esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Research Progress in Surface Ligand Exchange of PbS Quantum Dots for Solar Cell Application
## 2 Solid-State Ligand Exchange
### Halide Ligand to Eliminate Trap States  
When CQD surfaces are insufficiently passivated, CQDs with a high surface-area-to-volume ratio show an increase of trap state density near their midgaps. These trap states can promote charge recombination, and the elimination of trap states at their midgap is essential to realize highly efficient CQD PVs [21]. This goal can be achieved by robust organic-inorganic hybrid passivation via solid-state ligand exchange processes using mercaptopropionic acid (MPA) and CdCl\({}_{2}\)[22]. The MPA organic ligands cannot cover whole inter-atom trenches on the Pb-abundant surface due to the steric issue and lack of appropriate coordination numbers. Halide inorganic ligands are small enough to fill the trenches on hard-to-access sites, and are therefore used to complement the insufficient MPA passivation strategy as organic-inorganic hybrid passivation. The density of the midgap trap state in hybrid-passivated PbS CQD was observed to be 2\(\times\)10\({}^{16}\) cm\({}^{-3}\) eV\({}^{-1}\), indicating a five times lower value than that of conventional organic- and inorganic-only PbS CQDs. Grazing-incidence small-angle X-ray scattering (GISAXS) shows the average value of the inter-center spacing of unexchanged and oleic acid-capped PbS CQDs that is measured to be 4.4 nm, which is coherent with the nm-scale spacing originated from bulk oleic acid. The inter-dot spacing was reduced to 3.4 nm when employed with the short MPA ligands. Although the organic content of the PbS CQDs was removed after the solid-state ligand exchange using inorganic ligands, the resultant PbS CQD thin films showed the retained inter-dot spacing of 4.4 nm, which is consistent with that of the unexchanged PbS CQD thin films.  
Figure 3: Electrical performance of PbSe and PbS QD field-effect transistors (FETs) according to air exposure. (**a**) Transfer curves (I\({}_{\text{D}}\)-V\({}_{\text{G}}\)) of ethanedithiol (EDT)-treated PbSe and PbS FETs. (**b**) Transfer curves of formic-treated PbSe and PbS FETs. (**c**) Hole mobility changes over time of carboxylate-capped PbSe FETs in air. (**d**) Hole mobility changes over time of carboxylate-capped PbS FETs in air. Reprinted from reference [20]. Copyright 2010 American Chemical Society.  
films [20]. These findings confirm the importance of the bidentate organic crosslinker during solid-state ligand exchanges to achieve PbS CQD densification. As a result, the hybrid-passivated PbS CQD PV showed a certified _PCE_ of 7.0% owing to the above-mentioned merits.
# Gear Fault Detection Method Based on Convex Hull Clustering of Autoencoder's Latent Space
## 2 Materials and Methods
### Data Preparation  
Before applying analytical machinery, the data have to be prepared adequately, which is described in this section. First, the measured and acquired vibrational signal (Figure 6a) is split into 25 segments (Figure 6b).  
In the next step, a segment of the signal is transformed into the frequency domain with the aid of power spectral density (\(P_{aa}\)) estimation according to [PERSON]'s approach [30] (Figure 6c) with the Hann window function. Furthermore, the power spectral density is presented in dB scale by 10 log(\(P_{aa}\)) (Figure 6d). Finally, this logarithmic power spectral  
Figure 5: Macro photograph of pitting in gear pair II after the tests.  
Figure 6: Signal processing: (**a**) acquired acceleration signal; (**b**) segment of signal; (**c**) power spectral density; (**d**) logarithmic power spectral density; (**e**) normalized power spectral density.  
density is normalized (x at Figure 6e). The above-mentioned procedure performed for all measurements resulted in 880 normalized periodograms in the case of normal operation (gear pair I, load stages from 1 to 8), and 560 in the case of damaged gears (gear pair II, load stages from 2 to 5). Figure 7 shows the distribution of selected parameters of the obtained signals for two populations.  
It can be seen that simple analysis within the time domain (Figure 7a,b,d) does not provide sufficient information about the state of the gearbox operation. The density distribution of selected parameters varied over the two populations, however, failure could not be predicted since the parameter values overlapped. The same conclusions could be drawn in the case of simple analysis within the frequency domain. There was no significant difference between the amplitude of harmonics corresponding to the frequency of meshing (>900 Hz) for normal operation and failure (Figure 7b).  
After the applied preprocessing, we had two datasets, to clarify, one for normally operating gears and the other for malfunctioning. The idea was to expose the models only to the valid data and observe the reaction of the trained models to the faulty gear measurements. In this way, we did not want to impose a simple classification (on the particular type of damage), but rather see a more generic behavior of the relevant features extracted by the semi-supervised algorithms. Therefore, we split the first dataset into training and test subsets with a 70-30 ratio.
# Accuracy of Dynamic Modulus Models of Asphalt Mixtures Containing Reclaimed Asphalt (RA)
## 2 Theoretical Background: Predictive Models
### Generalized Huet-Sayegh Model (252p1d)  
The 252P1D model consists of two springs, two parabolic elements, and one dashpot, hence the name. It is an adaptation of the Huet-Sayegh model, with an additional linear dashpot placed in a series with the parabolic elements and the spring of rigidity (see Figure 4). This model is suitable for characterizing the viscoelastic properties of asphalt mixtures and asphalt binders, which exhibit both elastic and viscous responses under dynamic loading [19]. This simplified model can be used for analysis and design purposes in pavement engineering to predict a material's behaviour under various loading conditions.  
The dynamic module of the Generalized Huet-Sayegh model is given by the following expression:  
\[E^{*}(i\omega)=E_{0}+\frac{E_{\infty}-E_{0}}{1+\alpha\left(i\omega\tau\right)^ {-k}+\left(i\omega\tau\right)^{-h}+\left(i\omega\tau\beta\right)^{-1}}, \tag{4}\]  
The model consists of nine parameters, from which six are expressed explicitly in the previous equation: \(E_{0}\) is the high-temperature stiffness; \(E_{\infty}\) is the purely elastic modulus;  
Figure 4: Generalized Huet–Sayegh rheological model (252P1D).  
Figure 3: Parameterization of the sigmoid function [18].  
\(\alpha,\ k,\ \text{and}\ h\) are characteristics of the parabolic elements and set as \(0<k<h<1\); and \(\beta\) is the Newtonian viscosity of the dashpot. As for the other three parameters, they are determined implicitly using \(\tau\), the time characteristic, and calculated as follows.  
\[Ln(\tau)=a+bT+cT^{2}, \tag{5}\]  
\(a,\ b,\) and \(c\) are regression constants representing the material characteristics.  
The parameters of the 2S2P1D model were fitted by non-linear regression and successive approximations until a good visual fit was obtained between the curve given by the models and the experimental results. The method for determining these parameters involved minimizing the sum of the distances between the experimental dynamic modulus and that of the model, introduced at N points of pulsation \(\omega_{i}\) (Least Square Method). This minimisation was performed at the reference temperature \(T_{R}\) = 20 \({}^{\circ}\)C using the MS Excel SOLVER function as follows:  
\[Min\ \left(\sum_{i=1}^{N}\biggl{(}\left[E_{1}^{exp}(\omega_{i})-E_{1}^{model}( \omega_{i})\right]^{2}+\left[E_{2}^{exp}(\omega_{i})-E_{2}^{model}(\omega_{i}) \right]^{2}\right), \tag{6}\]  
\(E_{1}^{exp}\) and \(E_{2}^{exp}\) are, respectively, the real and imaginary parts of the experimental dynamic modulus \(E^{exp}\). \(E_{1}^{model}\) and \(E_{2}^{model}\) are, respectively, the real and imaginary parts of the dynamic modulus \(E^{model}\) given by the Generalized Huet-Sayegh model.
## 3 Efficient Network AOS Comprehensive Multiplexing Algorithm
### Asynchronous Non-Real-Time Traffffic  
#### 3.3.1 Frame Generation Algorithm  
In the packet multiplexing layer, a high-efficiency frame generation algorithm is employed for asynchronous non-real-time traffic, where a frame is generated only when the total length of the incoming user data packets exceeds the transmission frame data area length, resulting in a frame generation efficiency of 1. Assuming there are \(N_{e}\) virtual channels for asynchronous non-real-time traffic, the frame generation algorithm for the virtual channel \(p=(0,\;1,\;2\ldots N_{e}-1)\) can be expressed as follows:  
\[F_{e\_p}=\left\{\begin{array}{ll}1,&v_{e\_p}\geq V\\ 0,&v_{e\_p}<V\end{array}\right. \tag{14}\]  
In the formula, \(F_{e\_p}\) represents whether a frame is generated for the \(p\)-th VC at the current moment. A value of 1 indicates that a frame is generated, while 0 indicates that no frame is generated. \(v_{e\_p}\) denotes the length of the data packets that have arrived for the \(p\)-th VC at the current moment.
## 3 Methodology
### Preliminary
Based on Equation (3), \(\hat{p}\) comprises the signal of projection \(p\) and the noise of projection \(n\). Therefore, Equation (6) can be rewritten as follows:  
\[\hat{f}(x,y) =\int_{0}^{2\pi}((p+n)*g_{\infty})(\theta,ycos\theta-xsin\theta)d\theta \tag{7}\] \[=\int_{0}^{2\pi}(p*g_{\infty})(\theta,ycos\theta-xsin\theta)d \theta+\int_{0}^{2\pi}(n*g_{\infty})(\theta,ycos\theta-xsin\theta)d\theta\]  
From the above, we assume that the projection data comprise a continuous signal. However, in a practical situation, the projection data are the sampled signal. The noisy projection \(\hat{q}[i,\ k]\) sampled from the continuous projection \(\hat{p}(\theta,t)\) can be defined as follows:  
Figure 2.0  
where  
Figure 2.1  
In addition, the following is true:  
Figure 2.2  
where \(\Delta t\) indicates the pixel pitch of the detector and \(t_{max}\) denotes the half-length of the detector. Additionally, \(N_{\theta}\) means the number of angles sampled at intervals of \(\Delta\theta\), and \(N_{t}\) means the number of pixels sampled at intervals of \(\Delta t\). Thus, Equation (6) can be rewritten as follows using the noisy sampled projection \(\hat{q}\):  
\[ \begin{array}{rl}\hat{f}(x,y)&=\frac{2\pi}{N_{\theta}}\sum_{i=0}^{N_{\theta} -1}(\hat{q}*g_{\infty})[i,ycos\theta-xsin\theta]\\ &=\frac{2\pi}{N_{\theta}}\sum_{i=0}^{N_{\theta}-1}(q*g_{\infty})[i,ycos\theta -xsin\theta]+\frac{2\pi}{N_{\theta}}\sum_{i=0}^{N_{\theta}-1}(n_{d}*g_{\infty })[i,ycos\theta-xsin\theta],\end{array}  \tag{11}\]where \(n_{d}\) denotes the noise of the sampled projection \(\hat{q}\). Equations (7) and (11) indicate that the ramp-filtered projection and ramp-filtered noise within the projection are integrated into the reconstructed space along the ray. The results demonstrate why the noise in the LDCT reconstructed image is not pixel-wise independent. Therefore, the results of blind spot strategy-based noise reduction have a problem with emphasizing artifacts caused by photon noise. One method to solve this problem is noise reduction in the projection domain. Noise reduction methods based on the blind spot strategy satisfy the assumptions of the blind spot strategy, because noise in the projection domain is pixel-wise independent. These methods reconstruct the 3D image from projection images that are already noise-reduced. However, projection images for LDCT have extremely weak signals. Thus, it is quite challenging to perform noise reduction in the projection image domain without over-smoothing. To solve these problems, we propose a _DPS_-based noise reduction method. The following subsection describes the details regarding the proposed _DPS_.
# Designing a Conceptual Digital Twin Architecture for High-Temperature Heat Upgrade Systems
## 3 Digital Twin Architecture for Heat Upgrade Systems
### Tech Stack and Implementation Considerations
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Requirement** & **Potential Technology** & **Purpose** \\ \hline Bidirectional connection & MQTT, OPC-UA & Collecting real-time data from sensors and IoT devices, and pushing it to a cloud for further processing; \\  & & Receive commands and control parameters \\ Protocol translation & Eclipse Hono & Connect a large number of IoT devices in a unified way \\ Data Storage & InfluxDB, Prometheus & Time series database \\ Data analysis, AI, & Tensorflow (v2.16.1), & Prediction and optimization \\ and ML & Pytorch (v.2.4) & Integration of multiple simulation tools \\ Simulation Interface & FMI standard & Real-time 3D visualization of the physical system, interactive interfaces \\
3D Modeling and & Unity (v.6), Unreal & Real-time data dashboards and monitoring metrics \\ Rendering & Engine (v.5.3) & Containerization, orchestration, scaling, and management of distributed apps \\ Data Visualization & Grafana (v.10.4) & Cloud-based processing, storage, and infrastructure for digital twin \\  & & \\ Deployment and & Docker (v.28.0.0), & Containerization, orchestration, scaling, and management of distributed apps \\ Cloud Infrastructure & AWS, Microsoft Azure, & Cloud-based processing, storage, and infrastructure for digital twin \\  & & \\ \end{tabular}
\end{table} 
Table 2: Overview of digital twin requirements and technologies that can satisfy it.
## 3 Simulation
### Integrated Analysis  
Integrated analysis supports structure design and verification of high-level optical requirements for image quality and sensitivity [26; 27; 28]. It can also extrapolate the effect of microvibrations on opto-mechanical stability [29]. The finite element model (FEM) of the satellite was established by software (Patran), as shown in Figure 5. The lift-off vibration test was performed to verify the correction of FEM, focusing on the spectrum characteristics of optical components, as shown in Figure 6. The first order natural frequencies of the satellite in the \(X\!/\!Y\!/Z\) direction were 25, 30, and 135 Hz, respectively. The first order mode shapes in the \(X\!/\!Y\) direction were shown as the horizontal swing, and the first order mode shape in the \(Z\) direction was shown as axial translational motion. Moreover, unit force/moment was applied, respectively, at the flywheel mounting position, and the displacements of three specific points on each optical component were obtained. The analysis was performed using nominal 0.1% damping of the critical value in the satellite structure [6]. With the displacements of three specific points, the displacement and angular displacement of each optical component could be calculated by a three-point fitting algorithm. After multiplying the measured perturbation in each DOF by the displacement and angular displacement of the optical component, the results were transformed to time domain by IDFT. Finally, integrated with the optical amplification factor of each optical component and sum, the decentration of the central image point on the focal plane could be calculated by MATLAB processing. The pixel offset normal to the flight direction of the whole optical system after FFT is shown in Figure 7.  
Figure 5: Satellite finite element model (FEM).  
Figure 6: The lift-off vibration test of the satellite.  
As shown in Figure 7, the influence on pixel offset was mainly concentrated at 330 Hz due to axial perturbation and 390-450 Hz due to lateral perturbation, respectively. The maximum pixel offset reached 0.382 pixels at 419.8 Hz.
# Anomaly Detection of CAN Bus Messages Using a Deep Neural Network for Autonomous Vehicles
## 2 Related Work
### Triplet Loss Network  
The triplet loss is a loss function in deep learning which is used to train samples with small differences, such as handwriting, faces, etc. The feed data packet includes positive and negative anchor samples. The similarity calculation of samples can be achieved by optimizing the distance between anchor samples and positive samples to be less than that between anchor samples and negative samples. Nowadays, the triplet loss network is widely used in face recognition and object detection. For example, the literature [19] presents the use of triplet networks to solve the problem of local image descriptor learning. For the challenge of text-independent speaker verification against short utterances, the literature [20] presents an end-to-end system that directly maps learning speech features to compact fixed-length speech discrimination embedding. The system uses Euclidean distance to measure the similarity between experiments. In order to learn feature mapping, an improved priori network with residual blocks is proposed to optimize the triple loss function. In this paper, an advanced CAN bus anomaly detection system is presented for intelligent vehicles, which integrates DNN technology and a triple loss network. Firstly, the system extracts data features as a set of vectors through the deep network, and then calculates the similarity between two real-time extracted CAN data sequences and another calibrated data sequence from three data sequences using the triple loss to find out the abnormal data.
## 3 A Taxonomy of Measuring Methods, Techniques, and Sensors in Physiological-Based DMSs
### Infrared Thermography (IRT)  
Unlike visible and near-infrared imaging, far-infrared (FIR) thermography, also known as infrared thermography (IRT), is a passive method that relies solely on the thermal radiation from the driver depending on the temperature. This method has the advantage of independence from changes in ambient lighting. The beginning of physiological monitoring through the application of facial thermography starts with measuring the breathing dynamics (Figure 3f). In 2015, [PERSON] et al. [67] demonstrated that it is possible to track the nostrils and monitor the breathing dynamic not only in static subjects, usually sitting in a chair, but also during head movements. However, depending on the size of the subject, the angle of attack, and the individual tidal volume, the temperature changes during inhalation and exhalation are usually less than 1 degree Celsius. The applications of thermal cameras for continuous respiration monitoring could be extended to evaluation of cessation of breathing and respiratory rate. However, several factors, including the placement of cameras and the movement of the subjects, can influence their accuracy and reliability in these applications. Subsequently, [PERSON] et al. [68] found that the cyclic ejection of blood from the heart to the head through the carotid arteries leads to periodic, mechanical, and vertical micromovements of the head and can also be used to monitor the HR using FIR thermography. In 2020, [PERSON] et al. [69] presented a drowsiness detection system using facial thermal imaging to acquire the driver's respiration signal. Both the respiration rate and inspiration-to-expiration time ratio were calculated and used as input features, in order to train two machine learning classifiers. Based on the results from experiments conducted in a car simulator with thirty participants, they reported that both classifiers were able to detect drowsiness and achieved 90% accuracy, 85% specificity, 92% sensitivity, and 91% precision. Although not yet widely used in DMSs, the biggest advantages of this method are no energy is transmitted to the test subject, there is no harmful radiation, and thermal cameras can be used in complete darkness. Furthermore, by detecting infrared radiation generated by the body, non-contact continuous body temperature monitoring can be performed.
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 3 Proposed Search Algorithm, a Set of Behaviors: A1
### Search Behavior
#### 3.1.3 Layers of Pheromones  
The actual usefulness of pheromones is to transmit information about observed and non-observed cells along the search area, so that the agents are aimed at zones with more unobserved cells. This way, it is expected that the observed cells generate and transmit less pheromone levels than the unobserved ones. However, if there exist unobserved cells, surrounded by observed cells, it is possible that these last ones act like a barrier, avoiding an effective transmission of the information by the pheromones. On the other hand, it is our experience that it is useful to try to organize the search by prioritizing the observation of isolated (i.e., with high \(\theta\) values) cells surrounding each agent. In order to address these issues, we propose a multi-layer pheromone system made up by three independent layers:  
* Standard layer (L1): There is no impact of the isolation index on this layer, and it has different diffusion coefficients for observed and unobserved cells.
* Long-range layer (L2): Intended to transmit information from unobserved cells along the entire search area. The diffusion coefficients of observed and unobserved cells are equal, and the observed cells do not produce pheromones.
* Isolation layer (L3): This mainly considers pheromone creation due to the isolation index. Once the cells are observed, they lose their pheromone concentration at this layer.  
In Table 3, the parameters for the three layers are summarized. Each parameter has been named with a super-index to indicate the layer to which they belong, except for \(\rho_{\Delta\theta}\) and \(\rho_{\theta}\), which only apply to Layer 3.