esa-sceva/satcom-chunk-collection huggingface dataset:
# Bridge Damping Extraction Method from Vehicle-Bridge Interaction System Using Double-Beam Model
## 6 Conclusions  
The authors of this paper examined bridge damping evaluation in relation to VBI systems. The damping ratio calculation method was obtained by simplifying a VBI system into a double-beam system and then solving the simplified model using the extended dynamic stiffness method. The main results are as follows:  
1. Numerical tests showed that the damping ratios of the double-beam finite element model and the VBI model were basically consistent with the values calculated with the simplified model. In other words, the simplified method proposed in this paper can accurately calculate the damping ratio of a VBI system.  
Figure 16: Bridge damping identification curve (12:00–12:59).  
Figure 17: Bridge damping identification curve (13:00–13:59).  
2. The numerical simulation results showed that the equivalent traffic flow stiffness obtained using the proposed PSO estimation method (5.072 \(\times\) 10\({}^{5}\) N/m) was very close to the real value (5.070 \(\times\) 10\({}^{5}\) N/m). The feasibility of the proposed method was verified by plotting the bridge damping identification curves for different number of vehicles and obtaining the damping estimates of the bridge and the equivalent traffic flow from the intersection of the curves.
3. The damping ratios obtained using the proposed method were less discrete compared to those directly obtained using monitoring data. These results indicated that the proposed method can be applied to practical engineering projects to extract bridge damping values, eliminate the influence of the vehicle-bridge coupling effect on bridge damping identification, and reduce the dispersion of damping identification.  
Conceptualization, F.H. and Y.X.; methodology, F.G.; software, F.G.; validation, F.H., Y.W., and Y.X.; formal analysis, F.G.; investigation, F.G.; resources, Y.X.; data curation, F.G.; writing--original draft preparation, F.G.; writing--review and editing, F.H. and Y.W; visualization, F.G.; supervision, Y.X.; project administration, Y.X.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the National Key R&D Program of China (2019 YFB1600702), Transportation Science and Technology Program of Shandong Province (2021B51), National Natural Science Foundation of China (51978508), and Technology Cooperation Project of Shanghai Qizhi Institute (SYXF0120020109).  
Not applicable.  
Data Availability StatementData are contained within the article.  
We would like to acknowledge the reviewers who provided constructive suggestions that helped us improve our work. We also acknowledge [PERSON] from Tongji University for his suggestions for this paper.  
The authors declare no conflict of interest.
# Additive Manufacturing of Variable Density Lenses for Radio Frequency Communications in X-Band
## 4 Prototype Manufacturing
### Metallization Process  
The feeding horn antenna must be a conductive element, so it must be coated with a homogeneous layer of electrically conductive material. This was performed using two successive electroless coating processes of nickel and copper [33]. This method uses a chemical reaction to bond particles of the desired metal to the surface of the polymer pieces [34] without the use of an electric current.  
Initially, the surface of the piece must be prepared to allow strong bonding with the metal deposition. The most common method for this involves chemical etching with strong oxidizing compounds such as chromium salts, which are highly corrosive and polluting. In this work, the chromium salt etching was replaced by a combination of a first shot blasting and a subsequent chemical etching with sodium hydroxide solution. Then, a catalytic layer of palladium is deposited on the surfaces of the pieces by immersion in an activating bath consisting of a \(PdCl_{2}\) and \(SnCl_{2}\) acidic solution, as described in [35]. Before the final metallization bath, an acceleration step is performed by treating the sample in an acidic solution to remove the non-reacted salts and the \(Sn(IV)\) and expose the \(Pd(0)\) to the autocatalytic reaction. This layer is responsible for initiating the chemical reduction reaction in which the metal adheres to the surface.  
The initial nickel coating process results in a relatively uniform and stable metallic deposition, although it may leave some small uncovered areas, particularly in the corners. Despite the uniformity of the layer, the nickel coating presents very low conductivity, so an additional copper bath is applied to fix minor defects and increase the conductivity.
# A Probabilistic Hyperspectral Imagery Restoration Method
## 5 Experimental Results and Analysis
### Performance Evaluation on Simulated Noisy Dataset
Figure 5: Visual comparison on band 70 of Washington dataset: (**a**) Clean/noise-free image band, (**b**) Noisy band, (**c**) Restoration result from VBM3D, (**d**) Restoration result from Godec, (**e**) Restoration result from SLrank, (**f**) Restoration result from the proposed method. Figure 6: _Cont._  
Table 1 and Figure 7 give the PSNR and SSIM values. Those values are calculated with all original clean image band and the noisy image band. Figure 7 illustrates the PSNR and SSIM values varied with different bands for different restoration methods, where the vertical axis denotes the PSNR/SSIM values and the horizontal axis denote the band number. In Figure 7, the curves colored with red, blue, green, and black represent the results of VBM3D, Godec, SLrank, and the proposed low-rank, respectively. It can be seen from Figure 7 that the proposed low-rank method has highest PSNR and SSIM value among all competing methods in most bands. The average PSNR and SSIM for all bands are given in Table 1, from which we can see the proposed method obtains better PSNR/SSIM compared with any other competing methods.
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 3 Results
### Statistical Results
#### 3.2.3 Weight Strategy  
The improvement of height and 3D errors obtained using the weight strategy on 14 days at 3 stations are presented in Tables 5-7. The improved RMSEs are highlighted in bold. Scintillation and standard parameters, respectively, improved the height RMSE on 6 and 8 out of 14 days, where the best improvement rates were 93.1% and 86.1%, respectively. For 3D errors, scintillation and standard parameters separately improved 3D RMSE on 8 and 7 out of 14 days, where the best improvement rates were 85.5% and 73.4%, separately. Thus, in weight strategy, standard parameters were comparable to scintillation parameters for improving both height and 3D errors according to the proportion of days with improvement and the best improvement rate. Furthermore, the improvement in height error was generally larger than that in 3D error.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**Original RMSE**} & \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Weighted RMSE**} \\ \cline{3-4}  & & **Height** & **3D** & & **Type** & **Height** & **3D** \\ \hline \multirow{8}{*}{**SJICU**} & 4 September 2017 & 0.0260 & 0.0345 & \begin{tabular}{c} Scintillation \\ \end{tabular} & 0.0609 & 0.0615 \\ \cline{1-1} \cline{2-6}  & 7 September 2017 & 0.0247 & 0.0311 & \begin{tabular}{c} Scintillation \\ \end{tabular} & **0.0052** & **0.0227** \\ \cline{1-1} \cline{2-6}  & 8 September 2017 & 0.1355 & 0.3454 & \
\begin{tabular}{c} Scintillation \\ \end{tabular} & 0.9587 & 0.9870 \\ \cline{1-1} \cline{2-6}  & 13 September 2017 & 0.0219 & 0.0329 &
\begin{tabular}{c} Scintillation \\ \end{tabular} & 0.1220 & 0.1436 \\ \cline{1-1} \cline{2-6}  & & & & & & \\ \cline{1-1} \cline{
# Fast Fourier Asymmetric Context Aggregation Network:
## 4 Experiments  
### Dataset  
To the best of our knowledge, there is currently no publicly available dataset featuring diverse categories of paired design sketches and fashion images. To address this gap, we constructed a dataset named SKFashion, based on [70], which includes a wide range of fashion categories. This dataset enables the evaluation of our method's performance in generating images for various types of apparel.  
Additionally, to further validate the robustness of our proposed approach under different conditions, we conducted extensive experiments on two publicly available datasets: VITON-HD [71] and Fashion-Gen [72].  
1. **SKFashion:** The SKFashion dataset consists of 20,421 paired design sketches and fashion images, each with a resolution of 256 \(\times\) 256 pixels. It covers dozens of fashion categories, including long sleeves, short sleeves, dresses, shorts, skirts, padded coats, jackets, and vests. Figure 9 presents a selection of image pairs from SKFashion. Following the setup in [56], we divided the SKFashion dataset into a training set containing 17,396 image pairs and a test set comprising 3025 image pairs.
2. **VITON-HD:** The VITON-HD dataset includes images of various upper-body clothing categories and is widely used in fashion image generation tasks such as virtual try-on.  
Figure 9: Partial data demonstration of the SKFashion dataset.  
We standardized the resolution of the clothing images in VITON-HD to 192 \(\times\) 256 pixels and utilized the garment structure edge maps extracted using DexiNed (Dense Extreme Inception Network for Edge Detection) [73] as design sketches. We divided the dataset into a training set comprising 10,309 sketch-clothing image pairs and a test set with 1010 pairs. Figure 10 illustrates a selection of image pairs from the VITON-HD dataset.
3. **Fashion-Gen:** The Fashion-Gen is a large-scale multimodal dataset designed for fashion-related tasks and is widely used in multimodal fashion image generation. We extracted clothing images from Fashion-Gen using [74] and generated corresponding design sketches by applying DexiNed to obtain structural edge maps of the garments. Following the setup in [53; 56; 57], we randomly sampled 10,282 sketch-clothing image pairs as the training set and 800 pairs as the test set. Figure 11 showcases a selection of image pairs from the Fashion-Gen dataset.  
Figure 11: Partial data demonstration of the Fashion-Gen dataset.  
Figure 10: Partial data demonstration of the VITON-HD dataset.
## 1 Introduction
### Study Population and Data Collection  
The participants were pediatric patients, and their parents, treated by diabetes teams at pediatric clinics in south-eastern Sweden. Letters were sent in 2009 to geographic populations of two clinics with approximately 200 and 250 patients, respectively, below the age of 19 years. The invitation was also presented on the open portal without geographical restrictions. Patients aged 12-18 years and mothers and fathers of all children were invited. They could respond by e-mail or by mail to an independent researcher who was not their practitioner.  
Of the 21 responders (17 females, 4 males), 7 were mothers (ages 37-49 years), 1 was a father, 1 was a boyfriend (age 17 years) of a young patient, and 12 were young patients (ages 12-21 years, median 15 years). All responders reported some experience with using the portal.  
Figure 1: A sample of practitioner information entitled “I made a mistake with the insulin!” with advice for specific emergencies. The text at the bottom of the screen includes “Written by...”, “Revised by...” (names, affiliations).  
The respondents were asked to write an essay about their positive as well as their negative experiences with using the open portal in 2009, irrespective of frequency and/or success in doing so. The decision to use essays was related to the fact that young Internet users frequently write their comments and opinions. Two leading questions were asked, followed by some clarifying questions:  
_\"**Tell me about a situation when you succeeded in using the portal.** Has the portal made managing the disease easier in any way? Are there any advantages in using the portal?_  
_Tell me about a situation when you did not succeed in using the portal.** Has the portal become an obstacle in some way? Are there any disadvantages in using the portal?\"_
# A Novel Alternating \(\mu\)-Law Companding Algorithm for PAPR Reduction in OFDM Systems
## 2 System Model
### Peak-to-Average Power Ratio (PAPR) Definition  
The PAPR of an OFDM signal is defined as the ratio of the maximum peak power to the average power, which the following function can express [6]:  
\[PAPR=\frac{\max\limits_{0\leq n\leq N-1}[|x[n]|^{2}]}{E[|\mathbf{x}|^{2}]}, \tag{3}\]  
where \(\max[\cdot]\) and \(E[\cdot]\) denote the largest value in a certain range and the expectation operator, respectively.  
The performance of PAPR reduction techniques is generally evaluated using the complementary cumulative distribution function (CCDF). CCDF is defined as the probability of PAPR exceeding the \(\alpha\). This paper uses the following formula to evaluate PAPR [38]:  
\[CCDF=P(PAPR>\alpha)=1-(1-e^{-\alpha})^{N}, \tag{4}\]  
\(\alpha\) is the threshold.
## 2 Material and Method
### Spatial Analysis  
The obtained noise pollution measurements constitute a data set linked to several locations with its geographical coordinates, longitude and latitude. By denoting the acoustic noise level at a location \(x\) as \(Z(x)\), we can define this data set as \(\{Z(x),\,x\in\mathcal{D}\}\), where \(\mathcal{D}\) are all the locations of the modeling set (68 positions).  
In this context, the objective of our model is the prediction of \(Z(x_{0})\) in any location \(x_{0}\), particularly those in the validation set. The noise reports contain information (as shown in Table 1) of the set of covariables included. Consequently, \(Z(x)\) is modeled as a tendency function of the influencing covariables in the process that explains its variability in a large extent plus some random error explaining the short term variability, i.e.,  
\[Z(x)=\mu(x)+\delta(x), \tag{1}\]  
where \(\mu(x)=E[Z(x)]\) and \(\delta(x)\) is a Gaussian process intrinsically stationary with zero mean, whose spatial dependence characterization is given by the variogram \(\gamma\)[30]:  
\[2\gamma(h)=\text{Var}\left[Z(x+h)-Z(x)\right]=\text{Var}\left[\delta(x+h)-\delta (x)\right], \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Time Period**} \\ \cline{2-6}  & **7 h–10 h** & **10 h–13 h** & **13 h–16 h** & **16 h–19 h** & **19 h–22 h** \\ \hline Mean & 62.117 & 63.714 & 63.503 & 62.596 & 62.435 \\ Std. Dev. & 0.719 & 0.723 & 0.580 & 0.649 & 0.683 \\ Median & 61.900 & 64.350 & 63.550 & 62.001 & 62.450 \\ Variance & 40.265 & 40.738 & 26.241 & 32.878 & 36.391 \\ Minimum & 46.1 & 48.5 & 51.6 & 43.4 & 49.9 \\ Maximum & 77.1 & 80.6 & 73.5 & 73.7 & 78.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of noise levels in different time periods.  
Figure 5: Modelling set (**black** dots); and validation set (**white** dots).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Df** & **Sum. Sq.** & **Mean Sq.** & **\(F\)-Value** & **\(P\)-Value** \\ \hline Time Period & 4 & 151.7 & 37.9 & 1.0742 & 0.3689 \\ Std. Dev & 385 & 13,591.5 & 35.3 & 1.0052 & 0.2567 \\ \hline \hline \end{tabular}
\end{table}
Table 3: ANOVA (Analysis of Variance) of noise levels in different time periods.  
where Var denotes the variance and \(h\) is an offset. In this study, the variogram has been calculated with the R statistical package for variogram fitting [31].
# Hybrid Crow Search Algorithm-LSTM System for Enhanced Stock Price Forecasting
## 2 Materials and Methods
### Data Preprocessing
#### 2.2.1 Sliding-Window Algorithm  
As the name suggests, a sliding window of fixed length moves in a specified direction over a given distance at each step. An example is shown in Figure 2. The value of a certain point is expanded to an interval that includes it in order to calculate statistical values and filter out turbulent volatility. The technique used in this paper is applied to various features, such as technical indicators, to effectively enhance data accuracy. Currently, this algorithm is widely used across various domains, such as convolutional neural networks, pooling layer calculation, and image recognition [28], as well as for moving-average calculations in finance, filling missing values in machine learning, and adjusting time-series data [29; 30].
# Protecting Private Communications in Cyber-Physical Systems through Physical Unclonable Functions
## 4 Performance Evaluation and Results
### Experiment Description
Each packet was configured to have 100 information bits. Time was divided into 5-h slots. In this time period, \(N_{packets}=1.8\times 10^{6}\) packets (or \(N_{bits}=1.8\times 10^{9}\) information bits) are transmitted. Then, the resulting amounts are enough to evaluate the bit error rate (BER). Thus, the BER in the stablished communication link was evaluated and measured for each different slot. The third experiment was performed using a simulation scenario similar to the previous one. Nevertheless, in this case, for each PUF configuration, cipher and decipher were built using 'cloned' PUF. The similarity level between PUF and the resulting key streams are measured and evaluated. For these three initial experiments, simulations considering PRNG were configured as follows: \(N=16\) to represent a common current situation; and the applied excitation (challenge) to the PUF consisted of a composition of 100 harmonics whose frequencies were calculated using an automatic algorithm (24). \[f_{k}=1000\cdot\frac{2k}{3} \tag{24}\]  
Later, in the fourth experiment, some modifications in the scenario were applied. The cipher and decipher were reconsidered to implement two identical PUF (two samples from a unique EB activated material). The probability of the cipher and decipher not being able to operate correctly due to malfunctions in the PUF is then evaluated. Different configurations and numbers of EB devices in the PUF were considered. With this experiment, the probability of a magnetic material so anisotropic that two samples from the same material do not behave in the same manner is analyzed. The same configuration parameters were employed in this fourth experiment for the cipher and the decipher. Simulations for these fourth initial experiments were repeated 12 times for each case. Each simulation was configured to represent 360 h of operation. The fifth and last experiment was completely different from all already described. With the objective of evaluating the effect of hardware limitations, especially in the context of CPS where resource constrained devices are usually employed, in the proposed architecture; a real implementation of the proposed solution was executed. We evaluated the consumption in terms of RAM memory, program space, and computational time. The experiment was repeated for different values of \(N\) (the bit-length of number in the PRNG). As a hardware platform, we employed an Arduino Nano board. It includes an AVR microcontroller, the ATmega328 microcontroller. It also has 32 KB of flash memory, 2 KB of SRAM memory, and 1 KB of EEPROM (which is rarely employed). The implemented PUF for this experiment was manufactured as indicated by [PERSON] [45]. Four different EB devices were employed in the designed PUF.
## 1 Introduction
We evaluate the effectiveness of ARP by simulations and compare its performance with previous schemes under a mobility model which combines both the social activities and the geographic movements. Simulation results show that ARP achieves a relatively longer network lifetime and higher message delivery ratio with lower transmission overhead and data delivery delay than both flooding and LABEL schemes. The main contributions of this paper are to answer these questions:  
1. How does the variation in node transmission ranking help us in data forwarding? 2. Why and how to design the message queue management scheme? Since the buffer queue of a sensor node is limited, queue management is another challenge for effective data delivering. 3. How well does ARP work? How does it compare to other routing protocols in our network model that reflect the characteristics of human mobility? The rest of the paper is organized as follows: Section 2 discusses related works. Section 3 discusses the motivation and the mobility model used in our paper. Section 4 introduces ARP. Section 5 shows the effectiveness of ARP via simulation. Section 6 concludes the paper.
## 2 Materials and Experimental Methods  
### Materials, Mixture Proportions and Specimens' Preparation  
There are two main constituents for manufacturing geopolymer concrete: geopolymer binder and aggregate. In this study, geopolymer binder is a mixture of fly ash and alkaline liquid. Sodium silicate (Na\({}_{2}\)SiO\({}_{3}\)) along with sodium hydroxide (NaOH) are mixed in the ratio of 2.5 by mass to create an alkaline liquid. The modulus ratio (Ms) of the sodium silicates solution was 3.3 (where Ms = SiO\({}_{2}\)/Na\({}_{2}\)O, Na\({}_{2}\)O = 8.37%, SiO\({}_{2}\) = 27.63%). The concentration of NaOH was 8M, where 'M' stands for the molarity of the concentration solution. Class F fly ash was obtained from a local coal power plant with a density of 2.5 g/cm\({}^{3}\) and a particle size range of 5-10 \(\upmu\)m, which is used as a solid binder for geopolymer preparation. The chemical compositions of the fly ash are presented in Table 1. Both fine aggregates (FA) and coarse aggregate (CA) in a saturated surface dry condition were used in this experimental work. The ratio of coarse to fine aggregates was 70% and 30%. River sand with fineness modulus of 2.54 was used as fine aggregate, while a crushed stone was used as coarse aggregate with a mixture of three groups: 5-10 mm (45%), 10-20 mm (50%), and 20-25 mm (5%).  
Geopolymer concrete specimens were manufactured by mixing fly ash, alkaline liquid, coarse aggregate, fine aggregate, and superplasticizer. Firstly, the alkaline liquid was initially prepared by mixing a NaOH and Na\({}_{2}\)SiO\({}_{3}\) solution according to suggestions from a study by [PERSON] [34] to promote better polymerization. Fly ash and the alkaline liquid was mixed for five minutes. Then, both CA and FA were added to the slurry and mixed for five minutes. Finally, the superplasticizer was poured into the mixture and mixed for three minutes. Fresh geopolymer concrete was cast and compacted into the cylindrical mold (150 \(\times\) 300 mm) and cured in an oven at 60 \({}^{\circ}\)C for 4, 8, and 12 h. For specimens used for the pullout test, the reinforcing bars embedded in the geopolymer concrete were carefully wrapped by PVC tube to manage the bonded length, which is 100 mm. Later, the fresh geopolymer concrete was poured into prepared molds and cured in an oven with the same conditions. In this research, specimens were mixed and cast with three mixture proportions and cured at three different conditions as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Oxide** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **K\({}_{2}\)O \& Na\({}_{2}\)O** & **MgO** & **SO\({}_{3}\)** & **LOI \({}^{*}\)** \\ \hline (\%) & 52 & 31.9 & 3.48 & 1.2 & 1.02 & 0.81 & 0.3 & 9.6 \\ \hline \hline \end{tabular} \({}^{*}\)LOI: loss on ignition.  
\end{table}
Table 1: Chemical composition of fly ash.