esa-sceva/satcom-chunk-collection huggingface dataset:
# LightR: A Fault-Tolerant Wavelength-Routed Optical Networks-on-Chip Topology +
## 1 Introduction
More importantly, RobustONoC assumes that only one single fault will appear in each WRONoC topology, regardless of the network size, which is rather unrealistic. In fact, there can be multiple malfunctioning MRRs, especially for large networks consisting of many MRRs. Therefore, there remains a need for a more realistic fault model, as well as a scalable and fault-tolerant WRONoC topology. We summarize the main contributions of this paper as follows:  
* We propose a new WRONoC fault model addressing the different fault rates of MRRs, thereby removing the impractical assumption in the state-of-the-art fault model that only one malfunctioning MRR will exist regardless of the size of the networks. * We propose the first fault-tolerant WRONoC topology, LightR, as an extension of the Light topology [16]. Compared to Light, which reserves only one signal path for each master-slave pair, LightR enhances reliability by providing two independent signal paths to each master-slave pair that requires communication. * We greatly reduce the MRR usage compared to the state-of-the-art fault-tolerant WRONoC design method. We evaluate the performance of LightR by comparing it to three state-of-the-art topologies, \(\lambda\)-router [17], GWOR [18], and Light [16], and to the state-of-the-art fault-tolerant design method for topologies, RobustONoC [15]. The experimental results show that LightR provides higher reliability with a modest MRR usage. For example, considering the 3% fault rate for a 64-node network, LightR decreases the number of erroneous communications by 85-90% compared to the state-of-the-art topologies.
## 4 Experiments
### Ablation Study  
First, every component of MFFCESSC-SSD is evaluated by adopting the SSD as the baseline in all ablation experiments. Table 1 shows that the mAP increases by 3.3% and 2.8% on VisDrone2019 and ARH2000, respectively, by applying the ResNet as a backbone. However, incorporating the enormous parameters of ResNet, the inference speed significantly decreases by 32.7% and 25.3%. The proposed MFF component adds multi-scale semantic features to shallow feature maps and mitigates the impact of the background, which effectively improves the detection accuracy of the model for small objects. Therefore, combining multi-scale feature fusion with an attention mechanism increases the mAP by 13.5% and 5.5% on VisDrone2019 and ARH2000, respectively, while further decreasing the inference speed. The CESSC component reduces the parameters by 39.2% on VisDrone2019 and 52.5% on ARH2000 by adopting global contextual information and SC. Due to the sparsity of targets in the ARH2000 dataset, CESSC more significantly reduces the number of parameters and speeds up inference The CE-GN layer can generate contextual features and global correlations. Thus, the CESSC component further boosts the mAP by 8.7% and 11.2%. Focal loss increases the importance of small objects in the loss function of the MFFCESSC-SSD, and the mAP is slightly boosted by 6.4% and 1.0% on VisDrone2019 and ARH2000, respectively. The introduction of focal loss leads to a small decrease in the model's inference speed due to the fact that the loss function guides the optimization process of the model, and more complex loss functions tend to involve additional parameters or computations.
# Digitization of Medical Device Displays Using Deep Learning Models: A Comparative Study
## 4 Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**Standalone**} & \multicolumn{3}{c}{**With CNN**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline
0 & 0.98 & 0.96 & 0.97 & 0.97 & 0.99 & 0.98 \\
1 & 0.99 & 1.00 & 1.00 & 0.99 & 0.98 & 0.99 \\
2 & 0.95 & 0.96 & 0.96 & 0.89 & 0.81 & 0.85 \\
3 & 0.96 & 0.96 & 0.96 & 0.99 & 0.97 & 0.98 \\
4 & 1.00 & 1.00 & 1.00 & 0.98 & 1.00 & 0.99 \\
5 & 0.98 & 0.94 & 0.96 & 0.76 & 0.81 & 0.78 \\
6 & 1.00 & 0.98 & 0.99 & 0.93 & 0.99 & 0.96 \\
7 & 1.00 & 0.98 & 0.99 & 0.96 & 0.97 & 0.97 \\
8 & 0.93 & 0.99 & 0.96 & 0.99 & 0.98 & 0.98 \\
9 & 0.98 & 0.97 & 0.97 & 0.99 & 0.98 & 0.98 \\ \hline sys & 1.00 & 0.99 & 1.00 & 1.00 & 1.00 & 1.00 \\ dia & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ m & 0.99 & 1.00 & 1.00 & 0.98 & 0.97 & 0.97 \\ p & 1.00 & 1.00 & 1.00 & 0.95 & 0.92 & 0.94 \\ spO2 & 1.00 & 1.00 & 1.00 & 0.99 & 0.99 & 0.99 \\ \hline
**Accuracy** & \multicolumn{3}{c}{**0.98**} & \multicolumn{3}{c}{**0.96**} \\
**Macro avg** & 0.98 & 0.98 & 0.98 & 0.96 & 0.96 & 0.96 \\
**Weighted avg** & 0.98 & 0.98 & 0.98 & 0.96 & 0.96 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of classification metrics: YOLOv8l (640) standalone vs. YOLOv8l + CNN. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**YOLOv8l (640)**} & \multicolumn{3}{c}{**SSD (640)**} \\ \cline{2-7}  & **Precision** & **Recall** & **AP@50** & **Precision** & **Recall** & **AP@50** \\ \hline
0 & 0.986 & 0.908 & 0.985 & 0.829 & 0.797 & 0.767 \\
1 & 0.992 & 0.915 & 0.971 & 0.843 & 0.852 & 0.825 \\
2 & 0.952 & 0.903 & 0.973 & 0.779 & 0.740 & 0.777 \\
3 & 0.976 & 0.916 & 0.965 & 0.754 & 0.7 In comparison, the YOLOv8l model combined with the CNN classifier achieved a slightly lower overall accuracy of 0.96. While most classes maintained high performance metrics, some classes, such as class 5 and class 2, showed lower values of precision, recall, and F1-scores. Regarding the confusion matrices between the YOLOv8l standalone model and the YOLOv8l with the CNN, shown in Figure 5, we can once again observe that the performance of the standalone model was slightly superior to that of the model with the CNN incorporated. Figure 6 presents the precision-recall (PR) curve for the YOLOv8l standalone model. The PR curve illustrates the relationship between precision and recall across different classification thresholds. We can observe that all classes have good performance across various threshold levels. The curves for each class are positioned in the upper-right corner, indicating that the model achieves a good balance of precision and recall for all the analyzed classes. Finally, Figure 7 provides a qualitative analysis showcasing some of the results obtained with the YOLOv8l (640) standalone model. Figure 5: Confusion matrices for YOLOv8l (640): (**a**) standalone; (**b**) with CNN incorporated. Figure 6: Precision–recall (PR) curve for YOLOv8l (640) standalone model.
## 4 Analytical Framework for Heterogeneous Service Provisioning  
To address the challenges of latency-sensitive and dynamically evolving IoT services in smart healthcare, we develop an analytical framework for adaptive service provisioning across mist, edge, and cloud layers. The objective is to jointly optimize delivery rate and latency by allocating bandwidth and computing resources based on service priority, link conditions, and predicted demand. Unlike conventional static models, our framework supports heterogeneous healthcare workloads, ranging from real-time telemetry to elastic background services, by integrating ML modules with a mathematically grounded optimization engine. This model enables the system to dynamically route, throttle, and prioritize services with respect to utility and delay constraints, ensuring quality of service (QoS) across all tiers of the architecture.  
Figure 3 illustrates the operational flow of the proposed ML-RASPF framework, which incorporates dynamic decision-making, feedback loops, and exception handling. IoT data sources (e.g., wearables and sensors) initiate service requests evaluated for predicted load conditions. If forecast demand exceeds available capacity, the system triggers early adaptation via edge resource provisioning; otherwise, service paths are optimized centrally. A QoS classifier categorizes incoming requests into latency-sensitive or bandwidth-intensive classes, enabling priority-based control. The RL agents then adjust delivery  
Figure 3: ML-RASPF workflow from IoT input to rate-adaptive service provisioning across mist–edge–cloud.  
rates based on real-time performance metrics. The optimization engine jointly considers utility and delay, guiding service allocation across mist, edge, and cloud layers. A feedback mechanism continuously monitors service performance: exception handling is activated if quality falls below thresholds, and rate policies are re-optimized. This architecture ensures robust, context-aware, and adaptive service provisioning across distributed healthcare infrastructures.
# CMAG: A Mission to Study and Monitor the Inner Corona Magnetic Field
###### Abstract  
Measuring magnetic fields in the inner corona, the interface between the solar chromosphere and outer corona, is of paramount importance if we aim to understand the energetic transformations taking place there, and because it is at the origin of processes that lead to coronal heating, solar wind acceleration, and of most of the phenomena relevant to space weather. However, these measurements are more difficult than mere imaging because polarimetry requires differential photometry. The coronal magnetograph mission (CMAG) has been designed to map the vector magnetic field, line-of-sight velocities, and plane-of-the-sky velocities of the inner corona with unprecedented spatial and temporal resolutions from space. This will be achieved through full vector spectropolarimetric observations using a coronal magnetograph as the sole instrument on board a spacecraft, combined with an external occulter installed on another spacecraft. The two spacecraft will maintain a formation flight distance of 430 m for coronagraphic observations, which requires a 2.5 m occulter disk radius.  
The mission will be preferentially located at the Lagrangian L5 point, offering a significant advantage for solar physics and space weather research. Existing ground-based instruments face limitations such as atmospheric turbulence, solar scattered light, and long integration times when performing coronal magnetic field measurements. CMAG overcomes these limitations by performing spectropolarimetric measurements from space with an external occulter and high-image stability maintained over time. It achieves the necessary sensitivity and offers a spatial resolution of 2.5\({}^{\prime\prime}\) and a temporal resolution of approximately one minute, in its nominal mode, covering the range from 1.02 solar radii to 2.5 radii. CMAG relies on proven European technologies and can be adapted to enhance any other solar mission, offering potential significant advancements in coronal physics and space weather modeling and monitoring.  
corona; magnetic field; instrumentation; polarimeters; radiative transfer +
Footnote †: journal: Journal of Geophysical Research
## 9 Uncertainty Quantification for the Vibroacoustic Computational Model
### Hyperparameter of the Stochastic Reduced-Order Model (SROM) and Stochastic Solver  
The dispersion parameter \(\delta\)--also called hyperparameter--of each random matrix \([\mathbf{G}]\) allows its level of dispersion (statistical fluctuations) to be controlled. The hyperparameters (dispersion parameters) of random matrices \([\mathbf{G}_{M^{S}}]\), \([\mathbf{G}_{D^{S}}]\), \([\mathbf{G}_{K^{S}(0)}]\), \([\mathbf{G}_{M}]\), \([\mathbf{G}_{K}]\), and \([\mathbf{G}_{\mathbf{C}}]\) are represented by a vector-valued hyperparameter \(\delta\) such that  
\[\delta=(\delta_{M^{S}}\,,\,\delta_{D^{S}}\,,\,\delta_{K^{S}(0)}\,,\,\delta_{M }\,,\,\delta_{K}\,,\,\delta_{C})\,, \tag{98}\]  
which belongs to an admissible set \(\mathcal{C}_{\delta}\) and which allows the level of uncertainties to be controlled for each matrix introduced in the stochastic reduced-order model. Consequently, if no experimental data are available, then \(\delta\) has to be used to analyze the robustness of the solution of the vibroacoustic problem with respect to uncertainties by varying \(\delta\) in \(\mathcal{C}_{\delta}\).  
For a given value of hyperparameter \(\delta\) in \(\mathcal{C}_{\delta}\), there are two major classes of methods for solving the stochastic reduced-order model (SROM) defined by Equations (64)-(69). The first one belongs to the category of the spectral stochastic methods (see [76; 77; 83]). The second one belongs to the class of stochastic sampling techniques for which the Monte Carlo method is the most popular. Such a method is often called non-intrusive because it offers the advantage of only requiring the availability of classical deterministic codes. It should be noted that the Monte Carlo numerical simulation method (e.g., [104; 105]) is a very effective and efficient one because it has the four following advantages:  
* it is a non-intrusive method,
* it is adapted to massively parallel computation without any software developments,
* it is such that its convergence can be controlled during the computation,
* the speed of convergence is independent of the dimension.  
If experimental data are available, there are several possible methodologies (one is the maximum likelihood method [87]) to identify the optimal values of hyperparameter \(\delta\) (these aspects are not given here, and we refer the reader to [75; 84]). Several works have been published concerning experimental validation of the nonparametric probabilistic approach of both the computational model-parameter uncertainties and the model uncertainties induced by modeling errors (e.g., [96; 106; 107; 108; 109; 110; 111]).
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 2 Materials and Methods
### Point Cloud Data  
A point cloud is a vast collection of target surface information points that is typically acquired using multi-view geometry or laser technology. Point cloud data may accurately depict the actual condition of the ground surface, including the reflecting characteristics of ground objects and the ground state. Based on the 3D reconstruction technique of multi-view geometry, we were able to generate corn population point cloud data at the field scale for this study. The point cloud data includes 3D coordinate information (X, Y, Z), color information (RGB), and intensity information (Intensity).
# Enhancing Cache Robustness in Information-Centric Networks: Per-Face Popularity Approaches
## 3 Enhancing Cache Robustness via Per-Face Popularity  
In this section, we discuss three Per-Face Popularity, or PFP, approaches to increasing cache robustness by means of normalizing popularity contributions across all faces. Firstly, we discuss the original Per-Face Popularity algorithm, or simply, PFP. Then, we look at a more efficient and scalable approach that also considers the recency of requests, known as Per-Face Popularity with Dynamic Aging (PFP-DA). Finally, we discuss a parameterized approach in which exponentiation is used on contributions to reduce numeric distance among the overall popularity values of ranked contents, known as Per-Face Popularity with Beta-Parameterization, or PFP-\(\beta\) (also, PFP-Beta).
# Forest Fire Detection and Notification Method Based on AI and IoT Approaches
## 3 Fire Detection Approach  
### Proposed High-Level System Architecture  
An overview of the suggested method of forest fire detection and notification system is given in this subsection. A small error might result in unexpected wildfires, which can quickly lead to a catastrophic situation [31]. The major goal of this study is to develop a novel fire detection method based on IoT and AI that can reduce wildfires and a host of other issues. The system, which is seen in Figure 1, is designed to keep an eye on the risk of wildfires, accurately detect even the tiniest sparks of flame, and alert the fire brigade when necessary.  
It is hard to detect fires in the forest until it is too late. This can lead to the loss of lives that are within the wildfire region. Our goal is to identify the fire and the location of the fire in order to safely evacuate the people within a 1-mile radius by sending a notification alarm through their phone, as we explained in detail in the refs. [32; 33].
# Wintertime Local Wind Dynamics from Scanning Doppler Lidar and Air Quality in the Arve River Valley
## 3 Material: WLS200S Lidar
### Lidar Specifications
#### 3.1.2 Measured Quantities  
The WLS200S provides high resolution range-resolved measurements of:  
* the Line-Of-Sight velocity (\(V_{los}\)) in m\(\cdot\)s\({}^{-1}\). Negative velocities represent a flow toward the lidar while positive velocities indicate a flow away from the lidar. To facilitate the plot interpretation, a convention based on the north-south or west-east direction is applied in this study whenever possible, and is specified in the figure caption.
* the Carrier to Noise ratio (CNR) in dB, corresponding to the ratio of the power of the received heterodyne signal to the noise power. The CNR depends, among others things, on aerosol content and can be expressed by the Equation (1) [41]: \[CNR(R)=10 log_{10}\left(\frac{1}{R^{2}}I(R)\beta(R)T^{2}(R)\right),\] (1) where \(R\) represents the distance from the lidar in the LOS direction, \(\beta(R)\) is the backscatter coefficient, \(T^{2}(R)=exp(-2\alpha R)\) is the atmospheric transmission (with \(\alpha\) being the extinction coefficient) and \(I(R)\) gathers together the geometric dependences on \(R\), including the heterodyne efficiency, which mainly affects the signal in the nearest range gates. The CNR gives an indication of the measurement quality and is used for data quality checking (Appendix A).  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **WLS200S** & **WLS8-5** \\ \hline Wavelength (μm) & 1.54 & 1.54 \\ \hline Accumulation time (sec) & 1 & 3 \\ Nb. Pulses averaged & 10,000 & 30,000 \\ Scan speed (deg\(\cdot\)s\({}^{-1}\)) & 1 & - \\ \hline Range resolution (m) & 100 & 20 \\ Range gates & 59 & 24 \\ Azimuth Range (\({}^{\circ}\)relative to north) & 250 to 60 & - \\ Elevation Range (\({}^{\circ}\)relative to Horiz.) & 0 to 90 & - \\ Scan cone angle (\({}^{\circ}\)) & - & 14.93 \\ \hline Speed accuracy (from manufacturer) (m\(\cdot\)s\({}^{-1}\)) & 0.2 & 0.1 \\ Direction accuracy (from manufacturer) (\({}^{\circ}\)) & - & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of the 3D long range Doppler wind lidar WLS200S and profiler Doppler wind lidar WLS8-5 (both designed by Leosphere) used during the Passy-2015 field experiment.
# Real-Time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation
## 2 Related Works
### Lightweight Architecture  
In recent years, deep neural network model compression and acceleration methods have been rapidly developed. [PERSON] et al. [36]. summarized this work in 2018, including lightweight architecture to achieve model compression and acceleration.  
Following the pioneering work of grouping/deep convolution and separation convolution, lightweight architecture design has achieved rapid development, including Xception [23], MobileNet [22, 24], ShuffleNet [21], etc. The lightweight neural network is a lighter model, which has a performance no worse than that of the heavier model. It aims to further reduce the amount of model parameters and complexity on the basis of maintaining the accuracy of the model and make the operation speed faster, so as to realize a hardware-friendly neural network.  
Commonly used lightweight neural network technologies include distillation, pruning, quantization, weight sharing, low-rank decomposition, lightweight attention module, dynamic network architecture/training mode, lighter network architecture design, NAS (neural architecture search), etc. These methods achieve a valuable compromise between the speed and accuracy of the classification task. We propose a new lightweight operator, called single linear bottleneck, and apply it to our motion detection network. This is the first work to use the lightweight operator to solve the motion detection task.
# Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry
## 6 Conclusions  
In this paper, a novel method has been proposed by simultaneously employing two cross-track antennas to detect the deceptive targets in the interferogram, with the following two major findings:  
1. The interferogram produced by combining two SAR images can identify the position of false targets, and the interferometric phases of the false targets related to the jammer position are close to a constant.
2. Range frequency detection after phase filtering can effectively extract the false phase in the interferogram. With the proposed jamming detection algorithm, the jammed parts in a SAR image can be extracted.  
As presented by simulation results based on the TanDEM-X system, the proposed method can detect deceptive jamming effectively and shows great promise in the field of ECCM. Moreover, the detection results also provide a good starting point for possible deceptive jamming suppression.  
[PERSON] carried out all of the analyses and algorithms under the supervision of [PERSON]. [PERSON] performed the experiments. [PERSON] offered a lot of sound advice on the language. [PERSON], [PERSON], [PERSON] and [PERSON] contributed to writing and improving the paper.  
**Funding:** This research was funded by the National Natural Science Foundation of China (No. 61471020).  
**Acknowledgments:** This work is supported by the National Natural Science Foundation of China (No. 61471020) and the Program for New Century Excellent Talents in University.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Pixel number of SAR image & 1,093,016 \\ Pixel number of original false target & 58,978 \\ Pixel number of detected false target & 61,283 \\ Pixel number of correct detection & 57,116 \\ Pixel number of error detection & 4167 \\ Correct detection rate & 96.84\% \\ False alarm rate & 0.38\% \\ \hline \end{tabular}
\end{table}
Table 2: Quantitative Evaluation of our method.  
Figure 11: The detection error area in SAR image (absolute value of complex image).
# A Systematic Review on Design Thinking Integrated Learning in K-12 Education
## 3 Results
### What Intervention Approaches Were Employed in DTIL?  
#### 3.4.1 Study Design  
According to the existing taxonomy framework [63], we divided the research designs of these empirical studies into three types: experimental (using random assignment), quasi-experimental (no random assignment but with a control group or multiple measures), and non-experimental (no random assignment, no control group or multiple measures). Among the 44 studies, most used a non-experimental design (31 or 70.5%) (see Figure 9). These studies usually presented one or more cases where an intervention was applied and measured student performance through various instruments. Seven (or 15.9%) studies used quasi-experimental designs, and six (or 13.6%) used experimental designs.