esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Time Domain Optimization
### Time Domain Optimization Results  
Based on the PSDs and CPSD discussed in Section 6, wind time series with a length of 8192 s for the rotor effective wind speed \(u_{eff}\) and its lidar estimate \(\hat{u}_{eff}\), using the optimal circularly-scanning CW lidar configuration for minimizing measurement MSE presented in Table 1, are generated. Next, simulations are carried out with the wind time series, the combined feedback-feedforward controller, as well as the wind turbine model described above. Here, \(3\times 9\) simulations are performed with nine different buffer times \(T_{buffer}\) and three different cutoff frequencies \(f_{cutoff}\). For each simulation, the standard deviation of the rotor speed is calculated (see Figure 8).  
In this case, the lowest value of 0.0796 RPM and thus the optimal result is achieved with a buffer time of 3.5 s and a cutoff frequency of 0.1 Hz. The standard deviation in the feedback-only case is 0.2078 RPM and, thus, an improvement of over 60% is obtained with LAC.  
Finally, Figure 9 (left) compares the transfer function calculated using the frequency domain model described in Section 6 and the simplified filter used in the simulations. This confirms that the best filter in practice is close to the transfer function. Figure 9 (right) compares the PSDs of the rotor speed estimated from both the feedback-only and combined feedback-feedforward simulations to the  
Figure 8: Standard deviation of rotor speed for different buffer times and cutoff frequencies using optimal circularly-scanning continuous wave (CW) lidar measurements.  
theoretical values calculated using frequency domain methods presented in Section 6. The estimated spectra from the simulations fit well to the model. This confirms that the feedforward controller combined with the simplified filter is close to the optimal configuration predicted in Section 6.
# Thermo-Responsive Shape-Memory Effect and Surface Features in Polycarbonate (PC)
## 3 Characterization of Heating-Responsive SME
### Influence of Programming Temperature and Strain (High Temperature Programming)
#### 3.2.2 Shape Recovery Ratio  
Heating-responsive shape recovery test was conducted in two steps. In the first step, the sample was heated to the previous programming temperature for 20 min. In the second step, the sample was  
Figure 8: Typical stress versus strain curves in uni-axial tension at different temperatures to maximum programming strains of 30% (**a**) and 100% (**b**) and then unloading after cooling to room temperature.  
Figure 9: Shape fixity ratio (\(R_{\beta}\)) against programming temperature with three different maximum programming strains.  
further heated to 180 \({}^{\circ}\)C for 20 min. Figure 10 presents \(R_{r}\) upon two-step heating in samples with maximum programming strains of 20%, 30% and 100%.  
As we can see, for samples programmed to 20% maximum programming strain, significant recovery is observed upon heating to the previous programming temperature in all samples, as the corresponding shape recovery ratio ranges from 67 to 89% and the average is over 80%. Upon further heating to 180 \({}^{\circ}\)C, all of them reach 100% shape recovery.  
For samples programmed to 30% maximum programming strain, upon heating to the previous programming temperature the shape recovery ratio is lower than those samples with 20% maximum programming strain, only around 60% to 70%. A higher programming temperature seemingly reduces the shape recovery ratio slightly. However, 100% shape recovery is still obtained upon further heating to 180 \({}^{\circ}\)C in all samples.  
For samples programmed to 100% maximum programming strain, the shape recovery ratio does not monotonically increase or decrease with the increase in programming temperature as that in samples programmed with 30% programming strain. It increases with the increase in programming temperature till 150 \({}^{\circ}\)C, in which the corresponding shape recovery ratio is about 40%, and then decreases if the programming temperature is higher. Different from those programmed to 20% and 30% strains, upon further heating to 180 \({}^{\circ}\)C, full shape recovery is only observed in samples programmed at 150 \({}^{\circ}\)C or below. A higher programming temperature over 150 \({}^{\circ}\)C results in less shape recovery. According to [4], this is most likely due to permanent plastic deformation in the elastic component/chain/segment of the PC at high programming strain and high programming temperature.
## 2 Related Work
### SegNet Network  
SegNet network is an end-to-end convolution neural network to achieve pixel-level classification. It consists of two parts, i.e., the encoding structure and the decoding structure, as shown in Figure 2. The encoding structure continuously extracts features of various scales of the image through the convolution layer and the pooling layer. The pooling process of SegNet is different from the traditional pooling process. In addition, SegNet uses the encoder to save the location information corresponding to the maximum value in the original feature map. The location information will be used during up-sampling. The feature map of the decoding structure is up-sampled, and the image is gradually restored to the original image size in combination with the encoded feature map to achieve pixel-level classification.  
The SegNet encoding process consists of five phases. The first two phases consist of two \(3\times 3\) convolution layers and a \(2\times 2\) pooling layer. The next three down-sampling stages include three \(3\times 3\) convolution layers and a \(2\times 2\) pooling layer with ReLU. In the max-pooling process, the encoder is used to record the position index of the maximum value. The decoding process is symmetric with the encoding process, and it consists of five phases. The first three phases consist of a \(2\times 2\) up-sampling layer and three \(3\times 3\) convolution layers. The last two phases consist of a \(2\times 2\) up-sampling layer and two \(3\times 3\) convolution layers. At the end of the network, the classification prediction results for each pixel are output by the softmax function. By introducing fully convolution layers, SegNet greatly reduces network parameters. It also improves the execution efficiency of the entire network, and  
Figure 1: U-Net network structure. Note that the figure is designed according to Reference [30].  
Figure 2: SegNet network structure. Note that the figure is designed according to Reference [29].  
realizes end-to-end pixel-level classification. The SegNet network has achieved good results in the semantic segmentation, but it still needs to be further improved in the feature extraction structure.
## 3 Results and Discussion
### ASE Spectra  
The ASE spectrum of B-co-MP in THF at a 400 uM concentration pre-\(\gamma\)-irradiation is shown in Figure 8. An Nd:YAG laser (\(\lambda\) = 355 nm) was used to excite the solution. The ASE peak was centered at 560 nm with a spectral bandwidth of 5 nm at the full-width half maximum. At this concentration, the ASE at 560 nm did not coincide with the fluorescence peak (580 nm). Previously, we demonstrated that B-co-MP formed ASE only as a monomer (560 nm) due to the MEH-PPV segment [17]. We exposed the same solution to different gamma doses (5 to 20 kGy), followed by laser excitation at an energy of 12 mJ. In Figure 8, the ASE peak was observed at 554 nm with an FWHM of 8 nm at 5 kGy. Doubling the gamma dose to 10 kGy yielded a blue shift to 535 nm with an FWHM of 11 nm. At 15 kGy, the ASE peak shifted to 506 nm with an FWHM of 13 nm. Interestingly, for the 20 kGy, we observed laser-induced fluorescence with an FWHM of 25 nm and no ASE detected.  
Figure 7: The main fluorescence peak wavelength (\(\lambda_{\text{max}}\)) of the B-co-MP in THF versus gamma doses for different concentrations.
## 5 Simulation Experiments and Analysis
### Performance Analysis
The LCE, the LCD, and the prob 0.5 have a little performance improvement when the popularity increases, while they are insensitive to changes in content popularity. Finally, because the popular content using CARF is more concentrated than the OPP, the CARF outperforms the OPP. The request rate is the number of interest packets sent by a satellite node per unit time. We set the cache size to 40% and the Zipf parameter to 1. Figure 15 describes the trend for the average number of hops under different request rates. The performance metrics for each cache policy did not change significantly as the request rate changed, which shows that the current request rate is within the capability of the satellite nodes. The proposed CARF outperforms other caching approaches for all different request rates, and compared to the OPP, it can reduce the average number of hops by around 15%. This means that the proposed CARF can reduce the IoT devices' energy consumption and the data retrieval delay. Figure 14: Impact of \(\gamma\): (**a**) Average content access delay; (**b**) Average hit rate; (**c**) Throughput. Figure 13: Impact of cache size: (**a**) Average content access delay; (**b**) Average hit rate; (**c**) Throughput.
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 4 Classification of Nanofluids Based on Base Liquid
### Methanol-Based Nanofluid
[PERSON] et al., also investigated the absorption performance of different methanol-based nanofluids containing different concentrations of silica and alumina particles. It was observed that the highest CO\({}_{2}\) absorption compared to pure methanol is achieved when 0.01 vol\(\%\) Al\({}_{2}\)O\({}_{3}\) or 0.01 vol\(\%\) SiO\({}_{2}\) are dispersed in the pure methanol, in this case, 4.5% and 5.6% improvement will occur in the absorption process, respectively [104]. In another investigation, [PERSON] et al., utilized the transient hot-wire method to determine the thermal conductivity nanofluids in which SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) are dispersed in methanol. The maximum increase in thermal conductivity was 14.29% when the nanoparticle concentration was in the range of 0.005-0.5 vol\(\%\)[142]. [PERSON] et al., utilized a bubble column to evaluate the performance of nanofluids containing Al\({}_{2}\)O\({}_{3}\) dispersed in methanol-based fluids in the absorption rate of CO\({}_{2}\). They found that the absorption rate of the nanofluid is 8.3% higher compared to the pure base fluid [105]. In the end, different base fluids for nanofluids for the improvement of CO\({}_{2}\) absorption in the literature are compared in Table 5. The results indicate that the nanofluids in these fluids showed proper activity as compared with those reported in earlier studies.
# Deep Neural Network-Based Guidance Law Using Supervised Learning
## Problem Statement  
### Equations of Motion  
The missile is attempting to hit the stationary target. The missile has a constant speed and hits the target by controlling the lateral acceleration command. Figure 1 shows the two-dimensional engagement geometry between the missile and the target.  
Here, \(R\) is the relative distance from the missile to the target, \(V_{m}\) is the speed of the missile, \(a_{m}\) is the lateral acceleration command of the missile, \(\lambda\) is the look angle, \(\psi_{m}\) is the heading angle of the missile and \(\Omega\) is the LOS rate. Let us define the LOS angle (\(\sigma\) ) as a contained angle formed between the look angle and the heading angle. The motion of the missile is expressed via nonlinear differential equations, such as Equations (1)-(4).  
\[\dot{R}=-\ V_{m}\cos(\lambda-\psi_{m}) \tag{1}\]  
\[\dot{\lambda}=\ \frac{V_{m}}{R}sin(\lambda-\psi_{m}) \tag{2}\]  
\[\dot{\psi}_{m}=\frac{a_{m}}{V_{m}} \tag{3}\]  
\[\sigma=\lambda-\psi_{m} \tag{4}\]  
Figure 1: Two-dimensional engagement geometry.  
The allowable range of lateral acceleration of the missile is set as follows:  
\[-10g\leq a_{m}\leq 10g \tag{5}\]  
where the acceleration of gravity is \(g=9.81\) m/s\({}^{2}\). This paper addresses the engagement scenario between the anti-ship missile and the target. It is assumed that the missile flies at a constant speed between Mach 0.5 and 0.95, and that the target is stationary.
## 3 Experimental Results  
Using the measuring system described in the previous section, measurements of output voltage and output power were performed in the domain of variable operating frequency [44,45]. Also measurements were conducted in open loop (directly, without additional resistance). First tests showed that the movement of the pendulum is supported by frequencies from 10 Hz to 15 Hz. Experimental tests gave important information about which environment the system can succeed in. The disadvantage can be short broadband of operational frequency, but it is possible to broaden it by application of heavier masses on additional inertial pendulums. Output signals were displayed on the oscilloscope and its results were stored. Then, visualization and proper calculations were performed in Matlab software. Measured values of output voltage were stored with a sampling rate of 100,000 S/s per channel. For the tests, seven different values of additional resistance were considered mainly close to the value of internal resistance of 12 coils in the system connected in series which value was 21.5 \(\Omega\). The most relevant characteristics of performed tests are collected in Table 3.  
After collecting the raw signal of output voltage, we calculated its root mean square value (2). After such calculations output power was calculated (3). All the results were visualized in the operating frequency domain (Figure 8).  
\[U_{RMS}\ =\ \sqrt{\frac{1}{N}\sum_{k=0}^{N-1}u(k)^{2}} \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Test Feature** & **Input Data** \\ \hline Operational frequencies [Hz] & 10; 11; 12; 13; 14; 15 \\ Internal resistance of the system [\(\Omega\)] & 21.5 \\ Additional resistance [\(\Omega\)] & 3.3; 6.5; 12.3; 17; 21.5; 25.7; 30.3 \\ Sampling rate per channel [S/s] & 100,000 \\ \hline \end{tabular}
\end{table}
Table 3: Characteristics of the test.  
Figure 7: Comparison of total magnetic forces values in the system for: (**a**) x = 1 mm (**b**) x = 13 mm, x is the distance between the additional magnet and the pendulum. Note that, the secondary peaks visible in Figure 7a originated from the finite size of magnet dipoles.  
An increase of the additional resistance value causes the increase of the voltage RMS value from the excited system. However, it can be expected that the mentioned value would not be as great and reach almost the limit of 30.3 \(\Omega\). Cross-checking measurements must be performed for higher values of additional resistance or by wider range of operating frequency. Unfortunately, design constraints hindered it. The results of RMS voltage value in open loop show its linear change with operational frequency. Tests with the open loop are not in practical usage, but they can show the limits of the system. The above presented results were only performed for the frequency range, where rotational movement was supported. To expand it, pendulum's moment of inertia must be changed. Moreover, for the optimization of the system's power efficiency, extended experiments can be performed in wider range of additional resistance [46].
# GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector
###### Abstract  
As the significance of meticulous and precise map creation grows in modern Geographic Information Systems (GISs), urban planning, disaster response, and other domains, the necessity for sophisticated map generation technology has become increasingly evident. In response to this demand, this paper puts forward a technique based on Generative Adversarial Networks (GANs) for converting aerial imagery into high-quality maps. The proposed method, comprising a generator and a discriminator, introduces novel strategies to overcome existing challenges; namely, the use of a Canny edge detector and Residual Blocks. The proposed loss function enhances the generator's performance by assigning greater weight to edge regions using the Canny edge map and eliminating superfluous information. This approach enhances the visual quality of the generated maps and ensures the accurate capture of fine details. The experimental results demonstrate that this method generates maps of superior visual quality, achieving outstanding performance compared to existing methodologies. The results show that the proposed technology has significant potential for practical applications in a range of real-world scenarios.  
GAN; aerial image; map generation; edge detection; image-to-image translation Article  
G 2024 14 10963 10.3390/app142310963
# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 3 System Model
### Machine Learning for Wireless Indoor Localization
#### 3.1.1 Artificial Neural Networks  
Artificial Neural Networks considers a powerful ML technique that can be applied to indoor localization systems. The basic working of an ANN is like the human neural network in our brain. The ANN follows the same structure of how brain neurons are interconnected. The ANN has three layers: the input layer, the hidden layer(s), and the output layer. The ANN has many advantages. Once the ANN is trained, the model will provide the output even with inadequate data. Moreover, ANN has a memory distribution, manages fault tolerance, and has a flexible network structure. The ANN performance is highly dependent on the hardware it runs.  
A Feed-Forward Neural Network (FFNN) is considered a sub-branch of ANN. An FFNN consists of at least one layer of neurons and input and output layers. The network's intensity can be observed based on the collective behavior of the connected neurons. The output is chosen by evaluating the network's output in its input context. The main benefit of this network is that it learns to assess and identify input patterns. Generally speaking, an Artificial Neural Network has three layers, named the input layer, the hidden layer, and the output layer. Few works in the literature have used FFNN for indoor localization, giving considerably good results. However, it was observed that ANNs require a considerable computational time and capacity.
# Stimuli-Responsive Photonic Crystals
## 4 Active Tuning of the Photonic Band Gap
### Mechanical Stimuli  
Mechanically tuned photonic crystals exploit the elastic properties of the constituent materials. In general, they are usually compounded by an elastomeric matrix [17; 39; 44; 47; 51] that actively responds to mechanical stimuli. In this case, the mechanical force deforms the polymer and this changes the periodicity of the lattice, thus changing the optical response [16]. [PERSON] et al. fabricated a 400 nm periodical linear grating made of a nanostructured polydimethylsiloxane membrane by nanoimprinting replication. Subsequently, a high refractive index TiO2 nanoparticle layer was spin-coated, showing a guided-mode resonance. The elastomeric behavior of the membrane allows for a 20% elongation when subjected to stretching, varying the resonance peak position up to \(\approx\)80 nm. In Figure 2e, it is possible to see the change of structural colors brought about by the mechanical deformation [31].  
Figure 2: Different cases of tunable photonic crystals. (**a,b**) Reflectance spectra and visual effect of PMMA-co-PHEMA-co-PEGDMA) and titania photonic crystals (PhCs) in different solvents. Adapted from reference [32] by permission of The Royal Society of Chemistry. (**c,d**) Effect of the magnet-sample distance on polyacrylate-capped superparamagnetic magnetite (Fe\({}_{3}\)O\({}_{4}\)) colloidal nanocrystal clusters (CNCs). Adapted from reference [110] by permission of John Wiley & Sons. (**e**) Changing of structural color of grating nanostructured polydimethylsiloxane membrane/TiO\({}_{2}\) as the elongation increases. Adapted from reference [31].
# Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing
## 3 Experimental Evaluation
### Evaluation Methods for the Segmentation
#### 3.1.2 Intersection of Union (IoU)  
IoU is the most popular evaluation method for object detection benchmarks. Many object detection methods in computer vision use a bounding box to indicate the interest object's location. IoU is calculated using the bounding box of the ground truth and the bounding box inferred from the object detection method. Equation (5) shows the calculation for IoU.  
\[\text{IoU}=\frac{\text{Ground truth area}\ \cap\text{Inferred area}}{\text{Ground truth area}\ \cup\text{Inferred area}} \tag{5}\]  
Object segmentation methods-based deep learning describes object location using pixel-wise, not bounding box. So, we calculate IoU using the pixel count but with the bounding box's area. Figure 12 describes the comparison of two methods: the bounding box-based and pixel-based methods.
## 4 Simultaneous Utilization of Multiple RANs within a 3 GPP Framework
### Simultaneous Utilization of 3 GPP-Based and Wi-Fi Access Networks over 5 GC or EPC
#### 4.1.2 ATSSS Phase 2
Regarding the configuration of the connectivity between access networks and core networks, ATSSS Phase 1 establishes connections between both access networks and the 5 GC, which serves as the core network for the 5G system. ATSSS Phase 2 extends support for a different connectivity scenario, where a 3 GPP access network is linked to the EPC, the core network of the LTE system, while a non-3 GPP access network is connected to the 5 GC.
# Revealing the Spatial Pattern of Weather-Related Road Traffic Crashes in Slovenia
## 3 Results
### The GWR and the Regression Tree Models
The mean absolute error value estimates how well the models predicted our dependent variables (road traffic crash frequency by weather situation) on a yearly level. The best result was produced for the wind-related road traffic crash pattern. Model over- and under-predictions for all weather situations, represented with standardized residuals, were all normally distributed and free of spatial \begin{table}
\begin{tabular}{c c c c c c} \hline \hline MDC & 0.988 & 1.061 & \(-\)1.747 & 1.077 & 3.146 \\ \hline CHI & \(-\)0.005 & 0.019 & \(-\)0.06 & \(-\)0.003 & 0.031 \\ \hline \hline \end{tabular}  
FI = registered vehicle frequency, population (number of adult persons), and highway length; F2 = traffic loads and highway length; F3 = regional road length; MDC = municipality development coefficient; CHI = climate heterogeneity index.  
\end{table} 
Table 3: _Cont._  
Figure 3: Regression trees for road traffic crashes in (**a**) rain (**b**) snow, (**c**) wind, and (**d**) fog. autocorrelation (insignificant Moran's I values). Of course, despite the properly specified regression tree models, results could probably be further improved if additional linearly related predictors were considered.
## 3 Structure and Phase Error Analysis of Dual Antenna Tracking Loop
### Dynamic Stress Induced by Satellite Motion  
Dynamic stress appears when dynamic models in tracking loops cannot model the satellite/user motion properly, for instance, when using a low order tracking loop to track signals which experience high order dynamics. For a static receiver, satellite motion causes a maximum Doppler rate of 0.936 Hz/s [15]. This is a typical low dynamic scenario. Under this condition, the Doppler frequency estimated from the master loop reflects the dynamic of the satellite. Thus, in the dual antenna assistance tracking loop, the Doppler aiding could eliminate the dynamic completely in the slave loop. As a result, a 2 nd order loop could track the carrier signal of the slave antenna easily with the assistance from a 3 rd order master loop. Besides, the differential phase error caused by the dynamic will be zero due to coherence of the Doppler frequencies.