esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust Framework for Real-Time Iris Landmarks Detection Using Deep Learning
## 3 Materials and Methods
### Proposed Methodology
#### 3.4.1 Proposed Model  
Figure 2 shows the flow diagram of the proposed framework. An image is sent to the face detection model to predict the face bounding box after it has been rescaled and normalized as required. After scaling and normalizing, this prediction is provided as an input to the facial landmarks model to predict 68 facial landmarks. Finally, we acquire eye-cropped images from the landmarks model. These eye images are rescaled and normalized. The iris landmarks model is then fed to the eye-cropped images for prediction. A complete pipeline of the face bounding boxes, facial landmarks, and iris landmarks models is given in Figure 3.
# Optimization Procedure for Computing Sampling Time for Induction Machine Parameter Estimation
## 3 Optimal Induction Machine Poles and Sampling Time  
### Induction Machine Model  
The induction machine model is well known today. The model is derived by transforming the circuit equations from three phase system into two component system using the Park transformation, details found in [22; 23]. To create a single input state space model for the simplest implementation of the procedure described in the continuation of the paper, the IM model is written in the _voltage oriented_ (\(v_{q}=0\)) rotating (\(dq\)) reference frame with fluxes as state variables:  
\[\begin{split}\frac{\mathrm{d}}{\mathrm{d}t}\mathbf{\psi}_{s}& =\mathbf{v}-R_{s}G_{s}\mathbf{\psi}_{s}-R_{s}G_{m}\mathbf{\psi}_{r}-\omega_{k} T_{r}\mathbf{\psi}_{s}\\ \frac{\mathrm{d}}{\mathrm{d}t}\mathbf{\psi}_{r}&=-R_{r} G_{r}\mathbf{\psi}_{r}-R_{r}G_{m}\mathbf{\psi}_{s}-(\omega_{k}-p\omega)T_{r}\mathbf{\psi}_{r}\\ \frac{\mathrm{d}}{\mathrm{d}t}\omega&=\frac{G_{m}} {J}\frac{3}{2}p\big{(}\psi_{sd}\psi_{rq}-\psi_{sq}\psi_{rd}\big{)}-\frac{1}{J }M_{L}\end{split} \tag{13}\]  
The notation in Equation (13) is: \(\mathbf{v}=[v_{d}\quad 0]^{T}\)--\(dq\) components of stator voltage, \(\mathbf{\psi}=\big{[}\psi_{d}\quad\psi_{q}\big{]}^{T}\)--\(dq\) components of fluxes, subscripted '\(s\)' for stator and '\(r\)' for rotor quantities, \(R_{s}\)--stator resistance, \(R_{r}\)--rotor resistance, \(\omega_{k}\)--the \(dq\) reference frame rotating speed, grid synchronous speed in this case, \(\omega\)--the actual rotor speed, \(p\)--number of pole pairs, \(J\)--rotor inertia, \(M_{L}\)--the load torque, \(T_{r}\)--the rotational matrix defined in Equation (14) and parameters \(G\) are the elements of inverse of machine inductance matrix \(\mathbf{L}\) defined in Equation (15) with \(L_{s}=L_{ls}+L_{m}\), \(L_{r}=L_{lr}+L_{m}\), where \(L_{m}\) is the main magnetizing inductance and \(L_{ls},L_{lr}\) are the stator and rotor leakage inductances.  
\[\mathbf{T}_{r}=\begin{bmatrix}0&-1\\ 1&0\end{bmatrix} \tag{14}\]  
\[\mathbf{G}=\begin{bmatrix}G_{s}&0&G_{m}&0\\ 0&G_{s}&0&G_{m}\\ G_{m}&0&G_{r}&0\\ 0&G_{m}&0&G_{r}\end{bmatrix}=\begin{bmatrix}L_{s}&0&L_{m}&0\\ 0&L_{s}&0&L_{m}\\ L_{m}&0&L_{r}&0\\ 0&L_{m}&0&L_{r}\end{bmatrix}^{-1} \tag{15}\]
## 5 Discussion
### Research Gaps
#### 5.1.2 Ambiguity in APT Attack Path and Late Detection  
Concerning the second research question, it has been observed that numerous APT detection mechanisms have been implemented using various types of platforms, namely personal computers, mobile devices, and the IoT. Although many APT detection solutions and techniques have been designed and implemented, they have failed to provide a comprehensive solution for threat detection [11]. The reasons are the limited significance attached to human behavioral factors leading to APTs, unclear APT attack trail, or TTPs [12]. Additionally, the most recent solutions are generalized based on a group of users rather than single individual protection [194].  
The number of APTs has rapidly increased. The primary reason is that APTs are not concentrating on a single loophole within a system that may be detected and removed easily. Instead, they are utilizing a sequence of loopholes in several systems to access high-security areas within a corporate network. In this context, attackers regularly exploit the point where most security attempts go into perimeter protection. The attacker's chances of detection are markedly increased if they have gained access to the system's infrastructure [195].  
Additionally, identifying APTs before the attackers reach the final stage, with a low percentage of false alarms and missed detections, remains a concern due to their stealth and deception. According to recent reports, it took US organizations three to six months to discover and control a data breach and prevent further damage in 2018 [160]. Contrastingly, it has been observed that inadequate research has sought to identify APTs as a whole, from reconnaissance through clean-up, despite several studies being conducted to detect an APT assault in one or two phases [16,23].  
Such a solution necessitates sophisticated correlation and comprehensive behavior analysis of individuals and systems within and across networks. Finally, several attacks are modeled on APTs, such as the MITRE framework and the cyber kill chain, which comprises the APT lifecycle and the APT detection TTPs [70]. Consequently, security experts are unable to identify an attack as an APT [11].
## 2 Materials and Methods
### Numerical Modelling of Gaseous Fire-Extinguishing Systems  
Numerical representation of gaseous fire-extinguishing system operation with Computational Fluid Dynamics (CFD) models could be used for the design of better gas-mixtures, optimisation of shape and sizes of protected compartments, or to improve the location of nozzles. However, in the current state of the art, such modelling is not a trivial task. Following some of the best practice guidelines for CFD [26], steady-state RANS (Reynolds-Averaged Navier-Stokes) modelling of stratified gas mixtures for higher Rayleigh numbers shows deficiencies, as classical turbulence models use an isotropic approach for Reynolds stresses and Boussinesq approximation for the density-temperature dependency. Also based on [19], modelling of fire-extinguishing gas flow through minor gaps makes a great challenge for the existing mathematical models. The guidelines [26] recommend possible solutions for the modelling of such complex phenomena, which are the use of URANS (Unsteady RANS model used in this study), Reynolds Stress or Large Eddy Simulation models.  
The buoyancy of the layers must be resolved [26], as well as the mixing and diffusion in the intermediate layer. In the case of gas mixtures, which have a difference of density between gas and  
Figure 2: Diagram of the test rig used as the reference for the numerical experiments [11; 21].  
ambient air close to 0 (\(\Delta\rho\to 0\)), the buoyancy has a minor effect, and diffusion and turbulent mixing may be the most important phenomena. The buoyancy will also dive the flow through the leakages, where turbulent mixing of air and gas will occur. To accurately model the flows in the leakages, the minuscule details of the leakages had to be resolved explicitly, which means that the prepared numerical model had a significant difference of scales between the large details (e.g., compartment architecture) and the small details (e.g., nozzles, leakages).  
The flow of extinguishing gas inside and outside of protected compartment may be driven by following physical phenomena and interactions, which were included in our considerations:  
* Difference of buoyancy between gas and air, and the phenomena that take part at the interface of the buoyant layer (e.g., diffusion, turbulent mixing);
* Flow of clean air into to the protected compartment through leakages;
* Flow of extinguishing gas out of the protected compartment through leakages;
* The temperature gradient in and out of the protected compartment.  
Other significant phenomena and interactions that can possibly influence the retention of the extinguishing gas beyond the scope of this paper are, among others:  
* Forced ventilation inside of the protected compartment;
* The non-uniform release of the gas and the use of local pressure relief dampers;
* Heat sources in the compartment (e.g., server heat sinks);
* Pressure gradient outside of the chamber (e.g., due to wind effects);
* The source of fire itself.
# A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning
## 2 Related Works  
### Corridor-Based Trajectory Planning  
In recent years, corridor-based trajectory planning techniques have gained popularity as a means of ensuring safe quadrotor flights. [PERSON] et al. [23] rely on an underlying global occupancy grid that can be constructed from sensor data such as laser range finder, stereo cameras, or RGB-D sensors. A graph search algorithm can then find a valid collision-free path in the grid. Utilizing OctoMap [25], an occupancy map, [PERSON] et al. [22] use free cubes in the map as the spatial constraints. [PERSON] et al. [26] first build a KD-Tree with the obstacle point cloud, then inflate sphere-shaped corridors by a sampling process. These works must fuse an underlying occupancy grid upfront before generating SFCs.  
Utilizing only the latest depth image, [PERSON] et al. [12] invented an algorithm for inflating rectangular pyramid corridors when needed to check for collision in a sampled polynomial trajectory. Since they uniformly sample in the surrounding space and try to inflate a new pyramid around that sampled point, the generated SFC collection can overlap much and become unnecessary constraints of the same spatial boundary for path planning. Moreover, the random sampling technique itself is also not quite computationally efficient.
## 4 SMS-Based Telemetry Data Transmission Protocol
### Protocol Header  
The header is entrusted with the management and \"housekeeping\" of the protocol. It is the responsibility of an application using the protocol to construct the header appropriately. The format of the header is strictly defined, and can be written to a fixed length data segment. This has advantages in that the application using the protocol can extract the header from the user data quickly, find the start and the end points of the header, and decompile the header into component fields without worrying about variable length field data in the header itself. In effect, the header can be extracted and \"cookie cut\" into components fields using the same mechanism for any implementation of the protocol.  
The protocol header contains the following information:  
* Synchronisation marker--start of protocol message
* Message Format and Protocol ID
* Message Type ID
* Message Structure--Multipart or Single Message
* Application ID--Protocol user information
* Sender Device ID--unique application ID of the sender
* Recipient Device ID--unique application ID of the recipient
* Message ID--sequential ID of this message
* Generation Timestamp--when was the message sent by the remote device
* Validity Period--how long should the message remain valid for
* Receipt Required Flags--is a receipt required, and if so, what type of receipt
* Control Octets--containing control messages for the remote device
* User Data Segment Length--how many octets of user data to follow
* User Data Segment Encryption ID--is the user data encrypted, and by what mechanism
* Header Checksum--checksum for header data only
* User Data Segment Checksum--checksum for user data only
* Message Checksum--checksum for entire message, including the other checksums  
The header is encoded in the order in which the fields are defined above. Figure 3 shows the location of each header field, relative to the start of the message:  
The header requires 64 octets of the first message in a protocol message set. This leaves 76 octets of user data capacity in the first message.
# Hybrid Forecasting Models Based on the Neural Networks for the Volatility of Bitcoin
## 1 Introduction and Review of Models
### Review of Models
#### Higher Order Neural Network (HONN)  
HONN proposed by [PERSON] and [PERSON] [40] has been widely used to simulate the higher-order nonlinear inputs and to provide some basis for the simulations as 'open box' [41]. Because first-order networks do not take advantage of meaningful relationships between the input variables, the networks need a lot of training passes with a large training set. To improve this disadvantage, HONN has been developed. In general, with the selection of good input variables, it is known that HONN provides better forecasting performance than the classic ANN.  
In Equation (4), the independent variable is presented as the linear combination. Specifically, the variable is expressed by multiplying each input variable \((x_{i})\) by a weight \((w_{i})\) and adding the results. We can easily make out the higher-order terms of the inputs from the first-order terms. Here, we consider the second order HONN to improve the volatility forecasting. Let us define the input vector \(\vec{x}\) and the weight vector \(\vec{w}\) by  
\[\vec{x}=[x_{0},x_{1},\cdots,x_{n}]\ \ \text{and}\ \ \vec{w}=[w_{0},w_{1}, \cdots,w_{n}],\]  
respectively. Then the input vector \(\vec{x}_{h}\) and the weight vector \(\vec{w}_{h}\) in HONN are given by  
\[\vec{x}_{h}=[x_{0},x_{1},\cdots,x_{n},x_{0}^{2},x_{0}x_{1},x_{0}x_{2},\cdots, x_{n-1}x_{n},x_{n}^{2}]\ \ \text{and}\ \ \vec{w}_{h}=[w_{0},w_{1},\cdots,w_{n},w_{00},w_{01},w_{02},\cdots,w_{n-1n},w _{nn}],\]  
respectively. From these vectors, the output with the activation functions \(f\) can be calculated as follows.  
\[\text{output}=f\left(\vec{w}_{h}\cdot\vec{x}_{h}\right)=f\left(\sum_{i=0}^{n }w_{i}x_{i}+\sum_{i=0}^{n}\sum_{j=i}^{n}w_{ij}x_{i}x_{j}\right). \tag{5}\]  
The structure of a second-order HONN used in this paper is illustrated in Figure 2. We construct the hybrid models based on this second-order HONN for the accurate forecasting.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Activation Function** \\ \hline Sigmoid & \(f(x)=\frac{1}{1+e^{-x}}\) \\ Hyperbolic Tangent (Tanh) & \(f(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}}\) \\ Rectified Linear Unit (ReLU) & \(f(x)=\begin{cases}0&\text{for }x<0\\ x&\text{for }x\geq 0\end{cases}\) \\ Exponential Linear Unit (ELU) & \(f(x)=\begin{cases}\alpha(e^{x}-1)&\text{for }x<0\\ x&\text{for }x\geq 0\end{cases}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Activation functions used in this paper.
## 4 Bayesian Identification
### Likelihood Function  
When the data size \(N{\rightarrow}\infty\), the DFT of output measurement noise is circular (or proper) assuming that the measurement noise is independent and identically distributed in the time domain [19]. This implies that the relation matrix \(\mathbf{C}\) of the measurement noise process is a zero matrix. The measurement noise across a local AOI is assumed to be independently and identically distributed. The likelihood function of \(\mathbf{H}\), equivalently the probability of observing \(\mathbf{Y}\) given \(\mathbf{H}\), is  
\[p(\mathbf{Y}|\mathbf{H},\kappa)=\frac{1}{\pi^{D/2}\mathrm{det}^{1/2}\underline{ \mathbf{\Sigma}}_{N}}\exp\left(-\frac{1}{2}(\underline{\mathbf{Y}}-\mathbf{ \Psi}\mathbf{H})^{\mathrm{H}}\underline{\mathbf{\Sigma}}_{N}^{-1}(\underline{ \mathbf{Y}}-\mathbf{\Psi}\mathbf{H})\right) \tag{17}\]  
where \(D=(4n+2)F\), and the regression matrix \(\psi\) and the augmented covariance matrix \(\underline{\mathbf{\Sigma}}_{N}\) are, respectively, composed as  
\[\psi=\begin{bmatrix}\mathbf{\Phi}&\mathbf{0}\\ \mathbf{0}&\mathbf{\Phi}^{*}\end{bmatrix},\,\underline{\mathbf{\Sigma}}_{N}= \kappa\underline{\mathbf{P}}=\kappa\begin{bmatrix}\mathbf{P}&\mathbf{0}\\ \mathbf{0}&\mathbf{P}^{*}\end{bmatrix} \tag{18}\]  
The scaling coefficient \(\kappa\) is introduced to consider the unwanted effects such as the change of experiment condition and low-level nonlinearity of a light-weight structure. Recall that \(\mathbf{P}\) is obtained during the preprocessing experiment where the structure is in static equilibrium, while the scaling coefficient \(\kappa\) is unknown and needs to be properly determined from data.
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 4 Supervised Learning for Estimating the Bit Error Rate (BER)
### Data Splitting for Training, Testing, and Validation  
To ensure proper evaluation and validation of our model, the gathered data are split into distinct subsets. This step allows for the allocation of data for training, testing, and validation purposes, ensuring robust performance assessment. The following example illustrates the distribution of the data:  
* 70% of the data is allocated for the learning phase;
* 15% of the data is reserved for validation purposes;
* Another 15% is dedicated to testing the model's performance.
# Effect of Paper Sludge and Dendromass on Properties of Phytomass Pellets
## 2 Materials and Methods
### Analysis of Samples  
The proximate analysis of the samples was performed on a thermogravimetric analyzer TGA 701. The results of the measured moisture, volatile matter, ash, and fixed carbon are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Moisture (\%)** & **Volatile Matter (\%)** & **Ash (\%)** & **Fixed Carbon (\%)** \\ \hline
**S-100** & 6.87 & 71.30 & 5.02 & 16.81 \\
**SD-100** & 3.86 & 74.99 & 0.49 & 16.31 \\
**SSPS-45/45/10** & 5.83 & 70.90 & 6.8 & 16.47 \\
**SSPS-40/40/20** & 4.13 & 70.27 & 10.53 & 15.07 \\
**PS100** [18] & 5.61 & 55.71 & 42 & 0.93 \\
**Standard deviation** & \(\pm\)1.24 & \(\pm\)0.78 & \(\pm\)2.53 & \(\pm\)1.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proximate analysis of samples.  
Figure 1: Samples of pellets: (**a**) S–100 (**b**) SD–100 (**c**) SSPS–45/10 (**d**) SSPS–40/40/20.  
Figure 2: Ultimate analysis of pellet samples compared to paper sludge pellets (PS100).  
It can be noticed that sample SSPS 45/45/10 shows a similar concentration as the pellets made of pure straw. The highest value of ash was measured for the sample SSPS 40/40/20. For comparison, the paper sludge (PS100) contains a high ash concentration, and it is almost 100 times higher than that of SD-100.
# A Depression Recognition Method Based on the Alteration of Video Temporal Angle Features
## 3 Proposed Framework
### Robustness Analysis of Angle Features  
Facial expressions primarily manifest through variations in angle information. For instance, a smile is characterized by an upward movement of the corners of the mouth, while intense crying involves a downward movement of the corners of the mouth. In this paper, we extract features A\({}_{11}\) to A\({}_{14}\) to precisely capture these expression changes. Human head movement can be decomposed into six components: horizontal translation (x-axis), vertical translation (y-axis), depth translation (z-axis), yaw rotation (left/right tilt), pitch rotation (head up/down), and roll rotation (left/right head). It is important to investigate whether these six actions have an impact on our angle features and if the features extracted in this study effectively withstand the influence of these actions, thus demonstrating their robustness. The subsequent section presents an analysis and discussion on the robustness of these features.
# An Attention-Based Method for Remaining Useful Life Prediction of Rotating Machinery
## 3 Model Verification and Comparison
### Prediction Experiments Using the Attention-GRU Model  
#### 3.3.1 Model Parameter Configuration  
The original RUL estimation method of the XJTU-SY dataset calculates the time from the point when the bearing starts rotating to when the vibration amplitude reaches the level of failure. To verify our proposed model, we redefine the useful life range of the bearing. As RUL prediction is not performed on \"healthy-state\" samples, the weight assigned to these samples is 0 in feature extraction, and the point of the \"degradation-begun state\" is taken as the starting point of RUL prediction. The RUL, marked as \(R\), is represented by a percentage, and the RUL of the \(i\)-th sample in the \(j\)-th dataset \(R\left(x_{ji}\right)\) can be calculated by Equation (15):  
\[R\left(x_{ji}\right)=\left\{\begin{array}{cc}None,&i<T_{st}\\ \frac{i-S_{j}}{T_{st}-S_{j}}\times 100\%,&T_{st}\leq i\leq S_{j}\end{array}\right. \tag{15}\]  
Figure 9 shows the structure of the attention-GRU model for bearing RUL prediction experiments. The model consists of two attention-GRU layers and two dense layers (Dense1  
Figure 8: Clustering result of bearing useful life data under working condition 2.  
and Dense2), and each attention-GRU layer consists of 64 nodes; the first dense layer has 32 nodes, and the second dense layer has 1 node. The network model receives input vectors with a length of 64 and outputs one prediction result. The optimizer used for training is the Adam optimizer; 32 groups of samples are set as one training batch, and each training involves 30 epochs, as shown in Table 4.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 5 Experimental Evaluation
### Experimental Setting  
Both our Sparse Time-Series Clustering framework and the baseline, which applies \(k\)-means to the original datasets, are implemented in Python. For the application of \(k\)-means to the sparse (resp. the original) dataset, we use \(\beta\)-DTW (resp. the standard DTW) and DBA for updating the cluster representatives in each iteration. We use the GPyTorch library [35] for the implementation of Sparse Gaussian Process Regression and the Tslearn package [36] for the implementation of \(k\)-means. Our experiments run on an Intel(R) Xeon(R) Silver 4210 CPU (2.20 GHz) with 16 GB of RAM.
## 2 System Model and Problem Formulation  
Consider a half-duplex two-way cognitive relay network that consists of two source nodes \(S_{1}\) and \(S_{2}\) with a fixed power supply and a passive relay node with energy harvesting ability. All the terminals are equipped with a single omnidirectional antenna, and the antenna gain is normalized to 1. It is assumed that a direct link between the source nodes does not exist. We adopt the power splitting receiver architecture and DF protocol at relay. The system model is as shown in Figure 1.  
The information exchange of the whole transmission needs two time slots: a multiple access (MA) transmission phase and a broadcast (BC) phase. During the MA phase, source nodes \(S_{1}\) and \(S_{2}\) transmit their own information to relay \(R\). Due to the broadcasting nature, PU receives the information from source nodes \(S_{1}\) and \(S_{2}\) as interference. To ensure the performance of the primary network, an interference threshold to restrict the total transmission power of source nodes \(S_{1}\) and \(S_{2}\) is set. Once the relay receives the signal, it partitions it into two parts: one part for energy harvesting, the other for information decoding. In the BC phase, the relay forwards the decoded signals to source nodes \(S_{1}\) and  
Figure 1: System model for two-way cognitive relay network.  
\(S_{2}\) with the harvested energy. Similarly, to ensure the performance of the primary network, the transmission power of the relay should be under the interference threshold.