esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Presentation and Discussion of Findings
### Importance of the Validated Normalised Independent Variables  
The normalised variables identified site meetings (SIMP); identifying indicators of cost overrun (IDCOV); interim valuations (INTERIMV); monitoring equipment cost (MONEC) and historical (HDATA) as the main post-contract cost controlling techniques which are mostly influenced by the CPM. The abovementioned independent variables all have importance values of 0.152; 0.131; 0.080; 0.077; and 0.068 respective.  
A comparison of the validated post-contract cost controlling techniques and the level 2 ranking in Figure 6, shows a chart based on Table 10. In Figure 6, the x-axis is taken from the ranking in Table 10. The red line is the normalised values, while the blue line shows the values from level 2. The y-axis indicates the important values.  
Figure 5: Validated predicted pseudo-probability for the professionals.  
SIMP has a higher value in the validation when the CPM is involved in the post-contract cost controlling process. This reflects a change from 0.014 to 0.152. IDCOV is ranked second in the table with a value of 0.131 when the CPM makes most of the decisions and 0.02 when the QS is involved in choosing the post-contract cost controlling technique. INTERIMV has closer values of 0.080 and 0.038 in situations when the CPM and QS are the prime decision-makers. The values get closer for MONEC. In this variable, the CPM scenario provided a value of 0.077 and 0.060 for the QS. HDATA higher in the QS lead decision making process with a value of 0.126, while the CPM classification provided a value of 0.068.  
The result of this analysis further uncovers that MONEC, INTERIMV and HDATA are often adopted by the QS and CPM in most construction projects. The least applicable techniques from the graph are the COMPUNI and MONMC. The greatest discrepancy in this analysis is the importance QS place on PampL and CFLOW during construction projects and the diminishing relevance of these post-contract cost controlling techniques to the CPM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Importance** & **Normalised Percentage** & **Rank** \\ \hline SIMP & 0.152 & 100.00\% & 1 \\ IDCOV & 0.131 & 86.20\% & 2 \\ INTERIMV & 0.08 & 52.60\% & 3 \\ MONEC & 0.077 & 50.90\% & 4 \\ HDATA & 0.068 & 44.60\% & 5 \\ TCACT & 0.067 & 44.00\% & 6 \\ INCMILE & 0.067 & 44.20\% & 7 \\ COMPUNI & 0.062 & 41.00\% & 8 \\ WORKBUDG & 0.047 & 30.90\% & 9 \\ MONLC & 0.043 & 28.60\% & 10 \\ VARMAN & 0.039 & 25.70\% & 11 \\ MONOV & 0.029 & 18.80\% & 12 \\ COSTFOR & 0.029 & 18.80\% & 13 \\ COSTR & 0.027 & 17.80\% & 14 \\ MONMC & 0.023 & 15.00\% & 15 \\ FORCOMP & 0.02 & 13.30\% & 16 \\ PamL & 0.019 & 12.30\% & 17 \\ CFLOW & 0.018 & 12.10\% & 18 \\ \hline \hline \end{tabular} (Source: Authors’ compilation, 2020).  
\end{table}
Table 10: Validated normalised importance of the post-contract cost controlling techniques.  
Figure 6: A comparison of the normalised importance for level 2 and validate independent variable.
# Ambient LED Light Noise Reduction Using Adaptive Differential Equalization in Li-Fi Wireless Link
## 2 Ambient Light Noise Model and Adaptive Differential Equalization
### Interference and Shot Noise Reduction Using Adaptive Differential Equalization
As another method, the link budget of the optical wireless transmission system can be increased by increasing the output optical power of the white LED light. However, the intensity of the ambient light source also should be taken into account depending on the lighting environment in the building; as the intensity of the ambient light source increases, the intensity of interference noise increases. Accordingly, in a system having a high transmission rate, it is necessary to configure its link in consideration of the reduction of the SNR according to the increase in bandwidth and the intensity of interference noise generated from ambient light at the same time.
## 5 Time-Series Analysis of Application Results of ICT-Based Education Program
### Analysis of Satisfaction Based on Sub Elements of Education Participants by Year
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **No. of Cases** & **Elements** & **Mean** & **Standard Deviation** \\ \hline  & & Educational contents & 4.92 & 0.19 \\
2017 & 293 & Education operation & 4.68 & 0.35 \\  & & Detailed contents & 4.92 & 0.26 \\  & & Educational contents & 4.83 & 0.25 \\
2018 & 438 & Education operation & 4.79 & 0.26 \\  & & Detailed contents & 4.54 & 0.46 \\  & & Educational contents & 4.48 & 0.41 \\
2019 & 855 & Education operation & 4.63 & 0.33 \\  & & Detailed contents & 4.50 & 0.41 \\  & & Educational contents & 4.73 & 0.47 \\
2020 & 339 & Education operation & 4.71 & 0.39 \\  & & Detailed contents & 4.74 & 0.44 \\  & & Educational contents & 4.67 & 0.57 \\
2021 & 71 & Education operation & 4.57 & 0.67 \\  & & Detailed contents & 4.60 & 0.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Survey results of faculty member satisfaction based on sub-elements by year. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **No. of Cases** & **Elements** & **Mean** & **Standard Deviation** \\ \hline  & & Educational contents & 4.94 & 0.15 \\
2017 & 38 & Education operation & 4.91 & 0.18 \\  & & Detailed contents & 4.78 & 0.24 \\  & & Educational contents & 4.86 & 0.32 \\
2018 & 64 & Education operation & 4.93 & 0.14 \\  & & Detailed contents & 4.80 & 0.36 \\  & & Educational contents & 3.81 & 0.89 \\
2019 & 93 & Education operation & 4.02 & 0.78 \\  & & Detailed contents & 4.10 & 0.64 \\  & & Educational contents & 4.47 & 0.62 \\
2020 & 451 & Education operation & 4.46 & 0.62 \\  & & Detailed contents & 4.56 & 0.54 \\  & & Educational contents & 4.71 & 0.61 \\
2021 & 25 & Education operation & 4.79 & 0.41 \\  & & Detailed contents & 4.44 & 0.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Survey results of school administrator satisfaction based on sub-elements by year.
## 2 OGSMP-MIMO System Modeling  
### Channel Modeling  
In the visible-light indoor lighting scenario, to satisfy the lighting and communication requirements simultaneously, multiple LEDs were arranged on the roof to establish a VLC communication link model, as illustrated in Figure 1.  
In an indoor space with length L, width W, and height H, a three-dimensional coordinate system was established with a corner of the ground as the coordinate origin. \(M\) LEDs for lighting and communication were arranged on the roof, \(N\) PDs were arranged at the target end to be measured, and the communication between the target to be measured and each LED light source followed the VLC communication link model.  
The LED transmitter, wireless channel, and photodetector receiver form a multi-array visible-light MIMO communication system. Because a single light source layout causes uneven illumination, it is necessary to utilize a multi-LED array scattered layout in large scenes, which leads to obvious differences in the illumination intensity of each LED received by the PD receiver; multipath interference problems exist, which may affect the communication quality. Consequently, the VLC communication link model was divided into \(K\) small positioning areas, and only the LED light sources in this area were considered at the PD receiving end of the \(k\)-th area. In the \(k\)-th region, the MIMO communication system channel can be expressed as follows:  
\[H=\left(\begin{array}{cccc}h_{1,1}&h_{1,2}&\dots&h_{1,N_{t}}\\ h_{2,1}&h_{i,j}&\dots&h_{2,N_{t}}\\ \dots&\dots&\dots&\dots\\ h_{N_{t},1}&h_{N_{t},2}&\dots&h_{N_{t},N_{t}}\end{array}\right) \tag{1}\]  
where \(h_{N_{t},N_{t}}\) represents the illuminance generated by the \(N_{t}\)-th transmitting antenna and received by the \(N_{t}\)-th receiving antenna, and \(h_{i,j}\) represents the direct current gain of each channel. Accordingly, \(h_{i,j}\) is expressed as:  
\[h_{i,j}=\left\{\begin{array}{cc}\frac{A_{T}^{j}}{d_{ij}^{2}}I(\phi_{i})\cos \left(\varphi_{j}\right)&0\leq\varphi_{j}\leq\varphi_{c}\\ 0&\varphi_{j}\geq\varphi_{c}\end{array}\right\} \tag{2}\]  
where \(A_{T}^{j}\) denotes the receiving area of the \(j\)-th PD, \(d_{ij}\) is the distance from the LED to the PD, \(\varphi_{i}\) represents the light outgoing radiation angle between the LED and PD, \(\varphi_{j}\) is the light incident angle, \(\varphi_{c}\) denotes the field angle of the PD light receiving range, and \(I(\varphi_{i})\) represents the light intensity of the \(i\)-th LED along the \(\varphi_{i}\) direction.
## 2 TCAPLL Architecture
### Monitoring System
The right condition of Equation (20) is related to the DLL loop filter and ensures that, as long as the satellite is non-visible during the disabling time of the feedback component \(t_{Dis}\), the aiding frequency maintains a code phase error of less than 0.5 chips. If these two conditions are satisfied, tracking continues, relying solely on the feed-forward component, which will allow the PLL and DLL of a tracking channel to re-track a satellite once it is visible again, without the need for re-acquisition. If not, the feed-forward component is disabled, and re-acquisition starts with a frequency search span equal to \(\pm e_{{}_{f-f}}\).
## 2 Materials and Methods  
The metasensors proposed in this work are designed to operate in absorption within the THz band of 0.4-0.7 THz. All metastructures were configured as tri-layer designs consisting of a layer of aluminum (Al) metallic blades printed over a 150 \(\upmu\)m thick polypropylene (PP) substrate with back metallization acting as a ground plane. It is important to note that metals within the designed THz frequency range can still be considered as conductors that can be modeled using finite conductivity [4; 19], hence the back metallic sheet acts as a reflecting layer. The structure dimensions are: periodicity, \(d\) = 162.5 \(\upmu\)m; distance between metallic strips, \(a\) = 26 \(\upmu\)m; metallic strips width, \(g\) = 9.5 \(\upmu\)m; metallic layers thickness, \(t\) = 0.4 \(\upmu\)m; and blade width, \(w\) = 58.5 \(\upmu\)m; as shown in Figure 1.  
The designed metasurfaces are simulated using the commercial simulator CST Microwave Studio(tm). Floquet ports and periodic boundary conditions are applied to the designed unit cells to model the metasurfaces as an infinitely replicated array. The PP substrate was modeled as a low-loss dielectric with a complex relative permittivity \(\varepsilon_{PP}\) = 2.25(1 - \(j\)10\({}^{-3}\)). The aluminum used for the pattern and the ground plane was modeled as a lossy metal with electrical conductivity \(\sigma\) = 1.5 \(\times\) 10\({}^{7}\) S/m, as in [4, 19] with the nominal conductivity of aluminum reduced to account for losses due to roughness. The metasurfaces were illuminated under normal incidence using a vertically polarized (\(E_{y}\)) plane wave. To evaluate the performance of the designed metasurfaces working as thin-film sensors, their outer face was coated with different thin-film thicknesses, ranging from 200 nm to 800 nm. The dielectric permittivity of the analyte used for this purpose was \(\varepsilon_{a}\) = 8, as this value has been used in other studies dealing with biosensing applications [19, 20].
# A Microfluidic Approach for Biosensing DNA within Forensics
## 4 Biocompatibility of Materials for Forensic Microfluidic Devices
### Pdms  
PDMS is also reported as chip material for the DNA amplification reaction [85; 86; 4]. PDMS is stable over a temperature range of \(-\)50-200 \({}^{\circ}\)C [87]. Moreover, PDMS is biochemically compatible, easy to shape (by molding) and optically transparent [59; 88]. PDMS is very suitable for cell culturing, since the permeability for gases, like O\({}_{2}\) and CO\({}_{2}\), is higher than for any other polymer [39]. However, PDMS suffers from severe bubble formation during the thermal cycling protocol, due to evaporation of the PCR mixture (through the PDMS) at temperatures above 90 \({}^{\circ}\)C [40; 89]. PDMS is not very compatible with common solvents, such as chloroform, benzene, acetone and ethanol, since PDMS swells in these chemicals [59; 58]. Furthermore, it is reported that surface treatments on PDMS are often unstable over time [39].  
Although PDMS is used as coating for glass PCR chips [90; 91], an important drawback of the use of uncoated PDMS is that, because of its permeability, it might result in inhibition of the amplification reaction due to absorption or adsorption of components in the amplification mixture (in particular the enzyme). In fact, this drawback can be bypassed by coating the PDMS with bovine serum albumin (BSA) [42; 43; 44]. To coat PDMS chips, [PERSON] et al. and [PERSON] et al. used a parylene (dichlororaparxylylene) surface treatment [40; 41]. PVP is also applied by [PERSON] et al. to coat a PDMS/glass PCR chip [92].
## 4 Experiments, Results, and Analysis
### K-Fold Cross Validation  
We implemented a 10-fold cross-validation technique to use all training samples while performing robust validation [30]. Ten percent of the dataset was set aside for a hold-out test set. The remaining 90% of the dataset was divided into ten equal folds. We trained the DNN ten times, using nine different folds, leaving out one fold for validation each time.  
Mean Absolute Error (MAE) was used to quantify errors between the target values and the predictions at the output layer. The error values were then used to perform gradient descent to update the network's weights. Early stopping, set with a patience of 100 epochs and a precision threshold of \(10^{-4}\), was used to halt training when the validation set's MAE ceased to significantly improve. Figure 7 illustrates the training and validation set's average MAE trend, averaged across the ten folds, along with the minimum average validation MAE.
###### Abstract  
This article presents the conceptual design, operation principle, dynamic modeling, and simulation results of a discrete incremental hydraulic positioning system (DIHPS) intended for use in high-precision, heavy-load industrial automation solutions. An original solution for precise incremental step positioning using DIHPS is proposed, comprising an n-step linear double-acting hydraulic actuator (HA) with a double rod, cylinder sleeve with outflow gaps, binary valves (BVs) bleed-off into the tank, and fixed throttle valves (TRVs) at the inlets of the actuator chambers. The discrete incremental shift of the HA is determined by controlling the opening and closing of the individual BVs, and the step shift of the HA piston stops at the opening location of the BV. A dynamic model of incremental step shift for DIHPS is developed, considering the behavior and relationships of its individual elements. A dynamic model of HA with and without a hydropneumatic accumulator (HPA) is presented. HPA has been shown to effectively dampen piston vibrations and pressure peaks at the HA stop position. The design assumptions, dynamic models, and discrete incremental positioning of the DIHPS are verified by simulation tests and assessed with quantitative indicators. Innovative DIHPSs have significant practical potential in the discrete incremental positioning of heavy loads.  
digital incremental positioning; hydraulic positioning system; hydraulic resistance; hydraulic stiffness; damping model; heavy industry automation Article
# Power Quality Fault Identification Method Based on SDP and Convolutional Neural Network
## 4 Convolutional Neural Network
### Convolutional Layer  
The convolutional layers are used for feature extraction in the CNN. The convolution kernels of different sizes extract and enhance the features of patterns. The size of convolution kernels can influence the performance after feature extraction. If it is too small, the pattern feature recognition is poor. If it is too large, the computing time is too long. The calculation process is shown in Figure 4.
## 3 Results and Discussion
### Comparison of Encapsulation Methods in Terms of Efficiency, Yield and Powder Characteristics
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{3}{c}{**Encapsulation Methods**} \\ \cline{2-5}  & **SFD** & **SD-2 FN** & **SD-UN** & **FD** \\ \hline Encapsulation efficiency (\%) & 51.66 \(\pm\)4.16 \({}^{\text{a}}\) & 96.33 \(\pm\)2.08 \({}^{\text{b}}\) & 93.33 \(\pm\)2.30 \({}^{\text{b}}\) & 63.00 \(\pm\) 3.61 \({}^{\text{c}}\) \\ Drying yield (\%) & 84.40 \(\pm\) 1.75 \({}^{\text{a}}\) & 74.27 \(\pm\) 1.05 \({}^{\text{b}}\) & 45.27 \(\pm\) 1.80 \({}^{\text{c}}\) & 95.39 \(\pm\)0.82 \({}^{\text{d}}\) \\ Powder Properties & & & & \\ Moisture content (\%) & 0.23 \(\pm\) 0.01 \({}^{\text{a}}\) & 1.27 \(\pm\) 0.59 \({}^{\text{b}}\) & 1.31 \(\pm\) 0.27 \({}^{\text{b}}\) & 0.57 \(\pm\) 0.22 \({}^{\text{a}}\) \\ Bulk density (g\(\cdot\)mL\({}^{-1}\)) & 0.19 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.29 \(\pm\) 0.01 \({}^{\text{b}}\) & 0.27 \(\pm\) 0.02 \({}^{\text{b}}\) & 0.20 \(\pm\) 0.01 \({}^{\text{a}}\) \\ Tapped density (g\(\cdot\)mL\({}^{-1}\)) & 0.24 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.35 \(\pm\) 0.01 \({}^{\text{b}}\) & 0.33 \(\pm\) 0.02 \({}^{\text{b}}\) & 0.24 \(\pm\) 0.01 \({}^{\text{a}}\) \\ Carr index & 20.58 \(\pm\) 1.00 \({}^{\text{a}}\) & 18.68 \(\pm\) 4.28 \({}^{\text{b}}\) & 17.87 \(\pm\) 3.13 \({}^{\text{ab}}\) & 14.33 \(\pm\) 1.52 \({}^{\text{b}}\) \\ Hausner ratio & 1.25 \(\pm\) 0.01 \({}^{\text{a}}\) & 1.22 \(\pm\) 0.06 \({}^{\text{a}}\) & 1.21 \(\pm\) 0.05 \({}^{\text{ab}}\) & 11.6 \(\pm\) 0.02 \({}^{\text{b}}\) \\ Particle size D\({}_{\text{d}}\) (μm) & 3.18 \(\pm\) 0.18 \({}^{\text{a}}\) & 11.62 \(\pm\) 0.35 \({}^{\text{b}}\) & 6.64 \(\pm\) 0.35 \({}^{\text{c}}\) & 14.22 \(\pm\) 0.79 \({}^{\text{d}}\) \\ \hline \hline \end{tabular} All the given values are means of three (\(n=3\)) determinations \(\pm\) SD. \({}^{\text{abcd}}\) Means within a row with different letters are significantly different (\(p<0.05\)). SFD, Spray Freeze Drying. SD-2 FN, Spray Drying- 2 Fluid Nozzle. SD-UN, Spray Drying -Ultrasonic Nozzle. FD, Freeze Drying.  
\end{table} 
Table 4: The effect of drying method on encapsulation efficiency, drying yield, and powder characteristics. with different encapsulation methods. The freeze-dried encapsulates had the biggest droplet size among the others. Similarly, [PERSON] et al. [63] described that in comparison to other drying processes, such as spray drying and oven drying, freeze-drying produced the largest particle size of mung bean protein isolates.
# Transfer Learning from Deep Neural Networks for Predicting Student Performance
## 4 Research Methodology
### The Proposed Transfer Learning Approach
All the experiments were conducted using the Keras library in Python [31]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \(\{\)C\({}_{1}\),\(C_{2}\)\(\}\) & \(\{\)C\({}_{1}\),\(C_{3}\)\(\}\) & \(\{\)C\({}_{1}\),\(C_{4}\)\(\}\) & \(\{\)C\({}_{1}\),\(C_{5}\)\(\}\) & \(\{\)C\({}_{2}\),\(C_{3}\)\(\}\) & \(\{\)C\({}_{2}\),\(C_{4}\)\(\}\) & \(\{\)C\({}_{2}\),\(C_{5}\)\(\}\) & \(\{\)C\({}_{3}\),\(C_{4}\)\(\}\) & \(\{\)C\({}_{3}\),\(C_{5}\)\(\}\) & \(\{\)C\({}_{4}\),\(C_{5}\)\(\}\) \\ \hline Forum views & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ Page views & 7 & 7 & 8 & 7 & 0 & 0 & 2 & 0 & 2 & 2 \\ Recourse views & 17 & 17 & 17 & 17 & 11 & 11 & 10 & 12 & 12 \\ Folder views & 2 & 2 & 0 & 2 & 12 & 0 & 12 & 13 & 12 & 12 \\ URL views & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 1 & 1 & 1 \\ Assignments views & 13 & 8 & 9 & 8 & 9 & 9 & 9 & 9 & 8 & 9 \\ Submitted Assignments & 8 & 8 & 9 & 8 & 8 & 9 & 8 & 9 & 8 & 9 \\ Total views & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ Total activity & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ Gender & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ Total Number of Features & 51 & 46 & 47 & 47 & 44 & 33 & 47 & 46 & 47 & 49 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Features of the paired datasets. Figure 2: The three-step process of the proposed method. ```

**Algorithm 1** Transfer learning through paired course using deep neural networks. ## 5 Results

The averaged accuracy results (over the 10 folds) are presented in Table 4. For each pair, we conducted two experiments, using each course alternatively as the source course and the other one as the target course. Therefore, we evaluated 20 distinct combinations formed by the five courses. For each pair, we highlighted in bold the cases where the transfer model produced better results than the baseline. Overall, it is observed that the model \(C_{i,j}\) benefits the predictions of the source course \(C_{i}\), since the predictive performance of the transfer learning deep network is better than the baseline \(C_{j}\)A one-tailed, paired t-test (\(a\)=0.05) was conducted for verifying whether the improvement in the transfer model was statistically significant. Therefore, we compared the accuracy results obtained by the baseline deep network (using the target course dataset), with the results obtained by the transfer method, iteratively, for each number of epochs. Since the p-value is inferior or equal to 0.05, we conclude that the difference is significant in all cases except the starting point where the number of epochs equals zero (Table 5). Moreover, the \(p\)-value is gradually decreased as the number of epochs increase from every epoch from 10 to 100. The analysis of the experimental results, in question-and-answer format, underlines the efficiency of the proposed method for transferring knowledge from one course to a related one. 1. Can the weights of a deep learning model trained on a specific course be used as the starting point for a model of another related course?
## 5 Experiment  
### Multi-Source Data Acquisition and Preprocessing  
The data preprocessing stage encompasses comprehensive processing of both bike-sharing demand data and multi-source feature data.  
Regarding the bike-sharing demand data, the Divvy bike-sharing ride records data for Chicago from the year 2020 are utilized ([[https://divvy-tripdata.s3.amazonaws.com/index.html](https://divvy-tripdata.s3.amazonaws.com/index.html)]([https://divvy-tripdata.s3.amazonaws.com/index.html](https://divvy-tripdata.s3.amazonaws.com/index.html)) (accessed on 16 February 2023)). The initial steps involve data cleansing, including the removal of null and outlier records, alongside filtering out records outside the research area to ensure data accuracy and consistency. Subsequently, the raw ride records are transformed into demand data, with ride counts aggregated based on designated time intervals and station identifiers, thus generating spatiotemporal demand data for bike-sharing. For time series forecasting tasks, the demand data are further segmented into time windows to create historical observation data and actual label values for forecasting.  
Concerning the preprocessing of multi-source feature data, initial steps involve statistical analysis of historical ride records and their standardization for subsequent construction of multi-graph models. Subsequently, processing of POI data ([[https://www.openstreetmap.org](https://www.openstreetmap.org)]([https://www.openstreetmap.org](https://www.openstreetmap.org)) (accessed on 1 April 2023)) surrounding stations entails steps such as data acquisition, cleansing, classification, and matching to acquire information on points of interest around each station. Distance between stations is calculated using latitude and longitude information and applying spherical distance formulas, followed by standardization. Additionally, considering the influence of time, time features including weekdays, weekends, and holidays are extracted. Climate data ([[https://www.wunderground.com](https://www.wunderground.com)]([https://www.wunderground.com](https://www.wunderground.com)) (accessed on 22 April 2023)) and COVID-19 confirmed cases data (www.chicago.gov (accessed on 22 April 2023)) are also obtained, processed, and standardized to provide environmental factors relevant to bike-sharing demand. Through these steps, comprehensive preprocessing of multi-source feature data is conducted, furnishing a reliable data foundation for subsequent modeling and analytical tasks.