esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Assessment of Multi-GNSS PPP with Multipath Correction
Additionally, the sidereally filtered BDS-only solution (se Figure 7b) outperforms the unfiltered GPS solution in the east and up components (see Figures 9a and 11a), but is slightly worse than the sidereally filtered GPS solution in the north and up components (see Figures 10b and 11b). Figure 9: 12 h displacements (cm) with respect to daily solutions for the east component at station GSWX on DOY 219, 2018 and averaged power spectral density (PSD) (in dB) on a frequency band from 2 to 100,000 s over all 12 stations. (**a**) shows the solutions without sidereal filtering; (**b**) shows the solutions for which all satellites except for the GEO satellites are sidereally filtered; (**c**) shows the sidereally filtered solutions; (**d**) shows the sidereally filtered solutions excluding the GEO satellites from data processing. The yellow dashed-dotted horizontal lines denote the reference values. Figure 10: 12 h displacements (cm) with respect to daily solutions for the north component at station GSWX on DOY 219, 2018 and averaged power spectral density (PSD) (in dB) on a frequency band from 2 to 100,000 s over all 12 stations. (**a**) shows the solutions without multipath correction; (**b**) shows the solutions for which all satellites except for the GEO satellites are sidereally filtered; (**c**) shows the sidereally filtered solutions; (**d**) shows the sidereally filtered solutions excluding the GEO satellites from data processing. The yellow dashed-dotted horizontal lines denote the reference values.
# Framing Algorithm-Driven Development of Sets of Objectives Using Elementary Interactions
## 4 Platform Concept
### Use Case  
To illustrate the intended workflow of the platform and describe the development process of an SOO, the following use case includes the relevant steps using the example of creating an SOO for an MCDA application assessing sustainable water infrastructure.  
**Step 1: Defining the assessment goal, selecting and activating platform participants.** One or more persons--the initiators--recognize the need for an MCDA application. The initiators define the goal and the system boundaries of the real-world system. Further, the initiators identify relevant stakeholder groups. In the case of water infrastructure, typical stakeholder groups have been previously identified [11,22]. To reach a large number of potential participants, professional associations with the assessment topic should be identified.  
**Step 2: Starting the development process.** As soon as an invited participant creates an account on the platform, s/he is able to inform him-/herself about the purpose and aims of the MCDA application. During this introduction, the participant answers multiple-choice questions. These questions both inform the participant about the context and assess the status of the participant's knowledge. Thereafter, the participant can browse through the current SOO (which at the beginning of the development process comprises the goal only). Alternatively, the participant may answer a sequence of elementary interactions. The sequence is created on a semi-random base. The participant can stop answering EIs at any time. Dependent on the status of the SOO, not all proposed EIs might be available yet. For example, if there are no criteria, requests for indicators are not yet possible, because each indicator requires a criterion.  
**Step 3: Development process.** The development process for an SOO should run without the need for administrative intervention in most cases. Tasks such as evaluating the validity of the SOO elements proposed and generating the EI stream are performed using algorithms. However, initiators may monitor activities on the platform and intervene in situations when there is a lack of participants or when the goal has not been defined clearly.  
**Step 4: Evaluation of the resulting SOO.** After threshold values of validity have been reached for all SOO elements, a milestone version of the SOO is created. This version of the SOO can be integrated into an MCDA application.  
**Step 5: Evolution.** When external conditions have changed significantly, (e.g., civic preferences), the SOO developed may not be sound for the application any longer. In this case, the platform can be used for further development of the SOO based on the SOO elements already identified in the platform.  
In the following, specific core components of the platform concept, which facilitate the implementation of the given use case, are highlighted. Among them are the platform core components _Set of Objectives Designer_, _Participant Manager_, and _Model Aggregator_.
# Applications of Graphene-Based Nanomaterials in Environmental Analysis
## 2 Graphene-Based Nanomaterials and Nanocomposites
### Synthesis of Magnetic Nanoparticles  
The common methods for synthesizing MNPs are co-precipitation, thermal decomposition, hydrothermal synthesis, colloidal and high-energy ball mill. Depending on which method was used, a specific size and dispersion of size distribution of MNPs are obtained [48].  
The modified co-precipitation method introduced by [PERSON] includes the addition of FeCl\({}_{3}\)-6H\({}_{2}\)O and FeCl\({}_{2}\)-4H\({}_{2}\)O in deionized water, with the temperature rising to 60 \({}^{\circ}\)C in order to acquire a yellow and clear solution and stirred vigorously. Afterwards, aqueous ammonia is inserted drop by drop until the pH reaches 10. Then, it is left for the reaction to take place for about 30 min, under continuous and vigorous stirring. Throughout the whole experiment, N\({}_{2}\) is used as a protective gas. When the reaction finishes, the black precipitate is collected with the help of a strong magnet, it is washed many times with deionized water and ethanol. Lastly, the magnetite nanoparticles are lyophilized [49; 50].  
The thermal decomposition method carried out by [PERSON] et al. produced maghemite nanoparticles by the solid-state isothermal decomposition in air at 400 \({}^{\circ}\)C of Fe(C\({}_{2}\)H\({}_{3}\)O\({}_{2}\))\({}_{2}\cdot\)2H\({}_{2}\)O for 1 h. Before the synthesis, the solid was homogenized in an agate mortar, following in the precursor particles having a size distribution of 1-5 \(\upmu\)m [51].  
[PERSON] et al. [52] produced Fe\({}_{3}\)O\({}_{4}\) nanoparticles by hydrothermal synthesis, where 1 mmol of Fe(NO\({}_{3}\))\({}_{3}\cdot\)9H\({}_{2}\)O and 0.5 mmol of sodium bis(2-ethylhexyl)sulfosuccinate were added in 10 mL deionized water, following the addition of 2 mL of 50% hydrazine hydrate while stirring. This solution was put into a 20 mL Teflon-lined stainless-steel autoclave of and heated to 160 \({}^{\circ}\)C at a pace of 5 \({}^{\circ}\)C/min. For 10 h, the temperature was kept at 160 \({}^{\circ}\)C and then the mixture brought to room temperature naturally. The nanoparticles were filtered, washed several times with ethanol and deionized water and air-dried. The morphology and the structure of the MNPs were characterized by TEM, HRTEM and XRD.  
Another procedure for synthesizing Fe\({}_{3}\)O\({}_{4}\) magnetic nanoparticles is by the microwave-solvothermal method suggested by [PERSON] et al. [53], where 5 mmol of FeCl\({}_{3}\cdot\)6H\({}_{2}\)O, 400 mg of Na\({}_{3}\)C\({}_{6}\)H\({}_{5}\)O\({}_{7}\) and 50 mmol of NH\({}_{4}\)CH\({}_{3}\)CO\({}_{2}\) were added in a solution of 70 mL of ethylene glycol. The black solution was stirred and heated and then transferred into a microwave transparent PTFE-TFM-lined alumina ceramic vessel, with the capacity of 100 mL. Then, it was heated for 15 min to 260 \({}^{\circ}\)C for 2 h in a microwave reaction system. Finally, the black powder of Fe\({}_{3}\)O\({}_{4}\) was gathered with the help of a strong magnet and washed several times with ethanol.
## 3 Methods
### Target Broadside Radiation Pattern
#### 3.1.3 Target Broadside Radiation Pattern with Equal Sidelobe Level  
An \(N\)th-order Chebyshev polynomial of the first kind with respect to the variable can be defined as [11]  
\[T_{N}(z)=\begin{cases}\cos(N\cos^{-1}z),&|z|\leq 1\\ \cosh\Bigl{(}N\cosh^{-1}z\Bigr{)},&|z|>1\end{cases} \tag{24}\]  
Substituting \(z\) with \(a\cos^{2}\theta+b\), \(T_{N}(z)\) becomes a \(2N\)-th order polynomial with respect to \(\cos\theta\) and the \(2N\)-th order broadside beampattern that follows the Chebyshev polynomial is formed. The main lobe at \(\theta=\pi/2\) corresponds \(z_{1}=b\). The value of the Chebyshev polynomial is  
\[T_{N}(z_{1})=\cosh\Bigl{(}N\cosh^{-1}b\Bigr{)} \tag{25}\]  
The sidelobe at \(\theta=0\), \(z_{0}=a+b\), and we obtain  
\[T_{N}(z_{0})=1 \tag{26}\]  
Corresponding to  
\[z_{0}=a+b=-1 \tag{27}\]  
Setting \(S\) is the main lobe to sidelobe ratio, we have  
\[S=\frac{T_{N}(z_{1})}{T_{N}(z_{0})}=\cosh\Bigl{(}N\cosh^{-1}b\Bigr{)} \tag{28}\]  
Combing (27) and (28), we obtain  
\[\begin{array}{l}b=\cosh\Bigl{(}\frac{1}{N}\cosh^{-1}S\Bigr{)}\\ a=-1-\cosh\Bigl{(}\frac{1}{N}\cosh^{-1}S\Bigr{)}\end{array} \tag{29}\]Since the zeros of the Chebyshev polynomial are easily found, the nulls of the broadside Chebyshev beampattern are  
\[\theta_{N,n}^{null}=\arccos\left(\sqrt{\frac{\cos((2n-1)\pi/(2N))-b}{a}}\right), \quad n=1,2,\ldots,N. \tag{30}\]  
Figure 2 shows some examples of target broadside radiation pattern designed by the methods mentioned above. Figure 2a gives the second-order target broadside radiation pattern with the given nulls at \(15^{\circ}\) and \(165^{\circ}\). Figure 2b shows the fourth-order ideal beampattern with nulls at \(15^{\circ}\), \(45^{\circ}\), \(135^{\circ}\), and \(165^{\circ}\). Figure 2c,d illustrate the second-order and fourth-order broadside differential pattern with maximum directivity factor. Figure 2e,f show the fourth-order ideal radiation pattern for the \(-10\) dB and \(-20\) dB sidelobes, respectively.  
Figure 2: The target broadside radiation pattern was designed by multiple criteria. **(a)** The second-order broadside beampattern with nulls at \(15^{\circ}\) and \(165^{\circ}\). **(b)** The fourth-order target beampattern with nulls at \(15^{\circ}\), \(45^{\circ}\), \(135^{\circ}\), and \(165^{\circ}\). **(c)** The second-order target beampattern with maximum directivity factor. **(d)** The fourth-order target beampattern with maximum directivity factor. **(e)** The fourth-order ideal beampattern with equal sidelobe level, where S is set to \(10\) dB. **(f)** The fourth-order ideal beampattern with equal sidelobe levels, where S is set to \(20\) dB.
# Sparse Logistic Regression: Comparison of Regularization and Bayesian Implementations
## 3 Methods
### Least Absolute Shrinkage and Selection Operator (LASSO)  
In the context of classification with a logistic regression model, the \(\ell_{1}\) norm regularized logistic regression problem consists of estimating the model coefficients collected in \(\mathbf{\theta}\) by minimizing a cost function given by the sum of the log-likelihood (4) and a regularization term consisting of the sum of the absolute values of the model coefficients (i.e., the \(\ell_{1}\) norm of the vector of coefficients):  
\[l_{\mathcal{R}}(\mathbf{\theta})=\sum_{i=1}^{n}\{y_{i}log(\sigma_{\mathbf{\theta}}(x_{i} ))+(1-y_{i})log(1-\sigma_{\mathbf{\theta}}(x_{i}))\}-\lambda\sum_{j=1}^{p}|\mathbf{ \theta}_{j}| \tag{5}\]  
The trade-off between likelihood maximization and model complexity is governed by the regularization parameter \(\lambda\ \in\mathbb{R}^{+}\) and is usually selected by cross-validation (see Section 4). First, note that a quadratic approximation to the unpenalized cost function (4) about the current estimate ([PERSON] expansion about current estimates) is given by (see [33,41] for details)  
\[\ell_{Q}(\mathbf{\theta})=-\frac{1}{2n}\sum_{i=1}^{n}w_{i}(z_{i}-x_{i}\mathbf{\theta}) ^{2} \tag{6}\]  
where  
\[z_{i}=x_{i}\mathbf{\theta}+\frac{y_{i}-\sigma_{\mathbf{\theta}}(x_{i})}{\sigma_{\mathbf{ \theta}}(x_{i})(1-\sigma_{\mathbf{\theta}}(x_{i}))} \tag{7}\]  
and  
\[w_{i}=\sigma_{\mathbf{\theta}}(x_{i})(1-\sigma_{\mathbf{\theta}}(x_{i})). \tag{8}\]  
The LASSO algorithm is a popular regularization method for model coefficient estimation and variable selection [12] which can be used to solve (5) with the same quadratic approximation for the unpenalized cost function ([41]), i.e., to solve:  
\[\min_{\mathbf{\theta}}\{-\ell_{Q}(\mathbf{\theta})+\lambda\sum_{j=1}^{p}|\theta_{j}|\} \tag{9}\]  
cycling through all variables \(j=1,2,\ldots,p\) until a certain criterion is met. For each value of \(\lambda\), an outer loop computes the quadratic approximation about the current parameters \(\mathbf{\theta}\), then the coordinate descent path algorithm ([33,42]) solves the penalized weighted least-squares problem (9).
## 5 Concluding Remarks  
In this study, the effective compressive strength of a high-strength concrete (HSC) column intersected by normal-strength concrete (NSC) slabs was estimated by introducing the ANFIS algorithm. In addition, a simplified equation to facilitate practical applications was developed by means of extensive parametric studies and dimensional analyses. The accuracy of the ANFIS model and proposed equation was then verified by comparison with the collected test results. The following conclusions were obtained from the findings of this study.  
1. The effective compressive strengths of HSC corner and edge columns intersected by NSC slabs were estimated using ANFIS, a type of neuro-fuzzy system. The results showed that the ANFIS model offers a very accurate evaluation of the collected test results, with the COV calculated at 0.13.
2. The equations for effective compressive strength specified in ACI 318-19 and CSA A23.3-14 provided very conservative results, but with relatively large scatters.
3. The parametric study was performed based on the ANFIS model, and a simplified equation for effective compressive strengths was proposed by means of a dimensional analysis. The simplified equation showed an accuracy which was very similar to that of the ANFIS model.  
**Author Contributions:** Supervision, K.S.K. and H.-C.C.; formal analysis, H.-C.C. and S.-H.C.; methodology, K.S.K. and S.-H.C.; conceptualization, S.-J.H.; visualization, S.-H.L.; data curation, H.-Y.K. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Comparison of simplified equation and other approaches.  
**Funding:** This research was funded by the Urban Architecture Research Program funded by the Ministry of Land, Infrastructure and Transport of Korean government grant number 20 AUDP-B100356-06, for [PERSON]. Moreover, this work was supported by the Basic Study and Interdisciplinary R&D Foundation Fund of the University of Seoul (2020), for [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
# The Influence of Motion Data Low-Pass Filtering Methods in Machine-Learning Models
## 2 Materials and Methods
### Kinematic Measures  
Seven gait characteristics (gait speed, step length, step width, toe clearance, foot angle, shank angle, and thigh angle) were calculated from the filtered 3D motion data. Considering the high gait symmetry in healthy older adults [18], the gait characteristics were only calculated for a single side (left), which was the recovery side in perturbation trials. Step length and step width were calculated as the distance between heels at recovery foot touchdown (the step near the center of walkway) in the anteroposterior (AP) or mediolateral (ML) direction, respectively. Toe clearance was calculated as the maximum toeheight of the left limb from right foot touchdown to left foot touchdown. Gait speed was calculated as the mean center of mass (COM) velocity in one gait cycle. Further, all segment angles were calculated as the angle between the segment of left limb and horizontal plane at left foot touchdown. The COM kinematics were calculated using a 13-segment rigid body model with sex-dependent segmental inertial parameters using the position of all the 3D markers.
## 3 Methodology
#### 3.1.2 Model Verification  
ZEMAX is a software program primarily used for optical design, simulation, and analysis. It is widely used by engineers and scientists to design, analyze, and optimize optical systems, such as lenses, mirrors, gratings, and other related optical components. ZEMAX offers various tools for ray tracing, system optimization, and tolerance analysis, allowing users to simulate the performance of optical systems under different conditions. We used ZEMAX (Version 19.4) to simulate the structure of a rotating dual-grating optical system to verify the applicability of the derived formulas [23]. The optical structure of rotating dual grating is shown in Figure 4, and its observed planar section is shown in Figure 5.  
The incident beam wavelength was set to 1064 nm, and the grating period was 7.322 um. Rotating grating one and rotating grating two were placed with a common optical axis in parallel, as shown in Figure 4. The optical axis was viewed in reverse from the target position, with the grating lines aligned along the positive X-axis. The target position was uniquely determined in the rectangular coordinate system. The Z-axis was the common rotation axis of rotating grating one and rotating grating two, which was the system's optical axis. The rotating dual grating could freely rotate in the range of (0\({}^{\circ}\)360\({}^{\circ}\)) in either the counterclockwise or clockwise direction. In ZEMAX, a multi-structure setup was used to rotate both gratings. The incident beam was directed toward the target along the positive Z-axis. In ZEMAX, the incident beam was introduced with an incident angle, which could represent the off-axis displacement. After diffraction by the two rotating gratings, which could rotate at different angles, the deflection angle and azimuth angle of the beam directed toward the target were obtained. In ZEMAX, the incident azimuth and pitch angles were set, with the off-axis azimuth angle set to 0.1\({}^{\circ}\). The pitch angle was varied from 0\({}^{\circ}\) to 180\({}^{\circ}\) in 20\({}^{\circ}\) increments. Rotating grating one was rotated counterclockwise by 0\({}^{\circ}\), and rotating grating two was rotated counterclockwise by 20\({}^{\circ}\). In the proposed formulas, \(\alpha=0^{\circ}\), \(\beta\) increased by 20\({}^{\circ}\) increments up to 180\({}^{\circ}\), \(\theta_{1}=0^{\circ}\), and \(\theta_{2}=20^{\circ}\). The simulation results from ZEMAX tracing and substitution into the formulas yielded the data examples shown in Table 1.  
Figure 4: Optical structure of rotating dual grating. Figure 5: Observed planar section.
## 2 Materials and Methods
### Proposed Multi-Sensor-Based Detection Model
#### 2.2.1 Creating a Depth Map for Image Registration  
To effectively fuse data from heterogeneous sensors like cameras and LiDAR for object detection, it is essential to align the dimensions of 2D images and 3D PCD. This is typically achieved by transforming 3D PCD into a depth map. Furthermore, 3D PCD represent a set of 3D coordinate points, generated when laser signals emitted from LiDAR bounce off surrounding objects. To apply this transformation to a 2D setting, a calibration process is necessary to reconcile the disparate viewpoints of the two sensors beyond dimension transformation. This allows the \((x,\ y,\ z)\) coordinate of the PCD to be mapped to the \((u,\ v)\) coordinate of a 2D image, as expressed in the following equation [21]:  
\[\begin{pmatrix}u\\ v\\ 1\end{pmatrix}=\begin{pmatrix}f_{u}&0&u_{0}\\ 0&f_{v}&v_{0}\\ 0&0&1\end{pmatrix}\begin{pmatrix}R&t\\ 0&1\end{pmatrix}\begin{pmatrix}x\\ y\\ z\\ 1\end{pmatrix}\begin{pmatrix}x\\ y\\ 1\end{pmatrix} \tag{4}\]  
Here, \(f_{u}\) and \(f_{v}\) are the eigenvalues of the camera, representing the focal lengths in the horizontal and vertical directions, respectively; \(u_{0}\) and \(v_{0}\) denote the principal points of the 2D image; and \(R\) and \(t\) denote the rotational transformation and parallel translation matrices, respectively. They are computed using singular value decomposition as described in [22]. Consequently, a depth map, aligning with the camera's viewpoint, is created and employed for training YOLO. Figure 2 depicts the process of creating a depth map by aligning a LiDAR-acquired PCD point with a camera. Figure 1(a) presents an image acquired by an RGB camera, while Figure 1(b) displays a point map projected onto the image by aligning the 2D-transformed PCD with the camera. Moreover, Figure 1(c) illustrates the resulting depth map.  
Figure 2: \(Cont.\)
## 4 Experimental Results and Analysis
### Visualization of Feature Maps  
In this subsection, we use principal component analysis (PCA) to visualize feature maps to present the recognition results more intuitively. We extract the fully connected layer output feature maps of LACNet as input to the PCA method. PCA outputs two-dimensional data by the dimension reduction process. The visualization results of the feature map are shown in Figure 12, where Normal indicates the normal state and D1-D4 are samples with different areas of damage. The same classes of samples converge into clusters. It indicates that the differences between all kinds of samples are obvious and there is no confusion in the recognition of damage in the composite structure. Hence, the proposed method has a superior performance.
# A Reconfigurable Single-/Dual-Bandstop Filter with Controllable Equal-Ripple Performance
## 2 Design and Analysis of the Proposed Reconfigurable BSF
### State I: S-BSF
#### 2.1.2 Design Approach of State I  
To implement the arbitrary control of the equal-ripple level of \(S_{11}\) for State I, the following equations should be satisfied:  
\[\frac{\partial|F_{S}|}{\partial\theta}\big{|}_{\theta=\theta_{S}^{S_{11}}}=0 \tag{19}\]  
\[\left|F_{S}\right|\big{|}_{\theta=\theta_{S}^{S_{11}}}=\frac{1}{\sqrt{10^{RL_{S }/10}-1}} \tag{20}\]  
where (19) enables \(\theta_{S}^{S_{11}}\) to be employed as the pole of \(S_{11}\) and (20) is used to set the ripple level corresponding to \(\theta_{S}^{S_{11}}\) to \(RL_{S}\).  
Similarly, the following equations should be maintained for arbitrarily controlling the equal-ripple level of \(S_{21}\) for State I:  
\[\frac{\partial|F_{S}|}{\partial\theta}\big{|}_{\theta=\theta_{S}^{S_{21}}}=0 \tag{21}\]  
\[\left|F_{S}\right|\big{|}_{\theta=\theta_{S}^{S_{21}}}=\frac{1}{\sqrt{10^{SR_ {S}/10}-1}} \tag{22}\]  
where (21) enables \(\theta_{S}^{S_{21}}\) satisfy the condition of the pole of \(S_{21}\), and (22) is employed to ensure the ripple level corresponding to \(\theta_{S}^{S_{21}}\) equalling to \(SR_{S}\).  
In addition, in the conduct of the actual design, the S-parameter response exhibits an unexpected resonance. This resonance can be eliminated while the characteristic impedances for State I satisfy the following equation:  
\[Z_{1}=\frac{2Z_{2}Z_{4}}{Z_{3}} \tag{23}\]  
By substituting (23) into (11), the highest power of the numerator and denominator of \(F_{S}\) decreases by 2. In terms of the number of design parameters, the proposed S-BSF in State I has four variable parameters (\(Z_{1}\), \(Z_{2}\), \(Z_{3}\), and \(Z_{4}\)). A unique solution satisfying the desired performance (\(RL_{S}\) and \(SR_{S}\)) can be acquired by combining (19)-(23) and fixing a variable parameter. In State I, \(Z_{3}\) is selected to be fixed.