esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In 1971, [PERSON] proposed the singularity expansion method (SEM) in which a time domain radar backscattered signal can be approximated as a sum of damped exponentials and complex residues [1, 2]. He noticed how the frequencies (or poles) associated with these damped exponentials did not depend on the orientation of the object, and this led to the object identification as the poles can be used as its unique signature. Equation (1) shows how the input signal \(y(t)\) is constructed with the complex residues \(R_{i}\) and the poles \(s_{i}\), having \(M\) as the number of complex natural resonances (CNRs). The poles \(s_{i}=\alpha_{i}+jw_{i}\), which are called the natural resonances of the object, represent the natural frequencies of the illuminated target [3, 4]. In [5], SEM is mentioned as a useful method for determining experimental CNR of complex targets other than thin wires.  
\[y(t)\approx\sum_{i=1}^{M}R_{i}e^{-s_{i}t} \tag{1}\]  
The singularities in the time or frequency domain can be calculated using methods such as matrix pencil method (MPM), [PERSON]'s method, Cauchy's method, or vector-fitting Cauchy method (VCM) [6, 7, 8, 9, 10, 11, 12]. However, besides VCM, there is no reconstruction of the signal validated through numerical results that validate the poles for object identification scenarios or any analysis for real-time implementation. The main reason for this article is, then, to provide an insight into the advantages and disadvantages of each algorithm for several scenarios to extract useful information with the right procedure, and also obtain performance metrics such as execution time, number of samples, and accuracy. These metrics can subsequently serve as a basis for the selection or design of a hardware platform that conforms to the desired levels of accuracy, execution time, and number of samples required by the application.  
In Section 2, the CNRs calculation methods are presented. Then, Section 3 describes the simulation and experimental setup, and after that we present the comparison of the results and discuss accuracy, execution time, and overall suitability for a hardware implementation.
## 2 System Model and Configuration
### Link Budget Parameters  
This work adopts one sun-synchronous circular EO satellite with orbital height of 500 km [1], whose repeat period is 31 days. For all scenarios investigated herein, three typical G/Ss have been selected in China: Kashgar (latitude 39.50\({}^{\circ}\) N, longitude 75.92\({}^{\circ}\) E), Beijing (latitude 40.45\({}^{\circ}\) N, longitude 116.86\({}^{\circ}\) E) and Sanya (latitude 18.31\({}^{\circ}\) N, longitude 109.30\({}^{\circ}\) E), which are representative of temperate continental, temperate monsoon and tropical monsoon climate conditions, respectively [25]. For Kashgar, Beijing and Sanya G/S, the annual probability of rain is 0.38%, 1.94% and 2.46%, and the point rainfall rate for 0.01% of an average year is 9.42 mm/h, 42.56 mm/h and 81.09 mm/h, respectively. Therefore, the selected G/Ss can be representative of three typical states of drought, moderate rainfall and abundant rainfall in China.  
Based on engineering experience and on a technical basis [26; 27; 28; 29; 30], the link budget parameters are given in Table 2. The satellite-to-ground data transmission link is assumed as an additive white Gaussian noise (AWGN) channel in this paper.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**MCS**} & \multirow{2}{*}{**Mode Name**} & **Spectral Efficiency** & \(E_{\text{S}}/N_{\text{b}}\) **Threshold** & **Useful Data Rate** \\  & & **(bit/s/Hz)** & **(dB)** & **(Mbps)** \\ \hline
1 & QPSK 1/4 & 0.490 243 & \(-\)0.35 & 294.15 \\
3 & QPSK 2/5 & 0.789 412 & 1.70 & 473.65 \\
6 & QPSK 2/3 & 1.322 253 & 5.10 & 793.35 \\
12 & 8 PSK 3/5 & 1.779 991 & 8.50 & 1067.99 \\
14 & 8 PSK 3/4 & 2.228 124 & 10.91 & 1336.87 \\
20 & 16 APSK 4/5 & 3.165 623 & 15.03 & 1899.37 \\
25 & 32 APSK 4/5 & 3.951 571 & 18.64 & 2370.94 \\
28 & 32 APSK 9/10 & 4.453 027 & 21.05 & 2671.82 \\ \hline \end{tabular}
\end{table}
Table 1: Characteristic of MCSs recommended by DVB-S2.
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
###### Abstract  
As nodes in wireless sensor networks are usually powered by nonrenewable batteries, energy efficient design becomes critical. This paper considers a battery-powered transmitter using on-off keying (OOK) modulation and studies its energy efficiency in terms of the battery's energy consumption for per bit transmission (BECPB). In particular, the transmitter may use one of two distinct types of batteries with battery utilization factor (BUF) depending on discharge current. The first has an instantaneous discharge current (IDC)-based BUF, while the second has a mean discharge current (MDC)-based BUF. For each type of battery, a closed-form BECPB expression is derived under a Rayleigh channel when a prescribed symbol error rate (SER) is guaranteed. Then theoretical analysis is made to study the impact of battery characteristic parameter \(\gamma\), communication distance \(d\) and bandwidth \(B\) on the BECPB. Finally, the analysis is corroborated by numerical experimental results, which reveal that: the BECPB for each type of battery increases with \(\gamma\) and \(d\); the BECPB for the two batteries first decreases and then increases with \(B\), and there exists the optimal bandwidth corresponding to the minimum BECPB; the battery with IDC-based BUF corresponds to a larger BECPB. When \(\gamma\) and \(d\) are large, the BECPB for each type of battery is significantly higher than that for the ideal battery whose BUF is always 1. For instance, when \(\gamma=0.015\), \(d=90\) m and \(B=10\) kHz, the BECPB for IDC-based and MDC-based battery is nearly 60% amd 25% higher than that of the ideal battery, respectively.  
b
# Ergonomic Evaluation of Different Surgeon Positions for Total Knee Arthroplasty Surgery
## 1 Introduction  
Among the occupational risks faced by orthopaedic surgeons (e.g., radiation exposure, infectious diseases, and chemical agents), one of the most common and least studied is ergonomic risk [1; 2; 3; 4]. Orthopaedic surgery is a speciality with a particular risk of musculoskeletal disorders [5] because the orthopaedic surgeon's usual activities require physical strength, forced postures, repetitive movements, and prolonged standing [3; 6]. These are all risk factors for musculoskeletal disorders. In addition, the surgical environment should be more ergonomic [4; 7; 8]. The incidence of musculoskeletal disorders has been studied in some subspecialities of orthopaedic surgery such as: traumatology, paediatric surgery, in residents, and among surgeons performing arthroplasty [2; 3; 4; 9; 10; 11; 12]. The prevalence of work-related musculoskeletal pain has reached rates as high as 96.5% in arthroplasty surgeons [2]. Despite the growing concern in recent decades, to the best of our knowledge, no studies have provided information on the ergonomic habits and risk postures of orthopaedic surgeons at work.  
Total knee arthroplasty (TKA) is one of the most common surgical procedures in orthopaedics, and it must be performed ergonomically and safely. In Spain, more than65,000 total knee arthroplasties are performed annually (138.6 per 100,000 inhabitants in 2020) [13]. When performing a homolateral TKA with the surgeon's dominant hand, there are basically two positions: facing or on the same side of the knee to be operated on. However, when performing a total knee replacement contralateral to the surgeon's dominant hand, there are different positions that the surgeon tries to accommodate. We have not found any studies that define or investigate the different positions the surgeon takes when performing this procedure.  
Our study aims to determine which positions are ergonomically more favourable when orthopaedic surgeons perform contralateral knee arthroplasty using the dominant hand. Moreover, it aims to identify the surgical steps that require ergonomic corrections.
# Analysis of Synchronous Generators' Local Mode Eigenvalues in Modern Power Systems
## 3 Local Mode Analysis of Synchronous Generators without AVR
### Local Mode Boundaries' Assessment  
However, it is beneficial to estimate what the boundary values of damped frequencies \(\omega\) and time constants of amplitude decay \(\sigma\) of local oscillations are for synchronous generators of different types and different nominal powers. The boundary assessment aimed to estimate the frequencies and dampings of the local oscillations of the currently operating synchronous generators for all possible loadings. The obtained results allow us to predict local oscillations' frequencies and dampings and will be useful in PSS planning. The eigenvalues were calculated for more than one hundred real synchronous generators connected to the infinite bus (all synchronous generators in the Slovenian power system were analyzed, plus some synchronous generators for which data were attainable from the literature). It was assumed that generator loads range from 0 to 120% in active and reactive power (generators were over-excited, and they deliver reactive power at lagging power factor). The boundaries were estimated based on systematic calculations. The obtained values are presented in Table 3.  
Figure 8: Eigenvalue loci of local mode of a 158-MVA hydro-type synchronous generator where transmission line impedance was reduced to 50% of the nominal (real) impedance (comparable with Figure 3).  
In cases where the generator loads range from 0 to 100% in active and reactive power (generators were over-excited, and they deliver reactive power at lagging power factor), the calculated boundaries are presented in Table 4.  
Of course, the results are primarily valid for synchronous generators in the Slovenian power system. Still, due to similar generators in European power plants, the results are advantageous for engineers and researchers working in the European Network of Transmission System Operators.
# Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances
## 4 Deep Learning Methods for Nested NER
### Span Labeling Approaches
#### 4.2.2 Boundary Model  
The boundary approach involves additional layers, called boundary detection, to generate NE candidates, as illustrated in Figure 3c. These candidates are then assigned labels through the span classification layer. In this section, we discuss the research by focusing on the additional component, the boundary detection layer.  
[PERSON] et al. introduced a boundary-enhanced neural span classification (BENSC) model that employs two token classifiers within the boundary detection layer [88]. The two token classification layers predict whether the token is a head or tail word of the entity, respectively. Then, all valid head-tail pairs, i.e., where the tail follows the head, are labeled through the span classification layer. This model has the advantage of being able to predict longer entities, as it does not impose a length limitation. However, it presents the issue of error propagation, where inaccuracies in the boundary detection layer carry over to the next stages.  
In general, span-based models treat prediction results that do not exactly match the correct span boundaries as incorrect. However, [PERSON] et al. argue that this rigid consideration can introduce noise into the model [89]. To address this issue, they introduced boundary regression, inspired by object detection techniques in computer vision. Their model enumerates all spans that are shorter than a specific threshold and assigns them as candidate spans. The candidates go through two steps: filtration and refinement. In the initial step, the low-quality candidates that do not closely overlap with the actual NEs are filtered out using a binary classification layer. In the subsequent step, the boundaries of the remaining high-quality candidates are refined using a regression layer. The candidate spans adjusted in the two steps are finally classified through a span classification layer. This model is not free from the length limitation, as it employs the enumeration strategy for generating candidates in the initial stage. Although the excessive computational resource consumption associated with enumeration is mitigated through the filtration step, this introduces an error propagation issue. Nevertheless, the model remains notable for its use of approximate boundary refinement, allowing for adjustments in span boundaries even when initial predictions are not perfectly accurate.  
The primary advantage of these boundary models lies in their ability to narrow the candidate pool, thereby reducing computational time complexity and resource consumption. Although its overall performance may be lower compared to the enumeration approach, the model's resource efficiency makes it well-suited for real-world applications where computation constraints are a key consideration.
## 3 Results and Discussion  
### Surface Characterization  
The morphology and roughness of the Cu0, Cu1, Cu2, Cu3 and Cu4 surfaces observed by AFM are shown in Figure 2. It was obvious that Cu0, Cu1 and Cu2 displayed smooth surfaces, with their roughness \(0.40\pm 0.03\) nm, \(0.42\pm 0.02\) nm and \(0.43\pm 0.03\) nm, respectively. Nevertheless, Cu3 and Cu4 samples showed significantly rougher surfaces compared to Cu0, Cu1 and Cu2 (Ra: Cu0, Cu1 and Cu2 \(<\) Cu3 \(<\) Cu4). The AFM results suggested that a little amount of Cu doped in the TiO\({}_{2}\) could not remarkably change its morphology and roughness, while a large quantity of Cu doped in the TiO\({}_{2}\) could significantly increase its roughness and change its morphology.  
The crystalline structures of the Cu doped TiO\({}_{2}\) films (Cu0, Cu1, Cu2, Cu3 and Cu4) detected by XRD are displayed in Figure 3. The major phase was anatase when the films were calcined at 450 \({}^{\circ}\)C. Small diffraction peaks of CuO appear at \(26=35.6^{\circ}\) on Cu4 samples. No Cu peak was observed on Cu1, Cu2 or Cu3 samples, and it could be attributed to either small amount of Cu loading or very small Cu cluster size. The XRD results indicated that the doped Cu element mainly survived as a state of CuO.  
The micro BCA test in Figure 4 shows the amounts of Cu survived in the Cu0, Cu1, Cu2, Cu3 and Cu4. It was clear that the amounts of Cu on the surface increased with the amounts of doped Cu (Cu4 \(>\) Cu3 \(>\) Cu2 \(>\)Cu1, * \(p<0.05\)). Cu4 possessed almost three times' the amounts of Cu compared with Cu1 and Cu2, and possessed the most Cu on its surface, which was consistent with the XRD results.  
Figure 3: XRD results of the Cu0, Cu1, Cu2, Cu3 and Cu4 samples.  
Figure 2: The AFM characterization of the Cu0, Cu1, Cu2, Cu3 and Cu4 samples.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 3 Proposed Method
### Similarity Calculation for Embedded Vectors  
Two strategies for similarity calculation are introduced to measure the similarity between the embedded UAV feature vectors and satellite feature vectors. This paper adopts the widely used cosine similarity and Euclidean distance. A comparison of the different similarity calculation strategies is presented in the experiment section. The cosine similarity between the query embedded feature vector for UAV (\(\vec{V}_{uav}=[x_{1},x_{2},...,x_{n}]\)) and the reference embedded feature vector for satellite (\(\vec{V}_{sat}=[y_{1},y_{2},...,y_{n}]\)) is as follows:  
\[d=\frac{\vec{V}_{uav}\cdot\vec{V}_{sat}}{||\vec{V}_{uav}||\cdot||\vec{V}_{ sat}||} \tag{3}\]  
The cosine similarity approaching 1, 0, and \(-1\) means a positive correlation, no correlation, and negative correlation between the samples. Due to the disproportion in the numbers of UAV and satellite images, the cosine similarity of positive samples is optimized to 1, and the similarity of negative samples is optimized to 0 during model training.  
The Euclidean distance between \(\vec{V}_{uav}=[x_{1},x_{2},...,x_{n}]\) and \(\vec{V}_{sat}=[y_{1},y_{2},...,y_{n}]\) can be expressed as the following formula:  
\[d=\sqrt{x_{1}^{2}+x_{2}^{2}+...+x_{n}^{2}}-\sqrt{y_{1}^{2}+y_{2}^{2}+...+y_{n }^{2}} \tag{4}\]  
The Euclidean distance between a query feature vector and a positive reference feature vector needs to be minimized. The distance to the negative reference feature vector needs to be maximized. The optimization range for cosine similarity is \(-1\) to 1, and the optimization  
Figure 3: A color-space-based color transfer function is introduced to assimilate drone and satellite images. The color distribution of satellite is used as the reference for the UAV images.  
range for Euclidean distance is 0 to positive infinity. Therefore, using Euclidean distance can achieve a larger optimization range.
# Key Recovery for Content Protection Using Ternary PUFs Designed with Pre-Formed ReRAM
## 2 One Way Unclonable Functions with Ternary States
### One Way Unclonable Functions with PUFs
#### 2.2.1 Ring Oscillator PUFs  
Ring oscillator (RO) PUFs are designed typically with 16 to 256 CMOS-based circuits, each oscillate at a slightly different value due to small variations during fabrication [9]. The pairing of two rings generates a consistent response, 0 or 1, if the first ring oscillates slower or faster than the second one. RO-based PUFs are widely used to secure field programmablegate array circuits (FPGA), and some integrated circuits. They can, however, be subject to side-channel analysis through electromagnetic interference. The set of addresses **A** generated from **T** can point to a set of RO pairs in a particular order. The set of instructions **I** point to a subset of ROs, and to a targeted value of the power supply. The responses of the PUF are the stream **K**.
###### Abstract  
The traditional method used to determine the moisture content of tea leaves is time consuming and destructive. To address this problem, an effective and non-destructive prediction method based on near-infrared spectroscopy (NIRS) is proposed in this paper. This new method combines discrete wavelet transforms (DWT) with the bootstrap soft shrinkage algorithm (BOSS). To eliminate uninformative or interfering variables, DWT is applied to remove the noise in the spectral data by decomposing the origin spectrum into six layers. BOSS is used to select informative variables by reducing the dimensions of the sub-layers' reconstruction spectrum. After selecting the effective variables using DWT and BOSS, a prediction model based on partial least squares (PLS) is built. To validate effectiveness and stability of the prediction model, full-spectrum PLS, genetic algorithm PLS (GA-PLS), and interval PLS (iPLS) were compared with the proposed method. The experiment results illustrate that the proposed prediction model outperforms the other classical models considered in this study and shows promise for the prediction of the moisture content in Yinghong No. 9 tea leaves.  
a _Article_  
An Effective Prediction Approach for Moisture Content of Tea Leaves Based on Discrete Wavelet Transforms and Bootstrap Soft Shrinkage Algorithm  
1 College of Engineering, South China Agricultural University, Guangzhou 510640, China; [EMAIL_ADDRESS] (M.Z.); [EMAIL_ADDRESS] (J.G.); [EMAIL_ADDRESS] (J.R.); [EMAIL_ADDRESS] (F.Z.)  
2 Tea Research Institute, Guangdong Academy of Agricultural Sciences/Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation & Utilization, Guangzhou 510640, China; [EMAIL_ADDRESS]  
3 Public Monitoring Center for Agro-Product of Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-020-8528-2860 +  
Footnote †: journal: Accepted: 13 July 2020; Published: 14 July 2020
## 5 Applications of Laccases
Very often, the mechanism of the operation of biosensors is based on the ability of lacases to degrade undesirable compounds in the analyzed matrices [128; 129; 130; 131; 132]. This property of lacases is also exploited in enzyme-based bioremediation to remove xenobiotics present in urban, agricultural and industrial wastewater. The great interest of researchers in the field of potential applications of lacases in the bioremediation of newly emerging pollutants in the aquatic environment results from their beneficial biochemical properties and excellent diversity [93]. Lacase was used to remove emerging pollutants, such as nonylphenol, triclosan, BPA, ethinylestradiol, diclofenac, 2,4-dichlorophenol and others [90; 133; 134; 135; 136; 137; 138; 139; 140; 141; 142; 143; 144; 145; 146; 147; 148; 149; 150; 151; 152; 153; 154; 155; 156; 157; 158; 159; 160]. In the next chapter, we will summarize the current knowledge on the processes of removing pollutants of various origins from water with the use of lacase. Laccases show promising potential in a variety of environmental and industrial applications, which will be listed in Table 2.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Application** & **Source Organism** & **Activity** & **References** \\ \hline \multirow{6}{*}{**Pulp and paper industry**} & T. _versicolor_ & Demethylated and delignified the sulphate pulp & [161] \\ \cline{2-4}  & _Bacillus_ sp. & Decolorization of old newsprint and the biological & [162] \\  & & bleaching of eucalyptus kraft pulp & \\ \cline{2-4}  & _Fusarium equiseti_ VKF2 & Biological bleaching of newspaper waste & [163] \\ \cline{2-4}  & _T. versicolor_ & Immobilized lacase on copper ferrite magnetic & [164] \\  & & nanoparticles (CuMNPs) improve the delignification & \\ \hline \multirow{6}{*}{**Textile industry**} & _My disciplophthora thermophila_ & Altered the shape of wool yarn by lacase-assisted & [165] \\  & & tyrosine grafting & \\ \cline{2-4}  & _Cerena unicolor_ & Bicatalyst to transform & [95] \\ \cline{2-4}  & & 8-anilino-1-naphthalenesulffonic acid into a & \\ \cline{2-4}  & & water-soluble green dye with antibacterial and & \\ \cline{2-4}  & & & \\ \cline{2-4}  &
# Towards Optimizing Multi-Level Selective Maintenance via Machine Learning Predictive Models
## 4 Proposed Methodology
### Predictive Model for Multi-Level Selective Maintenance Using ANN and SVR  
#### 4.2.1 The General Concept of the ANN Model  
The Artificial Neural Network (ANN) is inspired by the architecture of neurons in the brain. To simplify the mathematical modeling, an ANN is composed of a single hidden layer, an input layer and an output layer. Nodes in one layer are linked to nodes in the next layer through weights. Initially, the weights and biases are randomly generated [36; 37]. The ANN model is based on the following equations:  
\[h_{j,inp}=\sum_{i=1}^{n}w_{ij}\cdot x_{i}+b_{inp} \tag{6}\]  
\[h_{j,out}=g(h_{j,inp}) \tag{7}\]  
\[y_{p}=\sum_{j=1}^{m}h_{j,out}+b_{hid} \tag{8}\]where:  
* \(x_{i}\): the \(i^{th}\) input node
* \(h\)\({}_{j,out}\): the output of the \(j^{th}\) hidden node
* \(w_{i,j}\): the weight connecting the input node \(x_{i}\) to the hidden node \(j\)
* \(g(\cdot)\): the activation function of the model
* \(b_{inp}\): the input layer bias
* \(b_{hid}\): the hidden layer bias
* \(y_{p}\): the model output: the estimated target value.  
In a neural network, an activation function (\(g(\cdot)\)) is used to choose how the weighted sum of the input is transformed into an output from nodes in another layer of the network. The selection of activation function has a big impact on the neural network performance, and several activation functions may be used in different parts of the model [38; 39].  
In general, four common functions used in hidden layers are considered as the activation function for the ANN algorithm:  
* Identity function \[g(x)=x\] (9)
* Logistic Sigmoid function \[g(x)=\frac{1}{1+e^{-x}}\] (10)
* Rectified linear unit function \[g(x)=max(0,x)\] (11)
* Hyperbolic tangent function \[g(x)=\frac{e^{x}-e^{-x}}{e^{-x}+e^{-x}}\] (12)