esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
DEIM [35] was originally introduced to estimate nonlinear terms in dynamical systems by selecting the interpolation indices using a greedy algorithm. The index selection process in this algorithm is based on trying to capture most of the variation of the sample set heuristically. DEIM is often used with POD to provide interpolatory projection approximation. Theoretical works on this approximation and its extension can be found in [35; 36; 37]. DEIM has been used in many applications, such as two-dimensional shallow-water equations [38], four-dimensional variational data assimilation [39], three-dimensional nonlinear aeroelasticity model [40], and other problems [41; 42]. This work is an extension of the result given in [43], which only considered one simple preliminary numerical test. This work provides substantially more materials with an additional improved method that incorporates a faster way to compute projection basis based on a randomized algorithm, as well as performs more numerical experiments with detailed analysis and comparisons to the standard approach. The main contributions of this work can be summarized as follows. * We propose an algorithm that accelerates the missing data reconstruction procedure based on the least-squares method, which can reduce computational time in two aspects. The first one is based on applying the rSVD to obtain an optimal low-dimensional basis used in the least-squares projection. The second aspect arises from applying a greedy point selection procedure that can extract a small subset of important components to reduce the size of the reconstruction problem and, therefore, speed up the computational time. * We provide numerical experiments that demonstrate the efficiency (in terms of accuracy and speedup) of the proposed method for some standard testing images with different amounts of missing data. * We perform numerical tests on a sequence of two-dimensional miscible flow images. The proposed method was shown to give 10 to 100 times reduction in reconstruction time with the same order of accuracy. This illustrates the extensibility of the proposed method on other large-scale problems, as well as the applications of video processing. This paper is organized as follow. Section 2 describes the approach for reconstructing missing components using the least-squares method. The connection between SVD and the projection basis, which is called POD basis, used in the approximation is discussedin Section 3. The rSVD algorithm is explained in Section 4. Section 5 presents a greedy point selection process using DEIM as well as shows how it is used to reduce the computational complexity of missing data reconstruction. Section 6 discusses more details on the complexity reduction of the proposed method in terms of floating-point operations when compared to the standard least-squares approach. The numerical tests demonstrating the effectiveness of the proposed method are performed on three experiments in Section 7. Finally, the conclusion and some possible extensions are discussed in Section 8.
## 1 Introduction
In this study, we hypothesize that TOA SW anisotropy changes significantly with cloud microphysical properties, represented through the cloud-top effective radius, and amount of moisture above clouds, represented through cloud-topped water vapor. Using several years of CERES SSF Edition 4 data, we extracted footprints of low-level and single-layer clouds above ocean which are overcast, horizontally homogeneous, and had mean cloud optical depth of about 10 (Section 2.1). As shown in Section 2.3, we built new empirical ADMs using CERES-measured SW reflectances, MODIS-retrieved cloud-top effective radii, and estimates of cloud-topped water vapor (Section 2.2). In Section 3, we demonstrate that refined ADMs had a sensitivity to above effective radii and water vapor, and that missing such sensitivity resulted in regional and systematic underestimation of TOA SW fluxes. Finally, Section 4 discusses results.
# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 2 Datasets  
We employed two sets of datasets to test the performance of the model. We created the GF-6 images dataset that contains 587 image-label pairs. The aerial image labeling dataset was the benchmark dataset [58].
# Review: A Survey on Configurations and Performance of Flow-Mode MR Valves
## 4 Analysis
### Shear Stress Metric Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline Ref. & Type & \(\tau_{exp.}\)/ ms & \(F_{detf}\) (min.), N (test) & \(F_{detf}\) (max.), N (test) & \(\tau\) (min.), k.Ps & \(\tau\) (max.), k.Ps & \(\Delta\tau\), k.Ps \\ \hline [PERSON] et al. [94] & Figure 7a & 0.23 & 400 & 1900 & 123.1 & 566.13 & 466.03 \\ [PERSON] et al. [99] & Figure 7a & 0.30 & 450 & 1900 & 110 & 570 & 460 \\ [PERSON] et al. [99] & Figure 7a & 0.2 & 1894 & 15600 & 291 & 2977 & 2106 \\ \hline [PERSON] et al. [61] & Figure 7d & 0.1 & 495 & 1350 & 381.9 & 1041.7 & 699.8 \\ [PERSON] et al. [60] & Figure 7d & 0.60 & 900 & 3500 & 24.4 & 833.3 & 693.3 \\ \(\circ\) [PERSON] et al. [78] & Figure 7d & 0.52 & 750 & 4000 & 225.2 & 1200.3 & 975.1 \\ \(\circ\) [PERSON] et al. [78] & Figure 7d & 0.52 & 430 & 2800 & 132.3 & 800.3 & 668 \\ \hline [PERSON] et al. [77] & Figure 7c & 0.008 & 1500 & -8000 & 84.2 & -840.1 & 365.9 \\ \hline [PERSON] [67] & Figure 7g & 1.0 & 430 & 2900 & 145.6 & 982.1 & 836.5 \\ \hline [PERSON] et al. [88] & Figure 8d & 0.06 & 69 & 3305 & 14.2 & 683.1 & 668.9 \\ \hline \(\circ\) Connected MK damage (mess.) & Figure 8a & 0.52 & 617 & 4126 & 161.1 & 1077.2 & 916 \\ [PERSON] et al. [99,97] & Figure 8a & 1.0 & 620 & 3100 & 55.7 & 278.75 & 223.1 \\ \hline [PERSON] [50] & Figure 8e & 0.52 & 790 & 4300 & 374.5 & 2038.6 & 1664.1 \\ [PERSON] et al. [99] & Figure 8f & 0.52 & 330 & 3000 & 88.8 & 808.9 & 720.1 \\ [PERSON] et al. [99] & Figure 8e & 0.132 & 215 & 4320 & 44.7 & 898.6 & 851.9 \\ \hline [PERSON] et al. [93] & Figure 8b & 0.05 & 920 & 6838 & 46.8 & 347.9 & 301.1 \\ [PERSON] et al. [96] & Figure 8b & 0.132 & 240 & 3250 & 53.1 & 719.4 & 666.3 \\ \hline \end{tabular}
\end{table} 
Table 2: Summary: estimated shear stress metrics for selected MR actuator configurations; the \(\circ\) symbols refer to studies using commercial flow-mode MR dampers.
# Preliminary Results of an Astri/UWM EGNSS Receiver Antenna Calibration Facility
## 6 PCO Accuracy Validation  
The accuracy and stability of the obtained models can be validated by checking the reproducibility of the obtained results and comparison to existing models (igs14.atx). The results obtained for PCOs from the three calibrations, presented in Table 2, show that there were some slight deviations between different session results, which is reflected in standard deviations of 0.19 mm, 0.25 mm, and 0.56 mm in the northern, eastern, and vertical components, respectively, for the LEIAR10/NONE antenna. For the TRM15000.00/NONE antenna, the standard deviations reached 0.26 mm, 0.22 mm, and 0.52 mm in the northern, eastern, and vertical components, respectively. Generally, as shown in Table 2, both tested geodetic antennas (TRM115000.00/NONE and LEIAR10/NONE) show good result reproduction. The standard deviations for the northern and eastern components were smaller than 0.3 mm. For the vertical component, we achieved standard deviations that were slightly larger, reaching almost 0.6 mm.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Calibration Date**} \\ \hline
11 September 2020 & 23 September 2020 \\ \hline \multicolumn{2}{c}{**Hardware Used**} \\ \hline Robot Mitsubishi RV-3S & Robot Mitsubishi RV-3S \\ GPS-Reference 2000 external oscillator, & GPS-Reference 2000 external oscillator, \\ Receivers: 2\(\times\) Topcon Net-G3A & Receivers: 2\(\times\) Topcon Net-G3A \\ Reference antenna: Topcon GR-G3 & Reference antenna: Topcon GR-G3 \\ Calibrated antenna: LEIAR10/NONE & Calibrated antenna: TRM115000.00/NONE \\ \hline \multicolumn{2}{c}{**Start/End of Calibration Sessions**} \\ \hline
07:41:15/12:44:05 & 06:55:05/12:03:40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of calibration campaigns.  
Figure 6: The antennas during calibration: (**a**) LEIAR10/NONE; (**b**) TRM115000.00/NONE.  
In comparison to the igs14.atx values (Table 2), it can be seen that differences do not exceed 0.6 mm, 0.5 mm, and 2.0 mm in the northern, eastern, and vertical components, respectively, for the LEIAR10/NONE antenna. In the case of the TRM115000.00/NONE antenna, the differences do not exceed 0.4 mm, 0.9 mm, and 1.7 mm in the northern, eastern, and vertical components, respectively. Such differences are acceptable and often occur when individual and mean-type calibration results are compared [28; 29].
## 5 Conclusions  
In this paper, we proposed a multi-user and fine-grained task migration caching strategy in MEC that satisfies the completion time constraints and the goal of minimizing energy consumption. First, we established a fine-grained task partitioning model to transform user tasks into directed graphs with multiple subtasks. Then, based on the fine-grained task model, we studied the impact of the caching strategy on task execution energy consumption. The genetic algorithm was used to solve the above optimization problem and to obtain the optimal task migration and task caching solution. The simulation results showed that the caching strategy can greatly reduce the time required for task execution and the energy consumption, and the fine-grained task migration caching algorithm proposed in this paper is more efficient in energy-saving than other strategies, such as all tasks being executed in the edge cloud or mobile devices.  
In future work, we plan to improve task migration in MEC in the following three ways: (1) The task migration strategy proposed in this paper only considers the environmental state whereby the mobile device is always within signal coverage, and the network status is stable. The mobility of the mobile device is not considered, which could be considered in future work with migration decisions made by predicting the movement and trajectory of devices. (2) This paper only considers the task characteristics  
Figure 6: The impact of the edge cloud cache size on energy consumption.  
Figure 7: The impact of average data size on energy consumption.  
when investigating the fine-grained task migration energy-saving strategy. However, channel changes and battery remaining were not considered. In future work, more factors should be considered, and the migration strategy should be dynamically adapted to more complicated situations. (3) This paper adopts GA to solve optimization problems. However, in practical applications, GA has a long running time. In the future, new, fast algorithms should be studied to improve task migration performance.  
Conceptualization, L.T. and B.T.; Data curation, L.Z.; Formal analysis, L.T. and L.K.; Funding acquisition, B.T.; Investigation, L.Z.; Methodology, L.T.; Project administration, B.T.; Software, L.T.; Supervision, B.T.; Validation, L.K.; Visualization, L.K.; Writing--original draft, L.T.; Writing--review & editing, B.T.  
This research is supported by the National Natural Science Foundation of China under grant no. 61602169, 61702181, and 61876062, and the Natural Science Foundation of Hunan Province under grant no. 2018J[2135 and 2018J][3190, as well as the Scientific Research Fund of Hunan Provincial Education Department under grant no. 18A186.  
The authors declare no conflicts of interest.
###### Abstract  
Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.  
object classification; deep learning; convolutional neural network; network architecture +
Footnote †: journal: Applied Sciences
# Gaussian Process Surrogates for Modeling Uncertainties in a Use Case of Forging Superalloys
## 3 Active Learning and Solving Inverse Problems
### Active Learning  
Active learning is already well researched in terms of optimal use of resources for parameter optimization of a model, i.e., generating training data, see [12; 32; 33; 34]. The process of generating training data means obtaining labels \(Y_{train}\) for an input \(X_{train}\), such that a dataset \(D_{train}=(X_{train},Y_{train})\) can be used to fit or optimize parameters of a model. Labels \(Y_{train}\) are obtained by an oracle, where an oracle can be a domain expert, results of real physical experiments or like in our case results of expensive numerical FEM simulations.  
In the following, we present the idea behind the researched optimization approach and highlight the applicability of active learning with our proposed surrogate model.  
In active learning, a number of \(n_{AL}\) datapoints connected to maximum epistemic uncertainty \(\sigma_{ep}\) are queried from a pool of candidates \(X_{pool}\) to build a training dataset \(D_{train}=(X_{train},Y_{train})\) that is used for training a surrogate model. Thus, we select ideal training data, i.e., we use a minimum amount of training data such that the overall epistemic uncertainty in respect to making prediction on \(X_{pool}\) is minimized. We define in (11) the active learning query strategy with loss function \(\mathcal{L}_{AL}=\mathcal{L}_{AL}(\sigma_{ep})=\sigma_{ep}(x)\) to select a new query datapoint \(d_{q}^{AL}=(x_{q}^{AL},y_{q}^{AL})\) with input \(x_{q}^{AL}\) and output \(y_{q}^{AL}\).  
\[d_{q}^{AL}=\underset{x\ in\ X_{pool}}{\text{argmax}}\ \sigma_{ep}(x) \tag{11}\]  
A query datapoint \(d_{q}^{AL}\) is then moved to the training dataset \(D_{train}\), the surrogate model is fitted and the iterative generation of training data starts again. In respect to our proposed surrogate model, we are able to utilize directly the epistemic uncertainty predictions of the two GPs, i.e., \(\sigma(\mu)\) and \(\sigma(\sigma_{al})\). Thus, we define \(\sigma_{ep}(x)=\sigma(\mu(x))+\sigma(\sigma_{al}(x))\) and select training data by utilizing (11).
###### Abstract  
In order to solve the problem of the lack of maneuverability of the conventional underwater glider, this paper proposes a hybrid-driven underwater glider equipped with a Kappel tip rake propeller, analyzes the propulsion performance of different types of Kappel tip rake propellers in the wake field of the hybrid-driven underwater glider, optimizes the overall propulsion performance of the hybrid-driven underwater glider, and realizes self-propulsion and gliding with high efficiency and low energy consumption. In the research process, the Schnerr-Sauer cavitation model and the cavitation simulation strategy of VOF two-phase flow were adopted, coupled with the SST k-\(\omega\) and \(\gamma\) transition turbulence model, and the control calculation error was not more than 3%. Based on the hydrodynamic performance study of the Kappel tip rake propeller, the self-propelled simulation was carried out under the working conditions of 6 kn, 5 kn, 4 kn, and 3 kn, and the gliding simulation was carried out under the working conditions of 1 kn, 0.5 kn, and a glide angle of 12\({}^{\circ}\). The propulsion performance of the hybrid-driven underwater glider with different models of Kappel tip rake propellers was analyzed. It was found that the maximum open water propulsion efficiency of the propeller Kap05 had the largest improvement, which was 3.07% higher than that of the reference base propeller. Under the self-propelled condition, the hybrid-driven underwater glider with the propeller Kap05 had the lowest wake fraction, and the propellers Kap04 and Kap05 had the best propulsion performance in the wake field of the hybrid-driven underwater glider. In the gliding condition, the form of the folding paddle can reduce the gliding resistance generated by the propeller by more than 45% and the gliding negative lift by more than 68%. A moderate tip rake can effectively improve the propulsion efficiency of the Kappel tip rake propeller in the wake field of the hybrid-driven underwater glider, reduce energy loss, and improve the overall performance of the hybrid-driven underwater glider.  
tip rake propeller; Kappel propeller; hybrid-driven underwater glider; open water propulsion performance; computational fluid dynamics (CFD); self-propulsion simulation +
Footnote †: journal: Applied Sciences
# Hybrid Arctic Puffin Algorithm for Solving Design Optimization Problems
### Research Contribution  
The primary contributions of this research are outlined as follows:  
1. Proposing of the Hybrid APO-JADE Algorithm: This study introduces the Hybrid Arctic Puffin Optimization with JADE (APO-JADE) algorithm, integrating the exploratory capabilities of APO with the adaptive evolutionary mechanisms of JADE to enhance optimization performance and reliability.  
2. Outperforming Performance on Standardized Benchmarks: Through extensive evaluations on the CEC2022 benchmark functions, APO-JADE is demonstrated to outperform existing algorithms, achieving superior convergence speed, solution accuracy, and computational efficiency.
3. Practical Applications in Engineering Design: The APO-JADE algorithm has been effectively applied to optimize the design of planetary gear trains and three-bar truss structures, validating its robustness and practical utility in solving complex engineering optimization problems.
4. Statistical Analysis and Comparison with the State of the Art: This research includes a thorough statistical comparison of APO-JADE with other state-of-the-art optimization techniques, highlighting its superior performance across various metrics.
5. Versatility and Applicability: The adaptability of APO-JADE is showcased through its ability to handle diverse optimization landscapes, emphasizing its potential as a general-purpose optimization technique for a variety of industrial applications.
## 3 Component Design and Modeling
### Mach-Zehnder Optical Modulators
As can be seen, the minimum insertion loss is only 2.26 dB, which includes the bidirectional grating coupling loss of 0.52 dB, the MMI coupler loss of 0.15 dB, the phase shifter optical loss of 1.5 dB and the rest of waveguide transmission and bending loss. From Figure 8b, it can be seen that the static extinction ratio (ER) of 7.54 dB can be obtained with a working wavelength of 1560 nm which is perfectly matched to one of the WDM channels. The excess optical loss for the operation wavelength is about 1.2 dB. In order to match the modulator working wavelengths with the WDM channels, the free spectra range (\(FSR\)) of the MZI is designed as 5 nm. According to the optical theory, the FSR is related to the arm length difference of the MZI:  
\[FSR=\frac{\lambda_{1}\cdot\lambda_{2}}{N_{gu}\cdot\Delta L}\approx\frac{ \lambda_{0}^{2}}{N_{gu}\cdot\Delta L} \tag{12}\]  
where \(\lambda_{0}\) is the vacuum wavelength of 1550 nm, and \(N_{gu}\) is the group refractive index of the single-mode ridge waveguide, which is set to 3.9629 in the simulation, and \(\Delta L\) is calculated to be approximately 121.12 \(\upmu\)m by the above formula. The dynamic performances of the MZ modulators were investigated with the data transmission simulation setup, which included the compact model of the modulators, the continuous wave laser, the PRBS generator, the NRZ/PAM4 pulse generator and eye diagram analyzer. Figure 9a,b show the eye diagrams at 100 Gb/s of OOK modulation and 50 Gbaud/s of PAM4 modulation. The peak-to-peak driving voltage of OOK and PAM4 modulation are set at 2 V. To improve the signal integrity and linearity, the bias condition of the working wavelength and the four voltage levels of PAM4 modulation are carefully chosen. The clear open eyes imply that single lane of 100 Gb/s can be supported with both OOK and PAM4 modulation formats. The corresponding ER are about 6.8 dB and 4.9 dB, respectively. Figure 8: (**a**) The normalized transmission spectrum of MZI. (**b**) The optical transmission spectrum varies with voltage. Figure 9: Simulated eye diagram results of eight channels of silicon-based electro-optical modulator at 100 Gbit/s. (**a**) The eye diagram obtained by the modulator during 100 Gbit/s NRZ signal transmission. (**b**) The eye diagram obtained by the modulator during 50 Gbaud/s PAM4 signal transmission.