esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiparameter Analysis of the Ultrasonic Transducer Transfer Function Using a Genetic Algorithm
## 5 Conclusions  
This article demonstrates that the application of a genetic algorithm allows the materials for the matching layer to be selected in such a way that enables an appropriate shaping of the transfer function of the piezoelectric transducer. Depending on the parameters of the proposed fitness function, the calculations may include the following factors: transmission bandwidth, mean value, and the unevenness of the characteristics in the analyzed band. The analysis was performed for a transducer operated in three different media: air, water and a biological medium (skull bone). The investigations focused on a transducer with either two or three matching layers. The experiments were performed on a database comprising 96 physically available materials with various acoustic properties. The materials in this database have a non-uniform distribution of impedances, with a relatively small representation of materials with low and high impedances. The potential for designing a transducer that has the required transfer function should be significantly improved after the database of materials is expanded.  
The main advantage of the proposed method, which employs a genetic algorithm, is the optimal selection of materials for matching layers from among the physically existing materials. This approach is fundamentally different from the existing methods of the matching layer selection. It assumes the calculation of the acoustic impedance of the matching layer based on a particular criterion and then the selection or development of a material with properties that meet the above criteria. However, it should be noted that the development of a material with specific acoustic properties is difficult for technological reasons. By using different matching functions, the genetic algorithm allows transducers with desired parameters to be designed to work in different media.  
Conceptualization, T.G. and D.B.; methodology, T.G. and D.B.; software, D.B.; validation, T.G. and D.B.; formal analysis, T.G.; investigation, D.B.; resources, T.G.; data curation, D.B.; writing--original draft preparation, T.G. and D.B.; writing--review and editing, D.B.; visualization, D.B.; supervision, T.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is available upon request to the authors.  
The authors declare no conflict of interest.  
Figure 7: Analysis of the transfer function for the biological medium (skull bone): (**a**) 2 matching layers, (**b**) 3 matching layers.
# YOLO-PWSL-Enhanced Robotic Fish: An Integrated Object Detection System for Underwater Monitoring
## 3 Experimental Results and Analysis
### Ablation Experiment and Comparative Experiments
#### 3.2.2 Comparative Analysis  
All models were trained on the same custom dataset for a fair comparison. The specific experimental comparisons were as follows:  
1. We conducted a comparison experiment between LGFB and common attention modules, such as SE (Squeeze-and-Excitation Networks) and CBAM (Convolutional Block Attention Module), and the experimental data are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Image Size & 640 \(\times\) 640 \\ Batch size & 4 \\ Learning rate & 0.01 \\ Momentum factor & 0.937 \\ Weight decay coefficient & 0.0005 \\ Iterations & 120 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameter settings.  
Figure 10: Tag information.  
The comparison showed that LGFB displayed better target detection performance compared to the other two modules due to its ability to adaptively fuse local details and global context information.
2. We conducted comparative experiments on Conv, DWConv, and PConv, and the results of the experiments are shown in Table 3.  
The comparison showed that PConv was able to deal with the problem of missing data efficiently because it focuses on the effective pixels instead of the entire convolution window, which significantly improves the performance and reduces the amount of computation compared to the other two convolutions.
3. In order to better validate and demonstrate the effectiveness of this improved model, we compared YOLO-PWSL with three target detection models to extend our comparison, including SSD, Faster R-CNN, and Yolov5s, and the results of the tests are shown in Table 4.  
The YOLO-PWSL model was compared with SSD, Faster R-CNN, and YOLOv5s. The experimental results demonstrated that the improved YOLO-PWSL model achieved higher [EMAIL_ADDRESS] with lower FLOPs and fewer parameters, showing higher effectiveness in detecting fish within complex backgrounds.  
However, its parameter count and computational cost remain relatively high, indicating the need for further lightweight optimization to enhance efficiency.
## 3 Methodology
### Umap  
Uniform Manifold Approximation and Projection (UMAP) is a nonlinear dimensionality reduction technique designed to preserve both local and global data structures. It reduces high-dimensional data to low-dimensional space by modeling its manifold structure through a weighted nearest neighbor graph and then optimizing its layout to reflect the intrinsic geometry of the data [51].  
Figure 3: Schematic diagram of the Principal Component Analysis (PCA) algorithm.  
UMAP operates in two main steps. First, it constructs a weighted graph in the high-dimensional space based on the local relationships between data points. Then, it maps this structure into a low-dimensional space using a cross-entropy loss function to maintain the topological similarity. Compared with t-SNE, UMAP tends to preserve more of the global structure, scales better with larger datasets, and offers faster computation (Figure 5).  
The similarity (edge weight) between the two high-dimensional data points \(x_{i}\) and \(x_{j}\) is computed as  
\[\omega\big{(}x_{i},x_{j}\big{)}=exp\left(\frac{-max\big{(}0,d\big{(}x_{i},x_{ j}\big{)}-\rho_{i}\big{)}}{\sigma_{i}}\right) \tag{4}\]  
where \(d\big{(}x_{i},x_{j}\big{)}\) is the distance between points, \(\rho_{i}\) adjusts for local connectivity, and \(\sigma_{i}\) is a normalization factor derived from the distribution of neighbors around \(x_{i}\).  
In lithology recognition, UMAP enables effective visualization and clustering of complex geological logging data, helping to identify distinct lithological patterns and improving the interpretability of classification models.
## 3 Results
### HPLC Analysis  
The experimental data of the concentration of peeled and shell chestnut seeds before and after irradiation by 1, 5, and 10 kGy gamma rays are represented in Table 3. The results showed the presence of rutin and caffe acid in significant amounts in the peeled seeds. Hesperidin, chlorogenic acid, and quercetin in moderate amounts were also found. Catechin, p-coumaric acid, and ferulic acid were determined only in small or trace amounts. According to the literature data, the phenolic fraction of the seed kernel extracts of horse chestnut was constituted mainly of O-glycosated quercetin, kaempfer, and isorhammeetin derivatives, but caffe acid and catechin derivatives were also present. The four aescin saponins were also detected [1]. In the current study it was established, after gamma irradiation, that the concentration of some of the detected substances changed significantly depending on the applied radiation dose. An increase in catechin concentration was observed after irradiation, most significantly at 10 kGy, opposite caffe acid decreases. Quercetin and ferulic acid concentration decreased, while chlorogenic acid concentration was enhanced after 10 kGy irradiation. A significant increase of p-coumaric acid was observed after gamma treatment by 1 kGy. It is interesting to note that morin and luteolin were not detected in non-irradiated samples. However, after irradiation by 1 and 5 kGy, morin was detected, while after irradiation by 10 kGy, luteolin was detected. Different concentrations of individual polyn phenolic substances were observed in the shell seeds compared to the peeled seeds (Table 3). Significant amounts of rutin and catechin were detected in shell chestnut seeds, many times higher than those in PS. Chlorogenic acid, luteolin, and quercetin were detected in moderate and small amounts in SS. The absence of hydroxycinamic acids (caffeic acid, p-coumaric acid, and ferulic acid), hesperidin, and morin were observed in the non-irradiated SS samples, while after irradiation, depending on the dose of high-energy radiation, these substances were detected in some of the samples. Ferulic acid, p-coumaric acid, hesperidin, and morin were detected in the samples irradiated by 5 kGy. Moreover, caffeic acid, p-coumaric acid, and morin were detected in SS after 10 kGy irradiation. In the literature it was reported that horse chestnut seed shell is rich in polyphenonic components, including dimers, trimers, tetramers, oligomers, and monomeric flavonoids, such as catechins and epicatechin derivatives, as these substances determine the strong radical-scavenging activity detected for reactive oxygen species (ROS) induced by UVB irradiation [9]. In Japanese horse chestnut seed coats, flavonoids, condensed tannins named proanthocyanidins and their glycosides were identified [8]. [PERSON] et al.
# A Compact Monopole Wideband Antenna Based on DGS
## 4 Fabrication, Measurements, and Comparison
### The Impedance Plot Analysis of the Antenna  
The impedance plot illustrates the frequency-dependent behavior of the antenna's input impedance, Figure 16. The impedance is represented as a complex quantity:  
\[Z_{in}(f)=R(f)+jx(f) \tag{9}\]  
where  
\(R(f)\) is the resistive component;  
\(jx(f)\) is the reactive component;  
\(f\) is the frequency in GHz.  
The ideal input impedance for most RF systems is 50 \(\Omega\) (purely resistive). A good impedance match ensures maximum power transfer and minimal reflection. The impedance plot confirms the effectiveness of the patch antenna design in achieving wideband  
Figure 16: Measured 2D radiation pattern at different frequencies.  
impedance matching. The low S\({}_{11}\) value, near-ideal VSWR, and broad frequency coverage are a testament to the design enhancements provided by the L-slot and semi-circular slot on the ground plane. This design approach successfully meets the requirements for high-performance broadband antenna applications (Figure 17). The wide bandwidth is attributed to the introduction of the L-slot and circular slot, which create multiple resonant modes and improve current distribution. The impedance plot indicates an efficient radiation across a broad frequency range and a stable impedance matching, which will consequently minimize return losses.
## 4 Analysis of Simulation Findings
### Simulation Analysis of Internal Parameter Variations in Motors  
To further explore the influence of internal parameter changes on the control effectiveness using the Fuzzy-ADRC strategy, this section compares and analyzes the speed curves. Specifically, we vary two specific internal parameters of the motor, namely, the magnetic flux coefficient and the stator inductance, for evaluation. Figure 11 illustrates the effect on motor speed as the magnetic flux coefficient or stator inductance value changes.  
The legend in Figure 11 represents the parameters of the corresponding motor. The measurements indicate that when the magnetic flux coefficient is set at 0.3277, 0.3477, and 0.3677, the system exhibits dynamic response times of 0.051 s, 0.019 s, and 0.017 s, respectively. Similarly, for stator inductance coefficient values of 0.002, 0.022, and 0.042, the dynamic response times are 0.031 s, 0.017 s, and 0.020 s. These measurements suggest that within a specific range of changes, increasing the magnetic flux coefficient or stator inductance can enhance the stability of the system, resulting in smoother operation and indirectly accelerating the dynamic response. Conversely, reducing these parameters may lead to a slower dynamic response of the system.
# Fingerprinting Indoor Positioning Based on Improved Sequential Deep Learning
## 2 Preliminaries
### Conventional WKNN Algorithm  
A widely used matching algorithm in fingerprint-based positioning is the WKNN algorithm [30]. WKNN selects \(k\) RPs with the smallest Euclidean distances based on RSSI measurements, and the user's estimated location is determined by calculating the weighted average of these \(k\) RPs:  
\[\left[\hat{\ell}_{o},\hat{\ell}_{a},\hat{\ell}_{f},\hat{\ell}_{b}\right]^{\top} =\sum_{k}^{i=1}w_{i}\Big{[}\ell_{oi},\ell_{ai},\ell_{fi},\ell_{bi} \Big{]}^{\top} \tag{3}\]  
where \(\left[\hat{\ell}_{o},\hat{\ell}_{a},\hat{\ell}_{f},\hat{\ell}_{b}\right]^{\top}\) is the estimated user location, and \(w_{i}\) is the weight assigned to the position of the \(i\)th RP, determined based on the RSSI Euclidean distance [31].  
The Enhanced Weighted K-Nearest Neighbor (EWKNN) algorithm [32] enhances the traditional WKNN by adaptively determining the value of \(k\) and incorporating an RSSI Euclidean distance threshold. This threshold ensures that only reliable RPs, with sufficiently small distances, are used in the localization process, thereby enhancing the precision and reliability of the positioning system. Additionally, a Bayesian localization algorithm was introduced in [33], which leverages Bayesian classification theory to provide a probabilistic approach to localization, maximizing the information obtained from the received RSSI vector.
## 3 Materials and Methods
### Proposed ATM System Design and Implementation
#### 3.3.4 Vehicle Communication with VANET  
The sensors monitor the vehicle's position in the current traffic situation by registering the motor vehicle and its communications equipment. Through the use of IoT sensor systems, data are transmitted and distributed among vehicles in such a way that helps prevent traffic and ensure safe travel [56]. The platform design is created to notify ahead of the vehicular situation and unsafe driving scenarios and handle the unpredicted injuries and deaths and scenarios with statistics to be acknowledged ahead to vehicles on the road for secure traveling. It is also necessary to communicate the messages to the drivers by extracting the incredibly massive amount of past reliable statistics based on current traffic conditions [57]. The Roadside Unit (RSU) plays a vital role in communication in VANET. Algorithm 2 represents the working Vehicle communication process in proposed ATM.  
```
Step-1 installed the RSU unit set the roadside at a specific distance Step-2 Vehicle connection setup with RSU 2.1 Neighboring vehicle receives a setup connection request from RSU 2.2 Vehicle sends the required data, i.e., location, velocity, start time to RSU  Step-3 Data storage: RSU stores all the received data in a data-based Step-4 RSU Interval: if RSU received more than one request from multiple vehicles, then apply the wait and synchronization method for data storage per the time interval. Step-5 call (Image processing in ITM) method is described in the previous section. Step-6 Vehicle synchronization: if Synchronization values are high (because of higher speed vehicle), send the alert data (priority) Step-7 Eliminate vehicle: remove the low-velocity vehicle and set the lower priority Step-8 RSU communication: RSUs communicate with each other and share alert messages to handle congestion
```  
**Algorithm 2**Vehicle communication process in proposed ATM
## 2 Integrated Navigation System Model  
This section first defines the coordinate systems in the integrated navigation system. The integrated navigation model uses the pseudo-range and pseudo-range rate as the basic observational quantities, adopts the classic integration pattern, and combines the vertical gyroscope algorithm to correct the errors of the gyroscope and accelerometer, allowing the carrier to maintain acceptable navigation accuracy even when the GNSS signal is weak or denied.
## 3 Results and Discussion
### Characterizations of MS/CID/ZrO\({}_{2}\) Composites
The O 1s and N 1s peaks of the MS/CID/ZrO\({}_{2}\) composites before and after the U(VI) adsorption were analyzed, and the results are shown in Figure 4c,e and Figure 4d,f, respectively. The high-resolution N 1s spectrum of the sample before the uranium adsorption was deconvoluted into two peaks at 399.97 and 400.35 eV, corresponding to C-NH\({}_{2}\) and C=N-OH [37], respectively. After the U(VI) adsorption, the N 1s spectrum shifted to higher binding energies (400.04 and 402.00 eV). This shift can be attributed to the formation of coordination bonds between the lone pair electrons of nitrogen atoms and uranium, which reduced the electron density around the N atoms and increased their binding energy [38; 39]. Similarly, as shown in Figure 4d, the high-resolution O 1s spectrum of the sample before the uranium adsorption was deconvoluted into two peaks at 531.30 and 532.72 eV, corresponding to -COOH and -OH groups in MS/CID/ZrO\({}_{2}\), respectively. After the adsorption, the positions of these peaks shifted to 531.51 and 532.87 eV, respectively. This shift, similar to the case for N atoms, indicates that O atoms also participated in the coordination with uranium. From the inset of Figure 4a, it can be seen that after adsorption, the Zr 3d peak of MS/CID/ZrO\({}_{2}\) shifted to the direction of higher binding energy, which may be due to the participation of F\({}^{-}\)- and Zr(IV)-bonded hydroxyl groups in the adsorption reaction, resulting in the formation of new zirconium species (Zr oxyfluoride or ZrF\({}_{4}\)) [40; 41].
# A Review of Multifunctional Antenna Designs for Internet of Things
## 2 Materials and Methods
### Smart Home Appliances
#### 2.2.1 Antenna Specifications
Achieving these requirements using only passive elements presents significant challenges, leading to the proposal of electrically and mechanically reconfigurable antennas as a solution [15]. Reconfigurability in antennas allows for adjustments in the electrical size and/or shape of the antenna, thereby controlling its operational characteristics, such as frequency, polarization, and radiation pattern. The polarization of an antenna is fundamentally linked to its physical configuration, as the direction of surface currents on its radiating elements is influenced by the shape of the antenna radiator and the manner in which it is excited. Recall that the electric field of the generated wave retains a direction (polarization) parallel (or following) to that of its source electric current density. An antenna can shift its polarization sensing between different types, such as from linear (LP) to circular (CP), or from RHCP  
Figure 5: Radiation pattern of (**a**) an omnidirectional and (**b**) an isotropic antenna. to LHCP, and so on [58]. A simple example is depicted in Figure 6, where a cross-dipole antenna can be operated in three polarization configurations. Various methods exist for tuning antenna characteristics, including mechanical adjustments (e.g., motor-based steerable systems) [59], pumping liquid metals into special hollow cavities to alter substrate permittivity and permeability [60], and the use of photoconductive switches activated by laser light on semiconductor materials [61]. However, electrical tunability remains particularly attractive for sub-6 GHz integrated antennas. The ease with which electronic components can be integrated into antenna systems allows for efficient and effective modification of antenna properties. This form of reconfigurability at RF often employs electronic switches, such as PIN diodes and varactor diodes, to alter an antenna's operational characteristics [12].
# Flexural Wave Propagation and Defect States of Periodic Slab Track Structure in High-Speed Railway
###### Abstract  
The unit slab track structure in high-speed railways exhibits multiple periodic characteristics, which result in bandgaps of elastic wave propagation within the track structure. Moreover, local defects inevitably occur in the ballastless track structure, disrupting its periodicity and leading to the generation of defect states. An analytical model for infinite periodic slab track structure was established using the Floquet transform and supercell method, accounting for local defects, to clarify the propagation of flexural waves in slab tracks. The formation mechanism of elastic wave bandgaps in periodic slab tracks can be explained by Bragg scattering and local resonance. In the low-frequency below 200 Hz, the local resonances of the slab interact with the flexural waves in the rail, forming an approximately broad coupling bandgap. The bandgaps expand significantly with the increasing fastening stiffness. Besides, when the stiffness of the isolating layer beneath the slab is within the range of 0.9 to 1.0 \(\times\) 10\({}^{9}\) N/m\({}^{3}\), a broad coupled bandgap is generated in the frequency range of 180-230 Hz. Local damage caused by contact loss between the composite slab and baseplate leads to defect states, and the frequencies of the defect states correspond to unique wave modes, demonstrating the localization of elastic waves near the defect location. The formation mechanism of defect states can be elucidated by the local resonance of the structure at the defect. The frequency of the first-order defect state is significantly affected by the defect size, the second-order defect state exhibits unidirectional propagation characteristics, and the third-order defect state shows localized vibration characteristics, which can provide a reference for defect identification.  
unit slab track; flexural wave propagation; Floquet transform; bandgap; local defect; defect states
# Ferroelectric Materials: A Novel Pathway for Efficient Solar Water Splitting
## 4 Ferroelectric Materials for Photoelectrochemical (PEC) Water-Splitting Devices
### General Requirements for Photoelectrode Materials
Photocorrosion is a major problem for long-term PEC cells because of water splitting, which leads to the decomposition and fluctuation of photocurrent in a short time interval, especially in harsh conditions (strongly acidic or alkaline). However, a high ionic environment (low or high pH) is essential to minimize other challenges such as ohmic losses and localized pH gradient overpotentials in the PEC systems of separate photoelectrodes [33; 34]. Therefore, ferroelectric materials with intrinsically stable properties even in harsh electrolyte are potential candidates for the generation of new PEC systems. Furthermore, the suitable alignment between the band edge potentials of the candidate materials and the relative decomposition potentials can promote stable PEC devices. Lastly, cost-effective materials along with earth-abundant elements are also required for a practical approach. Figure 5: The bandgap of various materials including ferroelectrics and the semiconductor.