esa-sceva/satcom-chunk-collection huggingface dataset:
# Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification
## 4 Experimental Result and Discussion
### Ablation Experiments  
To investigate the contribution of the modules designed in DIFSCL to HSI classification, we conducted comprehensive ablation experiments on three datasets. DIFSCL includes crop data augmentation, supervised contrastive loss (\(L_{con}\)), and domain discrimination loss (\(L_{d}\)), where crop data augmentation replaces the original noise data augmentation. Therefore, in the comparative experiments, the scenario without crop data augmentation represents the use of noise data augmentation. We validated the benefits they bring to the experiment by excluding some modules from DIFSCL as controls. In the MSAAN section, our contribution is the introduction of the MSAA module. To validate the effectiveness of this module, we conducted a comparative experiment using a network from which the MSAA module was removed.  
As shown in Tables 7-9, we can observe the impact of each module in the DIFSCL method on classification performance. Notably, crop data augmentation has the most significant influence on the classification results, which aligns with a consensus in contrastive learning that data augmentation is crucial. Our complete model, represented as \(v_{0}\) in Tables 7-9, performed the best, further proving that our proposed DIFSCL approach is effective.  
To validate the effectiveness of both our MSAA module and the DIFSCL method, we designed two comparative experiments. As shown in Tables 10, Method 1 represents the experimental setup using DIFSCL's feature extraction network without implementing the DIFSCL method, Method 2 represents the setup using the DIFSCL method but without the MSAA module, Method 3 represents a feature extraction network that uses the DIFSCL method but removes the channel attention module, and Method 4 represents our proposed complete DIFSCL method. The results demonstrate that the method with the MSAA module outperforms those without it, and the approach using DIFSCL achieves better results than the one without DIFSCL, confirming that both our proposed MSAA module and DIFSCL method contribute positively to the classification performance of HSI.  
Figure 12: Visualization of 2D features on the Salinas dataset. (**a**) DFSL. (**b**) DCFSL. (**c**) GIA-CFSL. (**d**) CDFSCL. (**e**) ADAFSL. (**f**) DIFSCL.
# Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage
## 4 Solver by Alternating Direction Method of Multipliers (ADMM)
### **F** Sub-Problem Solving  
The sub-function of the sub-problem of **F** is written as follows,  
\[J_{\textbf{F}}=\frac{1}{2}\|\textbf{H}\textbf{*}\textbf{F}-\textbf{G}\|_{2}^{2}+ \sum_{i=1}^{5}\big{(}\frac{\beta_{i}}{2}\|\textbf{Z}_{i}-\textbf{K}_{i\textbf{*} }\textbf{F}\|_{2}^{2}-\Big{<}\beta_{i}\overline{\textbf{Z}}_{i},\textbf{Z}_{i}- \textbf{K}_{i\textbf{*}}\textbf{F}\Big{>}\big{)}. \tag{6}\]  
A supplement to Equation (6) can be formulated as follows,  
\[0=\frac{\beta_{i}\|\overline{\textbf{Z}}_{i}\|_{2}^{2}-\beta_{i}\|\overline{ \textbf{Z}}_{i}\|_{2}^{2}}{2}(i=1,2,3,4,5). \tag{7}\]  
The sub-function then becomes:  
\[J_{\textbf{F}}=\frac{1}{2}\|\textbf{H}\textbf{*}\textbf{F}-\textbf{G}\|_{2}^{2 }+\sum_{i=1}^{5}\frac{\beta_{i}}{2}\|\textbf{Z}_{i}{}^{(k)}-\textbf{K}_{i\textbf {*}}\textbf{F}^{(k+1)}-\overline{\textbf{Z}}_{i}^{(k)}\|_{2}^{2}. \tag{8}\]  
The Fourier transform is performed on the above expression to obtain the frequency domain representation and transform Equation (8) into the following equation:  
\[\textbf{J}_{\overline{\textbf{F}}}=\frac{1}{2}\|\overline{\textbf{H}}^{ \circ}\overline{\textbf{F}}^{(k+1)}-\overline{\textbf{G}}\|_{2}^{2}+\sum_{i= 1}^{5}\frac{\beta_{i}}{2}\|\overline{\textbf{Z}}_{i}{}^{(k)}-\overline{ \textbf{K}}_{i}{}^{\circ}\overline{\textbf{F}}^{(k+1)}-\overline{\overline{ \textbf{Z}}}_{i}^{(k)}\|_{2}^{2}. \tag{9}\]  
where the symbol \({}^{\circ}\) represents the point multiplication operation and \(\overline{\textbf{X}}\) represents the spectrum of **X**.  
The derivative of Equation (9) to **F** is as follows,  
\[\frac{\partial\textbf{I}_{\overline{F}}^{(k+1)}}{\partial\overline{\textbf{F} }^{(k+1)}}=\overline{\textbf{H}}^{\circ}(\overline{\textbf{H}}^{\circ} \overline{\textbf{F}}^{(k+1)}-\overline{\textbf{G}})+\sum_{i=1}^{5}\beta_{i}{} ^{\circ}\overline{\textbf{K}}_{i}{}^{\circ}(\overline{\textbf{K}}_{i}{}^{ \circ}\overline{\textbf{F}}^{(k+1)}+\overline{\overline{\textbf{Z}}}_{i}^{(k) }-\overline{\textbf{Z}}_{i}^{(k)})=\textbf{0}. \tag{10}\]  
Let _Lhs_ and _Rhs_ be defined as Equations (11) and (12), respectively,  
\[Lhs=\overline{\textbf{H}}^{\circ}\overline{\textbf{H}}+\sum_{i=1}^{5}\beta_{i }{}^{\circ}\overline{\textbf{K}}_{i}{}^{\circ}\overline{\textbf{K}}_{i}. \tag{11}\]  
\[Rhs=\overline{\textbf{H}}^{\circ}\overline{\textbf{G}}+\sum_{i=1}^{5}\beta_{i }{}^{\circ}\overline{\textbf{K}}_{i}{}^{\circ}(\overline{\textbf{Z}}_{i}^{(k) }-\overline{\overline{\textbf{Z}}}_{i}^{(k)}). \tag{12}\]  
Then, according to Equation (10), \(\textbf{F}^{(k+1)}\) is defined as follows,  
\[\textbf{F}^{(k+1)}=ifft2(Rhs/Lhs), \tag{13}\]  
where \(ifft2\) denotes fast inverse Fourier transform.
## 3 Materials and Methods
### Data Extraction and Collection  
Two independent reviewers ([PERSON] and [PERSON]) separately examined all the identified studies and extracted data. During initial review of the data, the following information was collected for each study: title, first author, year of publication, study design, number of patients/specimens, joint of application, AR system, additional hardware required, clinical application, feasibility, accuracy, and precision. Risk of bias among the studies was assessed using the risk-of-bias visualization (robvis) tool [25]. Accuracy was intended as the mean difference from the planned targeted angle and summarized in mean values and standard deviations (SD), so that the smaller the value with the smaller SD, the more precise the AR device was in hitting the target.
## 5 Experimental Verification  
For further study of the wheelset or bogie dynamics, the full-size or scaled roller rig technique is widely used in many research institutions and universities [8; 13; 17; 19; 23]. We built a 1/9 scaled IRW system to verify the existence of the three dynamic characteristics of the IRW mentioned in Section 4. We collected and recorded the experimental phenomena as presented in the Supplementary Materials Video S1.  
As Shown in Figure 22c,d, the roller is driven by a large-torque electric motor, and the wheelset on the roller can be regarded as an IRW of a trailer truck. Figure 22b demonstrates that a pair of yaw suspensions are installed between the traction plate and the IRW. A thrust ball bearing is installed between the IRW and the traction plate, which makes the friction in the yaw direction negligible, so when disconnecting the yaw suspension, the IRW can yaw toward the traction plate freely. In the measurement system, two laser displacement sensors, one yaw angle sensor, and a pair of rotary encoders are installed, as shown in Figure 22a,b. Lateral displacement \(y\), yaw angle \(\psi\), and wheel rotational speeds \(\omega_{\mathrm{L}}\) and \(\omega_{\mathrm{R}}\) can be measured accurately, synchronously, and in real-time. With a rotary encoder installed on the roller's axle, the rotational speed of the roller can be stably controlled, varying from 0 to 300 r/min with a less than \(\pm 1\) r/min fluctuation. Correspondingly, the rotational speed of the wheels can vary from 0 to 2250 r/min.  
As shown in Figure 23a, the two wheels are solid and adopt a 1/10 linear conicity tread with a 4 cm length of the linear section in width. The surface profile of the roller near the wheel-roller contact patches is shown in Figure 23b. The fundamental physical parameters of this scaled IRW are shown in Table 5.  
Figure 23: The profile design for the experiments. (**a**) Wheel tread; (**b**) roller surface.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Symbol** & **Definition** & **Value** \\ \hline \(m\) & Total mass of IRW & 10.62 kg \\ \(m_{\text{W}}\) & Wheel mass & 2.92 kg \\ \(J_{Cx}\) & Carrier’s inertia moment in the \(x\) direction & \(5.53\times 10^{-2}\) kg\(\cdot\)m\({}^{2}\) \\ \(J_{Cy}\) & Carrier’s inertia moment in the \(y\) direction & \(5.94\times 10^{-3}\) kg\(\cdot\)m\({}^{2}\) \\ \(J_{Cz}\) & Carrier’s inertia moment in the \(z\) direction & \(5.32\times 10^{-2}\) kg\(\cdot\)m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: The scaled IRW’s parameters in experiments.  
Figure 22: The scaled IRW experimental roller rig. (**a**) Back view; (**b**) top view; (**c**) side view; (**d**) oblique view.
#### 3.1.1 Sleeper Production Process  
1. The sleeper mold is loaded on a rail conveyor, cleaned of any dirt and concrete residues, and a separation emulsion is applied to its walls. In 1 mold, 4-5 sleepers are produced, depending on the length of the sleeper;
2. Steel wires, stirrups, dowels, separating faces, and other equipment elements are placed in the mold. The longitudinal reinforcement consists of 40 wires with a diameter of 3 mm, which are separated and held by the jaws. The 8 \(\times\) 5 matrix consists of 8 wires in each row. The jaws are tightened with two flanges, using two bolts. The bolts are tightened using a pneumatic tightener with a prescribed torque of 23 Nm. In the first step, the bolts on one side of the mold are tightened and after tensioning the reinforcement, the bolts on the other side of the mold are tightened. The range of tests for prestressing reinforcement is specified in the standard PrEN 10138-2 [34];
3. The reinforcement is prestressed by a tension line to the prescribed value. The magnitude of the tensile force is recorded. Prestressed steel reinforcement must comply with the requirements of PrEN 10138-2, steel for other reinforcements must comply with EN 10 080 [35];
4. The produced concrete mix is dosed into the mold and then compacted vibrating the whole mold for the prescribed time. The concrete must meet the requirements of EN 206-01 [36] and EN 13230-1 [32], it must also meet the requirements for frost resistance T 200 according to STN 73 1322 [37]. The temperature of the cement when dosing into the mixer must not exceed 60 \({}^{\circ}\)C. The quality of the aggregate must comply with the requirements of EN 12 620 + A1: 2008-12 [38];
5. At the workstation for accelerating the hardening (hydration) of sleepers, the molds are transferred by crane from a rail conveyor into a steaming chamber with a capacity of 100 molds. The steaming process takes about 12 h. The tests must show the actual temperatures inside and around the sleeper. The temperature profile is recorded and should not exceed 45 \({}^{\circ}\)C.  
At the cutting and stacking workstation, the molds are overturned and the sleepers are pushed onto a conveyor, where they are separated. Subsequently, the surface and appearance of the product will be inspected. The surface of the sleeper must be well filled out. Isolated cavities with a closed surface up to 100 mm\({}^{2}\) and a depth up to 5 mm are permitted. Fragments on edges and surfaces are not permitted in places where they could interfere with the fastening function. The load-bearing surface of the sleeper (the surface that is in contact with the fastening) must be undamaged. The reinforcement must not protrude more than 8 mm from the sleeper front. The absence of longitudinal wire from the reinforcement or twisting of the wires is not allowed. The sleeper mold and failure point are shown in the Figure 1.
# Fault Diagnosis of Rolling Bearings Based on Improved Kurtogram in Varying Speed Conditions
###### Abstract  
Envelope analysis is a widely used method in fault diagnoses of rolling bearings. An optimal narrowband chosen for the envelope demodulation is critical to obtain high detection accuracy. To select the narrowband, the fast kurtogram (FK), which computes the kurtosis of a set of filtered signals, is introduced to detect cyclic transients in a signal, and the zone with the maximum kurtosis is the optimal frequency band. However, the kurtosis value is affected by rotating frequencies and is sensitive to large random impulses which normally occur in industrial applications. These factors weaken the performance of the FK for extracting weak fault features. To overcome these limitations, a novel feature named Order Spectrum Correlated Kurtosis (OSCK) is proposed, replacing the kurtosis index in the FK, to construct an improved kurtogram called Fast Order Spectrum Correlated Kurtogram (FOSCK). A band-pass filter is used to extract the optimal frequency band signal corresponding to the maximum OSCK. The envelope of the filtered signal is calculated using the Hilbert transform, and a low-pass filter is employed to eliminate the trend terms of the envelope. Then, the non-stationary filtered envelope is converted in the time domain into the stationary envelope in the angular domain via Computed Order Tracking (COT) to remove the effects of the speed fluctuation. The order structure of the angular domain envelope signal can then be used to determine the type of fault by identifying its characteristic order. This method offers several merits, such as fine order spectrum resolution and robustness to both random shock and heavy noise. Additionally, it can accurately locate the bearing fault resonance band within a relatively large speed fluctuation. The effectiveness of the proposed method is verified by a number of simulations and experimental bearing fault signals. The results are compared with several existing methods; the proposed method outperforms others in accurate bearing fault feature extraction under varying speed conditions.  
fault diagnosis; fast kurtogram; order spectrum correlated kurtosis; rolling bearing; non-stationary +
Footnote †: journal: Applied Physics Letters
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### Mission Analysis
#### 3.3.1 Constellation Design
Given that the system's requirements, whose definition is beyond the scope of this paper, dictate specific floor values for the PNT signal strength, TID, and the average GDOP, the Min PNT signal strength at ground level should be above \(-157.3\) dBW, ensuring that the PNT signals of the proposed system achieve a higher minimum signal strength than the Galileo E1 signal [11]. The TID should be less than 15 krad, based on heritage from ERMIS, CubeSat mission regarding the radiation tolerance of COTS, and the GDOP should be less than 6 so that the system's performance can be considered sufficient [12]. The optimal solution that conforms with those is a Walker Delta 61\({}^{\circ}\): 100/10/1 constellation at 550 km and is visualized in Figure 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Altitude** & **Total \# of S/C** & **Orbital** & \begin{tabular}{c} **Min PNT Signal** \\ **Strength at Ground** \\ **Level (dBW)** \\ \end{tabular} & \
\begin{tabular}{c} **TID in 3** \\ **Years * \\ **(krad)** \\ \end{tabular} &
\begin{tabular}{c} **Avg** \\ **GDOP** \\ \end{tabular} \\ \hline
750 & 80 & 10 & \(-158.9\) & 22.6 & 7 \\
700 & 81 & 9 & \(-158.5\) & 19.5 & 4.1 \\
650 & 90 & 10 & \(-158.1\) & 17 & 5.5 \\
600 & 90 & 10 & \(-157.7\) & 14.5 & 7.7 \\
550 & 100 & 10 & \(-157.2\) & 12.2 & 5.3 \\ \hline \hline \end{tabular}  
* It is assumed that the S/C components are shielded by aluminum with a thickness of 1.5 mm and that the Sun is at solar maximum. The calculation was conducted via SPENVIS [10].  
\end{table} 
Table 2: Tradeoffs between altitude, number of satellites, and Payload link budget for the inclination of 61\({}^{\circ}\). Figure 4: Constellation configuration tradeoff process.
## 4 Results and Discussion
### Analysis of PEM-FC through Assembly
#### 4.1.1 Channel Cross-Sections by an Equivalent Channel Height  
Figure 7 shows, during the assembly process, how displacement is divided in the PEM-FC. In a realistic FC, due to the bipolar plate's channels, this causes the friction pressure on the GDL to differ from the clamping pressure. In addition, it is difficult to predict the friction behavior in the interface without CFD analysis, because of the bipolar plate's rounded corners. It was seen that the triangular shape channel gives the minimum total displacement compared to the other channels, where, it was observed that the total displacement was about 2.57, 2.53, 2.51, 2.5, and 2.47 \(\upmu\)m for rectangular, circle, polygon, trapezoidal, and triangular shaped channels, respectively. Therefore, the triangular shape gives a better performance of the FC due to the smallest deformation during its operational life.  
Figure 7: The displacement (\(\upmu\)m) in the PEM-FC through the process of assembly at height = 1 mm, with scale increased 50 times.  
In Figure 8, the development of the pressure division in the FC is illustrated during the assembly process, and it also shows the figures for the pressure distribution (contour plots) and the cell deformation, as these figures show the influence of the repetitive simulation on the pressure division. In Figure 9, it is shown that during the assembly process, we can see the division of pressures in the FC, which was developed where the numbers show the division of pressure from von Mises (contour plots) and the shape of the cell distortion. The ranges of pressure subjected on each channel were about \(-\)1.71-8.24, \(-\)1.77-8.33, \(-\)1.75-8.38, \(-\)1.8-8.4, and \(-\)1.85-8.45 MPa for rectangular, circular, polygon, trapezoidal, and triangular shaped channels, respectively. Therefore, it is also evident that the FC with a triangular channel is the most durable and resistant to deformation resulting from the stress and pressure distribution during assembly. Therefore, it is considered the best in performance and lifetime as a result of the durability it possesses compared to other studied channels.  
Figure 8: Pressure variations (MPa) in the PEM-FC through the process of assembly at height = 1 mm, with scale increased 50 times.
# Force Fight and Its Elimination in a Prototype of a Redundant Direct-Drive Avionic Actuator
## 1 Introduction  
Nowadays industries require electrical rotating machines to be more and more efficient. Their use in embedded systems also needs mass optimization according to specifications. Among electrical machines, permanent magnet synchronous machines (PMSM) with inner magnets were selected as good technology candidates [1] for this type of application. The advantage of this technology is related to the high and constant torque in the operation range at low velocity. Besides, performance, robustness, and efficiency are higher in comparison to other technologies [2].  
In critical systems such as aircraft, satellites, robots, and special space vehicles, simplex structured actuators are unable to satisfy the reliability requirements. Therefore, an actuator with redundant motor architecture has been developed to deal with this problem [3, 4]. The redundancy can have several forms that depend on the safety requirements. In our case, redundancy in the designed direct-drive actuator is realized using permanent magnet synchronous machines (PMSM) as a main component, called BrushlessAlternative Current (BLAC) motor. According to the application specifications, a compound of four PMSM was designed [4]. Finally, for normal operation with any fault, each machine provides a quarter of the total motor torque of the designed actuator to compensate the load torque.  
The major problem of material redundancy resides in the force fight phenomenon between the active electric machines. In this case, electric machines may enter in conflict during their operation [5; 6]. Instead of all being in motor mode to provide a quarter of the total torque in normal operation, some of them can provide a resistive torque to the others. The position control loops associated with each machine provide the requested motor torque. This phenomenon creates abnormal operation of the direct drive actuator involving current consumption with thermal rise and possibly instability and/or decrease in performance.  
To eliminate this abnormal behavior, some faults are identified as responsible for this phenomenon and some monitoring approaches are suggested to create a communication protocol between information feedback and active lanes.  
In this paper, the first part is devoted to the designed direct-drive actuator and its specific control strategy. In the second part, two operation modes are presented; one in healthy operation mode and the second in faulty operation mode where the force fight phenomenon between lanes is described. In the third section, a monitoring mode with some approaches is suggested to eliminate this phenomenon. Finally, an experimental validation is carried out on a specific test bench to show the force fight phenomenon between the lanes and how to eliminate it in order to keep the same performance as in healthy operation mode.
# PSO-Based Voltage Control Strategy for Loadability Enhancement in Smart Power Grids
## 4 Problem Statement  
### Power Grid Q-V Control  
The steady-state model of power grid Q-V control can be considered as  
\[[\Delta\mathbf{Q}]=B[\Delta\mathbf{V}] \tag{3}\]  
where \(B\) is the system susceptance matrix, and \(\Delta\mathbf{Q}\) and \(\Delta\mathbf{V}\) are the vector of reactive power variation and vector of voltage magnitude variation, respectively. Expending Equation (3) gives  
\[\left[\begin{array}{c}\Delta\mathbf{Q_{G}}\\ \Delta\mathbf{Q_{L}}\end{array}\right]=\left[\begin{array}{cc}B_{GG}&B_{GL} \\ B_{LG}&B_{LL}\end{array}\right]\left[\begin{array}{c}\Delta\mathbf{V_{G}}\\ \Delta\mathbf{V_{L}}\end{array}\right] \tag{4}\]  
where the subscripts \(L\) denotes the load buses and \(G\) denotes the generator buses. Next, let \(\mathbf{q}=\Delta\mathbf{Q_{L}}\) and \(\mathbf{u}=\Delta\mathbf{Q_{G}}\), then substitute for \(\mathbf{q}\) and \(\mathbf{u}\) in Equation (4) yields  
\[\left[\begin{array}{c}\mathbf{u}\\ \mathbf{q}\end{array}\right]=\left[\begin{array}{cc}B_{GG}&B_{GL}\\ B_{LG}&B_{LL}\end{array}\right]\left[\begin{array}{c}\Delta\mathbf{V_{G}}\\ \Delta\mathbf{V_{L}}\end{array}\right] \tag{5}\]  
In the above equation, \(\Delta\mathbf{V_{G}}\) and \(\mathbf{q}\) can be expressed as  
\[\Delta\mathbf{V_{G}}=B_{GG}^{-1}\mathbf{u}-B_{GG}^{-1}B_{GL}\Delta\mathbf{V_{ L}} \tag{6}\]  
\[\mathbf{q}=B_{LG}\Delta\mathbf{V_{G}}+B_{LL}\Delta\mathbf{V_{L}} \tag{7}\]  
Substituting for \(\Delta\mathbf{V_{G}}\) in Equation (7) yields  
\[\mathbf{q}=B_{LG}B_{GG}^{-1}\mathbf{u}-B_{LG}B_{GG}^{-1}B_{GL}\Delta\mathbf{V_ {L}}+B_{LL}\Delta\mathbf{V_{L}} \tag{8}\]  
Equation (8) can be rearranged as  
\[\Delta\mathbf{V_{L}}=J_{1}\mathbf{q}-J_{2}\mathbf{u} \tag{9}\]  
Figure 3: Typical P-V curve.  
where  
\[\begin{array}{l}J_{1}=\left(B_{LL}-B_{LG}B_{GG}^{-1}B_{GL}\right)^{-1}\\ J_{2}=J_{1}\left(B_{LG}B_{GG}^{-1}\right)\end{array} \tag{10}\]  
Note that \(J_{1}\) and \(J_{2}\) are the matrices with respect to system configurations. In this case, \(J_{1}\mathbf{q}\) in Equation (9) represents uncontrolled \(\Delta\mathbf{V_{L}}\) in response to \(\mathbf{q}\), while \(J_{2}\mathbf{u}\) represents the impacts of control outputs \(\mathbf{u}\) on \(\Delta\mathbf{V_{L}}\).