esa-sceva/satcom-chunk-collection huggingface dataset:
# T-Type Vertical Wall for Decoupling and Pattern Correction of Patch Antenna
###### Abstract  
The requirements of 5G/6G promote progress in the miniaturization of the antenna array, which promotes the development of a closely spaced decoupling technique. However, the present techniques face the common problem of beam tilt if the spacing is close. Thus, a pattern-corrected, closely-spaced technique is proposed in this paper for the two patch antennas with the \(\lambda_{0}/20\) edge-to-edge distance of the H-plane. The corresponding structure, which is inserted at the center of the spacing, consists of a vertical wall with a single substrate and two symmetrical T-type metals, and a slot at the center is reserved to adequately accommodate the vertical wall. The vertical strip at the other end of the T-type metal is connected to the ground of the patch antenna, while the parallel strip is placed exactly above the patch substrate. After an exact analysis, a prototype was fabricated and measured, and the results showed that the measurements agreed well with those of the simulations, the decoupling coefficients in the 5.8 GHz band were below \(-20\) dB, and the measured radiation pattern at 5.81 GHz was corrected to the broadside from \(28^{\circ}\) and the maximum realized gain was 5.30 dB.  
5G/6G; miniaturization; patch antenna; vertical-wall decoupling; pattern correction +
Footnote †: journal: Applied Sciences
## 4 Cross-Linking of Polymer Membranes
Most often, divinylbenzene is used as cross-linker in ion-exchange membranes based on styrene copolymers. As shown in [164; 165; 132], the use of more flexible cross-linkers, for example, bis (vinylphenyl) ethane (Figure 4), allows manufacturing ion-exchange membranes with a better selectivity/conductivity ratio. In particular, at close transfer numbers, the ionic conductivity of membranes crosslinked with bis (vinylphenyl) ethane is four times higher than that of membranes cross-linked by divinylbenzene [132]. This effect is explained by the close reactivity of bis (vinylphenyl) ethane and styrene, which provides more uniform cross-linking and more optimal membrane structure. At this point, it is also worth considering IEMs based on strongly cross-linked matrices obtained by polymerization of aqueous or amide solutions of ionogenic monomers, such as functionalized acrylamides or syrenes [166; 151; 167; 168]. Well known Fujifilm ion-exchange membranes also belong to this class of materials [169; 104]. Due to the high cross-linker content, reaching 60%, a three-dimensional network is formed during the polymerization, which does not swell like ordinary membranes. Therefore, the maximum water uptake of such membranes is limited by the initial solvent volume, which determines the free volume (Figure 5). However, the hydration degree of such membranes is usually more than 18-20 water molecules per one functional group, due to the limited solubility of the monomers and cross-linkers in the reaction mixture. Of course, this to some extent limits the selectivity of such materials. Note that this approach leads to a paradoxical for other types of ion-exchange membranes effect--an increase in the functional group concentration in these membranes leads to a decrease in water uptake per functional group and an increase in selectivity [168; 170; 171]. Figure 4: Chemical structures of crosslinking agents: divinylbenzene and bis (vinylphenyl) ethane.
## 4 Performance Evaluation  
### Evaluation Metric  
In order to prove the effectiveness of our proposed approach, we calculate the normalized root mean square error (NRMSE) of the estimated inter-node distances as  
\[e=\frac{1}{N}\sum_{i=1}^{N}\frac{1}{\mathcal{C}(\kappa_{i})}\frac{\sum_{k\in \kappa_{i}}\sqrt{(\hat{d}_{ik}-d_{ik})^{2}}}{\bar{R}}, \tag{20}\]  
where \(\hat{d}_{ik}\) is the estimated inter-node distance, \(d_{ik}\) is its real value, and \(\mathcal{C}(\kappa_{i})\) is the cardinal of \(\kappa_{i}\), the set of node-\(i\)'s neighbors. The normalization is performed with respect to the the average communication range \(\bar{R}\). Other normalization metrics go far beyond the scope of this work. Also, we assess the localization NRMSE as  
\[e_{position}=\frac{1}{N_{u}}\sum_{i=1}^{N_{u}}\frac{\sqrt{(x_{i}-\hat{x}_{i})^{2 }+(y_{i}-\hat{y}_{i})^{2}}}{\bar{R}}, \tag{21}\]  
where \(N_{u}\) is the number of localized sensors.
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 2 Preliminaries
### Deep Q-Network Algorithm  
The Deep Q-Network (DQN) is an optimization algorithm that integrates deep learning with reinforcement learning, designed to address decision-making problems in high-dimensional state spaces. Its core concept lies in leveraging deep neural networks to  
Figure 2: The basic flowchart of the A* algorithm.  
approximate the Q-function, enabling the agent to learn the state-action value function Q(s,a) and thereby derive optimal decision policies within its environment.  
In conventional Q-learning, the storage and iterative update of a Q-value table are feasible only when the state space is relatively small. However, this approach becomes impractical in high-dimensional or continuous state spaces. DQN addresses this limitation by employing a deep neural network as a function approximator, replacing the Q-table with a parameterized network. This innovation significantly enhances the applicability of reinforcement learning in complex task domains.  
The primary procedure of DQN is as follows: at each time step \(t\), the agent receives the current state \(s_{t}\) from the environment and selects an action based on an \(\varepsilon\)-greedy policy--that is, with probability \(\varepsilon\), it selects a random action, and with probability \(1-\varepsilon\), it selects the action with the highest estimated Q-value. Upon executing the action, the environment returns the next state \(s_{t+1}\) and an immediate reward \(r_{t}\).  
A critical component of DQN is the experience replay mechanism. The agent stores each interaction tuple \((s_{t},a_{t},r_{t},s_{t+1})\) in a replay buffer and samples mini-batches randomly during training. This breaks the temporal correlations inherent in sequential data, thereby enhancing the stability and efficiency of the learning process, as illustrated in Figure 3.  
To further stabilize training, DQN introduces the target network mechanism. Specifically, it maintains two networks: a primary network \(Q(s,a;\theta)\), which is updated continuously, and a target network \(Q^{\prime}(s,a;\theta^{-})\), which periodically synchronizes its parameters with the primary network. The target Q-value is updated using the following formulation:  
\[y_{t}=r_{t}+\gamma\max_{a^{\prime}}Q^{\prime}(s_{t+1},a^{\prime};\theta^{-}) \tag{1}\]  
Here, \(\gamma\) denotes the discount factor, representing the importance of future rewards. The network parameters \(\theta\) are optimized by minimizing the mean squared error loss function:  
\[L(\theta)=\mathbb{E}_{(s,a,r,s^{\prime})\sim D}\Big{[}(y_{t}-Q(s,a;\theta))^{ 2}\Big{]} \tag{2}\]  
Through iterative updates, DQN refines the network parameters \(\theta\) to approximate the optimal Q-function, thereby improving and reinforcing the policy.  
By integrating mechanisms such as deep neural networks, experience replay, and target networks, DQN demonstrates robust learning capabilities in high-dimensional environments and has been successfully applied to complex tasks such as robotic path planning.  
Figure 3: Framework diagram of the DQN algorithm.
## 2 Model Establishment and Analysis
### Finite Element Analysis of the Original Fifth Wheel
#### 2.4.2 Modal Analysis  
Due to the operating conditions of the tractor trailer in high-speed and heavy-duty environments, various components of the vehicle will produce different mechanical vibrations depending on the road conditions. When a large mechanical resonance occurs, it will reduce the mechanical properties of the entire tractor trailer, thereby shortening its service life. Therefore, resonance caused by external factors needs to be avoided.  
The center holes of the four inner and outer baffles of the fifth wheel are constrained, limiting all six degrees of freedom. The first to tenth-order intrinsic frequency and vibration mode values of the original fifth wheel are calculated, and the results are shown in Table 7. Among them, the distribution diagrams of the first to third modes are shown in Figure 4.  
It can be seen in Figure 4 above that in the first-order vibration mode, the maximum deformation is located in the two ears at the lower end of the fifth wheel. The second-order vibration mode is relatively small, and most of the deformation is located at the edges of the carriages. The third-order deformation gradually increases, and the deformation of the left carriages bulging to the right is relatively large.  
Semi-trailers are subject to various external excitations in real-world operating conditions, primarily originating from the road surface, tires, driveshafts, and the engine during vehicle motion. According to relevant research [33], the excitation frequency of the road surface is lower than 5 Hz; the excitation frequency of tires and driveshafts ranges between 30 Hz and 40 Hz; and the engine's excitation frequency typically falls within 30 and 100 Hz. Typically, the speed of a semi-trailer ranges from 40 to 100 km/h.  
The first-order intrinsic frequency of the original fifth wheel is 486 Hz, which is more than four times higher than the working range of external excitations. Therefore, the fifth wheel does not resonate with external excitations in the constrained state.
# Astronomical Investigation to Verify the Calendar Theory of the Nasca Lines
## 3 Astronomical Investigations in the Nasca Project
### Statistical Studies on the Astronomical Alignment of the Geoglyphs  
#### 3.3.1 Preview  
As already mentioned, different types of geoglyphs can be found in the Pampa of Nasca and Palpa. The most common are lines and areas, and in addition, there are figures, stone formations and line centers. Examples of these different types of geoglyphs are visible in Figure 12. As already shown, the lines and areas are particularly relevant for astronomical investigations. However, there are also figures that have an astronomical orientation. For a first statistically reliable investigation, only the lines were selected. It must be considered that lines with a width of more than 0.5 m were recorded as areas in the NascaGIS (see Figure 12). Another point is that there are different types of lines. The NascaGIS distinguishes between the following types: straight, curved, meandering and zigzagging lines. Some examples are visible in Figure 12.  
The first aim was a fully semi-automatic and systematic analysis with all straight lines, which are stored in the NascaGIS, to provide a statistical statement on the astronomical alignment of these lines. A total of 2308 Nasca Lines are of the type \"straight line\" and only these were initially included in the calculations. In the NascaGIS, all coordinates are stored in the PSAD 56 UTM Zone 18S system. From the coordinates of the start point A and end points B of each line, the two geodetic azimuths from A to B and from B to A were calculated. They differ by approximately 180 degrees; deflections of the vertical were neglected. The elevation angles were obtained with the help of digital terrain models.  
Due to the atmosphere and the shape of the Earth, elevation angle corrections were necessary. The refraction as well as the curvature of the Earth was required. The point of extinction of a celestial body is more difficult to estimate. This effect depends on the Earth's atmosphere and the brightness of the object. There are very different statements in the literature: [PERSON] (2017) (p. 61) takes a value of 2 degrees, but there are examples that provide completely different values. Basically, the lighter a celestial body is, the longer it is visible. In these calculations, it was assumed that the Sun was always visible down to the horizon during the Nasca period.  
Figure 12: Extract from the NascaGIS: lines, areas, stone heaps and figures.  
Another problem is that nobody knows how the Nasca people used the sky and how competent they were at observing the celestial bodies. These questions cannot be answered because there are no written records. For this reason, the lines were examined for an astronomical signification in relation to the Sun and some brighter stars.
## 2 Study Area and Data Sets
### Data Sets  
The L-band ALOS satellite data sets covering the region of interest were provided by the Japan Aerospace Exploration Agency (JAXA). The SAR interferograms were computed from PALSAR fine-beam single-polarization (FBS) data taken on three different dates (20 January 2007, 10 December 2008, and 25 January 2009), and predominantly over the winter season to minimize the adverse impact that vegetation has on the accuracy of SAR interferometry. Observation parameters for all the images were as follows: reference system for planning (RSP) number 94; path number 501; and acquired in the ascending orbit with an off-nadir angle of 34.3\({}^{\circ}\). We also used twenty-one C-band ENVISAT-ASAR images provided by the European Space Agency (ESA) and acquired in the period from 30 September 2003 to 15 June 2010. These data sets were acquired in the descending orbit with an incidence angle of 22.9\({}^{\circ}\). A Shuttle Radar Topography Mission-3 (SRTM-3) version-4 (90 m resolution) was used to eliminate the topographic phase, which was downloaded from ([[http://www.cgiar-csi.org/](http://www.cgiar-csi.org/)]([http://www.cgiar-csi.org/](http://www.cgiar-csi.org/))). Figure 1 shows the cover ranges of the ALOS and ENVISAT data.
## 4 Conclusions  
We have implemented a hybrid grating with an FBG and Ex-TFG in a special fiber with a small diameter to achieve the temperature-calibrated high-sensitivity RI sensing. The hybrid grating in the RDF promised the strong core-to-core and core-to-cladding mode coupling. It showed a high RI sensitivity with its enhanced evanescent field for interacting with the surrounding medium, and was also capable of measuring RI at different temperatures. The device demonstrated an RI sensitivity up to \(\sim\)1224 nm/RIU at the RI of 1.38 and the thermal sensitivities of 9.68 pm/\({}^{\circ}\)C and 39.65 pm/\({}^{\circ}\)C for its Bragg and cladding mode resonances, respectively. By exploiting the RI-insensitive property of Bragg resonance, the temperature and RI were be monitored simultaneously, and the measurement of RI of  
Figure 5: As the temperature increased, (**a**) original wavelength shifts of the Bragg and cladding mode resonances when the hybrid grating is in water; (**b**) the wavelength shifts of the cladding mode resonance before and after temperature calibration; (**c**) variation of the calculated RI with the temperature.  
water was calibrated at different temperatures. Therefore, the proposed hybrid grating in RDF with a compact and integrated structure provides a new approach to enhancement of the RI sensitivity and simultaneous measurement of multi-parameter for many RI-based sensing applications.  
Conceptualization, B.J. and L.Z.; methodology, K.Z.; validation, B.J. and Z.H.; formal analysis, D.F.; investigation, B.J. and Z.H.; writing--original draft preparation, B.J.; writing--review and editing, D.F. and K.Z.; supervision, L.Z and J.Z.; project administration, L.Z. and J.Z.; funding acquisition, B.J. and L.Z.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 61775182, 61505165 and 61705185), the Natural Science Basic Research Plan in Shaanxi Province of China (Grant Nos. 2019J-330 and 2018 JQ6101), and the Marie Sklodowska-Curie Individual Fellowships in the European Union's Horizon 2020 Research and Innovation Programme (Grant No. 660648).  
This research was supported by the Fundamental Research Funds for the Central Universities (No. 3102019 PB005).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Transport Study of Cr(VI) through Aliquat 336/PolyHIPE Membrane
#### 3.4.1 Influence of the Stirring Rate  
The influence of stirring rate on the removal rate of Cr(VI) was studied in order to obtain a suitable stirring operation condition. Figure 5 shows that the Cr(VI) removal rate increases with increasing stirring rate. In the range of 450-500 rpm, the removal rate reaches a maximum of about 98% after 8 h of separation, indicating that the diffusion of Cr(VI) between the bulk phase and membrane interface (Steps 1 and 5) is minimized. The results of stirring rate higher than 500 rpm show only very slight decrease of the averaged removal rate. Nevertheless, the decrease is within experimental error. Therefore, the stirring rate for feeding and stripping phase was fixed at 450 rpm throughout the followed transport study.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Graph-Based Homology Mapping Methods
#### 2.3.5 Progressive Cactus  
Progressive Cactus employs a graph-based method for whole-genome alignment, introducing a novel methodology through a progressive alignment strategy [49]. This approach systematically aligns multiple genomes by constructing and manipulating a graph structure that represents relationships between different genomic sequences.  
1. Initial Pairwise Alignment: * The process begins with aligning two closely related genomes using a pairwise alignment algorithm; * This initial alignment is used to create a baseline graph structure, where nodes represent sequences and edges represent alignment matches, identifying regions of sequence similarity and dissimilarity;
2. Additional Genome Integration: * As additional genomes are integrated, the alignment graph is expanded iteratively, one genome at a time; * Each new genome is incorporated into the existing graph by identifying sequences and aligning these to the graph's nodes, updating the graph structure to include new alignment paths; * This step ensures optimal alignment quality across the entire set of genomes, leveraging the graph structure to maintain comprehensive genomic relationships;
3. Progressive Refinement: * The existing graph undergoes iterative refinement as new genomes are added, optimizing the graph structure based on sequence homologies and structural constraints; * This refinement process adjusts the graph's edges and nodes to enhance alignment accuracy, maintaining both sequence similarity and structural integrity across the graph;  
Figure 9: SibeliaZ algorithm pseudo-code.  
4. Iterative Interaction: * Progressive Cactus iterates through the alignment process multiple times, refining the alignment graph at each step; * Each interaction improves the graph's accuracy and consistency, ensuring that the final graph represents the most accurate genomic relationships;
5. Parallelization: * To enhance computational efficiency, Progressive Cactus employs parallelization techniques; * The graph manipulation and alignment processes are distributed across multiple computing nodes or cores, enabling faster processing of the alignment graph for large genomic datasets.  
The use of a graph-based method allows Progressive Cactus to efficiently manage and process complex genomic alignments through representing sequences and their relationships in a dynamic, scalable structure. This graph-based approach facilitates the handling of large-scale genomic datasets and supports detailed analysis of genomic similarities and variations across multiple species.  
The complexity of Progressive Cactus varies depending on the size and complexity of the input genomes. It generally has a time complexity of \(O(n^{2}\lg n)\) for aligning \(n\) genomes.
# The Power System and Microgrid Protection--A Review
## 3 Protection Scheme of Microgrid
### Adaptive Protection
For instance, the protection scheme and the type of the relays and DGs are completely similar in References [30; 120], while the former implemented a real network and the latter simulated a test case. The empirical results are according to real-time conformity. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Refs.**} & **Published** & \multirow{2}{*}{**DG Presence**} & \multirow{2}{*}{**Protection Scheme**} & \multirow{2}{*}{**Real/Simulation**} & \multirow{2}{*}{**Protective Relay**} \\  & **Year** & & & & \\ \hline
[75] & 2003 & Without DG & Impedance Based & Real &
\begin{tabular}{c} Current and \\ Voltage \\ \end{tabular} \\
[29] & 2004 & DG & Adaptive & Simulation & Fuse and Recloser \\
[108] & 2006 & Inverter Based & Voltage Based & Simulation & Voltage \\
[62] & 2008 & DG & Modification & Simulation & DOC \\
[104] & 2008 & Inverter Based & Harmonic Based & Simulation & IDM \\
[44] & 2010 & SE/WT & Differential Based & Real & Differential \\
[64] & 2010 & SM Based & Adaptive & Simulation & Current Based \\
[66] & 2010 & SM Based & Adaptive & Simulation & Current Based \\
[92] & 2011 & SM Based & Impedance Based & Real & Distance \\
[106] & 2011 & SM and Inverter & Modification & Real & OC \\
[55] & 2012 & Inverter Based & Modification & Real & Current and \\
[103] & 2012 & Inverter Based & Harmonic Based & Simulation & Current Based \\
[27] & 2013 & DG & Impedance Based & Real & Distance \\
[42] & 2014 & Inverter Based & Differential Based & Simulation & Differential \\
[57] & 2014 & DG & Adaptive & Real & Fuse and Recloser \\
[101] & 2014 & Inverter Based & Harmonic based & Real & IDM \\
[86] & 2014 & Inverter Based & Adaptive & Simulation & Current \\
[43] & 2015 & DG & Adaptive & Simulation & DOC \\
[56] & 2015 & DG & Modification & Real & Fuse and Recloser \\
[74] & 2015 & SM Based and WT & Impedance Based & Real & Distance \\
[111] & 2015 & SM and & Adaptive & Simulation & OC \\  & & Inverter-Based & & & \\
[112] & 2015 & SM and Inverter & Adaptive & Simulation & DOC \\
[58] & 2016 & SM Based & Modification & Real & Fuse and Recloser \\
[89] & 2017 & Inverter Based & Impedance Based & Simulation & Distance \\
[120] & 2017 & SM Based & Adaptive & Simulation & OC \\
[88] & 2018 & Inverter Based & Impedance Based & Real & Distance \\
[105] & 2018 & Inverter Based & Harmonic Based & Real & DOC \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of different items used in microgrid protection schemes in the literature. The main contributions of the literature are given in Table 2. Important works on different protection schemes from the beginning until now are summarized in Table 2.