esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In long-haul WDM (wavelength division multiplexing) optical communication systems utilizing the DP-16 QAM modulation scheme, traditional methods for removing chaos have exhibited poor performance, resulting in a high bit error rate of \(10^{-2}\) between the original signal and the removed chaos signal. To address this issue, we propose DeepChaos+, a machine learning-based approach for chaos removal in WDM transmission systems. Our framework comprises two key points: (1) DeepChaos+ automatically generates a dataset that accurately reflects the features of the original signals in the communication system, which eliminates the need for time-consuming data simulation, streamlining the process significantly; (2) it allows for the training of a lightweight model that provides fast prediction times while maintaining high accuracy. This allows for both efficient and reliable signal reconstruction. Through extensive experiments, we demonstrate that DeepChaos+ achieves accurate reconstruction of the original signal with a significantly reduced bit error rate of approximately \(10^{-5}\). Additionally, DeepChaos+ exhibits high efficiency in terms of processing time, facilitating fast and reliable signal reconstruction. Our results underscore the effectiveness of DeepChaos+ in removing chaos from WDM transmission systems. By enhancing the reliability and efficiency of chaotic secure channels in optical fiber communication systems, DeepChaos+ holds the potential to improve data transmission in high-speed networks.  
Keywords:wavelength division multiplexing; optical fiber communication; machine learning; variational auto encoder; knowledge distillation +
Footnote †: journal: _Photonics_
## 2 Related Work
### Tree-Based Data Indexing Methods  
With the advent of the information age, the tremendous amount of data has posed challenges to data management. Efficient data retrieval and management become crucial when dealing with large-scale data [17].  
Tree-based indexes, as a commonly used data structure, are widely applied in data management to achieve fast data retrieval and queries. As early as 1984, [PERSON] et al. [18] proposed the concept of R-trees, which is an extension of B+-trees and better solves the storage and retrieval problems of data. Based on this, [PERSON] et al. [19] improved the R-tree and proposed the R+-tree structure, effectively avoiding the overlap problem of intermediate nodes in the R-tree and improving the efficiency of spatial data retrieval. Due to the success of the R-tree, many variants have emerged subsequently, such as the R*-tree (1990) [20], parallel R-tree (1992) [21], Hilbert R-tree (1993) [22], and priority R-tree (PR-tree) (2008) [23].  
The quadtree [24] is another data structure used to represent and manage two-dimensional space, recursively dividing the data into four quadrants, each of which can be further subdivided to achieve a finer partition of space. Similar to the quadtree, the KD-tree [25] achieves fast nearest neighbor searches by dividing space into hyper-rectangular regions parallel to the coordinate axes.  
While a single index structure can effectively solve the problem of storing massive data, the application of composite indexes is more widespread in complex scenarios. To address the challenge of managing large-scale real-time trajectory data, [PERSON] et al. [26] proposed the HBSTR-tree index structure. By grouping continuous trajectory points into nodes and combining the spatial-temporal R-tree, B*-tree, and hash table methods, efficient management and queries of trajectory data are achieved. [PERSON] et al. [27] proposed a composite indexing method to address the problem of multidimensional queries in HBase. By dividing multidimensional space into grids and using z-order curves and pyramid techniques to generate GridID, efficient multidimensional range queries for floating-point datasets are achieved.
## 2 Materials and Methods
### Study Definition and Data Analysis  
The efficacy and feasibility of the use of SERs were assessed by the technical success rate, procedure time, number of times the endoscopy lens was cleaned, and presence of any mucosal injuries after SER removal, in the cadaveric specimens. Technical success was defined as successful TMF elevation under endoscopic guidance. The procedure time was measured from the insertion of the endoscope into the EAC (with or without the SER) to its removal after completion of the TMF elevation. The procedural feasibility and proximal end location of the SER were analyzed using endoscopic images.
## 3 Proposed Approach
### Real-Time Detection
#### 3.1.1 Finger Position Detection  
Among the various methods that may be used for finger detection, the use of the Xbox-one Kinect sensor is a popular approach. However, as shown in Figure 2, the Kinect skeleton detection only detects the skeleton of the hands, and not the position of the fingers. Furthermore, the Kinect confused the position of the left hand with the guitar's neck. This method was also tested by [PERSON] and [PERSON] [14], who observed the same issues. Due to these drawbacks, the Mediapipe framework was used instead [28]. This framework offers the blaze-palm detector and a hand landmark model. Blaze-palm is responsible for detecting the palm and the hand skeleton itself, similarly to the Kinect's functionality. Meanwhile, the hand landmark model (see Figure 3) segments each finger and retrieves the positions of the finger joints (i.e., 21 positions) in \(x,y,z\) format, which are called landmarks. Each landmark has an ID, referring to the positions extracted. For example, the IDs 5, 6, 7, and 8 refer to the positions of the index finger's joints.
###### Abstract  
This study focuses on the numerical modeling of one-dimensional consolidation of filtered copper tailings under saturated and unsaturated conditions using Plaxis2D(r). The modeling is based on 1D consolidation curves under saturated conditions and controlled matric suction. The Soft Soil and Hardening Soil constitutive models were employed, incorporating the soil-water characteristic curve (SWCC) to capture hydro-mechanical behavior and volumetric strain. The modeling strategy replicates the loading and drainage conditions of the experimental tests, using a single set of soil parameters to represent the transition from unsaturated to saturated conditions. Axisymmetric simulations were conducted to replicate the geometry of the experimental specimen, considering initial saturation degrees of 60%, 80%, and 99%, followed by the application of vertical stresses ranging from 10 to 3200 kPa. The results demonstrate that the proposed modeling approach accurately represents the compressibility curve for both constitutive models across different saturation levels. This provides flexibility for conducting numerical analyses tailored to specific soil conditions and geotechnical design requirements. Furthermore, modeling allowed for the evaluation of excess pore pressures and their dissipation time under both saturated and unsaturated conditions.  
one-dimensional consolidation; soft soil; hardening soil; Plaxis; SWCC; pore pressure Article
# Investigation of Visual Stimulus Signals Using Hue Change for SSVEP
## 2 Materials and Methods
### Preparation of Experiment  
The flash stimulus was altered using a pair of colors with hue differences of 5\({}^{\circ}\) to 45\({}^{\circ}\), based on the three primary colors of red (0\({}^{\circ}\)), blue (120\({}^{\circ}\)), and green (240\({}^{\circ}\)), to determine a signal with the highest SSVEP recognition accuracy and no discomfort. As described above, the visual stimulus consisted of nine signals at 30, 20, 15, 12, 10, 7.5, 6, 5, and 4 Hz displayed as a set.  
Two parts of the occipital region (O1 and O2) were used for EEG measurements. The application attached to Emotiv Epoc+ adjusted the contact condition of all the electrodes to the best (green) condition in order to optimally mount the electrode in contact with the scalp. This allowed for optimum EEG data to be obtained. Emotiv Epoc+ EEG data were transmitted to the PC via Bluetooth at a sampling rate of 256 Hz.  
To acquire the EEG data, the subject sits in front of the display showing the visual stimuli shown in Figure 3 and moves the gaze point from a visual stimulus of 30 to 4 Hz based on the examiner's cue. The EEG data were collected in 10 trials.  
The CCA-based analysis program, developed using MATLAB, extracted frequencies from the EEG data and calculated the percentage of correct answers.  
From the results identified from the EEG data, we compared the effect of the rate of correct answers and fatigue before and after the experiment among experimental participants. In accordance with the main purpose of the Declaration of Helsinki, the subjects were informed about the experiment in writing and orally in advance, and their signatures of consent were obtained. Six subjects (five males and one female, all in their twenties) participated in this experimental study, and EGG data were obtained from them. The subjects were all healthy, with either normal or corrected eyesight and normal color discrimination.
## 5 Research on the Frequency-Domain Characteristics of AE Signals
### Discussion on AE Signal Distribution and Peak Stress Precursor  
The peak frequency of the AE signal is a characteristic parameter of the signal in the frequency domain. It is defined as the peak value observed in the power spectrum. The frequency centroid is also a frequency feature calculated, in kilohertz, as the sum of magnitude times frequency divided by the sum of magnitude, as equivalent to the first moment of inertia. As shown in Figure 5, the amplitude represents the maximum amplitude of the signal waveform, which is usually expressed in dB.  
In addition to using AE parameters to predict fracture precursors, this section studies the failure mechanism and precursory characteristics of critical failure by statistical analysis of the distribution of peak frequency, frequency centroid, and amplitude in the deformation and failure process of granite samples.
# How Should the Transition from Underwater to Surface Swimming Be Performed by Competitive Swimmers?
## 1 Introduction
Although these kinematic parameters have been examined in the mid-pool swimming or in the underwater undulatory kicking phases, no information about their role in the transition from underwater to surface swimming has been explored as yet. For example, it is unknown whether great depth could be beneficial during transition given that it would require greater body angles (i.e., higher active drag) to reach water surface in a relatively restricted distance. Therefore, the aim of the present study was to examine the role of the segmental, kinematic and coordinative parameters on the swimming velocity during the pre-transition and transition phases of the push start. Considering the positive influence of underwater on the subsequent surface swimming, it could be expected that swimmers would decrease the time gaps between the propulsive arm actions, would minimize body inclinations and would maximize USS velocity for faster transitioning velocity.
## 2 Background
### Weather Data with RPA  
The proper storage and structuring of weather data are crucial for efficient capture and cleaning using the RPA protocol. To capture weather data effectively, it is essential to select reliable and reputable data sources. The RPA protocol should be configured to extract data at intervals that match the update frequency of the data source, ensuring that the information is both timely and relevant. Additionally, it is vital to ensure that the weather data accurately reflect the specific geographical area of interest.  
When cleaning the captured data, handling missing or incomplete information is a primary concern. Techniques such as inte\"polation can be employed to estimate missing values, while statistical methods help to identify and eliminate outliers. Formatting issues, including varying time zones and units of measurement, should be standardized within the RPA protocol to ensure consistency. To avoid redundancy, it is important to deduplicaterecords by maintaining unique identifiers for each data point. Normalization is also critical, involving the conversion of weather measurements into standardized units and the unification of categorical classifications across different sources.  
For handling weather data over time, applying rolling averages can smooth out short-term fluctuations and provide more meaningful insights. Historical data should be managed carefully to account for potential biases introduced by changes in weather observation methods over time. Real-time validation techniques, such as cross-source verification, can help ensure the accuracy of the captured data, while sanity checks prevent the recording of unrealistic values.  
In this research, data extracted from websites have been cleaned and formatted using the tools available in the RPA tool UiPath, such as [92] the data scrapping wizard for data structured in tables or lists, string manipulation activities (e.g., splitting through breaking down string based on specific delimiters like commas or spaces), and data table manipulation, filtering out unnecessary or invalid rows or columns based on certain conditions.  
Finally, in web-based automation, one of the key activities is monitoring the architecture of websites. This is important because changes made by the website owner can disrupt the bot's ability to extract and process data correctly. In such cases, the RPA tool sends an error message to the automation developer, notifying them that the bot needs to be updated to align with the website's new architecture.
# Onboard Centralized ISL-Building Planning for LEO Satellite Constellation Networks
## 4 Link-Building Planning
### Link-Building Planning Method Based on Topology Stability Optimization
In our simulation, when \(N=32\) and \(K=32\), the onboard computer took less than one second to complete the computation.
# Agreement Technologies for Coordination in Smart Cities
## 3 Applications
### Emergency Medical Service Coordination
#### 3.3.3 Experimental Results
As shown in the results, the proposed dynamic reassignment and redeployment methods clearly improve the efficiency of an EMA service in terms of reducing response time. However, the approaches, particularly dynamic redeployment, introduce an extra cost. Since the mechanism is based on an almost continuous repositioning of idle ambulances, the travel distances of the ambulances increase. Considering the 10 days, the average distance each ambulance has to cover each day in the SUMMA model is 95.48 km, whereas it is 299.97 km for the DRARD approach. That is, ambulances have to travel about three times the distance because of frequent location changes. It is a political decision whether this extra effort is acceptable in order to improve the quality of service. In any case, compared to augmenting the number of ambulances in order to reduce response times, the DRARD approach \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Day/\#Patients** & **1/221** & **2/152** & **3/199** & **4/124** & **5/137** \\ \hline SUMMA & 11:42 & 11:52 & 11:03 & 11:23 & 11:50 \\ DRARD & 9:46 & 9:50 & 9:29 & 9:39 & 10:09 \\ Improvement \% & 16.6 & 15.7 & 12.8 & 9.3 & 14.0 \\ \hline
**Day/\#Patients** & **6/175** & **7/96** & **8/160** & **9/144** & **10/201** \\ \hline SUMMA & 12:30 & 12:51 & 12:42 & 10:11 & 11:49 \\ DRARD & 10.51 & 9:48 & 10:05 & 9:05 & 10:08 \\ Improvement \% & 13.1 & 23.7 & 20.7 & 10.8 & 14.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average arrival times in minutes for 10 different days. Figure 9: Comparison of arrival times to patients. Here, the 1609 patients from the 10 analysed days are ordered with respect to the arrival time in each curve. appears to be a less costly alternative. In this sense, we have executed the DRARD method. Further, fewer ambulances achieve roughly the same average arrival time as that obtained in the SUMMA model. That is, with 29 ambulances in the SUMMA model, the same average arrival time can be obtained with 21 ambulances in the DRARD approach.
# Development and Application of Unesaled borehole Leakage Detection Device Based on Flow Method
## 3 Results and Discussions
### Air Leakage Disposal Technique  
#### 3.2.1 Principle of Air Leakage Disposal Technique  
When using the air leakage detection device described above to detect a test borehole, it is necessary to provide secondary sealing to the borehole if there are air leaks inside. Currently, most approaches for addressing air leakage around boreholes are primarily based on the \"three plugging and two injection\" technique. This approach involves pre-judging potential air leakage locations based on \"two plugging and one injection\", while reserving a bag in advance. During drilling and mining operations, the bag can be activated to perform secondary sealing if the concentration of seam gas extracted decreases. This approach has the disadvantage that air leakage disposal cannot achieve precise positioning and higher sealing costs [37; 38]. In view of the shortcomings, this paper proposes the \(\Phi\)10 mm sealing capsule bag secondary sealing method, which directly sends the secondary sealing capsular bag to the front end of the air leakage position. This method achieves precise disposal of the leakage position and reduces the cost of sealing boreholes.  
The specific usage is as follows. Firstly, the conventional \"two plugging and one injection\" sealing method is used to perform the primary sealing to the extraction borehole, and observe the changes in the flow rate and concentration; if there is an increase in the flow rate and a decrease in the concentration, the borehole is tentatively judged to be leaking. Then, use the air leakage detection device to detect the leakage, as shown in Figure 1, after accurately determining the leakage position; a 10 mm diameter extraction pipe with a capsular bag of sealing holes is passed through the \(\Phi\)50 mm extraction pipe commonly used in the borehole to the front end of the leakage position for secondary grouting and  
Figure 6: Air–gas mixture flow from 1#–6# boreholes.  
sealing. After secondary sealing, the diameter of the extraction pipe was changed from the original 50 mm to 10 mm. The principle of the secondary sealing of a \(\Phi\)10 mm sealing capsular bag is shown in Figure 7.
# Simulation Modelling of Silicon Gated Field Emitter Based Electronic Circuits
###### Abstract  
Vacuum transistors (VTs) are promising candidates in electronics due to their fast response and ability to function in harsh environments. In this study, several oscillator and logic gate circuit simulations using VTs are demonstrated. Silicon-gated field emitter arrays (Si-GFEAs) with 1000 \(\times\) 1000 arrays were used experimentally to create a VT model. First, transfer and output characteristics sweeps were measured, and based on those data, an LTSpice vacuum transistor (VT) model was developed. Then, the model was used to develop Wein and Ring oscillator circuits. The circuits were analytically simulated using LTSpice, where the collector bias voltage was 200 V DC, and the gate bias voltage was 30-40 V DC. The Wein oscillator circuit produced a frequency of 102 kHz with a magnitude of 26 Vpp. The Ring oscillator produced a frequency of 1.14 MHz with a magnitude of 4 Vpp. Furthermore, two logic circuits, NOR and NAND gates, were also demonstrated using LTSpice modeling. These simulation results illustrate the feasibility of integrating VTs into functional integrated circuits and provide a design approach for future on-chip vacuum transistors applied in logic or radio-frequency (RF) devices.  
field emitter arrays; vacuum transistors; oscillator circuits; logic circuits +
Footnote †: journal: Applied sciences
# Central and Periodic Multi-Scale Discrete Radon Transforms
## 7 Example of Periodic DRT Application  
Periodic DRT has a similar complexity to central DRT and adds the benefit of having a fast inversion algorithm. It would not have been of use in the previous demonstration because of the periodicity it recreates at image domain. However, another field that suits its properties has been found.  
An invertible, discrete Curvelet transform, running exclusively in integer arithmetics, was introduced in [38]. Inside Curvelet transform, conventional DRT was used at the Radon level. In the conclusions of that work, it was remarked that the computation times should be reduced, so that the important benefits of using Curvelets as a focus estimator in the shape-from-focus technique could be really useful.  
It is the case that conventional DRT can be safely replaced by periodic DRT, as the founding transform of the discrete, first generation Curvelet transform, without affecting the quality of inversion.  
With the very slight modifications suggested in Equations (7) and (8), the measured times show a speed-up of 1.35\(\times\) for the forward transform and 1.85\(\times\) for the inverse transform when using periodic over the conventional DRT pair. The measurement has been done in an implementation written in Python3, with the aid of Cython [39] to include a \(C\) implementation for those parts that take the most of the computation time. The Wavelet transform has been implemented using the library PyWavelets, available in [40]. The results are indistinguishable to those obtained when using conventional DRT as founding block of Curvelet. An example of reconstruction, after hard thresholding in the Curvelet domain, is shown in Figure 21. Notice that the original image, taken from the Press article [16], has a lot of compression artifacts that are efficiently removed with Curvelet thresholding. A lesser divergence has been found in the degree of smoothness, which seems bigger for the same factor of thresholding when using a periodic, instead of conventional, DRT. However, by simply readjusting this correction factor, the results coincide again.
# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 2 Primary Classifications of Electrical Energy Losses  
As previously mentioned, power losses are represented by the difference determined by the amount of energy that is injected into the distribution system and the amount of electricity that is finally billed to the end users [5].  
Figure 1: Sankey diagram of the processes in the global electrical energy system.  
As illustrated in Figure 2, power losses can be divided into two distinct categories: technical power losses and non-technical power losses. The determination of these categories is based on the factors that cause them, ranging from the elements of the electrical network to abnormal consumer operations connected to the power system.