esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Diagnosis and Control Methods
### Fast Fourier Transform Diagnosis Method  
The fast Fourier transform (FFT) is a sophisticated algorithmic technique widely employed in various fields of science, engineering, and mathematics. This transformation allows for the analysis of complex signals and the extraction of information about their frequency components [28; 29].  
The FFT technique finds applications in diverse fields, including signal processing, telecommunications, image processing, audio analysis, scientific computing, and many more. It allows researchers, engineers, and analysts to quickly analyze and manipulate signals in the frequency domain, enabling tasks such as filtering, spectral analysis, correlation, convolution, and data compression. Its efficiency has made it an indispensable tool in digital signal processing, providing the capability to handle real-time data streams and process vast amounts of information with minimal computational overhead [30; 31].  
The fast Fourier transform method is a groundbreaking algorithm that has transformed the landscape of digital signal processing and numerous related fields by providing an efficient means to compute the discrete Fourier transform. Its ability to drastically reduce the computational complexity of this operation has made it a cornerstone in modern scientific and engineering applications, allowing for the exploration and extraction of valuable insights from complex datasets in the frequency domain [32; 33].
# Internet of Things (IoT) Cybersecurity: Literature Review and IoT Cyber Risk Management
## 3 The Four-Layer IoT Cyber Risk Management Framework
### IoT Cyber Infrastructure Layer
#### 3.2.3 Cybersecurity Technologies  
The cybersecurity technologies at this layer refer to internal cyber technology assets. The IoT cybersecurity technology developers at the IoT cyber ecosystem are highly relevant and may help the organization find and establish strong IoT cybersecurity technologies. Internal cybersecurity technologies should support the organization's overall cybersecurity goals as well as the IoT cybersecurity goals.  
[PERSON] distinguishes three main classes of cybersecurity technologies [77]: technologies that ensure the confidentiality of information; technologies that detect and counter online threats and vulnerabilities; and technologies that detect and counter cybercrime. Cybersecurity technologies are accompanied by authentication, which involves certification and the management of credentials [78]. Common IoT cybersecurity technologies include intrusion detection systems/intrusion prevention systems, device authentication and management, secure communications, data encryption and tokenization, secure software and firmware, Public Key Infrastructure (PKI) technology, firewalls, DDoS protection, security analytics, and incidence response systems [5].
# TEDL: A Text Encryption Method Based on Deep Learning
## 6 Encryption and Decryption
### Word Vector Table  
After training, the word vector table is generated, where the word serves as an index unit whose corresponding value is a \(D\)-dimensional real vector. The word vector table based on \(C_{\alpha}\) is represented as \(T_{\alpha}\), and one based on \(C_{\gamma}\) is denoted as \(T_{\gamma}\). For a certain word \(w\), the corresponding vectors in \(T_{\alpha}\) and \(T_{\gamma}\) are \(\mathbf{v}_{\alpha}|_{w}\) and \(\mathbf{v}_{\gamma}|_{w}\), respectively.  
Let \(\mathbf{v}_{\alpha}|_{w}\) and \(\mathbf{v}_{\gamma}|_{w}\) be row vectors. If \(T_{\gamma}\) is not subsequently processed but directly used for encryption, the similarity between \(\mathbf{v}_{\alpha}|_{w}\) and \(\mathbf{v}_{\gamma}|_{w}\) should be low enough, otherwise, it is dangerous. The similarity can be measured by the cosine similarity, which is defined as  
\[sim_{xx}(w)=\cos\Bigl{(}\left.\mathbf{v}_{\alpha}\right|_{w}\cdot\mathbf{v}_{ \gamma}^{T}\right|_{w}\Bigr{)} \tag{9}\]  
The first condition to ensure security is:  
\[sim_{xx}(w)<limit_{xx},\quad 0<limit_{xx}<1 \tag{10}\]  
where \(limit_{xx}\) is a parameter, determined by the security requirements related to the specific application scenario.  
In addition, in \(T_{\alpha}\), there exist words with high similarity to \(w\). They are usually synonyms of \(w\) or words closely related to it. Rank them as \(w_{1}\), \(w_{2}\), \(w_{3}\), \(\cdots\) according to the similarity with \(w\). The similarity between \(w_{i}\) and \(w\) is defined as  
\[sim_{xy}(w,w_{i})=\cos\biggl{(}\left.\mathbf{v}_{\alpha}\right|_{w}\cdot \left.\mathbf{v}_{\alpha}^{T}\right|_{w_{i}}\biggr{)} \tag{11}\]  
Obviously, the following relationship is true.  
\[sim_{xy}(w,w_{i-1})>sim_{xy}(w,w_{i}),\quad i=2,3,\cdots \tag{12}\]Then we give the second condition for ensuring security.  
\[sim_{xx}(w)<sim_{xy}(w,w_{n}),n=limit_{xy}\in\mathbb{N} \tag{13}\]  
where \(limit_{xy}\) is a parameter. If the \(sim_{xx}(w)\) is too large, or even greater than \(sim_{xy}(w,w_{1})\), the attacker can easily conclude that the plaintext corresponding to the ciphertext \(\left.\mathbf{v}_{\gamma}\right|_{w}\) is exactly \(w\). Therefore, \(limit_{xy}\) should also be set properly according to the security requirements.  
In the case where hyperparameters are identical, \(sim_{xx}(w)\) is actually determined by the ratio of \(C_{\beta}\) to \(C_{\alpha}\). To meet both requirements for \(sim_{xx}(w)\), make sure a proper size of \(C_{\beta}\).
###### Abstract
This kind of analysis can provide input data and pivotal indications essential to evaluate the PFCs performance during the operation and maintenance phases, to estimate its feasible structural lifetime, as well as to estimate the type of treatment to which this component will be undergone in the decommissioning phase. The PFCs of a divertor cassette consists of three parts, namely, the straight leg of the vertical target, liner and reflector plates (replaced by the dome in some previous configurations) and baffle region [24, 25, 26]. The 2019 DEMO divertor cassette concept has been considered for this study and its layout is shown in Figure 2[27, 25]. In addition, the latest design of the ITER-Like PFCs has been taken into account [25, 26, 27, 28, 29, 30, 31] (Figure 3). This work is focused on an extensive neutronics comparative analysis mainly concerning the assessment of the spatial distribution of the principal nuclear loads on the DEMO divertor PFCs targets, considering both the WCLL and HCPB blanket configurations, during operations. The different type of blanket has an impact on the neutron spectrum (water vs. He as coolant) impinging on the targets [32]. Therefore, it is reasonable to expect an effect on nuclear loads levels and intensity distribution, if these two blanket options are compared. In particular, this study consists of the following parts:  
Figure 1: Main irradiation processes and primary neutronic effects with the consequent microscopic and macroscopic secondary responses of the irradiated materials [13]. * The calculation and assessment of the nuclear damage (dpa/FPY), nuclear heating (W/cm\({}^{3}\)) and He-production rate (appm/FPY) distributions on W-monblocks, Cut-interlayers\(\backslash\)CuCrZr-pipe and PFC-CB supports made of Eurofer steel, of inner and outer target, considering separately, both the WCLL and HCPB blanket configurations. * Direct comparison between the above-mentioned nuclear loads distributions among the two blanket concepts, in order to evaluate the blanket impact. * Assessment, both for WCLL and HCPB cases, of the total nuclear power (MW) on PFCs and breakdown in each component, comparing the results between the two DEMO configurations under analysis. Two MCNP models have been reproduced: WCLL [33; 34; 35; 36; 37; 38] and HCPB [39] Single Module Segment layered blankets, both with the new MCNP model of the divertor 2019 configuration. Detailed MCNP representations of the whole ITER-Like (IL) PFCs inner and outer targets have been recreated and integrated in both the above-mentioned models [25]. Neutron and gamma transport simulations have been performed using MCNP5v1.6 Monte Carlo code [40] and the Joint Evaluated Fusion File JEFF 3.3 nuclear data libraries [41].
## 4 Empirical Evaluations Utilizing Open Source Systems
### Threats to Validity  
Several issues may affect the validity of the results in our empirical study. The first issue is that we evaluated our approach by comparing the cohesion of extracted classes to the cohesion of the original classes and the metric used to measure cohesion (i.e., \(CBCS\)) is based on information that is used in our refactoring approach. \(CBCS\) measures the cohesion based on the client-based similarity between each pair of methods in the class and our approach uses the client-based similarity to identify pairs of methods that should belong to the same extracted class. Thus, it is most likely the extracted classes will have higher \(CBCS\) values than the original class. However, our main purpose was to show an empirical evidence based on real classes that extracted classes resulting from our algorithms have higher \(CBCS\) values than the original classes.  
Another issue is that we did not evaluate the extracted classes in terms of coupling. Coupling refers to the degree of relatedness between classes. Cohesion and coupling are two quality attributes that can have an inverse relationship, meaning the improvement of one quality attribute can lead to the deterioration of the other quality attribute [13; 14]. Thus, we need to keep balance between cohesion and coupling when performing the extract class refactoring [5; 30]. However, the focus of this paper is to use the client-based cohesion to support the extract class refactoring, as they have not been exploited in previous approaches. Employing metrics of coupling and other aspects of cohesion, such as structural and semantic cohesion, together with the metrics of client-based similarity to support the Extract Class refactoring, is left for future research.  
The last issue which may affect the validity of our results is that the extracted classes suggested by the proposed approach were not evaluated by software engineers. It is important to evaluate the solutions suggested by newly proposed refactoring approaches by a number of software engineers [5]. If the majority of the evaluators (i.e., Software Engineers) are not satisfied with solutions suggested by a refactoring approach, the applicability of the approach is questionable. However, solutions that improve important attributes such as cohesion are expected to satisfy software engineers. Thus, we evaluated our approach based on cohesion using the metric \(CBCS\). Several well-known papers in the literature (see, e.g., in [15; 16; 17; 18]) have used quality metrics to evaluate newly proposed refactoring techniques.
# Model-Based Adaptive Collaboration of Multi-Terminal Internal Force Tracking
## 2 Model Establishment of a Multi-Terminal Clamping System
### Absolute Relative Motion Control
According to Equation (18), we can obtain the expected translation velocity \({{}^{\vartheta}_{ha}}(t)\) and angular velocity \({\omega_{ha}^{d}}(t)\) of the master terminal as follows:  
\[{{}^{\vartheta}_{ha}}(t)={{}^{\vartheta}_{o}}(t)+(\ R_{ha}\ (t)\ {{}^{hi}l_{a}})^{ \times}\ {\omega_{o}^{d}}(t) \tag{21}\]  
\[{\omega_{ha}^{d}}(t)={\omega_{o}^{d}}(t) \tag{22}\]  
\[{e_{pha}}(t)={p_{ha}^{d}}(t-T)-p_{ha}\ (t) \tag{23}\]  
\[{e_{Qha}}(t)={Q_{hu}^{d}}(t-T)-Q_{ha}(t\ ) \tag{24}\]  
where \({e_{pha}}(t)\) represents the absolute position error of the master terminal, \({e_{Qha}}(t)\) indicates the absolute direction error of the master terminal, \(T\) is the sampling time step, and \({p_{ha}^{d}}(t-T)\) is the command position of the last sampling time. \({p_{ha}}\ (t)\\) is the measurement position of the current time, and \({Q_{ha}^{d}}(t-T)\) is the desired direction at the last sampling time, with \({Q_{ha}}(t)\) representing the direction measured at the current moment. Note that only \({Q_{ha}^{d}}(t-T)\) and \({Q_{ha}}(t)\) are consistent, while \({e_{Qha}}\) will be zero. Once the Cartesian velocity of the master terminal is established, the desired velocity of the slave actuator can be determined by  
\[{{}^{\vartheta}_{hj}}(t)={v_{ha}}(t)-{{}^{\left({hj}p_{ha}^{d}\right)^{ \times}\omega_{ha}}(t)} \tag{25}\]  
\[{\omega_{hj}^{d}}(t)={\omega_{ha}}(t) \tag{26}\]  
where \(j=b,c,d\), \({v_{ha}}(t)\) is the master terminal translation velocity and \({{}^{hi}p_{ha}^{d}}={{}^{hi}p_{ha}}(t_{0})\) is the relative position initialized from the terminal coordinate system extracted from the terminal gesture by (19). Consider relative errors:  
\[e_{prj}(t)={}^{w}R_{hj}(t)\left[{}^{hj}p_{ha}^{d}-{}^{hj}p_{ha}(t)\right] \tag{27}\]  
\[e_{Qrj}(t)={}^{w}R_{hj}(t)\left[{}^{hj}Q_{ha}^{d}-{}^{hj}Q_{ha}^{d}(t)\right] \tag{28}\]  
where \(e_{prj}(t)\) indicates the relative position error of each slave actuator under the robot coordinate system and \(e_{Qrj}(t)\) indicates the relative direction error of each slave terminal under the robot coordinate system. Similarly, \({}^{hj}p_{ha}^{d}={}^{hj}p_{ha}(t_{0})\) and \({}^{hj}Q_{ha}^{d}={}^{hj}Q_{hi}(t_{0})\) represent the expected relative position and direction expressed in the slave actuator coordinate system, respectively. With the above closed-chain constraint method, we can quantitatively calculate the absolute and relative motion of the four terminals accordingly for the desired position and orientation of the object. The advantage of using master-slave closed-chain constraint control is that multi-terminal systems can perform collaborative tasks without knowing the information about the shape, size, and stiffness of the object.
## 2 Experimental Section
### Determination of Constituents in Tea Samples  
Tea leaves were ground in a commercial grinder (Joyoung Co., Jinan, Shandong, China). Ground samples (1 g) were extracted twice with 80% methanol (50 mL) (Tedia Company, Inc., Fairfield, OH, USA) for 1 h, passed through a 0.45-um Millipore filter (Merck, Darmstadt, Germany), and analyzed by high-performance liquid chromatography (HPLC) (Agilent Technologies, Palo Alto, CA, USA). The contents of GA, CAF, and catechins, including (+)- catechin (C), (\(-\))-epicatechin (EC), (\(-\))-epigallocatechin (EGC), (\(-\))-epicatechin (ECG), (\(-\))-epilallocatechin 3-O-gallate (EGCG), 1,4,6-tri-O-galloyl-\(\beta\)-D-glucose (GG), and compound **1** were determined in an Agilent 1200 Series HPLC system equipped with an LC-20 AB solvent delivery unit, a SIL-20A auto sampler, a CTO-20A column oven (40 \({}^{\circ}\)C), a G1314B Variable Wavelength Detector (VWD) detector (280 nm), and an LC Ver1.23 workstation (Agilent Technologies, Palo Alto, CA, USA). Separation was achieved with a TSK-GEL ODS-80 TM column (4.6 mm \(\times\) 250 mm, Tosoh, Yamaguchi, Japan). The mobile phase consisted of solvents A (0.05 M H\({}_{3}\)PO\({}_{4}\)-H\({}_{2}\)O (Xilong Chemical, Guangzhou, China), 5% CH\({}_{3}\)CN (Tedia Company, Inc., Fairfield, OH, USA) and B (0.05 M H\({}_{3}\)PO\({}_{4}\)-H\({}_{2}\)O, 80% CH\({}_{3}\)CN). The elution conditions were the following, 0-22 min, 95%-65.5% solvent A; 22.5 min, 34.5%-100% solvent B; 22.5-27.5 min, 100% solvent B; 27.8 min, 0%-95% solvent A; and 28-34 min, 95% solvent A. The flow rate was 1 mL/min, the temperature of the column oven was maintained at 40 \({}^{\circ}\)C, and the injection volume was 10 \(\mu\)L. The chemical compounds were identified by comparing their retention times with those of standards. Each sample was extracted three times and analyzed in duplicate.
## 10 Simulation Results and Discussion
### Fraction of Empty Regions  
The fraction of empty nodes becomes compact, then the empty region is created inside the network so the result would be conducted according to empty regions occurrence and compared with L2-ABF. The heterogeneous relation comes into existence in all approaches like TANVEER and its sub-sections, and as the nodes increase the occurrence ratio of empty regions increased. Firstly, the maximum fraction can be observed with respect to DPD-TANVEER, which is under half of the network nodes.  
However, a small upward sliding was observed from 300 to 500 nodes. After this, an increasing trend presents in which a number of nodes remained constant in this area. The decrement is because of DPD-TANVEER when the empty regions are infrequent amount then, for the time being, data are shifted towards the watchman nodes and then again revert to the ordinary nodes. The ordinary nodes further transmit the data to sonobuoys. If there are no further empty nodes, the multiple occurrences of the empty node on the same path is tackled by the help of BIN-TANVEER as watchman nodes. Watchman nodes present in each layer in order to minimize the end-to-end delay. This will help to save not only the time delay but also achieve the purpose of nodes in layers order as it is done in LBA-TANVEER. Both techniques have little similarity in results due to the layer-by-layer angle approach used in L2-ABF and tri-angular approach in TANVEER. The BIN-TANVEER beats the baseline approach as the fraction of empty nodes does not handle by ABF, because of the extra supplementary nodes used like BIN. The direct transmission is sent to the forwarding nodes by calculating the angle, while on the other hand, the DPD-TANVEER handles this situation work with BIN-TANVEER and bypasses the empty regions, as shown in Figure 12.  
\begin{table}
\begin{tabular}{c c} \hline
**Simulation Constraints** & **Values** \\ \hline - & 600 \\ Experimented Nodes & \\ Network volume (m\({}^{3}\)) & 2000 m \(\times\) 2000 m \(\times\) 2000 m \\ \(T_{\text{range }}\)(m) & 300 \\ \(P_{tx}\)(W) & 2 \\ Initial Energy (J) & 100 \\ \(P_{r}\)(W) & 0.2 \\ Pi Idle power (W) & 0.02 \\ \(F\) (KHz) & 10 \\ \(P_{s}\)(Bytes) & 100 \\ Baseline Approach & L2-ABF \\ Under Study Protocol & TANVEER \\ \hline \end{tabular}
\end{table}
Table 2: Simulation Evaluation Constraints.
## 2 Materials and Methods
### Description of the Proposed Decision-Support Tool  
The proposed decision-support tool consists of three basic steps. Step 1: All required data and geospatial data (i.e., geodata) related to the topography, geology, seismicity, geotechnical conditions, and restricted parts (i.e., \"no-go\" zones) of the area under consideration are collected and organized into separate thematic layers within a GIS geodatabase. An overlay analysis is performed to generate a composite map that visually represents the spatial relationships between the collected geodata and provides stakeholders and experts with a rapid and detailed digital overview of the area. Having performed the overlay analysis, and after meticulous data processing, each thematic layer is then converted into raster format and transformed into a fundamental design criterion for the Multi-criteria Decision Analysis (MCDA) and subsequently for the LCPA technique.  
Step 2: Once the basic design criteria are defined, the MCDA is carried out through the implementation of a proper MCDM that converts the designer preferences into weight factors. Herein, the Analytical Hierarchy Process (AHP) has been employed, in which an n \(\times\) n comparison matrix (judgment matrix) is constructed, where n denotes the number of criteria. This matrix includes the ranking score that has been qualitatively assigned to each criterion by the designer based on an evaluation scale ranging from 1/9 to 9. Having defined the judgment matrix, the qualitative factors (i.e., ranking score) are then normalized and averaged in order to obtain an average weight for each criterion through pairwise comparisons. Additional numerical measures, such as the consistency ratio (CR), have been established with the aim of evaluating the consistency of the hierarchy. It should be noted that different scenarios can be created at this step based on the assigned weight factors.  
Step 3: Having derived the weighting factors, the LCPA technique is employed within the GIS environment to generate multiple length- and consequently cost-minimized (i.e., optimal) CI routes corresponding to each scenario.  
The flowchart shown in Figure 2 graphically illustrates the main steps of the proposed decision-support tool, with a comprehensive explanation of each step provided in the following sub-sections. It is worth noting that the proposed decision-support tool has been developed using the powerful capabilities of ArcMap 10.8, a basic application of ArcGIS Desktop [54]. The GIS-based LCPA technique has been automatically integrated with the AHP methodology through suitable scripts written in Python programming language.
# Targeted Lipid-Based Drug Delivery Systems for Lung Cancer Therapy
## 3 Drug Delivery Systems
### Liposomes/Proliposomes  
Liposomes (Figure 1b) were discovered by [PERSON] et al. [48]. They consist of a lipid bilayer consisting of phospholipids, cholesterol and an aqueous phase which is enclosed in the lipid bilayer [49]. Phospholipids are commonly obtained from natural or synthetic sources and are called amphiphilic molecules as they have a hydrophilic head group and a hydrophobic tail group. When these phospholipids come in contact with aqueous medium (above their phase transition temperature), they tend to self-assemble into vesicles, where, based on alike-likes-alike, the lipophilic moiety of phospholipids attracts the lipophilic part (tail-tail), and the lyophobic part towards the lyophobic moiety (head-head) [50; 51]. Their advantages include low toxicity and high biocompatibility, and entrapment of both hydrophobic and hydrophilic drugs. However, liposomes are associated with stability issues like vesicles aggregation, vesicles fusion and leakage of drugs. For this reason, proliposomes offer a better, more stable alternative, which is the dry formulation of liposomes, and it can be achieved by spray drying, freeze drying or rotary evaporation [52; 53]. Proliposomes also have an additional component called a carbohydrate carrier (i.e., sorbitol, mannitol, lactose or sucrose) [52]. Some drugs which have been entrapped within liposomes for lung cancer therapy as shown in Table 1 are paclitaxel [33], docetaxel [54] and triptolide [55]. A pharmacokinetic study revealed that the mean maximum concentration in the pleural fluid (Cmax,IP), the disposition half time (T\({}_{1/2}\)) and the area under the concentration-time curve in the pleural fluid (AUC\({}_{0\to 96,\text{IP}}\)) were approximately two times higher compared to free paclitaxel. Additionally, the area under the concentration-time curve in plasma during 96 h (AUC\({}_{0\to 96,\text{Pla}}\)) was significantly greater than that of free paclitaxel. However, the excretion rate in urine over 24 h was lower than that of free paclitaxel [56]. Immune responses included increased selectivity, site-specific delivery or co-delivery of APIs with different mechanisms of action [57]. Intravenous and intratracheal delivery have also been investigated for the delivery of docetaxel for lung cancer treatment, with the latter being more effective, showing a higher peak concentration and reduced clearance [58].