esa-sceva/satcom-chunk-collection huggingface dataset:
# Partitioning Evapotranspirationiration in a Cotton Field under
## 1 Introduction
[PERSON] [22] in 2018 used the \(uWUE\) to partition the \(ET\) components of maize farmland in the semi-arid region of the Loess Plateau of China. The \(uWUE\) method could reduce the time lag of different environmental factors on a daily scale, and the accuracy of its calculation results was particularly prominent under the condition of multiching film. [PERSON] et al. [23] in 2020 used the \(uWUE\) to simulate the \(ET\) components of the oak grassland in California, which had good applicability. [PERSON] et al. [24] in 2021 used the \(uWUE\) to partition the \(ET\) components of a paddy field in the middle and lower reaches of the Yangtze River in China, and used the Micro-Lysimeter/Eddy Covariance (ML/EC) Method to evaluate the \(uWUE\), and found that the \(uWUE\) had a good effect on partitioning \(ET\) components in the paddy field, and the \(T\)/\(ET\) of the early rice were significantly lower than those of the late rice. [PERSON] et al. [25] in 2022 used the Shuttleworth-Wallace Model, Priestley-Taylor Model, and \(uWUE\) to calculate \(T\)/\(ET\), and found that the calculation results of \(uWUE\) were larger than those of other methods. Then, [PERSON] et al. [26] used the \(uWUE\) and the measured data of the \(EC\) system to partition the \(ET\) components of the tallgrass prairie in Kansas in the United States and found that the precipitation had a direct impact on the \(ET\) components. It can be seen that the \(uWUE\) has become an effective method for estimating \(T\)/\(ET\). However, partitioning \(ET\) of cotton fields under mulched drip irrigation in an arid region by using the \(uWUE\) method lacks long-term observation and in-depth research, making it difficult to clarify the changes in different \(ET\) components. The main cotton-producing area is Xinjiang in northwestern China. However, water scarcity and strong ET pose a threat to the sustainable development of cotton, resulting in instability in cotton yield and quality due to the arid climate and low precipitation in Xinjiang. Clarifying the \(ET\) components of cotton fields under mulched drip irrigation in an arid region can provide a basis for the spatial and temporal distribution of agricultural water in cotton fields. With the help of the \(EC\) system, this study carried out a two-year continuous experiment on cotton fields under mulched drip irrigation at the Key Laboratoryof Modern Water-Saving Irrigation of Xinjiang Production & Construction Group. \(ET\) in cotton fields under multiched drip irrigation was partitioned into \(E\) and \(T\) by \(uW\)\(U\)\(\!\)E in order to clarify the law of water evapotranspiration and the impact of climate on it and scientifically understand the water cycle process of farmland for the purpose of providing scientific guidance for water-saving irrigation and achieving the goal of rational allocation of water resources.
# Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges
## 4 Challenges
### High-End Devices  
The authentication method that was presented in this study requires a high-end device that supports biometric recognition. However, there are still many low-end devices in widespread use. Even if all the devices support the GPS authentication method, it may expose them to location spoofing.  
The minimum requirement for this authentication method is that mobile devices with GPS support the anti-spoofing module, such as the selective availability spoofing module (SAASM). On the other hand, we believe that all devices supporting biometric recognition and anti-spoof geo-location modules will come sooner.
# Carrier Phase Dual One-Way Ranging Method Based on a Frequency Hopping Signal
## 4 Performance Analysis of the FH-DOWR Model  
In this section, we investigate the performance of the FH-DOWR model in terms of the acquisition time, tracking performance, and carrier phase ranging error. In addition, we provide the simulation results via a software receiver.
## 2 GFB-OFDM Transceiver
### Polyphase Filter  
The purpose of the polyphase filter is to filter each subband in order to eliminate the mirror interference between the subbands. The parameters of the filtering function need to be selected carefully, considering its impact on the overall performance of GFB-OFDM [23; 24]. In this paper, we adopt a dual root-raised-cosine (DRRC) filtering function, which employs the time-domain representation of the RRC function to window and truncate frequency-domain RRC signals. The procedure of generating this filter is as follows.  
The time-domain form of the frequency-domain RRC function can be expressed as  
\[\mathbf{p}(t)=\frac{(1-\alpha)T^{2}\text{sinc}(\frac{t}{T}(1-\alpha))+\frac{4}{ \pi}\alpha T^{2}\text{cos}(\frac{\pi t}{T}(1+\alpha))}{T^{2}-16\alpha^{2}l^{2}} \tag{8}\]  
where \(\alpha\) is the roll-off factor of the RRC filter and \(T\) is the length of the subsymbol mentioned earlier, which means that the half-value bandwidth of this frequency domain RRC function is \(\frac{1}{T}\), where \(\frac{1}{T}\) represents the subband frequency domain bandwidth.  
The time-domain RRC function is employed to truncate the temporal extent of the filter function. The time-domain RRC function is expressed as  
\[\mathbf{q}(t)=\begin{cases}1,&0\leq|t|<T_{b}(1-\beta)\\ \sqrt{\frac{1}{2}(1+\text{cos}(\frac{|t|-T_{b}(1-\beta)}{2T_{b}\beta}\pi))},&T _{b}(1-\beta)\leq|t|<T_{b}(1+\beta)\\ 0,&|t|\geq T_{b}(1+\beta)\end{cases} \tag{9}\]  
where \(\beta\) is the roll-off factor of the RRC function and \(T_{b}\) is half of the time-domain bandwidth of the RRC function. Therefore, the length of this time-domain RRC function is \(2T_{b}(1+\beta)\). Then, the time domain form \(\mathbf{g}(t)\) of the DRRC filter is expressed as  
\[\mathbf{g}(t)=\mathbf{q}(t)\mathbf{p}(t). \tag{10}\]  
Figure 2: GFB-OFDM data-processing procedure with the number of modulated symbols \(K=8192\), the number of subbands \(N=8\) and the number of subcarriers in each subband \(M=1024\).  
The time-domain length of the filtering function is equal to the length obtained by repeating each subsymbol in \(L\) times, i.e., \(2T_{b}(1+\beta)=LT\). As \(L\) increases, which means the time-domain length of the filtering function becomes larger, the filtering function approaches an ideal frequency-domain RRC function. This enables an ideal elimination of interference between the subbands.
###### Abstract  
Given the widespread use of mobile devices that track their geographical location, it has become increasingly easy to acquire information related to users' trips in real time. This availability has triggered several studies based on user's position, such as the analysis of flows of people in cities, and also new applications, such as route recommendation systems. Given a dataset of geographical trajectories in an urban metropolitan area, we propose a new algorithm to detect corridors. Corridors can be defined as geographical paths, with a minimum length, that are commonly traversed by a minimum number of different users. We propose an efficient strategy based on the Apriori algorithm to extract frequent trajectory patterns from the geo-spatial dataset. By discretizing the data and adapting the roles of itemsets and baskets of this algorithm to our context, we find the longest corridors formed by cells shared by a minimum number of trajectories. After that, we refine the results obtained with a subsequent filtering step, by using a Radius Neighbors Graph. To illustrate the algorithm, the GeoLife dataset is analyzed by following the proposed method. Our approach is relevant for transportation analytics because it is the base to detect lacking lines in public transportation systems and also to recommend to private users which route to take when moving from one part of the city to another on the basis of behavior of the users who provided their logs.  
GPS trajectory; frequent urban corridor; movement patterns; trajectory data mining 2020 10 5003; doi:10.3390/app10145003
# Fooling in Membrane Distillation, Osmotic Distillation and Osmotic Membrane Distillation
## 4 Characterization Techniques for Fouling Analysis
### Microscopy Techniques
#### 4.2.3 Atomic Force Microscopy (AFM)  
AFM is one of the advanced techniques used for characterization of the membrane surface without requiring any previous sample preparation. Unlike conventional optical or electron microscopy, AFM requires physical interaction between a probe with a sharp tip and the sample surface. It is possible to work in contact, non-contact or intermittent contact mode to get the three-dimensional topographical images of the membrane surface. The measurements can be made in different media: air, liquid, vacuum, controlled atmosphere, etc. The AFM probe scans the sample onto which a laser beam isdirected and reflected onto a four-quadrant photodiode. The feedback controller evaluates the signal coming out of the photodiode. Moreover, piezoelectric elements are included in the scan head to ensure precise nanoscale motion [100]. Comprehensive information on this technique can be found in [101; 102].  
In general, AFM technique is used to determine surface roughness parameters, mean pore size, pore size distribution, surface porosity and pore density of both new and fouled membranes [101]. This technique is commonly used for characterization of MD membranes. As an example, [PERSON], et al. [103] used AFM morphological analysis to understand the observed decrease in contact angle and surface charge of PTFE and PP membranes used in MD. The three-dimensional AFM pictures of virgin and fouled PTFE and PP membranes with model manure solution and pig manure after sieving with MF and UF showed that the foulants accumulate in the \"valleys\" leading to a drop of surface roughness [103].
## 4 AC/DC Converter
### MOSFET Selection  
The MOSFET selection is carried out with the same procedure as with diodes. However, in this case, simulations were made with Split N capacitor topology (Figure 25).  
The selection of the MOSFET's was done in the following conditions: at minimum input state (3 m/s wind velocity): 500 kHz switching frequency, 0.05/0.1 duty-cycle (D), \(R_{load}\) = 80/90 \(\Omega\) and the commercial diode model MBR0520L; and at maximum input state (10m/s): 1000 kHz switching frequency, \(R_{load}\) = 70/80 \(\Omega\) and maintaining duty-cycle values. With these test conditions, several commercial MOSFETs were selected based on their appropriate characteristics for the present use, i.e.,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Diode** & \(R_{load}\) **(\(\Omega\))** & \(f_{in}\) **(kHz)** & \(V_{in}\) **(Vp)** & \(V_{out}\) **(V)** & \(I_{out}\) **(mA)** & \(P_{out}\) **(mW)** & \(P_{in}\) **(mW)** & \(\eta\) **(\%)** \\ \hline MBR0520L & 30 & 6.717 & 2.575 & 1.995 & 66.572 & 132.957 & 248.648 & 53.47 \\ BAT15-03W & 30 & 7.629 & 3.9 & 1.753 & 58.451 & 102.497 & 190.125 & 53.91 \\ IPS79 BS31 & 30 & 6.857 & 2.751 & 1.972 & 65.649 & 129.646 & 283.80 & 45.68 \\ NSR20F30 NXT5G & 30 & 6.73 & 2.588 & 1.992 & 66.405 & 132.291 & 251.165 & 52.67 \\ NSVR351 SDSA3 & 30 & 7.106 & 3.1 & 1.915 & 63.961 & 122.73 & 360.375 & 34.06 \\ STPS20L15G & 30 & 6.497 & 2.321 & 2.047 & 68.261 & 139.789 & 202.014 & 69.20 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Comparative results between different diodes at 10 m/s.  
Figure 29: Comparisons between diodes at 3 m/s and 10 m/s. (**a**) Efficiency. (**b**) Input/output power.  
minimizing the losses. In Tables 19 and 20 and Figure 30, the achieved results with detailed conditions are shown.  
For the minimum wind velocity, the Si1553 CDLN MOSFET is the one that provides the maximum efficiency of 46.01%. Moreover, there is a couple of high losses devices. These are Si8466 EDB and IRLU3802, due to the low value of the income power. The high losses figure with the IRLU3802 MOSFET occurs because its consumption is higher than energy harvested by the system. When the income power is at its maximum, the efficiencies of all the devices are positive and range between 55% and 78%. In these conditions, the Si1553 CDLN MOSFET grants to the converter a maximum efficiency of 77.8%. Moreover, Si1553 CDLN has the lowest consumption in both work operating modes, providing the converter its maximum efficiency and output power. Therefore, Si1553 CDLN MOSFET was selected for the AC/DC converter. A close look at the Si1553 CDLN characteristics parameter of Tables 18 and 19 shows that it is the one with best relationship between Ron resistance and gate capacitance, thus, achieving lower switching and conduction losses.
## 2 Materials and Methods
### Study Selection Criteria  
The eligibility criteria were structured using the PICOS (i.e., population, intervention, comparison, outcome, and study type) framework. Table 1 shows the parameters of the PICOS question. We included studies in dentate patients over 18 years of age, of any race or gender, requiring one or more tooth extractions, rehabilitation with implants, and subjected to immediate, early or delayed prosthetic loading. Likewise, we included articles that compared bone loss and success and/or survival according to the time of loading of one or more post-extraction implants.  
For this study, the authors consider the following definitions:  
* Immediate loading: Dental implants are connected to the prosthesis within the first week after implant placement surgery.
* Early loading: Dental implants are connected to the prosthesis between the first week and two months after implant placement surgery.
* Conventional loading: Dental implants are connected to the prosthesis more than two months after implant placement surgery.  
The following exclusion criteria were applied: studies in patients with bone diseases or other systemic disorders contraindicating dental implant placement, patients rehabilitated with one or more implants in previously edentulous zones, studies involving less than 10 patients, studies with follow-up periods of less than 12 months, in vitro or animal studies, clinical cases, letters to the editor, narrative or systematic reviews or involving meta-analyses, and case-control studies. No restrictions in terms of language or date of publication were applied.
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 6 Investigating Reinforcement Learning-Based Routing Protocols
### Qgeo  
[PERSON] et al. [71] have introduced the Q-learning-based geographic routing method (QGeo) for flying ad hoc networks. The purpose of this method is to control routing overhead and improve the packet delivery rate in highly dynamic networks. In QGeo, the best  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Scheme** & **Advantage** & **Disadvantages** \\ \hline \multirow{6}{*}{QMR} & Designing a distributed routing process, high scalability, & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\  & managing the size of state space, appropriate & \\  & convergence speed, adaptive adjustment of hello & \\  & broadcast interval, designing a penalty mechanism to & \\  & prevent routing holes, balancing energy consumption in & \\  & the network, improving network lifetime, designing a new exploration and exploitation mechanism, reducing delay and energy consumption in the data transmission process, adjusting learning parameters dynamically. & \\ \hline \hline \end{tabular}
\end{table}
Table 11: The most important advantages and disadvantages of QMR.  
Figure 29: Learning process in QMR.  
route between the source and destination is discovered in a distributed manner without the need for global information. In the first step, each UAV periodically exchanges hello messages with its neighbors to share information such as the location of node, the current Q-value, and the link condition. The hello broadcast interval is adaptively adjusted based on node speed. As a result, QGeo is compatible with the dynamic network environment. According to the messages received from neighboring nodes, each UAV forms a neighborhood table to record neighbors' information in this table. Then, a Q-learning-based routing algorithm is designed to decide on routing paths. In this scheme, each UAV is defined as a state in the state space. Additionally, any action indicates the transition from the current node to a neighboring node. The learning process in QGeo is shown in Figure 30. This method introduces a new concept called the packet travel speed, which guarantees the reliable and fast data transmission process in the network. In this routing algorithm, the reward function is calculated based on the packet speed. Note that QGeo dynamically adjusts the discount factor based on the distance and speed of UAVs in the network. However, the learning rate has a constant value. Table 12 briefly describes the most important advantages and disadvantages of QGeo.
## 5 Experiments and Discussions
### Cutting Force Prediction
The parameters set in the frequency sweep experiment are shown in Table 7. At the same time, the dynamometer was used to measure the elastic force at the bottom of the spring and compare it with the estimated cutting force. Part of the comparison results is shown in Figure 10.  
Through statistical analysis of different cutting forces obtained by the model estimation, the dynamic frequency response diagram of force estimation as shown in Figure 11 can be obtained. The spring elastic coefficient obtained by the estimation method is the same as the actual elastic coefficient when the frequency range is lower than 70 Hz, while there is a maximum value at 70 Hz, which is the resonance caused by the drive frequency reaching the natural frequency of the device itself. When the frequency range is higher than 100 Hz, the elastic coefficient of the high-frequency part rises again due to the poor estimation ability of the estimation method for the high-frequency signal. It can be concluded that the bandwidth of the estimated force is about 70 Hz, and its bandwidth is mainly limited by the natural frequency of the system itself. \begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **1 g/mN** & **2 g/mN** & **5 g/mN** & **10 g/mN** \\ \hline
1 & 9.4229 & 20.1711 & 48.1308 & 101.2601 \\ \hline
2 & 10.0144 & 23.2481 & 46.5884 & 95.7065 \\ \hline
3 & 9.4357 & 21.7028 & 46.4835 & 101.0842 \\ \hline
4 & 8.4655 & 22.7166 & 46.1990 & 96.8934 \\ \hline
5 & 9.4652 & 19.4296 & 46.3317 & 101.0324 \\ \hline Avg. & 9.3607 & 21.4587 & 46.7467 & 99.1953 \\ \hline \end{tabular}
\end{table} 
Table 6: Estimated forces under different loads. Figure 10: Comparison in dynamic frequency and its repeatability evaluation. Comparing Figure 12 with Figure 11, it can be found that the cutting forces in Figure 12 differ greatly at the trough of the sinusoidal force. The MSE of the estimated cutting forces and the cutting forces collected by the Kistler dynamometer are 0.0027 and 0.0110, respectively. Thus, the cutting force estimation method based on HDL is more accurate. The reason is that the position of the tool changes all the time when machining sinusoidal morphology. The driving force of the voice coil motor is different from the linear model because of its nonlinearity, which leads to a larger error in the estimation of cutting force. The HDL model is more accurate when facing the nonlinear driving force of the voice coil motor because of its ability to deal with the nonlinear system.
## 5 Discussion
### How the Mother Wavelet and Scale Range Influence the Entropy  
As mentioned in Section 3.2, there are many families (types) of mother wavelets. They differ from each other because each type of wavelet has a different shape, smoothness, compactness, and is useful for different purposes. To assess the influence of different mother wavelets on the reconstructed NDVI, EVI, and GCVI time series.  
For each mother wavelet, the scales varied from one to a maximum scale, and the information entropy continued to be used as the criteria to assess the influence. Figure 9 illustrates the results of all four mother wavelets in the cases of the three VI and seven planting conditions; note that the maximum scale was iterated from 2 to 502 with an interval of 25.  
Figure 8: Comparison of the entropy variation in (**a**) the normalized difference vegetation index (NDVI), (**b**) enhanced vegetation index (EVI), and (**c**) green chlorophyll vegetation index (GCVI).  
Figure 9: Varied entropy of scalograms.  
Clearly, with the increase of the maximum scale, all the mother wavelets performed similarly in all cases; that is, the saturation phenomenon in the entropy also appeared in the _gaus_, _shan_, and _morl_ cases when the maximum scale was greater than 200. _Mexh_ and _shan_ had a similar speed to _gaus_ and _morl_ in reaching the saturation point, whereas their entropies were slightly lower than that of the latter two, particularly in the NDVI case. However, the differences between the four mother wavelets were not significant, particularly in the EVI and GCVI cases. From the view of planting conditions, for all three VIs, the four mother wavelets also demonstrated similar and uniform trends in different planting conditions, which further proved that the mother wavelet was not sensitive to the class. Therefore, it was safe to set _Morlet_ as the mother wavelet to apply the CWT to the reconstructed time series.
# Text Data Augmentation Techniques for Fake News Detection in the Romanian Language
## 4 Methodology
### Data Preprocessing  
To apply data preprocessing for our Factual.ro data set, we first employed normalization techniques to handle diacritics and other special characters in Romanian text. Secondly, we cleaned the text by implementing a function that makes the text lowercase, removes text in square brackets and links, and removes punctuation and words containing numbers.  
Additionally, we performed commonly used NLP operations, such as removing stop words, tokenization, and lemmatizing the text. This was done by using the Romanian stop words list and the tokenizer from the Natural Language Toolkit (NLTK) library and the Stanza library for lemmatization. Finally, we used the Stanza library to apply the lemmatization to Romanian text. This library has been evaluated in multiple languages, including Romanian, and performed well compared to other widely used toolkits [36].  
Term Frequency-Inverse Document Frequency (TF-IDF) is a widely used technique in NLP for vectorizing text data. We used TF-IDF as a measure that reflects the importance of a word in a document or corpus by considering both its frequency in the document and its rarity in the corpus. We computed the TF-IDF score by multiplying the term frequency (TF) of a word in a statement by the inverse statement frequency (IDF) of the word in the whole corpus. In this way, we used TF-IDF to extract features from text data that we can then use in ML models.  
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**Augmentation Type**} & \multicolumn{4}{c}{**Before Data Augmentation**} & \multicolumn{4}{c}{**After Data Augmentation**} \\ \cline{3-8}  & & **Total** & **Train** & **Test** & **Total** & **Train** & **Test** \\ \hline None \({}^{*}\) & All & 930 & 744 & 186 & 930 & 744 & 186 \\  & True & 501 & 413 & 88 & 501 & 413 & 99 \\  & False & 429 & 331 & 98 & 429 & 331 & 98 \\ \hline Back Translation & All & 930 & 744 & 186 & 1674 & 1488 & 186 \\  & True & 501 & 397 & 104 & 898 & 794 & 104 \\  & False & 429 & 347 & 82 & 776 & 694 & 82 \\ \hline Easy Data Augmentation & All & 930 & 744 & 186 & 3906 & 3720 & 186 \\  & True & 501 & 397 & 104 & 2089 & 1985 & 104 \\  & False & 429 & 347 & 82 & 1817 & 1735 & 82 \\ \hline \hline \end{tabular} \({}^{*}\)Original data set extracted from www.factual.ro (accessed on 17 June 2023).  
\end{table}
Table 5: Distribution of each class in training and testing data before and after data augmentation.