esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Validation of Automatic Refocusing in OCTA Images  
From a set of B-scan OCT images, using the ROI, the OCTA image was extracted by using Equation (1), as can be seen on the right side of Figure 3. The top slice is the OCT image and the slice  
Figure 2: Steps to reconstruct well-focused OCTA images. Complex OCT data are obtained from Fourier transform of the acquired fringes. In the process 2 shown in blue color, the optimal propagation distances, _d_optimal, giving the maximum contrast in the OCTA image, are searched for by evaluating the contrast of the OCTA image in the ROI (region of interest) while inserting numerous \(d\) values. The round structures in the B-scan OCT images represent vascular structures in the mouse brain.  
marked 3 is the corresponding OCTA image. With these OCT images, a series of OCTA images were reconstructed by using Equation (2) while changing the propagation distance \(d\). The OCTA images reconstructed using another \(d\) are depicted in the right side and marked with numbers 1, 2, and 4. In same manner, the image contrasts of the reconstructed OCTA images were calculated by using Equation (3) and plotted in terms of the propagation distance \(d\), as shown in the left graph in Figure 3. The calculated image contrast and the corresponding d values of the four OCTA images on the right side in Figure 3 are indicated with the red arrows in the left graph. The left graph shows the 2 nd OCTA slice with the maximum contrast, and the right figure confirms this observation. The blood flow signals in the area marked by the white arrows are clearer than in other images. This signifies that the contrast value can be utilized as a criterion for refocusing of the brain OCT images acquired in vivo.
# PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris
## 1 Introduction
However, the current deep-learning-based iris segmentation network models incur a high cost in terms of a large parameter search space and a long segmentation time, have requirements for hardware devices, and perform poorly in multi-source heterogeneous iris segmentation. Targeting these problems and motivated by the above observations, we propose a precise and fast segmentation network model, PFsegIris, for multi-source heterogeneous iris images that can accurately segment iris regions of different sizes; weaken the influence of different spectra and eyelid, eyelash occlusion noises; and enhance the discriminative ability of iris region pixels, thereby, having a better universality for iris images collected by different devices. First, the iris feature extraction module designed in this paper was embedded in each layer of PFsegIris to extract rich iris information, in which convolution kernels of different sizes obtain the information of iris regions of different sizes, reducing the amount of parameters and meanwhile taking accuracy into account. Then, an efficient parallel position and channel attention mechanism [19] was used, once in the middle, to focus on the iris region pixels of small targets at different spectra. Finally, a skip connection was added to make details such as edge features more precise. Images from one near-infrared iris database, JLU-6.0, and two visible iris databases, CASIA-Iris-Lamp-v4 and MICHE-I, were mixed for training. Several experiments were carried out for testing on four near-infrared iris databases, JLU-6.0 [20], JLU-7.0 [20], CASIA-Iris-Interval-v4 [21], and Mmu2 [22], and three visible iris databases, CASIA-Iris-Lamp-v4 [21], MICHE-I [23; 24], and UBIRIS.V2 [25; 26; 27]. The test results show that the iris segmentation algorithm proposed can realize fast and precise segmentation for multi-source heterogeneous iris images. Our main contributions can be summarized as follows:  
1. Different from traditional methods and other iris segmentation algorithms based on deep learning, a more precise segmentation algorithm, PFsegIris, was designed to segment multi-source heterogeneous irises without any preprocessing or post-processing. We proved that accurate iris segmentation can be realized for imageswith heterogeneous spectra (visible and near-infrared), different iris region sizes, and uneven quality. 2. While ensuring accuracy, PFSegIris is lightweight, significantly reducing the number of parameters, storage space, and calculations in comparison with other methods, and the average prediction time on seven heterogeneous databases was less than 0.10 s, which is a fast iris segmentation algorithm. In addition, the algorithm proposed can be applied to devices with low computing performance and storage capacity. Furthermore, the remainder of this paper is organized as follows. Section 2 presents the proposed PFSegIris structure in detail. Experimental results and comparisons are described in Section 3. Finally, Section 4 is the conclusion.
# Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest
## 4 Results
### Feature Selection and KW-ANOVA Test  
As mentioned before, the optimal subset of features was selected using the RFECV algorithm and the features were statistically analyzed by the KW-ANOVA test. The optimal subset of features reduces the complexity of the model while maintaining the classification accuracy. The relationship between the number of selected features and the classification accuracy is illustrated in Figure 4. In Figure 4, the cross-validation score fluctuates as the number of features decreases, which is caused by the change in the data distribution during the five-fold cross validation process. From Figure 4, the highest accuracy is obtained by selecting 13 features. The selected features are as follows: _RMSSD_, _pNN50_, _HR_, _VLF_1, _HF_1, _LFnorm_1, and _HFnorm_1 in the ECG feature set; _Smith_, _Smean_, _Svar_, _ApEn_, _LZC_, and _CTM_ in the SpO2 feature set. The number corresponding to each feature is presented in Table 3.  
Footnote 1: [[https://github.com/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/](https://github.com/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/)]([https://github.com/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/](https://github.com/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/en/))  
in Figure 6. Thus, in this study, the RF classifier is more suitable for SA detection than the other machine learning algorithms mentioned above.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **AUC** \\ \hline SVM & 96.1\% & 93.4\% & 97.8\% & 0.956 \\ LR & 97.4\% & 95.5\% & 98.2\% & 0.971 \\ KNN & 93.8\% & 89.3\% & 96.7\% & 0.940 \\ RF & 97.5\% & 95.9\% & 98.4\% & 0.992 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Per-segment classification results of different classifiers on the Apnea-ECG database.  
Figure 5: Box plots for the selected features corresponding to 0 and 1 classes, where 0 represents normal and 1 represents apnea.
## 3 Results and Discussion
### Effect of the Diameter of the Ball Seat on the Flow Characteristics
As shown in Figure 11, as the diameter of the ball seat increased from 30 to 40 mm, the pressure at the inlet of the differential motion assembly decreased from 31.4 to 30.3 MPa, and the pressure drop \(\Delta P\) of the differential motion assembly with a 40 mm ball seat diameter was only 0.2 times that of the assembly with a 30 mm ball seat diameter. In summary, when the ball seat diameter increased from 30 to 40 mm, the maximum velocity decreased to 0.55 times that of the original differential motion assembly with a ball seat diameter of 30 mm. The axial length of the vortex area and the vortex velocity near the wall decreased to 0.85 times and 0.67 times that for the original ball seat diameter, respectively. In addition, the pressure drop of the differential motion assembly with a ball seat diameter of 40 mm was only 0.2 times that of the original assembly. Thus, increasing the diameter of the ball seat could effectively reduce the erosion of the drilling fluid on the inner surface of the differential motion assembly and the risk of cutting the pin before the ball is dropped, thereby improving the reliability and service life of the differential motion assembly. Notably, the larger diameter of the ball seat in the differential motion assembly means that a larger drop ball should be used to trigger the differential motion assembly when a core enters the IPP-Coring system. However, due to the constraint of the inner diameter of the drilling tool, the diameter of the drop ball should not be too large; otherwise, it will be difficult to smoothly drop the ball onto the ball seat. As shown in Figure 2, the diameter of the inlet of the differential motion assembly was only 50 mm; therefore, a ball seat with a diameter of 40 mm may be the most appropriate.
## 7 Conclusions and Future Research  
OWC has already been proved to be a congruent complementary technology to RF, with the potential of being integrated into the next-generation of wireless technologies. OCC is a promising OWC system that can be employed to receive data from existing LED infrastructures using cameras. Despite its advantages, OCC suffers from some limitations that degrade its overall performance. In this study, several performance-improvement techniques for OCC mainly focusing on the transmitter and receiver parameters are discussed. A great deal of attention was given to enhancing the user data rate and SINR. Existing modulation techniques for OCC and their application environments were also discussed. In addition, an analysis of user satisfaction was performed to demonstrate the optimal OCC settings for users in different service scenarios. Our future work will include the implementation and testing of the optimality of OCC systems to achieve a high data rate in long-distance communication. Moreover, very few studies have investigated the effects of the communication channel parameters on OCC data reception, which could be another significant research topic for OCC performance improvements. Future research will also include the integration of OCC in optical hybrid infrastructures by utilizing the useful features offered by current OCC systems.  
All authors contributed to this paper. Specifically, [PERSON] and [PERSON] proposed the idea and wrote the paper; [PERSON] and [PERSON] performed the simulations; [PERSON] and [PERSON] analyzed and verified the simulation data; and [PERSON] supervised the work and provided funding support.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-0-01396) supervised by the IITP (Institute for Information & communications Technology Promotion).  
The authors declare no conflict of interest.
# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 5 Discussion
In addition, as more data collection is performed, supervised machine learning can be used to determine the expected prediction error rate and to understand the lowest obtainable prediction error level. Such investigation, however, is beyond the scope of the current research and can be performed in future work. The implementation and deployment of HSPs poses multiple challenges. One of the challenges is that the UAV requires both hardware and software skills for its maintenance, which can hinder its operation by users who do not have the required technical knowledge. In this study, during data collection, the UAV's electronic speed controller module of one of the motors was broken, which required the replacement of the module and recalibration of the UAV. In addition, at one instance, the UAV flight mode changed unexpectedly, which caused the UAV to become unstable and difficult to control and had to be corrected with a recalibration of the UAV controller channel. Moreover, after a certain flight time period, the UAV requires to be recalibrated to maintain control and low vibration. Another challenge is that the optical sensors used in this experiment require low altitude to provide valid data. Therefore, the UAV must be flown very close to the plants. This prevents the use of the UAV's automatic mission planning that enables the UAV to fly automatically based on the given path and collect the data. Since the UAV must fly at low altitude and the height of the crop changes in different areas, there is a need to always fly the UAV manually. Although this challenge can be addressed by installing an object detection and collision prevention system on the UAV, such a system may not detect small branches, which can cause UAV to crash. In addition, object detection and collision prevention systems can remarkably increase the costs of UAVs and energy consumption, which subsequently reduces the flight time.
# Use of Eoofriendly Glass Powder Concrete in Construction of Wind Farms
## 3 Results and Discussion  
### Characterization of Glass Powder  
Three grinding processes were carried out on the waste glass, each one for a different time, in order to obtain three samples of glass powders with different particle sizes.  
Three dimensions have characterized the glass powders--d10, d50 and d90. They represent, respectively, the diameter of the sample particles for which 10%, 50% and 90% of the particles have a diameter smaller than that dimension, as can be seen in Table 2. In  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Conctee Composition**} & \multicolumn{6}{c}{**Sample ID**} \\ \cline{2-7}  & **Control** & **G15** & **G30** & **G45** & **G60** & **G80** \\ \hline Cement substitution rate for glass powder (\%) & 0 & 15 & 30 & 45 & 60 & 80 \\ Cement CEM I 52,5 R (kg/m\({}^{3}\)) & 330 & 280.5 & 231 & 181.5 & 132 & 66 \\ Glass Powder (kg/m\({}^{3}\)) & 0 & 49.5 & 99 & 148.5 & 198 & 264 \\ Equivalent binder (kg/m\({}^{3}\)) & & & & 330 & & \\ Plasticizer (\% binder) & & & & 0.35 & & \\ Total water (L) & & & & 184 & & \\ Arid \textless{}4 mm & & & & 740 & & \\ Gravel 4-12 mm & & & & 310 & & \\ Gravel 12-20 mm & & & & 850 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of experimental conditions for the samples prepared.  
this work, the value of d50 was used for the characterization of the different batches of ground glass.  
The cumulative granulometric curves of the three samples (Figures 1 and 2) revealed considerable differences in the distribution of particle size.  
The glass powder subjected to X-ray diffraction tests resulted in a broad diffraction band between 15 and 45 degrees, which corresponded to its amorphous and disordered structure. The X-ray diffractogram of the glass powder of d50 = 16 \(\upmu\)m is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Glass Powder Used** & **Griding Duration** & **d\({}_{10}\)** & **d\({}_{50}\)** & **d\({}_{90}\)** \\ \hline T1 & 2 h 30 & 2.92 \(\pm\) 0.01 \(\upmu\)m & 33 \(\pm\) 1 \(\upmu\)m & 110 \(\pm\) 3 \(\upmu\)m \\ T2 & 4 h 15 & 1.96 \(\pm\) 0.01 \(\upmu\)m & 16 \(\pm\) 1 \(\upmu\)m & 59 \(\pm\) 2 \(\upmu\)m \\ T3 & 5 h & 1.65 \(\pm\) 0.01 \(\upmu\)m & 11 \(\pm\) 1 \(\upmu\)m & 43 \(\pm\) 2 \(\upmu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Granulometric characteristics of glass powders produced as a function of grinding time.  
Figure 1: Distribution of the accumulated granulometry of the 3 batches of glass powder [32].  
Figure 2: Differential particle size distribution of the 3 batches of glass powder [32].  
By means of the observation through the Scanning Electron Microscope (SEM), glass grains with particle size between 1 and 20 \(\upmu\)m can be observed. These grains showed acicular shape and cone-shaped fractures. It should be noted the absence of fine elements attached to these glass particles and their low porosity. A frontal and side views of a glass powder particle, obtained by SEM, are shown in Figure 4.
## 4 Results and Discussion
### Underground Implementation of a Digital-Twin-Based Spatial Object Extraction Algorithm
#### 4.1.2 Point Spatial Object Extraction Process  
The method of extracting point objects was improved using the 3D correspondence grouping algorithm. It facilitates grouping related points in preprocessed point cloud data to geometrically derive objects after key point extraction. This grouping provides geometric consistency by continually comparing the grouped reference data with the comparison data, adding them to a model subset when a geometric match is found. The method adds the benefit of circumventing pose estimation because it also calculates transformations (rotations and transformations) for all subsets, which are expected to align with the model instance. However, it consumes more computational resources and time, owing to the increase in the number of computations.  
The technique for identifying point objects entails preprocessing and executing correspondence grouping. This technique clusters the point correspondence set, which is acquired after description matching, into a model instance within the current scene. The algorithm identifies the feature points of both reference and comparison data and compares feature descriptors to carry out geometric matching. Figure 4 provides a representation of this point object extraction process.
# Wave-Shaped Microstructure Cancer Detection Sensor in Terahertz Band: Design and Analysis
###### Abstract  
For the quick identification of diverse types of cancer/malignant cells in the human body, a new hollow-core optical waveguide based on Photonic Crystal Fiber (PCF) is proposed and numerically studied. The refractive index (RI) differs between normal and cancerous cells, and it is through this distinction that the other crucial optical parameters are assessed. The proposed cancer cell biosensor's guiding characteristics are examined in the COMSOL Multiphysics v5.5 environment. The Finite Element Method (FEM) framework is used to quantify the display of the suggested fiber biosensor. Extremely fine mesh elements are additionally added to guarantee the highest simulation accuracy. The simulation results on the suggested sensor model achieve a very high relative sensitivity of 99.9277%, 99.9243%, 99.9302%, 99.9314%, 99.9257% and 99.9169%, a low effective material loss of \(8.55\times 10^{-5}\) cm\({}^{-1}\), \(8.96\times 10^{-5}\) cm\({}^{-1}\), \(8.24\times 10^{-5}\) cm\({}^{-1}\), \(8.09\times 10^{-5}\) cm\({}^{-1}\), \(8.79\times 10^{-5}\) cm\({}^{-1}\), and \(9.88\times 10^{-5}\) cm\({}^{-1}\) for adrenal gland cancer, blood cancer, breast cancer type-1, breast cancer type-2, cervical cancer, and skin cancer, respectively, at a 3.0 THz frequency regime. A very low confinement loss of \(6.1\times 10^{-10}\) dB/cm is also indicated by the simulation findings for all of the cancer cases that were mentioned. The straightforward PCF structure of the proposed biosensor offers a high likelihood of implementation when used in conjunction with these conventional performance indexes. So, it appears that this biosensor will create new opportunities for the identification and diagnosis of various cancer cells.  
microstructure fiber; PCF; cancer cell; confinement loss; effective material loss; sensitivity
# Blockwise Frequency Domain Active Noise Controller Over Distributed Networks
## 5 Conclusions  
In this paper, an experimental validation of an ANC system has developed over a network of distributed acoustic nodes using a collaborative incremental strategy. For this purpose, a distributed version of the FPBFxLMS algorithm has been introduced. In order to evaluate the performance of the proposed algorithms in a practical environment, we have implemented the adaptive control in a real-time audio toolbox provided by MATLAB(r). Results show that the distributed implementation of the algorithm exhibits the same performance as its centralized version when there are no communication constraints in the network.  
Moreover, the computational complexity of the distributed algorithm has been studied and compared with the centralized version. Since each node of the distributed algorithm can perform almost all the operations simultaneously, the computational complexity is split among the nodes and the processing time of the algorithm is significantly reduced. However, when we consider the use of physical nodes in a real network, we will have to analyze some aspects with regards to the communication capabilities among the nodes in the network. In this sense, we can conclude that the distributed ANC system performs well if the network has enough bandwidth to transfer the network information without violating the real-time condition.  
The performance of the proposed distributed FPBFxLMS algorithm in an ideal ASN and an ASN with a constant delay in the communications has been also shown. The behavior of the system worsens when delay increases, but, in this experiment, it shows an acceptable and stable performance despite data loss in the information exchange when the node uses its local information while waiting for network information. However, the influence of the degree of acoustic interaction between nodes on the network communication strategies must be considered in future works.  
Figure 9: Noise reduction obtained for the distributed FPBFxLMS algorithm using a four-node ASN for different latency values at the node with the best performance (**a**) (case 1) (**c**) (case 2) and with the worst performance (**b**) (case 1) (**d**) (case 2).  
**Acknowledgments:** This work has been supported by the European Union (European Regional Development Fund) together with Spanish Government through TEC2015-67387-C4-1-R project, the grant BES-2013-063783 and Generalitat Valenciana through the PROMETEOII/2014/003 project.  
**Author Contributions:** All authors discussed the results and implications and commented on the manuscript at all stages. [PERSON], [PERSON] and [PERSON] conceived the study, supervised and edited the work. All authors contributed to the formulation development. [PERSON] performed the experimental work, analyzed the data and wrote the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.