esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification
[24] introduced an ensemble method that uses Least Squares Support Vector Machine (LSSVM), Multilayer Perceptron (MLP), and Random Forest (RF) to construct a decision model based on shape, texture, and color features. [PERSON] et al. [19] made a comparison of several algorithms to classify cervical cancer cells into two classes: normal and abnormal. They used 20 morphologic features and found that the combinations of algorithms Bagging + MultilayerPerceptron and AdaBoostM1 + LMT were the best scenarios analyzed by them. [PERSON] and [PERSON] [20] presented a method to extract nuclei and cytoplasm features of Pap smear images. Attributes such as center, perimeter, area, and average intensity were considered. The method uses the expectation-maximization (EM) algorithm and a Gaussian mixture model (GMM). Finally, the authors state that the method can be used to determine the cancer stage and be efficient for classifying cervical cells that present low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). [PERSON] et al. [21] applied a median filter to the images to remove noise and used Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the contrast. The k-Means algorithm was implemented to segment the nucleus and cytoplasm regions of cervical cells. From these regions, 38 characteristics of texture, shape, and color were extracted. Attributes were selected using the Random Forest method. Next, the cells were classified into two and seven classes using the ensemble bagging method. The authors compared the approach with five classifiers (LD, SVM, k-NN, boosted trees, and bagged trees) and showed that their method performed better. [PERSON] et al. [22] explore AlexNet, VGG-16, VGG-19, ResNet-50, ResNet-101, and GoogLeNet for the classification of cervical lesions. The authors also proposed an ensemble method of the three best models. They found GoogLeNet to be the best individual architecture, and they showed that the ensemble improved by using the AUC-ROC curve. This article proposes the classification of pre-neoplastic cervical lesions based on features extracted from nuclei. The main contributions of this work can be summarized as:  
* investigation of handcrafted and biological features of nuclei images;
* proposal and analysis of a hierarchical classification with Random Forest to perform state-of-the-art cell nuclei classification;
* decision aid resource to decrease professionals' workload and increase the Pap smear result's reliability;
* comparison of oversampling techniques to balance data;
* introduction of the segmentation cervix collection of the CRIC Searchable Image database;
* statistical comparison of different hierarchical classifiers. The outline of the paper is as follows. Section 2 exhibits the materials and methods considered. Section 3 displays the computational experiments and their results and discussion. Finally, Section 4 presents the conclusions of this work.
## 3 Results  
### Implementation of the Open and Standardized I/O  
The use of EtherCAT prescribes the change of the architecture of the I/O to a master-slave architecture. This changes the architecture of the test bench from Section 2.1 to the one shown in Figure 8. The individual components are described below.  
EtherCAT Master--replaces the driver in its function as the open-source driver. The master configures the network and sends the messages to the slave. It receives the messages from the slave again and communicates them back to the upper layers of the software [34]. The EtherCAT Master is the exposed software interface to the test bench on the system level.  
EtherCAT Slave--Receives the messages of the master, codes them in the ARINC 429 and sends them via the designated channel [34]. The EtherCAT Slave is the exposed hardware interface to the test bench on the system level.  
Ethernet--is used as a medium for packing the EtherCAT packets into the Ethernet packets. The packets then pass through all nodes in the ring.  
There are no components available on the market that combine EtherCAT with the ARINC 429. In the presented work, an EtherCAT master was configured, according to the EtherCAT standard and integrated into the Airbus A350-1000 HighLift test bench. In addition, a slave for the ARINC 429 was implemented in hardware and software. The implementation is described in the following.
# Development of a Maintenance and Spare Parts Distribution Model for Increasing Aircraft Efficiency
## 4 Simulation Model Results
### Simulation Design  
#### 4.2.1 Time Restrictions and Assumptions  
First, the modeler defined a time horizon and time units based on how far into the future the simulation should run. For this study, the simulation was planned to run for  
Figure 8: Distribution network sub-model (own elaboration).  
forty years to evaluate the maintenance and distribution network performance for an airplane lifecycle. It is assumed that a year has fifty weeks of operation. As a result, the time boundaries for the model were as follows:  
* Initial time: 0 weeks;
* Final time: 2000 weeks;
* Time step: 1 week;
* Units for time: weeks.  
The following assumptions were made related to the points to be fulfilled in order to conduct a comparison between the simulation models for a defined simulation demand scenario:  
* The same demand using replication;
* The same replenishment methods used with the same parameters and the same definition of them;
* Same aircraft fleet production rate and total number of aircraft units;
* Same flight traffic volume and operating hours;
* Same failure probability for the spare part component;
* Same initial situation, that is, no backlog to begin with and the same conditions of WIP (products on their ongoing transportation to the customers), the same initial stock in the different warehouses, and initial stock ready to deliver to customers;
* Same number of employees with same initial distribution and same capacity to perform warehouse activities;
* Same supply chain distribution network (production facility, warehouses, etc.);
* Same production logic for all simulation models;
* Same material delays: 1 week of production process, 2 week of transportation lead time between PF and CW, 4 weeks of transportation lead time between CW and RWs, and 2 weeks of transportation between RWs and airport-maintenance locations;
* Minimum quantity to deliver from central to regional warehouses (trucks'minimum load is 50 units of spare parts). If this quantity is not reached, then the delivery does not take place;
* All models have the same forecasting method and the same changes between them;
* Same inventory management models, reorder-point for pull strategy, and the same coordination of the distribution of spare parts for the push strategy;
* Constant interest rates;
* The backlog of spare part units is treated with the same logic in all models. Airports and distributors with higher backlog levels are prioritized when organizing the delivery of the spare parts;
* Same production limitations: Minimum of 100 products/week, maximum of 300 products/week;
* The warehouses have no stock capacity limitations. It was assumed that outsourcing warehouses for stocks can be found in the nearby with extra-holding costs;
* There were no transport limitation between the different production stages. It was assumed that additional third-party logistics could be found;
* No experts in the field have directly supported the model construction to assign values or to assist the validation of the models.
## 4 Case Study  
Finally, the 3D PSO-GD inversion technique is employed to process and interpret SATEM measurements of the Qinshui coal mine in Jincheng, Shanxi Province, China. The primary objective of this survey was to investigate the goals in the No. 3 coal seam of the Permian Shanxi Formation within the survey area to provide a reference for subsequent mining. Water-bearing goals are a major safety hazard during coal-seam mining. During mining in this survey area of interest, we discovered that water-bearing karst collapse columns pose a threat to the mining project and the safety of the mine.  
Based on the available geological and drilling data, as well as the distribution of the formations, a total of 12 SOTEM survey lines, each with a length of 2000 m and an offset from 100-600 m, were designed for the survey area. The emitter had a length of 1700 m and emitted a current with an intensity of 40 A at a fundamental frequency of 12.5 Hz. The flight altitude and speed were set to 50 m and 8 m/s, respectively. The vertical-induced electromotive force dBz/dt was measured. Figure 12 shows the arrangement of the survey lines.  
The PSO-GD algorithm was used to perform a 3D inversion. Figure 13 shows the inversion result, which satisfactorily reflects the spatial location of the goals. The location of the abnormal body is clearly delineated. A vertical profile of the measured line S300 is shown in Figure 14. The known coal seam line is at an approximate height of 400 m, as shown in Figure 14. The results of the inversion made it possible to successfully delimit two  
Figure 11: Fitting error curves and uncertainty analysis. (a) Fitting error curves. (b) uncertainty analysis.  
Figure 12: Map of the survey area.  
solutions associated with inverse problems, while the GD algorithm successfully improves the convergence rate at the late stage of inversion. The new algorithm capably balances the speed and accuracy of the inversion. The PSO and GD algorithms are effectively integrated through a cosine probability function, and the \(\alpha\)-trimmed filter used in the PSO-GD algorithm successfully achieves model smoothing. Test results obtained based on simulation and field data show that the new algorithm is a feasible option for 3D inversion and interpretation of SATEM data.  
For research articles with several authors, Conceptualization, Y.H.; Funding acquisition, G.X.; Investigation, Y.H., W.C. and Z.T.; Methodology, Y.H. and W.C.; Resources, G.X. and Z.T.; Software, Y.H.; Writing--review & editing, G.X. and W.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific Instrument Developing Project of the Chinese Academy of Sciences (Z181100005718001). the Open Foundation of Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Land and Resources (KF2020-3). the Scientific Equipment Instrument and Development Project of the Chinese Academy of Sciences (YJKYYQ20190004). National Natural Science Foundation of China (42030106).  
The authors declare no conflict of interest.
# Unsupervised Offline Changepoint Detection Ensembles
## Appendix A Pseudocode of the Ensemble Algorithms  
Here the pseudocode of the proposed ensemble algorithms is shown, including:  
* [noitemsep,topsep=0 pt]
* OptEnsemble (Algorithm A1) based on the Opt algorithm;
* WinEnsemble (Algorithm A2) based on the Win algorithm;
* BinSegEnsemble (Algorithm A3) based on the BinSeg algorithm.  
```
Input: signal \(\{y_{t}\}_{t=1}^{T}\), cost functions \(c_{1}(\cdot),\ldots,c_{N}(\cdot)\), number of regimes \(K\geq 2\). for all\((u,v),1\leq u<v\leq T\)do for\(n=1,\ldots,N\)do  Initialize \(\hat{C}_{n}(u,v)\gets c_{n}\big{(}\{y_{t}\}_{t=u}^{v}\big{)}\) endfor endfor \(C_{1}\leftarrow\psi(\hat{C}_{1},\hat{C}_{N})\). for\(k=2,\ldots,K-1\)do for all\(u,v\in\{1,\ldots,T\},v-u\geq k\)do \(C_{k}(u,v)\leftarrow\min_{u+k-1\leq t<v}C_{k-1}(u,t)+C_{1}(t+1,v)\) endfor endfor  Initialize \(L\), a list with \(K\) elements.  Initialize the last element: \(L[K]\gets T\).  Initialize \(k\gets K\). while\(k>1\)do \(s\gets L(k)\) \(t^{*}\leftarrow\operatorname*{argmin}_{k-1\leq t\leq s}C_{k-1}(1,t)+C_{1}(t+1,s)\) \(L(k-1)\gets t^{*}\) \(k\gets k-1\). endwhile  Remove \(T\) from \(L\). Output: set \(L\) of estimated breakpoint indexes.
```  
**Algorithm A1**OptEnsemble  
**Remark 14**.: _The algorithm is based on the Bin
# Chlorobenzene Removal Using DBD Coupled with CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) Catalyst
## 3 Results and Discussion
### Effect of Flow Rate on the Performance of DBD Reactor  
Figure 8 shows the degradation efficiency of chlorobenzene at different flow rates while the SED of plasma is set to be 800 J\(\cdot\)L\({}^{-1}\). For the DBD reactor coupled with CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalyst, within the tested flow rate (3 to 5 L\(\cdot\)min\({}^{-1}\)), the removal efficiency of chlorobenzene increased gradually with flow rate. For the single DBD reactor and the DBD reactor packed with \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) powder, the removal efficiency of chlorobenzene increased significantly with flow rate when the flow rate is low (3 to 4 L\(\cdot\)min\({}^{-1}\)). However, under a high flow rate (4 to 5 L\(\cdot\)min\({}^{-1}\)), the removal efficiency of chlorobenzene in these reactors appeared to fluctuate slowly with flow rate.  
In the previous studies, [PERSON] et al. found that the removal efficiency of VOCs will gradually decrease with the increase in flow rate with fixed discharge power, which is attributed to the longer residence time at a low flow rate [58]. In this study, it was found that the increase in flow rate is beneficial for the removal of the chlorobenzene when  
Figure 8: Effect of flow rate on the removal efficiency of chlorobenzene SED = 800 J\(\cdot\)L\({}^{-1}\).  
Figure 7: Effect of SED on the concentration of O\({}_{3}\) in the single DBD reactor and the combined plasma catalysis reactor.  
the discharge was carried with the same SED. Thus, for per volume of gas, the total energy of electronics will not change with flow rate. Instead, the amount and energy of electronics will be influenced by different flow rates: with the increased flow rate and discharge power, the amount of electronics will decrease, and the energy of electronics will increase. As is known, the function of DBD discharge requires the break of chemical bonds under the attack of high-energy electronics. If the energy of electronics (proportional to discharge power) is not high enough, the possibility for the break of chemical bonds will be small, and the energy will be wasted. For the discharge carried out with a higher flow rate and voltage, the energy efficiency can be improved since the waste of energy was avoided. However, when the break of chemical bonds (production of active radicals) is not the reaction controlling step, the enhancing effect of energy efficiency improvement will be weakened (single DBD reactor). The beneficial effects of catalyst loading can be summarized into two parts: 1. reducing the energy required for the break of chemical bonds by the adsorption sites, which helps to improve the energy efficiency of the reactor (better performance of \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) and CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) loading when the flow rate is 3 L\(\cdot\)min\({}^{-1}\)); 2. providing more consumption paths for active radicals, which helps to use the improve of energy efficiency (better performance of CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) loading when the flow rate is 5 L\(\cdot\)min\({}^{-1}\)).
# Human Activity Classification Based on Angle Variance Analysis Utilizing the Poincare Plot
## 2 Materials and Methods
### Actigraphy Count Calculation Procedure  
Several researchers have tried to reproduce the working algorithm of the Actigraph. The validity of the Actigraph for the assessment of physical activity in a population has been extensively studied across many different ages, groups, gender, and patients [41].  
According to [PERSON] et al., in the calculation of the value that resembles the Actigraph, the count can be generated from raw accelerometer data with the following steps. At first, the raw data were resampled to 30 Hz, and then they were band-passed with a [0.1-5] Hz filter. The band-passed signal was down sampled to 10 Hz. Saturation and dead band were applied to the data such that no acceleration exceeds the maximum absolute value (truncation threshold), and any value lower than the minimum value (dead band) is set to 0, respectively, to avoid any non-human activity. Lastly, the data were divided according to the time window and the summation of the values in each time window is the final activity count [41]. The Actigraph count calculation process is shown in Figure 4.  
Poincare plot parameters SD1, SD2, and SDRR were calculated and classified with an SVM classifier with linear kernel, where C = 1 and gamma ='scale'. The mean and standard deviation for all activities are shown in Figures 7 and 8. The Actigraph count resembles that the value was analyzed where count calculation for static and dynamic activities was considered.  
Figure 6: Poincare plot of angle. The left panel denotes laying, sitting, standing, and walking Poincare plot of angle for the HAR dataset and the right panel walking and running Poincare plot of angle for mHealth.  
Figure 7: (**a–c**) Bar plot of SD1, SD2, and SDRR, respectively, for laying, sitting, standing, and walking. (**d**) Bar plot of the mean angle for static activities with the HAR dataset.  
Table 1 shows the result for static and dynamic activities for the angle- and count-based classification system with accuracy (100%), sensitivity (100%), and specificity (100%). The ROC curve for static and dynamic classification with the Actigraph count and Poincare plot-based classification is shown in Figure 9.  
In the case of angle-based classification, SD1, SD2, and SDRR were considered separately and together as a feature to classify. In contrast, the mean counts of X, Y, and Z were considered separately and together.  
Figure 8: (**a–c**) Bar plot of SD1, SD2, and SDRR, respectively, of dynamic activities (walking and running) with the MHealth dataset.  
Figure 9: ROC curve for static and dynamic classification. The left panel denotes the ROC curve for the Actigraph count-based static and dynamic activities’ classification. The right panel denotes the Poincare plot-based static and dynamic activities’ classification.
## 3 Experiment and Results
### Building Extraction Results and Analysis
#### 3.3.1 Esar  
For the extraction of buildings from ESAR data, the buildings with large dip angles are very similar to the vegetation, and the parking lot with vehicles is similar to the buildings. The building extraction results of the six methods are shown in Figure 4.  
In the ESAR data experiment results, it shows that the method using CNN is more effective than other methods. The CNN can accurately determine the location of the building. In the SVM method, vegetation is easily misclassified as a building because it has a similar scattering mechanism to buildings with large orientation angles. The method of using thresholds to extract buildings from the image is not effective. For all methods using CNN, the proposed method has a relatively high accuracy for the extraction of buildings, and the false alarm rate of it is lower than that of other non-CNN methods. In terms of details, the method proposed by us can extract large-angled buildings and suppress the interference of parking lots, which have a regular arrangement of vehicles. The accuracy comparison is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Feature** & **Method** & **AR (\%)** & **FAR (\%)** & **F1-Score (\%)** \\ \hline Eigenvalue & Threshold & 22.20 & 39.23 & 32.52 \\ PauliRGB + G\({}^{0}\) & SVM & 61.85 & 58.15 & 49.92 \\
6D-Vector [38] & CNN & 85.18 & 27.89 & 78.10 \\ Polarimetric Features [39] & CNN & 85.64 & 45.54 & 66.58 \\ PauliRGB + G\({}^{0}\) & CNN & **88.05** & 25.01 & 80.99 \\ PauliRGB + G\({}^{0}\) & CNN + Superpixel & 86.14 & **17.61** & **84.22** \\ \hline \end{tabular}
\end{table}
Table 1: Different methods of building extraction effects under ESAR data.  
Figure 4: ESAR image building extraction results. (**a**) [PERSON]’s threshold extraction method; (**b**) PauliRGB and G\({}^{0}\) statistical texture parameters using SVM results; (**c**) RVCNN; (**d**) FFDCNN; (**e**) PauliRGB and G\({}^{0}\) Statistical texture parameters using CNN; (**f**) as the result of introducing superpixel constraints in (**e**).
## 3 Experimental Results and Analysis  
The MATLAB R2021b simulation platform was used to simulate and verify the proposed IRS-RFL algorithm. The simulation environment is set to \(100\) m \(\times\)\(100\) m \(\times\)\(15\) m three-dimensional space as shown in Figure 4. In this study, the flat fading Rayleigh channel is used in the simulation verification. The fingerprint data in the set positioning space were sampled on a grid with a sampling interval of \(\gamma=1\) m. The parameters were set as follows: number of antennas \(BS\) of \(K=8\), spacing \(d=\frac{\lambda}{2}\), number of paths \(C=3\) of \(BS-IRS\), average power of noise \(-96\) dBm, carrier frequency \(\mathbf{f}_{q}=3.5\) GHz; the total number of \(IRS_{S}\)s is \(M=9\), where the coordinates of \(IRS_{1}-IRS_{9}\) are (0 m, 75 m, 15 m), (100 m, 0 m, 15 m), (100 m, 100 m, 50 m, 15 m), (75 m, 100 m,15 m), (0 m, 50 m, 15 m), (50 m, 0 m, 15 m), (25 m, 100 m, 15 m), (100 m, 75 m, 15 m). And the number of reflective elements \(W=8\) of each \(IRS\), and number of paths \(L=3\) of \(IRS-US\). The large-scale decay coefficient was \(\beta_{mUS}=-86.65-20\log_{10}(dis_{IRS_{m},US}\cdot dis_{BS,IRS_{m}})+ \chi_{mUS}[\text{dB}]\)[48], where \(dis_{IRS_{m},US}\) is the distance between \(US\) and \(IRS_{m}\), \(dis_{BS,IRS_{m}}\) is the distance between \(BS\) and \(IRS_{m}\), and \(\chi_{mILS}\sim\mathcal{CN}(0,64)\) is the shadow decay.  
As shown in Tables 3 and 4, an error existed between the reconstructed and original fingerprint databases, and the average relative error calculated from all the data was 1.85%; therefore, there was almost no effect on the localization error.
## 3 Data Collection and Analysis  
This research mainly integrates GPS data and SC transaction data from metro or bus systems to identify time-varying station-specific transfer behavior.