esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Design details of a single-layer rotationally symmetric metasurface ensuring decoupled and synchronized propagation of TM and TE modes has been presented in this work. The MTS was implemented by means of a dense layer of subwavelength metallic elliptical patches with an asymmetric cross-shaped aperture in the center, printed on top of a grounded dielectric. With such a pixel geometry, the dispersion properties of TM and TE SWs match at the working frequency (13.5 GHz), and the total field generated on the aperture by the combination of both modes is perfectly linearly polarized. The design of such structures has important applications in the development of dual circularized polarized MTS antennas based on surface impedance modulation, in which TM and TE mode propagation with similar dispersion properties is required.  
The authors would like to thank the financial support by the Spanish Ministry of Economy and Competitiveness, Project No. TEC2013-47753-C3-1-R and TEC2016-76997-C3-1-R and by the Gobierno de Navarra, Project PI017 COMUNICACIONES 5G and PI018 COMUNICACIONES 5G.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] y [PERSON] analyzed the data; [PERSON], [PERSON] y [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Figure 8: (**a**) \(Re(E_{x})\) field in the aperture (\(R=50u=157\) mm) at \(\varphi=0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\) as a combination of the TM and TE SW fields; (**b**) Zoom of the curves.
## 5 Mathematical Formulation Based on Linear Programming  
In this section, mathematical formulation is performed using linear programming that checks the feasibility of the proposed protocol (verified by performing the extensive simulations). To achieve the optimal solution for the objective function, we define some constraints. By using these constraints, the feasible regions for the network throughput and EC are drawn and their brief description is given below.
# Preliminary Study of Recycled Aggregate Mortar for Electric Arc Furnace Dust Encapsulation
## 2 Materials and Methods
### Characterization of Materials
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Element (mg/kg)** & \multirow{2}{*}{**EAFD**} & \multicolumn{3}{c}{**Criteria EU Landfill Directive 2003/33/EC**} \\ \cline{3-5}
**L/S = 10** & & **Inert** & **Non-Hazardous** & **Hazardous** \\ \hline Cr (Chromium) & 1.970 (a) & 0.5 & 10 & 70 \\ Ni (Nickel) & 0.053 & 0.4 & 10 & 40 \\ Cu (Copper) & 2.157 (a) & 2 & 50 & 100 \\ Zn (Zinc) & 24.047 (a) & 4 & 50 & 200 \\ As (Arsenic) & \(<\)0.05 & 0.5 & 2 & 25 \\ Se (Selenium) & 2.762 (b) & 0.1 & 0.5 & 7 \\ Mo (Molybdenum) & 20.494 (b) & 0.5 & 10 & 30 \\ Cd (Cadmium) & 0.138 (a) & 0.04 & 1 & 5 \\ Sb (Antimony) & 0.001 & 0.06 & 0.7 & 5 \\ Ba (Barium) & 6.935 & 20 & 100 & 300 \\ Hg (Mercury) & 0.180 (a) & 0.01 & 0.2 & 2 \\ Pb (Lead) & 5483.866 (c) & 0.5 & 10 & 50 \\ Fluoride & 65.8 (a) & 10 & 150 & 500 \\ Chloride & 24,100 (b) & 800 & 15,000 & 25,000 \\ Sulphate & 16,300 (a) & 1000 & 20,000 & 50,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Leached concentrations of EAFD (mg/kg) and acceptance criteria (WAC, EU Council Decision 2003/33/EC).
# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
## 6 Discussion and Findings
### DL and UL Cluster-Transport Assignment Process Results

Table 4: The composition of DL and UL traffic size to the transport network: Scenario 1.
in every instance proportionately shared among three transport links (see Table 6). The satellite network prevalently handles UL stream traffic, with 85% of the maximum traffic size in all 60 instances assigned to the satellite network (approximately for maximum DL in all 46 instances assigned to the satellite network). A total of 52% of the minimum traffic size for UL flows to DOCSIS cable. The simulation generated sixty instances, which comprised three clusters assigned to three transport network types (optical network, satellite network, and DOCSIS coaxial cable). Overall, in 60 instances, the composition of the clusters and assignment of the cluster to the transport network varied and showed no specific pattern or relations between the cluster and transport network. The assignment of a cluster to the transport network was performed unanimously across all recorded instances and was created based on the dynamic attributes of UEs.
## 4 Simulation  
In this section, the proposed UT-CLAT method is validated using synthetic data. Without loss of generality, the scenario contains four cooperative robots, labeled 1-4, tracking an uncooperative target in 2D space (as shown in Figure 1). The robots and the target are assumed to be subject to similar nonlinear unicycle models, as below:  
\[\mathbf{x}_{k+1}\triangleq\begin{bmatrix}x_{k+1}\\ y_{k+1}\\ \theta_{k+1}\end{bmatrix}=\begin{bmatrix}x_{k}+\Delta_{T}(v_{c}+w_{k}^{v}) \cos(\theta_{k})\\ y_{k}+\Delta_{T}(v_{c}+w_{k}^{v})\sin(\theta_{k})\\ \theta_{k}+\Delta_{T}(\omega_{c}+w_{k}^{\omega})\end{bmatrix},\]  
A subscriber \(i\) or \(t\) is used to distinguish the robots or the target. The state vector \(\mathbf{x}_{k}\) to be estimated contains three entries--\(x_{k}\), \(y_{k}\), and \(\theta_{k}\)--which represent the 2D position and the orientation of the robots and the target with respect to the global frame. It is assumed that at the initial time, the robots are randomly placed on different circles centered at \([-10,10]\), \([10,10]\), \([-10,-10]\), \([10,-10]\). The same control command \(\mathbf{u}=[v_{c},\omega_{c}]^{\top}=[0.3,0.0375]^{\top}\) is applied to each robot to form four approximated circles with radii 8. The velocity and angular velocity noise are assumed to be subject to Gaussian distributions with the covariance \(\mathbf{Q}_{i}=\text{diag}([0.1^{2},(0.5\pi/180)^{2}])\). The target is initialized at \([-15,-15]^{\top}\) in the global frame,and the control input is set as \(\mathbf{u}_{t}=[0.05,0]^{\top}\). Similarly, the target control is subject to zero-mean Gaussian noise with \(\mathbf{Q}_{t}=\text{diag}([0.02^{2},(2\pi/180)^{2}])\).  
In the simulation, robot 1 is assumed to be accessible to the global position and orientation in the global frame with the following measurement model:  
\[\mathbf{z}_{i,k}^{a}=\mathbf{x}_{i,k}+\mathbf{v}_{i,k}^{a},\]  
where \(\mathbf{v}_{i,k}^{a}\sim\mathcal{N}(\mathbf{0},\text{diag}\left[(0.5^{2},0.5 ^{2},0.5\pi/180)^{2}\right])\) is the control noise.  
Both the cooperative robots and uncooperative measurement are subject to a relative range measurement model as follows:  
\[z_{i,k}^{c} =\|x_{j}-x_{i}\|_{d}+v_{i,k}^{c}, \tag{31}\] \[z_{it,k}^{t} =\|x_{t}-x_{i}\|_{d}+v_{i,k}^{t}, \tag{32}\]  
\(\|\cdot\|_{d}\) is the operator that calculates the relative range between two robots or a robot and the target. The sensing range for the target is set as \(r_{t}=20\), and the sensing range for cooperative robots, as well as the communication range, is set as \(r_{c}=10\). The measurement noises are \(v_{i}^{c}\sim\mathcal{N}(0,0.05^{2})\) and \(v_{i}^{t}\sim\mathcal{N}(0,0.05^{2})\), respectively.
# Flavonoids as Promising Multitarget Agents in Alzheimer's Disease Therapy
## 2 Activities of Flavonoids from Natural Sources
### Inhibition of Acetylcholinesteraseerase (AChE)
#### 2.4.1 Catechins, Flavanones and 3-Hydroxylavanones
From _Humulus lupulus_ L. (hops), three prenylated flavanones were isolated, namely isoxanthhohumol (**16**), 8-prenilnaringenin or (25)-4\({}^{\prime}\),5,7-trihydroxy-8-(3-methylbut-2-en-1-yl)flavan-4-one (**120**), and 6-prenylnaringenin or 5,7,4\({}^{\prime}\)-trihydroxy-6-(3-methylbut-2-en-1-yl)flavan-4-one (**121**) that are inactive as inhibitors of AChE [64], suggesting the steric hindrance of the prenyl group. From the fruits of _Paulovonia tometosa_ Steud three geranylated 3-hydroxyflavanones were isolated, namely 4\({}^{\prime}\)-\(O\)-methyldiplacol or (2\(R\),3\(R\))-6-geraniol-3,3\({}^{\prime}\),5,7-tetrahydroxy-4\({}^{\prime}\)-methoxyflavan-4-one (**122**), 3\({}^{\prime}\)-\(O\)-methyldiplacol or (2\(R\),3\(R\))-6-geraniol-3,4\({}^{\prime}\),5,7-tetrahydroxy-3\({}^{\prime}\)-methoxyflavan-4-one (**123**), and 6-geranyl-3,3\({}^{\prime}\),5,5\({}^{\prime}\),7-pentahydroxy-4\({}^{\prime}\)-methoxyflavan-4-one (**124**) that inhibit AChE with IC\({}_{50}\) values of 31.90, 48.50, and 15.60 uM, respectively [100]. Ampelopsin, the (2\(R\),3\(R\))-3\({}^{\prime}\),4\({}^{\prime}\),5,5\({}^{\prime}\),7-pentahydroxflavoranol (**88**), was isolated from _Piper bavinum_, and presented an IC\({}_{50}\) of 59.47 uM [93]. These results show, once again, how important is the presence of two hydroxy groups on the ring B for the hydrogen bonding of the 3-hydroxyflavanone to the enzyme, as well as the presence of the geranyl group at C6 [100]. Figures 11 and 12 show the chemical structure of flavonoids **107-124** that inhibit AChE with IC\({}_{50}\) values ranging from 7.23 to 316.30 uM, except for compounds **107**, **109**, **120**, and **121** that were inactive.
## 3 Results and Discussion
### Distribution of Volatile Compounds in the Four Wine Grape Varieties  
#### 3.3.1 Free Volatile Compounds of the Four Wine Grape Varieties  
Free volatile compounds of the four wine grape varieties were analyzed using PCA to investigate the effect of varieties on aroma profile. As shown in Figure 3a1, the first (44.22%) and second (23.14%) components explained 67.36% of the total variance. The over-ripen samples of different varieties (D35 and D42) were well separated from the early samples by PC1. The main contributors to PC1 were aldehydes (hexanal, (E)-2-octenal, 2,4-heptalenal, 2,4-hexadienal, etc.) and acetophenone (Figure 3a2). These compounds and the over-ripen samples have opposite trend vectors in PC1, indicating that the contents of these compounds were negatively correlated with the ripeness of grapes.  
Figure 2: Clustering heat map of bound volatile compounds during grape ripening. ((**a**), Cabernet Sauvignon; (**b**), Cabernet Gernischet; (**c**), Cabernet Franc; (**d**), Merlot).  
In Figure 3a1, different varieties were also well separated by PC1 and PC2. The samples of Cabernet Sauvignon were distributed in the first and second quadrants. According to the loading plot (Figure 3a2), more types of free volatile compounds contributed to the aroma of Cabernet Sauvignon than the other three varieties. For Cabernet Sauvignon, samples picked at D0-D7 were mainly located in the first quadrant, contributed by hexanal, acetophenone, (E)-2-Ocenol, etc. D14-D28 distributed at the intersection of the first quadrant and the second quadrant were contributed by benzyl alcohol, 1-Hexenol, (Z)-3-Hexenyl acetate and other compounds. D35-D42 were contributed by (E)-3-hexenol, acetophenone, benzyl alcohol and other compounds and distributed in the second quadrant. Cabernet Gernischet samples collected at various dates were mainly located in the fourth quadrant, with contributors of octanal, 3-hexenal and decanal, etc. The sample distribution of Cabernet Franc was close to that of Cabernet Gernischet at the center of the coordinate axis. The Merlot samples were distributed in the third quadrant in Figure 3a1, far apart  
Figure 3: PCA scores plot for samples based on free (**a1**) and glycosidically bound (**b1**) volatile compounds of different varieties; PCA loadings plot of free (**a2**) and glycosidically bound (**b2**) volatile compounds of different varieties.  
from the three other varieties. These samples were characterized by the compounds of butyl acetate, ethyl caproate, ethyl caprylate, methyl heptanone and methyl isobutyl ketone.
# A Line Segment Detector for Space Target Images Robust to Complex Illumination
## 2 Methods
### Noise Removal Method Combined with Adaptive Bilateral Filtering  
Although the camera used in space may adopt a series of measures to suppress stray light and noise, for example, the spectral characteristics of the CCD sensor can be reasonably selected to make it work in the band where the sunlight irradiance is relatively weak and the sensitivity of the CCD sensor is relatively strong. A narrow-band filter is added in front  
Figure 1: The line segment fracture phenomenon is caused by the reduced number of anchors. (**a**) The original image. (**b**) Theoretical output image. (**c**) Anchor map after AG3 line processing. (**d**) Actual output image.  
Figure 2: The workflow of ST_LSD.  
of the lens to filter out some stray light outside the target. However, the impact of complex illumination and spatial noise on image quality still cannot be avoided.  
When the original image contains noise, the first step of line segment detection is to filter the image to remove the noise. Line segment detection algorithms generally use linear filtering methods, such as the most commonly used Gaussian filtering. However, linear filtering methods can suppress some edges while removing noise, resulting in edge blurring, which is not conducive to the extraction of subsequent anchors. To address the problem, we adopt a nonlinear filtering method, where a fast adaptive bilateral filter [43] is used instead of the traditional Gaussian filter.  
Let \(I:\mathbb{Z}^{2}\rightarrow\mathbb{R}\) represent the input image. The output image \(O:\mathbb{Z}^{2}\rightarrow\mathbb{R}\) after adaptive bilateral filtering is given by:  
\[O(x)=k(x)^{-1}\sum_{y\in\Omega}\eta(y)\phi_{x}(I(x-y)-\theta(x))I(x-y) \tag{1}\]  
the normalized parameter \(k(x)\) is expressed as:  
\[k(x)=\sum_{y\in\Omega}\eta(y)\phi_{x}(I(x-y)-\theta(x)) \tag{2}\]  
where the \(\theta(x)\) is the intensity of the image pixel of interest \(I(x)\), which is set to be \(\theta(x)=I(x)+f(x)\). \(f(x)\) is the offset image pixel. \(\Omega\) is a window centered at the origin, and the window in Equations (1) and (2) is usually set to be \(\Omega=\left[-3\rho,3\rho\right]^{2}\). \(\phi_{x}:\mathbb{R}\rightarrow\mathbb{R}\) is the local Gaussian range kernel, which is expressed as:  
\[\phi_{x}(i)=\exp\left(-\frac{i^{2}}{2\sigma(x)^{2}}\right) \tag{3}\]  
where \(\sigma(x)\) is the standard deviation of the Gaussian function. Importantly, the spatial kernel \(\eta:\mathbb{Z}^{2}\rightarrow\mathbb{R}\) in Equation (1) is a Gaussian kernel, which is calculated as:  
\[\eta(y)=\exp\left(-\frac{\|y\|^{2}}{2\rho^{2}}\right) \tag{4}\]  
where \(\rho\) is also the standard deviation of the Gaussian function.
# Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning
## 3 Methodology
### Model Framework  
Figure 2 illustrates the overall framework of the Enconv1d model, which primarily consists of three modules: the temporal feature extraction module, the spatial feature extraction module, and the dense layer module. Considering the spatio-temporal characteristics of single-differenced pseudorange data, we designed these modules to leverage the encoder structure and multi-head attention mechanism of the Transformer to capture long-term dependencies in the data. Simultaneously, the potential of multi-scale 1D CNNs is exploited to analyze complex spatial relationships and extract spatial information from the data. Finally, the spatial and temporal features are integrated through the dense layer to generate positioning results. The dense layer itself is composed of fully connected layers. The detailed design of the model is described in the following sections.  
Figure 2: The overall framework of the Enconv1d model. (The star represents the calculated two-dimensional positioning coordinates.)
## 3 Instrument Development
### AMULSE CO\({}_{2}\) Setup  
One lock-in multiplies the detector signal by a reference sine wave (an integer of the laser modulation frequency) in order to obtain the Y component of the signal while the other multiplies the detector signal by a corresponding cosine wave to obtain the X component of the signal. The root-sum-square of the X and Y components is the total magnitude of the selected harmonic.  
Assuming linear intensity modulation with a phase shift of \(\pi\), the 1\(f\)-normalized WMS-2\(f\) signal simplifies to the following equation [11; 12]:  
\[\frac{2f}{1f}=\frac{S(T)\times P\times x_{i}\times L}{i_{0}\times\pi}\int \limits_{-\pi}^{+\pi}\Phi\left(\
u_{peak}+a\cos\left(\theta\right)\right) \times\text{cos}2\theta d\theta \tag{1}\]  
where \(S(T)\) is the line strength at temperature \(T\) of the absorption transition, \(P\) is the partial pressure of the target species (CO\({}_{2}\) in this case), \(x_{i}\) is the target species concentration (to be fitted), \(L\) is the path length of laser beam travel through the uniform absorbing medium, \(i_{0}\) is the amplitude of the linear term of the laser intensity modulation (to be determined beforehand experimentally in the laboratory) [16], \(\
u_{peak}\) is the peak central frequency position, \(a\) is the amplitude of the frequency (wavelength) modulation, \(\theta\) corresponds to \(\omega t\) or \(2\pi t\) and \(\Phi\) is the line shape function at the absorbing frequency (in our case we used a Lorentzian profile since we are working at pressures greater than 200 mbar).  
The ratio of \(2f/1f\) signals is thus calculated for a range of temperatures at the measured pressure and at a nominal expected value of \(x_{i}\).
## 2 Equations of Motion for Flexible Multibody Systems
### Floating Frame of Reference Formulation with the Reduced Flexible Bodies  
The generalized coordinate vector \(\mathbf{q}^{i}\) can be approximated using the transformation matrix \(\overline{\mathbf{T}}_{0}^{i}\) as:  
\[\mathbf{q}^{i}\approx\overline{\mathbf{q}}^{i}=\mathbf{B}^{i}\left[\begin{matrix} \mathbf{q}_{R}^{i}\\ \overline{\mathbf{p}}_{F}^{i}\end{matrix}\right],\mathbf{B}^{i}=\left[ \begin{matrix}\mathbf{I}&\mathbf{0}\\ \mathbf{0}&\overline{\mathbf{T}}_{0}^{i}\end{matrix}\right]. \tag{20}\]  
The EOMs of a reduced FMBD system can be obtained by substituting Equation (20) into Equation (8) and premultiplying by \(\mathbf{B}^{i}{}^{T}\) as follows:  
\[\left[\begin{matrix}\mathbf{M}_{R}^{i}&\overline{\mathbf{M}}_{RF}^{i}\\ \overline{\mathbf{M}}_{FR}^{i}&\overline{\mathbf{M}}_{F}^{i}\end{matrix} \right]\left[\begin{matrix}\mathbf{q}_{R}^{i}\\ \overline{\mathbf{p}}_{F}^{i}\end{matrix}\right]+\left[\begin{matrix}\mathbf{0 }&\mathbf{0}\\ \mathbf{0}&\overline{\mathbf{K}}_{F}^{i}\end{matrix}\right]\left[\begin{matrix} \mathbf{q}_{R}^{i}\\ \overline{\mathbf{p}}_{F}^{i}\end{matrix}\right]+\left[\begin{matrix} \mathbf{C}_{\mathbf{q}_{R}^{i}}^{i}\\ \overline{\mathbf{C}}_{\mathbf{P}_{F}^{i}}^{i}\end{matrix}\right]\boldsymbol{ \lambda}^{i}=\left[\begin{matrix}\left(\mathbf{Q}_{e}^{i}\right)_{R}\\ \left(\overline{\mathbf{Q}}_{e}^{i}\right)_{F}\end{matrix}\right]+\left[ \begin{matrix}\left(\mathbf{Q}_{e}^{i}\right)_{R}\\ \left(\overline{\mathbf{Q}}_{e}^{i}\right)_{F}\end{matrix}\right], \tag{21}\]  
where the submatrices and vectors in Equation (21) are expressed as:  
\[\begin{split}\overline{\mathbf{M}}_{RF}^{i}=\overline{\mathbf{M} }_{FR}^{i}{}^{T}=\mathbf{M}_{RF}^{i}\overline{\mathbf{T}}_{0}^{i},\ \ \overline{\mathbf{C}}_{\overline{\mathbf{p}}_{F}^{i}}=\mathbf{C}_{\mathbf{q}_{F}^ {i}}\overline{\mathbf{T}}_{0}^{i},\\ \left(\overline{\mathbf{Q}}_{e}^{i}\right)_{F}=\overline{\mathbf{T} }_{0}^{i}{}^{T}\left(\mathbf{Q}_{e}^{i}\right)_{F},\ \left(\overline{\mathbf{Q}}_{v}^{i}\right)_{F}=\overline{\mathbf{T} }_{0}^{i}{}^{T}\left(\mathbf{Q}_{v}^{i}\right)_{F}.\end{split} \tag{22}\]  
It is clear that the accuracy of the reduced EOMs depends on approximation of the flexible bodies. Various approaches such as mode selection methods [14; 15; 16; 17; 19], modal compensation [4; 5; 10; 11; 12; 29], etc., have been studied to achieve better approximation of the reduced flexible bodies in structural dynamics, but increasing \(N_{d}^{i}\) may be the simplest and the most popular remedy to get more accurate \(\overline{\chi}_{n}^{i}\).
## 4 Data Validation
### Analysis of Results
bridge dataset is equivalent to the expansion of B bridge data, which is introduced to make it realize the effective classification of B bridge data and alleviate the problem of not being able to realize the accurate classification of six categories of data due to the unbalanced B bridge dataset. To illustrate the generality of this method, the expanded dataset is changed from A bridge to C bridge. C bridge expanded dataset is used to train the Resnet 50 model, and the training will divide the data set into training and validation sets in the ratio of 8.2. Figure 6 shows the accuracy curves obtained during the training process. From Figure 6, which can be seen that the fitting effect of the training and validation sets of the modified model is good, and the recognition accuracy of the validation set is over 90%. The training set is trained to obtain a model with high recognition accuracy, and this C bridge dataset can be used as an expanded dataset. From Table 1, the training data of A bridge is also imbalanced. Figure 7a shows the confusion matrix obtained by training using the training set of A bridge and then testing using the test set of A bridge. Figure 7b shows the confusion matrix obtained by using the expanded dataset of C bridge plus the dataset of A bridge and then testing with the same test set of A bridge. From the comparison of the two figures, we can see that the Recall of drift, local gain, noise, missing, and outlier is improved by 2.1%, 3.7%, 1.3%, 0.4%, and 34.6%, respectively. The normal data recognition accuracy fluctuates within the normal range, but there is no significant decline in adopting data migration; the overall recognition rate of the model for different categories of data can be improved, and the low recognition accuracy of certain categories caused by unbalanced data is alleviated. By using the C dataset as the expanded dataset, the same effect as the A bridge expanded dataset can be achieved, which illustrates the generality of this method. The bridge dataset can be used as an expanded training set if it satisfies the requirement of having good classification capability for its own bridge data. The effectiveness of data migration was verified by comparative validation, and two working conditions were set up separately. After data expansion of B bridge using A bridge, the normal data Recall is improved by 30%, and other categories of data also have a small improvement after data expansion of A bridge using C bridge data. The Recall of jump point data is improved by 34.6%, and other categories of data also have a small improvement. It illustrates that the adoption of migration learning effectively increases the data features of the dataset samples, improves the recognition accuracy of the  
Figure 5: Confusion matrix recognition accuracy comparison chart: (**a**) B bridge training; (**b**) B + A bridge training (1—normal, 2—drift, 3—local gain, 4—noise, 5—missing, and 6—outlier). convolutional neural network for different categories of data, and enhances its ability to detect abnormal data.