esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The nonlinear free vibration of a porous FG-GPLRC cylindrical panel with a central crack has been investigated by combining a phase field formulation and a virtual geometry rotation in the framework of 2-D XNEM. The curved neutral surface was transformed into a rectangular plane for the easy definition and manipulation of L/I functions, and the troublesome numerical locking was effectively suppressed by employing the MITC3+ shell concept and the stabilization factor. The developed nonlinear numerical method was verified through a comparison with the reference solutions, and the nonlinear free vibration characteristics of cracked porous FG-GPLRC cylindrical panels were profoundly examined. The numerical results led to the following major findings:  
* The present method shows stable convergence and good agreement with the reference solutions, with a maximum relative difference equal to 4.838%.
* The nonlinear-to-linear frequency ratio \(\omega_{NL}/\omega_{L}\) decreases with increasing crack inclination angle, but the decreasing slope becomes saturated. Meanwhile, it uniformly decreases as the relative crack length becomes larger.
* The frequency ratio \(\omega_{NL}/\omega_{L}\) increases in proportion to the GPL mass fraction \(g^{*}_{GPL}\), but it becomes saturated as \(g^{*}_{GPL}\) increases over a critical value. And, the saturation trend is different for different GPL distribution patterns.
* The frequency ratio \(\omega_{NL}/\omega_{L}\) increases proportionally to the porosity coefficient \(e_{0}\) even though it shows a saturation trend. Also, its variation to \(w^{\text{max}}_{c}/h\) is remarkably affected by the porosity distribution and becomes more diverse when the GPL distribution is combined.
* Compared with FG-GPLRC, FG-CNTRC produces a remarkably lower linear fundamental frequency but a slightly higher frequency ratio \(\omega_{NL}/\omega_{L}\), for the same volume fraction. In addition, its nonlinear free vibration is affected by the CNT alignment direction, differing from FG-CNTRC.  
This work was supported by the 2024 Hongik University Research Fund.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The author declares no conflict of interest.
# A Framework for Cloud Based E-Government from the Perspective of Developing Countries
## 3 Cloud Based E-Government Framework
### Cloud Based G2G E-Government Framework Stages and Assimilation Process
#### 7.3.1 Migration  
Once the activities in this stage are completed the system migration an integration process begins. The migration is a process of moving the existing electronic government system to the cloud environment. In relation to electronic government migration to the cloud (within the context of developing countries), the cloud vendor or third-party company could be employed to complete the migration process as it involves many technological issues that need to be handled by an expert. There are many issues that need to be considered while the traditional electronic government system is being migrated. The government needs to decide whether the legacy system needs to be re-engineered before migration to the cloud. The feasibility study should be done while re-engineering the legacy electronic government system to decide whether the system needs to be rebuild completely or the re-engineering would be sufficient for the migration. The migration process will be discussed in detail in the methodology section.  
**Stage 4: Evaluate:** The final stage of the cloud based framework involves evaluation of the outcomes of the previous stage. Value creation is an important parameter that we have used to evaluate the framework. Value creation is a process of adding value to e-government services that the stakeholders acquire. The value of e-government services could be increased by providing citizen-centric, simple and affordable services. The different stakeholders of the electronic government; citizen, business, government and organizations are linked together, thus adding the value for one stakeholder benefits all. These stakeholders have their own perspective on the value creation. The value creation for the government is to save cost and time, gain wider e-government adoption from the stakeholders and minimize the resource wastage. The outcomes obtained from these activities are mapped to the framework goals to make decision whether to continue the cloud based G2G e-government or not. The flowchart in Figure 5 shows the comparison process that supports the government decision to adopt the plan.  
Figure 5: Evaluation of the framework goals.
# DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection
## 5 Results and Discussion
### Visualization Results and Analysis  
Based on Pos (only defective image samples) and PosNeg (including both non-defective and defective image samples), we evaluated the network's performance. Figure 9  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **FLOPs (GMac)** & **mIoU (\%)** & **F1 (\%)** \\ \hline U-Net & 62.791 & 63.81 & 71.34 \\ U-Net+Mul & 63.034 & 65.14 & 73.69 \\ U-Net+MBConv & 31.584 & 64.98 & 73.18 \\ Ours & 36.675 & 66.94 & 74.89 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of our proposed model and the baseline models on the industrial magnetic tile defect dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **mIoU (\%)** & **F1** (\%)** \\ \hline FCN & 87.23 & 90.23 \\ U-Net & 89.23 & 92.35 \\ Deeplab v3+ & 91.73 & 94.56 \\ U-Net++ & 90.97 & 93.78 \\ AttentionU-Net & 91.73 & 94.34 \\ Trans U-Net & 91.42 & 93.98 \\ DEU-Net (Segmentation) & 91.59 & 94.76 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of segmentation results on the KSDD dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Loss Variants** & **mIoU (\%)** & **F1** (\%)** \\ \hline Cross-entropy loss & 64.71 & 72.94 \\ Combined loss & 66.94 & 74.89 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the proposed model on different loss functions.  
shows some visual results of correct and incorrect predictions. In the experiments, most of the samples were correctly segmented and classified. However, there are some false positives and false negatives due to insufficient lighting in certain corner areas. We also assessed the network's performance based on the absolute number of misclassified test examples. Table 6 describes the performance of our network on industrial magnetic tile defects. Due to the significantly larger number of non-defective examples compared to that of defective examples and the high recognition rate for non-defective examples, which can inflate the overall accuracy of PosNeg, we randomly selected 60 non-defective samples to add to PosNeg. It can be observed that our network performs well on Fray and Crack, with no misclassified examples, while maintaining a high accuracy in negative test examples (i.e., non-defective examples).
# Maritime Traffic Evaluation Using Spatial-Temporal Density Analysis Based on Big AIS Data
## 2 Materials and Methods
### Density Model
#### 2.4.1 Definition of Ship Density
This study calculates the density of maritime traffic from the occupancy time, defined as the time in which a ship crosses a unit area (therefore, its units are time per area). This new analysis method adopts the updates of the EU vessel density map detailed method of EMODnet Human Activities [44]. As the summed occupancy times are calculated over a span of 1 month, the density can be defined as the occupancy time in hours per square kilometer per month. This analysis method can transcend the limitations of the temporal AIS data in which different ships are sampled at different intervals. Even when multiple ships sail the same track or the same distance, this new method is sensitive to ship speed and thus best reflects the real traffic environment.
## 1 Introduction
The latter provides a full scan of a range of excitation wavelengths, performing for each of these excitation wavelengths a full emission scan. The resulting matrix of fluorescence intensities, sometimes referred to as 'optical fingerprint', is reflecting all fluorescing components and its peaks are associated with different components from the DOM pool [14; 29]. A classical labeling of the fluorophores in DOM identifies five main peaks A, C, B, T, and M. Those peaks are extensively used to identify the main composition and origin of FDOM. Previous reports indicate the correlation between microbial presence and freshly produced DOM (peaks M, B and T), while allochthonous DOM has been associated to the peaks A and C [29]. EEM spectra are frequently used in recent literature [27; 28] and several parameters are calculated from them, including the humification index [30], biological index [31], and recently produced material index [32]. A statistical analysis of EEM spectra is often performed by parallel factor analysis (PARAFAC), a method that identifies the underlying components from a set of spectra [33; 34]. EEM spectroscopy (EEMs) and its spectral analysis can be considered the state-of-the-art in FDOM assessment, however, to the best of our knowledge, no submersible sensor exists that enables the high-resolution (in terms of wavelengths intervals) measurement of excitation-emission matrix spectra with in situ equipment. Herein, we will present (i) design and application of a novel in situ EEMS sensor system (in memory of [PERSON] named the 'Kallemeter') that enables full optical fingerprints in a submersible design, and compare these results with (ii) flow-through data from two commercial FDOM sensors, and (iii) spectral components identified in surface water samples from a laboratory UV-VIS spectrofluorometer, based on a field campaign in the Trondheimsfjord, Norway.
# Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach
## 6 Conclusions  
Addressing the change characteristics of surface types with complex texture information such as rivers and lakes, this paper used an improved directional logarithmic variation function model to analyze the texture features of images and proposes an L-V-NSCT approach. This approach can not only effectively reflect the characteristics of land surface change in different periods but also has good anti-noise performance.  
Through our experiments, we can also see that the detection accuracy and other indicators of the proposed method are not stable for different types of images. The stability and parameter settings of the method merit further study.  
[PERSON] contributed to the conception of the study and wrote the manuscript; [PERSON] performed the experiments and data analyses; [PERSON] contributed significantly to algorithm design and manuscript preparation; [PERSON] helped perform the analysis with constructive discussions.  
National Defense Pre-Research Foundation of China (7301506); National Natural Science Foundation of China (61070040); Education Department of Hunan Province (17C0043).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Experimental Procedures  
The experimental setup included a uniaxial cyclic loading system, a high-resolution 3D X-ray microscopic imaging system, and a scanning electron microscope (SEM) system, as shown in Figure 2. The uniaxial cyclic loading system uses a WDW-300 electronic universal testing machine manufactured by Changchun Kexin, Changchun, China. CT scanning is conducted using a high-resolution 3D X-ray Microanalyzer (XRM), model Xradia 510 Versa, manufactured by Carl Zeiss, Jena, Germany. X-CT image 3D reconstruction and measurements are performed using the open-source software ImageJ 1.52p. The scanning electron microscope system employs a QuantaTM 250 scanning electron microscope manufactured by FEI, Hillsboro, OR, USA.  
Figure 1: Processed coal sample.  
(1) Six coal samples were selected from the same coal seam. Three samples were subjected to uniaxial compression under uniform loading to determine their compressive strength. The remaining three samples underwent uniaxial cyclic loading-unloading tests. The loading rate was set to 0.25 mm/min. (2) For the cyclic loading-unloading tests, the stress targets for the first three cycles were set to 20%, 40%, and 60% of the average compressive strength, respectively. The fourth cycle's target was set at 15% below the maximum stress value.  
Figure 3: Experimental flow chart.  
Figure 2: Experimental systems. (**a**) Electronic universal testing machine; (**b**) High-resolution 3D X-ray Microanalyzer; (**c**) Scanning electron microscope.  
3. X-CT scans were conducted at the initial state and after each of the four loading-unloading cycles, producing a total of five sets of images.
4. Surface morphology and elemental composition were analyzed using scanning electron microscopy and energy-dispersive spectroscopy (EDS).
## 2 Methods
### Signal Processing  
The ECG signals were resampled to 500 Hz solely to improve R-peak detection, since upsampling factors have been widely used in the literature for this purpose [42]. To mitigate high-frequency noise, a second-order Chebyshev filter with a cut-off frequency of 100 Hz was applied. Additionally, a second-order Chebyshev filter with a cut-off frequency of 0.5 Hz was used to reduce low-frequency baseline interference. Both filters were implemented under a zero-phase structure to maintain the original signal's phase and amplitude characteristics [43]. Next, the Pan-Tompkins algorithm, known for its effectiveness and  
Figure 3: Adaptation of the labeling system across three different databases: (**a**) the Apnea-ECG database uses an original labeling system, (**b**) the MIT-BIH database labels with inheritance every 30 s, and (**c**) the UCD-DB database employs real-time sampled inheritance. Each dashed block represents a one-minute segment. Respiratory episodes are labeled as: normal (N), apnea (A), obstructive apnea (O), central apnea (C), and hypopnea (H).  
computational efficiency, was employed for R-peak detection [44]. To account for slightly delayed or missed R-peak locations, a sliding R-peak correction window was applied. Subsequently, the ECG recordings were segmented into 1-min intervals to extract the measure of HRV by calculating the consecutive time differences between R-peaks [15].  
Eventually, a manual signal screening process was conducted to discard segments with a high presence of noise or artifacts, such as lead detaching, amplitude saturation, sneezes, and others. As a result, 81% of Apnea-ECG, 87% of MIT-BIH, and 91% of UCD-DB segments were deemed suitable for further analysis. Overall, a total of 40,993 ECG segments were obtained, providing approximately 683 h of valuable information. The distribution of these segments by databases and classes can be found in Table 1.
## 2 Passive Systems
### Energy Harvesting
The main goal of these systems is to increase the amount of generated power, which can be especially attractive in situations with low or intermittent power [67]. As mentioned, one reason to create hybrid energy harvest systems is to overcome the discontinuities of power. Solar energy is an excellent example of this problem, and, due to that, the work conducted in [86] developed a system able to extract energy from photovoltaic devices and piezoelectric. Both harvesters have individual rectifiers, being after connected to a conventional battery. It provides energy to a hydrogen sensor, a sensor interface, as well as a microcontroller, and a transmitter. Instead of using sun and vibration, a different approach combines sun, wind, and water. Each source has its conditioning block and comparator. When the generated voltage is above a particular value, a comparator allows transferring the charge stored at that capacitor to the battery. If the voltage is below the required level, the same comparator turns off the converter to avoid battery discharge [85]. The work presented in [87] focuses on combining systems regardless of the kinds of energy. It consists of three sub-systems: energy harvesting, reservoir capacitor array, and control/charger. Each energy harvester charges its reservoir capacitor, and each capacitor smooths the dynamic range, which can cause the system to operate unreliably or fail. After that, the control and charge sub-system determines which power source, either the battery or the reservoir capacitor array, should power the load by comparing the terminal voltage and defined threshold voltage. This sub-system has a window comparator, threshold detector, load switch, battery charger, and battery protector. The same logic decides if the battery is charged by the capacitors or not.
# A Deep Learning Approach with Feature Derivation and Selection for Overdue Repayment Forecasting
## 4 Experiments
### Ablation Study  
In this subsection, we explore the effect of the attention mechanism, feature derivation method, sequential input features and static input features; the results are listed in Table 3. In the table, \"w/o attention\" refers to the concatenation of the hidden vector of BiLSTM at the last moment with the DNN-extracted feature vector connected to the output layer; \"w/o derivation\" refers to the operation of basic feature engineering approaches such as normalization and one-hot encoding on the first record of each individual piece of raw data and directly connecting to the DNN layer, instead of feature derivation and selection. We can conclude from Table 3 that the attention mechanism and feature derivation improve the prediction performance of AD-BLSTM, but modestly. However, in the next subsection, we will show that the two components play an essential role in interpretability. Furthermore, in the table, \"w/o sequence\" refers to the isolation of BiLSTM and the attention layer, degenerating to the DNN baseline in Table 2, while \"w/o statistics\" isolates the DNN layer for the purpose of observing the effect of sequential behavior and static background information. The results  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Recall** & **AUC** & **KS** \\ \hline LR & 0.850 & 0.18 & 0.675 & 0.286 \\ XGBoost & 0.851 & 0.19 & 0.684 & 0.292 \\ FM & 0.852 & 0.21 & 0.684 & 0.304 \\ DNN & 0.852 & 0.23 & 0.685 & 0.315 \\ Multi-BiRNN & 0.798 & 0.43 & 0.779 & 0.432 \\ Multi-BiLSTM & 0.804 & 0.47 & 0.781 & 0.438 \\ AD-BLSTM & **0.855** & **0.59** & **0.844** & **0.481** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results with baseline models. The best is in bold. AUC: area under the curve; KS: Kolmogorov-Smirnov value; FM: factorization machine; Multi-BiRNN: multi-layer bi-directional recurrent neural network; AD-BLSTM: our model.  
indicate that the previous repayment label sequence has an influential impact on the next repayment target even without identification and background information, while the impact of static features is far less influential. Another observed phenomenon is that the accuracy has the opposite variation trend to the other indicators, perhaps for the reason that the model pays more attention to distinguishing overdue repayments, thus misclassifying some negative instances.
# Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things
## 5 Implementation and Results
### Deep-Learning Implementation  
As mentioned earlier, we used Python-based Keras machine-learning library for the implementation of the deep-learning algorithm. During classification stage, training dataset are read, stored in a data frame and converted into a matrix. Furthermore, these datasets are bifurcated into the training and testing datasets, where the training dataset comprised of 18,989 benign network transactions and 20,697 malicious network transactions, while the testing dataset comprised of 9494 benign network transactions and 10,349 malicious network transactions. The system was initially tested using the labeled testing dataset consisting of 19,843 (i.e., 33.333% of input dataset) transactions, wherein each record constitutes of the 6 values; transmission-rate, reception-rate, transmission-to-reception ratio, duration, transmission mode, source-IP, destination-IP, the data-value information, and the binary label information. After the initial training of DNN using labeled training dataset comprising of 39,686 (i.e., 66.667% of input dataset) transactions, it was run against the testing dataset, without any binary label information. Since, the testing dataset had 39,686 of unlabeled records, the deep-learning model produces 39,686 predictions in the form of \"0\" or \"1\".  
We then create and instantiate a \"Sequential\" DNN with 3 hidden layers, and equip the processing units within each layer with ReLU activation function. Thereafter, the deep-learning model is compiled and fitted with 150 runs, i.e., epochs, and the number of features. Finally, the deep-learning model is compiled, and the classifier is assigned and saved in the variable \"predictions\". Consequently, in every test, the results of the classifier are normalized to a binary value.
## 5 Security Issues and Cyber Attacks in Wireless Sensor Networks
### Attacks on Various Network Layers
#### 5.3.5 Sybil Attack  
In Sybil attack, a malicious sensor node takes multiple identities to perform an attack. In wireless sensor network, all the sensor nodes work complaisantly but this type of attack targets this cooperation and disturbs the routing and communication process [77; 78; 79; 80; 81; 82; 83].
## 5 Discussion and Conclusions
Additional processing is required for the reconstructed images before they can be evaluated using focusing criteria. The proposed configuration ensures that the reconstruction of 3D objects and the trajectory of moving objects will be free from distortions. In summary, the provided method is able to determine, with precision, the magnification of the reconstructed image using an inline holography system based on the double-sideband filter. We have demonstrated that the lateral and transverse magnifications depend on the focal length of the lens and the distance between the lens and the camera. We used different focusing criteria to obtain the reconstructed and focused images. The Tamura criterion was the most accurate, both in the simulations and in the experimental validation. This study is useful for real-time measurements of the movement of microparticles, in the 3D reconstruction of microscopic objects, in the characterization of flows in microfluidic systems, etc. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14125118/s1](https://www.mdpi.com/article/10.3390/app14125118/s1)]([https://www.mdpi.com/article/10.3390/app14125118/s1](https://www.mdpi.com/article/10.3390/app14125118/s1)). Conceptualization, C.R., J.C. and A.L. ; methodology, C.R. ; software, C.R. ; validation, C.R. and L.G.-C.; formal analysis, C.R. ; investigation, C.R. and L.G.-C.; resources, C.R. and A.L. ; data curation, A.L. ; writing--original draft preparation, C.R. ; writing--review and editing, A.L., J.C., I.E. and L.G.-C.; visualization, C.R. ; funding acquisition, C.R. and A.L. All authors have read and agreed to the published version of the manuscript. This research was funded by DGAPA-UNAM through projects PAPIIT: TA101020 and IG100121 and also by Ministerio de Ciencia e Innovacion and Fondos FEDER (PID2021-562 126509 OB-C21 and PDC2022-133332-C21) and Generalitat de Catalunya (2021 SGR00138). Not applicable. The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author. [PERSON] acknowledges her master's scholarship from CONAHCyT Mexico. IE acknowledges financial support from a Beatriu de Pinos Fellowship (2021-BP-00206). The authors declare no conflicts of interest.