esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Developments in Materials Science for the Conservation and Restoration of Historic Artifacts
## 3 Advanced Polymers for Artifact Stabilization
### Applications of Advanced Polymers in Artifact Stabilization  
The applicability of advanced polymers in artifact conservation is multifaceted, addressing both preservation and restoration needs. Among the various uses of advanced polymers, the most common is represented by their use as consolidants or as protective coatings.  
The use of advanced polymers as consolidants (the most encountered application) focuses on the development of restoration solutions having important properties for restoration specialists:  
* Mechanically compatible solutions: beyond mere adhesion, advanced polymer-based adhesives and consolidants can be formulated to mimic the mechanical properties of their substrates, providing balanced reinforcement without inducing stress that could lead to further damage [44].
* Microstructural enhancement: advanced polymer solutions can enhance the microstructural integrity of porous materials by fusing with capillary systems and binding loose particles together to restore mechanical strength [23]. For instance, consolidants like Paraloid B72 are widely used for wood consolidation due to their excellent penetration and bonding capabilities [45].
* Customizable viscosity: the viscosity of polymer adhesives can be modified according to application needs--allowing for easy application on vertical surfaces or intricate details without running or sagging [46].  
According the literature data, polymeric coatings can be successfully used as dynamic protection coatings or hydrophobic barriers, or even as multifunctional coatings. Protective coatings developed from polymers offer dynamic responses to environmental changes. For example, self-healing coatings can repair minor scratches or damages autonomously when exposed to specific stimuli [47]. Many advanced polymer coatings impart hydrophobic characteristics that prevent water ingress while allowing moisture vapor transmission, a crucial aspect for protecting porous materials like stone from water damage while preventing trapped moisture that could lead to deterioration [48]. Using such materials, the shortcomings of classic polymeric solutions (such as Paroloid or Acryl33) can be overcome; also, the selection of polymeric materials should be carefully made, considering the compatibility with the substrate (e.g., siloxane/silane/silicate-based solutions can exhibit low permeability and thus reduced compatibility when applied to stone artifacts). At the same time, the capability to incorporate multifunctional properties--including antimicrobial and anti-static functionalities--broadens the spectrum of protection available for sensitive artifacts [49]. This is particularly relevant in environments prone to biological growth or dust accumulation.
### Related Works  
Optimizing power allocation is a key challenge in wireless networks, particularly in terms of balancing SE and EE. In cell-free massive MIMO environments, a CPU coordinates power control for multiple distributed APs, which subsequently allocate power to UEs. This hierarchical structure highlights the importance of efficient CPU-to-AP and AP-to-UE power management in achieving system-wide performance and fairness. Prior research has investigated various power allocation schemes, with heuristic-based and AI-based approaches being most commonly explored for practical implementation. While heuristic methods offer simplicity, they often lack adaptability. In contrast, deep learning-based approaches demonstrate promising flexibility and spatial awareness, particularly in complex and dynamic network environments. Recent deep learning-based studies have applied hybrid architectures combining CNNs with long short-term memory (LSTM) networks to address power control and beamforming in massive and ultra-massive MIMO systems [8; 9; 10]. These works leverage temporal dependencies via LSTM and spatial features via CNN; however, they typically target only AP-to-UE power allocation or beamforming, and do not perform full-chain hierarchical optimization. In contrast, our proposed method performs joint end-to-end CPU-to-AP and AP-to-UE power allocation using a lightweight CNN structure designed for real-time inference and system-wide optimization.  
Table 1 summarizes the architectural and functional differences between our model and recent deep learning-based methods.
# Trapping and Methanation of CO\({}_{2}\) in a Domestic Microwave
## 3 Results and Discussion  
### \(Co_{2}\) Methanation under Continuous Flow Conditions under MW  
The sorption bed was left empty in this case to measure the activity of various combinations of SiC, Fe\({}_{3}\)O\({}_{4}\) and Ru/SiO\({}_{2}\) loaded in the catalytic reactor under a continuous flow of CO\({}_{2}\) and H\({}_{2}\) while irradiated by MW. The presence of only SiC led to no measurable activity (Figure 3A). A full conversion of CO\({}_{2}\) was obtained over the SiC + Fe\({}_{3}\)O\({}_{4}\) physical mixture, but the only product was CO. This observation is consistent with the well-known fact that iron oxides are active catalysts for the RWGS reaction (Equation (2)) but not for CO\({}_{x}\) methanation [31].  
Figure 2: Pictures of a physical mixture of 354 mg of SiC and 91 mg of Fe\({}_{3}\)O\({}_{4}\) under a N\({}_{2}\) stream after various times under 800 W MW irradiation. A minute-long video of the sequence is available at the following link: [[https://mycore.core-cloud.net/index.php/s/rhQAXNZGD5O7](https://mycore.core-cloud.net/index.php/s/rhQAXNZGD5O7) qq7]([https://mycore.core-cloud.net/index.php/s/rhQAXNZGD5O7](https://mycore.core-cloud.net/index.php/s/rhQAXNZGD5O7) qq7) (accessed on 2 November 2023).  
A layer of Ru/SiO\({}_{2}\) was added to catalytic reactor just after the SiC + Fe\({}_{3}\)O\({}_{4}\) mixture, separated with a 5 mm quartz wool plug. This configuration led to a full CO\({}_{2}\) conversion, with methane being the only product at steady-state (Figure 3A, third entry). However, the second run with the same configuration led to a CH\({}_{4}\) yield of about 90% and a yield of CO of 10%. This suggests that the Ru-based catalyst had been deactivated during the first run, likely due to sintering of the Ru particles exposed to the high temperatures of the red-hot glowing SiC + Fe\({}_{3}\)O\({}_{4}\) mixture, despite being located about 5 mm away.  
The time-resolved product analysis of the second run with the (SiC + Fe\({}_{3}\)O\({}_{4}\)) + Ru/SiO\({}_{2}\) configuration is shown in Figure 3B. The initial lag time before observing any conversion was both due to the time needed to purge the line dead-volume to reach the IR analyzer and the time needed to heat up the reactor under MW. CO was initially observed as the sole reaction product, before declining in yield, followed by the rise of the methane yield. This suggests that the initial activity was only due to the RWGS activity of the FeO\({}_{x}\) component and that the temperature of the Ru catalyst was too low to achieve methanation.  
Interestingly, the carbon balance was significantly lower than 100% just after the light off (Figure 3B). This suggests that part of the CO formed was initially trapped in the reactor, likely as carbonyl species formed at the surface of the Ru nanoparticles. This hypothesis was checked by carrying out an _operando_ DRIFTS analysis described in the subsequent section.
###### Abstract  
Knowledge about the biomass productivity of trees planted in a rainwater harvesting structure, i.e., a trench (\(T\)), relative to a normal pit (\(P\)) on degraded land is scarce. The objective of this research paper is to compare the effect of \(T\) with \(P\) on the growth and biomass production of the _Acacia saligna_ and _Casaurina equisefilia_ which were planted on degraded land. All the individual stems of both species in the \(T\) and \(P\), their diameters at breast height (DBH) and heights in 2016 and 2020 were measured. Species-specific allometric equations were used to quantify the biomass production of the studied species. The \(t\)-tests were used for data analysis; both _A. saligna_ and _C. equisefilia_ individuals planted in the \(T\) had higher DBH and height increment as compared with _A. saligna_ and _C. equisefilia_ that were planted and grown in a \(P\). The results also revealed significant differences on the mean DBH and height of _A. saligna_ and _C. equisefilia_ planted in a \(T\) and \(P\) (\(p<0.05\)). Between 2016 and 2020, the total biomass (TB) of _A. saligna_ planted in a \(T\) and in a \(P\) increased significantly (\(p<0.05\)) on average by 25.5 kg/tree and 7.7 kg/tree, respectively (\(p<0.05\)). Similarly, the mean TB values of the _C. equisefilia_ planted in a \(T\) and a \(P\) between 2016 and 2020 increased significantly (\(p<0.05\)) by 28.9 kg/tree and 13.1 kg/tree, respectively. Finally, establishing trenches to restore degraded lands was shown to facilitate growth and biomass production of planted species on degraded land.  
**Keywords:** conservation structures; degraded land; dry biomass; soil moisture; restoration; semi-arid region; _Casaurina equisefilia; Acacia saligna_
# Design of an Axial Turbine for Highly Downsized Internal Combustion Engines
## 3 Methodology
### Definition of Design Point and Boundary Conditions  
The data from the Ricardo Wave-simulated 1D engine model at full load were used to define the initial boundary conditions for the design of the turbine. The reason for choosing full load is that the axial turbine's main purpose is to reduce the inertia compared to a radial one to improve the response, and thus it is supposed to be more important in an aggressive driving style. The design point was moved at 2000 engine rpm because of the higher necessity of torque at lower regimes when accelerating from stationary. However, as explained in the Results section, the final design point was then definitively set to 4000 rpm, as the 2000 rpm one did not allow for a suitable turbine at maximum engine revolutions (revs).  
In order to ensure a proper match between the original compressor and the turbine, it was important to make sure that for a given engine operating point, the turbine was able to give enough power for the compressor at given shaft rpm values. Initially, this condition was checked for the design point. Then, as discussed in Section 3.5, a map was created to check the suitability of the turbine for the whole operative range.  
The main geometric constraint parameters to control in order to obtain a low-inertia axial turbine are those related to the mass of the turbine and its distribution or its moment of inertia relative to the rotation axis. Approximating the turbine rotor to a cylindrical element (as shown in Figure 4), there is a clear strong dependence of the moment inertia with the radius of the turbine and linear dependence with the length.  
\[I=\frac{1}{2}\pi\rho l\Big{(}r_{t}^{4}-r_{h}^{4}\Big{)} \tag{1}\]  
Figure 3: Engine on the dyno before testing.  
For this reason, the main geometric parameters to control in the design of the turbine will be the hub radius (\(r_{h}\)) and the blade height (\(r_{t}\)-\(r_{h}\)) (being important for the aerodynamic design of the blade). A simple Excel file was created to obtain further data as an input for the preliminary design of the turbine (Figure 5) through simple calculations.  
The data obtained from measuring/scanning the original turbocharger were used as a reference and as limitations to the size of the new turbine assembly, as it must interface with the existing system (exhaust manifold, turbocharger bearing and cooling housing, exhaust pipe, etc.).  
Figure 4: Simplified rotor blade model.  
Figure 5: Schematic of the turbine design methodology.
## 2 Methodology
### Theoretical Analysis  
The key strata theory is an assumption model for analyzing overburden failure induced by mining. [PERSON] et al. [22] developed this theory to investigate the law of strata movement. The key stratum is generally defined as the thick and hard rock layer. It plays a major role in controlling overlying strata behavior. This section mainly introduces how key strata theory is applied to this specific project. The position of the primary key strata (PKS) and sub key strata (SKS) is first determined and then compared with the previous judgement in the numerical model.  
The application of the key strata theory in this study is essential due to its strong theoretical foundation and practical relevance in analyzing overburden failure induced by underground mining. Originally proposed by [PERSON] et al. [22], the theory defines key strata as mechanically strong and thick rock layers that control the movement and failure of overlying strata. In this research, key strata positions are determined through a combination of stiffness criteria and breaking span calculations, allowing for a rigorous and quantifiable identification of primary and sub-key layers. This approach not only simplifies the complexity of stratigraphic modeling but also ensures that the mechanical behavior of overburden is accurately captured. Moreover, the predicted positions and failure patterns of the key strata provide the basis for numerical simulation settings and experimental validation. Given that mining-induced fractures are closely linked to the breakage and movement of these control layers, the key strata theory offers a highly suitable framework for studying the evolution of mining-induced seismicity and surface subsidence. PKS can control the behavior of the whole overburden strata above it, and SKS only controls part of the above strata. After defining the concept of key strata, the position of these special layers could be identified using a theoretical analysis method. According to the concept of key strata, the fine sandstone (PKS) has a great influence on other strata's behavior and should be paid more attention when analyzing the overburden failure. For example, one experiment group considers the deformation modulus \(E\) of PKS as the variable.
## 5 Modelling of Communication Measurement Systems
### Results and Analysis  
1. The data rate is fixed, and the QPSK BER simulation under different phase noise and different thermal noise conditions.  
It can be seen from Figure 13 that under the condition of a fixed signal-to-noise ratio, the greater the phase noise, the greater the bit error rate. When the root mean square error of the phase noise sequence is \(5^{\circ}\), to meet the bit error rate requirement of \(1\times 10^{-5}\), the required \(E_{b}/N_{0}\) needs to be increased by 0.7 dB. When the root mean square error is \(10^{\circ}\), \(E_{b}/N_{0}\) needs to be improved by about 5 dB. When the phase noise is large to a certain extent, continuously improving the signal-to-noise ratio cannot reduce the bit error rate. As shown in the top curve in the figure, the root mean square of the phase noise is \(20^{\circ}\). With the increase of \(E_{b}/N_{0}\), the slope of the curve gradually decreases, that is, the phase noise deteriorates to a certain extent, and the communication system will not work properly.
2. The phase noise is fixed, and thermal noise affects the bit error rate of different transmission rates.  
Taking the QPSK system as an example, the root mean square error of the phase noise in the time domain is \(5^{\circ}\), as in Figure 14, and the influence of the phase noise on the transmission of the system under different data transmission rates is simulated. The simulation results are shown in Figure 15. Under this simulation condition, when the transmission rate is greater than 100 kbps, the system error deterioration is small. When the transmission rate is less than 100 bps, the system bit error is seriously deteriorated. Therefore, when the system phase noise index is poor, the low bit rate data transmission system is more susceptible to the influence of phase noise than the higher bit rate data transmission system.  
Figure 13: Signal-to-noise ratio vs. bit error rate under different phase noise level.  
(3)  
Influence of phase noise on bit error rate under different modulation methods  
Taking the phase root mean square error of \(5^{\circ}\) as an example, the simulation calculation is carried out for the BPSK, QPSK, 8 PSK and 16 QAM modulation modes, and compared with the respective theoretical values under the AWGN channel; it can be seen from the results in Figure 16 effect of phase noise. Accordingly, when designing a high-order modulation system, the phase noise index must be strictly required.  
Figure 14: QPSK constellation diagram when phase noise RMS = \(5^{\circ}\).  
Figure 15: Bit error rate curves at different code rates using QPSK when phase noise RMS = \(5^{\circ}\).
## 4 Experiments and Results  
### Dataset Construction  
The dataset used for network training is the indoor lane dataset captured by the CMOS camera in varied illumination conditions. The camera is Sony IMX219 (pixel: 800, CMOS size: 1/4-inch, aperture: 2.0, focal length: 2.2 mm, size: 32 mm \(\times\) 32 mm). The lane dataset contains a total of 6000 images. The colors of the lane are red, yellow, and blue. The background color is white. The ratio of lanes in colors is 1:1:1. The constructed lane dataset contains a variety of different illumination scenarios. The illumination scenarios are with the uniform natural light, uniform indoor illumination, strong sunlight, and strong flashlight. The resolution of all the images in the dataset is 224 \(\times\) 224 and the lane images captured by the camera are manually labeled. The training dataset includes 15% overexposed images and 85% properly exposed images  
The testing datasets are also collected in a variety of illuminating environments. There are two types of testing datasets in the experiment, which are properly exposed images and overexposed images. In addition, properly exposed images for the same camera and viewing field as the overexposed images are also collected. In the following experiments, two testing datasets are employed to verify the segmentation of the network. One contains only properly exposed images, and the other includes 15% overexposed images and 85% properly exposed images.  
As shown in Figure 8, the images in the first column are properly exposed images, such lane images, which are clear and complete. Properly exposed images are used as a dataset in a properly exposed environment. The images in the second column are the lane images collected in the overexposed environment. The lane images of this kind are seriously defective, and the image cannot be completely extracted by the existing segmentation methods. All overexposed lane images have properly exposed images acquired with the same camera view. Labels of properly exposed lane images can be obtained by labeling all properly exposed images. These labels also apply to overexposed images from the same camera view. Labels are used to verify the lane segmentation results in the overexposed and properly exposed environments.  
Figure 8: Samples of the constructed lane dataset and the manual label.
# Improved Interpolation and Anomaly Detection for Personal PM\({}_{\mathbf{2.5}}\) Measurement
## 2 Methods
### Kernel Regression-Based Interpolation Using Linear Interpolation  
The method proposed in this paper is based on the kernel regression [14], but it took the advantages of the linear interpolation method [16]. The LI method can be used appropriately, especially when the time-series trend is clear. For instance, when the time-series pattern appears to be rising up or decreasing, the LI method can be applied for the interpolation of the corresponding data pattern more appropriately. We used this property of the LI method in order to improve the performance of the KRBI method. In other words, we used the LI method for the bursty missing data a priori and then applied the KRBI method for the final interpolation of the missing data.  
The kernel regression algorithm can be summarized as follows. First, we defined the time-series data as (\(t_{i}\), \(y_{i}\)) where \(t_{i}\) and \(y_{i}\) represented the time and measurement of data at time \(t_{i}\). Kernel regression was to set the representative value \(\hat{y}\) of \(y_{i}\)s where \(p\leq i\leq q\) and the bandwidth \(h\) are defined as \(h=p-q\). In this case, the representative value \(\hat{y}\) might be calculated as a weighted average value of \(W_{i}y_{i}\) where the weight \(W_{i}\) could be generated by following well-known statistical models such as Gaussian or uniform distributions. The algorithm can be expressed mathematically as follows.  
\[\hat{y}=\frac{\sum_{i=1}^{n}K_{h}\Big{(}\frac{-x_{i}}{h}\Big{)}y_{i}}{\sum_{j= 1}^{n}K_{h}\Big{(}\frac{-x_{j}}{h}\Big{)}}=W_{i}y_{i}\]  
where the choice of weight \(W_{i}=\frac{\sum_{i=1}^{n}K_{h}\Big{(}\frac{-x_{i}}{h}\Big{)}}{\sum_{j=1}^{n} K_{h}\Big{(}\frac{-x_{j}}{h}\Big{)}}\) and \(K_{h}(\cdot)\) is a kernel chosen [14].  
In order to apply the KRBI method, a proper bandwidth calculation must be done. For this purpose, we used a part of the data that had no missing data to estimate the proper bandwidth for the data interpolation. A detailed description is provided in the next section.
## 2 Mathematical Modeling and Equations
\begin{split}\bar{v}+c_{v}\bar{v}-\chi_{\Delta T}^{2}\bar{v}^{ \prime\prime}-\Theta\left(\frac{y^{\prime\prime}}{\chi_{\Delta T}^{2}}+\bar{ v}^{\prime\prime}\right)\int_{0}^{1}\left[\frac{y^{\prime}\bar{v}^{\prime}}{ \chi_{\Delta T}^{2}}+\frac{1}{2}\Big{(}\bar{v}^{\prime 2}+\bar{w}^{\prime 2}\Big{)} \right]\mathrm{d}x=F_{v}\cos(\Omega_{v}t+\theta_{v}),\\ \bar{w}+c_{w}\bar{w}-\chi_{\Delta T}^{2}\bar{w}^{\prime\prime}- \Theta\bar{w}^{\prime\prime}\int_{0}^{1}\left[\frac{y^{\prime}\bar{v}^{\prime }}{\chi_{\Delta T}^{2}}+\frac{1}{2}\Big{(}\bar{v}^{\prime 2}+\bar{w}^{\prime 2}\Big{)} \right]\mathrm{d}x=F_{w}\cos(\Omega_{w}t+\theta_{w}),\end{split}  \tag{4}\]  
where the eigenvalue solutions are derived by disregarding the non-linear, damping, and forcing terms (Appendix A). Figure 1: Suspended cable’s mathematical model in thermal conditions. To investigate internal resonances between in-plane and out-of-plane modes, we utilize two discretization equations  
\[ \begin{split}\bar{v}(x,t)&=\varphi_{n}(x)q_{v}(t),\\ \bar{w}(x,t)&=\phi_{n}(x)q_{w}(t),\end{split}  \tag{5}\]  
where \(\varphi_{n}(x)\) (\(\phi_{n}(x)\)) denotes the \(n\)-th eigenfunction for the transverse (out-of-plane) motions. \(q_{n}(t)\) and \(q_{w}(t)\) represent the in-plane and out-of-plane generalized coordinates. By inserting Equation (5) into (4), integrating from 0 to 1, and multiplying by the mode shapes \(\varphi_{n}(x)\) and \(\phi_{n}(x)\), we obtain two ordinary differential equations  
\[ \begin{split}\bar{q}_{v}+\omega_{v}^{2}q_{v}+2\mu_{v}q_{v}=\Lambda _{v1}q_{v}^{2}+\Lambda_{v2}q_{w}^{2}+\Gamma_{v1}q_{v}^{3}+\Gamma_{v2}q_{v}q_{w }^{2}+f_{v}\cos(\Omega_{v}t+\theta_{v}),\\ \bar{q}_{w}+\omega_{w}^{2}q_{w}+2\mu_{w}q_{w}=\Lambda_{w1}q_{v}q_{ w}+\Gamma_{w1}q_{v}^{2}q_{w}+\Gamma_{w2}q_{w}^{3}+f_{w}\cos(\Omega_{w}t+ \theta_{w}),\end{split}  \tag{6}\]  
where details regarding the damping, excitation, linear, and non-linear coefficients can be found in Appendix B.