esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion of Findings
### Comparative Analysis of Selected Features for All Methods
The performance on the Colon dataset for the BEOSA and BIEOSA methods was also very impressive compared with related methods; the BWOA, BGWO, BDMO, BSNDO, BEOSA and BIEOSA yielded 1016.1 (50), 1301.5 (100), 546.2 (25), 1374.3 (25), 157.1 (75) and 316.5 (100), respectively. We observed that both BEOSA and BIEOSA yielded a low average number of selected features, with values of 157.1 and 316.5, respectively, for methods with population sizes of 75 and 100. Note that the population was not so relevant to the obtained result, since BGWO, which obtained its best average number of selected features with a population size of 100, yielded a far worse result. The performance of BEOSA and BIEOSA regarding the average number of selected features showed that the proposed method is suitable for selecting the optimal set of features required to achieve improved classification accuracy. An interesting finding revealed by this performance analysis was that BEOSA and BIEOSA are very suitable methods for high-dimensional datasets with a larger number of features to start with. The result also showed that both BEOSA and BIEOSA were very competitive approaches, even when dealing with low-dimensional datasets. In the following subsection, we evaluate and compare the classification accuracy of the selected features by each of the methods discussed in this section.
## 1 Introduction
In [18], the bio-inspired Cuckoo search algorithm (CSA) was adopted to effectively tackle PSC by the use of Levy flight with fast convergence. Moreover, a social behaviour motivated algorithm named teaching-learning-based optimization (TLBO) was adopted to achieve the accurate tracking of GMPP under PSC, the advantages of this algorithm are simple structure and fast convergence [19]. Furthermore, the generalized pattern search (GPS) optimization algorithm [20] was devised to resolve PSC, which has superior performance, such as high convergence speed, excellent dynamic, and steady state efficiencies, as well as simple operation. In reference [21], an ant colony optimization (ACO) combined with a novel strategy of pheromone updating was developed for MPPT, which can effectively improve the speed of tracking, accuracy, stability, and robustness under various weather conditions and different partial shading patterns. However, all of these meta-heuristic algorithms have two main deficiencies as they are independently utilized for MPPT under various scenarios, as follows:  
* High convergence randomness: Unlike the deterministic optimization algorithms, since the meta-heuristic algorithms adopt random searching mechanisms, the final optimal solutions may be different in different runs, which will cause the output power to fluctuate greatly and is undesirable to the operation of PV systems;
* Difficult to balance the optimum quality and computation time: To obtain a high-quality optimum, the meta-heuristic algorithms usually need to establish a larger size of initial population and carry out many iterations, which results in huge computational burden and long computing time. However, considering that the MPPT's control cycle is extremely short, it is inevitable to lessen the size of population and the iteration numbers, which will lead to a significant reduction in the quality of optimization. Rapid development of artificial intelligence in recent years, especially Google DeepMind's AlphaGo [22], which has easily defeated two world champions in two world-renowned Go matches in 2016 and 2017, respectively, has boosted a tide of artificial intelligence. In fact, the model-free reinforcement learning (RL) is one of the core algorithms of AlphaGo, which can rapidly construct an optimal action policy at each state, according to its current knowledge or experience [23]. Motivated from this outstanding characteristic, a new transfer reinforcement learning (TRL) with space decomposition for MPPT of PV systems under PSC is proposed in this paper. In comparison with the aforementioned meta-heuristic algorithms, TRL has the following two advantages:  
* Capability of knowledge transfer: Through a positive knowledge transfer from past optimization tasks, the optimal knowledge matrices of the new optimization task can be approximated by TRL, hence this method can efficiently harvest an optimum of high quality;
* Capability of online learning: TRL can continuously learn new knowledge from interactions with the environment based on RL, which can rapidly adapt to MPPT under different solar irradiation, temperatures, and PSC.
# Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
## 3 Result
Figure 6: Zones in middle field were misclassified as alfalfa in June (**a**) but were correctly labeled as potato in August (**b**). To ensure classification performance, which is a function of classification reliability, the method of quantifying classification accuracy was used. To accomplish this, all samples will be used, all data will be validated, the RF classifier will be used, and all samples will be averaged at a certain level. In terms of Gini, this function calculates the characteristics of the input data. The significance of the two radar and optical input sources' characteristics must be examined in the data [29]. We use the Gini significance feature as a reduction of impurities when using the random forest classifier, which is used for all forest trees. Gini's personality is extremely valuable. Its high importance indicates that it plays a key role in the forecasting process; on the other hand, if the feature's importance is low, it means that, according to Sentinel 1 sensor data, this information was limited for prediction before May, despite being consistent with plant structure. Early April and May play a critical role in the longitudinal development of winter crops and the ability to distinguish summer crops from one another. Plant development shows a difference in their NDVI values over time and during the growing season. Changes in the amount of NDVI will cause plants to differentiate due to differences in their phenological structure. This distinction is particularly noticeable between July and August, when the crops are distinguishable from one another due to growth and development.
# The DREAM Endstation at the Linac Coherent Light Source
###### Abstract  
Free-electron lasers (FEL), with their ultrashort pulses, ultrahigh intensities, and high repetition rates at short wavelength, have provided new approaches to Atomic and Molecular Optical Science. One such approach is following the birth of a photo electron to observe ion dynamics on an ultrafast timescale. Such an approach presents the opportunity to decipher the photon-initiated structural dynamics of an isolated atomic and molecular species. It is a fundamental step towards understanding single- and non-linear multi-photon processes and coherent electron dynamics in atoms and molecules, ultimately leading to coherent control following FEL research breakthroughs in pulse shaping and polarization control. A key aspect for exploring photoinduced quantum phenomena is visualizing the collective motion of electrons and nuclei in a single reaction process, as dynamics in atoms/ions proceed at femtosecond (10\({}^{-15}\) s) timescales while electronic dynamics take place in the attosecond timescale (10\({}^{-18}\) s). Here, we report on the design of a Dynamic Reaction Microscope (DREAM) endstation located at the second interaction point of the Time-Resolved Molecular and Optical (TMO) instrument at the Linac Coherent Light Source (LCLS) capable of following the photon-matter interactions by detecting ions and electrons in coincidence. The DREAM endstation takes advantage of the pulse properties and high repetition rate of LCLS-II to perform gas-phase soft X-ray experiments in a wide spectrum of scientific domains. With its design ability to detect multi-ions and electrons in coincidence while operating in step with the high repetition rate of LCLS-II, the DREAM endstation takes advantage of the inherent momentum conservation of reaction products with participating electrons to reconstruct the original X-ray photon-matter interactions. In this report, we outline in detail the design of the DREAM endstation and its functionality, with scientific opportunities enabled by this state-of-the-art instrument.  
Keywords:COLTRIMS; REMI; gas phase; CEI; TMO; AMO; LCLS II; FEL +
Footnote †: journal: Applied Physics Letters
# Optimal Sliding Mode Control for an Active Suspension System Based on a Genetic Algorithm
## 1 Introduction
Compared with the traditional sliding mode control, the OSMC optimizes the adjustable parameters of the switching function and control law by means of a genetic algorithm (GA). GA [22; 23] is closely related to industrial applications, which consider the optimization of parameters with constraints, targets, and dynamic components [24]. [PERSON] [25] proposed a GA-based design and optimization method for micro-electromechanical systems unimomorph piezoelectric energy harvesters that demonstrated an enhancement of energy harvesting efficiency by 31% in comparison with un-optimized harvesters. [PERSON] et al. [26] optimized the fractional order PID (proportion, integral and differential) controller based on multi-objective GA. [PERSON], [PERSON]. [27] presented a GA for the optimization of the stacking sequence to improve the strength of a cylindrical shell under internal pressure. In order to improve the control effect, this paper designs an OSMC controller tuned using a GA to control the active suspension system of the vehicle. The rest of this paper is organized as follows: Section 2 overviews the structure and control principle of the active suspension system, Section 3 studies the active suspension system modelling, Section 4 focuses on the SMC controller design, Section 5 describes the simulations, and the final section give the conclusions.
# A New Fault Diagnosis Method for Unbalanced Data Based on 1 DCNN and L2-SVM
## 4 Experimental Verification
### Case Study 2: Application to the Lab-Measured Bearing Datasets
#### 4.2.2 Diagnostic Results and Analysis  
Figure 11 shows the verification results of the three datasets in Table 5, operated by the improved model. In Dataset A, all samples are identified correctly. In Dataset B, two samples of FIX are incorrectly identified as NORMAL. In Dataset C, six samples of FIX  
Figure 10: Time domain and frequency domain diagram of test bearing: (**A**) time-domain waveform; (**B**) frequency-domain waveform.  
Figure 9: Accelerated bearing life tester.  
are incorrectly identified as NORMAL. It can be seen that in the two unbalanced datasets, the model mistakenly identifies some FIX samples as NORMAL. Due to the minimum number of FIX training samples, the feature learning is insufficient. At the same time, since NORMAL is the majority class, the features extracted by the model during the training process are more biased toward NORMAL, which coincides with the idea that the model may give rise to more bias in features learned toward the majority class samples when the data are unbalanced, as we predicted in the previous section. Despite these difficulties, the proposed method overcomes this difficulty well and achieves an overall discrimination accuracy of 99.2% on the two unbalanced datasets, with an average discrimination accuracy of 99.0% for the minority class samples. The results further confirm that the modified method obtains better precision for the minority class samples in the lab-measured bearing datasets and has a certain generalization performance.  
Figure 11: The confusion matrix of different datasets: (**a**) the result of Dataset A; (**b**) the result of Dataset B; (**c**) the result of Dataset C.
# Dynamic Demand-Responsive Feeder Bus Network Design for a Short Headway Trunk Line
## 3 Methodology  
The authors previously developed an algorithm for demand-responsive feeder transit services for a long headway trunk line [8]. The previous research aimed to find an optimal routing solution for feeder buses assigned to train stations, where they were supposed to pick up and drop off passengers from and to train stations. The objective function of the algorithm was minimizing total costs, including vehicle operating costs and passenger travel time, while limiting individual passengers' maximum travel times. This differentiated the algorithm from typical delivery-pickup algorithms, which do not consider individual passengers' travel times. Furthermore, the study's other innovations included accommodating the relocation of buses and the dynamic nature of the operation involving multiple stations and trains.  
While the previous algorithm was developed based on a long headway for a trunk line (20 min), this study reduces the headway of trains from 20 min to 5 min. Like the algorithm for the previous research, this algorithm minimizes the total cost, including vehicle operating costs and passenger travel time, while individual passengers' maximum travel times are limited within given maximum travel times. The main challenge of the model lies in the short headway of trains while considering passengers' time windows. When the rail service's headway is long enough for feeder buses to return before the next train arrives, the feeder network algorithm is relatively simple because the maximum feeder service cycle time is determined by the rail headway, and matching between feeder buses and trains is unnecessary. However, if the rail service's headway is not long enough for the feeder buses to return before the next train, the algorithm must find matches not only between passengers and feeder buses but also between feeder buses and trains.  
This algorithm applies the simulated annealing (SA) algorithm to solve the proposed model. Figure 1 represents a conceptual operating framework of the proposed demand-responsive feeder transit in our study.  
Figure 1: Conceptual operation of the proposed demand-responsive feeder transit service.
# JUMRv1: A Sentiment Analysis Dataset for Movie Recommendation
## 4 Methods and Materials
### Feature Selection
#### 4.4.1 Filter Methods  
Chi-Squared  
Chi-squared is a statistical test that measures the divergence from the expected distribution if the occurrence of a feature is assumed to be independent of the class value ([PERSON] et al. [35]). The chi-squared test measures dependence between stochastic, well-defined variables; hence, using this function eliminates the features that are the most likely to be independent of class and therefore irrelevant for classification. It does so with the use of the chi-squared metric. In the case of continuous variables, the range needs to be divided into intervals ([PERSON] et al. [36]). This chi-squared metric, which is also treated as the value of each feature, is given in Equation (2):  
Figure 4: Workflow of the (**a**) filter-based and (**b**) wrapper-based feature selection methods.  
\[\chi^{2}_{f}=\sum_{j=1}^{r}\sum_{s=1}^{c}\frac{(n_{j}-\mu_{js})^{2}}{\mu_{js}} \tag{2}\]  
where, \(r\) is the number of distinct values in the feature, \(c\) is the number of distinct values in a class, \(n_{js}\) is the frequency of \(j\)th element with \(s\)th class and \(\mu_{js}=\frac{n_{ss}n_{js}}{n}\), \(n_{js}\), is the frequency of \(j\)th element, and \(n_{ss}\) is the total number of elements with \(s\)th class. A higher chi-square value indicates that the feature is more informative.  
F-Classifier  
This is used to find the Analysis of Variance (ANOVA) f-value. ANOVA can determine whether the means of three or more groups (features in this case) are different. ANOVA uses F-tests to statistically test the equality of means.  
F-tests are named after its test statistic. The F-statistic is simply a ratio of two variances. Variances are a measure of dispersion, or how far the data are scattered from the mean. Larger values represent greater dispersion. Dispersed data means the feature will not be that useful because this can be an indication of noise in the data. Therefore, basically, it is used to filter out co-related features.  
More importantly, ANOVA is used when one variable is numeric and one is categorical, such as with numerical input variables and a classification target variable in a classification task. The results of this test can be used for feature selection where those features that are independent of the target variable can be removed from the dataset.  
Mutual Information  
MI is based on the concept of entropy. Entropy is a quantitative measure of how uncertain an event is. This means that if an event has a greater probability of occurring than another, then its entropy is lower than the second event. In classification, MI between two random variables shows dependency between them. Minimum dependency gives zero MI, and as dependency rises, so does the
## 4 Azimuth-Variant Doppler Characteristics Analysis
### Doppler Characteristics
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Configuration A**} & \multicolumn{2}{c}{**Configuration B**} & \multicolumn{2}{c}{**Configuration C**} \\ \cline{2-7}  & **Transmitter** & **Receiver** & **Transmitter** & **Receiver** & **Transmitter** & **Receiver** \\ \hline Carry Frequency (GHz) & & 9.5 & & 5.33 & & 1.25 \\ Signal bandwidth (MHz) & & 150 & & 16 & & 500 \\ Flying altitude (km) & 515 & 20 & 800 & 20 & 10 & 20 \\ Flying velocity (m/s) & 7,600 & 5 & 7,450 & 5 & 100 & 5 \\ Beam incidence angle (\({}^{\circ}\)) & 45 & 60 & 30 & 60 & 60 & 60 \\ Beamwidth in range (\({}^{\circ}\)) & 2.3 & 10 & 2.3 & 15 & 15 & 15 \\ Beamwidth in azimuth (\({}^{\circ}\)) & 0.33 & 10 & 0.28 & 15 & 13.75 & 13.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example configuration parameters for the HAP-borne passive BiSAR system. algorithms such as the chirp-scaling and wavenumber-domain may be not suitable to accurately focus the collected data. It is necessary to develop an image formation algorithm that is usable for azimuth-variant BiSAR configurations. However, most existing BiSAR imaging algorithms are only usable to handle azimuth-invariant BiSAR configurations [36,37] and only a few azimuth-variant BiSAR algorithm can be found [19,20]. Before developing an imaging algorithm for the HAP-borne passive BiSAR system, it is necessary to analyze the two-dimensional spectrum model of the returned signal. Figure 2: Azimuth-variant Doppler characteristics. (**a**) Configurations A; (**b**) Configuration B; (**c**) Configuration C.
# Lempel-Ziv Parsing for Sequences of Blocks
## 2 LZ77 Parsings
: _If a string \(s\) is produced by an SLP grammar of size \(g\), then there exists a phrase-aligned LZ77 parsing \(f_{1}f_{2}\ldots f_{z}\) for \(s\) of size at most \(g\)._  
By a non-constructive argument [8; 10; 16], one can show that the converse equivalent reduction from LZ77 parsings to SLP grammars is not possible: in some cases, the size of the minimal SLP grammar can be \(\Omega(\frac{\log n}{\log\log n})\)-times larger than the size of the greedy (i.e., minimal) LZ77 parsing. For completeness, let us show this by repeating here the counting argument essentially used in [10; 17]. Consider all SLPs of size \(g\) that produce strings over the alphabet \(\{a,b\}\) and contain the rules \(A\to a\) and \(B\to b\). Since each rule \(C\to DE\) can be constructed in, at most, \(g^{2}\) ways by choosing a pair \((D,E)\), there are at most \(g(g^{2})s^{-2}\) possible configurations of the SLPs (\(g-2\) choices of the pairs \((D,E)\) and the choice of initial rule). Therefore, all such SLPs produce, at most, \(g(g^{2})s^{-2}\leq g^{2g}\) different strings. Further, for given \(n\) and \(k\in[1\ldots n-1]\), there are exactly \(\binom{n}{k}\) strings of length \(n\) consisting of \(n-k\) letters \(a\) and \(k\) letters \(b\), and each such string has an LZ77 parsing of size \(O(k+\log n)\). If each such string can be produced by an SLP of size \(g\), then \(g^{2g}\geq\binom{n}{k}\) and, hence, \(2g\log g\geq\log\binom{n}{k}\geq k\log\frac{n}{k}\). Choosing \(k=\lceil\log n\rceil\), we deduce that \(g\log g\geq\Omega(\log^{2}n)\) and, therefore, \(g\geq\Omega(\frac{\log^{2}n}{\log\log n})\), which implies that \(g\geq\Omega(k\frac{\log n}{\log\log n})\), an \(\Omega(\frac{\log n}{\log\log n})\)-blow up in size compared to the LZ77 parsing of size \(O(k+\log n)=O(k)\). Albeit there is no an equivalent reduction from LZ77 parsings to grammars, a slightly weaker reduction described in the following lemma still holds. **Lemma 3** (see [4; 10; 18; 19]). : _If a string \(s\) has an (overlapping or non-overlapping) LZ77 parsing of size \(z\), then there exists an SLP grammar of size \(O(z\log\frac{n}{z})\) producing \(s\)._
# A Study on a Prediction Model of E-Bike Expansion Degree at Irregular Signalized Intersections
## 1 Introduction
The cellular automata model, social force model, and particle dispersion model have often been used for the simulation of motor vehicles and e-bikes [17; 18; 19]. The influence of e-bikes on motor vehicle traffic has been evaluated by simulating the running track of e-bikes and the interaction characteristics with motor vehicles at a typical signalized intersection. However, scenarios of irregular signalized intersections have not been considered in previous studies. Since the intersection angle of an irregular signalized intersection is a non-right-angle, the trajectory of traffic flow in each direction deviates and the expansion effect of e-bikes is particularly prominent. The main purpose of this study is to study the expansion effect of straight-going e-bikes at irregular signalized intersections. The specific focus of this study is the flow and spacing of e-bikes during the green light release period at irregular signalized intersections. Furthermore, the concept of expansion degree is introduced based on a clustering method. Finally, the beetle antennae search-back propagation (BAS-BP) neural network model is established to predict the expansion degree of e-bikes going straight at irregular signalized intersections. This study can provide theoretical guidance for the channelization design of irregular signalized intersections and e-bike control.