esa-sceva/satcom-chunk-collection huggingface dataset:
# From NFT 1.0 to NFT 2.0: A Review of the Evolution of Non-Fungible Tokens
## 2 Background and Preliminaries
### NFT Key Properties  
At a high level, NFTs contain two parts: the smart contract and the metadata for the digital artwork [37]. The smart contract exists on a blockchain, and contains a set of rules or standards that facilitates the transaction and serves as a digital description of the content. NFT smart contracts also keep track of the owner of each token using appropriate data structures. Each token, stored in the smart contract, includes a link that uniquely identifies the asset represented. NFTs can represent any asset with specific characteristics [38]. In the following, we highlight the key properties of NFTs (Figure 3):  
* Indivisibility. NFTs have been tailored to be indivisible by default for serving their utility. Indeed, this property comes from the non-fungible nature of NFTs, and means that NFTs can not be divided into smaller tokens, but the whole NFT can be purchased for owning an item.
* Scarcity and uniqueness. These properties are strictly related to the previous one. Indeed, NFTs are unique by definition. This means that NFTs cannot be replicated or reproduced, which provides a verifiable scarcity property that is notoriously difficult to obtain online.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Non-Fungible Token** & **Fungible Token** \\ \hline _Interchangeability_ & Non-Interchangeable & Interchangeable \\ \hline _Uniformity_ & Unique & Identical \\ \hline _Divisibility_ & Non-Divisible & Divisible \\ \hline _Standard_ & ERC-721 & ERC-20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between fungible and non-fungible tokens.  
* Security. The support of blockchain technology guarantees a high security level. Indeed, NFTs are stored on the blockchain network, and for this reason, they are tamper-proof. Furthermore, the blockchain can be used to verify ownership.
* Traceability. When an NFT is issued, the holder is recorded on the blockchain, and all the transactions are also recorded. For this reason, all the history involved in a specific NFT can be retrieved.
* Interoperability. NFTs can be easily moved across multiple systems.
## 3 Results and Discussion
### Optimization and Benchmarks of ANN
The points with an error greater than the mean value \(\pm\) standard deviation were excluded, as they can be affected by objective problems resulting from the operating conditions in a normal outside environment. Only eight points fulfilled these conditions. After refining the dataset, a high improvement in accuracy was noted. The MRE was just 10.78%, which is a satisfying result given such diverse and general input data. Compared to other studies, it is possible to achieve even lower MRE values in the range of 4-5%, but mostly on synthetic data, which can be potentially more prone to interferences, while values from full forecasts rarely reach that low level of inaccuracy [56, 57]. The visualization of raw, unrefined comparison between the experimental and ANN outputs is presented in Figure 6. It is also worth mentioning that the distribution of errors in the dataset was close to the normal distribution, with a slight shift toward positive values. Figure 5: The results of ANN training regression.
## 2 CNN Deployment in Automotive  
As technology advances, the expectations of car buyers evolve, with a growing demand for vehicles that seamlessly integrate with their digital lives while prioritizing safety. Automakers are undergoing a paradigm shift from emphasizing horsepower to prioritizing digital technology, particularly in the creation of self-driving cars, commonly referred to as autonomous driving systems (ADSs). Anticipated in the upcoming years is the next generation of ADSs, poised to deliver an elevated level of self-driving experience. To achieve this, various technologies must be seamlessly integrated into a singular system capable of communication, collaboration, and ultimately emulating human functions across almost all driving scenarios. Intel suggests [14] that achieving this level of sophistication necessitates the incorporation of several sensors such as radar, LIDARs, GPS (Global Positioning System), and advanced cameras. These sensors are tasked with collecting crucial information at a rate of approximately 1 GB/second and processing it in real-time to ensure sound driving decisions. While LIDARs offer advantages like high precision and 3D mapping, their high cost renders them impractical for widespread commercial use in the industry. Consequently, some companies, including Tesla [15] and Mobileye [16], have opted for optical cameras and radars as their preferred sensing devices in the vision-based components of ADSs. In this context, convolutional neural networks (CNNs), as an image-based technique, have demonstrated remarkable effectiveness and are widely employed by automakers in the object detector modules of vehicles.  
Despite the notable advancements in object detection using convolutional neural networks (CNNs) for various applications, the implementation of such networks in autonomous driving systems (ADSs) remains a considerable challenge. This challenge arises from the stringent real-time constraints imposed on safe decision-making and data processing latency within ADSs. As a result, the development of an end-to-end ADS design has not been fully resolved, and ADSs continue to be subjects of ongoing experimentation [17].
## 4 Discussion
### Correcting Problematic \(\beta\) Values  
Near-zero SMAP/CYGNSS \(\beta\) values yield low TB spatial heterogeneity, and positive \(\beta\) values are unrealistic. Before calculating global SMAP/CYGNSS TB and NSSM, we plan to replace erroneous near-zero and positive SMAP/CYGNSS \(\beta\) values with realistic, representative \(\beta\) values. Since most near-zero and all positive \(\beta\) values have poor (near-zero or positive) correlations, we will implement a correlation cutoff to determine which \(\beta\) values to replace. We chose a preliminary correlation cutoff of \(-0.4\), to ensure the replacement of erroneous \(\beta\) values while minimizing the number of replaced \(\beta\) values. Future work will include testing a variety of correlation cutoffs during NSSM validation.  
Unlike the SMAP radar, CYGNSS does not include any cross-polarization data, so we are unable to replicate the regression model between cross-polarization backscatter (representative of vegetation level) and \(\beta\) used to create representative \(\beta\) values for the SMAP active-passive product [2]. Instead, we will use the relationships between SMAP/CYGNSS \(\beta\) values and mean annual precipitation, mean NDVI, and NSSM variability to determine representative \(\beta\) values.  
Since the variation in \(\beta\) values produced by different landcover classes can be explained by the relationships between \(\beta\) values and mean annual precipitation, mean NDVI, and NSSM variability (Section 4.1), we will use landcover classes to create representative \(\beta\) values. Unfortunately, the correlation between \(\beta\) values and landcover class is not particularly high, as can be seen by the spread of the box and whisker plots in Figure 9e. We therefore removed \(\beta\) values with correlations greater than \(-0.4\) from the analyses. We calculated the median \(\beta\) value for each landcover class, including only 9 km grid cells with \(\beta\) values with R \(<-0.4\). We then replaced \(\beta\) values where R \(>-0.4\) with their median landcover class \(\beta\) values. Since MODIS landcover class changes annually, SMAP/CYGNSS \(\beta\) values will change annually. Figure 11 shows a comparison of uncorrected and corrected \(\beta\) values.
## 4 Materials and Methods
### Quality Analysis
#### 4.3.12 Determination of Volatile Profile  
The volatile compounds in the drinking jelly were determined based on the method described for HS-SPME-GC/MS by [PERSON] et al. [73], with some modifications. For the analysis, 1 g of homogenized drinking jelly was weighed into a 20 mL headspace vial, to which 0.5 mL of saturated NaCl solution was added to increase ionic strength and drive the volatiles into the headspace. The vial was sealed using a plastic cap with a polytetrafluoroethylene-silicone septum. The headspace vial was equilibrated at 60 \({}^{\circ}\)C and stirred at 700 rpm for 5 min to reach homogeneous volatilization. A 50/30 \(\upmu\)m DVB/CAR/PDMS (divinylbenzene/carboxen/polydimethylsiloxane) fiber needle assembled on a 57330-U SPME handle (Supelco, Bellefonte, PA, USA) was exposed to the headspace for 50 min at 60 \({}^{\circ}\)C for the adsorption of volatile compounds. The fiber needle was then immediately introduced into the injection port of a 7890B gas chromatograph coupled with a 7000C mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). In the injection port, volatiles were thermally desorbed at 250 \({}^{\circ}\)C for 3 min in split mode (3:1). The separation was performed on an HP-5 MS quartz capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) using helium (99.999%) as the carrier gas. The flow rate of the carrier gas was 3.6 mL per min. The oven temperature was programmed as follows: initially set at 40 \({}^{\circ}\)C for 3 min, ramped to 180 \({}^{\circ}\)C at 4 \({}^{\circ}\)C per min, held for 2 min, and then increased to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C per min. The electron impact ionization energy was 70 eV, the ion source and the quadrupole temperature were kept at 230 and 150 \({}^{\circ}\)C, respectively, and the auxiliary heating temperature was 250 \({}^{\circ}\)C. For the mass scanning, a range of 30 to 500 \(m/z\) was followed. Identification of volatile compounds was carried out by matching their mass spectra with the NIST 14 database and confirming their RIs. The relative concentrations of the volatile compounds in the drinking jelly samples were calculated using the peak area of each compound relative to the total peak area of all detected volatiles. The results were expressed as a % of relative concentrations.
## 2 Fuzzy and Jellyfish Optimization-Controlled Solar PV Battery System
### Solar PV System  
Photovoltaic solar cells are semiconductor-based electric devices that convert solar energy into usable electricity. When solar power was paired with a photovoltaic (PV) system, issues arose, such as initial cost, dependability, and generation efficiency. Therefore, simulation and modeling significantly contribute to the investigation, design, and development of PV performance [14].  
Due to the low power of photovoltaic solar cells, multiple cells, connected in series or parallel, a PV module connected to the desired current and voltage values, which form a PV array, should be created [15]. Photovoltaic cells have nonlinear properties that vary with temperature and radiation intensity. The PV characteristics curve becomes more complicated when the solar array is partly shaded and more than one peak arises. Because of this, the efficiency of solar cells decreases [16]. There are several reasons why a PV array may become shaded. As an illustration, consider the dust on the panels' surface and the nearby buildings, trees, and chimneys [17]. Using MPPT algorithms, the optimum efficiency can be achieved for the photovoltaics at different load operating points. The solar module's power converter's controller performs MPPT calculations. Figure 2 shows the curves (current-voltage and power-voltage) at the operating position. The use of solar power temperature and irradiation have a nonlinear and unpredictable effect on this property. Typically, two distinct variables must be introduced:  
Figure 2: Current–voltage and power–voltage curves of a PV panel.  
Figure 2 shows the power-voltage and current-voltage characteristics of the PV array under uniform irradiance. The red colored curve shows the maximum power that obtained from the 1000 radiation.  
When the current through the PV cell is 0, open circuit voltage (VOC) is the PV cell voltage.  
When the voltage through the PV cell is 0, short circuit current (ISC) is the PV cell current.  
On the I-V curve, a unique point (maximum power point (MPP)) exists where the PV array works at maximum efficiency. The panel's rating is 100 W, 17.9 V, and 5.59 A.
# Effect of C-S-H Nucleating Agent on Cement Hydration
## 2 Materials and Methods
### Methods  
#### 2.4.1 Hydration Heat Test and Calculation of Hydration Kinetics  
The hydration heat test was obtained by a TAM air eight-channel microcalorimeter, and the test temperature was controlled at 20 \({}^{\circ}\)C. The weighed water was mixed with the early strength agent to form a wet component, and then the wet component and cement were added to the 20 mL plastic bottle. This mixture was stirred with an egg beater for 60 s at a slow speed and then for 60 s at a fast speed to form a uniform cement slurry. Finally, the plastic bottle was placed into the tester, and thermal data were recorded.  
The isothermal hydration heat curves are used to calculated hydration kinetic parameters based on the Krstulovic-Dabic formula. The cement hydration process can be divided into three stages, including nucleation and crystal growth stage (NG), interactions at phase boundaries diffusion stage (I) and diffusion stage (D), and the corresponding kinetic equations are [19]:  
\[\text{NG stage}:\ [-\text{In}1-\alpha]^{1/n}=K_{1}(t-t_{0})=K_{1}^{\prime}(t-t_ {0}) \tag{1}\]  
\[\text{I stage}:1-(1-\alpha)^{1/3}=K_{2}r^{-1}(t-t_{0})=K_{2}^{\prime}(t-t_{0}) \tag{2}\]  
\[\text{D stage}:\ \left[1-(1-\alpha)^{1/3}\right]^{2}=K_{3}r^{-2}(t-t_{0})=K_{3}^{ \prime}(t-t_{0}) \tag{3}\]  
where \(dQdt\) represents the hydration exothermic rate of mixtures, \(Q\) is the hydration exothermic quantity and \(t\) is the hydration time; these parameters can be obtained through the hydration heat test. \(\alpha\) is the hydration degree of cement, \(K_{1}\), \(K_{2}\), \(K_{3}\), \(K_{1}^{\prime}\), \(K_{2}^{\prime}\), \(K_{3}^{\prime}\) are the reaction rate constant, \(n\) is reaction exponent, \(t_{0}\) is the end time of cement hydration induction period, \(r\) is the particle size participating in the reaction and \(Q_{max}\) is the theoretical maximum hydration heat of infinite age, which is obtained by the instrument's own software. In addition, according to Formulas (4) and (5), the hydration heat data can be converted into the hydration degree \(\alpha\) and hydration rate \(da/dt\) required by the kinetic model.  
\[\alpha(t)=Q(t)/Q_{\max} \tag{4}\]  
\[da/dt=dQ/dt \tag{5}\]  
The hydration degree \(a(t)\) obtained from Equation (4) is substituted into Equation (1), draw a double logarithmic curve of \(ln[-ln\left(1-\alpha\right)]-\ ln(t-t_{0})\) (as the formula shows \(ln[-ln\left(1-\alpha\right)]=nlnK_{1}^{\prime}+nln(t-t_{0})\)), then the kinetic parameters \(n\) and \(K_{1}^{\prime}\) in the NG stage are obtained by linear fitting. In the same way, the same calculation is made for Equations (2) and (3) to obtain the reaction rate constants \(K_{2}^{\prime}\) and \(K_{3}^{\prime}\) in the I and D stage, then, the kinetic parameters obtained are substituted into the differential expressions of Equations (1)-(3), the kinetic curves of reaction rate \(F_{1}(\alpha)\), \(F_{2}(\alpha)\), \(F_{3}(\alpha)\) and reaction degree \(\alpha\) for the NG, I and D stage are obtained, respectively.
## EMD_CS_ST Denoising Method
### EMD_CS_ST Denoising  
#### 2.4.1 The Shortcomings of CS Denoising  
To illustrate the problem of poor denoising performance when CS is facing a signal with a low signal-to-noise ratio. This paper verifies by digital simulation experiments. The Picker wavelet of the simulated seismic signal is used in the simulation experiment. The Picker wavelet can be expressed as:  
\[f(t)=(1-2\pi^{2}f_{p}^{2}t^{2})\exp(-\pi^{2}f_{p}^{2}t^{2}) \tag{10}\]  
where \(f(t)\) is the amplitude, and \(f_{p}\) is the peak frequency. In the simulation analysis, \(f_{p}=30\) Hz, the sampling number is 1000, the sampling frequency is 1000 Hz, and random noise is added. The noise belongs to Gaussian distribution, and the variance is 0.09. Their waveforms and spectrums are shown in Figure 1.  
Figure 1: Ricker waveform, noisy Ricker waveform, and their spectrums.  
Compressed sensing reconstruction of the noisy Ricker wavelet is shown in Figure 2. It can be known from Figure 2b that the original signal does not have sufficient sparsity in the sparse domain. After passing the CS algorithm, it can be seen from Figure 2c,d that the noise is removed, but the effect is not obvious, and the NF phenomenon (red circle) appears.  
If soft-thresholding filtering is performed on the reconstruction coefficient \(\theta\) and the sparse coefficient corresponding to the noise is removed, the obtained result is as shown in Figure 3. Although significant denoising was achieved, some NF phenomena (red circles) still appeared.
## 2 Results and Discussion  
### Optimization of Microwave Conditions  
Translation of the previously developed solid-supported Glaser-Hay reaction to the microwave was initiated utilizing a propargyl alcohol immobilized resin and phenylacetylene as a soluble alkyne due to the rapid monitoring of the reaction via TLC analysis and UV detection. While other solvents including DCM, acetone, and toluene were briefly investigated, THF was found to be optimal under microwave conditions. This correlated to the previously optimized thermal conditions and was ideal due to the microwave transparency of THF. Microwave conditions were assessed under varying irradiation times in both power mode (100, 200 and 300 W) and in temperature mode with set temperatures. Interestingly, when attempted in power mode at any setting for 5-20 min, little diyne product was detected. When examining the temperature profiles, temperatures ranging from 140-180 \({}^{\circ}\)C were  
Figure 1: Chemoselectivity of the Glaser-Hay reaction.  
observed. At these temperatures it is possible that resin degradation may be occurring, resulting in diminished yields. Consequently, we shifted our focus to temperature mode, which involves pre-setting a maximum temperature the reaction can reach, and varying the reaction times. Temperatures between 40 \({}^{\circ}\)C and 100 \({}^{\circ}\)C were examined with irradiation times of 5-20 min (Table 1).  
Based on these preliminary results, it appears that a minimum of 10 min of reaction time is required in order to yield significant product independent of temperature conditions. Prolonged reaction times did not increase product yield. Optimal conditions were observed when irradiating for 10 min at 80 \({}^{\circ}\)C, affording a 77% yield after resin cleavage. Increased temperatures resulted in decreased yields perhaps due to resin or catalyst degradation under microwave conditions. However, these conditions represent a significant time enhancement relative to the previously developed thermal conditions that required 16 h of reaction time.
###### Abstract  
Coral reefs are biologically diverse and economically important ecosystems that are on the decline worldwide in response to direct human impacts and climate change. Ocean color remote sensing has proven to be an important tool in coral reef research and monitoring. Remote sensing data quality is driven by factors related to sensor design and environmental variability. This work explored the impact of sensor noise, defined as the signal to noise ratio (_SNR_), on the detection uncertainty of key coral reef ecological properties (bottom depth, benthic cover, and water quality) in the absence of environmental uncertainties. A radiative transfer model for a shallow reef environment was developed and Monte Carlo methods were employed to identify the range in environmental conditions that are spectrally indistinguishable from true conditions as a function of _SNR_. The spectrally averaged difference between remotely sensed radiance relative to sensor noise, \(\varepsilon\), was used to quantify uncertainty in bottom depth, the fraction of benthic cover by coral, algae, and uncolonized sand, and the concentration of water constituents defined as chlorophyll, dissolved organic matter, and suspended calcite particles. Parameter uncertainty was found to increase with sensor noise (decreasing _SNR_) but the impact was non-linear. The rate of change in uncertainty per incremental change in _SNR_ was greatest for \(SNR<500\) and increasing _SNR_ further to 1000 resulted in only modest improvements. Parameter uncertainty was complicated by the bottom depth and benthic cover. Benthic cover uncertainty increased with bottom depth, but water constituent uncertainty changed inversely with bottom depth. Furthermore, water constituent uncertainty was impacted by the type of constituent material in relation to the type of benthic cover. Uncertainty associated with chlorophyll concentration and dissolved organic matter increased when the benthic cover was coral and/or benthic algae while uncertainty in the concentration of suspended calcite increased when the benthic cover was uncolonized sand. While the definition of an optimal _SNR_ is subject to user needs, we propose that _SNR_ of approximately 500 (relative to 5% Earth surface reflectance and a clear maritime atmosphere) is a reasonable engineering goal for a future satellite sensor to support research and management activities directed at coral reef ecology and, more generally, shallow aquatic ecosystems.  
remote sensing; coral reef; sensor noise; retrieval uncertainty