esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### Mesh Mapping  
The most critical step in the proposed approach to calculating the acoustic attenuation of silencers is to set a mesh mapping for transferring data. The mesh mapping is used to transfer the node data of the CFD mesh (source mesh) into the acoustic mesh (target mesh). However, there is not usually a one-to-one correspondence between the nodes of different meshes. Thus, an appropriate mapping algorithm should be employed.  
A maximum distance algorithm is often applied in Virtual.lab acoustics software to set a mesh mapping between different meshes with the same geometrical shape, but a different density of nodes. The algorithm includes the following two necessary parameters [19]:  
1. Number of nodes (\(N\)): The maximum number of nodes from the source mesh that are considered for mapping with one node of the target mesh.
2. Maximum distance (\(R\)): Only the nodes of the source mesh that lie inside a sphere with a radius R--centered at the node of the target mesh--are taken into account.  
The \(N\) closest nodes to a given source node are used to transfer data to the target node. The data value assigned to the target node is a weighted average of the values at the \(N\) source nodes. The weights are:  
\[W_{i}=\frac{1}{d_{i}}\bigg{/}\sum_{i=1}^{N}\frac{1}{d_{i}}\,. \tag{1}\]  
The transferred value of the target node is then:  
\[P_{\text{Target}}=\sum_{i=1}^{N}\frac{P_{i}^{\text{Source}}}{d_{i}}\bigg{/} \sum_{i=1}^{N}\frac{1}{d_{i}}\,, \tag{2}\]  
where \(d_{i}\) is the distance between the source node and the target node, and \(P_{i}^{\text{Source}}\) is the value of the source node.  
For example, when the target node is defined as a center, there are three source nodes (\(N=3\)) lying inside a sphere with \(R=100\) mm. Thus, the mapping data transfer relation is depicted in Figure 1. The value at \(A\) is given by:  
\[P_{\text{Target}}=\frac{\frac{1}{d_{1}}P_{1}^{\text{Source}}+\frac{1}{d_{2}}P_ {2}^{\text{Source}}+\frac{1}{d_{3}}P_{3}^{\text{Source}}}{\frac{1}{d_{1}}+\frac{1 }{d_{2}}+\frac{1}{d_{3}}}. \tag{3}\]  
After transferring data by the mesh mapping method, Virtual.lab software is used to conduct a numerical simulation on the internal sound field of the silencer, and the \(TL\) is then determined by  
\[TL=10\log_{10}\bigg{(}\frac{W_{1}}{W_{2}}\bigg{)}, \tag{4}\]  
with  
\[W_{1}=|p_{1}|^{2}A_{1}/Z_{1},W_{2}=|p_{2}|^{2}A_{2}/Z_{2}. \tag{5}\]  
Substituting (5) into (4) yields  
\[TL=10\log_{10}\bigg{(}\frac{p_{1}\overline{p_{1}}Z_{2}}{p_{2}\overline{p_{2}} Z_{1}}\frac{A_{1}}{A_{2}}\bigg{)}, \tag{6}\]where \(W_{1}\) and \(W_{2}\) are the acoustic power of the inlet and outlet of the silencer, respectively; \(p_{1}\) and \(p_{2}\) are the sound pressure of the incident and the transmitted waves, respectively; \(\overline{p_{1}}\) and \(\overline{p_{2}}\) are conjugate complex numbers of \(p_{1}\) and \(p_{2}\), respectively; \(Z_{1}\) and \(Z_{2}\) are the acoustic impedance of the inlet and outlet of the silencer, respectively; and \(A_{1}\) and \(A_{2}\) are the cross-sectional areas of the inlet and outlet of the silencer, respectively.
## 4 Discussion
### Diurnal and Seasonal Variation in NEE
The cumulative NEE at our site in the rice-wheat system was higher in wheat (\(-\)576 g C m\({}^{-2}\)) as compared to rice (\(-\)368 g C m\({}^{-2}\)). A much higher NEE in rice of \(-\)448 g C m\({}^{-2}\) was observed by [PERSON] et al. [19] in eastern India in a rice-rice system. [PERSON] et al. [34] and [PERSON] et al. [24] reported an NEE of \(-\)258 and \(-\)398 g C m\({}^{-2}\) in rice. Higher NEE values have been reported in winter wheat in Germany by [PERSON] et al. [43] at \(-\)627 and \(-\)537 g C m\({}^{-2}\) in (Table 4) because of the longer crop growth duration compared to spring wheat. The NEE at the harvest of rice was positive at 0.35 g C m\({}^{-2}\) d\({}^{-1}\) due to the respiration losses by the root residues (Table 1). The NEE was more positive at 0.46 g C m\({}^{-2}\) d\({}^{-1}\) after the wheat harvest due to higher soil temperatures resulting in higher respiration losses (1.04 g C m\({}^{-2}\) d\({}^{-1}\)) in wheat as compared to rice. More weed growth in the fallow period after rice resulted in a higher average GPP at 0.74 g C m\({}^{-2}\) d\({}^{-1}\) as compared to 0.54 g C m\({}^{-2}\) d\({}^{-1}\) in the fallow period after wheat growth (Table 1). Higher soil temperatures and low soil moisture in the fallow period after wheat resulted in lower GPP values as compared to the fallow period after rice. [PERSON] et al. [19] also reported a resultant NEE of 22 g C m\({}^{-2}\) in the summer fallow period as the RE was balanced by photosynthetic CO\({}_{2}\) assimilation. [PERSON] et al. [44] reported a contribution of 20 g C m\({}^{-2}\) due to residues, which represents 5% of the seasonal carbon budget. The RE and GPP during the fallow periods contributed to 17% and 5% of the annual cumulative RE and GPP.
# Fleet Resilience: Evaluating Maintenance Strategies in Critical Equipment
## 4 Case Study
### System-Level Resilience  
The analysis interval begins on 1 January 2018 and ends on 25 June 2019. The monthly systemic availability of the extraction fleet is shown in Figure 9. The occurrence of an  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Equipment** & **MTBF(h)** & **MTTR(h)** & **Equipment** & **MTBF(h)** & **MTTR(h)** & **Equipment** & **MTBF(h)** & **MTTR(h)** \\ \hline
**D08** & **17.84** & **11.46** & H40 & 23.06 & 2.27 & H57 & 98.30  
external disruption, as well as the recovery period of availability at the system level, is represented by an arrow in the aforementioned figure. This goes from each local minimum to the next local maximum of the series.  
As can be seen in Figure 9, there are four \"shocks\" in the series that affected availability in the Extraction Fleet. That is to say, the availability presents four local minimums indicated by the marks \(t_{2_{i}}\). In addition, four recovery points are identified \(t_{3_{i}}\). The resilience index obtained through Equation (1) for the period of analysis is 0.41 or 41%.
###### Abstract  
Due to their low power consumption, high modulation speed, and low cost, vertical-cavity surface-emitting lasers (VCSEL) dominate short-reach data communications as the light source. In this paper, we propose a compact equivalent circuit model with noise effects for high-speed multi-quantum-well (MQW) VCSELs. The model comprehensively accounts for the carrier and photons dynamics of a MQW structure, which includes separate confinement heterostructure (SCH) layers, barrier (B) layers, and quantum well (QW) layers. The proposed model is generalized to various VCSEL designs and accommodates a flexible number of quantum wells. Experimental validation of the model is performed at 25 Gb/s with a self-wire-bonded 850 nm VCSEL.  
VCSEL; equivalent circuit model; short-reach optical interconnects; relative intensity noise Article  
A Compact High-Efficient Equivalent Circuit Model of Multi-Quantum-Well Vertical-Cavity Surface-Emitting Lasers for High-Speed Interconnects
# Seismic Responses of a Cable-Stayed Bridge with Consideration of Uniform Temperature Load
###### Abstract  
The effects of temperature load on the dynamic responses of cable-stayed bridges have attracted the attention of researchers in recent years. However, these investigations mainly focus on the influence of temperature on the dynamic characteristics of structures, such as vibration mode and frequency. This paper discusses the effects of uniform temperature changes on the seismic responses of a cable-stayed bridge. A three dimensional finite element model of a cable-stayed bridge using OpenSees is established for nonlinear time history analysis, and uniform temperature load is applied to the prototype bridge before the conducting of seismic excitation. Three ground motion records are selected from the PEER strong motion database based on the design spectrum. Case studies are then performed considering the varying temperature and the connections between the deck and pylons of the bridge. The result shows that the seismic responses of the bridge are significantly increased with the consideration of temperature load. Meanwhile, the types between the deck and pylon also have notable impacts on the seismic responses of the bridge with and without temperature changes. This research could provide a reference for designers during the design phase of cable-stayed bridges.  
cable-stayed bridges; uniform temperature; earthquake; nonlinear analysis; deck to pylon connections +
Footnote †: journal: Applied Sciences
## 3 Two-Echelon Two-Indenture System Modelling  
### Mathematical Model of LORA  
The characteristics of fleet system repair level and spare parts allocation are evaluated in this section. We introduce maintenance capability factor and maintenance decisions (repair, move, discard) into LORA and METRIC models. This method can reflect the influence of multiple failure modes on the joint optimization model.  
We consider the structural dependence relationship between system components, as well as the maintenance resources in the model. LORA is used to analyze the optimal repair level of components and determine the type and quantity of maintenance resources. Therefore, the key issues of repair level analysis are as follows:  
1. Analyze the maintenance decision of failure components based on failure mode: move, repair, discard;
2. Analyze the level of repair decision-making;
3. Determine the maintenance resources needed to implement the maintenance decision.  
According to the above two-echelon two-indenture system structure logic analysis, an LRU and its SRUs are taken as an example to build a LORA model. The variables and descriptions involved in this paper are shown in Table 1.  
Figure 2: Two-echelon service logistics system.  
(1) If the LRU fails, the possible maintenance decisions are repair, move, and discard:  
\[X_{0,j,repair}+X_{0,j,move}=\lambda_{0,j},j=1,2,\cdots \tag{1}\]  
\[X_{0,0,repair}+X_{0,0, discard}=\sum_{j=1}^{J}X_{0,j,move} \tag{2}\]  
Constraint (1) states that the number of repairs and moves at Base location is equal to LRUs' annual failure times.  
Constraint (2) states that the number of repairs and discards at Depot location is equal to the number of LRUs moves.  
(2) If the failure is determined to be caused by SRU, there are:  
\[X_{i,j,repair}+X_{i,j,move}=\lambda_{i,j} \tag{3}\]  
\[\sum_{j=1}^{J}X_{i,j,move}=\sum_{d\in\mathcal{D}}X_{i,0,d} \tag{4}\]  
\[X_{0,j,repair}=\sum_{i=1}^{I}X_{i,j,repair} \tag{5}\]  
Constraint (3) states that the number of repairs and moves at Base location is equal to SRUs' annual failure times.  
Constraint (4) states that the number of repairs and discards at Depot location is equal to the number of SRUs moves.  
Constraint (5) states that the number of LRU repairs at Base location is equal to the sum of all SRUs repairs.  
(3) Maintenance decisions need the corresponding maintenance resources. So \(d=repair\) needs the following constraint:  
\[X_{i,j,repair}\leq Y_{r,j}\cdot\lambda_{i,j},i\subset\Omega_{r} \tag{6}\]  
where \(\Omega_{r}\) is a collection of all the components that need the resource \(r\) for repair.
# Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
## 4 Experimental Validation
### Analysis of Prediction Results of Normal Data of Gearbox Operation
#### 4.2.1 Optimize the Model Parameter Setting  
Standardized data are fed into the IALO-GRU model, which is trained by adjusting the GRU hyperparameters (such as hidden-layer cells, GRU cell layers, training batch size, and learning rate), whose parameter ranges are detailed in Table 2.
## 3 Acquired Data
### Obtained Wheel Signatures  
Signals were gathered at the test stand (Figure 6) which enabled moving a wheel at a constant speed of v = 0.028 m/s (0.1 km/h) with its precise lateral and vertical position (Section 3.1, method \"b\").  
Figure 6: The test stand for acquiring wheel signatures.  
The head of a wheel detector (sensor) was installed on the rail and connected to the trackside electronic unit, which provided signal u\({}_{1}\) and u\({}_{2}\) for registration. Sampling frequency according to Formula (1) was equal to f\({}_{\rm m}\) = 1000 sps (for sampling rate of detector f\({}_{\rm i}\) = 10 ksps).  
The created database contains records (passage in both directions) of u\({}_{1}\) and u\({}_{2}\) signals for the assumed configurations:  
* a wheel profile \(\textbf{W}\in\{\) k2, k4, k5, k6, k8, k10, k11, k13, k15, k16\(\}\) (see Table 1)In the case of decreasing the speed, it can be achieved by means of interpolation (up-sampling) method. The same sample from the original signal appears \(\Delta\)n times in the new buffer, and it decreases the speed by \(\Delta\)n times. Such a mechanism includes zero-hold interpolation for filing the gaps between the following n samples and can be used for small \(\Delta\)n.  
For higher level of up-sampling, it is recommended to fill the gaps with (\(\Delta\)n--1) zero values and use an output filter (Figure 8). For this method each u[n] sample shall be multiplied by \(\Delta\)n to obtain the proper output signal.  
The pass band of output filter (f\({}_{\text{p}}\)), presented in Figure 8 shall be limited to the maximum frequency existing in the signal, which is about 300 Hz (taking into consideration the wheel signature) for the speed of 450 km/h (400 km/h is currently required for axle  
Figure 8: Idea of up sampling of signal with output filter.  
Figure 7: Idea of (**A**) down and (**B**) up-sampling of signal, to emulate speed change.  
counters for highspeed lines in Europe). An example of increasing the speed is shown in Figure 9 and reducing the speed in Figure 10.
###### Abstract
In addition, traditional FMEA does not take into account the interrelationships between failure modes or the influence of multiple factors interacting with each other, making the assessment of risks in modern, interconnected systems even more complex [14]. To overcome these challenges, hybrid approaches have been developed that combine FMEA with advanced decision-making methods. By integrating techniques with MCDM methods, these hybrid models can address the problem of equal weighting in traditional FMEA. For example, AHP allows decision makers to assign a different weighting to each factor depending on its relative importance to ensure that the most critical aspects of the risk are adequately addressed. This improves the accuracy of risk assessments as it more realistically reflects the true impact of each failure mode [15]. In addition, hybrid approaches help to mitigate the subjectivity problem by introducing more structured and systematic procedures for evaluating expert judgment. Methods such as AHP allow decision makers to make pairwise comparisons, reducing biases and inconsistencies in evaluation. These hybrid models provide a more nuanced, flexible framework for prioritizing risks that takes into account both the relative importance of the different criteria and the interactions between them. This ultimately leads to more reliable and effective risk prioritization in complex systems [16]. The integration of FMEA and MCDM increases complexity, especially when the number of failure modes or criteria increases. Mastering this complexity often requires advanced software tools and a deep understanding of both methods. As a result, the practical applicability of the model can be limited, making it difficult for users without specialized training to effectively apply and interpret the results. To address this challenge and improve the weighting process in the FMEA approach, we propose a novel hybrid FMEA-ROC-CoCoSo model. This model aims to overcome the limitations of traditional FMEA, especially in assigning appropriate weights to the most important risk factors: severity, occurrence, and detectability. By incorporating the Rank Order Centroid (ROC) to more accurately assign weights based on relative importance and the Combined Compromise Solution (CoCoSo) to effectively rank and prioritize failure modes, this hybrid approach improves the robustness and precision of risk assessments. The proposed model improves the consistency and accuracy of risk prioritization and provides a more reliable framework for decision making in complex systems. The results of this study will demonstrate how the hybrid FMEA-ROC-CoCoSo model outperforms traditional methods and provides a valuable tool for industries requiring advanced risk management strategies.
# An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
## 3 Experimental Results and Analysis
### Experimental Setup
#### 3.1.2 Experimental Settings  
The Bernoulli sampling probability \(p\) in (3) was fixed as 0.3. The Adam algorithm [46] was employed as the gradient descent optimizer to update the network weights and biases, with momentum \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), \(\varepsilon=10^{-8}\). The network was trained with 50 epochs with a batch size of 64 at an initial learning rate of \(10^{-4}\). After first 25 epochs, the learning rate was reduced by being multiplied by a descending factor of 0.1. The number of average times \(K\) was set as 100 to output the predicted despeckled image in the testing process. Our proposed method was implemented in the PyTorch framework [47] and run on an Intel i9-10900K CPU and an NVIDIA GeForce GTX 3090 GPU.
# A Thorough Evaluation of GaN HEMT Degradation under Realistic Power Amplifier Operation
## 3 Implementation and Definition of the Stress Measurement  
The software controlling the measurement setup allows the user to organize a set of automatic measurements that will be carried out sequentially on the DUT. The user can choose between _stress measurements_ and _control measurements_. In the first case, a defined  
Figure 3: Block diagram of the low-frequency time-domain load-pull setup used for the characterization of microwave devices.  
operating condition is applied to the DUT for a certain period of time, during which the main electrical quantities of interest are periodically monitored and sampled. The control measurements, instead, are thought to be carried out in between stress sessions to check the status of the DUT under operating conditions different from the one selected for the stress. Stress measurements can be chosen among:  
* DC stress: a bias point is applied to the DUT for a time \(T_{stress}\), during which the DC currents and voltages are periodically measured with a sampling time \(T_{s,DC}\). In this case, no signal is applied to the DUT.
* Low-frequency stress: a large-signal operating condition at a low frequency is applied to the DUT for a time \(T_{stress}\), during which both average currents and voltages as well as the incident and reflected waveforms are periodically measured. The sampling time for the DC quantities (\(T_{s,DC}\)) and for the waveforms (\(T_{s,AC}\)) can be different. The available control measurements are:
* DC measurements: DC I/V characteristics are measured over a grid of bias points defined by the user.
* Low-frequency measurements: a large-signal low-frequency characterization is carried out on the DUT. The investigated condition can be different with respect to the one used for the stress measurement and the user can choose among a single load line, a power sweep, a load sweep or both.  
Although the software provides great flexibility in the definition of the measurement sequence, the typical choice, which has been used also for the characterization presented in this paper, is schematically depicted in Figure 4. A preliminary set of control measurements is carried out on the DUT to obtain its initial state. Then, the total stress time is divided into several stress sessions followed by intermediate control measurements to evaluate the degradation of the DUT under different conditions and to monitor the evolution of its state. Eventually, a final set of control measurements is carried out to characterize the final state of the DUT.
## 4 Validation
### Validation Results
#### 4.3.5 Sensitivity Analysis  
Due to the high degree of complexity and randomness of the FSWNS, and the general lack of related data, the present simulation model rests on several assumptions. These concern the prevailing ice conditions, the IB transfer times, and the presence of pre-existing ice channels, among others. To assess the sensitivity of the simulation outcome to those assumptions, a sensitivity analysis is carried out. As per Figure 8, the sensitivity analysis indicates that the simulated number of instances of IB assistances is sensitive to variations in the assumed \(H_{\alpha q}\)-values, but especially to assumptions concerning the presence of pre-existing ice channels. Specifically, by increasing or decreasing the assumed \(H_{\alpha q}\)-values by 20%, the instances of assistance increase by 13% or decrease by 30%, respectively. By assuming that all those brash ice channels that as default are assumed to be present with a 50% probability (see Section 4.2.3) are present at all times, the instances of assistance decrease by 98%, whereas by assuming that the same ice channels are absent at all times, the instances of assistances increase by 45%.  
The sensitivity analysis further indicates that the mean IB waiting time is sensitive to all the analyzed assumption variations. Specifically, by increasing or decreasing the assumed \(H_{\alpha q}\)-values by 20%, the simulated mean waiting time for IB assistance increases by 6% or decreases by 23%, respectively. By increasing or decreasing the assumed IB transfer times by 50%, the simulated mean waiting time for IB assistance increases by 9% or decreases by 47%, respectively. By assuming that all those brash ice channels that as default are assumed to be present with a 50% probability (see Section 4.2.3) are present at all times, the mean waiting time decreases by 86%, whereas by assuming that the same ice channels are absent at all times, the waiting time increases by 69%.