esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experiment and Result Analysis
### Analysis of Feature Extraction Effectiveness
#### 6.3.1 Analysis of Facial Geometric Feature Point Extraction  
In the experiment, the Landmark method is utilized for extracting facial geometric features. Initially, the Lucas-Kanade algorithm is employed to track facial landmark points, followed by noise filtering using a Kalman filter. Research demonstrates the effectiveness of the Landmark method in extracting facial geometric landmarks for deepfake face detection. Figure 15 illustrates a schematic of facial landmark tracking on selected forged face images.
# Cost-Sensitive Variational Autoencoding Classifier for Imbalanced Data Classification
## 4 Experimental Setup and Analysis of Results
### Experimental Evaluation Criteria  
Appropriate performance measures need to be selected to evaluate the algorithm model correctly. The error rate and accuracy are commonly used evaluation criteria for classification problems. The error rate refers to the proportion of misclassified samples to the total number of samples, whereas accuracy refers to the proportion of correctly classified samples to the total number of samples. The error rate and accuracy can reflect the overall classification performance of the dataset, but cannot accurately reflect the classification performance of the imbalanced dataset. For imbalanced datasets, we sometimes pay more attention to the ability of the classifier to identify important samples (minority class samples), so we need measures other than the error rate and accuracy. In this experiment, the evaluation criteria Recall, Precision, and F1-score of each category in the dataset were evaluated to comprehensively analyze the classification performance of the imbalanced dataset. Recall represents the proportion of the predicted positive examples among the  
Figure 5: Structure of the cost-sensitive variational autoencoding classifier. Based on the variational autoencoding classifier, a cost-sensitive module is added to adapt to the imbalanced data. \(X\) represents the input data, \(Y\) represents the label of the input data, \(\hat{X}\) represents the reconstruction of the input data, and \(z\) represents hidden variable.  
real positive examples, whereas Precision represents the proportion of the real positive examples among the samples judged as positive examples. The calculation formulas for the Recall and Precision rates are given as Equations (10) and (11), respectively, where _TP_ represents the true positive, _FP_ the false positive, _TN_ the true negative, and _FN_ the false negative samples.  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
\[Precision=\frac{TP}{TP+FP} \tag{11}\]  
Precision and Recall are usually a pair of relative contradictory measures. When the Precision ratio is high, the Recall ratio tends to be low and vice versa. To comprehensively consider the two, we recorded the F1-score of all types of categories. The calculation method for the F1-score is given by Equation (12).  
\[F1-score=\frac{2*Precision*Recall}{Precision+Recall} \tag{12}\]
## 3 Performance in Selective Fading Conditions  
A comparative analysis of the QCPSK and FM-CSK systems presented in the previous section aims to evaluate their consistency for usage in WSNs. The mentioned application requires resilience to the phenomena that appear in wireless communications. The principal feature that distinguishes such systems is inter-symbol interference caused by multipath propagation; therefore, this section is mainly devoted to performance analysis in selective fading conditions. A comprehensive description of the test setup is given at the beginning of the section. Since the setup is relevant to the investigation of noise immunity analysis, the performance of the systems in additive white Gaussian noise (AWGN) is also analyzed in this section, which ends with the noise immunity study in selective fading conditions.
# GNSS Signal Acquisition Algorithm Based on Two-Stage Compression of Code-Frequency Domain
## Abbreviations  
The following abbreviations are used in this manuscript:  
GNSSGlobal Navigation Satellite System  
CSCompressed sensing  
FFTFast Fourier Transform  
IFFTInverse Fast Fourier Transform  
RIPRestricted Isometry Property  
CFCCode-frequency compression  
CCCode-compression  
FCRFrequency Compression Ratio  
SNRSignal-to-Noise Ratio
## 1 Introduction
Meanwhile, the power transfer quality and efficiency are affected by changing the circuit parameters and operating frequency. For the carrier modulation mode, the baseband signal spectrum is broadened by sinusoidal carrier modulation techniques. It facilitates the transmission of signals on the channel. Literature [33] proposed a SWIPT system, based on FD, and provided the circuit model and measurement results. Among them, the carrier frequency of power and information is 22.4 kHz and 1.67 MH, respectively. In order to reduce the influence of the information transfer frequency on the power transfer efficiency, the information transfer frequency is at least an order of magnitude higher than the power transfer frequency. In [34], information and power are simultaneously transmitted in a single inductive link by using different carrier frequencies. Taking forward transmission as an example, the modulated high-frequency data carrier is injected into the primary side of the system. Then, the superimposed signal, composed of the power carrier and the information carrier is transmitted to the secondary side, and finally the data carrier in the superimposed signal is extracted by the demodulation module on the secondary side. This method is essentially a frequency division multiplexing (FDM) technology. Literature [35] proposed a full-duplex communication by modulating dual carrier using quadrature phase shift keying. In [36], frequency shift keying is used to achieve full-duplex communication through partially coupled coils. However, complex modulation and demodulation methods may limit their application space. Although this transmission method has many advantages, there are also challenges. In full-duplex operation, the forward transmission signal and the reverse transmission signal circulate in the circuit simultaneously, and the signal is injected into the power loop, that is, there are three frequencies in the system at the same time, so there are inevitable interferences on the three coupling channels, such as the forward/reverse information transmission channel and the power transmission channel. A huge challenge to system design is posed when these complex coupling relationships are intertwined. Taking into account the complexity of the coupling relationship in the full-duplex communication SWIPT system with a shared coupling channel, this paper proposes an analysis method based on the transmission channel, and studies the crosstalk between the power channel and the information channel, and between the forward and reverse transfer of information. A design method of full-duplex communication SWIPT system based on shared coupling channels is provided. Finally, the effectiveness of the proposed SWIPT method is verified by the experiment.
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 4 Experimental Evaluation and Analysis
### Threshold-Based Adjustment Function for Weighting Factor Stability
#### 4.3.3 Performance Evaluation of IMIS
In stark contrast to conventional mutual information (MI)-based feature selection techniques, which operate within a static evaluation paradigm, IMIS employs a dynamic and adaptive reassessment of feature relevance based on the influx of new data. This dynamic adaptation guarantees the selection of only the most pertinent features, resulting in enhanced classification performance against the ever-evolving landscape of ransomware threats. Figure 14: Classification performance (F1-score) comparison of IMIS, RCGU, EMRMR, and MIFS across feature set sizes (5–50). Figure 13: DR of IMIS vs. RCGU, EMRMR, MIFS, and JMI across 5 to 50 features to evaluate its effectiveness in identifying threats [72]. As shown in Figures 11-14, F1-score consistently served as a more balanced and robust metric across varying feature sizes. This was particularly important given the dataset's class imbalance. That ensures a fair assessment of model performance on both benign and ransomware samples. Table 5 presents the computational efficiency of IMIS compared to other techniques based on Python profiling. IMIS demonstrates significant performance improvements in terms of execution time, total runtime, and training duration. IMIS exhibits the shortest per-call execution time (0.01 s) and lowest total runtime (3.5 min), demonstrating a substantial performance advantage over other methods. This reduction in computational overhead is attributed to IMIS's selective feature update mechanism, which circumvents superfluous re-computation by dynamically reassessing only pertinent features upon the arrival of new data. Table 6 presents the top 10 API call features identified by IMIS, categorized by functionality. These features correspond to critical ransomware behaviors, including encryption, file manipulation, and network communication. These findings underscore IMIS's capability to prioritize ransomware-specific features, thereby enhancing detection accuracy by focusing on API calls most frequently exploited in ransomware attacks.
# Ranking of Electricity Accumulation Possibilities: Multicriteria Analysis
## 2 Comparative Description of Selected Energy Storage Technologies
### Lithium-Ion Battery  
The energy storage technology of lithium-ion batteries seems to be particularly well-known, given that they are used in more than 50% of the market for small, portable electrical devices, especially mobile phones, because they can provide twice the operating time of a conventional battery. Thus, the response time is much smaller, they charge faster, can last longer, and have the highest efficiency [11]. Lithium-ion batteries are based on electrochemical charge/discharge reactions that occur between a positive electrode containing lithium metal oxide and a negative electrode composed of carbon material. In recent years, the large-scale integration of lithium-ion batteries has been accelerating, particularly driven by the development of automotive and energy storage innovations. It is possible to conclude that lithium-ion batteries can unequivocally be considered the main, potentially the most widely used storage technology of the future. Technological development further increases energy density, operation, and cycle times. Additionally, the cost of the system will continue to decrease, which is currently the largest part of the capital expenditure of lead-acid, flow, or sodium-sulphur batteries. This can be improved by increasing the industrial capacity and promoting mass production. Already, lithium-ion batteries are used for self-consumption in residential and commercial buildings, which are distributed for emergency support and frequency regulation, respectively, as well as for the integration of large renewable energy equipment into power systems [12]. Recycling processes and equipment are also being introduced, which ensure the recycling efficiency is already well over 50% [13].
# Working State of ECC Link Slabs Used in Continuous Bridge Decks
## 5 Characterization of the ECC Link Slabs' Working State
### Working State Modes Based on Strains and Displacements
It is explicitly shown in Figure 11c that the two mutations take place at those measuring points close to the mid location of Type II ECC link slab, such as G1 and G2. Most of the strains develop steadily and change with small increments. The CFT grid alone is less sensitive to the working state leaps originating from the evolution of ECC material properties. Besides, the overall strain behavior of reinforcing steels encounters the mutation at M1 though a few measured strains are inconsistent with it as shown in Figure 11d. The other mutation feature of S\({}_{\text{steel}}\)-based working mode is distinctly observed at M2 and the mutated forms can be particularly expressed by the change of curve profile and incremental amplitude. As for the working state mode based on D\({}_{\text{m}}\) as shown in Figure 12, an evident state leap can be found at M2. However, the mutation characteristic at M1 is not clearly shown in the D\({}_{\text{m}}\)-\(L_{j}\) curve, implying that the plastic deformation at M1 cannot match the influence of initial structural failure on the macroscopic displacement. In general, the working state modes focusing on the local strains of components and the structural deflection can be employed as a valuable demonstration of the evolving structural working state. It could be reasonable to define M1 as the yielding point signifying that the working state mode of ECC link slab begins to change from elastic state into elastic-plastic state, and M2 as the initial structural failure point after which the structural working performance is badly weakened. Figure 12: D\({}_{\text{m}}\)-based working state mode.
## 1 Introduction
In this paper, we propose a method for detecting this degradation in stability, which can then be applied to detecting PPD based on observing the progression of the tendency of the sensorimotor loop to develop instabilities before the instability manifests itself as symptoms. Here, we do not concern ourselves with the source or mechanism of the instability and develop a method that could be utilized for any instability in general. This proposed method is further fleshed out through a numerical study. We employ a simple pendulum with a PID controller as a conceptual representation of the sensorimotor loop to simulate motor control tasks in healthy and PPD individuals for this analysis. We alsopresent representative human subject data collected through such an example clinical test, thereby demonstrating the feasibility of the proposed method. The main contribution of this study is to propose a method to track the tendency of a feedback control system to transition to an unstable state and using machine learning algorithms for its robust detection. Specifically, we employ a simple simulation example consisting of a simple pendulum with a PID controller as a conceptual representation for both healthy and PPD individuals with a noise variance of 0.01 and a noise variance of 0.1. To show the efficacy of the proposed method, we compare our results of the SVM algorithm for machine learning with two sets of proposed features, C1 and C2, with the results of the LSVM algorithm for machine learning. The results of SVM with feature C2 are comparable with the results of LSVM for a noise variance of 0.01. These results are almost unaffected by a noise variance of 0.1. All of the methods showed a high sensitivity of above 96% and specificity of above 98% on a training data set. In addition, they perform very well with the synthetic validation data set with a sensitivity above 95% and specificity above 98%. Thus, our proposed method is evaluated on a synthetic data set, and the machine learning results show promise and potential for use in detecting PPD through an early diagnostic device. In addition, we have shown the feasibility of the proposed method to estimate a pole from a clinical test that may be used as a regular health checkup to track the progression of the poles and hence the probability of developing [PERSON]'s disease in near future. The conceptual framework of the proposed method is described in detail in Section 2.2, the simulation example used for our numerical study in Section 2.3, and potential methods to classify whether particular data are from healthy individuals or an individual with PPD in Sections 2.3.1 and 2.3.2. The results of the numerical study are described in Section 3. An example task and associated data collected from human subjects are presented in Section 3.2. The longitudinal study needed to validate the method will be taken up as future work. Finally, we discuss further steps needed to validate the proposed method in Section 4 and close with the conclusion in Section 5.
# TR-GPT-CF: A Topic Refinement Method Using GPT and Coherence Filtering
## 3 Results and Discussion
### Limitations  
This work has several limitations, which provide important directions for future research. First, selecting an appropriate Z-score threshold for detecting misaligned words was challenging. An improperly chosen threshold may result in correctly associated topic words being erroneously classified as misaligned. Second, the effectiveness of word replacement depends on GPT-generated word suggestions, which are influenced by the prompt design used to query the model. Although our method guarantees contextual relevance, further optimizations in prompt engineering for topic coherence could enhance the accuracy of word replacement. Third, the evaluation in this study primarily relies on the _C_-_v_ coherence metric and manual inspection to assess the interpretability of the topics. Incorporating additional coherence metrics may yield a more thorough evaluation. Fourth, this study leverages a relatively small dataset and applies sampling to larger datasets, such as Yahoo Answers. Consequently, there is a trade-off in computational cost when scaling to larger datasets. The primary trade-offs involve an increased processing time due to Z-score computation, GPT-based replacements, and embedding similarity calculations.
# Satellite SAR Data-based Sea Ice Classification: An Overview
## 4 SAR Data-based Ice Classification
The new iteration of sea ice concentration algorithm based on Neural Networks (NN) utilizing S1 double-polarization data was developed for the Baltic Sea. Backscattering coefficients, several texture features, and spectral gradient and polarization ratios extracted from the Advanced Microwave Scanning Radiometer-2 (AMSR2) data are used as input. The sea ice concentration estimates are computed for each SAR segment after a SAR segmentation, i.e., the NN inputs are segment-wise median values [35]. The fully automated sea ice concentration algorithm produces a daily mosaic covering the Baltic Sea, and it is planned to apply it over the Barents and Kara Seas. MAP-Guided Sea Ice Classification (MAGIC) system developed at the University of Waterloo [36; 37] has been extended for automated ice-water discrimination on RS-2 dual-polarization images based on a combination of an Iterative Region Growing using Semantics (IRGS) classification and a pixel-based Support Vector Machine (SVM) approach. IRGS, developed at the University of Waterloo in the System Design Engineering department, performs the segmentation of the polygons via a watershed algorithm [38]. SVM classifier uses backscatter and SAR texture features to assign ice-water labels after IRGS segmentation. The MAGIC system has been tested on the groundtruthed RS-2 dual polarization SAR scenes of the Beaufort Sea containing a variety of ice types and water across melt, summer, and freeze-up periods. The average classification accuracy was found to be up to 96.47% [37; 39; 40]. MAGIC has been developed as a modular system of an operational tool to be inserted into CIS operations. The Norwegian Meteorological Institute developed an automated system for sea ice classification based on cross-polarization SAR data implemented at the Norwegian Ice Service [41]. Pre-processed S1 SAR images are used to automatically reveal OW and sea ice concentration [42]. The Norwegian Ice Service currently provides the automatically generated daily ice charts for the Arctic in experimental test regime. The operational daily ice maps continue to be provided by sea ice experts using RS-2, S1 SAR and other data.
## 2 Experimental Configuration
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **KNL** & **SKL** & **CLS** \\ \hline Manufacturer and model & Intel Xeon Phi 7250 & Intel Xeon 6148 & Intel Xeon Gold 6140 \\  & KnightsLanding & Skylake & Intel Xeon Gold 6140 \\ \hline Number of nodes & 8305 & 132 & 16 \\ \hline CPU \(\times\) cores per node & \(1\times 68=68\) & \(2\times 20=40\) & \(2\times 18=36\) \\ \hline Clock speed & **1.4 GHz** & **2.4 GHz** & **2.3 GHz** \\ \hline Main Memory & 16 GB (MCDRAM), 96 GB & 192 GB & 252 GB \\ \hline File System & Lustre & Lustre & Lustre \\ \hline Compiler & Intel v17.0.5 & Intel v17.0.5 & Intel v19.0.4 \\  & Intel v19.0.4 & Intel v19.0.4 & Intel 18.0.4 \\ \hline MPI Library & openmpi v3.1.0 & openmpi v3.1.0 & openmpi v3.1.0 \\  & impi v19.0.4 & impi v19.0.4 & openmpi v3.1.1 \\ \hline NetCDF Library & netcdf v4.6.1 & netcdf v4.6.1 & netcdf v4.4.4 \\ \hline \multirow{4}{*}{Compiler Options} & -fp-model consistent & -fp-model consistent & -fp-model consistent \\  & -ip–O3–no-prec-div & -iq–O3–no-prec–div & -ip–O3–no–prec–div \\  & -static-intel & -static-intel & -static -intel \\  & **xMIC-AVX512** & **-XCORE-AVX512** & **-XCORE-AVX512** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summarized specifications for Nurion (Knights Landing (KNL) and Skylake (SKL)) and a general cluster system (CLS). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Ideal Experiments** & **Real Experiments** \\  & **(Case: Grid Resolution)** & **NWP, Tyhoon Soulik and Cimaron \\ \hline \multirow{2}{*}{WRF} & Large Eddy Simulation & NWP, Tyhoon Soulik and Cimaron \\  & : (3200 \(\times\) 3200) grids \(\times\) 10 layers & : (2048 \(\times\) 2048) grids \(\times\) 33 layers \\ \hline \multirow{2}{*}{ROMS} & Benchmark & NWP, Tyhoon Soulik and Cimaron \\  & : (8192 \(\times\) 1024) grids \(\times\) 30 layers & : (2048 \(\times\) 2048) grids \(\times\) 33 layers \\ \hline \multirow{2}{*}{FVCOM} & Benchmark & NWP, Tide case \\  & : 10,398,925 nodes \(\times\) 31 layers & : 8,369,391 nodes \(\times\) 10 layers \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental model configurations.
# Structural Methodologies for Distributed Fault Detection and Isolation
## 5 Case Study
Subsystems 2 and 3 are first order connected, and their shared variables are \(V_{a2}\cap V_{a3}=\{v_{dc},i_{inv}\}\). Subsystems 1 and 3 are second order connected because they have no shared variable, and they are both first order connected to the second subsystem. _Subsystem 4 (AC load)_: Like the DC load, the AC load is modeled as an electric resistance, \(R_{ac}\). The set of equations for this subsystem is:  
\[ \begin{split} e_{a20}:v_{4}&=v_{rms}\\ e_{a21}:i_{ac}&=f_{ac}\frac{v_{4}}{R_{ac}}\\ e_{a22}:i_{rms}&=\frac{1}{\sqrt{2}}|\sqrt{2}i_{ fan}(cos\phi+jsin\phi)+\sqrt{2}i_{ac}|\\ e_{a23}:y_{IT267}&=i_{rms},\end{split}  \tag{16}\]  
where \(V_{a4}=\{v_{4},v_{rms},i_{ac},i_{rms},i_{fan}\}\) are unknown variables, \(M_{a4}=\{y_{IT267}\}\) is the measurement, \(F_{a4}=\{f_{ac}\}\) is a fault, and \(\{R_{ac},\phi\}\) are parameters of the subsystem. Subsystems 3 and 4 are first order connected, and their shared variable is \(V_{a3}\cap V_{a4}=\{v_{rms}\}\). _Subsystem 5 (electric fan)_: The fan rotational speed, \(\omega\), is a function of the fan current, \(i_{fan}\). The last subsystem equations are:  
\[ \begin{split} e_{a24}:i_{fan}&=f_{fan}\frac{v_{4}}{R _{fan}}\\ e_{a25}:\dot{\omega}&=\frac{1}{f_{fan}}(\frac{i_{ fan}}{B_{fan}}-\omega)\\ e_{a26}:\omega&=\int\dot{\omega}dt\\ e_{a27}:y_{ST516}&=\omega,\end{split}  \tag{17}\]  
where \(V_{a5}=\{i_{fan},v_{4},\dot{\omega},\omega,i_{fan}\}\) are unknown variables, \(M_{a5}=\{y_{ST516}\}\) is a measurement, and \(F_{a5}=\{f_{fan}\}\) is a fault of the subsystem. Fan electrical resistance, \(R_{fan}\), fan inertial, \(f_{fan}\), and fan mechanical resistance, \(B_{fan}\), are the parameters. Subsystems 4 and 5 are first order connected, and \(V_{a4}\cap V_{a5}=\{i_{fan},v_{4}\}\) is the set of shared variables among these subsystems.