esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Genetic Algorithm for Parameter Estimation of Sinusoidal Signals
## 1 Introduction
[35] proposed an HGA to estimate the amplitude, frequency, range, elevation angle, and azimuth angle of near field sources; the proposed schemes achieved good results in terms of accuracy, convergence rate, and robustness, but the complexity increased a lot. In the light of these facts, authors are motivated in this work to design an enhanced genetic algorithm (EGA), through a prejudice-free selection mechanism for preserving population diversity, diversified individuals with a two-step crossover (TSC) operator, and a good cooperation of fast convergence and searching performance with an adaptive mutation strategy, for estimating the parameters of sinusoidal signals. The prominent features of the proposed method are briefly summarized as follows:  
* Validation through the results of a comparative analysis in terms of performance-monitoring metrics based on the mean, coefficients of determination (\(R^{2}\)), and the sum of squared residuals value (\(SSR\)) of each algorithm;
* An intelligible concept with easy realization and a worry-free tradeoff between global and local search in comparison to general hybrid techniques. The rest of the work is organized as follows. The mathematical ecological theory foundation of the proposed approach is presented in Section 2. Section 3 describes the proposed EGA in detail, including the prejudice-free selection, the two-step crossover operator, and the adaptive mutation operator. In Section 4, benchmark function based numerical analyses are carried out to verify the validity of the EGA. The parameter estimations of real-life sinusoidal signals are performed and discussed in Section 5. At last, we summarize our work and highlight the main contributions in Section 6.
# Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor
## 4 Experiments and Results  
Firstly, the experimental data are obtained by the Simulink simulation program in MATLAB, and the collected data are sorted out. Secondly, the effectiveness of the improved method is verified by ablation experiments and comparative analysis.  
Figure 6: The flowchart of using the whale algorithm to optimize the hyperparameters of the Bi-LSTM diagnostic model.  
Under normal circumstances, there is no short-circuit current in the PMSM, so its value is zero; after an inter-turn short-circuit fault occurs, a short-circuit loop is generated, thereby generating a short-circuited current. The above simulation results show that the stator current of the PMSM changes significantly after the inter-turn short-circuit fault occurs.
## 3 CFD Modelling as Digital Twin
### CFD Models for Filling and Emptying Processes
#### 3.3.2 Two/Three Dimensional Modelling  
Geometric discretisation plays an essential role in 2D and 3D CFD models when simulating two-phase flows in water pipelines since the quality and refinement of the elements determine the resolution of the information captured concerning the air-water interface, as well as the numerical stability, the processing runtime step, and the Courant number if the Courant number is not variable in time. Refinement is usually required in zones near the walls to simulate the flow stratification in the viscous layer and logarithmic layer zone. In the case of 2D CFD models, the geometrical domain is discretised as a mesh, which can be divided into rectangular (structured) or triangular (unstructured) elements. In contrast, in the case of 3D CFD models, the geometrical domain is divided into finite  
Figure 6: Spatio-temporal evolution of turbulent kinetic energy (\(k\)) and dissipation frequency (\(\omega\)) during an emptying process.  
elements with hexahedral (structured) or tetrahedral (unstructured) shapes. Figure 7 shows the discretisation methods of two- and three-dimensional geometric domains.  
The pipe network configuration also influences the choice of 2D vs. 3D modelling. In single-pipe systems, two-phase flow primarily develops along the longitudinal axis, making 2D models suitable for capturing essential dynamics while reducing computational costs. However, 3D CFD models are necessary for scenarios involving significant changes in the boundary conditions, such as bends, bifurcations, or variations in pipe cross-sections, where flow behaviour is dominant in all three dimensions.  
While 3D CFD modelling provides greater capacity to represent complex hydraulic phenomena, it also has limitations, including higher computational costs, longer simulation times, and more intricate calibration requirements than 2D models. In branched or complex pipeline systems, unstructured meshes in 3D models are often preferred, as structured hexahedral grids may be challenging to implement.
# Quantum Key Distribution with Post-Processing Driven by Physical Unclonable Functions
## Appendix A The Size of the Pre-Shared Key
### Error Reconciliation  
For the sake of simplicity, let us consider a BB84-type protocol where the two bases are equally likely. We will also assume that the error rate in the sifted key will be the same for both bases, and it will be denoted by \(q_{\mathrm{e}}\). Let \(n_{\mathrm{s}}\) denote the length of the sifted key to be reconciled. According to [PERSON]'s theory, [PERSON] and [PERSON] have to exchange at least \(n_{\mathrm{er}}=n_{\mathrm{s}}H(q_{\mathrm{e}})\) bits for error reconciliation, where \(H(q_{\mathrm{e}})\) is the binary entropy. To prevent the leakage of information to an adversary, the data can be encrypted by means of an OTP encryption using the pre-shared key of length \(k_{\mathrm{er}}=n_{\mathrm{s}}H(q_{\mathrm{e}})\) bits [10; 11]. Assuming \(n_{\mathrm{s}}=10^{4}\) and \(q_{\mathrm{e}}=1\%\), we have \(k_{\mathrm{er}}\sim 800\) bits, while for \(q_{\mathrm{e}}=2\%\), we have \(k_{\mathrm{er}}\sim 1400\) bits. In a protocol where this information is not encrypted, one has to pay the cost in the privacy amplification by shortening further the final key.
# Improved ECG Watermarking Technique Using Curvelet Transform
## 2 Curvelet Transform
### Transform ECG Image into Curvelet Transform  
A fast discrete curvelet transform (FDCT) is applied to transform the host ECG image into the frequency domain. After applying FDCT, we obtained the number of scales k scale = log\({}_{2}\) k \(-\) 2, where k x k is the dimensions of the ECG image. Subsequently, the decomposition Coarse, fine and detail level of coefficients are obtained. From these three levels, we can embed our data at any level. The invisibility and robustness can be decided from the selection of the level. In a coarse-level maintain most of the energy of image to give more invisibility.
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Deposition-Associated Issues
#### 3.2.5 Inconsistent Extrusion  
Inconsistent extrusion of the material may result in a bumpy surface that may affect the quality and dimensional accuracy of the printed object (Figure 9). The contributing factors could be tangled or stuck filament, a semi-clogged nozzle, nozzle height set too low, incorrect extrusion width, poor quality filament, and/or having an overtigthened extruder idler wheel. Solutions include the following:  
1. Making sure that the filament is feeding into the printer. There should not be too much resistance to stop the filament spool from moving freely and pulling into the printer (Refer to 3.1.6: Grinding filament).
2. Remove any foreign debris in the nozzle (refer to 3.1.4.: Clogged nozzle).
3. Recalibrate the build platform (refer to 3.1.2: (1) Incorrect built platform levelling).
4. Make sure that the extrusion width is set at 100% or 150% greater than the default nozzle diameter of 0.4 mm. The printer will struggle to extrude any width less than the nozzle diameter (refer to 3.2.4: Small features not printed).
5. Make sure that the filament is not degraded. Replace the filament that may be at fault and use a better-quality filament (refer to 3.2.1: (3) Poor quality filament with diameter fluctuations).
6. Make sure that the tension of the extruder idler wheel is correct (refer to 3.1.6: (4) Overtigthened extruder idler wheel.
## 5 Experimental Results and Discussion
### Performance Evaluation and Analysis
#### 5.3.4 Decoding Energy Reduction Performance  
Next, the actual energy consumption performance for the bit streams generated by the proposed and state-of-the-art algorithms is compared for a video streaming use case. First, the generated bit  
Figure 5: An illustration on the visual quality impact of the reconstructed video sequences of bit streams prepared by the (**a**) HM 16.0 encoder, (**b**) proposed algorithm, and other state-of-the-art algorithms proposed by (**c**) [PERSON] et al. [20], (**d**) [PERSON] et al. [46], and (**e**) [PERSON] et al. [34]. The figures correspond to the frame number 36 of the “Kimono HD” sequence encoded at 2 Mbps. streams are decoded using the openHEVC video decoder with Linux ondemand as the frequency scaling governor [61]. It can be observed in the Table 4 that the proposed and state-of-the-art algorithms demonstrate an energy-consumption reduction in the range of \(\approx\)4% compared to HM 16.0 encoded video bit streams. Moreover, forcing the decoder to skip in-loop filters enables the proposed algorithm to increase the energy-consumption reduction up to 5.65%. Changing the Linux ondemand governor to a more application-specific DVFS algorithm [10] that alters the CPU's operational frequency based on the estimated complexity of the next video frame improves the energy-consumption reduction of all the algorithm. In this case, the proposed algorithm has achieved 7.77% and 9.10% decoding energy-consumption reductions compared to the HM encoded bit streams--a non-trivial performance with only \(-\)1.44 dB and \(-\)2.03 dB quality impacts for with and without in-loop filter operations, respectively. The decoding energy reduction achieved per 1 dB PSNR video quality loss for the proposed and state-of-the-art algorithms is presented in the Table 5, and the \(\widehat{\Delta E}(\%/\mathrm{dB})\) achieved for a 1 dB quality loss is graphically demonstrated for three different test sequences in Figure 6. These results further corroborate that the energy reductions achieved by the bit streams generated with the proposed algorithm result in smaller impacts on quality compared to the state-of-the-art approaches. Thus, the decoding energy consumption reduction achieved for each 1 dB PSNR loss is also relatively large for the proposed encoding algorithm.
## 3 Design and Architecture of the Network Slicing System  
The network slicing system consists of four major modules named IBN tool, OSM network orchestrator, FlexRAN controller, and deep learning model. This system can perform the E2E slicing mechanism for both the core network and the access network. Moreover, our first module IBN is an automated tool to deploy higher-level network configurations in deployment and execution mode. It enables the network service providers to configure the network resources by just providing higher-level network configurations for both the core network and access network. It also contains a hybrid deep learning model GAN that continuously monitors the network resources statistics and stores them to the IBN database resource repository. Another module is the OSM framework which provides core network slicing support. The OSM policy configurator of the IBN tool can convert the higher-level configurations into JSON string format because the OSM orchestrator accepts the slice configurations in the form of JSON strings. The core NFVs are deployed by providing the information to the OSM orchestrator. After that, the OSM NFVO dispatches those slice configurations to OpenStack for the deployment of network functions (NFs). The core network slicing is managed by the OSM framework, where we deployed three separate core network EPC (Evolved Packet Core) functions such as MME, HSS, and SPGW for each slice. On the other hand, the FlexRAN controller which is an SDN-based controller that is used for managing and creating the access network slicing. We develop a specific RAN slicing policy configurator for access network to convert the high-level slice configuration provided at the IBN tool to JSON slice template format and send it to the underlying FlexRAN controller. Further, the FlexRAN controller deploys these configurations at eNodeB for slice creation. The complete E2E design and architecture of network slicing system is presented in Figure 3.  
Figure 3: End to End network slicing architecture with intent-based networking.
# Conversation-Based School Building Inspection Support System
## 4 Implementation
### Result  
An example of the post-event inspection accomplished with the chatbot's support is demonstrated with the series of screenshots shown in Figure 3. The chatbot interacts with the assessor using diverse message formats adapted from the LINE Messaging API. The system first informs the assessor to start an inspection after an earthquake occurs. The assessor sends a command to the chatbot for initiation. Next, the chatbot provides a series of cascaded, clickable maps for the assessor to specify a room that requires inspection. By pressing on the desired region on each map, the chatbot narrows down the map display area and leads the assessor to one of the rooms. The assessor then selects one of the question categories and starts to fill in the report. Entire questions are displayed as confirmation buttons. If the assessor selects \"no problem\", the next checkpoint for inspection pops up sequentially. If \"require improvement\" is chosen, the chatbot is switched into input mode. The assessor makes detailed textual or multimedia descriptions. After completing the inspection, the system summarizes the current contents and presents it to the assessor for final confirmation. If modification is required, the assessor may select the checkpoints to be modified and correct the answers. Finally, the corrections are completed and confirmed, and the system saves the report to the database.  
For the web-based dashboard for managers, the summary view is shown in Figure 4 and the detailed view is shown in Figure 5. Each view includes an interactive map on the left and a panel on the right, providing an overview or a detailed table. In the summary view, the manager first selects an earthquake event using the selection bar, then clicks one of the districts on the map. The summary of inspection reports of schools related to the earthquake event in the district is then revealed. The summary includes a bar chart of the distribution of answered checklists over the checklist categories, showing a brief overview of the building damage in the district. In the detailed view, similar to the summary view, the manager selects an earthquake event and a district, and a table lists inspection reports of the schools with links to the detailed results. The color demonstrates the  
Figure 3: Screenshots of the chatbot.
mechanism that involves signing content by its producer and the authority who provided the certificate to the producer. To validate content, a user checks the authenticity of all the certificates involved within the certificate chain till it encounters a trusted or a self-signed network entity (usually a trusted authority). This process helps to enhance the trustworthiness of the data by verifying their authenticity. However, vehicular networks are prone to issues related to their highly dynamic nature. The data verification process employed by NDN may result in significant delays, which is unsuitable for sensitive applications, such as sharing safety messages. In traditional centralized authentication systems, data authentication is often associated with high overheads due to the need to communicate with a central authority. To this end, we propose an approach that enables local data verification while providing a secure authentication mechanism for producer vehicles. Our approach involves leveraging blockchain technology to decentralize the authentication process, making it more efficient for vehicular environments. By using blockchain, we can avoid the need for centralized authorities, thereby reducing the associated overheads. To the best of our knowledge, this study is the first attempt to use blockchain for decentralized data authentication in vehicular NDN. The validation process that we conducted showed that our proposal is secure against attackers.  
The paper is structured as follows: Section 2 provides an overview of the NDN architecture. Then, Section 3 examines the related work. The threat model and assumptions for our study are outlined in Section 4. In Section 5, we describe our proposed solution, NDN-BDA. Our work is evaluated and validated in Section 6, and we conclude the paper and discuss future work in Section 7.
# Channel Modeling and Analysis for the Sensor Network Inside Tower Buildings
## 3 Channel Clustering and Performance Analysis
### Cluster-Based Channel Parameters  
After the ray-based channel is clustered into cluster-based one, it is an important step to calculate the equivalent cluster parameters according to the ray parameters. According to the law of energy conservation, the total power of \(n\)th cluster \(\bar{P}_{n}\) can be equal to the vector superposition of the ray powers, which can be expressed as  
\[\bar{P}_{n}{=}\bar{a}_{n}^{2}=20\text{log}_{10}\left(\sum\limits_{m=0}^{M(n)} 10^{\frac{a_{m}^{2}}{10}}\right) \tag{6}\]  
where \(a_{n,m}\) denotes the fading factor of \(m\)th ray within \(n\)th cluster. In addition, the delay of \(n\)th cluster \(\bar{\tau}_{n}\) can be calculated based on the power weight of each ray, which can be expressed as  
\[\bar{\tau}_{n}=\frac{\sum\limits_{m=0}^{M(n)}a_{n,m}^{2}\cdot\tau_{n,m}}{\sum \limits_{m=0}^{M(n)}a_{n,m}^{2}}. \tag{7}\]  
Taking a specific receiver inside tower building as an example, the ToA, AAoA, and EAoA of each ray are obtained from RT simulation data, and the channel data is clustered based on the improved clustering algorithm. With the Clustering number \(k\) variing from 2 to 10, the corresponding SSE is recorded in Table 1. It can be found that the SSE has the decrease trend with the increase of cluster number. According to the elbow method, we choose \(k=7\) is the optimal cluster number since the SSE would not change too much afterwards.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Clustering Number \(k\)** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Normalized SSE value of k-means algorithm & 1.00 & 0.52 & 0.26 & 0.29 & 0.32 & 0.16 & 0.18 & 0.17 & 0.16 \\ Normalized SSE value of improved k-means algorithm & 1.00 & 0.35 & 0.37 & 0.33 & 0.48 & 0.11 & 0.14 & 0.13 & 0.12 \\ \hline \end{tabular}
\end{table}
Table 1: The Clustering Number and its Normalized SSE.  
Meanwhile, the original rays, the clustering results of traditional k-means algorithm and improved k-means algorithm are shown in Figure 2, respectively. It can be found that the channel is well clustered with the optical clustering number \(k=7\), and the improved k-means algorithm outperforms the traditional one. Meanwhile, the cluster delay \(\tilde{\tau}_{n}\) and power \(\tilde{P}_{n}\) was calculated according to (6) and (7). It should be mentioned that the rays with same color belong to the same cluster.
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 3 Results
### Quantitative Analysis of Main Active Ingredients  
The retention of bioactive compounds is critical for the functional properties of AS fruit. This study evaluated the impact of five drying methods on phenolic acids, flavonoids, and eleutherosides (Table 2). Method validation results are summarized in Table S3. Representative chromatograms of standards and samples are shown in Figures S1 and S2. VFD exhibited superior preservation of key phenolic compounds, including chlorogenic acid (161.66 mg/100 g), rutin (601.20 mg/100 g), and quercetin (96.64 mg/100 g). In contrast, HD retained higher levels of eleutheroside E, isofraxidin, and protocatechic acid. VD, despite reduced pressure, resulted in substantial losses of protocatechic acid, chlorogenic acid, rutin, hyperoside, and quercetin. These findings highlight the significant influence of drying methods on the stability and degradation of phenolic and glycoside compounds in AS fruits. The enhanced retention of phenolics in VFD-treated samples is attributed to its low-temperature, low-pressure conditions, which effectively mitigate polyphenol degradation. Among AS fruit bioactive compounds, eleutheroside B exhibited the highest content in VFD (161.66 mg/100 g), significantly exceeding levels observed with other drying methods. According to the Arrhenius equation, reaction rates increase exponentially with temperature [35]. In VFD, extremely low temperatures minimize reaction rate constants, significantly slowing chemical degradation. Under standard conditions, the oxidation and decomposition of eleutheroside B require sufficient activation energy. However, in VFD, energy input remains below this threshold, preserving structural integrity. Additionally, glycosides such as eleutheroside E and isofraxidin undergo hydrolysis at elevated temperatures due to glycosidic bond cleavage. The low-temperature environment of VFD prevents hydrolysis, enhancing glycoside retention and stability. Furthermore, VFD's high-vacuum conditions limit oxygen exposure, effectively reducing oxidation-induced phenolic degradation. The highest levels of eleutheroside E (20.48 mg/100 g) and isofraxidin (2.32 mg/100 g) were observed in HD-treated samples. At high temperatures, eleutheroside E may undergo thermal decomposition; however, HD's rapid moisture removal limits hydrolysis, maintaining its stability. In contrast, VFD's low-temperature environment slows chemical reactions, preventing hydrolysis and oxidation, thereby preserving glycosidic bonds. Isofrakidin,