esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Followed Steps  
In this investigation, a mathematical model is created by using the equations of Section 2.2 and it is validated with literature data which are presented in Section 2.3. The first step in this analysis is the examination of the unit in the default design scenario and the results are presented in Section 3.1. The next step is the presentation of a simple parametric analysis which is given in Section 3.2. The third step is a detailed parametric analysis with the most interesting parameters; the turbine inlet temperature and pressure levels. These results are presented in Section 3.3 and also an optimization analysis is presented in this subsection. Section 3.4 includes results about the daily, monthly and yearly performance of the examined system. The last part of Section 3 is Section 3.5 which regards the financial analysis, the environmental analysis and further discussion of the found results. Table 6  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Inputs**} & \multicolumn{3}{c}{**Collector Efficiency**} \\
**G\({}_{\text{b}}\) (W/m\({}^{2}\))** & **T\({}_{\text{in}}\) (K)** & **This Study** & **Literature [29]** & **Deviation** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 4: Validation evidence for the PTC model [29].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Literature [17]** & **This Study** & **Deviation** \\ \hline Thermodynamic cycle efficiency (-) & 0.0475 & 0.04723 & 0.568\% \\ CO\({}_{2}\) mass flow rate (kg/s) & 174.3 & 176 & 0.975\% \\ Net cycle work (kW) & 1620 & 1612 & 0.494\% \\ Turbine work (kW) & 3178 & 3184 & 0.189\% \\ Pump work (kW) & 1558 & 1572 & 0.899\% \\ Heat rejection (kW) & 32,510 & 32,518 & 0.025\% \\ Temperature after the pump (\({}^{\circ}\)C) & 29.71 & 29.76 & 0.168\% \\ Turbine temperature outlet (\({}^{\circ}\)C) & 35.09 & 34.73 & 1.026\% \\ Low cycle pressure (bar) & 58.57 & 58.65 & 0.137\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Validation evidence for the thermodynamic cycle [17].  
includes the studied parameters, their default values and the examined range of their values. During the optimization of the cycle, the conjugate directions method is applied with the goal to maximize the system efficiency. The optimization variables are the pressure level in the turbine inlet and the temperature level in the turbine inlet. The optimization is conducted with the EES [32] by using its proper module.  
The daily analysis is done for the city of Athens in Greece. The data about the solar direct beam irradiation have been taken by Ref. [36] and 12 mean monthly days are examined. Moreover, for each month, a proper number of sunny days have been used, as they have been presented in Ref. [44]. More specifically, for the months from January to December, the respective sunny days are the following: 13-10-15-18-20-22-29-20-18-17-15. Totally, 226 days are assumed that the examined system can operate which is a reasonable number for the climate conditions of Athens.
## 2 Theory Background and Methodology  
The method proposed in this paper is based on the ESM and CS theory. Firstly, rooted on the principle of ESM, the sound field model can be established. And then, two parts called an equivalent dictionary under the sparse assumption and a series of orthogonal basis obtained by SVD are combined to form an equivalent redundant dictionary. On this foundation, ERDCESM is presented under the CS framework. Moreover, to further improve the dynamic range of sound source identification and location, a reformative method which takes advantage of FB is developed.
## 5 Experiments
### Observation Time Window Study  
To provide a more comprehensive answer to whether our model is more robust than other models regarding RQ 1, we conducted experiments using observation time windows of various sizes. For the observation time window \(T\), we made four settings, namely \(T\) = 0.5 h, 1 h, 2 h, and 3 h; these corresponded to the time when the popularity reaches roughly 50%, 60%, 65%, and 70% (Fake) (45%, 55%, 60%, and 65%, Real) of the final popularity, respectively, as depicted in Figure 9.  
* Observation time window \(T\) = 3 h, time interval \(\lfloor\frac{T}{n=3}\rfloor=1\) h;
* Observation time window \(T\) = 2 h, time interval \(\lfloor\frac{T}{n=3}\rfloor=40\) min;
* Observation time window \(T\) = 1 h, time interval \(\lfloor\frac{T}{n=3}\rfloor=20\) min;
* Observation time window \(T\) = 0.5 h, time interval \(\lfloor\frac{T}{n=3}\rfloor=10\) min. \(\lfloor\cdot\rfloor\) means the floor operation. The evaluation results are shown in Table 9. We visualize this table for illustration. Focusing on just ExoFIA, as visualized in Figure 15, we observe that the value of 1P (prediction on larger cascades) is most affected by observation time. This is because larger \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Variants** & **HR@IP** & **HR@IP** & **HR@IP** & **HR@IP** & **NDCG@IP** & **NDCG@SP** & **NDCG@IP** & **NDCG@1 SP** & **MSE** \\ \hline ExoFIA-Trend & 0.724 & 0.787 & 0.791 & 0.753 & 0.797 & 0.831 & 0.849 & 0.869 & 0.056 \\ ExoFIA-Social & 0.729 & 0.792 & 0.791 & 0.748 & 0.765 & 0.807 & 0.830 & 0.857 & 0.061 \\ ExoFIA-Diff & 0.715 & 0.786 & 0.792 & 0.756 & 0.774 & 0.817 & 0.843 & 0.869 & 0.059 \\ ExoFIA & **0.753** & **0.799** & **0.797** & 0.754 & **0.825** & **0.853** & **0.868** & **0.882** & **0.049** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Prediction performance of variants of ExoFIA when observation time is set to 3 h. The best result in each column is in boldface.
## 4 Discussion  
The following sections present the findings of our study, focusing on the relationship between typeface shape and reading outcomes. This research explored how cross-modal correspondences between typeface shapes and taste (specifically sweetness) influence perceived pleasantness, reading fluency, and text comprehension. This analysis was conducted across two age groups--children (elementary school students) and adults (university students)--to examine potential differences in their perception and performance.  
By integrating subjective ratings (sweetness and pleasantness ratings) with objective indicators of reading fluency (reading speed and text comprehension), our study provides a more comprehensive insight into cognitive and emotional processes involved in reading. In this section, we address the role of typeface shape in facilitating reading fluency, the limitations of this study, and suggestions for future research directions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source of Variability** & \(\mathbf{b}\) & \(\mathbf{SE_{b}}\) & **OR** & **95\% CI for \(\mathbf{OR}\)** & \(\mathbf{z}\) & \(\mathbf{p}\) \\ \hline Intercept & 2.19 & 0.13 & 8.90 & [6.95, 11.37] & 17.39 & \textless{}0.001 \\ \hline Typeface sweetness & 0.14 & 0.10 & 1.15 & [0.95, 1.39] & 1.46 & 0.14 \\ \hline Age group & 0.67 & 0.25 & 1.96 & [1.20, 3.21] & 2.67 & 0.008 \\ \hline Typeface sweetness & \(\times\) Age group & 0.14 & 0.19 & 1.15 & [0.79, 1.68] & 0.73 & 0.47 \\ \hline \hline \end{tabular} Note. \(OR=\) odds ratio.  
\end{table}
Table 4: Results of generalised mixed modelling (with binary dependent variable) used for predicting text comprehension based on typeface sweetness ratings and age group.  
Figure 6: Effect of typeface sweetness and age group on indicators of reading fluency: (**A**) reading speed and (**B**) text comprehension.
# A Survey on Network Optimization Techniques for Blockchain Systems
## 5 Network Optimization Techniques Used in Blockchain Networks
### Consensus Layer
#### 5.2.4 Congestion  
[PERSON] et al. [91] designed a framework for blockchain-based IoT systems. They artificially adjusted the PoW algorithm's difficulty as a means to limit congestion in the network. They used a queuing system between the side chains, and the main blockchain decides the optimal number of blocks that must be admitted and mined at any particular time in the main blockchain. By reducing PoW's difficulty, an increase in transactional throughput was realized. However, this work suffers from bandwidth inefficiencies of a traditional blockchain. Since this approach uses a reduced PoW difficulty to prevent queue saturation, there will be a higher occurrence of forking rates at high scales.
# Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion
## 2 Literature Review
### Temperature Inversion  
Air pollution is more serious under particular atmospheric stagnation conditions. Normally, the temperature in the troposphere (from 0 to 11,000 m elevation) decreases by 0.65 degrees Celsius for every 100 m of elevation. The temperature variation phenomenon promotes the convection of aerosols (see Figure 1a). However, at occasional weather conditions such as the radiation cooling at night, the cold air plunges down through the warm air and the warm air forms a cap above the ground-level cold air, causing the temperature layer to be inversed. Such temperature inversion will trap the convection of the surface air limited below the warm-air cap and exacerbate the air pollution as shown in Figure 1b [16,17]. It was indicated in [18] that the PM\({}_{2.5}\) concentration is raised up by 54% due to the temperature inversion at the three observation sites investigated in their study. [PERSON] and [PERSON] [19] investigated the pollution days observed in Fairbanks, Alaska, and they found that all of the 128 winter pollution days during the period between 2004 and 2009 are related to phenomenon of the temperature inversion.  
Puli in Taiwan is a basin township surround by mountains 1000 to 2000 m tall, easily encouraging temperature inversion at nights. Air pollution is therefore hard to dissipate if the wind is calm. This motivates us to explore the relationship between meteorological variables (air pressure, temperature, relative humidity, and wind speed) of temperature inversion and the PM\({}_{2.5}\) concentrations, in order to enhance the prediction accuracy by discriminating between the days with and without temperature inversion. We hypothesize that such a prevailing predictor for PM\({}_{2.5}\) concentrations can be trained by partitioning the training data into the two salient classes.
# A Robust Intelligent Controller for Autonomous Ground Vehicle Longitudinal Dynamics
## 3 Controller Development
### Robust Adaptive RBNN-SMC
#### 3.3.3 Robust Law
\begin{array}{l}\dot{\mathcal{L}}\leq-q\mathcal{Z}^{2}-o|\mathcal{Z}|+\left| \overline{\mathfrak{M}}\right||\mathcal{Z}|+\left|\frac{1}{q_{1}}\widetilde{ \omega}^{T}\dot{\widetilde{\omega}}+\frac{1}{q_{2}}\overline{\mathfrak{h}}^{T} \dot{\overline{\mathfrak{h}}}+\frac{1}{q_{3}}\widetilde{c}^{T}\dot{\widetilde {c}}\right|\\ =-q\mathcal{Z}^{2}-o|\mathcal{Z}|+\left|\widetilde{\omega}^{T}\mathcal{H}( \mathcal{Z})+\omega^{T}\mathcal{H}_{\mathfrak{h}}^{\prime}(\overline{ \mathfrak{h}})\overline{\mathfrak{h}}+\omega^{T}\mathcal{H}_{\mathfrak{h}}^{ \prime}(\widetilde{c})\widetilde{c}+\mathfrak{J}_{1}+\mathfrak{J}_{2}\right|| \mathcal{Z}|\\ +\left|\frac{1}{a_{1}}\widetilde{\omega}^{T}\dot{\widetilde{\omega}}+\frac{1}{ a_{2}}\overline{\mathfrak{h}}^{T}\dot{\overline{\mathfrak{h}}}+\frac{1}{a_{3}} \overline{c}^{T}\dot{\widetilde{c}}\right|\end{array}  \tag{34}\]  
According to Equation (24), Equation (34) becomes:  
\[ \begin{array}{l}\dot{\mathcal{L}}\leq-q\mathcal{Z}^{2}-o|\mathcal{Z}|+| \mathfrak{J}_{1}+\mathfrak{J}_{2}||\mathcal{Z}|+\left|\widetilde{\omega}^{T} \right|\left(\left|\mathcal{H}(\mathcal{Z})||\mathcal{Z}|-\frac{1}{a_{1}} \middle|\dot{\omega}\right|\right)\\ +\left|\overline{\mathfrak{h}}^{T}\right|\left(\omega^{T}\mathcal{H}_{ \mathfrak{h}}^{\prime}(\overline{\mathfrak{h}})\middle||\mathcal{Z}|-\frac{1}{ a_{2}}\middle|\dot{\mathfrak{h}}\right|\right)+\widetilde{c}^{T}\left(\omega^{T} \mathcal{H}_{\mathfrak{h}}^{\prime}(\overline{\mathfrak{h}})|\mathcal{Z}|- \frac{1}{a_{3}}\middle|\dot{\widetilde{c}}\right|\right)\end{array}  \tag{35}\]  
From Equation (27), we can achieve:  
\[ \begin{array}{l}\dot{\mathcal{L}}\leq-q\mathcal{Z}^{2}-o|\mathcal{Z}|+| \mathfrak{J}_{1}+\mathfrak{J}_{2}||\mathcal{Z}|\\ =-q\mathcal{Z}^{2}-(o-|\mathfrak{J}_{1}+\mathfrak{J}_{2}|)|\mathcal{Z}|\end{array}  \tag{36}\]  
Using the condition \(|\mathfrak{J}_{1}+\mathfrak{J}_{2}|\leq o\) results in \(\dot{\mathcal{L}}\leq 0\). Asymptotic stability is guaranteed under [PERSON]'s theory. The proof is thus complete. To conclude this conceptual section, the new proposed process is outlined visually in Figure 5.
## 4 Results
### Signal Processing Results  
#### 4.2.1 Signal-to-Noise Ratio Calculation  
A detailed description of the method that we use to estimate the quality of EBI signals can be found in [23]. To summarize the main steps of the method, it works as follows:  
1. The continuous wavelet transform is carried out on the measured signal.
2. Wavelet ridges are identified.
3. Both energy and duration of wavelet ridges are computed and normalized to the total energy and duration of the measured signal.
4. Obtaining the weighted ridge energy by multiplying both quantities from step 3.
5. Median filter is applied to the weighted ridge energy to estimate the extent of noise.
6. Finally, an SNR is calculated as a ratio of squared weighted ridge energy summed over all ridges divided by the estimated signal noise. SNR is expressed in decibels through the log10 transformation.
# Performance and Evaluation of GNSS Receiver Vector Tracking Loop Based on Adaptive Cascade Filter
## 1 Introduction
This paper is organized as follows: Section 2 explains the design of cascaded adaptive vector tracking loop. It mainly includes the basic principles of the signal tracking loop method based on the KF and the extended Kalman navigation filter. The method of adaptively adjusting the noise covariance matrix is also mentioned. Section 3 mainly covers experiments using a field vehicle and analyzes the navigation and positioning solution results, demonstrating the validity of the proposed method. Discussion and conclusions are drawn in Section 4.
# Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery
## 2 Blister Defect Detection for PLB Based on CNN
### Detection Scheme Based on CNN  
Blister defect detection problem of PLB is to judge whether PLB sheet has blister from images. It can be considered as an image classification problem. CNN method has achieved great success in the image classification applications. In this paper, an optimization based CNN method is proposed to detect blister defect of PLB sheet.  
As an important deep learning model, CNN uses an architecture of multi-layer stack. Each layer in the stack can be considered an input-to-output conversion that is used to achieve selective extraction of image feature representations. CNN learns the mapping relationship between a large number of input samples and outputs with a combination of input layer, convolution layer, ReLU layer, pooling layer and fully connected layer. Input layer completes image preprocessing. The convolution layer  
Figure 1: Image capture of PLB sheets.  
Figure 2: Blister defect samples of PLB sheet.  
implements the perception of local feature information of the image. These local information will be combined at a higher level to get global information. The convolution layer also greatly reduces the amount of computation through parameter sharing, and it extracts different features by employing multiple kernels. ReLU layer performs a nonlinear mapping of the output of convolutional layer. The pooling layer is used in the middle of a continuous convolutional layer to reduce overfitting and compress the amount of data and parameters. Fully connected layer is used to achieve the final classification using the advanced features extracted from the previous layers. The major feature of CNN is the shared convolution kernel, which works well for high-dimensional data processing. Another feature is that it is not necessary to manually select features and train weights.  
Thus far, many successful architectures have been proposed for CNN, which include DenseNet [46], ResNet [47], VGG16, RCNN, etc., and some of them have been applied to defect detection [37; 38; 39; 40; 41; 42; 43; 44; 45]. VGG16 architecture is employed for pavement crack detection in [43]. Single shot multibox detector network is adopted to detect surface defects of container [44]. RCNN architecture is used for polymeric polarizer detection of liquid crystal display panel [45].  
In this paper, an efficient CNN based detection method is provided for blister detection in PLB sheets. The experimental results indicate that the proposed method is superior to other machine learning based methods.
# Super-Resolution Multicomponent Joint-Interferometric Fabry-Perot-Based Technique
## 4 Results and Discussion  
### Simulation Conditions  
In the simulation experiment, it is assumed that the \(B(v)\) range of the ground object spectrum is 1.0-2.0 \(\upmu\)m, that is, 5000-10,000 cm\({}^{-1}\). Let the maximal OPD of the two-beam interferometer be \(L\) = 3 cm; that is, the spectral resolution is 0.167 cm\({}^{-1}\), and the FPI satisfies 2\(nd\) = 2 \(L\), where the OPD scan step is \(l\) = 0.5 \(\upmu\)m. We consider this as an example for simulation verification. Because the input continuous spectrum cannot be used in the simulation process, the input spectrum is a discrete spectrum with an interval of 0.001 cm\({}^{-1}\), which is approximated as a continuous spectrum. MATLAB was used for this simulation.  
As shown in Figure 6, we used three input spectra: a cosine function with spectral period frequency \(\omega\) = 5 cm, a Gaussian function with a half-height width of 0.075 cm\({}^{-1}\), and atmospheric absorption spectrum data calculated from the US standard atmosphere (1976) and the HITRAN API.
## 3 Method
### Core of UAV-NeRF  
Based on Sat-NeRF [10], we propose UAV-NeRF, an end-to-end 3D surface reconstruction method specifically designed for drone imagery scenarios. This method samples points in the radiance field according to the geometric imaging model of the drone. It then performs radiance decomposition calculations based on the angles of light incidence at the moment of capture, allowing for a precise differentiation of energy among the incoming light rays. Furthermore, we introduce incident angle processing during both the model input and light ray stepping decoding stages, effectively embedding the geometric structure into the 3D reconstruction process.  
With radiance as a key factor, UAV-NeRF (as shown in Figure 2) can incorporate additional inputs and outputs. For a single point on a given ray, the inputs are as follows:  
* \(\mathbf{x}\): a three-dimensional vector of the location of a point.
* \(\mathbf{\omega}\): a three-dimensional vector encoding the direction of solar rays, which can be extracted from the elevation and azimuth angles \((\mathbf{\theta},\mathbf{\phi})\) according to the time and geographical position.
* \(\mathbf{t}_{f}\): an \(\mathbf{N}^{(\mathbf{t})}\)-dimensional learnable embedding vector, which learns the features of transient objects such as cars (cannot be explained by the shadow change in \(\mathbf{\omega}\)).  
* \(\lambda\): a three-dimensional vector of the incident angle: unlike in satellite remote sensing, the incident angle in UAV images fluctuates more at low altitudes, which may lead to more radiance variations.  
The decoding process of each point on the ray can be written as \(\mathcal{F}\colon(\mathbf{x},r\omega,\mathbf{t}_{j},\lambda)\mapsto(\sigma, \mathbf{c}_{a},s,\mathbf{a},\beta)\), and the outputs are as follows:  
* \(\sigma\): the volume density at location \(\mathbf{x}\).
* \(\mathbf{c}_{a}\): the albedo RGB color, which reflects the radiation properties of the current point \(\mathbf{x}\); in the UAV imaging process, decoding this item should also be related to the incident angle \(\lambda\).
* \(\mathbf{s}\): a shadow-aware shading scalar, which represents the probability that the current point is in the shadow and is decoded by \(\mathbf{x}\) and \(\mathbf{\omega}\).
* \(\mathbf{a}\): the ambient RGB color, which is independent of geometry and only related to the solar direction \(\mathbf{t}\).
* \(\mathbf{\beta}\): a transient scalar, which indicates the probability that \(\mathbf{x}\) is part of a transient object.  
To this end, we first studied how to construct a neural radiation field by sampling rays under the UAV view, and we then studied the NeRF model by considering the incident ray angle according to the UAV characteristics.  
Finally, we employed the same loss function as Sat-NeRF to optimize the model. The evaluation metrics for the model primarily included the PSNR and SSIM, which are related to novel view synthesis, as well as the MAE for the 3D surface model, detailed in Section 3.1. Both the ground truth DSM and the predicted DSM were resampled to a uniform resolution of 0.1 m \(\times\) 0.1 m.
# Cotton Terry Textiles with Photo- and Bio-Activity in a Model Study and Real Conditions
## 1 Introduction
It was found that the photocatalytic activity of TiO\({}_{2}\) NPs onto textile materials is influenced by crystal structure [29], particle size and shape [18], TiO\({}_{2}\) content [9], light type (UV and/or visible light [9], light intensity and irradiation time [10; 11; 30], as well as doping method [11]. The self-cleaning properties of textiles treated with ZnO NPs depend on ZnO concentration on the fibers and fabric preparations [31], relative humidity and water content on the fabric surface [17], as well as doping method [12]. In model studies, it was found that the efficiency of photocatalysis in the air is mainly influenced by parameters such as the type and surface of the photocatalytic material, the type, intensity and time of lighting, as well as the relative humidity of the air [3; 32; 33; 3; 34; 35]. Thus far, research on the efficiency of photocatalysis of modified textile materials has focused mainly on model conditions, concerning water and air disinfection [4; 16; 34]. Studies on TiO\({}_{2}\) modified photocatalytically active textiles concerned chitosan or cotton-chitosan and polyester, which showed biological activity against the following bacteria: _E. coli_, _S. aureus_, _B. cereus_[33; 34]. Photocatalytic solutions of textile materials, especially towels in visible light have not been described in real conditions. Therefore, the purpose of the present study included: (1) the assessment of selected self-cleaning properties of modified textiles; (2) the assessment of the antimicrobial activity of photocatalytically (TiO\({}_{2}\) and ZnO NPs) modified textile materials under model conditions; (3) the assessment of the microbiological contamination of the surface of photocatalytically modified towels, the microbiological contamination of air, and volatile organic compound and gas (carbon dioxide, carbon monoxide and nitrogen dioxide) concentrations in air under real conditions. Modification of cotton Terry textiles with above mentioned oxides is an attempt to obtain new kind of safe and environmentally friendly textile materials for multipurpose applications.