esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
We proposed the three-step methodology for modeling user satisfaction from online user reviews, shown in Figure 1.  
First, the usefulness of online user reviews containing information related to UX and usability from the corpus collection will be identified. Before applying the user review analysis, the framework checks the quality and reliability of user reviews through three user review quality filters: spam detection, relatedness, and subjectivity of review documents. These three classifiers function in a sequential format. They filter spam reviews, access UX-related reviews, and then select the subjective reviews. The relatedness classifier, also known as the UX multi-criteria qualifier (UXMCQ), uses a mainly unsupervised method requiring minimal configuration of domain seed words to auto-label the data based on the context window (see Section 3.1 for more details).  
The second step process consists of the following: (i) UX dimensions (UXDs) extraction using the proposed user experience word-embedding LDA (UXWE-LDA), an improved knowledge-based topic modeling methodology, and (ii) sentiment analysis and its orientation for each extracted UXD from online user reviews. UXWE-LDA is an improved LDA version that automatically learns the domain knowledge from the given text corpus. UXWE-LDA resolves the problems of existing LDA, often generating semantically incoherent topics. UXWE-LDA improves the existing knowledge-based topic models by UGC extracting more domain-dependent dimensions in the UX area. UXWE-LDA combines topic modeling, specifically LDA, with word embedding that automatically learns the domain knowledge from a large amount of textual data. This model automatically learns the domain knowledge from the given text corpus and extracts more coherent topics to assign labels as UXD to each extracted topic using a dictionary-based approach. Additionally, it identifies the user's positive and negative sentiment association towards each UXD. To identify the sentiment orientation, we employed the BERT-based sentiment transformer pipeline.  
The third step consists of two parts: (i) casual relation analysis of UXDs with respective sentiment orientation and (ii) mapping the UXDs' causal relationship on the user satisfaction model. This overcame the problem of existing models based on measuring satisfaction from online reviews. The Bi-LSTM model combines the user rating and extracted dimensions to measure user sentiment's causal relationship on user satisfaction. In addition, we employed the two-dimensional Kano model for user satisfaction. Developed by [PERSON] et al. [31], this model categorizes the product features into different classes: must-be, performance, excitement, indifferent, and reverse. These features' values are associated with user satisfaction [14]. The subsequent section describes each step in greater detail.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 7 Performance Analysis of the Proposed ECSSP-SC
### Comparison of Security Attributes  
It has been explained in the Section 6 that the proposed ECSSP-SC satisfies mutual authentication, the confidentiality of messages, the integrity of messages, non-repudiation, anonymity, availability, and forward security attributes. A comparison of the security properties of the ECSSP-SC and other protocols is shown in Table 5. From this analysis, one can conclude that the proposed mutual authentication protocol offers all the necessary security attributes of a smart card-based system.
## 2 Materials and Methods
### PLSR Modeling  
Partial least squares regression (PLSR) is a modeling method used in visible and near-infrared spectral analysis, capable of handling the challenges of high dimensionality, collinearity, and noise in spectral data. By simultaneously decomposing the spectral matrix (X) and reference matrix (Y) into latent variables that maximize the covariance between the two, PLSR enhances model stability and prediction accuracy. Selecting the appropriate number of latent variables is crucial in the modeling process and is typically determined through cross-validation, such as 10-fold cross-validation, to minimize the root-mean-square error of cross-validation (RMSECV) and avoid overfitting or underfitting. Additionally, preprocessing methods such as smoothing and normalization can affect the model performance, requiring experimentation with different preprocessing techniques and latent variable combinations to optimize the model configuration. PLSR has demonstrated its effectiveness in handling complex spectral data, providing accurate and reliable prediction results. A comparison of diffuse transmission and reflection spectral analysis is presented in Figure 3.
# I-BIM Applied in Railway Geometric Inspection Activity: Diagnostic and Alert
## 4 Railway Inspection Activity
### Rail Inspection Metodologies
#### 4.2.2 Data Evaluation and Diagnostic  
The evaluation of the obtained measurements was categorized according to the technical standard GR.IT.VIA.018, concerning the tolerances and defects of the geometric track parameters [49]. The document defines the deviation tolerances applicable over the track's geometric parameters, considering the Iberian gauge. The guideline expresses several levels of diagnostics:  
* The alert level refers to a parameter value that did not exceed the tolerance;
* The intervention level indicates that a value did not reach the tolerance;
* The immediate action alert is considered when a value exceeds the tolerance.  
For irregularity that exceeds the established tolerances, the type of intervention or immediate action to be implemented is defined by the function of the type of anomaly and the range of values considered in each geometric parameter, established in the standard. Therefore, in the situation of an immediate correction alert, a train speed reduction must be applied or a closure for the affected rail segment can be required.  
Table 1 lists the interval of values for each geometric parameter, related to the alert level, considering the circulation speed and the tolerance level [49]. In this study, the circulation speed range between 80 km/h and 120 km/h was considered.  
The data used in the inspection activity were collected during the process and actually carried out on site, as part of the regular maintenance work on railways, in the national context. The set of data selected to illustrate the application of the developed script concerned only a restricted number of anomalies, having been selected as the most frequent and striking in relation to the more or less imminent danger they may cause.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Class IV—Velocity 80 \textless{} V \textless{} 120 (km/h)** & \\ \hline
**Gauge (mm)** & **Cant (mm)** & **Longitudinal Level (mm)** & **Alignment (mm)** \\ \hline \(-\)7/+25 & \(-\)12/+12 & \(-\)13/+13 & \(-\)10/+10 \\ \hline \end{tabular}
\end{table}
Table 1: Tolerance of rail geometric parameters for alert level and velocity [80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 99; 11; 11; 11; 11; 11; 11; 11; 112; 113; 114; 115; 116; 117; 118; 119; 120].
## 3 Results and Discussion
### Water-Soluble Anions
Average values of 0.41 \(\pm\) 0.20 \(\upmu\)g m\({}^{-3}\) within the classroom C3 and 1.41 \(\pm\) 0.95 \(\upmu\)g m\({}^{-3}\) on the GRS were measured for the cold days, whereas 0.48 \(\pm\) 0.33 \(\upmu\)g m\({}^{-3}\) and 0.99 \(\pm\) 0.72 \(\upmu\)g m\({}^{-3}\), respectively, were measured for the warmer days of the year. Elevated outdoor concentrations occurred for both periods but mainly during the cold period (almost 3.5 times higher than indoors), where apart from traffic load, there was a strong contribution from biomass burning from residential heating. Nevertheless, the influence of regionally transported external particles of NO\({}_{3}\)\({}^{-}\), which penetrate into the classroom, was obvious, as a significant Spearman correlation between the indoor and outdoor NO\({}_{3}\)\({}^{-}\) was observed for all experimental days with a correlation coefficient of 0.48 (\(p\) = 0.02). This is in accordance with the publications of [PERSON] et al. (2018) and [PERSON] et al. (2019), which highlight that NO\({}_{3}\)\({}^{-}\) particles are strongly related to large scale regional transport processes, as they are formed in the atmosphere (SOA formation) mainly by the oxidation of nitrogen dioxide (NO\({}_{2}\)), which is a primary product of combustion by vehicle exhausts, fireplaces and industrial activities. Similar results were found for SO\({}_{4}\)\({}^{2-}\), which was the most abundant anionic element for both the indoor and outdoor environments compared to NO\({}_{3}\)\({}^{-}\) and Cl\({}^{-}\). For all experimental days, higher ambient concentrations were recorded than those indoors. More specifically, amid the colder days, the mean value of SO\({}_{4}\)\({}^{2-}\) in classroom C3 was 1.45 \(\pm\) 0.82\(\upmu\)g m\({}^{-3}\) and 1.78 \(\pm\) 0.79 \(\upmu\)g m\({}^{-3}\) on the GRS, respectively. Interestingly, during the warm period, SO\({}_{4}\)\({}^{2-}\) mean concentrations were elevated in both experimental points (1.57 \(\pm\) 1.10 \(\upmu\)g m\({}^{-3}\) and 2.57 \(\pm\) 1.18\(\upmu\)g m\({}^{-3}\)). A possible explanation could be the frequent regional transport episodes of airborne particles during the summer. As [PERSON] et al. (2019); [PERSON] et al. (2019) and [PERSON] et al. (2020) report, sulfate anions are formed in the ambient environment as SOA, following heterogeneous or homogeneous sulfur dioxide (SO\({}_{2}\)) reactions, which is emitted in the ambient environment as a primary product of combustion processes. The influence of ambient SO\({}_{4}\)\({}^{2-}\) to internal concentrations (penetration from windows and doors) was clearly significant, as for all PM\({}_{10}\) samples, [PERSON]'s rho correlation coefficient between the indoor and outdoor SO\({}_{4}\)\({}^{2-}\) was found to be 0.66 (\(p\) = 0.00). This is in agreement with the study of [PERSON] et al. (2019), which highlighted that indoor sulfate mainly originated from external penetration rather than indoor sources.
## 2 Materials and Methods
### Hydrodynamic Model
#### 2.1.2 Numerical Method  
The integrated coupling model is implemented on the basis of the 2D hydrodynamic model TRITON. The TRITON model employs a numerical approach to solve the two-dimensional shallow water equation (Equation (1)) on a square Cartesian grid with grid spacing \(\Delta x\). This is accomplished using a finite-volume method with an upwind strategy. The grid cell interface fluxes are computed by employing an approximate Riemannian solver that incorporates an upgraded Roe method. The average approximate solution method of [PERSON] et al. [40], was employed to handle the flux and bottom slope source terms of each edge grid. This approach effectively prevents negative water depth values without the need to decrease the time step, thereby ensuring the accuracy of the solution. The friction source term employs local implicitness to enhance stability, while the CFL number is utilized to regulate the time step. The numerical method efficiently resolves the calculation instability and the nonconservation of material momentum resulting from unrealistic physical phenomena, such as negative water depth and artificially high flow velocity at the dry-wet interface of complex terrain. Additionally, it can effectively address flow issues under diverse conditions. Simultaneously, the model uses CUDA programming language to provide parallel computing technology, enabling the transition from a single CPU and GPU to numerous ones, hence achieving high-speed operation.
# Illuminant Adaptive Wideband Image Synthesis Using Separated Base-Detail Layer Fusion Maps
## 2 Related works
### Joint Bilateral filter  
In this study, a joint bilateral filter was used to detect luminance saturation. The bilateral filter, which is a low-pass filter with boundary-preservation characteristics, has excellent performance and is used in many fields. The bilateral filter uses two Gaussian functions for each distance difference and intensity difference from adjacent pixels [9]. The bilateral filter (B) is obtained as follows:  
\[\begin{split} B[I(i,j)]=\frac{1}{W_{p}}\sum\limits_{y=-hw}^{hw} \sum\limits_{x=-hw}^{hw}I(i+x,j+y)\\ g_{r}(\|I(i+x,j+y)-I(i)j\|)g_{d}(\|x,y\|)\\ W_{p}=\sum\limits_{y=-hw}^{hw}\sum\limits_{x=-hw}^{hw}g_{r}(\|I(i +x,j+y)-I(i,j)\|)g_{d}(\|x,y\|)\\ g_{r}(x)=\exp\left(\frac{-x^{2}}{2\sigma_{r}^{2}}\right)\\ g_{d}(x)=\exp\left(\frac{-x^{2}}{2\sigma_{d}^{2}}\right)\end{split} \tag{1}\]where \(I(i,j)\) is the input image, \(hw\) is the kernel size, \(g_{r}\) is a Gaussian function for boundary preservation that adjusts the blur weight by color difference with adjacent pixels, and \(g_{d}\) is a Gaussian function that adjusts the blur weight according to the distance difference from adjacent pixels in the spatial domain. In this case, the widths of the Gaussian functions \(g_{r}\) and \(g_{d}\) are determined by the standard deviations \(\sigma_{r}\) and \(\sigma_{d}\), respectively. \(W_{p}\) stands for the normalization term.  
However, the bilateral filter has limitations in denoising because it uses a single image. [PERSON] and [PERSON] et al. used an additional flash image with a strong signal-to-noise ratio (SNR) adjacent to the existing two Gaussian functions. A joint bilateral filter that applies a term to the displacement difference with a pixel has been proposed [10; 11; 12]. This joint bilateral filter receives two images and applies the filter to the image by adjusting the blurring weight using the boundary information of the remaining image in which the boundary information is relatively well-preserved [13]. The joint bilateral filter was obtained as follows:  
\[\begin{array}{c}JB[I(i,j)]=\frac{1}{W_{p}}\sum\limits_{y=-hw}^{hw}\sum \limits_{x=-hw}^{hw}I(i+x,j+y)\\ g_{r}(\|F(i+x,j+y)-F(i,j)\|)g_{d}(\|x,y\|)\\ W_{p}=\sum\limits_{y=-hw}^{hw}\sum\limits_{x=-hw}^{hw}g_{r}(\|F(i+x,j+y)-F(i,j )\|)g_{d}(\|x,y\|)\end{array} \tag{3}\]  
where \(g_{r}(\|F(i+x,j+y)-F(i,j)\|)\), which adjusts the blur weight according to the color difference between adjacent pixels, uses the intensity difference of image \(F\) instead of that of image \(I\), unlike the bilateral filter. Image \(F\) is an image in which the boundary information is relatively well-preserved.
# Fast Fault Line Selection Technology of Distribution Network Based on MCECA-CloFormer
## 5 Simulation Results and Discussion
### Analysis and Testing of Actual Waveform Recordings  
To further illustrate the feasibility of the method proposed in this paper, actual waveform data collected on-site were used to test the algorithm. Figure 11 shows a simplified topology diagram of the actual 10 kV distribution network test site, where the fault occurred in line L1. Figure 12 displays the zero-sequence current waveform exported from the waveform viewing software (CAAP2008X) and the fault image generated using the MAF-MDF method.  
From Figure 12, it is evident that the zero-sequence current signals acquired from actual measurement points contained noise, with varying levels of intensity across different lines. In particular, the signals collected by the measuring device from line L3 exhibited notably prominent noise. However, after the application of moving average denoising to both healthy and faulty feeder signals, the resulting MDF images still displayed significant differences. There was a high degree of similarity observed between MDF images of healthy feeders. Testing shows that the pre-trained MCECA-CloFormer model can accurately select fault lines.  
Figure 11: Topology of actual 10 kV distribution network test site.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data Missing Rate** & **Accuracy** \\ \hline
5\% & 99.67\% \\
10\% & 100\% \\
20\% & 99.00\% \\
30\% & 94.68\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Data missing test results.
## 4 Results and Discussions
### Cloudy, Cloud Shadow, and Haze Pixels Masks  
Using the Sentinel-2 MSI observations shown in Figure 6 and their corresponding synthetic images generated by PSRFM, we tested the cloudy, cloud shadow and haze pixel identification methods proposed in Section 2. For a comparison, we also applied the well-developed FMASK method [28] to the same images for clouds and cloud shadows detection. Figure 7 displays the six cloud contaminated images with their clouds and cloud shadow masks resulted from the two clouds and cloud shadow detection methods. The cloud and cloud shadow identification method proposed by this study is called image fusion cloud masking (IFCM). Here, cloud mask includes clouds, cloud shadows, and haze elements.  
It is evident, as shown in Figure 7, that the cloud, cloud shadow, and haze masks by IFCM matched well to the cloudy, cloud shadows, and haze pixels of the images. In comparison to the results of FMASK, IFCM looked better visually, especially for the thin clouds and haze detection. For example, IFCM masked the thin clouds in the images on 3-5 October 2018 (the last two rows) more accurately. The image on 3 October 2018 had both thin and thick clouds; IFCM identified both to a certain extent while FMASK only detected thick clouds. For the image on 5 October 2018, the FMASK mask showed more omissions while the IFCM mask more accurately reflected the reality.  
For the two images (the third and fourth rows) with the haze contamination, IFCM could identify the haze pixels to a certain extent, though some non-haze pixels were over identified. The over identified non-haze pixels were a result of compromise in determining the threshold value \(T_{h}\) for the haze detection. The bigger the threshold value \(T_{h}\), the less the identified haze pixels, including the over identified non-haze pixels. Unlike the clouds and cloud shadow pixel detection, the difference between a haze contaminated pixel and its corresponding synthetic pixel can be smaller than the top image fusion modelling bias or errors, so that the over identified non-haze pixels are not avoidable to keep the real haze pixels detected. As FMASK was not specially developed for haze detection, it over detected the haze pixels as cloud and cloud shadow pixels more considerably. From this point of view, IFCM was more flexible than FMASK for haze detection.  
Figure 6: Sentinel-2 observations with cloud and haze contamination acquired on (**a**) 16 May, (**b**) 15 June, (**c**) 30 July, (**d**) 21 August, (**e**) 3 October, and (**f**) 5 October 2018.  
Figure 7: The six clouds and haze contaminated Sentinel-2 images acquired on (**a**) 16 May, (**b**) 15 June, (**c**) 30 July, (**d**) 21 August, (**e**) 3 October, and (**f**) 5 October 2018, and their haze, cloud, and cloud shadow masks generated by the image fusion cloud masking (IFCM) and the FMASK methods respectively. White indicates clouds, dark grey shows cloud shadows, light grey represents haze, and the rest is cloud-free.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 4 Discussion
### Future Directions
#### 4.3.1 Fusion of Different Techniques and Technologies  
The combination of different techniques and technologies may provide flexible, powerful, and robust location systems in the future [102]. For example, the RSS and AoA estimations, together with the VLC technology, have been effectively combined for reducing the mean localization error in [129]. Additionally, in [120], the combination of the multilateration localization technique with the acoustic signals provided promising resultsin the 3D plane. Moreover, the CSI estimations can be utilized with the OFDM standard, as they are more stable in the different environments and can, thus, help maintain the system's performance over time [46].  
Although fingerprinting has been widely used in the literature, its usage is limited for indoors applications (i.e., [30; 41; 130; 14]). Therefore, there is great opportunity for applying fingerprinting outdoors, i.e., in an open distributed collaboration of many users for building or refining a location system, such as the \"OpenStreetMap\" [135]. In addition, the ambient geomagnetism field can be used for distinguishing between indoors and outdoors environments, as well as creating a geomagnetic map for outdoors, by using the compass, in combination with the smart device's accelerometer and gyroscope.  
Furthermore, BLE- and Bluetooth-based ad hoc networks that promote the collaboration of the different Bluetooth-equipped devices can be investigated in the future, as well as other packet parameters, apart from RSS measurements. For SLAM, a future direction can include the estimation of the starting point, which is currently a limitation. Wi-Fi is one of the most popular localization technologies today, with no additional software and hardware requirements. However, the Wi-Fi FTM (i.e., IEEE 802.11 mc amendment) is still not widely available; therefore, alternative amendments could be evaluated with time-based ranging estimations. The RFID is another promising technology with high localization accuracy [118]; therefore, smart devices' applications with RFID readers can be developed in the future. Similarly, the UWB is recently deployed on smartphones [103], so it can be used for indoor navigation.
## 4 Experiments and Results
### Parameter Sensitivity  
We attempt to identify the model parameters in our experiment to determine the optimal model. In this section, we demonstrate how different model parameters affect the resulting model performance.  
**Impact of the number of DeepCA layers.** The number of layers is a significant parameter in the DeepCA structure that directly affects the model performance. Each layer directly executes with all channels of the input image and the rule vector that aims to tune the data values for classification. Therefore, if the number of layers is too small, the data tuned by the model are insufficient for efficient classification. On the other hand, if the number of layers is too large, the data tuned by the model can be classified. However, this barely increases the classification accuracy, and the required computation time is too long. In this regard, we test the layer number parameter from 1 to 10. Figure 14 shows the impact of the number of layers on the BreakHis dataset in terms of classification accuracy. The appropriate number of layers chosen in the experiment is five based on the maximal achieved classification accuracy.  
**Impact of the size of the fully connected layer.** This parameter is related to the interconnection within the fully connected layer of each FCNN. Due to the performance of the FCNNs, even if the fully connected layers have small sizes, e.g., 16, they are sufficient for small classification tasks. However, larger fully connected layers tend to yield better classification performance. As shown in Figure 14, we vary the size of the fully connected layer to test the resulting classification performance. In this regard, we fix the number of DeepCA layers to five layers while keeping other parameters fixed in the FCNNs asfollows: the number of dataset training iterations is set to 30 epochs, the minibatch size is set to 1, the momentum is defined as 0.9, and the learning rate is set to 0.000001.  
From the experimental results, the fully connected layer size of 64 is chosen as the best choice for further use. However, we extend the experiment by combining several FCNN sizes to achieve better classification performance (see Table 2), as shown in the performance report of this paper.