esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design of the Integrated Optical Prism
The design takes the advantages of prism deflection that does not reduce the beam intensity and transmission grating high integration, providing a high precision, high brightness and easy to assemble and debug solution for reference grating and scale grating diffractive light deflection. The effect of the integrated prism and the deflection of four \(\pm 1\) order diffractive beams is shown in Figure 2. The key parameters in the design are the integration of the prism angle, performance parameters for the diffraction light horizontal/vertical emergent spacing, and variable parameter to the top of the prism and grating spacing and the beam radius. Figure 1: (**a**) Principle of a two-grating encoder; (**b**) diagram of employing a collimation lens to parallel diffracted beams; (**c**) diagram of employing prims to parallel the diffracted beams; (**d**) diagram of employing gratings to parallel the diffracted beams. The design and calculation of parameters are described in Figure 2b. The grating diffraction angle is denoted as \(\theta\). The refractive angle of diffraction light is denoted as \(\beta\). The reflection angle of diffraction light is denoted as \(\gamma\). The angle between the normal side of the prism and the top plane is denoted as \(\alpha\). The design requires that the diffraction light emitted is parallel to the incident light, then \(\alpha\) and \(\gamma\) should satisfy Equation (1). \[\alpha=\gamma. \tag{1}\]  
In triangle ABC, there is a geometric relationship expressed by:  
\[\gamma+(90^{\circ}-\beta)+(90^{\circ}+\alpha)=180^{\circ}. \tag{2}\]  
Diffraction angle \(\theta\) and refractive angle \(\beta\) of diffraction light meet the refractive law of Equation (3), where \(n\) is the refractive index of optical materials used for prism:  
\[\frac{\sin\theta}{\sin\beta}=n. \tag{3}\]  
Grating diffraction angle \(\theta\) is determined by grating distance d and incident light wavelength \(\gamma\):  
\[\theta=\arcsin\frac{\lambda}{d}. \tag{4}\]  
\[\alpha=\frac{\beta}{2}. \tag{5}\]  
\[\alpha=\frac{\beta}{2}=\frac{1}{2}\arcsin\frac{\sin\theta}{n}=\frac{1}{2} \arcsin\frac{\lambda}{nd}. \tag{6}\]  
In our design, the optical material of a prism is H-K9 Lglass (Union Optic Incorporated, Wuhan, China), and the wavelength \(\lambda=660\) nm, refractive index \(n=1.5128\), grating pitch \(d=1000\) nm. Then, the key parameter of the integrated prism \(\alpha\) can be calculated:  
\[\alpha=\frac{1}{2}\arcsin\frac{\lambda}{nd}=\frac{1}{2}\arcsin\frac{660}{1.51 38\times 1000}=12.924^{\circ}. \tag{7}\]
## 1 Introduction
The requirements that have to be met by such a platform can be summarized as follows:  
* automated set up and execution of computational scientific workflows and co-simulations
* reusability of integrated executables with their specific dependencies and runtime environments
* configurable communication between executables without the need for changing executables or specific implementations of interfaces and adapters by the users
* availability of an easy-to-use web interface to build, operate and manage scenarios
* parallelization and coordination of workflows for increasing performance  
The remainder of this article is organized as follows: Section 2 presents the main results of a literature review on already existing solutions for efficiently automating complex computational scientific workflows. Since no solution (either commercial or open source) was found that fulfills the discussed requirements, further research on developing a flexible framework as a solution for automating computational scientific workflows within the energy research software environment was conducted. Within this context, a background literature review was performed for finding state-of-the-art methodological approaches and reusable frameworks that could be used as building blocks for a comprehensive solution. The thereby selected technologies and used basic approaches are described in Section 2.2. In Section 3, the architectural design of the workflow automation framework is described. The architecture addresses the goals of a generic, modular, and highly scalable framework supporting the loose coupling of executables. Additionally, by instrumenting cluster computing environments and modern runtime automation technologies, it is capable of efficiently performing complex data processing and co-simulation workflows with high levels of performance and automation. Furthermore, a web-based editor and runtime interface is presented, providing an easy-to-use user interface for defining and managing scientific workflows. In Section 4, for the purpose of testing and evaluating the presented framework, a typical scientific model chain within the field of Energy Systems Analysis is used as an example workflow and implemented within the framework. The setup of this example workflow and the overall procedure for evaluating and benchmarking the framework are explained. The implications of the chosen architectural design and the benchmark results are then discussed in Section 5. Section 6 summarizes key results discussed in the article and gives an outlook on future work.
# A Carrier-Based Gardner Timing Synchronization Algorithm for BPSK Signal in Maritime Communication
## 3 Materials and Methods
### Loop Filter  
For the \(n\)th symbol, once the synchronization error \(err_{n}\) is detected by the error detector, it is fed into the loop filter. The loop filter here is identical to the one used in the Gardner algorithm, which is a second-order proportional integral filter. The output of the filter can be obtained by  
\[E_{LF,n}=c_{1}err_{n}+c_{2}{\sum_{i=1}^{n}err_{n}} \tag{13}\]  
where \(c_{1}\) and \(c_{2}\) are scaling and integration coefficients, respectively. The filter output \(E_{LP,n}\) is then fed to the NCO.
## 4 Methodology
### Model Optimization  
To optimize the anomaly classifier model's (LightGBM) hyperparameters, drift detection and adaptation thresholds and sliding window sizes in TWIN-ADAPT, we employ Particle Swarm Optimization (PSO). PSO, well known for its efficient global search capability and speed, is an ideal choice for this task due to its ability to operate without the need for gradient information, making it well suited for complex, non-linear optimization problems often found in real-world applications [49; 50].  
The integration of PSO in our system focuses on optimizing several critical hyperparameters of the LightGBM model, such as the number of tree leaves, maximum tree depth, minimum number of data samples in one leaf, learning rate and number of base learners. By adjusting these parameters, the model can be finely tuned to accurately model the data streams from various cleanroom CPS components, each represented by a dedicated digital twin. Our implementation uses PSO to iteratively explore the hyperparameter space. Each particle in the swarm represents a potential solution, i.e., a set of hyperparameters, and moves through the solution space by updating its velocity and position based on its own experience and that of its neighbors. This collective intelligence approach helps quickly converge to the best solution, which is the set of hyperparameters that result in the highest predictive accuracy for our LightGBM model. This approach not only improves the performance of the LightGBM classifier model in detecting and adapting to concept drift in the data streams of cleanroom's CPS but also enhances the model's efficiency by reducing computational overhead.  
In addition to optimizing the LightGBM model parameters, PSO is also utilized to optimize the hyperparameters of TWIN-ADAPT's adaptive and sliding window algorithm. This includes finding the optimal values for the thresholds used to determine drift and trigger adaptation by comparing the model accuracy across consecutive windows of samples in a data stream (refer to Algorithm 2 in Section 5.1).
## Results and Discussions
### FTWR Desorption Mechanism Analysis  
According to kinetic analysis and SEM morphology analysis, it was speculated that the desorption process of RFTW in FTWR is shown in Figure 9. As shown in Figure 9a,b, the porous structure and rich specific surface area of FTC make it easier for small molecules in FTC, such as saturated light hydrocarbons and aromatic compounds, to enter the pores of FTC. After the pores were blocked by small molecular compounds, more FTW could only be attached to the surface of the FTC. Attributed to the high temperature of the Fischer-Tropsch reaction system, FTW was in a fluid state, and a large amount of FTW and FTC were bonded and wrapped to form WFTR. Figure 9c,d depicted the desorption process of FTW in FTWR. Owing to the principle of similar compatibility, the FTW on the surface of the FTC was first desorbed into the solvent, which is related to the surface diffusion of the particles, and the desorption rate at this stage was relatively slow. The second stage was related to the intra-particle diffusion. The interaction between FTW molecules was greater than the effect of pores on FTW molecules, and FTW desorption in the pores of the spent catalyst. According to the desorption isotherm and kinetic analysis, this stage was multi-molecular layer desorption, with lower heat of desorption and faster desorption rate. The desorption rate in the third stage was relatively gentle, which is because the heat of desorption of the underlying molecules was less than the heat of liquefaction of FTW. After reaching the desorption equilibrium, RFTW and WFTC were obtained.  
Figure 8: SEM images of the FTWR (**a**) fresh FTWR, (**b**) 1 h, (**c**) 2 h, (**d**) 4 h.  
Figure 9: _Cont._
# Multidisciplinary Aerodynamic Design of a Rotor
## 4 Results and Discussion
### Effect of Blade Taper  
Within the framework of actuator disk theory and blade element theory, chord distribution of the optimum hovering rotor is inversely proportional to the radius. However, it is not practical to put this design into production. In general, a linear chord variation is adopted to improve rotor performance, since it is a close approximation of an inversely-proportional rotor over the outer portions of the blade, with little manufacturing cost penalty. The blade taper is defined as a ratio of blade tip chord to root chord in this study. Effects of blade taper on rotor induced power and profile power losses are investigated. The range of taper is confined to between 0.2 and 1. In order to eliminate the effect of rotor solidity, thrust-weighted solidity is required to be a constant, following Equation (27):  
\[\int_{0}^{1}r^{2}cdr=Const \tag{27}\]  
In Figure 10, it is shown that the induced power and profile power losses are both affected by blade taper. In both hover and forward flight conditions, profile power loss decreases with taper increases. This is because a shorter chord in the blade tip region can reduce section drag there. Although the chord will elongate near the blade root under the constraint of constant thrust-weighted solidity, the majority of profile power is consumed near the tip, due to the high dynamic pressure. In both hover and forward flight conditions, an increase in blade taper makes lift distribution of blade section more uniform, thus the induced power loss is reduced. In hover, the minimum induced power loss is obtained near 0.4 blade taper.
## 4 Trusted Network Secure Routing Algorithm
### Node Reputation
#### 4.1.2 Communication-Behavior Updating  
In [15], node communication behavior is updated by simply adding 1, but a malicious node may initially exhibit normal communication behavior to obtain a high reputation value, or initially normal nodes may later be captured by an attacking node; thus, normal early statistics may mask the abnormal behavior of such nodes in a later period. To avoid this situation, this paper introduces the weakening factor \(\eta\), which takes values in the range between 0 and 1 and can effectively weaken the statistical information from earlier times. Under the assumption that the update cycle of the network system has a period of \(\Delta T\), the normal and abnormal communication behaviors are updated as follows:  
\[\left\{\begin{array}{l}\alpha_{ij}=\eta\cdot\alpha_{ij}+\alpha_{ij}(\Delta T )\\ \beta_{ij}=\eta\cdot\beta_{ij}+\beta_{ij}(\Delta T)\end{array}\right. \tag{10}\]
# Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing
## 3 Results and Discussion
### Daily \(R_{n}\) Validation  
To estimate and validate the daily net radiation (Rn24), the measured values of incoming short-wave radiation were used. Using measured data in the estimate does not reduce the method's operability, as these values are available from meteorological stations. It is not necessary to set up an experiment to use them. The results are shown in Table 1. The two estimates are presented: Rn24 A, which used a value of 98.208, calibrated by the authors of [40] for northeastern Brazil, and Rn24 B, which uses the value of a (in Equation (5)) equal to 110, according to [39]. The authors of [40], in a study conducted in the Quixere region, state of Ceara, using Landsat 5/TM images, observed MPE values of the order of 7.69% and 9.11% for Rn24 A and Rn24 B, respectively. Here, the MPE for DC was 13.2% for Rn24 A and 17.0% for Rn24 B. [59], in a study over a cerrado area in Sao Paulo State, found an MPE of 10.6% when using Rn24 B. The authors of [56] observed errors between 12.5% and 16.4% and 11.3% and 15.9% for instantaneous and daily Rn, respectively, by comparing MODIS and eddy flux towers in the Brazilian Amazonia.  
Again, greater energy availability in DC is observed from the results of the estimates, in agreement with the instantaneous Rn values. However, instantaneous radiation balance estimates are necessary for some studies. Nevertheless, they do not have as much practical applicability as the daily Rn since they estimate evapotranspiration from remote sensing data [57]. Table 1 shows the coefficient of determination (R\({}^{2}\)) between estimates and observations, and despite Rn24 A having a lower MPE, Rn24 B showed a better correlation with the observed data. This result was crucial to determine the choice of the latter to estimate daily evapotranspiration in the following sections.  
However, the estimates underestimated the field measurements, a result contrary to the one obtained for the Caatinga area by [25]. They used the sinusoidal model to extrapolate the instantaneous values of Rn on a daily scale. Here, it is important to note that in [25], the surface temperature was used as a substitute for the air temperature in the computation of the downwelling longwave radiation. This component of the radiation balance may be responsible for an increase in the Rn value.
## 3 Study Methods and Evaluation Process
### Fuzzy C-Means Clustering Algorithm
#### 3.3.1 Specific Principles
The constraints are as follows:  
\[\sum\limits_{i=1}^{c}U_{ij}=1 \tag{5}\]  
The classification problem is, therefore, mathematically modeled by converting the optimal classification problem into a problem of finding the extreme value of min(J) subject to constraints. By expanding the constraints via the Lagrangian and combining the two equations above, it is possible to find the extreme value when there exists:  
\[c_{i}=\frac{\sum\limits_{j=1}^{n}\left(x_{j}u_{ij}^{m}\right)}{\sum\limits_{j =1}^{n}u_{ij}^{m}} \tag{6}\]  
\[U_{ij}=\frac{1}{\sum\limits_{k=1}^{c}\left(\frac{\left\|x_{j}-c_{i}\right\|}{ \left\|x_{j}-c_{k}\right\|}\right)^{\left(\frac{2}{m-1}\right)}} \tag{7}\]  
Therefore, it can be seen that the value of the cluster center \(C_{i}\) is directly related to the fuzzy similarity matrix \(U_{ij}\), i.e., determining the cluster center \(C_{i}\) determines \(U_{ij}\), and determining \(U_{ij}\) determines the cluster center \(C_{i}\). Therefore, given an initial cluster center \(C_{i}\) or matrix \(U_{ij}\), the minimum extreme value of the objective function J(x) can be found by iteration. The specific weighted Euclidean distance expression is as follows:  
\[\left\|x_{j}-c_{i}\right\|=\sqrt{\sum\limits_{k=1}^{n}\omega_{k}\cdot\left(x _{jk}-c_{ik}\right)^{2}} \tag{8}\]
# Noninteracting Control Design for 6-DoF Active Vibration Isolation Table with LMI Approach
## 2 System Modeling
(**a**) Schematics of the 6-DoFs AVIS; (**b**) The AVIS’s orthogonal projection and allocation of spring forces and actuator’s forces; (**c**) The AVIS’s orthogonal projection and allocation of the vibration sensors. motion of the first actuator generates a translation along the \(x\)-axis, and at the same time, rotations about the \(y\)- and \(z\)-axes. The translation along the \(y\)-direction is coupled with the rotation about the \(x\)-axis, as seen in the second columns of \(\mathbf{A}_{1}\) and \(\mathbf{A}_{2}\). Additionally, the system output obtained from sensors' measurements is as follows:  
\[\mathbf{Y} = \mathbf{R}^{+}\mathbf{Y}_{m},\mathbf{R}^{+}=(\mathbf{R}^{T}\mathbf{R})^{-1}\mathbf{R}^{T},\] \[\mathbf{Y}_{m}=\left[ \begin{array}{cccccc}x_{1}&x_{2}&y_{1}&y_{2}&z _{1}&z_{2}&z_{4}&z_{4}\end{array} \right]^{T},\] \[\mathbf{R} = \left[ \begin{array}{cccccc}1&0&0&0&-L_{xz}&L_{xy}\\ 1&0&0&0&-L_{xz}&-L_{xy}\\ 0&1&0&L_{xz}&0&L_{zx}\\ 0&1&0&L_{xz}&0&-L_{zx}\\ 0&0&1&-L_{zy}&-L_{zx}&0\\ 0&0&1&L_{zy}&L_{zx}&0\\ 0&0&1&-L_{zy}&L_{zx}&0\end{array} \right] \tag{6}\]  
The elements of \(\mathbf{Y}_{m}\) are the measurements of two horizontal sensors along the \(x\)-axis, two horizontal sensors along the \(y\)-axis, and four vertical sensors along the \(z\)-axis, respectively. The subscript of each measurement corresponds to the nearest actuator. Proofs for Equations (4)-(6) are given in Appendix A. Moreover, let an alternative input vector consisting of six elements be as follows:  
\[\mathbf{U}=\left[ \begin{array}{cccccc}f_{x}&f_{y}&f_{z}&\tau_{x}&\tau_{y}&\tau_ {z}\end{array} \right]^{T}\in\mathbf{R}^{6} \tag{7}\]  
where each element of \(\mathbf{U}\) is the total force/torque in the corresponding direction. Consequently, the control inputs to the eight actuators are computed according to their location and are given by:  
\[\mathbf{U}_{a}=\mathbf{B}_{a}{}^{+}\mathbf{U},\] \[\mathbf{B}_{a}{}^{+}=\mathbf{B}_{a}{}^{T}\left(\mathbf{B}_{a}\mathbf{B}_{a}{}^{T} \right)^{-1} \tag{8}\]  
The system dynamics with the new input are given by:  
\[\dot{\mathbf{X}}=\mathbf{A}\mathbf{X}+\mathbf{B}\mathbf{U}+\mathbf{D}_{\mathbf{x}} \tag{9}\] \[\mathbf{Y}=\mathbf{C}\mathbf{X}\]  
with \(\mathbf{B}=\left[ \begin{array}{cc}\mathbf{O}_{6}&\mathbf{I}_{6}\end{array} \right]^{T}\). The noninteracting control system is then designed so that the interactions between the system states are isolated and the reference in each direction controls the corresponding output.
_Appl. Sci._**2015**, \(5\), 1102-1116; doi:10.3390/app5041102  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**A New Mechanism for THz Detection Based on the Tunneling Effect in Bi-Layer Graphene Nanoribbons**  
**[PERSON] 1,2**  
1 Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, via G. Di Biasio 43, Cassino 03043, Italy; E-Mail: [EMAIL_ADDRESS]; Tel.: +39-0776-299-3691; Fax: +39-0776-299-3707  
2 National Institute for Nuclear Physics, INFN, via E. Fermi 10, Frascati 00044, Italy  
Academic Editor: [PERSON]  
_Received: 14 October 2015 / Accepted: 3 November 2015 / Published: 12 November 2015_  
**Abstract:** A new possible mechanism of signal detection in the THz range is investigated, based on the excitation of resonances due to the tunneling effect between two graphene nanoribbons. A simple detector is proposed, where two graphene nanoribbons are used to contact two copper electrodes. The terminal voltages are shown to exhibit strong resonances when the frequency of an external impinging field is tuned to the characteristic tunneling frequency of the graphene layer pair. An electrodynamic model for the electron transport along the graphene nanoribbons is extended here to include the tunneling effect, and a coupled transmission line model is finally derived. This model is able to predict not only the tunneling resonance, but also the well-known plasmon resonances, related to the propagation of slow surface waves.  
**Keywords:** graphene; graphene nanoribbons; terahertz electronics; tunneling effect
# Protection of Digital Elevation Model--One Approach
## 3 Results
### Testing of the Proposed Model
#### 3.2.3 Testing of Encryption  
Testing of the secure transmission of DEM data from the LiDAR radar to the end user was carried out using the e-Doc Encryptor application encryption module. This module also uses an electronic signature module to simultaneously ensure integrity, authenticity, and non-repudiation.  
A symmetric key was generated on the LiDAR side to encrypt the data. Encryption of the symmetric DES 256-bit key was performed by the public key from the end user's certificate, which is delivered to the end user during the transfer of encrypted data.  
The end user decrypts the symmetric key using his private key located in the means for qualified electronic signature (electronic card chip) and authenticated with his PIN code. The resulting content is decrypted using a symmetric key.  
A malicious user cannot gain insight into the contents of a protected file because he does not have the corresponding private key. Figure 8 shows an attempt to maliciously access the file content using a fake electronic card.  
The decryption module started with the procedure of decrypting the encrypted symmetric key by the private key from the electronic card. Since the private key in the attacker's electronic card chip does not correspond to the public key, as well as the end user's certificate for encryption does not correspond to the certificate on the attacker's electronic card, the card software detected an irregularity and refused to carry out the decryption operation. An attacker or malicious person cannot now access the contents of the DEM.
## Special Issue on Body Area Networks
[48] describe a novel adaptive method for data stream allocation to increase the available time-frequency resource usage in heterogeneous WBANs with dual radio interfaces (narrow band and ultra-wide band). The communications application area ends with the proposal of an enhanced temperature-aware routing protocol, with efficient mobility, that is based on a multi-criteria decision making method. The authors suggest this as a new option to deal with the challenge of rises in node temperatures (hot spot regions) in WBANs [49]. **Applications:** [PERSON] et al. [50] describe a novel lossless electroencephalography (EEG) compression Very-Large-Scale Integration (VLSI) circuit to increase both efficiency and effectivity of EEG signal transmission over WBANs with very promising results. [PERSON] et al. [51] present technology for low-power high data-rate digital capsule endoscopy with human body communication. This special issue ends with the presentation of a novel method based on an ensemble of heterogeneous classifiers that is able to recognize static, dynamic and transitional user activities [52]. In summary, this special issue includes a series of excellent research papers on body area networks, covering three important application areas. This issue would not be possible without the contributions of the authors who submitted their valuable papers. We would like to thank all reviewers and the editorial team of Applied Sciences for their great work. This work was partially supported by Portuguese Fundacao para a Ciencia e a Tecnologia--FCT, I.P., under the project UIDB/04524/2020\", by Spanish Ministerio de Ciencia, Innovacion y Universidades, Agencia Estatal de Investigacion (AEI)/European Regional Development Fund (FEDER, UE) under DPI2016-80894-R and PID2019-106084 RB-I00 grants, by Biomedical Research Networking Centre in Mental Health (CIBERSAM) of the Instituto de Salud Carlos III, and by Portuguese National funds through FITEC-Programa Interface, with reference CIT \"INOV-INESC Inovacao-Financiamento Base\". The authors declare no conflicts of interest.