esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Objective Function
#### 2.1.2 Weight Calibration  
Having characterized the spatial objective values, the next step is to identify appropriate values for their corresponding weights. All objective values have been described to give values between zero and one, where the lower the value, the closer the objective value is to the mathematically ideal configuration.  
These four objective functions could be equally weighted; however, we know observability is of paramount importance. Therefore, \(W_{ob}\) has been overweighted with a value of 100, which leads one of the satellite pairs in the resulting constellation to be a polar or near-polar pair. The subsequent three weights \(W_{w}\), \(W_{ew}\), and \(W_{ns}\) represent the weights regarding repeat observations and the ground track directionality per spatial grid cell. We started with an equal value of 1 for these weights, which is a valid selection for celestial objects such as the moon or Mars, or when one would like to consider a static gravity field solely. Through a literature search on the importance of geophysical variables [7], as well as previous design points [11], we calibrated these weights based on this prior information to capture equally hydrology, ocean, and ice mass variations. The North-South ground track directionality helps obtain information about these areas of interest to a higher degree than the East-West ground track directionality and repeat observations, which is why it is more heavily weighted. The main drive for this is the recovery of ice mass variations present at high-latitude areas driving the solution away from mathematically optimal solutions should all areas of interest be uniformly distributed on Earth. The resulting weight values can be found in Table 1.
## 3 Performance Assessment
### Experiment  
#### 3.2.1 Experimental Setup  
In order to assess the validity of the proposed method in the real underwater acoustic channel, experiments are designed and conducted in a pool. The length, width and depth of the pool were 45 m, 5 m, and 6 m, respectively. The bottom of pool was covered with sand and the sides of pool were covered with acoustic absorbent. The transmit sensor was a hemispherical transducer which was placed at 1.5 m below the surface. The receive sensor was a spherical hydrophone which was deposed at 1.5 m away from the surface. The communication distance between the transmitter and receiver was 5.5 m.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**The Proposed**} & \multicolumn{2}{c}{**The TR Acoustic Communication**} \\  & **Method** & **Using SC Modulation** \\ \hline Communication frequency band & \(B\) & 7.5–15.5 kHz & 7.5–15.5 kHz \\ The number of subcarriers & \(M\) & 8 & 1 \\ Roll-off factor & \(\alpha\) & 0.5 & 0.5 \\ Symbol interval & \(T\) & 1.5 ms & 0.1875 ms \\ Number of total symbols & \(8\times 800\) & 6400 \\ Number of training symbol & \(8\times 100\) & 800 \\ Number of equalizers & 8 & 1 \\ Total number of taps & \(N_{t}\) & \(8\times 8\) & 64 \\ Forgetting factor of RLS algorithm & \(\lambda\) & 0.999 & 0.999 \\ Proportional tracking constant in DPLL & \(K_{1}\) & 0.1 & 0.1 \\ Integral tracking constant in DPLL & \(K_{2}\) & 0.002 & 0.002 \\ Constellation & BPSK & BPSK \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for two communication methods in the experiment.  
Figure 7: Comparison of the frequency spectrum of the information packet between two communication methods in the experiment: (**a**) the proposed method; and (**b**) the TR acoustic communication using SC modulation.  
Experiment results of the proposed method are compared with that of the TR acoustic communication using SC modulation. In the experiment, parameters for the proposed method and the TR acoustic communication using SC modulation are illustrated in Table 1. Phase tracking for each communication method was based on the second-order PLL [35]. Adaptive equalizers used in the experiment were linear decision-direction equalizers and the sample interval was equal to the symbol interval \(T\). Each information packet individually for two communication methods was flanked by a start linear frequency modulation (LFM) chirp marker used for packet synchronization. The LFM chirp was a 50 ms, 7.5-15.5 kHz chirp with a Hamming window. A guard time interval was set between the LFM chirp and the information packet to avoid interference, and the duration of which was 100 ms. Figure 7 illustrates the frequency spectrums of information packets respectively for two communication methods in the experiment.
## 5 Results and Discussion  
### Warehouse Racking Simulation Results and Discussion  
To validate the model without the addition of any payload complexities, the pallet segment was loaded with a steel weight box, as shown in Figure 7. The average initial deflection recorded for the plate under load was 9.40 mm with a standard deviation of 0.56 mm. To replicate this, the finite element model was modified to have only a single box matching the size of the weight box centered along the board.  
For this initial validation, the main response of interest after running the simulation was the deformation of the pallet segment along the Y-axis. The largest deflection was identified at the midspan of the pallet analogue board. The predicted deflection was 9.25 mm (0.364 in). Figure 9 shows the Abaqus model results of the deformed structure. The color scheme corresponds to the deformation along the negative region of the Y-axis, with sections shaded blue as the ones with larger displacements. When compared with the experimental test measurement, the result had a deviation of \(-1.62\%\), which was deemed acceptable for the validation of the test setup and confirmed the model's ability to accurately replicate the racking across the width pallet support test analogue.  
Figure 8: Picture of unit load segment with 2 layers of large boxes, fully supported **(a)** and showing deflection under load (**b**).
# Asymmetric Dual-Band Tracking Technique for Optimal Joint Processing of B1I and B1C Signals
## 3 Reception Model of B1-ADS Signal
\begin{array}{ll}s_{\mathrm{B1-ADS}}(t-\tau)=&A_{\mathrm{B1I}}s_{\mathrm{B1I }}(t-\tau)\cos(2\pi(f_{\mathrm{c}}-f_{\mathrm{s}})(t-\tau)+\theta_{c}-\theta_ {s})\\ &+A_{\mathrm{B1C}}s_{\mathrm{B1C}}(t-\tau)\cos(2\pi(f_{\mathrm{c}}+f_{\mathrm{ s}})(t-\tau)+\theta_{c}+\theta_{s})\end{array}  \tag{12}\]  
Equation (12) is the basis for the subsequent design of a reception and processing algorithm. This representation of the B1-ADS signal can further reveal the similarity between itself and the regular BOC signal. Assuming that the baseband waveforms and the amplitudes of both the B1I and B1C signals are the same in Equation (12), the B1-ADS signal can be considered as a special BOC signal with a carrier frequency of 1568.259 MHz and a subcarrier frequency of 7.161 MHz. This explains the similarity of the ACFs of the B1-ADS and BOC(7,2) signals in Figure 3.
## 1 Introduction  
With the rapid development of consumer products such as mobile phones, notebook computers, and MP4 players, audio transducers have been recognized as multifunctional assets with which to increase the convenience of multimedia products. According to the working-principle difference, there are electrostatic [1, 2], balanced armature [3, 4] piezoelectric [5, 6, 7], and moving coil speakers. A speaker enclosure is used to separate the sound from the front and back to avoid sound cancelation by speakers. A closed speaker enclosure uses a vent structure or passive radiator to improve the low-frequency response.  
Tools for analysis of speaker performance were investigated in previous works. Physically, the loudspeaker consists of electromagnetic, mechanical, and acoustic domains. The three domains can be described with second-order differential equations that are modeled using a combination of resistance, capacity, and inductance; thus, the equivalent-circuit method is used in the electromagnetic, mechanical, and acoustic domains [8]. Based on the equivalent-circuit method, [PERSON] et al. analyzed the passive radiator loudspeaker [9, 10]. In addition, [PERSON] et al. came up with the two DOF modeling methods to analyze speaker boxes with passive radiators [11]. The acoustic part is modeled as the piston model. The inner shape of speaker enclosure was not considered.  
The originality of the proposed work is the analysis method used to consider the standing wave of the speaker box. Based on the analysis tool, a screen is used to reduce the effect of the standing wave and improve the frequency response.  
The main content of the paper is as follows. First, the electromagnetic and mechanical domains are described by a lumped-parameter method that uses an equivalent circuit. The acoustic domain uses the finite element method to consider the inner shape and structure of the enclosure. Second, based on the combination of lumped-parameter method and finite element method, a screen is used and analyzed in the enclosure to reduce the peak caused by the standing wave. Finally, the speaker enclosure with a passive radiator and screen sample is manufactured. The sound pressure level (SPL) simulation result is matched with the experimental result. The dip and peak in the frequency response curve are canceled using a screen, and thus the SPL performance is improved.
# Practical Performance Assessment of Water Vapor Monitoring Using BDS PPP-B2b Service
## 3 Methodology
### GNSS Data Processing  
To achieve the monitoring of water vapor based on GNSS observations, the PPP technique is applied for processing the original code and phase observations from GPS and BDS. There are two processing modes included in this study, i.e., the post-processing mode and NRT processing mode. The post-processing mode is to conventionally process GNSS observations with final satellite ephemeris (e.g., WUM) on a daily basis (24 h processing length). It is widely employed for generating ZTD products (e.g., [36]). The detailed strategies for post-processing mode are given in Table 1. The priori values for tropospheric parameters are obtained from the Saastamoinen model [37] and the Global Pressure and Temperature 3 (GPT3) [38]. The Vienna Mapping Function 3 (VMF3) is adopted for mapping the tropospheric delay from the zenith direction to the slant signal paths [38]. All observations are processed with the interval of 30 s, and the tropospheric parameters are estimated every 3600 s.  
Figure 2: Flowchart of the experiment methodology.  
Different from the post-processing mode, the NRT processing mode only adopts 6 h data for each processing to estimate ZTD for the final epoch. The length of 6 h is selected due to the consideration for both ZTD and ambiguity convergence [39]. The general schematic diagram for the NRT processing mode is shown as Figure 3. Apart from the data length, the key difference between two modes is the satellite ephemeris. The NRT mode applies two RT ephemeris (i.e., CAS and B2b) for data processing to achieve timeliness for water vapor monitoring. The processing software is secondary developed on the basis of the Geodetic SpatioTemporal data Analysis and Research (GSTAR) software (v.1.0) developed by Beihang University [40]. It is installed on the IPC of the monitoring system and will be launched every hour for water vapor monitoring.
## 4 Discussion  
This study addresses the problem of denoising gravity and gravity gradient data by proposing an enhanced U-Net network method that incorporates multi-scale feature fusion, attention mechanisms, and physical a priori constraints. (1) The multi-scale feature fusion module introduced in this research aggregates features from different depths in the encoder, effectively capturing the multi-scale spatial features of geological anomalies. This approach accurately models both shallow edge and deep semantic information. Combined with a spatial attention mechanism, the network adaptively focuses on critical regions within the gravity field. This design achieves a 10-20% improvement in PSNR metrics compared to traditional methods, highlighting its superiority in feature learning. (2) An innovative theoretical advancement of this study is embedding the Laplace equation from geophysics into the loss function to construct physical consistency constraints (the Laplace term). This integration notably enhances the physical smoothness of denoised data and increases the conformity with theoretical models by over 30% in passive regions such as oceans. (3) The study extends the improved U-Net network to multi-component scenarios (Txx, Tyy, Tzz). Through the synergy of multi-scale features and attention mechanisms, the network preserves high-frequency details and suppresses noise amplification in the gradient data's second derivatives, thus preventing edge blurring issues associated with traditional filtering methods. This approach results in a 15-25% improvement in structural similarity (SSIM) for gradient component denoising compared to wavelet transforms.  
Regarding the limitations, the model may encounter performance constraints when dealing with extremely complex or uniquely physical geological structures. The complexity and irregularity of such special data could exceed the model's learning capabilities, leading to suboptimal denoising results. Therefore, future research will focus on enhancing the integration of physical constraints with deep learning to improve the model's ability to process high-dimensional, complex geological scenes. Meanwhile, this method can be compared and explored for fusion with the latest transformer-based architectures. Leveraging the potential of transformers in global physical consistency modeling, a hybrid architecture can be constructed, which not only retains the U-Net's ability to finely capture local geological anomalies but also utilizes transformers to strengthen the learning of correlations among large-scale geological structures.
# Explainable Machine-Learning Predictions for Peak Ground Acceleration
## 3 Establishment of the Machine Learning Model
### Feature Selection
The coefficient of determination (\(R^{2}\)), mean absolute error (MAE), and root mean squared error (RMSE) were used as the evaluation indices of the prediction effect. The expressions are given by Equations (10)-(12):  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{(\hat{y}_{i}-y_{i})}^{2}}{\sum\limits_{i=1} ^{n}{(\overline{y}_{i}-y_{i})}^{2}}, \tag{10}\]  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}{|y_{i}-\hat{y}_{i}|}}, \tag{11}\]  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{(y_{i}-\hat{y}_{i})}^{2}}}, \tag{12}\]  
where \(n\) is the sample size, \(y_{i}\) is the true value, \(\hat{y}_{i}\) is the predicted value, and \(\overline{y}\) is the average of the true values. We used SciKit-Learn [46] in machine learning to randomly divide the data into a training dataset and testing dataset, of which 80% of the seismic records were used as the training dataset for model training and feature selection, and the remaining 20% were used as the testing dataset for model performance testing. In the 10-fold cross-validation, the training dataset was randomly divided into 10 groups, in which 9 groups were used for training, the remaining group was used for verification; this process was repeated until all 10 groups had been used as a validation set. The average error of 10 verifications was calculated, and the hyper-parameters that make the average performance of the model best in 10 verifications were selected as the best hyper-parameters for the model. Figure 3 shows the performance of the verification dataset of the model in the optimal hyper-parameter when different feature combinations are used as model inputs. The number of horizontal coordinates in Figure 3 is the number of input features, representing the 10 different feature combinations in Section 3.2. It can be seen from Figure 3 that when the number of features is 4 (the feature combination is PBA, Bedrock \(V_{s}\), FP, and D800), the \(R^{2}\) value of the model is the largest, the MAE and RMSE values are the smallest, and the model performance is the best. The data distribution of the 4 input features, PBA, Bedrock \(V_{s}\), FP, and D800, are shown in Figure 4.  
Figure 2: Importance score of features. The correlation matrix of the four input features is shown in Figure 5. This matrix is symmetrical, with the same correlation shown above the main diagonal being a mirror image of the values below the main diagonal. The number in the correlation matrix and the color of the rectangle represent the strength of the correlation. The correlation between the four input features is weak. Figure 4: The data distribution for (**a**) PBA, (**b**) Bedrock \(V_{\text{s}}\), (**c**) FP, and (**d**) D800. \[V_{\text{se}}=\frac{d_{0}}{\sum\limits_{i=1}^{n}(d_{i}/v_{\text{si}})}, \tag{9}\]0 The performance of the model in the optimal hyperparameter with different features combinations as input.
## 3 Selection of Materials for Wearable Antennas
### Flexible Substrate Material for Wearable Antennas  
To improve the radiation characteristics of an antenna, substrates are used to provide the antenna with mechanical stability. A thicker dielectric substrate with a low dielectric constant is recommended to achieve ideal antenna performance, including increased efficiency, a broader bandwidth, and enhanced radiation. Rather than using stiff materials as a substrate, textile material is employed since it is more flexible and can reduce signal loss while also mitigating concerns associated with bending and stretching [50]. Most textile materials that are used as flexible substrates have a low relative permittivity, a low tangent loss, and an adequate thickness to cause a fringing effect at the margins. As a result, flexible substrates are applied to enhance the effectiveness of antenna technologies [59]. An antenna operates at two specific frequency bands used in wireless network communications and the substrate material is used to enhance the performance of the antenna [60]. Figure 5 demonstrates how flexible antennas are created by combining non-conductive materials like cotton, wool, and silk with different polymer materials and metallic conductive fibers [54]. These materials are used to create antennas, and the relative permittivity is determined by the fabric's thickness and the type of fabric itself and whether it is knitted or woven. Polymers are highly resistant, non-conductive materials that are monomers which are repeatedly occurring hydrocarbon subunits that comprise the structure [42]. Because they are thin, affordable, and very flexible, these materials also have a low loss tangent. Paper, on the other hand, is a material that is both environmentally friendly and inexpensive. It can be modified to be a fire retardant, making it appropriate for use as a substrate for flexible antennas. Table 6 presents several different types of flexible materials that are frequently employed as substrates in flexible wearable antenna, highlighting their extensive use in applications of this kind. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Conductive Materials** & **Conductivity (S/m)** & **Thickness (mm)** \\ \hline Egaln Liquid & \(2.5\times 10^{8}\) & 0.08 \\ \hline Polyleurethene–Nanoparticle Composite & \(1.1\times 10^{6}\) & 0.0065 \\ \hline Zoflex + Copper & \(1.93\times 10^{8}\) & 0.175 \\ \hline Silver Flakes Fluorine Rubber & \(8.5\times 10^{4}\) & — \\ \hline AgNW/PDMS & \(8.1\times 10^{5}\) & 0.5 \\ \hline PANI/CCo Composite & \(7.3\times 10^{3}\) & 0.075 \\ \hline Copper Coated Tafita & \(3.4\times 10^{6}\) & 0.15 \\ \hline Graphene & \(3.3\times 10^{4}\) & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Conductive materials used in wearable antennas for the ISM band [55]. Since research has been conducted, many patterns in different sizes are accessible. Other frequencies, gains, and bandwidths separate these systems. For testing purposes, an
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 5 Challenges and Open Research Topics
### Open Research Topics
#### 5.2.4 EMF-Constrained Radio Network Planning  
RIS-assisted 5G networks demand new radio network planning methods that incorporate EMF limitations. Planning must consider reflection properties, placement, the number of RIS elements, clustering, interference, and trade-offs between performance and EMF safety [111; 112].
## 4 Conclusions  
The present study investigated the ability of a sustainable pharmaceutical drug, named _Fulinucil_, which contains N-acetylcysteine (NAC, 300 mg/3 mL) to protect a bronze substrate against corrosion in two aggressive electrolytes, i.e., 3.5 wt.% NaCl solution and simulated acid rain (pH = 3.4).  
The results of the electrochemical and surface analysis led to the following conclusions:  
1. The drug acted as a good cathodic inhibitor of bronze corrosion in both tested electrolytes; the maximum values of the inhibition efficiency were 86.7% in the presence of 36 mM NAC in the saline solution and 90.2% obtained by the addition of 6 mM NAC in simulated acid rain. Generally, the inhibition performance of the drug was higher in the simulated acid rain solution than in the saline environment.
2. EIS results showed that the adsorption of NAC on bronze surface hindered the charge transfer reaction and impeded the formation of corrosion products, which became less prone to participate in redox reactions.
3. The inhibition mechanism was attributed to the spontaneous adsorption of NAC on the bronze surface via physical and chemical interactions. In both tested electrolytes, the adsorption process followed the Langmuir isotherm.
4. SEM and EDX analysis confirmed that the drug was able to effectively suppress corrosion product formation on the bronze surface exposed to both corrosive environments.  
In conclusion, the reuse of expired _Fulinucil_ could offer a green solution for mitigating bronze corrosion, which respects the principles of environmentally conscious corrosion control and the circular economy by extending the life cycle of pharmaceutical products, while simultaneously reducing pharmaceutical waste and its associated disposal costs. However, further research is needed to investigate the long-term stability and effectiveness of the drug, under different conditions, for cultural heritage and industrial applications.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15126637/s1](https://www.mdpi.com/article/10.3390/app15126637/s1)]([https://www.mdpi.com/article/10.3390/app15126637/s1](https://www.mdpi.com/article/10.3390/app15126637/s1)), Figure S1: Optical micrographs of bronze sample after chemical etching; Table S1: Corrosion-inhibiting performances of several pharmaceutical drugs on various metallic materials exposed to saline or acidic environments. Refs. [84; 85; 86] are included in Supplementary Materials file.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author.  
The author thanks [PERSON] for performing some of the experimental measurements.  
The author declares no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} EIS & Electrochemical impedance spectroscopy \\ NAC & N-acetylcysteine \\ \end{tabular}