esa-sceva/satcom-chunk-collection huggingface dataset:
# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 3 Materials and Methods
### Biometric Human Identification  
After extracting the ensemble features (combining VGG16 and LSTM representations) from the 2D spectrogram images, we can proceed with the classification step using the LightGBM model. LightGBM is a gradient-boosting framework that excels at handling large-scale datasets and offers fast and efficient training. Using the LightGBM model for classification complements the initial feature extraction steps using the VGG16 and LSTM models. LightGBM's ability to handle large-scale datasets efficiently and its excellent predictive performance make it a valuable tool for the classification stage in your cardiac biometrics system.  
The LightGBM uses a tree-based learning algorithm. In this algorithm, trees are grown vertically using a leaf-wise algorithm [58]. Since LightGBM generates complicated trees, it's more accurate than other tree-boosting algorithms. Additionally, LightGBM has a high speed with low memory usage because it uses the GOSS and EFB algorithms [59]. The model was applied to the optimized features from the LSTM step, and the data were split into 80% training and 20% testing. The pseudocode for this step is shown in Algorithm 4.  
A biometric human identification system is a critical application in the field of security and surveillance. We used several signal processing techniques, such as the Discrete Wavelet Transform (DWT) and the Short-Time Fourier Transform (STFT), for feature extraction from ECG signals. These features are then fed into machine learning models to classify  
Figure 6: LSTM architecture for features optimization.  
and identify individuals. In addition to signal processing techniques, feature engineering plays a crucial role in biometric identification. Feature engineering involves selecting and transforming the relevant features that can improve the accuracy of the machine learning model. The features were extracted from spectrogram images using the VGG16 pre-trained model and optimized using the LSTM model.  
A LightGBM model was used for the biometric identification task. This model is trained on the ECG-ID dataset and optimized using hyperparameter tuning techniques to improve its performance. Overall, the combination of signal processing techniques, feature engineering, and machine learning models has shown promising results in biometric human identification and has the potential to significantly improve security and surveillance systems. As shown in Figure 7, the system is able to recognize humans from their heartbeats (if human heartbeats are in the dataset).  
Figure 7: A visual example of the biometric human identification System.
## 3 Proposed OUM Model and Research Methodology
### Sampling and Measurement  
The survey utilised expert sampling to non-randomly select a group of respondents based on their direct engagement in a specific domain [48], i.e., BIM implementation and adoption. Email invitations were sent to 500 professionals working for the sponsoring organisation. Recommended by Kwong & Wong [49] and [PERSON] et al. [47] for PLS SEM researchers, the sample size for this study was based on the maximum amount of independent variables pointing at a given dependent variable in the proposed model. For example, there are three independent variables pointing at 'User Competency', which is a dependent variable in the proposed OUM; therefore, a minimum of 37 and 59 respondents are required, respectively. Accordingly, and for enhanced robustness, a minimum of 59 respondents were sought, while 73 complete responses were received, thereby exceeding the minimum response benchmark of 59 or 20%. The demographics of the 73 respondents (14% return rate) represent various architectural and engineering disciplines operating from the distinct office locations of the sponsoring organisation within the United Kingdom (Table 1). Forty (55%) respondents are operating at a junior level with seven or less years' industry experience and 33 (45%) at a senior level with over seven year's industry experience. Furthermore, 67 (92%) respondents are hands-on architects and engineers working directly on BIM projects, in addition to six (8%) senior management directors who are key decision makers in the organisation's uptake of BIM.  
In terms of the questionnaire measurement instrument, respondents were asked to rate their level of agreement based on a five-point Likert-scale (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree) in conjunction with the studies of [PERSON] et al. [36], [PERSON] & [PERSON] [21], [PERSON] [24], [PERSON] et al. [44], [PERSON] [50] and [PERSON] et al. [51], and the recommendation by [PERSON] [52] on identifyingthe relative importance of a set of variables. The survey was conducted using Google Forms; a free online documentation platform that allows for Likert scale measurements and the rapid export of data in Excel format, thus enabling effortless data entry into the statistical software. In terms of coding, the variables and corresponding associated indicators (questionnaire statements) were abbreviated based on common research methods proposed by [PERSON] et al. [47], e.g., the '_User Competency_' variable was abbreviated as '_UC_' with associated indicators '_UC1_, _UC2_, _UC3_... _UCI_'.
# High-Accuracy Clock Offsets Estimation Strategy of BDS-3 Using Using Multi-Source Observations
## 2 Materials and Methods
### The New Strategy
#### 2.2.2 Estimation of Clock Offsets with Respect to BDT
The RMS of the 9-day residual for the least square estimation is 0.1 ns, which are given in Figure 7. Figure 7 preliminary suggests that the observations of different large-diameter antennas present a good consistency in short timescale. Their long-time consistency needs to be further verification in the future. Considering the high-accuracy inter-satellite clock offsets already obtained by 3.1. the clock offsets of all other satellites can be further traced to BDT by Equation (5). \[clk_{A}(t_{0})=clk_{Q}(t_{0})+clk^{\rm Two-way\ {estimated}}{}_{AQ}(t_{0}) \tag{5}\]
# Future Scenarios for Software-Defined Metro and Access Networks and Software-Defined Photonics
## 5 Architectures
### Converged Fixed-Mobile Networks  
In Section 2.2, the reasons to adopt the SDN model in the metro and access networks was described with reference to the requirement of future 5G cellular networks. The re-configurability required by mobile RAN systems will be reflected in the fixed line access networks. Therefore, similarly to the convergence of metro and access segments, a convergence between the fixed and mobile service environments and networks, sometimes referred as fixed mobile convergence (FMC), can be envisioned.  
The schematic representation of a generic fixed-mobile metro-access network architecture is shown in Figure 14. The figure shows metro switches and both MBH and MFH links. A centralized  
Figure 13: The SARDANA architecture [83].  
SDN controller can drive all the switches involved to provide connections at the different required granularities and ensure the cooperation among BBUs, as will be required by 5G.  
SDN concepts applied to efficient 5G MBH and MFH networks have been proposed by [PERSON] et al. [88], in order to enable multiple operators to share the same physical infrastructure. In particular, for the use of SDN in the MFH network, CPRI over Ethernet (CoE) is proposed as a new transport protocol.  
Several concrete proposals have been brought forward in recent years to merge fixed and mobile access networks.  
[PERSON] and [PERSON] [51] have proposed a metro-access network architecture which makes use of PON-based access, optical cross-connect metro, and WR-OBS transport. Flexible resource allocation mechanisms and mobility management procedures allow the native support of mobile services in a fully integrated mode.  
[PERSON] et al. [89] have presented a trial demonstration based on flexible operating access nodes within a shared metro-access reconfigurable infrastructure. Virtual paths are proposed to transport mixed data format/rates carrying different services. One of the services provided by the network is an OFDM transmission system operating at 2 Gbit/s per channel for interconnecting neighboring 4G mobile nodes using coordinated multi-point processing [89].
## 1 Introduction
CFM uses marginal distribution functions to obtain joint distribution functions; this is a convenient method for adding relevance among variables with good precision, so it is now widely adopted. The Nataf transformation [31], which is a type of Gaussian copula, is one of the most important CFMs and famous for its high precision and speed; it can link any distribution space to the normal distribution space, and this is extremely beneficial for the PEM. The estimated points and weights can be easily obtained through the Gauss-Hermite integration. Moreover, because the calculation of PEF is extremely time consuming, the PEM can considerably reduce the computation time. Therefore, the combination of the Nataf transformation with PEM is an excellent choice for PEF calculations. However, the probability variables can be bounded by constraints. For instance, wind farms have cut-in and cut-out wind speed constraints, and when the estimated points are outside the range of these constraints, they lose their physical significance and cause errors. In particular, when dealing with 2-point and 3-point estimation situations, because of the small number of estimated points, the errors can become considerable. Hence, it is necessary to limit the estimated points to remain within the boundaries of the constraints. The goal of this study is to solve the PEF in an IES. We comprehensively consider the uncertainty regarding wind energy, solar energy in power systems, and gas loads in natural gas systems to establish a model for calculating the PEF. Considering the correlation between different wind-driven generators and solar energy generators in reality, we propose using the Nataf transformation to solve the correlation problem accurately and efficiently. As is well known, solving the PEF in an IES is more time consuming than solving the PPF in a power system. The Monte Carlo method is an inefficient solution to the PEF; hence, the method developed in our study improves the solution efficiency using the multipoint estimation method (MPEM). This paper proposes an extension to the existing MPEM that reduces the computational complexity and cost. For cases where the estimated points are outside of the range, the approach proposed in this paper uses the power transformation method and equality constraint transformation to improve 2-point and 3-point estimations and reduce the calculation error The remainder of this paper is organized as follows: A PEF model for IESs is described in Section 2. A PEM based on the Nataf transformation is detailed in Section 3, and 2-point estimation methods considering boundary constraints are discussed in Section 4. Section 5 presents a case discussing PEF calculations, and Section 6 discusses the ideas proposed in this paper and directions of research worth studying in future. The concluding remarks are given in Section 7. Notations and a list of abbreviations are provided in the Appendix A.
## 2 Materials and Methods  
### Algorithm Outline  
The ultimate aim of the algorithm is to estimate accurately the instantaneous phase and frequency of alpha oscillations using the AR and LMS-based AR model. The algorithm is composed of the following sequential preprocessing steps:  
Preprocessing for both the AR- and LMS-based AR model:  
1. Re-reference the raw data and then downsample to 500 Hz.
2. Optimize the frequency band (8-13 Hz) based on the peak/central frequency of each individual. The individual alpha frequency (IAF) is linked to the maximum EEG power within the alpha range. After finding the IAF, a passband for a band-pass filter is chosen. The low cutoff frequency for the band-pass filter is IAF-1 and high cutoff frequency is IAF+1.
3. Apply a two-pass finite impulse response (FIR) band-pass filter with a filter order of 128 and the passband chosen in step 2 [28].
4. Segment the data into 500 ms epochs.  
AR model (replicating [PERSON]'s method) [27]:  
1. Compute the optimal AR model order using Akaike's Information Criterion (AIC).
2. Compute AR coefficients using the Yule-Walker equations.
3. Estimate the instantaneous phase at the boundary of the sliding window in the AR forward prediction method (prediction length, 128 ms) [21; 27] (see Figure 1).  
LMS-based AR model:  
1. Select the adaptation size/learning rate of LMS.
2. Select the number of filter taps (same as the AR model order).
3. Compute the coefficients using LMS and then use those coefficients to predict the next sample until the prediction length in the AR equation.
4. Calculate the time-series forward prediction for twice the prediction length (256 ms) for the LMS-based AR model (See Figure 1).  
Figure 1: Autoregressive (AR) forward prediction. (**A**) The length of the actual data segment is 500 ms; (**B**) From both ends, a 64 ms data segment was trimmed; (**C**) The remaining 372 ms data segment was used to generate AR coefficients to forecast 128 and 256 ms data segment.  
Assessment of performance of AR model and LMS-based AR model:  
1. Compute the means of the predicted segment and original segment and subtract the respective means from the predicted segment and original segment.
2. Estimate the instantaneous phase and frequency of the original and predicted data segments by calculating the analytic signal via the Hilbert transform.
3. Compute the phase difference between the original and predicted data segments.
4. Calculate the phase locking value (PLV) between the original and predicted data.  
The last four steps are to be performed for both methods. A flow chart of these steps is shown in Figure 2.
## 5 Discussion  
In this paper, we adopted the double-projector structured-light three-dimensional measurement system. In [2], the authors revealed the advantages of this system: (1) it reduces occlusions; (2) it increases the signal-to-noise ratios for its double brightness compared to a single projector system; (3) it decreases the number of images required for scanning; and (4) it tests multi-path interference. Our method showed the third advantage again; that is, the number of images required was reduced, and the measurement speed was significantly improved. Taking the dual-frequency four-step phase-shifting method as an example, our method only needs to obtain eight superimposed gratings to obtain the unwrapped phase for both sides. When using a traditional single-projector and single-camera system, 16 pictures must be captured to acquire the same results. The measuring efficiency was  
Figure 14: Red-channel image phase unwrapping: (**a**) unwrapped phase; (**b**) cross profile of the blue line in (**a**); (**c**) cross profile of the red line in (**a**).  
Figure 15: Blue-channel image phase unwrapping: (**a**) unwrapped phase; (**b**) cross profile of the blue line in (**a**); (**c**) cross profile of the red line in (**a**).  
improved by 50%. Second, our method projected the most primitive sinusoidal grating without complex coding patterns, and the separation method was simple. Third, our method only replaced the monochrome camera with the color camera in the system, and did not add other devices. Finally, our experimental process is not complicated, has no special requirements for the placement of cameras and projectors, and is easy to operate.
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 2 Materials and Methods
### Method
#### 2.3.5 Mapping Spatial Distribution of Cropland Abandonment  
On the basis of the definition of abandoned cropland and annual land-cover classification, the spatial distribution of cropland abandonment with rainfed cropland and irrigated cropland was mapped. Abandoned cropland was considered for areas out of cultivation for 2 years, and strong land policy support is required to improve the situation. Special attention is required for the potential abandonment of intermittent cropland. Active cropland represented areas always under cultivation, which serve as an important safeguard for food production. According to the area and proportion of cropland abandonment, reasonable adjustment and planning of the cropland will be carried out in future work.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
###### Abstract  
The automated analysis of medical diagnostic videos, such as ultrasound and endoscopy, provides significant benefits in clinical practice by improving the efficiency and accuracy of diagnosis. Deep learning techniques show remarkable success in analyzing these videos by automating tasks such as classification, detection, and segmentation. In this paper, we review the application of deep learning techniques for analyzing medical diagnostic videos, with a focus on ultrasound and endoscopy. The methodology for selecting the papers consists of two major steps. First, we selected around 350 papers based on the relevance of their titles to our topic. Second, we chose the research articles that focus on deep learning and medical diagnostic videos based on our inclusion and exclusion criteria. We found that convolutional neural networks (CNNs) and long short-term memory (LSTM) are the two most commonly used models that achieve good results in analyzing different types of medical videos. We also found various limitations and open challenges. We highlight the limitations and open challenges in this field, such as labeling and preprocessing of medical videos, class imbalance, and time complexity, as well as incorporating expert knowledge, k-shot learning, live feedback from experts, and medical history with video data. Our review can encourage collaborative research with domain experts and patients to improve the diagnosis of diseases from medical videos.  
deep learning; echocardiography; ultrasound; endoscopy; medical diagnostic videos; classification; segmentation; detection
# Sound Localization Framework for Construction Site Monitoring
## 4 Construction Sound Localization Framework (CSLF)  
New workspaces are constantly created at construction sites, and thus, are necessary to cope with various situations. Therefore, Internet of Things (\"IoT\") devices, which are easy to install and dismantle without needing a separate power supply, are required at construction sites. Moreover, due to high damage risks in the field, devices with low unit costs will be suitable in the consideration of economic efficiency. Under these conditions at construction sites, a system was designed considering usability and economic efficiency in this study, and a more efficient method was presented through preliminary and field tests.  
When developing a framework to estimate the location of a sound source, it is necessary to analyze the characteristics of the space. Existing studies monitored the characteristics of sound sources while the research method and scope were determined based on whether the space was linear, indoor, or external. Therefore, developing a CSLF system to estimate the location of sound sources generated within a construction site should be preceded by a spatial characteristics analysis of the construction site. Furthermore, the characteristics of the device setting and layout (e.g., position, distance, direction, and number of microphones) should be considered for accurate location estimation.
###### Abstract  
Flight time is widely used to calculate jump height because of its simple and inexpensive application. However, this method is known to give different results than the calculation from vertical velocity at takeoff. The purpose of this study is to quantify the effect of postural changes between takeoff and landing on the jump height from flight time. Twenty-seven participants performed three vertical jumps with arm swing. Three-dimensional coordinates of anatomical landmarks and the ground reaction force were analyzed. Two methods of calculating jump height were used: (1) the vertical velocity of the whole-body center of mass (COM\({}_{\text{wb}}\)) at takeoff and (2) flight time. The jump height from flight time was overestimated by 0.025 m compared to the jump height from the takeoff velocity (\(p<0.05\)) due to the lower COM\({}_{\text{wb}}\) height at landing by \(-0.053\) m (\(p<0.05\)). The postural changes in foot, shank, and arm segments mainly contributed to decreasing the COM\({}_{\text{wb}}\) height (\(-0.025\), \(-0.014\), and \(-0.017\) m, respectively). The flight time method is reliable and had low intra-participant variability, but it cannot be recommended for a vertical jump when comparing with others (such as at tryouts) because of the potential \"cheating\" effect of differences in landing posture.  
flight time; vertical jump; center of mass; landing +
Footnote †: journal: Accepted: 20 January 2020; Published: 22 January 2020
## 3 Results and Discussion
### Evaluation of Antifungal Activity of Allicin
#### 3.1.1 Determination of Zone of Growth  
The zones of growth of fungi were determined at different concentrations (50, 100, 150 and 200 \(\upmu\)M) of allicin. A strong concentration dependent inhibition of all target fungi was observed. The growth of fungal colonies reduced significantly as compared to the control at each concentration of allicin employed. The growth of _V. dahliae_ was completely inhibited by allicin at a concentration of \(\geq\)150 \(\upmu\)M. Similarly, the growth of _B. cinerea_ and _A. brassicicola_ was strongly inhibited at a concentration of 200 \(\upmu\)M. In contrast, _V. longisporum_ was least inhibited by allicin even at the highest concentration used (200 \(\upmu\)M) (Figure 2a). These findings reveal the excellent antifungal potential of allicin at a 200 \(\upmu\)M or lower concentration against model plant pathogens.  
In the second assay, allicin was employed at concentrations of 1.5 and 3 mM to elucidate the potential antifungal activities. Allicin (1.5 or 3 mM) was added to the paper discs evenly distributed around the plug of mycelium. A concentration-dependent fungal growth inhibition was observed in  
Figure 2: Zones of growth of fungi after treatment with allicin at different concentrations. The fungal growth zone is greater in respective control as compared to plates containing specific concentration of allicin. (**a**) Allicin was added to PDA to achieve final concentrations of 50, 100, 150 and 200 \(\upmu\)M. (**b**) The 20 \(\upmu\)L of 1.5 or 3 mM allicin was pipetted into each disc. The plates were then incubated at 22 \({}^{\circ}\)C for one week and photographed. In the control plate, the fungus grew normally from center towards the corners of the plate. Fungal growth was restricted in allicin treated plates. The concentration dependent growth inhibition was observed in case of test fungi.  
allicin treated fungi. At a concentration of 1.5 mM, allicin resulted in a decrease in the growth of test fungi. A strong antifungal activity was observed at the highest concentration (3 mM), particularly in the cases of _V. dahliae_ and _V. longisporum_ (Figure 2b). These findings are in accordance with the previous investigations reporting the antifungal activity of allicin [15; 20; 21; 22; 50].