esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Envelope Detector-Aided, Machine Learning-Based Fault Type Detection Model
### Machine Learning-Based Fault Presence and Type Detection Models
#### 3.2.2 Machine Learning-Based Classification Layers  
Both classification layers in Figure 9 are tested with various ML algorithms. The considered ML algorithms are well-known techniques, including CNN, SVM, DT, and RFC. Each ML algorithm is trained with the created dataset. For the training dataset of the ML models, a simulation model is designed in a MATLAB Simulink environment by taking into account the parameters of the 46 kV substation, which is different from the Figure 2. Basically, we train our model with another substation and apply the trained model to detect the faults in the testbed utilized in this study. The substation model used in the training model has feeders with transformers, capacitor banks, line impedances, and individual loads. As the purpose of ML models is either finding the presence of fault or classifying the type of fault, various fault types are used at the end of each feeder. As we need more different datasets for each specific type of fault, we change the main components of each training datum such as the analog-to-digital converter noise, the load percentage of network, bit number, and start/end sample points of faults. The training process for each ML algorithm used 2113 sample data created using parameters of the real 46 kV substation. However, the training dataset is divided into 70% for training and 30% for validation because the validation data are used to monitor overfitting during the training process. If there is an increasing gap between validation and training performance, the training operation is automatically stopped to avoid overfitting. Because both classification layers are used for different purposes, the training labels and datasets can be different even though they have the same architectural design. The first classification layer is designed to identify a fault in the power signal and separate the fault signal from the switching and motor signal waveforms commonly available in power grids. Therefore, the labels for the first classification layer's dataset are prepared considering the Normal Condition, Motor, Switching, and Fault. The second classification layer aims to identify the common types of faults, so the labels for this layer are designed as SLG, LL, LLG, and 3 LG. Because of the changes in the size of the feature extraction outcomes and label number, the structure of the ML architecture is adjusted, and models are created for each possible variation to achieve the maximum performance in this study.
# SmartX Multi-View Visibility Framework
## 3 SmartX MVF with Flow-Centric Visibility: Design and Implementation
### Network Packets Flow Classification  
As visibility data is already integrated by following flow-layer characteristics, grouping of processed data into various classes becomes a relatively simplified job. For the classification of network packets flows, in this initial version of flow-centric visibility, we define four classes (attack, identified, known, and unknown). Attack class contains network packet flows that belong to previously attacked IP addresses. These IP's can be obtained from the internet, as known attack databases. Identified class contains network packets flow from playground user tenant and already notified applications to the playground operators via SmartX tagging mechanism. Known class contains network packets flow from the playground user tenant, but the playground operator does not know the application. Unknown class contains network packets flow from unknown IP addresses and unknown applications (i.e., both user tenant and application are not known). After this initial classification, further analysis can be performed at the sec center by considering other metrics.  
Figure 8: Design of multi-layer visibility data integration component.  
Figure 7: Selected list of key identifiers for visibility data integration.  
The design of network packets flow classification component is shown in Figure 9. Classify network packet flows is the main function that loads already integrated visibility data into a Spark data frame. A pre-downloaded data file with attack IP addresses is loaded, and contents are validated before further execution. After data validation, both integrated visibility data frame and attacker IP addresses data frame are joined together on source and destination IP addresses. The resultant data frame is stored to the Elasticsearch and Parquet datastores. Find identified flows analyzes the tenant name and application name fields in the integrated data frame, if both the fields have values and these data records are not in the attacked flows data frame, then it is classified into the identified class. Find known flows then analyzes tenant name and application name fields and if the tenant name is present and application name is absent, then it is classified into the known class. Find unknown flows analyzes tenant name and application name fields and if both tenant name and application name are absent, then it is classified into the unknown class.  
Visibility classification also leverages Apache Spark SQL and implemented as a separate project in Scala. Again, a jar file is created and deployed at the vis center. By using Spark-submit, this jar file is executed at regular time intervals of five minutes.
## 3 Experimental Results
### Dynamic SSSI Effects on S1 Structure
#### 3.2.1 SSSI Effects on Responses of S1 Structure in Frequency Domain
Note that the negative value of \(\Delta\)PSD in Table 4 denotes a decrement in \(\Sigma\)PSD of S1 structure response. As shown in Figure 7, the effect of SSSI phenomena on \(f_{\textit{flex}}\) of S1 structure could be negligible because almost no change was observed in frequency when compared with the reference case. Similar results were also observed in frequency shifting at other input earthquake excitation levels (i.e., L_Input and H_Input of both artificial and Hachinohe earthquakes), thus illustrating similar trends of SSSI effects in frequency shifting.
## 6 Evaluation
### Ciphertext Inference
#### 6.2.1 Analysis and Comparison of Ciphertext Inference Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Method** & **Model** & **Accuracy** & \
\begin{tabular}{c} **Amortized** \\ **Time** \\ \end{tabular} &
\begin{tabular}{c} **Memory** \\ **Usage** \\ \end{tabular} & \(\lambda\) \\ \hline \multirow{6}{*}{MNIST} & CryptoNets [9] & CNN-4 & 98.95 & 249.6 & N/A & 80 \\  & FCryptoNets [37] & CNN-4 & 98.71 & 39.1 & N/A & 128 \\  & CryptoDL [12] & CNN-4 & 99.25 & 148.9 & N/A & 80 \\  & HCNN-CPU [39] & CNN-3 & 99.00 & 90.32 & N/A & 76 \\  & **LHDNN** & **CNN-6** & **99.62** & **2.23** & **6.91G** & **128** \\ \hline \multirow{2}{*}{Cancer} & Approx-ReLU [21] & AlexNet & 75.85 & 45.68 & 32.89G & 128 \\  & **LHDNN** & **AlexNet** & **81.10** & **30.61** & **24.58G** & **128** \\ \hline \multirow{6}{*}{CIFAR-10} & FCryptoNets [37] & CNN-8 & 75.99 & 22372 & N/A & 128 \\  & CryptoDL [12] & CNN-9 & 91.50 & 11686 & N/A & 80 \\ \cline{1-1}  & PACN [14] & ResNet-20 & 92.43 \(\pm\) 2.65 & 10602 & 172G & 111.6 \\ \cline{1-1}  & LDCN [38] & ResNet-20 & 91.31 & 79.46 & N/A & 128 \\ \cline{1-1}  & **LHDNN** & **ResNet-20** & **92.28 \(\pm\) 2.58** & **64.25** & **34.63G** & **128** \\ \hline \hline \end{tabular} N/A indicates that this value is not given in the reference.  
\end{table} 
Table 6: Comparison of ciphertext inference results. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Option** & **Dataset** & **Model** & \begin{tabular}{c} **Initialization** \\ **Context** \\ \end{tabular} & \begin{tabular}{c} **Model** \\ **Encoding** \\ \end{tabular} & \
\begin{tabular}{c} **Input** \\ **Encryption** \\ \end{tabular} &
\begin{tabular}{c} **Output** \\ **Decryption** \\ \end{tabular} & **Inference** \\ \hline \multirow{3}{*}{Time} & MNIST (b = 64) & CNN-6 & 1.39 & 0.11 & 0.41 & 0.0045 & 142.62 \\  & Cancer (b = 8) & AlexNet & 2.97 & 2.70 & 0.75 & 0.0049 & 244.90 \\  & CIFAR-10 (b = 16) & ResNet-20 & 8.18 & 0.53 & 0.18 & 0.0233 & 1027.96 \\ \hline \multirow{3}{*}{Memory} & MNIST (b = 64) & CNN-6 & 1.67 & 0.0021 & 2.26 & 0.0005 & 19.12 \\  & Cancer (b = 8) & AlexNet & 5.13 & 0.1735 & 3.94 & 0.0005 & 71.91 \\ \cline{1-1}  & CIFAR-10 (b = 16) & ResNet-20 & 11.62 & 0.0035 & 1.05 & 0.0002 & 83.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ciphertext inference time (s) and memory usage (GB) consumption. 3.5 GHz with 16 GB RAM. The MNIST CNN-4 of FCryptoNets was run on a machine with an Intel Core i7-5930K CPU at 3.5 GHz with 48 GB RAM, while its CIFAR-10 CNN-8 was run on an n1-magemem-96 instance on the Google Cloud Platform, with 96 Intel Skylake 2.0 GHz vCPUs and 1433.6 GB RAM. The CPU evaluation of HCNN [39] was conducted on a machine with an Intel Xeon Platinum CPU at 2.10 GHz with 187.5 GB RAM. The experimental environment for CryptoDL was a machine with Intel Xeon E5-2640 at 2.4 GHz with 16 GB RAM. The experimental environment for PACN was a machine with dual Intel Xeon Platinum 8280 CPU (112 cores) with 512 GB RAM. The experimental environment for LDCN was a machine with AMD Ryzen Threadripper PRO 3995 WX at 2.096 GHz (64 cores) with 512 GB RAM.
# The Euler-Type Universal Numerical Integrator (E-TUNI) with Backward Integration
## 6 Analysis of the Numerical Accuracy of E-TUNI Using Landau Symbols
### The E-TUNI Numerical Accuracy  
In the modified Euler method, i.e., in E-TUNI, we employ the mean derivative functions \(\bar{f}_{n}\), which can be obtained by an Artificial Neural Network (ANN):\[y_{n+1}=y_{n}+h\tilde{f}_{n}, \tag{25}\]  
where the mean derivative is defined as:  
\[\tilde{f}_{n}=\frac{1}{h}\int_{t_{n}}^{t_{n+1}}f(t,y(t))dt. \tag{26}\]  
If the mean derivative is estimated by an Artificial Neural Network (ANN), we will have:  
\[\tilde{f}_{n}^{\text{ANN}}\approx\tilde{f}_{n}+\varepsilon_{\text{ANN}}, \tag{27}\]  
where \(\varepsilon_{\text{ANN}}\) represents the error of the neural network. Expanding the exact solution \(y(t)\) around \(t_{n}\), we have that:  
\[y(t_{n+1})=y(t_{n}+h)=y(t_{n})+hy^{\prime}(t_{n})+\frac{h^{2}}{2}y^{\prime \prime}(t_{n})+O(h^{3}). \tag{28}\]  
The mean derivative can be approximated by expanding \(y^{\prime}(t)\):  
\[y^{\prime}(t)=y^{\prime}(t_{n})+(t-t_{n})y^{\prime\prime}(t_{n})+O(h^{2}). \tag{29}\]  
Thus, substituting Equation (29) into the integral (26) and integrating over the interval \([t_{n},t_{n+1}]\), we have:  
\[\tilde{f}_{n}=y^{\prime}(t_{n})+\frac{h}{2}y^{\prime\prime}(t_{n})+O(h^{2}). \tag{30}\]  
Thus, substituting the mean derivative Equation (30) into the modified Euler method, we finally have:  
\[y_{n+1}=y_{n}+hy^{\prime}(t_{n})+\frac{h^{2}}{2}y^{\prime\prime}(t_{n})+O(h^{ 3}). \tag{31}\]  
Equation (31) shows that the local error of the method has increased to \(O(h^{3})\), improving accuracy. On the other hand, if the mean derivative is approximated by an Artificial Neural Network (ANN):  
\[\tilde{f}_{n}^{\text{ANN}}=\tilde{f}_{n}+\varepsilon_{\text{ANN}}. \tag{32}\]  
So, the modified method becomes:  
\[y_{n+1}=y_{n}+h(\tilde{f}_{n}+\varepsilon_{\text{ANN}}). \tag{33}\]  
So, the total error will be:  
\[y(t_{n+1})-y_{n+1}=O(h^{3})+O(h\varepsilon_{\text{ANN}}). \tag{34}\]  
Thus, if the artificial neural network is accurate enough, we can make \(\varepsilon_{\text{ANN}}\to 0\), making the integration error close to \(O(h^{3})\). Thus, the local error being equal to \(O(h^{3})+O(h\varepsilon_{\text{ANN}})\) will make the global error, summed over \(N=O(1/h)\) steps, equal to \(E_{\text{global}}=O(h^{2})+O(\varepsilon_{\text{ANN}})\). Therefore, the modified Euler method with mean derivatives improves the global accuracy to \(O(h^{2})\). If an artificial neural network is used to estimate the mean derivative, its error \(\varepsilon_{\text{ANN}}\) can be reduced to a minimum, making the method highly accurate. Thus, this method can be seen as a transition to higher-order Runge-Kutta methods.
# Application of Improved MFDFA and D-S Evidence Theory in Fault Diagnosis
## 5 Conclusions  
In this paper, we proposed combining an improved MFDFA with BP neural network under multi-sensor data based on the D-S evidence theory and applying it to centrifugal pump fault diagnosis, and subsequently resulted in the following conclusions after investigation.  
The improved MFDFA can effectively extract the multiple fractal spectrum feature parameters \(\Delta f\), \(\alpha_{0}\), \(\Delta\alpha\), \(\alpha_{\min}\), and \(\alpha_{\max}\) and use them as the centrifugal pump fault feature vectors, overcoming the difficulties of the traditional signal analytical method to accurately extract the fault features, in which \(\alpha_{0}\) and \(\alpha_{\max}\) have better differentiation for the pump operation status.  
After applying this method, the correct diagnosis rate of centrifugal pump faults reached 92.5%, with improvements of 3.33%, 2.08%, 2.50%, and 7.50% over the rates demonstrated by single sensor systems focused on the pump vibration, torque, motor vibration, and oscillation sensors, respectively. The method effectively avoids the defects typical of single sensor systems containing single fault information and poor accuracy, thereby improving our ability to make reliable diagnoses. This validates the use of this novel method as a stable fault diagnosis model to provide a meaningful reference for fault diagnosis research.  
Conceptualization, X.L.; methodology, X.L.; software, Y.L. ([PERSON]); validation, Y.L. ([PERSON]), F.D. and Y.L. ([PERSON]); formal analysis, Y.L. ([PERSON]); investigation, Y.L. ([PERSON]); resources, F.D.; data curation, Y.L. ([PERSON]); writing--original draft preparation, X.L.; writing--review and editing, Y.L. ([PERSON]); visualization, Y.L. ([PERSON]); supervision, F.D.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by the Jiangxi Education Department Science Foundation of China (GJ)170988) and the National Natural Science Foundation of China (No. 51969017).  
The authors declare no conflict of interest.
# Modeling and Simulation of a Wave Energy Converter _Nwave_
## 2 Explanation of the Device  
As shown in Figure 1, _INWAVE_ (INGINE Inc., Seoul, Korea) consists of the buoy and the power take off (PTO) module, which is located on land. The buoy is discus shaped and is connected to ropes by three points on the bottom, and this rope is connected to the PTO module located on land via a pulley fixed to the sea-floor. The PTO module consists of a counterweight, the gearbox module (which includes the ratchet gears) and the generator. Individual ropes that are connected to the buoy are connected to a counterweight. The counterweight provides tension on the rope consistently, and this tension gives the buoy dynamic stability.  
In calm water conditions, the buoy maintains equilibrium due to the weight, buoyancy and tension from the weights. The buoy goes through a complex movement that can be divided into heave, surge, sway, roll, pitch and yaw, as the wave passes over it. Depending on the movement of the buoy, ropes are pulled, and the counterweights either rise or lower. Each rope is wound on the rope drum, and the rope drum is connected to the generator. The ratchet gear, which is mounted on the rope drum, only sends the power to the generator when the buoy pulls the ropes, and as such, the counterweight is rising. Since the motion of the three ropes is not in the same phase, the time point for the sending of the power by the individual rope drum is different, which results in a mechanical smoothing effect. The generator uses a 20-kW permanent magnet synchronous generator (Yaskawa, Tokyo, Japan) and is linked to the grid through an AC/DC converter (LSIS, Gyeonggi-do, Korea) and a DC/AC inverter (LSIS, Gyeonggi-do, Korea). As shown in Figure 2, currently, three modules are installed on Jeju Island's northern coastal region for a test run. It was installed in September of 2015 and seeks to start operating commercially in the latter half of 2016.  
Figure 1: Schematics of the _INWAVE_ device model configuration.  
Figure 2: _INWAVE_ prototype installed in the northern port of Jeju Island: (**a**) panorama of the device; (**b**) buoys; (**c**) counterweights; and (**d**) electric generator [7].
###### Abstract  
This paper discusses the potential of using lightweight nature-inspired cellular structured designs as energy absorbers in crashworthiness applications for electric vehicles (EV). As EVs are becoming popular with their increased battery capacity, these lightweight cellular structures have regained research interest as they may increase mileage by reducing vehicle mass in addition to protecting the battery during collisions. In this paper, a novel lightweight cellular structure for EV battery protection and crashworthiness is designed and simulated. In designing the cellular structure, four different ways of applying the shell thickness have been considered that affects the collapse behavior and the crashworthiness. A numerical study was conducted on 45 samples with varying length, shell thicknesses, and thickness application methods. Four types of shell thickness application methods were investigated: Uniform thickness, strut-wall thickness, gradient thickness, and alternate thickness. Force-displacement curves, energy absorption, specific energy absorption, and collapse behaviors are some of the metrics used for evaluating the crashworthiness of the structures. Shell thickness is found to affect both the collapse behavior and energy absorption capabilities. Energy absorption results are similar to other studies on designed cellular structures. The highest performing cellular structure is reported to have a specific energy absorption of 35 kJ/kg, which is comparable to cellular structures reported in the literature.  
lightweight cellular materials; energy absorption; electric vehicle; battery compartment crashworthiness; protective devices; passive safety
## 2 Literature Review
utilized DRL to effectively balance tourist satisfaction against regional capacity constraints, demonstrating substantial improvement in managing urban tourism flows. Similarly, [PERSON] et al. [35] showed how DRL frameworks adaptively optimize real-time tourism services in response to fluctuating demand scenarios. Complementarily, [PERSON] [36] introduced an integrated recommendation system that employs blockchain technology to significantly enhance data security and align with modern tourism governance frameworks. Lastly, SHapley Additive exPlanation (SHAP) [37] values have gained prominence as interpretable measures of feature importance, facilitating clear and actionable insights into predictive models. SHAP analysis explicitly quantifies the contributions of individual features to prediction outcomes, supporting transparent and informed decision-making in tourism forecasting contexts. This interpretability is crucial for stakeholders to trust and effectively apply predictive models in practice. While the above studies have made significant strides in improving tourism demand forecasting through hybridization, ensemble learning, and the integration of auxiliary data sources, most existing models either (i) focus on single-task prediction; (ii) treat feature dimensions statically without dynamic recalibration; or (iii) lack robustness in sparse, multi-regional scenarios. In contrast, the proposed SE-TFF introduces a more comprehensive forecasting architecture that explicitly addresses these limitations. First, it leverages a multi-task learning design that simultaneously predicts multiple tourism-related indicators (e.g., arrivals, revenue, lodging demand), thereby capturing inter-variable dependencies. Second, it integrates a Squeeze-and-Excitation (SE) mechanism to dynamically recalibrate multi-scale feature inputs--spanning environmental, economic, and social channels--enhancing the model's responsiveness to contextual heterogeneity. Third, SE-TFF demonstrates improved performance under sparse data settings by incorporating reinforcement-driven optimization and cross-channel attention, which traditional hybrid models often overlook. These innovations jointly contribute to enhanced forecasting accuracy, better generalization across regions, and stronger model interpretability--marking a substantive advancement over earlier hybrid approaches.
# Climate-Smart Agro-Hydroological Model for a Large Scale Rice Irrigation Scheme in Malaysia
## 1 Introduction
The study authors found that the adopted water management in the area leads to excessive irrigation and leaching, as well as elevated groundwater salinity. The agro-hydrological SWAP model was applied under deficit irrigation to assess water cycles in an irrigation area. The optimal practices in irrigation management for hydrologic years were obtained and the average percentage of water saving and groundwater recharge under the optimal irrigation schedules were simulated [20]. Similarly, the agro-hydrological SWAP model was applied to address the salinization of soil and deterioration of water quality. The tool effectively predicted water and salt concentrations of soil, and the reduced effectiveness of relative crop output and water use efficiency [21]. Models could be useful in the assessment of impacts of agricultural management and environmental shifts to support sustainable water management in agricultural, at scales ranging from field to catchment [22]. Despite the availability of a number of available tools, there are still limited studies of the coupling of hydrological and crop models to simulate processes within agro-hydrological watersheds [23]. An integrated system is crucial to address the complexity in various agricultural water management phases within the context of the agro-hydrological watershed, particularly with the emerging issue of climate change. Agricultural use of water is based on various variables including climatic circumstances, topography, lithology, soil, management methods, and plant type. Understanding these parameters enables crop water requirements to be estimated and crop management processes to be established. Existing water management methods might not be sufficient to deal with climate change effects on reliable water supplies for irrigation. Spatial and temporal climate variations have affected water availability in different water catchments globally. Notably, the Tanjung Karang Rice Irrigation Scheme in Malaysia often experiences shortages of water due to unusual spatial and temporal distributions of rainfall. However, the scheme is yet to model agro-hydrological systems for effective water distribution under climate change impacts. To date, the increasing pressure of demand for water remains a challenge for paddy irrigation planning based on projected multi-models climate data series. Irrigation managers need a tool that helps with quick decisions on water management to mitigate the risk of water and food safety. The aim of this study was to develop a climate-smart agro-hydrological model for adaptive irrigation and wise water resource management towards water security under the new realities of climate change.