esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Adopted Low-Cost Receivers and Software  
The following problems of low-cost receivers, not experienced by geodetic receivers, must be mentioned. Firstly, they use narrow-band filters to limit the sampling frequency that could cause noisier measurements. Additionally, their antennas do not have the multipath mitigation techniques that are present in geodetic antennas, such as vision correlators from Novatel [19] or the a posteriori multipath estimator (APME) technique from Septentrio (Leuven, Belgium) [20]: this could cause significant problems in locations with reflection sources, and can lead to errors above the accepted threshold for monitoring applications [21]. For high accuracy applications, the antenna calibration and its phase center variations (PCV) are needed [22; 23]: in particular, the vertical estimates are clearly affected by the accuracy of available PCV. However, available PCVs from International GNSS Service do not include corrections for antennas of low-cost GPS receivers. All of these shortcomings could cause noisier time series of estimated coordinates, especially in height.  
In both experiments, u-blox receivers [24] are used: u-blox is a Swiss-based company (Thalwil) and a leading provider of wireless and positioning sensors and modules for the automotive, industrial, and consumer markets.  
As previously stated, the comparison of two GNSS processing packages was a secondary task: the two chosen programs were Bernese GPS Software Version 5.2 (BSW5.2) from the AstronomicalInstitute of University of Bern (Switzerland) and Leica Geo Office Version 8.3 (LGO) from Leica Geosystems (Heerbrugg, Switzerland).  
BSW5.2 [25; 26] is one of the leading software packages for the international geodetic community. To optimally tune the processing options, the users should possess very good theoretical knowledge and experience in GNSS data processing. BSW5.2 was used as benchmark, to compare the results of LGO. The main processing parameters are reported in Table 1. LGO is a commercial package that supports many surveying sensors and techniques, for example, GNSS, terrestrial positioning systems, and leveling data [27]. Where a choice is possible, the adopted processing parameters in LGO (Table 1) were identical to those of BSW5.2: in LGO, the ambiguity resolution algorithm is fixed and cannot be chosen by the user.  
Usually, a time window of approximately 15-60 min is considered for local monitoring applications in quasi-real-time [28; 29]: we decided to process hourly sessions and data acquired at 1 Hz. The session length and the adopted sampling rate allow a good number of available observations under any conditions. At the same time, in the most of the monitoring applications, an hourly processing guarantees an acceptable latency of the results.
## 4 Experimental Results
### Subjective Measures
#### 4.3.2 Apparatus and Procedure  
We orchestrated the experiment to elapse 1 h per subject a day. Prior to the experiment, each participant was supplied with an overview of what the SDO is and the two different education methods over 20 min. After this, each participant filled out a survey prior to the experiment. The education method using the Helliviewer for SDO images and IVR-SDO distance education system were randomly assigned to the participants to examine the satisfaction of the proposed system. The order of the two approaches was fully counterbalanced across the 20 participants. The education method using the Heliviewer allowed the viewer to observe solar events and SDO images based on the timeline, as shown in Figure 14. The participants can exchange questions and answers while selecting and observing the SDO images of various spectra. In testing the IVR-SDO distance education system, two remote students and one expert participated simultaneously while wearing a Vive HMD and earphones and using a controller for hand interaction. From the SDO image server, 10 types of images were streamed to the relay server at 30 fps, and the students and expert accessing the relay server can see the SDO images through the HMD. They can ask questions and receive explanations via voice conversation and feel the presence of other users through the head avatars. After all tests were completed, each participant was asked to answer a 16-question questionnaire with a 5-point Likert scale from 1--strongly disagree to 5--strongly agree.
# Radiometric Method for Determining Canopy Stomatal Conductance in Controlled Environments
## 2 Materials and Methods
### Canopy Sensible Heat Flux  
In the steady state, H is the energy exchanged by conduction and convection between the canopy and the chamber air. The canopy energy balance equation was rearranged for calculating H by residual (Equation (5)), where R\({}_{\rm net}\) = net radiation, LE = latent heat flux, G = soil heat flux, and P = energy storage in photosynthesis.  
\[\rm H=R_{\rm net}-LE-G-P. \tag{5}\]  
LE includes water vapor fluxes mostly due to canopy E\({}_{\rm can}\) because evaporation was only 2% of R\({}_{\rm net}\). The soil heat flux, G, is a component of land surface feedbacks that depends on the amount of energy available below the canopy. G was assumed to be zero due to a poor transfer of heat through the dense canopies (high planting densities and leaf area indices \(>\) 15) used in this study, but this may not be a valid assumption during early development when the plants are seedlings. P was determined from canopy photosynthesis, which can be as much as 10% of R\({}_{\rm net}\) at high light intensities. For example, if P\({}_{\rm net}\) = 60 umol m\({}^{-2}\) s\({}^{-1}\) at a PPF\({}_{\rm o}\) of 1400 umol m\({}^{-2}\) s\({}^{-1}\), then P = 29 W m\({}^{-2}\). Equation (5) allows for a comparison of energy fluxes in common energy units (W m\({}^{-2}\)) and allows H to be determined by residuals. However, Equation (5) ignores the thermal storage within the canopy, which is small for the short vegetation used in this study, but this storage can be as high as 5-10% of the net radiation in forest canopies [13].
## 2 Materials and Methods
### Trained Model Evaluation  
During training and cross-validation, the calculation and plotting of metrics was performed to assess each model's performance.  
* **Accuracy**: The percentage of the correctly classified samples.
* **Precision**: The percentage of true positives out of all positive classifications.
* **Recall**: The percentage of true positives out of all actual positive classifications.
* **F1 Score**: Harmonic mean of precision and recall.
* **ROC Curve and AUC**: A graphical representation of the trade-off between the true positive rate and false positive rate at various thresholds. They are used as a model's ability to discriminate between classes.
* **Confusion Matrix**: The number of true-negative, true-positive, false-negative and false-positive classifications of the model.
* **Learning Curves**: A graphical representation of the model's performance on the training and validation datasets over time. This graph represents the model's accuracy or loss during training.  
All metrics above were calculated for each model. Learning curves for each model displayed training and cross-validation scores against the number of training examples, providing an estimate of whether the model was overfitting or underfitting. The best model was selected based on the F1 score criteria.
## 2 Structure  
### Modeling and Characterization of Hybrid Plasmonic Waveguide  
The designed antenna is based on the hybrid plasmonic structure. Figure 1a shows the basic geometry of the waveguide and standpoint view of the final design. Figure 1b presents the TM mode of the waveguide. These structures are configured by implementing a material with a low refractive index (such as SiO\({}_{2}\)), between a metal (Ag) and another material with a higher refractive index (Si). Plasmonic TM modes are the supporting mode, having high confinement inside a low refractive index material [40]. Fundamentally, the hybrid plasmonic waveguide (HPWG) is the combination of dielectric and plasmonic waveguides. Although dielectric waveguides can be considered practically lossless, the mode size in such guides can be limited by the amount of diffraction. On the other hand, plasmonic guides can squeeze light far below the diffraction limit, but consequently cause a large propagation loss. By combining both of these guides, an HPWG can be formed. As compared to purely plasmonic waveguides, HPWG suggests a better compromise between loss and confinement, compatible with silicon on the insulator technology [41]. Different types of waveguides have been analyzed recently. Among these, a hybrid dielectric-loaded plasmonic waveguide (HDLPW) has been proposed as a potential candidate for the optical transmission and its waveguide properties were numerically analyzed to verify the principle technique [42]. In [43], the plasmonic slot nano-antenna is shown to provide a 100 times smaller size than the area of the diffraction-limited spot, to control the optical fields at nano-scale.
# Modeling the Impact of Atmospheric Warming on Staple Crop Growth in China in the 1960s and 2000s
###### Abstract  
Respenses of crop growth to climate warming are fundamental to future food security. The response of crops to climate change may be subtly different at their growing stages. Close insights into the differentiated stage-dependent responses of crops are significantly important in making adaptive adjustments of crops' phenological optimization and cultivar improvement in diverse cropping systems. Using the Agro-C model, we studied the influence of past climate warming on crops in typical cropping systems in China. The results showed that while the temperature had increased distinctly from the 1960s to 2000s, the temperature frequency distributions in the growth season of crops moved to the high-temperature direction. The low temperature days during the crop growth periods that suppress crop growth decreased in the winter wheat area in North and East China, rice and maize areas in Northeast China, and the optimum temperature days increased significantly. As a result, the above ground biomass (AGB) of rice and maize in Northeast China and winter wheat in North and East China increased distinctly, while that of rice in South China had no significant change. A comparison of the key growth periods before and after heading (silking) showed that the warming before heading (silking) made a great contribution to the increase in the AGB, especially for winter wheat.  
Agro-C model; climate change; crop productivity; temperature response 1
###### Abstract  
Due to the inevitable acquisition system noise and strong background noise, it is often difficult to detect the features of weak signals. To solve this problem, sparse representation can effectively extract useful information according to the sparse characteristics of signals. However, sparse representation is less effective against non-Gaussian white noise. Therefore, a novel SRSVD method combining sparse representation and singular value decomposition is proposed to further improve the denoising performance of the algorithm. All the signal components highly matched with the dictionary are extracted by sparse representation, and then each component of singular value decomposition is weighted by the evaluation index, PMI, which can indicate the component with useful information in the signal, so that the signal denoising performance of the algorithm is greatly improved. The performance of the proposed method is verified by the processing of weak signals in the circuit and early fault signals of bearings. The results show that SRSVD can successfully suppress noise interference. Compared with other existing methods, SRSVD has better denoising performance.  
singular value decomposition; sparse representation; weak signal detection +
Footnote †: journal: Applied Physics Letters  
1
Footnote 1: Copyright** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 1 Introduction
Owing to the hydrogen bonding and \(\pi\)-\(\pi\) electron donor-acceptor interactions between the adsorbent and analytes, Fe\({}_{3}\)O\({}_{4}\)-MWCNTs-OH@poly-ZIF67 shows good selective extraction of aromatic acids [36]. This research indicates that magnetic nanocarbon-material-based ZIFs have good application prospects for adsorbing pesticides because of their good adsorptive properties derived from the magnetic carbon nanocomposite. Organochlorine pesticides (OCPs) are ubiquitous in the environment due to their extensive application and persistent organic pollutants characteristics, which pose great risks to human health and ecosystems [37]. On account of their bioaccumulation, degradation resistance and carcinogenesis, teratogenesis, and mutagenesis to human, 15 OCPs, including hexachlorocyclohexane (HCHs) and dichloroiphenyltrichloroethane (DDTs), were banned with the issue of the Stockholm Convention in 2004. However, the current study showed that the concentration level of OCPs in aqueous environment around Beijing were ranged from 9.81 to 32.1 ng L\({}^{-1}\) (average 15.1 \(\pm\) 7.78 ng L\({}^{-1}\)) [38]. Therefore, it is necessary to develop sensitive and accuracy analytical methods for continuous monitoring trace level of the OCPs in water samples. Inspired by the abovementioned studies, a novel magnetic Co-based ZIF composite was synthesized by organic-inorganic coordination. The obtained M-M-ZIF-67 composite possessed porous surfaces and super-paramagnetism due to the utilization of Fe\({}_{3}\)O\({}_{4}\)-MWCNTs as the magnetic kernel and support. In addition, due to the large surface area and excellent adsorption capacity of MWCNTs, the prepared hybrid material could be good adsorbent for MSPE of OCPs. In the end, an M-M-ZF-67 based MSPE method was established and applied for the extraction of OCPs from agricultural irrigation water samples prior to gas chromatography-tandem triple quadrupole mass spectrometry (GC-MS/MS).
## 5 Conclusions  
This study used an autoencoder that integrated a bidirectional Long Short-Term Memory (BiLSTM) layer with a Maximal Overlap Discrete Wavelet Transform (MODWT) layer. This configuration aimed to identify anomalies in Lamb wave signals as they propagated  
Figure 16: (**a**) is the pristine signal, (**b**) represents the damage signal, (**c**) are the signal differences, (**d**) illustrates the energy envelop obtained by the SPWVD.  
Figure 17: Damage D1 position on the plate.  
Figure 18: Damage D2 position on the plate.  
through a composite structure. Our deep learning approach was trained on 720 raw baseline signals, encompassing 36 distinct exciter-sensor paths. This proposed method was validated using unseen datasets comprising 72 randomly selected signals. One of the primary strengths of our approach is its ability to work directly with raw oscilloscope signals, thus eliminating the need for extensive data preparation. To ascertain the effectiveness of our method, we compared the validation accuracy with that of a pure LSTM-based deep model and a 1D Convolutional Neural Network (CNN) model. The results demonstrated that our proposed method performed admirably. To detect anomalies, we introduced damaged signals to the trained encoder model and established threshold values derived from the error generated by the model on the baseline signals. We observed that datasets involving multiple exciter-damage paths exceeded these threshold values, indicating structural damage. Interestingly, paths that passed close to or directly across the damage produced higher error values. This observation proved instrumental in accurately identifying the precise location of the damage within the structure. This approach has the potential to greatly enhance our ability to detect and locate structural damage in composite structures. One limitation of using the proposed scheme is its high sensitivity. MODWT + Bi-LSTM is sensitive to the signal variation which enables models to detect anomalies at an early stage; however, it also increases the chances of detecting false positive results. For such a case, careful model calibration and parameter tunning as well as pre-processing of the input signals are recommended to optimize the model sensitivity and performance.  
[PERSON] conducted the primary research work and wrote the manuscript. [PERSON] provided valuable assistance and supervised the project. [PERSON] contributed resources, technical guidance, and supervision. [PERSON] offered technical support and assistance. [PERSON] provided crucial technical support, supervision, and funding for the project. All authors have read and agreed to the published version of the manuscript.  
Major APC was funded by the National University of Science & Technology (NUST).  
Not Applicable.  
The data presented in this study are available on request from the corresponding author.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Multi-Step Traffic Speed Prediction Based on Ensemble Learning on an Urban Road Network
## 3 Prediction Methodology  
Previous studies show that ensemble learning can be used for traffic prediction with good performance. Thus, this paper will develop an ensemble learning model for multi-step traffic speed forecasting with direct strategy, namely DDSELM, to process the given time series data. Firstly, the correlation analysis is conducted to identify the key factors affecting speed forecasting. Then, a detrending algorithm is developed to divide the speed dataset into trend part (i.e., mean set) and residual one, and then this study employs direct strategy and ensemble learning of stacking to predict multi-step residuals. Finally, the multi-step residuals combined with the trend sets can form the final predict results.
# Differential Privacy in Federated Learning: An Evolutionary Game Analysis
## 2 Related Work and Preliminaries
### Gaussian Mechanism Differential Privacy in Federated Learning
In other words, when two datasets, \(\mathcal{D}\) and \(\mathcal{D}^{\prime}\), with minor differences are perturbed by the randomized algorithm, \(\mathcal{M}\), the resulting output dataset, \(\mathcal{O}\), should have minimal differences. These differences are controlled by \(\varepsilon\). The smaller the \(\varepsilon\), the stronger the privacy protection. The implementation of differential privacy is illustrated in Figure 2.  
In this study, different clients have different privacy budgets. This is because different clients may handle datasets with varying levels of sensitivity. For instance, some clients might possess highly sensitive personal data, while others deal with less sensitive information. Furthermore, clients may have different privacy requirements based on their specific contexts, regulations, or agreements. Since we implement a Gaussian mechanism, this translates to different powers of Gaussian noise. According to [29], during the \(T\)th round of aggregation, the relationship between Gaussian noise power and privacy budget can be expressed as \(\sigma=\Delta\sqrt{2T\log(1.25/\delta)}/\varepsilon\), where \(\Delta=\max_{\mathcal{D},\mathcal{D}^{\prime}}||\mathcal{O}(\mathcal{D})- \mathcal{O}(\mathcal{D}^{\prime})||\). When the range of gradient updates is limited, we can always find a finite constant, \(C\), such that \(\Delta=2C/D\). Then, we can obtain  
\[\sigma=\frac{2C\sqrt{2T\log(1.25/\delta)}}{D\varepsilon}. \tag{2}\]  
Figure 2: Implementation of differential privacy. The equation calculates the standard deviation, \(\sigma\), of the Gaussian noise, which is computed based on each client's privacy budget. This calculation is based on their dataset size, \(D\), the global training round number, \(T\), the pre-defined privacy failure probability, \(\delta\), and the gradient clipping threshold, \(C\). This calculated \(\sigma\) value is then used within the Gaussian mechanism to add noise to the gradient updates uploaded by that client, ensuring differential privacy protection.
# Barriers and Chemistry in a Bottle: Mechanisms in Today's Oxygen Barriers for Tomorrow's Materials
## 3 Passive Barriers
### Composites
Several inventions have been filed to reduce the effect by increasing the affinity of the two polymers (covalently or electrostatically), reducing the island size to nano-sized structures that interfere much less with visible light [58,116,117,118]. A similar approach to the improvement of oxygen retention in PET is to blend in inorganic nano-particles. The structure of the particles is relatively inert to the PET-processing, consistently providing low haze [119,120]. Additionally, these solid fillers will provide for a lower free volume in the PET, lowering the solubility of oxygen in the composite material as well [87,89]. There have been multiple developments in this area with fillers of 1 nm thickness and 100-1000 nm in diameter. In these dimensions, interference with light is minimal, offering an improvement in haze to conventional polymer blends. A platelet-form is preferred to achieve very high aspect ratios in Equation (9). Clays are very popular as the fillers in these systems, but also graphene and silicates have been investigated [121, 122, 123, 124]. Despite the inert nature of these particles to the process, these systems are still struggling with dispersion and orientation of the particles. The layered particles prefer stacking, even when processed in PET. Certain techniques for impregnating the particles into the polymer can cause the platelets to separate into differing compositions. In order to be effective as a barrier, the particles should be spread out (exfoliated) and should be oriented in parallel to the stretching of the polymer, similar to MXD6 after deformation [125, 126, 127]. In addition to external fillers, the strategy of increasing tortuosity has been found to proceed even with PET itself. Crystallization of the structural polymer can be induced by entering nucleating agents to create dense areas, lowering solubility and diffusion of gases as well [56,128]. So far, this is still an emerging technology and has not yet reached high commercial successes. However, oxygen transmission rates below that of SiOx-coatings have been achieved by a multilayered barrier with exfoliated nano-clays and therefore shows great promise. The barrier performance for both oxygen and water still needs to be improved though to reach the strict requirements for the electronics applications. Additionally, some areas of the visible light are still absorbed by the film, which also requires optimization [129].