esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### N-glycan Analysis by Mass Spectrometry Nano-LC-Chip Q ToF  
Samples were transferred to polypropylene vials and 6 \(\upmu\)L were injected into an Agilent 6520 nano-LC-Chip quadrupole time-of-flight mass spectrometer (Q-ToF MS, Agilent Technologies, Santa Clara, CA, USA). Glycans were further purified on a 9 mm \(\times\) 75 \(\upmu\)m PGC enrichment column and then separated on a 43 mm \(\times\) 75 \(\upmu\)m PGC analytical column with binary solvents, gradient, and data collection based on [PERSON] et al. [7]. The following minor modifications were applied: scan range for MS was 450 to 2500 \(m\)/z, and for MS/MS was 100 to 3200 \(m\)/z. Spectra in both MS and MS/MS modes were collected at a rate of 0.63 spectra/s. The collision energy in the collision cell corresponded to a slope of 1.8/100 Da.  
Chromatograms from the nano-LC-Chip Q ToF were curated and compounds identified in Masshunter Qualitative Analysis software (version B.07.00 Agilent Technologies) using a previously described bovine milk \(N\)-glycan bioinformatic library adjusted for one fewer \(N\)-acetylglucosamine in the chitobiose core. The library assumed compositions including hexose (Hex), \(N\)-acetylhexosamine (HexNAc), fucose (Fuc), \(N\)-acetylneuraminic acid (NeuAc), and \(N\)-glycolyneuraminic acid (NeuGc) [43]. Additionally, structures included a tetrasaccharide core containing three hexose (e.g., mannose moieties and one HexNAc (N-acetylglucosamine) moiety. Structures were confirmed using MS/MS fragmentation patterns and isotopic distribution. A library of glycan structures specific to the sample set was assembled based on retention time, molecular formula and confirmed \(m\)/z. Relative abundances of \(N\)-glycans were obtained using Masshunter Profinder software (version B.06.00, Agilent Technologies). The batch targeted feature extraction algorithm with the following parameters was applied using the generated database: minimum abundance of 750 counts, charge state up to +3, retention time window of 2 min, and a glycan isotope model. Peak areas were verified for accurate integration and isotopic distribution.
## 4 Experiments
### Experimental Design
#### 4.2.5 Model Comparison Experiments
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Accuracy** & **Other** & **House** & **Forest** & **Water** \\ \hline
2016–2017 Subset & 92.7\% & 92.4\% & 76.8\% & 96.0\% & 96.5\% \\
2018–2019 Subset & 92.1\% & 92.1\% & 76.8\% & 96.9\% & 96.0\% \\
2020–2021 Subset & 91.2\% & 91.8\% & 76.4\% & 96.0\% & 96.0\% \\ Full Validation Dataset & 91.6\% & 93.0\% & 76.1\% & 96.0\% & 96.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification Performance on Validation Subsets (2016–2021). Figure 8: Normalized confusion matrix of segmentation results for full fine-tuning validation set and its subsets of different periods. accuracy scores below 75%. Figure 8 further supports these findings, showing the normalized confusion matrix for the full validation set and its temporal subsets. The matrix demonstrates that the cross-attention-based model consistently performs well across different periods, with minimal variation in segmentation performance. This reinforces the conclusion that cross-attention fusion not only improves accuracy but also enhances the stability and reliability of predictions across varying data distributions. The results of this model can be locally analyzed and visualized. Using standard mapping software, Figure 9 demonstrates that at the finest level, the results of the model can be locally analyzed and visualized using QGIS software; the model is validated. The results in Figure 10 show that the identified building, water, and forest domains. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy** & **Macro F1 Score** & **Cross-Entropy Loss** \\ \hline Cross-Attention Fusion & 81.5\% & 78.4\% & 0.22 \\ Concatenation Fusion & 78.2\% & 75.9\% & 0.26 \\ Sum Fusion & 74.6\% & 73.2\% & 0.30 \\ Average Fusion & 75.1\% & 74.8\% & 0.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of different fusion methods. Figure 10: The example of change identification: (**a,b**) Original images of periods 2016–2017 and 2018–2019; (**c,d**) images with a hatch layer that represents mismatch of the building class in segmentation results. Figure 9: The segmentation results on the finest level: (**a**) OSM map view of central Kaunas city; (**b**) processed data of the selected time period (2019–2020) with segmented buildings (magenta), water (blue), forest (brown), and other (white) categories.
# Styrene-Based Copolymer for Polymer Membrane Modifications
## 3 Results and Discussion  
### Study of Crystal Size and Crystallinity of Blend Membranes  
The XRD patterns of modified PVDF are shown in Figure 3. Figure 3a shows the pattern for 100% PVDF membrane, which has the largest crystallite size of 174.4 A\({}^{\circ}\). It shows three significant peaks at 20 angles of 18.59\({}^{\circ}\), 20.12\({}^{\circ}\), and 26.61\({}^{\circ}\) with intensities of 738.42, 552.76, and 242.90, respectively. These correspond to the Miller indices of (0, 2, 0), (1, 1, 0), and (0, 2, 1), respectively [21]. Two of these indices--(0, 2, 0) and (0, 2, 1)--correspond to an \(\alpha\)-phase structure while (1, 1, 0) is a \(\beta\)-phase structure [22]. A comparison of the data for the 100% PVDF membrane with styrene-acrylonitrile blend membranes shows two prominent 20 peaks at 18.59\({}^{\circ}\) and 20.12\({}^{\circ}\). The intensities of the three diffraction peaks at 18.59\({}^{\circ}\), 20.06\({}^{\circ}\), and 26.61\({}^{\circ}\) vary progressively for the 10%, 20%, and 60% styrene-acrylonitrile blends membranes, while the amorphous halo becomes more pronounced as can be seen in Figure 3. In the 10% styrene-acrylonitrile and 60% styrene-acrylonitrile blend membranes, the 18.59\({}^{\circ}\) peak intensity reduces progressively to 716 and finally to 288 from a value of 759 for 100% PVDF (Figure 3a-d). This indicates the emergence of strain in the polymer crystallites with the blending of a second polymer and is given in Table 1. The peak intensities also increase at the Miller indices (1, 1, 0) for the styrene-acrylonitrile blend membranes. It reflects the increase in \(\beta\) phase structure for these blend membranes. The peak at 26.61\({}^{\circ}\) is the most affected and almost disappeared in the 60% styrene-acrylonitrile blend membrane. Strong crystalline peaks of PVDF (Figure 3a), were replaced by weaker peaks in the XRD patterns of blend membranes as shown in Figure 3b-d. This also confirms the blending of styrene-acrylonitrile in PVDF and signifies the decrease in crystallite size and increase in residual strain in the structure of the blend membranes with an increase in styrene-acrylonitrile content [23]. The XRD pattern of styrene-acrylonitrile (Figure 3e) shows two amorphous halos at 20 angles of 13.54\({}^{\circ}\) and 19.6\({}^{\circ}\) with intensities 289 and 188, respectively. These halo sections have the Miller indices of (0) and (3, 2, 1), respectively. These XRD results are in order for phenyl acrylonitrile. Crystallinity of 100% PVDF membranes is the highest at 25.11% and decreases with an increase in styrene-acrylonitrile content. The crystallinity analyzed with the Lorentz approach for 10% and 60% styrene-acrylonitrile blend membranes reduces to 18.25% and 13.51%, respectively, and is given in Table 2. Crystallinity data achieved from both the Lorentz and Gauss approaches also indicate the increase in amorphous character of membranes with styrene-acrylonitrile content.  
Figure 3: X-ray diffraction of styrene-acrylonitrile-blended PVDF membranes.
# Quasi-Isotropic Hybrid Dielectric Resonator Antenna--Bow-Tie Patch with Harmonic Suppression
## 4 Conclusions  
In this paper, a quasi-isotropic hybrid DRA and bow-tie patch with harmonic suppression is proposed. The DRA and bow-tie patch antenna were utilized to implement the quasi-isotropic pattern. The quasi-isotropic pattern was realized by combining two omnidirectional patterns with an orthogonal relationship. For harmonic suppression, a seventh-order Chebyshev low-pass filter was designed in the feed line of the proposed antenna. In the case of the antenna without a filter, harmonic radiation occurred in a section separate from the operating band. However, the proposed antenna effectively removed harmonics generated in a section away from the operating band, suppressing the radiation due to harmonics. The measured gain isolations of the second and third harmonics of the proposed antenna are 10.10 and 18.94 dB, respectively, and the simulated gain difference considering all radiation areas (\(0^{\circ}\leq\varphi\leq 360^{\circ}\) and \(0^{\circ}\leq\theta\leq 180^{\circ}\)) is at least 7.211 dB.  
Figure 14: Comparison of the simulated and measured maximum gain of the proposed antenna.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Simulated Gain**} & \multicolumn{3}{c}{**Measured Gain**} \\ \cline{2-7}  & **3.1** & **3.13** & **3.15** & **3.1** & **3.13** & **3.15** \\ \hline Frequency (GHz) & 3.1 & 3.13 & 3.15 & 3.1 & 3.13 & 3.15 \\ Maximum gain (dBi) & 2.080 & 2.358 & 2.428 & 2.145 & 1.908 & 2.282 \\ Minimum gain (dBi) & \(-\)4.254 & \(-\)5.061 & \(-\)5.812 & \(-\)6.396 & \(-\)7.449 & \(-\)8.798 \\ Gain difference (dB) & 6.334 & 7.419 & 8.240 & 8.541 & 9.357 & 11.080 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the simulated and measured gain difference of the proposed antenna (\(0^{\circ}\leq\varphi\leq 360^{\circ}\) and \(0^{\circ}\leq\theta\leq 90^{\circ}\)).  
Therefore, the proposed antenna applies to various wireless communication fields and is expected to improve the size and performance of the RF-system.  
Conceptualization, [PERSON] and K.C.H.; methodology, T.J. and J.H.K.; software, T.J.; validation, T.J. and J.S.L.; formal analysis, [PERSON] and [PERSON]; investigation, [PERSON], [PERSON]; filter analysis [PERSON], [PERSON] and [PERSON]; resources, [PERSON]; data curation, [PERSON]; draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Defense Challenge Future Technology program of Agency for Defense Development (UD200035 RD).  
Data Availability Statement: Data sharing is not applicable.  
The authors declare no conflict of interest.
## 5 Opportunities Provided by VSN
### Reducing Complexity and Cost of Sensor Overlay Proliferations  
It is very difficult to maintain different sensor networks for individual purpose. It increases the complexity of any application. On the other hand, it is very expensive and difficult to deploy overlay network upon a particular physical sensor network. In both cases VSN can provide a viable alternative which is suitable in terms of complexity and cost of proliferations. Sensor network providers may introduce virtualization of sensor networks for a variety of reasons like organizational issues, regulatory challenges, security, scalability, and quality of experience. If an organization does not implement VSN technology within its own domain, it needs to build separate networks for different services to maintain quality-of-service requirements.
## 3 System Model  
In this work, a typical smart grid communication model that is primarily based on a 5G network system is considered. We also consider a sub-system in the network, which is composed of a cellular access network, Next Generation Node B (gNodeB), a radio bearer, and user equipment (UE), as shown in Figure 3. The smart grid user or UE as the case maybe is meant to establish a wireless link with the gNodeB to secure direct access to the evolved packet core, and in a smart grid communication setting, the UEs are considered as AMI devices. In our system model, the following assumptions have been made:  
* That the gNodeB employs a static beam-forming technique;
* That each of the UEs can be served by a lone optimal beam;
* That each of the packets is specifically targeted to a particular UE;
* That the number of UEs in the system is finite;
* That the UEs are randomly distributed around the gNodeB.
# Application of Mammalian Nudix Enzymes to Capped RNA Analysis
## 2 Diversity of 5\({}^{\prime}\) RNA Cap Structures--Natural Caps
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cap Structure** & **RNA Type** & **Cell Type** & **Ref.** \\ \hline m\({}^{7}\)GpppN\({}_{1}\) & & & \\ m\({}^{7}\)Gppp\({}^{2\prime}\)O\({}_{1}\) & & & \\ m\({}^{7}\)Gppp\({}^{6,2\prime}\)O\({}_{2}\) & mRNA & Eukaryotic & [8; 19; 20] \\ m\({}^{7}\)Gppp\({}^{2\prime}\)O\({}_{1}\),\({}^{2\prime}\)O\({}_{2}\) & & & \\ \hline  & snRNA (U1, U2, U4, U5), & & \\  & gnRNA & Eukaryotic & \\ m\({}_{3}\)[2; 2; 7]GpppN & primary miRNA (priiRNA) & HPFs (human foreskin fibroblasts) & [20] \\  & selenoprotein encoded & HEK293, OSCA-40 canine & [34] \\  & transcripts, _junD_, _dltx9_ and _tgs1_ & osteosarcoma, HEK293 FT & [22; 23; 24] \\  & mRNA & & \\ \hline CH\({}_{3}\)pppN & U6 and 7 SK snRNAs & Eukaryotic & [35; 36] \\
5\({}^{\prime}\)-gamma-methyl (tri)phosphate cap & U\({}_{2}\) and 7 SK snRNAs & Eukaryotic & [35; 36] \\ \hline  & (CH\({}_{3}\))\({}_{2}\)pN & Precursor RNA of miR-145 & MCF-7 and MDA-MB-231 breast & [21] \\ Alfa-dimethyl-phosphate cap & (pre-miR-145) & cancer cells & \\ \hline m\({}^{7}\)GpppA(G) & & & \\ m\({}_{2}\)[2; 7]GpppA(G) & pre-tRNA & \(S.\)_cerevisiae_ & [25] \\ m\({}_{3}\)[2; 2; 7]GpppA(G) & & & HeLa \\ \hline m\({}^{7}\)GpppA(G) & & & \\ mGpppC & Fraction of short RNAs & & \\ GpppG, GpppA & (\(\sim\)20-200 nt), obtained from & & \\ m\({}_{3}\)[2; 7]G, m\({}_{2}\)[2; 7]G, m\({}_{2}\)[2; 7]G, m\({}_{2}\)[2; 2]Gp & total RNA preparation & & \\ m1 Gp, m2 Gp, mCp & & & \\ \hline NAD\({}^{+}\) & mRNA & \(S.\)_cerevisiae_ & [28] \\ (5\({}^{\prime}\) nicotinamide-adenine & & Hercul snoRNA & [29] \\ dinucleotide cap) & mRNA & Human (CCRF-SB cell line), mouse & [37] \\ \hline FAD & mRNA & Human (CCRF-SB cell line), mouse & [37] \\  & (C57 BL/6 tissues), \(S.\)_cerevisiae_ & [37] \\ \hline dpCoA & Mouse liver RNA & Mouse liver RNA & [32] \\ \hline  & & HEK293T & \\ AppppA & RNA (\(>\)200 nt) & RBL-2H3 (rat basophilic leukemia cells) & [33] \\ \hline UDP-GlcNAc & mRNA & Human (CCRF-SB cell line), mouse & \\ UDP-GlcNAc & mRNA & (C57 BL/6 tissues), \(S.\)_cerevisiae_ & [37] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Diversity of naturally occurring cap structures on Eukaryotic cellular RNAs.
# New Orthogonal Transforms for Signal and Image Processing
## 5 Proposed Orthogonal Transform and Its Modifications
### Two-Dimensional (2D) Transform
For different submatrices we deal with a matrix of coefficients instead of coefficient \(C\) for the inverse transform. Figure 3 shows: a--the input image for \(N=256\) and its spectra obtained for various transform matrices; b--full exponential matrix (\(N=256,\ a=0.3\)); c--full exponential matrix (\(N=256,\ a=0.7\)); d--full non-exponential matrix (\(N=256\), basis sequence: \(1,2,3\)); e--full exponential matrix after channel grouping (\(N=8,\ a=0.3\); \(8\times 8\) channels); f--full exponential matrix after channel grouping (\(N=8,\ a=0.7\); \(8\times 8\) channels), g--sparse exponential matrix (\(N=256,\ M=4,\ a=0.7\)); h--sparse exponential matrix (\(N=256,\ M=16,\ a=0.7\)); i--sparse non-exponential matrix with the same submatrices (\(N=256,\ M=8\), basis sequence: \(1,2,3\)); j--sparse non-exponential matrix with the same submatrices (\(N=256,\ M=16\), basis sequence: \(1,\ 2,\ 3\)); k--full non-exponential matrix with arbitrary \(\beta\) parameter (\(N=256,\ \beta=0.75\) basis sequence: \(1,\ 2,\ 3\)); and l--full non-exponential matrix with arbitrary \(\beta\) parameter (\(N=256,\ \beta=1.25\) basis sequence: \(1,\ 2,\ 3\)). In all cases after performing the inverse transforms we obtain the original input image.
## 3 Hardware Development
### Sensors
#### 3.1.2 Obtaining the Position of the Magnetic Field  
Given the sine and cosine waveform outputs, the angular position of the magnetic field can be calculated using the atan2 function [25]. Nevertheless, for correct operation of the trigonometric function in a digital system, the signals acquired by the analog to digital converter (ADC) must be normalized to maximum values of \(\pm 1\).  
The minimum and maximum values of the analog signals are determined by continuously reading voltages while rotating the magnet from 0\({}^{\circ}\) to 360\({}^{\circ}\) and vice versa. Performing multiple rotations ensures that the min and max values are more accurate. Then, during normal operation, each new measured voltage is normalized to \(\pm 1\) as follows [26]:  
\[Y_{norm}=\frac{V_{measured}-V_{min}-\left(\frac{V_{max}-V_{min}}{2}\right)}{ \frac{V_{max}-V_{min}}{2}} \tag{1}\]  
where \(Y\in\{A,Z\}\). After that, the angle in degrees is calculated as:  
\[\theta=\left\{atan2(A_{norm},Z_{norm})\right\}\cdot\frac{180}{\pi} \tag{2}\]  
Figure 4: (**a**) Physical position of the Hall-effect sensors. (**b**) Analog output signals when the mechanical float was moved manually, initially considering the tank empty and gradually moving the float upwards until simulating a full tank, and vice versa.
# Path Algorithms for Contact Sequence Temporal Graphs +
## 2 Materials and Methods  
### TRG Data Structures  
A sample contact sequence temporal graph \(G=(V,E)\) is shown in Figure 1a. Each edge is labeled with \(<\!t,w\!>\) (timestamp and weight). For this graph, \(|V|=3\) and \(|E|=4\). Each contact sequence temporal graph has a corresponding _static graph_ generated by removing the \((t,w)\) labels and coalescing the edges with the same source and destination to a single edge. In the example graph of Figure 1a, the corresponding static graph will have only one edge from vertex \(A\) to vertex \(B\). _Edge activity_ is defined as the ratio \(|E|/|E_{s}|\) where \(|E_{s}|\) is the number of edges in the static graph. Our example graph has \(|E_{s}|=3\) and edge activity equal to 1.33. Some paths in the graph of Figure 1a are time-respecting (e.g., the \(ABC\) path that uses the edge \((A,B,4,1)\)), and others are not (e.g., the \(ABC\) path that uses the edge \((A,B,3,4)\)).  
Figure 1b demonstrates the OSE representation of our example graph. Each edge in Figure 1a is represented by a quadruple \((u,v,t,w)\) (source, destination, timestamp and weight). Using this representation, one needs to examine all the edges regardless of whether the objective is to find a time-respecting path from \(u\) to \(v\) or to find the one-hop neighbors of \(u\). OSE-based time-efficient, one-pass algorithms have been developed in [8] for several single-source all-destinations optimal paths.  
Figure 1: Ref. [1] (**a**) A temporal graph G, (**b**) OSE(G), (**c**) TRG_Wu(G), (**d**) TRG_Zschoche(G), and (**e**) TRG_Ours(G). For (**d,e**), as all the nodes belong to sets \(\phi(u)\) and \(V_{out}(u)\), respectively. Therefore, we have not labeled them with the set memberships as we have conducted for (**c**).  
We already mentioned in Section 1 that all the paths are time-respecting in a time-respecting graph (TRG). We first describe the TRGs of [8; 9] and then describe our proposed TRG.
# Co-Channel Interference Suppression for LTE Passive Radar Based on Spatial Feature Cognition
## 4 Simulation Analysis
The degradation compared with the case where the interference signals are from the sidelobe is not apparent.
## 4 Experimental Investigation  
### Experimental Setup  
Experiments were carried out to verify the proposed method. As shown in Figure 13, the equipment named the magnetostrictive guided wave detector (MSGW30, Zheda Jingyi Tech, Ltd., Hangzhou, China) was used in the experiments. The equipment has the ability to generate and receive guided waves with the modules including signal generation, power amplification, pre-amplification  
Figure 11: Comparisons of the results by the B-scan (**Left**), DAS (**Middle**), and CSAFT (**Right**) methods for different angular steps: (**a**) \(\theta_{s}=1^{\circ}\); (**b**) \(\theta_{s}=1.5^{\circ}\); (**c**) \(\theta_{s}=2.0^{\circ}\). The excitation signal is a tone-burst signal with three cycles.  
Figure 12: Comparisons of the results by the CSAFT method for excitation signals with different cycles: (**a**) five cycles; (**b**) seven cycles; (**c**) nine cycles. \(\theta_{w}\) is the angular bandwidth of the defect spot.  
and A/D conversion under the control of the computer. The coils in the magnetostrictive sandwich transducer (MST) were processed as a flexible printed circuit (FPC), and the material of the magnetostrictive strip is Fe-Co alloy. The circumferential scanning is shown in Figure 14. The iron plate is the same size as that in the simulation. An iron mass scatterer with the additional cross-sectional area of 100 mm\({}^{2}\) was coupled on the iron plate at the radius 0.825 m from the original point \(O_{2}\) and 12\({}^{\circ}\) from the start position of the scanning as the artificial defect using the epoxy coupling agent. The MST moved around the original point \(O_{2}\) at a radius of 1.3 m with the angular step 1.5\({}^{\circ}\) to scan the fan area with an angle of 45\({}^{\circ}\). The MST was used both as an actuator and sensor. The excitation signal used in the experiment is the same as that in the simulation, which is shown in Figure 8a. The sampling ratio is 2 MHz. Thirty-one signals were collected in the scanning. It should be mentioned that the coupling agent used for the coupling between the transducer and the plate is the shear wave coupler (UGW30, Zheda Jingyi Tech, Ltd., Hangzhou, China).  
Figure 14: Circumferential scanning in the iron plate using guided waves.  
Figure 13: The magnetostrictive guided wave inspection system for circumferential scanning in the plate.
# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
## 3 Terminologies Used in WSNs
### Homogeneous and Heterogeneous WSNs  
WSNs can be classified based on their infrastructure as homogeneous WSNs or heterogeneous WSNs. In homogeneous WSNs, all the SNs have similar hardware components such as the sensing subsystems, processing subsystem, radio subsystem, and power supply unit. On the other hand, the hardware components of two or more SNs are different in heterogeneous WSNs. A major reason for the design of heterogeneous WSNs is to equip some SNs with bigger sensing range and more battery power to attain longer transmission [46]. Although deploying homogeneous WSNs can be quite easy in comparison to heterogeneous WSNs, heterogeneous WSNs are more useful in factual deployments because they are close to practical scenarios [14]. Different energy-efficient and energy-balanced routing protocols have been proposed over the years assuming homogeneous or heterogeneous WSN structures. In some cases, the proposed energy-efficient and energy-balanced routing protocols are verified to be suitable for both homogeneous or heterogeneous WSN scenarios. We studied these energy-efficient and energy-balanced routing protocols and subsequently grouped them based on their infrastructural design as shown in Tables 2 and 4-6.