esa-sceva/satcom-chunk-collection huggingface dataset:
_Agronomy_**2014**, \(4\), 108-123; doi:10.3390/agronomy4010108  
_Article_  
**Elements of an Integrated Phenotyping System for Monitoring Crop Status at Canopy Level**  
**[PERSON], [PERSON], [PERSON], [PERSON] *, [PERSON] and [PERSON]**  
Center for Advanced Land Management Information Technologies (CALMIT), School of Natural Resources, University of Nebraska-Lincoln, 302 Hardin Hall, Lincoln, NE 68588-0973, USA; E-Mails: [EMAIL_ADDRESS] (D.R.); [EMAIL_ADDRESS] (A.G.); [EMAIL_ADDRESS] (B.L.); [EMAIL_ADDRESS] (A.Z.); [EMAIL_ADDRESS] (R.P.); [EMAIL_ADDRESS] (G.K.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-402-472-3424.  
_Received: 4 December 2013; in revised form: 15 January 2014 / Accepted: 30 January 2014 / Published: 17 February 2014_  
**Abstract:** Great care is needed to obtain spectral data appropriate for phenotyping in a scientifically rigorous manner. This paper discusses the procedures and considerations necessary and also suggests important pre-processing and analytical steps leading to real-time, non-destructive assessment of crop biophysical characteristics. The system has three major components: (1) data-collection platforms (with a focus on backpack and tractor-mounted units) including specific instruments and their configurations; (2) data-collection and display software; and (3) standard products depicting crop-biophysical characteristics derived using a suite of models to transform the spectral data into accurate, reliable biophysical characteristics of crops, such as fraction of green vegetation, absorbed photosynthetically active radiation, leaf area index, biomass, chlorophyll content and gross primary production. This system streamlines systematic data acquisition, facilitates research, and provides useful products for agriculture.  
**Keywords:** proximal sensing; vegetation; hyperspectral; spectral indices
# Analysis on the Spatial-Temporal Distribution Patterns of Major Mine Debris Flows in China
## 3 Analysis of the Time Variation Pattern of Mine Debris Flow  
### Intra-Year Variation Pattern of Mine Debris Flow  
The distribution of major mine debris flow disasters in each month from 1954 to 2019 were collated to obtain Figure 1. Statistics show that most of the major mine debris flow disasters in China occur from May to September, with the number of occurrences accounting for more than 90.1% of the total number of 12 months. The frequency of occurrence is higher from June to August, and the largest number of disasters occurs in July, accounting for 29.6% of the total. Major mine debris flows rarely occur from January to March and December. The fitted trend lines show that the distribution of disaster occurrence periods are concentrated between July and August. Considering that China is mainly influenced by continental monsoon climate, with warm and humid summer winds blowing from the east and south oceans from April to September every year, rainfall is mostly concentrated in summer. The trend of precipitation in each month overlaps highly with the trend of major mine debris flow disasters. The historical monthly precipitation totals are low during the low frequency of major mine debris flow in January to March and December, while the total amount of precipitation is at a high level during the high frequency of major mine debris flow in May to September, which confirms that the intra-year distribution trend of major mine debris flow disasters is largely influenced by the precipitation factor during the flood season (China's flood season is from May 1 to October 20 each year).
# A New Rotor Position Measurement Method for Permanent Magnet Spherical Motors
## 3 Rotor Position Measurement for a PMSM
### Analysis of Rotor Motion in a PMSM
#### 3.3.3 Calculation of the Yaw angle_\(\varphi\)  
When the rotor moves within the maximum angle, the high-speed camera captures the image of the three OFPs, which are located within a circle at the radius of \(VR_{\max}\) (the value of \(VR_{\max}\) is correlation with the experimental parameters), as indicated in Figure 10.  
Figure 10: Calculation of yaw angle_\(\varphi\).  
Similarly, the angle \(\varphi\prime\) can be computed by  
\[\varphi\prime=\arctan\frac{d_{v}}{d_{u}} \tag{12}\]  
where: \(d_{u}\) and \(d_{v}\) are the distances between the midpoint LED and the origin in the ICF.  
With the calculated \(\varphi\prime\), the tilting angle_\(\varphi\) in the range of 0-2\(\pi\) can be expressed as:  
\[\left\{\begin{array}{ll}\varphi=\varphi\prime&\text{The first quadrant}\\ \varphi=\pi-\varphi\prime&\text{The second quadrant}\\ \varphi=\pi+\varphi\prime&\text{The third quadrant}\\ \varphi=2\pi-\varphi\prime&\text{The fourth quadrant}\end{array}\right. \tag{13}\]  
Thus, the rotor position expressed by the tilting angle_\(\theta\) and yaw_\(\varphi\) in a spherical coordinate frame can be converted to a position \([x,y,z]\) in a rectangular coordinate frame as  
\[\left[\begin{array}{c}x\\ y\\ z\end{array}\right]=R_{\mathrm{s}}\left[\begin{array}{c}\sin\theta\cos \varphi\\ \sin\theta\sin\varphi\\ \cos\theta\end{array}\right] \tag{14}\]  
where \(R_{\mathrm{s}}\) is the distance between the tip of the rotor shaft and sphere center.
## 3 Performance Analysis of the MPS Beamformer  
In this section, the performance of the MPS beamformer is investigated and discussed. The beamformer parameters are examined for array pattern optimization and SLL reduction. These parameters include the number of subtracted multibeams (\(P\)), the convergence control factor for SLL reduction (\(\beta\)), the number of iterations for SLL reduction (\(I\)), and the rounded corners planar array structures using threshold normalized distance (\(\delta_{T}\)). In addition, the scanning performance of the MPS beamformer is examined at several mainlobe directions.
# Research on the Application of THz-TDS in Coal-Rock Interface Recognition
## 2 Experimental Detection  
### Experimental Instruments  
The experimental setup used in this study was the THz time-domain spectrometer. The device was purchased from BATOP GmbH. The model was THz-TDS 1008. The experimental instrument is shown in the Supplementary Materials. The working principle of the THz time-domain spectrometer involves the generation of laser pulses by the external laser source. These laser pulses are then divided into two parts (the pump pulse and the probe pulse) by the beam-splitting mirror. The pumped pulse enters the THz emitter to produce THz pulses. These THz pulses propagate free space for a certain distance and then contact the THz detector together with the detection pulses to drive the THz detection device for measurement. By controlling the time delay system to adjust the time delay between the pump pulse and the probe pulse, the complete time-domain waveform of the THz pulse can be detected. Through Fourier transform, the frequency domain spectrum of the measured sample can be obtained, which can provide essential optical parameters including absorption coefficient and refractive index, among others [19; 20; 21]. It is worth noting that the laser source used in this setup has a central wavelength of 800 nm and a pulse duration of 100 fs. In order to ensure experimental conditions are close to the actual environment and avoid potential effects on the measurement results, the experiments were carried out at room temperature (23 \({}^{\circ}\)C). In addition, to obtain valid experiment results, the moisture value of the experiment environment was set under 5% relative humidity because water can be extremely absorbent to the THz radiation. (No dry air or nitrogen was in the system).
## 2 Materials and Methods
### Estimation of the Number of Signals
#### 2.3.2 Diagonal Loading Technique  
In Equation (8), the numerator and the denominator represent the geometric and arithmetic means of the eigenvalues, respectively. Smaller eigenvalues therefore make the estimates unstable. To stabilize the estimation procedure, we thus use a diagonal loading technique that adds a diagonal matrix to the covariance matrix [25; 26; 27].  
The DL process is expressed as follows:  
\[\mathbf{R}^{\prime}(\omega)=\mathbf{R}(\omega)+\eta(\omega)\mathbf{I} \tag{9}\]  
where \(\eta\) is a diagonal loading factor and \(\mathbf{I}\) is the identity matrix. The process that was shown in the previous equation can then be rewritten as follows:  
\[l_{i}^{\prime}(\omega)=l_{i}(\omega)+\eta(\omega) \tag{10}\]  
where \(l_{i}^{\prime}(\omega)\) is the \(i\)-th eigenvalue that is calculated from \(\mathbf{R}^{\prime}(\omega)\). Therefore, even when a different DL factor is used with the same covariance matrix, we do not need to perform the eigenvalue decomposition process again. In other words, we use the DL technique to estimate \(M\) as an offset value and do not add the DL matrix to the covariance matrix directly. By replacing \(l_{i}\) in Equation (8) with \(l_{i}^{\prime}\), we can then obtain the modified estimates.
## 2 Materials and Methods
### Training of Classifiers  
#### 2.5.1 Cross-Validation  
Data samples were divided into five stratified folds in which 80% and 20% were data subdivisions for the training and test sets, respectively. Every stratified fold presents the same relative frequency between positive and negative classes as that belonging to whole dataset. In order to make fair comparisons among the classifiers without biasing the results, the k-folds are the same for all the tested classifiers.  
For ML classifiers, i.e., DT, KNN, and SVM, the best hyperparameters have been set with an exhaustive search over specified parameter values. The search consists of finding proper parameters related to the number of units processing information (tree nodes in DT), weighting observation related to a similarity measure (KNN), and constructing a separation hyperplane in feature dimension space exploiting kernel trick (SVM).
## 3 Deepfake Technologies and the Emergence of Deepfake-Driven Social Engineering
### Relevant Threat Vectors  
Although deepfake technologies originate from advancements in generative modeling, their rapid adoption for illegal activities spans multiple threat vectors [11]:  
* **Financial fraud:** Attackers can impersonate high-level executives or managers, instructing employees to authorize payments or disclose sensitive financial data;
* **Espionage and data exfiltration:** By impersonating key personnel, cybercriminals may gain privileged access to systems or coax employees into divulging intellectual property or other strategic information;
* **Disinformation campaigns:** Deepfakes can create fake public statements or events, damaging a corporation's reputation and eroding trust among stakeholders;
* **Phishing and vishing (voice phishing):** These consist of deepfake audio calls or video calls that mimic IT support staff or external vendors, tricking employees into resetting passwords or installing malware.  
As outlined in the following sections, these attack vectors exploit the trust placed in digital communication, ultimately underscoring the urgency of robust detection tools and well-informed defensive strategies [11].
## 3 Materials and Methods
### Sensitivity Analysis  
As described in Section 2.2, a sensitivity analysis was performed to evaluate how the LCA inputs uncertainties affect the final outcomes distributions obtained through the MC method. The analysis is focused on GWP, then Table 7 summarizes the obtained distributions of kgCO\({}_{2}\)eq for all materials/processes for REF1 and DELTA mortars and Table 8 for REF2 and HP mortars.  
As previously observed, including in this stochastic LCA, cement is the most impacting phase. The following Figures 3 and 4 then represents the calculated sensitivity indices excluding cement, which is present in the same quantity in all the analyzed mortars. Transport of natural aggregate in mortar REF1 affects results uncertainty by 64% (main effect) and 78% (total effect); while transport of recycled aggregate DELTA affects them by 29% (both main and total effect). Even if the impact of benefits relating to non-occurrence of landfill disposal of DELTA waste is higher than the incidence of transport of DELTA waste (58%), it can be said that the transport of aggregates is a fundamental aspect that can influence the uncertainty of results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**REF1**} & \multicolumn{4}{c}{**DELTA**} \\ \cline{2-9}  & **Distribution** & **Mean** & **Median** & **SD** & **Distribution** & **Mean** & **Median** & **SD** \\ \hline Cement, limestone 6–20\% & Lognormal & 0.33213 & 0.31031 & - & Lognormal & 0.33213 & 0.31031 & - \\ Conveyor belt & Lognormal & 0.00004 & 0.00003 & - & Lognormal & 0.00004 & 0.00003 & - \\ Industrial machine & Lognormal & 0.00001 & 0.00001 & - & Lognormal & 0.00001 & 0.00001 & - \\ Packing & Normal & 0.01045 & - & 0.005386 & Normal & 0.00736 & - & 0.00428 \\ Rock crushing & - & - & - & Lognormal & 0.00029 & 0.00029 & - \\ Transport of cement & Lognormal & 0.00357 & 0.00356 & - & Lognormal & 0.00357 & 0.00356 & - \\ Transport of DELTA waste & - & - & - & Normal & 0.05035 & & 0.02458 \\ Transport to construction site & Lognormal & 0.01192 & 0.01188 & - & Lognormal & 0.00893 & 0.00890 & - \\ Tap Water & Lognormal & 0.00010 & 0.00010 & - & Normal & 0.00010 & - & 0.00004 \\ Benefits transports & - & & & & Normal & \(-\)0.00238 & - & 0.00008 \\ Benefits waste plastic & - & & & & Normal & \(-\)0.10558 & - & 0.05798 \\ Electricity (medium) & Lognormal & 0.01374 & 0.01374 & - & Lognormal & 0.01374 & 0.01374 & - \\ Electricity (grid mix) & Normal & 0.04277 & - & 0.00000 & Normal & 0.04277 & - & 0.00000 \\ Sand & - & 0.00333 & 0.00333 & - & - & - & - & - \\  & Min & Max & & & & & \\ Transport of sand & Uniform & 0.01140 & 0.08841 & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 7: GWP distributions (kg CO\({}_{2}\) eq) of materials/processes in REF1 and DELTA mortars LCA. SD = Standard Deviation.
# Fast One-Step Synthesis of Anisotropic Silver Nanoparticles
###### Abstract  
The shape control of metal nanoparticles, along with the size, is critical for most of their applications as they control their optical properties. Anisotropic metal nanoparticles show superior performance in a number of applications compared to spherical ones. Shape control is usually achieved by a two-step process, where the first involves the formation of spherical nanoparticles and the second is about the actual shape transformation. In this paper, we report on a fast and facile synthesis of silver nanoplates in a single step, involving laser ablation of a silver target in a liquid medium while this is exposed to light irradiation and hydrogen peroxide flow. We obtained anisotropic particles with a mixture of shapes, of 70-80 nm in size and 10-20 nm in thickness, which showed a plasmon sensitivity greater than 200 nm/RIU.  
laser ablation; silver nanoplates; photochemistry; plasmon sensitivity +
Footnote †: journal: Applied Sciences
## 5 Application of ZPLTS-ELECTRE II Method in Ontology Ranking
### Discussion
#### 5.4.2 Comparison with Existing Fuzzy ELECTRE II Methods  
The ZPLTS-ELECTRE II method is compared with other recent developments of ELECTRE II in Table 30.  
Table 30 compares the ZPLTS-ELECTRE II method with existing enhanced ELECTRE II methods. It is shown in Table 30 that some of these methods share some similar [45,62] and dissimilar [34,44] features with the ZPLTS-ELECTRE II method. In particular, Table 30 shows that none of the existing enhanced ELECTRE II methods enables the measurement  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Enhancement** & **Credibility** & **Structure** \\ \hline PL-ELECTRE II [44] & PLTS & No & Possibility Degree \\ PLTS-ELECTRE II [45] & PLTS & No & Score and Deviation \\ PLTS ELECTRE II [62] & PLTS & No & Score and Deviation \\ HF-ELECTRE II [34] & HFLTS & No & Score and Deviation \\ ZPLTS-ELECTRE II [_this study_] & ZPLTS & Yes & Score and Deviation \\ \hline \hline \end{tabular}
\end{table}
Table 30: Comparison of ZPLTS-ELECTRE II with other ELECTRE II enhancements.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **Quantitative** & **Qualitative** & **No. of** & **No. of** \\  & **Criteria** & **Criteria** & **Criteria** & **Ontologies** \\ \hline ELECTRE I [15] & Yes & No & 8 & 70 \\ ELECTRE I/III [14] & Yes & No & 5 & 12 \\ WLCRT [16] & Yes & No & 8 & 70 \\ TOPSIS/WSM/WPM [17] & Yes & No & 8 & 70 \\ ZPLTS-ELECTRE II [_this study_] & Yes & Yes & 10 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 29: Comparison of ZPLTS-ELECTRE II with other MCDM methods for ontology ranking.  
of the credibility level of a decision-maker's evaluation as the proposed ZPLTS-ELECTRE II method does. The differences between the ZPLTS-ELECTRE II method and the other enhancements of ELECTRE II are that (1) the ZPLTS-ELECTRE II method offers better modeling capabilities in decision-making problems through the ability of decision-makers to better express their credibility and confidence levels with the use of Z-numbers as well as the ability of decision-makers to specify an individual discordance level for each criterion, and (2) the ZPLTS-ELECTRE II method provides the capability to model both quantitative numerical criteria as well as qualitative linguistic criteria, unlike the existing enhancements of ELECTRE II methods.
## 2 Background
### Positioning, Navigation, and Timing (PNT) Systems  
Numerous PNT systems provide various services using a variety of techniques. The GNSS is the most ubiquitous PNT service, which provides reliable positioning and timing services to a massive coverage area [23; 24]. Unfortunately, broadcast systems are becoming increasingly vulnerable to spoofing cyberattacks [20; 21; 22], so many alternative approaches have been considered for safety-critical transport applications, such as self-driving vehicles or \"flying cars\" [25]. In [10], the authors discussed five common APNT technologies: DME [10; 26]; P-WAM [27; 28; 29; 30; 31]; PL [32]; VOR [10]; and the LDACS1 [33; 34]. In Table 1, we broadly compare the ranging precision of CHP2 to other traditional PNT systems such as the WAAS [35]; RTK GPS [36]; the UHARS [37]; and UWB systems [38; 39; 40]. Please  
Figure 2: Initial hardware prototype of the CHP2 system: (**left**) the ZCU102 evaluation kit and FMCOMMS5 RF evaluation board installed in a folded sheet metal case, with dimensions 20 cm \(\times\) 20 cm \(\times\) 5 cm, and (**right**) the CHP2 assembly mounted to the DJI S1000+ UAV for flight testing.  
note that this is a highly diverse set of technologies that are subject to numerous different design decisions and variables that may affect their performance. We do not present this information to make any claim that our proposed solution is necessarily better or worse than related systems; rather, we aim to broadly categorize similar technologies to provide a general idea of what they are capable of and to highlight some key similarities and differences.
## 3 Moon Cold Traps Exploration Challenges
It is expected that nuclear reactors will be mainly used in human bases and large industrial installations, and not to directly power rovers. The need to use photovoltaic cells to charge rover batteries introduces many limitations. They can only be charged during the day in sunny areas. If the rover performs any operations in shaded areas, it must move from these areas to sunny areas to recharge the batteries before the battery discharges. A separate issue is the survival of the device at extremely low temperatures that occur in shaded areas and during the long lunar night. One day on the Moon lasts 27 (exactly 27.3) Earth days, including day and night lasting 14 Earth days each. Protecting the batteries of rovers and mining trucks against complete discharge, especially during the lunar night, will be a great challenge. The Moon is seismically active [58], which may also affect the stability of crater slopes. Due to the complex terrain and the rotation of the Moon, the movement of shadows [59] at the South Pole is very complex in time. The problem of selecting the optimal route for rovers and mining dump trucks should be viewed as a time optimization problem. Given routes may be impassable during certain time periods due to the fact that they are in a shaded area, and moving along them during this time may involve an increased drain on the battery charge. The time of the simulation of the movement of shadows at the South Pole [59], which was performed for the years 2025-2028, is not accidental, as this is the period of the NASA Artemis program. Another problem will be the safety of places where rockets will land and take off. The test launch of the SpaceX Starship rocket in April 2023 showed how big a problem of rock fragments generated during rocket takeoff and landing can be. On the Moon, due to lower gravity, the problem will be even more serious. Therefore, it will be necessary to choose appropriate landing places where there will be no large rock fragments and the surfaces can be easily hardened to create launch pads. To paraphrase the saying \"measure your strength according to your intentions\", in the case of the Moon you can say \"choose cold traps and their resources according to the technical capabilities of the equipment you have\". Therefore, analyzing craters and the cold traps located in them in terms of their easy accessibility is crucial. It is not enough that they contain valuable deposits--they must be deposits that can be easily accessed. The authors of publication [60] made an extensive analysis of cold traps for both poles of the Moon in terms of their availability.