esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study of Eye-Tracking Gaze Point Classification and Application Based on Conditional Random Field
## 2 Proposed Method
### Expression of Gaze Point Feature  
During the gazing, the gaze point in each gaze image is \(D=\{d_{1},d_{2},\ldots,d_{t}\}\), where \(d_{t}\) is the position of the gaze point falling in the image task target at moment \(t\). A certain relationship exists between the gaze points, and it is possible to predict the image area where the next attention appears by the previous gaze point. Considering the different gaze differences of different people, \(14\times 14\) image block at this gaze point location is extracted as the visual feature, and the color features of RGB, Lab and HSV color spaces of the image block are extracted as the visual feature representation of this gaze point. The image block color space features corresponding to each group of the sequence of the attention point can be expressed as \(G^{a}=\left\{G_{1}^{a},G_{2}^{a},\ldots,G_{t}^{a}\right\}\), where \(G_{t}^{a}\) denotes the image block color features corresponding to the attention point at moment \(t\). The color features are calculated as described in Section 2.3.
## 2 Channel Model and Signal-to-Noise Ratio
### Channel Impulse Response
Therefore, the total received power in the \(k\)-th time interval (width \(\Delta t\)) is calculated as the sum of the power of the \(N_{k}\) rays that contribute in that interval:  
\[p_{k}=\sum_{i=1}^{N_{k}}p_{i,k}=\sum_{i=1}^{N_{k}}\frac{1}{d^{2}}R_{S}(\varphi,\varphi^{\prime})A_{eff}(\psi) \tag{6}\]  
Letting \(M=t_{max}/\Delta t\), and assuming as the time origin the arrival of the LOS component, the impulse response after multiple reflections is given by:  
\[\sum_{k=1}^{\infty}h^{(k)}(t;E,R)=\sum_{j=0}^{M-1}p_{k}\delta(t-j\Delta t) \tag{7}\]  
Replacing Equations (3) and (7) in Equation (2), the channel impulse response can be expressed as:  
\[h(t;E,R)=\frac{1}{d^{2}}R_{E}(\theta,n)A_{eff}(\psi)\delta(t)+\sum_{j=1}^{M-1 }p_{k}\delta(t-j\Delta t) \tag{8}\]  
Once the impulse response \(h(t)\) is computed for a fixed emitter \(E\) and receiver \(R\) position, the main parameters that characterise the IR channel--the path loss (\(PL\)) and the rms delay spread (\(D\))--are easily calculated as [4]:  
\[ \begin{split} PL=-10\text{log}H(0)&\quad H\left(f \right)=\int_{-\infty}^{\infty}h(t)e^{-j\Delta tf}dt\\ D=\left[\frac{\int_{-\infty}^{\infty}\left(t-h\right)^{2}h^{2}( t)dt}{\int_{-\infty}^{\infty}h^{2}(t)dt}\right]^{\frac{1}{2}}&\quad\mu= \frac{\int_{-\infty}^{\infty}th^{2}(t)dt}{\int_{-\infty}^{\infty}h^{2}(t)dt} \end{split}  \tag{9}\]
# Quality Enhancement of MPEG-H 3 DA Binaural Rendering Using a Spectral Compensation Technique
## 1 Introduction
Panning gain compensation is performed in the low-frequency band where the bandwidth of the QMF is wider than the critical frequency scale. And a signal compensation similar to the active downmix is performed in the high-frequency band, where the QMF band has a narrower bandwidth than the critical frequency scale. In such a way, the proposed algorithm compromises the algorithm's complexity with performance within a framework of MPEG-H 3 DA. This paper is organized as follows. Section 2 reviews the MPEG-H 3 DA decoder and BR. Section 3 demonstrates spectral artifacts in the MPEG-H 3 DA BR, and Section 4 proposes a binaural gain normalization and its implementation structure on MPEG-H 3 DA BR as well as an empirical analysis based on our implementation. Section 5 presents experimental results that include both objective and subjective tests. In Section 6, we present the conclusion.
## 3 Recent Developments and Progress
### Environmental Remote Sensing by GNSS-R  
Environmental remote sensing by space-borne GNSS-R was conducted and assessed, specifically, data from CYGNSS (Cyclone GNSS from NASA). This was accomplished by assessing models that utilize nearly simultaneous observations from a pair of CYGNSS satellites using a double-difference method. For a more comprehensive examination of the findings from this intriguing study, the reader is encouraged to explore the results further [49]. In recent years, GNSS-R has emerged as a robust tool for remote sensing, proving indispensable in numerous applications. This technology utilizes signals that are reflected off the Earth's surface and analyzes them to accurately measure a variety of critical parameters; another relevant study focused on evaluating the retrieval of snow depth in permafrost regions. These areas experience significant changes in ground surface elevation,  
Figure 11: The differences in ZTD RMSE between La Nina and non-La Nina IGS stations (Reprinted with permission from Ref. [48]. 2024 [PERSON]).  
Figure 10: Estimation and evaluation of ZTD from single and multiple GNSS observations (Reprinted from Ref. [24]).  
as they are subjected to annual cycles of subsidence and uplift caused by the thawing and freezing processes that occur in the active layer of the soil.
# Spatial Performance Indicators for Traffic Flow Prediction
## 3 Materials and Methods
### Spatial KPIs
#### 3.1.3 Node Centrality Measure (Adapted PageRank Algorithm Modified)
The univariate analysis of APAM will help to analyze the distribution of the APAM values of all nodes and provide insights, such as the spread of APAM values, asymmetry of the distribution, and prevalence of outliers. For example, if the univariate analysis reveals that there are some nodes with significantly higher APAM values than the rest of the nodes (which we consider as outliers), we could perform targeted improvement targeted towards those nodes. In this study, we only showed the results of variance, skewness, and kurtosis as part of the univariate analysis. The mean of the APAM value distribution is not informative because the APAM value is derived from the eigenvector of the dominant eigenvalue \(\lambda_{1}=1\); hence, the value is dependent on the number of nodes.
# A Quantum Detectable Byzantine Agreement Protocol Using Only EPR Pairs
## 2 Preliminaries  
### \(|\Psi^{+}\rangle\) EPR Pairs  
Quantum entanglement is one of the most celebrated properties of quantum mechanics and, without exaggeration, serves as the core of the majority of quantum protocols. Mathematically, entangled states of composite systems must be described as a linear combination of two or more product states of their subsystems, as a single product state will not suffice. The famous Bell states are special quantum states of two qubits, also called EPR pairs, that represent the simplest form of maximal entanglement. These states are succinctly described by the next formula from [33].  
\[\left|\beta_{x,y}\right\rangle=\frac{|0\rangle|y\rangle+(-1)^{x}|1\rangle| \bar{y}\rangle}{\sqrt{2}}\, \tag{1}\]  
where \(|\bar{y}\rangle\) is the negation of \(|y\rangle\).  
There are four Bell states and their specific mathematical expression is given below. The subscripts \(A\) and \(B\) are used to emphasize the subsystem to which the corresponding qubit belongs, that is, qubits \(|\cdot\rangle_{A}\) belong to [PERSON] and qubits \(|\cdot\rangle_{B}\) belong to [PERSON].  
\[\left|\Phi^{+}\right\rangle=|\beta_{00}\rangle=\frac{|0\rangle_{A}|0\rangle_ {B}+|1\rangle_{A}|1\rangle_{B}}{\sqrt{2}} \tag{2}\]  
\[|\Phi^{-}\rangle=|\beta_{10}\rangle=\frac{|0\rangle_{A}|0\rangle_{B}-|1 \rangle_{A}|1\rangle_{B}}{\sqrt{2}} \tag{3}\]  
\[\left|\Psi^{+}\right\rangle=|\beta_{01}\rangle=\frac{|0\rangle_{A}|1\rangle_{ B}+|1\rangle_{A}|0\rangle_{B}}{\sqrt{2}} \tag{4}\]  
\[|\Psi^{-}\rangle=|\beta_{11}\rangle=\frac{|0\rangle_{A}|1\rangle_{B}-|1 \rangle_{A}|0\rangle_{B}}{\sqrt{2}} \tag{5}\]  
For existing quantum computers that use the circuit model, it is quite easy to generate Bell states. The proposed protocol relies on \(|\Psi^{+}\rangle=\frac{|0\rangle_{A}|1\rangle_{B}+|1\rangle_{A}|0\rangle_{B}}{ \sqrt{2}}\) pairs. Apart from \(|\Psi^{+}\rangle\) pairs, we shall make use of qubits in another well-known state, namely \(|+\rangle\). For convenience, we recall the definition of \(|+\rangle\)  
\[|+\rangle=H|0\rangle=\frac{|0\rangle+|1\rangle}{\sqrt{2}}\, \tag{6}\]  
which can be immediately produced by applying the Hadamard transform on \(|0\rangle\). Let us also clarify that all quantum registers are measured with respect to the computational basis \(B=\{|0\rangle,|1\rangle\}\).
# Clinical Trial Classification of SNS24 Calls with Neural Networks
## 4 Experiments
### Preliminary Experiments  
#### 4.2.1 CNN-Based Architecture  
Among a number of the convolutional neural networks (CNN) that have presented excellent performances in many natural language processing tasks, one typical CNN architecture for text classification is proposed by [PERSON] [18]. This type of CNN architecture generally includes these layers: embedding layer (input layer), convolutional layer, pooling layer, and fully connected layer.  
We build our CNN model based on this type of CNN architecture. When applying CNNs in text analysis, the input has a static size and text lengths can vary greatly [18]. So, as the input, the texts from the \"contact reason\" field are tokenized, and all words are integer encoded. These pre-processed train data are then fed to the CNN architecture. In the embedding layer, each word is embedded into a 200-dimension vector. A one-dimensional convolution layer is then added. Filters perform convolutions on the text matrix and generate feature maps. We depict one filter region size in our experiments. The max-pooling is performed over each feature map to select the most prominent feature (the largest number). We choose max-pooling as the pooling strategy since it presents much better performance compared to average pooling in the text classification tasks [29]. Then, the max-pooling results of word embeddings are concatenated together to form a single feature vector. To prevent the built CNN model from overfitting, we add a dropout layer after the fully connected layer [30]. The final Softmax layer receives the concatenated feature vector as input and uses it to classify the texts. The main hyperparameters settings in the built CNN model are shown in the second column of Table 3.  
The training and validation results are presented in Figure 3. The left figure shows the accuracy's on the train and validation datasets. As it can be observed, the accuracy increases slowly on the training dataset after 6 epochs. The accuracy on the validation dataset tends to saturate after certain epochs, and the curve doesn't show the trend of increasing accuracy after more epochs within the setting one. Figure 3 right plots the loss curves of the train and validation datasets over the setting epochs. The loss curve on the training dataset decreases rapidly at the beginning of 3 epochs and slowly after the 3 epochs. The loss curve on the validation dataset saturates gradually after 6 epochs, and the loss hasbeen reduced to a minimum level at epoch 8 and then begins to increase. As observed from both figures, the curves have been basically stable after 6 epochs within the setting epochs.  
Using this trained CNN model, the accuracy obtained on the test dataset is 76.56%.
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 2 Domain Adaptation Techniques
### Overview of Domain Adaptation Techniques
#### 2.1.4 Hybrid Methods  
To make use of each method's advantages, hybrid domain adaptation strategies incorporate both conventional and deep learning-based approaches. Deep Adaptation Networks (DAN) represent one such method that combines deep neural networks with multiple kernels learning to provide efficient domain adaptation [94]. DAN has been used for a variety of computer vision applications and has been proven to perform better under circumstances including domain adaptability. Another such efficient hybrid model is CDAN-SA-MTL (Conditional Domain Adversarial Network with Self-Attention and Multi-Task Learning), which combines self-attention and multi-task learning strategies to improve domain adaptation. By simultaneously considering multiple tasks and utilizing self-attention mechanisms, it achieves enhanced adaptation results and robustness in scenarios involving domain variations [95].  
DANN-SA-MTL (Domain Adversarial Neural Network with Self-Attention and Multi-Task Learning) integrates self-attention and multi-task learning into the domain adaptation process. This approach boosts model performance by incorporating self-attention mechanisms for improved feature extraction and multi-task learning to handle diverse adaptation tasks, making it a versatile choice for domain adaptation challenges [96].
## 5 Experimental Results
In the 2D spatial-temporal spectrum the spatial frequencies in the range [0.05-0.1] mm\({}^{-1}\) correspond to the wave propagating in air. The propagation paths of this wave are shown in Figure 20.  
The spatial distribution of the A\({}_{0}\) mode phase velocity obtained from the experimentally collected B-scan in the case of the contact excitation is presented in Figure 23. The B-scan was measured along the \(x\) axis from the centre of the excitation aperture of the piezoelectric transducer. The spatial reconstruction of the phase velocity was performed by the proposed zero-crossing method. From the presented distribution follows that variations of the phase velocity are in the range 91-109 m/s and the highest values correspond to the positions of the optically darker spots (Figure 13) with higher values of the Young's modulus of \(E_{\rm D}\) = 13 GPa. Correspondingly, in the lighter zones the Young's modulus is \(E_{\rm L}\) = 9 GPa. The Young's moduli values were found using Equation (2). Figure 22: 2D spectra of the contact (**a**) and of the air-coupled B-scans (**b**). For more detailed analysis the \(A_{0}^{\prime}\) and \(A_{0}^{\prime\prime}\) modes obtained in the case of the air-coupled excitation were selected (Figure 24). The phase velocities of different A\({}_{0}\) modes propagating in the sample obtained from the 2D spectrum presented in Figure 24 are presented in Table 1. The modes \(A_{0}^{\prime\prime}\) propagate with a higher velocity and are caused by the darker zones in the mica specimen. Figure 19.0 Spatial distribution of the phase velocity reconstructed from the contact B-scan. Figure 19.1 2D spectrum of the air-coupled B-scan of the sample. It is necessary to point out that the B-scans recorded in different zones of the MICA sample are slightly different due different number and dimensions of the optically darker zones (Figure 13) the physical properties of which differ from the optically lighter zone.
## Network Traffic Features for Anomaly Detection in Specific Industrial Control System Network  
**[PERSON] *, [PERSON] and [PERSON]**  
VTT Technical Research Centre of Finland, Kaitovayla 1, Oulu 90571, Finland;  
E-Mails: [EMAIL_ADDRESS] (M.S.); [EMAIL_ADDRESS] (S.N.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel: +358-40-570-8720.  
_Received: 27 June 2013; in revised form: 5 August 2013 / Accepted: 10 September 2013 /_  
_Published: 25 September 2013_  
**Abstract:** The deterministic and restricted nature of industrial control system networks sets them apart from more open networks, such as local area networks in office environments. This improves the usability of network security, monitoring approaches that would be less feasible in more open environments. One of such approaches is machine learning based anomaly detection. Without proper customization for the special requirements of the industrial control system network environment, many existing anomaly or misuse detection systems will perform sub-optimally. A machine learning based approach could reduce the amount of manual customization required for different industrial control system networks. In this paper we analyze a possible set of features to be used in a machine learning based anomaly detection system in the real world industrial control system network environment under investigation. The network under investigation is represented by architectural drawing and results derived from network trace analysis. The network trace is captured from a live running industrial process control network and includes both control data and the data flowing between the control network and the office network. We limit the investigation to the IP traffic in the traces.  
**Keywords:** industrial control systems; anomaly detection; machine learning; network security
# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
## 4 Experiments
### Real Data Experiments
#### 4.2.4 Performance of Root Mean Square Error  
Our method employs geometric constraints of the triangular pyramid target to calibrate the extrinsic parameters of the camera and LiDAR system. There are no feature points for flash LiDAR to detect and we cannot use the reprojection error to evaluate the  
Figure 11: Extrinsic parameters of the camera and LiDAR system: (**a**) rotation from LiDAR to camera and (**b**) translation from LiDAR to camera.  
Figure 10: Average reprojection error and root mean square error (RMSE) of 20 frames of the camera and LiDAR: (**a**) reprojection error and (**b**) RMSE.  
calibration result, as the conventional method did. Therefore, we apply root mean square error (RMSE) in this experiment.  
\[e_{RMSE}=\sqrt{\frac{\sum\limits_{k=1}^{K}\sum\limits_{i=1}^{3}\sum\limits_{j=1}^ {M(ijk)}\left(N_{i,j,k}^{T}(\mathbf{R}P_{i,j,k}^{L}+T)-d_{ijk}\right)^{2}}{\sum \limits_{k=1}^{K}\sum\limits_{i=1}^{3}M(i,k)}}, \tag{25}\]  
where \(e_{RMSE}\) represents the RMSE of all points to their corresponding chessboards. We also calculate the RMSE of 20 pairs of frames, and the result is illustrated in Figure 10b. It shows that the RMSEs of most frames are under 25 mm.
# Post-Quantum Authentication in the MQTT Protocol
## 2 Problem Statement
### Contributions  
This paper outlines how existing postquantum cryptography fits within the existing communication protocol, MQTT, to integrate authentication.  
* We discuss the use of MQTT for IoT and provide background on its use.
* We discuss postquantum cryptosystems and test MQTT with the postquantum KEM and digital signature algorithms CRYSTALS-Kyber and CRYSTALS-Dilithium, respectively.
* We discuss the opportunity to use KEMs for authentication rather than digital signatures.
* We create a skeleton implementation of MQTT authenticated with the KEM CRYSTALS-Kyber to act as a proof of concept for the feasibility of the use of a KEM for authentication in MQTT.
* We implement a comparison of unauthenticated MQTT, MQTT authenticated with digital signatures (CRYSTALS-Dilithium) and authenticated with KEM (CRYSTALS-Kyber).
* We then compare and discuss our results in the context of the use of postquantum authentication in MQTT for IoT devices in the future.
# Serial Decoders-Based Auto-Encoders for Image Reconstruction
## 3 Theory
### Wasserstein Auto-Encoders
#### 3.6.2. Proposed Cascade Decoders-Based Wasserstein Auto-Encoders  
The proposed cascade decoders-based Wasserstein auto-encoders can be resolved by the following optimization problem:  
\[\begin{split}(\theta;\mathbf{z};\mathbf{y}_{1},\cdots,\mathbf{y }_{\mathrm{N}})=\underset{\theta,\mathbf{z}\mathbf{y}_{1},\cdots,\mathbf{y}_ {\mathrm{N}}}{\operatorname{argmin}}&\bigg{(}\alpha W_{ \mathrm{z}}(\mathrm{p}(\mathbf{z}),\mathrm{q}(\mathbf{z}_{\mathrm{h}}))+ \beta\sum\limits_{n=1}^{\mathrm{N}}W_{\mathrm{y}}(\mathbf{y}_{n},\mathbf{x}) \bigg{)}\\ \text{s.t.}\ \mathbf{z}=\mathrm{E}(\mathbf{x});\mathbf{y}_{1}= \mathrm{D}_{1}(\mathbf{z});\mathbf{y}_{n}=\mathrm{D}_{n}(\mathbf{y}_{n-1}), \mathbf{n}=2,\ldots,\mathrm{N};\mathrm{C}_{\mathrm{y}_{n}},\mathbf{n}=1, \ldots,\mathrm{N}\end{split} \tag{22}\]  
For the purpose of gradually and serially training cascade decoders-based auto-encoders, the optimization problem in Equation (22) can be divided into the following suboptimization problems:  
\[\begin{split}(\theta_{1};\mathbf{z};\mathbf{y}_{1})=\underset{ \theta_{1}\mathbf{z}\mathbf{y}_{1}}{\operatorname{argmin}}&\big{(} \alpha W_{\mathrm{z}}(\mathrm{p}(\mathbf{z}),\mathrm{q}(\mathbf{z}_{\mathrm{ h}}))+\beta W_{\mathrm{y}}(\mathbf{y}_{1},\mathbf{x})\big{)}\\ &(\theta_{2};\mathbf{y}_{2})=\underset{\theta_{2}\mathbf{y}_{2}} {\operatorname{argmin}}& W_{\mathrm{y}}(\mathbf{y}_{2},\mathbf{x}) \\ &\cdots\cdots\\ (\theta_{\mathrm{N}};\The aforementioned architecture represents general cascade decoders-based Wasserstein auto-encoders (GCDWAE), and it can be easily be expanded to residual cascade decoders-based Wasserstein auto-encoders (RCDWAE), adversarial cascade decoders-based Wasserstein auto-encoders (ACDWAE), and residual-adversarial cascade decoders-based Wasserstein auto-encoders (RACDWAE).
## 5 Results and Discussions
### Solid Particle Distribution  
The proper quantification of solid dispersion is indispensable for a detailed understanding of the hydrodynamics of solid-liquid stirred tanks, as it strongly impacts the mass transfer between the liquid and the particles. Figure 11 reports the solid volume fraction of vertical cross-section (\(z=0.1H\), \(z=0.4H\), \(z=0.7H\)), and Figure 12 shows the axial distribution of the solid volume fraction at \(R/r=0.5\). As depicted in Figure 11, there was particle accumulation under the impeller and around the baffle. The novel impellers, however, may function to reduce the deposition of solid particles at the bottom of the stirred tank and the thickness of the clear liquid layer at surface was lower than that of the PBT. The PBPT, BCBT and BCPBT improved the solid suspension quality in the stirred tank compared with the PBT. The dispersion degree of solid particles in the PBPT and BCBT impeller systems were superior to that of the PBT impeller system under the same operation condition, and the BCPBT could further promote the dispersion degree of solid particles on the basis of PBPT and BCBT. This phenomenon can also be observed in Figure 12. Obviously, a conclusion could be drawn that the capacity of BCPBT for solid suspension is superior to that of other impellers. As previously mentioned, it might be explained by the fact that the apertures and gaps around the novel impellers causes a series of high-speed jets generation around the blade, and that the shear effect of the jets on the surrounding liquid generated many stable small-scale vortices, thus advancing the local energy dissipation rate around the liquid and enhancing the mixing effect.  
Figure 10: Contour plots of pressure on the center plane of impellers at \(z=0.25\)\(H\), (**a**) PBT, (**b**) PBPT, (**c**) BCBT, (**d**) BCPBT.