esa-sceva/satcom-chunk-collection huggingface dataset:
# Pansharpening by Complementing Compressed Sensing with Spectral Correction
## 2 Materials and Methods
### Compressed Sensing  
Compressed sensing (CS) is a technique used to reconstruct unknown data from a small number of observed data. In theory, the original data can be estimated when the data is sparse [28].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Image** & **Nihonmatsu** & **Yokohama** \\ \hline \multirow{2}{*}{Original image} & PAN image & \(1024\times 1024\) & \(1792\times 1792\) \\  & MS image & \(256\times 256\) & \(448\times 448\) \\ \multirow{2}{*}{Training image} & PAN image & \(256\times 256\) & \(448\times 448\) \\  & (for high-resolution dictionary) & \(256\times 256\) & \(448\times 448\) \\ \multirow{2}{*}{Test image} & PAN image & \(256\times 256\) & \(448\times 448\) \\  & MS image & \(64\times 64\) & \(112\times 112\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the original, training, and test images of the image datasets. MS: multispectral, PAN: panchromatic.  
We considered the problem of reconstructing an image \(z^{h}\in\mathbb{R}^{n_{0}}\) with a higher resolution than the observed low-resolution image \(z^{l}\in\mathbb{R}^{m_{0}}\) (\(m_{0}<n_{0}\)). The relationship between the high-resolution image and the low-resolution image can be expressed by Equation (1).  
\[\begin{array}{c}z^{l}=SHz^{h}=Lz^{h},\\ L=SH,\end{array} \tag{1}\]  
where \(S\) is a downsampling operator and \(H\) is a filter that lowers the resolution. At this time, since the dimensionality of \(z^{l}\) is smaller than that of \(z^{h}\), the solution cannot be uniquely determined.  
Based on the compressed sensing theory, the high-resolution image \(z^{h}\) is estimated by Equation (2) from the image element \(D_{h}\) and sparse representation \(a\). The element for reproducing the image is called atom \(d_{i}\in\mathbb{R}^{n_{0}}\), and the set of atoms is called the dictionary \(D_{h}\in\mathbb{R}^{n_{0}\times N_{d}}\).  
\[z^{h}=\ D_{h}a\ s.t.\|a\|_{0}\leq m_{0}, \tag{2}\]  
Using Equation (2), Equation (1) can be expressed as  
\[\begin{array}{c}z^{l}=Lz^{h}=\ D_{l}a,\\ D_{l}=LD_{h}.\end{array} \tag{3}\]  
\(z^{h}\) can be reproduced by \(z^{l}\) using the sparse representation \(a\) obtained from Equation (4), in which the sparsity constraint is added to Equation (3).  
\[\min_{a}\|a\|_{0}\ s.t.\ \|z^{l}-LD_{h}a\|_{2}^{2}\leq\varepsilon. \tag{4}\]  
Equation (4) can be solved using optimization methods.
# Extracting Sentence Embeddings from Pretrained Transformer Models
## 5 Conclusions
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model** & **T0** & **T4** & **BERT** & **BERT + Avg.** & **Avg.** & **B2S** & **B2S-100** & **RE** \\ \cline{2-9}
**Layer** & \multicolumn{3}{c}{**Last**} & \multicolumn{3}{c}{**First + Last**} & \multicolumn{3}{c}{**-**} \\ \hline avg. & 85.4 & 80.7 & 81.2 & 84.4 & 79.0 & 85.0 & 85.0 & 28.0 \\ + normalize & 85.5 & 80.6 & 85.6 & 86.1 & 79.6 & **85.7** & 85.9 & 27.2 \\ + quantile-u & 85.2 & 86.9 & 84.0 & 85.1 & 79.7 & 85.2 & 85.2 & 27.5 \\ + quantile-u & 85.4 & 86.7 & 85.3 & 85.7 & 80.6 & 85.4 & 85.4 & 27.2 \\ + whiten & 34.3 & 31.3 & 31.3 & 31.5 & 30.5 & 29.9 & 29.9 & 28.8 \\ + whiten & 75.2 & 75.0 & 72.7 & 72.8 & 58.6 & 70.6 & 69.0 & 28.4 \\ + zscore & 86.0 & 81.2 & 81.3 & 83.8 & 79.8 & 84.8 & 85.2 & 27.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Clustering accuracy dependence on token aggregation and post-processing techniques for the agnews dataset. The best result for each model is bolded, while underlined result is the best across all the models. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Model** & **T0** & **T4** & **BERT** & **BERT + Avg.** & **Avg.** & **B2S** & **B2S-100** & **RE** \\ \cline{2-9}
**Layer** & \multicolumn{3}{c}{**Last**} & \multicolumn{3}{c}{**First + Last**} & \multicolumn{3}{c}{**-**} \\ \hline avg. & 32.7 & 32.4 & 34.8 & 36.1 & 30.7 & 33.1 & 37.0 & 19.4 \\ + normalize & 32.8 & 32.0 & 35.1 & 36.3 & 30.3 & 33.8 & **37.6** & 20.7 \\ + quantile-u & 33.6 & 32.5 & 37.0 & 39.1 & 33.1 & 32.9 & 36.7 & 21.0 \\ + quantile-u & 33.4 & 32.5 & 35.4 & 37.9 & 34.5 & 33.0 & 37.0 & 21.1 \\ + whiten & 29.4 & 30.2 & 27.2 & 23.2 & 18.2 & 12.7 & 13.2 & 15.9 \\ + whiten & 34.8 & 34.7 & 40.2 & **42.7** & 36.5 & 35.0 & 34.4 & 29.4 \\ + zscore & 33.5 & 33.0 & 36.1 & 37.4 & 32.1 & 32.4 & 36.1 & 20.0 \\ \hline idf\({}_{t}^{W}\) & 31.0 & 30.0 & 32.3 & 32.3 & 28.1 & 34.5 & 34.5 & 32.1 \\ + normalize & 31.2 & 30.2 & 32.5 & 32.6 & 28.7 & 34.7 & 34.8 & **35.2** \\ + quantile-u & 32.5 & 31.3 & 33.6 & 33.6 & 29.8 & 33.9 & 35.0 & 32.4 \\ + quantile-u & 31.7 & 30.7 & 31.9 & 33.6 & 30.7 & 33.7 & 34.4 & 33.4 \\ + whiten & 27.3 & 27.6 & 26.4 & 20.6 & 17.1 & 11.8 & 12.4 & 16.7 \\ + whiten & 32.8 & 32.1 & 37.0 & 38.7 & 35.4 & **37.2** & 37.2 & 30.8 \\ + zscore & 32.0 & 30.8 & 32.6 & 32.5 & 28.2 & 34.3 & 34.3 & 31.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Clustering accuracy dependence on token aggregation and post-processing techniques for the biomedical dataset. The best result for each model is bolded, while underlined result is the best across all the models.
# Mapping Server Collaboration Architecture Design with OpenVSLAM for Mobile Devices
## 2 Related Works
### FastAPI-Based Mapping Server Architecture  
FastAPI is one of the high-performance web frameworks that are fast enough to be comparable to NodeJS or Go based on Starlette and Pydantic and is designed based on JSON Schema, an open API standard. In addition, FastAPI has fewer bugs due to the minimization of code duplication, so debugging time can be reduced, and code can be written quickly. [28] shows the results of several tests that take multiple rows from a simple database table and serialize them into JSON messages in order to compare the performance of various web frameworks. Per request, 1, 5, 10, 15, and 20 queries were tested. FastAPI showed the second-best performance after atreugo, and, in this paper, FastAPI implemented in Python was used for development convenience.  
Figure 1(a) shows the relationship between Uvicorn, an ASGI server, and FastAPI, one included ASGI framework. FastAPI can be composed of Starlette, a microweb framework, and Pydantic for data validation. Starlette, a subclass of FastAPI, is a web application that can be executed asynchronously. It runs on the Uvicorn server and uses Pydantic, the fastest Python validation tool, for all data processing of FastAPI. Uvicorn is a high-speed ASGI web server that performs asynchronous processing based on uvloop in a single process. Because parallel processing is not possible with Uvicorn alone, FastAPI uses Gunicorn, which can handle everything that happens between a web server and a web application as a server and process manager, making parallel processing possible. Figure 1(b) shows the multi-process architecture based on Gunicorn. FastAPI based on parallel processing using Gunicorn has all the performance advantages of Uvicorn and can also execute ASGI applications [28].  
To implement the OpenVSLAM algorithm in the server, instead of the communication method using Node.js and SoketIO, this paper focused on the ease of maintenance and on the asynchronous process communication for the background process implementation of the server. This requires a server with the same level of performance as Node.js, which was used in the existing OpenVSLAM. In the case of FastAPI, the SLAM algorithm composed of C++ is applicable, and it is an asynchronous process communication and a high-performance Python web framework. Therefore, it meets the requirements of the system in this paper.
# RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR
## 4 Results and Discussion  
### Datasets  
In recent years, numerous datasets for remote sensing target detection have emerged. In this study, we evaluate our proposed model on two publicly available remote sensing image datasets: SIMD and TGRS-HRRSD. The TGRS-HRRSD [41] dataset was released by the University of Chinese Academy of Sciences in 2019 and contains 21,761 images obtained from Google Earth and Baidu Maps with spatial resolutions ranging from 0.15 m to 1.2 m. The dataset includes 55,740 target instances across 13 categories. The sample sizes are relatively balanced between categories, with approximately 4000 instances per category. The SIMD [42] dataset contains 5000 satellite remote sensing images of size 1024 \(\times\) 768, totaling 45,096 instances across 15 target classes. The distribution of each category in these two datasets is shown in Figures 6 and 7. As illustrated in Figure 6, the distribution of object categories in the SIMD dataset varies significantly. Notably, the examples in the categories of car, van, and boat account for over 90% of the total instances, while other categories, particularly helicopter and fighter, have very few instances. This characteristic of class imbalance is common in real-world remote sensing object detection scenarios. Therefore, it is crucial to verify whether our method can achieve favorable results on this dataset to ensure practical applicability. The category distribution in the TGRS-HRRSD dataset is shown in Figure 7, where the number of instances for each category is relatively balanced. In our experiments, we split the two datasets into training, validation, and testing sets according to their original proportions.
## 7 Results and Discussion
### Anomaly Detection Performance  
One of the core goals of Edge-FLGuard is to improve anomaly detection across heterogeneous IoT nodes without compromising data privacy. Table 10 summarizes the detection performance of both autoencoder- and LSTM-based models under two training settings: local-only learning and federated learning (FL).  
Across the board, federated models significantly outperformed their local-only counterparts. For instance, the federated autoencoder achieved an F1-score of 0.89 and an AUC-ROC of 0.94, compared to 0.79 and 0.85, respectively, for the local version. Similarly, the federated LSTM achieved an even stronger F1-score of 0.91 and AUC of 0.96, reflecting its ability to capture sequential patterns more effectively through global knowledge sharing.  
In addition to standard metrics, we also evaluated the False Positive Rate (FPR) and False Negative Rate (FNR). For federated models, the FPR remained below 6%, and FNR under 8%, indicating a well-calibrated decision boundary that balances detection sensitivity and reliability--a critical requirement for minimizing alert fatigue in operational IoT systems.  
These results demonstrate that federated learning enables better generalization to unseen threats by aggregating knowledge across diverse edge environments--something that local models, constrained by their narrow training scope, cannot achieve.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Training** & **Precision** & **Recall** & **F1-Score** & **AUC-ROC** & **FPR** & **FNR** \\ \hline Autoencoder & Local-only & 0.84 & 0.76 & 0.79 & 0.85 & 0.09 & 0.24 \\ Autoencoder & Federated FL & 0.91 & 0.88 & 0.89 & 0.94 & 0.06 & 0.12 \\ LSTM & Local-only & 0.87 & 0.82 & 0.84 & 0.89 & 0.07 & 0.18 \\ LSTM & Federated FL & 0.93 & 0.90 & 0.91 & 0.96 & 0.05 & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Anomaly detection performance of autoencoder and LSTM models under local vs. federated training.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Global Rounds & 50 \\ Clients per Round & 10 (from 50 total) \\ Aggregation Method & FedAvg (optionally: trimmed mean) \\ Client Participation Rate & 20\% \\ Non-IID Strategy & Clients grouped by traffic type \\ Adversarial Setup & 30\% poisoned clients (FL rounds) \\ Communication & Encrypted gRPC (TLS 1.3) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Key configuration values used in the federated learning experiments, including the number of clients, aggregation method, participation rate, and privacy-related parameters.  
To assess the statistical significance of the observed performance improvements, we conducted paired two-tailed \(t\)-tests comparing the F1-scores and AUC-ROC values of local-only and federated models over five independent experimental runs. The differences in both metrics for autoencoder and LSTM models were statistically significant at the 95% confidence level (\(p<0.05\)), confirming the robustness of the performance gains introduced by federated learning. Please refer also to Appendix B for a full description.
## 4 Prospective
### Challenges of Existing Optimization Methodologies  
#### 4.1.1 Simulation Time Budget  
Gradient-free algorithms such as heuristic algorithms and DBS are widely used in inverse design. However, these methods require a great number of EM simulations to find the parameters set for an acceptable FOM. Heuristic algorithms need to perform EM simulations several times the DOF at each iteration to find the parameters update directions. DBS as a brute-force searching scheme is mainly used for devices with QR-code structures. Each simulation will only update one pixel, thus the required number of simulations to update the whole QR code during one round is the same as DOF. Usually, several rounds of DBS are needed for a device to reach an acceptable FOM. [PERSON] et al. have shown that it takes more than 5000 simulations to optimize a mode convertor with 1120 DOF [115]. With increasing DOF of devices, the time budget of required simulations will be extremely large. Therefore, they proposed a new method which combined DBS with gradient-based algorithms. In this way, the total number of simulations could be cut down at the cost of some decrease in performance. Improving the efficiency of gradient-free algorithms is a prerequisite for further applying them to the design of more complicated devices.  
In addition to cutting down the number of EM simulations required for the iterative optimization, a time-efficient EM simulator can also reduce the time budget for inverse design. Even though 3D-FDTD is computationally intensive, the simulation speed scales with multiple processors. High-performance computing techniques including parallelization and distributed computing are already adopted for commercial [116] and open-source [117] FDTD solvers, allowing the designers to reduce simulation time by additional computing resources. Implementation of FDTD or FEM using graphics processing unit (GPU) has also been investigated for higher simulation speed [118; 119]. Advances in these attempts will also help reduce the time budget for inverse design.
## 3 State of the Art
### Analysis and Discussion
Overhead was explored in relation to the computing expenses of the proposed approaches in [55], which emphasized the necessity of striking a balance between accuracy and computational efficiency in the methods offered. The research presented in [56] investigated the overhead caused by the consensus process in blockchains and suggested that using a lightweight consensus method can reduce overhead and increase scalability. The topic of overhead was discussed in the context of data transmission in [57], which emphasized the significance of effective data-sharing systems to reduce overhead. Lastly, ref. [60] provided a comparative analysis of various solutions. This research suggested that FL approaches often incur less overhead than alternative distributed learning modes. The study also discussed processing overhead in the context of incentive mechanisms for FL. Taken together, these papers highlight the importance of properly managing overhead costs to guarantee the efficiency, scalability, and effectiveness of distributed and federated information systems. Upon reviewing the collection of work relevant to IDSs based on FL in IoV, it becomes apparent that the realm of security within vehicular networks is experiencing a significant and fundamental change. FL has emerged as a promising solution for effectively addressing the intertwined issues of safeguarding data privacy and enhancing threat detection efficiency. In conclusion, Table 10 presents a comparative analysis of the advantages and drawbacks of each one of these proposed solutions that we discussed.
## 8 Limitations and Future Work  
This research has had several limitations regarding important aspects such as cross-validation and thorough comparison. Some possibilities for future work are as follows:  
1. Using a cross-validation dataset along with a training and testing dataset could improve the testing accuracy of the models.
2. Hyperparameter tuning using the cross-validation dataset to choose the best hyperparameters and higher accuracies.
3. Using newer and more advanced machine-learning algorithms and testing unsupervised deep-learning models on the dataset such as graph neural networks to improve accuracy.
4. More configurations of hidden layers and nodes and activation functions can be experimented with to achieve higher accuracy.  
**Author Contributions:** K.J.: data curation, investigation, writing--original draft, C.B.: methodology, project administration, writing--review and editing, K.S.: data curation, investigation, software, D.P.: data curation, investigation, software, J.M.C.: methodology, writing--review and editing, A.B.: methodology, P.L.M.: writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 3 Experimental
### Detectors  
A critical component for XPCS experiment is the detector. On the one hand the repetition rate of the detector defines the accessible time scale of the dynamics, on the other hand it should provide a single-photon sensitivity because of the low count rate in many XPCS studies. Furthermore, speckles need to be resolved for optimum contrast. During the last decades XPCS was typically performed using CCD detectors with repetition rates in the range of seconds. Therefore, slow dynamics of condensed matter have been investigated frequently, with special attention on colloidal glasses, gels and polymer melts [47, 66, 67, 68, 69, 70, 71, 72]. Faster dynamics have been probed using fast diodes, such as for studying capillary waves [73, 74, 75, 76, 77] or hydrodynamic functions [41, 42, 43, 44]. Although microseconds could be reached, such experiments are characterized by long data collection times to reach sufficient statistics. In addition, as an effective single-pixel detector, only one \(q\) can be probed at one time.  
With the rise of commercial 2D counting and hybrid detectors, the sub-s regime became accessible for real-time XPCS [78]. Nowadays, Eiger detectors and Medipix-based developments are used at XPCS beamlines, enabling repetition rates up to several kHz. The detectors used at current XPCS beamlines are outlined in Table 1 highlighting their size in pixel units, pixel size \(p\) and maximum repetition rate \(f\). For comparison, the current version of the PI PIXIS CCD detector is added as well, which provides a significantly smaller pixel size on the cost of a slow repetition rate. Further detector developments point towards even faster repetition rates [79, 80, 81], such as the recently developed Rigaku XSPA detector or the Sydor Keck-PAD, at least in burst-mode operation collecting a limited number of patterns. A different detection scheme may become available using event-baseddetection with Timepix3 [82] and its successor Timepix4 that is currently under design. With Timepix3, a single event can be detected with a time resolution of 1.56 ns in the event-based detection scheme. However, using such a detector demands new types of analysis routines, tracking single events via their time stamps.  
At FEL sources, special integrating detectors have been developed that are adapted to both the high peak brilliance and the particular repetition rate at the different instruments. This includes the AGIPD at European XFEL, the ePix detector at LCLS and the MPCCD at SACLA, listed as well in Table 1. A commercialized version of the AGIPD is currently designed and named SPARTA, allowing the collection of several 100 patterns with MHz repetition rate.
# Multi-Lidar System Localization and Mapping with Online Calibration
## 3 Back-end Optimization and Map Construction
### Map Construction and Update  
The map construction in the laser SLAM system outlined in this paper involves registering the feature point cloud of each key frame onto the global map. In comparison to the inter-frame matching of the odometer, this approach boasts higher matching accuracy; however, it is accompanied by a decrease in matching speed. With the continuous expansion of the map, the computational resources required rise significantly. Therefore, the use of key frames, local map construction, and point cloud downsampling is employed to bolster scan-to-map matching speed and prevent excessive computational burden on the overall system performance.  
The subsequent step entails the construction and update of the point cloud global map. The specific process is illustrated in Figure 9, where \(Q_{k}\) represents the map point cloud constructed at time \(k\), \(Q_{k}\) is the feature point cloud at time \(k+1\), \(T_{k}^{W}\) is the pose estimation of the vehicle relative to the global coordinate system at time \(k\), and \(T_{k+1}^{L}\) is the pose estimation of the laser odometer at time \(k+1\).  
Consequently, the pose estimation of the vehicle relative to the global coordinate system at time \(k+1\) is expressed as follows:  
\[T_{k+1}^{W}=T_{k+1}^{L}T_{k}^{W} \tag{36}\]  
Figure 8: Ground-constrained residual model.  
Figure 9: Map update model.  
Based on the pose transformation relationship, the feature point cloud of the current frame can be projected into the global coordinate system, as expressed in the following equation:  
\[P_{k+1}=T_{k+1}^{W}\overline{\mathcal{Q}}_{k+1} \tag{37}\]
## 3 Theoretical Foundations
### Topic Model Evaluation  
Topic model evaluation is based on model fit metrics, such as held-out likelihood or \"perplexity\" [12; 13], which assess the generalization capability of the model by computing the model likelihood on unseen data. Model fit metrics of unseen documents estimate the model capability for generalization or predictive power. \"Perplexity\" is a metric that measures how effectively a probability model predicts a set of unknown (or known) data. A lower \"perplexity\" indicates that the topic model is better at predicting the sample. Mathematically, the \"perplexity\" measure is computed as follows.  
\[\text{Perplexity}=-\frac{\log P(\mathbf{w}^{\prime}|\Phi,\boldsymbol{\kappa})} {N^{\prime}}, \tag{6}\]  
where \(\mathbf{w}^{\prime}\) is a set of unseen words in a document, \(N^{\prime}\) is the number of words in \(\mathbf{w}^{\prime}\), \(\Phi=[\phi_{1},\phi_{2},\ldots,\phi_{K}]\) is a posterior estimate or draw of topics, and \(\boldsymbol{\kappa}\) is the posterior estimate or draw of the Dirichlet hyperparameters.