esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Response Surface Methodology (RSM) Analysis  
The UAE conditions on TALC from raw materials are shown in Figure 1A (peel), Figure 1B (seed), Figure 1C (columella), and Figure 1D (pulp). There is a significant interaction effect between the factors evaluated (\(p<0.05\)). The elliptical shape of the plots indicates the significant effect of interactions on the TALC [17]. All the conditions of the UAE caused an ALK extraction in all raw materials. The effect (negative or positive) of the factors on the extraction of ALKs depended on each matrix, and the significant effect of the independent variables (and their interactions) on each raw material was as follows: for peel, \(\mathrm{X_{PC}}>\mathrm{X_{ET}}>\mathrm{X_{AS}}\), seed \(\mathrm{X_{ET}}>\mathrm{X_{AS}}>\mathrm{X_{PC}}\), columella, \(\mathrm{X_{ET}}\)\(>\mathrm{X_{PC}}>\mathrm{X_{AS}}\), and pulp, \(\mathrm{X_{ET}}>\mathrm{X_{PC}}>\mathrm{X_{SA}}\). Figure 1: Response surface plots and pareto charts of total alkaloid content from _A. muricata_ peel (**A**), seed (**B**), columella (**C**), and pulp (**D**) after the ultrasound-assisted extraction. DW = dry weight. \(\mathrm{X_{SA}}\) = sonication amplitude (%). \(\mathrm{X_{ET}}\) = extraction time (min). \(\mathrm{X_{PC}}\) = pulse cycle. On the other hand, RSM was performed using multiple regression techniques. Second order polynomial equations (Table 2) were obtained to calculate the predicted TALC for pulp, peel columnella, and seeds, which describe the effect of the test factors as well as the effect of all interactions on the response variable. Regression analysis showed that the TALC experimental data from peel (R\({}^{2}\) = 0.98), seed (R\({}^{2}\) = 0.88), columella (R\({}^{2}\) = 0.88), and pulp (R\({}^{2}\) = 0.97) have a reasonable correlation and adequate adjustment with the model (Lack of fit, \(p>0.05\)) (Table 2). The lack of fit ensures the fitness of the model, and this demonstrates the approximation to a real system. Similar results were shown by [PERSON] et al. [20] in the optimization of UAE of ALKs from _Stephania cambolica_. The extraction of ALKs from peel, seed, columella, and pulp can be calculated with predicted mathematical models (Table 2). However, some coefficients (Table 3) were not significant (\(p>0.05\)) for the model. ALKs from peel, seed, columella, and pulp can be calculated with predicted mathematical models (Table 2).
# An Efficient Finite-Difference Stencil with High-Order Temporal Accuracy for Scalar Wave Modeling
## 5 Numerical Experiments
### Numerical Accuracy and CPU Execution Time Analyses  
An increase in the temporal order \(N\) will significantly increase the computational cost. In this part, we set a series of temporal orders \(N\) to analyze the relative errors and CPU execution times (CPU: AMD 5600h) of the cross-rhombus stencil and our new stencil. Here, we adopted \(h=6\) (m), \(\Delta t=0.0015\) (s) and \(v=1500\) (m/s) for all FD methods. We set up three receivers at positions \((768,1536)\) (m), \((1536,768)\) (m) and \((768,768)\) (m), which represent the simulations of different propagation angles. We calculated their total relative errors with the reference method [7], and the total relative error was defined as  
\[E=\sum_{i=1}^{3}\|\mathbf{t}_{i}-\mathbf{r}_{i}\|_{2}, \tag{29}\]  
Figure 5: Numerical experiments of the homogeneous model with grid sampling \(h=4\) (m) and \(h=6\) (m). The left panel shows the snapshots with the grid sampling \(h=4\) (m), and the right panel represents the grid sampling \(h=6\) (m): (**a,b**) TE-C method, (**c,d**) TE-R method and (**e,f**) DRP-R method.  
where \(\mathbf{t}_{i}\) represents the records of the three receivers and \(\mathbf{r}_{i}\) represents the records of the references.  
Table 2 lists the total relative errors and CPU execution times of the TE-R and TE-CR methods with different orders \(N\). It can be seen that the relative errors of the TE-CR method were slightly less than that of the TE-R method (Table 2). However, the CPU execution times of the TE-CR method were much larger than that of the TE-R method. Table 2 demonstrates that the numerical accuracy of the new method was slightly lower than that of the standard cross-rhombus stencil, but its computational efficiency was greatly improved, especially in the high-order cases.
# Quantitative Deterioration Assessment of Road Bridge Decks Based on Site Inspected Cracks
## 5 Training Artificial Neural Networks
### ANN Performance Evaluation  
Bayesian regularized artificial neural networks (BRANN) are more powerful and more robust than the standard backpropagation networks. BRANN is hard to overfit the data since the algorithm provides a Bayesian criterion for stopping the training. Then, it usually leads to a generalized solution [31].  
In this study, BRANN is used for training the RACP simulation results which are shown previously in Figure 7. The training parameters such as effective one of BRANN, performance gradient and mean square errors reach stable states as shown in Figure 15. If the training parameters for BRANN reach stable states, it typically means the solution is truly converged. In order to ensure that a generalized solution is achieved by Bayesian regularization technique, k-fold cross-validation is conducted [34; 35], where the dataset (RACPs) is divided into 10 subsets. Each subset will be treated as a validation dataset, while the rest is used as a training dataset. Then the prediction accuracy is checked for the 10 validation subsets, where it resembles the total dataset. Figure 15(a) shows the results of the k-fold cross-validation, where the regression coefficient of prediction (R\({}^{2}\)) is 0.913 and the mean square errors' (mse) value is 0.0073. The results demonstrate the generalization of the network, where it maps almost all the validation subsets with good accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ANN Input Variables** & **Variables for Each FEM Element** & **No. of Elements** & **Total Number of Variables** & **Cracks Direction** \\ \hline Case (3) & \(\epsilon_{\text{xx}}\), \(t_{\text{yy}}\), \(\epsilon_{\text{yy}}\) & 84 & 252 &
\begin{tabular}{c} Included \\ ”Indirectly” \\ Not included \\ \end{tabular} \\ Case (5) & \(\epsilon_{1}\) & 84 & 84 & Not included \\ \hline \hline \end{tabular}
\end{table}
Table 4: Two sets of input variables to show cracks direction significance.  
Figure 14: Relationship between the fatigue life of “multiscale simulation program” & “ANN” for the training dataset.  
Moreover, the authors decided to test the network with independent real crack patterns besides their mirror cases (1056 data) to check the robustness of the network to treat unknown data. The relation of the fatigue life by the multi-scale simulation and the one of the built ANN for the test dataset is shown in Figure 16b. The validation around the best-fit line is with 5.1% of the coefficient of variation of prediction (C.O.V).
# Design of Micro-Phasor Measurement System to Identify Phasor of Distribution Transformers
## 2 Design of the Micro-Phasor Measurement System
### Voltage Phasors Comparing  
To obtain the phase angles, the proposed system comprises two parts. Specifically, the reference side involves the main transformer located in the substation, and the measurement side involves the distribution transformers [3]. Data were sent from the reference side to the server through mobile communication network and measurement results are stored in the database. On-site portable device sent measurement statistics to a mobile device via Bluetooth. Finally, the mobile device is connected to mobile network to retrieve data from server database and compare the statistics acquired from both sides, and the phasor of the distribution transformer at the on-site measurement could be determined. Figure 4 shows the framework of the voltage phasor comparison.
# Research on Safety Prediction of Sector Traffic Operation Based on a Long Short Term Memory Model
## 3 Time Series Forecasting Model  
### Time Series Forecasting  
A time series is a sequence \(S\) of historical measurements \(y_{t}\) of an observable variable \(y\) at equal time intervals [30]. Through a sequence of time, the method of time series forecasting predicts future events by analyzing the trends of the past, on the assumption that future trends will hold similar to historical trends. Forecasting future values of an observed time series plays an important role in nearly all fields of science and engineering, such as economics, finance, business intelligence, meteorology, and telecommunication [31]. It is also an important area of Machine Learning (ML) and Deep Learning (DL), which can be cast as a supervised learning problem. Methods, such as Support Vector Machines (SVM), Ridge Regression (RR), Random Forests (RF), and Long-Short Term Memory (LSTM), can be applied to it. These data-driven models with time series are examples of non-parametric nonlinear models which use only historical data to learn the stochastic dependency between the past and the future [30].  
Based on the flight conflict time series, which is the data sequence of flight conflict amount obtained from equal time intervals according to time sequence. We establish different prediction models, in which the required parameters in the last time instant \(t-1\) are introduced as input variables and the number of conflicts at the current time instant \(t\) is introduced as an output variable.
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 4 Joint Optimization Framework for RIS Configuration and UAV Mobility
### Real-Time RIS Phase-Shift Optimization
#### 4.1.1 Objective Function  
The objective is to maximize the SINR at the user, which is defined as  
\[\text{SINR}=\frac{\left|h_{d}+\mathbf{g}^{H}\mathbf{\Phi}\mathbf{h}\right|^{2}P}{\sigma^{2 }+I}, \tag{7}\]  
where \(h_{d}\) denotes the direct complex channel coefficient from the NTN node to the user, \(\mathbf{h}\in\mathbb{C}^{M}\) represents the channel vector from the NTN node to the RIS, and \(\mathbf{g}\in\mathbb{C}^{M}\) is the channel vector from the RIS to the user. The RIS phase-shift matrix \(\mathbf{\Phi}\in\mathbb{C}^{M\times M}\) is a diagonal matrix given by  
\[\mathbf{\Phi}=\text{diag}\Big{(}e^{i\phi_{1}},e^{i\phi_{2}},\dots,e^{i\phi_{m}} \Big{)}, \tag{8}\]  
where \(\phi_{M}\in[0,2\pi)\) denotes the phase shift applied by the \(m\)-th RIS element. The variable \(P\) denotes the transmit power of the NTN node, and \(\sigma^{2}\) is the noise variance experienced at the user.  
The interference power \(I\) from other users or operators is modeled as a log-normal random variable, expressed as  
\[I=10^{\frac{N(0,\sigma_{I}^{2})}{10}}\cdot I_{0}, \tag{9}\]  
where \(N(0,\sigma_{I}^{2})\) represents a zero-mean Gaussian random variable with variance \(\sigma_{I}^{2}\), and \(I_{0}\) is the average baseline interference power.
# Constructing a Microstrip Phase Shifter with Low Phase Error Using a Y-Resonator
## 1 Introduction  
With the rapid development of wireless communications, phase shifters have been used in a real sense in several technological fields, such as phased-array radars [1], phased-array antennas [2], beam-forming networks [3], and satellite communication systems [4].  
In addition, various types of phase shifters have been proposed as key devices for phased array systems [4, 5, 6, 7, 8, 9]. The phase shifter is located between the feed power division network and the antenna array and makes the phase of the antenna unit change by adjusting the feed phase of the antenna unit so as to achieve the purpose of beam scanning and the performance of which determines the advantages and disadvantages of the phased array antenna's scanning operating bandwidth, scanning accuracy, and the volume of the performance. Therefore, it is of far-reaching significance to study phase shifters with a small structure size, wide phase shift band, simple structure, and small phase deviation.  
Currently, wideband phase shifters are mainly realized by Schiffman structures [10, 11], branch-and-branch loaded transmission lines [12, 13, 14, 15], microstrip-slot transitions [16], multimode resonators [17], coupling structures [18], and loaded aggregate units [19]. In RF, front-end phase shifters usually work together with bandpass filters. [PERSON] of Nanjing University of Posts and Telecommunications proposed a phase shift theory based on multimode resonance theory and a broadband phase shifter based on multiple resonance networks. For the first time, filtered multiplexed multi-phase-shifted phase shifters andfiltered broadband phase shifters were realized. However, compared with simple-parallel-mode dual-mode resonators, the design using multimode resonators has the limitations of high complexity, difficult tuning, high isolation requirements, high influence by errors, and a narrower bandwidth range.  
Therefore, a Y-type dual-mode microstrip phase shifter using input-output cross-coupling is proposed in this paper. The equivalent circuit of the Y-type resonator is analyzed, and the results show that two suitable resonant frequencies can be obtained by choosing appropriate branch lengths to control the bandwidth.  
The two-port network of the bandpass filter of the main line is directly combined with the two-port network of the reference line to form a four-port phase shifter, the insertion of the two two-port networks is analyzed, and, finally, a synthesis process and the design and fabrication of a practical phase shifter are realized, and the measurement results verify the proposed design.
## 3 Development of the EMS and Obtained Results for a Specific Case Study
### Optimization of the RB and FL EMSs
#### 3.5.2 FL EMS Optimization  
The optimization of the FL EMS has been carried out as explained in Section 2.8.3. For the GA optimization, 90 chromosomes and 250 maximum generations have been used. The operation cost with this EMS is 15.4% better than in the original case. With fewer generations than in the other methods, the operational cost of the EV charging is lower.
# Fast 3D Semantic Mapping in Road Scenes +
## 4 3D Semantic Mapping
### Map Regularization
We employ two Gaussian kernels for the pairwise potentials following the previous work [9]. The first one is an appearance kernel as follows:\[k^{(1)}(\mathbf{f}_{i},\mathbf{f}_{j};\theta)=\exp\!\left({-\frac{|\mathbf{P}_{i}- \mathbf{P}_{j}|^{2}}{2\theta_{\mathbf{P},c}^{2}}-\frac{|\mathbf{c}_{i}-\mathbf{ c}_{j}|^{2}}{2\theta_{c}^{2}}}\right), \tag{18}\]  
where \(\mathbf{c}\) is the RGB color vector of the corresponding 3D points. This kernel is used to build long range connections between 3D points with a similar appearance. The second one, a spatial smoothness kernel, is defined to enforce a local, appearance-agnostic smoothness among 3D points with similar normal vectors. \[k^{(2)}(\mathbf{f}_{i},\mathbf{f}_{j};\theta)=\exp\!\left({-\frac{|\mathbf{P}_ {i}-\mathbf{P}_{j}|^{2}}{2\theta_{\mathbf{P},n}^{2}}-\frac{|\mathbf{n}_{i}- \mathbf{n}_{j}|^{2}}{2\theta_{n}^{2}}}\right), \tag{19}\]  
where \(\mathbf{n}\) are the respective surface normals. The surface normal are computed using the Triangulated Meshing using Marching Tetrahedra (TMMT) proposed in [35]. Note that the original method is towards producing a dense labeling with the stereo vision. Since the LSD-SLAM only generates semi-dense 3D point clouds, we modify the TMMT to extract a triangulated mesh within limited ranges of short distance between 3D points. _High Order Potential_: The higher order term \(\psi_{c}(X_{c}|\mathbf{c})\) encourages the 3D points (voxels) in the given segment to take the same label and penalizes partial inconsistency of supervoxels as described in [40]. It is defined as  
\[\psi_{c}(X_{c}|\mathbf{c})=\min_{l\in\mathcal{L}}(\gamma_{c}^{\max},\gamma_{c }^{l}+k_{c}^{l}N_{c}^{l}), \tag{20}\]  
where \(\gamma_{c}^{l}\) represents the cost if all voxels in the segment take the label \(l\). \(N_{c}^{l}=\sum_{i\in\mathbf{c}}\delta\) is the number of inconsistent 3D points with the label \(l\) which is penalized with a factor \(k_{c}\), regarded as the inconsistency cost. All parameters \(\theta_{\mathbf{P},c},\theta_{c},\theta_{\mathbf{P},n},\theta_{n},\theta_{ \mathbf{P},s},\theta_{s}\) specify the range in which points with similar features affect each other, respectively. They can be obtained using piece-wise learning.
# Comparison of Tensile and Fatigue Properties of Copper Thin Film Depending on Process Method
## 2 Experimental Setup
### System Configuration  
We organize a test machine for both the tensile and fatigue tests as shown in Figure 4. The test machine consists of a force-translator, a load measurement sensor such as a load cell, and a displacement measurement sensor. In addition, the test machine was equipped with a 12x zoom CCD for acquiring the surface image of specimen during the tensile or fatigue tests.  
The voice-coil actuator is used as the force-translator. Since the voice-coil actuator can apply tension and compression, it is suitable for tensile test as well as fatigue test. The voice-coil actuator can be repeatedly loaded with an operational frequency of up to 10 Hz, with a peak force of 15.57 N and a continuous stall force of 5.07 N. Also, the stroke range is \(\pm\)3.18 mm when applying cyclic loading.  
The load cell is used to measure the load. It can measure up to 10N and the resolution is 1 mN. A capacitive sensor, one of the non-contact displacement sensors, is used to measure the displacement of the specimen. The displacement sensor can measure up to 1250 \(\upmu\)m with a resolution of 0.1 \(\upmu\)m.  
The specimen of thin film is difficult to handle when performing tensile and fatigue tests. The thin film may frequently be fractured before the experiment. In addition, it is difficult to align the specimen with loading direction. Therefore, in this study, the specimen is aligned and fixed as shown in Figure 5. For fixing and aligning the thin film, we designed the aluminum plates and the specimen is mounted with these plates to make the specimen easier to handle. In addition, to align the specimen, the holes in the same position on the aluminum plate and the tester are made and pins are fixed.  
Figure 4: The system configuration for tensile and fatigue testing.  
Figure 5: Alignment and attaching process to fix specimen to aluminum plate.  
Figure 6 shows fracture image when the alignment is correct. The fracture occurs near the center of the reduced section at the specimen. Therefore, the results of testing are better when using a fixed jig of a thin film.
# A Distance-Encoded Bloom Filter for Fast NDN Name Lookup
## 1 Introduction
In this prefix trie, there are two types of nodes: prefix nodes and intermediate nodes, which are non-prefix nodes. To find the longest matching prefix for a given content name, a Bloom filter must include all nodes as members, not just prefix nodes but also intermediate nodes. As a result, the matching node identified by the Bloom filter test may be an intermediate node. Therefore, the hash table must store all nodes, including intermediate nodes. If the hash table contains only prefix nodes, additional backtracking may be required due to hash table lookup failures. The proposed Bloom filter determines the next search range based on two distance values, which define the possible length range where descendant prefixes may exist. Consequently, the Bloom filter test indicates the existence of a matching prefix at a specific length, allowing the hash table to store only prefix nodes. This proposed method not only reduces the number of Bloom filter accesses by narrowing the search range based on distance values but also helps mitigate hash collisions by reducing the number of entries stored in the hash table. The rest of this paper is organized as follows. Section 2 reviews related work on name lookup and introduces trie-based, hash-based, and Bloom filter-based approaches. Section 3 introduces the proposed scheme, which incorporates a distance-encoded Bloom filter. In Section 4, the proposed scheme is evaluated through experiments and comparedto previous approaches. The experimental results are also discussed. Finally, Section 5 concludes this paper.
# A Comparison between Two Reduction Strategies for Shrouded Bladed Disks
## 2 Background
### Craig-Bampton Method  
According to the CB method [21], the model dofs are divided into master dofs \(\mathbf{q_{M}}\) and slave dofs \(\mathbf{q_{S}}\) and the system response is approximated as a linear combination of constraint modes and of fixed-interface normal modes, where the constraint modes \(\mathbf{\
abla}\) represent the static deformed shapes due to a unitary displacement of one master dof, while the other master dofs are set to zero, and the fixed-interface modes \(\mathbf{\phi}\) are the normal modes of the system with fixed (i.e., zero displacements) master dofs.  
The resulting reduction matrix \(\mathbf{R_{CB}}\) is defined as  
\[\mathbf{R_{CB}}=\left[\begin{array}{cc}\mathbf{I}&\mathbf{0}\\ \mathbf{\
abla}&\mathbf{\phi}\end{array}\right] \tag{5}\]  
and, when \(\mathbf{R_{CB}}\) is applied to the system, the governing equations  
\[\left[\begin{array}{cc}\mathbf{M_{MM}}&\mathbf{M_{MS}}\\ \mathbf{M_{SM}}&\mathbf{M_{SS}}\end{array}\right]\left\{\begin{array}{cc}\tilde{q}_ {M}\\ \tilde{q}_{S}\end{array}\right\}+\left[\begin{array}{cc}\mathbf{K_{MM}}&\mathbf{K_{ MS}}\\ \mathbf{K_{SM}}&\mathbf{K_{SS}}\end{array}\right]\left\{\begin{array}{c}\mathbf{q}_{M}\\ \mathbf{q}_{S}\end{array}\right\}=\left\{\begin{array}{c}\mathbf{0}\\ \mathbf{0}\end{array}\right\} \tag{6}\]  
become  
\[\mathbf{M_{CB}}\left\{\begin{array}{c}\tilde{q}_{M}\\ \tilde{q}_{S}\end{array}\right\}+\mathbf{K_{CB}}\left\{\begin{array}{c}q_{M}\\ \eta_{S}\end{array}\right\}=\left\{\begin{array}{c}\mathbf{0}\\ \mathbf{0}\end{array}\right\} \tag{7}\]  
with  
\[\mathbf{M_{CB}}=\mathbf{R_{CB}}^{T}\mathbf{M}\mathbf{R_{CB}}, \tag{8}\]  
\[\mathbf{K_{CB}}=\mathbf{R_{CB}}^{T}\mathbf{K}\mathbf{R_{CB}}, \tag{9}\]  
\(\mathbf{\eta_{s}}\) being the vector of fixed-interface modal amplitudes.  
When the CB method is applied to systems with contact interfaces to generate reduced order models suitable for the nonlinear forced response, the set of master dofs must include the set of contact dofs of the model.