esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
First, we examined whether the proposed model learned the SO timing determined by the VD method. The mean difference was 5.07 ms (SD = 24.0). In 98.6% of the trials (53,432 trials; all trials used for the analysis = 54,177), the difference in the SO timing between the proposed and VD methods was within \(\pm\)50 ms. Figure 2 displays a histogram of the difference for all trials. The Kolmogorov-Smirnov test demonstrated that the distribution of the difference deviated significantly from the uniform distribution (D = 0.543, \(p\) < 0.001). This result indicates that the proposed model successfully learned the SO timing of the VD method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **MinPeakWidth** & **MaxPeakWidth** & **MinPeakDistance** & **MinPeakHeight** & **MinPeakProminence** \\ \hline Blink time & 20 ms & 320 ms & 100 ms & 90-percentile of & 85-percentile of \\  & 20 ms & 320 ms & 100 ms & \(\underline{dEOG}\) & \(\underline{dEOG}\) \\ Saccade time & 4 ms & 40 ms & 100 ms & 90-percentile of & 90-percentile of \\  & 4 ms & 40 ms & 100 ms & \(\underline{dEOG}\) & \(\underline{dEOG}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of parameters of _findpeaks_() functions used in the PV method.  
Figure 3 shows the grand-average EFRP waveforms based on the three methods. Note that each waveform was shifted to align the peaks of the lambda response, as the PV method detects the saccade time rather than the SO timing. The lambda response was identified at approximately 80 ms in all methods. Figure 4 shows the mean amplitude of the lambda response based on each method. The mean amplitude was 0.050 \(\upmu\)V (SD = 1.10) for the PV method, 0.273 \(\upmu\)V (SD = 1.30) for the VD method, and 0.545 \(\upmu\)V (SD = 1.26) for the proposed method. The mean amplitude of the lambda response based on the proposed method was significantly larger than that based on the VD method (t(49) = 2.88, \(p\) = 0.018 (Bonferroni-corrected)) and that based on the PV method (t(49) = 5.03, \(p\) = 2.12 \(\times\) 10\({}^{-5}\) (Bonferroni-corrected)). The larger mean amplitude of the lambda responses in the proposed method suggests that the SO timing detected by the method was more consistent across trials compared to those detected by the VD and PV methods. There was no significant difference between the VD and PV methods (t(49) = 1.77, \(p\) = 0.250 (Bonferroni-corrected)).  
Figure 3: Grand-average waveforms based on the three methods. Each waveform was shifted to align the peaks of the lambda response.  
Figure 2: Histogram of the difference between SO timing detected by the proposed and VD methods.
## 3 Simulated and Measured Results
Several RF-DC rectifiers have been studied in the past few years; however, studies on system integration, such as RF-energy-harvesting ICs, have been rare. Furthermore, the IC proposed in this study exhibited low power consumption and a small chip area. It is possible to decrease the labor cost significantly by eliminating the future maintenance efforts to replace batteries. At close range, this proposed IC can be used to trickle charge for low power devices including GPS, tracking tags, wearable sensors, and consumer electronics. At long range, this transmitted power can be used for battery-based or battery-free remote sensors for factory automation, structural health monitoring, and industrial control. In future work, the power conversion efficiencies (_PCEs_) of the rectifier and RF-energy harvesting chip can be improved by reducing the energy consumption of each proposed circuit. Figure 22: Measured _PCE_ of the proposed RF-energy-harvesting IC (system _PCE_) with respect to the input power (dBm) and load resistor (\(\Omega\)).
# Recent Developments in Modulation Spectroscopy for Methane Detection Based on Tunable Diode Laser
## 3 Tunable Diode Laser
A wide tuning range between 7960 and 8840 nm can be performed by continuous-wave operation of EC QCL [67].
## 3 Experiments and Results  
Firstly, the collected experimental data are sampled and sorted out, and three types of fault signal are selected for time-frequency analysis. Time-frequency images are input into the proposed neural network, and then ablation experiments and comparative analysis are carried out. Finally, to verify the robustness of the proposed algorithm, tests are carried out under different sample sizes, sampling frequencies and sampling time.
# Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
## 3 Evolving Computing Paradigms and Related Concepts
### Fog Computing  
The idea of processing at the edge has been around since the 2000s [99; 100]. A related concept of cloudlets has been presented in 2009 [101]. The phrase 'Fog Computing' has been propounded by Cisco researchers in 2012 [42; 102]. Fog computing and cloudlets are related concepts operating at the edge level, with cloudlets deployed at mobile networks and fog computing dealing with connected things [103].  
Fog computing is frequently deliberated as a form of edge computing [7; 8; 104]. Fog computing literally delivers distributed processing, networking, and storage potential nearer to the user [105]. Fog computing is more than a mere deployment of the edge computing concept; it is the pinnacle of reinforcing edge computing concepts [9]. It is not an extension or replacement of the Cloud; instead, it is a new paradigm operating in between IoT and the cloud, with the intent of supporting and enhancing Interaction and integration of the cloud, edge, and IoT.  
Cisco describes fog computing [44] as a highly virtualized setup that caters to services of computing, storage, and networking between conventional cloud data centers and end devices, generally but not solely deployed at the network's edge. According to the OpenFog Consortium [10], fog computing is delineated as \"a system-level horizontal architecture that distributes resources and services of computing, storage, control and networking anywhere along the continuum from Cloud to Things, thereby accelerating the velocity of decision-making\". Fog computing adopts a distributed tactic originating from the edge computing model to outdo the limitations of the centralized cloud computing approach [25], with fog nodes positioned anyplace between cloud and end devices. The computing paradigms associated with fog computing are depicted in Figure 7.
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 4 Experiments
### State-of-the-Art Comparison
#### 4.2.1 Performance on the Meningioma Dataset  
This section compares ECA with SOTA methods on the meningioma test set and visualizes the segmentation performance of ECA against other attention mechanisms. Table 2 shows a performance comparison of SOTA methods on the meningioma test set; the evaluation metrics include DSC, HD95, FLOPs, and Params. As far as DSC is concerned, the higher DSC value represents the higher segmentation accuracy, and ECA performs the best among all the methods (89.22), which is significantly better than the other models. The Transformer is very effective in extracting global information; however, it has a natural disadvantage in extracting local information and cannot perform fine segmentation of tumor edges. Although dResU-Net and CU-Net perform well in terms of accuracy, they have significant issues in edge extraction, which can also be observed from the HD95 metric. nnUNet is very effective at capturing local information, but because it predicts on patches, it lacks the ability to effectively extract global information. ECA, by compressing the whole image, realizes segmentation in the whole image while retaining the global information. In terms of HD95--the lower HD95 represents the more accurate boundary prediction of the model; ECA also performs the best in this metric (2.88), which is significantly better than the other methods. Compared to other 3D attention mechanisms, ECA achieves the best performance in DSC and HD95, with FLOPs of 977.02 and Params of 22.38, while consuming relatively moderate computational resources and showing high segmentation accuracy and boundary processing capabilities. This reflects its superiority and efficiency. This is due to the fact that ECA compresses 3D images into 2D, greatly reducing the computation amount and memory burden while maintaining high performance with low computation.
## 2 Remaining Useful Life (RUL) Prognostics Methodologies
### RUL Prognostics Methodologies Based on the Stochastic Degradation Process  
RUL prediction based on stochastic process degradation is degraded by a stochastic model on the performance degradation data modeling, and then infers and predicts the distribution of the RUL, making it easy to quantify the uncertainty of prediction results. Given that the lithium batterydegradation process is essentially an uncertain stochastic process, many scholars have proposed stochastic process models (such as Gaussian process, Wiener process, _etc._) to predict the RUL of lithium-ion batteries. The Gaussian process is based on the statistical learning theory and adapts well to high dimensions, small samples, and nonlinear and other complex problems. Its generalization ability is also strong. [PERSON] _et al._[55] put forward a Gaussian process model to assess battery capacity degradation. [PERSON] _et al._[46] constructed a battery data Gaussian process. They not only adopted the Gaussian process regression (GPR) method to give the uncertain interval of the RUL prediction and build the method system of lithium-ion battery online RUL prediction, but also set a RUL prediction verification and assessment experiment through NASA lithium battery validation data. However, none of the above experiments has fully explored the degradation model. Therefore, [PERSON] _et al._[47] built a mixed Gaussian battery degradation model through the data of charging and discharging at room temperature and a constant current, and used PF for model parameter identification, rather than assuming a particular state-space capacity degradation model, which can better predict battery SOH. The experiments and comparative analysis of the method can be obtained with high efficiency. [PERSON] _et al._[48] put forward the multi-scale Gaussian process modeling method in wavelet analysis, which was based on the degradation data of lithium batteries in constant-current discharge to predict the RUL. [PERSON] _et al._[49] proposed a RUL prediction method based on measure errors of the Wiener process, which can better predict the battery RUL.  
Based on the above studies, stochastic process modeling can better characterize the lithium battery degradation processes, and the results illustrate that these methods can predict the RUL of lithium-ion batteries. However, none of these studies has considered the self-recharge characteristics of lithium-ion batteries, which mainly aimed at the prediction of lithium battery RUL in situations where the temperature is fixed and the discharge current is constant. These studies have not involved the widespread problems of lithium-ion batteries in operation, such as the influence of a time-varying environment, a random varying current, self-recharge characteristics, and system configuration.
# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles
## 4 Results
### Overhead Evaluation  
The measurement of protocol overhead proceeds as follows. Simulation with two protocols is performed to analyze the accumulated message amount per unit time.  
The CPB protocol requires that all nodes send and receive discovery messages periodically, whereas PCDP does not have to do all nodes; only the cluster head periodically issues announcements. However, the PCDP must request and transmit path information whenever it encounters a new node. This is longer than discovery, but it is expected that overhead will be reduced in the long term because it is only required to be performed once per node.  
The results are shown in Figure 20. If the node density is very low or the execution time is short, the overhead of the existing CPB may be smaller but insignificant, and the amount of overhead accumulated in the CPB is gradually increased.
# Multi-Information Fusion Indoor Localization Using Smartphones
## 3 System Workflow  
In this part, the overall scheme of the multi-information localization system is presented, as shown in Figure 1.  
In this scheme, the acoustic signal and inertial measurement unit (IMU) data are first captured by a smartphone. We recruited volunteers to collect data with different equipment and scenes. The volunteers held smartphones that were preinstalled with the client application and were asked to move at a constant speed. These collected data  
Figure 1: Localization system scheme combining ultrasonic signals and inertial signals.  
are automatically saved as formatted *.txt and sent to the server intermission. In the location solution terminal, the target location estimation acoustic base was determined using the CHAN method, and in the inertial measurement unit-based localization, the data from acceleration, gyroscope, and magnetometer were extracted from the server and preprocessed. Afterward, a dynamic improved PDR method was used to determine the pedestrians' locations. Finally, localization was achieved via the motion model with CHAN estimation and dynamic improved PDR estimation.
## 4 Experimental Results and Analysis
### Computational Complexity Analysis  
We analyze the computational complexity of our proposed KBDSP method. KBDSP mainly includes two steps. The first step is to construct the kernel matrix, which has \(O(n^{2})\) complexity. The second step is to implement Algorithm 1. The computational complexity of the second step is mainly determined by complexity of updating \(J\), \(G\), \(H\), \(A\), \(Z\) and \(B\). The complexities of updating \(J\), \(G\), \(H\), \(A\) and \(Z\) are \(O(n^{3})\),\(O(n^{3})\), \(O(n^{3})\),\(O(n^{3})\), respectively. Updating B requires \(O(n)\). Therefore, the total time complexity of KBDSP is \(O(tn^{3})\), where \(n\) is the number of data points and \(t\) is the number of iterations in KBDSP.
## 6 Discussion  
In this section, the relationships between data reconstruction accuracy using TVS-MH as well as the signal compression ratio and the reconstruction performances of the TVS-MH algorithm for different types of gas signals were tested; additionally, the reconstruction performances and computation complexities using different signal reconstruction algorithms were compared.  
Comparison of Reconstruction Performances and Computation Complexities of the TVS-MH Algorithm for Different Gas Signals
## 5 Experimental Results
### Quantitative Results
#### 5.3.2 Sensitivity to Hyperparameters  
We study the effect of two control parameters here: the time variation and the number of rescaling factors selected by the agent at once. Experiments were conducted on a workstation within the amd_512 queue of the ZC-M6 supercomputing system. The workstation is equipped with an NVIDIA GeForce RTX 3090 GPU (24 GB GDDR6X; NVIDIA Corp., Santa Clara, CA, USA) and an AMD EPYC 7H12 64-core processor (2.6 GHz, 512 GB RAM; Advanced Micro Devices, Inc., Santa Clara, CA, USA). The VarDA DRL-AST method was implemented using PyTorch (v1.10.0, [[https://pytorch.org](https://pytorch.org)]([https://pytorch.org](https://pytorch.org)), accessed on 30 January 2024) with Python 3.7 [61].  
* Time variation: seven controlled experiments were conducted to compare and evaluate the effect of time variations on assimilation performance and the training cost of neural network models, including 6 h, 12 h, daily, semi-monthly, monthly, seasonal, and annual intervals. The experimental results are shown in Figure 4a, revealing that assimilation performance is optimal when the variance is rescaled daily (as indicated by the value in the solid red dot). There is no notable change in assimilation performance with higher rescaled frequency, which may be related to the inherent stability of the Lorenz-96 model itself. Thus, the daily change is deemed sufficient to reflect the flow-dependent characteristics of the model. Then, our study opts for daily variation in follow-up experiments.
* The number of rescaling factors: guided by the inherent characteristics of the Lorenz-96 model variables, where 40 variables are equidistant around the equator, we explore a concept called 'chunking', in which a block corresponds to a rescaling factor and adjacent variables share the same factor. Our experimentation encompasses block sizes of 5, 8, 10, 20, and 40 because there are 40 variables in our study for even distribution. Remarkably, as shown in Figure 4b, an escalation in the number of blocks positively correlates with improved assimilation performances. Nevertheless, transitioning from 20 to 40 blocks yields only marginal gains in assimilation performance, while increasing model training costs. Consequently, this study adopts a balanced approach, opting for 20 blocks (\(CK=20\)).  
We take 5 times training with different random seeds, and the mean returns of the model in the training phase are shown in Figure 5. This result shows that our method can achieve stable convergence every time.
# Focal Muscle Vibration for Stroke Rehabilitation: A Review of Vibration Parameters and Protocols
###### Abstract  
In this review, we present a narrative synthesis of studies on the use of focal muscle vibration (FMV) in stroke rehabilitation with a focus on vibration device, parameters, and protocols. A search was conducted via PubMed, SCOPUS, PEDro, REHABDATA, and Web of Science using the keywords \"stroke and focal vibration\" or \"focal muscle vibration\". Inclusion and exclusion criteria to select the articles were determined. Twenty-two articles involving FMV and stroke were included in this review. Eight different vibration devices were used in the 19 articles that reported the vibration apparatuses. The vibration frequencies ranged from 30 Hz to 300 Hz with amplitudes ranging from 0.01 mm to 2 mm. The vibration treatment frequency ranged from a single treatment to 5 days/week. The session duration ranged from 14 s to 60 min/session with a duration of a single treatment to eight weeks. Twenty different muscles were targeted with 37 different outcome measures used to assess the effects of FMV. The clinical applications of FMV were not confirmed based on available evidence. More research is needed to improve the FMV technology, guide the selection of vibration parameters, optimize the vibration dosage, and develop standardized protocols for FMV therapy in patients with stroke.  
focal muscle vibration; FMV; stroke; vibration frequency; vibration amplitude; vibration duration; vibration device(s) +
Footnote †: journal: Applied Sciences
## 1 Introduction
The dataset is enhanced using augmentation methods like rotation and adjustments to brightness and contrast to mimic real-life conditions. The model employs a liquid neural network with Neural Circuit Policy (NCP) [21], improving performance over traditional LSTM models. This approach results in better generalization while remaining sensitive to rare disease cases. The technological innovation of this study for real-life agricultural applications lies in the deployment of a lightweight CNN-RNN model directly on IoT devices. This approach enables real-time image processing on-site, eliminating the need to send images to remote servers for analysis and wait for results. As illustrated in Figure 1, the system captures images of tomato plants using cameras installed on the field. These images are then processed locally by an optimized CNN-RNN model deployed on an IoT device, such as a Raspberry Pi. The lightweight nature of the model ensures that it can run efficiently on these resource-constrained devices, allowing for rapid image analysis directly at the point of capture. This on-site processing capability offers several significant advantages:  
* Due to the lightweight nature of the model, the system can handle multiple camera devices with only a few IoT devices, making it easy to scale. The elimination of the need for powerful central servers reduces infrastructure costs and simplifies deployment in large agricultural fields;
* By processing images locally, the system can quickly send results to user devices, enabling timely interventions. This rapid feedback loop is crucial for effective disease management, allowing farmers to address issues as soon as they are detected;Since the image processing is performed on-site, the system is less dependent on stable and high-speed internet connections, which can be a limitation in remote farming areas. This independence enhances the reliability of the system;  
Processing data locally on IoT devices reduces the risk associated with transmitting sensitive data over networks. This approach ensures that data remain secure and private, addressing potential concerns related to data breaches and unauthorized access. The same approach used in this study has been previously published in [22], where we discuss the related methodologies and initial findings. However, this current research goes beyond the previous by exploring and validating the performance of the model on a deeper scale, providing a more comprehensive analysis and testing under a wider range of conditions. This extended investigation offers greater insight into the model's capabilities and its potential applications, thereby enhancing the novelty and contribution of this work.
# Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm
## 4 Results
### SAR Image  
The raw FMCW radar data collected from Flint Hall Farm are post-processed using the RMA to produce the first SLC SAR image from the drone radar system (Figure 17). The SAR image does not utilise multilook processing and thus maintains the raw radar spatial resolution and speckled effect. The drone flight path aligns with the right-hand axis of the SAR image--left-looking from the bottom-right to the top-right of the image. Within the radar Field of View (FOV) are several strong and stable radar reflectors, which are discrete and separate targets on the slope. This includes the corner reflectors (red) and other fenced areas for in situ instrumentation (yellow, blue and pink). These targets are visible as bright spots within the SAR image, with their size and positioning as expected. The corner reflectors provide the brightest pixels of these targets, thus indicating a strong radar signal return. The grass and vegetated areas surrounding the targets and towards the top half of the SAR image are shown as a natural speckle.  
Figure 16: Photograph of the drone radar system in-flight at Flint Hall Farm.