esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In a clinical context, there are increasing numbers of people with voiding dysfunction. To date, the methods of monitoring the voiding status of patients have included voiding diary records at home or under dynamic examinations at hospitals. The former is less objective and often contains missing data, while the latter lacks frequent measurements and is an invasive procedure. In light of these shortcomings, this study developed an innovative and contact-free technique that assists in clinical voiding dysfunction monitoring and diagnosis. Vibration signals during urination were first detected using an accelerometer and then converted into the mel-frequency cepstrum coefficient (MFCC). Lastly, an artificial intelligence model combined with uniform manifold approximation and projection (UMAP) dimensionality reduction was used to analyze and predict six common patterns of uroflowmetry to assist in diagnosing voiding dysfunction. The model was applied to the voiding database, which included data from 76 males aged 30 to 80 who required uroflowmetry for voiding symptoms. The resulting system accuracy (precision, recall, and f1-score) was around 98% for both the weighted average and macro average. This low-cost system is suitable for at-home urinary monitoring and facilitates the long-term uroflow monitoring of patients outside hospital checkups. From a disease treatment and monitoring perspective, this article also reviews other studies and applications of artificial intelligence-based methods for voiding dysfunction monitoring, thus providing helpful diagnostic information for physicians.  
voding dysfunction; urodynamics; artificial intelligence; mel-frequency cepstrum coefficient italics. Article
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
###### Abstract
A method for estimating vehicle state parameters using IMM-MCCKF is developed for environments with non-Gaussian noise. (2) Under conditions of double lane-change and sinusoidal steering, simulation studies were performed to confirm the efficacy of the suggested strategy. The outcomes prove that the suggested IMM-MCCKF approach can produce precise estimates of vehicle state parameters from inexpensive sensor signals while effectively mitigating the effects of unknown statistical properties of noise. When tested in environments with non-Gaussian noise, it outperforms MCCKF and CKF in estimating accuracy and robustness. This study is organized according to the following structure:  
Section 2 completes the derivation and modeling of the vehicle and tire models. Section 3 provides a detailed derivation of the IMM-MCCKF algorithm. Section 4 designs the simulation experiments under two working scenarios and completes the co-simulation based on CarSim-Simulink. Section 5 presents a quantitative analysis and discussion of the simulation results. Section 6 summarizes the experimental results and the entire paper.
# Multimodal Classification of Anxiety Based on Physiological Signals
## 4 Results
### Anxiety Classification  
We compared the estimated performance of the classification models when using RFECV for feature selection and four different data balancing techniques--Random Oversampling (Over), SMOTE (SMO), ADASYN (ADA), and Borderline SMOTE 2 (Border). For comparison purposes of the different balancing techniques between the different classifiers, the parameters were chosen for each technique and applied to all classifiers.  
To better understand the performance and behavior of the data balancing techniques, we look at how the observations are distributed in the data space without the use of supervised feature selection in Figure 5. It can be seen that in the original imbalanced dataset, there is a slight overlap of classes where the instances might be easily misclassified and that there is no relevant noise or small disjuncts in the data. Nevertheless, although the class overlap persists and is even slightly increased by the data balancing techniques, the number of minority examples increased, and therefore the classification of those difficult cases might be improved, especially along the decision border.  
The anxiety classification performance of each classifier, using RFECV with the different data balancing techniques, is presented in Figure 6 and Table A3.  
Using the imbalanced dataset, some of the classifiers showed relatively lower measures of FI-score because of low recall or precision, while maintaining good accuracy values. This may reflect the impact of data imbalances on the classification performance of the models. Considering our problem, recall is more relevant than precision since misclassifying severe cases of high anxiety is more prejudicial.  
Concerning the F1 metric, Random Oversampling (Over) improved all classifiers' performance except DT and SVM, and SMOTE (SMO) showed improvements in all classifiers' performance aside from LDA, DT, and SVM. At the same time, ADASYN (ADA) appeared to increase the performance of all classifiers, and Borderline SMOTE 2 (Border) did not improve the score of ADB. The best F1 result was achieved using SMOTE and ADB, with a mean score of 0.864 (SE = 0.057). Regarding ROC-AUC, Over showed improved scores for LDA, SVM, and XGB, while SMO increased the evaluation scores of all classifiers except LR. ADA improved all models' performance apart from LR and RF, and Border boosted the performance of all classifiers. The best ROC-AUC score was obtained with Border and RF, with a mean score of 0.980 (SE = 0.017).  
The execution time of our pipeline (without the data balancing techniques) is presented in Figure 7. It is worth noticing that the classifiers with the worst performance results had lower execution times: 1.91 s, 1.07 s, 0.47 s, and 0.58 s, respectively. As for the best-performant algorithms, ADB presented intermediate runtimes of 14.73 s, whereas RF and XGB presented higher execution times.
# Forecasting Taxi Demands Using Generative Adversarial Networks with Multi-Source Data
## 4 Methodology
### Features Extraction and Analysis
#### 4.2.2 Taxi Demands and Temporal Factors  
* Taxi demands distribution in Weekdays and Weekends  
Figure 6 shows taxi demands distribution in Weekdays and Weekends of three study areas in the city of Wuhan.  
We can see that these areas have similar taxi demand distributions (especially on Fridays, indicating that people take taxis on the last weekday for reasons such as going to travel or for entertainment or coming back on Sundays) and similar changes in taxi demands. Therefore, we can leverage the weekdays and weekends data to help in forecasting taxi demands.  
* Taxi demands distribution in Holidays  
The collected dataset contains eight holidays (National days 1-7 October and 24 November Christmas day). Here we study the taxi demand distribution in the three study areas on the holidays, as shown in Figure 7.  
In general, there is a noticeable pattern during holidays in the three areas. Days 1 and 2 October showed high taxi demands followed by a decrease in the other days and finally reached another high demand on7 October. It can be explained that in the beginning holiday period, and 24 November Christmas day, people may take taxis to travel. In contrast, taxi demands increase again on 7 October when people may come back home.  
* Taxi demands distribution in daily hours  
Figure 8 reports taxi demand distribution during a day (24 h) in the areas in Wuhan city.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Attribute** & **Description** \\ \hline Occupied trip ID & represents Taxi ID and trip number such as 28784651\_10 \\ Pick up time-stamp & Format 20131112181211 represents year, month, date and time. \\ Pick up coordinates & includes longitude, latitude of a pick up event. \\ Drop off time-stamp & Format 20131112181211 represents year, month, date and time. \\ Drop off coordinates & includes longitude, latitude of a drop off event. \\ Occupied trip distance & Distance by kilometers \\ Occupied trip period & Period by minutes \\ GPS Record number & The number of GPS records in an occupied trip. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Attributes of an occupied trip.  
Figure 5: Three study areas in the city of Wuhan, **(a)** Jiedaokou area, **(b)** Guanggu area, **(c)** Wuhan railway station area.  
Figure 6: Taxi demands distribution in Weekdays and Weekends in the city of Wuhan. Weekday show high taxi demands as they related to working time, Sunday has a high number of Taxi demands as well.  
Figure 7: Taxi demands distribution in the city of Wuhan during holidays. Holidays show high taxi demands in the three areas.  
As we can see in Figure 8, the three study areas have some shared patterns. For example, all of them showed the minimum rates at 4 am and peak-rush at 8 am and 5 pm.  
The taxi demand shows high periodicity at long terms, due to the various changes of taxi demands on Thursdays and Fridays in many weeks are almost similar, in the three areas. The periodicity of short-term and long-term can assist in forecasting taxi demands in city areas in the future.
# Conceptual Framework for Implementing Temporal Big Data Analytics in Companies
## 7 Discussion, Conclusions, and Future Research Directions
### Limitations and Future Research Directions  
The presented framework also has some limitations. The most important of these is the lack of a feedback loop. Such a loop would make it possible to continuously improve the implemented TBDA ecosystem. Therefore, one of the main directions of future research will be to expand the framework with a feedback loop. The use of ML is planned to be utilized, as suggested by the participants in the focus study. As claimed in [87], ML techniques are particularly appropriate for temporal big data analytics due to their flexibility. The second limitation of the framework is to move away from the waterfall approach. It is therefore necessary to check whether the waterfall approach will be appropriate in the case of TBDA implementation projects. This is the second line of future research. Other research directions emerging from this study include:  
* Implementation of the framework in practice--case studies in selected companies. The purpose of such studies is to make the proposed solution practical and to verify the correctness of KPIs.
* Popularization of the concept of temporality among business. Showing how the time dimension of big data analytics affects the competitiveness of a company.  
* Research on the implementation requirements for BDA applied in companies. Such research may result in the creation of a model set of requirements.
* Conducting market research--will companies and data scientists be interested in the described framework?
# Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection
## 4 Experiments and Discussion
### Parameter Sensitivity  
Determination of hyperparameters is an open problem for many algorithms. We conducted parameter sensitivity study by testing different settings of parameters \(\alpha\) and \(\beta\). Since these parameters help HMVFS perform hierarchical feature selection, it is clear that HMVFS will be sensitive to parameter change, and this study may reveal a hierarchical feature relationship. The candidate set was \(\{10^{-2},\,10^{-1},\,1,\,10,\,10^{2},\,10^{3}\}\) for each parameter. Classification performance and average running time are recorded and illustrated in Figure 3.  
It is observed that \(\alpha=10\) is beneficial to final selection and maintains relatively high classification performance, among which lower \(\beta\) has slight advantages. View importance is different in multiview learning. From the perspective of view importance, when only two views exist and one of them is generally better, acceptable performance can be achieved by one view, and additional features are expected for improvement. High-level feature selection is weak because the other view has relatively more redundant features and will be ignored with higher \(\beta\). Meanwhile, appropriately higher \(\alpha\) enhances low-level feature selection to exploit the most representative features from the unimportant view. Moreover, acceptable performance is achieved with \(\alpha=10^{-2},\,\beta=10^{2}\) and \(\alpha=10^{-1},\,\beta=10^{2}\). High-level selection is enhanced, and low-level selection is restrained, which results in limited performance approximating in single-view learning and short convergence time.
# A Systematic Review of the Trends and Advances in IFC Schema Extensions for BIM Interoperability
## 5 Evaluation of IFC Schema Extension Cases
### Detailed Analysis of Extension Implementation and Validation Approaches
#### 5.4.2 Analysis of Validation Approaches  
The validation approaches used by the mentioned studies had varying degrees in their approaches to verify their extensions. The first and basic approach, termed'representation-centric', was used to demonstrate the interoperability of the extensions. That is, these studies implemented the extensions in a BIM modeling tool and, after export, showed that the entities and properties were correctly manifested in the receiving BIM application. The second approach, termed 'utility-centric', went one step further by demonstrating that the imported entities and properties could be used properly in the receiving application to conduct relevant analyses. This latter approach was deemed a more exhaustive validation as it also demonstrated the utility and purpose of their extensions. Other studies did not provide a validation of their extensions and were classified as 'not implemented'.  
Table 6 presents the corpus of cases classified in accordance with these validation approaches and also further categorized based on the aforementioned extension approaches, i.e., entity and property versus property extensions. The table shows that for entity and property extension cases,'representation-centric' approaches outnumber those of 'utility-centric' validations. As these cases focused on defining novel products currently absent in the IFC schema, they focused on demonstrating that the new extensions could be correctly exported and represented in receiving BIM applications. For instance, ref. [64] introduced new entities to represent detailed parts of a tunnel boring machine and then verified that these entities and related properties were correctly displayed in open IFC tools, an open source IFC viewer.  
In contrast, cases limited to property extensions adopted more 'utility-centric' approaches for their validations. As noted in Section 5.3.1, these cases involved defining properties to enable process representations, including inspections, operation and maintenance procedures, or engineering and management analyses. Thus, these studies demonstrated the use and results of the specific properties exported to the receiving applications. For example, ref. [52], who extended processes pertaining to code compliance, demonstrated  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Validation Approaches**} & \multicolumn{3}{c}{**Extension Approach**} \\ \cline{2-4}  & **Entity + Property Extension** & **Property Extension** & **Total** \\ \hline Representation centric & 25 & 2 & 27 \\ Utility centric & 13 & 7 & 20 \\ Not implemented & 17 & - & 17 \\ Total & 55 & 9 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Validation approaches by extension purposes.  
that actual code compliance checking became possible once the extensions were realized in ArchiCAD, a BIM authoring tool.
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 2 Methodology
### On-Site Personnel: The Reflectance-Based Approach  
#### 2.3.1 Overview  
The reflectance-based approach to vicarious radiometric calibration is a well-understood technique that has been used successfully over the past 35 years for airborne and space-borne sensors [12; 13; 14; 18; 81; 94; 95; 96; 97]. Like the RadCaTS process, measurements of the atmosphere and surface BRF are made during an overpass time of interest, and the results are used in a radiative transfer code to determine the TOA spectral radiance (or reflectance). The results are then compared to the imagery for the sensor under test.
## 3 System Architecture
### Cloud Platform  
The proposed remote maintenance service is developed as a cloud-based platform. The Cloud platform enables the communication of the shop-floor/field technicians and the expert engineer as well as the distribution of information between the involved parties. The Cloud platform is comprised of two domains: (i) the communication domain and (ii) the data storage domain. As discussed in the previous paragraphs, the communication domain is used for matching shop floor technicians for expert engineers and for establishing the communication channels between them. On the other hand, the data storage domain in essence is the Cloud Database where all the data are saved in SQL tables as well as the 3D geometries uploaded.
## Materials and Methods
### Characterization
#### 2.4.2 X-ray Crystallography  
Crystalline type and degree of crystallinity of the VPE and shear-treated samples were evaluated using X-ray diffraction (XRD-6100, Shimadzu Corp., Kyoto, Japan) with a Cu X-ray tube which operated at 40 kV and 30 mA. The scanning angle (20) was 10\({}^{\circ}\)-80\({}^{\circ}\) with 2\({}^{\circ}\)/min of scan speed. The degree of crystallinity was calculated from the ratio between the area under the crystalline peaks and the total area under crystalline and amorphous halo which can be determined by Equation (1).  
\[\text{Degree of crystallinity}\ (\%)=\frac{F_{1}}{F_{1}+F_{2}}\times 100\% \tag{1}\]  
where \(F_{1}\) is the area under crystalline peaks at 20 = 21\({}^{\circ}\) and 24\({}^{\circ}\). \(F_{2}\) was indicated to the area under amorphous halo at 20 = 19.8\({}^{\circ}\)[17; 18].  
The thicknesses of crystalline, amorphous, and intermediate layers and the degree of crystallinity were characterized by a small-angle X-ray scattering (SAXS) at the beamline BL11 of a SAGA light source (Kyushu synchrotron light research center, Saga, Japan with the proposal no. 1909068R/BL11). The beam strength was 8 keV, with an exposure time of 300 s and distance between sample and detector of 1140 mm. The data analysis was carried out by the Fit2d software package, version V18.002. From the intensity (\(I(q)\)) and the scattering vector (\(q\)), which were obtained from SAXS profile, the long period (\(L_{0}\)) can be evaluated by Equation (2) [19].  
\[L_{0}=\frac{2\pi}{q_{max}} \tag{2}\]  
where \(q_{max}\) indicates the peak position of the scattering curves.  
The thickness of lamellar structure and degree of crystallinity were also calculated by the electron density correlation function [20; 21] in Equation (3).  
\[C(z)=\int_{0}^{z_{max}}I(q)\cdot q^{2}\cdot\cos(q\cdot z)dq \tag{3}\]  
where \(C(z)\) is the correlation of electron density difference at a distance of \(z\). \(I(q)\) indicates the scattered intensity and \(q\) identifies the scattering vector [22]. The electron density correlation function of a semi-crystalline polymer with a degree of crystallinity which was higher than 50% is shown in Figure 2a [20]. The obtained factors can be determined as the long period (\(L_{0}\)), thickness of intermediate layer (\(d_{tr}\)), thickness of total amorphous layer (\(d_{(Mem-Amorphous)}\)) and degree of amorphousness (\(\omega_{a}\)). From these factors, the thickness of total crystalline layer (\(d_{(Mem-Crystalline)}\)) can be further obtained by Equation (4).  
\[L_{0}=d_{(Mean-Crystalline)}+d_{(Mean-Amorphous)} \tag{4}\]  
The thickness of core amorphous layer (\(d_{a}\)) was determined by the subtraction of the thickness of intermediate layer from the thickness of total amorphous layer, as shown in Equation (5).  
\[d_{a}=d_{(Mean-Amorphous)}-d_{tr} \tag{5}\]  
A schematic diagram of the lamellar structure based on the analysis of the electron density correlation function is shown in Figure 2b.
## 3 Proposed SLM PAPR Scheme  
In this section, a novel low-complexity SLM scheme without the transmission of SI is proposed to reduce the PAPR of OFDM signals. The proposed method properly designs the time domain conversion vectors based on the equivalent wireless channel and the transmitter does not transmit the SI. At the receiver, the effect of the carrier-wise multiplication of the phase rotation vector and the data vector can be removed by using the conventional channel equalization method. Also, it is shown that the proposed scheme has very low computational complexity.
## 3 Findings
### Prognostics and Health Management (PHM) for Unit Systems  
In recent years, PHM has emerged as an essential approach in the global competitive market, achieving advantages over others by improving system maintainability and reliability. However, the application of PHM to flexible unit systems is a challenging task as systems are more complex. Specifically, small and medium-sized ventures experienced difficulty in applying PHM, because of the lack of resources and time for research and development.  
[PERSON] et al. (2018) explored how the Prognostics method is an intelligent answer for enhancing the availability of unit systems and fault prognosis to evaluate residual life. A PHM model for manufacturing systems integrated with different online sensors with different flexible structures has been developed by ([PERSON] et al., 2019), and [PERSON] et al. (2019) proposed a contemporary sign partition as well as prognostics structure for multi-section systems with non-resolute segment signals, and [PERSON] et al. (2019) developed a prognostic procedure that uses multi-stream signals for predicting the residual life of partially degraded manufacturing systems.  
\begin{table}
\begin{tabular}{c l c c} \hline
**SI. No.** & **Name of the Journal** & **Number of Articles** & **Year of Publishing** \\ \hline
1 & Reliability Engineering and Systems Safety & 5 & 2012,14,17,19 \\
2 & IEEE Transactions on Automation Science and & 4 & 2015,16 \\  & Engineering & & \\
3 & International Journal Advanced Manufacturing & 3 & 2015,18 \\  & Technology & 3 & 2014,15,17 \\
4 & IEEE Transactions on Reliability & 3 & 2017,19 \\
5 & CIRP Annals: Manufacturing Technology & 3 & 2017,19 \\
6 & Journal of Intelligent Manufacturing & 3 & 2009,2014 \\
7 & IFAC online & 3 & 2017,19 \\
8 & Journal of Manufacturing Systems & 2 & 2018 \\
9 & International Journal of Production Research & 2 & 2015,17 \\
10 & IIE Transactions & 2 & 2014,15 \\
11 & Procedia Manufacturing & 2 & 2017 \\
12 & Computers \& Industrial Engineering & 2 & 2017,19 \\
13 & IEEE Transactions on Power Systems & 2 & 2015 \\
14 & IEEE Systems Journal & 2 & 2019 \\
15 & European Journal of Operation Research & 2 & 2018 \\
16 & Journal of Precision Engineering and & 1 & 2009 \\  & Manufacturing Technology & 1 & 2018 \\
17 & Materials Today: Proceedings & 1 & 2018 \\
18 & International Journal of Productivity and Quality & 1 & 2016 \\ \hline \end{tabular}
\end{table}
Table 5: List of journals related to the parameters related to the flexible unit systems.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### Signal Frequency Dependencies  
Simulation analysis across test frequencies (1, 5, 10, 20, 50 Hz) reveals optimal performance in the low-frequency range. Signal frequencies below 20 Hz maintain reasonable SNR performance, while frequencies above 50 Hz show improved noise characteristics but may approach system bandwidth limitations. The maximum derivative amplitude varies proportionally with frequency, reaching 309.02 for 50 Hz signals compared to 6.28 for 1 Hz signals. This scaling affects VCO sensitivity requirements and system dynamic range considerations.