esa-sceva/satcom-chunk-collection huggingface dataset:
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Minimizing Transmission Energy Costs
#### 5.2.3 Compressed Learning  
In contrast to the CS approach, which has reconstruction overhead at the receiving end, compressed learning (CL) focuses on directly inferring information from compressed measurements using machine learning tools [110]. This approach mitigates the reconstruction overheads and associated power consumption. It combines signal processing and machine learning to infer information from a signal using a small number of measurements. It leverages deep learning techniques to optimize both the sensing and inference processes during the training phase. Moreover, it also eliminates the need for additional computations, resources, time, and power required for the reconstruction process. The authors of [111] proposed a compressed learning approach in their abnormal ECG detection system. In the CL approach, the features are directly extracted from compressed data while ensuring acceptable levels of accuracy. CL reduces the amount of data required to be stored and processed, thereby also reducing storage, memory accesses, and computations, which is highly important in WMDs.  
Similarly, the authors of [112] proposed a CL method for on-device ECG signal classification, which operates directly within the compression domain, eliminating the need for reconstruction. This approach achieved a 2.6\(\times\) reduction in model size at a compression ratio (CR) of 0.2, with only a loss of 0.028 in the macro F1 score compared with the uncompressed scenario. It significantly reduced the dimensions of raw data, thereby decreasing the required processing power. In another instance [113], the authors also performed feature extraction and classification directly on the compressed data in their low-power seizure detection processor. All of the computations are performed on the sensor node, and only the 1 bit result is transmitted. This significantly reduces the transmission load and power consumption. Likewise, the authors of [20] employed a similar approach to analyze compressed data received from the sensor node without reconstruction to detect anomalies. They were able to achieve acceptable accuracy, with a compression ratio of 16:1 and a significant reduction in transmission power.  
CL is a relatively new approach which can significantly reduce the transmission power at the sensor node and computation power at the receiving node. However, the accuracy of the results can be affected by the quality of the compressed measurements.
# Flexible-Link Multibody System Eigenvalue Analysis Parameterized with Respect to Rigid-Body Motion
## 4 Results
The approximated eigenpairs have been compared with the exact ones, which are computed linearizing the model at each sample time (\(\Delta t=0.01\) s) and solving the corresponding eigenvalue problem. In particular, for comparison, the following indices have been employed,  
* relative percentage error on the undamped natural frequency, \(\epsilon_{f}\): \[\epsilon_{f}=\frac{|f-\vec{f}|}{f}\cdot 100\] (30) Let \(\lambda=\lambda_{r}+i\lambda_{i}\) be a system eigenvalue, it holds: \[\omega=\sqrt{\lambda_{r}^{2}+\lambda_{i}^{2}};\qquad f=\frac{\omega}{2\pi}\] (31)
* relative percentage error on the damping factor, \(\epsilon_{\xi}\): \[\epsilon_{\xi}=\frac{|\xi-\vec{\xi}|}{\xi}\cdot 100;\quad\xi=-\frac{\lambda_{r} }{\omega}\] (32)
* modal assurance criterion (MAC): \[MAC=\frac{|\phi^{*}\vec{\phi}|^{2}}{(\vec{\phi}^{*}\vec{\phi})\;(\phi^{*}\vec {\phi})}\] (33)  
In Equations (30), (32) and (33), the over-set tilde denotes the approximated quantities. The target value for the comparison of the adopted indices is 1 for the \(MAC\) and 0 for \(\epsilon_{f}\) and \(\epsilon_{\xi}\). The comparison indices for the first three elastic pairs of complex conjugate eigenvalues and eigenvectors are shown in Figures 3-5. The same figures also show the trend of the undamped eigenfrequencies and of the damping ratio along the trajectory, to provide an evidence of the variability of the modal parameters due to system configuration change. All the three figures show a good agreement between the exact and the approximated modal parameters along almost the entire trajectory. The biggest discrepancies (but still bounded) occur on the tails of the trajectory (at the beginning and the end), where the expansion parameters differ significantly from the values they assumed at the expansion point. Overall, the results appear very satisfying for all the three comparison metrics adopted. Finally, note that, although the system has been parameterized around a dynamic equilibrium configuration, the computational time has been drastically reduced. Indeed, the solution of the eigenvalue problem in the 51 operating points in which the trajectory has been discretized requires approximately 0.135 s with a Matlab implementation on a machine with one processor of the type Intel Core i5-6200U CPU at 2.4 GHz with 8 gigabyte of RAM. Conversely, the computation of the approximations of the analyzed eigenpairs in the same points takes on average 0.008 s, leading to a reduction of the computation time of 94%. Figure 4: _Cont_. Figure 3: Second vibration mode along the planned trajectory. Figure 4: Third vibration mode along the planned trajectory. Figure 5: Cont.
# [PERSON]: An HPC Job Scheduler Using Deep Reinforcement Learning and Remaining Time Prediction
## 6 Conclusions and Future Work  
This study presents RLSchert, an HPC job scheduler based on deep reinforcement learning and remaining time prediction. RLSchert is a scheduler that integrates runtime prediction and job scheduling. Accurate prediction of the remaining runtime of the job provides a guarantee for grasping the status of the cluster, and the scheduling strategy based on reinforcement learning can make correct decisions based on the status of the cluster. The kill policy based on imitation learning further guarantees the efficiency of the scheduler, and when the optimization goal changes, the scheduler can still learn the best policy. In the selection of the dataset, we chose the most common and most complex HPC job: VASP. We conducted extensive evaluations and confirmed that RLSchert performs well  
Figure 13: Visualization of tasks killed by RLSchert.  
on real-world workloads and different optimization goals. The lowest average slowdown demonstrated high stability, and the shortest job completion time illustrated a reasonably low computational cost.  
There are two derived interesting questions that could be studied in the future: the first is extending this study to other types of iterative computing jobs in real HPC platforms to prove the universality of our model; secondly, we would like to study multi-index optimization more and integrate RLSchert into a real HPC management system.  
Conceptualization, Q.W.; methodology, Q.W.; software, Q.W., H.Z. and C.Q.; data curation, Y.S. and X.L.; writing, Q.W. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences, Grant No. XDA19020102.  
Not applicable.  
Data Availability StatementData produced by the experiments conducted in this paper can be found in [[https://www.dropbox.com/sh/s08](https://www.dropbox.com/sh/s08) pqhxa6 psyl33/AACFzEeQ0 xcG5 wsj534 GIVNYa?dl=0]([https://www.dropbox.com/sh/s08](https://www.dropbox.com/sh/s08) pqhxa6 psyl33/AACFzEeQ0 xcG5 wsj534 GIVNYa?dl=0) accessed on 10 October 2021.  
We are thankful for the support from the Supercomputing Center (SCC) of USTC. We are thankful to the administrators of the SCC for the data and professional knowledge support. We gratefully acknowledge the computing resources provided by the Network and Information Center to run our experiments.  
The authors declare no conflict of interest.
## 5 Implementation and User Scenario
### Technical Validation and Experimental Results
#### 5.4.5 Results of UI/UX Performance
In finance, investors had a higher score (9.4) than casual users (8.2), suggesting the system is more beneficial for those with deeper engagement. For retail stores, repeat customers scored higher (9.0) than first-time users (8.3), indicating that familiarity with the system leads to better experiences. As illustrated in Figure 14, user satisfaction mirrored \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Platform Name** & **CTR (\%)** & \begin{tabular}{c} **Conversation** \\ **Rate (\%)** \\ \end{tabular} & \begin{tabular}{c} **Session** \\ **Duration(m)** \\ \end{tabular} & \
\begin{tabular}{c} **User** \\ **Experience** \\ \end{tabular} &
\begin{tabular}{c} **User** \\ **Satisfaction** \\ \end{tabular} \\ \hline E-commerce Web & 12.5 & 42.3 & 5.8 & 9.0 & 91.2\% \\ Mobile App & 10.3 & 39.2 & 4.9 & 8.5 & 88.4\% \\ VR Store & 14.7 & 48.6 & 7.4 & 9.4 & 94.7\% \\ Smartwatch UI & 8.9 & 32.1 & 2.7 & 8.1 & 85.5\% \\ Automotive UI & 11.1 & 37.8 & 6.1 & 8.8 & 90.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance evaluation of AI-MDD-UX for different UI instances. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Platform Name** & **CTR (\%)** & \begin{tabular}{c} **Conversation** \\ **Rate (\%)** \\ \end{tabular} & \begin{tabular}{c} **Session** \\ **Duration(m)** \\ \end{tabular} & \
\begin{tabular}{c} **User** \\ **Experience** \\ \end{tabular} &
\begin{tabular}{c} **User** \\ **Satisfaction** \\ \end{tabular} \\ \hline Power User & 15.2 & 50.1 & 3.5 & 9.6 & 93.8\% \\ Cautious Browser & 10.4 & 41.5 & 6.8 & 9.1 & 90.1\% \\ First-Time User & 9.6 & 34.2 & 7.2 & 8.6 & 86.7\% \\ Accessibility User & 8.8 & 30.5 & 8.3 & 8.9 & 88.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance evaluation of AI-MDD-UX for different user personas.
# Laser-Assisted Etching of EagleXG Glass by Irradiation at Low Pulse-Repetition Rate
## 2 Materials and Methods  
The adopted source is an Ytterbium laser (Pharos, Light Conversion, Vilnius, Lithuania), consisting in a mode-locked oscillator followed by an amplifier. This system delivers 190-fs duration pulses at 1030-nm wavelength, with a maximum output power of 10 W and a tunable repetition rate up to 1 MHz. In the experiments discussed in this work, we fix the repetition rate of the amplifier section to 1 MHz and, unless specified differently, we set the internal pulse-picker to produce an output pulse-train at 25 kHz. In our microfabrication apparatus, the pulse energy \(E_{p}\) used for irradiation is finely tuned by an attenuation stage, consisting of a half-waveplate followed by a linear polariser. An additional half-waveplate is used to control the impinging laser polarisation, keeping it parallel or orthogonal to the writing direction. The laser beam is finally focused in the EagleXG glass samples by a 40\(\times\) (NA = 0.75) water-immersion objective, at 500 \(\upmu\)m depth from the surface. The maximum available pulse energy, measured before the focusing objective, is 8.6 \(\upmu\)J. The glass sample is fixed onto a three-axis air-bearing translation stage (ABL1500, Aerotech Inc, Pittsburgh, PA, USA), allowing smooth motion and translation speeds \(v_{t}\) up to 300 mm/s, while maintaining precision in positioning better than 10 nm.  
Wet chemical etching of the irradiated samples is performed with two different etching agents: HF or KOH. In the first case, the glass sample is immersed in a beaker containing an aqueous solution of HF at 10% concentration; the beaker is then placed in an ultrasonic bath, which also keeps the temperature constant at 35 \({}^{\circ}\)C. The high etching rates achievable with HF make this chemical agent particularly convenient for a fast and efficient investigation of the etching process. In the second case, we use an aqueous solution of KOH at 10 M; during the etching process, the beaker is kept on a hotplate set at 90 \({}^{\circ}\)C, and a magnetic stirrer is used to continuously mix the solution. In this work, we do not investigate the dependence of the etching selectivity on the etchant concentration; the chosen concentrations of HF and KOH (reported here above) are comparable to those typically used in the literature to study laser-assisted etching of glass materials (see e.g., Refs. [3; 5; 7; 23; 25]).  
For the characterization of the etched structures, we use an optical microscope (DM2700M, Leica, Wetzlar, Germany), equipped with a computer-connected CCD camera (PL B623 CU, Pixelink, Ottawa, Canada). With the aid of a properly calibrated image-acquisition software, it is possible to take accurate dimensional measurements of the fabricated structures.
###### Abstract  
Our work discusses the investigation of 75 peptide-based drugs with the potential ability to break the \(\beta\)-sheet structures of amyloid-beta peptides from senile plaques. Hence, this study offers a unique insight into the design of neuropeptide-based drugs with \(\beta\)-sheet breaker potential in the amyloid-beta cascade for Alzheimer's disease (AD). We started with five peptides (\({}^{15}\)QKLVFF\({}^{20}\), \({}^{16}\)KLVFF\({}^{20}\), \({}^{17}\)LVFF\({}^{20}\), \({}^{16}\)KLVFF\({}^{19}\) and \({}^{15}\)QKLV\({}^{18}\)), to which 14 different organic acids were attached at the N-terminal. It was necessary to evaluate the physiochemical features of these sequences due to the biological correlation with our proposal. Hence, the preliminary analysis of different pharmacological features provided the necessary data to select the peptides with the best biocompatibility for administration purposes. Our approaches demonstrated that the peptides \({}^{17}\)LVFF\({}^{20}\), NA-\({}^{17}\)LVFF\({}^{20}\), \({}^{16}\)KLVIF\({}^{19}\) and NA-\({}^{16}\)KLVIF\({}^{19}\) (NA-nicotinic acid) have the ability to interfere with fibril formation and hence improve the neuro and cognitive functions. Moreover, the peptide conjugate NA-\({}^{16}\)KLVIF\({}^{19}\) possesses attractive pharmacological properties, demonstrated by in silico and in vitro studies. Tandem mass spectrometry showed no fragmentation for the spectra of \({}^{16}\)KLVIF\({}^{19}\). Such important results suggest that under the action of protease, the peptide cleavage does not occur at all. Additionally, circular dichroism confirmed docking simulations and showed that NA-\({}^{16}\)KLVIF\({}^{19}\) may improve the \(\beta\)-sheet breaker mechanism, and thus the entanglement process of amyloid-beta peptides can be more effective.  
Alzheimer's disease; amyloid-beta peptides; \(\beta\)-sheet breaker potential; neuropeptide-based drugs; pharmacological activity; solid-phase peptide synthesis; matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy; circular dichroism +
Footnote †: journal: Molecular Sciences
# Spatiotemporal Variations in Vegetation Canopy Interception in China Based on a Revised Gash Model
## 3 Results
### Environmental Factors Affecting Canopy Interception  
The results of stepwise multiple regression analysis exhibited precipitation, NDVI, temperature, relative humidity, and solar radiation explained 82% of the variance in canopy interception in China over the past 20 years (Table 1). Among these factors, precipitation, NDVI, and temperature were the main contributors to the variations, and they were also significantly correlated with interception based on the partial correlation analysis (Table S1).  
Specifically, the spatial distributions of the relative contribution of environmental factors have been explicitly visualized to show the main factors influencing canopy interception across the entire country (Figure 8). Precipitation was the dominant variable in Northwestern China, with a relative contribution of over 40% in most of the areas (Figure 8a). In some regions of Xinjiang Province, the relative contribution of precipitation reached even 80-100%. The NDVI was the main environmental factor in the Loess Plateau and some regions of the Tibet Plateau, with relative contribution values approaching 40-60% (Figure 8b). Temperature contributed more than 40% in some Southern regions of China (Figure 8c). Other factors, including wind speed, relative humidity, and solar radiation, contributed less than 20% to the variation of interception in most parts of China (Figure 8d-f).  
Overall, the spatial patterns of the dominant environmental factors were obtained to display which ones affected canopy interception the most in China (Figure 9). Based on the results, precipitation, temperature, and NDVI were the predominant variables affecting 47.09%, 25.12%, and 16.29% of China, respectively.  
Figure 8: Spatial pattern of the relative contributions of (**a**) NDVI, (**b**) P, (**c**) RH, (**d**) SR, (**e**) T, and (**f**) WS to canopy interception in China. P: precipitation; NDVI: normal difference vegetation index; T: temperature; WS: wind speed; RH: relative humidity; SR: solar radiation. I: cold temperate coniferous forest; II: temperate mixed forest; III: deciduous broad-leaved forest; IV: subtropical evergreen broad-leaved forest; V: tropical rain forest; VI: temperate steppe; VII: temperate desert; VIII: alpine vegetation of Qinghai-Tibet Plateau.
# T-Move: A Light-Weight Protocol for Improved QoS in Content-Centric Networks with Producer Mobility
## 1 Introduction
Besides these, proactive content-pushing [9; 10], where the producer proactively pushes data to the content router even if no interest is received, is a popular solution to send a continuous stream of data even if a producer is moving. Again, pushing all content to all neighboring routers proactively may not be possible in networks with large amounts of data (for example, audio and video streaming in V2X networks). In this paper, we propose T-Move, which is a producer mobility solution for content-centric applications with edge caching. T-Move uses a novel metric called the _trendiness_ of a data object, which is indicative of the popularity of similar content. T-Move contains two separate strategies--one for cache replacement and another for (producer) mobility management. Both these strategies are simple and especially suitable for applications that require low response times. In T-Move, when a producer moves, a copy of its content is maintained at an edge router that is most likely to receive an interest for it and Forwarding Information Base (FIB) entries of routers are updated with minimum update messages to point to the new location of the producer or the cached content. Simulation results show that T-Move results in reduced traffic and increased cache hit ratio, even when the content producers are mobile, leading to better QoS than MAP-Me, an anchorless producer mobility solution for content-centric networks [11]. The rest of the paper is organized as follows: Section 2 explains the caching and mobility management policies in CCN, which is a popular ICN architecture. Section 3 deals with work related to cache replacement policies and mobility management in the web and ICN. Section 4 discusses the T-Move policy proposed by us. Simulation results using ndnSIM are presented in Section 5 and we conclude the paper in Section 6.
# Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms
## 5 Related Works  
Image copy detection (ICD) is a well-known research area focused on detecting transformed versions of images. One of the earliest approaches to image copy detection involves perceptual hashing. Techniques such as Average Hashing (aHash) [33], Difference Hashing (dHash) [34], and the more robust Perceptual Hashing (pHash) [35] generate compact representations of images, enabling efficient comparison and duplicate detection. Microsoft PhotoDNA [36] is another significant development in this field, designed to detect and identify altered images through robust hashing techniques. These methods are computationally efficient but may struggle with highly transformed copies, such as those subjected to geometric distortions or heavy compression.  
Figure 12: Minimum false detection rates for COCO 161,062 images.  
Figure 13: False detection rates in cases satisfying 80% True Detection Ratio.  
More recent research has leveraged deep learning to improve robustness against complex transformations. Convolutional Neural Networks (CNNs) [18] have been widely used to learn high-level feature representations that remain invariant to various image manipulations. Models such as ResNet [21] and VGG [19] have been fine-tuned on large-scale datasets to enhance their discriminative power in detecting copied images. Siamese and triplet networks have also been employed to learn similarity metrics for matching transformed versions of the same image.  
Hybrid approaches combining traditional and deep learning methods have also been explored to achieve better accuracy and robustness. Some studies [37; 38] integrate hand-crafted features with CNN-extracted embeddings, leveraging both low-level texture patterns and deep feature representations to enhance detection performance. Others [39; 40] utilize transformer-based architectures, capturing spatial and geometric relationships between image regions through self-attention mechanisms, enabling more effective copy detection.  
While deep learning-based ICD methods perform well in controlled laboratory environments, they often struggle in large-scale production settings. DISC2021 [41] introduced a large-scale dataset comprising one million images to better reflect real-world workloads. This dataset simulates query conditions by including numerous distractor images that are not equivalent to the target images. However, it does not sufficiently address hard negative cases, which are crucial for distinguishing near-duplicates from true copies. To overcome this limitation, NDEC [42] has focused on developing benchmark datasets that explicitly incorporate hard negative cases, enabling more robust evaluation and adaptation to real-world scenarios.
# Study on the Anti-Interference Performance of Substrate-Free PEDOT:PSS ECG Electrodes
## 2 Materials and Methods  
### Experimental Electrodes  
The experimental electrodes used in this paper are the substrate-free PEDOT:PSS electrode and commercial Ag/AgCl wet electrodes as reference, as shown in Figure 1. The method of connecting the substrate-free dry electrodes is as follows: Using the preparation method in the previous study to prepare a sheet of PEDOT:PSS film [7], the film was cut into electrodes of the appropriate size and shape, as well as an auxiliary connecting film, using a programmable cutter (SILH-Cameo4 Silhouette). The PEDOT:PSS film electrodes were transferred to the skin with the help of DI water, during which process the electrode would turn into a gel and then dry on the skin again [24]; the electrodes were connected to the wires with the help of connecting film. To prevent the connecting film from contacting the skin, non-conductive medical tape was used for isolation.