esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This paper proposes a systematic modal identification method for ultralow-frequency flexible structures. Through the initial displacement excitation of the ultralow-frequency flexible structure, the DIC method is used to obtain the response signal of the structure, and the ERA is used to identify the ultralow-frequency mode of the structure. Through the study of two examples, the following conclusions are obtained:  
1. For the modal test of the solar wing array structure, the DIC method and the traditional contact measurement method were used to identify the modal parameters. The structure frequency identification error of the two modal identification methods is below \(\pm 3\%\), which preliminarily verifies the feasibility and accuracy of the ultralow-frequency flexible structure modal test method based on the DIC method.
2. Aiming at the ultralow-frequency flexible cantilever beam structure, the modal measurement is carried out by using the DIC method. The experimental test results are in good agreement with the theory results, which proves the accuracy of the modal test method based on the DIC method proposed in this paper.  
Flexible structures have the characteristics of ultra-low frequency and the first mode frequency can be less than 0.5 Hz. The novelty of this paper is that the low frequency of the structure is difficult to be excited by the pulse excitation obtained by using a force hammer. A frequency below 0.5 Hz can be excited by using the Initial Displacement Excitation Method. It is difficult to identify modal frequencies below 0.5 Hz using accelerometer measurements. Besides, the contact sensor will cause additional mass to the structure, which will greatly affect the modal analysis results of flexible structures. The Digital Image Correlation method can be used to measure ultra-low frequency without additional mass. This paper proposes a modal identification method based on Digital Image Correlation method for modal analysis of large ultra-low frequency flexible structures. The proposed method can identify flexible structures with a modal frequency of 0.183 Hz.  
Conceptualization, Y.W.; methodology, Y.W. and H.Q.; software, H.Q.; validation, H.Q. and R.Z.; formal analysis, H.Q.; investigation, H.Q.; resources, D.J.; data curation, Y.W.; writing--original draft preparation, H.Q.; writing--review and editing, H.Q. and Y.W.; visualization, D.Z.; supervision, R.Z.; project administration, D.J.; funding acquisition, D.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number No. 11602112. This research was funded by The Natural Science Foundation of the Jiangsu Higher Education Institutions of China, grant number 20 KJB460003. This research was funded by Qing Lan Project.  
Figure 14: Comparison of the MAC value of the identified mode shape and the theoretical mode shape.
# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 1 Introduction  
### 1.1 Medical Significance of EEG Signal Acquisition  
Electoreencephalography, a non-invasive signal acquisition technique in Neuroscience [1], is employed for recording the brain's electrical activity via electrodes placed on the scalp. These captured signals are distinguished by their unique frequency, amplitude, and source location characteristics, playing a pivotal role in the diagnosis and monitoring of neurological disorders such as epilepsy, sleep disturbances, brain tumors, cerebral injuries, etc. The high temporal resolution of the electroencephalograph (in this study, it is denoted by the abbreviation EEG), in contrast with other neuro-imaging modalities such as fMRI, enables the real-time analysis of brain activity, offering valuable insights into cognitive functions and mental states. As a final point, EEG is instrumental in the advancement of Brain-Computer Interfaces (BCIs) [2], contributing significantly to our comprehension of neural dynamics in both healthy and pathological states by enabling the telemonitoring of patients, as well as enabling biomedical engineers to create telekines applications and neurofeedback training BCI systems [3; 4]. In clinical settings and in research, particularly in the domains of neurology and neurosurgery, the precise signal acquisition needs to be as accurate and noise-free as possible to determine the patterns of brain waves and make diagnostic analyses through EEG [5; 6].  
The technology that entails EEG is an enormously relevant tool in neurological assessments [7], whereby an evaluation and assessment of the electrical activity taking place within the brain is carried out. It helps to identify some kinds of seizures, which helps develop an accurate protocol of treatment. EEG identifies the patterns of brain waves for different stages of sleep [8]; it is very important in the diagnosis of sleep abnormalities like narcolepsy. In patients with head injury who have fallen into a coma, EEG is of paramount importance in the assessment of brain responsiveness and the quantification of damage [9]. It may be used in the determination of early abnormal loci within the brain, loci that may be reflecting the onset of ailments such as epilepsy, dementia, and Alzheimer's [10; 11].
# Edge Computing in Healthcare: Innovations, Opportunities, and Challenges
## 4 Literature Review
### Edge Offloading and Computational Distribution
The Distributed Collaborative Dependent Task Offloading Strategy based on Deep Reinforcement Learning (DCDO-DRL) is specifically designed to address the challenges in radiomics-based medical image diagnosis model (RIDM) tasks in healthcare environments [94]. The authors address the NP-hard nature of task offloading, particularly for sequential and dependent medical subtasks. Modeling the task dependencies as a directed acyclic graph (DAG) and putting the offloading decisions within the DAG managed using a Sequence-to-Sequence (S2S) neural network, the authors' solution predicts the optimal placement of tasks either locally or on nearby edge servers. Comparing the DCDO-DRL approach with traditional heuristic and model-free deep reinforcement learning algorithms on RIDM tasks, a significant improvement is presented and accomplished by efficiently balancing the trade-off between reducing latency and conserving energy during the execution of such tasks. [PERSON] et al. [95] propose an edge computing-based architecture for an IoT-enabled Clinical Decision Support System (CDSS), designed to improve the overall latency and bandwidth in the healthcare sector. The system uses statistical feature extraction and Support Vector Machines (SVMs) for detecting the main cardiac diseases, hypotension and arrhythmia, and a decision tree (DT) algorithm for predicting cardiovascular diseases. Using edge computing at the gateway layer, the system processes data closer to where it is generated, rather than relying on cloud-based processing, resulting in a latency reduction of an average of 87.66 times. The overall network efficiency seems to have a major improvement as well, a 2.59 times reduction, which is mandatory for scaling such healthcare systems, considering the continuously increasing number of IoT devices connected to the internet and the volume of generated data. FedCure [96], a personalized federated learning framework, is tailored to address the heterogeneity challenges in the IoMT. It integrates a variety of personalization methods including Personalized Federated Hypernetworks, Federated Meta-Learning, Per-FedAvg, Knowledge Distillation, and FedGKT that work together to tackle heterogeneity at the data, device, and model level. By deploying customized models for specific application needs, each device operates with models uniquely adapted to its needs, effectively approaching the issues of non-IID data. It incorporates edge computing to handle task offloading optimization by moving complex tasks to edge nodes found in proximity. The results showcase the adaptability and performance of this system while using different healthcare-related use cases, and improved accuracy compared to traditional federated learning methods.
## 2 Results
### Identification of miRNAs in C. sativa  
The small RNA library was constructed using pooled RNA of stems, roots and leaves of _C. sativa_ and sequenced by the HiSeq 2000 platform. After filtering, a total of 22,164,487 clean reads ranging from 18 to 30 nucleotide (nt) in size were obtained. Among them, the clean reads with counts \(\geq\)5 were further analyzed by the psRobot and Mireap software to predict the miRNAs in _C. sativa_. After checking manually, a total of 563 miRNAs were predicted. Among them, 183 miRNAs were identified as conserved miRNAs by comparing with miRbase, which could be divided into 36 families in which miR156 was the largest family, with 16 members. The remaining 380 miRNAs were adjudged to be _C. sativa_-specific miRNAs, which were grouped into 60 families. Among them, CsmiRNA-n4 was the largest family that possessed 34 members (Table S9).
# ISAR Imaging Analysis of Complex Aerial Targets Based on Deep Learning
## 1 Introduction
Compared with the research presented in [14], this study further addresses the practical challenges of super-resolution and noise suppression, designing a CapsNet that incorporates two convolution types--AKConv and GSConv--with the objective of generating and denoising ISAR images. Additionally, this paper investigates the robustness of the proposed algorithm against noise. The remainder of this paper is structured as follows: Section 3 provides a detailed explanation of our proposed CapsNet-assisted ISAR imaging method for maneuvering targets, including an in-depth description of the neural network architecture, the introduction of the two novel convolution kernels, the generation of training data, and the network training process. In Section 4, we conduct numerical experiments to validate the effectiveness of the proposed method, making comparisons with other existing algorithms using simulation and several sets of real radar data. Finally, conclusions are presented in Section 5.
## 4 Self-Healing Approaches
### Network Architecture
#### 4.2.1 Strategy Network  
A network comprising tens of thousands of faults solving strategy libraries was proposed by [1]. They argued that the essence of the strategic network in their study is to proffer a combination of solutions when dealing with risks of power grid failures. The global strategy network in a power grid scenario is not accurate in solving power failure problems; hence, the sub-strategy of each node is combined to solve a global fault based on the general direction of the global strategy. The combined sub-strategy then constitutes a strategy network. The framework for the network consists of several layers and each with its strategy:  
* Perception layer: This layer monitors the network to detect failures and anomalies. Strategies in this layer include using sensors to monitor network traffic, analysing system logs, and applying ML techniques to identify abnormal patterns;
* Analysis layer: This layer is responsible for analysing the data collected by the perception layer to identify the root cause of failures. Strategies in this layer include using ML algorithms to analyse the data and identify patterns that indicate the cause of a failure;
* Planning layer: This layer is responsible for developing a plan to address the identified failures. Strategies in this layer include using ML algorithms to determine the optimal recovery strategy and selecting the appropriate recovery mechanism;
* Execution layer: This layer is responsible for executing the recovery plan. Strategies in this layer include using automation to execute the recovery plan and providing feedback to the other layers to optimise the recovery process;
* Knowledge layer: This layer stores and manages knowledge about the network and the recovery process. Strategies in this layer include using databases to store information about the network topology and previous failures and using algorithms to learn from previous failures and improve the self-healing process.
## 2 Geologic Background and Methods
### Geometric Modeling and Meshing  
The study area exhibits an undulating surface with a maximum elevation difference of approximately 1.4 km. The length of the tunnel in the mathematical model established according to the geological conditions of the study area is 12,547 m, and the depth of the tunnel is 106-936 m. Preliminary investigations classify the surrounding rocks along the tunnel as conglomerate, siltstone, granodiorite, diorite, slate, tuff, and other lithologies. The internal fractures in the model are the Qinghai Nanshan North Fracture, the Daotang River Fracture, the Hacheng Mountain Fracture, the South Edge of Laji Mountain Fracture, the North Edge of Laji Mountain Fracture and other deep and large fractures. To ensure calculation accuracy, a free triangle mesh was adopted for discretizing the model, with mesh refinement applied to the tunnel surface and its adjacent areas. The final model consists of 1,364,978 domain cells and 33,552 boundary cells (Figure 5). Using Formula (1), a finer grid division with a local grid size of 0.5 m was applied to the high-thermal-conductivity zones of the granite, while a grid size of 2 m was chosen for areas with lower thermal conductivity, such as gravel-rich rocks.  
\[\mathrm{h}=\frac{\mathrm{k}\cdot\Delta\mathrm{T}}{\mathrm{q}} \tag{1}\]  
Here, \(\mathrm{h}\) is the local grid size, \(\Delta\mathrm{T}\) is the critical temperature gradient, and \(\mathrm{q}\) is the heat flux density threshold (taken as the average geothermal heat flow value of 80.4 mW/m\({}^{2}\) for the study area).
# A Deep Learning Approach for Maximum Activity Links in D2D Communications
## 5 Deep Learning-Based Link Scheduling
### General Deep Learning  
Figure 2 is the general architecture of the deep learning-based solution, in which the input layer represents the features of the D2D pairs, and the output layer reports the D2D link activation result. In the most general case, the parameters of hidden layers which consist of weights and the values of neurons are used to connect the input layer and the output layer. The parameters of each hidden layer are determined by the previous layer. In the process of training a typical learning model, we first collected labeled input data to be used as a training set. Then the training set was fed to input layers during the training phase by acquiring the results from the output layer, which may differ from the expected values. The difference between the output value and the expected value can be calculated by a loss function. Weights can be modified by a backpropagation (BP) method in order to minimize the above difference or loss function. The training process of the learning network is meant to adjust the weights through the training samples. When entering a test data set, the learning framework  
will generate a vector, which is an estimate of labeled value. The performance of a trained learning model depends on the differences between the output vector and real value. Due to the fact that the performance of a deep learning model is greatly influenced by the model parameters, it is difficult to train a deep learning network [41]. For instance, in presence of a limited data set, if the training model is designed in a way that is too complicated and tries to approximate a complex data relationship by using a noise sample, it results into overfitting [42]. Conversely, if the design of the model is too simple to fully simulate data correlation, the model can lead to underfitting. Both of these issues affect the generalization ability of the learning model. When the deep learning model faces large-scale data, both a high computational complexity and a long time-spending are introduced by the sum of the loss functions using all the data as the training objects, so it is important to select an appropriate batch size for training. In this paper, a series of parameters were compared through experiments to optimize the performance of the learning model.
## 2 Materials and Methods
### UHPLC-MS Analysis of the Anthocyanic Phenolic Profiles in Pinot Noir Wines  
LC-MS parameters for the analysis of anthocyanins of Pinot Noir were as follows: injection volume = 5 \(\upmu\)L; mobile phase composition, phase A = 4.5% formic acid degassed in ultrapure water; phase B = 4.5% formic acid in MS-grade acetonitrile; the gradient program was as follows: 5% B from 0 to 1 min, 5-15% B from 1 to 10 min, 15 to 25%B from 10 to 15 min, 25 to 40%B from 15 to 18 min, 40 to 95% from 18 to 21 min, 95% from 21 to 24 min, 95 to 5% from 24 to 25 min, and 5% from 25 to 28 min. The ESI source was run in positive mode with the following parameters: gas temperature 340 \({}^{\circ}\)C, nitrogen gas flow = 13 L min\({}^{-1}\), nebulizer pressure 50 psi, nitrogen sheath gas heater = 350 \({}^{\circ}\)C, sheath gas flow = 12 L min\({}^{-1}\), capillary voltage +3500 V, and nozzle voltage = + 1000 V. ESI-MS parameters for anthocyanins were as follows: mass range = _m_/_z_ 250-700, scan time = 500, size fragmentor potential = 135 V, and cell acceleration voltage = 5 V. DAD detector parameters were as follows: wavelength 1 range = 700-210 nm, response time = 4 s, acq. frequency = 1.25 Hz and slit width = 4 nm (with 1 nm spectrum steps). For anthocyanins in Pinot Noir, at first a targeted selection was run, selecting only those features with an absorbance band between 450 and 550 nm (Supplementary File S1--Table S17). Then, the corresponding MS traces were identified, looking for the precursor ions and for specific in-source fragments, e.g., _m_/_z_ 493 and _m_/_z_ 331 for malvidin-3-O-glucose and its aglycone, respectively. An example for the targeted analysis of ponidin-3-O-glucose (_m_/_z_ 463 and _m_/_z_ 301) is represented in the Supplementary File S1--Figure S1.  
The relative concentrations (mg.L \({}^{-1}\)) of anthocyanins in the Pinot Noir samples were calculated against a calibration curve of the reference standard of malvidin chloride (MvCl) prepared at different concentrations (0.0, 0.05, 0.2, 0.5, and 1.0 gL\({}^{-1}\)) in duplicates.
# An Efficient Internet-Wide Scan Approach Based on Location Awareness
## 7 Limitations and Future Work  
Although the location-aware scanning-based approach proposed in this study has achieved good results in improving scanning efficiency, there are still some limitations. First, the geographic location of IP addresses may be inaccurate or not updated in time, which affects the effectiveness of task assignment. Second, the metrics constructed based on average scanning time cannot fully reflect the dynamic changes of the network. Further validation of its generality under a wider range of network conditions is still needed in the future.  
Based on our results, there are several future research directions worth exploring. First, although our current approach uses average scan time as a location-based clustering metric, the accuracy of task matching can be further improved by incorporating more granular metrics such as packet loss, jitter, or hop count. Second, we could extend the geo-aware framework to support adaptive scanning strategies in dynamic network environments. Thirdly, integrating deep learning models to predict scanning results or optimise cluster configurations in real time is a promising direction. Finally, conducting further experiments on more diverse network infrastructures will help to generalise our approach.  
**Author Contributions:** Conceptualization, W.S. and H.H.; methodology, H.H.; software, H.S.; validation, W.S. and Q.G.; formal analysis, W.S. and H.S.; investigation, H.S.; resources, H.H.; data curation, Q.G.; writing--original draft preparation, W.S.; writing--review and editing, H.H.; visualization, W.S.; supervision, H.S.; project administration, H.S.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Shandong Provincial Natural Science Foundation under Grant No_ZR20231_ZH011,No_ZR2022 LZH015; the National Natural Science Foundation of China (NSFC) via grant 62401304; project ZR2022 QF040 supported by Shandong Provincial Natural Science Foundation; the Young Talent of Lifting Engineering for Science and Technology in Shandong, China via SDAST2025 QTA077; and the QLU Talent Research Project under grant 2023 RCKY138.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** During the preparation of this study, the author used Nmap 7.95 for the purposes of provision of scanning services. The authors have reviewed and edited the output and take full responsibility for the content of this publication.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 6 Conclusions  
In this study, the influence of SDIEC on the optimization results of mixed flow pump impeller was quantitatively studied by using the combined optimization system. During the optimization process, the impeller was parameterized by the inverse design method, the optimization objectives were the pump efficiencies at 1.2Q\({}_{\text{des}}\) and 0.8Q\({}_{\text{des}}\), and the constraints were the pump head and efficiency at 1.0Q\({}_{\text{des}}\). The conclusions of this research are as follows:  
1. In the first case, the influence of SDIEC was ignored in the optimization process and only the stacking condition and blade loading were used as design variables, but satisfactory results were still obtained. Taking optimized impeller F1 as an example, the pump efficiencies at 1.2Q\({}_{\text{des}}\), 1.0Q\({}_{\text{des}}\), and 0.8Q\({}_{\text{des}}\) are 80.32%, 87.62%, and 80.54%,  
Figure 18: Pressure distribution at different spans: (**a**) 1.2Q\({}_{\text{des}}\); (**b**) 0.8Q\({}_{\text{des}}\).  
respectively. These values correspond to 6.48%, 2.41%, and 0.06% improvement over the baseline impeller.
2. In the second case, the influence of SDIEC was considered in the optimization process, and the stacking, blade loading, and circulation were used as the design variables and the upper limit of optimization was further improved. Taking optimized impeller S2 as an example, the pump efficiencies at 1.2\(Q_{\text{des}}\), 1.0\(Q_{\text{des}}\), and 0.8\(Q_{\text{des}}\) are 81.08%, 88.87%, and 81.75%, respectively. These values correspond to 0.76%, 1.24%, and 1.21% improvement over the impeller F1.
3. SDIEC also has a significant influence on the blade loading distribution of the optimized impeller. In impeller F1, the blade loading at the shroud and hub are aft-loaded and fore-loaded, respectively. While in impeller S2, the blade loading at the shroud and hub are both fore-loaded.  
Based on the above findings, to further improve the optimization upper limit of the mixed flow pump, it is necessary to consider the influence of SDIEC in the optimization process. Additionally, the research results can provide guidance for the optimization of other rotating machinery.  
Conceptualization, M.W.; funding acquisition, Y.L., J.Y., and F.K.O.; methodology, M.W.; project administration, Y.L., J.Y., and F.K.O.; validation, M.W.; writing--original draft, M.W.; writing--review and editing, M.W. All authors have read and agreed to the published version of the manuscript.  
This work supported by the Science and Technology Plan of Wuhan (grant No. 2018060403011350) and the National Key Research and Development Plan (grant No. 2018 YFB0606103).  
Data Availability Statement: Data is contained within the article.  
The authors thank [PERSON] for English revision of this paper, the reviewers for their constructive comments, and the editors for their enthusiastic work.  
The authors declare no conflict of interest.