esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Inverse Solution for Risley Prisms
### Geometric Method, Ellipse Approximation
#### 2.2.2 Calculation of the Rotation Angles of the Prisms  
In this approximation, the geometric shape that describes the beam, when the second prism \(\pi_{2}\) rotates, approximates an ellipse \(e_{1}\) with centre \(C\) at any point of the circle \(c_{1}\).  
Since the sum of the focal distances of any point \(P\) on an ellipse is constant and equal to the length of the major axis of the ellipse (\(b\)):  
\[PF1+PF2=2b\to d_{1}+d_{2}=2b \tag{13}\]  
For simplicity, a rotation of the coordinate axes through the angle \(\delta\) is made, Figure 5, so that the target point P is on the X' axis, being now the point P' (\(x_{0}^{\prime},y_{0}^{\prime}\)).  
Figure 5: Rotation of the coordinate axes of the ellipse approximation. (**a**) represents the original axes (X, Y); (**b**) represents the rotated axes (X’, Y’) though the angle \(\delta\).  
Knowing the target point P\({}^{\prime}\) and the points of the focal distance \(F1^{\prime}\) (\(x_{1}^{\prime}\), \(y_{1}^{\prime}\)) and \(F2^{\prime}\) (\(x_{2}^{\prime}\), \(y_{2}^{\prime}\)), the distance between them is calculated as:  
\[\begin{split} d_{1}&=\sqrt{\left(x_{1}^{\prime}-x_{ 0}^{\prime}\right)2+\left(y_{1}^{\prime}-y_{0}^{\prime}\right)^{2}}\\ d_{2}&=\sqrt{\left(x_{2}^{\prime}-x_{0}^{\prime} \right)^{2}+\left(y_{2}^{\prime}-y_{0}^{\prime}\right)^{2}}\end{split} \tag{14}\]  
where:  
\[\begin{split} x_{1}^{\prime}&=r_{\text{F}}^{1}\cdot cos (\gamma^{\prime})\\ y_{1}^{\prime}&=r_{\text{F}}^{1}\cdot sin(\gamma^{ \prime})\\ x_{2}^{\prime}&=r_{F2}\cdot cos(\gamma^{\prime})\\ y_{2}^{\prime}&=r_{F2}\cdot sin(\gamma^{\prime}) \end{split}\]  
The radius \(r_{\text{F1}}\) and \(r_{F2}\) are the radii of the circles formed with the centre at the origin and passing through focus points \(F1\) and \(F2\), respectively. The focus points \(F1\) and \(F2\) are at the same distance from the primary radius. This distance is \(\sqrt{b^{2}-a^{2}}\).  
Considering \(y_{0}^{\prime}=0\) and on the basis of Equations (13) and (14), it is possible to obtain the solution of the angle \(\gamma^{\prime}\) between the X'-axis and the line that passes through the centre point \(C^{\prime}\) of the ellipse:  
\[\gamma^{\prime}=-acos\big{(}-(c_{4}-4b\cdot c_{7}+c_{5})/x_{0}^{\prime}\cdot c _{3}\big{)} \tag{15}\]  
where:  
\[\begin{split} c_{1}&=-4b^{2}\cdot r_{F1}-4b^{2} \cdot r_{F2}\\ c_{2}&=r_{\text{F1}}^{3}-r_{\text{F1}}^{2}\cdot r_{ F2}-r_{F1}r_{F2}^{2}+r_{F2}^{3}\\ c_{3}&=2\cdot(r_{F1}-r_{F2})^{2}\\ c_{4}&=-c_{1}\\ c_{5}&=-c_{2}\\ c_{6}&=4b^{2}r_{F1}r_{F2}-r_{F1}{}^{3}r_{F2}+2r_{F1 }{}^{2}r_{F2}{}^{2}-r_{F1}r_{F2}{}^{3}\\ c_{7}&=\sqrt{c_{6}+r_{F1}{}^{2}\cdot{x_{0}^{\prime}} ^{2}-2r_{F1}\cdot r_{F2}\cdot{x_{0}^{\prime}}^{2}+r_{F2}{}^{2}\cdot{x_{0}^{ \prime}}^{2}}\end{split}\]  
Finally, the rotation angle of the first prism \(\theta_{1}\) and the second prism \(\theta_{2}\) can be determined following the same process as the circumference approximation (undoing the change of coordinate axes) through Equations (6)-(10).
# Denoised Improved Envelope Spectrum for Fault Diagnosis of Aero-Engine Inter-Shaft Bearing
## 1 Introduction
After all, the frequency distribution of vibration sources is very large since the high-pressure rotor and low-pressure rotor operate with significant differences in rotational speed. Then, the dual-frequency excitation problem is easily formed in the monitoring signal of inter-shaft bearings. Consequently, the vibration information excited by the inter-shaft bearing is easily overwhelmed by background noise of the same frequency. To effectively identify inter-shaft bearing faults from casing signals of an aero-engine test platform, a novel method referred to as DIES is proposed in this paper. First, this method computes a subtracted normalized improved envelope spectrum (NIES) employing a spectrum subtraction technique. This technique is applied to subtract the healthy signal spectrum from the faulty signal spectrum ([PERSON] and [PERSON], 2016). In this process, NIES is used to extract weak fault information on the inter-shaft bearing under the interference of dual-frequency excitation. Then, the spectrum subtraction technique is used to suppress the frequencies of deterministic interferential components in the NIES of the fault vibration signal. Subsequently, an improved adaptive-thresholding method is proposed and applied to the subtracted NIES, in order to eliminate its high background noise. Compared to these methods mentioned above, the DIES can not only diagnose a bearing fault accurately under high background noise but also be more effective in handling the multi-source interference with a wide-band distribution problem under dual-frequency excitation. To demonstrate the effectiveness and superiority of the proposed method, comparative evaluations are conducted against existing classical methods. The classical methods used for comparison in this study include spectral kurtosis-envelope spectrum (SK-ES), IES, and ITD-SVD. Experimental results show that the proposed DIES method is more robust than IES, SK-ES, and ITD-SVD in handling the fault diagnosis problem of inter-shaft bearings. The reasons are as follows. While IES can extract cycle frequencies to a certain extent, it lacks the capability to eliminate interference frequency components. SK-ES essentially extracts target frequency bands through narrowband filtering, so it cannot effectively extract hidden cyclostationary periodic signals. Although the ITD-SVD method has strong denoising ability, it fails to effectively extract weak fault signal components. In contrast, the proposed DIES method is capable of extracting hidden cycle frequency components. Moreover, it can eliminate interference frequency components through spectrum subtraction and an improved adaptive thresholding technique. These advantages render it more effective than other comparative methods. The remainder of this paper is organized as follows. Section 2 presents the proposed DIES framework in detail. Section 3 evaluates the effectiveness and generalizability of the proposed method through experiments on two real-world datasets. Finally, conclusions are provided in Section 4.
## 2 The Development of the Fluoropolymer Industry from 1930s \(\sim\) 1990s
### 1940s  
In the beginning, PTFE polymers found no use for any application due to its insolubility, high melting temperature and extremely high melt viscosity. During World War II people realized the importance of this new material. At the time, the Manhattan Project was carrying out the development of the atomic bomb. Practically, U-235 needs to be separated from U-238 using differential diffusion of UF\({}_{6}\). UF\({}_{6}\) is highly corrosive to most metals and it was difficult to purify this material. As a result, the Manhattan project was looking for new corrosion resistant materials to meet the novel needs of purifying UF\({}_{6}\). The then strange new material and its resistance to chemicals proved that PTFE could survive the extremely corrosive purification conditions. Therefore, PTFE got its first business order from the Manhattan project. After the war, PTFE was commercially available in 1947 with the trademark Teflon from DuPont to meet the growing market needs of the US and the world. The rate of market growth for PTFE has been 3%-5% per year for the past 30 years. In the late 1990s, annual consumption worldwide for PTFE was over 55,000 tons, which is almost doubled now. Some typical commercially available PTFE products are Teflon (DuPont), Polyflon (Daikin), Dyneon PTFE (Dyneon), and Fluon (Ashai Glass).  
Noticeably, even now PTFE still remains the largest type of fluoropolymers with about 70% of the total fluoropolymer market worldwide.
## 4 Results and Discussion
### Improved RF-Based Models
Compared with the regular RF model, for the training set, the RF-WOA model increases the R\({}^{2}\) value of the RF from 0.973 to 0.986 and the VAF of the RF from 97.298 to 98.603 and simultaneously reduces the RMSE of the RF model from 0.164 to 0.118 and the MAE of the RF from 0.123 to 0.093. For the testing set, the RF-WOA model increases the R\({}^{2}\) value of the RF from 0.923 to 0.932 and the VAF value of the RF from 93.994 to 95.032 and simultaneously reduces the RMSE of the RF model from 0.262 to 0.246 and the MAE of the RF from 0.209 to 0.188. Finally, the measured and predicted PPV by the RF-WOA model on both training and test sets are depicted in Figures 12 and 13, respectively. Figure 11: Taylor diagram of the testing results for the six developed models.
# A Novel Variable Step Size Incremental Conductance Method with an Adaptive Scaling Factor
## 4 Simulation and Experimental Results
### Experimental Results  
In this paper, a 600 W prototyping circuit is implemented from which experiments are carried out accordingly. Figure 7 shows the block diagram of the proposed system. From Figure 7, a low-cost DSC dsPIC33 FJ16 GS502 from Microchip Corp. (Chandler, AZ, USA) is used to realize the MPPT algorithms mentioned above, and the experiments are performed with an AMETEK TerraSAS DCS80-15 Solar Array Simulator (San Diego, CA, USA) in SAS mode as a power source. The specifications of the utilized power converter are listed in Table 1 and the parameters of the utilized PV panel are listed in Table 2. A photo of the experimental setup and the testing environment is shown in Figure 8.  
Figure 9 shows the measured tracking performance of different \(M\) values when the irradiance level increases. Figure 10 shows the measured tracking performance of different \(M\) values when the irradiance level decreases. As shown in Figures 9 and 10, the experimental MPP tracking curves correspond well to the MPP tracking curve obtained by simulations (Figures 4 and 5). The obtained experimental results are summarized in Table 5. Compared to the conventional VSS INC MPPT method, the proposed method can improve the tracking energy loss by 43.9% under increasing irradiance level condition and can improve the tracking energy loss by 29.9% under decreasing irradiance level condition.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **tt of** & **tt of** & **tt of** & **ta of** & **ta of** & **ta of** & **Total** \\  & **500 W/m\({}^{2}\)** & **800 W/m\({}^{2}\)** & **300 W/m\({}^{2}\)** & **500 W/m\({}^{2}\)** & **800 W/m\({}^{2}\)** & **300 W/m\({}^{2}\)** & **Energy Loss** \\ \hline VSS INC (\(M=0.65\)) & 4.7 s & 0.4 s & 0.6 s & 99.8\% & 99.9\% & 99.9\% & 3145.6 W \\ VSS INC (\(M=1.08\)) & 2.9 s & 0.4 s & 0.4 s & 99.8\% & 98.2\% & 99.9\% & 2175.7 W \\ VSS INC (\(M=1.8\)) & 2.9 s & 0.4 s & 0.4 s & 97.5\% & 97.8\% & 99.9\% & 2446.3 W \\ Proposed method & 2.9 s & 0.4 s & 0.4 s & 99.8\% & 99.9\% & 99.9\% & 1970.2 W \\ \hline \hline \end{tabular} Note: tt: tracking time, ta: tracking accuracy.  
\end{table}
Table 4: Summarized simulated results of different \(M\) values.  
Figure 7: The block diagram of the proposed system. A/D: Analog-to-Digital; PID: Proportional-Integral-Derivative.  
Figure 8: Experimental setup and the testing environment.  
Figure 9: Experimental results of different \(M\) values (**a**) \(M=0.65\), (**b**) \(M=1.08\), (**c**) \(M=1.08\)/0.65.
## 2 Material and Methods
### Preparation of Stock, Calibration Standard (CS) and Quality Control (QC) Samples  
The stock solution of 5-AIQ and pantoprazole were prepared by dissolving the accurately weighed amount of the neat powders in methanol to achieve a 200 \(\mathrm{\SIUnitSymbolMicro g}\)/mL solution. The stock solution of 5-AIQ was further serially diluted by 50% ACN to prepare a working standard for CSs 30-2000 \(\mathrm{ng}\)/mL and for QCs 105-15,000 \(\mathrm{ng}\)/mL concentration. This working standard solution was further spiked into plasma to achieve CSs in plasma between 1-666 ng/mL. Similarly, the QC samples were prepared by spiking their working standards into plasma to achieve 3.5, 50 and 500 ng/mL concentration and was treated as low, middle and high QC (LQC, MQC and HQC) samples. The IS working solution of 200 ng/mL was prepared by diluting its stock standard with 50% ACN and was used during sample preparation procedure. All of the aqueous solution was stored in a refrigerator maintained at 4 \(\pm\) 2 \({}^{\circ}\)C; however, the spiked plasma CSs and QCs samples were stored in a deep freezer maintained at \(-80\)\(\pm\) 5 \({}^{\circ}\)C.
## 3 Materials and Methods
### Rainfall Datasets
#### 3.1.3 CGDPA Data  
The China Gauge-based Daily Precipitation Analysis (CGDPA) is produced and routinely calibrated by the National Meteorological Information Center (NMIC) of China Meteorological Administration (CMA). The CGDPA dataset has a daily spatiotemporal resolution of \(0.25^{\circ}\) and is used as a reference for evaluating the GPM products. This dataset includes daily precipitation from 2419 stations (including the National Climate Observatory, National Meteorological Observation Station I, Second-level station), and the optimal interpolation method based on \"climate background field\" is applied to generate a grid product of daily precipitation in China in real time [36].  
Considering that the GPM satellite datasets started in April 2014 and the TRMM data were stopped in April 2015, we chose the overlap period of the two satellite data from April 2014 to March 2015 as the study period. On the daily scale, a more detailed and comprehensive assessment was made in different subregions.
Sensors_**2012**, _12_, 1529-1543; doi:10.3390/s120201529  
_Series of_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**An LDPC Decoder Architecture for Wireless Sensor Network Applications**  
**[PERSON], [PERSON] * and [PERSON]**  
Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Corso Duca degli Abruzzi 24,  
Torino 10129, Italy; E-Mails: [EMAIL_ADDRESS] (A.D.G.B.);  
[EMAIL_ADDRESS] (G.M.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +39-011-090-4205; Fax: +39-011-090-4217.  
_Received: 18 November 2011; in revised form: 25 January 2012 / Accepted: 31 January 2012 / Published: 6 February 2012_  
**Abstract:** The pervasive use of wireless sensors in a growing spectrum of human activities reinforces the need for devices with low energy dissipation. In this work, coded communication between a couple of wireless sensor devices is considered as a method to reduce the dissipated energy per transmitted bit with respect to uncoded communication. Different Low Density Parity Check (LDPC) codes are considered to this purpose and post layout results are shown for a low-area low-energy decoder, which offers percentage energy savings with respect to the uncoded solution in the range of 40%-80%, depending on considered environment, distance and bit error rate.  
**Keywords:** LDPC decoder architecture; wireless sensor networks; power consumption
# Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability
## 4 Discussion
### Terrestrial Ecosystems' Drought Resistance
[51] found that steppic ecosystems also showed weak resistance against drought. Our results are more consistent with the research above.
## 3 Results
### Correlation Analysis  
The correlations between the physicochemical characteristics were investigated using Pearson correlation analysis calculated through International Business Machine Statistical Package for Social Sciences (IBM SPSS). The r and p values were presented. The r value expresses the relationship between variables. The p value expresses the significance of the relationship. A lower \(p\)-value denotes statistical significance, whereas a higher \(p\)-value denotes the opposite. A negative correlation was found between pH and the other variables, while a positive correlation was found between the other parameters. At the 0.01 p level, all relationships were significant. The correlation matrix for the physicochemical parameters are presented in Table 3.  
A substantial negative correlation was observed between pH and temperature (r = \(-\)0.665), pH and EC (r = \(-\)0.602), and pH and TDS (r = \(-\)0.603) which is similar to the findings of [PERSON] et al. in 2017 [66] and [PERSON] and [PERSON] in 2019 [67]. On the other hand, a substantial positive correlation was observed between temperature and EC (r = 0.657), temperature and TDS (r = 0.664), and EC and TDS (r = 0.995). This correlation values agreed to the findings of [PERSON]  
Figure 7: Concentration of Zn in GW samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Temp** & **pH** & **EC** & **TDS** \\ \hline Temp & 1.000 & & & \\ pH & \(-\)0.665 ** & 1.000 & & \\ EC & 0.657 ** & \(-\)0.602 ** & 1.000 & \\ TDS & 0.664 ** & \(-\)0.603 ** & 0.995 ** & 1.000 \\ \hline \hline \end{tabular}  
* Correlation is significant at the 0.01 level (2-tailed).  
\end{table}
Table 3: Correlation matrix of the physicochemical parameters.  
Similar to physicochemical parameters, Pearson correlation analysis for the relationships between Ba, Cu, Fe, Mn, and Zn was also taken. Fe was positively correlated with Mn and Zn; and Mn was positively correlated with Zn. Positive substantial correlations between these metals indicated the same origin, are mutually dependent, and have similar transport characteristics [69]. The positive p with higher r values of this study illustrates relationship between metals; however, this relationship was not significant. The presence of these metals in GW is attributed to natural weathering of rocks. The correlation matrix for metals concentrations is shown in Table 4.
## 5 Connected Vehicles
### Benefits and Business Opportunities
#### 5.2.3 Fleet Management for Commercial EVs  
Fleet management involves vehicle finance management, vehicle dynamic maintenance, vehicle Telematics (real-time tracking and trouble-shooting), and the management of speed, driver, fuel, and safety. The location information, health information, and fuel storage of individual vehicles will be fed back to the cloud-based server of the central management system in real-time for decision processing and real-time dispatching to maximize the efficiency of the logistics network operation and maintenance [92]. The vehicle database will record the health index, violation, and insurance status of specific vehicles, while the driver database will record the average speed, lane change frequency, operation specification, and gear change frequency of particular drivers. Rational analysis of the Big Data formed by the recorded parameters can optimize fleet management significantly [93]. When a malfunction or accident occurs, the real-time recorded vehicle damage status and pre-accident maneuvering data can assist the relevant departments in quickly determining accident liability and insurance payouts [94].
# Integrated Design of Process-Tolerance for Remanufacturing Based on Failure Feature
## 2 Materials and Methods
### Multi-Objective Function Construction
#### 2.3.2 Constraints Functions  
IDP-T should be conformed to the actual machining capacity of the remanufacturing companies and assembly precision requirements of the product. Based on this, the constraint function should be established.  
Tolerance Constraint of Closed-Loop  
The tolerance of a closed-loop reflects the precision of the manufacturing stage and the assembly stage of the dimension chain. For remanufacturing the dimension chain, the tolerance of the closed-loop should meet the requirements of the closed-loop tolerance of the original dimension chain. However, due to the uncertainty of the part reused mode of remanufacturing the dimension chain, the original tolerance recovery of a single part cannot meet the requirements. It is necessary to include this uncertainty in the integrated tolerance design of remanufacturing.  
The reused mode of used parts can be divided into new parts, reused parts, and remanufactured parts. The tolerance of the new part meets the requirements of new product suppliers; the reused parts had absolute dimensions, and the tolerance is 0; the tolerance of remanufactured parts needs to be optimized.  
Therefore, when considering different part reused modes of remanufacturing dimension chains, the tolerance constraint of a closed-loop is as in Equation (15).  
\[\sum\xi_{i}T_{i}+\sum\xi_{new}T_{new}\leq T_{0} \tag{15}\]  
where \(T_{i}\) and \(T_{new}\) are the tolerance of the remanufacturing surface \(i\) and new parts, respectively; \(\xi_{i}\) and \(\xi_{new}\) are the transfer coefficient of surface \(i\) and new parts, respectively; \(T_{0}\) is the closed-loop tolerance of the original dimension chain.  
Constraint of Machining Capability  
There are differences in machining capability among different remanufacturing companies, and the actual machining capability of companies needs to satisfy Equation (16).  
\[T_{minij^{-}}\leq T_{ij^{-}}\leq T_{maxij^{-}} \tag{16}\]  
where \(T_{minij^{-}}\) and \(T_{maxij^{-}}\) are the minimum and the maximum machining precisions of the removal process of the company in economic processing parameters.
# Automatic Short Text Summarization Techniques in Social Media Platforms
## 5 Approaches to Summarization
### Abstractive Summarization  
Abstractive summarization involves the generation of new sentences that capture the essential information from the original text. Unlike extractive summarization, this approach can rephrase and paraphrase the content, allowing for more coherent and concise summaries. One of the significant advantages of abstractive summarization is its ability to produce more fluent and cohesive summaries by synthesizing information in a manner similar to human language. Furthermore, it excels at handling overlapping information,as it can consolidate similar ideas into concise statements. However, abstractive summarization does have some limitations. It requires a deeper understanding of the input text to generate contextually accurate and meaningful summaries. Additionally, it is prone to introducing factual inaccuracies or misinterpretations, especially when dealing with out-of-vocabulary words or rare entities. To mitigate these challenges, ongoing research focuses on improving the context comprehension and semantic understanding capabilities of abstractive summarization models. With advancements in NLP and deep learning, efforts are being made to enhance the accuracy and coherence of abstractive summaries while maintaining their conciseness and relevance to the original text. Here are some common abstractive approaches in short text summarization:  
* Sequence-to-sequence (Seq2 Seq) models: Seq2 Seq models, based on recurrent neural networks (RNNs) or transformers, are widely used in abstractive summarization. These models encode the input short text into a fixed-size vector and then decode it to generate a summary. The decoder generates new sentences by predicting the next word based on the context learned from the encoder [27].
* Transformer-based models: Transformers, particularly pre-trained models like GPT-2 and BERT, have shown promising results in abstractive summarization. These models use self-attention mechanisms to capture contextual relationships between words and generate coherent and contextually appropriate summaries.
* Reinforcement learning: Abstractive summarization can also be approached as a reinforcement learning problem. The model generates candidate summaries, and a reward mechanism is used to evaluate their quality. The model is then fine-tuned using policy gradients to optimize the summary generation process.
* Pointer-Generator networks: Pointer-Generator networks combine extractive and abstractive methods. These models have the ability to copy words from the input text (extractive) while also generating new words (abstractive) to create the summary. This approach helps to handle out-of-vocabulary words and maintain the factual accuracy of the summary.