esa-sceva/satcom-chunk-collection huggingface dataset:
# Boron Nitride Nanotubes for Curcumin Delivery as an Anticancer Drug: A DFT Investigation
## 1 Introduction  
Non-carbon nanostructures, as well as BNNTs, have attracted a lot of attention due to their thermal and chemical stability, together with their mechanical properties [1]. Boron nitride nanotubes (BNNTs) were originally introduced in 1994 [2], and the arc discharge procedure was used to produce them a year later [3]. Applications in various fields are extremely relevant for sensing applications [4], hydrogen storage [5], capture neutron therapy [6], drug delivery [7], and biomaterial applications [8]. Due to the theory of the adsorption of different molecules such as urea [9], CO\({}_{2}\)[10], NH\({}_{3}\)[11], Ni [12], and anticancer drugs [13] in nanotubes, various experiments have occurred in recent years.  
For several years, one of the most significant nanomaterials studied in the pharmaceutical drug system has been carbon nanotubes (CNT) [14]. As CNTs are used as drug carriers, many biochemical problems arise concerning the interactions between drug molecules and carbon nanotubes. Experimentally and theoretically [15], there are many studies on this subject. One of the key challenges of nanocarriers is to encapsulate and release the drug in a nanoparticular form without any chemical modifications at the proper site [16]. BNNT is a high inner volume nanocarrier that encapsulates more drugs and allows them to penetrate open ends [17]. The BNNTs are non-cytotoxic compared to carbon nanotubes (CNTs), with their large bandgap and the independence of electronic properties from the nanotube diameter, which is very chemically stable [18]. Curcumin is a tautomeric compound present in organic solvents as an enolic form and water as a keto form [19].  
In history, curcumin was used as a medicinal herb for its different purposes, including antioxidant [20], anti-inflammatory [21], anti-mutagenic [22], anti-microbial [23], and other therapeutic attributes [24]. Curcumin is distributed as capsules, tablets, and ointments in many forms [25]. The U.S. Food and Drug Administration has accredited curcuminoids as \"generally recognized as safe\" (FDA) (GRAS) [26].  
Curcumin is poorly absorbed and easily metabolized [27]. Functionalized single-walled carbon nanotubes (SWCNTs) have been used as a delivery system for curcumin molecules experimentally by [PERSON] et al. to protect curcumin from degradation and to increase its solubility [28]. They concluded that SWCNTs significantly enhance the solubility and stability of curcumin. To increase bioavailability, several agents were added. The most interesting is piperine; it increases curcumin bioavailability by blocking the metabolic pathway [29]. The goal of this paper is to investigate the adsorption of curcumin drugs on the inside and outside surfaces of armchair (5,5) BNNTs using density functional theory (DFT) calculations. As seen in Table 1, the details of the BNNT configuration (5,5) employed in this study together with the model in Figure 1, are shown.
# Modeling of Integrated Nanoneedle-Microfluidic System for Single Cell Temperature Measurement
## 1 Introduction
Two factors were analyzed to effectively characterize the intracellular temperature of a single cell. The first factor was to test the effect of the cell conductivity on the nanoneedle performance, and the second was to evaluate the sensor sensitivity and response time to temperature change in the range of 24-60 \({}^{\circ}\)C. Lastly, the integration of the nanoneedle to a microfluidic channel required a study on the cell flow rate which was influenced by the microfluidic channel cross-sectional area and the fluid velocity.
## 1 Introduction
The circuits were simulated in Cadence(r) Virtuoso using 90 nm CMOS technology. The proposed array multiplier and Wallace multiplier offer reductions of approximately 56% and 91% in power consumption and approximately 48% and 90% in the product of power and delay, respectively. [PERSON] et al. examined the reduction in delay time in Wallace, Dadda, and grouping and decomposition multipliers [19]. They designed 4- and 8-bit multipliers with 180 nm CMOS technology using the Cadence(r) tools. It was concluded that grouping and decomposition multipliers reduced the number of computation steps and, hence, achieved a higher multiplication speed. The delay time was reduced by 56%, and the power-delay product was reduced by 53% compared to the [PERSON] and Dadda multiplier architectures. Previous studies have employed commercial electronic design automation (EDA) tools such as Cadence(r) and Synopsys(r) in the very-large-scale integration (VLSI) design of multipliers. These professional tools require the payment of annual licenses, which presents a challenge for public educational institutions. This study focuses on the VLSI design of 11-, 16-, 24-, 32-, 53-, and 64-bit multipliers using 350 nm CMOS technology. The synthesis results are utilized to present a delay/area performance comparison among the traditional array multiplier, Wallace tree multiplier, and radix-4 Booth multiplier. Furthermore, this comparison was extended to the VLSI design of floating-point multipliers. The novelty of this work is demonstrated by a successful design methodology of multipliers using Alliance open-source EDA tools in the entire process from the design stage, where we performed conversions on the register-transfer level (RTL) and ran simulations on it, to the auto-place and route stage and to the physical layout creation [20].
## 3 Collaborative Knowledge-Based WSN Proposal
### Application Protocol  
The application protocol is designed to transfer data and KBs between the computer and sensors and among sensors using transport layer services. Although KBs are usually edited in the computer, transmitted to sensors and executed in sensors, it is also possible for information generated in sensors (e.g., values of inferred output) to be sent to other sensors or the computer.  
Figure 1: Structure of the knowledge-based sensor.  
In order to reduce battery consumption, we propose that the application protocol work incrementally (_i.e._, the complete KB is first sent from the computer to the sensors, only after which can a part of the KB (variables, fuzzy sets, _etc._) be updated) and that the protocol allow the sensors to operate in a sleep mode.  
The main functions of the communication protocol are the following:  
1. Transmit elements of a DB to sensors, including the number of variables in the KB, number of fuzzy sets in each variable, the fuzzy set associated with each variable, the type of each variable (local or remote) and the data range of each crisp value;
2. Transmit elements of a RB to sensors, including the number of rules, number of propositions in the antecedent as well as the antecedent and consequent themselves;
3. Transmit values of input and output variables among sensors or between a sensor and the computer.
4. Perform operations such as add, update, delete or activate a KB; define the interval between inferences and remain in sleep mode or in active mode between inferences.
5. Register the state of system variables, so as to register a variable and obtain the value of variables in order to represent them in the computer, _etc._  
The protocol allows the user to change the knowledge of the system in real time, to incorporate different knowledge in different sensors and to verify the evolution of some variables in the system.  
The main aspects of the implementation of the application protocol are the following:  
1. The computer and every sensor have a communication agent, which has been developed in Java;
2. The application protocol utilizes transport layer services which are available in WSNs (connection-oriented, connectionless and broadcast);
3. An Application Protocol Data Unit (APDU) has been defined to support the protocol commands and responses between nodes (computer and sensors). In this work, the following commands have been utilized: * KBs transmission. Based on a connection-oriented service, this command allows the computer to send a complete KB to the sensors. * Variable values transmission. This command allows the sensor to send the value of an input variable or the value of an inference. * Work cycle. The sleep mode interval between inferences is sent from the computer to sensors using a connection-oriented transport service. * Alarm notification. Based on a connectionless broadcast service with only one hop, each sensor can notify remarkable variable values to neighbor sensors (e.g., when a variable surpasses a threshold).
# Bat Optimized Link State Routing Protocol for Energy-Aware Mobile Ad-Hoc Networks
## 3 Research Methods and Materials
### Quality of Services Parameters  
General network quality is described or measured by the QoS and presents the amount of data sent from a source node and received by a destination node within a given period of time. Different parameters, such as throughput, packet delivery ratio, E2E delay, packet loss, jitter, and energy are used to quantitatively measure QoS and evaluate the network quality. The behavior of the MANET routing protocol is influenced by all these parameters [6]. Energy consumption, routing overhead, and delay are used in this study because of their comprehensive assessment to the performance of the MANET.  
* Routing overhead parameter: this value is the total number of routing packets divided by the total number of delivered data packets. The additional bandwidth, which has been consumed by the overhead for delivering data network, can be measured using this parameter. The robustness of the network is influenced by the routing overhead in terms of the battery power consumption of the nodes and the utilization of the bandwidth [6].
* EC parameter: route discovery is implemented to calculate every probable path from the source to the destination node. The optimum path is then selected by the protocol based on its criteria, such as the minimum number of hops and the shortest path. The selected path is used until it gets destroyed. Thus, the node energy in this route decreases. In a situation when a node loses its energy, the messages cannot be sent and consequently leads to the exclusion of the node from the network. This occurrence negatively affects the lifespan of the ad hoc network. Part of the initial energy is taken as an energy constraint [9].
* Delay parameter: it is considered as one of the most important parameters in the telecommunication system. Delay refers to the total time that is spent to send the packets from source to destination nodes via the network. Different aspects of the network are responsible for increasing/decreasing the delay: (1) processing, (2) queuing, (3) transmission, and (4) propagation.
## 2 Research Significance  
The occurrence of slope instability is a long-term, nonlinear, and complex process [53]. Due to the complexity of slope structure and the discontinuity of mechanical properties of geotechnical materials, as well as the variability of control factors acting on the slope, slope stability is comprehensively affected by geological and engineering factors [54]. As an indispensable key mechanical parameter in slope engineering design and stability analysis, FoS was incorporated into various standards and engineering codes, and its research significance cannot be ignored. FoS evaluation involves many variables and requires the calculation of slope geometry data, geological material parameters, and pore water pressure. These uncertain factors have different influence weights on slope stability, and there is a complex nonlinear relationship between them [55].  
The potential economic benefits to be gained from solving FoS by the ML model are huge (e.g., allowing steeper slopes, better support system design, etc.). In practice, it is usually necessary to estimate FoS at the preliminary stage of an engineering project. When sufficient historical cases are available, a machine learning approach provides an alternative model for slope stability assessment by establishing the input-output relationship between the FoS and related parameters [55]. To this end, this paper proposes an intelligent FoS prediction method combined with the PCA method, which uses ML methods to train and develop a new hybrid ML model for FoS prediction. The input-output relationship between the variables and FoS is identified, and potential explicit or implicit relationship functions are determined to predict FoS for a given set of input variables. The new method can process the data without depending on the measurement scale, and the arrangement of the data is not affected [56]. In this model, the PSO algorithm is used to Improve the predictive performance and generalization capability of the ANN model. To the best of the author's knowledge, this is a preliminary exploration in the field of slope stability research using the PCA-PANN model. Compared with the traditional LEM and qualitative evaluation methods, the proposed model can find and deal with the implicit nonlinear relationship between variables in depth. Therefore, it is a promising method for slope stability prediction.
## 2 System Model and Problem Statement
### Non-Reconciled Physical-Layer Key Generation  
There are two mainstream approaches for legitimate users to generate keys from the wireless channel in TDD wireless communication systems. One involves estimating the CSI using the received pilot signals, and the other involves sending random signals in order to mix the random signals and the channel gains [21]. The former is selected in our scheme.  
During the key generation process, [PERSON] and [PERSON] first send pilot signals to each other to extract the CSI from the wireless channel. The transmitting pilot power values of [PERSON] and [PERSON] are \(\sigma_{A}^{2}\) and \(\sigma_{B}^{2}\), respectively. The channel noise is additive white Gaussian noise (AWGN) with zero mean and variance \(\sigma_{n}^{2}\). Hereafter, [PERSON], [PERSON], and [PERSON] use the least-squares (LS) algorithm to estimate the channel. The channel estimates can be expressed as  
\[\left\{\begin{array}{l}\hat{h}_{AB}=h_{AB}+w_{A}\\ \hat{h}_{BA}=h_{BA}+w_{B}\\ \hat{h}_{AE}=h_{AE}+w_{E}\end{array}\right. \tag{2}\]  
where \(w_{A}\), \(w_{B}\), and \(w_{E}\) denote channel estimation errors. They are independent identically distributed complex Gaussian random variables, with zero mean and variance \(\sigma_{n}^{2}/\sigma^{2}\), i.e., \(w_{B}\) and \(w_{E}\) are \(\mathcal{CN}\big{(}0,\sigma_{n}^{2}/\sigma_{A}^{2}\big{)}\) and \(w_{A}{\sim}\mathcal{CN}\big{(}0,\sigma_{n}^{2}/\sigma_{B}^{2}\big{)}\).  
Then, [PERSON] and [PERSON] quantize the channel estimates \(\hat{h}_{AB}\) and \(\hat{h}_{BA}\). In order to make the distribution of the quantized sequences in each quantization interval as uniform as possible, we consider using an equal probability quantization algorithm to quantize the channel estimates [17]. The probability distribution function (PDF) of the channel estimates is used to divide the quantization interval. To extract more keys, we quantize the real part and the imaginary part of the channel estimates. The quantized sequences are converted into Gray code with length \(q\), where \(q\) represents the quantization precision. After that, the non-reconciled keys \(K_{A}\) and \(K_{B}\) can be obtained.
# Process Parameter Optimisation in Laser Powder Bed Fusion of Duplex Stainless Steel 2205
## 6 Conclusions  
In the present work, the optimal printing parameters for the manufacture of components with DSS-2205 by SLM have been defined and an optimal austenitization treatment has been chosen. Aiming to obtain these parameters, the Design Expert software has been used for the analysis of the density and mechanical properties (\(\sigma_{0.2}\), \(\sigma_{\text{uts}}\), \(\varepsilon_{\text{r}}\), and A) of the DSS-2205 material, applying the methodology of the response surfaces. At the same time, uniaxial tensile tests have been carried out to experimentally verify the real behavior of the analyzed steel. The specimens used were produced with the SLM technique and then treated by quenching at 1050 \({}^{\circ}\)C and quenching in water. The properties of additively manufactured DSS-2205 have been compared with a base material produced by traditional methods, which has served as a reference. In this way, it has been shown that it is possible to reproduce its behavior, with the dispersion of the resulting properties being less than 4% and a relative density of 99.82%, which is very close to the desired density. The optimal printing parameters P = 174.9 W, S = 639.6 mm/s, and H = 0.12 mm are thus defined, with a manufacturing energy density of 45.57 J/mm\({}^{3}\). With the manufacturing factors bounded, the variation of the mechanical properties of DSS-2205 was experimentally analyzed, depending on the heat treatment applied. The achieved austenite content with each of the treatments has been fundamental in this analysis. Finally, quenching at 1000 \({}^{\circ}\)C with water quenching is marked as optimal, providing an austenite percentage of 38.53% and the mechanical properties shown in Table 8. The procedures, methodology, and experimental tests used in this article are replicable and can be repeated in the optimization of other materials in a similar way.  
Conceptualization, I.I.C.; Investigation, N.M., L.M., R.R.-A., A.D., J.M.A. and I.I.C.; Writing--original draft, N.M.; Writing--review & editing, A.D., J.M.A. and I.I.C.; Supervision, J.M.A. and I.I.C.; Funding acquisition, J.M.A. and I.I.C. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the Spanish Government for the financial support received to perform the research project PID2021-124768 OB-C21 and TED2021-130413B-I00. This work was also supported by the Regional Government of Castilla y Leon (Junta de Castilla y Leon) and by the Ministry of Science and Innovation MICINN and the European Union NextGenerationEU/PTRR through the project MA2 TEC (C17.I01).  
The data presented in this study are available on request from the corresponding author (privacy).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Electronic Nose Detection
#### 3.1.5 A Classification Model Based on SVMs
Different dimensionality reduction methods were applied (Table 3) to the oyster sample data at varying temperatures, with an 60% training set and a 40% test set divided using SVM for the reduced data. The final processing results are summarized in the following table. Accuracy is employed to evaluate the results, revealing that the processing outcomes obtained through LDA for dimensionality reduction at the same temperature were significantly better than those achieved using PCA. Furthermore, the results obtained using the same processing method at different temperatures improved with increasing temperature. During the data dimensionality reduction stage, a comparison of scatter plots indicated that the scatter plot derived from LDA outperformed that of PCA, exhibiting  
Figure 6: Confusion matrix. (**a**) Confusion matrix generated by SVM using PCA dimensionality reduction at 12 \({}^{\circ}\)C. (**b**) Confusion matrix generated by RF using LDA dimensionality reduction at 12 \({}^{\circ}\)C.  
smaller intra-class distances among the various scatter points. After LDA dimensionality reduction, the data features more effectively represented the changes in oyster freshness under different temperatures.
## Further Security and Privacy Insights  
### Resilience against Single-Point Failures  
The proposed decentralized VANET framework is resilient to single-point failures compared with traditional centralized ones. This is crucial for vehicle networks, as uninterrupted functioning is essential for optimal efficiency and safety. A single server or node failure in a centralized system can result in a complete system shutdown. However, our decentralized architecture guarantees that the network will remain operationally sound even if many nodes fail. This is accomplished via distributed ledger technology, enabling vehicles to communicate information seamlessly and autonomously without requiring a central coordinating node. This setup ensures a strong and reliable communication system for vehicles, greatly decreasing the chances of network failure and improving the overall dependability of vehicle communications.  
The architecture is designed to adapt and reorganize itself based on network changes, such as adding or losing nodes. This self-repairing feature guarantees that data routing is consistently optimized, ensuring constant connection even in challenging circumstances. The system can rapidly compensate for lost nodes by utilizing suitable algorithms for network routing and data redundancy, enhancing its resilience. This decentralized and self-adaptive method improves the system's resilience and scalability, making it suitable for the growing network of connected vehicles.
## 3 Results and Discussion
### Extract Quantification  
Quantification of polyphenol compounds was carried out by LC-MS/MS for the three DES systems and for 70% EtOH. The results are presented in Table 6. The analysis of Table 6 demonstrates an overall polyphenol content that is higher in the case of the DESs than that of the ethanolic solution used, which is in agreement with the total phenolic determination performed by Folin Ciocalteu method, as described above. As regards each compound, the results showed important quantitative differences in tyrosol contents with respect to the values reported in the literature, which are in a range of 90 \(\upmu\)g g\({}^{-1}\) to 660 \(\upmu\)g\({}^{-1}\)[59]. Moreover, the amounts of tyrosol in this study were higher than those of hydroxyytrosol and oleupropein. This outcome is not in accordance with previous investigations that reported an opposite trend [60]. This is likely to be explained by the stability of tyrosol, compared with the other phenolic compounds, and probably the selectivity of the solvents used to extract tyrosol [61]. It has been reported that polyphenols can interact with DESs through hydrogen bonds and therefore increase their stability and solubility in DESs whose composition (HBD/HBA) plays a critical role in the extraction efficiency [62]. Additionally, a high and linear correlation was found between tyrosol content and the total phenol content (TPC) in all DES extracts under study (Figure 4). Many factors have been mentioned in relation to the variation in phenolic content of olive leaf extracts such as the extraction procedure, hydric deficiency, salinity, fertilization, geographical zone, sampling time, light exposition, frost stress, leaves age, the olive variety, bacteria, and fungi [63; 64].  
The amounts of hydroxyytrosol from the OLL extracts in the present study were found to be lower than most of the values found by other authors [65; 66; 67], but higher than the amounts recorded in [PERSON] et al. (2013) [68]. Similarly, the values of oleupropein content were lower than expected with GL-Lys extract and GL-Arg extract in which oleupropein was not found, but close to some published values in the case GL-Pro and the ethanolic solution [69] and even higher with respect to the findings in [70; 71]. Furthermore, the results of this study exhibited a simultaneous increase in hydroxyytrosol concentration and a decrease in oleupropein concentration, which is attributed to the degradation of oleupropein into hydroxyytrosol due to the chemical and enzymatic reactions during olive leaves processing [72]. Regarding luteolin-7-O-glucose and rutin, their concentration values were within the range of the upper and lower values reported in the literature [73; 74; 75; 76; 77; 78]. It  
Figure 3: Polyphenol extraction yield using conventional and green solvents. Extractions were carried out under optimal conditions (\(R_{L/S}=150\) mL g\({}^{-1}\), \(\mathcal{C}_{\text{DES}}=90\%\), \(T=80\)\({}^{\circ}\)C) and stirring (900 rpm) for 150 min.  
should be noted that the observed differences in the content of the phenolic compounds herein are attributed to the above-mentioned factors.