esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
## 1 Introduction
However, with the increase in network layers, there are challenges such as the gradient problem, network degradation, and large system resource requirements, which limit the further development of this approach in the field of fault diagnosis [10]. Solving these problems is the key to promoting a more robust and feasible application of deep neural networks in fault diagnosis. [PERSON] et. al. proposed a fault diagnosis method for rolling bearing faults based on ICEEMDAN combined with the Hilbert transform (ICEEMDAN-Hilbert) and a residual network (ResNet) [11]. Based on acoustic and vibration data, [PERSON] et al. constructed a domain adaptive residual neural network (DA-ResNet) model based on maximum mean difference (MMD) and residual connections for the cross-domain diagnosis of rolling bearings [12]. [PERSON] et al. proposed replacing the fully connected layer portion of the traditional RENET with global average pooling (GAP) technology. This approach effectively solves the problem of there being too many parameters in the traditional RESNET model [13]. Current data-driven fault diagnosis methods based on deep neural networks mainly focus on the learning of deep information. These network models usually have a single input and the final recognition accuracy is relatively low. To enhance the accuracy of fault identification, a multi-transform domain processing method is proposed in this paper. In this approach, the original signal, optimal modal components obtained from variational mode decomposition, and continuous wavelet transform time-frequency graphs are combined to form multi-channel signals. This enables the input signal to contain multiple-domain information simultaneously. This signal is then fed into a residual attention network for deep learning to better extract hidden fault information. Considering that different features have varying degrees of information transmission as the network depth and number of features increase, this paper suggests utilizing the Efficient Channel Attention mechanism to assign weights to learned features in order to differentiate them effectively. Finally, Softmax is employed for fault identification.
## 3 Therapeutic Management of EGPA
### Anti-IL-5-Targeted Therapies  
Lung involvement is not included among the factors defining severe EGPA (Table 1); however, the lung is the most commonly involved organ in EGPA (\(\geq\)90% of cases) [1, 2, 8, 11]. Moderate-to-severe asthma is the main clinical manifestation and chest X-ray may show transient pulmonary infiltrates, which are due to the accumulation of eosinophils. Upper airway involvement is also common, in the form of chronic rhinosusitis with nasal polyps [1, 2, 8]. Asthma is managed according to international guidelines [16]; however, the severity of symptoms may require prolonged courses with oral steroids, which are difficult to taper. Therefore, IL-5-targeted therapy may be implemented to spare glucocorticoid side effects. To date, therapeutic monoclonal antibodies capable to interfere with the IL-5 pathway and, consequently, on eosinophil growth, recruitment, activation, and survival, include mepolizumab, reslizumab, and benzizumab [17, 18]. Appolizumab and reslizumab act by sequestering soluble IL-5, making it not available to eosinophils, whereas benzalizumab binds to the IL-5 receptor, thus blocking access to IL-5, but is also able to induce eosinophil killing by natural killer cells through antibody-dependent cell toxicity (ADCC) (Figure 3) [17, 18]. All three monoclonal antibodies have been approved for treatment of moderate-to-severe eosinophil asthma as add-on therapy in patients unsatisfactorily controlled by standard therapy [19], thus being theoretically administitable in EGPA-associated eosinophil asthma as well.  
\begin{table}
\begin{tabular}{l l} \hline \multicolumn{1}{c}{**Original 1996 FFS**} & \multicolumn{1}{c}{**Revised 2011 FFS**} \\ \hline Cardiac involvement & Age \textgreater{} 65 years \\ Gastrointestinal disease (bleeding, perforation, infarction, or pancreatitis) & Cardiac insufficiency \\ Renal insufficiency (plasma creatinine concentration \textgreater{}1.6\) & Renal insufficiency \\ (stabilized peak creatinine 1.7 mg/dL [150 micromol/L]) & Gastrointestinal involvement \\ Proteinuria (\textgreater{}1 g/day) & Absence of ENT manifestations (presence is associated with a better prognosis) \\ \hline \end{tabular} ENT: ear, nose, and throat. The presence of each factor is given one point. The FFS score ranges from 0 to 2: a score of 0 is given when none of the factors is present, a score of 1 for 1 factor, and a score of 2 for 2 or more factors. The 2011 set of criteria has now replaced the original 1996 items.  
\end{table}
Table 1: Comparison of 1996 and 2011 five factor score (FFS) items.  
Moreover, apart from the upper and lower airways, eosinophils are also believed to play a crucial role in determining tissue damage in other typical manifestations of EGPA, such as peripheral neuritis and vasculitis [7]. Therefore, IL-5-targeted therapies may also result in control of non-asthma features of EGPA.
## 3 Experimental Results
### Histograms  
In view of the results obtained for the number of freezes and freeze intensities, in the sequel, the histograms of the freeze length \(x\) accumulated over all four quadrants are provided and discussed for the distances \(d=10\,\mathrm{m}\), \(12.5\,\mathrm{m}\), and \(15\,\mathrm{m}\).  
The histograms of freeze length shown in Figure 9a-c indicate a few short freezes. The minimum freeze length of \(x_{\mathrm{min}}=1\), i.e., two subsequent video frames are identical, is observed for the cases of \(d=12.5\,\mathrm{m}\) and \(15\,\mathrm{m}\) while \(x_{\mathrm{min}}=2\) is the minimum freeze length for \(d=10\,\mathrm{m}\). On the other extreme of large freeze lengths, these are obtained as \(x=56\) for \(d=12.5\,\mathrm{m}\) and \(x=59\) for \(d=15\,\mathrm{m}\). Given the minimum freeze length of \(x_{\mathrm{min}}=1\), the histograms are right-skewed with the respective distributions leaning to the left and the tails being drawn out to the right.  
To assess the central tendencies of the freeze length data shown in the histograms, the mean (average value of freeze length), the median (separating the higher from the lower half of freeze lengths), and mode (most frequently occurring freeze length) are shown in Table 2. The standard deviation (SD), skewness, and kurtosis are also provided in the table. Depending on the distance \(d\), the most frequently occurring freeze lengths are 3 to 4 frames while a median freeze length of 5 frames was observed for all distances. The mean varies between 5.0737 and 7.0909 frames depending on the distance \(d\) with the related SDs of 2.2467 and 6.0678 indicating higher spread of freeze length values for the larger distances. The asymmetry of the unimodal histogram of freeze length about the mean is minor for the distance \(d=10\,\mathrm{m}\) with a positive skewness of 0.6938. Pronounced tails on the right of the histograms develop with increasing distance, i.e., skewness of 3.1341 for \(d=12.5\,\mathrm{m}\) and 4.1254 for \(d=15\,\mathrm{m}\). In view of the statistical modeling of the freeze length characteristics conducted in Section 4, it should be noted that an exponential distribution has a skewness of 2 while geometric and shifted geometric distributions have a skewness greater than or equal to 2. The kurtosis value is largely determined by the tails (outliers) of the histogram of the data [26]. As can be seen from the histograms in Figure 9a-c, the tailedness increases with increased distance. The small sample kurtosis of 0.1249 at \(d=10\,\mathrm{m}\) characterizes a light tail while the tails become heavier at \(d=12.5\,\mathrm{m}\) and \(d=15\,\mathrm{m}\) with significantly larger sample kurtosis of 13.8458 and 24.8990, respectively. It should be noted that heavier tails are an indication of outliers which, in the context of this work, are due to single occurrences of long freezes.  
Figure 8: Freeze intensities observed in each quadrant for the different distances.  
Figure 9: Histograms of freeze length \(x\) measured in frames for different transmitter-receiver distances.
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Congestion Control Mechanisms in CoAP
#### 4.1.9 FASOR  
This congestion control mechanism works in the case of bufferbloat condition and copes up with high link error rates. In Fast-Slow RTO (FASOR) [26], RTO computation is separated into two categories. Fast RTO computation is employed for unambiguous RTT samples while the Slow RTO computation is performed for ambiguous RTT samples to overcome deep bufferbloat and heavy congestion. This avoids extra delays and also helps under link errors by reducing flow completion.  
For retransmission timer backoff, FASOR introduces a novel and self-adaptive timer containing three transitive states i.e., FAST / FAST-SLOW-FAST / SLOW-FAST. Each of these states have different backoff logic and it adapts to the dynamic network states. This enables FASOR to prevent extra power consumption due to unnecessary retransmissions and balance between the tradeoff of aggressive and conservative retransmissions.  
The major issue with the proposed scheme is that it does not include special logic for senders remaining idle that is typical for CoAP. Moreover, the upper bound of slow RTO is kept 60 s, which can be improved further.
## 3 Description of the Launcher System Modules
### Power Circuit  
The power circuit is the power source for the launcher system. It generates five isolated DC sources to energize the control circuit with all needed supplies and to provide a high-voltage direct current to charge the capacitor bank, as illustrated in Figure 4. The voltage level can be changed by using a multi-output high-frequency transformer. The power circuit is based on the principle of energy exchange between a primary coil of the high-frequency transformer and a capacitor to generate a high-frequency alternating current. This process is controlled by two high-frequency high-current MOSFETs and some diodes. Then, four step-down coils and a step-up coil are wrapped around the same high-frequency transformer core and, finally, these output sources are rectified as listed in Table 2.
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 2 System Design
### Function Design  
The observation of point targets in the deep-space background by the EOIR module poses a challenge to the normal operation of the simulation scenario owing to the complex processing flow and high-resolution image output. Even at 64 \(\times\) 64 resolution, the entire system can only generate 1 frame per second (FPS). For real-time target recognition algorithms, this is not enough to support their analysis of target micromotion. To balance accuracy and performance, we divided the process into two parts: target area and background area. The EOIR module simulates the high-confidence targets, while the stars and background are produced by an external system model based on a Central Processing Unit (CPU).  
Figure 3 illustrates the functional flow of the system. Our fast simulation strategy addresses the performance challenge by improving the simulation process and optimizing the program execution, while optimizing the time complexity of the algorithms in the background generation to further enhance the simulation efficiency. At the onset of the simulation, we connected with STK [25] and established the target and seed satellite that carries the infrared sensor to complete the scenario initialization as input. Next, we generated the satellite constellation from the seed satellite, filtered the sensors involved in imaging through chain analysis, recorded their imaging periods, and removed other satellites from the scenario. During the simulation process, the target part contacts the STK data provider service to obtain the SNR of the sensor for each target in bulk. The background part first performs a preliminary screening of stars contained in the nearby table through sensor pointing and field of view (FOV), and then calculates their positions in parallel [18]. Finally, we merge the target and background and introduce diffraction effects and noise [35] asynchronously to complete the image simulation. After the imaging period ends, the system generates infrared image sequences and analyzes them in terms of constellation, waveband, and target micromotion.  
Figure 3: System process.
# Dynamic and Active THz Graphene Metamaterial Devices
## 2 Fundamentals of Graphene
### Nonlinear Optical Property  
Due to its gapless band structure and linear energy-momentum dispersion for its electrons, graphene has a robust nonlinear response [8; 10; 47]. The incident field can induce intraband transition or interband transition of carriers in graphene with the relationship between the Fermi level and the energy of incident photons. The THz frequency is much lower, making the interband transition happen at a low temperature and with undoped graphene (\(E_{F}\approx 0\)) [15]. Thus, compared with near-infrared and visible light, the incident THz field could generally induce the intraband transition of graphene carriers, as shown in Figure 1a,b. Additionally, for the THz field from free space into the substrate with graphene, the THz field transmission could be described by the Tinkham equation [25]:  
\[\widetilde{E}_{t}(w)=\frac{2}{1+n_{s}+Z_{0}\widetilde{\sigma}(w)}\widetilde{E} _{in}(w) \tag{2}\]  
where \(\widetilde{E}_{in}(w)\) is the incident THz field, \(n_{s}\) is the refractive index of the substrate, \(Z_{0}\) is the free-space impedance, and \(\widetilde{\sigma}(w)\) is the intraband conductivity of graphene at THz band. With the incident THz field, the intraband conductivity \(\widetilde{\sigma}(w)\) is nonlinear, it could be described by solving of the Boltzmann equation as [25; 48; 49]:  
\[\widetilde{\sigma}(w)=-\frac{e^{2}v_{F}^{2}}{2}\int_{0}^{\infty}D(E)\frac{ \tau(E)}{1-iw\tau(E)}\frac{\partial f_{FD}(E,\mu_{c},T_{e})}{\partial E}dE \tag{3}\]  
where \(v_{F}=1\times 10^{6}\) m s\({}^{-1}\) is the Fermi velocity of the relativistic Dirac fermions in graphene, \(\tau(E)\) is the energy-dependent electron scattering time, \(D(E)=2|E|/\pi(\hbar v_{F})^{2}\) is the density of states, and \(f_{FD}(E,\mu_{c},T_{e})=\{\exp[(E-\mu_{c})/k_{B}T_{e}]+1\}^{-1}\) is the Fermi-Dirac distribution function for chemical potential \(\mu_{c}\) and electron temperature \(T_{e}\).  
From Equation (3), we could find that the intraband transition conductivity of graphene is determined by \(\mu_{c}\), electron temperature (\(T_{e}\)), and density of states of the graphene energy bands \(D(E)\). As shown in Figure 1c, the energy of the incident THz is transferred to electrons in graphene, leading to an increase in \(T_{e}\), and the concomitant decrease of \(\sigma\). It means that the rise of THz frequency and THz intensity could both lead to a decrease in \(\sigma\) (Figure 1d). In the following section, based on the different responses of graphene THz nonlinearity, we briefly introduce the THz nonlinear mechanism by THz nonlinear absorption, THz high harmonic generation, and different frequency generation (DFG).
# A Novel Adaptive Gain of Optimal Sliding Mode Controller for Linear Time-Varying Systems
## 2 Design of Optimal Control Law  
In this study, a general type of the linear system with varied time is described as follows [12]:  
\[\dot{x}_{L\xi}(t)=G_{L\xi}(t)x_{L\xi}(t)+H_{L\xi}(t)u_{L\xi}(t) \tag{1}\]  
where, \(\dot{x}_{L\xi}(t)\in R^{n}\) is the state of system, \(u_{L\xi}(t)\in R^{m}\) is the input control, \(G_{L\xi}(t)\) and \(H_{L\xi}(t)\) are continuous time functions, \(t_{0}\) is initial time, and \(T\) is final time. The cost function is defined as below with the chosen value \(\gamma\):  
\[J=\sum_{i=1}^{n}\frac{\Xi_{i\xi i}\big{|}x_{L\xi i}(T)\big{|}}{\gamma}+\frac{1 }{2}\int\limits_{t_{0}}^{T}\Bigl{(}u_{L\xi}^{T}(s)M_{L\xi}(s)u_{L\xi}(s)+x_{L \xi}^{T}(s)N_{L\xi}(s)x_{L\xi}(s)\Bigr{)}ds \tag{2}\]Using Equation (2), the optimal control law is then determined as follows:  
\[u_{L\xi}^{*}(t)=-M_{L\xi}^{-1}(t){H_{L\xi}}^{T}(t)q_{L\xi}(t) \tag{3}\]  
where the function \(q_{L\xi}(t)\) is defined as follows:  
\[q_{L\xi}(t)=\gamma Q_{L\xi}(t)sign(x_{L\xi}(t)) \tag{4}\]  
Substituting Equation (4) into Equation (3), Equation (3) can be rewritten as follows:  
\[u_{L\xi}^{*}(t)=-\gamma M_{L\xi}^{-1}(t){H_{L\xi}}^{T}(t)Q_{L\xi}(t)sign(x_{L \xi}(t)) \tag{5}\]  
In the above equations, \(Q_{L\xi}(t)\) is the symmetric matrix (\(n\times n\)). This matrix is called gain matrix function/or gain matrix of the system. Substituting Equation (5) into Equation (1), the system (1) is rewritten as follows:  
\[\dot{x}_{L\xi}(t)=G_{L\xi}(t)x_{L\xi}(t)-H_{L\xi}(t)M_{L\xi}^{-1}(t){H_{L\xi}} ^{T}(t)(\gamma Q_{L\xi}(t)sign(x_{L\xi}(t))) \tag{6}\]  
From Equation (6), in this work, a new gain matrix function \(Q_{\Delta L}(t)\) is suggested as follows:  
\[\dot{Q}_{\Delta L}(t)=N_{L\xi}x_{L\xi}(t)^{2}-\gamma x_{L\xi}(t)Q_{\Delta L}(t) \tag{7}\]  
Substituting Equation (7) into Equation (5), the control \(u_{L\xi}^{*}(t)\) is rewritten as:  
\[u_{L\xi}^{*}(t)=-M_{L\xi}^{-1}(t){H_{L\xi}}^{T}(t)Q_{L\xi}^{*}(t)sign(x_{L\xi} (t)) \tag{8}\]  
where, \(Q_{L\xi}{}^{*}(t)=\gamma Q_{\Delta L}(t)\). The function \(Q_{\Delta L}(t)\) can be found from the following derivative function:  
\[\dot{Q}_{\Delta L}(t)=-N_{L\xi}(t)\big{|}x_{L\xi}(t)\big{|}-\gamma{G_{L\xi}}^ {T}Q_{\Delta L}(t)\big{|}x_{L\xi}(t)\big{|} \tag{9}\]  
In order to derive the above equation, consider the optimal control problem for linear time-varying system (1) with respect to the Bolza criterion without a non-integral term:  
\[J_{L\xi}=\frac{1}{2}\int\limits_{t_{0}}^{T}\Big{(}u_{L\xi}^{T}(s)M_{L\xi}(s) u_{L\xi}(s)+x_{L\xi}^{T}(s)N_{L\xi}(s)x_{L\xi}(s)\Big{)}ds\]  
In this derivation, Equation (5) is simplified as \(q_{L\xi}(t)=\gamma Q_{L\xi}(t)x_{L\xi}(t)\). This simplification is used for objective to find a range of gain values for the system in optimal control law. The appearance of \(sign(x_{L\xi}(t))\) will limit the boundary of the gain value, so this expression must be replaced by the original variable \(x_{L\xi}(t)\).
## 2 Vehicle Detection Framework Using Selective Multi-Stage Features
### Designing and Training the CNN Model  
The CNN model could be freely designed depending on the application. The purpose of the model in the proposed vehicle detection framework was to selectively extract features reflecting the characteristic vehicle image information from each layer. The feature map aspects learned in the upper  
Figure 1: Proposed vehicle detection framework.  
and the lower layers differed considerably; therefore, the CNN model had to have sufficient depth to ensure that the lower and the upper layers could be distinguished. The AlexNet [10] characteristics were analyzed by [PERSON] and [PERSON] [11] using [PERSON]'s visualization technique and clarified that more than five convolution layers are required to distinguish between the lower and the upper layers. Subsequent CNN research has suggested that very deep CNN models are preferable [12; 13]. However, the proposed CNN model was not intended for image classification, but for selectively extracting features representing the vehicle image information from each layer. Therefore, we designed the CNN model with five convolution layers, similar to AlexNet [10] or ZFNet [11].  
The factors considered important in the design of the CNN model were the number of filters in the convolution layers and the filter size and stride. More filters meant that more features could be learned from each layer. Furthermore, visualization techniques could be used to systematically identify the patterns of the learned feature maps. Therefore, we designed a CNN model with 60 filters for each convolution layer, each having a size of \(5\times 5\) pixels and a stride of 1, on the basis of the recent CNN model design trends towards smaller filter sizes and strides [12]. Rectified linear unit (Relu) layers were used to accommodate non-linearity and two max-pooling layers. Table 1 and Figure 2 show the proposed CNN model details.  
The CNN model was then trained with the prepared vehicle image training dataset, using data augmentation and dropout techniques to prevent overfitting. For data augmentation, we randomly flipped the vehicle images horizontally with 50% probability and took a random crop from the training image with the same size as the input data. The dropout technique omitted some neurons in the input or the hidden layers. For other training options, we set max epochs = 77, initial learning rate = 0.001, and mini batch size = 128. We also used cross-entropy as the cost function and softmax as the activation function.
## 2 System Model and Problem Formulation
### Problem Formulation  
In this work, we aim to maximize the achievable sum rate of all terrestrial UEs by optimizing the transmit power, receive beamformer, and UE association. The proposed problem is formulated as  
\[\begin{split}&\max\limits_{\mathbf{v}_{m,k}\cdot p_{m,k}\cdot \mathbf{\alpha}_{m,k}}R_{sum}=\sum\limits_{k\in\mathcal{K}}R_{k}\\ &\text{s.t.}\quad\text{C1}:p_{m,k}\leq T_{k}^{max},\quad\forall k \in\mathcal{K},m\in\mathcal{R},\\ &\quad\text{C2}:\alpha_{m,k}\in\{0,1\},\quad\forall k,m,\\ &\quad\text{C3}:\sum\limits_{m\in\mathcal{R}}\alpha_{m,k}=1, \quad\forall k,\\ &\quad\text{C4}:R_{m}\leq C_{m},\quad\forall m\in\mathcal{M},\\ &\quad\text{C5}:R_{k}\geq r_{k},\quad\forall k,\end{split} \tag{7}\]  
where C1 limits the maximum transmit power of UE \(k\), C2 constrains the UE user association indicators, and C3 denotes that a UE can only access one AP. Constraint C4 denotes the  
Figure 1: The NOMA-based terrestrial–satellite network architecture.  
achieved sum rate of BS \(m\) should be less than its backhaul link capacity \(C_{m}\). Constraint C5 guarantees the UE's QoS. Note that this work primarily considers communication scenarios where the access capacity of terrestrial BSs is limited, necessitating the introduction of satellites to provide service for terminals unable to connect to BSs. Consequently, satellites are specifically employed to guarantee normal communication in this region, and typically, satellite downlink communication rates are high. Thus, the backhaul link capacity constraint is not imposed on the satellite.  
The optimization problem (7) is a mixed-integer non-linear programming problem, characterized by its pronounced non-convexity and NP complexity. Additionally, the update of the user association strategy triggers alterations in user channel conditions, making the direct transformation of the optimization problem (7) into a convex form exceedingly difficult. To address this complexity in a more efficient manner, the problem (7) has been strategically partitioned into three stages for resolution.