esa-sceva/satcom-chunk-collection huggingface dataset:
# Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks
## 1 Introduction
### Our Work  
In this paper, we aim to develop, for the first time, a federated [16] training framework for Deep Operator Networks, which we refer to as Fed-DeepONet. To this end, we propose an efficient stochastic gradient-based algorithm that enables the distributed optimization of the DeepONet parameters by averaging first-order estimates of the DeepONet loss gradient. Then, we propose a moment-enhanced (i.e., adaptive) stochastic gradient-based strategy that increases the speed of training convergence of Fed-DeepONet, i.e., an Adaptive Fed-DeepONet framework. Finally, as a proof of concept, we verify the performance of Fed-DeepONet using two experiments with different configurations of the number of clients and different fractions of available clients. The experiments aim at training Fed-DeepONets for learning (i) the solution operator of a pendulum and (ii) the dynamic response of a parametric library of pendulums.  
We remark that compared to the traditional federated training of neural networks (FedAvg), the proposed Fed-DeepONet algorithms target the more complex problem of approximating, in a distributed/federated manner, the infinite-dimensional response of nonlinear operators. As a result, we can use a trained Fed-DeepONet to tackle complex engineering and scientific computing tasks (e.g., learning digital twins or materials science discovery). These tasks are inherently infinite-dimensional, and we can model them as input-output nonlinear operators. For these infinite-dimensional tasks, however, the traditional FedAvg for training neural networks, which only approximates mappings between finite-dimensional spaces in a distributed manner, will fail unless the designer makes stringent/unrealistic assumptions (e.g., computes the output response for only a fixed parameter value or a single initial condition) and uses enormous datasets. In addition,we will demonstrate in our numerical experiments that Fed-DeepONet significantly reduces the generalization error, similar to the centralized DeepONet. This generalization capability makes the proposed Fed-DeepONet framework more robust to non-independent and identically distributed scenarios than the traditional FedAvg training strategy for deep neural networks. Finally, the proposed Fed-DeepONet has the following unique feature: It can effectively tackle data heterogeneity problems in the output functional space (i.e., when the parametric responses of the clients are different) by learning all possible parametric responses.  
We organize the rest of the paper as follows. Section 2 provides a brief review of the centralized training of Deep Operator Networks (DeepONet). Section 3 proposes gradient-based and moment-enhanced (i.e., adaptive) algorithms for the federated training of DeepONets (Fed-DeepONet). In Section 4, we demonstrate the effectiveness of the proposed Fed-DeepONet by learning (i) the solution operator of a gravity pendulum and (ii) a library of pendulums. Finally, Section 5 discusses our results and limitations, and Section 6 concludes the paper.
# Feasibility of Obtaining Surface Layer Moisture Flux Using an IR Thermometer
## 4 Potential Flux Measurement Method
[PERSON] and [PERSON] offer another method to quantify \(Q_{H}\) and \(Q_{E}\) that does not require values for the eddy diffusivities of heat and water vapor [8; 18]. This is the Bowen's Ratio (\(\beta\)) method that combines the ratio of \(Q_{H}\) over \(Q_{E}\) with a measurement of the net all-wave radiation obtained by a net pyrradiometer device (\(Q^{\star}\)) and with the ground/surfacetemperature flux (\(Q_{G}\)) measured with a ground heat flux plate or soil temperature probes at different depths. In this case, \(\beta\) can be found without \(K_{H}\) and \(K_{w}\) from:  
\[\beta=\frac{Q_{H}}{Q_{E}}=\frac{c_{pd}\Delta T}{L_{\sigma}\Delta\overline{q}}\, \tag{9}\]  
and \(Q_{H}\) and \(Q_{E}\) are obtained from:  
\[Q_{H}=\beta(Q^{*}-Q_{G})/(1+\beta)\quad\text{and} \tag{10}\]  
\[Q_{E}=\frac{Q^{*}-Q_{G}}{1+\beta} \tag{11}\]  
According to previous research, which suggests the physical basis for applying an IR Thermometer for moisture flux measurements, Mazad successfully measured temperature gradients and heat flux using an IR Thermometer for tribological applications [19]. Perhaps the strongest published evidence indicating our proposed inexpensive and simple handheld IR Thermometer has definite merit for assessing moisture flux is that authored by [PERSON] and [PERSON] [10], and noted earlier in this paper. In the course of design and demonstration of a more efficient wick central to their digital psychrometer commonly used in greenhouses, [PERSON] and [PERSON] developed a comprehensive evaporative model using an IR digital thermometer to quantitatively track moisture flux in their instrument's captive wick, thus predicting when the wick's water reservoir needed to be filled. Although the ultimate objective herein is to use the proposed IR Thermometer Psychrometer to assess the moisture flux between the Earth's surface or surface canopy and the atmosphere, [PERSON] and [PERSON] highlighted the practicability of achieving relatively accurate moisture flux using an IR Thermometer and fundamental evaporative and psychometric relationships. There are several interesting challenges to be explored using the IR Thermometer Psychrometer for earth-atmosphere moisture flux measurements, one of those being the determination of emissivity, with sufficient accuracy, for the various earth surface types that one is likely to encounter. To sum up the above, a combination of an IR Thermometer together with instrumentation to measure pressure (e.g., a Kestrel) appears to be an inexpensive alternative configuration with capabilities comparable to traditional soil/air moisture and temperature instrumentation suites normally employed for moisture flux measurements (see Appendix A of Oke [8]). In summary, the previous discussion supports the hypothesis that IR sensors/detectors can be used for determining near-surface moisture and heat fluxes.
# Enhanced Photocatalytic Performance of Bi2O2 CO3 Loaded Activated Carbon for Toluene Removal in Air
## 4 Conclusions  
In summary, a novel BOC/AC nanocomposite photocatalyst was fabricated by in-situ growth of flower-shaped BOC nanosheets on the surface of activated carbon through a facile chemical method. The as-synthesized BOC/AC nanocomposite demonstrated excellent removal efficiency for toluene, with more than 90% degradation in 3 h, which is comparable and even higher than reported catalysts. Moreover, CO\({}_{2}\) and H\({}_{2}\)O were confirmed as the major products. We also found that the catalytic activity was highly dependent on the loading content of BOC, with the highest performance obtained for 100 g loading. Furthermore, toluene could be completely removed after 7 h. Importantly, the photocatalytic efficiency remains constant after three cycles, demonstrating the good recyclability of the BOC/AC for photocatalytic toluene removal. The excellent photocatalytic degradation activity of the BOC/AC composite catalyst can be attributed to the synergistic effect of the adsorption ability of AC and the photocatalytic activity of both the BOC nanosheets and the AC. This work may provide useful guidance for indoor air purification, particularly for trace harmful gases such as VOCs.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122312500/s1](https://www.mdpi.com/article/10.3390/app122312500/s1)]([https://www.mdpi.com/article/10.3390/app122312500/s1](https://www.mdpi.com/article/10.3390/app122312500/s1)), Figure S1: N2 adsorption\(-\) desorption isotherms (a) and pore size distributions (b) of AC and BOC/AC composites. Figure S2: Chromatograms of organic products at 3 h for BOC/AC_100 (a) and AC (b). Figure S3: Chromatograms of organic products at 7 h for BOC/AC_100 (a) and AC (b); Figure S4: Spectrum of the xenon lamp used.  
Investigation, X.W., L.C. and J.D.; Methodology, X.W., C.L., Y.G. and Y.L. ([PERSON]); Supervision, Y.L. ([PERSON]) and J.D.; Writing and original draft, X.W. and L.C.; Review and editing, Y.L. ([PERSON]) and Y.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Institutional Review Board Statement: Not applicable.  
Informed Consent Statement: Not applicable.  
Data Availability Statement: Not applicable.  
Figure 9: Photocatalytic mechanism of toluene removal using BOC/AC composite.
## 4 Design Considerations
### Determination of Duty Cycle  
According to the LED V-I curve shown in [22], the forward voltage \(V_{F}\) and forward current \(I_{F}\) at rated condition are 3.45V and 0.35A, respectively, whereas the cut-in forward voltage \(V_{FLED}\) at zero current is 2.7 V. Hence, the equivalent resistance \(R_{FLED}\) is 2.143 \(\Omega\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Circuit Operating Mode** & **CCM** \\ \hline Input voltage (\(V_{in}\)) & \(12\pm 10\%\) \\ Output voltage (\(V_{o}\)) & \(27.6\text{V}(=8\times 3.45\text{V})\) \\ LED Rated current (\(I_{o,rated}\))/Rated Power (\(P_{o,rated}\)) & 350 mA/29 W \\ LED Minimum current (\(I_{o,min}\))/ Min. Power (\(P_{o,min}\)) & 87.5 mA/7.25 W \\ Switching frequency (\(f_{s}\))/Period (\(T_{s}\)) & 100 kHz/10 \(\upmu\)s \\ LED forward voltage (\(V_{F}\)) and current (\(I_{F}\)) at rated condition & 3.45 V/0.35 A \\ LED cut-in forward voltage (\(V_{FLED}\)) at zero current & 2.7 V \\ LED strings & 6 strings with 4 LEDs per string \\ \hline \hline \end{tabular}
\end{table}
Table 1: System specifications.  
Figure 6: System block diagram of the proposed circuit.
## 4 Problem Definition and Optimization Approaches  
When a task arrives at a mobile device, it is the responsibility of the mobile device to determine whether the task should be executed locally or be sent to the connected edge device. Similarly, when a task arrives at an edge device, it is the responsibility of the edge device to decide whether to further offload the task. Ultimately, we want to make these decisions at mobile devices and edge devices so that the total time from the moment a task arrives at a mobile device to the moment the task is executed is minimized. Additionally, we want to minimize the energy consumption at mobile devices for processing and transmitting tasks.  
We can formulate these requirements into the following minimization problem:  
\[\begin{split} Minimize:\alpha&\sum_{t\in T}(c_{1 }(t).d_{l,et}(m(t),t)+\\ & c_{2}(t).d_{r,o}(m(t),e(m(t)),t)+\\ & c_{3}(t).d_{r,o2}(m(t),e(m(t)),e_{k}(t),t))+\\ &\beta.\sum_{t\in T}(c_{1}(t).e_{e}(m(t),t)+(c_{2}(t)+c3(t)).e_{s} (m(t),t))\end{split} \tag{22}\]  
subject to the following constraints  
\[c_{1}(t),c_{2}(t),c_{3}(t)\in\{0,1\} \forall t\in T \tag{23}\] \[c_{1}(t)+c_{2}(t)+c_{3}(t)=1 \forall t\in T\] (24) \[e_{k}(t)\in c(e(m(t))) \forall t\in T \tag{25}\]where \(c(e_{i})\) is a set of edge nodes to which edge node \(e_{i}\) has a connection. \(\alpha\) and \(\beta\) are arbitrary coefficients used to modify the importance of time delay and energy consumption with respect to each other.  
In the proposed solutions, it is assumed that the workload of a task \(d(t)\) is not provided to the algorithm that is making the offloading decisions. The reason for this restriction is that in the real world, the workload of a task is not available before a task is executed in most cases.
## 4 30 cm Model Cavity Simulation
### Post-Treatment Processes
#### 4.2.1 BCP Processes  
BCP is one of the most important steps in the post-processing of SC cavities, used to remove impurities, contaminants, and mechanical damage on niobium surfaces. This process can significantly enhance the RF performance of SC cavities. Employing CST MWS and the COMSOL Electromagnetic Waves (EMW) module, we conducted precise calculations for the BCP processes within the cavity, assuming a uniform polishing of the inner surface. The simulations focused on the changes in cavity frequency resulting from polishing the inner surface by 150 \(\upmu\)m and an additional 30 \(\upmu\)m, as depicted in the calculation models presented in Figure 16. In order to minimize the simulation errors between the two programs, we set the CST mesh to a tetrahedral mesh which is consistent with COMSOL for simulation calculations, and the results are shown in Figures 17 and 18. Polishing the inner surface by 150 \(\upmu\)m yielded a frequency increase of 1356 kHz by CST and 1450 kHz by COMSOL, while an additional 30 \(\upmu\)m polishing resulted in a frequency increase of 268 kHz by CST and 180 kHz by COMSOL. Due to differences in calculation methods and mesh density used by the two programs, there are also differences in the frequency variations of the resonator obtained from them. However, this total difference is only 0.003% of the resonant frequency, which indicates that the simulation results of these two programs are basically consistent. The frequency sensitivity to the polishing thickness is about 9.04 kHz/\(\upmu\)m.  
Figure 15: The flow chart of the simulation procedure.
# A Model-Assisted Combined Machine Learning Method for Ionospheric TEC Prediction
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Data Collection  
The modeling data used in this paper are divided into three parts: the TEC observation data of Athens Station, the solar radio wave flux of 10.7 cm wavelength (F10.7), and the sunspot number (SSN). In this paper, we can download the required TEC observation data by selecting the corresponding time, observed station, and data type through the official websites of the National Oceanic and Atmospheric Administration (NOAA) [29] and the Global Ionospheric Radio Observatory (GIRO) [30; 31]. The downloaded data can be saved as a text file that records 24 h of TEC data for each day of the year at a station. In order to meet the modeling requirements of the Long-term regional ionospheric TEC prediction model, the monthly 24-h TEC mean values of each TEC observation station are calculated, and one observation station corresponds to a standardized modeling data file. As shown in Figure 1, by observing data, it can be found that TEC presents a certain periodic fluctuation, and the intensity of different years of fluctuations varies in different years. After the statistics of the above data, 5184 sets of data were obtained.  
Figure 1: VTEC data curve for 24 h from 2004 to 2022 in Athens, Greece.
# A Cross-Platform Personalized Recommender System for Connecting E-Commerce and Social Network
## 1 Introduction
In fact, the social network contains abundant, detailed, time-resolved, real-world user data, e.g., tweets (microblogs), tags and relations with other users, which motivates us to extract users' information and capture users' interest profiles for cross-platform recommendations. In this paper, we propose a novel cross-platform recommender system (CPRec) to make full use of the abundant information on social networks to improve recommendation accuracy. In CPRec, we build both a user profile and a commodity profile from data on social networks and e-commerce. An improved topic model for detecting users' interest profiles from their historical released information is designed, which is based on Latent Dirichlet Allocation (LDA) [13]. The users' tags and their followees' profiles will be used when we are building the users' profiles. After obtaining a user profile, we make recommendations based on the recommended score as calculated from the users' profiles and the commodity profiles. Considering that each user will take different actions after he/she receives the recommended products, a feedback mechanism is designed for the CPRec. Since a user-commodity-score matrix will be obtained if the CPRec starts to work, we develop an improved collaborative filtering algorithm that combines user profiles to make further recommendations. Finally, in order to show the performance of the proposed system, we evaluate and analyze the CPRec based on two platforms, Sina Weibo and Alibaba. The contributions of this paper are summarized as follows:  
* We propose a novel cross-platform personalized recommender system, CPRec, for recommending e-commerce commodities to users on social network platforms. * An interest mining process is proposed to build the user interest profiles, which makes full use of users' information on social networks. * We propose three subdivisions for CPRec, i.e., recommendations for individuals, a feedback mechanism and an improved collaborative filtering algorithm. * The experimental results validate the feasibility of the CPRec, the veracity of user profiling and the superior performance of our improved collaborative filtering algorithm compared with some existing algorithms. The remainder of the paper is organized as follows. In Section 2, an overview of our proposed cross-platform recommender system is given. System building and user profiling are presented in Section 3, and commodity profiling and recommendation subdivisions are described in detail. Section 4 discusses the experimental results. Section 5 concludes this paper and outlines future work.
## 4 ARION: Experiment and Scenario of Use
All users connected to the teacher are shown, and the teacher can enable their user-specific modalities such as the chat (if enabled), the camera, and the screen video modalities. After selecting the platform's available modalities, the U.I. places them in the central part of the open Studio and arranges them in a grid-like view where each modality is a tile, as shown in Figure 2:1 Open Studio with all the possible modalities. The grid view at the current state supports a dynamic grid with a maximum size of \(2\times 3\). The grid also has dynamic features like drag and drop that allow changing the order of the available modalities. These enable a teacher to create a specific order of modalities, allowing him, for example, to have the videos of the users' camera or screen, helping him to evaluate the learning process. After completing the process of adding modalities and ordering them, the teacher has a synchronized timeline based on the first and the last event from the enabled modalities, as seen in Figure 2:2 Open Studio with ordered modalities. Furthermore, the teacher can start the timeline from the player of Open Studio and move the timeline to a specific time. For example, if the teacher wants to move the time at the start of the video, the teacher moves the timeline button. Every modality has a red indicator in the sub-timeline of the modality tile to help the user spot the available events and locate the video starting point, as shown in Figure 2:3 Camera Video modality with start and end event. Figure 2:4 Arion Platform Modalities. (**a**) Generic View modalities; (**b**) User Specific Modalities. Figure 2:5 Open Studio with all the possible modalities. Figure 2:6 Open Studio with ordered modalities. In addition to the timeline setup and the navigation, the teacher can note the timeline at specific times that he/she considers necessary. A red line appears in the timeline while a note is being created, indicating the note's appearance. That indicator allows the teacher to recognize the frame in which the note was created and aids the teacher in remembering the remarkable events. The note creation is able through a button in the bottom right area of the U.I. (Figure 2:7 Open Studio with all the possible modalities) that opens a modal, as Figure 2:8 Note Creation Modal shows, and allows the teacher to type a message and save the note. Finally, the teacher can access all the created notes and edit them through the My Notes bottom right area of the U.I. (Figure 2:9 Open Studio with all the possible modalities), which opens a modal (Figure 4:0 Available Notes Modal) that shows the available notes and provides the edit and delete functions.
## 3 Data and Methods
### Accuracy Assessment  
To further evaluate the model's performance with different data combinations as input, we calculated not only the overall accuracy but also the \(F1\)_scores_ for each vegetation class, as well as the weighted average \(F1\)_score_ to assess overall classification performance.  
The F1 score, ranging from 0 to 1, is the harmonic mean of precision and recall, making it ideal for imbalanced datasets. It effectively balances high precision and low recall (or vice versa). Higher F1 scores indicate better classification performance. The formula is as follows:  
\[P=\frac{TP}{TP+FP} \tag{2}\]  
\[R=\frac{TP}{TP+FN} \tag{3}\]  
\[F1 score=2\times\frac{PR}{P+R} \tag{4}\]  
\(P\) (precision) and \(R\) (recall) are calculated for each class by treating that class as the target class and all others as non-target. \(TP\) (true positives) refers to the number of instances correctly predicted as belonging to a given class. \(FP\) (false positives) refers to the number of instances incorrectly predicted as belonging to that class, and \(FN\) (false negatives) refers to the number of instances that actually belong to the class but were incorrectly predicted as another class.  
The Weighted Average \(F1\)_score_ is a metric that accounts for class imbalance by assigning a weight to each class based on its support (i.e., the number of true instances of that class). In this study, where the number of samples per vegetation class is uneven, the Weighted Average \(F1\)_score_ provides a more realistic measure of the model's overall classification performance [88]. A high Weighted Average \(F1\)_score_ indicates that the model performs well not only on rare classes but also consistently across dominant vegetation types. The formula for the Weighted Average \(F1\)_score_ is as follows:  
\[WeightdF1 score=\sum_{i=1}^{n}\left(\frac{N_{i}}{N}\times F1_{i}\right) \tag{5}\]\(n\) is the number of categories, \(N_{i}\) is the number of samples in category \(i\), \(N\) is the total number of samples, and \(F1_{i}\) is the \(F1\)score of category \(i\).  
We generated a confusion matrix for the training results of each dataset, calculated the above indicators based on the confusion matrix, and evaluated the effectiveness of different data in classification tasks by comparing the differences in indicators.
# Enhancement of Sentinel-2A Images for Ship Detection via Real-ESRGAN Model
## 3 Results and Discussion
### Training Object Detection Model
This misidentification occurs because the enhancement process sharpens the images. Overall, enhanced Sentinel-2A satellite images positively affect ship detection, making ships more visually distinguishable. However, factors such as sea conditions and the ships' position, size, and color can still impact the detection accuracy independent of image enhancement. Future work can focus on developing a more robust dataset that eliminates the temporal and radiometric differences observed in the images. LR-HR image pairs can be rearranged by experimenting with different interpolation methods for generating LR images. The challenges of monitoring ship detection over time persist [63]. While satellite images  
Figure 5: Comparison of ship detection with the Sentinel 2A image (**left**) and the enhanced Sentinel 2A image (**right**): (**a**) Port of Zeebrugge; (**b**) Port of Zeebrugge; (**c**) Port of Ningbo-Zhoushan; (**d**) Port of Ningbo-Zhoushan. Figure 6: Comparison of ship detection with the Sentinel 2A image (**left**) and the enhanced Sentinel 2A image (**right**): (**a**) Port of Amsterdam; (**b**) Port of Zeebrugge. can provide data for real-time surveillance, this method is expensive and can be affected by weather conditions, such as cloud cover. As indicated in recent studies, unmanned aerial vehicles (UAVs) may help address these limitations [64; 65; 66]. To ensure effective monitoring, it is essential to evaluate dynamic accuracy metrics that assess both accuracy and movement during these examinations. Additionally, although the Real-ESRGAN model enhances ship detection in Sentinel-2A images, its effectiveness in low-contrast situations--such as poor lighting or inclement weather--still needs further investigation. A more in-depth analysis of these conditions could reveal both limitations and opportunities for improvement.