esa-sceva/satcom-chunk-collection huggingface dataset:
# Road Scanner: A Road State Scanning Approach Based on Machine Learning Techniques
## 5 Experiments
### Classification Models
#### 5.2.6 Gradient Boosting  
Gradient Boosting is an ML technique that provides a prediction tool in the form of a collection of relatively weak prediction models, most commonly decision trees [36]. It relies mainly on the boosting technique. The boosting technique begins by fitting the data to a starting model. Then, a second model is constructed that concentrates on properly forecasting instances in which the first model performs badly. It is expected that the combination of these two models will be superior to each model alone. Then, this procedure is repeated several times and every subsequent model aims to address the deficiencies of the boosted ensemble of all preceding models.
# Metric for Estimating Congruity between Quantum Images +
## 3 A Measure for Fidelity between Quantum Images
### Fidelity Check Operation (FCO)  
The peculiar nature of quantum computation imposes certain constraints on how quantum information can be manipulated and its final state recovered [17; 24]. In trying to circumvent the violation of these requirements, for example, on the circuit-model, which most QIP literature adopt, the quantum image state is prepared from a classical copy of the same image. After it is prepared and initialised, the quantum image can only be manipulated, and recovered using quantum circuit elements and components, such as the basic unitary gates that were highlighted earlier in Section 2. Many copies of the same image are usually prepared and different circuits designed to manipulate and recover them as required for an intended application [14; 15; 16; 17; 18; 19; 20], albeit, as we shall demonstrate later, oftentimes, this is an expensive undertaking.  
Throughout the rest of this paper, we assume that all images (reference and test images) are encoded as FRQI quantum images using procedures that were highlighted earlier in Section 2 and discussed in detail in [11; 12; 17; 19]. Consequently, the geometric (GTQI) and colour (CTQI) transformations and their restricted variants, which are widely used in FRQI quantum image processing (QIP) to manipulate the spatial and chromatic content of images [17; 19], are the main resources employed to manipulate our images.  
Therefore, the foregoing aphorism enuncates the requirements to formulate our proposed QIFM fidelity metric as a pixel difference-based metric, whose formulation is tailored towards effective quantification of fidelity between a pair of quantum images in a manner that guarantees correlation with subjective evaluation of the notion of fidelity [1]. It is also designed to minimise the cost, in terms of basic quantum gates, required to execute the operations.  
These two yardsticks (i.e., acceptable levels of fidelity and minimised cost of quantum resources) form the main benchmark on which the performance of our proposed metric will be evaluated.  
Figure 5: Flowchart for executing the proposed QIFM framework to compare two (or more) quantum images.  
Based on this stipulation, we present, in the sequel, the two operations that make up the proposed quantum image fidelity operation to quantify \"likeness\" between two (or more) images (our QIFM protocol). For simplicity, we shall, henceforth, refer to this operation as the fidelity check operation, \(FCO\).  
Relying on the rudimentary requirements of quantum computation, which are already established in the literature (such as [2]), we highlight two algorithms that each produces its own sub-circuit, which combine to produce the \(FCO\) operation that facilitates the QIFM assessment as presented in Figures 4 and 5.
# Field Measurements of Free Ascending Behavior of Occupants along Medium-Long Stairway
## 3 Results
### Ascending Speed
The mean ascending speeds of each group are summarized in Table 2. Comparing with the mean ascent walking speed along the super long stairs in other studies, e.g., [PERSON]'s [20] 40 floors, [PERSON]'s [15] 50 floors, and [PERSON]'s 26 floors [23], the mean ascent speeds of males and females in this research are a little higher, and the fatigue effect on ascent behavior is not so obvious when walking on medium-long stairs. Due to the upward walking speed being related to the walking distance along stairs, it is more convenient for practical application to find the relationship between the ascent speed and the walking distance. Using linear regression analysis for the mean ascent speed of each half floor, we can obtain the relationship of the mean ascent speed with the traveling vertical height (Figures 12 and 13): \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Participants**} & **Male** & **Female** & **Male** & **Boys** & **Female** \\  & **(Aged 20–24)** & **(Aged 20–24)** & **(Aged 30–60)** & **(Aged 13–17)** & **(Aged 40–60)** \\ \hline Maximum mean ascent speed (4 levels)/m/s & 1.87 & 1.40 & - & - & - \\ \hline Mean ascent speed (4 levels)/m/s & 1.24 \(\pm\) 0.28 & 1.02 \(\pm\) 0.20 & - & - & - \\ \hline Minimum mean ascent speed (4 levels)/m/s & 0.72 & 0.62 & - & - & - \\ \hline Maximum mean ascent speed (8 levels)/m/s & 1.75 & 0.92 & 1.11 & 1.22 & 0.99 \\ \hline Mean ascent speed (8 levels)/m/s & 0.97 \(\pm\) 0.28 & 0.78 \(\pm\) 0.11 & 0.91 \(\pm\) 0.13 & 0.92 \(\pm\) 0.22 & 0.70 \(\pm\) 0.13 \\ \hline Minimum mean ascent speed (8 levels)/m/s & 0.57 & 0.56 & 0.59 & 0.75 & 0.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the mean ascent speed. Figure 12: Variation in the mean ascent speed of males with vertical height. For males:  
\[v_{m}=1.064-0.007\mathrm{H} \tag{2}\]  
where \(v_{m}\) is the mean ascent speed of males, \(\mathrm{m/s}\); \(H\) is the traveling vertical height, \(\mathrm{m}\). The coefficient of determination \(R^{2}\) was 0.87. For females:  
\[v_{f}=0.852-0.006\mathrm{H} \tag{3}\]  
where \(v_{f}\) is the mean ascent speed of females, \(\mathrm{m/s}\). The coefficient of determination \(R^{2}\) was 0.92. Due to decrease in walking speeds with height caused by physical exertion, the above two equations can describe the impact of fatigue indirectly to some extent.
## 5 Data Imbalance
### Data Imbalance Handling Techniques Future Research Directions
#### 5.5.8 Strategy of Combining Data Augmentation with Contrastive Learning  
Future research could explore combining data augmentation techniques with contrastive learning to enhance the handling of imbalanced data. Contrastive learning is a self-supervised learning method that learns more discriminative feature representations by maximizing the similarity between similar samples and the difference between dissimilar samples [133]. When dealing with imbalanced data, data augmentation techniques can be used to generate diverse minority class samples, and contrastive learning can effectively distinguish these samples from majority class samples [134,135].
## 3 Results
### Whce (Brt, Frt and Ext) and Temporal Units (TUs) Considering the Rounds  
As can be observed in Table 7, the chi-square analysis showed an association between Whce and rounds (\(p<0.001\); \(V=0.160\)) for the whole sample and for both men (\(p<0.001\); \(V=0.187\)) and women (\(p=0.001\); \(V=0.161\)) separately.  
To simplify the interpretation of these results, it was decided to group the rounds into two blocks, PR and FR, considering all the TUs. The chi-square overall analysis also showed an association (\(p<0.001\); \(V=0.172\)) between variables (TUs and grouped rounds). The SR indicated an overrepresentation of TU-FRT and TU-GS in FR (SR = 3.1 and 4.8, respectively) and an underrepresentation of TU1, TU2, TU3 and TU-GS in PR rounds (SR = \(-3.3\), \(-2.2\), \(-2.6\) and \(-2.4\), respectively). As seen in Figure 1, the contest durations were longer in FR.  
Results by sex also confirmed the association between TUs and grouped rounds, both for women (\(p<0.001\); \(V=0.152\)) and for men (\(p<0.001\); \(V=0.204\)). For women, the SR for the FR showed an overrepresentation of TU-GS and an underrepresentation of TU1-BRT (SR = 2.8 and \(-2.1\), respectively). For men, the SR showed for the FR an overrepresentation of TU-FRT and TU-GS (SR = 3.2 and 4.2, respectively) and an underrepresentation of TU1-BRT and TU3-BRT (SR = \(-2.9\) and \(-2.5\), respectively).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**WC18**} & \multicolumn{2}{c}{**WC19**} & \multicolumn{2}{c}{**WC21**} & \multicolumn{2}{c}{**Total**} \\ \hline  & **N** & **Mean \(\pm\) SD** & **N** & **Mean \(\pm\) SD** & **N** & **Mean \(\pm\) SD** & **N** & **Mean \(\pm\) SD** \\ \hline
**Men** & n = 104 & 100 \(\pm\) 84.4 & n = 105 & 96.8 \(\pm\) 79 & n = 109 & 76 \(\pm\) 66.5 & n = 318 & 90.7 \(\pm\) 75.9 \\ \hline
**Women** & n = 60 & 101 \(\pm\) 88.3 & n = 60 & 127.8 \(\pm\) 101 & n = 66 & 137.9 \(\pm\) 127.3 & n = 186 & 122.7 \(\pm\) 108.1 \\ \hline
**Overall** & n = 164 & 100.4 \(\pm\) 83.1 & n = 165 & 108.1 \(\pm\) 88.6 & n = 175 & 99.4 \(\pm\) 98.5 & n = 504 & 102.5 \(\pm\) 90.4 \\ \hline \hline \end{tabular} Data are means \(\pm\) standard deviation (SD).  
\end{table}
Table 6: Golden score (GS) period analysis in temporal terms (seconds-s) by the championship (WC18, WC19 and WC21) and sex (men vs. women) (n = 2340).  
Figure 1: Descriptive analysis and associations between temporal units (TUs) and grouped rounds (preliminary and final rounds, PRs and FRs, respectively) (n = 2340). Asterisk highlights the variables leading to the most important contributions to the difference, according to the SR; SRs = standardised residuals.
# Analysing Social Media Discourse on Electric Vehicles with Machine Learning
## 3 Materials and Methods
### Sentiment Analysis  
Sentiment analysis is a text mining technique used to identify and classify emotional content in texts. This analysis is widely used to understand users' opinions about products, services, political events, or general social issues [46]. Although its main purpose is to determine whether a given text contains positive, negative, or neutral sentiment, in more detailed analyses, different categories of sentiment (e.g., happiness, anger, sadness, fear) can also be evaluated. Nowadays, sentiment analysis has become an important tool for understanding public sentiment, especially in social media, customer feedback, news comments, and forum discussions [47].  
Sentiment analysis methods are generally divided into three main categories: rule-based, machine learning-based, and deep learning-based approaches [48]. Rule-based approaches rely on identifying emotional expressions in texts using lexicons and grammatical rules. However, they may fail to understand context and complex elements of language such as irony [49]. Machine learning-based methods, on the other hand, enable the development of models capable of emotion classification using a given training dataset. Algorithms such as support vector machines (SVM), logistic regression, and random forests are examples of this category. In recent years, deep learning models (e.g., long short-term memory (LSTM), convolutional neural networks (CNN), and transformer-based models), especially those that work with large datasets, have become prominent with high accuracy rates in sentiment analysis [50].
## 3 Results Analysis and Discussion
### Analysis of the Horizontal Displacement of the Pile Top  
Figure 17 illustrates the change curves of the horizontal displacement of the pile top in the field test.  
Figure 16: Comparison of the measured and finite element analysis bending moments of the pile body.  
Figure 17: Change curves of the horizontal displacement of the pile top.  
In this figure, the steel-pipe pile top experienced displacement towards the foundation pit during the excavation process. Then, the pile top reached the maximum displacement deformation (at 5.63 mm). The overall deformation was not large and was smaller than the alarm value (50 mm) stipulated in the technical code for the monitoring of building excavation engineering [31]; the supporting effect of steel-pipe piles was favorable. Pile-top displacement deformations were larger in piles No. 2 and 3 than in piles No. 1 and 4 because piles No. 1 and 4 were side piles. Furthermore, the embedding effect of the surrounding unexcavated soil for piles No. 1 and 4 reduced the pile-top displacement toward the foundation pit.  
In addition, the pile-top displacement rate was low after completing the first step of the foundation pit excavation. In the first excavation step, the active earth pressure of the soil at the rear side of the supporting structure was not large. The pile body also experienced a small deformation under the constraining effect of the cap beam and such an active earth pressure. The active earth pressure and pile-top displacement gradually increased with the excavation depth.
## 5 Utilization Assessment and FGI Results of AROS_\(\alpha\)  
A single-spanned bridge of 20.39 m in length and 5.12 m in height was selected as the target for our study. The bridge was selected as it would provide a single target to test the functionalities of AROS_\(\alpha\). The bridge was first converted into a BIM model using 2D drawings, then converted into an AR model.  
Figure 3 shows the results of the utilization assessment and FGI for the individual functions provided in AROS_\(\alpha\). The assessment was conducted by requiring each expert to evaluate the individual functions with respect to their practical applicability in the field. They were asked to assign scores using a 5-point Likert scale, where 1 indicates \"very low\" and 5 indicates \"very high.\" The figure shows the average and standard deviations of the scores. The standard deviations were calculated as a value around 1 and represent the spread of the individual scores.  
The functions 'Checklist provisions via BIM model projection', 'dimension measurement,' and 'BIM model and LiDAR analysis' ranked highest, with mean values over 4.0; the functions with the highest utility. The results revealed that the experts deemed these functions to have the highest utility in common and periodic inspection tasks. The lowest score was recorded in the 'BIM model viewer,' with a mean value of 3.36. Therefore, separately displaying a BIM model is useful, but not on a frequent basis. The differences in the standard deviations were not large (i.e., 0.72-0.96), confirming that the results represented a consensus between the experts as to the utility of the individual AR functions.  
Figure 2: Research methodology.  
Discussions during the FGI revealed that, despite the high utility of the main functions, the experts noted potential shortcomings that would limit their use in the field. Specifically, they noted that the overall quality of the BIM model projections needed to be improved. This problem was further divided into two separate technical issues, and their details are discussed further in the following sections.
# Device-Free Crowd Size Estimation Using Wireless Sensing on Subway Platforms
## 5 Results and Discussion
### Combined Model  
The use of a reliable vehicle detection system and two separately trained models for the two environment states enables us to combine both models in a real-time environment. When we overlay the output of the combined model with the ground-truth data, we obtain the graph shown in Figure 5. The trend and magnitude of the predicted crowd size have a clear correlation with the trend that can be observed in the test data.  
When we look at more statistical metrics, we obtain a median error of 2.856 people, an MAE of 3.567 people, and an RMSE of 4.706. These numbers are a clear improvement over the use of a single polynomial regression model trained on both states, which results in a median error of 4.617 people, an MAE of 6.192 people, and an RMSE of 8.250.  
If we calculate the F-test and \(p\)-value between absolute errors of the proposed switching model and the non-switching model, we achieve an F-test value of 35.394 and a \(p\)-value of \(5.861\cdot 10^{-9}\), confirming the statistical significance of this improvement.  
The data as presented are the raw output of the regression models and are not yet filtered or smoothed in any way. Due to the lack of filtering, the movement of people in the environment causes fluctuations based on their positions and movement patterns. Since the data used within this paper were collected during busy periods in an uncontrolled environment, we obtained a thorough benchmark on how the system will perform in similar environments.  
Switching between models when not perfectly aligned with changes in the environment can result in discontinuities in the combined output. These discontinuities can become visible as upward or downward spikes in the final graphs during the arrival or departure of rail vehicles.  
Figure 8: This graph displays the cumulative error distribution of the absolute crowd estimation error expressed in the number of people for both with (orange dashed) and without (green dash-dotted) a rail vehicle present, as well as the final results using the switched model (blue solid) and without using a switching model (black dotted). The black arrow indicates the improvement.  
Another important note is that the ground-truth samples for both training and testing are combined with the closest measurement sample collected every 10 s. This allows for a mismatch of up to 5 s between the ground-truth samples and the corresponding attenuation values. In a very lively public transportation environment, large changes in the number of people and their location can occur in the time between collecting the ground truth and the measurement by the system. An example of this is people disembarking the rail vehicle and walking to the exit. These short events of 10 to 20 s can result in a significant increase in crowd size for the duration of only one or two samples. This poses challenges in collecting reliable training data during these periods.
## 4 Case Study
### Field Data Examples  
The ESG system has been operating in the main powerhouse of the Shuangjiangkou hydropower station for two years, during which thousands of MS events were obtained. Three groups of effective MS signals were randomly selected for EMD_CS_ST de-noising processing and analysis. To facilitate calculation and comparison, the effective MS signals are intercepted 1000 ms from each time-window of 1500 ms. Firstly, three groups of signals are denoised by the method of EMD_CS_ST, and then using the MATLAB platform to perform FFT spectrum analysis, it can more intuitively judge the denoising effect of MS signals. The waveform and time spectrum before and after denoising are shown in Figure 11.  
It can be seen from Figure 11 that the three groups of original MS signals all contain a large amount of random noise. Even if the main spectrum distribution of these signals can be seen from the time spectrum, it is not obvious due to noise interference. After the denoising processing of this method, the waveform and time spectrum of the MS signal are clearer, the dominant spectrum is more visible, and there is almost no NF phenomenon. The EMD_CS_ST method was adopted to denoise MS signal, and the signal denoised has the advantages of high _SNR_ and stable waveform. The effectiveness and practicability of the proposed method are proved qualitatively.
## 4 Conclusions  
In summary, this paper reported the facile synthesis of MnO2 NFs/NrGO nanocomposites and their application on the simultaneous determination of DA and UA. The electrochemical measurements indicated that the MnO2 NFs/NrGO composites possess a large, effective electroactive area and low-charge transfer resistance. MnO2 NFs/NrGO/GCE showed superb catalytic capacity toward the electrooxidation of DA and UA, attributing to the synergistic effect from MnO2 NFs and NrGO sheets. The anodic peak currents of DA and UA increased linearly with their concentrations varying from 0.2 uM to 6.0 uM. However, their anodic peak currents were highly correlated to the Napierian logarithm of their concentrations, ranging from 6.0 uM to 100 uM. The LODs were 0.036 uM and 0.029 uM for DA and UA, respectively. Furthermore, the proposed sensor successfully realized DA and UA detection in human serum samples with satisfactory recovery. Combining with prominent advantages such as facile preparation, high sensitivity, good selectivity, repeatability, and reproducibility, the proposed MnO2 NFs/NrGO nanocomposites have become the most competitive candidates for the simultaneous determination of DA and UA in various real samples.  
The following are available online at [[http://www.mdpi.com/2079-4991/9/6/847/s1](http://www.mdpi.com/2079-4991/9/6/847/s1)]([http://www.mdpi.com/2079-4991/9/6/847/s1](http://www.mdpi.com/2079-4991/9/6/847/s1)), Figure S1: The anodic peak currents of 1 uM DA and UA in the presence of 100-fold alanine (AL), glutamic acid (GA), ascorbic acid (AA), lysine (LY) and citric acid (CA).  
The authors declare no conflicts of interest.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 2 Interference in Hotspot Scenarios
### Classic Solutions for Interference in Hotspot Scenarios
#### 2.1.2 Small Cells  
Heterogeneous Network (HetNet) is an extended concept of Hierarchical cell structure (HCS) that has been discussed even before the standardization of LTE [11]. It is usually associated with urban scenarios, where the traffic demand and the number of users are constantly increasing.  
In HetNets, different categories of cells coexist with each other, and they usually differ in coverage and capacity [12]. Still, they do not necessarily need to share the same radio access technology. In such deployments, small cells are an alternative to offload the macrocells traffic, especially on hotspots and cell-edge [13], due to severe interference.  
Dense urban areas that already have limited capacity may require big investments to deploy another conventional access point. In such cases, the use of small cells is attractive, since they can improve coverage and transmit with lower power, given the smaller distance between user and transmitter. These cells usually operate in the same frequency band as the macrocells, transmitting with lower power to reduce interference.  
Recent studies involving HetNets usually focus on two categories of small cells: femtocells and picocells [14; 15; 16]. The former is typically deployed by the user, it is not planned, and it may be private, being called a Closed Subscriber Group (CSG). The latter has the functionality of a regular eNB, but with smaller coverage and lower transmission power. Picocells are usually deployed to cover areas with a high density of users and they can also be deployed indoors, if necessary.  
The use of small cells is also indicated as a key technology for 5G. The densification of the network has been indicated in the literature as an efficient solution to help satisfy the requirements for 5G [17].  
However, the use of small cells also imposes some challenges. Its deployment demands investment in planning and hardware, and it may increase the amount of unnecessary hand-offs [18]. The large power difference between cell tiers can leave small cell users at a disadvantage. On the other hand, CSGs can create holes in coverage for the macrocell users, as they do not have access to the CSG.