esa-sceva/satcom-chunk-collection huggingface dataset:
# Brain-Inspired Healthcare Smart System Based on Perception-Action Cycle
## 5 Healthcare Smart System for Measuring Dolphin-Assisted Therapy Efficiency
### EEG Acquisition
#### 5.1.3 Time Series Acquisition  
The possible brain stimulation by the emitted dolphin-frequency should change the patient's brain activity, which would be reflected in an EEG. So, we employed a ThinkGear ASIC Module electroencephalographic sensor (TGAM1, Figure 8), which is an electroencephalographic biosensor responsible for amplifying, processing and digitizing brain activity signals by means of three electrodes--EEG, reference and ground, allowing to collect unprocessed brain wave data.  
Besides, we used an algorithm patented by NeuroSky [20] to generate the spectra of the alpha, beta, theta, delta, gamma signals, amplifying the signal of the raw data and eliminating the noise captured during the sampling to apply said algorithm to the remaining signal. On the other hand, the attention and meditation indicators range from zero to 100 depending on how concentrated, stressed, restless, etc. is found the person during the collection of the samples.  
The TGAM1 sensor is physically composed of a single channel EGG electrode. Its Bluetooth is used for data transmission, its transmission speed is set to 57,600 bauds. This chip operates within the range 2.97-3.63 V, its maximum input voltage is 10 mV peak to peak, it also has 4 kV electrostatic discharge protection arrangements for contact discharge and 8 kV for air discharge. The transmission speeds at which it can be configured are 1200, 9600 and 57,600 bauds. Its sampling frequency of 512 samples per second, a frequency ranging from 3 to 100 Hz, serial communication, low power consumption 15 mA at 3.3 V. It is also provided with a low-signal quality warning, in addition to having a reduced size and be low-cost [20].  
Likewise, the TGMA1 sensor has different connections, but among the most important are the power, transmission, reception and electrode connections. Figure 8 shows the red wire as the main measuring electrode, the green wires are from the ground and reference electrodes, the black wires as the shield of the system electrodes to mitigate as much as possible the ambient noise, the yellow and blue wires are the power and purple is the communication, respectively. The module requires a transmission speed of 9600 bauds and by default it is set to 57,600 bauds because it lacks resistance between pin B1 and pin B0 of the rear part explained below.  
The biosignals acquired by TGAM1 are represented in RAW data. The range of Raw Data varies from \(-\)32,768 to 32,767, signed 16 bits precision. The first frontal polar electrode, or \(F_{p}1\), samples in real-time the discretization of voltage.  
This RAW-Volts data relation is defined by Equation (4). Figure 9 shows the representation of the RAW data in \(\upmu\)V.  
\[Volts=RAWdata\times\frac{\frac{1.8}{4006}}{2000}\quad[\upmu\text{V}]. \tag{4}\]  
Figure 8: ThinkGear ASIC Module electroencephalographic sensor (TGAM1).
## 2 Materials and Methods
### Design Phase
#### 2.2.1 Problem Classification  
Before going into detail about the algorithm selection model, the problem must be classified. The determination of the exact problem type to solve is important because the problem type significantly influences the algorithm selection. One of the most discussed problems within the literature on scheduling is the JSSP. It is considered one of the hardest combinatorial optimization problems. The JSSP considers a job shop with a number of machines, where each job would have its own predetermined route to follow [5]. Each job has a series of tasks that requires the usage of a particular machine for a known duration. Additionally, the tasks need to be completed in a specified order. The objective is to schedule the jobs on the machines to minimize the time necessary for processing all jobs. Building on this, the FJSSP generalizes the job shop and parallel machine environment [5]. The difference to the job shop is that the flexible job shop considers work centers with a number of identical machines in parallel. Each job follows its own route and must be processed at each work center, but any machine can perform the job. Therefore, the scheduling problem becomes computationally more difficult to solve. This article focuses on the JSSP and FJSSP. This is because the JSSP is one of the most complex scheduling problems of all scheduling classes and is therefore a suitable initial scheduling scenario for testing the functionality of an algorithm selection approach. The reason for FJSSP is the flexibility it offers for scheduling. It is an extension to the JSSP, and especially as this article focuses on dynamic AGV scheduling, the characteristics of FJSSP align with the research objectives. Moreover, the FJSSP represents one of the most practically relevant problem settings and is of interest for implementation in real-world productions [17].  
Figure 2: Theoretical concept of the algorithm selection approach.  
As the JSSP and FJSSP mainly focus on production scheduling, the problem classes must be transferred to AGV scheduling. AGV scheduling shows similarities to the production scheduling problem. Instead of machines as resources, there are AGVs within the manufacturing environment that need to process a certain number of transport orders with suboperations. Therefore, the AGV scheduling can be derived from the production scheduling problem in order to consider different possible AGV scenarios. Depending on the use case, several scheduling layouts can be displayed and tested. Within JSSP-oriented scheduling, each job has a series of tasks that require the usage of a particular AGV for a known duration. For the FJSSP, the processing of a transport order can be performed on any AGV of a certain AGV group, and the allocation of AGVs to jobs is executed during the scheduling process.
## 3 Materials and Methods
### Expression for Estimating the Average Project Duration \(\mu_{p}\)
\(K_{1}\) is another empirical coefficient whose recommended value is 1.12 when the activity duration distributions are symmetrical (for example, normal, uniform). If the activity duration distributions are asymmetrical (for example, lognormal, most triangular distributions), then we suggest taking a slightly higher value (\(K_{1}=1.22\)). It must be borne in mind, however, that coefficients \(K_{0}\) and \(K_{1}\) are somehow in equilibrium with each other. If \(K_{0}\) in Equation (6) was higher (accept more activities as \(j\)), then \(K_{1}\) would have to be smaller (to account for a potentially lower impact of those \(j\) activities on \(\mu_{p}\)), and vice versa. After some experimentation, we found the values proposed here for both coefficients seem to work well in almost all cases. \(N_{p}\) represents an estimate of the equivalent number of parallel critical + subcritical paths (assuming they all spanned from the project start to the project end). We calculate it as follows:  
\[N_{p}=\frac{\sum d_{j}}{PD}. \tag{10}\]  
It is worth noting that, although the numerator and denominator of \(N_{p}\) could have been integrated and simplified within Equation (9), they are presented separately for a reason; \(N_{p}\) shows a high correlation on its own with \(\mu_{p}\), despite it not being linear. Future studies may want to improve our equations by revising, for example, how \(N_{p}\) is mathematically included in Equation (9). \(\sum\sigma_{j}\) corresponds to the sum of all critical + subcritical activity duration standard deviations. We present it separately because, in cases where we were using coefficients of variation (\(CV_{j}\)), we could easily replace this term with \(\sum\bigl{(}CV_{j}\cdot d_{j}\bigr{)}\). \(LN(\cdot)\) corresponds to the natural (Euler's) logarithm. \(S\) is the slack tightness coefficient, whose values always remain in the range \(0<S\leq 1\); it is calculated as  
\[S=\frac{\tau-\frac{\sum s_{j}}{n_{j}}}{\tau}=1-\frac{\sum s_{j}}{\tau\cdot n_{ j}}. \tag{11}\]  
\(S\) represents the average (total) slack per activity. When \(S=1\), all paths have to be critical (there is not a single activity with a slack \(s_{j}>0\)). When \(S=0\) (an extreme which is not mathematically possible), all subcritical paths would (theoretically) not be causing an impact on \(\mu_{p}\) (impossible project duration extension beyond \(PD\)). Hence, like \(SP\) in Equation (6), the variable \(S\) allows fine-tuning the representativeness of \(N_{p}\) by considering how _loose_ the subcritical activities are. With all these terms, Equation (9) for calculating \(\mu_{p}\) was fully defined and can be easily implemented.
###### Abstract  
In this work, we present a resource allocation scheme for managing trade-offs between total throughput maximisation and system fairness in a non-orthogonal multiple access (NOMA) system for 5G networks. Our proposed approach is designed to improve throughput and fairness as performance metrics of NOMA in 5G networks. We apply integer linear programming for user pairing and adopt particle swarm optimisation as the power allocation scheme for reducing resource allocation complexity. To formulate the multi-objective problem, we use scalarisation of multi-objective optimisation, which exhibits flexibility in assigning different weights to a single objective--in the case of this study, either sum rate or fairness. Moreover, the problem is formulated with a penalty function to prevent optimisation violating the constraints of the optimisation function. Simulation results show that the proposed model outperformed the conventional approach by at least 17% in terms of throughput maximisation and fairness rate.  
5G communications; wireless communications; integer linear programming (ILP); successive interference cancellation (SIC); multi-objective optimisation (MOO); weighted sum method (WSM); particle swarm optimisation (PSO); Jain's fairness index (JFI)
## 1 Introduction
While previous research emphasized the necessity of models for pre-analyzing booming noise [12], most studies attempted problem analysis by interpreting finite-element models encompassing the entire vehicle and tailgate [8; 12; 24; 25]. Although modeling methods that target the entire completed system have the advantage of precision, they consume a significant amount of time and resources. Moreover, numerous constraints arise during the modification process, given that most designs are already in their final stages. To address these limitations, we employed simplified tailgate finite-element models suitable for the design phase, along with a careful selection of essential design factors that consider the vehicle body and boundary conditions. This approach allowed us to efficiently construct a model within an accuracy range suitable for analysis without exceeding the necessary precision. This enabled us to analyze the dynamic characteristics related to booming noise before physical production, thereby reducing the time and cost required for modeling. The remainder of this paper is structured as follows. In Section 2, the mechanism of booming noise generation is analyzed through experimental methods. The vehicle interior noise components were obtained through road tests and the ODS analysis was performed using the vibration signals of the tailgate. The relationship between the tailgate and the booming noise was thus identified. Additionally, the mechanism of booming noise generation was elucidated by selectively determining the major modes of the tailgate related to the booming noise through modal tests. In Section 3, a commercial finite-element analysis software (ANSYS 2022 R1) is used to present the modeling process of the tailgate, representing the modes related to booming noise. The modeling elements and properties of the tailgate were thus defined. Simultaneously, the coupling conditions between an inner panel, an outer panel, and reinforcements were addressed. Based on the evaluation results of various boundary conditions within the vehicle body, the major boundary conditions were selected to construct the final analysis model, which was validated using the experimental results. In Section 4, our design proposals for reducing booming noise in the tailgate are presented. The effects of the improvements we made were quantified by comparing the natural frequency changes and maximum deformations under random excitation between the existing and improved models. In Section 5, experimental and analytical results are discussed. Finally, Section 6 summarizes the major findings of this study and provides conclusions.
# Multimodal Video Analysis for Crowd Anomaly Detection Using Open Access Tourism Cameras
## 5 Discussion  
The results obtained reveal that the utilization of time series decomposition through STL has been effective in identifying seasonal patterns and flexible trends. Specifically, a notable shift in trend is observed around 9 October and 12 October, coinciding with regional and national festivities, respectively. This observation reinforces the usefulness of decomposition in capturing seasonal events and abrupt changes in the data. Additionally, by analyzing the residual of the decomposition in Figures 10 and 11, points that are distant from the rest are identified, indicative of the presence of anomalous values. The detection of collective anomalies through the application of a threshold to the trend, as shown in Figures 12 and 13, confirms the presence of anomalous periods in both series, these being the festive periods mentioned previously. On the other hand, when using the SESD algorithm to detect point anomalies (Figures 14 and 16), the validity of the collective anomaly detection method is confirmed, as the intervals previously identified as anomalous also stand out. This analysis further suggests that other points detected as anomalies indicate significant changes across different time intervals, as shown in Figures 15 and 17. These findings  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **PySAD** &
\begin{tabular}{c} **Trend Threshold +** \\ **SESD (Ours)** \\ \end{tabular} & **Cohen’s Kappa Score** \\ \hline Detection & & & \\ Series & 14 & 545 & 0.0287 \\ \hline Heatmap & & & \\ Series & 19 & 582 & 0.0289 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of punctual anomalies detected in both data series by PySAD and the proposed approach, along with a comparison based on Cohen’s Kappa Score.  
Figure 17: Justification of anomalies in heatmap series. (a) 1 October 2023 10:45:00 (Previous day) [0.001601]. (b) 2 October 2023 10:45:00 (Anomaly) [0.012045]. (c) 3 October 2023 10:45:00 (Next day) [0.008394].  
reinforce the effectiveness of the approach used for anomaly detection in the studied time series. These results enable the detection of anomalies in images, such as identifying an unexpected increase in the presence of individuals during periods that would typically be expected to remain nearly empty.
## 2 Materials and Methods
### Localization and Classification of Contacts  
We determined the anatomical location of iEEG contacts in the patient's pre-operative MRI space (see Figure 1a) by co-registering the post-implantation CT with the pre-operative MRI using statistical parametric mapping [29] within the Brainstorm software (April 2022) [30]. Each contact coordinate was determined on the co-registered CT-MRI image. To account for the brain shift that occurs after electrocorticographic implantation [31], subdural electrodes were projected onto the cortical surface (reconstructed via FreeSurfer). When both subdural and depth electrodes were implanted, depth electrodes were also adjusted to compensate for brain shift [31].  
Figure 1: Methodological pipeline. (**a**) Input intracranial EEG (iEEG) data. Different colors correspond to different grids or depth electrodes. (**b**) Time-frequency (TF) images extracted from each iEEG contact in three frequency ranges (TF analysis was performed every 3 s). (**c**) Design of the deep learning (DL) tool that was used to extract values of visual complexity from each TF image. The input image is deconstructed into 13 layers (13 different images). For each layer of the image, we computed the unsupervised activation energy (UAE), which is a measure of the image’s visual complexity.
## Appendix A Simulation Environments
### Checking the Quality of the Simulation Environment Base Model  
#### a.4.1 Checking Moments  
Using the chosen user-specific models, we simulate 100 trials. In each trial, for each user in ROBAS 2, we use their respective model to generate a data trajectory \((S_{i,t},R_{i,t})_{t=1}^{56}\) (note that the ROBAS 2 study had two brushing windows per day for 28 days for a total of 56 brushing windows). We then compute the following metrics for each of the trials and averaged across trials:  
1. Proportion of Missed Brushing Windows: \[\frac{1}{N}\sum_{i=1}^{N}\frac{1}{T}\sum_{t=1}^{T}\mathbb{I}[D_{i,t}=0]\]
2. Average Nonzero Brushing Duration: \[\frac{1}{N}\sum_{i=1}^{N}\frac{1}{\sum_{t=1}^{T}\mathbb{I}[D_{i,t}>0]}\sum_{t=1 }^{T}\mathbb{I}[D_{i,t}>0]D_{i,t}\]
3. Variance of Nonzero Brushing Durations: \[\overline{\text{Var}}(\{X_{k}\}_{k=1}^{K})\] represent the empirical variance of \(X_{1},X_{2},...,X_{K}\). \[\overline{\text{Var}}(\{D_{i,t}:t\in[1\colon T],D_{i,t}>0\}_{i=1}^{N})\]
4. Variance of Average User Brushing Durations: This metric measures the degree of between-user variance in average brushing.  
Average of Variances of Within-User Brushing Durations:  
This metric measures the average amount of within-user variance.  
\[\frac{1}{N}\sum_{i=1}^{N}\widehat{\text{Var}}\big{(}\{D_{i,t}\}_{t=1}^{T}\big{)}\]  
The base models slightly overestimate the proportion of missed brushing windows in the ROBAS 2 data set. Our base models also slightly underestimate the average brushing duration. Our base models also for the most part slightly overestimate the between-user and within-user variance of rewards.
## 4 Multi-Module Measurement Validation of MANTiS-32
### Evaluation of Modal Analysis Performance Using a Scaled Cable-Stayed Bridge Model
#### 4.1.1 Design and Construction of the Scaled Bridge Model  
The full-scale prototype is a three-span, continuous steel-box girder cable-stayed bridge with a total length of 450 m, a width of 11.7 m, and a pylon height of 62 m. The scaled model, shown in Figure 18, was fabricated at a 1:30 geometric scale, resulting in a total length of 1640 mm, a pylon height of 2120 mm, and a deck width of 400 mm. To ensure that the model accurately reflects the dynamic behavior of a flexible, small- to medium-span bridge, structural stiffness was deliberately minimized, and additional mass blocks were installed on the deck, as illustrated in Figure 18, to lower the fundamental frequency.  
The scaled cable-stayed bridge model was constructed using conventional structural steel, with material properties summarized in Table 6. The total mass of the deck-mounted lumped masses is 1480 kg. The steel exhibits a Poisson's ratio of 0.30, a shear strength of 40 kgf/mm\({}^{2}\), and a Young's modulus of 2.15 \(\times\) 10\({}^{4}\) kgf/mm\({}^{2}\), ensuring appropriate stiffness and mass distribution for simulating the dynamic behavior of a medium-span bridge.
## 4 Analysis of Control Strategy Based on Steering Stability  
The steering safety performance of tracked vehicles refers to the ability of vehicle steering to avoid possible skidding or rollover under the influence of centrifugal force and terrain slope factors, which can be avoided by formulating a suitable steering control strategy. During the control process, the real-time conditions of vehicle steering are analyzed to determine dangerous operation conditions such as skidding and rollover, and the instructions for avoiding skidding and rollover are transmitted to the driving system controller.
###### Abstract  
We present a novel photon-acid diffusion method to integrate polymer microlenses (MLs) on a four-channel, high-speed photo-receiver consisting of normal-incidence germanium (Ge) p-i-n photodiodes (PDs) fabricated on a 200 mm Si substrate. For a 29 \(\upmu\)m diameter PD capped with a 54 \(\upmu\)m diameter ML, its dark current, responsivity, 3 dB bandwidth (BW), and effective aperture size at \(-3\) V bias and 850 nm wavelength are measured to be 138 nA, 0.6 A/W, 21.4 GHz, and 54 \(\upmu\)m, respectively. The enlarged aperture size significantly decouples the tradeoff between aperture size and BW and enhances the optical fiber misalignment tolerance from \(\pm 5\upmu\)m to \(\pm 15\)\(\upmu\)m to ease the module packaging precision. The sensitivity of the photo-receiver is measured to be \(-9.2\) dBm at 25.78 Gb/s with a bit error rate of \(10^{-12}\) using non-return-to-zero (NRZ) transmission. Reliability tests are performed, and the results show that the fabricated Ge PDs integrated with polymer MLs pass the GR-468 reliability assurance standard. The demonstrated photo-receiver, a first of its kind to the best of our knowledge, features decent performance, high yield, high throughput, low cost, and compatibility with complementary metal-oxide-semiconductor (CMOS) fabrication processes, and may be further applied to 400 Gb/s pulse-amplitude modulation four-level (PAM4) communication.  
g remarkation photodiode; polymer microlens; photon-acid diffusion; reliability test; high speed; optical communication +
Footnote †: journal: Sensors
# GPS/BDS RTK Positioning Based on Equivalence Principle Using Multiple Reference Stations
###### Abstract  
Reliable real-time kinematic (RTK) is crucially important for emerging global navigation satellite systems (GNSSs) applications, such as drones and unmanned vehicles. The performance of conventional single baseline RTK (SBRTK) with one reference station degrades greatly in dense, urban environments, due to signal blockage and multipath error. The increasing use of multiple reference stations for kinematic positioning can improve RTK positioning accuracy and availability in urban areas. This paper proposes a new algorithm for multi-baseline RTK (MBRTK) positioning based on the equivalence principle. The advantages of the solution are to keep observation independent and increase the redundancy to estimate the unknown parameters. The equivalent double-differenced (DD) observation equations for multiple reference stations are firstly developed through the equivalent transform. A modified Kalman filter with parameter constraints is proposed, as well as a partial ambiguity resolution (PAR) strategy is developed to determine an ambiguity subset. Finally, the static and kinematic experiments are carried out to validate the proposed algorithm. The results demonstrate that, compared with single global positioning system (GPS) and Beidou navigation system (BDS) RTK positioning, the GPS/BDS positioning for MBRTK can enhance the positioning accuracy with improvement by approximately (45%, 35%, and 27%) and (12%, 6%, and 19%) in the North (N), East (E), and Up (U) components, as well as the availability with improvement by about 33% and 10%, respectively. Moreover, the MBRTK model with two and three reference receivers can significantly increase the redundancy and provide smaller ambiguity dilution of precision (ADOP) values. Compared with the scheme-one and scheme-two for SBRTK, the MBRTK with multiple reference receivers have a positioning accuracy improvement by about (9%, 0%, and 6%) and (9%, 16%, and 16%) in N, E, and U components, as well as the availability improvement by approximately 10%. Therefore, compared with the conventional SBRTK, the MBRTK can enhance the strength of the kinematic positioning model as well as improve the positioning accuracy and availability.  
kinematic positioning; multi-baseline solution; equivalence principle; multiple reference stations; positioning availability +
Footnote †: journal: Remote Sensing, 2020, 12, 3178; doi:10.3390/rs12193178