esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Radiation Dose Implications in Obese Abdominal Computed Tomography Imaging
## 4 Discussion
However, noise values were only reduced by 25-30% due to the inversely proportional relationship to the square root of change of the tube current. For instance, a 100% increase of the tube current will improve the image noise by approximately 40% [24]. Regardless of the reduced radiation dose, in this study, it was still unknown if such a difference was clinically detectable or significant, especially when IR was used for abdominal CT studies for obese patients when compared to FBP, even with low-dose scans. Our findings showed up to a 40% dose reduction with a comparable noise level for the obese condition when IR was used for the protocol at 100 kVp compared to the protocols using 120 kVp and FBP for the matched tube current values. These findings are similar to both clinical and phantom studies, which have reported image noise reductions with IR techniques for CT scanning of obese patients [25, 26, 27]. Additionally, it has been shown in a prior study by [PERSON] et al. that different iterative reconstruction techniques can achieve comparable noise levels with up to nine-fold reductions in radiation dose [1, 28]. Indeed, IR application in this study has shown its usefulness in improving image quality with constant dosage or for dose optimization with constant image quality. The limitations of this study stem from the subjective evaluation of image quality in our phantom, which was not possible owing to the absence of abdominal organs (Figure 4). Another limitation is the use of noise only as an image quality metric, particularly with IR algorithms and the subsequent noise texture consideration. Additionally, the experiments were carried out on a CT scanner from a single manufacturer. Image quality differences among CT studies due to the use of different manufacturer protocols is anticipated owing to the variations in delivered dose, image reconstruction, iterative reconstruction types, and exposure parameter modulation methods [29, 30]. Oursimulation of obese individuals was limited due to difficulties in covering all obesity classifications. The animal fat used in our simulation was a useful and accurate surrogate for subcutaneous fat and is useful for optimization studies. The advantage of using such fat tissue is its affordable price and easy manipulation using the layering approach as compared to other phantom simulation approaches, such as using a fat ring of epoxy resin material, whereby covering the whole abdomen and pelvis is technically unfeasible due to the curved nature of the phantom at the lower abdominal level [31].
## 3 Results and Discussion
### Comparison of Vibration Frequency Structure Using Third-Octave Filtering  
To facilitate a more detailed comparison of the vibration structures induced by two different sources, an analysis was conducted using third-octave filtering. The resulting effect, in the form of a comparison of histograms of maximum vibration velocities for the center frequencies of each third-octave band, is presented in Figures 12 and 13.  
Figure 11: Comparison of the number of events in various ranges of horizontal vector values for vibrations induced by blasting works in the open-pit mine and events induced by underground mine activities.  
Figure 10: Presentation of the horizontal vector values for events induced by mining tremors in the underground mine.  
As evident from the figures, the vibration structure induced by blasting works is dominated by frequencies ranging from 5.01 Hz to 7.98 Hz, while the range of dominant frequencies in the structure of vibrations induced by underground mine tremors is broader, ranging from 3.98 Hz to 10.00 Hz. It is also apparent that the maximum vibration velocities (PPVs) for dominant frequencies are nearly twice as high in the case of events.
# Effect of Subject-Specific Region of Interest on Motor Imagery Brain-Computer Interface
###### Abstract
To overcome the above-mentioned problems, various techniques, such as the time-frequency domain, have been introduced to transform non-stationary signals into stationary ones [22,23]. [PERSON] et al. [22] described time-frequency energy representations using the intrinsic time-scale decomposition (ITD) method. Proper rotation components in the time-scale domain were provided. Furthermore, the trade-offs between time information and frequency information were balanced. [PERSON] et al. [24] investigated dyadic time segmentation and adaptive selection of frequency domain features with equal bandwidth for the classification of EEG signals corresponding to left or right hand movement imagery for BCI tasks. However, these methods were found to be complex for online BCI studies. [PERSON] et al. [25] used multiple time segments via a band-pass filter on each time segment. The feature from multiple time segments of each sample was extracted using a CSP spatial filter and then combined. However, the problem of this study is that different subjects have different optimal time segments. According to the literature review, none of these techniques have studied the features of time-frequency techniques. Moreover, the investigation was conducted for offline studies, and the accuracy still needs improvement. Furthermore, no studies were found on signal decomposition techniques to extract subjects' ERD and ERS based on intrinsic time-scale decomposition features. The aim of our paper is to examine the effect of subject-specific regions of interest on the discrimination of four classes (both feet, both hands, right hand and left hand) of motor imagery using time-frequency techniques. The parameters of ERD and ERS were investigated and optimized using the intrinsic time-scale decomposition method. The intrinsic time-scale decomposition method was validated using the discreet wavelet transform. Furthermore, comparisons with other existing methods from the literature were also performed. This study is a continuation of our previous work [26,27], which successfully studied the discrimination of four classes of simple limb motor imagery movements for brain-computer interfaces and the comparison of EEG signal decomposition using time-frequency methods. Hence, by utilizing a subject-specific region of interest (ROI), BCI accuracies could be improved.
# Improvements in Rock Mass Description for Stope Design by Geophysical and Geochemical Methods
## 5 Ground-Penetrating Radar (GPR) for Representation of Fracture Surface Geometry  
Stoping in mining operations usually requires prior exploration drilling from the production tunnels. Exploration drilling typically produces good-quality holes that are also suitable for geophysical measurements. Traditionally, the suite of downhole geophysical methods has been limited to properties such as density, magnetic susceptibility, and resistivity, with the addition of downhole imaging methods. In this project, focus was given to downhole ground-penetrating radar (GPR), which has so far rarely been used in mining despite being widely utilized in subsurface imaging. The goal was to combine GPR with other methods in order to determine the location, size, and orientation of geological features such as major fractures.
## 3 Results
### Correlation of \(Re\) Changes and Other Measures  
Table 4 shows the [PERSON]'s correlation coefficients between individual relative RE changes %\(RE\) at the three different running speeds \(v_{1}\), \(v_{2}\), and \(v_{3}\), and additionally between individual %\(RE\) (averaged across the individual speeds \(v_{1}\), \(v_{2}\), and \(v_{3}\)), individual body mass, mean individual running speed, and individual VO\({}_{2,\text{peak}}\). A significant correlation between %\(RE\) at \(v_{1}\) and \(v_{2}\) was found only for the SAU footwear condition (ECT: \(r=0.558\), \(p=0.018\)). For speeds \(v_{2}\) vs. \(v_{3}\), %\(RE\) correlated significantly for the MIZ (ECT: \(r=0.631\), \(p=0.006\)), HOK (ECT: \(r=0.575\), \(p=0.014\)), and PUM (ECT: \(r=0.472\), \(p=0.05\)) footwear conditions. There were no significant correlations between %\(RE\) for speeds \(v_{1}\) vs. \(v_{3}\). As for the other parameters, %\(RE\) showed a small negative correlation with body mass in terms of ECT (\(r=-0.262\), \(p=0.049\)) and with VO\({}_{2,\text{peak}}\) (ECT: \(r=-0.311\), \(p=0.022\)) only for HOK. For all other CPRSs, no significant correlations were observed in this respect.  
Figure 6: Relative changes in energy cost of transport (ECT) at each running speed. Connected dots represent individual runners. \(v_{1}\) = speed 1, \(v_{2}\) = speed 2, and \(v_{3}\) = speed 3. HOK = Hoka Rocket \(\uplambda\), MIZ = Mizuno Wave Rebellion Pro, PUM = Puma Fast-R Nitro Elite, and SAU = Saucony Endorphin Pro 3.
## 1 Introduction and Motivation
### Motion Prediction-Based Decision Making Algorithms  
The proposed algorithm uses pre-recorded data to find and identify similarities for modeling the motion of the vehicles. [PERSON] et al. [3] presents a method to predict the motion of the human-driven vehicles using a data-driven approach, and [PERSON] et al. [4] present a motion prediction for longitudinal and lateral directions. The pre-recorded dataset consists of the positions, velocities and acceleration of the participating vehicles. Using this data, the algorithm orders the measurements into clusters. Each cluster describes a specific behavior of human drivers. Using the data of each cluster, density functions can be fitted. During the evaluation of a given traffic situation, the probabilities are computed to given road segments. The goal is to guarantee that the probability of collisions in the given positions is smaller than the previously defined maximum. Using the computed density functions, collision-free trajectories are can be defined. These results are used during the trajectory planning. The planned trajectory must guarantee safe cruising and comfort requirements at the same time. The generation of the trajectory can be made by a model predictive approach, where constraints can be taken into account. Finally, the trajectory is tracked by a robust Linear Parameter-Varying (LPV) controller.
# Developing Machine Learning-Based Models for Railway Inspection
## 4 Deep Learning-Based Methods
### Time Series Classification  
A time series is a sequence of data points (measurements) which has a natural temporal ordering. The goal of time series classification is to derive a label to an interval of data points over time based on its behavior. The acceleration data used in this study is a type of time series data as shown in Figure 3. Detecting joints from the acceleration data can be fulfilled through the time series classification approach [25].  
Conventional time series classification approaches can be grouped under two main types: similarity-based and feature-based methods [26]. Similarity-based approaches are based on similarity measurements--e.g., Euclidean distance--such that the testing samples are assigned with class labels of the corresponding training samples where their similarity measurements are minimized. 1-nearest neighbor (1-NN) and dynamic time warping (DTW) are two widely used methods of this category. Feature-based methods transfer the time series instances to a feature space where a set of features are analyzed to represent the patterns of the time series instances. Classification systems run on these features to label the time series instances. Both of these two approaches conduct heavy computations to assess similarity measures for the similarity-based approach and feature extractions for the feature-based approach.  
Recently some works have demonstrated deep neural networks, especially convolutional neural networks (CNN), for end-to-end time series classification [27; 28]. Unlike the traditional feature-based classification framework, CNN does not require handcrafted features. Both features and classifier are learned jointly in one model. There are three basic components to define a basic CNN: (1) the convolutional layer, (2) the pooling layer, and (3) the output layer. Interested readers please refer to [29] for a more detailed introduction to CNN. A convolution layer applies a sliding a filter over the time series [17]. The filter is a generic non-linear transformation of a time series. The output of a convolution layer (one filter) on an input time series can be considered as another time series that underwent a filtering process to obtain discriminative features useful for classification tasks. When applying several filters on an input time series, multiple discriminative features are learned automatically. The pooling operation aggregates the time series over the entire time dimension resulting in a single real value. Usually a global aggregation is useful to reduce the number of parameters in a model thus decreasing the risk of overfitting. The final discriminative layer takes the features from the convolution and pooling layers and generates a probability distribution over the class labels for the input time series. In this paper, the state-of-the-art technology of the CNN is investigated to build an end-to-end time series classification model to detect joints from acceleration data.
## 1 Introduction
To minimize the overall system losses, it is critical to ensure that the reflected light is within the acceptance angle of the fiber at the output port requiring that the 3D MEMSs are used in pairs. The first 3D MEMS reflects the light at an angle toward the second unit, which corrects that angle to align the beam orthogonal to the output port fiber. A schematic representation of this configuration is presented in Figure 2. Although the control electronic of such an analog MEMS is more complex than for a digital system, the advantage of that configuration is that a non-blocking configuration for \(N\) ports can be obtained with only \(2N\) mirrors. This allows for building a switch with a much larger number of ports [11; 12]. The speed at which the mirrors can be repositioned in a reflection-based optical switch is in the order of a few milliseconds. This speed depends on the mirror mass \(m\): the larger the mirror, the more time it takes to re-orient it. This can be understood by comparing the mirror to an harmonic oscillator whose frequency \(\omega\) is given by \(\omega\propto\sqrt{k/m}\), with \(k\) the spring constant. In this configuration, the mass of the mirror is proportional to its area which is dictated by the beam diameter. Figure 1: Schematic representation of a two-dimensional micro-electro-mechanical system (2D MEMS). The 3D MEMS mirrors have a diameter of about 500 microns. Smaller beam diameter and mirror sizes could be achieved with faster optics (shorter focal length), but that requires larger angular amplitude from the mirror. In these conditions, the hinge mechanism is subject to higher mechanical stress and has an increased failure rate. In the 3D MEMS design, the system imposes a trade-off between the mirror's angular amplitude and its mass, which translates into a trade-off between the number of ports accessible by the switch and its reconfiguration speed [13]. It is worth noting that the mirror size is also limited by the energy density of the beam, and the damage threshold of the mirror coating. Breaking the millisecond reconfiguration speed limit requires the introduction of another paradigm: instead of using reflection from a mirror, it is possible to use diffraction from several elements.
## 2 Related Works
[26] discussed securing the sensitive data in Caesar cipher by converting the plaintext data into ciphertext, which boosted the security high by revisiting the shift cipher method. The experimentation results revealed that it improved the security performance in decrypting the data. On the other hand, decrypted data was hacked easily. [PERSON] et al. [27] developed a method to reduce the consumption of time while using the android application to access information. The developed android application acquired various details of the users while registering the license number and the required information was shared through the web service. As a result, it improved the bandwidth while sharing the information with the user. Meanwhile, it paved the way to hack information easily. Detailed literature work presented in the Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author and Year** & **Technique** & **Objective** & **Pros** & **Cons** \\ \hline [PERSON] et al. (2021) [11] & Sub-cluster head (SCH) & \begin{tabular}{c} Improving resource \\ constraints in IoT devices \\ \end{tabular} & \begin{tabular}{c} Increased throughput \\ and energy consumption \\ \end{tabular} & Low-speed applications \\ \hline [PERSON] et al. (2021) [12] & Geographic multipath routing & Eliminate hidden node & Improved performance & \\  & routing & problem & and network lifetime & Cost rate was high \\ \hline [PERSON] et al. (2019) [13] & Energy-effective multipath routing (EMR) & \begin{tabular}{c} Decrease energy \\ consumption while \\ communicating \\ \end{tabular} & \begin{tabular}{c} High performance, \\ minimum energy \\ consumption \\ \end{tabular} & Not suited for mobile \\ \hline [PERSON] et al. (2019) [14] & \begin{tabular}{c} Firefly load balancing \\ based energy optimized \\ routing (FLB-EOR) \\ \end{tabular} & \begin{tabular}{c} Minimum weightage for \\ multimedia data \\ transmission \\ \end{tabular} & \begin{tabular}{c} Enhanced LBE and \\ throughput \\ \end{tabular} & Energy consumption \\ \hline [PERSON] et al. (2018) [15] & Gaussian distribution framework & Multipath routing for delay node optimization & \
\begin{tabular}{c} Improve packet delay, \\ data distribution, power \\ consumption, and \\ network life \\ \end{tabular} & Transmission delay \\ \hline [PERSON] et al. (2019) [16] & Evolutionary game-based routing (EGR) & Reduce data redundancy &
\begin{tabular}{c} Enhanced network \\ performance and \\ energy efficiency \\ \end{tabular} & Failed to apply in \\  & real time \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literary works of various authors.
# Quality of Experience Experimentation Prediction Framework Through Programmable Network Management
## 5 Discussion and Data Analysis
### Machine Learning Classification  
Furthermore, a data analysing process had to happen on two different levels after obtaining raw data from the virtual test bed and the vMOS from the users. QoE scoring factors and network data had to go through data cleaning and normalisation. Then, the features stated before were placed through a training process against the MOS and vice versa to make a collection of classification predictions and use a neural network to compare and contrast them. All features were trained with fine tree, medium tree, coarse tree, kernel naive Bayes, linear SVM, quadratic SVM, cubic SVM, fine Gaussian SVM, medium gaussian SVM, fine KNN, medium KNN, coarse KNN, cosine KNN, cubic KNN, weighted KNN, boosted trees, bagged trees, subspace discriminant, subspace KNN and RUSBoosted trees. With no over-trained attempts, which model is best fitted for the data and the feature classified [22] by comparing and contrasting. Figure 9 shows an example of the data analysis process.  
In Table 8, all classification methods mentioned above are used to classify the five features and predict their outcome if a new stream of data is inserted. The table shows the percentage of the predicted class against true class. From these models, the researcher selected the models with the highest accuracy along with the fastest prediction time. Bagged trees was selected for bit rate downloading and MOS and fine KNN for the buffer length, the dropped frames and the resolution. Table 9 shows the most ideal machine learning classification prediction methods on highly noisy data, such as our generated network data and MOS user feedback technique proposed in our previous paper [22]. It is important here to discuss the prediction speed along with the training time for the prediction of MOS. The training time, prediction speed and misclassification cost columns are representations of the different machine learning classifications to predict MOS. The highest accuracy using  
Figure 8: Results of vMOS experimentation trials.  
Figure 7: Results of resolution network feature experimentation trials.
## 7 Results and Analysis
### Performance Under Varied Network Conditions  
The results in Table 19 summarize system performance across varying network scenarios, while Figure 19 illustrates the latency distribution under bursty traffic, highlighting the comparative advantages of the proposed LSTM scheduler.  
Figure 19: Latency distributions under bursty traffic with O-RAN timing constraints. The LSTM scheduler maintains 95% of inferences below the 5 ms URLLC threshold (red dashed), while staying fully within the O-RAN Near-RT window (gray region: 10 ms to 1 s). The green curve corresponds to the rule-based scheduler, which shows significantly lower compliance with low-latency requirements compared to the LSTM and PID controllers.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **CSS** & **Latency Impact** \\ \hline
50 ms Token Refresh & 91.2 & +5.1\% \\ \hline
200 ms Token Refresh & 86.7 & +3.8\% \\ \hline Baseline (100 ms) & 89.7 & +4.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 18: Security–performance trade-off analysis [65].
# Economic and Environmental Impact of Energy Saving in Healthcare Buildings
## 3 Results
### Optimization of Electric Energy Installations  
The electric installation in a healthcare building is of major importance, since these buildings play a vital role in public health service [17]. [PERSON] et al. (2009) valued that the annual average consumption of electricity in a hospital with less than 300 beds was 8.88 kWh, and 10.04 kWh for a hospital with more than 300 [18]. [PERSON] et al. (2011) evaluated that the annual energy consumption in a healthcare centre in Spain--in normal operational conditions and functioning--was 90 kWh/m\({}^{2}\) for this type of building and 150 kWh/m\({}^{2}\) for hospitals [19].  
With the objective of determining the positive economic and environmental impacts, thanks to optimizing electric energy installations, the annual average saving and investment for the whole set of buildings was quantified for a three-year period.  
Figure 1 shows the potential mean energy saving per built surface area (m\({}^{2}\)) through the optimization of the electric installations in healthcare buildings with less than 5000 m\({}^{2}\) (no beds) and hospitals larger than 5000 m\({}^{2}\) (with beds). An annual average energy saving of 0.45 kWh and 0.55 kWh per built surface area (m\({}^{2}\)), with a payback lower than 3.5 years, was achieved, respectively.  
Figure 1: Potential energy saving derived from optimizing the electric energy installations.  
The specific energy saving measures applied were the following: installation of power capacitors for reactive power compensation, improvement of the electricity contract conditions, and appropriate choice of electric power.
# Travel Time Prediction on Long-Distance Road Segments in Thailand
###### Abstract  
This study proposes a method by which to predict the travel time of vehicles on long-distance road segments in Thailand. We adopted the Self-Attention Long Short-Term Memory (SA-LSTM) model with a Butterworth low-pass filter to predict the travel time on each road segment using historical data from the Global Positioning System (GPS) tracking of trucks in Thailand. As a result, our prediction method gave a Mean Absolute Error (MAE) of 12.15 min per 100 km, whereas the MAE of the baseline was 27.12 min. As we can estimate the travel time of vehicles with a lower error, our method is an effective way to shape a data-driven smart city in terms of predictive mobility.  
GPS data analytics; machine learning; smart mobility; travel time prediction +
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