esa-sceva/satcom-chunk-collection huggingface dataset:
# Face Morphing, a Modern Threat to Border Security: Recent Advances and Open Challenges
## 5 Morphing Attack Detection
### Deep vs. Non-Deep Classification  
This categorization concerns mainly the approach used for feature extraction (i.e., whether features are automatically learned as in Figure 5 or engineered as in Figure 6) and is inherent to almost all classification tasks since the advent and widespread use of DCNNs in recent years. Nowadays, DNNs have established themselves as prominent tools for solving Machine Learning (ML) and Artificial Intelligence (AI) problems [54] (e.g., face recognition, scene analysis and driver state monitoring, autonomous driving etc.), achieving unprecedented results in challenging competitions such as the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [55], and even exceeding the accuracy of human experts in certain classification tasks [56]. As it is to be expected, DCNNs have been extensively used for morphing attack detection, with the majority of the techniques relying on the learnt features of well-known, pre-trained networks from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [55], such as VGG [57], AlexNet [58], ResNet [59], and GoogleNet [60]. The main downside of DCNN-based approaches is the requirement of large morphing datasets, for DCNN training (i.e., feature learning).  
Figure 4: In the off-line scenario, MAD is based only on a test image that is provided via the eMRTD.  
Figure 5: In deep classification, feature extraction relies on an automatic learning procedure that utilizes deep learning.  
Figure 3: In the on-line scenario, MAD is based on a test image that is provided via the eMRTD and a live capture of the person’s face.  
On the other hand, the techniques following an \"non-deep\" approach, rely on engineered features for classification, with the majority of them employing general-purpose texture descriptors, widely used in Computer Vision tasks, such as the Local Binary Patterns (LBP) and the Scale-Invariant Feature Transform (SIFT).  
A categorization of the MAD-related works included in the following sub-section, is presented in Table 1.
# An Enhanced Mechanism for Fault Tolerance in Agricultural Wireless Sensor Networks
## 3 WSN Topology
### The Structure of the WSN  
In this sub-section, we describe the structure of the WSN along with its key components and assumptions (for our model).  
**Sensor Nodes:** These nodes are deployed by the farmers in the agriculture field at specific places; hence, each node has specific coordinates. Each node has a unique ID (identification number). The main role of the sensor node is to collect environmental data (e.g., temperature and humidity) in the field of interest and then transmit the collected data with its proper ID to the CH with a single hop.  
**Cluster Head (CH):** It is a particular sensor node with relatively higher energy resource, and with a better computing and storage capacity. There are several CHs in the WSN. A CH has deterministic coordinates; it acts as an interface for communication between its members and the Base Station. The CH manages the communications within its cluster according to the _Time Division Multiple Access (TDMA)_ protocol [42]. The CH must perform an aggregation processing of the received data in order to minimize the number of messages transmitted to the Base Station. The communication between the CH and the Base Station is performed with a single hop.  
**Base Station (BS):** It manages the entire network; it receives the data sent by different CHs and retransmits data to the final user. This transmission can be performed via the Internet while taking the advantage of the Internet of Things (IoT) environment. In other words, crop management, monitoring, and control can be carried out remotely via the Internet using a PC or a smartphone. In fact, the user knows the geographical map of all sensor locations, which allows them to intervene to possibly replace a defective sensor or a depleted battery. In order to make the nodes equidistant from their CH in our model, we used the deployment strategy that divides the monitored field into circular regions where the CHs are located at the center of each circle. The BS is also placed in the center of crop field (see Figure 1).  
As illustrated in Figure 1, a simple clustering mechanism was applied, in which, the agricultural field is divided into several circular zones with the CHs located in the center of the circles and all the clusters contain the same number of sensor nodes. This is possible to implement for an agricultural field as the farmer can physically access the land. Therefore, each sensor node has the same distance from the associated CH as any other sensor node in the same cluster. We can say that the clustering topology used in our model guarantees network scalability and is an effective energy-saving technique (by the nature of the deployment).
###### Abstract  
This paper reviews receivers that feature low-loss multimode-output arrayed waveguide gratings (MM-AWGs) for wavelength division multiplexing (WDM) as well as hybrid integration techniques with high-speed throughput of up to 100 Gb/s and beyond. A design of optical coupling between higher-order multimode beams and a photodiode for a flat-top spectral shape is described in detail. The WDM photoreceivers were fabricated with different approaches. A 10-Gb/s photoreceiver was developed for a 1.25-Gb/s baud rate and assembled for eight-channel WDM by mechanical alignment. A receiver with 40-Gb/s throughput was built by using visual alignment for a 10-Gb/s baud rate and four-channel WDM. A 100-Gb/s receiver assembled by active alignment with a four-channel by 25-Gb/s baud rate is the basis for beyond-100 Gb/s and future multi-wavelength integrated devices toward data-centric communications and computing.  
arrived waveguide grating; fiber optic communications; data centric; Ethernet; planar lightwave circuit; optical sub-assembly; integrated photonics +
Footnote †: journal: Applied Sciences
# Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
## 3 Results
### The Correlation Analysis of SDR and CST for an Individual Tree Canopy  
Due to the influence of LAI, the SDR data of 30 trees with high LAIs (>1) were used to determine the relationship of SDR with CST. The canopy temperatures were extracted from TIR images using canopy areas generated from LiDAR data. The actual field data revealed the crown SDRs of the affected Yunnan pine was mainly between 10% and 20%, and there was fewer affected pine when the SDR was between 50% and 80%. This was because the survival time of an affected tree was shorter when the crown contained many dead needles as this reduced the volume of water obtained from the roots as the draw of transpiration decreased. Therefore, trees with a SDR exceeding 50% can die quickly, leading to a low number of samples in this stage. The CST increased with a rising SDR, as shown by the significant correlation in Figure 7. This strong correlation (R\({}^{2}\) = 0.7890) indicated that UAV-based thermal remote sensing could effectively monitor the SDR of the canopy in individual trees.  
Figure 6: The correlation between leaf area index (LAI) and canopy surface temperature (CST) in 15 trees in the plots (**a**). The regression analysis of shoot damage ratio and CST with the same sample of trees (**b**). The red points represent the abnormal CST points of three trees with low LAIs.
# A Survey on Change Detection and Time Series Analysis with Applications
## 4 Change or Breakpoint Detection within Non-Stationary Time Series
### Jumps Upon Spectrum and Trend (JUST)  
The JUST is established based on ALLSSA or the Ordinary Least-Square (OLS) with an appropriate windowing technique [76]. Using ALLSSA, JUST simultaneously searches for trends and statistically significant spectral components of each time series segment to identify the potential jumps by considering appropriate weights associated with the time series. Within each translating window, a sequential approach is implemented to find the jump location in the trend component within the window. Then all the potential jump locations are sorted according to their maximum occurrences, then the minimum distance to the window locations where they were estimated. The flowchart of JUST is given in [76]. Although using OLS instead of ALLSSA in JUST reduces the computational cost, it can significantly reduce the accuracy of breakpoints due to over/under-fitting issues.  
One of the advantages of JUST over BFAST is the simultaneous estimation of trends and seasonal components. In BFAST, removing the seasonal component estimated by the harmonic functions of fixed frequencies can propagate error into the residual series. The error propagation in BFAST can also occur when removing the estimated trend from the time series. In other words, the correlation between the trend and seasonal components is not carried forward in BFAST, making it less accurate and less compatible with unequally spaced time series or even equally spaced time series with unknown signal frequencies [76].
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 3 Implementation and Evaluation of SM9 Identity-Based Encryption
### Setup and Key Extraction
#### 3.2.1 Setup (\(1^{k}\))  
Given input \(k\), the output of this algorithm is the master public key \(M_{pk}\) and master secret key \(M_{sk}\). Algorithm 4 gives its pseudocode.  
```
1 Generate three groups \(G_{1},G_{2}\) and \(G_{T}\) of prime order \(r\)
2 Generate a bilinear pairing map \(e\ :\ G_{1}\times G_{2}\to G_{T}\)
3\(P_{1}\xleftarrow{\xi}\ G_{1}\)
4\(P_{2}\xleftarrow{\xi}G_{2}\)
5\(ke\xleftarrow{\xi}\mathbb{Z}_{r}^{*}\)
6\(P_{pub-e}\leftarrow[ke]P_{1}\)
7\(g_{e}\gets e\big{(}P_{pub-e},P_{2}\big{)}\)
8 Pick a cryptographyic hash function \(H_{v}\)
9\(hid_{e}\gets 3\)
10\(M_{pk}\leftarrow\left(\begin{array}{c}G_{1},G_{2},G_{T},e,P_{1},P_{2},P_{pub-e },g_{e},\\ H2 RF_{1}(H_{v},\cdot),H2 RF_{2}(H_{v},\cdot),hid_{e}\end{array}\right)\)
11\(M_{sk}\leftarrow(ke)\)
12 output\(\big{(}M_{pk},M_{sk}\big{)}\)
```  
**Algorithm 4**\(Setup\big{(}1^{k}\big{)}\)  
2.2 Private-Key-Extract (\(\text{M}_{\text{pk}}\), \(\text{M}_{\text{sk}}\), \(\text{ID}_{\text{A}}\))  
Given an identity string \(ID_{A}\in\{0.1\}^{*}\) of entity \(A\), \(M_{pk}\) and \(M_{\text{sk}}\), the operation outputs \"error\" if  
\[ke+H2 RF_{1}(H_{v},ID_{A}\|hid_{e},r)\ mod\ r=0 \tag{3}\]  
otherwise, it outputs decryption private key  
\[DE_{A}=\Bigg{[}\frac{ke}{ke+H2 RF_{1}(H_{v},ID_{A}\ \|\ hid_{e},r)}\Bigg{]}P_{2} \tag{4}\]
## 2 Materials and Methods
### Laboratory Analysis  
1. Major ions and trace metals analysis: three sets of water samples were analysed. Samples labelled (A) were prepared by collecting 10 ml of each sample in a centrifuge polyethylene tube and arranged serially for Inductively Coupled Plasma (ICP-MS) analysis of cations. The same arrangement was used for the sample set labelled (B) for Ion Chromatography (IC) for the anions analysis in the Environmental Laboratory, Department of Civil and Environmental Engineering, University of Strathclyde. Alkalinity (HCO3-) was determined using a Digital Titrator (Model: 16900, HACH International, Loveland, CO, USA) and 1.6 N H2 SO4 cartridge.
2. Stable isotopes analysis in groundwater: samples labelled (IS) were shipped to the Ministry of Agriculture, Irrigation and Water Development Isotope Laboratory, Blantyre, Malawi under a temperature below 4 \({}^{\circ}\)C for the stable isotope of \(\delta^{2}\)H and \(\delta^{18}\)O analysis. The analysis of groundwater samples was carried out following the same method of isotope water samples as described in [18] and laboratory analysis was conducted in line with International Standard Procedures with appropriate quantification and validation of results.  
Figure 3: Hydrostratigraphic section across: (**a**) profile line AB and (**b**) profile line CD, showing different aquifer layers in part of the study area.
## 4 Simulation and Analysis
### Analysis of Algorithm Positioning Performance  
The analysis of satellite positioning accuracy is carried out from two aspects: planar positioning accuracy (2D) and three-dimensional positioning accuracy (3D). Figure 18 presents a comparative analysis of the 2D and 3D positioning accuracy for various satellite selection algorithms when four, five, and six LEO satellites are selected. Table 7 systematically summarizes the positioning accuracy comparisons of different satellite selection algorithms under the same conditions of selecting four, five, and six LEO satellites.  
Figure 18: The 2D and 3D position accuracy (m) under different numbers of selected satellites.  
From the data analysis presented in Figure 18 and Table 7, it is evident that as the number of selected satellites increases, the positioning accuracy calculated by each algorithm exhibits a downward trend. The SFGWO-A algorithm demonstrates relatively superior positioning performance compared to the other three intelligent optimization algorithms, whereas the SFGWO-B algorithm exhibits relatively inferior positioning performance. This is because the SFGWO-A algorithm integrates the perturbation strategy of the VNS algorithm and the mutation strategy of the GA algorithm into the traditional GWO algorithm, thereby enhancing the global optimization capability of the algorithm. The SFGWO-B algorithm incorporates the EWM algorithm into the traditional GWO algorithm, trading a portion of positioning accuracy for an extended continuous observation time of satellites. It is evident that the trend of the positioning result closely resembles the trend of the DGDOP value. The MSFGWO algorithm not only enhances the positioning accuracy but also simultaneously extends the continuous observation time of the satellite combination. With the increase in the number of selected LEO satellites, the 3D positioning accuracy shows a downward trend: the traversal method improves from 239.93 m to 189.36 m, the MSFGWO algorithm improves from 240.97 m to 192.86 m, the GWO algorithm decreases from 253.12 m to 195.79 m, the SFGWO-A algorithm decreases from 239.89 m to 189.73 m, and the SFGWO-B algorithm decreases from 261.86 m to 196.42 m. When selecting six satellites, compared with the three-dimensional positioning accuracy of the traversal method, the positioning accuracy of the MSFGWO algorithm, the GWO algorithm, the SFGWO-A algorithm, and the SFGWO-B algorithm is 3.5 m, 6.43 m, 0.37 m, and 7.06 m lower, respectively.  
The aforementioned experimental results demonstrate that incorporating the perturbation strategy of the VNS algorithm and the mutation strategy of the GA algorithm into the GWO algorithm can significantly enhance the positioning accuracy of the algorithm. However, upon the introduction of the EWM algorithm, a certain degree of positioning accuracy must be traded off to optimize the continuous observation time of satellites. In contrast, the MSFGWO algorithm demonstrates an effective ability to strike a balance between positioning accuracy and the continuous observation time of satellites.
# Can OCT Angiography Be Made a Quantitative Blood Measurement Tool?
## 8 Can OCTA Be Made a Quantitative Tool?  
OCTA systems can observe dynamic signal power (variance) and decorrelation rate [19; 83], based on the dynamics of light scattering. As algorithms, imaging system performance, and motion tracking/compensation continue to improve, OCTA observables, particularly decorrelation rate, can be precisely and accurately measured. These observables may generate useful diagnostic information, even if their underlying hemodynamic correlates remain unclear. However, if OCTA observables can be directly linked to hemodynamic parameters such as blood flow, volume, hematocrit, and speed, OCTA diagnostics could aid understanding of pathogenesis. This effort requires an appropriate model to describe OCTA signals. The model may be empirical (Section 6), but ideally, should have a theoretical foundation (Section 7.1). Current theoretical models are very simple, and only account for single scattering [18; 19; 84; 85]. Improvements in OCTA theory to include multiple scattering [85] and orientation effects [37] are needed. Empirical models have been developed for flow phantoms [67], but they may be limited to in vitro conditions, and their applicability in vivo remains uncertain. Better in vivo validation experiments, perhaps in well-controlled and stable animal preparations, are needed. Last, due to differences in light scattering and hemodynamics (Figures 1 and 2), models for capillaries and macrovessels must be developed independently.  
In spite of these proposed efforts, the inherent complexity of the rheology and light transport in microvasculature may prevent reliable quantification of OCTA. Therefore, we propose that alternative optical properties (aside from light scattering) may enable more quantitative OCTA. For instance, visible light OCTA [86] enables direct absorption-based measurements of hemoglobin concentration, which is expected to correlate well with hematocrit (RBC volume fraction) under most conditions [87]. Yet another way to circumvent the pitfalls of RBC scattering is to introduce an exogenous contrast agent with more desirable scattering properties into the bloodstream [88]. If a more isotropically scattering contrast agent such as Intralipid\({}^{\text{\textregistered}}\)[89; 90] is used, angiograms derived from the contrast agent signal alone do not suffer from multiple scattering tails [89; 90]. Microbubbles [91; 92] are promising for enhancing intravascular scattering signals, and may present more well-defined decorrelation characteristics than blood. Moreover, if the contrast agent behaves like plasma and the signal can be calibrated and related to concentration [89; 90], plasma flow, transit time, and volume can all be measured.
## 3 Blind Source Separation Algorithm Based on BAS-BCA
### Bas
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Number** & **Function** & **DIM** & _scope_ & \(f_{min}\) \\ \hline
**F1** & \(f_{1}(x)=\sum\limits_{i=1}^{D}x_{i}^{2}\) & 30 & \([-100,100]\) & 0 \\
**F2** & \(f_{2}(x)=\sum\limits_{i=1}^{D}\lvert x_{i}\rvert+\prod\limits_{i=1}^{D}\lvert x _{i}\rvert\) & 30 & \([-10,10]\) & 0 \\
**F3** & \(f_{3}(x)=\sum\limits_{i=1}^{D-1}\left[100(x_{i}^{2}-x_{i+1})^{2}+(x_{i}-1)^{2}\right]\) & 30 & \([-30,30]\) & 0 \\
**F4** & \(f_{4}(x)=\frac{1}{4000}\sum\limits_{i=1}^{D}x_{i}^{2}-\prod\limits_{i=1}^{D} \cos(\frac{x_{i}}{\sqrt{i}})+1\) & 30 & \([-600,600]\) & 0 \\
**F5** & \(F_{5}(x)=\sum\limits_{i=1}^{n}\left[x_{i}^{2}-10\cos(2\pi x_{i}+10)\right]\) & 30 & \([-5.12,5.12]\) & 0 \\
**F6** & \(f_{12}(x)=20+\epsilon-20\text{exp}(-0.2\sqrt{\frac{1}{D}\sum\limits_{i=1}^{D} x_{i}^{2}})-\exp(\frac{1}{D}\sum\limits_{i=1}^{D}\cos(2\pi x_{i}))\) & 30 & \([-32,32]\) & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Test function. As shown in Table 2, it can be seen that this BAS algorithm has basically converged to the minimum value in the calculation of functions F1, F2, F4, F5, and F6, and the standard deviation is also close to 0, proving the high stability of the algorithm. Although the F3 function did not converge to the minimum value, overall, the BAS algorithm has better convergence performance than other algorithms. As for the calculation time in Table 2, the BAS algorithm of this paper has a medium execution time. According to the data in the table, the test time for BAS under the three test functions of F2, F3, and F4 is less than that of the GWO and WOA.
# Acute Effects of Whole-Body Vibration on Gait Kinematics in Individuals with Parkinson's Disease
## 2 Materials and Methods
### Data Analysis and Statistics  
A priori power analysis calculation (G*Power 3.1.9.2 software, Institut fur Experimentelle Psychologie, Heinrich Heine Universitat, Dusseldorf, Germany) (effect size: 0.3; \(\alpha\) = 0.05) showed that 20 participants were sufficient to achieve a statistical power of \(1-\beta\) = 0.8. The calculation of the effect size was based on a previous study with a similar sample size and population characteristics, focusing on the gait duration [30]. To ensure a conservative estimate, the effect size was approximated from 0.37 to 0.30. This approach was chosen to maintain statistical rigor while accounting for potential variability in gait measurements. All statistical analyses were performed with this sample size calculation to validate the study's sensitivity to meaningful differences in gait parameters. Mean and standard deviation (SD) and median and interquartile range (IQR) were used for description of normally and non-normally distributed data, respectively. Statistical analysis was carried out using IBM(r) SPSS statistics software version 23.0 (SPSS Inc., Chicago, IL, USA), with the significant level set at \(p\leq 0.05\). The normal distribution of the dependent variables was tested using the Shapiro-Wilk test. Based on this preliminary analysis, parametric tests were adopted to analyze gait kinematics parameters and the time of the 10-meter walk test. One-way ANOVA for repeated measures was conducted to detect the effects of the condition on gait parameters (walk time, cadence, step length, stance phase, swing phase, propulsion, and pelvic tilt). The assumption of sphericity for each ANOVA model  
Figure 1: Flowchart of the study protocol. WBV, whole-body vibration condition; HS, half-squat condition; CC, control condition.  
was checked with [PERSON]'s sphericity test. When significant differences were detected, Bonferroni post hoc analysis was run.
## 4 Discussion  
### Vegetation Indices as General Predictors of GPP  
Previous studies have found that no single VI provides a universal predictor of GPP and that the relationships between GPP and VIs are often site- or biome-specific [8; 10; 18]. We found that the within-site relationships between NDVI and GPP varied between ecosystem types and differed from that observed in the across-site comparison (Figure 2). EVI and NIR\({}_{\rm v}\), on the other hand, provided more general predictions of GPP, with temporal relationships that were comparatively consistent across sites. Even at evergreen forests, where all VIs were weaker predictors in our study and others [7; 8; 11; 18], the GPP relationships with EVI and NIR\({}_{\rm v}\) fall within the range of other sites/biomes. These more universal relationships underscore the utility of EVI and NIR\({}_{\rm v}\) for extrapolating GPP in the absence of further site- or biome-specific information.  
Our analyses revealed strong similarities between EVI and NIR\({}_{\rm v}\), and subsequent comparisons showed a tight correlation between EVI and NIR\({}_{\rm v}\) (Figure 7 and Supplementary Material 6). Previous studies have also pointed to a similar performance between EVI and NIR\({}_{\rm v}\)[10]. This correlation was unexpected given the mathematical differences between the two VIs. EVI was designed to reduce cross sensitivity to soil and atmospheric conditions, while reducing saturation in dense canopies [22]; previous studies have shown EVI has enhanced sensitivity to variation in NIR reflectance [23]. In a similar way, the design and derivation of NIR\({}_{\rm v}\) arose from an attempt to isolate the NIR reflectance of the vegetation from that of the soil [6; 58]. Our analyses confirm the strong similarity between EVI and NIR\({}_{\rm v}\) is a consequence of the shared sensitivity to traits influencing NIR, and the reduced influence from Red reflectance and soil reflectance. We conclude that the information provided by EVI and NIR\({}_{\rm v}\) is largely redundant, and that both are similarly useful for inferring GPP, phenology, and other ecosystem properties [5; 6; 58; 59].