esa-sceva/satcom-chunk-collection huggingface dataset:
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 4 Fuzzy Q-Charging Algorithm
### Reward Function
First, the energy factor \(\mathbf{E}(D_{i})\) is defined by \(\sum\
olimits_{j=1}^{n}p_{j}^{i}\times\xi_{j}\), which sums up the products of energy charged to the sensors and their severity indexes. According to the fact mentioned above, \(\mathbf{E}(D_{i})\) tends to achieve the maximal value when \(p_{j}^{i}\) is proportional with \(\xi_{j}\). It means that \(\mathbf{E}(D_{i})\) will increase if we increase the value of \(p_{j}^{i}\) for sensors with a large value of \(e_{j}\) and a small value of \(E_{j}\). In other words, our algorithm encourages charging more to sensors with a large energy consumption rate and small remaining energy. The priority factor \(\mathbf{P}(D_{i})\) is calculated by \(\sum\
olimits_{j=1}^{n}w_{j}p_{j}^{i}\), where \(w_{j}\) is the priority of sensor \(S_{j}\). Obviously, the second factor is maximized if \(w_{j}\) is proportional with \(p_{j}^{i}\). Therefore, our algorithm will prioritize the sensors that play important roles in guaranteeing the network's target coverage and connectivity. Third, the target monitoring factor \(\mathbf{T}(D_{i})=\frac{t_{i}}{m}\) depicts the ratio of the number of targets that are monitored (i.e., \(t_{i}\)) to the total number of the targets (i.e., \(m\)). By including this factor, our algorithm will choose the next charging location that maximizes the number of monitored targets. The reward of a charging location \(D_{i}\) (denoted by \(r(D_{i})\)) is the normalized sum of the three factors. \(r(D_{i})\) can be calculated as follows. \[r(D_{i})=\frac{\sum\limits_{j=1}^{n}\frac{1}{E_{j}}p_{j}^{i}e_{j}}{\sum \limits_{k=1}^{l}\sum\limits_{j=1}^{n}\frac{1}{E_{j}}p_{j}^{i}e_{j}}+\frac{ \sum\limits_{j=1}^{n}w_{j}p_{j}^{i}}{\sum\limits_{k=1}^{l}\sum\limits_{j=1}^{ n}w_{j}p_{j}^{k}}+\frac{t_{i}}{\sum\limits_{k=1}^where \(e_{j}^{k}\) is the energy consumption rate of \(e_{j}\) at timing \(t_{k}\) in the past. The more frequently the sensors update the information to the base station, the more accurately the MC can estimate the sensors' energy consumption rate. However, sending such information too often may consume significant energy of sensors. Therefore, in our algorithm, the sensors only update their remaining energy in the following scenarios.
## 3 Fiber-Reinforced Concrete (FRC)
### Kenaf Fiber-Reinforced Concrete (KFRC)  
The kenaf plant is able to grow to heights of 3.5-4.5 m within 4-5 months [126]. Studies indicate that the kenaf plant had the optimal CO\({}_{2}\) absorption among the investigated plants. Kenaf plant can absorb 1.5 times the carbon dioxide by its weight [127]. The findings show that the tensile strength of kenaf fibers vary between 223 MPa and 1191 MPa and the elastic modulus and final tensile strain of the kenaf fiber vary within 2860 MPa to 60,000 MPa and 0.012 to 0.1, respectively [120]. Kenaf fiber shows a linear stress-strain diagram [120; 128]. Currently, kenaf fiber is used in bio-materials with a wide application area [94; 129; 130; 131; 132]. Table 3 presents the mechanical properties of some natural fibers.  
The elastic modulus of some natural fibers such as hemp, kenaf, and flax are comparable to glass fibers, while the density of these natural fibers is one half the density of glass fiber. According to the nature of bio-fibers, the properties may differ in different origins, so the range of properties are reported in the following table.  
Surface treatment of kenaf fibers by sodium hydroxide (NaOH) can reduce its hydrophilic properties [120]. This reduction in the fiber water sorption characteristic causes an improvement of fiber durability and reduces its biodegradability [147]; however, it may affect the bond strength with the cementitious matrix.  
According to the literature, higher toughness is exhibited by NFRC (such as KFRC) generally compared to the normal concrete [94, 104, 124]. Also, microstructural analysis by scanning electron microscopy (SEM) shows a good bonding between the kenaf fibers and concrete matrix [124]. [PERSON] and [PERSON] conducted research on KFRC by changing the fiber volume content and the fiber length. They stated the indirect tensile and flexural strength increased by an increment of FC and fiber length [94]. This statement seems to be in contrast with the previous statement about coconut FRC in terms of the effects of fiber length [106] but both studies suggested that the 50 mm fiber length was suitable. Moreover, the ductile failure mode was observed compared to plain concrete, which resulted in an enhancement in cracking behavior and ductility. Moreover, another study reported a toughness index in KFRC almost 3 times higher than the OPC concrete control samples [124]. Use of natural fibers such as kenaf fiber to cast NFRC (specifically KFRC) can result in not only economic profit in terms of production cost and material weight, but also in terms of health benefits for society when compared to synthetic fibers [104, 147]. The green concrete developed has an environmental benefit which is of immense importance in the present context of the sustainability of natural resources [127, 147]. Moreover, the quantity of CO\({}_{2}\) would be reduced in the atmosphere by using kenaf fibers in concrete. It may decline the high CO\({}_{2}\) released within the manufacturing of Portland cement. Thus, kenaf fiber-reinforced concrete (KFRC) is a potential green material for various construction purposes [94, 124].
## 4 Results
### Land Cover Percentage Grading Model Results  
After obtaining the land cover classification data for 2010, it is used as a common input along with the quantitative remote sensing products in 2010. The optimal state is achieved through multiple iterations of the model. Therefore, the land cover percentage grading model of 10 categories in 2010  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Reference** & \multicolumn{10}{c}{**Classified**} \\ \cline{2-13}
**Classes** & **CR** & **FR** & **SR** & **GR** & **WT** & **GC** & **WL** & **CT** & **DS** & **TD** & \\ \hline Crop (CR) & 43,835 & 3366 & 814 & 3497 & 341 & 9 & 139 & 223 & 369 & 2 & 83.3\% \\ Forest (FR) & 3359 & 47,280 & 2395 & 3011 & 43 & 14 & 94 & 42 & 174 & 19 & 83.7\% \\ Shrubs (SR) & 301 & 821 & 7778 & 544 & 2 & 0 & 10 & 0 & 27 & 2 & 82.0\% \\ Grass (GR) & 3185 & 3254 & 1521 & 72,173 & 212 & 165 & 217 & 32 & 5467 & 596 & 83.1\% \\ Water (WT) & 82 & 20 & 0 & 55 & 2592 & 0 & 20 & 29 & 20 & 0 & 92.0\% \\ Glacier (GL) & 0 & 8 & 0 & 21 & 0 & 1497 & 0 & 0 & 57 & 6 & 94.3\% \\ wetland (WL) & 41 & 22 & 13 & 19 & 7 & 0 & 1414 & 0 & 19 & 2 & 92.1\% \\ City (CT) & 40 & 13 & 0 & 7 & 8 & 0 & 2 & 860 & 2 & 0 & 92.4\% \\ Desert (DS) & 368 & 219 & 107 & 5787 & 111 & 332 & 88 & 29 & 46,179 & 363 & 86.2\% \\ Tundra (TD) & 0 & 0 & 0 & 91 & 0 & 5 & 0 & 0 & 35 & 2332 & 94.6\% \\ Overall & & & & & & & & & & & 84.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Long time series land cover classification model precision confusion matrix.  
Figure 8: Box diagram of each land cover type and the overall accuracy of the test set in 1980, 1990, 1995, 2000, 2005, 2010 and 2015. Crop (CR), Forest (FR), Shrubs (SR), Grass (GR), Water (WT), Glacier (GL), Wetland (WL), City (CT), Desert (DS), Tundra (TD) and OA (Overall).  
was obtained. The training method and training process of the model are roughly the same as those of the land cover classification model.  
After the test set are input into the trained model, the classification accuracies of the different levels of each category are obtained. According to the statistics, the results of the land cover percentage grading model training are relatively good. As shown in Table 5, the overall classification accuracy reached more than 85%, and the accuracies of 8 categories reached more than 90%.
## 4 Simulation  
### Identification of \(T_{p}\) and a  
In order to realize the identification of \(T_{p}\) and \(a\), this paper adopted the recursive least squares algorithm with auxiliary variable in MATLAB 7.1 to carry out simulation research. In simulation, the pure delay time of the system was not considered, so the identified system can be obtained by Equation (10)  
\[G_{1}(s)=\frac{5650}{3.91s+12.08} \tag{26}\]  
The discretization of Equation (26) can be obtained as follows by using \(z\) transform  
\[G_{1}(z)=\frac{b_{1}z^{-1}}{1+a_{1}z^{-1}}=\frac{124.3z^{-1}}{1-0.73z^{-1}} \tag{27}\]  
where \(a_{1}\) and \(b_{1}\) are estimated values and need to be identified.  
The dynamic characteristics of gas temperature in the combustor can only be displayed when the system is in dynamic state, so the research object can only be identified in the state of being stimulated. In simulation, a continuous excitation signal sequence was taken as the input signal, and this signal has the characteristics of zero mean, uncorrelated unit variance, and is measurable; a white noise sequence was taken as the noise; its mean value is zero, and its variance is \(\sigma^{2}=1.00^{2}\); 0.1 s was taken as the sampling period, and 1000 were taken as the sampling points. Under the above simulation conditions, the identification of \(a_{1}\) and \(b_{1}\) were simulated, and simulation curves were obtained, as can be seen in Figures 2 and 3.  
As shown in Figures 2 and 3, the values of \(a_{1}\) and \(b_{1}\) fluctuate greatly when the data is small, which may be influenced by noise. However, with the increase of the data length, the values of \(a_{1}\) and \(b_{1}\) gradually tend to be stable, and the final values of \(a_{1}\) and \(b_{1}\) are \(-0.73\), \(123.5\), respectively.  
The identification model can be obtained by taking the above parameters into Equation (27) and converting them into time domain by using inverse \(z\)-transform.  
\[G_{2}(s)=\frac{5650}{3.92s+12.47} \tag{28}\]  
Since the choice of the model largely depends on the subjective judgment of the individual, and the errors of the noise measurement and processing methods will lead to identification errors, it is necessary to verify the identification results. To display the simulation results more intuitively, the step response curve of the identified object \(G_{1}\) and the identification model \(G_{2}\) were compared on one figure, as can be seen in Figure 4. In Figure 4, the deviation between the rising time and the adjusting time of \(G_{1}\) and \(G_{2}\) is small, approximately 5%, so the feasibility of the identification method is proven effectively.  
Figure 4: The identified object and the identification model.  
Figure 3: Identification curves of \(b_{1}\).  
Figure 2: Identification curves of \(a_{1}\).
# Phenolic Profiling of Five Different Australian Grown Apples
## 3 Results and Discussion
### Phenolic Compounds Profile by LC-MS/MS Analysis
#### 3.4.4 Other Polypphenols  
In other polyphenols, curcuminoids (1), furanocumarins (1), hydroxybenzaldehyde (2), hydroxycumarins (1), hydroxyphenylropenes (1), phenolic terpen (2), tyrosols (3) and other polyphenols (2), while tyrosols was the dominant subclass were identified in apple samples.  
Compound **88** was tentatively characterised as 4-hydroxybenzaldehyde based on the precursor ion at ([M-H]\({}^{-}\) at \(m/z\) 121.0301 and confirmed based on the MS\({}^{2}\) fragmentation, which exhibited the loss of CO\({}_{2}\) (44 Da) from the precursor, resulting in the product ion at \(m/z\) 77 [78]. Rosmanol (compound **93**) was found in positive modes, and tentatively characterised according to the precursors [M+H]\({}^{+}\) at \(m/z\) 347.1844. In the MS\({}^{2}\) experiment, peaks at \(m/z\) 301 (loss of H\({}_{2}\)O) and \(m/z\) 231(loss of CO\({}_{2}\)) achieved the identification of coumarin [79]. Meanwhile, compound **94** (carnosic acid with ([M-H]\({}^{-}\) at \(m/z\) 331.1905) was confirmed by the fragments at \(m/z\) 287 and \(m/z\) 296, resulting from the loss of CO\({}_{2}\) and further loss of H\({}_{2}\)O from the precursor [80]. To best of our knowledge, this is the first time it has been detected in apple samples.  
Compounds **95** and **96** detected in negative mode were detected as hydroxytyrosol 4-\(O\)-glucoside and 3,4-DHPEA-AC, precursor ion at \(m/z\) 315.1090 and \(m/z\) 195.0658, respectively. On further analysis, hydroxytyrosol 4-\(O\)-glucoside was confirmed by the product ions at \(m/z\) 153 and \(m/z\) 123, corresponding to the loss of glucoside (162 Da) and glucoside-CH\({}_{2}\)O (192 Da) from the precursor ion, respectively [78] and 3,4-DHPEA-AC was confirmed by the product ions at \(m/z\) 135 [M-H-C\({}_{2}\)H\({}_{4}\)O\({}_{2}\)] [81].  
Compounds **91** and **92** were found in negative ionization mode and identified as salvianolic acid C and salvianolic acid B with precursor ions at \(m/z\) 491.0963 and \(m/z\) 717.1436, respectively. Salvianolic acid C was confirmed by the product ion at \(m/z\) 311 [M-H-caffeic acid], \(m/z\) 267 [M-H-caffeic-CO\({}_{2}\)] and \(m/z\) 249 [M-H-CO\({}_{2}\)-H\({}_{2}\)O][82], while salvianolic acid B was confirmed by the intensive peaks at \(m/z\) 519 [M-H-Danshensu, loss of 198 Da], \(m/z\) 339 [M-H-Danshesu-caffeic acid, loss of 378], \(m/z\) 321 [M-H-2 \(\times\) Danshensu, loss of 396 Da] and \(m/z\) 295 [M-H-Danshensu-caffeic acid-CO\({}_{2}\), loss of 422 Da][82]. Previously, both compounds were detected in _Salvia uniformiza_[83]. Salvianolic acid, known for its antioxidant potential, can effectively remove oxygen free radicals in the human body. This compound is one of the natural products with the strongest antioxidant effect [84]. However, these compounds have been discovered for the first time in apple varieties to the best of our knowledge.
## Data and Methodology  
### Remote Sensing Data  
We used two very different remote sensing datasets: emitted terrestrial microwave radiation and reflected solar shortwave radiation. The microwave dataset was based on the Advanced Microwave Scanning Radiometer (AMSR-E) enhanced land parameters developed by the Numerical Terradynamic Simulation Group at the University of Montana [26]. These parameters include near surface air temperature (ta; -2 m height) and vegetation canopy transmittance (tc) at three microwave frequencies (6.925 GHz, 10.65 GHz, and 18.7 GHz). Onboard the NASA-EOS Aqua satellite, AMSR-E recorded data twice daily (daytime, ~13:30; and nighttime, ~01:30) from mid-June 2002 to October 2011, when the rotating antenna failed. The second dataset included optical reflectance data from the Moderate-resolution Imaging Spectroradiometer (MODIS). We used MODIS collection 5 level 3 data product in a 0.05 degree (~5.6 km) Climate Modeling Grid (CMG), specifically the Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) data that combines observations from Terra and Aqua (MCD43C4; [27]). NBAR provides 16-day retrievals (updated every 8 days) of surface reflectance normalized to a nadir view using BRDF models of surface anisotropy [28; 29].  
Croplands for this study were identified based on the International Geosphere Biosphere Programme (IGBP) global land cover classification scheme in the MODIS land cover CMG product (MCD12C1; [27]), and the USDA Foreign Agriculture Service (FAS) crop layers by oblast (for Russia [30], Kazakhstan [31], and Ukraine [32]).  
We used tree cover data (ca. 2010) from the 30 m Global Land Cover dataset, which was derived from Landsat 7 ETM+ data by USGS and the Department of Geographical Sciences at University of Maryland [33]. We used these data to calculate the proportion of tree cover within our study sites.
# Accuracy of Guided Endodontics in Posterior Teeth
## 2 Materials and Methods
The results for posterior tooth location (maxilla vs. mandible), type (molar vs. premolar) and number of main root canals per tooth (one, two or three) were calculated as mean values with 95% confidence intervals. In addition, a multivariate analysis of variance (MANOVA), followed by Bonferroni post hoc tests for the variable \"number of main root canals\", were performed using SPSS V. 28.0.1 (IBM Corp, Armonk, NY, USA) in order to assess the influence of different variables with regard to the accuracy of the prepared access cavities in different and angular spatial dimensions. The level of significance was set at \(\alpha\) = 0.05. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Deviation** & **Angle** & **Mesio-Distal, at Base** & **Bucco-Oral, at Base of** & **Mesio-Distal, at Tip** & **Bucco-Oral, at Tip** \\  & **[Degrees]** & **of the Bur [mm]** & **the Bur [mm]** & **the Bur [mm]** & **of the Bur [mm]** \\ \hline Mean & 1.39 & 0.26 & 0.29 & 0.24 & 0.31 \\ Median & 1.10 & 0.20 & 0.21 & 0.18 & 0.21 \\ Minimum & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ Maximum & 7.8 & 1.19 & 1.96 & 1.61 & 1.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Deviation between planned and prepared access cavities regarding angle deviation and meso-distal and bucco-oral deviation at the tip and base of the bur. Figure 2: Access cavity preparation: (**A**) CAD/CAM-fabricated template placed on a model, (**B**) sleeve inserted for static navigation of the bur, (**C**) access cavity preparations in maxillary premolars and molar, (**D**) locating three main root canals with endodontic hand files.
# Locating the Epidemic Source in Complex Networks with Sparse Observers
## 2 Model and Method  
In this section, we will provide a detailed description for the epidemic source localization algorithm. First, we will give a brief introduction to epidemic source localization problem formulation; for a tree network, we will next put forward PESL algorithm as a Bayesian and Maximum Likelihood estimator that can simultaneously estimate the probability that a node becomes the epidemic source and the initial time; finally, we will give an example in the tree network.
# University Student Dropout Prediction Using Pretrained Language Models
## 2 Related Work  
### Research on Student Dropout in Higher Education  
The amount of information on individual students and that recorded during their education have increased rapidly [18]. Several studies have been conducted to extract and utilize meaningful information from such data. Some studies [19; 20; 21] propose methods to predict dropout based on student attendance records in online environments (e.g., MOOC and Coursera). Since these studies are conducted in an online environment, there is no need to consider behavior patterns outside attendance records, such as club activities. Machine learning techniques are effective in the field of education for evaluating student performance, and several studies [4; 5; 6; 7; 8; 9; 10; 22; 23] compare the performance of machine learning methods on the dropout prediction task. The study [6] employed a logistic regression and artificial neural networks to predict the student dropout rate using data of high school students. In a different approach [7], the researchers focused on tree-based decision classification to predict student dropout considering students' economic circumstances. Meanwhile, the study [5] used various machine learning methods to analyze the dropout rate of freshmen engineering students and found that entering university score was a critical variable in predicting dropout. However, these studies only utilize demographic information available before admission, whereas predicting student dropout often requires academic information from universities.  
Recent studies [8; 24] have shown that academic information can help predict dropout probability. The study [24] applied a generalized mixed-effects random forest to predict the probability of engineering students dropping out based on student-level information and degree programs. Another study [8] used machine learning techniques to predict freshmen dropout using secondary school academic records and first-year course credits. Several researchers [11; 25] predict student dropout using unstructured data. The study [25] extracted student sentiment from advisor notes written by student advisors to predict student dropout. In a recent study [11], the authors utilized temporal structured data and unstructured counseling data from all semesters to predict college student dropout using a PLM. However, because these studies train on structured and unstructured data separately, they lack the ability to capture the relationships between the data. In this study, we propose a method for formulating structured data in the format of unstructured data and concatenating them to understand the relationships between the data. To the best of our knowledge, this is the first attempt to capture the relationships between structured and unstructured data in a dropout prediction task.
## 2 Related Works
The difference between multi-step forecast data and the actual measured data is analyzed to check for anomalous patterns in the data under leakage conditions. A voltage fault diagnostic scheme was proposed by [PERSON] et al. (2019) using a Gated Recurrent Unit  
Figure 2: Univariate multi-step forecasting. (GRU) neural network multi-step-ahead voltage prediction. The GRU predicts the battery cell voltages six time steps (one minute) in advance from 30 previous time step values. Predicted values are compared with a predefined threshold to make decisions about fault occurrences. Another attention-based multi-step prediction model was investigated in [39] to predict tool wear in a CNC router machine. Performance from various prediction horizons ranging from five steps to twenty steps was examined. Sensor fault diagnosis in a wind turbine blade by mapping spatiotemporal relationships among sensors was proposed in [40] by using a CNN model. The CNN predicts individual sensor readings employing all sensor readings, and the predicted value is compared with the actual reading to detect faults. More advanced variants of convolutional neural network-based models, such as Generative Adversarial Networks (GANs), have also been implemented for sensor fault analysis [41]. Given that sensor signals are highly prone to noise, other adversarial techniques, as proposed in [42,43], could be explored in future research for sensor fault analysis. A summary of significant works in sensor fault analysis is presented in Table 1.
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## 4 Incremental Frequent Itemset Mining with Map-Reduce
### Experimental Evaluation
#### 4.3.4 Experiment Internals  
We compare the performance of three algorithms described in Section 4.2. We denote the algorithm from Section 4.2.2, incremental algorithm with IMRApriori and early pruning optimization--as \"Delta\". The algorithm from Section 4.2.4 with minimum steps/jobs is called \"DeltaMin\". The algorithm from Section 4.2.5, that also keeps count of non-frequent itemsets, is called \"DeltaMinKeep\". The baseline for the comparison of the algorithms is the previously published non-incremental FIM algorithm IMRApriori [10] on \(\mathcal{D}^{+}\). It is called \"Full\".
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 2 Design Methodology
### Phase 4: Design of Triple Band-Notched SWB Antenna (Proposed Antenna)  
To alleviate the interference at X band, a C-shaped resonator was placed at the bottom of the patch beside the feedline. The addition of the C-shaped resonator led to the obstruction of the X band (9.8-10.4 GHz) without influencing the efficacy of Antenna 3. Figure 5 illustrates the proposed antenna configuration and VSWR result of the triple band-notched antenna. The designed antenna exhibited a wide bandwidth, including a triple notch band feature with a very compact design.  
Figure 4: Phase 3: VSWR of the dual band-notched antenna.  
Figure 5: Phase 4: VSWR result of the triple band-notched antenna.  
Figure 3: Phase 2: VSWR of the single band-notched antenna.