esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots
## 6 Conclusions  
We proposed HomographyFpnNet, which uses multi-scale deep visual features, and obtained a smaller homography estimation error for center-aligned image pairs compared with the state of the art. The proposed hierarchical HomographyFpnNet for non-center-aligned image pairs significantly outperforms ORB/SIFT+ RANSAC methods. The experiments conducted with a trained three-stage hierarchical HomographyFpnNet model on wall images of climbing robots showed that our proposed hierarchical HomographyFpnNet is suitable for estimating the homography matrix between the wall map and camera images. In future work, we will focus on solving the problem of increasing estimation error of homography caused by the change in illumination, viewpoint, etc. between the camera image and the fixed wall map.  
Figure 12: Experiment 2. Prediction results of our three-stage hierarchical HomographyFpnNet on test wall images where image_patch_b is only warped from the first wall image.  
Figure 13: Experiment 2. Prediction errors of our three-stage hierarchical HomographyFpnNet on test wall images.  
**Author Contributions:** Conceptualization, Q.Z. and X.L.; methodology, Q.Z.; software, Q.Z.; validation, Q.Z.; formal analysis, Q.Z.; investigation, Q.Z.; resources, X.L.; data curation, Q.Z.; writing--original draft preparation, Q.Z.; and writing--review and editing, X.L.  
**Funding:** The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (No. U1613203), Shenzhen Science and Technology Plan (No. JCYJ20170816172938761), and the Fundamental Research Funds for the Central Universities (No. 51221004).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Python-Based Automation Script to Mark Computer-Aided Design Assessments
## 2 Materials and Methods  
### Automation Tool Development  
The methods employed in this study were designed to develop and evaluate a Python-based automation tool for assessing student CAD submissions. The tool was built using PythonTM v3.9 and leveraged software API to interact with CAD files. The workflow (Figure 1) of the script involved several key steps: (1) Input Files and Setup: A solution file containing the expected parameters (P\({}_{1-n}\)) was used as a reference. Student submissions were provided as CAD files, located in a specified directory. The script required an output directory for intermediary files and generated a final CSV file summarising marks for each submission. (2) Solution File Processing: The CAD solution file was exported to a STEP file format using a COM automation interface via the win32 com.client module. The STEP file was analysed using the Open Cascade (OCC) library to extract the expected parameters for comparison. (3) Student Submissions: For each CAD file in the submissions folder, the script executed Step 2 above. (4) Marking Scheme: Each property was evaluated against the expected value using a percentage difference method. (5) Output: Results, including calculated parameters and individual marks, were saved to a CSV file for quick review and further analysis. To ensure scalability and usability, the script processed all CAD files in a designated directory, generating a CSV file summarising the results for each submission. Intermediate files, such as temporary STEP files and logs, were automatically deleted after processing to maintain a clean output directory.  
Figure 1: Workflow of the Python-based automation tool for assessing student CAD submissions.
# GC4 MRec: Generative-Contrastive for Multimodal Recommendation
## 4 Experiment
### Experimental Settings
#### 4.1.2 Baselines  
To demonstrate the effectiveness of our proposed GC4 MRec, we compare it with the following state-of-the-art recommendation methods, spanning both general collaborative filtering and multimedia recommendation approaches.  
**General Collaborative Filtering:**  
\({}^{\star}\)**MF-BPR**[43]: This method employs matrix factorization (MF) as its core model and optimizes the user and item latent representations using Bayesian Personalized Ranking (BPR) loss.  
\({}^{\star}\)**LightGCN**[8]: This method streamlines the conventional graph convolutional network (GCN) by eliminating non-linear activation and feature transformation layers, focusing on efficient neighborhood aggregation for recommendation.  
**Multimedia Recommendation:**  
\({}^{\star}\)**VBPR**[26]: This method incorporates visual features as side information to enhance MF-BPR, improving recommendation performance by leveraging item visual characteristics. For a fair comparison, we extend VBPR to include textual modality in our experiments.  
\({}^{\star}\)**MMGCN**[12]: This method constructs modality-specific graphs and employs a GCN for each modality. The final prediction is obtained by fusing the representations learned from each modality's GCN.  
\({}^{\star}\)**LATTICE**[13]: This method leverages multimodal features to construct an item semantic graph, capturing latent semantic correlations between items and improving recommendation quality. As our proposed method builds upon and improves LATTICE, we highlight these improvements in the Results section.  
\({}^{\star}\)**DGVAE**[14]: This method maps multimodal data into textual representations and employs VGAE for modeling and reconstructing the item-item graph, thereby enhancing the accuracy of the model's recommendations.  
\({}^{\star}\)**FREEDOM**[19]: This method refines the user-item graph used in LATTICE by removing potential false-positive edges and freezes the item-item graph to boost recommendation performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset \({}^{\ddagger}\)** & **\# Users** & **\# Items** & **\# Interactions** & **Density** \\ \hline Clothing & 39,387 & 23,033 & 237,488 & 0.00026 \\ Sports & 35,598 & 18,357 & 256,308 & 0.00039 \\ Baby & 19,445 & 7050 & 139,110 & 0.00101 \\ \hline \hline \end{tabular} \({}^{\dagger}\)Datasets can be accessed at [[https://ceweb.ucsd.edu/](https://ceweb.ucsd.edu/)]([https://ceweb.ucsd.edu/](https://ceweb.ucsd.edu/))\(\sim\)jmcualeey/datasets/amazon/links.html (accessed on 18 January 2025).  
\end{table}
Table 1: Statistics of the datasets.  
* **SLMRec**[44]: This method integrates self-supervised learning into multimedia recommendation, utilizing data augmentations to uncover multimodal patterns and improve contrastive learning.
* **MMSSL**[30]: This method introduces a modality-aware interactive structure learning paradigm using adversarial perturbations and employs cross-modal contrastive learning to disentangle shared and modality-specific features.
## 3 Empirical Research
### Analysis of Results
#### 3.3.2 Analysis of the Results of the Two-Stage Dynamic Network Model
This suggests that the efficiency results for these provinces may not be scientifically sound and that the model may need to be further optimized in order to reveal the relative efficiency differences and potential problems of the provinces at different stages more accurately. Qinghai Province has demonstrated the highest efficiency in the stage of technological research and development. The reasons mainly lie in its unique resource advantages; the promotion of industrial upgrading by scientific and technological innovation, ecological restoration, and sustainable development; the transformation of the innovation system and mechanism; as well as modern agricultural innovation. At the same time, the efficiency of its technology transfer stage is also relatively high, with an overall efficiency of 1. It can be temporarily considered that the result is in line with the factual basis. However, Henan Province has a relatively complete industrial chain and a good infrastructure and has also increased investment in innovation and technology, promoting cooperation between enterprises and research institutions. It shows the highest efficiency in the stage of technology transfer, but its efficiency in the research and development stage is relatively low. Nevertheless, the overall efficiency is still 1, which can also indicate that this model still overestimates the evaluation results. The following takes Hebei Province as an example to analyze its overall efficiency, stage-one efficiency, and stage-two efficiency in the two-stage dynamic network model, as shown in Table 8 and Figure 4. From the data, it can be seen that with the continuous development of the high-tech manufacturing industry in Hebei Province, the efficiency of the technology R&D stage (stage one) has been on an upward trend year by year, showing certain positive changes. This trend indicates that Hebei Province has gradually achieved results in technological innovation and R&D investment, especially in the construction of scientific research and technological infrastructure. However, it is worth noting that the overall efficiency of the high-tech manufacturing industry in Hebei Province is as high as 0.866948 over the entire assessment cycle, which means that Hebei Province has demonstrated high overall efficiency in the overall dimension over the entire assessment period. However, further analysis reveals that the overall efficiency in each year does not exceed 0.5, a phenomenon that highlights certain limitations of the dynamic network DEA in dealing with efficiency evaluation. Meanwhile, when breaking down the overall efficiency of each stage, it is found that the overall efficiency of stage one is only 0.308851, and the efficiency of stage two is 0.366886, and the efficiencies of the two stages are not up to the ideal level, which indicates that Hebei Province is still facing a lot of bottlenecks in the innovation chain of the high-tech manufacturing industry. Figure 4: Efficiency values of various categories in Hebei Province in dynamic network modeling.
## 2 Materials and Methods
### Theoretical Analysis of the Short-Circuit Current
At this time, the voltage and current \(\rm i_{g}\) of the DC-link are 6 positive pulsating waveforms. The total impedance \(\rm Z\) in the fault circuit is:  
\[\rm Z=\left[(R+j\omega_{g}L)||\frac{1}{j\omega C}\right]+\left(2R_{g}+j2\omega _{g}L_{g}\right) \tag{4}\]  
where \(\omega_{g}\) is the synchronous angular frequency of the grid. The amplitude of the DC-link current \(\rm i_{g}\) is  
\[\rm I_{g}=\frac{\sqrt{2}V_{g}}{|Z|} \tag{5}\]  
In addition, \(\rm i_{g}=i_{g}\frac{1}{j\omega_{gC}}/(R+j\omega_{g}L+\frac{1}{j\omega_{g}C})= \frac{i_{g}}{1-\omega_{g}^{2}LC+j\omega_{g}RC}\)  
Therefore, the amplitude of \(\rm i_{s}\) is  
Figure 2:0  
Steady-state value of each pulse of short-circuit current is:  
Figure 2:1  
Figure 2:2 Equivalent model of each stage of a short circuit. (**a**) Stage 1, capacitor discharge. (**b**) Stage 2, diodes turn on because of the inductor freewheeling. (**c**) Stage 3, uncontrolled rectification. It can also be approximated that the steady-state value is DC, \((3/\pi)\) I\({}_{\rm s}\). The steady-state short-circuit current can be found with Equations (4)-(7), but it is complicated to manually calculate and can be obtained through simulation. 3.2 Under the Condition R \(>\) 2\(\sqrt{\frac{\rm k}{\rm c}}\), When It Is Overdamped, the Short Circuit Current Goes through Two Stages  
Stage 1: Similarly to Stage 1 in Section 2.3.1, the capacitor discharges, and the equivalent circuit is shown in Figure 4a. The state Equation (1) is solved as follows:  
Figure 2:3  
Figure 2:4  
Figure 2:5  
The time for the discharge current to reach its peak is  
Figure 2:6  
The peak of the discharge current (i\({}_{\rm p}\)), i.e., the peak of the short-circuit current, can be obtained by bringing Equation (9) into Equation (8). Stage 2: When the capacitor voltage drops below the AC-link voltage, the diodes turn on, the circuit enters a state of uncontrolled rectification, and it tends to be steady. The equivalent circuit is shown in Figure 4c. Note that the capacitor voltage at this time is not zero, which is different from Figure 4b. Because it is overdamped, the short-circuit current does not go through the stage in Figure 4b. The analysis and calculation in this stage are the same as those in Stage 3 in Section 2.3.1. 3.3 Under the Condition R \(=\) 2\(\sqrt{\frac{\rm k}{\rm c}}\), When It Is Critically Damped, the Short Circuit Current Goes through Two Stages  
Stage 1: Similarly to Stage 1 in Section 2.3.1, the capacitor is discharged, and the equivalent circuit is shown in Figure 4a. The state Equation (1) is solved as follows:  
Figure 2:7  
Stage 2: The stage is the same as that in Stage 3 in Section 2.3.1. Critical damping can hardly be encountered in practice but exists only in theory.
## 2 Material and Methods
### Performance Metrics  
The outcomes of streamflow modeling with CHAID, OPELM, and hybrid ARCH-CHAID and ARCH-OPELM models were evaluated by five statistical metrics. The performance metrics utilized in this study were Correlation Coefficient (R), Root Mean Square Error (RMSE), Nash-Sutcliffe model  
Figure 3: Schematic structure of the hybrid (OPELM/CHAID-ARCH) models integrating the autoregressive conditional heteroskedasticity (ARCH) time-series approach with standalone extreme learning machine (OPELM) and Chi-square automatic interaction detector (CHAID) models.  
efficiency coefficient (NSE), Mean Absolute Error (MAE), and the ratio of RMSE to the standard deviation (RSD).  
(a) R: The closer is the value to 1, the higher the accuracy.  
(b) RMSE: The smaller the RMSE, the more precise the prediction will be [61].  
(c) NSE: The closer the value is to 1, the higher the accuracy. The NSE is utilized to evaluate the predictive power of models [62].  
(d) MAE: The smaller the MAE, the more precise the prediction will be.  
(e) RSD: The smaller the RSD, the more precise the prediction will be [63].  
\[R\ =\ \frac{\sum_{i\ =\ 1}^{M}\left(O_{i}-\overline{O}\right)\left(P_{i}- \overline{P}\right)}{\sqrt{\sum_{i\ =\ 1}^{M}\left(O_{i}-\overline{O}\right)^{2}\sum_{i\ =\ 1}^{M}\left(P_{i}-\overline{P}\right)^{2}}} \tag{10}\]  
\[RMSE\ =\ \sqrt{\frac{1}{M}\sum_{i\ =\ 1}^{M}\left(P_{i}-O_{i}\right)^{2}} \tag{11}\]  
\[NSE\ =\ 1\ -\ \frac{\sum_{i\ =\ 1}^{M}\left(P_{i}-O_{i}\right)^{2}}{\sum_{i \ =\ 1}^{M}\left(O_{i}-\overline{O_{i}}\right)^{2}} \tag{12}\]  
\[MAE\ =\ \frac{\sum_{i\ =\ 1}^{M}\left|Pi-O_{i}\right|}{M} \tag{13}\]  
\[RSD\ =\ \frac{RMSE}{STDEV_{i}}\ =\ \frac{\left[\sqrt{\sum_{i\ =\ 1}^{M}\left(O_{i}-P_{i}\right)^{2}}\right]}{ \left[\sqrt{\sum_{i\ =\ 1}^{M}\left(O_{i}-\overline{O_{i}}\right)}\right]} \tag{14}\]  
Equations (10)-(14) give evaluations for assessing the accuracy of the models. In these equations, \(M\) is the number of observations, \(O_{i}\) is the actual observations, and \(P_{i}\) is the estimated values. Also, \(\overline{O}\) and \(\overline{P}\) denote the mean of the observed and predicted dataset, respectively, and \(STDEV\) represents standard deviation, in the studied period.
## 2 Materials and Methods
### Data Collection and Pre-Processing
#### 2.1.1 Data Collection  
* The WikiArt Dataset  
This dataset is a collection of more than 140,000 paintings which can be accessed from the Wikiart.org website. However, the website does not provide an easy means, such as a public API, to perform data collection. We designed a custom script based on the Python library _beautifulsoup_[34] to capture the most relevant data fields directly from the site's webpages. The data were then inserted into a MySQL database for further filtering while preserving the integrity of the original data scheme. The data fields selected were the artist, the media, the genre, the year and the style. Some samples from the dataset are shown in Figure 1.  
* The Metropolitan Museum of New York (MET) Dataset  
The Metropolitan Museum of New York (MET) recently published a collection of more than 200,000 cultural artifacts under the Creative Commons open access license. The data represent images of assets along with their metadata, however the metadata are not fully available for the majority of the assets. The collection is accessible from a CSV file, nevertheless, the downloading of the images required the writing of a custom script to harvest them from the asset webpages on the museum website. The script was also based on the Python library _beautifulsoup_[34]. Some samples from the dataset are shown in Figure 2.  
* The Rijksmuseum Dataset  
More than 100,000 assets have been published by the Rijksmuseum of Amsterdam (which is often referred to as the [PERSON] museum, as it hosts a large collection of the artist's works). The collection is accessible via an API and has been the subject of multiple research contributions [11; 33]. As a result, many researchers have hosted the collection on archival platforms. However, we noted that the museum had updated its collection with multiple new assets, and thus we relied on the museum's API to collect and organize the harvested assets. In this work, we only focused on paintings. Some samples from the dataset are shown in Figure 3.  
Figure 1: Examples of paintings from the WikiArt dataset.  
Figure 2: Some artworks from the Metropolitan Museum of New York (MET) dataset.
## 2 Materials and Methods
### Numerical Experiments Using the SCOPE Radiative Transfer Model  
At the canopy scale, leaf rolling leads to reduced canopy coverage and increased canopy gap fraction levels, which in turn leads to a reduction in the leaf area involved in photosynthesis and transpiration, referred to as the LAI. Consequently, the LRR can be indirectly represented by the LAI. To analyze the relationships among leaf rolling, the quantum yield of photosystem fluorescence, and canopy fluorescence, the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model was applied to simulate changes in SIFy as a function of the LAI and fluorescence quantum efficiency (Fqe) [30; 31].  
The parameter settings of the SCOPE model closely match the experimental conditions. The canopy LAI varied between 1 and 3.5 m\({}^{2}\)/m\({}^{2}\), and the photosystem Fqe varied between 0.01 and 0.03. Other vegetation properties and the solar observation geometry were held constant, with the solar zenith angle fixed at 30\({}^{\circ}\) and the observation angle fixed at 0\({}^{\circ}\), matching the real measurement setup.
## 2 Study Area, Dataset, and Methodology
### Vegetation Data Sources and Preprocessing  
This study utilizes MODIS NDVI data (One specific data product of the Moderate Resolution Imaging Spectroradiometer: a space remote sensing instrument developed and built by NASA) to represent vegetation growth status and coverage in Aksu, with a spatial resolution of 1 km [35; 36]. NDVI is widely chosen for its sensitivity to green vegetation cover, simplicity, and ability to effectively represent vegetation health, especially in arid and semi-arid regions like Aksu. Although alternative indices such as the Enhanced Vegetation Index (EVI) and Soil-Adjusted Vegetation Index (SAVI) offer specific advantages--such as improved performance in high-biomass areas or reduced soil influence--NDVI remains the most suitable choice for Aksu's sparse and low-contrast vegetation landscape. Comparative tests showed NDVI's greater consistency and interpretability under Aksu's conditions, reinforcing its selection for this study.  
Data were downloaded at monthly intervals from 2000 to 2020 via the Google Earth Engine platform (GEE), using a maximum value composite method to minimize cloud and aerosol interference [37; 38]. To further smooth the data and eliminate short-term fluctuations, Savitzky-Golay (S-G) filtering was applied, focusing on seasonal (1 May-30 September) NDVI to capture the growing season.  
To enhance data transparency, Table 1 summarizes the primary datasets used in this study, including data sources, spatial resolution, temporal range, and preprocessing steps, facilitating a clear understanding and replication of the study's methodology.
## 2 Materials and Methods
### Controller Node Failure-Recovery
#### 2.7.4 Auction Sequence  
The whole auction sequence is visualized in Figure 10 and follows the process described in Section 2.7.1. Losses over the network are either compensated by repeated message broadcasts for some time, e.g., the task offer message during the auction announcement period, or are guarded with explicit acknowledgement (ack) messages, which trigger a resend of the original message if no ack message was received from the recipient for some time.
## 3 Helicopter Emergency Medical Services and Air-MSU  
### HEMS Applications and Potential  
Helicopter emergency medical services (HEMS) were traditionally used for trauma transfers and have increased popularity for use in providing triage and transportation for stroke patients to the nearest medical facilities or interfacility transportations since the approval of perfusion treatment for stroke by the FDA [17; 18].  
HEMS have been proven to improve the accessibility of stroke care for a wide range of communities from highly rural to urban areas by providing timely transportations within the treatment window. A study aggregated the data of HEMS dispatch between 2004 to 2011 in the United States of America and reported that the majority of the transported patients arrived at a primary stroke center in less than 2 h after dispatch [19]. However, a recent study on aernectical retrievals for stroke patients residing in rural Australia revealed that the transfer times are still largely outside of the recommended time window for reperfusion treatments; this is due to the scarcity of relevant stroke centers in rural and remote areas, where the establishment of additional stroke centers remains unfeasible due to the sparsely distributed population across vast areas [20].  
Thus, the invention of MSU has opened opportunities to evolve the technology into an air version of the MSU (hereafter named the Air-MSU). Air-MSU can potentially deliver diagnosis and administer tPA where applicable to stabilize the stroke patient's condition on site before transporting to a specialized stroke center for more sophisticated medical evaluation and treatment. It is worth noting that the CT scanning procedure for diagnosing is only carried out once the helicopter has landed onto a suitable terrain with a minimal slope.