esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Raman Spectroscopy  
Raman spectroscopy is a versatile and non-destructive technique used to probe the phase and vibrational properties of materials. CuO belongs to C62n symmetry and has twelve optical phonon modes (i.e 4A\({}_{\text{u}}\) + 5B\({}_{\text{u}}\) +A\({}_{\text{g}}\) + 2B\({}_{\text{g}}\)) in the Raman spectrum; Of these only three (A\({}_{\text{g}}\) + 2B\({}_{\text{g}}\)) are Raman active modes [20,54]. Figure 9a-d shows the Raman spectra of green synthesized CuO nanocoatings recorded over 200-700 cm\({}^{-1}\) at room temperature using 532 nm radiation. The Raman spectrum exhibited the characteristic peaks of CuO at 305.5, 344.3, and 642.7 cm\({}^{-1}\). The first peak at 305.5 cm\({}^{-1}\) is related to the A\({}_{\text{g}}\) mode, and the two peaks at 344,3, and 642.7 cm\({}^{-1}\) are attributed to the B\({}_{\text{g}}\) mode [54,55]. As shown in Figure 9a-d, at lower RS of the spin coater, the peak intensity is more intense and sharper which confirms that the thickness, particle size, and crystallinity are improved [56], complementing the obtained XRD results. [PERSON] et al. [56] reported that the Raman spectra peaks become stronger as well as sharper and shift to higher waves as the grain size increases. Moreover, the Raman spectrum shows a weak peak at 596.5 cm\({}^{-1}\), which indicates the presence of the Cu\({}_{\text{2}}\)O phase [56]. From the Raman analysis, both CuO and Cu\({}_{\text{2}}\)O phases coexist in the coatings and also, the XPS spectra indicats the presence of the Cu\({}^{+}\) chemical state. However, the XRD spectra do not reveal the presence of the Cu\({}_{\text{2}}\)O phase, and this confirms that Raman spectroscopy and XPS are more surface-sensitive than XRD, and provides more surface information [54].  
Figure 8: RBS spectra of CuO (**a**) at 700 rpm, (**b**) 800 rpm, (**c**) 900 rpm, and (**d**) 1000 rpm.
## 3 QSAR Today
### Selection of Chemical Compounds: Identify Relevant Compounds for Analysis  
The first step in QSAR model development is gathering a set of chemical compounds (molecules). These compounds can be sourced from earlier in-house efforts, synthesized as needed, or obtained from chemical vendors specializing in early-phase drug discovery. Several databases are widely used in QSAR modeling. PubChem [69], ChemSpider [70], and DrugBank [71] provide extensive data on small molecules. Interaction databases like ChEMBL [72], NCI60 [73], STITCH [74], and BioAssay [75] offer valuable insights into biological and chemical entity interactions, bioactivities, and ADMET properties, supporting compound selection and modeling efforts. The use of database sources or literature references for selecting active chemical compounds should be approached with caution, as discussed in the next section. Relying on diverse sources of biological activity data or improperly preprocessed information--such as data that require recalibration between different sources to align with a common reference compound--can result in QSAR models that are unreliable, statistically unsound, and lack meaningful insights.  
Regardless of the source, this is where Statistical Molecular Design (SMD) [38; 76] becomes essential for selecting chemical compounds that sufficiently cover a large volume of the chemical space (see Section 2.1).  
The compounds in the QSAR chemical library may be structurally diverse (lacking a common chemical scaffold) or structurally similar (sharing a common chemical scaffold). To minimize collinearity between variables, the molecules should be selected to maximize their independent behavior and orthogonality [77]. This approach enables the selected compounds to map significant descriptor space with a minimum number of compounds. Additionally, the selected compounds should be synthetically feasible.
## 2 Active Wearable Body Area Networks and Antennas  
This paper presents active antennas (AAs) for medical, sports, and IoT applications. Active antennas are devices combining antennas with active components. The radiating element is designed to provide the optimal load to the active elements. The integration of the antenna and the active components drastically reduce the system volume, system weight, and complexity of the matching network. In the last decade, active antennas are employed in wireless and in medical communication systems [25; 26; 27; 28; 29; 30; 31]. The major applications of active antennas are electronically scanning arrays and phased arrays. Indeed, arrays of active antennas are well suited for mobile communication systems requiring dynamic satellite tracking. The most common approach toward achieving fast-beam scanning is through the integration of monolithic microwave integrated circuit (MMIC) phase shifters, low noise amplifiers, and solid state power amplifiers with the radiating elements. In some cases, hybrid electromechanical arrays, combining mechanical steering with electrical steering shaping, are used. This architecture is often used to reduce the number of active control elements by limiting the electrical scanning in only one plane. This is often the case for mobile terminals, where azimuth scanning is performed by mechanical rotation, and elevation agility is realized by a linear phased array. Developments in RF technologies, such as MIC, MMIC, LTCC, MEMS, and other fabrication processes allowed automated low-cost production process of phased arrays with a high integration level. Phased arrays are much faster for beam switching than mechanically scanned antennas. Early phased array antennas were passive antennas. The front end of the antenna was composed of array elements with phase shifters. With the great progress of GaAs MMIC technology in the last twenty years, solid state device dimensions have been minimized to the size of the array elements, enabling distributed phased array architectures. High-power amplifiers and low noise amplifiers (LNAs) could be placed close to the front end, and connected to each radiating element. This phased array architecture allows significant power efficiency improvement and better receive sensitivity. The only loss before the first LNA is the antenna. The amplifiers may be packaged in transmit (receive) modules with power dividers (combiners), phase shifters, and attenuators. Development of compact wideband active antennas may improve, significantly, the performance of wearable systems. The design and development of compact printed active loop, dipole, and slot antennas is presented in this paper.
## 2 GFB-OFDM Transceiver
### Transmitter
The second-level IFFT process can be written in matrix form as  
\[\mathbf{y}_{(N_{\text{cp}}+M)\times N_{2}}=\frac{1}{N_{2}}\mathbf{Y}_{(N_{\text{cp}}+M )\times N_{2}}\mathbf{B}_{N_{2}\times N_{2}} \tag{5}\]  
where \(\mathbf{B}\) is the inverse Fourier transform matrix. The output of the second-level IFFT \(\mathbf{y}\) is repeated \(L\) times column-wise, which is given as  
\[\mathbf{y}_{(N_{\text{cp}}+M)\times N_{2}}\xrightarrow{\text{Repeat $L$ times}}\mathbf{y}_{(N_{\text{cp}}+M)\times LN_{2}} \tag{6}\]  
Then, the output is filtered by the polyphase filter \(\mathbf{\mathrm{g}}\), which is written as  
\[\mathbf{z}_{(N_{\text{cp}}+M)\times LN_{2}}=\mathbf{y}_{(N_{\text{cp}}+M)\times LN_{ 2}}\odot\left[\mathbf{\mathrm{g}},\mathbf{\mathrm{g}},\dots,\mathbf{\mathrm{g}}\right]_{(N _{\text{cp}}+M)\times LN_{2}} \tag{7}\]  
where \"\(\odot\)\" is the Hadamard product operation. The column vector \(\mathbf{\mathrm{g}}\) is the discrete form of the polyphase filtering function with the length of \(LN_{2}\). Since each column of \(\mathbf{y}\) is filtered by the same \(\mathbf{\mathrm{g}}\), the filtering matrix is obtained by repeating \(\mathbf{\mathrm{g}}\) in \(N_{\text{cp}}+M\) times row-wise. Then, the \(N_{\text{cp}}+M\) subsymbol is summed over and the \(m\)th subsymbol is placed with the lag of the \(N(m-1)\) samples. The sum can be elaborated by Figure 2. Finally,the sum of the time-domain samples can be transmitted. Thus, the overall number of samples is \(K+N(2L+N_{\text{cp}}-1)\). In contrast, if the frequency-domain data symbols at the \(K\) subcarriers are transmitted in practice using the traditional CP-OFDM waveform, it generally requires at least \(N_{\text{ift}}\)-point IFFT, where \(N_{\text{ift}}=2K\). The aforementioned procedure demonstrates that GFB-OFDM can accomplish the IFFT of \(N_{\text{ift}}\) points by utilizing \(N\) first-level IFFTs of \(M\) points and \(N_{\text{cp}}+M\) second-level IFFTs of \(N_{2}\)-points, thereby enabling the realization of a large-point IFFT through two small-point IFFTs. Moreover, the number of subbands and the number of subcarrier in each subband can be chosen according to the required traffic on demand.
###### Abstract  
In this paper, we propose a compact four-port coplanar antenna for cognitive radio applications. The proposed antenna consists of a coplanar waveguide (CPW)-fed ultra-wideband (UWB) antenna and three inner rectangular loop antennas. The dimensions of the proposed antenna are 42 mm \(\times\) 50 mm \(\times\) 0.8 mm. The UWB antenna is used for spectrum sensing and fully covers the UWB spectrum of 3.1-10.6 GHz. The three loop antennas cover the UWB frequency band partially for communication purposes. The first loop antenna for the low frequency range operates from 2.96 GHz to 5.38 GHz. The second loop antenna is in charge of the mid band from 5.31 GHz to 8.62 GHz. The third antenna operates from 8.48 GHz to 11.02 GHz, which is the high-frequency range. A high isolation level (greater than 17.3 dB) is realized among the UWB antenna and three loop antennas without applying any additional decoupling structures. The realized gains of the UWB antenna and three loop antennas are greater than 2.7 dBi and 1.38 dBi, respectively.  
cognitive radio (CR); ultra-wideband (UWB); narrowband (NB) antenna; isolation; spectrum sensing; communication +
Footnote †: journal: Applied Sciences
# NGLSFusion: Non-Use GPU Lightweight Indoor Semantic SLAM
## 1 Introduction
Therefore, the algorithm given in this paper consists of a total of three modules: a VO module based on RGBD, a lightweight semantic segmentation module with real-time loops, and a 3D reconstruction module that fuses semantic information to generate maps. In conclusion, the algorithm in this paper is modular, and the modules can be freely combined to meet the requirements according to the work required when using the robot. Considering the portability integrity on robotic platforms, NGLSFusion supports both online builds using the Robot Operating System (ROS) [20] and test validation using offline datasets. The main contributions made by the algorithms in this paper are summarized as follows:  
1. A VO module with improved ORB-SLAM2 for fast extraction of image feature points and excellent robustness. The module firstly initializes the system with ORB features for the first frame of the front input, secondly tracks the feature points on subsequent frames using optical flow method, eliminates mismatching using forward and reverse optical flow and creates new key frames, then extracts the feature points on key frames using direct method and calculates their descriptors. Finally, the camera pose is optimized by using the PnP-Ransac algorithm for keyframes and feature points, which can speed up the extraction of feature points and optimize the camera pose. 2. A GPU-independent optimized inference lightweight model for constructing semantic point clouds. A file containing a specific network topology and a binary file containing weights and deviations are obtained by optimizing the lightweight LinkNet model, and the model is repredicted using an inference engine that reads both files and implements parallel operations using SIMD to meet real-time performance on a CPU-only device. Two-dimensional image segmentation is performed on the objects in the indoor environment acquired by the camera, and the segmented semantic colors and their confidence levels are compared using the optimal value comparison method to generate a three-dimensional point cloud with semantic information by combining the depth information corresponding to each pixel in the image. 3. A lightweight semantic SLAM system for fusing semantics based on optimized models in indoor environments. The system can run in real time on devices without GPU and build maps using globally consistent pose and semantic information to ensure robot localization accuracy and map construction, and the entire system has complete debugging and visualization tools. The other sections of this paper are structured as follows: In Section 2, the related work is presented. In Section 3, the methods used for the three modules are described. In Section 4, the proposed modules are tested experimentally and the results are analyzed rationally. In Section 5, the experimental conclusions and future perspectives are given.
# Computational Modeling of Passive and Active Cooling Methods to Improve PV Panels Efficiency
## 3 Results and Discussion
### Hybrid Method  
To analyze the effect that the two methods applied simultaneously on the solar panel can produce, a perforated panel with a layer of water on it (hybrid panel) was simulated. For the performance analysis, the fluid flow, heat transfer, and radiation physics modules were used. Figures 11 and 12, and Table 9 show the results obtained for the temperature  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multirow{2}{*}{**Amb. T. (\({}^{\circ}\)C)**} & **Act. Panel Wat.** & **Act. Panel Wat.** & **Act. Panel Wat.** & **Diff. Std.—Act. Panel** \\  & & **Amb. T. (\({}^{\circ}\)C)** & **10\({}^{\circ}\)C** & **5\({}^{\circ}\)C** & **Wat. Amb. T. (\({}^{\circ}\)C)** \\ \hline
6:00 & 26.12 & 27.90 & 26.04 & 10.62 & 6.15 & 1.86  
Table 9 shows the different values of the temperature reached by the standard panel (Std. Panel) and the hybrid panel at different times of the day, considering the variation in ambient temperature (Hyb. Panel Wat. Amb. T.). The final column shows the temperature difference between the standard panel and the hybrid panel with water at ambient temperature (Diff. Std.--Hyb. Panel Wat. Amb. T.).  
As can be corroborated from the data recorded in Table 9, the combined passive and active cooling methods (hybrid panel) produce a drop in temperature compared to the standard panel. It is highlighted that this decrease is on average 2.2 \({}^{\circ}\)C less than the decrease caused by the active method (Table 8).  
As in the previous cases, in this system, the data obtained by the mathematical model of the panel power were obtained (See Table 10).
## 1 Introduction
Therefore, this study models cracks as open discontinuities without contact to ensure conservative and computationally efficient estimation of ultimate compressive strength. Stiffened panels generally exhibit superior ultimate strength compared to unstiffened plates due to the additional support provided by stiffening elements. Various stiffening techniques, including longitudinal and transverse stiffeners, corrugated panels, and sandwich structures, have been explored to enhance load-carrying capacity and resistance to crack propagation. The optimization of the design and arrangement of stiffening elements is a major focus in the ongoing endeavor of improving the ultimate strength of stiffened panels. The development of numerical and analytical methods is crucial for advancing the understanding of the ultimate strength of plates and stiffened panels with cracks under uniaxial compressive loads. Numerical models, such as finite element (FE) and analytical models, have been used to predict the structural performance of cracked panels and identify the key factors influencing their residual ultimate strength. These methods have also contributed to the development of accurate, efficient design guidelines and standards. Recently, [PERSON] et al. and [PERSON] et al. [29; 30] assessed the ultimate strength of cracked structures using artificial neural networks (ANNs). While substantial research has been conducted on the residual ultimate strength of cracked hull structures, most studies focus on the effects of cracks under uniaxially applied compression. However, the combined effects of crack size, orientation, and biaxial loading conditions remain insufficiently explored. The aims of this study are as follows:  
* To identify key factors affecting the ultimate compressive strength of cracked plates and stiffened panels under uniaxial and biaxial loading conditions;
* To perform detailed nonlinear finite element analyses that capture the effects of various crack sizes, orientations, types, and loading directions;
* To assess the strengths and limitations of existing predictive models and identify areas for improvement;
* To provide recommendations for enhancing the accuracy and reliability of ultimate strength prediction in cracked marine structures.
## 2 Underwater Wireless Sensor Network
### Acoustic Link and Its Constraints
#### 2.1.1 High Propagation Delay  
Underwater, the delay of acoustic signal is about 0.67 s/km, and the speed of acoustic signal is about 1500 m/s. Compared to traditional WSNs, these indicators causes high propagation delay due to the speed of radio frequency in the space of about \(3\times 10^{8}\) m/s [17; 18; 19; 20; 21].
## 2 Heavy-Hau Vehicle-Track Coupled Dynamics Model
### Description of Operating Conditions of Track Structure Stiffness Analysis  
#### 2.6.1 Selection of the Stiffness Range of the Track Structure  
The LVT structure adopts the double-layer vibration damping form of the under-rail rubber pad and under-block rubber boots, which can well attenuate the wheel-rail impact and effectively reduce the dynamic damage to the track and basic auxiliary structure. Reasonable track stiffness settings can improve the wheel-rail contact state and increase the safe service life of the track structure. Although the pad with greater static stiffness is beneficial to maintain track geometry, the increase in the stiffness of the pad will also increase the vibration of the track structure, increase the pressure at the sleeper node, and shorten the service life of the pad components. The stiffness of the LVT structure in the lateral direction of the track is mainly provided by the under-rail pad and the elastic rubber boots around the support block to slow down the lateral dynamic impact of the train and maintain the lateral position of the track; the vertical stiffness is mainly determined by the stiffness of the under-rail fastener and the stiffness of the under-block rubber boots and pad.  
Although rubber material has a good function of alleviating the impact of the wheel and rail, its fatigue durability and reliability under the reciprocating load of the large axle remain untested. The rubber pad has long been subjected to heavy-haul trains with large axle loads and large volumes of reciprocating effects, and coefficients such as environment, climate, and line conditions have led to fatigue and aging, which greatly shortens its safe service life. Existing investigations of the Daqin heavy-haul railway show that the stiffness value of the under-rail pads can reach 229.1 kN/mm after 5 years of operation and service [25]. Considering that the stiffness characteristics of rubber materials will change  
Figure 5: Schematic diagram of wheel-rail contact model.  
greatly, it is necessary to study the influence of the changes in the stiffness characteristics under the LVT rail and under block of the heavy haul railway on the dynamic performance of the heavy haul train-LVT structure system. Aiming at the double-layer stiffness of the LVT structure, the single-coefficient variable method is adopted to study its influence on the dynamic performance of the rail system. When a certain stiffness is a non-analytical coefficient, its value is selected as the middle value of the value range. The value range and increment of each influencing coefficient are shown in Table 2.
## 3 Results and Discussions
### Hardened Properties
The first reason for this was the relatively fast loss of consistency observed for those pastes during the molding process (i.e., 1.50M-6% and 2.22M-6%), which was in line with the high initial heat release (orange curves in Figure 7). This may have compromised the casting and directly affected the strength. The second reason was an excess amount of silicate and OH\({}^{-}\) in the mixture, which inhibited the activation reaction by limiting the diffusion of ions [70]. Thus, the reaction of Ca\({}^{2+}\) with Si\({}^{4+}\) was negatively affected, along with the strength development. Figure 8 presents the average results for the apparent density and porosity of pastes at 28 days. The standard deviation is indicated by the error bars. The green triangles represent the apparent density. Regarding the porosity, the pastes made with 4% Na\({}_{2}\)O are represented with hatched bars, while the 6% Na\({}_{2}\)O pastes are represented with solid bars. The apparent density values lay between 1.38 and 1.61 g/cm\({}^{3}\), and the porosity mean values were between 33.92 and 36.62%. Unlike the compressive strength, it is possible to observe that the activation parameters' change did not significantly affect either the porosity or the apparent density. Regarding the formulations made with 4% Na\({}_{2}\)O, a rise in the silica modulus (higher silicate content) increased the apparent density and porosity. This result may have been due to the fact that sodium silicate has a higher density than sodium hydroxide, yet its high viscosity negatively affected the workability of the pastes [71] and consequently increased the incorporated air in the paste. In contrast, pastes activated with 6% Na\({}_{2}\)O presented a more coherent behavior: the increase in silica modulus increased the porosity (same as the 4% pastes) and slightly decreased the apparent density. Overall, the paste porosity was highly related to the water-to-binder ratio (w/b), which played an important role in the consistency of the mixes [72]. It is important to state that the highest mean porosities were found for the 2.22M-4%, 1.50M-6% and 2.22M-6% pastes. They also had higher w/b values of 0.38, 0.40 and 0.40, respectively. [PERSON] et al. [73] found that the use of SS as a binder increased the total porosity of GGBPS-based alkali-activated mortars. In contrast, [PERSON] and [PERSON] [40] found that an increase in the SS percentage of a PFA-based alkali-activated paste increases the apparent density. To the author's knowledge, no previous comprehensive study presented conclusive results on the influence of the activation parameters on the apparent density and/or porosity of alkali-activated SS paste. Figure 7: Influence of the activator parameters on the relation between the initial heat release and compressive strength (blue: 4% of Na\({}_{2}\)O; orange: 6% of Na\({}_{2}\)O; squares: initial heat release; triangles: compressive strength).
# Terahertz Raman Measurements Using a Spatial Heterodyne Raman Spectrometer
## 4 Results and Discussion
### THz Raman Spectra of Nucleobases
#### 4.5.2 Spectra of Liquid Mixtures  
Aqueous solutions were also measured. In Figure 10c,d, the spectral lines of the mixtures contain almost all the characteristic peaks of the components, except that a small number of characteristic peaks are masked owing to the mutual cancellation between the characteristic peaks of the components. This provides an experimental basis and technical support for developing THz Raman technology in such fields as water quality, agriculture, and medicine.  
Figure 10: (**a**) THz Raman spectra of BA powder and 2-hydroxy BA at a laser power of 128 mW with an integration time of 50 s. (**b**) THz Raman spectra of cytosine powder and uracil powder at a laser power of 128 mW with an integration time of 50 s. (**c**) THz Raman spectra of 0.1 mol/L 2-hydroxy BA aqueous solution and 0.1 mol/L theophylline aqueous solution (ratio 1:5) at a laser power of 128 mW with an integration time of 100 s. (**d**) THz Raman spectra of 0.1 mol/L theophylline aqueous solution and 0.1 mol/L uracil aqueous solution (ratio 2:1) at a laser power of 128 mW with an integration time of 100 s.