esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
## 2 Wind Turbine Parameter Description and Feature Selection
### Selection of Feature Parameters  
The oil temperature monitoring of the wind turbine gearbox is an important means of wind turbine fault early warning, The reason is that the oil temperature change of the gearbox can reflect the internal mechanical state and lubrication of the gearbox. As a key component of the wind turbine, the normal good operation of the gearbox depends on the normal circulation of the lubricating oil. Too high or too low oil temperature may indicate insufficient mobility of the lubricating oil, which will affect the lubrication effect of the gearbox, lead to excessive wear or lag, and increase the risk of failure. Generally, the gearbox-lubricating oil temperature is selected as the monitoring point of the fault-warning model.
## 3 LDAF Background and Overview
### LDAF Architecture
#### 3.1.3 Services  
Services address the needs of end device applications and therefore affect the size and/or number of messages for the end device. Two levels of application adaption are possible:  
* Transcoding of messages: The service receives messages from the external APIs in format and protocol imposed by the API and transcodes the payload to the LDAF message format described in Section 3.1.1. In this case the application does not affect the number of messages from/to the device.
* Application adaptation: The service in this case actively modifies the application logic. This affects the number of messages and further reduces the traffic for the end device. The service can determine whether a particular message is actually required for the device application and filter it out. It can, e.g., aggregate duplicated messages, negotiate and confirm message exchange with the API, and only pass the success notification to the end device, or build an aggregated response for the device that includes data collected from various APIs.  
Application adaptation helps LDAF servers distribute the application logic among the end device, cloud services, and the edge of the network.
# Ka-Band Doppler Scatterometry: A Strong Wind Case Study
## 4 Sea Surface Doppler Centroid
### Moderate Incidence Angle (Record #7)  
For moderate incidence angles, \(\theta=45^{\circ}\), (Record #7, Figure 13), the line-of-sight DC increases up to \(\approx\)0.6 m s\({}^{-1}\) due in part to larger projection factor, \(\sin\theta\). The gradual wind decay during Record #7 is reflected in the corresponding DC decay. Noticeably, DC variations better correlate with wind variations (Figure 13) compared to the \(\theta=20^{\circ}\) case (Figure 11). The magnitude of surface current and scatter velocity combined contribution (first two terms in Equation (11)) is below the observed DC, with the difference equal to the WIDV (green and blue lines in Figure 13a). The WIDV (Figure 13c) is smaller in magnitude than for \(\theta=20^{\circ}\) due to the MTF magnitude decrease with the incidence angle [22; 25]. An alternative WIDV estimate, \(V_{\mathrm{D}}-\overline{\hat{v}}\), is almost zero because the dominant 0.5-Hz fetch-limited waves are not well resolved by the radar footprint (\(\approx\)4 m). Eastward swell also present in the scene comes obliquely to the northward radar look direction and thus does not affect the DC significantly (Figure 12c,d).  
If wave gauge array measured 2D directional wave spectra are used in Equation (11) for WIDV estimate, the KaDOP prediction is about zero (Figure 13c, purple line) because such young, short waves are not resolved by the triangle array with relatively long (\(\approx\)10 m) base. If this unresolved part of the wave spectrum is simulated based on the JONSWAP parameterization (Figure 12d), the WIDV fits the measurements better, although not perfectly (Figure 13c, green line). The remaining discrepancy may be attributed to uncertainties in surface currents (which are estimated from measured 10 m currents but parameterized 0-10 m current shear, 1.5% \(\cdot\)\(U\)) and simulated wave spectra.  
It could be noted that the relative WIDV contribution decreases for slanted look geometries due to smaller MTF magnitude and related weaker NRCS modulations. The DC becomes more closely governed by the sum of surface current and Bragg contributions as the incidence angle increases.
# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## 3 Results
### Comparison 1: LAI-RM to MODIS LAI (Mc5; Mc6) at 1.0 km
#### 3.1.1 LAI-RM (1.0 km) Creation from In Situ LAI
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & **In Situ** & \multicolumn{4}{c}{**LAI-RM**} \\ \hline \multirow{2}{*}{**Date**} & \multirow{2}{*}{**EPA Site**} & & **n** & \(\overline{x}\) & \(\sigma\) &
\begin{tabular}{c} **Area** \\ **(\%)** \\ \end{tabular} & \(\overline{x}\) & \(\sigma\) \\ \hline
23 May & Appomattox & HDWD & 3 & 2.87 & 0.82 & 21.8 & 0.63 & 0.18 \\  & & PINE Unthinned & 21 & 1.88 & 0.20 & 49.6 & 0.93 & 0.10 \\  & & PINE Thinned & 8 & 1.01 & 0.19 & 18 & 0.18 & 0.03 \\  & & *** OV & 0 & 0.38 & 0.27 & 10.6 & 0.04 & 0.03 \\ \hline \multicolumn{8}{c}{Total} & 1.78 & 0.34 \\ \hline
5 March & Appomattox & HDWD & NA & 0.00 & 0.00 & 21.8 & 0.00 & 0.00 \\  & & PINE Unthinned & 24 & 1.49 & 0.14 & 49.6 & 0.74 & 0.07 \\  & & PINE Thinned & 0 & ** 0.81 & 0.14 & 18 & 0.15 & 0.02 \\  & & *** OV & 0 & 0.00 & 0.00 & 10.6 & 0.00 & 0.00 \\  & & & & & & Total & 0.88 & 0.10 \\ \hline
5 March & Hertford & HDWD & NA & 0.00 & 0.00 & 17.8 & 0.00 & 0.00 \\  & & PINE Unthinned & 20 & 1.73 & 0.16 & 72.3 & 1.25 & 0.12 \\  & & AG & 0 & 0.00 & 0.00 & 9.9 & 0.00 & 0.00 \\ \hline \multicolumn{8}{c}{Total} & 1.25 & 0.12 \\ \hline
30 April & Fairystone & HDWD & 54 & 1.99 & 0.34 & 100 & 1.99 & 0.34 \\
25 June & Fairystone & HDWD & 84 & 2.54 & 0.38 & 100 & 2.54 & 0.38 \\
4 September & Fairystone & HDWD & 59 & 2.67 & 0.46 & 100 & 2.67 & 0.46 \\ \hline
9 May & Umstead & HDWD & 21 & 2.32 & 0.49 & 55.1 & 1.28 & 0.27 \\  & & PINE Unthinned & 32 & * 3.41 & 0.75 & 44.9 & 1.53 & 0.34 \\  & & & & & & Total & 2.81 & 0.61 \\ \hline
23 October & Umstead & HDWD & 25 & 2.81 & 0.58 & 55.1 & 1.55 & 0.32 \\  & & PINE Unthinned & 32 & 3.69 & 0.06 & 44.9 & 1.66 & 0.03 \\  & & & & & & Total & 3.21 & 0.35 \\ \hline \hline \end{tabular} Note: * LAI estimated by comparing SR LAI change between the 23 October and 9 May estimates (7.49% decrease). ** LAI estimated by comparing SR LAI change between the 23 May and 5 March estimates (19.8% decrease). ** OV—2–3 year pine regeneration—used SR LAI to estimate LAI.  
\end{table} 
Table 5: 1.0 km upscaled in situ LAI across four research sites (HDWD—hardwood, OV—other vegetation).
## 2 Review of the PAP Algorithm
### PAP Algorithm  
From the inspiration of the well-known PNLMS algorithm, the PAP algorithm integrates the proportionate idea into the AP algorithm to modify the gain allocation method, and realizes a dynamic  
Figure 1: Typical CE schematic diagram.  
step size (STS) based on the magnitudes of the channel coefficients that are included in the unknown channels. The iteration equation of the PAP is modified to be  
\[\hat{\mathbf{w}}(m+1)=\hat{\mathbf{w}}(m)+\mu_{\text{PAP}}\mathbf{G}(m)\mathbf{X }(m)\left(\mathbf{X}^{T}(m)\mathbf{G}(m)\mathbf{X}(m)+\delta_{\text{PAP}}\mathbf{ I}_{M}\right)^{-1}\mathbf{e}(m), \tag{9}\]  
where \(\mu_{\text{PAP}}\) is still used as a STS, \(\delta_{\text{PAP}}\) denotes the regularization factor in the PAP, and \(\mathbf{G}(m)\) acts as the gain controlling matrix, which is written as  
\[\mathbf{G}(m)=\text{diag}\left\{\,g_{0}(m),g_{1}(m),\ldots,g_{K-1}(m)\right\}\,, \tag{10}\]  
where  
\[g_{k}(m)=\frac{\varphi_{k}(m)}{\sum\limits_{i=0}^{K-1}\varphi_{i}(m)}\,, \tag{11}\]  
and  
\[\varphi_{k}=\max\left\{\,p\max\left\{\,q,\left|\psi_{0}\right|,\left|\psi_{k} \right|,\cdots,\left|\psi_{K-1}\right|\right\}\,,\,\left|\psi_{k}\right|\right\}. \tag{12}\]  
In Equation (12), parameters \(p>0\) and \(q>0\) are used to prevent the update process from stalling. In practice, \(p=\frac{5}{K}\) is usually chosen [26].
# Improved U-Net with Residual Attention Block for Mixed-Defect Wafer Maps
## 4 Experiments and Results
### Results
#### 4.5.2 Single-Defect Result  
Figure 10 shows the accuracy graph and confusion matrix for single defects. The test accuracy was more than 0.99 starting from epoch 5, after which it was possible to obtain results that maintained accuracy above a certain level.  
Table 2 details the single-detection performance of the model. Accuracy for single defects reached 0.997. Because Center and Local defects have similar shapes, they may be misclassified depending on the defect location. A Scratch defect was sometimes predicted as an Edge-Loc defect. Nevertheless, since all single defects show an accuracy close to 1, it can be evaluated that our model accurately detects them.  
Figure 9: Training loss graph. The loss of the model according to the epoch is shown.  
Figure 10: Testing results of single defects: (**a**) testing accuracy graph. The accuracy of the model according to the epoch is shown; (**b**) normalized confusion matrix (Center, Donut, Edge-Loc, Edge-Ring, Loc, Scratch).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Defect Type** & **Accuracy** & **F1-Score** & **IoU** \\ \hline Center & 1.000 & 0.987 & 0.742 \\ Donut & 1.000 & 1.000 & 0.721 \\ Edge-Loc & 1.000 & 0.974 & 0.650 \\ Edge-Ring & 1.000 & 1.000 & 0.686 \\ Loc & 0.995 & 0.976 & 0.712 \\ Scratch & 0.987 & 0.982 & 0.720 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Single testing result.  
Figure 11 shows some examples of segmentation results of single defects. It consists of the original image, ground truth, and result of segmentation for each defect. You can see that it accurately predicts a single defect when compared to the ground truth mask. Because ground truth acts as a label, the segmentation result is output as being as similar to ground truth as possible. Because the masking operation was performed automatically, there were some parts that did not match the defect, but there was no problem in detecting the defect.
## 5 Results and Discussion  
This section will show the results that were obtained from the simulations to evaluate the performance of the proposed RALAR algorithm. The outcomes were compared with the conventional LAR (LAR without model) and the KALAR (Benchmark) approach in terms of PDR, average E2E delay, routing overhead, and energy consumption concerning the simulation time of 1500 s. The experimental simulation was carried out in VANET to enhance the GPS location data, which can lead to improvements in the performance of the VANET environment.
# Uncertainty-Aware Time Series Anomaly Detection
## 2 Related Work
### Take Away  
Uncertainty quantification is crucial for making informed decisions based on time series data. However, most existing anomaly detection approaches primarily focus on epistemic uncertainty, which reflects model limitations, neglecting the inherent randomness or noise in the data themselves (aleatoric uncertainty). This work proposes a novel approach that uses separate techniques to quantify both aleatoric and epistemic uncertainty in time series data. Unlike existing works that solely aim for highly accurate anomaly detection, our approach tackles noise in data by quantifying aleatoric uncertainty. This allows for more robust anomaly detection and addresses model uncertainty to enhance the overall reliability of predictions.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### INS Components
#### 3.1.13. Indicators  
Many indicators are proposed by the IMO, namely propeller and main engine revolutions, pitch value for Controllable Pitch Propellers (CPP), torque, starting air, lateral thrust, speed, rudder angle, gyro-compass heading, magnetic compass heading, heading reminder, water depth, time, air and water temperature. Moreover, Rate-of-Turn indicator, wind direction, and velocity indicator (i.e., anemometer) are also proposed [55].
# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 4 Hybrid-Precision Circuit Simulation Validation and Stress Analysis
### Hybrid-Precision Circuit Stress Analysis  
The transient electrical characteristics of the FPGA and SRAM, extracted from simulation results, form the basis for analyzing the electrical stress on key components under dynamic operating conditions. In the case of the FPGA, the I/O drive signal at 20 MHz generates a peak transient current of 185 mA, leading to a core voltage drop between 1.15 V and 1.25 V. This dynamic current fluctuation is mainly caused by simultaneous switching noise during high-speed signal transmission. For the SRAM, at a baud rate of 921,600, the address decoding circuit exhibits a maximum transient current of 62 mA, with static power consumption at around 48.49 mW. These dynamic power characteristics are closely linked to localized thermal stress accumulation, making them essential for multi-physics coupling analysis.  
A further analysis of power and signal integrity under voltage stress shows that power supply noise, such as ripple on the 1.2 V rail, can cause fluctuations in the gate voltage of PMOS transistors [29]. In high-temperature environments, like 85 \({}^{\circ}\)C, these fluctuations accelerate threshold voltage drift due to NBTI, potentially reaching 15-20%. This mixed-precision simulation framework can be used to optimize decoupling capacitors and adjust the power-plane balance, ensuring that the gate voltage stress remains within a range of 1.2 V \(\pm\) 1%, thus reducing NBTI-related degradation and preserving timing margins on critical paths.  
Additionally, during frequency switching, the clock node experiences a peak voltage overshoot of 1.32 V, which is 10% higher than the nominal value. If the transient suppression circuitry degrades or control strategies fail, this overshoot can trigger latch-up in PMOS devices or lead to oxide breakdown [30; 31].
## 2 System Model  
### Proposed Integrated Architecture  
The proposed solution aims at optimising the overall energy consumption of a clustered IIoT scenario, leveraging the WPT concept and, consequently, maximising the services availability and system durability to support typical industrial applications. Specifically, this ha been accomplished by introducing the following elements: (i) an Energy Supplier (ES) node, which is in charge of handling the WPT procedure and possibly acting as a communication gateway toward external networks, (ii) a Rechargeable Node (RN) which represent a IIoT able to be properly recharged thank to the harvester block, and (iii) the Power Transfer Manager (PTM) which optimise the power transfer by enforcing a suitable strategy.  
In Figure 1, the high level architecture is depicted, where each IIoT cluster is arranged in a tree-wise topology, according to the Pv6 Routing Protocol for Low-Power and Lossy Networks (RPL) [15], which has been modified to select the root and lower _rank_ nodes among the RNs. It is worth noting that the power consumption is mainly due to packet transmission and reception; as a consequence in each cluster the lower rank nodes are mainly affected by power constrains. Typically, the root node (rank = 0) that forwarda all the messages from/to all the other nodes is likely to firstly deplete their battery. For this reason, it is fundamental that the lower rank nodes were possibly placed closer to ES in order to be recharged when needed to handle an amount of messages greater than the other nodes. According to our proposal, PTM leveraging on the publish-and-subscribe message exchanging, can monitor the residual battery level for the devices that subscribed to the WPT services (i.e., RNs), schedule a WPT cycle on a weighted round robin basis for each cluster, and properly coordinating the ES circuitry. In order to implement the proposed system architecture two specific configurations for both the ES and RN have been conceived, as pointed out in the following.
# Horizontal Wind Effect on the Aerodynamic Performance of Coaxial Tri-Rotor MAV
## 2 Theoretical Analysis
### Force Analysis  
Taking a rotor as an example, the airflow model with the presence of the horizontal airflow is shown in Figure 3.  
In Figure 3, \(v\) is the horizontal wind velocity, \(W\) is the weight of rotor, \(T\) is the thrust of rotor, \(L\) is the relative lift of rotor and \(\alpha\) is the angle of attack. With the horizontal airflow, the rotor disk will tilt  
Figure 3: The airflow model of the rotor disk in the horizontal wind.  
Figure 2: Flow model of coaxial Tri-rotor MAV in the horizontal wind: (**a**) No wind; (**b**) Horizontal wind.  
at a certain angle. Because the rotor is required to generate relative force (to balance the horizontal force) and lift (to overcome the rotor gravity), resulting in stable hovering state. Clearly, the induced power changes with the angle of attack. To derive the effect of the horizontal wind velocity on induced power, the induced velocity \(v_{i}\) for a rotor can be obtained as follow [26]:  
\[v_{i}\,=\,\frac{{{v_{h}}^{2}}}{{\sqrt{{{\left({v\,\cos\,\alpha}\right)}^{2}}+{ \left({v\,\sin\,\alpha\,+\,v_{i}}\right)}^{2}}}}, \tag{1}\]  
where \(\alpha\) is the angle of attack, \(v\) is the horizontal wind velocity, \(v_{h}\) is the induced velocity in hover [26].  
\[v_{h}=\sqrt{\frac{{T_{1}}}{{2\rho A}}}, \tag{2}\]  
where \(\rho\) is the air density, kg/m3; \(A\) is rotor disk area, m\({}^{2}\). By applying the energy conservation, the power required is obtained as follow:  
\[P=T(v_{i}+v\sin\alpha). \tag{3}\]  
Therefore, the horizontal wind may cause more power consumption resulted by the induced velocity and affect the flight efficiency eventually.
## 3 Results and Discussion
### Data Processing and Arithmetic
#### 3.3.3 FCE Calculation Process

Table 11: FCE index weights and consistency tests for the quality of the fresh agricultural produce supply chain platform, the “Fresh Networking” service. = (0.006, 0.027, 0.246, 0.364, 0.039)T  
S\({}_{1}\) was normalized, and the specific scores of B\({}_{1}\), B\({}_{2}\), B\({}_{3}\), and B\({}_{4}\) were 71.82, 72.18, 67.34, and 70.22, respectively. In accordance with the first-level fuzzy evaluation, the first-level index evaluation matrix was obtained. \[R= \begin{cases}0.009&0.039&0.361&0.534&0.057\\ 0.006&0.067&0.332&0.502&0.093\\ 0.007&0.121&0.409&0.424&0.039\\ 0.008&0.092&0.372&0.437&0.091\end{cases} \]  
Finally, the results of the comprehensive evaluation of the service quality of the fresh agricultural produce supply chain platform, \"Fresh Networking\", were calculated and obtained. \[\text{B}=0.008\ {}^{*}\,20+0.069\ {}^{*}\,40+0.362\ {}^{*}\,60+0.488\ {}^{*}\,80+0.073\ {}^{*}\,100=70.98\]
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 4 Discussion  
### Impact of Land Subsidence on Java Island  
LS poses numerous consequences in multidimensional aspects. In this study, the impacts are classified into three categories: social, economic, and environmental aspects. These impacts may influence each other, for example, LS in urban areas that rely on groundwater for daily needs can lead to a decline in groundwater quality, resulting in higher water treatment costs. Moreover, the quantity of groundwater diminishes over time,  
Figure 14: Correlation between GNSS and InSAR cumulative displacement from 9 CORSs.  
Figure 13: Time series from GNSS and InSAR data in BAKO, CSBY, CLMG, CJKT, CANG, CLDO, CTGL, CROL, and CPKL stations.  
reducing the carrying capacity of that area and potentially limiting economic and social activities. Table 1 summarizes the global effects of LS.  
The impacts of LS in large cities, such as Jakarta, Bandung, and Semarang, are significantly evident. In Jakarta, there is a high rate of LS in the northern part, reducing the land level to lower than the mean sea level. Therefore, to prevent inundation, dam structures were built along the coast of Muara Angke, and more have been added since they were first built in 2007. At the time of this study, the land level in Muara Angke was observed as low as 1.5 m below sea level during high tide (see Figure 4(a.2)).  
In the last decade, LS in Semarang has expanded to western and eastern areas, resulting in coastal flooding and loss. Inundations have been reported to reach up to 8 km from the shoreline in several areas. Groundwater over exploitation, as well as natural compaction, have been reported as the primary factors of LS in Semarang [72]. Damages to infrastructure have increased significantly: buildings are frequently flooded (with some cases of permanent flooding), railways are inundated, and even the newly built airport is now at high risk of flooding. Repurposing inundated areas for fishponds and the support of industries by some individuals has exacerbated the situation. People are not only losing their properties, including houses, but also their livelihood.  
Similar situations have also been reported in Bandung, with significant infrastructure damage. The most prominent evidence is a cracked bridge with a gap of approximately 80 cm (Figure 6(b.2)). According to a road construction company responsible for routine maintenance of the bridge, the last repair was performed in 2013, which aligns with the result of LS occurring at a rate of 80 mm/year. Cracks on buildings and roads have also been reported in other studies, [64]. Worsening of coastal flooding [47] and damages to the drainage system have also been reported in Pekalongan [73].
## 3 Validation of CCPD Model
### Grid Independence Test  
According to the characteristic of blood vessel geometry in the DSC model[5], the reconstructed blood vessels buried at a depth of 0.5 mm with an average diameter of \(100\pm 10\)\(\upmu\)m were embedded in the skin model to verify the rupture damage mode and validate the CCPD model (Figure 5). As mentioned in Section 2.2, only the dermal layer and the blood vessel in it were considered and compared with those in the in vivo experiment. The skin depth was 1 mm, and the cross section of the skin was 1.5 mm \(\times\) 1.5 mm-2 mm \(\times\) 2 mm, depending on the size of each blood vessel. A 595 nm PDL with a pulse width of 6 ms and a spot diameter of 1 mm was used in the in vivo experiment and the numerical simulation. The optical and thermal properties of dermis and blood corresponding to the wavelength of 595 nm are presented in Table 1.  
Figure 4: Procedure of the 3D reconstruction of real blood vessels. (**a**) Taking a photo of the DSC model; (**b**) extracting the main blood vessels and transforming them to a binary image; (**c**) extracting the contour and skeleton lines; (**d**) smoothing the line and outputting it in a vector form; (**e**) conducting 3D angiogenesis.  
Figure 5: Geometric skin model with a 3D reconstructed blood vessel. (**a**) Vessel image of the dorsal skin chamber model (the main vessel of reconstruction is indicated by the yellow arrow). (**b**) Skin model with a 3D reconstructed vessel.  
Six different tetrahedron grids with average interval sizes of 20, 15, 12.5, 10, 7.5, and 5 \(\upmu\)m in the vessel region were tested for the reconstructed blood vessel (Figure 5) to check the grid independence of the present algorithm. Figure 6 shows the local Gaussian curvature at the vessel bifurcation. Figure 7 depicts the Gaussian curvature variation with different grid intervals of points A and B on the vessel wall (Figure 6). The average deviation in GC between the results with cellular sizes of 7.5 and 5 \(\upmu\)m at the two points was less than 1.1%. The grid independence test showed that 7.5 \(\upmu\)m was the appropriate size for temperature and pressure computation. Therefore, the grid with a cell size of 7.5 \(\upmu\)m was adopted in the rest of our simulations.