esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results
### Model Learning Ability Analysis  
**Setting:** In this section, we assess the learning capabilities of the proposed model using two frequently employed datasets, namely, CVC-ClinicDB and Kvasir. As illustrated in Table 1, CVC-ClinicDB comprises \(612\) open images extracted from \(31\) colonoscopy images, while \(1000\) polyp images were selected from the Kvasir dataset. For our experimentation, \(90\%\) of these images were allocated for training purposes, with the remaining \(10\%\) reserved for the test set.  
**Results:** As depicted in Tables 2 and 3, the BMFA-Net model showcased its impressive learning capabilities. On the Kvasir dataset, the BMFA-Net model exhibited a mean Dice score that was \(2.5\%\) greater than the classical PraNet model. Meanwhile, on the CVC-ClinicDB dataset, the BMFA-Net model demonstrated a remarkable \(0.1\%\) advantage over the best performing CFA-Net model, surpassing it. Moreover, it also exceeded the performance of PraNet by \(3.5\%\) in terms of the mean Dice score. These comparative results underscore the continued effectiveness of the BMFA-Net model design introduced in this paper. These experimental findings collectively demonstrate the outstanding learning capabilities of BMFA-Net, thereby affirming the viability and effectiveness of its design.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**CVC-ClinicDB**} & \multicolumn{2}{c}{**Kvasir**} & \multicolumn{2}{c}{**CVC-ColonDB**} & \multicolumn{2}{c}{**ETIS-LaribPolypDB**} & \multicolumn{2}{c}{**CVC-300**} \\  & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** \\ \hline CaraNet & 0.921 & 0.876 & 0.913 & 0.859 & 0.775 & 0.700 & 0.740 & 0.660 & 0.902 & 0.836 \\ SPIEM/722 & & & & & & & & & & \\ BoxPolyp & 0.904 & 0.849 & 0.910 & 0.857 & 0.820 & 0.741 & **0.829** & **0.742** & 0.903 & 0.835 \\ MICCAI22 & & & & & & & & & & \\ FFPA & 0.931 & 0.885 & **0.928** & **0.880** & 0.837 & 0.759 & 0.822 & 0.746 & 0.905 & 0.839 \\ MICCAI723 & & & & & & & & & & \\ CFA-Net & 0.933 & 0.883 & 0.915 & 0.861 & 0.743 & 0.665 & 0.732 & 0.655 & 0.893 & 0.827 \\ PR’23 & & & & & & & & & & \\ Ours & **0.934** & **0.887** & 0.923 & 0.875 & **0.841** & **0.764** & 0.825 & 0.744 & **0.922** & **0.843** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Segmentation results of the recent models on five datasets.
## 6 Experiment and Result Analysis
### Experimental Visualization and Analysis  
The algorithm proposed in this study underwent preprocessing on four distinct subsets of FaceForensics++ before being fed into the FG-TEFusionNet network for training. Upon completion of training, it was subsequently tested on the Deepfakes, Face2 Face, FaceSwap, and NeuralTextures datasets. The experimental results are visualized and analyzed using the AUC evaluation metric, with ROC curves for the AUC values obtained across the four datasets shown in Figures 21 and 22.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **FLOPs (M)** & **Parameters (M/106)** &
\begin{tabular}{c} **Inference Time** \\ **(ms/frame)** \\ \end{tabular} \\ \hline XceptionNet [13] & 22.5 & 20.8 & 45.2 \\ EfficientNetB4 [18] & 18.7 & 19.3 & 38.9 \\ TEFusionNet (Ours) & 19.5 & 18.6 & 28.7 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Comparison of the complexity and inference time of the deepfake detection models between the method proposed in this paper and the baseline models.  
Figures 21 and 22 depict ROC curves obtained from testing on four categories of manipulated face datasets, evaluating the detection performance of our FG-TEFusionNet. The ROC-AUC values were used to assess our model's performance across these datasets. These four AUC curves clearly demonstrate the outstanding performance of the model on different experimental datasets. The model achieved AUC values of 99.87%, 99.42%, 99.29%, and 99.78%, respectively, indicating its robust capability in detecting deepfake faces and reinforcing its overall superiority in the field. The results highlight the model's optimal performance on the Deepfakes and NeuralTextures datasets, with good detection results on Face2 Face and FaceSwap as well. This further validates the effectiveness and superiority of our approach.  
Additionally, we utilized the dataset for predictions and visualized the results as shown in Figure 23. This figure displays precise classification results when provided with images of both real and fake faces, accurately distinguishing between the two types of facial images.  
As shown in Figure 23, both real and manipulated fake face videos were separately inputted for classification prediction, accurately discerning between genuine and fake facial images. The results displayed in the figure present two sets of predictions, one containing genuine faces and the other fake faces.  
Figure 21: The ROC curves were obtained from testing on the Deepfakes (**a**) and Face2 Face (**b**) datasets.  
Figure 22: The ROC curves were obtained from testing on the FaceSwap (**a**) and NeuralTextures (**b**) datasets.
## 1 Introduction
### Related Works
While effective for web attacks, this model is limited in handling high-dimensional IoT traffic and lacks deep contextual modeling found in transformer-based or generative architectures. In [27], the authors presented a stacking ensemble ML-based IDS using the ToN-IoT dataset, where ensemble learning led to high classification performance with MCC values over 0.99 in both binary and multi-class modes. Though promising, the framework is conventional in its reliance on traditional ML models and does not incorporate synthetic augmentation or attention-based learning, which limits its flexibility in handling dynamic IoT environments. In [28], the authors introduced the CIC-IoT-2023 dataset, a real-time benchmark comprising 33 attack types across seven categories, executed over a topology of 105 IoT devices. This dataset addresses the limitations of previous datasets by providing realistic traffic patterns and complex attack scenarios. It forms the foundation for our work and several others, offering a robust testbed for evaluating IDS performance under real-world IoT conditions. Finally, in [29], the authors applied several DL models, including Transformer, to the CIC-IoT-2023 dataset for multi-class intrusion detection. Their results show that while the Transformer performs well in multiclass tasks (up to 99.40% accuracy), it lags behind in binary classification compared to hybrid models. Unlike our Transformer-GAN approach, their work did not leverage adversarial learning or evolutionary optimization to address class imbalance or model generalization. A summary of the key characteristics of the reviewed studies is presented in Table 1, highlighting differences in dataset usage, learning models, optimization techniques, and performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Dataset** & **Classification** & **Method/Model** & **Accuracy** \\ \hline
[20] & CIC-IoT-2023 & Binary & Federated DNN & 99.00\% \\ \hline
[21] & CSE-CIC-IDS2018 & Binary/Multiclass & CNN, LSTM & \textgreater{}98.00\% \\ \hline
[22] & ToN-IoT & Multiclass & Transformer & 87.50\% \\ \hline
[23] & CSE-CIC-IDS2018, KDD99 & Binary/Multiclass & TCN + U-Net & \text
## Appendix A Singular Value Decomposition  
The projection function has to be linear and is based on singular value decomposition. Let us denote real matrix \(A\) presented in Formula (A1), where \(U\) is an orthogonal matrix of size \(M\times M\), \(V\) is an orthogonal matrix of size \(N\times N\), \(V^{T}\) is a transposition of matrix \(V\) and \(\Sigma\) is a diagonal matrix of size \(M\times N\) containing singular values.  
\[A=U\Sigma V^{T}.\] (A1)  
Columns of matrix \(U\) and \(V\) are called, accordingly, left and right singular vectors of matrix \(A\). Elements \(\sigma\) of matrix \(\Sigma\) are sorted in non-increasing order.  
The calculation of singular value decomposition consists of two steps: computation of \(AA^{T}\) and \(A^{T}A\). Firstly, matrix \(A_{\lambda}\) is computed on the basis of Formula (A2), where \(\lambda\) is unknown and \(I\) is an identity matrix.  
\[A_{\lambda}=AA^{T}-\lambda I.\] (A2)  
Afterwards, the determinant of a matrix \(A_{\lambda}\) is calculated, for example, using Gaussian elimination. The obtained determinant is a characteristic equation of the matrix from which roots, denoted by \(\lambda_{1},\lambda_{2},\ldots\), are calculated. Singular vectors are computed by solving Equation (A3), where \(X\) is the expected vector.  
\[A_{\lambda}X=0.\] (A3)  
Singular vectors of matrix \(A^{T}A\) are columns of matrix \(V\) and singular vectors of matrix \(AA^{T}\) are columns of matrix \(U\). Singular values of \(\Sigma\) have squared roots of singular values of \(AA^{T}\) and \(A^{T}A\).
## 5 Conclusions  
In this paper, we introduced MVAN for the visual dialog task. MVAN could effectively determine the semantic intent of the current question and capture question-relevant information through complementary modules and sequential alignment processes. We used VisDial v1.0 to empirically evaluate our model, and as a result, our model outperformed existing state-of-the-art models. Moreover, we not only suggest plausible factors affecting a trade-off relationship of the evaluation metrics, but we enhanced the interpretability of multi-level attention through detailed visualization. As the main focus of our paper is on encoders, it limits in an in-depth exploration of decoders. In future, we plan to adopt the latest generative model for building decoder and incorporate vision-language pre-trained models [18; 20] if the performance of MVAN can be further improved. In addition, we will study an efficient ranking method to improve performance both in NDCG and non-NDCG metrics.  
Conceptualization, S.P. and T.W.; data curation, S.P. and T.W.; formal analysis, T.W. and Y.Y.; funding acquisition/project administration/supervision, H.L.; investigation, S.P.; methodology/software, S.P. and T.W.; visualization, S.P.; review and editing, Y.Y.; original draft, S.P. and T.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2018-0-01405) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation), Institute for Information & communications Technology Planning & Evaluation (IITP), grant funded by the Korean government (MSIT) (No. 2020-0-00368, A Neural-Symbolic Model for Knowledge Acquisition and Inference Techniques) and MSIT(Ministry of Science and ICT), Korea, under the ICT Creative Consilience program(IITP-2021-2020-0-01819) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation).  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. The data can be found here: VisDial v1.0: [[https://visualdialog.org/data](https://visualdialog.org/data)]([https://visualdialog.org/data](https://visualdialog.org/data)) (accessed on 26 March 2021).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 6: Error analysis on the VisDial v1.0 validation set. We analyze the examples for which the model scored 0 with respect to the R@10 metric. The Errors are categorized into (1) subjective judgment, (2) Ambiguous questions, and (3) Wrong multimodal alignment. (**a,b**) belong to the first, (**c**) to the second, and (**d**) to the third, respectively.
# Behavioral Modeling of Memristor-Based Rectifier Bridge
## 3 Forming the Sets of Input and Output Signals of the Memristor-Based Rectifier Bridge  
The circuit of the memristor-based rectifier bridge simulated in LTspice is shown in Figure 2. A sinusoidal voltage source with variable amplitude A and frequency f acts as the excitation (the input signal). A simple 1 k\(\Omega\) resistor acts as a load. The voltage of the resistive load is the output signal. At a positive half period of the sinusoidal input voltage, the memristance of the two P-xsv-N memristors (see Figure 2) is decreasing, while the memristance of the two N-xsv-P memristors is increasing until saturation is reached. At a negative half period of the input voltage the process is reversed. During one period there is a change of processes between both memristances. Thus, in the ideal case the output voltage is only positive, and this pulsed voltage has double the frequency of the input signal. In the real case there are the temporary short negative voltage-time areas in the output signal, due to the time shift in the state variable change. The circuit comprises memristors described by the Yakopcic model. The parameters of the Yakopcic model are represented in LTspice and have the following values: \(a_{1}=\ 0.076\ \mathrm{A}\), \(a_{2}=\ 0.06\ \mathrm{A}\), \(b=\ 3\ \mathrm{V}^{-1}\), \(x_{0}=\ 0.001\), \(x_{\mathrm{p}}=0.15\), \(x_{\mathrm{n}}=0.25\), \(A_{\mathrm{p}}=0.1\), \(A_{\mathrm{n}}{=}10\), \(V_{\mathrm{p}}=0.9\ \mathrm{V}\), \(V_{\mathrm{n}}=0.2\ \mathrm{V}\), \(\alpha_{\mathrm{p}}=1\), \(\alpha_{\mathrm{n}}=4\), \(\eta=1\)[54; 55]. The equations of the memristor model are implemented in a sub-circuit called memristor_yakopcic.sub. The Yakopcic model will be explained in detail below. The directives (.param and.tran) seen in Figure 2 are only used as an example.  
For the investigation of the rectifier, we have set the input signal parameters to the amplitude/frequency pairs (12 V/1 Hz), (13 V/2 Hz), (14 V/3 Hz), (15 V/4 Hz) and (16 V/5 Hz). The time window for simulation was set depending on the frequency and the number of signal periods \(N_{\rm p}\). We have chosen \(N_{\rm p}=~{}3\) in order to let startup transients settle. The LIspice output signal does not have equally spaced timestamps, as the program adjusts the time steps automatically dependent on the temporal derivative of the signal. Only the final period of the output voltage was kept for post-processing. For interpolation we used \(N_{\rm s}=~{}2001\) equally spaced samples. As a final step, all waveforms were normalized to the same angular frequency \(\omega~{}=~{}6\pi\) rad. By this the different maxima and minima are aligned.
# Detecting Deception from Gaze and Speech Using a Multimodal Attention LSTM-Based Framework
## 4 Discussion
#### Limitations and Future Research  
The automatic detection of deception is a very challenging task for several reasons. Firstly, it is inherently a hard task even for humans, because deceptive behavior is often subtle as liars try to hide that they are not telling the truth. Secondly, there is not a general consensus about what are the physical or behavioral characteristics that must be observed for detecting if a person is lying, and besides, the way people deceive varies across different subjects. Thirdly, there is a lack of training data due to the difficulty of collecting databases in realistic conditions. Fourthly, in real scenarios the presence of noise can deteriorate the quality of the recordings, leading to the system performance degradation. In fact, in this particular database, audio is very noisy and this could be the reason for the worse performance of this modality in comparison to the gaze one, as eye-tracking data has been recorded in better environmental conditions (see Table 1).  
All these factors determine that the performance of the ADD systems are far to be optimal, opening new research lines for addressing these issues. In particular, for future work, we plan to extend our research towards the exploration of suitable data augmentation techniques for alleviating the problem of scarce training data, the use of advanced denoising techniques for improving the quality of the audio recordings and the incorporation of the visual modality.
## 4 Experimental Results and Discussion
### Subjective Evaluation
#### 4.5.4 Results and Analysis  
Figure 11 shows the DMOS of the ten testing stereoscopic images reconstructed with the three different SR methods. It is obvious that the SC method [13] was poorer than the other two SR methods. Most of the participants thought that the stereoscopic images reconstructed by the SC method were more obscure. Benefiting from the correlation between views, the proposed method also outperformed the SRCNN [15], demonstrating its better stereo visual quality.
# Bilateral Teleoperation System with Integrated Position/Force Impedance Control for Assembly Tasks
## 5 Experimental Validation
### Precision and Effectiveness of Teleoperation System  
#### 5.2.1 Positioning Accuracy of the Tracker  
In the experiment, the lighthouses were posed as described in Section 3. The positioning sensor (tracker) was mounted on a rigid plate, with the assumption of no wobbling. The 6D coordinate information of the tracker was recorded in real-time. The positioning accuracy was illustrated with the data of the \(X\)-axis, and the results are shown below.  
From the tests, it can be seen that the jump of the position signal in the resting state was about 0.2 mm, which is consistent with the results tested in the literature [35; 36]; the VIVE tracker showed a mean rotational error of 0.13 \(\pm\) 0.08\({}^{\circ}\) and 0.3 \(\pm\) 0.07\({}^{\circ}\), and a mean translational error of 0.336 mm. The position trailing was precise enough for a teleoperation system.
# Recent Developments in Modulation Spectroscopy for Methane Detection Based on Tunable Diode Laser
## 4 Application of Modulation Spectroscopy for Methane Detection
### Two-Tone Frequency Modulation Spectroscopy Applied for Methane Detection
#### 4.3.3 Optimization of Methane Detection Based on TTFMS  
In absorption systems based on TTFM technique, optical interference fringe is also a limiting factor for detection sensitivity. A convenient fringe suppression method was employed to improve the sensitivity in a CH\({}_{4}\) detection system, as shown in Figure 6[70]. Modulation depth optimization and TTFM technology is applied in this system. In addition, the MDL at 1.654 \(\upmu\)m is enhanced to 130 ppb.m for a 50-min period. Observed detection sensitivity is given in the conclusion in Table 2.  
Figure 5: Schematic drawing of the experimental set-up of a portable spectrometer. Reproduced with permission from [122], [PERSON], 2006.
## 1 Introduction
One of the methods is the Chirp-Z transform [26], also written as CZT [27; 28; 29], which allows for the analysis of a selected fragment of the signal spectrum obtaining greater spectral resolution and more precise indication of the frequency value which corresponds to a local maximum of the spectrum.
# Analysis of Total-Forms of Cyanotoxins Microcystins in Biological Matrices: A Methodological Review
## 2 Quantification of Total MC by the MMPB Method
### Lemieux Oxidation Step
#### 2.2.2 Sediment Matrices  
For sediments, three studies have focused on improving the oxidation conditions. First, [PERSON] et al. [68] reported that the best oxidation conditions were 0.025 M of KMnO\({}_{4}\) and 0.093 M of NaIO\({}_{4}\) for one hour, which yielded 35% of the MMPB. [PERSON] et al. [69] tested a range of KMnO\({}_{4}\) between 12.5 and 100 mM and concluded that the optimal concentration was 50 mM or more of KMnO\({}_{4}\). Another study by [PERSON] et al. [70] demonstrated optimal yield conditions with a reaction time of 3 h with a 0.1 M saturated solution of both KMnO\({}_{4}\) and NaIO\({}_{4}\), giving an 89% oxidation yield, which is much better than those obtained earlier (33 to 45%) [68; 69].
# An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs
## 2 Related Works
### Data Transmission and Routing Paths
At the second level, the algorithms optimized the sojourn positions of mobile BSs using a particle swarm optimization technique. Each mobile BS's ant colony optimization technique represents the high-level Anycast routing tree. The data is then sent from CHs to BSs using a hybrid compressive sensing approach. Nevertheless, as the number of nodes grows, the runtime complexity grows. The network becomes expensive when using several sinks. In this regard, succeeding in choosing their best possible location is increasingly challenging as the number of sinks increases. By dividing the network into balanced clusters, the Hybrid Energy-Efficient Multi-Path (HEMP) routing algorithm improves the energy efficiency of WSN networks [39]. It also solves the issue of imbalanced cluster formation. In order to reduce the energy used during data transmission from the SNs to respective CHs, HEMP has employed a multi-hop communication technique inside the cluster. Recently, a technique [61] was proposed based on the DP clustering algorithm that allows nodes to be grouped based on their coordinate locations. It also enables SNs to self-generate decisions depending on the data flow condition in the network and builds an automated path plan that extends the network's lifetime. Although the study improves the lifetime, choosing a low-energy node as a CH results in a non-uniform distribution of CHs. Table 2 shows the most recent studies on routing protocol and sink placements.
## 3 The End-to-End Instrument Performance Simulation System (EIPS) Framework
### EIPS Components
#### 3.2.4.1 Theory of Optimal Estimation  
This section briefly describes the theory of OE applied in context of atmospheric sounding for developing the information and error analysis model. There are at least two notations used, but we adopt the standard data assimilation notation of [28] to briefly to explain the OE theory. We start with the mathematical notations for the forward model, which is a radiative transfer model in the context of atmospheric sounding.  
A forward model (F) can be mathematically written as:  
\[\mathbf{y}=\mathrm{F}(\mathbf{x})+\mathbf{\epsilon} \tag{1}\]  
where \(\mathbf{y}\) is the measured signal spectrum, \(\mathbf{x}\) is the true state of the atmosphere to be estimated and \(\mathbf{\epsilon}\) is the error in the measurements. Solutions to the Equation (1) can be estimated in the maximum a-posteriori (MAP) framework, from which the equation for the retrieved atmospheric state (\(\mathbf{\hat{x}}\)) can be derived as:  
\[\mathbf{\hat{x}}=\mathbf{x_{a}}+\left(\mathbf{H}^{\mathrm{T}}\mathbf{R}^{-1} \mathbf{H}+\mathbf{B}^{-1}\right)^{-1}\mathbf{H}^{\mathrm{T}}\mathbf{R}^{-1} (\mathbf{y}-\mathrm{F}(\mathbf{x_{a}})) \tag{2}\]  
\[\mathbf{\hat{x}}=\mathbf{x_{a}}+\mathbf{G}(\mathbf{y}-\mathrm{F}(\mathbf{x_{a }})) \tag{3}\]  
where \(\mathbf{H}\) is the Jacobian matrix, \(\mathbf{R}\) is the observation error covariance matrix, and \(\mathbf{G}\) is the gain matrix and \(\mathbf{A}\) is the analysis error covariance matrix. With proper specification of an a priori error covariance matrix \(\mathbf{B}\) (which often comes from the short numerical range forecast), the retrieval errors can be estimated by the \(\mathbf{A}\) matrix which is given by the following Equation (4):  
\[\mathbf{A}=\left(\mathbf{B}^{-1}+\mathbf{H}^{\mathrm{T}}\mathbf{R}^{-1} \mathbf{H}\right)^{-1} \tag{4}\]  
Figure 5: Flow diagram showing the process of reconstruction of instrument simulated spectra by the convolution of very high spectrally resolved radiances with instrument spectral response function.  
This analysis error covariance matrix is very useful for estimating the retrieval error without exactly calculating the retrievals and for comparing the retrieval error performance of different instrument configurations.