esa-sceva/satcom-chunk-collection huggingface dataset:
# Surfactant-Modified Construction Waste Bricks for the Removal of Diclofenac from Aqueous Solutions
## 3 Results and Discussion
### Adsorption Thermodynamics  
The experiments were carried out at various temperatures (298, 308, and 318 K) to investigate the thermodynamics of the adsorption of DCF onto the PRBPs and CCBPs. The results in Figure 10 demonstrate that there was an inverse relationship between the capacity to adsorb DCF onto the waste bricks and the temperature. Specifically, as the temperature increased from 298 K to 318 K, the capacity to adsorb DCF onto both the PRBPs and the CCBPs decreases. This finding suggests that the process of adsorbing DCF onto the waste bricks is exothermic. Additionally, the results indicate that the adsorption of DCF onto waste bricks is more favorable at lower temperatures. This finding is consistent with the results of previous studies in this area [40].  
To investigate the adsorption thermodynamics process further, the thermodynamic parameters of the adsorption of DCF onto the PRBPs and CCBPs can be calculated by the following equations [41; 42]:  
\[\Delta G=-\;nRT \tag{5}\]  
\[lnC_{e}=-\Delta H/\text{RT}\;+lnK_{d} \tag{6}\]  
\[\Delta G= \Delta H+T\Delta S \tag{7}\]  
where \(H\) is the adsorption enthalpy change (kJ/mol), \(S\) is the adsorption entropy change (J/mol K), \(R\) is the ideal gas state constant (8.314 J/mol K), \(T\) is the absolute temperature (K),  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Adsorbents**} & \multicolumn{2}{c}{**Langmuir**} & \multicolumn{4}{c}{**Freundlich**} \\ \cline{2-7}  & \(B\) (L/mg) & \(q_{max}\) (mg/g) & \(R^{2}\) & \(K_{f}\) (mg/g) & \(n\) & \(R^{2}\) \\ \hline PRBP & 0.096 & 0.19 & 0.97 & 0.029 & 2.17 & 0.90 \\ CCBP & 0.021 & 33.0 & 0.99 & 0.86 & 1.25 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The parameters and correlation coefficients of adsorption isotherm models.  
\(n\) is the Freundlich adsorption exponent, \(G\) is the adsorption free energy change (kJ/mol), and \(K_{d}\) is the adsorption rate constant.  
The thermodynamic parameters of the adsorption of DCF onto the PRBPs and CCBPs are listed in Table 5. The estimates of \(\Delta H\) and \(\Delta S\) could be obtained from the slope and intercept of the Van't Hoff plot, in \(K_{d}\) vs. 1/T, as shown in Figure 10c. The negative value of \(\Delta H\) of the PRBPs (-28.64 kJ/mol) and CCBPs (-18.49 kJ/mol) confirms that the adsorption of DCF is an exothermic process. The positive value of \(\Delta S\) for both adsorbents (112.37 and 80.06 J/mol\(\cdot\)K) indicates that the randomness increases at the solid-liquid interface during adsorption. The negative values of \(\Delta G\) for the PRBPs and CCBPs indicate that the adsorption is spontaneous.
## 3 Results
The modelling performed for different combinations of groups of features showed that when taking three or more groups of features, the highest accuracies are mainly observed for the combinations involving either Group 2, Group 5 or both, whereas for a smaller number of groups, the highest accuracy is in general found for the combinations enclosing Group 1. Table 3 provides additional details about the performance of the models with the best accuracy regarding different numbers of features' group, whereas Table S1 in the Supplementary provides the accuracy of all the tested combinations together with the corresponding most discriminative features used by the XGB algorithm. When analyzing each group individually, the highest accuracy, 82.56%, was observed for Group 1, which is considerably greater than for the other cases. For instance, the next in order was the model built on features from Group 5 whose accuracy reached only 67.34%. Additionally, for the models with the highest accuracy for different numbers of groups, there is a considerable difference between recall and the specificity when the Group 1 is not involved in the combination. For instance, the model built with groups 2, 3, 4, 5, and 6 performs with the highest accuracy among all of the other models, however, is fairly more efficient in predicting the stressed state than the normal state. In the case involving all of the features' groups, the accuracy (78.78%) was slightly lower than the best results obtained with a smaller number of groups. In terms of the XGB gain-related ranking of discrimination power, the Hjorth complexity and crest factor are overall appearing as predominant features. Additionally, when Group 2 is not enclosed, the variance is in general among the most discriminative features. \begin{table}
\begin{tabular}{c c c} \hline
**Window Length \(I\)** & **Number of Windows \(N\)** & **Most Discriminative Features (XGB Gain Measure)** \\ \hline \multirow{3}{*}{**30 s**} & 5 & Variance, Shape factor, Hjorth complexity, Minimum, Minimum of WD1 * \\  & 10 & Variance, Shape factor, Minimum, Minimum of WD1 * \\  & 15 & Variance, Shape factor, Hjorth complexity \\ \hline \multirow{3}{*}{**1 min**} & 5 & Crest factor, Hjorth complexity, Frequency center \\  & 10 & Hjorth complexity, Shape factor, Crest factor, Minimum, \\  & 15 & Crest factor, Hjorth complexity, Brown noise \\ \hline \multirow{3}{*}{**2 min**} & 5 & Hjorth complexity, Impulse factor, Minimum of WD4 *, \\  & 10 & Shape factor, Blue noise \\  & 10 & Hjorth complexity, Margin factor, Shape factor, Minimum of \\  & 10 & WDA *, Purple noise \\  & 15 & Hjorth complexity, Variance, Crest factor, Impulse factor \\ \hline \multirow{3}{*}{**5 min**} & 5 & Hjorth complexity, Pink noise, Shape factor \\  & 10 & Hjorth complexity, Minimum of WD8 *, Shape factor, \\  & 15 & Hjorth complexity, Frequency RMS, Pink noise, Shape factor \\ \hline \end{tabular}  
* WDn decomposed signal at level n resulting from the performed wavelet decomposition of order 8.  
\end{table} 
Table 2: Most discriminative features for different window lengths.
###### Abstract  
Predicting yield at an early growth stage allows one to optimize the amount and timing of nitrogen fertilization to maximize crop yield. The aim of the study was to determine how different nitrogen doses would affect the nutrition of two maize varieties (_Zea mays_ L) with this element and their yields, and to demonstrate the potential of using the SPAD index as an early-stage predictor of dry matter yield (DMY) of maize biomass. The field experiment, with different pre- and post-sown N fertilizer doses, was conducted for two years in Poland. The two-factor experiment was set up in a randomized block design with three field replicates. There were four mineral fertilization variants: (I) 120 kg N\(\cdot\)ha\({}^{-1}\) without P and K fertilization, (II) 120 kg N\(\cdot\)ha\({}^{-1}\) + PK, (III) 160 kg N\(\cdot\)ha\({}^{-1}\) + PK, and (IV) 200 kg N\(\cdot\)ha\({}^{-1}\) + PK. The second-order factor was two stay-green maize varieties: Delitop (FAO 240) and Magitory (FAO 270). The mean values of the SPAD index of maize depended on the nitrogen dose. When the dose was increased from 120 kg\(\cdot\)ha\({}^{-1}\) to 200 kg\(\cdot\)ha\({}^{-1}\) with constant P and K fertilization, the value of the leaf greenness index increased significantly. The plants of both maize varieties fertilized with nitrogen at a dose of 120 kg N\(\cdot\)ha\({}^{-1}\) without P and K were characterized by the lowest values of the SPAD index, regardless of the year of the experiment. When the nitrogen dose was supplemented with P and K, the plants' nutrition with nitrogen improved significantly. The values of the SPAD index in the maize plants of the variety Magitory were significantly greater than in the plants of the variety Delitop. The coefficients of correlation calculated for both maize varieties were highly significant at all measurement dates. Therefore, it is possible to conclude that the SPAD leaf greenness index can be used as an important, reliable, and non-invasive indicator for maize yield prediction. Nutrition of maize plants with nitrogen proved to be the main determinant of variability of the biomass yield. For this reason, it was the basis for the verification of individual mineral fertilization variants. The lack of phosphorus and potassium in the dose of mineral fertilizer limited the yield regardless of the variety, as compared to the variant that was optimally balanced with the nitrogen dose.  
SPAD; dry matter yield; mineral fertilization; type of maize variety +
Footnote †: journal: Applied Sciences
## 4 Factors Affecting the UHPC-NC Interface Adhesion
### Fiber Addition  
The interface bonding properties of UHPC-NC can be improved by adding different types of organic or inorganic fibers [65; 66]. Figure 12 shows the theoretical and analytical results of the fiber-reinforced interface characteristics. Fibers can reduce UHPC shrinkage and stress caused by poor shrinkage. Moreover, the risk of interface cracking caused by material shrinkage is effectively reduced with a thin reinforcement surface. Moreover, under an applied load, the bridged effect between fibers can effectively inhibit microcrack development in UHPC, redistribute the stress around the main crack, increase the residual stress after cracking, and change the force transfer and failure modes of the bonding surface. Subsequently, the fibers distributed in the disordered direction can improve the UHPC interface roughness, and those at the bonding surface can extend into the gap of the NC interface, improving the mechanical interlock force between UHPC and NC. [PERSON] [67], [PERSON] [68], [PERSON] [69] and [PERSON] [39] discovered the effect of interface factors, such as fiber distribution direction and dosage, on the interface bonding performance. [PERSON] [46] conducted the interface bonding test of UHPC-NC using the split tensile test method. It was discovered that two specimens with 1.5 vol.% fiber experienced split failure at the NSC layer, whereas the remaining specimens underwent bond failure at the ultrahigh-performance fiber-reinforced concrete (UHFFRC)-NSC interface. The NSC layer demonstrated a lower failure load than that of the UHFFRC-NSC/HSC interface.  
[PERSON] [70] compared the interface bonding shear strength of UHPC-UHPC and UHPC-NC specimens through an L-shaped shear test, whereby the specimens were mixed with steel fibers. UHPC-UHPC was noted to have higher ductility and bearing capacity than that of UHPC-NC, as shown by the load-displacement curve in Figure 13. [PERSON] [55] discovered that a steel fiber dosage of 1-2% can considerably improve the shear-bearing capacity of UHPC-NC. In [65], the effect of 2, 3, and 4 vol.% steel fiber dosages on the interface bonding strength of UHPC-NC was tested. The tensile strength within the interface bonding zone clearly increased with an increasing volume content of steel fibers. At 4 vol.% fiber, the interface split strength reached 92% of the overall split strength of cast NC. Through a flexural test, It is discovered that the split tensile strength within the interface bonding zone increases with the steel fiber volume dosage. The tensile strength of the interface bonding split increased by 80% from that of the specimen without steel fiber under a 2.0% steel fiber volume dosage [71; 72].  
Figure 12: Principle of the fiber-reinforced interface properties [39].
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 4 High-Level System Design
### Smart Contracts  
Smart contracts are self-executing contracts with the terms of the agreement directly written into code. They are stored and executed on the blockchain, ensuring trustworthiness and automation. In our system, smart contracts will be used to manage the creation, ownership, and transfer of NFTs representing unique identifiers for digital resources in global registers. Additionally, they will handle the rules and restrictions associated with the tokenization process, ensuring compliance with relevant regulations and policies.  
To enhance the flexibility and efficiency of our system, we implemented the following advanced smart contract features:  
1. Multi-token Standard Support: In addition to the ERC-721 standard for unique NFTs, we incorporate the ERC-1155 standard [39]. This multi-token standard allows for more efficient batch transfers and the management of both fungible and non-fungible tokens within a single contract. This is particularly useful for handling different types of digital assets and their associated metadata within various global registries;
2. Upgradeable smart contracts: To ensure long-term flexibility and the ability to address future requirements or security updates, we implement upgradeable smart contract patterns. This approach allows for the modification of contract logic without disrupting the system's operation or requiring the migration of existing data;
3. Role-Based Access Control (RBAC): We implement a granular RBAC system within our smart contracts to manage different levels of permissions for various stakeholders, such as registry administrators, content creators, and end-users;
4. Royalty and Revenue Sharing: For use cases involving intellectual property rights, we incorporate automated royalty distribution mechanisms. These allow for the seamless allocation of revenues to multiple stakeholders based on predefined rules.
5. Cross-chain Interoperability: To facilitate interaction with multiple blockchain networks, we design our smart contracts with cross-chain compatibility in mind. This includes the use of bridge contracts and standardized interfaces for cross-chain asset transfers and data verification;
6. Gas Optimization: We employ various gas optimization techniques in our smart contract development to minimize transaction costs, which is particularly important for high-volume operations in global registries;
7. Event Emission: Our smart contracts emit detailed events for all significant actions, enabling efficient off chain indexing and real-time updates for user interfaces and external systems.  
These advanced smart contract features ensure that our system can handle the complex requirements of various global digital registries while maintaining security, efficiency, and adaptability to future needs.
## 2 Pitting Model
### Multi-Parameter Localized Random Pitting Model  
#### 2.1.1 Depth and Size  
The depth distribution of pitting pits conforms to the Weibull distribution [40; 41]. The probability density function can be described as Equation (1), where \(\alpha\) is the shape parameter, \(\beta\) is the scale parameter, and h is the depth of the pits. The probability distribution function Equation (2) can be obtained through the integration of Equation (1). [PERSON] et al. [42] determined \(\alpha\) and \(\beta\) as 1.432 and 2.319, respectively, by statistical calculation. Then Equation (1) can be described as Equation (3). The depth of a single pitting pit can be confirmed by a randomly generated probability in the range of [0,1]. Thus, the inverse function of the probability distribution function is required as Equation (4), where \(P\) is the probability of a certain value of \(h\).  
\[f_{(h)}=\left\{\begin{array}{ll}\frac{\alpha}{\beta}\left(\frac{h}{\beta} \right)^{\alpha-1}&e^{-\left(\frac{h}{\beta}\right)^{\alpha}}&h\geq 0\\ 0&h\leq 0\end{array}\right. \tag{1}\]  
\[F_{(h)}=\int\limits_{-\infty}^{h}\left[\frac{\alpha}{\beta}\left(\frac{h}{ \beta}\right)^{\alpha-1}e^{-\left(\frac{h}{\beta}\right)^{\alpha}}\right]dh=1- e^{-\left(\frac{h}{\beta}\right)^{\alpha}}h\geq 0 \tag{2}\]  
\[F_{(h)}=1-e^{-\left(\frac{h}{\Gamma\log 2}\right)^{2.319}}h\geq 0 \tag{3}\]  
\[h=1.432[\ln(1-P)]^{\frac{1}{2319}} \tag{4}\]  
[PERSON] et al. [43] investigated the relationship between the diameter-depth ratio and time and obtained the time-varying function of the diameter-depth ratio, which can be described as Equations (5)-(7), where \(\lambda\) is the diameter-depth ratio and \(t\) is the corrosion time. The tower limit of ultimate strength in a 95% confidence interval is taken as \(F_{u}\), and the diameter of the \(K_{th}\) in the pit can be calculated by Equation (8), where \(D_{k}\) is the diameter of the \(K_{th}\) pitting pit.  
\[\lambda_{r}(t)=0,t\in[0,\,7] \tag{5}\]  
\[\lambda_{r}(t)=8.16+115.15\times\left[1-e^{\left(-\frac{t-6.6}{7.27}\right)} \right]^{1.12}\times e^{\left(-\frac{t-6.6}{1.74}\right)},t\in[7,\,12] \tag{6}\]  
\[\lambda_{r}(t)=\begin{cases}8.34+1.92\times e^{\left(-\frac{t-120.0}{2.99} \right)}&65\%\,\,conf\,idence\\ 8.34+4.00\times e^{\left(-\frac{t-120.0}{2.02}\right)}&80\%\,\,conf\,idence \,,\,t\in[12,\,\infty)\\ 8.34+5.04\times e^{\left(-\frac{t-120.0}{2.07}\right)}&95\%\,\,conf\,idence \end{cases} \tag{7}\]  
\[D_{\text{k}}=\lambda_{r}(t)\cdot h_{\text{k}} \tag{8}\]
# A Survey of Secure Time Synchronization
## 2 Time Synchronization
### Common Challenges for Time Synchronization
#### 2.3.1 Nondeterministic Delays  
Nondeterministic delays in the message delivery and confound latency estimations contribute directly to the challenges in secure time synchronization [26]. For example, the physical layer access time can be magnitudes larger than the required synchronization precision of the network. Several nondeterministic delays have been analyzed first by [PERSON] and [PERSON] [27] and added to by [28], according to the process of the message delivery. To illuminate the synchronization nondeterministic delays, it is necessary to analyze the sources of these message delivery delays [26]. As shown in Figure 1, the following components are presented in message delivery delays [27; 28; 29]:  
* Send time is the building time of a message at application layer. It also includes the delays introduced by the operating system overheads and protocol processing.
* Access time is the time you have to wait, after reaching the medium access control (MAC) layer, for accessing the transmission channel.
* Transmission time includes the time at physical (PHY) layer for transmitting a message. Delay is deterministic and can be calculated by the packet size.
* Propagation time is a time which is the actual time for a message to be transmitted from the sender to the receiver. It is deterministic, depending on the distance and in most cases, small enough to be ignored from latency estimation.
* Reception time includes the time at the physical (PHY) layer for receiving a message which is the same as transmitting a message.
* Receive time is the time which is taken at the receiver to construct and send the received message to the application layer.  
The medium access control (MAC) layer time-stamping technique can be used to eliminate these sources of nondeterministic delays by using the TPSN and FTSP [30]. The TPSN reduces the uncertainties by using time stamping in the MAC layer but, in the case of root node failure, the whole network needs to be resynchronized which increases the overhead and takes a large amount of bandwidth [31]. The FTSP achieves high accuracy by utilizing customized MAC layer timestamps to reduce the nondeterministic message delays; however, each time, it needs access to actual hardware which requires increasedcomputational resources and energy consumption because it is not a purely software-based protocol [32].  
The RBS protocol eliminates the largest sources of errors, i.e., send time and access time, from the critical path by using the receiver-to-receiver technique. This protocol saves energy consumption on time updates but there is still propagation time and receiver time uncertainty. If the receiver stores the time messages received which require a large number of storage space, this algorithm will provide better results [21; 33].
## 3 Analysis of Carrier Phase Recovery Approaches  
### One-Tap Normalized Least-Mean-Square (LMS) Carrier Phase Recovery  
As a feed-back carrier phase recovery approach [27; 28], which is schematically shown in Figure 2, the transfer function of the one-tap normalized LMS algorithm in the \(n\)-PSK coherent optical communication systems can be expressed using the following equations:  
\[y\left(k\right)=w_{NLMS}\left(k\right)x\left(k\right), \tag{5}\]  
\[w_{NLMS}\left(k+1\right)=w_{NLMS}\left(k\right)+\mu e\left(k\right)x^{*}\left( k\right)/\left|x\left(k\right)\right|^{2}, \tag{6}\]  
\[e\left(k\right)=d\left(k\right)-y\left(k\right), \tag{7}\]  
where \(x(k)\) is the complex input symbol, \(k\) is the index of the symbol, \(y(k)\) is the complex output symbol, \(w_{NLMS}(k)\) is the tap weight of the one-tap normalized LMS equalizer, \(d(k)\) is the desired output symbol after the carrier phase recovery, \(e(k)\) is the estimation error between the output symbol and the desired output symbol, and \(\mu\) is the step size of the one-tap normalized LMS algorithm.  
It has been verified that the optimized one-tap normalized LMS carrier phase recovery in the QPSK optical transmission systems behaves similarly to the ideal differential carrier phase recovery [24; 37],and the BER floor of the one-tap normalized LMS carrier phase recovery in the QPSK transmission systems can be approximately described as follows [37]:  
\[BER_{floor}^{NLMS\_QPSK}\approx\frac{1}{2}erfc\left(\frac{\pi}{4\sqrt{2}\sigma_{T }}\right). \tag{8}\]  
Therefore, the BER floor of the one-tap normalized LMS carrier phase recovery for the \(n\)-PSK optical fiber communication systems can be derived accordingly, and can be expressed using the following equation:  
\[BER_{floor}^{NLMS}\approx\frac{1}{\log_{2}n}erfc\left(\frac{\pi}{n\sqrt{2} \sigma_{T}}\right), \tag{9}\]  
where \(\sigma_{T}^{2}\) is the total phase noise variance (or effective phase noise variance) in the long-haul high-speed \(n\)-PSK optical transmission systems.