esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 System Performance Optimization
### CSI-Based Localization for Wireless Transmitters
#### 7.2.3 Location Using DTW and SVM  
As mentioned above, DTW (dynamic time warping) is widely used for classification and clustering tasks in isolated word speech recognition, gesture recognition, data mining and information retrieval. DTW calculates the distance between two samples, and the shorter the distance is, the more similar the two samples will be. For example, [PERSON] et al. [5] used DTW techniques in keystroke classification and achieved an accuracy of 93.5%. [PERSON] et al. [8] used DTW to achieve 87% accuracy for mouth recognition. Thus, in this paper, DTW is used to locate the public APs. Besides, we build a classifier to locate the wireless transmitters based on their waveform shapes and the DTW distances, and our classifier adopts the SVM classification scheme, which allows all the positions to be differentiated based on the training dataset. Support vector machines (SVMs) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis [43]. It has been proven to be an effective method for sequence classification. For example, [PERSON] et al. has established a gait recognition system called WifiUthat uses LibSVM to classify the human and achieves an accuracy of 93.05% [44]. The system can also use other machine learning algorithms to do the localization.
# Improving Localization of Deep Inclusions in Time-Resolved Diffuse Optical Tomography
## 2 Methods
### Temporal Windows
#### 2.3.3 Generalized Gaussian Window  
The Gaussian function and its Fourier transform are  
\[w(t)=e^{-t^{2}/(2\sigma^{2})}\longleftrightarrow W(f)=\sigma\sqrt{2\pi}e^{-2 \sigma^{2}\pi^{2}f^{2}}. \tag{15}\]  
As expected from the uncertainty principle, the higher \(\sigma\), the more spread the window at time domain will be and, therefore, the narrower it will be in the frequency domain, see Figure 5. Note that the centralized Gaussian windows gives the lower bound of the uncertainty principle.  
The Laplacian or exponential window (see Figure 6) are defined as  
\[w(t)=\exp(-p|t|)\longleftrightarrow W(f)=\frac{2p}{p^{2}+(2\pi f)^{2}}, \tag{16}\]  
Figure 4: (**Left**) Fifth order (\(n=4\)) Mellin-Laplace window with different \(p\) values. (**Right**) Fourier transform of those windows; as the p value is increased, the magnitude of the Mellin-Laplace window spectrum is more spread.  
Figure 3: (**Left**) Different \(n\) orders of Mellin-Laplace windows for \(p=2\). (**Right**) Fourier transform of those windows; as the order \(n\) of the Mellin-Laplace moment is increased, its spectrum magnitude is narrower and has less dispersion.  
where \(p\) is a parameter.  
Both Gaussian and Laplacian windows can be described by the generalized Gaussian window (also known as the generalized normal window),  
\[w(t)=\frac{\beta}{2\sigma\Gamma(1/\beta)}e^{-(|t|/\sigma)^{\beta}} \longleftrightarrow W(f)=\Gamma^{-1}\left(\frac{1}{\beta}\right)\sum_{n=0}^{ \infty}\frac{(-1)^{n}\pi^{2n}f^{2n}}{(2n)!}\sigma^{2n}\Gamma\left(\frac{2n+1}{ \beta}\right). \tag{17}\]  
where \(\beta\) is a free parameter and \(\Gamma\) is the gamma function. If \(\beta=2\) the function is Gaussian and if \(\beta=1\) is Laplacian. As \(\beta\to\infty\) the window converges to a square window.
###### Abstract  
This paper proposes a review of the previous research work and the representative publications regarding the seismic behavior of the concrete-encased steel (CES) columns. Concrete-encased steel sections are composed of steel sections encased in reinforced concrete members. The research work recently showed increased attention to this type of column due to its advantages compared to conventional reinforced concrete columns. Firstly, the analytical studies of the behavior of the CES columns under axial loads, including comparative studies between different research works, are presented. Then, the behavior of the CES columns under combined axial and flexural loads is also highlighted. An overview of the analytical confinement material models is addressed. In addition, the discussion and summary of the seismic behavior of the CES columns and the important parameters affecting the seismic behavior of these types of columns are included. Although important progress has been made by the previous studies in the CES columns under the axial load and the combination of axial and seismic loads, they fundamentally focused on the building columns, and little attention was paid to the impact of lateral reinforcement and their configuration in bridge piers. Due to the lack of studies on the parameters affecting the seismic behavior of the bridges, more studies should still be made to better understand the behavior of the CES bridge piers. This paper provides a reference for the research and engineering practice of concrete-encased steel bridge piers. It also concludes with suggestions for future studies to integrate the seismic requirement of the CES bridge piers in Canada.  
bridge seismic behavior; concrete-encased steel pier; ductility; confined concrete; plastic hinge; failure mode
# Positron Emission Tomography in Cerebral Amyloid Angiopathy: A Scoping Review
###### Abstract  
Background/Objectives: Cerebral amyloid angiopathy (CAA) is one of the most prevalent small vessel diseases (SVDs). Its diagnostic criteria rely mainly on neuroimaging markers, in particular using Magnetic Resonance Imaging (MRI), as pathology-based diagnoses are only occasionally available. Amyloid PET is frequently used to assess parenchymal amyloid deposition in Alzheimer's disease (AD), but amyloid tracers are not specific to vascular and parenchymal amyloids. The aim of this scoping review is to assess the usefulness of amyloid PET imaging in CAA. Methods: A systematic literature search was performed, aiming to assess amyloid PET performance in the following situations: (I) CAA-related intracerebral hemorrhage (ICH) and convexal subarachnoid hemorrhage; (II) pathology-proven CAA; (III) CAA-related inflammation; (IV) hereditary CAA. Results: A total of 52 studies were retrieved, including three systematic reviews, and from these, a specific selection was taken according to each objective, confirming the diagnostic value of amyloid PET added to MRI and clinical information in all the selected situations, although with some limitations. Conclusions: Amyloid PET reliably detects increased global and region-specific amyloid deposition in CAA patients, with a characteristic occipital-predominant pattern. Continued advancements in tracer development and imaging methodologies are needed to increase specificity.  
c pacs: 11 February 2025  
Revised: 30 March 2025  
Accepted: 2 April 2025  
Published: 3 April 2025  
Cerebral amyloid angiopathy; CAA; amyloid PET; MRI; cortical superficial siderosis; microbleeds; WMHs; CAA-related inflammation; hereditary CAA
## 2 System Architecture
### Software
#### 2.2.2 Human-Robot Interface (HRI)  
The use of an HRI for robot applications is crucial for the robustness and operability of the robot, enabling the user to visualize the real-time data of the sensors and, at the same time, plan and execute autonomous behaviors easily. For the OPTIHROV [20] (Optically Linked Hybrid Autonomous/Remotely Operated Vehicle) project, a Unity-based HRI was developed. The main features of this HRI are listed below.  
* High-definition render pipeline project.
* ROS communication.
* Localization data 3D representation.
* Mission planning.
* Mission Execution.
* Camera parameter control.
* VLC modem parameter control.
* Compressed image visualization.
* Arm control.  
An exhaustive description of the HRI is outside of the scope of this article, However, in Section 5.2, a screenshot of the HRI and the compressed images that the operator can visualize are presented.  
Figure 1: Hardware and communications diagram.
## 3 The 6G Technology
### Sixth-Generation Requirements
#### 3.2.4 Seamless Integration  
The seamless integration with other networks and paradigms is one of the core requirements of the future 6G network. Users will leverage the ease of integration to switchbetween networks and coverage as they move from one point to another [77]. The 6G network will interconnect all terrestrial technologies, such as mm Wave, m-MIMO, and beamforming, and non-terrestrial technologies, such as satellites, UAVs, etc. together [15; 78].
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 2 Nanofluid
They compared the diffusivity of fluorescein dye droplets in deionized water and a mixture of nanofluids in water [41]. Now, the focus of the studies is on using nanoparticles to increase the rate of absorption, mass transfer, and carbon loading in nanofluids [42]; this way, the resistance against mass transfer between liquid and gas phases will be decreased. The application of nanofluid as CO\({}_{2}\) absorbent could reduce the cost of energy in CO\({}_{2}\) capture systems by the enhancement of absorption rate. As an example, one way of reducing the energy consumption and cost of a CO\({}_{2}\) capture plant is to utilize nanographene oxide (NGO) nanofluids in CO\({}_{2}\) capture and sequestration (CCS) recovery that benefits from the gas hydrate formation process [43]. The dispersion of nanoparticles in an absorption solvent can improve the absorption performance due to the high surface area of these particles that creates Brownian motion, micro convection, and shuffle effects. The small size of nanoparticles also increase the rate and capacity of CO\({}_{2}\) absorption compared to the same particles with larger sizes [44]. However, if the concentration of nanoparticles exceeds the optimal point, nanoparticles will be agglomerated and prevent gas absorption into the liquid phase. Several gas absorption investigations were carried on using nanofluids while an external magnetic field was also applied to the gas absorption in a column [45]. In this respect, the convective mass transfer under the influence of nanoparticle dispersion in the fluid was also investigated in several liquid-gas absorption systems such as a tray and packed absorption column, wetted-wall column (WWC), gas-liquid hollow fiber membrane contactor, liquid-liquid extraction (LLE), gas-spared stirred absorption vessel, bubble-type column absorber, and three-phase airlift reactor [46].
# Multi-Source Information Fusion Diagnosis Method for Aero Engine
## 3 Case Study
### Information Fusion Diagnosis Analysis
#### 3.3.2 Information Fusion Based on D-S Evidence Theory  
Based on the basic principle of D-S evidence theory, the gas path and vibration information are fused. The actual diagnosis requires the membership function for the initial basic probability assignment. The construction method of the membership function varies depending on the situation. According to [23], a normal-type function is adopted in this paper considering the requirement for a high sensitivity in sensor fault diagnosis.  
\[m(\mathrm{A})=\mathrm{e}^{-(\frac{x-\mu}{\mu})^{2}} \tag{6}\]  
where \(\mu\) represents the mean value, \(\sigma\) represents the standard deviation, and \(Z\) represents the vibration amplitude signal or gas path degradation results. The mean value of \(m(\mathrm{A})\) of the gas path system is set as \(-0.05\), the same as the probability distribution in the Bayesian network. The mean value of \(m(\mathrm{A})\) of the vibration system is also set to 220 \(\upmu\)m.  
The diagnostic results of different systems and fusion diagnoses are shown in Figure 8. For single-source diagnosis, the gas path fault probability exhibits significant fluctuations during the early stage. Due to the fan flow degradation occurring before engine shutdown, gas path monitoring is able to detect the fault in advance, achieving a fault probability of up to 95%. In contrast, the vibration-based diagnosis shows a substantial delay: no alarm is triggered before shutdown, and only when the vibration amplitude exceeds 200 does the fault probability rise sharply, reaching a maximum of 97%.  
Bayesian network-based fusion methods, calculated following the model in [29; 30], achieve a diagnostic confidence of 87.2% without prior knowledge and 91.2% when prior knowledge is incorporated. However, Bayesian network diagnosis relies heavily on the availability and quality of prior information; under insufficient prior conditions, the fault confidence rises later and exhibits fluctuations near the decision threshold, indicating limited robustness. In contrast, D-S evidence theory effectively integrates gas path and vibration fault features without prior knowledge, achieving a higher fault confidence of 99.6%. Moreover, D-S fusion retains the early warning capability of gas path parameters while significantly suppressing diagnostic volatility during normal operation. Compared to Bayesian network, the D-S-based method demonstrates better stability, faster fault probability growth, and a higher final confidence level, making it more suitable for practical aero engine fault diagnosis under uncertain and variable conditions.
# Low-Cost Dual-Frequency GNSS Receivers and Antennas for Surveying in Urban Areas
## 3 Results and Discussion
### RTK Positioning Performance  
Low-cost dual-frequency GNSS receivers were tested in RTK in both open-sky and urban areas. Firstly, the RMSE for easting, northing, and ellipsoid height was estimated, then the horizontal and spatial RMSE were obtained for all stations by considering 24 sessions (Table 8). From the obtained results, it can be concluded that the horizontal and spatial RMSE in the open sky (ST 1) is less than 1 cm. Similar results were obtained for other stations (ST 2, ST 3, and ST 4) in urban areas, where the horizontal and spatial RMSE does not exceed 2 cm. To evaluate the quality of positioning of low-cost GNSS receivers in RTK mode, the positioning accuracy was estimated as the absolute difference in horizontal (d\({}_{\text{2D}}\)), vertical (\(\lambda\)h), and spatial absolute (d\({}_{\text{3D}}\)) positions from the true position (Figure 11).  
At ST 1, in the open sky, a horizontal positioning accuracy of 2 cm is achieved in 96% of the sessions and a spatial positioning accuracy of 2 cm in 75% of the sessions. At other stations (ST 2, ST 3, and ST 4) located in urban areas with obstacles to satellite signals, the horizontal positioning accuracy of 2 cm is achieved in 100%, 75%, and 62.5% of the sessions for ST 2, ST 3, and ST 4, respectively. The spatial accuracy is less than 2 cm in 54.2% of the sessions for ST 2 and ST 3, while for ST 4 this stands true only in 37.5% of the sessions. In addition to the statistics presented, it can be highlighted that the spatial accuracy of low-cost GNSS receivers under open-sky conditions is less than 3 cm in all sessions, while in the case of urban conditions, an accuracy of 3 cm is achieved in 95% of the sessions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Station** & **e (mm)** & **n (mm)** & **h (mm)** & **d\({}_{\text{2D}}\) (mm)** & **d\({}_{\text{3D}}\) (mm)** \\ \hline ST1 & 3.4 & 5.3 & 5.1 & 6.4 & 8.1 \\ ST2 & 6.5 & 6.5 & 13.5 & 9.2 & 16.3 \\ ST3 & 7.2 & 6.7 & 7.3 & 9.8 & 12.2 \\ ST4 & 6.0 & 8.4 & 10.0 & 10.3 & 14.4 \\ \hline \hline \end{tabular}
\end{table}
Table 8: RTK positioning precision in the open-sky (ST 1) and urban areas (ST 2, ST 3, and ST 4).  
Figure 10: Horizontal, vertical, and spatial positioning accuracy in the open-sky (ST 1) and urban areas (ST 2, ST 3, ST 4): (**a**) LC\(-\)1 at ST 1; (**b**) LC\(-\)1 at ST 2; (**c**) LC\(-\)1 at ST 3; and (**d**) LC\(-\)1 at ST 4.
# Unequal Interval Dynamic Traffic Flow Prediction with Singular Point Detection
## 7 Conclusions  
As information and communication technologies advance signal processing, it becomes crucial to utilize real-time and historical traffic information for predicting traffic flow while balancing prediction accuracy and computing time. In this paper, we analyzed the relationship between singular points and the intrinsic properties of traffic flow. By identifying the location of singular points, we divided the continuous traffic flow process into different stable statuses for traffic prediction. Our proposed method employed an improved multiscale wavelet transform to detect and predict singular points for both real-time and historical data. We then utilized the Vasicek model with unequal time slots to meet the accuracy requirements. Furthermore, the proposed method dynamically adjusted the prediction interval using the multiscale wavelet, achieving a balance between prediction accuracy and computing time. The reproduction interval was determined based on the traffic condition and its intrinsic property, rather than relying on a fixed prediction interval set by the management system. The simulation results demonstrated the effectiveness of the proposed method. Compared with four related algorithms, our method achieved a significant reduction in computing time with only a marginal sacrifice in prediction accuracy. Moreover, the majority of singular points were successfully detected. The proposed method considered the prediction accuracy and computing cost comprehensively on the rapid dynamic traffic flow status, which had the application prospect in the realistic transportation scenario. However, the present work has limitations: (1) the spatial relation among jointed road segments is seldom discussed, and (2) the impact factor only considered the singular point. In future works, we aim to extend our approach from a single road segment to the entire road network and explore a multiscale spatial division and consider more impact factors for the prediction performance, i.e., the signalized and unsignalized junctions on a road segment.  
Conceptualization and supervision, D.L.; methodology, formal analysis, validation, funding acquisition, and writing--original draft preparation, C.G.; formal analysis and writing--review and editing, X.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant No. 6 2201350 and No. 71171045, the Shanghai Sailing Program under Grant No. 21 YF1432800, and the National Key Research and Development Program of China under Grant No. 2022 YFB4501704.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
We acknowledge the support given by the Research Base of Online Education for Shanghai Middle and Primary Schools (Shanghai) and the education big data and education decision laboratory (China).  
The authors declare no conflict of interest.  
Figure 10: Traffic flow prediction performance comparison.
## 5 Results  
The working parameters for the PMT&T board, which include the preamplifier's gain and the attenuation values for the input signal conditioning circuit, are reported in Table 3.  
The flight model of the HEPD-02 detector successfully used these values during the environmental tests and the beam tests.  
As an example of the acquisition made by the Flight Model of the HEPD-02, with the proposed parameters, Figure 10 shows the ADC distribution produced by all the scintillators of the calorimeter, during a beam test with 228 MeV protons. For each scintillator, only one of the two PMTs has been taken into account.  
The path of the protons can be followed by observing the peak of the ADC distributions: in TR1, most of the particles passed in the second segment (TR1\(2\)1), while the peaks in nearby scintillators (TR1\(1\)1 and TR1\(3\)1) are less pronounced, and almost no signal is produced in TR1\(4\)2 and TR1\(5\)2.  
In TR2, the central slabs are mainly affected by the beam particles, as can be seen by the peak distribution of TR2_2 and TR2\(3\)2.  
The presence of the peaks in all the RAN scintillators indicates that the energy of the particle is sufficiently high to pass through the whole detector.  
The distributions from the last two segmented planes of LYSO crystal show that the particles exit mainly through EN1\(1\)1 and EN1\(2\)1 and EN2\(2\)1.  
Figure 9: (a) S-curve acquired with a signal of 40 mV, 50 ns at 1 kHz, (b) threshold DAC values (THR) as a function of the input signal (\(V_{thr}\)). The green line represents the linear fit of points in the descending region of the curve.
## 2 SRPC Compensation Principle
### Active Power Balancing  
The first step to achieving power quality improvement is to balance the active power between sections, considering that the section \(x\) load is higher than the section \(y\) load. Consequently, phase \(A\) current magnitude, which corresponds to section \(x\) catenary, will be higher than phase \(B\) current magnitude, as shown in Figure 2a. According to Equation (4), phase \(C\) current has the highest value in that case. By assuming negligible power losses in the SRPC power switches, SRPC system should shift half of the load currents difference from the heavily to the lightly loaded section, as in Equation (5) [26]. As a result, SRPC system partially contributes to feeding active power required by traction loads. The worst-case scenario for the SRPC to balance active power between sections is when one of the load sections without load (no locomotives supplied by catenary section \(x\) or catenary section \(y\)) [27].  
\[\Delta I\ =\frac{1}{2}(I_{Lx}-I_{Ly}) \tag{5}\]  
Figure 2: Diagrams of the three-phase power grid currents: (**a**) When both catenary sections are unequally loaded without compensation; (**b**) When balancing the active power between sections.  
After shifting the active power difference, the three-phase power grid currents are established as in (6). Accordingly and as shown in Figure 2b, phase \(A\) and phase \(B\) currents now have the same magnitude, but phase \(C\) current still has the highest magnitude \(\left(I_{C1}=\sqrt{3}\ I_{A1}=\sqrt{3}\ I_{B1}\right)\). However, phase \(C\) current is in phase with its phase voltage \(\dot{U}_{C}\), while the other two phases currents of \(\dot{I}_{A1}\) and \(\dot{I}_{B1}\) are shifted \(30^{\circ}\) angle with the corresponded phase voltages of \(\dot{U}_{A}\) and \(\dot{U}_{B}\), since the reactive power is not yet compensated [26; 27].  
\[\begin{array}{l}\dot{I}_{A1}=\dot{I}_{A}-\frac{\Delta I}{K_{V}}\ e^{-\beta 30^ {\circ}}=\frac{1}{2K_{V}}(I_{Lx}+I_{Ly})\ e^{-\beta 30^{\circ}}\\ \dot{I}_{B1}=\dot{I}_{B}+\frac{\Delta I}{K_{V}}\ e^{-\beta 90^{\circ}}=\frac{1}{2K_{V}} (I_{Lx}+I_{Ly})\ e^{-\beta 90^{\circ}}\\ \dot{I}_{C1}\ =\ -\dot{I}_{A1}-\dot{I}_{B1}\end{array} \tag{6}\]