esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting
## 2 Forecasting Model and Missing Data Imputation Methodology
### Missing Data Imputation Methods
#### 2.2.3 Imputation Using KNN  
The KNN imputation is based on machine learning, which has been extensively used for classification, regression, and imputation [29]. The method considers the \(k\) most similar data of the uncompleted dataset [29; 30; 31; 32]. In other words, these k-nearest neighbors aid in replacing missing data with estimates. Given a broken data pattern \(x\), the method adopts the \(k\)-closest instances that are complete values in the features to be imputed (i.e., attributes with missing values in \(x\)) such that they decrease by a certain distance value as far as possible [29]. In our study, \(\mathrm{\
u}=\ \{v_{k}\}_{k=1}^{K}\) represents the set of KNN of \(x\) arranged in ascending order of distance. Although the KNN can be chosen for objects without missing values, it is also encouraged to substitute corrected data for missing data [30; 32].  
To process missing values with the use of KNN, let us suppose that \(x\) includes a missing value on the \(j^{\mathrm{th}}\) input feature. After its KNN are selected, we set \(\mathrm{\
u}=\ \{v_{k}\}_{k=1}^{K}\), and the unknown value is estimated using the corresponding \(j^{\mathrm{th}}\) feature values of \(\mathrm{\
u}\). If the \(j^{\mathrm{th}}\) input feature is a numerical or continuous variable, different estimation procedures can be implemented in the KNN approach.  
Figure 5: Datasets with missing data.  
We weigh the contribution of each \(v_{k}\) according to its distance to \(x\), i.e., d(\(x\), \(v_{k}\)), assigning higher weights to closer neighbors. The equation for the estimation of the values is given below.  
\[\widetilde{x}_{j}=\frac{1}{K}\sum_{k=1}^{K}w_{k}v_{kj} \tag{2}\]  
where w\({}_{k}\) represents the weight related to the \(k^{\text{th}}\) neighbor. A suitable option for w\({}_{k}\) corresponds to the inverse square of the distance, as described by Equation (3).  
\[\text{w}_{k}=\ \frac{1}{d(x,v_{k})^{2}} \tag{3}\]  
In this study, we use this weighted method to estimate missing values in the cases of numerical and distinct variables, and further, we arbitrarily set \(k=3\). It has been shown that this method is robust for missing data estimation [29; 30; 31; 32]. Its primary limitation is that whenever the method searches for the most similar instances, the algorithm has to estimate the entire dataset. The method is particularly problematic for application to large databases because it is time consuming and computationally expensive for big data.
## 3 Reduced Dynamic Orbit Determination of COSMIC-2
### Assessment of Orbit Accuracy
#### 3.3.2 Overlap Orbit Validation  
In order to evaluate the orbit determination accuracy for the six satellites using the overlap validation method, two-orbit determination arc sections are set every day, and the time spans are 00:00:00-17:59:59 and 12:00:00-23:59:59 respectively. The overlap time is 6 h. Although the observations of the two overlapping orbits are the same, the two orbits are solved by two independent orbit determination processes [43]. Therefore, the overlapping orbits of the two orbit determination arcs for 6h are not related to each other. In order to avoid the boundary effect, the overlapping arc of 4h from 13:00:00 to 16:59:59 are selected for comparison [39; 44]. The overlapping orbit difference of six COSMIC-2 satellites in R, T and N directions is shown in Figure 8, and the mean RMS values of orbit difference between overlapping orbits during DOY 138-142 are given in Table 6.  
It can be seen from Figure 8 that the RMS values in the R, T and N directions of the 4 h overlapping orbit difference in the five days are smaller than 3 cm, and the RMS in the T direction is significantly larger than that in R and N directions. Table 6 and Figure 8  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **R** & **T** & **N** & **3D** \\ \hline Sat–1 & 0.60 & 1.20 & 0.93 & 1.66 \\ Sat–2 & 0.92 & 1.02 & 0.53 & 1.51 \\ Sat–3 & 0.71 & 1.12 & 0.73 & 1.53 \\ Sat–4 & 0.60 & 1.07 & 1.03 & 1.64 \\ Sat–5 & 0.79 & 1.33 & 0.70 & 1.75 \\ Sat–6 & 0.65 & 0.98 & 0.62 & 1.38 \\ \hline \end{tabular}
\end{table}
Table 6: Mean RMS of differences between overlapping orbits for COSMIC-2 (cm).  
Figure 8: RMS of differences between overlapping orbits for COSMIC-2.  
show that the mean RMS values of the overlapping orbit differences of the six COSMIC-2 satellites during DOY 138-142 fluctuate between 1.38 cm and 1.75 cm in the 3D direction, and the mean RMS of Sat-6 in the 3D direction is the smallest and that of Sat-5 in the 3D direction is the largest. The overlapping orbit validation indicates that mean the RMS values in 3D of the six COSMIC-2 satellites are all better than 2 cm. Obviously, our orbit determination accuracy is higher than that of TACC of 15 cm, and the result meets the orbit determination accuracy requirements of COSMIC-2.
## 4 Discussion and Conclusions
that ties computational studies together with experimental evidence [26]. Using photothermal deflection spectroscopy, they probe sub-band gap states in the visible (SG-Vis) spectra and at longer wavelengths (SG-IR). Combined with transient absorption and photocurrent measurements they were able to connect the experimentally observed results to computationally calculated point defects. Similar thorough analysis of defect states should also be performed on doped systems in order to compare defect reducing properties of specific dopants. Furthermore, the comparison of the defect states in pristine perovskite and doped systems using DFT methods is still missing in literature. To conclude, there still remain a lot of open questions that must be resolved to fully understand the phenomena of defect induced recombination and the potential role of compositional engineering. Recent work is inconclusive on the result of targeting the growth of specific grain orientations or larger grains. The complicated interplay of different effects seems to be a barrier to unravel the mechanisms behind compositional engineering. In this regard, future work must unify the various reports. This requests more systematic structural and photophysical characterization coupled with DFT calculations [50] to create a deeper understanding of doped perovskite and the dynamics of defects. This should result in a doping strategy that rationally reduces structural disorder and increases defect formation energy. In this review, we have tried to shed some light on the interplay of defect chemistry, photovoltaic performance and the influences of compositional engineering [51; 52; 53]. These findings may contribute towards developing rational fabrication methods to create materials approaching the theoretical limits. Conceptualization, R.M.W. ; writing--original draft preparation, draft based on literature thesis within the MSc program Molecular Sciences at UvA, R.B. ; writing--review and editing, R.M.W. ; supervision, R.M.W. All authors have read and agreed to the published version of the manuscript. This research has received no external funding. The authors are grateful to the UvA (Universiteit van Amsterdam) for the structural support. The authors declare no conflict of interest.
# Spoofing Detection Algorithm Based on Pseudorange Differences
## 3 Simulation Tests
### Beidou Open Laboratory Test  
To further verify the effectiveness of the algorithm, we conducted Experiment No. 2 in Beidou Open Laboratory. We used the antenna on the roof of the building to introduce the satellite's signal into the room and assumed this signal to be real. The signal is input into the spoofing signal simulator, and the spoofing signal is output after the computer calculation. The output spoofing signal from the simulator moves circularly. The receiver is adopted in the whole duration. In this duration, the signal is real at first, and this process lasts about 90 s. Then, the spoofing signal simulator is turned on. The absolute power advantage guarantees that the receiver can receive the spoofing signal. The circular motion signal is then acquired. This process lasts about 135 s. Finally, the simulator is turned off, and the signal becomes real. This process lasts about 90 s.  
We apply the traditional least squares method and the spoofing detection algorithm to the datasets and use (\(-\)2,185,955.407, 5,181,417.961, 2,999,272.014) as a reference point for coordinate transformation in the results shown in Figure 5.  
From Figure 5, we can see that, for the real signal, the results of the two algorithms are basically the same. For the spoofing signal with circular motion, the difference of the results of the two algorithms is obvious. In Figure 6, we can see that the position deviations in the first segment and the third segment are always small. The position deviations perform a significant change and remain large when the signal is switched from real to spoofing. These differences can help us to validate the signal authenticity. The false alarm rate when the signal is real is 2.91%, and the missed detection rate when the signal is spoofing is 2.04%.  
Figure 4: Position deviations of the two algorithms (Experiment No. 1).
## 5 Conclusions  
In this paper, we propose a deep-learning framework for crop prediction using remote sensing data. Since the spatial information of an image alone cannot help to estimate the crop yield well, the temporal information of consecutive images should be considered. Thus, a series of multispatial images with change information was used as the input for the 3D convolutional neural network proposed in this paper. Moreover, the dimensionality reduction method used in this paper can map the different images to the same dimensional feature and prevent the loss of useful information. In addition, the metric learning and multitask learning strategies make regions with similar crop yields have similar features learned by the network, which can make the learned features better for crop yield prediction. Experimental results on county-level soybean yield prediction in the United States also show the effectiveness of the proposed method. Accurate crop yield estimation has important research significance in the field of agriculture. And the proposed method provides new ideas for crop yield estimation and effectively improves the accuracy of crop yield estimation.  
In addition, the effectiveness of the proposed method has been validated on soybean yields in major producing states in the United States. Due to the differences in the characteristics of different crops and regions, it is possibly not entirely applicable and has limitations if directly applying the proposed method to estimate yields of other crops and regions. But, we believe that the strategy of using the temporal network and the feature constraint of the proposed method is still effective. Thus, it is necessary to modify the model based on the target characteristics of different crop types and different regions in order to achieve better yield estimation performance. This is one of the tasks we need to do in the future. For additional future work, using more information from the images and looking for a network with better performance to further improve the accuracy of the crop prediction are considered.  
Methodology, N.W. and P.H.; resources, Z.M.; software, N.W. and P.H.; writing--review and editing, N.W., P.H., Z.M., X.L., Z.H. and K.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Year**} & \multirow{2}{*}{**Avg**} \\ \cline{2-2} \cline{4-8}  & **2016** & **2017** & **2018** & **2019** & **2020** & **2021** \\ \hline Ridge regression & 8.586 & 7.983 & 8.485 & 7.235 & 6.683 & 7.995 & 7.828 \\ \hline Decision trees & 8.937 & 6.752 & 8.324 & 6.589 & 7.536 & 7.563 & 7.617 \\ \hline The proposed method & 6.428 & 4.406 & 6.058 & 4.664 & 5.198 & 5.243 & 5.333 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSE comparison of different methods.
## 3 Proposed Circuit Implementation
### Design of Input-Stage Operational Amplifier  
The input-stage op amp is a fully differential input rail-to-rail op amp. The design of the input-stage operational amplifier is shown in Figure 7. As the input-stage common-mode voltage changes from positive to negative, the input pair experiences three states: only PMOS is off, NMOS and PMOS work at the same time, and only NMOS is off. In this process, the input-stage transconductance change curve is a bell-shaped curve, the maximum value is twice the minimum value, and the frequency response curve is also the same. This requires good frequency compensation, and it is necessary to pay attention to the transient distortion caused by the fast-changing common-mode voltage modulating the input-stage transconductance.  
To overcome the above problems, the input-stage differential pair circuit of the op amp adopts tail current control technology. By using current-switch MP15, as well as current-mirror MN2 and MN3 to provide bias for the input NMOS differential pair, the total current of the NMOS and PMOS differential pairs is a constant value. In this way, when the common-mode voltage is high and the PMOS differential pair is off, the current generated by the current source MP1 can still make the NMOS work. When the common-mode voltage is less than the reference level, the input-stage differential pair is dominated by PMOS. When the common-mode voltage is equal to the reference level, the NMOS and PMOS differential pairs work simultaneously. At this time, the input-stage transconductance is the  
Figure 6: Overall circuit structure composition.  
largest, which is \(\sqrt{2}\) times that of NMOS or PMOS working alone, reducing the influence of the common-mode voltage on the input-stage transconductance [41].  
The NMOS differential input pair features two common-gate stages, while the PMOS differential input pair has only one common-gate stage. This design choice is primarily aimed at increasing the output impedance. To prevent significant variation in the overall gain with changes in the input common-mode voltage, a common-gate gate voltage adjustment circuit is also incorporated. This circuit ensures that the VBP1 voltage increases as the input common-mode voltage rises, thereby adjusting the intrinsic gain of transistors MP8 and MP9.
###### Abstract  
Turbulent energy cascade and intermittency are very important characteristics in the turbulent energy evolution process. However, understanding the temporal-spatial features of kinetic energy transfer and quantifying the correlations between different scales of turbulent energy remains an outstanding challenge. To deeply understand the spatial-temporal features in the energy transfer process, an integrated features identification and extraction method is proposed to quantitatively investigate the correlations using the ocean shear turbulence measured by an autonomous vertical reciprocating profiler (AVRP). The proposed integrated method mainly contains two parallel features analysis modules: first, temporal multiscale features structures of the nonlinear and nonstationary turbulent cascade are identified by Variational Mode Decomposition (VMD); then, the ocean microstructure shear fluctuation data are decomposed into a series of intrinsic mode functions (IMFs), which are characterized by different time scales and frequency bandwidths. The local features of energy transfer are identified when the local intermittency peaks overlap and the phase-synchronization case occurs between two neighboring scales; second, the spatial statistical characteristics of the turbulent energy dissipation are quantitatively studied. The cumulative probability distribution functions (CPDFs) of kinetic energy dissipation are approximated well by a normal distribution, indicating that the turbulent dissipation process exhibits a robust spatial scaling correlation and a few intense dissipation locations dominate the integrated process. Finally, the proposed integrated method is evaluated through experiments using an autonomous vertical reciprocating profiler deployed in the South China Sea. Preliminary experimental results show that the proposed novel method is useful to improve our understanding of turbulent energy transfer and the evolution process in the ocean dynamic systems.  
ocean turbulence; energy cascade; dissipation rates; spatio-temporal features; VMD +
Footnote †: journal: Applied Sciences
###### Abstract  
In this paper, a tactical Production-Distribution Planning (PDP) has been handled in a fuzzy and stochastic environment for supply chain systems (SCS) which has four echelons (suppliers, plants, warehouses, retailers) with multi-products, multi-transport paths, and multi-time periods. The mathematical model of fuzzy stochastic PDP is a NP-hard problem for large SCS because of the binary variables which determine the transportation paths between echelons of the SCS and cannot be solved by optimization packages. In this study, therefore, two new meta-heuristic algorithms have been developed for solving fuzzy stochastic PDP: Ant Colony Optimization (ACO) and Genetic Algorithm (GA). The proposed meta-heuristic algorithms are designed for route optimization in PDP and integrated with the GAMS optimization package in order to solve the remaining mathematical model which determines the other decisions in SCS, such as procurement decisions, production decisions, etc. The solution procedure in the literature has been extended by aggregating proposed meta-heuristic algorithms. The ACO and GA algorithms have been performed for test problems which are randomly generated. The results of the test problem showed that the both ACO and GA are capable to solve the NP-hard PDP for a big size SCS. However, GA produce better solutions than the ACO.  
production-distribution; genetic algorithm; ant colony optimization; fuzzy; stochastic
# ISAR Image Quality Assessment Based on Visual Attention Model
## 4 Discussion of the Method
### Potential Applications of Gram-T  
The Gram-T model, with its ability to capture global information and feature interactions, holds promise for applications beyond ISAR image quality assessment. In medical  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Methods** & **SRCC** & **PLCC** & **Score** \\ \hline Gram–T & 0.8831 & 0.8477 & 1.730 \\ Gram–T+CAB & 0.8874 & 0.8567 & 1.744 \\ Gram–T+IRAB & 0.9072 & 0.8645 & 1.771 \\ Gram–T+CAB+IRAB & 0.9103 & 0.8627 & 1.773 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation studies of our modules.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Scale Factor** & **SRCC** & **PLCC** & **Score** \\ \hline
0 & 0.8874 & 0.8567 & 1.744 \\
0.5 & 0.9071 & 0.8560 & 1.763 \\
1 & 0.8972 & 0.8551 & 1.752 \\
0.8 & 0.9103 & 0.8627 & 1.773 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation studies of scale factor.  
Figure 6: Attention heatmap analysis. The attention heatmap is on the left, and the original image is on the right. In our experiments, the blue color in the attention heatmap represents the high weight, and the red color represents low weight.  
imaging, the model could be used to enhance the quality assessment of MRI scans, aiding in the diagnosis of various conditions. For instance, Mayo Clinic's Neurology AI Program has demonstrated significant improvements in the accuracy of brain image interpretations by integrating AI with clinician expertise. Similarly, in general image quality assessments, the Gram-T model could be leveraged to extract robust image representations, which are beneficial for image assessment tasks.
## 2 Literature Review  
Traditional methods for fault diagnosis in gas turbines, such as physics-based models and rule-based expert systems [26], have played a fundamental role in our understanding and diagnosis of faults in these critical machines. These approaches are respected for their accuracy in identifying and characterizing faults, providing a robust framework for fault analysis. However, they have inherent limitations when applied to real-time scenarios, mainly due to their computational requirements and difficulty in adapting to emerging or unconventional fault patterns [27]. Another class of methods, signal processing techniques, has been widely used in the analysis of sensor data from gas turbines. Techniques such as Fourier analysis and wavelet transform [28] have shown their effectiveness in detecting anomalies in data. However, these methods often struggle with the challenge of distinguishing benign operational changes from actual faults when operating in real-timescenarios. In the following, the details of different algorithms for gas turbine fault diagnosis are discussed in Table 1.  
In recent years, there has been a significant shift towards the adoption of machine learning (ML) algorithms, especially deep learning models, for real-time fault diagnosis in gas turbines. This transition is driven by the potential of these advanced techniques to significantly increase fault detection accuracy and adaptability. Models such as Recurrent Neural Networks (RNNs), Autoencoders, Convolutional Neural Networks (CNNs), and Temporal Convolutional Networks (TCNs) have emerged as powerful tools for time-series data analysis in various applications. Real-time anomaly detection in time-series data is a multifaceted challenge, and choosing the most appropriate deep learning method depends on specific characteristics of the data and unique application requirements. TCNs have been highlighted for their ability to process sequences in parallel, dissimilar to RNNs that work in a stepwise manner, making them particularly efficient for real-time scenarios. Furthermore, their architecture, which uses dilated convolutions, enables them to capture long-range dependencies in the data, which is a critical factor for accurate anomaly detection in time-series data. Choosing the best method for fault detection in gas turbines depends on various factors, including data complexity, availability of labeled anomaly data, desired trade-offs between false positives and false negatives, and computational resources. Considering the advantages offered by TCNs, they present a compelling case for real-time fault diagnosis in