esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### ArcFace Loss-Based Classification Neural Network  
The Softmax function is widely used in image classification tasks. It distinguishes features between different categories by converting the output of the original classification problem into a probability distribution [35]. However, the Softmax function lacks direct constraints on intra-class compactness and inter-class separability, which to some extent limits the accuracy of image classification.  
The Softmax function is:  
\[L_{s}\ =-\ \frac{1}{m}\sum\limits_{i=1}^{\infty}\ln\left(\frac{\mathbf{e}^{ W_{ij}^{T}x_{i}+b_{\gamma_{i}}}}{\sum\limits_{j=1}^{n}\mathbf{e}^{W_{ij}^{T}x_{i} +b_{j}}}\right) \tag{13}\]  
In Equation (13), \(m\) is the size of each training batch; \(i\) is the spectrogram number; \(j\) is the true label; \(W_{y_{i}}^{T}x_{i}+by_{i}\) represents the output of the fully connected layer; and \(W_{y_{i}}^{T}\), \(x_{i}\), \(by_{i}\), and \(y_{i}\) are the weight of the \(i\)-th image category, the feature of the image, the bias of the image category, and the true label of the image, respectively.  
The Softmax function only considers the correctness of sample classification. For multi-classification tasks, it lacks effective constraints on intra-class compactness and inter-class separability. ArcFace Loss was proposed as an improved method of Softmax [36]. By adding a reward angle margin \(t\) (\(t>0\)) to the angle \(\theta\) between \(x_{i}\) and \(w_{y_{i}}\), the angle between the deep features and their corresponding weights is punished in an additive manner, thereby increasing the intra-class compactness and inter-class difference. In order to learn more distinguishable angular characteristics, the feature weights are set to a fixed value \(s\), and for the consideration of simplifying the model parameters, the bias value is set to zero. The schematic diagram of the ArcFace Loss classification structure is shown in Figure 10. The ArcFace Loss formula can be expressed as:  
\[L_{\text{ArcFace}}\ =-\ \frac{1}{m}\sum\limits_{i=1}^{m}\ln(\frac{\mathbf{e}^{s (\cos(\theta_{y_{i}}+t))}}{\mathbf{e}^{s(\cos(\theta_{y_{i}}+t))}\ +\ \sum\limits_{j=1,j}^{N}\mathbf{e}^{s \cos\theta_{j}}}) \tag{14}\]
# Joint Multi-Scenario-Based Earthquake and Tsunami Hazard Assessment for Alexandria, Egypt
## 3 Multi-Scenario Physics-Based Seismic Hazard Analysis  
The main target of a seismic hazard study is to estimate the ground motion intensity (e.g., PGA and spectral acceleration (SA) at a given period) for a given area/site of interest. Seismic hazard analysis primarily consists of two steps: (a) characterizing seismic sources of hazard (e.g., sizes and locations of possible earthquakes; (b) characterizing the natural effects of those sources that may result in significant ground motion intensity at a particular location (e.g., [39; 40]). In this work, seismic hazard assessment is carried out using the multi-scenario and physics-based methodology known as NDSHA, which has been used for more than two decades (see [41; 42]) for a recent review) at regional, national, and local scales. It is worth emphasizing that ground shaking computations within the NDSHA framework are based on multi-scenario physics-based modeling, and therefore no return period is associated with them, unlike standard DSHA and PSHA methods.  
The NDSHA method is based on the computation of realistic synthetic time histories of ground motion using basic physical principles of seismic wave generation and propagation in anelastic geo-structural models [43]. It represents a very effective approach, even in regions with incomplete earthquake catalogs available, such as North Africa and Middle Eastern regions [8]. The NDSHA can accommodate any reliable information that enhances hazard understanding and fills knowledge gaps. This includes data from seismogenic nodes derived from morphobstructural zonation (MSZ) studies, mechanical models of the lithospheric structure, and paleo-seismological and GPS data (e.g., [44]). This information enables the accurate computation of earthquake ground motion maps, including PGA, PGV, and PGD, or any other seismic engineering parameter extractable from computed synthetic signals (e.g., [2; 8]). Also, it may provide a comprehensive dataset of synthetic seismograms,  
Figure 3: (a) Spatial distribution of historical earthquakes (colored squares) from 2000 BCE to 1899 [8; 35; 37] and regional instrumental seismicity from 1900 to 2020 (colored circles) around Alexandria [8; 38]. The location of Alexandria is indicated by the black outlined square and the black lines represent the main tectonic elements in and around Egypt. (b) Spatial distribution of significant earthquakes is listed in Table 1.  
which is particularly important for the areas that suffer from the endemic lack of proper, strong motion time histories, such as Egypt.
## 4 Methodology  
The proposed methodology consists of the following steps:  
1. Data Preprocessing to normalize and prepare the data for analysis.
2. Feature Compression Using Autoencoder to train an autoencoder to learn a compressed representation of the input data.
3. Unsupervised Classification Using GMM: GMM application to classify the compressed features into distinct engine states.
4. RUL Prediction: Training separate LSTM models for each engine state to predict RUL.
5. Evaluation: Engine classification performance evaluation and RUL prediction using RMSE, R-squared, and Score metrics.  
Figure 1 presents a designed neural network model for the unsupervised classification using autoencoders and Gaussian mixture models for turbofan engine RUL prediction.  
Figure 1: Turbofan engine unsupervised classification and RUL prediction model.
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 4 Simulation Results and Analysis
### Comparison Algorithms
#### 4.2.1 Classical Algorithm: Network Performance with the Numbers of Nodes  
The MPEC algorithm chooses a path for data transmission from source to sink. Nodes in this path consume minimum energy but have a more massive load. Hence, MPEC's NL is the shortest because of the higher packet loss ratio and easy premature failure. At the same time, due to the minimum energy consumption path being also the shortest, MPEC has the lowest AH. The MTEC algorithm selects the next hop with the shortest single-hop distance in the forward neighbor set. It is a selfish algorithm with \"self-interested behavior\", that is, it only considers the energy of the current node in the routing process, but ignores the remaining energy and cache occupation of the next hop. Therefore, in the end, the NL and PLR values of MTEC are not ideal. For AE and AH, because the single-hop distance is the shortest each time, the final path is almost the longest compared with other algorithms, resulting in higher AE and AH. The MRE algorithm takes the residual energy as the routing standard and selects the high-energy node to forward data depending on the node energy change as far as possible. Therefore, the NL of this algorithm is the longest. However, since MRE chooses the node with the most residual energy to forward data, its routing path is usually long, resulting in the maximum AE of the network, and the AH is second only to MTEC.  
In conclusion, due to the one-sided consideration, it is difficult for the three classical comparison algorithms to compare with other algorithms in some partial performance. In contrast, this paper considers the algorithm from various aspects and selects the optimal forward neighbor as the forwarding node according to the parameter fusion strategy. Therefore, as we can observe from the simulation results, the performance of MPEC in terms of AE and AH is slightly inferior to that of MPEC, the comprehensive property of the algorithm in this paper has reached better results when it compares with the four performance indicators.
## 3 Mobility Management Architecture Using CoAP  
In this section, we describe the mobility management architecture for a mobile CoAP node that is based on CoMP. We also describe the detailed mobility management procedure and message format of CoMP.
# Dynamic Network Resource Autonomy Management and Task Scheduling Method
## 4 Simulation Experiments
### Numerical Comparative Analysis
Because of the higher efficiency of our proposed dynamic scheduling algorithm, it can have faster decision-making capabilities in complex situations and under multi-task conditions. In addition, the method can be adapted to the constraints of satellite resources. Multiple objectives, such as task completion time and resource usage efficiency, can be considered simultaneously, thus ensuring the efficiency and fairness of task allocation and satisfying scheduling requirements. The remaining algorithms, such as machine learning-based scheduling algorithms, require a large amount of data for training, which can be time-consuming and may also suffer from low training efficiency. The above experimental procedure mathematically involves mathematical optimization problems and some algorithms. In our proposed approach, network resources are considered finite and allocatable, and tasks are entities that need to use these resources. Therefore, the scheduling problem between tasks and resources can be abstracted as an optimization problem, i.e., how to allocate tasks with limited resources to achieve scheduling purposes. Beyond that, mathematical modeling and algorithms are the core of solving optimization problems. Mathematical models such as linear programming can be used to represent task and resource constraints and specify optimal scheduling policies. Thus, our research is very closely related to mathematics.
# Research on Pore Pressure Detection While Drilling Based on Mechanical Specific Energy
## 5 Conclusions  
1. A mechanical-specific energy-based drilling parameter method is proposed and validated to provide reasonable pore pressure estimates in deep complex lithologic intervals. The computation of MSE uses downhole parameters as far as possible. When reliable downhole measurements are lacking, a method based on drill string mechanics is proposed to estimate friction losses along the drill string by considering wellbore geometry and surface drilling parameters. In this way, more accurate drilling parameters at the bit can be obtained in real-time.
2. Torsional energy is the primary source of contribution to MSE, and MSE-based pore pressure detection is highly sensitive to downhole torque. More attention and effort should be paid to the measurement and estimation of downhole torque. It should also be noted that when using PDM, the contribution of the PDM to the total MSE needs to be fully considered.
3. The new method relies on trend lines and requires correction of the Eaton exponent, which has the influence of subjectivity. Moreover, all factors that are not related to pore pressure but can lead to significant changes in MSE may cause the overpressure conditions to be masked. At this point, the MSE-based method needs to refer to log data and actual drilling conditions from offset wells.  
Figure 5: Comparison of the MSE-based method and dc-exponent method for the pore pressure detection in well M. (**a**) The NCT of dc-exponent method; (**b**) the NCT of MSE-based method; (**c**) comparison of pore pressure detection between the dc-exponent method and MSE-based method.  
**Author Contributions:** Methodology, H.Y., H.C. and J.G.; investigation, H.Y., H.C. and J.G.; resources, H.Y.; writing--original draft preparation, H.C.; writing--review and editing, H.Y. and H.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China (grant number 2019 YFA0708302).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Data and Methods
### Study Methods
#### 2.2.3 BARC--Surface Reflectance at USDA Site with Derivative Analysis  
To study the stability of reflectance for differing types of vegetation targets, Hyperion data acquired for the USDA BARC in Maryland were collected and used. The Hyperion Level 1T data were acquired in the summer and fall from 2005-2015 and calibrated to surface reflectance by the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) software [34] using default model parameters for typical summer/fall clear-sky atmospheric conditions. The acquisition time, sensor look angle, center location of the data, and a visibility of 40 km were used as inputs for MODerate resolution atmospheric TRANsmission (MODTRAN) parameters.  
Derivative analysis is a powerful tool that enhances the interpretation of data. Derivatives of second order or higher should be relatively insensitive to variations in illumination intensity whether caused by changes in sun angle, cloud cover, or topography [35]. [PERSON] et al. [36] used spectral derivatives and single or multiple classifiers in land classification and achieved overall classification accuracy (expressed in percentage) that was significantly greater than was achieved when exploiting only the reflectance information. In a study by [PERSON] et al. [37], the classification results in the spectral as well as derivative domains were fused by a logarithmic-opinion-pool rule and the results demonstrated that the algorithms improved classification accuracy even in cases with small training sample sizes.  
When combined with time-series analysis, derivative analysis reveals whether the signal is consistent, regardless of seasonal factors.  
Derivatives are very sensitive to noise, so smoothing or minimizing random noise is important. [PERSON] and [PERSON] [35] examined several methods for smoothing of hyperspectral data. In this study, the local polynomial regression fitting [38] was used to smooth the reflectance data before derivatives were calculated. First, second, and third derivatives were calculated on the Hyperion spectra (Equation (2)), considering the full width at half maximum (FWHM) value of each wavelength interval.  
\[\frac{\Delta r_{i}}{\Delta\lambda}=\frac{r_{i}-r_{i-1}}{(FWHM)_{i}} \tag{2}\]  
where \(\Delta r_{i}/\Delta\lambda\) is the derivative and \(\Delta r_{i}\) is the change in reflectance, \(\Delta\lambda\) is the change of wavelengths, and FWHM is the full width at half maximum for that wavelength  
Various targets (each having 10 to 30 pixels) were identified from Hyperion data using the Region of Interest (ROI) tool of ENVI [39]. The mean reflectance of the targets were calculated for 152 calibrated bands of the Hyperion data. The calibrated bands consists of five pieces: 427 nm-925 nm; 973 nm-1114 nm; 1175 nm-1326 nm; 1497 nm-1790 nm and 2032 nm-2355 nm. The width for the local filtering was 30 nm.
## 1 Introduction
First, in the preprocessing stage, we proposed a novel local edge-preserve algorithm to remove redundant details and reduce useless edges (such as ships, rocks, buildings, etc.) on the sea and on the ground. Then we proposed EC-SIFT to extract harbor target keypoints; this method greatly reduces the redundancy of SIFT keypoints to further reduce the required calculations. Next, we use the GOM-SIFT algorithm and the QT algorithm to extract harbor candidates. Finally, these harbor candidates are input to the support-vector machine (SVM) to get the harbor target extraction results. The experimental results show that the proposed algorithm reduces the detail redundancy effectively and improves the accuracy and efficiency of harbor target extraction. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Content** & **Strength** & **Weakness** \\ \hline HDD [27 ] & This method is designed according to the characteristic that the harbors have a number of docks that extend to the sea and are pulsed at the junction with the sea. & The detection speed of this method is fast. & It can only extract the docks, so it is impossible to extract the entire harbor targets. \\ HDC [27 ] & This method extracts the harbor targets by detecting the semi-closed characteristic of the harbors. & This method can detect artificial/natural harbors, and it has good versatility. & This method has a high rate of false positives: it may mistake a section of the coast \\ HDB [27 ] & This method is designed according to the characteristic of water surrounding both sides of the harbor breakwaters. & The characteristics of the characteristics of the breakwater are remarkable, and it is easier to extract a complete harbor target. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The advantages and disadvantages of some methods. The rest of the paper is organized as follows: In Section? ?, we introduce the preprocessing method, the LEPA-processing method, and the methods of harbor target candidate extraction. The results of harbor target extraction are shown in Section??. The discussions of experimental results are provided in Section? ?, and the conclusion is provided in Section? ?.
## 3 Mathematical Concept and Modeling
### Proposed Model: CV-CNN-TCN  
The proposed architecture, a Complex-Valued CNN-TCN, was designed to process IQ data directly as complex-valued inputs, leveraging their inherent structure for effective feature extraction and classification. The input layer accepts data in the form of 2 x 128, representing the in-phase (I) and quadrature (Q) components, which are transformed into a complex representation to preserve the spatial and temporal correlations. The architecture incorporates two parallel convolutional pathways, each comprising 32 filters with a kernel size of 3, applied independently to the I and Q components. This design facilitates the capture of unique features from each component while ensuring nonlinear patterns are extracted using ReLU activation. Max-pooling layers with a size of 2 follow the convolutions to reduce dimensions and emphasize dominant features. The outputs of the convolutional blocks are flattened and concatenated, forming a unified feature representation. These features are then passed through a TCN block, which captures long-range dependencies in the data, enabling the model to learn temporal relationships critical for classification tasks. The architecture concludes with dense layers, employing softmax activation for classification. An overview of the proposed model is illustrated in Figure 3.
## 4 Simulation and Practical Application
### Basic Ideas of Microscopic Traffic Simulation  
The microscopic traffic simulation system is a complex and random dynamic system. The realization of the simulation system is a cyclic process. It includes problem analysis, data acquisition, model establishment, parameter selection, simulation operation, and result analysis [31; 32].  
Problem analysis is the overall analysis, description and characterization of traffic problems. The basic data required for microscopic traffic simulation modeling include the following: road geometry data (such as number of lanes, lane width, barrier width, non-motor vehicle lane width, lane division, and road intersection form), traffic flow characteristic data (such as traffic volumes, traffic composition, ratio of turning vehicles, vehicle arrival distribution, vehicle speed distribution, vehicle queuing and dissipation), traffic control data (such as signal timing of each intersection and traffic management), which depend on the research objectives [33; 34; 35].  
To obtain the best simulation results, simulation parameters are selected according to sites, traffic status as well as evaluation targets in the microscopic traffic simulation system. The parameters of the simulation are continuously adjusted to ensure the accuracy of the microscopic model [36].  
The operation of different random seeds can be regarded as different observations of the system. To ensure the reliability of the simulation output, multiple random seeds are utilized for multiple simulations. The results of the simulation are recorded generally after the system is stable. The average value of each operation result is also used as the final result. In regard to the statistics and analysis of the simulation results, multiple schemes can be compared.
## 1 Introduction  
Acoustic motion in nanoscale objects induced by light is of importance for both fundamental science and applications. This interest is due to various potential applications in acousto-optical deviceswhich can be used for ultrafast manipulation and control of electromagnetic waves by hypersonic (GHz) acoustic waves [1]. Colloidal crystals, formed by the self-assembly method, have shown phononic band gaps in the GHz frequency range [2; 3; 4; 5; 6; 7]. Due to the recent progress in the fabrication of high-quality colloids, this provides an opportunity to produce inexpensive phononic crystals.  
Dynamics in submicrometer colloidal crystals was investigated using different techniques such as Raman scattering [8], Brillouin light scattering [2], and optical pump-probe spectroscopy [4; 5; 6; 7]. However, these methods have limited spatial resolution and are not suitable for the observation of the detailed structure of the sample. Furthermore, for some materials obtaining a sufficient refractive index is a challenging task, if not impossible.  
At the same time, newly-developed X-ray free-electron lasers (XFELs) produce X-rays with unprecedented brightness, pulse duration and coherence, which are ideal for performing time-resolved experiments on various materials with a time resolution that outperforms that of third-generation synchrotron sources [9; 10; 11]. Moreover, the femtosecond X-ray pulses from free-electron laser sources allow an investigation of transient states of time-dependent processes [12; 13; 14; 15; 16; 17]. In this respect, XFELs are especially well suited for investigating ultrafast structural dynamics of colloidal crystals.  
The vibrations of the colloidal crystals were recently measured at free-electron laser FLASH in Hamburg [18]. The polystyrene colloidal crystals were pumped by an infrared (IR) laser of 800 nm and probed by an X-ray pulse with a wavelength of 8 nm. This pump-probe experiment indicated the possibility of observation of the dynamics in the colloidal crystals by diffraction with a FEL. To give a reliable interpretation of the observed results, it was necessary to investigate the dynamics with a larger time interval and better statistics.  
Here we present the results of a time-resolved pump-probe diffraction experiment in which the ultrafast dynamics in colloidal crystals induced by an IR laser was investigated. A theoretical analysis of the vibrations of the colloidal crystals was performed, and the vibrational frequency was calculated. This analysis was applied to the experimental results.