esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Converter and PTZ Panorama
### JPEG-1 to JPEG 360 Converter  
Here, it was found that all the existing spherical imaging services follow GPano metadata format, and JPEG 360 metadata format may cover the GPano format. The conversion of 360-degree images to standard formats or the insertion of JPEG 360 metadata is needed to allow viewers to benefit from standard metadata for realistic and immersive rendering. We developed a sample application that converts conventional JPEG-1 images to JPEG 360. We hope that this converting tool will help 360-degree-images users and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Property Supported** & **GPano** & **JPEG 360** \\ \hline
1 & Full ERP projection & Yes & Yes \\ \hline
2 & Partial ERP projection & Yes & Yes \\ \hline
3 & Cylindrical projection & Yes & No \\ \hline
4 & Image codestream in different box (box reference) & No & Yes \\ \hline
5 & Gravity & No & Yes \\ \hline
6 & Compass & Yes & Yes \\ \hline
7 & Multiple viewports & No & Yes \\ \hline
8 & Viewport width and height & No & Yes \\ \hline
9 & Viewport center & Yes & Yes \\ \hline
10 & Viewport roll & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 6: GPano metadata and JPEG 360 properties comparison.  
Figure 4: Graphical overview of ERP image: (**a**) spherical and cartesian coordinates, (**b**) generalized description of ERP mapped on 2D plane, and (**c**) generalized description of the viewport.  
camera and software vendors to provide immersive services and avail full benefits of the standard format in various markets.  
In the conversion software, first, we investigate the existing metadata to find whether the input image is spherical or not. Specifically, we look for GPano metadata, capturing camera, and stitching software, through which we can identify that the image is spherical. We call this part of the converter tool the \"Input and Investigation Part\". Besides this programmatic detection, we also allow users to embed customized JPEG 360 metadata if the image has no existing metadata information and users still want to convert it. If the image is qualified for conversion by finding projection format information or 360-degree camera name or stitching software in existing metadata, then, in the second part, 'Generating Metadata', the metadata values are read from input text file or generated from GPano Metadata values. These metadata values are then formatted in JPEG-360-standardized XML. Next, in the 'Embedding Part' of the software, we embed standard JPEG 360 metadata using the JPEG-defined standard JUMBF box. Finally, the new bitstream, including standard JPEG 360 metadata, is written to a new file.  
Our proposed tool can also be used to see the existing JPEG 360 metadata in the file; in this case, the APP11 marker from the bitstream is parsed to the metadata extractor for the decoding metadata values from the JUMBF box containing JPEG 360 XML. Thus, our tool allows users to see and update existing metadata.  
A detailed description of the tool's architecture and its implementation, along with a flowchart, is provided in Section 4.1 below.
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 4 Experiments
### Training and Inference  
This subsection provides an examination of how the MMCL framework performs throughout both training and inference stages. Figure 3 juxtaposes the framework's outcomes in loss optimization, test accuracy, signal learning, and noise memorization.  
Figure 3: Comparison between single-model learning and multi-model collaborative learning in terms of training loss, test accuracy, signal learning, and noise memorization.  
Observing the training loss curve reveals that MMCL demonstrates a noticeably swifter reduction in loss compared with the baseline model. In addition, as training epochs progress, the loss steadily converges to a more stable, lower plateau. This expedited convergence owes much to the framework's bidirectional knowledge transfer design, which allows the two models to exchange learned representations and leverage pseudo-labels as supplementary supervisory signals, thus steering clear of local optima. In the testing phase, the cooperative learning facilitated by the MMCL framework markedly enhances the accuracy of both models. Especially during the final training iterations, VLSNet's contextual reasoning and 2D-TAN's temporal boundary localization both become more refined, indicating that this collaborative mechanism can successfully broaden each model's performance ceiling. The signal learning trajectory reveals that the MMCL framework incrementally fortifies the cooperative models' capacity to capture relevant signals, subsequently stabilizing and outperforming any single-model baselines. CLIP's guidance mechanism, leveraging semantic priors to refine pseudo-labels' precision and coherence, plays a key role in these gains. The juxtaposition of noise memorization curves offers additional evidence of the efficacy of CLIP-guided pseudo-labels. By more effectively suppressing spurious or low-fidelity time segments, the MMCL framework supplies purer supervisory signals for training.
# BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services
## 2 Related Work
### Privacy of Zero Knowledge and Localization
Although ZKP is mainly desirable for defending the confidentiality of a user and the content of a message, computer-assisted support in modeling the security protocols is not available. However, for these intentions, this protocol is still vulnerable [45]. [PERSON] et al. [46] proposed a new category for localization method in wireless sensor networks using localization algorithms that contained based on multiple key features such as Anchor Based, Anchor Less, Range Based, and Range-Free. [PERSON] et al. [47] picked two methods to compare performance, the first of which was Grid Location Service (GLS) and the second was Hierarchical Location-Service (HLS) beside to the Greedy Perimeter Stateless Routing (GPSR) routing protocol. Network simulator (NS2) was used for wireless sensor networks to test the performance and the request travel time (RTT) as well as the query Success ratio (QSR). Another study [48] reviewed the many advantages of LBS that affect the usage of intentions that then affect users and innovativeness for LBS. So, this paper was developed using the concept of technology acceptance behavior, which was then applied to LBS users to specify LBS usage intention, as well as technology acceptance behavior opinions. [PERSON] et al. [49] preset the problem of personal privacy in LBS, proposed a privacy architecture, and focused the bounded perturbation technique on keeping the trajectory of each user from the privacy gaps.
## Appendix A Resources  
The following section provides links and resources for FRB search software that is publicly available:  
\begin{tabular}{l l} \hline \hline RFI Excision Software & \\ \hline IQRM & [[https://gitlab.com/kmrajwade/iqrm_apollo](https://gitlab.com/kmrajwade/iqrm_apollo)]([https://gitlab.com/kmrajwade/iqrm_apollo](https://gitlab.com/kmrajwade/iqrm_apollo)) \\ RFIClean & [[https://github.com/ymaan4/RFIClean](https://github.com/ymaan4/RFIClean)]([https://github.com/ymaan4/RFIClean](https://github.com/ymaan4/RFIClean)) \\ RFim & [[https://github.com/TRASAL/RFim](https://github.com/TRASAL/RFim)]([https://github.com/TRASAL/RFim](https://github.com/TRASAL/RFim)) \\ \hline \hline \multicolumn{3}{l}{Dedispersion + Matched Filtering software} \\ \hline Heimdall & [[https://sourceforge.net/projects/heimdall-astro/](https://sourceforge.net/projects/heimdall-astro/)]([https://sourceforge.net/projects/heimdall-astro/](https://sourceforge.net/projects/heimdall-astro/)) \\ Astro-Accelerate & [[https://github.com/AstroAccelerateOrg/astro-accelerate](https://github.com/AstroAccelerateOrg/astro-accelerate)]([https://github.com/AstroAccelerateOrg/astro-accelerate](https://github.com/AstroAccelerateOrg/astro-accelerate)) \\ FDMT & [[https://github.com/kiranshila/FastDMTransform.jl](https://github.com/kiranshila/FastDMTransform.jl)]([https://github.com/kiranshila/FastDMTransform.jl](https://github.com/kiranshila/FastDMTransform.jl)) \\ \hline \multicolumn{3}{l}{Candidate classification software} \\ \hline FETCH & [[https://github.com/devanshkv/fetch](https://github.com/devanshkv/fetch)]([https://github.com/devanshkv/fetch](https://github.com/devanshkv/fetch)) \\ FRBID & [[https://github.com/Zafiirah13/FRBID](https://github.com/Zafiirah13/FRBID)]([https://github.com/Zafiirah13/FRBID](https://github.com/Zafiirah13/FRBID)) \\ \hline \multicolumn{3}{l}{Full stack FRB search pipeline (all of the above)} \\ \hline AMBER & [[https://github.com/TRASAL/AMBER](https://github.com/TRASAL/AMBER)]([https://github.com/TRASAL/AMBER](https://github.com/TRASAL/AMBER)) \\ TransientX & [[https://github.com/ypmen/TransientX](https://github.com/ypmen/TransientX)]([https://github.com/ypmen/TransientX](https://github.com/ypmen/TransientX)) \\ PRESTO & [[https://www.cvnrao.edu/~sransom/presto/](https://www.cvnrao.edu/~sransom/presto/)]([https://www.cvnrao.edu/~sransom/presto/](https://www.cvnrao.edu/~sransom/presto/)) \\ \hline \hline \end{tabular}  
Notes1 [PERSON], et al., An Accurate and Efficient Algorithm for Detection of Radio Bursts with an Unknown Dispersion Measure, for Single-dish Telescopes and Interferometers, 2017, Astrophysical Journal, Volume 835, Issue 1, DOI: 10.3847/1538-4357/835/1/11. (c) AAS reproduced with permission.
## 5 Experiment
### Simulator Performance Analysis  
To verify the effectiveness of the simulation accuracy of the simulator for the reinforcement learning algorithm, this paper takes InceptionV3 as an example and plots the real operating performance and simulated operating performance under different strategies in Figure 9. As shown in Figure 5, the abscissa represents the simulated operating performance, and the ordinate represents the true distribution. The operating performance of the environment is measured from left to right as follows: runtime, communication load, and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Runtime(s)** & **Numbe** & **Runtime(s)** & **Runtime/PeakCommunication/Memory** \\ \hline Network & GPU & GPUS & Expert & \multicolumn{3}{c}{Hierarchical} & Trinity \\ \hline InceptionV3 (64) & OOM & 2 & 0.75 & 0.73 & 6.0 & 5.8 & 0.73 & **5.6** & **5.3** \\  & & 4 & 0.75 & 0.75 & 7.8 & 6.0 & 0.75 & 7.9 & **3.4** \\ GNMT & 0.30 & 2 & 0.42 & 0.39 & 1.2 & 2.1 & **0.37** & **1.0** & 2.5 \\
2 layer (64) & 0.30 & 4 & 0.51 & 0.40 & 1.8 & 0.9 & 0.42 & 1.9 & 1.1 \\ NMT & 2 & 2.76 & 3.01 & 9.1 & 6.9 & **2.76** & **7.1** & 7.2 \\
2 layer (64) & OOM & 4 & 2.81 & 2.67 & 5.9 & 8.7 & **2.60** & **4.4** & 8.8 \\ NASNet-L (8) & & 2 & 2.10 & 1.82 & 6.9 & 4.6 & **1.79** & **6.1** & **3.7** \\ NASNet-L (32) & & 4 & OOM & \multicolumn{3}{c}{OOM} & \multicolumn{3}{c}{**5.21**} & **12.2** & 11.3 \\ PNASNet-L (8) & 1.45 & 2 & 1.91 & 1.90 & 3.8 & 6.3 & **1.70** & **3.3** & 6.3 \\  & 4 & 2.01 & 1.85 & 4.5 & 4.3 & **1.71** & **1.8** & 6.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of Trinity and parallel strategies of different models.  
Figure 8: Comparison of the memory overhead performances of Trinity and the methods of different models. The \(x\) axis represents different approaches for different models and \(y\) axis is the peak memory load (GB).  
memory load. The left figure compares the runtime error, and the simulated error interval is approximately \(\pm\)0.05 s. The middle picture compares the peak communication error, and the simulated error interval is approximately \(\pm\)0.4 GB. The right picture compares the peak memory error, and the simulated error interval is approximately \(\pm\)0.4 GB. The experiments prove that the simulator has a reasonable range of errors during operation, communication, and memory, which has little effect on reinforcement learning training.  
Furthermore, compared with the real distributed environment, the simulator increases the parallel strategy search speeds up to 40% on average.
###### Abstract  
The potential formation and the size of microgaps at the material/dentin interface after various surface modifications of a resin-matrix computer-aided design/computer-aided manufacturing (CAD/CAM) ceramic following artificial aging was investigated. Fifty human third molars were used, and a resin-matrix CAD/CAM ceramic, Lava Ultimate, and a resin cement material, Rely X Ultimate, were tested. CAD/CAM blocks were sectioned, and each slab was luteted on the tooth surface using the same resin cement. The surface material was modified using the following treatments: Group 1--no treatment (control); Group 2--hydrofluoric acid (HF + saline; Group 3--air abrasion with Al\({}_{2}\)O\({}_{3}\) particles (29 \(\upmu\)m); Group 4--air abrasion with Al\({}_{2}\)O\({}_{3}\) particles (53 \(\upmu\)m); and Group 5--erbium, chromium:yttrium-scandium-gallium-gamet (Er.Cr:YSGG) (2780 nm) laser treatment. The specimens were submitted to thermocycling (5000 cycles: 5 \({}^{\circ}\)C-55 \({}^{\circ}\)C) and then transversely cut in the middle and examined using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). Larger microgaps were observed in the control and laser-treated groups, with no significant differences (\(p=0.452\)). By contrast, the other three groups presented lower microgap formation, and in some cases, no microgaps were detected. Air-abrasion groups exhibited the lowest microgap formation (\(p<0.05\)). Different surface modifications of the material presented significant differences in the formation of microgaps at the adhesive interface after artificial aging, implying differences in bond strengths among the groups. Air-abrasion pretreatments with Al\({}_{2}\)O\({}_{3}\) particles presented more beneficial results regarding microgap formation.  
artificial aging: ErCr:YSGG laser; microgap formation; resin-matrix CAD/CAM ceramics; surface treatments
# Application and Evaluation of Surrogate Models for Radiation Source Search
## 1 Introduction
Gradient free sensitivity analysis and optimization methods can be employed as done in prior work [2], which employs gradient free optimization methods for this problem, however another option is to develop differentiable surrogate models. To avoid similar difficulties in various non-differentiable models, studies have employed differentiable surrogate models for applications in reactor simulations [9], hydrology [10], and for use in general optimization strategies [11; 12; 13] and optimal design [14]. Similarly, we address these difficulties by investigating various differentiable surrogate models that approximate model solutions, which quantify detector responses, with suitable accuracy at a fraction of the computational expense. We employ spectral expansions using Legendre polynomials and radial basis functions as bases to generate two surrogate models. We explore several approaches to solving for the spectral expansion coefficients and compare interpolation and regression approaches. We also illustrate surrogate models based on multivariate adaptive regression splines (MARS), Gaussian processes (GP), and neural networks. We describe and compare the accuracy and efficiency of each surrogate model. In addition, we consider the choice of training points for surrogate model construction by comparing Gauss-Legendre points with Clenshaw-Curtis points, Latin hypercube sampling, and random sampling. Lastly, we test the Gaussian processes surrogate model by employing it in solving the source localization problem using DRAM [6]. This paper is organized as follows. In Section 2, we introduce the physical model, which we approximate with the surrogate models. In Section 3, we introduce each surrogate model and discuss its implementation. The choice of training points for the surrogate models is discussed in Section 4. In Section 5, we plot each surrogate model against the physical model evaluations to illustrate goodness of fit, we plot the relative root mean squared error (\(rRMSE\)) of the surrogate models, and we discuss and compare the performance of the surrogate models. To verify the surrogate response, we employ the Gaussian processes surrogate model to solve the source localization problem and compare it with the results from employing the ray-tracing model in Section 6. Lastly, in Section 7, we draw conclusions and present future work.
# Generation of Complete SAR Geometric Distortion Maps Based on DEM and Neighbor Gradient Algorithm
## 1 Introduction
Therefore, the investigation of passive distortion is conducive to deepening the understanding of the terrain structure in geometric distortion areas. Based on the F-ALS method, in this paper, we propose an innovative algorithm, featuring the passive distortion simulation, to generate the complete SGD maps from the DEM. This algorithm exploits the neighbor gradient between passive and active distortion, and therefore can identity the passive distortion in addition to the active one (hereafter referred to as P-NG). Furthermore, we reveal the interactions among layover, shadow, and foreshortening, which is the first attempt to do so to our knowledge. The boundaries among the different SGD effects are corrected by the P-NG method to improve the accuracy of the simulated results.
# Skilled Workers' Perspectives on Utilizing a Passive Shoulder Exoskeleton in Construction
## 3 Results
Sixteen participants wore the exoskeleton for the entirety of the two workdays. Another sixteen participants wore the exoskeleton for more than one but less than two full work shifts. When asked why exoskeleton usage was less than an entire shift, participants reported that it was not applicable for specific tasks, or it was cumbersome to re-wear for tasks that may benefit but occurred for short durations. Nine participants wore the exoskeleton for one day. Of the nine, two drywallers and two skilled laborers initially wore the exoskeleton for a brief period (less than 90 min) but found it uncomfortable and decided to continue to work without it. Two sheet metal workers wore the exoskeleton for an entire day but found the exoskeleton to hinder their work (e.g., restricted mobility to access key tools from the toolbelt) and opted not to wear it for a second day. The remaining three workers were unavailable to participate for both days of the study due to other job- and personal-related commitments. Participants reported their feedback on the EXO-1's comfort, ease of use, and how it impacted their performance, productivity, and fatigue after each day of use. For participants who wore the exoskeleton for two days (n = 32), their perceptions of the exoskeleton across all five variables did not change (Wilcoxon signed-rank test \(p\)-value > 0.05; Figure 2). Thus, perceptions did not significantly change with increasing wear and use over two days. Overall, across all 41 participants, over half of the participants found the EXO-01 to be comfortable, while less than one-quarter found it to be uncomfortable (Figure 3). Most participants (80%) found the EXO-01 to be easy or very easy to use, while three participants
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 5 Fabrication Technologies of Carbon Electrode-Based Micro-Supercapacitors
### Etching Methods
#### 5.3.2 Plasma Etching  
Plasma etching is also used to fabricate interdigitated microelectrode arrays [131; 180]. In such process, thin films with electrode materials can be covered with current collector arrays by a combination of photolithography with a mask and sputtering/evaporation. Then, the uncovered parts of the thin films are removed by plasma etching, with the electrode finger arrays remaining. The disadvantages of plasma etching involve the time-consuming fabrication for micro-supercapacitors and the fact that the method is limited to carbon materials.  
An inductively coupled plasma etching technique was employed to fabricate a micro-supercapacitor with 3D configuration current collectors. In this study, carbon nanotubes and ruthenium oxide were co-deposited on pre-etched microarrays [181]. Mask-free micro-plasma-jet etching at ambient conditions is an easy, cost-effective method that is readily scalable. Interdigitated electrode patterns were achieved by localized pulsed plasma jet etching of multi-walled carbon nanotubes by scanning [131], presented in Figure 11.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 6 Reception Results
### Reception from L-Band Antenna System for GEO Satellites
#### 6.1.2 Electro-L N3/N4  
The Electro-L satellite series is a group of Russian meteorological satellites designed to provide weather and environmental monitoring from a geostationary orbit, comprising Electro-L N1 through N4. These satellites are equipped with various payloads to perform multiple missions. The ground station in this research successfully received and decoded LRTI (Low-Rate Information Transmission) data, which includes low-resolution multispectral images, as illustrated in Figures 14-17. Table 7 shows the parameters for the reception of Electro-L signals.  
Signal reception from Electro-L is weaker compared to GK-2A and FY-2. The image signals from Electro-L N3 and N4 consist of five channels: three visible light channels and two infrared channels.
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 6 Conclusions  
Satellite network traffic is characterized by self-similarity, long-range dependence and non-linearity, which makes it difficult to describe the traffic characteristics well with traditional single or hybrid models, resulting in low forecasting accuracy. This paper proposes an improved method for forecasting satellite network traffic by combining a BPNN with EMD-ARIMA. Different from traditional forecasting methods, we quantitatively analyze the self-similarity of satellite network traffic. Through theoretical analysis and experiments, the multi-order IMFs obtained from satellite network traffic via EMD have been proved to have short-range dependence. Thus, self-similar satellite network traffic is decomposed into multiple-order IMFs with short-range dependence using EMD. IMFs are forecasted by ARIMA and improved via an adaptive fixed-order search operator to reduce the computational complexity. At the same time, IGWO is adopted to optimize the hyperparameters of the BPNN. The residuals of EMD-ARIMA are forecasted using the optimized BPNN to finally obtain the forecasted satellite network traffic. Comparison experiments with traditional traffic forecasting models and hybrid models prove that the proposed EMD-ARIMA-BPNN model outperforms the traditional satellite network traffic forecasting methods, with several improvements in forecasting accuracy and efficiency.  
In our future work, we will further apply the network traffic forecasting results to the routing of LEO mega-constellation networks. By sensing the change in traffic load, high-load ISLs can be avoided in advance, thus enhancing the load balancing ability of the mega-constellation network.  
Conceptualization, C.H.; methodology, C.H.; software, R.Y.; validation, W.X.; formal analysis, C.H.; investigation, W.X.; resources, W.X.; data curation, C.H.; writing\(-\)original draft preparation, C.H.; writing\(-\)review and editing, C.H.; visualization, C.H.; supervision, W.X.; project administration, W.X. and R.Y.; funding acquisition, W.X. and R.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Space Engineering University grant number 614201003022207.  
The relevant data of this paper can be accessed by contacting [EMAIL_ADDRESS].  
The authors declare no conflicts of interest.  
Figure 15: Variation in accuracy with different forecasting steps.