esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of Response-Influencing Factors
### Effect of Tailings Sand Friction Angle on Dam Stability  
During the calibration of the simulation parameters, it was discovered that the equivalent internal friction angle significantly impacts the simulation results [31; 32]. Figure 12 shows the time course of the horizontal displacement at monitoring point C4 on the slope under different friction angles, and it can be seen that the permanent displacement value decreases significantly with the increasing friction angle, and the extreme value of displacement also decreases significantly, which indicates that its stability increases with the increase in the friction angle of the tailings sands. At friction angles of 35\({}^{\circ}\) and 40\({}^{\circ}\), the differences in the horizontal displacement curves at the same point are relatively minor, and there is minimal accumulation of horizontal displacement. This observation suggests that the stability of the uranium tailings impoundment improves when the friction angle of the tailings exceeds 35\({}^{\circ}\). The horizontal displacement curves at monitoring point C4 exhibit minimal variation for friction angles of 35\({}^{\circ}\) and 40\({}^{\circ}\). Upon the application of a seismic load, the permanent horizontal displacement is markedly smaller compared to that observed at a friction angle of 30\({}^{\circ}\), with the displacement values decreasing, which indicates that the tailings dam remains stable. This finding suggests that when the friction angle exceeds 35\({}^{\circ}\), the stability of the tailings dam is effectively maintained. Furthermore, increasing the friction angle beyond 35\({}^{\circ}\) has a diminishing impact on the dam stability. However, it is important to note that during earthquakes, heavy rainfall tends to reduce the friction angle of the tailings sand. At a friction angle of 20\({}^{\circ}\), both the peak and permanent displacement values increase significantly. Following the application of a seismic load, the horizontal displacement at the monitoring point reaches 0.47 m and continues to rise, signaling instability in the tailings dam.  
Figure 13 shows the change rule of the acceleration response along the vertical direction under different friction angles of the model: with the increase in the tailings sand friction angle, the acceleration amplification coefficient continues to increase. As the friction angle of the tailings increases, the acceleration amplification factor also rises, thereby enhancing the dynamic response of the tailings dam. The friction angle of the material is a crucial factor affecting the acceleration amplification factor, which tends to increase proportionally with the friction angle. This suggests that, within a certain range, increasing the friction angle of uranium tailings improves the overall stability of the dam.  
Figure 12: X-displacement time course of monitoring point C4 under different friction angles.
# A Compact Quad-Element UWB-MIMO Antenna System with Parasitic Decoupling Mechanism
## 4 Results and Discussions
### Impedance Matching  
The impedance matching response with and without decoupling structures for antenna elements is shown in Figure 6. The modification in the patch design and partial ground plane helps in achieving impedance matching in the desired frequency band. The decoupling structures on the other hand also improve the impedance matching especially in the lower and upper band. The simulated and measured return loss results are both below 10 dB in the entire UWB frequency band.  
Figure 5: Surface current distribution of UWB-MIMO antenna: (**a**) 4 GHz; (**b**) 6 GHz; (**c**) 10 GHz.  
Figure 6: Simulated and Measured reflection coefficient (**a**) antenna 1 and 2; (**b**) antenna 1 and 3.
# NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning
## 3 Materials and Methods
### Networks  
Many problems in the literature can be modeled with networks and solved with the help of various network analysis techniques and learning algorithms. In this section, we provide an overview of the different categories of datasets we used in the experiments such as protein interaction networks, social networks, and citation networks.  
Protein-protein interaction networks depict the interactions between proteins in a biological system. These networks reveal the connections and communication among proteins, shedding light on cellular processes ([PERSON] et al., 2017). By mapping experimental or predicted interactions, these networks provide insights into signaling pathways, protein complexes, and regulatory networks. They help identify key proteins and modules, aiding in disease research and drug target identification ([PERSON] et al., 2018). Protein-protein interaction networks are valuable resources for understanding cellular systems and developing therapeutic interventions.  
Social networks are online platforms that revolutionize communication, connecting individuals, communities, and businesses. Users create profiles, connect with others, and share posts, photos, and messages ([PERSON] et al., 2018). These networks form virtual communities based on shared interests and facilitate interactions. They also serve as powerful tools for businesses to engage with a wide audience and promote their offerings [20]. Social networks have transformed how we connect and communicate in the digital age.  
Citation networks capture the connections between academic papers through citations. They reveal patterns of influence, track knowledge dissemination, and aid in research evaluation [21]. By analyzing these networks, researchers gain insights into scholarly impact and the flow of ideas, fostering knowledge discovery and advancement. Citation networks are valuable tools in bibliometrics and information retrieval, facilitating the identification of influential papers and research trends [22].
# Quantum Circuit Design of Toom 3-Way Multiplication
## 4 Cost Analysis
### Gate Count  
In the implementation, Toom-Cook circuit (as well as other fast multiplication circuit such as Karatsuba) is generally performed recursively until a certain criteria is met (e.g., until a specified multiplication size is attained, until a balanced time and space complexity is achieved, etc.). For multiplication, the basic assumption is that one Toffoli gate can be used to perform one-bit number multiplication [8; 11], which will be the base scenario of our cost calculation. In addition, the cost of an in-place adder \(A_{n}\) can be bounded by at most \(2n\) Toffoli gates [8], with \(n\) is the bit size of the larger addend. In terms of division circuit, we consider the implementation of constant multiplication in [24] with the Toffoli count of \(4n(n+1)\), which is competitive to the lowest cost of performing division in quantum, as found in other implementations [20; 21; 22; 23].  
Let \(T_{n}\) denote the cost of Toom 3-way multiplication for two large \(n\)-bit numbers. Accordingly, \(A_{n}\) and \(D_{n}\) represent the cost of \(n\)-bit addition/subtraction and \(n\)-bit division, respectively. As discussed in the Section 3, realizing \(n\)-bit Toom 3-way multiplication requires a total of five \(\frac{n}{3}\) submultiplications, three adder types of different length (ten \(\frac{n}{3}\)-bit, eight \(\frac{2n}{3}\)-bit, and one \(2n\)-bit addition/subtraction), and one \(\frac{2n}{3}\)-bit division (which is carried out by the constant multiplication circuit). Then, the Toffoli cost of \(n\)-bit Toom 3-way multiplication can be calculated as in Equation (21).  
\[T_{n}=5T_{\frac{n}{3}}+10A_{\frac{n}{3}}+8A_{\frac{2n}{3}}+A_{2n}+D_{\frac{2n} {3}} \tag{21}\]  
For a recursive implementation, the cost expands to Equation (22).  
\[\begin{split} T_{n}&=5^{\log_{3}n}T_{1}+10(A_{\frac {n}{3}}+5A_{\frac{n}{3}}+...+5^{\log_{3}(n)-1}A_{1})\\ &\quad+8(A_{\frac{2n}{3}}+5A_{\frac{2n}{3}}+...+5^{\log_{3}(n)-1 }A_{2})\\ &\quad+(A_{2n}+5A_{2n}+...+5^{\log_{3}(n)-1}A_{6})\\ &\quad+(D_{\frac{2n}{3}}+5D_{\frac{2n}{3}}+...+5^{\log_{3}(n)-1 }D_{2})\end{split} \tag{22}\]  
Substituting the Toffoli cost of \(A_{n}=2n\) and \(D_{n}=4n(n+1)\), the equation can be rewritten as  
\[T_{n}=5^{\log_{3}n}+\sum_{i=0}^{\log_{3}(n)-1}\left[24n\left(\frac{5}{3}\right) ^{i}+\frac{16n^{2}}{9}\left(\frac{5}{9}\right)^{i}\right] \tag{23}\]  
Using geometric series \(\sum_{i=0}^{m-1}r^{i}=\frac{1-r^{m}}{1-r}\), we obtain the Toffoli cost of recursive implementation as presented in Equation (24).  
\[\begin{split} T_{n}&=5^{\log_{3}n}+24n\left(\frac{1- (5/3)^{\log_{3}n}}{1-5/3}\right)+\frac{16n^{2}}{9}\left(\frac{1-(5/9)^{\log_{3 }n}}{1-5/9}\right)\\ &=4n^{2}+33n^{\log_{3}5}-36n\end{split} \tag{24}\]  
This obtained result is still not considering the uncomputation, which is essential in the workings of quantum computer. Taking it into account will require roughly doubling the previously acquired cost [8; 11]. In this case, the \"clean\" cost would be as presented in Equation (25).  
\[T_{n(clean)}=8n^{2}+66n^{\log_{3}5}-72n \tag{25}\]
## 4 Conclusions  
In this study, various types of WPDs have been developed and experimentally investigated by using slow-wave structures. For this purpose, WPDs having narrow-slit-loaded transmission lines were investigated, and novel meandered-transmission-line-based WPDs were introduced for the first time. The proposed meandered-line-based two-, four-, and eight-way power dividers have been fabricated and measured successfully. The proposed meandered transmission lines have also been used to design new types of WPDs with new input/output port arrangements. In this context, two three-way WPDs have been located on the same plane for similar power-splitting processes. Next, this circuit has been developed by locating one of the power dividers to another layer. The proposed circuits can behave like a power-divider bank to be used for alternative antenna array applications. Moreover, a five-way WPD has been designed by using meandered transmission lines in two layers. These alternative WPD topologies allow halving of the input power at the next output port. These WPDs have been implemented and their measurements show an acceptable agreement with the simulated results.  
Conceptualization, M.C., A.K.G., C.K. and A.G.; methodology, M.C., A.K.G., C.K. and A.G.; software, M.C., A.K.G., C.K. and A.G.; validation, M.C., A.K.G., C.K. and A.G.; investigation, M.C., A.K.G., C.K. and A.G.; resources, M.C., A.K.G., C.K. and A.G.; writing--original draft preparation, M.C., A.K.G., C.K. and A.G.; writing--review and editing, M.C., A.K.G., C.K. and A.G.; visualization, M.C., A.K.G., C.K. and A.G.; supervision, C.K. and A.K.G.; project administration, A.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 120E101.  
Not applicable.  
Data Availability Statement: Data and simulation files will be shared upon request from the authors.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref** & **\(\mathbf{f_{o}}\) (GHz)** & **Size (\(\mathbf{\lambda_{g}^{2}}\))** & **FBW (\%)** & **Structure Topology** \\ \hline
[2] & 1.32 & - & 103.60 & Three-section TL lines \& three resistors \\ \hline
[6] & 2.1 & 0.6386 & 80 & Two-stage with coupled lines \\ \hline
[10] & 2.6 & 0.096 & 18.2 & ITPD \\ \hline
[28] & 2 & 0.0156 & 36.26 & Slit-loaded transmission lines \\ \hline Meandered line (this work) & 2.03 & 0.0156 & 56.79 & Meandered transmission lines \\ \hline
3-way WPDs (this work) & 1.95 & 0.12 & 52.5 & Microstrip transmission lines \\ \hline
3-way WPDs with common ground plane (this work) & 1.74 & 0.079 & 52.29 & Microstrip transmission lines \\ \hline
5-way WPDs with common ground plane (this work) & 1.97 & 0.073 & 51.26 & Microstrip transmission lines \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of the proposed WPD with previous works.
## 2 Materials and Methods
### Hyperspectral Data Transformations
#### 2.5.1 Wavelet-Based Feature Extraction  
To extract meaningful features from the spectral data, we employed a wavelet-based feature extraction technique using the Discrete Wavelet Transform (DWT). This method is particularly effective for analysing signals across multiple scales and capturing both time-domain and frequency-domain characteristics, making it suitable for the spectral data used in this study.  
The wavelet decomposition was performed using the Daubechies 4 (db4) wavelet, a commonly used wavelet family known for its ability to effectively capture both smooth and abrupt changes in the signal. The DWT performs a multilevel decomposition of the input signal, where the signal is split into approximation and detail coefficients at each level. The approximation coefficients capture the low-frequency components of the signal, while the detail coefficients capture the high-frequency components. For a given signal \(S(t)\), the DWT decomposes the signal into approximation and detail coefficients up to a specified level \(L\). The decomposition can be represented as:  
\[S(t)=A_{L}+D_{1}+D_{2}+\ldots+D_{L}, \tag{1}\]  
where \(A_{L}\) represents the approximation coefficients at level \(L\), containing the low-frequency components. \(D_{1},D_{2},\ldots,D_{L}\) are the detail coefficients at level 1 to \(L\), capturing the high-frequency components. In this study, we decomposed each spectral signal up to level 4, meaning that the signal was decomposed into one set of approximation coefficients (\(A_{4}\)) and four sets of detail coefficients (\(D_{1},D_{2},D_{3},D_{4}\)). This approach allows us to retain information from different scales of the data, which is essential for analysingcomplex patterns in the spectra. The process began by applying the DWT to each spectrum in the dataset, resulting in a set of wavelet coefficients for each decomposition level. These coefficients represent different frequency bands of the signal, with lower levels corresponding to higher frequency details and higher levels capturing lower frequency, smoother components. The wavelet coefficients from all decomposition levels were then concatenated to form a feature vector of length 699 for each spectrum. In this study, we used the PyWavelets module in Python (version 3.11.13) for wavelet transformation. Given the varying scales of the wavelet coefficients, we applied MinMax scaling from the sklearn module to normalize the feature values, transforming them to a range between 0 and 1. This ensures that all features are treated uniformly during model training and prevents any single feature from disproportionately influencing the learning process. This wavelet-based approach provides a comprehensive representation of the spectral data, capturing both local variations and global trends. The resulting features are used as input for the classification model, enhancing its ability to differentiate between classes based on the spectral characteristics of the data.
# Flexible UHF RFID Tag for Blood Tubes Monitoring
## 3 Design of the Tag Antenna
### Optimization
In the iterative process, first the real part of the input impedance \(Za\) of the tag antenna is adjusted with the gap \(g\), but this also modifies the imaginary part. Then, the imaginary part is readjusted by controlling the value of \(W1\). Several iterations are performed following this strategy until the input impedance \(Za\) of the tag antenna is adjusted to the IC chip (NXP UCODE G2 XL) impedance (\(Z_{c}=16-j156\)\(\Omega\)), in both real and imaginary parts, in order to minimize the magnitude of the generalized reflection coefficient given in Equation (1). The values of the optimized parameters obtained by means of the previous iterative procedure and the electromagnetic simulator (CST Microwave Studio) are presented in Table 1. Due to the limited accuracy prototyping technique available for fabrication-in-lab of tags, the optimization procedure was stopped when a generalized reflection coefficient value less than \(-10\) dB was reached at the center frequency \(f_{c}=867\) MHz of the UHF band. In general, the impedance variation shown in Figures 3 and 4 can be modeled with an equivalent circuit composed of a single resonator coupled on one side to the chip and on the other to free space, thus producing radiation. With a fixed unloaded quality factor of the resonator, the bandwidth will be controlled with the amount of coupling, and the center frequency with the resonant frequency of the resonator. However, it will be difficult to derive specific formulas relating the equivalent circuit parameters to the actual geometrical variables of the physical design. Finally, a sensitivity analysis of the tag antennas regarding to its main geometrical parameters \(W1\) (Figure 3) and \(g\) (Figure 4) has been performed to ensure the robustness of this design. In Figure 3, the resonant frequency of the tag varies by 100 MHz when the gap \(g\) varies 6 mm. This gives a sensitivity of the resonant frequency with the gap of 16.6 MHz/mm. On the contrary the sensitivity of the resonant frequency with the length \(W1\) is higher, as it can be observed in Figure 4. In this case, the resonant frequency varies by 350 MHz when the length varies 8 mm, leading to a sensitivity of 43.7 MHz/mm.
# Detection of Rock Mass Defects Using Seismic Tomography
## 2 Methodology
### Ray Tracing  
Once the arrival times were computed at all points, it was relatively easy to compute the ray paths a posteriori. In the isotropic case, ray tracing is straightforward because the ray angle at every point is perpendicular to the wavefront. In the anisotropic case, the rays are no longer perpendicular to the wavefront since the phase angle is not the same as that of the group angle. Therefore, at every point, it was necessary to compute the group angle (from the phase angle, which was given by the gradient of the arrival time field) to obtain the ray orientation [21].  
Figure 1: Arrival times and ray tracings calculated for a heterogeneous medium using the fast-marching method on a 2D plane. (**Left**) Synthetic active ultrasonic survey source (the sensor is located in the lower-left corner). (**Right**) Synthetic acoustic emission source (the sensor is located at the center of the plane). The dashed lines indicate the ray paths from the source to the sensors. The different colors lines indicate the arrival times at each point. The red star indicate synthetic active ultrasonic or synthetic acoustic emission source sensor, and the black triangles indicate signal reception sensors.
# The Effect of an Ag Nanofilm on Low-Temperature Cu/Ag-Ag/Cu Chip Bonding in Air
## 2 Materials and Methods  
A 700-nm-thick silicon oxide (SiO\({}_{2}\)) film using an oxidation furnace was grown on an 8 inch silicon wafer (p-type, 100). Then, the Cu wafer was singulated into 1.5 cm (W) \(\times\) 1.5 cm (L) chips. The 50-nm-thick Ti and 1-\(\upmu\)m-thick Cu thin films were sequentially deposited on diced Si wafers by DC sputtering at a power of 2500 W under a pressure of 3.7 \(\times\) 10\({}^{-3}\) torr. Thereafter, a porous Ag nanofilm with an average thickness of about 15 nm was deposited over the Cu film using an e-beam evaporator; the deposition rate of Ag was about 1 nm/s. After dissolving about 70% of the Ag in 3-5 nm granules in the e-beam evaporator, Ag nanofilm deposition was undertaken at a working pressure below 2.7 \(\times\) 10\({}^{-6}\) torr. A low working pressure is required for preventing contaminations and minimizing scattering due to Ag colliding with molecules in the vapor phase. The solid-state diffusion behavior of the Ag nanofilm on Cu was evaluated by heating each sample on a hot plate in air and by analyzing depth profiles using high performance X-ray photoelectron spectroscopy (HPXPS; K-Alpha, Thermo Fisher Scientific, Waltham, MA, USA). HPXPS was pe rformed at KBSI (Korea Basic Science Institute) and the measurement resolution was less than 0.5 eV FWHM (full width at half maximum).  
For the Cu bonding using the Ag nanofilm, the top and bottom chips were bonded using a heating press (D3p-05j, Daeheung Science) at 150 \({}^{\circ}\)C and 180 \({}^{\circ}\)C under 30 MPa pressure for 30 min as shown in Table 1. The sample preparation process is schematically illustrated in Figure 1. The quality of the bonding interface was analyzed by shear measurement (DAGE 4800, DAKO). For the shear measurement, the bonded chips were singulated into 2 \(\times\) 2 mm size. Photographic images of the shear test are presented in Figure 2. In addition, the bonding interface was evaluated structurally by field-emission scanning electron microscopy (FESEM; Apreo S HiVac, FEI), energy dispersive spectroscopy (EDS; Xflash 6-100, BRUKER), and transmission electron microscopy (TEM; TM 80-300, Titan).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Temperature (\({}^{\circ}\)C)** & **Pressure (MPa)** & **Time (min)** & **Annealing** \\ \hline
150 & 30 & 30 & No \\
180 & 30 & 30 & No \\ \hline \end{tabular}
\end{table}
Table 1: Chip bonding conditions.
## 3 Results and Discussion  
### Reference Values  
Reference compositional average, standard deviation, and ranges obtained for all quality parameters are summarized in Table 1, based on the tomato types. The values were similar to those reported previously in the literature [30]. The wide range in the compositional levels was due to the great diversity of tomato types, cultivar genotypes, maturity index, harvest from different seasons, and growing under different environmental and soil conditions. The composition data showed that grape and cherry tomatoes had higher SSC, sugar, and organic acids levels, similar to reported by [PERSON] et al. [30]. These tomatoes are small and have less water content, making them more concentrated, mainly in relation to sugars [31].  
Glucose and fructose are the main sugar components in tomatoes with similar concentrations; however, there is a particular advantage with the higher fructose content varieties in terms of fruit quality and consumer preference since fructose provides more sweetness than its isomer glucose [32]. The balance between sugars, acids and aroma volatile compounds are responsible for the final flavour [31; 33]. The perception of fruit acidity is due mainly to citric acid level, which is one of the mainly organic acids in tomato. Besides taste, organic acids can be nutritionally important compounds, such as ascorbic acid (or vitamin C). The levels of ascorbic acid found in this work were wide, ranging from 3.8 to 77.9 mg/100 g slightly above than found in similar works [30; 34]. The average ascorbic acid content in all 4 tomatoes types was 29.3 mg 100 g\({}^{-1}\); these results are similar to [PERSON] et al. [30].