esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Load Identification Method
The FBG signal, which is the Bragg wavelength shift from a reference condition, is typically converted to strain values by using a wavelength-strain coefficient; however, the physical unit was irrelevant in this instance of machine learning. Therefore, we used the raw Bragg wavelength shifts in wavelength units. The reference Bragg wavelengths were the resting state of the wing without wind. We used sigmoid transfer functions (hyperbolic tangent) in the hidden layer and a linear function in the output layer. The eight outputs corresponded to the lift load on the eight span-wise wing segments. The Levenberg-Marquardt backpropagation algorithm [25] was used as an optimizer, and the Nguyen-Widrow initialization method [26] was used to initialize weights and biases. The learning parameters used are identical to those used in a previous study [25]. Test cases of \"aligned\", \"random\", and \"single\" were used as training data to train the neural network. The \"controlled\" cases were excluded so that they could be treated as new conditions for the neural network after training. For the training data, we used \"aligned\", \"single\", and 80% (232 cases) of the \"random\" test cases for optimization iterations. The balance of the \"random\" test cases was used for validation purposes in order to avoid overfitting. The loss of the validation data was monitored  
Figure 7: Architecture of neural network for load identification. during the optimization iterations. When the loss of the validation data increased across five sequential iterations, the optimization process was stopped and the weights and biases at the minimum loss were selected.
## 2 Theoretical Models of Light Propagation and Light Scattering  
### Radiative Transfer Equation (RTE)  
The RTE, a linear Boltzmann equation, can describe light propagation [23] on a length scale of the mean free path between light and a medium. On the length scale, a colloidal suspension is considered as a scattering medium (continuous medium) with the optical properties as shown in Figure 1a. In this study, we considered homogeneous scattering media. For a 3D medium, the steady-state RTE is given by  
\[[\mathbf{\Omega}\cdot\
abla+\mu_{a}(\mathbf{r})+\mu_{s}(\mathbf{r})]I( \mathbf{r},\mathbf{\Omega})=\mu_{s}(\mathbf{r})\int_{\mathbf{\Omega}\cdot \mathbf{\Omega}^{\prime}}p(\mathbf{\Omega}\cdot\mathbf{\Omega}^{\prime})I( \mathbf{r},\mathbf{\Omega}^{\prime})+q(\mathbf{r},\mathbf{\Omega}), \tag{1}\]where \(I(\mathbf{r},\mathbf{\Omega})\) in J cm\({}^{-2}\)str\({}^{-1}\) represents the light intensity as a function of spatial position vector \(\mathbf{r}=(x,y,z)\in\mathbb{R}^{3}\) in cm; and angular direction (unit direction vector) \(\mathbf{\Omega}=(\Omega_{x},\Omega_{y},\Omega_{y})\) in str. \(\mu_{a}(\mathbf{r})\) and \(\mu_{s}(\mathbf{r})\) in cm\({}^{-1}\) are the absorption and scattering coefficients, respectively. \(p(\mathbf{\Omega}\cdot\mathbf{\Omega}^{\prime})\) in str\({}^{-1}\) is the normalized phase with \(\mathbf{\Omega}^{\prime}\) and \(\mathbf{\Omega}\) denoting the incident and scattering directions, respectively; \(J_{4\pi}\,d\mathbf{\Omega}^{\prime}\) represents the integration over the whole solid angle; and \(q(\mathbf{r},\mathbf{\Omega})\) in J cm\({}^{-3}\) str\({}^{-1}\) is a source function. The anisotropy factor \(g\) characterizes the anisotropy of light scattering and is defined as \(\int_{4\pi}d\mathbf{\Omega}^{\prime}p(\mathbf{\Omega}\cdot\mathbf{\Omega}^{\prime})\cos \mathbf{\Omega}\cdot\mathbf{\Omega}^{\prime}\). The steady-state light propagation model based on the RTE has been widely discussed in various research fields, including photoacoustic imaging [24; 25] and photocatalyst [26; 27; 28]. The light propagation model based on the RTE has been validated by light reflectance measurements for tissue phantoms [29].  
We consider the refractive-index mismatched boundary condition (RMBC) at the boundary [21; 29]. The boundary condition treats the light reflection and refraction at a medium-air interface induced by a difference in the refractive indices between the medium and air as shown in Figure 1b. [PERSON]'s law and [PERSON]'s law give the reflectivity and transmissivity coefficients.
# Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization
## 4 Experiment and Results
### Experiment Setup  
The experiments are conducted in a three-story building of Yeungnam University whose dimensions are 90 \(\times\) 36 m\({}^{2}\) for each floor. Figure 7 shows the area used to conduct the experiments. The localization path is different for each floor and so do the image collection points. The area contains three types of spaces: Area surrounded by walls at a distance of roughly 2 m, the area having a glass at the boundaries, and the wide area where walls are at a distance of 5 to 6 m on each side. Figure 8 contains a few indoor scenes from the building where the experiment is performed. The ground truths are manually marked on the ground and measured by both lasers as well as the measuring tape.  
Figure 7: The path used for experiments, floor 3 (**top**), floor 2 (**middle**), and floor 1 (**bottom**).  
The camera images are captured during different time of the day and lighting conditions may vary slightly. The smartphone is held in front of the user to collect the data. The magnetic data are collected with Samsung smartphone SM-G950N (Galaxy S8) to make the fingerprint database. Galaxy S8 contains a 3-axis magnetometer (AK09916C) which provides the magnetic \(M_{x}\), \(M_{y}\), and \(M_{z}\) values [79]. Total magnetic field intensity is calculated as:  
\[F=\sqrt{M_{x}^{2}+M_{y}^{2}+M_{z}^{2}} \tag{9}\]  
Galaxy S8 contains 3-axis accelerometer (LSM6 DSL) and gyroscope sensors (LSM6 DSL) [80]. The magnetic database is built using three components of magnetic field including \(M_{x}\), \(M_{y}\), and \(M_{z}\). While magnetic field has a total of seven components but not all of them are suitable to make a fingerprint. For example, declination (D), and inclination (I) of the magnetic field are angles which are vulnerable to sudden abrupt movements of the smartphone. The data are collected over a sampling rate of 10 Hz from magnetometer, accelerometer, and gyroscope. The magnetic database is built using Galaxy S8 while the testing is performed using two devices: Galaxy S8 and LG G6 (LGM-G600L).
# Multi-Granularity User Anomalous Behavior Detection
###### Abstract  
Insider threats pose significant risks to organizational security, often going undetected due to their familiarity with the systems. Detection of insider threats faces challenges of imbalanced data distributions and difficulties in fine-grained detection. Specifically, anomalous users and anomalous behaviors take up a very small fraction of all insider behavior data, making precise detection of anomalous users challenging. Moreover, not all behaviors of anomalous users are anomalous, so it is difficult to detect their behaviors by standardizing with single rules or models. To address these challenges, this paper presents a novel approach for insider threat detection, leveraging machine learning techniques to conduct multi-granularity anomaly detection. We introduce the Multi-Granularity User Anomalous Behavior Detection (MG-UABD) system, which combines coarse-grained and fine-grained anomaly detection to improve the accuracy and effectiveness of detecting anomalous behaviors. The coarse-grained module screens all of the user activities to identify potential anomalies, while the fine-grained module focuses on specific anomalous users to refine the detection process. Besides, MG-UABD employs a combination of oversampling and undersampling techniques to address the imbalance in the datasets, ensuring robust model performance. Through extensive experimentation on the commonly used dataset CERT R4.2, we demonstrate that the MG-UABD system achieves superior detection rate and precision. Compared to the suboptimal model, the accuracy has increased by 3.1% and the detection rate has increased by 4.1%. Our findings suggest that a multi-granularity approach for anomaly detection, combined with tailored sampling strategies, is highly effective in addressing insider threats.  
Keywords:insider threat detection; UEBA; anomaly detection; random forest +
Footnote †: journal: Applied sciences
# Multi-View Synthesis of Sparse Projection of Absorption Spectra Based on Joint GRU and U-Net
## 3 Settings for Simulative Studies
### Network Training and Implementation  
The GMResUNet proposed in this study was trained using the PyTorch framework with an L2 loss function, defined as follows [33]:  
\[L_{2}=\frac{1}{N}\sum_{n=1}^{N}\sum_{k=1}^{K}(y_{k}^{*}-y_{k})^{2} \tag{9}\]  
where \(N\) is the total number of samples in each batch of projected data, and \(K\) is the total number of pixels in each distribution. The variables \(y_{k}^{*}\) and \(y_{k}\) refer to the ground truth and predicted values of the \(k\)-th pixel, respectively. The training process consisted of repeating the batch until the loss function converged.  
The optimizer used was the adaptive moment estimation (Adam) [34] optimization method, which combines the concepts of the RMSprop algorithm and the momentum-based approach to not only implement momentum-driven parameter updates but also to adaptively adjust the learning rate to improve the gradient descent. Compared to other adaptive learning rate algorithms, Adam converges faster and learns better. The network was trained on an NVIDIA GeForce RTX 3090 GPU with a learning rate of 2.6 \(\times\) 10\({}^{-4}\) and a training time of nearly 10 h.  
The validation set was used during training to assess the model convergence to avoid network overfitting. In the validation set, the network parameters were not updated. In Figure 9, two plots show the evolution of the loss function for the training and validation sets under four sparse projection data. It can be observed that the model converged quickly in the first 300 periods on the training set and gradually stabilized after 300 periods; the validation set shows a similar pattern, so the network was not overfitted. In particular, the model converged slowly when N = 2 and quickly for N > 2 (N = 4, 8, 16). The results show that the model converged faster as the richness of the projected data increased; the difference in the convergence of the loss function was smaller for projection angles N above 4.
## 2 Key Challenges of Vactrain's Wireless Communications
Therefore, the time selectivity is enhanced due to the large Doppler frequency shift and expansion. For example, if the carrier frequency is 3.3 GHz, the maximum Doppler frequency shift is 3055 Hz at a speed of 1000 km/h, whereas the frequency shift for a pedestrian who moves at a speed of 10 km/h is only 24 Hz. Note that most of the current wireless communication systems are designed for the Doppler frequency shift on the order of hundreds of Hz. Due to the high Doppler frequency shift, the carrier frequency offset (CFO) between the transmitter and receiver will occur. However, even for systems without Doppler shifts, small CFO may also occur due to the instability of oscillators in wireless transceivers. Thus, in the multi-carrier systems, such as Orthogonal Frequency Division Multiplexing (OFDM), the large Doppler frequency shift will destroy the orthogonality among the sub-carriers, and introduce the Inter-Carrier Interference (ICI). ICI will seriously degrade the synchronizer performance and increase the system bit error rate. In addition, the movement speeds of the terminals will change with respect to time, resulting in the time-varying Doppler spreads and non-stationary fading coefficients. Thus, the accurate modeling and analysis of high mobility channels inside the vacuum tube will be a challenging task. 4. High penetration loss: As is mentioned above, the vactrain is operating in the hermetically sealed vacuum tube. To ensure the air tightness, the tube walls are made of special alloys or reinforced concrete, which are difficult for wireless signals to penetrate. According to the measured date, the penetration loss caused by the train body is usually between 20 dB to 35 dB [31]. Under this superposed penetration losses, the Signal-to-Noise Ratio (SNR) on the received signal in both uplink and downlink will be seriously degraded. 5. Frequency spectrum-free environment: The co-channel interference is a serious problem in the current wireless communication for railway. For example, GSM-R in China uses the 883-889 MHzfrequency band for the uplink and the 930-934 MHz frequency band for the downlink. This band is shared with services offered by the operator of China Mobile, and thus yield serious co-channel interference. In addition, Wireless Fidelity (WiFi) technology is used in the subway of China for train-to-ground communication and operates at 2.4 GHz [32], which is an unlicensed frequency band, causing serious interference and security problem. However, in scenarios of enclosed metal vacuum tube, the spectrum space in the tube is relatively free from the external environment. Thus, there will be no more the limitation of the frequency and bandwidth for wireless communication in the tube. Based on this, the frequency band and bandwidth can be selected unlimitedly to avoid interference from the user signal and then ensure the stability of wireless transmission of operational signal. Besides, the frequency/ space division multiplexing technologies [33] can also be adopted without limitation to increase the transmission capacity.
## 3 Results
The Reynolds number values required under normal conditions (no vibration) to achieve the Nusselt number value under vibration conditions are presented in Figure 10 at each frequency and amplitude. Using the presented Reynolds number values, the percentage reduction in Reynolds number (\(PR_{Re}\)) is calculated. It is used as an indicator to perform the comparative analysis of the reduction in Reynolds number under vibrating conditions. In this regard, investigating \(PR_{Re}\) eventually estimates the possible reduction in the size of the cooling system based on the vibrational characteristics at each wave shape. Figure 7:0  
Figure 12a,b show the variations in \(PR_{Re}\) with vibrational frequency and amplitude at both sinusoidal and square wave shapes. Given that no heat transfer enhancement with a lower frequency range (\(f\) < 30 Hz) occurs, no opportunity for reductions in Reynolds number arises in this frequency range. However, after exceeding the critical frequency (\(f\) = 30 Hz), \(PR_{Re}\) shows a steady rise with an increase in frequency for each type of wave shape. Given that the square wave-shaped vibration shows a higher heat transfer enhancement than the sinusoidal vibration at each frequency level, a higher percentage reduction in Reynolds number is obtained with increasing frequency under square wave-shaped excitation. As observed from Figure 12a, 25% and 36.5% reductions in Reynolds numbers are obtainable by applying sinusoidal and square wave-shaped excitation, respectively, at \(f\) = 100 Hz and \(Amp\) = 0.003 m compared to the conventional cooling system. When the peak-to-peak amplitude increases at a constant frequency of 100 Hz, \(PR_{Re}\) also increases. As expected, an increase in \(PR_{Re}\) is found to be higher with square wave-shaped vibration. Figure 12b shows that \(PR_{Re}\) rises to 28.8% with sinusoidal wave-shaped and 42.2% with square wave-shaped vibration at \(f\) = 100 Hz and \(Amp\) = 0.005 m. This result indicates that using vibration on heat sink structures can achieve a similar thermal performance at lower Reynolds numbers than can the conventional Reynolds numbers re  
Figure 7:1 Variation in percentage reduction in Reynolds number with vibrational parameters: (**a**) frequency at constant \(Amp\) = 0.003 m; (**b**) peak-to-peak amplitude at constant \(f\) = 100 Hz. quired to achieve the same thermal performance under nonvibrating conditions. Moreover, applying square wave-shaped vibration helps to achieve a further reduction in the size of the cooling system compared to sinusoidal vibration at every investigated frequency (\(f>30\) Hz) and amplitude level. Thus, the square-shaped vibration can achieve similar thermal performance with a smaller cooling system than a static heat sink with a much larger cooling system. Thus, this enhancement method might be well suited to the electronic industry because it is coherent with the challenges of compactness experienced in most modern electronic systems.
# Self-Isolated Dual-Mode High-Pass Birdcage RF Coil for Proton and Sodium MR Imaging at 7 T MRI
## 3 Results  
### EM Simulation  
Figure 5 shows the B\({}_{1}^{+}\)-field and SAR maps normalized to 1 uT of the total input power for the tapered high-pass birdcage RF coil at its resonant frequency for \({}^{1}\)H and \({}^{23}\)Na. The B\({}_{1}^{+}\)-field uniformity measured at the central axial plane of the RF coil was quantified to be 64.5% at the \({}^{1}\)H frequency. In contrast, at the \({}^{23}\)Na resonant frequency, the field uniformity was 90.2%.
# Real-Time Location-Positioning Technologies for Managing Cart Operations at a Distribution Facility
## 4 Case Study: Tracking of Carts in a Large and High-Traffic RFID-Enabled Air Mail Centre
### Tracking of Cart Movements  
Figure 9 demonstrate the paths that selected carts were moving in a selected hour (left) and on a selected day (right). Again, the carts are represented by different colors. In the figure, we observed that the movements of carts are very frequent. We are also able to identify some relocation movements of carts (i.e., those movements that are not for delivering the mail bags from the sorting machine to the ULD loading area), which are those paths that are bounded within a small area. Again, if such relocations of carts are very frequent, the AMC management should reconsider the staging rules. The platform is also able to calculate the number of moves and distance traveled by carts within particular time periods. For example, Figures 10 and 11 depict the total number of moves and the total distance traveled by all tagged carts within the implementation period in different hours. These figures could reflect how busy the AMC was in different hours. The higher is the number, the busier was the facility. These figures, therefore, could potentially be used as an important piece of information for resource allocation and planning. We observe that both figures have similar patterns; the peak period was from 07:00 to 20:00 where the carts moved very frequently and traveled long distances. Note that, in our calculation for distance, we assume that the cart moved from one point to another in a straight line. Therefore, the actual distances traveled could be longer than what we presented, but we do not expect the errors are significant due to our very frequent collection of location data. By using the distance traveled from one point to another and the time required, the system is  
Figure 8: Offline visualization of cart movements. The locations of the carts: for a selected hour (**left**); and for a selected day (**right**). The different colors represent different carts. The circle size is directly proportional to the total duration that the cart was staying at the same location point.  
also able to estimate the speed of the cart. Figure 12 depicts the average speed of all tagged carts in different hours. We also observe that the speed was higher during the peak hours. That might be due to the fact that during the peak hours the AMC was much busier and the staff members had to rush at moving carts for loading mail bags to ULDs. Similarly, this speed could be underestimated as we assumed that the carts moved in a straight line from each point to another.  
Figure 10: The total number of movements of all tagged carts in different hours.  
Figure 9: Paths that selected carts were moving: in a selected hour (**left**); and on a selected day (**right**). The different colors represent different cars.
# Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors
## 1 Introduction
### Motivation for Using VAEs in WSNs  
The performance of WSNs is not satisfied in several applications, where it is required to operate with huge data generated by multiple sensors simultaneously. The performance is degraded due to limited energy availability, inefficient computational model, and reduced bandwidth allocation. These constraints can be addressed by implementing an efficient compression technique, and that reduces the size of data to be transmitted effectively. The nature of VAEs indicates that they can compress the information available on any data by preserving the important features. The encoding option of VAEs can provide the compressed sensor data in a lower dimension latent space, to enable reduced data communication, to achieve a better energy conservation on limited bandwidth resources. Moreover, the robustness and intelligence of a WSN model can be improved with VAEs, and that can result in anomalies, faults, and intrusion detection. The latent space representation of data in VAEs can provide privacy to the sensitive data that are used for communication. In general, the integration of WSNs with VAEs can improve the overall integrity of WSNs including energy efficiency, security, and fault tolerance. The motive of the proposed work is as follows:  
* Provide an efficient compression technique for sensor data without losing valuable information;
* Improve the routing process for the transmission of data from the source to the destination;
* Enhance the energy efficiency of the overall sensor network to improve the network lifetime.  
Figure 1: Transmission efficiency improvement techniques in WSNs.
# An Optimal Power Control Strategy for Grid-Following Inverters in a Synchronous Frame
## 3 Discrete LQR-ORT Controller Design
### Computation of Suboptimal LQR Controller  
Unlike classic LQR approaches, the LQR controller in this work must achieve a non-zero final state [18]. To achieve this, the power tracking error is defined by  
\[e_{\rm dq}[k]=\ CX[k]-r[k] \tag{11}\]  
Figure 3: Control scheme for the LQR-ORT with known external input.  
where \(r[k]\) is the reference for the LQR controller and \(X[k]\) is the augmented state vector defined in (8). The discrete LQR-ORT cost function is given by [18]:  
\[J[k_{0}]=e[T]^{T}S_{p}[T]e[T]+\sum_{k=k_{0}}^{T}\left\{e[k]^{T}Q_{p}e[k]+E_{dq}^ {T}[k]R_{p}E_{dq}[k]\right\} \tag{12}\]  
where \(S_{p}\) and \(Q_{p}\) are both symmetric and positive semi-definite weighting matrices and \(R_{p}\) is a symmetric positive definite weighting matrix. Note that unlike the classic LQR cost function, (12) includes only input and error signals and does not include the state vector \(X[k]\). To solve this minimization problem, the modified discrete algebraic Riccati equation (DARE) must be solved [18]:  
\[S_{p}=\overline{A}_{T}^{T}S_{p}\left(\overline{A}_{T}-\overline{B}_{1T}K_{d} \right)+C^{T}Q_{p}C. \tag{13}\]  
The suboptimal controller matrix \(K_{d}\) is given by  
\[K_{d}=\left(\overline{B}_{1T}^{T}S_{p}\overline{B}_{1T}+R_{p}\right)^{-1} \overline{B}_{1T}^{T}S\overline{A}_{T}. \tag{14}\]