esa-sceva/satcom-chunk-collection huggingface dataset:
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 5 Architecture and Implementation  
The architecture of the CMUA (Continuous Mobile User Authentication) framework can be split up into three main components. A recording subsystem is the first component utilized by the framework and is used to gather data from users for initial model training. When the data are gathered successfully, it will be transferred to the second system, which trains a specific classifier using various extracted features from the dataset. This trained model will then be employed in the third and final system of the framework, which is the authentication software used to make predictions on new data seen from the same device, as was used to record the training data in the first place. A general outline of the high level architecture can be seen in Figure 1. The diagram shows the input of the user data to the recording application, which in turn saves it to an appropriate database. There it is used by the HMM to optimize the training parameters, and as a result the classifier is generated. New input data are then used in conjunction with the authentication application for making predictions on the users authentication state. The following chapter will explain every component in more detail.
###### Abstract
They used ortho-rectified images of Landsat Operational Land Imager OLI band8 (15 m resolution), Landsat7 Enhanced Thematic Mapper Plus (ETM+) band 8 (15 m resolution), and Landsat4-5 Thematic Mapper TM band3 (30 m resolution), to analyze front position and ice speed, and found no substantial fluctuations of both during the 15-year period. The aims of the present study are (i) to estimate ice flow velocity of the Perito Moreno Glacier by means of SENTINEL2 and LANDSAT images, applying image data treatment as proposed in the present literature [24]; (ii) to quantify calving rates from 1985 to 2017; and (iii) to verify whether PMG is varying in size and flow dynamics or some recent changes occurred, possibly in response to climate change. Our results here complement the work of other authors by (i) extending estimation of ice speed and calving rate of the PMG until year 2017 and (ii) using information from the more recent SENTINEL2 constellation of satellites, working at a finer resolution (10 m), which is promising for glacier mapping [25; 26].
## 4 Results
### Feature Reduction
#### 4.1.4 Boruta  
Figures 4 and 5 illustrate the result of implementing the Boruta approach on the final train set. In Figure 4, the X axis shows each feature, and the Y axis depicts the value of each feature's importance based on the Boruta technique. According to this technique, eleven features are suggested to be omitted (depicted as Rejected in Figure 5). The result of running different classifiers on the reduced set suggested by Boruta will be discussed later.
###### Abstract  
In order to solve the problem of the poor adaptability of the TBM digging process to changes in geological conditions, a new TBM digging model is proposed. An ensemble learning prediction model based on XGBoost, combined with Optuna for hyperparameter optimization, enables the real-time identification of surrounding rock grades. Firstly, an original dataset was established based on the TBM tunneling parameters under different surrounding rock grades based on the KS tunnel. Subsequently, the RF-RFECV was employed for feature selection and six features were selected as the optimal feature subset according to the importance measure of random forest features and used to construct the XGBoost identification model. Furthermore, the Optuna framework was utilized to optimize the hyperparameters of XGBoost and validated by applying the established TBM dataset of the KS Tunnel. In order to verify the applicability and efficiency of the proposed model in surrounding rock grade identification, the prediction results of five commonly used machine learning models, Optuna-XGBoost, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Decision Tree (DT), XGBoost, and PSO-XGBoost, were compared and analyzed. The main conclusions are as follows: the feature selection method based on RF-RFECV improved the accuracy by 8.26%. Among the optimal feature subset, T was the most essential feature for the model's input, while PR was the least important. The Optuna-XGBoost model proposed in this paper had higher accuracy (0.9833), precision (0.9803), recall (0.9813), and F1 score (0.9807) than other models and could be used as an effective means for the lithological identification of surrounding rock grade.  
TBM; tunnel; surrounding rock; tunneling parameters; RFECV; XGBoost; Optuna; machine learning; ensemble learning Article
## 3 Biosensor Application of Nanostructures Electrodeposited onto a Template
### Other Templates for Electrodeposition
The modified electrode was also used as a support for an immunoassay against the toxin microcystin, which is found in drinking water, with an immobilized antibody and HRP-labeled microcystin used for the assay. A recent study used coccoliths, which are biomineral shells isolated from _Emiliania huxleyi_, a photosynthetic bacterium, as templates for the electrodeposition of gold to make an aptamer-based sensor for vaspin, a 42.5-kDa adipose tissue-derived serine protease inhibitor [64]. The coccoliths were about four \(\upmu\)m in size, with many pores able to provide an increased surface area. The coccoliths were drop cast onto screen-printed gold electrodes, sputter coated with gold, and the gold was electrodeposited at -1.2 V (versus Ag) from an HAuCl\({}_{4}\) solution (concentration not given) for 70 s to create a roughened surface. The gold surfaces were modified with a first thiolated aptamer that would bind vaspin; then, a second aptamer would bind from solution to create a sandwich structure of vaspin between the two aptamers. The reduction in chronoamperometric current from five mM of K\({}_{3}\)[Fe(CN)\({}_{6}\)] with vaspin concentration in human serum was used to determine a limit of detection of 2.58 nM for the electrodes modified first with the coccoliths. Without the increase in surface area provided by the coccoliths, a limit of detection of 7.72 nM was found.
###### Abstract
The combination of X-ray computed tomography (CT) and areal surface texture parameters, according to ISO 25178-2:2012 [21], are increasingly used to characterize internal AM surfaces [15; 22; 23]. Areal surface texture characterization is often adequate for general characterization but may lack the ability of functional characterization for some application-specific purposes such as fluid flow, where the flow typically goes in one direction. Using X-ray CT, work has been conducted displaying how roughness varied randomly along the length of LPBF channels [24]. Other work utilizes the ability of X-ray CT to capture undercuts on a surface. This ability is unique for X-ray CT and the applicability of classic roughness parameters in the setting of including re-entrant features has been explored in the literature [25]. Additionally, an advantage of X-ray CT over conventional profilometer measurements is the abundance of data, allowing for a full characterization of the surface. A detailed characterization may be used to generate more accurate prediction models for surface texture. Such prediction models have been shown useful in the generation of predicted geometries of conformal cooling channel designs [26]. As new methods for manufacturing plastic injection holds with conformal cooling channels emerge, such as hybrid processes combining machining and laser powder bed fusion [27], adequate characterization techniques become crucial for quality inspection. X-ray CT inspection may be used for this purpose, once a proper understanding of the usage has been achieved. This work seeks to qualitatively and quantitatively characterize aluminum LPBF channels. The characterization includes the cross and surface texture together with the actual channel diameter. The nominal channel diameters were \(D_{nom}=1\) mm, 2 mm, 3 mm, 6 mm, and 9 mm, covering a range typically used for cooling applications in tools and molds. The goal of this work is to improve upon the understanding of the appropriate use of profile parameters and the connection between the calculated parameters and the formation of dress and roughness in LPBF aluminum channels. In this work, profile parameters are used, with profiles parallel to the flow direction of the channels, because it is the texture along the flow direction that is relevant towards influencing flow dynamics. It is also the intention to shed light on the possible size effects of LPBF aluminum channels and provide simple methodologies for generating prediction models for roughness and channel diameters. Section 2 presents the investigated channel artifacts together with the chosen characterization approach. Furthermore, the section includes a proposal of a novel methodology for predicting the equivalent diameter of LPBF channels as a function of channel length. Section 3 presents the obtained results together with a discussion of the shown observations, revealing both redundancy and unique characterization properties among the calculated profile parameters. Finally, Section 4 presents the conclusions of this work.
## 5 Conclusions  
In this paper, a low profile frequency reconfigurable UHF antenna is presented. Depending on the PIN diode configuration, the antenna operates at 401/868 MHz when the PIN diode is reverse-biased; and at 466 MHz when the PIN diode is forward-biased. The antenna has an acceptable gain compared with its dimensions. In the ON state, the antenna has a gain of \(-5.2\) dB at the resonant frequency (466 MHz). In the OFF state, it has gains of \(-8.5\) and \(-2.8\) dB at the resonant frequencies (401 MHz and 868 MHz, respectively). The antenna is fabricated and its input reflection coefficients are measured to validate the simulation results. A practical method is proposed to compensate the frequency shift between measurement and simulation in order to reduce cost and time development for designing new connected devices.  
Figure 16: Simulated and measured S11: step 3.  
Figure 17: Measured S11 in steps 3 (with PIN diode) and 4.  
**Author Contributions:** Conceptualization, A.B.; methodology, A.B.; software, A.B., B.B.; validation, B.B.; formal analysis, A.B.; investigation, A.B.; resources, B.B. and L.G.; writing--original draft preparation, A.B.; writing--review and editing, A.B.; visualization, A.B.; supervision, B.B.; project administration, B.B. and L.G.; funding acquisition, L.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Laser Absorption Sensing Systems: Challenges, Modeling, and Design Optimization
## 4 Strategies for Noise Reduction and Design Optimization
### Sources of Noise
#### 4.1.3 Optical Interference Fringe Noise
However, these techniques will inevitably increase the computation complexity, and the filter parameters need to be carefully designed on the basis of an appropriate understanding of the signal and noise characteristics. The extent of improvement that such techniques can achieve is ultimately limited by the quality of the light signal measured, so it is still of utmost importance to devote the best effort in designing a better optical system. \begin{table}
\begin{tabular}{c l l} \hline \hline
**Strategy** & **Solutions** & **Feature** \\ \hline Optical design & \begin{tabular}{l} Wedged or AR-coated windows; \\ Optical isolators; \\ Etalon immune distance \\ \end{tabular} & \begin{tabular}{l} Difficult to minimize etalon \\ in complex optical systems \\ \end{tabular} \\ \hline Mechanical modulation & \begin{tabular}{l} Dithering or rotating of mirrors; \\ Brewster-plate spoiler \\ \end{tabular} & \begin{tabular}{l} Increased system complexity; \\ Limited detection bandwidth \\ \end{tabular} \\ \hline Balanced detection schemes & \begin{tabular}{l} Split laser beam into the sample \\ beam and reference beam \\ \end{tabular} & \begin{tabular}{l} Difficult to replicate all optical \\ effects in reference path \\ \end{tabular} \\ \hline WMS & \begin{tabular}{l} Modulation index optimization; \\ Use of higher harmonics \\ \end{tabular} & SNR sacrificed \\ \hline Digital filtering technique & \
\begin{tabular}{l} Wavelet transform; \\ Kalman filtering; \\ EMD \\ \end{tabular} &
\begin{tabular}{l} Increasing computation cost; \\ Parameters need to be set carefully \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of strategies to minimize interference fringe noise. AR: anti-reflective, EMD: empirical mode decomposition.
# A Novel Wavelet-Based Algorithm for Detection of QRS Complex
## 5 Conclusions  
This study proposes a QRS detection algorithm based on a four-level biorthogonal spline wavelet transform. A noise evaluation approach was developed for selecting a lower-noise wavelet detail signal to reduce the noise interference instead of pre-filtering out the high-frequency noise in the signal preprocessing. If the noise amount in d3' is lower than that in d4', the detection of QRS peaks is based on d3'; otherwise, it is based on d4'. The identification of QRS peaks was based on the extremum pairs in the selected wavelet detail signals and the proposed decision rules. The proposed algorithm was evaluated by the MIT-BIH arrhythmia database. It presents a high accuracy to detect QRS peaks. The results show a 0.25% detection error rate, 99.84% sensitivity, and 99.92% positive prediction value. The performance of the proposed algorithm is better than that of several wavelet-based methods and non-wavelet-based algorithms.  
[PERSON] and [PERSON] provided the research ideas. [PERSON], [PERSON], and [PERSON] designed the algorithms, performed the experiments, and analyzed the experimental data. [PERSON] and [PERSON] wrote the paper. All authors reviewed and approved the final manuscript.  
This research was funded by National Chin-Yi University of Technology, Taiwan, Republic of China, grant numbers NCUT 18-R-CE-020 and NCUT 18-T-CE-010.  
The authors declare no conflict of interest.
## 5 Discussion
### Calculation of the Modal Responses
However, in the other cases shown in Figure 35c,d, the plots of the time-history analysis results were below those of the bidirectional \(V_{\Delta E}\) spectrum. From the comparisons between the predicted \(V_{\Delta E1U}\)\({}^{*}\) and that obtained from the time-history analysis results, the predicted \(V_{\Delta E1U}\)\({}^{*}\) provided a conservative estimation, except in the case of the Art-2 series shown in Figure 35b. This is because the predicted response points correspond to the \"valley\" of the bidirectional \(V_{\Delta E}\) spectrum, therefore making the predicted \(V_{\Delta E1U}\)\({}^{*}\) smaller. Figure 36 shows the prediction of \(V_{\Delta E1U}\)\({}^{*}\) from the bidirectional \(V_{\Delta E}\) spectrum for Model-Tf44. Similar observations to those made for Model-Tf34 can be made for Model-Tf44. Based on the above results, it can be concluded that that bidirectional \(V_{\Delta E}\) spectrum can approximate the upper bound of the equivalent velocity of the maximum momentary input energy of the first mode \(V_{\Delta E1U}\)\({}^{*}\). As shown in Equation (25), \(\Delta E_{1U}\)\({}^{*}\)/\(M_{1U}\)\({}^{*}\) was calculated from the ground acceleration component of U-axis, while the contribution of V-axis was none. Therefore, it is easily expected that the upper bound of the maximum momentary input energy of the first mode can be approximated by the bidirectional maximum momentary input energy. Comparisons between the unidirectional and bidirectional \(V_{\Delta E}\) spectra can be found in Appendix B. Therefore, the upper bound of \(V_{\Delta E1U}\)\({}^{*}\) can be properly predicted using the bidirectional \(V_{\Delta E}\) spectrum.
# A Fast Point Clouds Registration Algorithm for Laser Scanners
## 2 Proposed Method
### Intrinsic Shape Signatures Algorithm  
Compared with Harris, Normal Aligned Radial Feature (NARF), and SIFT feature point extraction algorithms, the ISS is faster and the extracted feature points distribute more evenly. It has two main parts: a local reference frame (LRF) and a feature vector used to describe the points in the neighborhood around the basis point [16]. Figure 3 shows the schematic of ISS algorithm.  
The steps of ISS algorithm are as follows:  
Figure 1: Flow chart diagram of the proposed method.  
Figure 3: Schematic of intrinsic shape signatures (ISS).  
Figure 2: Down sampling of the “Bed”; (**a**) the point cloud to be registered, (**b**) the down sampling result.  
Step 1, weighting the basis point. In order to establish the LRF of the basis point, the basis point and its adjacent points are weighted to compensate for uneven sampling of the point cloud, which can be described as  
\[w_{i}=1/\|\Big{\{}p_{j}:\big{|}p_{j}-p_{i}\big{|}<r_{density}\Big{\}}\| \tag{1}\]  
where \(w_{i}\) is the weight of the \(p_{i}\), and \(r_{density}\) is the radius of the sphere with \(p_{i}\) as the center, which represents its weighted range.  
Step 2, basis point eigenvalue analyzing. Do eigenvalue analysis at basis point \(p_{i}\) with its neighbor points at a supporting radius \(r_{iss\_frame}\). The covariance matrix can be calculated as  
\[COV(p_{i})=\frac{\sum_{|p_{j}-p_{i}|<r_{iss\_frame}}w_{j}\big{(}p_{j}-p_{i} \big{)}\big{(}p_{j}-p_{i}\big{)}^{T}}{\sum_{|p_{j}-p_{i}|<r_{iss\_frame}}w_{j}} \tag{2}\]  
Step 3, LRF establishing. The eigenvalues \(\{\lambda_{i}^{1},\lambda_{i}^{2},\lambda_{i}^{3}\}\) and eigenvectors \(\{\overrightarrow{e}_{max},\overrightarrow{e}_{med},\overrightarrow{e}_{ min}\}\) of \(COV(p_{i})\) were calculated and arranged in the order of decreasing magnitude. As shown in Figure 4, using \(p_{i}\) as the origin, \(\overrightarrow{e}_{max},\overrightarrow{e}_{med}\) and their cross product \(\overrightarrow{e}_{max}\times\overrightarrow{e}_{med}\) as the \(x,y,\) and \(z\) axes, the LRF can be established.  
Step 4, feature points extracting. Set thresholds \(\varepsilon_{1}\) and \(\varepsilon_{2}\), the points satisfying Equation (3) are regarded as ISS feature points. Figure 5 shows the feature points extracted by the ISS algorithm from the \"Bed\".  
\[\lambda_{i}^{2}/\lambda_{i}^{1}\leq\varepsilon_{1},\lambda_{i}^{3}/\lambda_{ i}^{2}\leq\varepsilon_{2},\ (0<\varepsilon_{1},\varepsilon_{2}<1) \tag{3}\]
# Citrus Pest Identification Model Based on Improved ShuffleNet
## 3 Results and Discussion
### Heatmaps  
To provide a concrete representation of the focal points in our model, we employed Grad-CAM [31] to visualize class activation maps for pest detection. These visualizations were compared with those generated by the original model. The heatmaps in Figure 10 suggested that the ShuffleNet V2 model may display a tendency to either shift its focus areas for pests or cover a wider area. Conversely, the SCHNet model introduced in this study demonstrated a more concentrated focus on pests with fewer notable positional shifts.
# Synchronization of Heterogeneous Multi-Robotic Cell with Emphasis on Low Computing Power
## Problem Area Definition  
The multi-robotic cell consists of a set of \(n\) robotic manipulators \(M=\{M_{1},\,\ldots\,,\,M_{i},\,\ldots\,,\,M_{n}\}\). In the organizational structure of cell control, manipulator M\({}_{1}\) represents the master element and a set of manipulators \(MS\subset M\), \(MS=\{M_{2},\,\ldots\,,\,M_{i},\,\ldots\,,\,M_{n}\}\) represents the slave elements. Each manipulator \(M_{i}\) performs a set of \(m\) operations \(O=\{O_{1},\,\ldots\,,\,O_{j},\,\ldots\,,\,O_{m}\}\) cyclically. Each operation \(O_{j}\) is in one cycle executed by manipulator \(M_{i}\). The duration time of this operation is \(T_{i,j}\in T=\left\{\begin{array}{ccc}T_{1,1}&\ldots&T_{1,m}\\ \ldots&\ldots&\ldots\\ T_{n,1}&\ldots&T_{n,m}\end{array}\right\}\).  
It is clear that duration times \(T_{x,j}\) for \(x=(1,\,\ldots\,,\,n)\) of operation \(O_{j}\) for every manipulator \(M_{i}\) are different without using a synchronization algorithm. Duration time depends on a movement speed \(V_{i,j}\in V=\left\{\begin{array}{ccc}V_{1,1}&\ldots&V_{1,m}\\ \ldots&\ldots&\ldots\\ V_{n,1}&\ldots&V_{n,m}\end{array}\right\}\) of manipulator \(M_{i}\) endpoint, defined as a parameter of a movement instruction.  
The goal is to modify the movement speed of manipulators \(MS\) separately for every operation \(O_{j}\) in such a way that the duration times of the same operation for every slave manipulator \(MS_{i}\) will be equal or very similar.  
The main requirements for the synchronization algorithm design are listed in Figure 1.
# Radiation-Balanced Lasers: History, Status, Potential
## 4 Development of Radiation-Balanced Lasers
### Athermal Fibre Lasers
#### 4.3.2 Athermal Silica Fibre Lasers
Ten spectra, collected for 100 ms each, were averaged to obtain the final fluorescence spectrum. The temperature-calibrated fluorescence spectra were obtained using a cryostat (Janis, ST500, Westerville, OH, USA) in which the sample temperature was maintained at various points from 300 K to 350 K by a temperature controller (Lake Shore, 335, Westerville, OH, USA) with resolution 0.01 K. Fluorescence spectra were normalized to the peak at 960 nm. P1 and P2 are electronic transitions E6-E2 and E5-E2/E3, respectively. The integrated intensity ratio of P1 and P2 peaks is temperature dependent following a Boltzmann distribution (Figure 18c). The integration ranges for P1 and P2 are from 952 nm to 968 nm and from 986 nm to 1000 nm, respectively. It was shown that the temperature of the Yb\({}^{3+}\):YLF crystal decreases by 6.6 K (Figure 18c). At each laser irradiance, a mean P1/P2 ratio was obtained by averaging six measurements, and the error bars represent one standard deviation, which are smaller than 1% of the mean values. In [60], [PERSON] et al. proposed and theoretically analyzed the fibre laser with two claddings (Figure 19). One of these claddings (the inner one) is composed of glass with Yb\({}^{3+}\):YLF nanocrystals. It covers the Yb\({}^{3+}\)-doped fibre glass core. The outer cladding is made of the same glass as the inner cladding, but without Yb\({}^{3+}\):YLF nanocrystals. The authors of this paper expect that the optically pumped Yb\({}^{3+}\):YLF nanocrystals in the inner cladding will lower the local temperature through anti-Stokes fluorescence. They proposed to reduce Rayleigh scattering by index matching the composite host material with the index of YLF. Their model shows a significant temperature decrease of 19 K when the Yb\({}^{3+}\):YLF nanocrystals within the composite cladding have a volume fraction of 10%. Figure 18: (**a**) Optical microscope image of Yb\({}^{3+}\):YLF microcrystals on an etched fibre. (**b**) Yb\({}^{3+}\):YLF fluorescence spectra at various laser irradiances, normalized to the P1 peak. At higher irradiance the P2 peak increases in intensity. (**c**) The integrated intensity ratio of P1 and P2 peaks with the corresponding calibrated temperature at each laser irradiance [60].