esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effect of Observation Modes and Bands on Correlating OCO-2 SIF with GPP\({}_{\text{EC}}\)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Site ID**} & \multicolumn{3}{c}{**SIF757**} & \multicolumn{3}{c}{**SIF771**} \\ \cline{2-7}  & **Nadir** & **Glimt** & **Target** & **Nadir** & **Glimt** & **Target** \\ \hline \multicolumn{7}{c}{**Mixed Forest**} \\ US-PFa & 0.99 & 0.32 & 0.67 & 0.95 & 0.48 & 0.59 \\ US-Syv & 0.67 & 0.57 & – & 0.71 & 0.49 & – \\ \multicolumn{7}{c}{**Permanent Wetlands**} \\ US-Los & – & 0.66 & 0.42 & – & 0.77 & 0.32 \\ US-PHM & 0.70 & – & 0.55 & – & – \\ US-Tw1 & 0.11 & 0.01 & – & 0.38 & 0.01 & – \\ US-Tw4 & 0.08 & 0.01 & – & 0.41 & 0.01 & – \\ US-ALQ & & 0.41 & – & 0.32 & \\ \multicolumn{7}{c}{**Evgreen Needleleaf Forests**} \\ CA-SCC & 0.74 & – & – & 0.48 & – & – \\ CA-TP1 & – & 0.08 & – & – & 0.05 & – \\ CA-TP3 & 0.56 & 0.20 & – & 0.44 & 0.20 & – \\ CA-TP4 & 0.54 & 0.76 & – & 0.46 & 0.74 & – \\ US-GLE & 0.48 & 0.52 & – & 0.47 & 0.50 & \\ \multicolumn{7}{c}{**Croplands**} \\ US-Twt & 0.26 & – & – & 0.12 & – & – \\ US-Ro2 & 0.43 & – & – & 0.35 & – & – \\ US-Tw3 & 0.18 & 0.06 & – & 0.35 & 0.07 & – \\ \multicolumn{7}{c}{**Deciduous Broadleaf Forests**} \\ CA-TPD & – & 0.49 & – & – & 0.48 & – \\ US-UMd & & 0.52 & – & 0.16 & \\ US-WCr & – & 0.49 & 0.53 & – & 0.22 & 0.50 \\ \multicolumn{7}{c}{**Grasslands**} \\ US-KFS & – & 0.51 & – & – & 0.47 & – \\ US-KLS & 0.38 & – & – & 0.56 & – & – \\ US-Ro4 & 0.85 & – & – & 0.91 & – & – \\ \multicolumn{7}{c}{**OSH**} \\ US-EML & 0.69 & 0.15 & – & 0.41 & 0.29 & – \\ \multicolumn{7}{c}{**CSH**} \\ US-RIs & – & 0.36 & – & – & 0.40 & – \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparisons of correlation coefficients for SIF and EC GPP for the different observation modes and bands (parameter: R\({}^{2}\)). stringent temporal-spatial registration between remotely sensed products and ground-based in-situ measurements. In particular, for different vegetation types, the R\({}^{2}\) of SIF757 is slightly lower than MODIS GPP with GPP\({}_{\text{EC}}\). For a single flux tower, the R\({}^{2}\) of SIF757 is higher than that of MODIS GPP (Table 4). The main reason for this is that the amount of data for SIF757 is small. Moreover, the growth environment of different longitudes and latitudes is inconsistent, which leads to differences in the value of SIF757 of the same vegetation type. The average SIF757 data of different flux towers of the same vegetation type may contain errors.
## 6 Conclusions and Future Work  
The paper proposes a shared control framework that integrates both spatial and temporal considerations into the decision-making process of a robot for navigating in a crowd and providing walking support to a patient. The presented work addresses the gap between robotic crowd navigation and robotic walking assistance. Our proposed models enable co-navigation between a patient and a robot. Two models are proposed and evaluated; the first model enables the patient-robot co-navigation in environments with dynamic obstacles, and the second enables co-navigation in environments with static and dynamic obstacles. Both models were evaluated and compared with baseline methods with respect to collision rates. The first model outperforms the baseline model with respect to collisions with humans. In contrast, the second model outperforms the baseline model with respect to both the success rate and collisions with humans. Additionally, the results show that our models can maintain contact with the patient. Finally, our model, which avoids both dynamic and static obstacles, was implemented in Gazebo successfully. This is a promising step towards deploying the model on a real-world mobile platform. As of now, our system is designed to assist patients with a minimum MMT level of 4/5. Our next step will be to evaluate our model in a real-world laboratory setting (e.g., a crowded hallway at our university) and then in a clinic or hospital setting. We are also interested in extending our work to other environments where co-navigation may be needed. Additionally, we plan to expand the system to accommodate patients with a lower rating than 4 for rehabilitation purposes.  
Conceptualization, M.K. and K.K.; methodology, K.K and M.K.; software, K.K.; experiments, K.K.; formal analysis, M.K. and K.K.; writing--original draft preparation, M.K. and K.K.; writing--review and editing, K.K. and M.K.; supervision, M.K.; project administration, M.K.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
This material is based upon work supported partially supported by the National Science Foundation under Grant Number 2226165.  
Not applicable.  
Figure 8: Flowchart of how the Model V2 is used to control the robot in a realistic Gazebo simulation.
# Machine Learning Methods in Weather and Climate Applications: A Survey
## 5 Short-Term Weather Forecast
### Model Design
**(c)** The decoder component maps the processed multigrid features (purple nodes) to the grid representation. **(d)** A multi-scale grid set. PanGu's [49] innovative 3D neural network architecture [87] offers a groundbreaking perspective for integrating meteorological data, and its suitability for three-dimensional data is distinctly pronounced. Moreover, PanGu introduces a hierarchical time-aggregation strategy, an advancement that ensures the network with the maximum lead time is consistently invoked, thereby curtailing errors. In juxtaposition with running a model like FourCastNet [47] multiple times, which may accrue errors, this approach exhibits superiority in both speed and precision. Collectively, these novel attributes and methodological advancements position PanGu as a cutting-edge tool in the domain of high-resolution weather modeling, promising transformative potential in weather analysis and forecasting. MetNet, FourCastNet, GraphCast, and PanGu are state-of-the-art methods in the field of weather prediction, and they share some architectural similarities that can indicate converging trends in this field. All four models initiate the process by embedding or downsampling the input data. FourCastNet uses AFNO, MetNet employs a Spatial Downsampler, and PanGu uses Block Embedding to manage the spatial resolution and complexity of the datasets, while GraphCast maps the input data from the original latitude-longitude grid into a multi-scale internal mesh representation. Spatio-temporal coding is an integral part of all networks; FourCastNet uses pre-training and fine-tuning phases to deal with temporal dependencies, MetNet uses ConvLSTM; PanGu introduces a hierarchical temporal aggregation strategy to manage temporal correlations in the data; and GraphCast employs GNNs to capture and address spatio-temporal dependencies in weather data. Each model employs a specialized approach to understand the spatial relationships within the data. FourCastNet uses AFNO along with Vision Transformers, MetNet utilizes Spatial Aggregator blocks, and PanGu integrates data into a 3D cube via 3D Cube Fusion, while GraphCast translates data into a multi-scale internal mesh. Both FourCastNet and PanGu employ self-attention mechanisms derived from the Transformer architecture for better capturing long-range dependencies in the data. FourCastNet combines FNO with ViT, and PanGu uses Swin Encoding. Figure 5: Network training and inference strategies. (**a**) 3 DEST architecture. (**b**) Hierarchical temporal aggregation. We use FM1, FM3, FM6 and FM24 to indicate the forecast models with lead times being 1 h, 3 h, 6 h or 24 h, respectively.
## 4 Experimental Results
### Optical Spectrum  
First of all, the CS-DSB modulation of the proposed microwave photonic converter is analyzed in the optical domain. The modulated optical spectrum of upconversion with an HF signal of 20.2 GHz and LO signal of 35.2 GHz is measured by an optical spectrum analyzer (OSA, YOKOGAWA, AQ6370D). The HF and LO signals are generated by two analog signal generators (ASG, KESYIGHT E8267D, and ROHDE&SCHWARZ) and the power of both the HF and LO signals is 0 dBm. The optical spectrum under both the MITB and quadrature bias (QB) point is shown in Figure 6. The selected MZM does not exhibit a very strong suppression ability of the carrier because the MZM has a relatively small extinction ratio (30 dB). Compared with the double-sideband (DSB) modulation described with the short-dashed line, the CS-DSB modulation delineated with the solid line demonstrates a suppression (16.8 dB) of the central optical carrier. It is beneficial to the amplification of sideband signals by the EDFA. The orange lines in Figure 6 display a strength improvement of 13.5 dB for the +1-order sideband signal of 20.2 GHz through the EDFA. The optical spectrum indicates that the MZM can output a good CS-DSB modulation signal after the EDFA. The optical carrier is suppressed by simply controlling the bias voltage of the modulator, which is different from the use of optical filter. In [27], optical filters are used to filter out unexpected optical carriers. However, the approach is limited by the requirement of the high stopband rejection level filter at the optical carrier wavelength and a very narrow bandwidth. Thus, the drawbacks will result in effects on the RF and LO sidebands when filtering. On the other hand, the modulation sidebands of the LO 35.2 GHz signal exhibit power attenuation of around 10 dB compared to the HF 20.2 GHz signal. This situation is due to two reasons, one is the increased loss of electrical devices including electrical cable and electrical combiner at the high frequency, the other is that the modulation capability of the modulator itself is weakened when frequency increases. The power responses of the electrical devices and MZM will be described and analyzed later.  
Figure 5: Experimental link setup based on the proposed converter (PS: power source; PC: polarization controller).
## 2 Temporal Characteristics of Near-Surface Atmospheric Electrostatic Abnormalities before an EQ
Among these EQ cases, the magnitude was Ms 3-8 and the proportions of sensed EQs (4.5 > Ms \(\geq\) 3.0), medium-strong EQs (6.0 > Ms \(\geq\) 4.5) and strong EQs (Ms \(\geq\) 6.0) were 13%, 52% and 35%, respectively. All these inland EQs occurred on fair-weather days with negative electrostatic field anomalies appearing 2-48 h before the EQ. Most of these anomalously negative Ez signals lasted for a short time (approximately 1 h). The values of the negative electrostatic field signals ranged from a few hundred to tens of thousands of volts per meter, which may have been related to the distance between the station and epicenter or the local geological structure. All 23 cases had a shock time of 2-48 h, and 83% of the anomalies occurred no more than one day before the EQ, which is a critical and effective time scale for EQ disaster prevention. According to these case studies and EQ list analysis, it is considered possible that if an inland EQ is impending, the crustal movements near the related faults cause the near-surface atmospheric electrostatic field to become abnormal. Additionally, other authors [4, 9] have found that precursors appear at a time scale of 2-48 h before an EQ. With these observation-based descriptions from all over the world, it is likely that fair-weather anomalously negative Ez signals ubiquitously appear 2-48 h before an inland EQ occurs.
## 2 Background Study on Pipeline Leak Detection Methods
### Ensemble Empirical Mode Decomposition (EEMD)  
Ensemble empirical mode decomposition (EEMD) helps to resolve the mode mixing problem in EMD by introducing white noise to the signal [51]. The mode mixing problem occurs in EMD when the IMF includes significantly different scales during sifting. The added white noise helps extract the original signal from the data, even when the data are intermittent.  
The introduction of a series of white noise nullifies each other in Algorithm 2, step 4. As a result, the necessary outcome stays within the dyadic filter windows, substantially reducing the likelihood of mode mixing and preserving the dyadic property. The disadvantage of EEMD is that the decomposition is incomplete, and there is a possibility of producing different number modes due to different signal-to-noise realizations.  
```
Step 1: Generate \(S^{i}(t)=S(t)+\beta Wn^{i}\), where \(\beta>0\), and \(Wn^{i}\) is a white noise with zero mean unit variance Step 2: Decompose each \(S^{i}(t)\)\((i=1,\dots,1)\) completely by EMD and obtain the modes \(\tilde{I}_{k}\), where \(k=1,\dots K\) indicated the mode. Step 3: Assign \((\tilde{I}_{k})\) as the \(k^{th}\) mode (average of corresponding modes \(\tilde{I}_{k}=\frac{1}{\sum_{i=1}^{1}}\tilde{I}_{k}^{i}\)) of \(S\)
```  
**Algorithm 2** EEMD algorithm steps
## 2 Materials and Methods
### Procedure
#### 2.4.7 Evaluation Criteria and Data Analysis  
The analysis of the collected data was conducted using various methods and criteria:  
Figure 3: Interaction process of participants with the IVR environment in “Parabolic Basketball VR”.  
* Improvement in scores: The statistical significance of improvements in total pre- and post-test scores was calculated using the Wilcoxon signed-rank test [51], providing a solid basis for interpreting the results. Additionally, the average scores for each question were analyzed to understand the behavior of the scores in detail;
* Normalized gain ([PERSON]'s gain): To assess overall improvement, normalized gain was utilized, calculated using the formula g = (\(\text{Ppost}-\text{Ppre}\))/(\(\text{Pmax}-\text{Ppre}\)), where Pmax is the maximum possible score. This measure offers insight into the overall effectiveness of learning facilitated by the serious game [14];
* Game performance: Game performance, based on metrics such as points earned and levels completed, was correlated with improvement in conceptual knowledge. This multidimensional approach allowed for a more comprehensive understanding of the relationship between serious game interaction and student learning.  
This comprehensive methodological procedure not only enables a rigorous evaluation of the educational impact of \"Parabolic Basketball VR\" but also provides a framework for future research on gamification in learning complex concepts (See Figure 4).
# Practical Estimation of Machine Tool Spindle Dynamics for Maintenance Decision Making
## 4 Discussion  
### Practical Usage of the IG Device  
The force plate dynamometer in this experiment was used to characterize the input force profile of the IG. However, it should be noted that the force plate dynamometer suffers from many of the same drawbacks of the other FRF measurement devices discussed in Section 1, namely, the need for a skilled operator, the large table space required, and the high cost of the device. For these reasons, the force plate dynamometer is not practical in a production environment and should not be used in practice to measure the output force from the IG. Instead, the output force spectrum of the IG can be assumed to be constant given that it was found to produce a very reliable input force, as shown in Section 3.1.  
It remains to be demonstrated that this assumption does not compromise the validity of the computed FRF or modal characteristics. This result can be observed in Figure 22, which shows the average magnitude plot of the FRF for 50 trials, calculated using both the directly measured force plate input and the assumed force input, which were characterized by the Gaussian impulse.  
Figure 21: Detailed view of the imaginary component of the FRF for each of the dynamic spindle test conditions. Dashed lines indicate the critical feature for modal characterization.  
Figure 20: Detailed view of the real component of the FRF for each of the dynamic spindle test conditions. Dashed lines indicate the critical feature for modal characterization.  
The two FRF curves show close agreement over the considered range. Furthermore, the modal characteristics extracted from the real and imaginary components of the FRF were compared for both methods. Detailed views of the real and imaginary components are shown in Figures 23 and 24, respectively. Table 3 shows the modal characteristics, which were calculated based on these FRF results.  
These results indicate that while some characteristics of the input force profile from the IG must be assumed in practical usage, the resultant modal characteristics, which were calculated from the FRF, were not significantly affected by this assumption. As a result, the modal characteristics of the spindle can still be monitored with sufficient accuracy. The validity of this assumption is the key factor that enables the proposed method to be used for practical FRF data collection for CBM tasks.  
Figure 23: Detailed view of the real components of the FRF for each of the directly measured and Gaussian-fitted static spindle test conditions.  
Figure 22: Average magnitude plot of the FRF recorded from 50 trials for the static spindle test condition for the directly measured input force and the assumed input force fitted to the Gaussian impulse.
# Discrete-Time Design of Dual Internal Model-Based Repetitive Control Systems
## 2 Problem Statement  
In this work, we consider the following discrete-time LTI system :  
\[y(k)=[P(z)]u(k)+v(k), \tag{1}\]  
where \(y(k),u(k),v(k)\in\mathbb{R}\) are, respectively, the discrete-time plant output, control input, and external disturbance, and \(P(z)\) is the plant model. Throughout this paper, we use notation \(z\) denoting the \(\mathcal{Z}\)-transform variable, and also the forward shifting operator, e.g., \(zu(k)=u(k+1)\). Hence, the notation \(z^{-1}\) denotes a backward shifting operator, e.g., \(z^{-1}u(k)=u(k-1)\). To ease the writing, the operation \([P(z)]u(k)\) is used to equally represent \(\mathcal{Z}^{-1}\{P(z)u(z)\}\). Here, \(P(z)\) can be either a polynomial or transfer function in the \(\mathcal{Z}\) domain, and \(u(z)\) is \(\mathcal{Z}\)-transform of the discrete-time control signal \(u(k)\).  
The plant model of the LTI system (1) is expressed as  
\[P(z)=\frac{\mathcal{N}(z)}{\mathcal{D}(z)}, \tag{2}\]  
where \(\mathcal{N}(z),\mathcal{D}(z)\in\mathbb{C}\) are numerator and denominator polynomials of the plant. The desired trajectory to be tracked is referred to as a reference signal \(r(k)\), and the tracking error \(e(k)\) is defined by  
\[e(k)=r(k)-y(k). \tag{3}\]  
In the design of the proposed controller, the following assumptions are used:  
**Assumption 1**.: _The polynomials \(\mathcal{N}(z)\) and \(\mathcal{D}(z)\) in (2) are known. In addition, the plant model \(P(z)\) (2) is stabilized with a conventional controller \(C(z)\), resulting in a stable transfer function. The stabilized plant model can be either a minimum or non-minimum phase system._  
**Assumption 2**.: _The reference \(r(k)\) is a repetitive signal with a fixed and known fundamental frequency \(f_{r}\). The disturbance \(v(k)\) is periodic with an uncertain frequency. However, its nominal fundamental frequency \(\bar{f}_{v}\) is known. The actual frequency of disturbance \(v(k)\) may slightly vary from its nominal value._  
**Assumption 3**.: _The reference frequency \(f_{r}\) and disturbance frequency \(\bar{f}_{v}\) are uncorrelated. In other words, they are not harmonics, i.e., \(f_{r}\
eq nf_{v}\), where \(n\) is an arbitrary positive integer number._  
The research objective is to synthesize control law \(u(k)\) to simultaneously track the repetitive reference \(r(k)\) and suppress the effect of the uncertain periodic disturbance \(v(k)\) of the LTI system (1), such that the tracking error \(e(k)\) (3) converges to a small steady-state value, and the resulting closed-loop system is stable. In addition, the proposed controller is realizable and applicable for both minimum and non-minimum phase systems.
# A Deep Learning Based Dislocation Detection Method for Cylindrical Crystal Growth Process
## 3 Experimental Results  
### Data Preparation  
In order to test the accuracy of the proposed algorithm, image collection was firstly carried out on the production site which covers multiple different crystal furnaces and different phases of the cylindrical growth step since it lasts too long. The amount of data is crucial for CNN training. Insufficient data would affect the generalization ability of the network. As a result, the accuracy of the classification would decrease when the network receives new data. A total number of 3480 images of the cylindrical growth step with a resolution of \(2592\times 1944\) were collected as the experimental data set. After preprocessing, each image was labelled with no target (1630 images), disappeared habit line (780 images), or normal label (1070 images), no target means that it is hard to say whether there exists a habit line in the image or not. Since labeling is a laborious task, we manually labeled part of the images, and trained the model, then used the trained model to annotate the rest of the images, and readjusted the annotations. Referring to the COCO dataset annotation format, annotated json files were generated. Annotations were presented in the form of coordinates and sizes of the ground truth bounding boxes. The image datasets were randomly divided into training set, validation set and test set according to the ratio of 7:1.5:1.5. The training set and test set were mutually exclusive, test samples were not involved in the training process. In order to avoid overfitting, a broad range of image augmentations were applied to the training set, including rescaling, rotation, height and width shift, horizontal flip. Model training was performed on the training set to update the weights, the hyperparameters were adjusted on the validation set, and the test set was used to evaluate the detection performance of the model.
# Lightweight End-to-End Speech Enhancement Generative Adversarial Network Using Sinc Convolutions
## 7 Results
### Performance and Parameter Comparisons with Baseline Systems  
Table 4 demonstrates the performance and parameter comparisons between the proposed Sinc-SEGANs (+augment) and the previous SEGAN variants [19; 30; 31] on the Valentini [39] benchmark. These results indicate that the substitution architecture outperforms baseline systems on PESQ, CBAK, and COVL. Considering the designs of these criteria, the results suggest that for speech signals enhanced by Sinc-SEGAN-sub (_substitution_ is shortened to _sub_), the general perceptive quality is higher, and they are reasonably comprehensive for users. Comparable results are achieved on CSIG, SSNR and STOI. Please note that although Sinc-SEGAN-sub underperforms DSEGAN [30] on CSIG and SSNR or SASEFAN-all [31] on STOI, it outperforms SEGAN [19], ISEGAN [30], and SASEGAN-avg [31] across all criteria. Additionally, the number of Sinc-SEGAN-sub parameters is merely 31.0% as compared to SEGAN, ISEGAN, or SASEGAN-avg, 29.4% compared to SASEGAN-all, and 17.7% compared to DSEGAN. In contrast, Sinc-SEGAN-add (_addition_ is shortened to _add_) outperforms all baseline systems, with the parameter scale that is 71% as compared to SEGAN, ISEGAN, or SASEGAN-avg, 67% compared to SASEGAN-all, and 41% compared to DSEGAN. Moreover, data augmentation methods deliver further improvements, leading to the best performance across all evaluation metrics, without additional parameters. These results validate the efficacy of Sinc convolution.
## 4 Experiments
### Implementation Details
#### 4.1.3 Test Dataset  
The dataset used for matching experiments was the multi-modality image matching dataset [15], which contains computer vision, medical, and remote sensing. The remote sensing dataset contains a multimodal remote sensing image dataset with optical and SAR images, optical and infrared images, and other remote sensing data. We used optical and SAR image data from above dataset (hereafter referred to as OS data) for experiments. The image coverage scenarios in OS data included urban areas, mountainous areas, rivers and plains, and their texture information and nonlinear radiation varied greatly. Therefore, we used OS data for matching comparison experiments. The OS data are shown in Figure 5.