esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Nanohydroxyapatite on Silk Fibroin-Chitosan Interactions--Molecular Dynamics Study
## 3 Results and Discussion
Notably, both SER and THR play an important structural role in silk fibroin. Since SER and THR residues contain active hydroxyl groups, they serve as attachment sites for carbohydrates. Furthermore, SER serves as the phosphorylation site [21]. In hydrogen bonding, SER and THR can act as both hydrogen donors and acceptors. Moreover, SER is often found in tight bends or loops, faces outward, forming hydrogen bonds with molecules of the  
Figure 6: Visualization of (**a**) intermolecular fibroin ion bridges with chitosan carried by phosphoric ions; (**b**) intramolecular arginine–lysine ion bridge; (**c,d**) calcium ion-mediated ion bridges. Molecules are represented by a ball–stick model with colors representing the following elements: carbon—turquoise, nitrogen—dark blue, phosphorus—yellow, oxygen—red and hydrogen—white, calcium—pink. medium (water) [21]. THR residue, which is more hydrophobic than SER, often occurs in an antiparallel \(\beta\)-sheet, facing the inside of the molecule [21]. Unlike ionic interactions and hydrogen bonds, hydrophobic contacts exhibit less significant differences in distribution between complexes stabilized by \(\text{HPO}_{4}{}^{2-}/\text{Ca}^{2+}\) and acetic acid (Figure 7). Nevertheless, their contribution to complex stabilization is much less important compared to previously discussed short-distance interactions. According to the applied default criteria [115] used for the detection of hydrophobic contacts in the YASARA software (2021), this type of weak contact involves carbon atoms surrounded by three or two hydrogen or carbon atoms, or C-H moieties in carbon rings [116]. As expected, the majority of hydrophobic contacts are formed by aromatic residues in the fibroin chain (PHE and TYR, Supplementary Table S4). The interatomic distances determined for these interactions range from 4.33 to 4.54 A (CH3 COOH) and from 4.25 to 4.66 A (Ca2+/HPO42-). It is worth noting that, in addition to the aforementioned main types of interactions, a relatively weak but identifiable cation-\(\pi\) contact can also be distinguished. The distances determined for these contacts are relatively large and range from 3.80 to 4.19 A (in the case of the complexes containing Ca2+ and \(\text{HPO}_{4}{}^{2-}\)) and from 3.91 to 4.23 A (in the case of the complexes containing CH3 COOH). These interactions involve the positively charged ammonium group in chitosan and the moieties in fibroin, containing \(\pi\) electrons. Our analysis reveals that \(\text{HPO}_{4}{}^{2-}/\text{Ca}^{2+}\)-influenced complexes involve cation-\(\pi\) contacts formed by HIS (11.0%), PHE (39.3%), and TYR (49.7%). Interestingly, complexes formed with CH3 COOH exhibit interactions with a wider range of residues, including ASP (7.0%), GLU (4.1%), GLY (0.2%), HIS (4.3%), PHE (27.3%), and TYR (57.1%).
###### Abstract  
The workspace of a robot provides the necessary constraint information for path planning and reliable control of the robot. In this paper, a workspace visualization method for a multijoint industrial robot is proposed to obtain a detailed workspace by introducing the 3D-printing layering concept. Firstly, all possible joint-angle groups of one pose in the joints' ranges are calculated in detail according to the POE (product of exponential) theory-based forward-kinematics expressions of the multijoint industrial robot. Secondly, a multisolution selection method based on the key degree of the joint is proposed to select the appropriate joint-angle groups. The key degrees of all joints and their key order are obtained according to the sensitivity expressions of all joint angles, calculated from the Jacobian matrix of the robot. One principle based on the smallest differences of the nominal angle is established to select the possible solutions for one joint from the possible solutions for the joint with the smaller key order. The possible solutions for the joint with the highest key order are the appropriate joint-angle group. Thirdly, a workspace visualization method based on the layering concept of 3D printing is presented to obtain a detailed workspace for a multijoint industrial robot. The boundary formula of each layer is derived by forward kinematics, which is expressed as a circle or a ring. The maximum and minimum values of the radius are obtained according to the travel range of the joint angles. The height limitations of all layers are obtained with forward kinematics. A workspace boundary-extraction method is presented to obtain the array of path points of the boundary at each layer. The proposed postprocessing method is used to generate the joint-angle code of each layer for direct 3D printing. Finally, the effectiveness of the multisolution selection method and the workspace visualization method were verified by simulation and experiment.  
multijoint industrial robot; visualization method; 3D-printing layering concept; multisolution selection; Jacobian matrix +
Footnote †: journal: Applied Soft
# Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA
## 5 Simulation Results and Analyses
### West Delta Network (WDN) System Planning  
WDN is a 66 kV transmission network and a section of the Unified Egyptian Network. The initial configuration of WDN with proposed routes is shown in Figure 3[24]. It comprises a 52-bus system connected by 55 double circuits, 8 generation units, and 44 load buses. The actual data of WDN are introduced in [24].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Optimization Algorithm**} \\ \hline  & \multicolumn{2}{c}{**LP [4]**} & \multicolumn{6}{c}{**LSHADE-SPACMA**} \\ \hline  & \multicolumn{2}{c}{**Terminal**} & \multicolumn{2}{c}{**No. of**} & \multicolumn{2}{c}{**Power Flow**} & \multicolumn{2}{c}{**Terminal**} & \multicolumn{2}{c}{**No. of**} & \multicolumn{2}{c}{**Power Flow**} \\ \hline
**From** & **To** & **Circuits** & **(MW)** & **From** & **To** & **Circuits** & **(MW)** \\ \hline
1 & 2 & \(1\) & \(-51\) & \(1\) & \(2\) & \(1\) & \(-51.25\) \\
1 & 4 & \(1\) & \(-32\) & \(1\) & \(4\) & \(1\) & \(-31.74\) \\
1 & 5 & \(1\) & \(53\) & \(1\) & \(5\) & \(1\) & \(52.99\) \\
2 & 3 & \(1\) & \(62\) & \(2\) & \(3\) & \(1\) & \(62.00\) \\
2 & 4 & \(1\) & \(4\) & \(2\) & \(4\) & \(1\) & \(3.63\) \\
3 & 5 & \(2\) & \(187\) & \(3\) & \(5\) & \(2\) & \(187.00\) \\
2 & 6 & \(3\) & \(-357\) & \(2\) & \(6\) & \(4\) & \(-356.88\) \\
4 & 6 & \(3\) & \(-188\) & \(4\) & \(6\) & \(2\) & \(-188.12\) \\ \hline \multicolumn{2}{c}{Cost} & \multicolumn{2}{c}{200} & \multicolumn{2}{c}{Cost} & \multicolumn{2}{c}{200} \\ \multicolumn{2}{c}{(million USD)} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{(million USD)} & \multicolumn{2}{c}{200} \\ \multicolumn{2}{c}{Time (s)} & \multicolumn{2}{c}{NC *} & \multicolumn{2}{c}{Time (s)} & \multicolumn{2}{c}{6.68} \\ \hline \hline \end{tabular}  
* NC donates not calculated.  
\end{table}
Table 2: New configurations of Garver network using LP [4] and LSHADE-SPACMA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**[PERSON] et al. [17]**} & \multicolumn{2}{c}{**LSHADE-SPACMA**} \\ \hline  & \multicolumn{2}{c}{**Terminal**} & \multicolumn{2}{c}{**No. of**} & \multicolumn{2}{c}{**Terminal**} & \multicolumn{2}{c}{**No. of**} \\ \hline
**From** & **To** & **Circuits** & **From** & **To** & **Circuits** \\ \hline
45 & \(81\) & \(1\) & \(45\) & \(81\) & \(1\) \\ \(55\) & \(57\) & \(1\) & \(55\) & \(57\) & \(1\) \\ \(55\) & \(62\) & \(1\) & \(55\) & \(62\) & \(1\) \\ \(56\) & \(57\) & \(1\) & \(56\) & \(57\) & \(1\) \\ \(5
# Investigation on Eddy Current Sensor in Tension Measurement at a Resonant Frequency
## 4 Experimental Results and Discussion  
### Experimental Setup  
The HP4294A precision impedance analyzer was used to produce a driving current of 20 mA and the sensor coil was operated through a swept frequency signal from 0.1 to 1.6 MHz. The absolute coil was constructed with 68 turns of a 1 mm diameter wire wound over three layers on the skeleton with a radius slightly larger than that of the test specimen. A digital phosphor oscilloscope with multiple channels was used to acquire signals across the sensor at a sampling frequency of 1 GHz/s. The experimental system consisted of an eddy current sensor, a precision impedance analyzer (Dongguan  
Figure 4: Magnetic field lines distribution.  
Figure 5: Magnetic field strength distributions (**a**) with and (**b**) without the sleeve structure (H/m).  
Feiyu Electronics Co., Ltd., Dongguan, China), a digital phosphor oscilloscope (Tektronix, Beaverton, OR, USA), a digit multimeter (Shenzhen Wangbo Instrumentation Co., Ltd., Shenzhen, China), an acquisition circuit and a universal testing machine (Shanghai Xinrenda Instrumentation Co., Ltd., Shanghai, China) as shown in Figure 6.  
Equations (6)-(8) suggest that the impedance of the coil tends towards a maximum at the resonant frequency [29]. Thus the system can be separated into two regions where the inductive and capacitive components are dominant, respectively. The impedance phase will change at the resonant frequency from a positive to a negative regime. Before this frequency, the impedance of the coil is dominated by the inductive component. Thus, the system appears to be almost inductive. However, after this frequency the capacitive component in the system is dominantly contributing to the impedance of the coil and the sensor will cease to measure variation in the inductive component. Figure 7 shows the impedance magnitude and phase curves when the test specimen is under no external tension.  
Figure 6: Photo of the experimental system.  
Figure 7: Impedance magnitude and phase curves.  
According to the theoretical derivation, the resonant frequency can be influenced by the capacitance. The LRC parameters are listed in Table 1. When the external capacitance is 2.2 nF, 10 nF and 100 nF, the experimental results can be shown in Figure 8.  
It is clear from Figure 8 that the resonant frequency declined with the increase of the external capacitance, which was consistent with the theoretical derivation. The sensor performance was carried out on a universal testing machine at room temperature. The experimental specimen was tested with the diameter of 12 mm and the length of 500 mm, the material of which was 45\(\mathrm{\SIUnitSymbolMicro}\) carbon steel. During testing, the tension and displacement of the test specimen were recorded by an acquisition system, which was directly connected to the universal testing machine [30]. The loading tension was increased by increments of 3 kN up to 15 kN, which was in the elastic range of the test specimen.
## 4 Parameter Identification  
### State Space Representation and Discretization  
To enhance the utility of the LPTN model within the VML for parameter identification, the LPTN framework is first reformulated into a continuous state-space representation. Concurrently, the governing state equations are discretized to align with implementation requirements. Given that bearing losses and certain TR calculations are inherently linked to radial load \(F_{r}\) and rotational speed \(n_{s}\), a loss vector \(Q(F_{r},n_{s})\) is defined encompassing all heat sources within the LPTN model. Let \(R_{cons}\) and \(R_{var}(F_{r},n_{s})\) denote matrices of known and unknown TRs, respectively. Under steady-state conditions, the node temperatures are formulated as:  
\[\frac{T}{R_{cons}+R_{var}(F_{r},n_{s})}=Q(F_{r},n_{s}) \tag{17}\]  
For transient analysis, time parameters are explicitly incorporated into the governing equations. The heat transfer dynamics adhere to the energy conservation principle: the net heat flux at any node equals the internal energy accumulation within the corresponding component volume [51]. This relationship is mathematically expressed as:  
\[\rho cV\frac{dT}{dt}+\frac{T}{R_{cons}+R_{var}(F_{r},n_{s})}=Q(F_{r},n_{s}) \tag{18}\]  
where \(\rho\), \(c\) and \(V\), respectively, represent the vectors formed by the specific heat capacity, density of the material, and component volume. is the temperature change vector in a time period \(dT\), and \(T\) is the temperature vector at the current time. Write the above formula as the state space form as:  
\[\begin{split}\dot{T}&=A(F_{r},n_{s})T+B(F_{r},n_{ s})U(F_{r},n_{s})\\ Y&=CT+DU(F_{r},n_{s})\end{split} \tag{19}\]  
In the formula, \(A(F_{r},n_{s})\) and \(B(F_{r},n_{s})\) are the system matrix and input matrix composed of TRs and heat capacity, respectively. \(U(F_{r},n_{s})\) and \(Y\), respectively, are system input variables and output variables, where \(U(F_{r},n_{s})\) is related to bearing loss and ambient temperature. \(Y\) represents the reference node temperatures corresponding to the sample datasets (real parameters) obtained from the PEL during identification, that is, the inner surface temperature \(T_{ir}\) of the inner ring and the outer surface temperature \(T_{or}\) of the outer ring in this paper, which can be obtained by the temperature of adjacent nodes.  
To enable the identification of unknown parameters and real-time processing in thermal modeling, the continuous state-space equations require discretization. Employing the Euler discretization method with a fixed sampling period \(t_{s}\), and assuming the input matrix \(U(F_{r},n_{s})\) remains constant during each sampling period, the discretized state-space representation is formulated as [51]:  
\[T^{k+1}=\left(I-A^{k}t_{s}\right)t+B(F_{r},n_{s})^{k}U(F_{r},n_{s})^{k}t_{s}+T ^{k} \tag{20}\]  
where the superscript \(k\) and \(k+1\) represent the two moments before and after the discrete time, respectively.
## 2 System Model
### Mobility of Devices Estimation  
Devices move randomly in the network; unpredictably change the network topology and affect the routing performance. Thus, the mobility of the device needs to be considered for the selection of the optimal route. For this, the well-known Random Way Point (RWP) mobility model is utilized to predict the mobility pattern of the devices [45]. The RWP model estimates the device mobility based on the position, directional angle, and trace time of the devices. The mobility \([M^{c}_{(x,y)}(t)]\) of the intermediate device \(c\) can be calculated as below:  
\[M^{c}_{(x,y)}(t)=\frac{\sum_{T}\sqrt{\left\{pos^{T}_{c}(t)-pos^{T}_{c}(t-t_{0} )\right\}^{2}+\left\{\theta^{T}_{c}(t)-\theta^{T}_{c}(t-t_{0})\right\}^{2}}}{ \sum_{c}n(T)} \tag{4}\]  
where \(T\) refers to the total tracing time of the devices. \(pos^{T}_{c}(t)\) and \(pos^{T}_{c}(t-t_{0})\) refers to the current and initial positions of the devices \(c\) in trace time \(T\), respectively. Moreover, \(\theta^{T}_{c}(t)\) and \(\theta^{T}_{c}(t-t_{0})\) are defined as the current and initial directional angle of the mobile device \(c\) in trace time \(T\), respectively. Besides, \(n(T)\) refers to the number of time samples taken during the trace time \(T\). Therefore, the device mobility value, estimated based on the RWP mobility model, enhances the route robustness while maintaining the stability of the established route.
# Estimation of Structural Deformed Configuration for Bridges Using Multi-Response Measurement Data
## 3 Validation Issues and FEM Model  
### Validation Process and Issues  
The FEM model (beam and truss) was used to verify the developed algorithm, and the validation flow is shown in Figure 2. A real deformed shape (RDS) is assumed by applying various static load conditions to the FEM model, and the MD matrix is constructed from the displacement, slope, and strain values at a specific location in the RDS. Due to the fact that the data measured from the on-site structure include measurement errors, differences from the FEM model may occur. However, because the algorithm proposed in this paper is developed by extending the existing algorithm already verified in various studies, and the objective of this study was to validate the algorithm, measurement errors that occur in the field were not considered here.  
As multi-response data are used in the developed algorithm, the following three issues are analyzed and reviewed to derive appropriate estimation results: shape function type, sensor placement method, and EDS by multi-response data. First, an appropriate shape function type should be selected to properly estimate the deformed shape. The MSF derived from the frequency analysis and SSF derived from the static analysis can be used as a shape function. An appropriate shape function is selected by comparing the estimation results of MSF and SSF through the data at an arbitrarily selected position. Second, when multi-response measurement data are used for shape estimation, interference between the data may affect the estimation results. Various methods for optimal sensor placement (OSP) have been proposed, but most of them consider only the data of one response. To solve this issue, a new sensor placement method is proposed in this study, and the estimation results of various sensor placement methods were compared. Third, the effectiveness of the algorithm developed in this study was verified by comparing the estimation error through multi-response data and that using only displacement data.  
The mean absolute percent error (MAPE) and mutual correspondence criterion (MCC) are usually used as indices to indicate the error of the estimated deformed shape. However, MAPE is sensitive to small values and insensitive to large values, whereas MCC is sensitive to large values and insensitive to small values. Therefore, in this study, normalized MAPE (NMAPE) was used as the error index to analyze the absolute estimation error between RDS and EDS.  
\[NMAPE(\%)\ =\ \frac{100}{N_{dof,u}}\sum_{i=1}^{N_{dof,u}}\frac{|RDS_{i}\ -\ EDS_{i}|}{|RDS|_{max}}. \tag{18}\]
## 2 Uncertainty Processing Based on Clustering Technique  
It has been discussed above that an effective set of scenarios can be employed to guide the TNEP design of a smart grid under uncertainty. Obviously, the accuracy of uncertainty simulation has a direct effect on the validity of planning results, and the number of scenarios is also related to the computational efficiency of TNEP problem solving. When dealing with large uncertainties, the conventional scenario-generation methods can hardly guarantee simulation accuracy while controlling the scenarios' number within an acceptable range. In addition, the difficulty of uncertainty simulation in the smart grid is also significantly increased due to the large-scale utilization of renewable energy. Therefore, an effective scenario-generation method is of great importance.  
Since it is one of the major characteristics of a smart grid to have massive amounts of data collected from various measuring devices [13], this paper proposes a novel scenario-generation method based on clustering analysis to cope with the uncertainty in the form of large datasets. As one of the most commonly used data mining techniques, clustering analysis has been proven to be effective to recognize the accurate distribution characteristics of large datasets [14]. In this paper, the power dataset to be clustered consists of the raw data for each uncertain factor, such as hourly data of wind power, photovoltaic power, load demand, _etc._ Through the clustering analysis, the power dataset can be clustered into several clusters where data samples belonging to the same cluster have similar values of renewable power output and load demand. This means that each cluster can represent a typical combination of renewable power output and load demand, while the correlation among the uncertain factors is considered intuitively. Therefore, a scenario set, which contains the typical combinations of renewable power output and load demand, can be selected from the clusters by data sampling.  
This paper proposes a novel hybrid clustering method that thoroughly investigates the inherent characteristics of the TNEP problem. Moreover, an effective strategy for scenario selection is applied to ensure the typicality of the selected scenario set. The specific procedures are described in the following subsections.
## 3 Methodology
### Document Classification  
Document classification is known as the process which builds and evaluates the model of classification. In this study, a set of classifiers were utilized, namely, Logistic Regression (LR) [60], Random Forest(RF) [61], k-nearest neighbors algorithm (KNN) [61], Decision Tree (DT) [62], Neural Networks (NN) [63], Support Vector Machine (SVM) [61], Linear Support Vector Machine (LSVM) [64], and Stochastic Gradient Descent (SGD) [65].  
Particle Swarm Optimization (PSO) is generally known as a population-based intelligence algorithm which was suggested by [33]. PSO is grounded on the interaction and the elementary behavior among agents (swarm individuals) to find the optimal solution and solve complex problems. PSO begins with a swarm of random particles. Through the optimizing procedure for a particular challenge with \(N\) variables, the \(i\)-th particle has a specific velocity vector, and a position vector presented as \(V_{i}=[V_{i1},V_{i2},\ldots,V_{iD}]\) and \(X_{i}=[X_{i1},X_{i2},\ldots,X_{iN}]\), respectively. The vector \(X_{i}\) is considered as a candidate solution to solve the given problem while the vector \(V_{i}\) is considered as the particle's search direction and step size. During the process of optimization every particle decides its trajectory according to its personal historical best position \(Pb_{i}=[Pb_{i1},Pb_{i2},\ldots,Pb_{iN}]\) and the global best-so-far position \(Gb=[gb_{1},gb_{2},\ldots,gb_{N}]\). In the canonical PSO, the update rules of \(V_{i}\) and \(X_{i}\) are defined as (12) and (13), respectively.  
\[V_{ij}^{t+1}=Y\times V_{ij}^{t}+c_{1}\times\mathit{rnd}_{1}\times(pb_{ij}^{t} -X_{ij}^{t})+c_{2}\times\mathit{rnd}_{2}\times(gb_{j}^{t}-X_{ij}^{t}) \tag{12}\]  
\[X_{ij}^{t+1}=X_{ij}^{t}+V_{ij}^{t+1} \tag{13}\]  
where \(Y\) is an inertia weight presenting how the previous velocity is preserved; \(c_{1}\) and \(c_{2}\) are known as two acceleration coefficients determining relative learning weights for \(Pb_{i}\) and \(Gb\), referred to as \"self-cognitive\" and \"social learning\", respectively; \(\mathit{rnd}_{1}\) and \(\mathit{rnd}_{2}\) are two random numbers uniformly distributed over \([0,1]\).  
Algorithm 1 presents the pseudo-code of PSO.  
```
Begin  Initialize parameters: \(Y=0.9,c_{1}=c_{2}=2.0\);  Generate initial population's positions \(X_{i}\) and velocity \(V_{i}\) where \(i=1,2,\ldots,N\);  Evaluate all \(X_{i}\) by computing the fitness function (fit);  Determine \(Pb_{i}\) and \(Gb\) according to the fitness function values;  While (not meet the stop conditions) For\(i=1:N\)  Update \(V_{i}\) and \(X_{i}\) according to Equations (12) and (13), respectively ;  If fit (\(X_{i}\)) \(<\) fit (\(Pb_{i}\)) \(Pb_{i}\) \(=\)\(X_{i}\) ; fit (\(Pb_{i}\)) \(=\) fit (\(X_{i}\));  If fit (\(X_{i}\)) \(<\) fit(\(Gb\)) \(Gb\) \(=\)\(X_{i}\) ; fit (\(Gb\)) \(=\) fit(\(X_{i}\));  End If End For\(Y=0.9-0.5\) End While  Return \(Gb\) End
```  
**Algorithm 1** Pseudo-code of the PSO algorithm.