esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Associations of Muscle and Arterial Stiffness Measures  
Spearman's correlation analysis showed muscle strain rate significantly correlated with carotid compliance (r = 0.49, \(p\) = 0.02), \(\delta\) stiffness (\(-\)0.84, \(p\) = \(<\) 0.0001), and carotid distensibility (r = 0.54, \(p\) = 0.01) (Table 3). The _u_/PWVs at systolic (r = 0.64) and diastolic phase (r = 0.67) were also moderately correlated with carotid \(\delta\) stiffness (\(p\) = 0.01). Muscle strain rate and _u_/PWVs did not correlate with cfPWV and baPWV. As shown in Figure 5, muscle strain rate negatively correlated with _u_/PWV-sys (r = \(-\)0.71, \(p\) = 0.001), _u_/PWV-dia (r = \(-\)0.74, \(p\) = 0.001) respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Carotid** & **Carotid** & **Carotid** & **cfPWV** & **baPWV** \\  & **Compliance** & \(\delta\)**-Stiffness** & **Distensibility** & **cfPWV** & **baPWV** \\ \hline Muscle strain rate & 0.49 * & 0.84 * & 0.54 * & \(-\)0.10 & \(-\)0.02 \\  & (\(p\) = 0.02) & (\(p\) \(<\) 0.0001) & (\(p\) = 0.01) & NS & NS \\ _u_/PWV-sys & \(-\)0.26 & 0.64 * & \(-\)0.26 & 0.11 & 0.14 \\  & NS & (\(p\) = 0.01) & NS & NS & NS \\ _u_/PWV-dia & \(-\)0.27 & 0.67 * & \(-\)0.30 & 0.15 & 0.09 \\  & NS & (\(p\) = 0.01) & NS & NS & NS \\  & 0.24 & \(-\)0.26 & 0.30 & \(-\)0.11 & \(-\)0.18 \\ \(\Delta\)_u_/PWV & NS & NS & NS & NS & NS \\ \hline \hline \end{tabular} * = significant, \(p\) \(<\) 0.05. NS = no significance.  
\end{table}
Table 3: [PERSON]’s correlation between muscle strain rate, carotid vascular function, and pulse wave velocity measures.  
Figure 4: Pre- and post-exercise _u_/PWV-sys (**A**) and _u_/PWV-dia (**B**) in response to different exercise intensity. * = vs. Pre.  
Figure 5: [PERSON]’s correlation between muscle strain rate and _u_/PWV at systolic (**A**) and diastolic (**B**) phase.
## 3 Results  
### Study Selection  
The PRISMA flow chart for the selection of is shown in Figure 1. A total of 63 studies were retrieved after the initial search from four databases. No additional record was found by manual search. 38 studies were selected after duplicate studies removal. Selected articles were screened thoroughly, and 28 were excluded based on predetermined criteria. The remaining 10 studies [15; 16; 17; 18; 19; 20; 21; 22; 23; 24] were included in qualitative analysis, and meta-analysis was performed on eight articles only [15; 18; 19; 20; 21; 22; 23; 24].
## 7 Conclusions  
From the given observations, it is definitely clear that random forest classification and the XGBoost classifier show higher accuracy values than the rest of the machine-learning models used in classifying phishing attempts as either harmful or safe. Random forest classification obtained a 98.5% of accuracy rate, while XGBoost achieved an accuracy percentage of 98.33, with the difference between the two being only 0.17%. But this small number becomes significantly larger in real-time scenarios, involving attempts at phishing where the range is usually greater than a million attempts per hour.  
Still, XGBoost classification should be preferred in real-time scenarios for two main reasons: It features a lower number of false negatives and executes faster than random forest classification. XGBoost classification shows a 1.77% rate of false negatives, while random forest classification scores a 1.83% rate of false negatives on the given dataset. Although the difference seems small, in real time it is a huge deciding factor because false positives may be accepted in the model, and false negatives must be filtered out.  
Also, with the number of estimators as 200, datapoints as 10,000, and extracted features as 15, random forest classification provides output in 3.01 s, while XGBoost classification outputs the same result in 2.03 s. This is a huge time gap in performance between the two models which needs to be selected for real-time analysis. Hence, it can be concluded that XGBoost classification is the best model for real-time analysis and prediction of whether an attempt in the blockchain is a phishing attempt or not.
# Audio Time Stretching Using Fuzzy Classification of Spectral Bins
## 3 Novel Time-Scale Modification Technique
### Transient Detection and Preservation  
For transient detection and preservation, a similar strategy to [23] was adopted. However, the proposed method is based on the estimated transientness of the STFT bins. Using the measure for transientness, the smearing of both the transient onsets and offsets is prevented. The transients are processed so that the transient energy is mostly contained on a single synthesis frame, effectively suppressing the transient smearing artifact, which is typical for the phase vocoder based TSM.
# A Review of the Optimal Design of Neural Networks Based on FPGA
## 4 Neural Network Optimization Technology Based on FPGA
### Memory and Access Optimization
#### 4.3.2 Data Reuse  
Data reuse is simply the reuse of data. As can be seen from the roof-line model, when the data reuse rate was low, bandwidth became the bottleneck affecting performance, and the computing resources of FPGA were not fully utilized. Therefore, through data reuse, the actual application bandwidth of memory is greater than the theoretical bandwidth, which increases the upper limit of the bandwidth and also reduces the storage pressure of memory and the amount of data cache and data exchange, so as to reduce the unnecessary time spent.  
As shown in Figure 11, in the data operation of neural networks, data reuse is usually divided into input feature map reuse, filter reuse, and convolutional reuse. An input feature map reuse is the reuse of the same input and replacing it with the next input after all the convolution kernels are computed. A filter reuse means that if there is a batch of inputs, the same convolution kernel for the batch is reused and the data are replaced after all the inputs in the batch are calculated. Convolutional reuse uses the natural calculation mode in the convolutional layer and uses the same convolution kernel to calculate the output of different input map positions. In general, these three data reuse modes reuse data at different stages in order to achieve the purpose of increasing performance. Of course, data reuse can also be divided by time reuse and space reuse. For example, if the data in a small buffer is repeatedly used in multiple calculations, it is called time reuse. If the same data are broadcast to multiple PEs for simultaneous calculation, it is called space reuse.  
In the FPGA-based deep neural network acceleration experiments, a large part of the acceleration was based on data reuse to increase the bandwidth upper limit and reduce the amount of data cache and data exchange. For example, in 2020, [PERSON] et al. [112] deployed the scheduling algorithm and data reuse system MEM-OPT on a Xilinx XCZ020 FPGA and evaluated it on LeNet-5, MobileNet, VGG-16, and other CNN networks. The total on-chip memory required to store input feature maps and cumulative  
Figure 10: Ping-pong cache description.  
output results was reduced by 80% compared to the traditional scheme. In 2021, [PERSON] et al. [110] proposed an adaptive channel chunking strategy that reduced the size of on-chip memory and access to external memory, achieving better energy efficiency and utilizing multiplexing data and registering configuration in order to improve throughput and enhance accelerator versatility. In 2022, [PERSON] et al. [111] applied the data reuse technology that were based on time and space to the convolution operation in order to improve data utilization, reduce memory occupancy, reduce latency, and improve computational throughput. The same convolution calculation was about 2.78 times faster than the Intel Core i7-9750H CPU and 15.69 times faster than the ARM Cortex-A53.
# Hypergraph Analysis of Functional Brain Connectivity During Figurative Attention
## 2 Mathematical Basis
### Cross-Correlation  
Unlike previous studies that rely on Pearson correlation for constructing functional connectivity networks [57; 58], our approach employs cross-correlation analysis. The Pearson correlation has several limitations, including sensitivity to delays in neural responses and potential confounding effects from other brain regions. To address these issues, we used cross-correlation analysis, selecting the optimal time delay at which correlation is maximized.  
Cross-correlation measures the similarity between two time series as a function of the time lag between them. This method provides a robust and sensitive approach for analyzing EEG signals recorded simultaneously from different channels, independent of their amplitudes [40]. By accounting for time lags, cross-correlation allows for the evaluation of relationships between signals not only at the same moment but also at different time points, offering deeper insights into temporal dependencies and synchronization patterns in neural activity.  
The normalized cross-correlation between two EEG channels \(x(t)\) and \(y(t)\), at a given time lag \(\tau\), is calculated as follows:  
\[Corr_{xy}(\tau)=\frac{\sum_{t}(x(t)-\overline{x})(y(t+\tau)-\overline{y})}{ \sqrt{\sum_{t}(x(t)-\overline{x})^{2}\sum_{t}(y(t)-\overline{y})^{2}}}, \tag{10}\]  
where \(\overline{x}\) and \(\overline{y}\) demote the mean values of \(x(t)\) and \(y(t)\), respectively. For each pair of channels, we identify the time lag \(\tau\) at which \(Corr_{xy}\) attains its maximum value and use this peak correlation value in our subsequent analysis. This approach ensures that we capture the strongest temporal relationship between the signals, providing a robust basis for reconstructing a functional connectivity network.  
Cross-correlation values range from \(-1\) and \(1\). To analyze changes in cross-correlation induced by attention, we define the difference in cross-correlation (\(\Delta Corr\)), which can take values between \(-2\) and \(2\). For clarity, we refer to cases where \(\Delta Corr>0\) as _correlation_ and cases where \(\Delta Corr<0\) as _anticorrelation_. This distinction helps characterize neural connections that emerge (increased synchrony) and disappear (decreased synchrony) due to attention.
## 4 Verification
### Case Study
** & **Dimensions** & **Unit** & **RP-1-E** & **RP-2-E** & **RP-3-E** & **RP-4-E** & **RP-5-E** \\ \hline Specimen & Attached plate & mm & 640 \(\times\) 340 & 640 \(\times\) 340 & 640 \(\times\) 340 & 640 \(\times\) 340 & 640 \(\times\) 340 \\ Attached plate thickness & mm & 1.3 & 1.3 & 1.3 & 1.3 & 1.3 \\ Stiffener & mm & 40.36 \(\times\) 1.1 & 46.25 \(\times\) 1.2 & 50.82 \(\times\) 1.22 & 57.56 \(\times\) 1.26 & 59.01 \(\times\) 1.26 \\  & Sectional area & mm\({}^{2}\) & 148.4 & 159.5 & 166.0 & 176.5 & 178.4 \\  & Sectional static moment & mm\({}^{3}\) & 952.4 & 1343.7 & 1637.5 & 2151.8 & 2258.6 \\  & (\(M_{S}\))\({}_{d2}\) & t & 0.00305 & 0.00326 & 0.00338 & 0.00358 & 0.00361 \\  & (\(M_{S}\))\({}_{d2}\)/(\(M_{S}\))\({}_{m}\) & - & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0004 \\ \hline Load case & \(R_{I}\) & mm & 80 & 80 & 80 & 80 & 80 \\  & (\(M_{I}\))\({}_{d2}\)/(\(M_{I}\))\({}_{m}\) & - & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0004 \\  & \(M_{I}\) & t & 0.4000 & 0.4000 & 0.4000 & 0.4000 & 0 As discussed in Section 4.1, during the initial stage, when the bending action predominates, the behavior was primarily governed by the static moment. As the deformation progressed, the stiffened plates primarily resisted the application of the membrane forces \(N_{0}\). Therefore, the distorted models in group E, designed to maintain both equivalent sectional areas and static moments, consistently exhibited a behavior similar to that of their reference models, as shown in Figure 18. In conclusion, this series of comparisons robustly demonstrates the efficacy and applicability of the proposed criterion for addressing prototypes equipped with common T-type stiffeners.
## 2 The Algorithm
### Adaptive Consistency Filtering
#### 2.4.1 Smoothness Consistency Filter  
The object surface within a small area is usually smooth. Using the above conditional-double-surface-fitting method, it is possible to approximate a local surface by quadratic fitting and identify an outlier based on its distance from the fitted surface. If a point \(p\) sits at a distance larger than a threshold \(th_{r}\) from the fitted surface, then it is filtered out. The threshold  
Figure 3: Double surface fitting. Solid line: object surface. Dotted line: fitted surface on the first iteration. Dashed line: fitted surface on the second iteration. Solid black points: reconstructed 3D points. \(p_{1-3}\): 3D points with large residual error.  
\(th_{r}\) is set adaptively, in consideration of the density of neighbor points and the curvature of the true surface. It adopts a larger value in cases of sparse neighbor points or high curvature, as described by Equation (17)  
\[th_{r}=a_{1}+\overline{d}\times a_{2}/th_{d}+a_{3}\times\delta_{n} \tag{17}\]  
where \(a_{1},a_{2}\) and \(a_{3}\) are fixed constants. The surface curvature is represented by \(\delta_{n}\), which is the variance of the normal of \(N_{1}\) neighbor points computed by Equation (18). As indicated by Equation (17), the threshold \(th_{r}\) becomes bigger as \(\overline{d}\) and/or \(\delta_{n}\) increase to deal with sparse neighbor points and high local curvature of the surface.  
\[\delta_{n}=\left(\sum_{i=0}^{N_{1}}\left|\mathbf{n_{i}}-\sum_{i=0}^{N_{1}} \mathbf{n_{i}}/\left(N_{1}+1\right)\right|^{2}\right)^{1/2} \tag{18}\]  
In Equation (18), \(\mathbf{n_{i}}\) is the normal of the \(i\)th neighbor point when \(i\) = 0, \(\mathbf{n_{0}}\) being the normal of point \(p\).
# Prediction of Weights during Growth Stages of Onion Using Agricultural Data Analysis Method
## 5 Conclusions  
In the study, we applied a statistical functional regression model to investigate the relationship between various environmental factors and onion weights during the growing season. To solve this problem, we performed the following two tasks. In the first one, we identified the six most important environmental factors among those that affect onion weight during the growing season. In the second, we proposed an optimal cultivation strategy that could suggest how to manage the six identified environmental factors to maximize the onion weights.  
From the analysis results so far, we could note the following facts. First, through the graphical and the correlation analysis, we can see that ground temperature, mean temperature, and humidity are positively correlated with onion weights, while wind speed, sunshine, and rainfall have a small negative correlation with the onion weights. Second, from the functional regression analysis for six environmental variables and onion weight, we note that the ground temperature, sunshine, and rainfall have a statistically significant effect on the onion weights, but other environmental factors such as wind speed, mean temperature, and humidity have little effect on the onion weights. In conclusion, to promote onion weights, the appropriate ground temperature, amount of sunshine, wind speed, and mean temperature must be maintained, and rainfall and the humidity must be low.  
Future works should utilize functional data analysis to investigate how these environmental factors affect onion yields. In addition, an overall study is needed to understand how environmental and growth factors and weights and yields of onions affect each other.  
Conceptualization, methodology and software, M.H.N.; validation and investigation, D.H.K.; writing--original draft preparation, Y.P.; formal analysis and writing--review and editing, W.C.; supervision and project administration, Y.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This paper was partially supported by the Research Program of the RDA (Rural Development Administration) (Project No. PJ0138672020) and the Korea National Research Foundation (Project No. 2017R1D1A1B03028808) of the Korea Grant funded by the Korean Government. The authors declare no conflict of interest.
# A Review of Flying Ad Hoc Networks: Key Characteristics, Applications, and Wireless Technologies
## 8 Main Components of Cloud-Based UAV Managing Systems (CBUMS)  
As already mentioned, CBUMS include three main layers, the UAV layer, cloud layer, and control layer [20], as shown in Figure 8.  
* _UAV Layer_: As shown in Figure 8, in the UAV layer (i.e., the physical layer), UAVs that are connected with the IoT cloud using short- and long-range wireless technologies can perform different tasks, ranging from traffic monitoring to delivery. The cloud layer sends control information and signals about the traffic situation to the UAV layer to guide responses based on the desired GBS in the control layer. In the UAV layer, multiple network components such as drone-to-target (D2T) and drone-to-drone (D2D) are attached [20; 118].
* _Cloud Layer_: The cloud layer, which is the heart of CBUMS, transfers the data between the UAV and control layers. As can be seen in Figure 8, the storage, computation, ML techniques, and interface are the main components of the cloud layer [20; 118]. _Storage_: The cloud layer captures streams of data about the location, environment, and UAV mission information, storing the captured data in a regular SQL database or NoSQL database based on the application's requirements. _Calculations and ML techniques_: Several computation algorithms, such as map/reduce, data analytics, image processing, ML techniques (including supervised and unsupervised learning algorithms), RL and DRL-based algorithms, and FL-based techniques are executed in the cloud to improve the system performance and fix existing open issues. _Interface:_ The interface contains web and network services that make connections between control and UAV layers. Interfaces in the cloud layer take advantage of various communication protocols, including wireless personal area networks (WPAN), wireless local area networks (WLAN), low-power wide-area networks (LPWAN), and cellular networks. In applications that require UAVs to directly communicate with the central station, a WiFi transmission system is used. However, long-term evolution (LTE) and long-range area networks (LPWAN) provide lower-latency communication systems than WIFI.
* _Control Layer_: As Figure 8 shows, the control layer includes GCSs that remotely register, control, manage, and monitor UAVs from a location close to or inside the flying field. The GCS contains application software that receives collected data from UAVs and sends control signals to them. The users can monitor UAVs, set task parameters, and modify them through the data analysis implemented by the cloud based on the application software [20; 118].  
Figure 7: The basic model of cloud, fog, and edge computing.
# Energy-Aware System Design for Autonomous Wireless Sensor Nodes: A Comprehensive Review
## 5 Energy Saving on Network Level
### Sleep/Wake-Up Protocols  
Sleep/wake-up protocols offer a significant contribution to energy conservation by adapting the node activity. The basic idea of sleep/wake-up protocols is to put the radio of a wireless sensor node into a sleep state during the period of inactivity and wake it up just before transmitting or receiving a packet. Each transmitter node must know the sleep/wake schedule of the receiver node to communicate. Therefore, synchronization between the transmitter and receiver nodes is needed to enable them to wake-up at the same time. This can be carried out by applying different schemes including duty cycle and topology control. Duty cycling schemes aim to save energy by reducing the idle listening state and promoting the sleep mode. These schemes turn off the radio of a node most of the time and wake it up only if needed or at scheduled moments [115; 116]. However, data latency issue occurs because the transmitter node needs to wait for the receiver node to be awake. To alleviate this problem, some parameters need to be settled, such as the time slot for each node, both idle listening and sleep periods and the length of the preamble.  
The topology control techniques reduce the energy consumption by inactivating the nodes that are not needed for ensuring the coverage or connectivity. These techniques can be divided into location-driven and connectivity-driven approaches [117]. The location-driven approach determines the state of the node as either in active or in sleep mode and assigns which node to wake up and when depending on its location, which is known previously. The connectivity-driven approach is used to activate and deactivate dynamically a sensor node to ensure a full coverage and maintain the network connectivity.
# Ensemble Radar-Based Rainfall Forecasts for Urban Hydrological Applications
## 3 Results
### Rainfall Forecasting  
To assess the predictability of the ensembles rainfall forecasts, the Receiving Operating Characteristic (ROC) curves were calculated. ROC curves have been widely used to analyse uncertainties in probabilistic forecasting systems and can measure the ability of a model to identify the occurrence of an event correctly. The method is based on a binary system, where yes/no forecasts and yes/no observations are computed [9]. For a sequence of threshold, a 'hit-rate' (HR) (proportion of events correctly forecasted) and a 'false-alarm rate' (FAR) (proportion of events that were not forecasted) are computed and used to define the ROC curve [55]. The better the forecast, the higher the HR (and lower the FAR). The area beneath the ROC curve should be above 0.5 (random forecast) and is equal to 1 when the model can perfectly forecast an event that occurs or not [9].  
Some of the results obtained for a selection of events with rainfall intensities equal or higher than 0.1 mm/h, 1.0 mm/h and 3.0 mm/h, are presented in Figures 4-7 at different forecasting lead-times.  
To summarise the results from the events analysed, the area beneath the ROC curves for different rainfall thresholds and forecasting lead-times are plotted in Figure 8. The results show that the forecast skill decreases with both, forecasting lead-time and higher rainfall intensities.  
Figure 3: Radar ensemble forecasts at time \(t=30\) min (**a**,**d**), \(t=60\) min (**b**,**e**), \(t=90\) min (**c**,**f**) starting on 1 August 2008 at 02:00.  
Figure 4: Receiving Operating Characteristic (ROC) curves for event on 7 July 2008 starting at 15:00 for Radar ensembles and rainfall thresholds (th) equal to 0.1 mm/h (a), 1.0 mm/h (b) and 3.0 mm/h (c). and Short-Term Ensemble Prediction System (STEPS) ensembles and rainfall thresholds equal to 0.1 mm/h (d), 1.0 mm/h (e) and 3.0 mm/h (f).  
Figure 5: ROC curves for radar and STEPS ensembles for event on 1 August 2008 starting at 01:30 for Radar ensembles and rainfall thresholds (th) equal to 0.1 mm/h (a), 1.0 mm/h (b) and 3.0 mm/h (c). and STEPS ensembles and rainfall thresholds equal to 0.1 mm/h (d), 1.0 mm/h (e) and 3.0 mm/h (f).  
Figure 6: ROC curves for radar and STEPS ensembles for event on 4 October 2008 starting at 15:00 for Radar ensembles and rainfall thresholds (th) equal to 0.1 mm/h (a), 1.0 mm/h (b) and 3.0 mm/h (c). and STEPS ensembles and rainfall thresholds equal to 0.1 mm/h (d), 1.0 mm/h (e) and 3.0 mm/h (f).  
Figure 7: ROC curves for radar and STEPS ensembles for event on 14 October 2008 starting at 15:00 for Radar ensembles and rainfall thresholds (th) equal to 0.1 mm/h (a), 1.0 mm/h (b) and 3.0 mm/h (c). and STEPS ensembles and thresholds equal to 0.1 mm/h (d), 1.0 mm/h (e) and 3.0 mm/h (f).