esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Experiment
### Parameter Calibration  
Based on the measured traffic data, some parameters (the average parking distance, the number of observable vehicles, and so on) of the car-following model, lane change model, and route decision model in VISSIM are calibrated and adjusted to make the simulated traffic volume close to the actual measurement ([PERSON] and [PERSON] 2009). In the subsequent simulation experiments, the calibrated model parameters are used for the simulation analysis.  
To the calibrated model can simulate the basic operation status of road traffic and simulate the actual road conditions, such as signal timing scheme and geometric elements of the road, including the number of lanes at the entrance, the width of each lane, and the length of the weaving section. The simulation system provides a perfect car-following model and lane-changing model. Users can change parameters in the model parameters according to the actual situation so that the state of the vehicle is closer to the actual situation. The following will describe the specifics of the experiments in terms of route decision, expected speed, and vehicle composition:  
* Routing Decision  
In VISSIM, the traffic flow is generated by the edge of the road network and is collected by relevant upstream sections. The total traffic volumes are defined for all links and time intervals in vehicles, and then the routing decision is made for each lane. In VISSIM, \"routing decision\" is used to set the relative traffic flow ratio in each direction of the entrance. The traffic volume of roads with numerous vehicles merging and diverging is allocated according to the actual proportion of vehicles turning left, going straight, and turning right. In addition, the traffic flow in the mixed lane is distributed in different directions according to the actual proportion of vehicles turning left turn, going through, or turning right turn.
* Traffic Compositions  
Figure 6: Experimental procedure.  
The velocity data of different samples measured in the field have little difference. According to the data from samples, the desired speed of trolleys and cranes in the VISSIM system are set between 40 km/h and 60 km/h and between 20 km/h and 25 km/h, respectively.
## 2 Theoretical Background of NMRI Analysis
In the present study, a well approved permeability-based empirical equation, known as the Schlumberger-Doll Research (SDR) equation was adopted to calculate the surface relaxation coefficient (\(\rho_{2}\)), as expressed in Equations (4) and (5) [35]. \[K_{S}=C\phi^{4}T_{2 LM}^{2}, \tag{4}\]  
where the constant (\(C\)) is approximately equated with the square of the surface relaxation coefficient (\(C={\rho_{2}}^{2}\)). Given the research conclusion drawn by [PERSON] and [PERSON] (2009) [35], the equation is written as:  
\[K_{S}=\rho_{2}^{2}\phi^{4}T_{2 LM}^{2}, \tag{5}\]The equation can also be expressed as:  
\[\rho_{2}=\left(k_{s}\phi^{4}T_{2 LM}^{2}\right)^{1/2}, \tag{6}\]  
where \(K_{S}\) (\(\upmu\)m\({}^{2}\)) represents the permeability of the soil; \(\phi\) denotes the porosity of the soils and \(T_{2 LM}\) (ms) denotes the weighted geometric mean value of the \(T_{2}\) spectrum. Thus, the diameter of the cylindrical pore \(D\) (\(\upmu\)m) in thawed soils can be calculated as expressed in Equation (7). \[D=4\rho_{2}T_{2}. \tag{7}\]  
Then, the pore volume \(V_{i}\) with a certain diameter is given as [36]:  
\[V_{i}=\frac{A_{i}}{\sum A_{i}}\cdot\frac{m_{w}}{\rho_{w}}=\frac{A_{i}}{\sum A_{ i}}\cdot\frac{m_{s}-m_{d}}{\rho_{w}}, \tag{8}\]  
where \(A_{i}\) denotes the amplitude of the corresponding signal in a \(T_{2}\) spectrum, \(m_{w}\) denotes the mass of pore water in the tested sample, \(m_{s}\) denotes the total mass of saturated soil sample and \(m_{d}\) denotes the mass of thoroughly dried soil sample after dry-out treatment in an air oven. \(\rho_{w}\) denotes the density of water, which is 1.0 g/cm\({}^{3}\) approximately. Equation (3) provides the basis to convert the measured \(T_{2}\) spectrum to the pore volume distributions of the tested sample. To describe the pore structure characteristics as comprehensive as desired, the images from NMRI scans were measured by a spin-echo (SE) sequence, which is particularly dedicated to materials with short transverse relaxation time (\(T_{2}\)) such as porous media. The MRI scans allow selecting several cross-sectional slices along the axis of the tested sample. These slices reflect the distributions of pore water at different heights of the sample.
## 1 Introduction
Deep neural networks can be used for monocular depth and camera motion estimation to regress the camera pose [73; 74; 75]. An essential factor in the success and precision of object 3D reconstruction is the accurate localization of the camera in a three-dimensional space [76]. As the SfM technique depends on determining the three-dimensional structure of a scene from a series of two-dimensional images taken from different viewpoints, accurately determining the camera's spatial position and orientation at each viewpoint is paramount [41; 77]. Inaccuracies in camera 3D localization can lead to errors in the derived depth information, the resulting three-dimensional reconstruction, and any subsequent analyses [78]. Such errors can propagate and magnify, ultimately undermining the fidelity of the reconstructed model. This can, in turn, adversely impact the usability of the ear's 3D model to derive precise HRTF. This work addresses challenges related to camera 3D localization while reconstructing an ear's 3D model. Efforts are put towards developing and refining methodology that enhances the precision of camera 3D localization. This will pave the way for more accurate, reliable, and robust 3D reconstruction of the ear. The novelty and contributions of this work can be summarized as follows:  
* Proposed a simple solution for the improvement of camera 3D localization for the ear 3D reconstruction tasks. The proposed solution is based on enriching the background texture. * Presented a dataset construction approach for the evaluation of camera 3D localization. * Performed comparative evaluation of different cap textures' influence on the precision of camera 3D localization. * Presented comparative results of camera 3D localization improvement solutions; the results show that the proposed method is effective and using a cap with texture allows a reduction in camera localization error. The results also show that caps with sparse dot patterns and a regular knitted patterned winter hat are the preferred patterns. The problem of and proposed solution for camera 3D localization improvement is summarized in Figure 1.  
The outline of the paper is as follows. In Materials and Methods (Section 2), the proposed solution for improving camera 3D localization via enhancing the background texture around the ear is described; an elaboration of the experimental scenarios used to investigate the background textures is provided; the data collection and software tools used in the research are reported. Results (Section 3) gives experimental comparison results of the background enhancement approaches using various caps and the overall usefulness of the solution and provides an interpretation of the findings and practical implications. Finally, Section 4 gives the conclusions of this work.
# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 3 Experimental Results and Comparisons of Various Algorithms
### Real vs. Synthetic Speech Identification  
Previous experiments revealed that an excessively high proportion of synthetic speech data degrades model performance on the RHS dataset. To investigate this phenomenon, we employed Proposed-I and Proposed-II, without statistical mechanisms, on a combined RHS and synthetic speech dataset, with labels redefined as \"real\" or \"synthetic\" to learn feature distinctions. As shown in Figure 22, both Proposed-I and Proposed-II exhibit rapid convergence and low loss. Proposed-I achieved 97.54% accuracy on the mixed test set, with its confusion matrix (Figure 23) indicating consistent classification across training, validation, and testing. Proposed-II outperformed it, achieving 98.66% accuracy, with its confusion matrix (Figure 24) showing superior consistency. The AUROC curves indicate consistent result distribution due to the absence of statistical mechanisms. Proposed-II performed even better, achieving an accuracy of 98.66%, with its confusion matrix being consistent with previous experiments and superior to Proposed-I in showing results. The AUROC curves in Figure 25 confirm both models' exceptional ability to distinguish real from synthetic speech, demonstrating significant feature-level differences. These results suggest that excessive synthetic speech impairs real speech identification accuracy. This approach validates real-synthetic speech distinguishability and supports applications like detecting fraudulent impersonation in voice authentication systems, enhancing security and reliability.  
Figure 21: AUROC analysis of Proposed-II. The model achieves near 1.00 AUC in most categories, outperforming Proposed-I particularly in overlapping classes, confirming its superior discriminative capability. The dotted line represents the baseline; the closer the solid line is to the dotted line, the poorer the classification performance.  
Figure 23: Confusion matrix of Proposed-I in real vs. synthetic speech identification: (**a**) Training, (**b**) validation, and (**c**) test. The model achieves a stable classification distribution, with 97.54% accuracy on the mixed test set, confirming identification consistency.  
Figure 24: Confusion matrix of Proposed-II in real vs. synthetic speech identification: (**a**) Training, (**b**) validation, and (**c**) test. The model exhibits fewer misclassifications and achieves 98.66% accuracy, outperforming Proposed-I.  
Figure 22: Loss and accuracy of (**a**) Proposed-I and (**b**) Proposed-II in real vs. synthetic speech identification. Both models demonstrate rapid convergence and low loss, confirming their efficiency in distinguishing between real and synthetic speech.
# Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset +
## 2 Background
### Intrusion Detection Systems  
An intrusion is a malicious activity that aims to compromise the confidentiality, integrity, and availability of network components in an attempt to disrupt the security policy of a network [11]. The National Institute of Standards and Technology (NIST) defines the intrusion detection process as \"the process of monitoring the events occurring in a computer system or network and analyzing them for signs of intrusions, defined as attempts to compromise the confidentiality, integrity, availability, or to bypass the security mechanisms of a computer or network\" [12]. An IDS is a tool that scans network traffic for any harmful activity or policy breaches. It is a system for monitoring network traffic for malicious activities and alerting network administrators to such abnormal activities [13]. IDSs achieve this by gathering data from several systems and network sources and analyzing those data for possible threats.  
Unlike firewalls, which are used at the perimeter of the network and play the role of gatekeeper by monitoring incoming network traffic and determining whether it can be allowed into the network or endpoint at all, IDSs monitor internal network traffic and mark suspicious and malicious activities. Consequently, an IDS can identify not only attacks that pass the firewall but also attacks that originate from within the network.
## 3 Application for Failure-Recall Data with DCLNN Method  
### Description of Failure-Recall Data  
The current maintenance activities of an escalator are mainly based on two modes: Periodical maintenance and failure-recall maintenance. The maintenance of different components in periodical maintenance is carried out in different cycles (for example, the four types of maintenance items for the escalator with a cycle of 4, 12, 24, and 52 weeks). The failure-recall maintenance aims at the problems discovered by the operator to perform inspection and repair. The failure-recall record can directly reflect the operational safety of the escalator to a certain extent. The failure-recall records of six escalators during 2012.10-2018.10 are investigated. The statistics of failure-recall reasons is shown in Figure 8. There are five main situations in which the failure is recorded, and the cause of component failure accounts for the most.  
The failure of the escalator is related to the failure of the components, the time of use, and the inherent performance degradation of the system. The collection and analysis of relevant data can provide scientific predictions for escalator failure. In addition, predicting the possible time of escalator failure and implementing maintenance in advance can reduce the chance of escalator failure and reduce casualties. The failure-recall record can reflect the failure occurrence rule and safety condition of an escalator. However, data in the failure-record have the characteristics of short samples, non-uniform samplings, and random interference, which brings difficulty in predicting the failure time of an escalator.  
In this paper, failure-recall records of six escalators with the same brand are collected. In the records, some details about every failure time are recorded and shown in Appendix A. Then, the failure time is processed into the corresponding failure interval days, and the failure time curves of the escalators are calculated by the accumulation idea. The processed failure time curve is shown in Figure 9.  
Figure 8: The statistics of failure-recall reasons.  
Figure 9: Failure time curve of six escalators.
## 6 A Simulation Study: Optimization of Micro-Algae Lipid Productivity  
Microalgae growth in a bioreactor can be described by the Droop model [37], which assumes that extracellular nutrient \(S\) is stored in a so-called internal quota \(Q\), before being used for biomass \(X\) growth. The Droop Model is usually the cornerstone of more elaborated models accounting for light-related phenomena [38].  
For the model under consideration, nutrient uptake \(\rho\) is described by a Michaelis-Menten type kinetics with internal quota limitation:  
\[\rho(S,Q)=\rho_{max}\frac{S}{S+K_{S}}(1-\frac{Q}{Q_{max}}) \tag{28}\]  
whereas the growth rate is modelled by Droop kinetics and photo-inhibition is incorporated through Haldane kinetics:  
\[\mu(Q,I)=\mu_{max}\bigg{(}1-\frac{Q_{min}}{Q}\bigg{)}\frac{I}{I+K_{SI}\,I+\frac {I^{2}}{K_{II}}} \tag{29}\]  
In these expressions, \(K_{S}\) is the half-saturation constant with respect to substrate \(S\), \(Q_{min}\) is the quota threshold below which no growth is possible, and \(Q_{max}\) is the maximum nitrogen quota. \(\rho_{max}\), \(\mu_{max}\), \(K_{sl}\) and \(K_{II}\) are, respectively, the maximum uptake and growth rates, the half-saturation and the inhibition constants with respect to incident light intensity.  
To keep the model simple, photo-incidimension is described by a first-order dynamics:  
\[\left\{\begin{array}{rl}\tau_{I}\frac{d\bar{I}}{d\bar{I}}&=I-\bar{I}\\ \mu(Q,\bar{I})&=\mu_{max}\Big{(}1-\frac{Q_{min}}{Q}\Big{)}\frac{I}{I+K_{sl}\,I +\frac{I^{2}}{K_{il}}}\end{array}\right. \tag{30}\]  
Mass balances in the chemostat lead to the following set of ordinary differential equations:  
\[\frac{dX}{dt} =\mu(Q,I)X-D\,X \tag{31a}\] \[\frac{dS}{dt} =-\rho(S,Q)X+D(S_{in}-S)\] (31b) \[\frac{dQ}{dt} =\rho(S,Q)-\mu(Q,I)Q \tag{31c}\]  
where \(S_{in}\) represents the concentration of inorganic substrate (nitrogen) in the inlet flow and D the dilution rate.  
The model parameters are partly taken from [39] and listed for convenience in Table 2.  
A classical performance index for continuous bioreactor is biomass productivity:  
\[P=D\,X \tag{32}\]  
However, this index does not reflect the lipid productivity. As lipid and carbohydrate production increases under nitrogen stress, and thus under internal quota limitation, the following performance index is introduced:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\mu_{max}\) & 5.1 d\({}^{-1}\) \\ \hline \(\rho_{max}\) & 0.0730 gN\(\cdot\)gC\({}^{-1}\cdot\)d\({}^{-1}\) \\ \hline \(K_{S}\) & 0.0012 gN\(\cdot\)m\({}^{-3}\) \\ \hline \(K_{sl}\) & 200 \(\mu\)E/m\({}^{2}\cdot\)s \\ \hline \(K_{il}\) & 220 \(\mu\)E/m\({}^{2}\cdot\)s \\ \hline \(Q_{min}\) & 0.05 gN\(\cdot\)gC\({}^{-1}\) \\ \hline \(Q_{max}\) & 0.15 gN\(\cdot\)gC\({}^{-1}\) \\ \hline \(\tau_{I}\) & 4 d \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model parameter values.  
\[P_{l}=\frac{D\,X}{Q}, \tag{33}\]  
which combines biomass productivity and internal quota depletion. The objective of ESC is to find in real-time and without prior knowledge about the model, with the inputs \((I,D)\) maximizing the performance index \(P_{l}\).
# FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
## 2 The NSBAS Processing Chain Used in the FLATSIM Service
### Stratified Atmospheric Correction  
The final steps are performed on the mosaicked interferograms. The atmospheric phase screen of interferograms can be divided into a pseudo stratified contribution, that is at first-order dependent upon elevation, and a more random contribution often named \"turbulent\". The dry and wet stratified atmospheric delays, that derive from the temporal variations of the layered atmospheric variables (pressure, P, temperature, T, humidity, q), can be easily predicted from global atmospheric models with the assumption of a mainly horizontally layered atmosphere [48; 49]. The turbulent contribution is difficult to model from independent atmospheric information as it requires to place the atmospheric patterns at the right place in space and time.  
In the FLATSIM service, the stratified atmospheric phase screen is computed in 8 \(\times\) 2-looks using the geopotential height of pressure levels, and T and q given for each of the 37 pressure levels by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 meteorological reanalysis [50]. The delay is first interpolated as a function of elevation with a very fine vertical spacing at regularly spaced ERA-5 nodes (every 0.25 degree). Then, for each pixel in radar coordinate, characterized by its elevation, latitude, longitude, and look angle given in separate grids, we compute the delay by bilinear interpolation of the surrounding four ERA-5 nodes. This procedure is very efficient and easily parallelized.  
Figure 3: Network-inversion of spectral diversity (**a**) and overlap between sub-swath (**b**). Error bars correspond to the averaged network discrepancy per acquisition. These two figures are part of the delivered products.  
In addition to an elevation dependent contribution, the stratified APS also includes large-scale phase delay gradients along range and azimuth. The correction before unwrapping (e.g., [51]) strongly reduces fringe rates in areas of high relief (Figure 4). The ERA-5 model, given with a temporal sampling of one hour, is found more accurate than other global atmospheric models (e.g., the high resolution global analysis of ECMWF, given every six hours). However, the ERA-5 modeled surface elevation is a smooth surface that stands above deeply incised valleys. As a result, the phase delay as a function elevation below the smooth surface is an extrapolation that is not very well constrained.
## 4 Experiments and Results
### Experimental Results and Comparisons
(For fair comparison, as introduced in Section 3, we have improved on the basis of SSD and only improved the network structure and the size of the default boxes.). In CISPNet, we use the CISP module to make the feature map understand more contextual information from different scales, and use the FFM module to fuse the features of the shallower layers to enhance the semantic information of the shallower layers. It solves the problem that SSD lacks understanding of context information and semantic information. From Table 2 and Figure 7, the proposed CISPNet method outperforms the other methods. Specifically, our CISPNet obtained 6.37%, 6.83%, and 5.78% performance gains in terms of mean AP over the aircraft, oiltanker, and ship, compared with the SSD model, respectively. Our CISPNet performs much better than the original SSD and other detectors. Specifically, our network can achieve 80.34% mAP at the speed of 50.7 FPS.
## 2 Proposed Methodology
### One-Dimensional Convolutional Neural Network  
Over recent years, there has been significant research interest in the Two-Dimensional Convolutional Neural Network (2D CNN) model-based fault diagnosis because it has a remarkable ability to automatically extract features from the images [57; 58]. However, 2D CNN requires various data preprocessing techniques, especially when used with vibration signals. For this application, the signal has to be converted into spectral images, making the fault diagnosis procedure more complex and laborious. Also, 2D CNN takes a longer time for training and testing, which may not be suitable for real-time fault diagnosis. For these reasons, this research employs the One-Dimensional Convolutional Neural Network (1D CNN), using time-series vibration signals directly as input for fault diagnosis.  
The 1D CNN can directly extract features from time-series signals without data preprocessing. Contrary to 2D CNN, convolution and pooling size can extract one-dimensional features from a time-series signal. Also, the training speed of the 1D CNN is much quicker than that of the 2D CNN because the input data size for the 1D CNN is smaller, and 1D CNN does not need additional preprocessing techniques since time-series data are directly used as input. This makes the 1D CNN more resource-efficient, leading to cost savings without decreasing the fault diagnosis performance. Owing to these advantages,1D CNN is continuously applied for fault diagnosis in various systems [59; 60]. Details of the structures of the 1D CNN used in this research are later discussed in Section 4.2.
# Global Power Dynamics in the Contemporary Space System
## 6 Systems Structure--New Balance of Power in Space
### Potential Scenarios  
At this moment in the century, as the first quarter draws to a close, predominant academic and scientific interests are focused on predicting or anticipating a future scenario of power relations in space systems. At this point, three potential scenarios of repolarization among states can be considered, as follows: (i) unipolar; (ii) bipolar; and (iii) multipolar.