esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
To evaluate the performance of the queue length estimation computing model proposed in this paper, a mixed traffic scenario is constructed in a popular platform VISSIM, where a signalized intersection is set. An architecture of ITS is built according to Figure 1. The signal transformation scheme is designed and the time used in the model can be delivered to the edge server. The real time status of connected vehicles and upstream traffic volume are collected by the roadside unit and then sent to the edge server. At the same time, the data from VISSIM is transferred to MATLAB, where the proposed algorithm is working and gives the result about the queue length. As shown in [12], the penetration rates may impose a great effect on the estimate accuracy and can be estimated by V2X [51]. Therefore, several penetration rate levels are validated in the simulation.  
**Remark 1**.: _The queue length estimation result of the proposed methodology is related to the positions of the connected vehicles, the penetration rate of connected vehicles, and the types of waiting vehicles, while not related to the typology and phases of the intersection. It can be concluded by analyzing the mechanism of the proposed model. Therefore, a traditional orthogonal four bidirectional branches intersection with four phases was used in this section to validate the performance._
## 3 Data and Method
### Spatial Econometric Tests  
In the field of spatial econometrics, particularly in handling panel data, several tests are conducted to determine the most appropriate model. A key test is the Hausman test, which is essential for choosing between fixed or random effects models. This test evaluates the consistency of an estimator under random effects in comparison to its efficiency under fixed effects. The Hausman test produced a chi-square statistic of 34.016 with 19 degrees of freedom, leading to a significant \(p\)-value of 0.0183. This result suggests that the fixed-effects model is more suitable for this analysis.  
The [PERSON], [PERSON], and [PERSON] LM2 marginal test, applied using the \"splm\" package in R ([PERSON] and [PERSON] 2012), is utilised to detect spatial lag or autoregression in the data ([PERSON] et al. 2003). Specifically, it aids in addressing RQ2 by allowing us to detect the extent to which economic outcomes in one province are potentially conditioned by those in its vicinity. The robust statistic of 27.854 and the highly significant \(p\)-value (<0.001) from the LM2 test in our analysis provide compelling evidence that spatial dependencies are present and influential.  
Lastly, the Granger causality test for the panel data, as suggested by [PERSON] and [PERSON] (2012), is employed to assess potential causal relationships. This test is crucial in determining whether one variable can predict another. Identifying these relationships is vital, as they significantly influence the choice of model estimation method. The panel Granger causality test is available in the \"plm\" package in R ([PERSON] and [PERSON] 2008). The test uncovered complex interactions between average household income and various economic sectors, exhibiting direct, inverse, and bi-directional Granger-causality relationships. A notable limitation of this analysis is the biannual frequency of household income data, necessitating its pairing with biannual sectoral GDP data. This limitation restricts the availability of figures to the year immediately preceding the year of the outcome variable. While the results are insightful, caution should be exercised in interpreting their implications due to the discontinuity in data. Nevertheless, the findings suggest potential \"simultaneity\" between sectoral GDP and household income, highlighting the importance of incorporating appropriate spatial econometric estimators.
# Overcoming Individual Discrepancies, a Learning Model for Non-Invasive Blood Glucose Measurement
## 4 Circuit of Blood Glucose Sensor  
Earlight is the blood glucose sensor used in this paper for collecting absorbance data of different wavelengths. In this section, we focus on the circuit design of the Earlight, which is composed of three parts, i.e., light emitter, light receptor, and controller, as shown in Figure 2a.  
The light emitter is used to emit incident light of different wavelengths, and contains five different wavelengths of LED light source (490 nm, 660 nm, 730 nm, 850 nm, and 930 nm), and a power dissipation of 120 mW. These LEDs are connected by using shared anode, Figure 2b.  
Figure 1: The absorption spectrum of aqueous glucose solution, deducted water background, measured by UV-3600 Plus (Shimadzu, Kyoto, Japan, 2018) ultraviolet-near infrared (UV-NIR) spectrometer, which has an absorption peak at about 930 nm.  
The light receptor is used to receive transmitted light and measure its intensity. Light receptor includes two parts, current to voltage convertor (I-V converter) and filter amplifier circuit (F-A circuit), Figure 2c. In I-V converter, the silicon photodiode (PD) whose bandwidth spans from 300 nm to 1100 nm is able to produce a current whose intensity is proportional to the received light intensity, and the current signal is transformed to a voltage signal (\(Vout_{1}\)) by MAX471 (Maxim, San Jose, San Jose, CA, USA), which is a current sensing IC presented by MAXIM company. In F-A circuit, the \(Vout_{1}\) is filtered and amplified to \(V_{return}\) that can be loaded into an analog to digital converter (ADC) via a low-pass first order filter and a proportional amplifier.  
The controller is applied to control the LEDs and calculate measurement result. The controller is a SCM STM32f103 (STMicroelectronics, Geneva, Switzerland) minimum system with a small liquid-crystal display. There is a 12-bit ADC in this system, that can be used to measure the voltage signal produced by the light receptor and calculate absorbance of light according to Equation (2). There are two reasons for using a 12-bit ADC: 1. The 12-bit ADC provides 4096-level resolution, and we found that the acquired voltage signal varies from 0 to 3.6 V with an accuracy of 0.001 V, which needs a resolution of \((3.6-0)/0.001=3600\). So a 4096 resolution of a 12-bit ADC is sufficient. 2. The STM32f103 microcontroller we use has a 12-bit ADC, so there is no need to add an external ADC, which will reduce the cost.  
The demonstration of using the prototype of blood glucose sensor is showed in Figure 3. Using this prototype, the data of absorbance can be collected, and the algorithm to estimate the blood glucose concentration will be presented in Section 5.  
Figure 2: Structure and circuit design of the Earlier device: (**a**) Structural design; (**b**) Light emitter circuit; (**c**) Circuit of I-V Converter; (**d**) Filtering and amplifying circuit. VCC: Volt Current Condenser, GND: Ground, SHND: Shut down end, SIGN: Signal end, RS-: Resistance end.
# Personalized Contextual Information Delivery Using Road Sign Recognition
## 2 Related Work
### Image Captioning Model  
Image captioning models aim to generate textual descriptions of images by analyzing their visual content. These models typically combine convolutional neural networks (CNN) for visual feature extraction with recurrent neural networks (RNN) or transformer-based architecture for sequence generation. Recent advancements in image captioning, such as attention mechanisms, have further improved these models' capabilities to identify and highlight relevant regions within images. However, despite these improvements, they still lack the integration of contextual user data, which are crucial for applications like personalized road sign recognition [4; 5].  
Despite their success in general-purpose applications, image captioning models face challenges when applied to road sign recognition. These models are primarily designed to describe static visual features and often lack the ability to interpret or contextualize data. For example, while an image captioning model might accurately describe a road sign as \"A green sign with the text '27 km to Mokpo\", it cannot provide additional insights or interpretations relevant to a specific user's journey [3; 11]. This limitation makes traditional image captioning models less effective in dynamic and user-centric applications such as real-time navigation.
# A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model
###### Abstract  
Recently, a lot of efforts have been devoted to developing more precise Modal Parameter Estimation (MPE) techniques. This is explained by the necessity in civil, mechanical and aerospace engineering of obtaining accurate estimates for the modal parameters of the tested structures, as well as of determining reliable confidence intervals for these estimates. The Non-linear Least Squares (NLS) identification techniques based on Maximum Likelihood (ML) have been increasingly used in modal analysis to improve precision of estimates provided by the Least Squares (LS) based estimators when they are not accurate enough. Apart from providing more accurate estimates, the main advantage of the ML estimators, with regard to their LS counterparts, is that they allow for taking into account not only the measured Frequency Response Functions but also the noise information during the parametric identification process and, therefore, provide the modal parameters estimates together with their uncertainties bounds. In this paper, a new derivation of a Maximum Likelihood Estimator formulated in Pole-residue Modal Model (MLE-PMM) is presented. The proposed formulation is meant to be used in combination with the Least Squares Frequency Domain (LSCF) to improve the precision of the modal parameter estimates and compute their confidence intervals. Aiming at demonstrating the efficiency of the proposed approach, it is applied to two simulated examples in the final part of the paper.  
Keywords:modal analysis; system identification; modal identification; maximum likelihood; frequency domain; confidence intervals; modal parameters; pole-residue modal model +
Footnote †: journal: Applied
## 4 Results and Discussion
### Operational Monitoring of Radar Differential Reflectivity Using the Sun  
The issue of differential reflectivity calibration is crucial for successful applications of polarimetric weather radar [10]. Offline Sun measurements, in which operational scanning is stopped and the radar antenna is pointing towards the Sun, are generally employed to calibrate the polarimetric receiving chain ([10,11]). [PERSON] [12] has shown that ZDR should be estimated within 0.2 dB for improved rainfall estimates based on Z and ZDR. In the present paper, the approach introduced by [PERSON] _et al._[3] is followed. The same volume data available from the Swiss weather radars described in Section 4.2 have been analyzed on a daily basis, and the corresponding solar-related results are presented in Table 4.  
Twenty values (from 6 June to 25 June) have been discarded for the Lema radar, because of a problem with the calibration of the vertical channel: a residual, positive bias of approximately +0.2 dB is clearly evident. The daily differential reflectivity values from the solar monitoring in fact are consistently positive; only some minor random fluctuations are present, and as a result, the dispersion is as small as 0.06 dB. For the Albis radar, 220 (out of 224) observations are available; the system did not work over two weekends in July. Unfortunately, despite the new-generation calibration unit, a significant negative Bias can be observed: the daily differential reflectivity values from the solar monitoring are systematically negative. However, a positive fact is offered by the limited dispersion: both the standard deviation and spread are 0.05 dB, the smallest value of the three radars. The Dole radar also shows a negative bias of approximately -0.4 dB (21 values have been discarded because of a problem with the calibration of the vertical channel). The dispersion is as small as 0.06 dB.  
The limited dispersion found for all three radars is an important result: it underlines the stability of the relative calibration between the horizontal and vertical receivers. It also makes it feasible to plan an adjustment of one channel with respect to the other in order to eventually reach a residual bias as close as possible to the ideal value of 0.0 dB.
# Realisation of a Novel Functionally Redundant Actuation System for a Railway Track-Switch
## 3 Development of Simulation Models
### The Switch Rail Model in Simpack  
The rail elements of the system are modelled using FEA first to check the bending of the switch rails during switching action (shown in Figure 6a). The material properties of the rail are assumed to be isotropic steel with [PERSON]'s modulus and Poisson's ratio of 200 GPa and 0.3, respectively. A second-order quadratic tetrahedral element, C3D10 [24] is used to create the finite element mesh of the rail bodies. The switch rail boundary conditions are that the rails are fixed in all dimensions at the heel position. The rail is free to bend and twist throughout the remaining length as a result of the forces applied at the bearer positions.  
The Finite Element (FE) model is used first to determine the actuator bearer positions in the switch layout. It was determined that the first actuator position should be the first bearer (B1) to ensure the closed position at the front-toe of the switch (see Figure 6b). The switch rails have to be lifted minimum to the cam radius at those bearer locations to allow free movement. The cam radii of the three actuator bearers are determined using the required switch rail movement, which is a function of the switch geometry of the switch. These cam radii are 65 mm (Bearer 1), 50.6 mm (Bearer 2) and 38.2 mm (Bearer 3). The actuator locations are selected as 1 st, 3 rd and 5 th bearer positions. The selected configuration ensures that the rail carrier moves in a semi-circular way without interference with any other cams.  
These finite element bodies of the switch and stock rails are then converted into flexible body files (.fbi files) using the model reduction technique in Abaqus. An MBS model of the switch panel is then developed using Simpack. This MBS model is then connected to the actuator model developed in Simulink to form a co-simulation in Simulink as shown in Figure 7. This co-simulation is constructed so that the designed controller, which will be explained in the following section, can be connected to the MBS as shown in Figure 4. The sensor data are fed to the actuator and controller modelled in Simpack and the actuation  
Figure 5: Forces acting on a single cam-A.  
torque is fed to the MBS model. The data exchange between Simulink and Simpack in the co-simulation environment is carried out at a fixed time step of 0.001 s.
## 3 Results
### Carbon Nanotube-Based Sensors  
#### 3.1.1 Synthesis Methods and Fabrication Techniques  
Carbon nanotube-based sensors have gained considerable attention in the field of bridge maintenance due to their exceptional mechanical properties and sensitivity to strain and deformation [31; 32]. Several synthesis methods and fabrication techniques have been developed to create carbon nanotube-based sensors with tailored properties.  
One commonly used method for carbon nanotube synthesis is chemical vapor deposition (CVD) [33]. In this approach, carbon-containing gases, such as methane or ethylene, are decomposed on a heated substrate, resulting in the growth of carbon nanotubes [34]. CVD offers control over nanotube diameter, length, and alignment, allowing for customization of sensor properties to meet specific requirements [35].  
Another fabrication technique involves the assembly of carbon nanotubes using template-guided growth [36]. In this method, a porous template, such as anodized aluminum oxide or a self-assembled monolayer, is used to guide the growth of carbon nanotubes in desired patterns or arrangements. This technique enables the fabrication of carbon nanotube-based sensors with precise geometries and controlled nanotube alignment.  
To enhance the sensing capabilities of carbon nanotube-based sensors, functionalization techniques are employed. Functionalization involves modifying the surface of carbon nanotubes with various molecules or functional groups [37]. These modifications can improve the adhesion of nanotubes to the substrate, enhance their electrical properties, or enable specific interactions with target analytes.  
Figure 2: A schematic overview of the primary nanosensor types utilized in bridge SHM, illustrating key characteristics and categories of each sensor type.
## 2 Description of the Numerical Tools
### Lagrangian Solver--Compressible Vortex Particles Approximation  
The Lagrangian solver is configured based on particle approximations. Particles are centered at \(\vec{Z}_{p}\); occupy a volume \(V_{p}\); and carry volume integrals of density \(\rho_{p}\), vorticity \(\vec{\omega_{p}}\), dilatation \(\theta_{p}\), and pressure \(p_{p}\):  
\[q_{p}(\vec{x},t)=\sum_{p}Q_{p}(t)\zeta_{e}(\vec{x}-\vec{Z}_{p}(t)),\qquad Q_{p} (t)=\int_{V_{p}(t)}q(\vec{x},t)dV=q_{p}(t)\,V_{p}(t) \tag{8}\]  
In the above, \(q_{p}\) is any of \((\rho_{p},\vec{\omega}_{p},\theta_{p},p_{p})\) and, respectively, \(Q_{p}\) is any of \((m_{p},\vec{\Omega}_{p},\Theta_{p},\Pi_{p})\) (e.g., \(\vec{\Omega}_{p}=\vec{\omega}_{p}\,V_{p}\)). Furthermore, \(\zeta_{e}\) denotes a smooth approximation of the Dirac function [28].  
In Lagrangian (material) coordinates, the flow equations for inviscid conditions take the form  
\[D/Dt\begin{Bmatrix}Z_{p}\\ V_{p}\\ M_{p}\\ \Omega_{p}\\ \Theta_{p}\\ \Pi_{p}\end{Bmatrix}=\begin{Bmatrix}\vec{U}_{p}\\ V_{p}\,\theta_{p}\\ 0\\ V_{p}\Big{(}(\vec{\omega}\cdot\
abla)\vec{u}+\
abla\times\frac{-\
abla p}{\rho} \Big{)}_{p}\\ V_{p}\Big{(}2\|\
abla\vec{u}\|-\
abla\cdot\frac{\
abla p}{\rho}\Big{)}_{p}\\ V_{p}((1-\gamma)p\theta)_{p}\end{Bmatrix} \tag{9}\]  
where \((\cdot)_{p}\) denotes evaluation at the particle position \(Z_{p}\). The velocity vector is not explicitly given by the above equations. Using the Helmholtz velocity decomposition it follows that  
\[\vec{u}=\vec{U}_{\infty}+\
abla\phi+\
abla\times\vec{\Psi},\quad\
abla^{2} \phi=\
abla\cdot\vec{u}=\theta,\quad\
abla^{2}\vec{\psi}=-\vec{\omega} \tag{10}\]and therefore velocity is defined by its scalar and vector potentials, \(\phi\) and \(\vec{\psi}\), respectively, that are both solutions of the Poisson equation with free far field conditions. Both potentials can be directly obtained through the Biot-Savart law; however the corresponding cost may be prohibitively high and therefore instead the particle mesh technique is employed.
## 4 Materials and Methods
### Genetic Techniques  
The primers used in this work are listed in Table S1 (Supplementary Materials). Different gene alleles were transferred by P1 transduction using the following donor strains: CF1693 for _relA251::Km\({}^{\text{R}}\)_ and _spoT207::Cm\({}^{\text{R}}\)_, RH90 for _rpoS359::Tn10_, EC3954 for _rpoB3370 thii:Tn10_, BSN27 for _\(\Delta\)lns-trp::Tn10_, SKC 1.8 for _rf4h::Tn51.8_, and MG1655 \(\Delta\)zur_ for \(\Delta\)zur::Cm\({}^{\text{R}}\). The deletion mutant strain MG1655 \(\Delta\)zur was generated using the primer pair zurP1/zurP2 and the method described by [PERSON] and [PERSON] [36]. The strain AAG16 carries a _relA_ disruption from codon 6 to 743, obtained as described by [20]. The plasmid pMGP-1 carries the same fragment as in pSF4000 in pBR322. In all studies undertaken, cells carrying either pSF4000 or pGMP-1 showed apparently identical phenotypes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strain** & **Relevant Characteristics** & **Reference** \\ \hline J96 & Pathogenic isolate & [37] \\ J96 _relA spoT_ & J96 _relA251::Km\({}^{\text{R}}\)_, spoT207::Cm\({}^{\text{R}}\)_ & [19] \\ JFV16 & _relA3744_ & [28] \\ JFV18 & JFV16 _relA251::Km\({}^{\text{R}}\)_, spp207::Cm\({}^{\text{R}}\)_ & This study \\ JFV21 & 96 _hulyA1_ & [28] \\ JFV22 & JFV21 _relA251::Km\({}^{\text{R}}\)_, spoT207::Cm\({}^{\text{R}}\)_ & This study \\ AAG1 & MG1655 \(\Delta\)lacZ_ & [38] \\ CF1693 & MG1655 _relA251::Km\({}^{\text{R}}\)_, spoT207::Cm\({}^{\text{R}}\)_ & [39] \\ JFV2 & AAG1 _ArelA spoT_ & [20] \\ JFV23 & AAG16 _spoT207::Cm\({}^{\text{R}}\)_ & This study \\ \hline \hline \end{tabular}
\end{table}
Table 1: Strains and plasmids used in this work.
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 5 Experiments and Results
### In-Depth Analysis
#### 5.3.6 Influence of Video Compression  
Pixel-level noise caused by common video compression formats can be a major source of errors for rPPG methods. In principle, the cleanest signal is obtained from uncompressed video frames, but this can be impractical due to their large size. To study this further, we evaluate our methods on a range of video compressions levels and formats to determine the trade-off between rPPG accuracy and video size/bitrate. The results can be seen in Figure 12 for the PURE dataset. The result show that among lossy encodings (denoted by circle), H.265 format best retains the rPPG pulsating component in the skin pixels of the video, resulting in high HR estimation accuracy, while maintaining the lowest bitrate. For example, accuracy only drops by \(\sim\)0.3 bpm while the bitrate reduces by two orders of magnitude (26 Mbps to 0.26 Mbps). These results agree with the findings in [64]. Note that even under the lowest compression settings, these formats are not lossless and they result in a change in rPPG accuracy. Among the truly lossless codecs, FFV1 is able to encode the videos most efficiently. It results in zero drop in rPPG accuracy while reducing bitrates by almost an order of magnitude w.r.t raw videos. This can make rPPG dataset storage management much easier: for example, ECG-Fitness originally takes up 1.05 TB, but can be compressed with FFV1 to under 150 GB without losing any information.  
Figure 10: rPPG performance (in terms of MAE; error bars represent standard deviation) somewhat degrades when subjects perform facial activity like taking (**a**) or expressing emotions (**c**). This can be partly attributed to non-trivial facial deformations causing changes in skin appearance. However, no significant performance degradation is noticed due to chewing motion of the mouth. Error while eating (**b**) is primarily caused by facial occlusion when subjects take a bite or a sip.  
Figure 11: rPPG performance for (**a**) rested vs. post-workout conditions in terms of MAE (error bars represent standard deviation); and (**b**) HR/HRV ranges in terms of MAE ratio w.r.t the average mean absolute error per dataset. With a few exceptions, HR estimation in higher ranges (including during post-workout conditions) is more often less accurate than in lower ranges.  
Figure 12: Effect of video compression on rPPG performance (MAE; PURE dataset). As videos are compressed to reduce their bitrate and storage size, rPPG accuracy decreases. H.265 offers the best trade-off between them among lossy encodings (denoted by circle). Among truly lossless encodings (denoted by diamond), FFV1 stores videos most efficently without affecting rPPG accuracy at all.