esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Presently, conservation and restoration are still based on non-standardized techniques. In recent years, innovative environmentally and operator-friendly products for conservation and restoration have been developed [29; 32; 33]. [PERSON] et al. [34] investigated green conversion of cultural heritage, which refers to eco-sustainable practices used as alternatives to traditional methods. The choice of consolidants for restoration has to take into consideration their adhesive strength, the absence of significant chromatic variations and their permeability [35]. Over time, some materials used in restoration, such as consolidants, age and lose their properties. New restorations include the removal of the aged or biodeteriorated consolidants with toxic solvents alone or in combination with non-toxic or innovative green compounds [36]. The thick layers of aged acrylic coating on the surfaces of wall paintings in San Salvador church in Venice were removed with highly viscous gel-like matrices: hydrophobically modified hydroxyethyl cellulose loaded with an oil-in-water microemulsion [37]. Paraloid(r) B72 is a copolymer of ethyl methacrylate and methyl acrylate (ratio 70:30) and is a very stable resin, with a Tg of 40 \({}^{\circ}\)C and a refractive index3 of 1.49. The solvents most commonly used in combination with Paraloid(r) B72 are acetone, toluene, xylene, ethyl acetate and diacetone alcohol [38]. Paraloid(r) B72, thanks to its adhesive and consolidating properties, is one of the most frequently used acrylic polymers, and it is employed for the conservation of a wide range of materials due to its reversibility, mechanical characteristics and ease of application [39; 40]. It has been suggested that fungal growth on Paraloid(r) B72 may interfere with the structural properties of the polymer due to modification of the water affinity [41]. Inaddition, it has been proved that aged synthetic acrylics can be colonized by melanised fungi, which accelerate the decay process [41; 42]. Degradation of consolidants occurs at the surface and leads to the fragility and loss of the pictorial layer. Transparent dispersion of casein (TDC) is an artisanal product obtained from lime and casein that is used as a consolidant in the restoration of mural paintings in Romanian churches [43]. The present study aimed to: (I) identify the main fungal metabolites involved in the decomposition of Paraloid(r) B72 and TDC; (II) evaluate in vitro the deteriorative potential of the fungi that colonize and decompose Paraloid(r) B72 and TDC; and (III) improve the susceptibility of consolidants to degradation through treatments with biocides.
# 5G Beyond 3 GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
## 2 5G Technologies beyond Release 15
### Edge Computing  
Advanced vehicular applications and services require ultra-low latency and high bandwidth computing. To alleviate core networks from these demanding requirements and efficiently serve local queries, multi-access edge computing (MEC) technologies bring computing capabilities at the edge of the network [30; 31]. Under this connect-compute environment, MEC allows telecommunication operators to flexibly deploy and enhance the computing scalability of networks. Rel-16 provides further enhancements to improve the coordination of mobility procedures with the application. Newer releases will introduce further enhancements to support edge computing for V2X [11].  
The value added by each of the previously described 5G features is summarised in Table 1.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 5 Multiple Input Multiple Output Technology
### Receive Diversity  
Receive diversity is considered as one of the most widely deployed multi-antenna configurations in MIMO technology [132; 133]. Receive diversity can come in different patterns of single input single output (SISO) with one receive antenna as well as a single input multiple output (SIMO) involving multiple receive antennas. A schematic diagram of receive diversity is shown in Figure 14. From the context of UAV deployment, the transmitter (Tx) will be the GCS, while the receiver (Rx) will be the aerial airborne platform. \(\omega\) denotes the channel weights assigned to different antenna array in the MIMO antenna configuration. The different signals from multiple wireless links are combined via maximum ratio combining (MRC) [134; 135] and selection combining (SC) [136; 137]. The number of antenna array that can be fitted into the airborne platform depends on the transmitting radio frequency. The goal of receive diversity is more towards improving link reliability than increasing the signal rate. This scheme does not maximize the spectrum.
## Materials and Methods
### Location Selection, System Design and Dimensioning, System Components, and Scenarios  
#### 2.2.1 Location Selection  
The following criteria apply to the selection of two locations in different European climate zones. They are listed in order of significance (Figure 1):  
1. Close to a large European functional urban area [12] or city with at least 50,000 inhabitants, preferably in one of Europe's five most populous countries [103].
2. Located in different European climate zones, as defined by the Koppen-Geiger climate classification [44] and supplement of [44].
3. Located in a region with underground salt formations suitable for underground gas storage [104].
4. One location should have a relatively high, and one location should have a relatively low solar global irradiation compared to European measurements [59; 60; 62].  
One location should have a relatively low annual precipitation compared to European measurements [105].
6. All required statistical and hourly modeling data should be available for the selected locations (wind velocity, solar irradiation, precipitation, building energy consumption, etc.).  
The urban area of Hamburg in Germany and Murcia in Spain were selected, see Figure 1. Hamburg is the cooler, windier, and rainier area; Murcia is the warmer, sunnier, and dryer area. In Appendix A.1, Table A1 shows key figures characterizing Hamburg in Germany and Murcia in Spain and their climates.
# HS-SPME Gas Chromatography Approach for Underivatized Acrylamide Determination in Biscuits
## 2 Materials and Methods
### Fiber Selection for AA Headspace-Solid Phase Microextraction  
To evaluate the most appropriate fiber for AA HS-SPME, 6 different fibers with 1 cm length were used, followed by GC with a flame ionization detector (FID): polydimetylsiloxane (PDMS, 100 \(\upmu\)m); polyacrylate (PA, 85 \(\upmu\)m); carboxax/divinylbenzene (CW/DVB, 65 \(\upmu\)m), carboxen/polydimetylsiloxane (CAR/PDMS, 75 \(\upmu\)m); polydimetylsiloxane/divinylbenzene (PDMS/DVB, 65 \(\upmu\)m); and divinylbenzene/carboxen/polydimetylsiloxane (DVB/CAR/PDMS, 50/30 \(\upmu\)m) covering different coating characteristics. Tested fibers had different ranges of coating length (65-100 \(\upmu\)m), polarity (nonpolar, polar, bipolar) and extraction mechanism (absorption or adsorption) (Table S2--Supplementary Data) [30].  
A 120 mL sealed screw cap glass vial (Schott Duran) containing 2 g of NaCl was used for headspace generation. The aqueous solution was prepared by transferring the solvents in the following order: 19 mL of distilled water, 20 mL of propanol, and 1 mL of AA stock solution A (5.0 g/L) and stirred for 15 min at room temperature (magnetic bar, 40 \(\times\) 0.9 mm, at 200 rpm). The screw cap glass vial was placed in a thermostatized bath (VWR International-Material de Laboratorio, Lda, Alfragide, Portugal) adjusted to 60.0 \(\pm\) 0.1 \({}^{\circ}\)C for 15 min, and then the SPME fiber was inserted in the vial headspace for 45 min, in a total of 90 min. All measurements were performed at least in triplicate, each one corresponding to one independent aliquot.
## 4 Joint Power Allocation for Co3P JDT
Then, we can obtain the LLA CTP power expression for \(P_{3}^{*}\) versus \(\tilde{\tau}_{3}[\mathrm{dB}]\), connecting the corresponding CTP3 powers at the boundary point between the Co3P JDT mode and Co2P JDT mode by CTP1 and CTP2, and at the settled point of \(P_{3}^{*}=P_{2}^{*}\) at \(\tilde{\tau}_{3}=\tilde{\tau}_{2}\), as  
\[+\left\{{2P_{3}^{*}}^{\prime}{\tilde{\gamma}}_{3}-\frac{{P_{3}^{ *}}^{2}{\tilde{\gamma}}_{3}\left({\tilde{\gamma}}_{3}-{\tilde{P}_{1}^{*}}^{ \prime}{\tilde{\gamma}}_{1}\right)}{{P_{3}^{*}}^{\prime}{\tilde{\gamma}}_{3}-{ P_{1}^{*}}^{\prime}{\tilde{\gamma}}_{1}}-\frac{{P_{3}^{*}}^{2}{\tilde{\gamma}}_{3} \left({\tilde{\gamma}}_{3}-{P_{2}^{*}}^{\prime}{\tilde{\gamma}}_{2}\right)}{{P _{3}^{*}}^{\prime}{\tilde{\gamma}}_{3}-{P_{2}^{*}}^{\prime}{\tilde{\gamma}}_{ 2}}-1\right\} \tag{23}\]5 (40a) or \[+\left\{{2P_{3}^{*}}^{\prime}{\tilde{\gamma}}_{3}-\frac{{P_{3}^{ *}}^{2}{\tilde{\gamma}}_{3}\left({\tilde{\gamma}}_{3}-{\tilde{P}_{1}^{*}}^{ \prime}{\tilde{\gamma}}_{1}\right)}{{P_{3}^{*}}^{\prime}{\tilde{\gamma}}_{3}-{ P_{1}^{*}}^{\prime}{\tilde{\gamma}}_{1}}-\frac{{P_{3}^{*}}^{2}{\tilde{\gamma}}_{3} \left({\tilde{\gamma}}_{3}-{P_{2}^{*}}^{\prime}{\tilde{\gamma}}_{2}\right)}{{P _{3}^{*}}^{\prime}{\tilde{\gamma}}_{3}-{P_{2}^{*}}^{\prime}{\tilde{\gamma}}_{ 2}}-1\right\} \tag{23}\]6  
Finally, in the condition \(\tilde{\tau}_{1}\geq\tilde{\tau}_{2}\geq\tilde{\tau}_{3}\), we calculate the CTP3 power to be actually allocated to CTP3, \(\hat{P}_{3}^{*}\) by using one of the LLA CTP3 power expressions of Equations (40a) and (40b) as  
\[\hat{P}_{3}^{*}=\left\{ \begin{array}{ll}0,&\text{if }P_{3}^{l,o*}<0,\\ P_{3}^{l,o*},&\text{if }P_{3}^{l,o*}\geq 0.\end{array} \right. \tag{41}\]  
Substituting \(\hat{P}_{3}^{*}\) obtained from Equation (41) instead of \(P_{3}^{*}\) into Equation (24b), we can obtain the LLA CTP2 power expression for Co3P JDT as  
\[P_{2}^{l,o*}=\tilde{\beta}\left(\tilde{\tau}_{1}\right)\left(\ln\tilde{\tau}_{2 }-\ln\tilde{\tau}_{1}\right)+0.5\left(P_{S}-\hat{P}_{3}^{*}\right). \tag{42}\]We calculate the CTP2 power to be actually allocated to CTP2, \(\hat{P}_{2}^{*}\) by using the LLA CTP2 power expression of Equation (42) instead of Equation (24b) as  
\[\hat{P}_{2}^{*}=\left\{ \begin{array}{ll}0,&\text{if }P_{2}^{l,\infty}<0,\\ P_{2}^{l,\infty*},&\text{if }P_{2}^{l,\infty*}\geq 0.\end{array} \right. \tag{43}\]  
Subsequently, we can obtain the CTP1 power to be actually allocated to CTP1, \(\hat{P}_{1}^{*}\) as \(\hat{P}_{1}^{*}=P_{S}-\hat{P}_{2}^{*}-\hat{P}_{3}^{*}\). In conclusion, we can decide whether Co3P JDT or Co2P JDT is performed or not by using Equation (41) for Co3P JDT or Equations (41) and (43) for Co2P JDT. That is, in the condition \(\hat{\gamma}_{1}\geq\hat{\gamma}_{2}\geq\hat{\gamma}_{3}\), Co3P JDT is performed if \(\hat{P}_{3}^{*}>0\); Co2P JDT by CTP1 and CTP2 is performed if \(\hat{P}_{3}^{*}=0\) and \(\hat{P}_{2}^{*}>0\); and single-point transmission by CTP1 is performed if \(\hat{P}_{3}^{*}=0\) and \(\hat{P}_{2}^{*}=0\).
# Medium Transmission Map Matters for Learning to Restore Real-World Underwater Images
## 4 Conclusions  
In this paper, to solve the difficulties existing in underwater image enhancement at this stage, we demonstrated the value of the physical prior, in particular, the medium transmission map, for restoring the real-world underwater images. By formulating a very simple network for learning both the prior and restoration results jointly, and encapsulating the knowledge interaction process across these two tasks at both feature and output levels, much better restoration features were learned, guaranteeing better results. In addition to producing the best results on two real-world benchmarks, our model also processed the underwater images at real-time speed, making it a potential framework to be deployed in intelligent underwater systems.  
In the future, we will explore the upper bound of the benefits of the medium transmission map and also continue the exploration for a suitable knowledge interaction design for better fusing the physical prior.  
Conceptualization, G.W., K.Y. and L.L.; methodology, K.Y. and L.L.; writing--original draft preparation, K.Y. and L.L.; writing--review and editing, G.W. and Z.Z.; supervision, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under grant 62102069 and U20B2063, the Sichuan Science and Technology Program under grant 2022 YFG0032.  
Not applicable.  
Our experiment was conducted on the UIEB Dataset [5], the UWCNN Dataset [8], and SAUD [29].  
Our code extends this github repository [[https://github.com/xahidbuffon/FUnIE-GAN/tree/master/Evaluation](https://github.com/xahidbuffon/FUnIE-GAN/tree/master/Evaluation)]([https://github.com/xahidbuffon/FUnIE-GAN/tree/master/Evaluation](https://github.com/xahidbuffon/FUnIE-GAN/tree/master/Evaluation)) (accessed on 7 April 2022), and we extend our thanks to the group.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **PSNR** & **SSIM** \\ \hline MTUR-Net & 22.60 (+0.00) & 0.9008 (+0.0000) \\ \hline Basic & 22.17 (\(-\)0.43) & 0.8893 (\(-\)0.0115) \\ w/o skip connection & 22.15 (\(-\)0.45) & 0.8897 (\(-\)0.0111) \\ w/o concat & 21.63 (\(-\)0.97) & 0.8820 (\(-\)0.0188) \\ w/o conv2d after concat & 22.23 (\(-\)0.37) & 0.8950 (\(-\)0.0058) \\ DRB blocks addition & 21.75 (\(-\)0.85) & 0.8890 (\(-\)0.0118) \\ DRB blocks concat & 21.92 (\(-\)0.67) & 0.8911 (\(-\)0.0097) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Component Analysis. The Basic Model is MTUR-Net without the MT-Guided Non-Local Module.
## 2 Study Area
### Geographical, Climate, and Water Management Overview
For this purpose, the reclaimed wastewater would be pumped to the SAT infiltrat  
Figure 1: (a) Location sketch of the studied area; (b) landscape image of Port de la Selva (1956) showing the extensive agricultural use of soil in the 1950s compared to the current urbanization setting; (c) location of main hydrological features and water management facilities over current landscape image of Port de la Selva. tion ponds where the subsoil acts as a low-cost/low-energy filtration/disinfection scheme including seasonal storage. To close the circle, artificially recharged water--diluted into the natural groundwater flow--is pumped on wells placed on the same alluvial aquifer about 1 km downstream. Risk analysis results derived from the DEMOWARE project showed that the system was efficient for reducing mononuclear phagocyte system (MPS) bacteria content below the World Health Organization (WHO) and Spanish regulations and removed after 50 m of flow through the subsoil. Nevertheless, the start-up of the recharging system is still dependent on obtaining authorization from the appropriate health care authorities. Handling a flow and transport model, the migration of the water infiltrated in the aquifer from the recharge ponds was simulated by Amphos21 to analyze the sensitivity of travel times and dilution factors related to different scenarios [38]. Moreover, the numerical model was capable of simulating aquifer response to rainfall events and pumping in water supply wells and reproduces the observed hydraulic heads with reasonable accuracy [35]. The model has been constrained from 12 monitoring wells. Nevertheless, it is expected that geophysical data could improve the reliability of the groundwater models representing the heterogeneity of the shallow aquifer. Figure 2: (**a**) Seasonal fluctuations of treated and abstracted water volumes and measured chloride concentration in Port de la Selva’s public water supply well [39]. The graph shows the correlation between water demand and wastewater generation and the deferred effect between maximum exploitation and salinization of the water supply well. The dashed line represents the chloride concentration limit defined by the Spanish sanitary guidelines for the quality of water for human consumption [37]; (**b**) rainfall for the period 2011–2016. The summer season corresponds to the dry months of the year and the highest water demand.
# Quantum-Secure Coherent Optical Networking for Advanced Infrastructures in Industry 4.0
## 7 Discussion
### Research Result Contributions and Positioning
For example, DRPE (Double Random Phase Encoding) was historically championed as \"theoretically unbreakable\" under certain conditions, yet we confirm the more recent stance that advanced machine learning can unravel DRPE unless phase masks are updated frequently--a constraint that complicates usage at 400 Gbps speeds. By contrast, MHCD side-steps some of DRPE's vulnerabilities by continuously embedding data in noise-like signals. Some studies [17] argue that classical encryption, if properly implemented, remains sufficiently secure in the near term. However, our post-quantum perspective challenges the complacency that might arise from that viewpoint. We posit that a malicious actor could be capturing high-value data now for future decryption. The window of vulnerability suggested by the previous work in [8], where data can be stored indefinitely, means that classical encryption strategies, even at the optical layer, risk obsolescence. We do not dispute the viability of classical encryption in immediate operational contexts, but we join the previous conclusion of [10] in asserting that ignoring post-quantum risks could be highly problematic in industries where long-term data confidentiality is paramount. By embedding optical-phase mask encoding with MHCD at the physical layer and coupling it with QKD at the key-management layer, our proposed framework offers a practical strategy to balance immediate network performance with resilience against future quantum adversaries. Compared to previous suggestions [3], which focus on quantum key delivery alone, we argue the following for a dual-plane protection model: quantum-based key distribution handles the ephemeral key process, while physically encoded signals thwart advanced eavesdropping attempts. This approach ensures that even if quantum key exchange is momentarily compromised or suffers from hardware limitations [9], the data plane retains a robust line of defense via optical-layer encryption.
## 4 Conclusions  
The conclusions drawn from the synthetic data testing of the 3D time-lapse electric field inversion algorithm for CSAMT are as follows:  
The algorithm, incorporating dual constraints in both temporal and spatial dimensions, demonstrates stability and feasibility, exhibiting robust performance in inverting monitoring data across various noise levels. The algorithm demonstrated its reliability by adjusting the size and number of discrete grid cells without significant inversion crime issues. By implementing time-lapse inversion on the observational data at each time step, the algorithm effectively addresses the challenge posed by the separate inversion of the monitoring data, enhancing the comparability of the results. Independent inversion, particularly when handling noise pollution data, is susceptible to generating inaccurate monitoring outcomes. Conversely, time-lapse inversion can be executed seamlessly, showcasing commendable performance under the combined influences of temporal and spatial constraints.  
Directly inverting a single electric field component, without the need for calculating the Cagniard apparent resistivity, yields reliable inversion results. This implies that during field operations, the monitoring objectives of the controlled source audio-frequency magnetotelluric method can be attained by observing only a single component of the electric field. The technology of CSAMT monitoring combined with this algorithm is well-suited for large-scale three-dimensional measurements in a diverse environment.  
Methodology and writing--original draft preparation, Q.S.; writing--review and editing, project administration, H.T.; software, W.W.; validation, Q.H. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation (Grant Nos. 41830429 and 42004123) and the Hubei Zigui-Changyang Shale Gas Strategic Area Survey Project (Grant No. DD20179623).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the author [PERSON] on request.  
No conflict of interest exists in the submission of this manuscript.
# OneMap Drought Monitoring Analysis Based on Statistical Models
## 2 Materials and Methods
### OneMap Drought Index Combining Meteorological, Agricultural, and Hydrological Drought Indices  
#### 2.3.1. Integration of Drought Indices by Statistical Methods  
Drought phenomena are related to several other variables, and thus, drought analyses based on a single index are insufficient in monitoring complex drought phenomena. Therefore, an OneMap drought index for accurate drought monitoring was developed using a combination of information related to various types of drought indices (meteorological, agricultural, and hydrological). The OneMap drought index was based on ground and satellite data using multivariate statistical models. The OneMap drought index developed in this study was compared to and verified with a drought index calculated using drought history data, and the degree of the drought was expressed using the verified OneMap drought index. This study sought to evaluate the applicability of the OneMap drought index, determine the drought stage (attention, caution, alert, or severe), and evaluate the applicability of the drought index and drought reproduction ability based on ground and satellite data.  
In this study, the drought index for each application method was evaluated by applying four linear models and two nonlinear models. The OneMap drought index was developed via multivariate statistical modeling applying Bayesian principal component analysis (BPCA), developed by [PERSON] et al. [28], to estimate high-dimensional datasets with missing data. The modeling involved three procedures: principal component regression analysis, Bayesian estimation, and the expected value maximization algorithm. The first two procedures were conducted to derive and determine appropriate parameters, and the third was used to estimate missing values.
## 3 Results
### Electrical Properties  
The internal resistance of the system was measured at 0.00 \(\pm\) 0.00 k\(\Omega\). Figure 7A shows that samples with greater salinities showed lower levels of resistivity. The average value of resistivity measured for pig myocardium was 7.6 \(\pm\) 0.6 \(\Omega\)m (Figure 7B). Layfomm40 soaked for 24 h in 1.35% saline had a resistivity of 7.0 \(\pm\) 0.3. It was the only salinity analyzed which showed no statistically significant difference to porcine myocardium (\(p\) = 0.0530).  
[PERSON] et al. [22] define 131-180 \(\Omega\) to be the intermediate impedance for RCA simulations, and 90-130 \(\Omega\) to be low impedance. Figure 7C shows that impedance and salinity followed an inverse relationship. Intermediate impedance was consistently achieved when the tank was filled with 0.25% saline (impedance = 150.0 \(\pm\) 5.8 \(\Omega\)). A tank salinity of 0.4% (110.4 \(\pm\) 4.2 \(\Omega\)) consistently achieved values of low impedance.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Simulator Fluoroscopy** & **Patient Fluoroscopy** \\ \hline Mean Vertebral Intensity & 107.2 & 106.0 \\ Mean Background Intensity & 108.0 & 111.8 \\ Contrast & 6.2\% & 5.3\% \\ \hline \end{tabular}
\end{table}
Table 2: Average fluoroscopy contrast between the vertebral bodies and the background.  
Figure 6: **(A)** The final, fully assembled spine. **(B)** The fully assembled base with the X-ray spine phantom. **(C)** High-dose fluoroscopy image of the fully assembled simulator, including the spine. **(D)** The mean CT number acquired from the vertebrae (92.8 \(\pm\) 10.5 HU) compared to spongy bone (148 to 661 HU) and the intervertebral disks (47.6 \(\pm\) 9.4 HU) compared to soft tissue (\(-\)700 to 225 HU) [21].