esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Solar Control Films on Building Glazing: A Literature Review
###### Abstract
The main issues involved in the application of smart windows are: the high cost and associated long payback period; the materials and devices must be highly durable; the optimization of the solar modulation properties to achieve maximum energy savings; the potential presence of the haze effect, usually caused by disperse particle technology. The installation of static solar control films (SCFs) is an alternative and effective solution to reduce heat gains through glazing systems by altering their solar optical and thermal properties. With an easy application method that does not require facade alteration, these films act as refurbishment solutions that promote the improvement of the thermal, luminous and energy performance of building glazing, while reducing potential glare and the transmittance of ultraviolet (UV) radiation. Furthermore, these static films present a lower economic cost than the dynamic window technologies previously mentioned. This paper presents a literature review of the performance of static SCFs when installed in building glazing systems consisting of: an overview of window films with the characterization of the different types, installation position and main properties; the characterization of SCFs in terms of existing types and their impact on the properties of the glazing system they are applied to; an in-depth literature review of research studies that assess the performance of SCFs in buildings; a discussion of the data, methodology and findings of the studies; and the main conclusions and recommendation of future studies on the topic. To the best of the authors' knowledge, there is no evidence reported in the literature of a study addressing a comprehensive review of the performance of static SCFs when installed in the glazing systems of buildings. Innovative window coatings [13] are not considered in the present review. The added value of the present work lies in collecting and analyzing the existing studies in the literature that address the performance of static SCFs applied to building glazing, comparing research approaches and the main findings. Moreover, research gaps and future studies are listed to help establish guidelines for research and design on the topic.
## 5 Discussion
However, the response to external factors, such as rapid reservoir water level drops or continuous heavy rainfall, is dynamic. Using a constant lag value may not accurately reflect these dynamic changes, and variations in the lag effects based on rainfall intensity and duration or changes in reservoir water levels are significant. Additionally, landslide deformation is a complex process influenced by both internal and external factors. Internal factors such as rock properties, slope geology, and internal structure were not considered in this study, which limited the comprehensiveness of the model. Future research should focus on accurately capturing the dynamic lag effects of external factors and their influence on landslide displacement, with the aim of improving predictive accuracy.
## 3 Results and Discussion
### Sorption of the Mixture of Zn(II), Cu(II) and Pb(II) by Bean Peel in Rainwater
The model for Cu(II) predicted removal percentages of between 61% and 66% for the spike of 100 \(\mathrm{\SIUnitSymbolMicro g}\)/L and between 74% and 80% for the spike of 500 \(\mathrm{\SIUnitSymbolMicro g}\)/L. Sample 1 was the closest to the predicted value for the spike of 500 \(\mathrm{\SIUnitSymbolMicro g}\)/L since it was able to remove 78% of Cu(II), close to the predicted value by the model (77%). Concerning Pb(II), for the spike of 100 \(\mathrm{\SIUnitSymbolMicro g}\)/L, sample 1 revealed a removal of 89%, and samples 2-5 showed removals between 11% and 48%. For the spike of 500 \(\mathrm{\SIUnitSymbolMicro g}\)/L, the removal was substantially higher than for the spike of 100 \(\mathrm{\SIUnitSymbolMicro g}\)/L for all samples, between 35% and 97%, with the exception of sample 3, which slightly decreased. For sample 6, it was not possible to obtain the removal percentage due to a high decrease in the concentration of the respective control (39%). Considering the data obtained for rainwater samples, it was highlighted that the real matrix of rainwater, with the presence of several species (organic and inorganic), reduces the sorption capacity of biomaterial on the removal of PTEs when compared with the predicted values by the models (which were obtained in ultrapure water). However, besides the concentrations obtained in the removal being higher than the levels recommended for drinking water, with the exception of Cu(II), which already was (5 \(\mathrm{\SIUnitSymbolMicro g}\)/L for Zn(II) [9], and 2000 \(\mathrm{\SIUnitSymbolMicro g}\)/L and 5 \(\mathrm{\SIUnitSymbolMicro g}\)/L for Cu and Pb, respectively [8]), they correspond on average to removals of 53% for Zn(II), 23% for Cu(II), and 45% for Pb(II) for the initial concentration of 100 \(\mathrm{\SIUnitSymbolMicro g}\)/L, and removals of 54% for Zn(II), 41% for Cu(II), and 60% for Pb(II) for the initial concentration of 500 \(\mathrm{\SIUnitSymbolMicro g}\)/L. Therefore, these findings suggest that the peel of white bean may be used as a sorbent for PTEs removal from rainwater.
# Content Adaptation and Depth Perception
## 5 User Studies
### Results
#### 5.5.3 Subjective Questionnaires  
The System Usability Scale (SUS) [29] questionnaire had seven questions to be answered in a 5-point Likert scale; we selected the ones relevant to our evaluation, removing the ones related to interaction. Q1: I think that I would like to use this system frequently, Q2: I found the system unnecessarily complex, Q3: I thought the system was easy to use, Q4: I think that I would need the support of a technical person to be able to use this system, Q5: I would imagine that most people would learn to use this system very quickly, Q6: I found the system very cumbersome to use, and Q7: I felt very confident using the system. The average results can be seen in Figure 11.  
Wilcoxon tests were Q1 (W = 46, \(p\) = 0.264), Q2 (W = 52, \(p\) = 0.014), Q3 (W = 34.5, \(p\) = 0.151), Q4 (W = 16, \(p\) = 0.292), Q5 (W = 7, \(p\) = 0.269), Q6 (W = 112, \(p\) = 0.003) and Q7 (W = 3.5, \(p\) = 0.710). There were significant differences in Q2 and Q6, meaning that the users found SliceView more complicated to use than LookingGlass. We could explain these differences by the displayed visual aspect, as the users were only requested to observe the displays, and not to directly interact with them. SliceView is made with cardboard and affordable materials, whereas LookinGlass is a sleek final commercial product.  
The NASA TLX questionnaire [28] had six questions. The participants were asked to give a numerical value from 0 to 20 for their Q1: Mental demand, Q2: Physical demand, Q3: Temporal Demand, Q4: Perceived performance, Q5: Effort, and Q6: Frustration. The average values are shown in Figure 11. Wilcoxon tests were Q1 (W = 45.5, \(p\) = 0.251), Q2 (W = 11, \(p\) = 1), Q3 (W = 11, \(p\) = 0.408), Q4 (W = 41.5, \(p\) = 0.872), Q5 (W = 27.5, \(p\) = 0.376) and Q6 (W = 20.5, \(p\) = 0.284). The difference was not significant for any question.
## 5 Case Study
### Experimental Results  
The prototype system was implemented in the C++ programming language using the g++ 11 compiler, NOP C++ Framework 4.0, and Linux Ubuntu 20.04 Kernel 5.4.0-125-generic. The experiments were carried out on a computer with an Intel Core i7-8750H CPU and 16 GB of RAM. It is important to note that the GPU was not used in this current experiment version. GPUs were not used because our method only focuses on query processing, in which GPUs could be useful but are not fundamental. Actually, focusing just precisely on query processing, the activities of data ingestion and object detection were not considered, being by nature those activities the GPUs demand.  
We use the operators mentioned earlier for two different experiments. The first is aimed at establishing some comparison with the literature. Therefore, it is limited to the scope and operators of the time window size found in [54]. In that work, the authors tested six different time windows (5 s, 10 s, 25 s, 1 min, 5 min, 10 min). The second experiment uses a broader range of time windows, from 10 min to 190 min, and it is aimed to estimate the computational complexity of the proposed approach experimentally.
# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 3 Experiments and Discussions
### 3D Displacement Measurement of Log Specimen  
The next set of validation experiments involved the same log, whose surfaces were imaged using a set-up of 360-deg cameras and analyzed using panoramic DIC. The experimental system was evaluated by analyzing the merging errors of adjacent camera pairs, the accuracy of the reconstructed shape, the panoramic displacement errors, and the performance of the panoramic DIC algorithm.
## 2 Materials and Methods
### Multi-Frequency GFIF Model
Obviously, the PCO errors at either satellite or receiver end will be projected into line-of-sight observations. The estimation of either satellite clock or IFCB will then be contaminated by the PCO errors. Once the geometric distance for each frequency is known precisely, the GFIF combination of geometric distances can be safely reduced from Equation (7). Then, the GFIF combinations for B1I/B3I/B1C and B1I/B3I/B2a signals can be derived as:  
\[\left\{ \begin{array}{l}C261_{r}^{s}=\frac{f_{1}^{2}}{f_{1}^{2}-f_{2}^{2}}IFCB_{ r,L1}-\frac{f_{1}^{2}}{f_{1}^{2}-f_{2}^{2}}IFCB_{L1}^{s}+\epsilon_{r,C261}^{s}\\ L261_{r}^{s}=\frac{f_{1}^{2}}{f_{1}^{2}-f_{2}^{2}}IFCB_{r,L1}-\frac{f_{1}^{2}}{f _{1}^{2}-f_{2}^{2}}IFCB_{L1}^{s}+B_{L261}+\epsilon_{r,L261}^{s}\\ C265_{r}^{s}=\frac{-f_{1}^{2}}{f_{1}^{2}-f_{2}^{2}}IFCB_{r,L5}-\frac{f_{2}^{2}}{ f_{2}^{2}-f_{2}^{2}}IFCB_{L5}^{s}+\epsilon_{r,C265}^{s}\\ L265_{r}^{s}=\frac{-f_{2}^{2}}{f_{2}^{2}-f_{2}^{2}}IFCB_{r,L5}-\frac{f_{2}^{2}}{ f_{2}^{2}-f_{2}^{2}}IFCB_{L5}^{s}+B_{L265}+\epsilon_{r,L265}^{s}\end{array} \right. \tag{10}\]  
With IFCBs and ambiguity terms remaining, Equation (10) becomes the GFIF model for IFCB estimation. While the IFCB can be generated from either the UC model as Equation (6) or the GFIF model as Equation (10), the results will be identical only if the applied corrections, mainly PCO corrections, are consistent. With the GFIF combination commonly formed without correcting PCOs, the resulting IFCB will derivate from that with the UC model. The disagreement may further increase once incorrect PCOs are applied in the UC model.
## 3 Dynamic Forecasting Model Based on VMD-SLSTM Network  
### Variational Mode Decomposition  
Unlike EMD, the intrinsic mode function (IMF) in VMD is redefined as an amplitude modulated-frequency modulated signal \(u_{k}(t)\), as follows [31]:  
\[u_{k}(t)=A_{k}(t)\cos(\phi_{k}(t)) \tag{3}\]  
where \(A_{k}(t)\) and \(\phi_{k}(t)\) are the instantaneous amplitude and phase.  
In VMD, to obtain IMF components, the signal stripping method used in EMD is discarded. Instead, after setting the parameters such as the mode number \(K\), the penalty parameter \(a\), and the rising step \(\tau\), the signal is decomposed by finding the optimal solution of the model shown below in the variational framework:  
\[\left.\begin{array}{l}\min\limits_{\{u_{k}\},|\alpha_{k}|\}\left\{\sum \limits_{k=1}^{K}\left\|\partial_{t}\Big{[}(\sigma(t)+\frac{j}{nt})u_{k}(t) \Big{]}e^{-j\omega_{k}t}\right\|_{2}^{2}\right\}\\ s.t.\quad\sum\limits_{k=1}^{K}u_{k}=f(t)\end{array}\right\} \tag{4}\]  
where \(\{u_{k}\}\) is the component that is finally obtained by decomposition; \(\{\omega_{k}\}=\{\omega_{1},\ldots,\omega_{K}\}\) is the actual center frequency of each IMF component, \([\sigma(t)+j/(\pi t)]u_{k}(t)\) is the analytical signal of each IMF component, \(e^{-j\omega_{k}t}\) is the estimated center frequency of each analytical signal, and \(f(t)\) is the original signal.  
To solve for the optimal solution of the constrained variational model, Equation (4) needs to be transformed into an unconstrained model, as follows:  
\[\begin{array}{l}L(\{u_{k}\},\,\{u_{k}\},\,\lambda)=\\ a\sum\limits_{k}\left\|\partial_{t}\Big{[}(\sigma(t)+\frac{j}{nt})u_{k}(t) \Big{]}e^{-j\omega_{k}t}\right\|_{2}^{2}+\left\|f(t)-\sum\limits_{k}u_{k}(t) \right\|_{2}^{2}+\left\langle\lambda(t),\,f(t)-\sum\limits_{k}u_{k}(t)\right\rangle \end{array} \tag{5}\]  
where \(\lambda\) is the Lagrange multiplier.  
The saddle point of the unconstrained model is obtained by using the alternating direction multiplier algorithm, which is the optimal solution of the constrained variational model. Hence, the original signal \(f(t)\) is decomposed into \(K\) IMF components.  
Figure 2: Architecture of the stack long short-term memory (SLSTM) network.
## 3 Results
#### 3.1.3. Energy Efficiency  
The records of energy consumption on winter days show very important differences in the heating energy needs with the \"glass curtain\" system closed and without it. In winter, the daily heating energy needs are reduced by around 56.2% with the \"glass curtain\" system closed (Figure 10). In summer, the cooling energy needs are the same in both cases because the glass curtain remains retracted and open.  
These results reflect a significant reduction in the heating system operating hours with the closed glass curtain system because the indoor temperatures fall below the setpoint temperature for many fewer hours during the year.
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 2 Research Methodology
### EMF and 5G Exposure in the Scientific Literature  
An analysis of the Scopus database revealed a growing body of research related to 5G technology and EMF exposure since 2015, reflecting the increasing importance of this topic. Figure 2a shows that 54,835 publications include the keyword \"5G\". However, when focusing specifically on 5G-related studies using keywords such as \"EMF\", \"exposure\", \"electromagnetic field\", \"radiation\", \"specific absorption rate\", \"power density\", or \"electric field strength\", the number of results decreased to 1030 publications (Figure 2b). This highlights a comparatively narrower focus on EMF exposure within 5G research, although this number is expected to grow significantly in the coming years given the relevance of this topic.  
When further narrowing the scope to research involving \"radio resource management\", \"resource allocation\", or \"optimization\" combined with the previous EMF-related keywords, only 141 relevant publications (Figure 2c) were identified. Moreover, introducing \"reconfigurable intelligent surfaces\" or \"RIS\" into this search criteria reduced the number to a mere 8 papers (Figure 2d). This stark contrast underscores a significant gap in the literature regarding EMF-aware radio resource management strategies for RIS-assisted 5G networks, highlighting both the novelty and the potential of this research domain.
## 4 Modeling and Optimization Concerns
### Z-Score (Standard Score)  
Because each dimension of the performance index is different, the accuracy of the trained model might be biased. To address this problem, a normalization and standardization process is generally used in machine learning. The Z-score is used to convert the raw data according to \(z=(x-\mu)/\sigma\), where \(\mu\) and \(\sigma\) are the mean and standard deviation of the population, respectively. For the testing and training data, the \(\mu\) and \(\sigma\) of the MCoE were 46.08 and 21.17 \(\mu\)m, respectively. The \(\mu\) and \(\sigma\) of the MT were 12.69 and 7.45 s, respectively. The \(\mu\) and \(\sigma\) of the CVib were 7.62 and 6.16 mm/s\({}^{2}\), respectively.
## 5 Conclusions  
This paper proposed a two-stage unsupervised DIP-based denoising method. Unlike the DIP method that uses a given noisy image as the learning target, the proposed method developed a two-target DIP learning strategy to generate intermediate denoised images. The cleaner preliminary image used in two-target DIP learning can help reduce the search space, thereby improving the denoising result. Furthermore, to utilize the uncertainty of the DIP learning, an up- and down-sampling strategy was adopted to generate multiple cleaner preliminary images, which were used to produce multiple intermediate denoised images that were complementary to the image details. Finally, this paper proposed an unsupervised fusion network to fuse the intermediate denoised images into one denoised  
Figure 11: Visual comparison of the denoised images for the camera RAW image. The quantitative PSNR results are listed behind the noisy and denoised images.  
Figure 12: Visual comparison of the denoised images for the low-dose X-ray image. The quantitative PSNR results are listed behind the noisy and denoised images.  
image to further improve the denoising effect. Extensive experiments on public datasets indicate that the proposed method can considerably improve the denoising effect and perform significantly better than state-of-the-art denoising methods, particularly in real-world image denoising. The proposed method used both external and internal priors. The external prior can recover the general contents of the given noisy image, and the internal prior can effectively restore specific details. Therefore, the proposed method can significantly improve the denoised effect and provide a flexible solution for various noisy images of real scenes.  
Conceptualization, S.X.; Funding acquisition, S.X. and Y.T.; Methodology, S.X. and S.J.; Software, X.C. ([PERSON]) and N.X.; Validation, X.C. ([PERSON]) and X.C. ([PERSON]); Supervision, S.X.; Writing--original draft, Y.T.; Writing--review and editing. Y.T. and S.X. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China under Grant No. 62162042 and No. 62162043, and in part by the Jiangxi Postgraduate Innovation Special Fund Project under Grant YC2022-s033.  
Not applicable.  
Data Availability StatementData sharing not applicable to this article as no datasets were generated during the current study.  
The authors declare that they have no conflict of interest. The manuscript was written with contributions from all authors. All authors have approved the final version of the manuscript.
## 5 Optimization of Scan Parameters
### Overlap Factor  
Scanning the uncertainty area is performed using a step size \(n\), which defines the scan spots (or dwell points). To ensure a safety margin against jitter, an overlap factor is introduced into the step size, as defined in Equation (12), where \(a\) is the overlap factor, and \(\theta_{b}\) is the beam divergence that illuminates a portion of the uncertainty area at each spot. The presence of satellite platform vibrations can result in missed target scan spots, causing either excessive overlap or missed scan areas. The LOS link between the transmitter and receiver satellites is subject to jitter, leading to pointing errors. Since the radial pointing error follows a Rayleigh distribution, its probability density function can be written as  
\[f(\gamma)=\frac{\gamma}{\sigma_{\text{vib}}}\exp\left(-\frac{\gamma^{2}}{2 \sigma_{\text{vib}}^{2}}\right), \tag{69}\]  
where \(\gamma\) is the deviation from the intended scan spot, and \(\sigma_{\text{vib}}\) is the standard deviation of the scanning beam due to vibrations. When \(\gamma\) exceeds a certain threshold \(\alpha\) in both the elevation and azimuth directions, scan gaps are created. Therefore, the probability \(P_{m}\) of missing a scan spot can be derived as follows [40]:  
\[P_{m}=\int_{\alpha}^{\infty}f(\gamma)d\gamma, \tag{70}\]  
\[P_{m}=\int_{\alpha}^{\infty}\frac{\gamma}{\sigma_{\text{vib}}}\exp\left(- \frac{\gamma^{2}}{2\sigma_{\text{vib}}^{2}}\right)d\gamma=\exp\left(-\frac{ \alpha^{2}}{2\sigma_{\text{vib}}^{2}}\right). \tag{71}\]  
Thus, the acquisition probability \(P_{c}\), which represents the probability of successfully acquiring the scan spot (i.e., the scan point remaining within the acceptable threshold), can be written as  
\[P_{c}=1-P_{m}=1-\exp\left(-\frac{\alpha^{2}}{2\sigma_{\text{vib}}^{2}}\right) \tag{72}\]  
The limit for the pointing error deviation \(\alpha\) can be expressed using Equation (72) as  
\[\alpha=\sigma_{\text{vib}}\sqrt{-2\ln(1-P_{c})}. \tag{73}\]  
To ensure that there are no gaps between the scan beam footprint when scanning each point, the step distance \(n\) must satisfy the following condition:  
\[n\leq\theta_{b}-2\alpha. \tag{74}\]Substituting \(n=\theta_{b}(1-a)\) into the inequality, we obtain  
\[\theta_{b}(1-a)\leq\theta_{b}-2\alpha. \tag{75}\]  
By simplifying this inequality, we can express the required overlap factor \(a\) as follows:  
\[1-a\leq\frac{\theta_{b}-2\alpha}{\theta_{b}}, \tag{76}\]  
\[a\geq\frac{2\alpha}{\theta_{b}}. \tag{77}\]  
Finally, substituting \(\alpha=\sigma_{\text{vib}}\sqrt{-2\ln(1-P_{c})}\) in Equation (77), we can derive the final expression for \(a\):  
\[a\geq\frac{2\sigma_{\text{vib}}\sqrt{-2\ln(1-P_{c})}}{\theta_{b}}. \tag{78}\]  
To avoid missed scan areas and gaps, the overlap factor must satisfy the condition in Equation (78).
## 3 Results and Discussion
### Interactions of Hydrolysates of Empty Capsules with Insulin Receptor  
Fluorescence spectroscopy tests were performed to determine if the peptides resulting from the digestion of the empty capsules interact with the insulin receptor, for which the InsR insulin receptor domain 1101-end was used. Fluorescence was used as a tool to monitor the folding of proteins. [PERSON] et al. [40] reported that the interaction of insulin with the insulin receptor can be observed by the increase in fluorescence intensity in the range of 250-410 wave lengths. Figure 11 shows the change in fluorescence intensity for the insulin receptor, insulin receptor-insulin complex, and insulin receptor-hydrolysate complex.  
Figure 10: Detail of the mass spectrum of commercial insulin (**a**) and the hydrolysate of empty microcapsules (**b**) in the 0–700 m/z region.  
Figure 9: Mass spectrum of commercial insulin (**a**) and of the hydrolysate for empty capsules (**b**).  
Figure 11 shows, on the one hand, the fluorescence of the insulin receptor caused by the presence of tryptophan and tyrosine residues, and on the other, the increase in fluorescence intensity when the interaction of the insulin receptor and insulin occurs, which coincides with a report by [PERSON] et al. [40; 41].  
Previous studies propose changes in the secondary structure of the receptor upon binding to insulin [40; 42; 43], which possibly implies the formation of more rigid helices and tertiary and quaternary structural changes that occur when two protomers come into contact.  
Regarding the response of the insulin receptor upon contact with the hydrolysate of in vitro digestion of the empty capsules, an increase in fluorescence intensity was also observed, which suggested that the hydrolysate also produces a conformational change on the insulin receptor, but of lower intensity than that obtained with insulin (40% at 300 nm). Judging from the florescence intensity response shown in Figure 11, it can be concluded that the peptides in the hydrolysate of the empty capsules are interacting with the insulin receptor in a somehow similar manner to insulin.