
#### 2.1.3 Pearson V (IPV)  
The Pearson V distribution represents the inverse of the Pearson III distribution [3; 20].  
\[f(x)=\frac{\exp\Bigl{(}-\frac{\beta}{x-\gamma}\Bigr{)}}{\beta\cdot\Gamma( \alpha-1)}\cdot\biggl{(}\frac{x-\gamma}{\beta}\biggr{)}^{-\alpha} \tag{11}\]  
\[F(x)=\frac{\Gamma\Bigl{(}\alpha-1,\frac{\beta}{x-\gamma}\Bigr{)}}{\Gamma( \alpha-1)} \tag{12}\]  
\[x(p)=\gamma+\frac{\beta}{\Gamma^{-1}(p;\alpha-1)}=\gamma+\frac{\beta}{qgamma (p,\alpha-1)} \tag{13}\]  
where \(\alpha,\beta,\gamma\) are the shape, the scale, and the position parameters, respectively; \(\alpha,\beta>0;\gamma<x_{\min}\), and \(x\) can take any values of range \(\gamma<x<\infty\).
## 3 Simulation Results and Discussion
### Simulation Results for Establish Time of Route  
We simulate the network establishment time of TSR, TSBR, gossiping protocol, polling algorithm and flooding protocol. We can see from the results in Figure 8, TSBR takes more time to establish a network than TSR, but still less than the other protocols.
# Autonomous Cyber-Physical Systems Enabling Smart Positive Energy Districts
## 3 Emerging Technologies Enabling Positive Energy Districts
### Technological Challenges in Positive Energy Districts
The aim is to enhance the reliability of environmental monitoring and to provide a more comprehensive understanding of environmental conditions. * IoT challenges [8]:
* The IoT enables the collection and analysis of real-time data from various energy sources [45]. However, IoT devices are vulnerable to cyber-attacks, which can lead to security threats, such as intellectual property theft, data breaches, and disruptions to critical infrastructure. * Data confidentiality, security, and privacy: Security approaches aim to protect networks (data and devices) against malicious attacks and unauthorized alterations, as well as protecting the privacy of the users (protection of personal information from unauthorized access and misuse) [86]. The backbone of IoT network security consists of cryptography-based methods ensuring data privacy, integrity, and authentication. Resilience to various forms of attack is provided, whilst low scalability, usability, and efficiency are drawbacks. * Functionality, safety, and fault tolerance: Industry 4.0 has introduced functional safety networks that provide enhanced production reliability, scalability, and flexibility. New contemporary applications have emerged that provide reliable coordination between ICT, sensors, and actuators [87]. * Quality of Service (QoS): Taking QoS characteristics into account, several quality approaches have been proposed at various layers of the IoT architecture [88]. QoS approaches must exist at every layer of the IoT architecture to guarantee a satisfactory level of QoS concerning safety critical applications of IoT. * Standardization activities, protocols, and architecture: IoT platforms that integrate different element types provided by diverse vendors that use different protocols, different data formats, and different communication technologies is needed to monitor and control the device requirements [89]. Combining various technologies in PEDs is challenging. Hence, several tools are increasingly being used to manage energy more efficiently [14]. At the district level, tools such as \"Geographic Information Systems\" (GIS) and \"Supervisory Control and Data Acquisition\" (SCADA) are commonly used, while, at the building level, tools including \"Building Management Systems\" (BMS), \"Building Energy Management Systems\" (BEMS),\"Computer Aided Facility Management\" (CAFM), and \"Integrated Workplace Management Systems\" (IWMS) are often utilized [14]. For example, a new GIS platform has been developed to quantify the energy savings gained through application of refurbishment measures [4]. For each postal district, the GIS platform provides estimates for buildings with specific parameters and provides solutions for low-enthalpy geothermal energy and solar thermal energy in urban areas. Solar irradiation maps assess solar resources, whilst Google Maps estimates geothermal potential. Urban data has been derived from official cadastral (land registration) databases.
###### Abstract  
Smart sensor nodes can process data collected from sensors, make decisions, and recognize relevant events based on the sensed information before sharing it with other nodes. In wireless sensor networks, the smart sensor nodes are usually grouped in clusters for effective cooperation. One sensor node in each cluster must act as a cluster head. The cluster head depletes its energy resources faster than the other nodes. Thus, the cluster-head role must be periodically reassigned (rotated) to different sensor nodes to achieve a long lifetime of wireless sensor network. This paper introduces a method for extending the lifetime of the wireless sensor networks with smart nodes. The proposed method combines a new algorithm for rotating the cluster-head role among sensor nodes with suppression of unnecessary data transmissions. It enables effective control of the cluster-head rotation based on expected energy consumption of sensor nodes. The energy consumption is estimated using a lightweight model, which takes into account transmission probabilities. This method was implemented in a prototype of wireless sensor network. During experimental evaluation of the new method, detailed measurements of lifetime and energy consumption were conducted for a real wireless sensor network. Results of these realistic experiments have revealed that the lifetime of the sensor network is extended when using the proposed method in comparison with state-of-the-art cluster-head rotation algorithms.  
wireless sensor network; smart node; lifetime; cluster-head rotation; energy consumption; transmission suppression Article
## 3 Results and Discussion
### Surface Morphology of Waterborne UV/Al Coatings with Low Infrared Emissivity  
From the SEM in Figure 6, it can be seen that the continuous and dense flake Al powder structure with a good spreading property was formed on different coating surfaces, and the morphology of the three was not different. However, compared with other Al powders, when the concentration of the Al powder was 25.0%, there was no gap on the surface of the coating, the distribution of the flake Al was more uniform and the coating performance was better.  
The performance of the waterborne UV/Al coating was compared with those of epoxy-lacquer-based coatings [31], PU-based coatings [32; 33; 34] and waterborne acrylic-based coatings [16], as shown in Table 9. Compared with other coatings, the infrared emissivity of the waterborne UV/Al coating was slightly higher than other coatings with a value of 0.361. The gloss of the waterborne UV/Al coating was lower than that of the PU-based coatings, which was like that of the epoxy-lacquer-based coatings and Al/waterborne acrylic coating. The adhesion of the waterborne UV/Al coating was as high as the Al/waterborne acrylic coating, which was at level 0. The impact  
Figure 5: Electrochemical curve of the coating with an Al powder concentration of 20.0%, 25.0% and 30.0%.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Al Powder Concentration (\%)** & **Ecorr (V)** & **R\({}_{\text{p}}\) (\(\Omega\)/cm\({}^{2}\))** & **Icorr (A/cm\({}^{2}\))** & \(\beta_{\text{a}}\) (mV/dec)** & \(\beta_{\text{c}}\) (mV/dec) \\ \hline
20.0 & \(-\)0.856 & 883.6 & \(2.992\times 10^{-5}\) & 139.0 & 108.0 \\
25.0 & \(-\)0.809 & 1534.4 & \(1.250\times 10^{-5}\) & 70.8 & 117.0 \\
30.0 & \(-\)0.837 & 1026.2 & \(2.276\times 10^{-5}\) & 107.0 & 108.0 \\ \hline \end{tabular}
\end{table}
Table 8: Influence of the Al powder concentration on corrosion resistance.  
Figure 6: SEM of coating with Al powder: (**A**) 20.0%, (**B**) 25.0% and (**C**) 30.0%.  
resistance of the waterborne UV/Al coating was the largest, which exceeded 50.0 kg cm. The impact resistance of the waterborne UV/Al coating was better than the other coatings.
# An Overview of CNN-Based Image Analysis in Solar Cells, Photovoltaic Modules, and Power Plants
## 3 Exhibition of Recent Literature
### Model Performance Metrics
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Author** & **Reference** & **Date** & **Accuracy** & **Precision** & **Recall** & **F1N/Assore** & **IoU/mAP** \\ \hline [PERSON] et al. & (2018) & 2024 & 91.67\% (val) & 0.91 & 0.89 & 0.9 & N/A \\ \hline [PERSON] et al. & (2018) & 2024 & N/A & N/A & N/A & N/A & \begin{tabular}{c} qualitative \\ comparison \\ only \\ \end{tabular} \\ \hline [PERSON] et al. & (2017) & 2024 & N/A & N/A & N/A & N/A & \begin{tabular}{c} mAP: 85.7\% \\ (YOLOv5m), \\ 86.5\% \\ (YOLOv5s), \\ 86.7\% \\ (YOLOv5x) \\ \end{tabular} \\ \hline [PERSON] et al. & (2019) & 2024 & N/A & N/A & N/A & 0.697 & \begin{tabular}{c} mAP50: \\ 77.9\%, \\ mAP50:95: \\ 49.6\% \\ \end{tabular} \\ \hline [PERSON] & (2018) & 2024 & N/A & N/A & N/A & N/A & \begin{tabular}{c} mAP50: \\ 77.9\% \\ \end{tabular} \\ \hline [PERSON] et al. & (2019) & 2024 & 94.11\% & 94.7\% & 96.7\% & 95.7\% & N/A \\ \hline Al-Otum & (2018) & 2024 & 2024 & 93.95\%, & LwNet: 95.2\% & LwNet: 94.8\% & LwNet: 95.0\% & n.a. \\  & & & 94.6\% & & & \\ \hline Yousif & (2018) & 2024 & \begin{tabular}{c} Better than \\ 6 previous \\ models \\ \end{tabular} & N/A & N/A & N/A & N/A \\ \hline Imak & (2018) & 2024 & 92.19\% & 0.92 & 0.9 & 0.91 & N/A \\ \hline [PERSON] and & (2019) & 2024 & \begin{tabular}{c} detection: 92\%, \\ fault \\ classification: 82\% \\ \end{tabular} & N/A & N/A & N/A & N/A \\ \hline [PERSON] & (2019) & 2024 & 84.75\% & N/A & N/A & N/A & N/A \\ \hline [PERSON] & (2018) & 2024 & 92.37\% & N/A & N/A & N/A & N/A \\ \hline [PERSON] et al. & (2018) & 2024 & \begin{tabular}{c} 98.30\% (2 \\ classes) \\ \end{tabular} & N/A & N/A & N/A & N/A \\ \hline [PERSON]- & (2018) & 2024 & N/A & N/A & N/A & N/A & \
\begin{tabular}{c} IoU (keypoint \\ detection): \\ 85.3\% \\ \end{tabular} \\ \hline [PERSON] et al. & (2019) & 2024 & 91.46\% & N/A & N/A & 91.67\% &
\begin{tabular}{c} Specificity: \\ 98.29\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: List of research results with various models in 2024 along with published metrics. the same time, there were examples of custom CNN development by [PERSON] et al. [35], with 98.39% accuracy; [PERSON] et al. [64] with 99.36% precision and 98.77% recall; and [PERSON] et al. [81] with 95.28% accuracy. After studying the available literature and reviewing the published results, the classification and localization of cracks and fractures using CNN models can be found to be highly effective. Researchers have usually used large-scale samples to train the models; in [PERSON] et al. [80], in 2020, around 46,000 EL images of solar cells were used, most of which were from open access databases. There have also been studies that used images from their own collection, such as [PERSON] et al. [71] in 2021.