esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Statistical Physics Analysis (Monolayer Model)  
The monolayer model [60] can be considered as the general case of Langmuir model. It is applied to describe an entire set of experimental data. The monolayer model with single energy assumes that a site can accept \"n\" number of molecules. The equation of this model is:  
\[\mathrm{q_{e}}=\frac{n\cdot N_{M}}{1+\left(\frac{C_{1/2}}{C_{e}}\right)^{n}} \tag{10}\]  
where, n is the number of molecules fixed per adsorbent surface, C\({}_{1/2}\) (mg L\({}^{-1}\)) is the concentration at half saturation, N\({}_{M}\) (mg g\({}^{-1}\)) is the receptor site density effectively occupied, \(\mathrm{q_{e}}\) (mg g\({}^{-1}\)) and C\({}_{e}\) (mg L\({}^{-1}\)) are the MB adsorption capacity and concentration, respectively, at equilibrium time.  
According to the study reported by [PERSON] et al. [62], if the number of dye molecules is lower than 1.0, it indicates the multi-anchorage adsorption process, which reflects a parallel position of the dye molecule adsorbed on the surface. This means that the dye molecules can simultaneously interact with several functional groups present on the adsorbent surface. On the contrary, if n value is higher than 1.0, adsorption can be considered as a multi-molecular process in which the molecules can interact in vertical position with the functional groups of the beads surface.  
In this study, n parameter took a value of 1.62. Hence, the adsorption process can be considered as a multi-molecular process, assuming a vertical geometry of the dye molecule in the adsorption process. Based on this model, it is possible to characterize the interactions between the MB molecules and the beads surface by the adsorption energy determination. The energetic parameter can be calculated from:  
\[\Delta E=R\cdot T\cdot In\left(\frac{C_{s}}{C_{1/2}}\right) \tag{11}\]  
where, C\({}_{s}\) (mg L\({}^{-1}\)) is the solubility of MB in water and C\({}_{1/2}\) (mg L\({}^{-1}\)) is the half saturation concentration determined by the fitting method.  
The value of the energy of adsorption was found of 19.1 kJ mol\({}^{-1}\), suggesting that the adsorption is of endothermic and physical nature. The same result was obtained by [PERSON] et al. [62] for the adsorption of MB onto an agricultural Algerian olive cake waste.
## 3 Results
### Machine Learning Modelling
MRI shows a lesion in the right palatine tonsil, characterized by a homogeneous and moderate signal intensity on the T2-weighted image and a hyperintense signal on DWI obtained with \(b=800\) s/mm\({}^{2}\). Consistently, both ADC and D maps (the latter was cropped in the upper right yellow box) indicate strongly decreased values in the user-defined lesion contour (yellow line), with 50 th percentiles of 0.87 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s and 0.76 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s, respectively (**a**). Bottom: the same patient had a pathological lymph node at the right IIa level, showing a homogeneous signal intensity on the T2-weighted image, a mildly hyperintense signal on the DWI obtained with \(b=800\) s/mm\({}^{2}\), and intermediate and uniform values on the ADC map (yellow line), with an Inverse Difference Moment of ADC\({}_{\rm LN}\) equal to 0.46 (**b**). Figure 5: HPV-negative OPSCC in a 72-year-old non-smoker non-alcoholic male. MRI shows a lesion into the base of the tongue with floor of the mouth involvement, characterized by a hyperintense signal  
on both T2-weighted image and DWI obtained with \(b\) = 800 s/mm\({}^{2}\). ADC and D maps (the latter was cropped in the upper right yellow box) indicate slightly decreased values in the user-defined primary tumor contour (yellow line), with 50 th percentiles of 1.52 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s and 1.29 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s, respectively. The pathological lymph node into the IIa level (green line) shows a quite homogeneous signal intensity on the T2-weighted image and rather inhomogeneous signal intensity on DWI, consistent with a low Inverse Difference Moment of ADC\({}_{\text{LN}}\) of 0.28 (**a**). These patients were correctly classified as HPV-negative by imaging-based or combined models (Model 1-Model 6) but incorrectly classified as HPV-positive by the clinical model (Model 7), smoke and drinking habits being peculiar to a patient affected by an HPV-related malignancy. Bottom: HPV-positive OPSCC in a 67-year-old heavy smoker and alcoholic male. MRI shows a lesion into the base of the tongue, with left palatine tonsil involvement, characterized by a quite homogeneous and moderate hyperintense signal on T2-weighted image and DWI (**b**). ADC and D maps indicate moderately decreased values in the user-defined primary tumor contour (yellow line), with the 50 th percentile equal to 1.33 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s and 1.12 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s, respectively. This patient was misclassified by all the proposed models, having smoke/drinking habits and ADC/D characteristics more indicative of an HPV-negative tumor.
## 3 Public Transport-Assisted Data-Dissemination System
### Multi-Attribute Decision Making
#### 3.1.1 Initialization Step  
The initialization step is the first-most step of the MADM process, which gathers the required information and triggers the process. In this step, there are the following options to consider:  
* Service's Requirement: The most important aspect is the user's requirements. For different users, they have different demands and objectives. In our proposed system, we categorize users' requirements into three categories, such as Service 1, Service 2, and Service 3. Different services have different levels of sensitivity to the same networking attribute. For example, considering bandwidth as an attribute, if its service 1, a lower bandwidth will be used. However, if it is a large data transfer, a higher bandwidth will be used. In addition to that, it is assumed that a user can select any one service at one time. Users can select the priority of services used. They can select the urgency or non-urgency of data delivery, which relates to the data type, such as delay-tolerant or delay-sensitive, and helps the controller to make optimal network-selection decisions;
* Data Type: Data types belong to the type of application selected by users. It can be delay-tolerant or delay-sensitive. Some of the services, such as video or data type, can be categorized as a real-time or non-real-time application and can, accordingly, be delayed for some time. This is another important piece of information to consider for optimal network selection;
* Network Alternatives: In our proposed work, we are demonstrating the offloading of data from traditional networks to road networks with delay-tolerant conditions. Therefore, to choose among a list of networks, we will be considering WLAN, UMTS, and Vehicular Networks. The controller will choose the best optimal network among these networks based upon user requirements and data type. Three of these networks have different properties. The vehicular network is used for all delay-tolerant applications, such as emails, data backup, video download, and photos, which significantly contribute to energy efficiency without a negative effect on user satisfaction. We assume that all vehicles are equipped with On-Board Units (OBU) to carry data. If we compare the other two networks, WLAN networks are managed for higher bandwidths and lower delay applications, although UMTS networks are the most energy-efficient with lower bandwidth requirements and large delays.  
Next, considering all the requirements in the process of network selection, we integrate utility theory with the AHP process to design our network-selection algorithm, as shown in Figure 4. We consider the characteristics of different types of services and their respective weights to define utility functions and the scores of a user's preferences by defining rank preference through AHP. Therefore, we are providing a comprehensive structure for users to give their preference, which the controller can use to make decisions based upon their requirements.  
Figure 3: MADM for network selection.
## 4 Experimental Design and Results
### Findings and Analysis from the Experiments
#### 4.3.2 ROC Curve  
The receiver operating characteristic (ROC) curve is a widely employed tool in machine learning and statistics for assessing the performance of classification models. It is constructed by plotting the true positive rate (\(TPR\)) on the \(y\)-axis against the false positive rate (\(FPR\)) on the \(x\)-axis [37].  
To generate the ROC curve, the classification model is applied to predict the samples within the test set, obtaining either predicted probabilities or decision scores for each sample. These scores are then utilized to arrange the samples in descending order. Starting from the lowest threshold, all samples are initially labeled as the negative class, and the TPR and FPR are calculated accordingly.  
\[TPR=\frac{TP}{TP+FN} \tag{11}\]  
\[FPR=\frac{FP}{FP+TN} \tag{12}\]  
In this context, \(TP\) refers to the count of true positives, which represents the instances correctly predicted as the positive class and which are actually positive. \(FN\) corresponds to the count of false negatives, indicating the instances that are incorrectly predicted as the negative class but are actually positive. \(FP\) represents the count of false positives, signifying the instances erroneously predicted as the positive class but which are actually negative. Lastly, \(TN\) denotes the count of true negatives, which indicates the instances correctly predicted as the negative class and which are actually negative. By gradually decreasing the threshold, the steps are repeated, and the \(TPR\) and \(FPR\) are calculated for different thresholds. All computed \(TPR\) and \(FPR\) values are plotted to form the ROC curve.  
When the model can perfectly distinguish between positive and negative classes, the curve will pass through the points (0,0) and (1,1), forming a straight line with a unit slope. When the model cannot distinguish between positive and negative classes, the ROC curve will approach the diagonal line. By observing the ROC curve, we can choose an appropriate threshold based on the specific requirements to balance the true positive rate and false positive rate. In general, the larger the area under the curve (AUC), the better the model performance. The Area Under the Curve (AUC) varies between 0.5 and 1, with 0.5 representing a model's performance equivalent to random guessing, and 1 indicating flawless classification by the model.  
Figure 9: Swin Transformer’s accuracy curve.  
We have generated the ROC curves for each class, as depicted in Figure 10. The graphs clearly demonstrate that the classification performance for each class is exceptional, indicating an outstanding model performance.
## 1 Introduction
applied the EEG-based BMI to their exoskeleton robot and a gait training platform to confirm the effectiveness of the long-term training [26]. Although the BMI intention recognition method is being studied intensively, it is not yet reliable and suitable for powered exoskeletons, which require 100% detection reliability. Malfunctions of the exoskeleton robot due to incorrect detection may add a significant risk to user safety [10]. Therefore, these methods are not yet suitable for the UI of a robot for complete paraplegics for mobile use; a new UI system that makes the user reliably interact with the robot with minimal distraction is necessary. In this paper, a new UI of the WalkON Suit, a powered exoskeleton for complete paraplegics, developed by Angel Robotics Co. Ltd. (formerly SG Robotics Co. Ltd., Singapore), is introduced. The new UI consists of see-through display (STD) glasses, as well as an OLED display and two tact switches installed on the handle of a crutch. The STD glasses are usually utilized for augmented-reality applications [27; 28], but are utilized as a head-up display to transfer necessary information from the robot to the user in this paper. The wearer of the WalkON Suit can check the overall operation state, such as the foot displacement, the trunk inclination, the current gait mode, the elapsed time and the total step counts through the STD glasses, as well as through the OLED display of the crutch. The proposed UI system improves the usability of the WalkON Suit, such that the wearer's field of view is widened and the training period with the robot is shortened, resulting in improvement of the overall safety and walking speed. The WalkON Suit with the proposed UI system was utilized for a complete paraplegic to carry the torch of the Pyeongchang Paralympics 2018, as shown in Figure 1. In addition to the new UI, the WalkON Suit revealed through the Pyeongchang Paralympics was different from the previous version introduced at the Cybathlon 2016 [29] in many aspects. In particular, the design of the WalkON Suit has been improved aesthetically while fulfilling the role of a torch bearer. In this paper, the proposed UI system of the WalkON Suit and its verification by a human subject are introduced. This paper is organized as follows. Section 2 introduces the mechanical and electrical configuration of the WalkON Suit briefly. Section 3 explains the motivation and the realization method of the proposed UI system. The experimental results are provided and discussed in Section 4. Section 5 concludes this paper and proposes future works.
## 3 Results and Discussion
### RAMAN Analysis  
Characterization using Raman spectroscopy is used for the determination of vibrational modes. Based on Figure 7, for pure TiO\({}_{2}\), it can be seen that the peak spectrum of 148.06 cm\({}^{-1}\) is very clear. Additionally, there are peaks at 399, 520, and 639 cm\({}^{-1}\). The data generated are in accordance with the results of previous studies [30], which showed one sharp peak and three other peaks that follow. The types of vibrational modes identified via Raman spectra are shown in Table 2.  
The authors of [30] reported the vibrational modes of the wave numbers seen in Table 2.  
In Figure 7, we can see the structural similarity of the dye-soaked TiO\({}_{2}\) substrate (DN-F05 and DN-F05-Ag) with pure TiO\({}_{2}\). There is a peak at 592.09 cm\({}^{-1}\) indicating COO deformation. This is possible because the concentration of dye DN-F05 is too large, which causes the dye's aggregation. Dye DN-F05 raises the peak to above 1000 cm\({}^{-1}\). Unexpectedly, the Raman spectra of TiO\({}_{2}\):DN-F05:Ag have no effect on the change in the shape of the graph. However, the most interesting phenomenon from the spectra is the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Wave Number (cm\({}^{-1}\))** & **Vibration Mode** \\ \hline
148 & O-Ti-O symmetrical stretching vibration \\
405 & Symmetrical buckling of O-Ti-O \\
520 & Asymmetric buckling of O-Ti-O \\
642 & O-Ti-O symmetrical stretching vibration \\ \hline Source: [PERSON] et al. [30]. \\ \end{tabular}
\end{table}
Table 2: Raman TiO\({}_{2}\) vibration mode.  
Figure 7: Raman spectra of TiO\({}_{2}\), TiO\({}_{2}\):DN-F05, and TiO\({}_{2}\):DN-F05:Ag.  
decrease in the intensity of the peak at 1000 cm\({}^{-1}\) following the immersion of TiO\({}_{2}\):DN-F05-Ag. This proves the interaction between Ag and TiO\({}_{2}\) after immersion, which influences the effect of Raman resonance for TiO\({}_{2}\)[29]. In addition, the Ag weakens the peak, and the sample presents a local lattice defect [28]. Therefore, high addition of Ag will decrease efficiency because it blocks the absorption of photons.
# Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans
## 3 Training Terrain Traversability
### Supervised Learning  
Different classification algorithms from the Python library _Scikit-learn_[24] have been chosen to predict 3D point traversability using the above set of spatial features. This machine learning library was designed to operate with the numerical and scientific libraries of Python _NumPy_ and _SciPy_, respectively [24].  
Taking into account that it is not necessary to employ complex classification methods to extract ground accurately from 3D Lidar scans [13], seven relevant supervised learning techniques have been selected for training: Decision Trees (DT), Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Linear Support Vector Machine (LSVM), Bagged Decision Trees (BDT), Random Forest (RF) and Gradient Boosted Trees (GBT). The last three are ensemble methods that combine various base estimators.  
Ten of the fifteen generated 3D point clouds are dedicated exclusively to the training process. This error-free synthetic data contains a total of 743,346 points, where 721,616 comply with the minimum neighbourhood restriction. The training data is unbalanced because about 70% of points belong to the traversable class. This happens mainly because most of the laser points are acquired from the ground near Andabata.  
Table 1 shows the training times required by each estimator tuned with its default options. The most time-demanding methods are LSVM and GBT, whereas the less demanding methods are GNB and KNN. It is remarkable the big gap of 136 s between the best and the worst times. Nevertheless, these times are not critical for navigation because training is only performed once off-line.
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 3 Sensor Localization
### Time-Based  
The estimation of the distance between two nodes is known as time of arrival or time of flight and can be calculated by precisely measuring the propagation time of the signal. Considering the high value of this speed compared to the metric distances, especially indoors, it is hard to achieve extreme precision and consistency because it is hard to have nodes perfectly synchronized and also because common devices are built with low-cost components.  
An interesting approach still based on time is the is known as time difference of arrival (TDoA). In this case, the emitter node transmits two signals that have different speeds, namely a radio frequency signal and an ultrasound pulse, as represented in Figure 2. Receivers infer the distance from the differences between the instant of ultrasound signal arrival \(T_{2}\) and that of radio signal arrival \(T_{1}\).  
The big advantage of the TDoA method over simple measurements of time of arrival is the lack of synchronization requirements since, in this case, it does not need to be absolute.
## 4 Results
### Spatiotemporal Distribution of the Estimated MDA8 in the NCP  
Figure 4 indicates the results of the spatial distributions of the seasonal averaged and annual averaged MDA8 in the NCP. Among these four seasons for the averaged MDA8, as shown in Figure 4a-d, O\({}_{3}\) pollution was highest in summer, followed by spring, then autumn, and the O\({}_{3}\) pollution was the lowest in winter. This is mainly due to the fact that during summer, the ultraviolet radiation is the strongest and the temperature is the highest, which is suitable for the photochemical reaction for the generation of ozone [46; 47; 48]. From the perspective of the spatial pattern of O\({}_{3}\) pollution, the most serious O\({}_{3}\) pollution area is in the border area of Hebei, Shandong, and Henan Provinces, that is, the southern part of Hebei, the northern part of Henan, and the western part of Shandong. This is due to the dense population of these areas [15; 49; 50], the high level of human activities [51; 52], and the resulting high anthropogenic emissions, while the strong ultraviolet radiation and high temperature in summer [46; 47; 48] greatly enhances the photochemical reaction level of O\({}_{3}\), and the ozone pollution level is also the most serious. Furthermore, it was observed that the concentrations of O\({}_{3}\) pollution were comparatively reduced during the spring season when analyzed against those prevalent in the summer period. This is because the temperature in spring is lower than that in summer, while the intensity of ultraviolet radiation in spring is weaker than that in summer. The levels of O\({}_{3}\) pollution in winter and autumn were the lowest among the four seasons due to the lowest temperatures and UV radiation intensity in winter and autumn, which affected the photochemical reaction of ozone. For the annual averaged MDA8, Figure 4e-g shows the spatial distribution patterns and temporal variations in the annual averaged MDA8. The serious O\({}_{3}\) pollution is mainly concentrated in the border area of the NCP, that is, the southeast of Hebei, the north of Henan, and the northeast and west of Shandong, which is consistent with the results shown in Figure 4b. In terms of the temporal variations of annual average MDA8, O\({}_{3}\) pollution  
Figure 3: Comparison of the relative importance of explanatory variables to the surface ozone estimation model in the NCP. (**a**) denotes the results derived from the surface ozone estimation model using S5P NO\({}_{2}\) and CAMS HCHO while (**b**) indicates the results for the model using CAMS NO\({}_{2}\) and CAMS HCHO.  
in the NCP showed a downward trend from 2019 to 2021, which may be related to the reduction in emissions caused by the impact of COVID-19 in 2020 [53; 54].
# High Resolution Brillouin Sensing of Micro-Scale Structures
## 1 Introduction
Therefore, mm- and sub-mm spatial resolution have been achieved using BOCDA [46; 47; 48; 45], which opened up a new paradigm by bringing SBS distributed measurement to much shorter and more compact devices such as on-chip waveguides [46; 48; 49] and micro-structures [50]. Therefore, the functionality of the distributed Brillouin measurement in micro-scale structures has changed from the more common temperature and strain monitoring in optical fibres to mapping the uniformity of these devices. In this paper, we review Brillouin sensing techniques for micro-scale waveguides in the form of (a) non-distributed backward scattering to highlight the effects of waveguide dimensions--which can reach the sub-wavelength limit--on the overall opto-acoustic responses, (b) non-distributed forward scattering, which enables core diameter measurement, and (c) distributed backward scattering, which enables detection of fine longitudinal features along the waveguides with cm- and mm-length scales. Although there are already some insightful reviews in the field of Brillouin-based fibre sensors [51; 52; 53; 54] and in general fibre optic-based distributed sensing [55; 56], the motivation of this work is to provide a more focused review of Brillouin-based micro-structure and on-chip waveguides monitoring. The structure of this paper is as follows: The second section discusses the basic SBS principle and the dependence of the opto-acoustic response on environmental variables. We then discuss various distributed Brillouin measurement techniques with an emphasis on the spatial resolution evolution in the time domain and correlation domain and provide deeper details and the state-of-the-art of correlation domain Brillouin sensing in Sections 3 and 4, respectively. In Section 5, we discuss opto-acoustic interactions in micro-structures and short-length scale devices. Finally, we provide an outlook for mm and sub-mm spatial resolution Brillouin sensing based on the recent advancements in the field.