esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Metasurfaces for OAM Beam Generation
### Reflective Metasurfaces
It can be demonstrated that the analytical solutions for reflective dual-helicity decoupled wavefront control contains the \(x\)-polarized reflection phase \(\phi_{x}\), the \(y\)-polarized reflection phase \(\phi_{y}\), and the rotation angle \(\alpha\). Besides, \(\phi_{x}\) and \(\phi_{y}\) have a phase difference of \(\pi\). The unit cell consists of dual-layer cross dipole and a ground plane (Figure 11b). In order to simplify the design, the unit cells are transformed into digital coding cells. The phase coverage is discretized by \(45^{\circ}\) gradient into 64 unit cells for two CP waves and each polarization has eight states. A complex coding  
Figure 10: Digital coding OAM reflective metasurface. (**a**) One-bit passive OAM metasurface [61]. (**b**) One-bit active OAM metasurface [62]. (**c**) One-bit dual polarization OAM metasurface [63]. (**d**) Five-bit multi-polarization OAM metasurface [64]. distribution was realized according to the demand. The metasurface generating the vortex beam with OAM mode \(l=1\) in _xoz_ plane under LHCP wave and two pencil beams in the _yoz_ plane for RHCP wave at 16 GHz was fabricated to demonstrate the theory. In Reference [67], independent control of polarizations, beam numbers, OAM modes and orientations were proposed by phase compensation. The unit cell is composed of two orthogonal I-shaped dipoles able to control the phase shift of \(x\)- and \(y\)-polarized waves independently. To verify the proposed approach, a metasurface generating triple vortex beams was designed; one has the mode \(l=-1\) for the linear polarized (LP) wave and mode \(l=+1\) for the RHCP and LHCP waves (Figure 11c). A decoupled method for CP wave is proposed in Reference [68] which excites vortex beams under single CP wave. The unit cell consists of two split ring resonators (SRRs) which are twisted to each other by 45\({}^{\circ}\), as shown in Figure 11d. Both SRRs can introduce PB phase with 100% efficiency under illuminating CP wave. Moreover, they exhibit symmetric and asymmetric modes parallel and perpendicular to the symmetry axis. According to Jones matrix, the in-phase constructive interference for co-polarized CP wave and out-of-phase destructive interference for cross-polarized CP wave are realized when the phase difference between two CP waves is \(\Delta\phi=-90^{\circ}\) and the twist angle of two SRRs is 45\({}^{\circ}\). A metasurface able to generate two RHCP vortex beams at 9.5 GHz and four LHCP vortex beams at 10.5 GHz was designed.
###### Abstract  
Automatic modulation recognition (AMR) stands as a crucial core technology within the realm of signal processing and perception, playing a significant part in harsh electromagnetic environments. The time-frequency image (TFI) of communication signals can manifest modulation characteristics and serve as a foundation for signal modulation recognition and classification. However, under the influence of the electromagnetic environment, communication signals are exposed to varying degrees of interference, which poses a challenge to the recognition of modulation types. Taking into account the effects of interference and channel fading, this paper introduces a communication signal modulation recognition algorithm based on deep learning (DL) and time-frequency analysis. This approach employs short-time Fourier transform (STFT) to generate time-frequency diagrams from time-domain signals. Subsequently, it binarizes the image and feeds it as input data to the neural network. Our research presents a composite deep convolutional neural network (CNN) architecture known as the composite dense-residual neural network (CDRNN). This architecture focuses on enhancing the feature extraction and identification, aiming to achieve accurate recognition of modulation types in harsh electromagnetic environments. Finally, simulation results validate that the proposed deep learning algorithm holds remarkable advantages in boosting the accuracy of modulation type recognition with better adaptability. The algorithm shows better performance even in harsh electromagnetic environments. When the signal-to-noise ratio (SNR) is 18 dB, the recognition accuracy can reach 92.1%.  
automatic modulation recognition; time-frequency analysis; deep learning; composite dense-residual neural network 2024  
2024 164550[[https://doi.org/10.3390/rs16234550](https://doi.org/10.3390/rs16234550)]([https://doi.org/10.3390/rs16234550](https://doi.org/10.3390/rs16234550))
# Long-Range Acoustic Communication Using Differential Chirp Spread Spectrum
###### Abstract  
Here, we propose a new modulation method using chirp spread spectrum (CSS) modulation to indicate the result of long-range acoustic communication (LRAC). CSS modulation had outstanding matched filter characteristics even though the channel was complex. The performance of the matched filter depends on the time-bandwidth product. We studied the method of using the same modulation method while increasing the amount of the time-bandwidth product. When differential encoding is applied, the de-modulation is made using the difference between the current symbol and the previous symbol. If the matched filter is applied using both the current and the previous symbol, such as the use of two symbols, the amount of the time-bandwidth product can be doubled, and this method can make the output of the matched filter larger. The proposed method was verified in lake and sea experiments, in which the experimental environment was analyzed and compared with the result using the channel impulse response (CIR) of the lake and sea. The lake experiment was conducted over a distance of about 100-300 m between the transmitter and receiver and at a depth of \(\sim\)40 m. As a result of the communication, the conventional method's bit error rate (BER) was \(1.22\times 10^{-1}\), but the proposed method's BER was \(1.98\times 10^{-2}\). The sea experiment was conducted over a distance of \(\sim\)90 km and at a depth of \(\sim\)1 km, and the conventional method BER in this experiment was \(1.83\times 10^{-4}\), while the proposed method's BER was 0.  
chirp spread spectrum; differential coding; long-range acoustic communication; time-bandwidth product; lake experiment; sea experiment +
Footnote †: journal: Applied Sciences
## 2 Application Scenarios and Key Concepts/Enablers for RF Hardware in JCRS
### Scenarios
#### 2.1.1 Traffic Monitoring  
The combination of communication and sensing information extracted from different sources and further data processing can provide a real-time reliable picture of traffic condition; hence, it can improve transportation management and public safety. In this scenario, the data collected from different sensors (e.g., radars) are communicated to the central processing unit or to a cloud-based system. The system then can interactively adjust traffic control parameters, such as traffic signals or dynamic lane management settings, based on traffic congestion levels, vehicle counts, average speeds, and traffic flow patterns [11].  
With a reliable traffic monitoring system in place, incidents such as accidents or road closures can be detected in a timely manner and communicated to the relevant authorities, which will enable faster emergency response, thus, improving public safety and reducing response times. This aligns with the broader concept of smart cities. By integrating traffic data with other services, such as public transportation systems, parking management, and environmental monitoring, cities can enhance their overall efficiency and improve transportation networks. In combination with deep learning, big data can be used to implement predicative traffic control based on traffic monitoring patterns. This scenario is illustrated in Figure 1.
## 3 Methodology
### MSWT-DN Structure
#### 3.2.3 Model Parameter Analysis  
The MSWT-DN is a complex cascade structure consisting of several deep modules, such as RSENet, IMSAR, U-Former, and DRANet. Due to its depth and multi-module design, the model has a relatively large number of parameters, making parameter analysis essential. This analysis not only helps us understand the computational complexity of the model, but also uncovers potential issues. By examining the parameters, we can gain insight into the contribution of each module to the network's complexity and evaluate the computational overhead of the entire network across different tasks. The number of parameters reflects both the model's expressive power and computational cost, providing valuable insights for future model optimization and improvement. The total number of parameters in the MSWT-DN network is approximately 45 million, with the following detailed breakdown:  
IMSAR: due to the introduction of multi-scale convolutions and attention mechanisms, this module is responsible for extracting rain streak features across multiple scales. As a result, it has a high number of parameters, accounting for the largest proportion of the network's total parameters.  
U-Former module: based on the Transformer architecture, U-Former is designed to enhance long-range dependencies and global feature modeling. While this module significantly improves the model's performance, it also leads to a larger parameter count.  
RSENet and DRANet modules: these two modules have relatively fewer parameters. RSENet focuses on extracting rain streaks, while DRANet specializes in restoring background details. Their parameter counts are more compact and focused on specific tasks.  
Despite the high parameter count of the MSWT-DN model, it provides a significant advantage in feature extraction and representation, enabling the model to handle complex deraining tasks effectively. However, the large number of parameters also implies higher computational demands, which may affect its efficiency and scalability in real-world applications. Therefore, further optimization and adjustment of the model are necessary during deployment to balance performance with computational resource requirements, ensuring its efficient application in practical scenarios.
# Native Smartphone Single- and Dual-Frequency GNSS-PPP/IMU Solution in Real-World Driving Scenarios
## 2 Mathematical Models and Data Processing Strategies  
This section introduces the theoretical background behind single- and dual-frequency PPP-IC and IMU tightly-coupled strategy, as well as the York-PPP user processing engine parameter settings.
## 2 Clouds Properties from Infrared and Microwave Satellite Observations
### Microwave Observations Only
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{3}{*}{**Authors**} & \multirow{3}{*}{**Year**} & \multicolumn{3}{c}{**Satellite**} & \multirow{3}{*}{**Main Features**} & \multirow{3}{*}{**Uncertainty or Accuracy of the Method (When Declared/Applicable)**} \\ \cline{3-4}  & & & & **Channel Frequency** & & **Main Features** \\ \cline{3-4}  & & & & **[GHz]** & & **[Declared/Applicable]** \\ \hline [PERSON] et al. [9] & 1989 & SSM/I & - & Retrieval includes ocean surface wind & ACC = \(\pm\) 2 m/s \\ [PERSON] et al. [10] & 1990 & SSM/I & - & CLWC & RMSD\({}_{\%}\) = 30\% \\ [PERSON] and [PERSON] [11] & 1993 & SSM/I & - & CLWP, CIWP, TRW, RR & RMSD\({}_{\mathrm{WV}}\) \(<\) 0.78 g/cm\({}^{2}\) \\ [PERSON] and Westwater [14] & 1995 & SSM/I, AMSU, TMI & - & Several retrieval comparisons & - \\ [PERSON] et al. [12] & 1997 & SSM/I & - & CLWP, cloud frequency & - \\ Wentz and Spencer [24] & 1998 & SSM/I & - & TPW & RMSD\({}_{\mathrm{WV}}\) = 5 mm \\ [PERSON] et al. [13] & 2001 & AMSU & 23.8, 31.4 & TPW, CLWP over ocean & RMSD\({}_{\mathrm{TPW}}\) \(<\) 3 mm \\ [PERSON] [15] & 2002 & AMSU & 89, 150 & CIWP, D\({}_{\mathrm{e}}\), CB & 5\% \(<\) RMSE\({}_{\mathrm{De,\%}}\) \(<\) 20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Microwave observations-only recap table.
## 4 Range-Based Algorithms
### Bayesian Filters  
When estimating the dynamic states, which is the case in robotic applications, filters can work effectively. They work by alternating between two steps, i.e., prediction and update. For example, the classic Kalman filter is the optimal estimator for a linear system in the presence of Gaussian noise. They can also be employed readily to fuse the observations of various sensors. In robotics, the movement dynamic is considered as constant velocity or constant acceleration, which usually performs acceptably in practice.  
While the process model is linear, the observation model becomes non-linear. In this case, sub-optimal non-linear filters are exploited. These include the Extended Kalman filter [44], Particle Filter [45], and Unscented Kalman Filter [46]. The Extended Kalman filter is the first-order Taylor series expansion of the non-linearity widely used in diverse applications with an acceptable outcome. However, such a first-order approximation might introduce errors in the posterior distribution estimation [47]. The unscented Kalman filter (UKF) and particle filter (PF) can outperform EKF by removing the linearisation step and carrying out deterministic sampling. Based on unscented transform (UT), UKF transforms states to weighted sigma points based on which prediction and update are executed. PF performs sequential importance sampling, drawing the particles and their corresponding weights from the probability density. We will review works based on Bayesian filters in Section 7.
## 2 Materials and Methods
### Data Processing  
The primary outcome measures in this study include the following: 1 The joint angles of the hip, knee, and ankle in the sagittal and frontal planes at the initial contact (IC) and at the peak vertical ground reaction force (vGRF), measured in degrees (\({}^{\circ}\)); 2 The peak vGRF normalized to body weight (BW); 3 The time to peak vGRF (T_vGRF), measured in milliseconds (ms); 4 The vertical load rate (LR), measured in body weight per millisecond (BW/ms); 5 The lower-limb stiffness (k\({}_{\text{leg}}\)), measured in body weight per meter (BW/m). The specific calculation formulas for LR and k\({}_{\text{leg}}\), as indicated in [2], are shown in Equations (1) and (2) below, where \(\Delta\)L represents the change in lower-limb length, measured in meters (m):  
\[\text{LR}=\text{peak vGRF}/\text{T\_vGRF}, \tag{1}\]  
\[\text{k}_{\text{leg}}=\text{peak vGRF}/\Delta\text{L}. \tag{2}\]  
The raw data collected by the Vicon system were subjected to kinematic and inverse dynamic analysis using Visual3D (Version 6, C-Motion, Inc., Germantown, MD, USA) software. A fourth-order low-pass Butterworth filter was utilized to smooth the three-dimensional coordinate data at 10 Hz and the force platform data at 50 Hz. The calculation of the lower-limb joint angles was performed using the Euler angle method. The instant of ground contact was defined as the initial moment when the vGRF exceeded 10 N [9].
# Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition
## 3 Methodology
### Transfer Learning for ArSL Classification
This approach enables the model to leverage the knowledge gained during pretraining, leading to improved performance and efficiency for the classification of sign language alphabets. This study utilized two variants of BiT architectures namely BiT-m-r50x1 and BiT-m-r50x3 for ArSL recognition. Additionally, two pretrained vision transformers were utilized for ArSL classification in this study as follows. ViT, introduced in ref. [76], is a vision transformer which treat an image as a sequence of patches, similar to how words are treated in NLP tasks. The image is divided into small patches, and each patch is flattened into a 1D vector. These patch embeddings serve as the input to the transformer model. The transformer architecture consists of multiple layers, including multihead self-attention and feed-forward neural networks. The self-attention mechanism allows the model to attend to different patches and capture the relationships between them. It helps the model understand the context and dependencies between different parts of the image. ViT also incorporates positional embeddings, which provide information about the spatial location of each patch. Two variants of ViT namely ViT_b16 and ViT_132 were used in our work. Swin transformer was also utilized in this study. According to ref. [77], Swin was built on the Transformer architecture, which consists of multiple layers of self-attention mechanisms to process and understand sequential data. Swin has shifted windows that work by dividing the input image into smaller patches or windows and shifting them during the self-attention process. This approach allows Swin to efficiently process large images without relying on computationally expensive operations, such as sliding window mechanisms or convolutional operations. By shifting the patches, Swin extends the receptive field to capture global dependencies and enables the model to have a better understanding of the visual context. We employed in this work the variant named SwinV2 Tiny256 for ArSL classification.
# Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment
## 4 Results and Discussions
### Standard Deviation of Concentration, \(\sigma_{c}\)  
The results obtained from the model for the standard deviation of the concentration are shown in Figure 4. The Figure reports the simulated values (\(y\)-axis) of \(\sigma_{c}\), plotted towards the field data (\(x\)-axis). It should be reported that, in certain experiments, the modelled values at the fast-response detector locations of the mean concentration are equal to 0. For this reason, the standard deviation is forced to 0 by the model. In the graph of Figure 3, only nonzero values are shown. The bisector line 1:1, the 1:0.5, and the 1:2 lines are also graphed. It is possible to note that 60% of all the points are within the _FAC2_ range. The linear trendline was added, obtaining an angular coefficient of 3.14 (showing a relevant overestimation of the standard deviation by the model), an intercept of \(-6.19\) (exhibiting negative bias) and a \(R^{2}\) = 0.3 (denoting a moderate linear relationship).  
Figure 3: Scatter plot of the mean concentrations \(C_{m}\). 1:1, 1:0.5, and 1:2 lines are added.  
It is important to make some considerations regarding this comparison. The modelled standard deviation of concentration theoretically represents the standard deviation of the instantaneous sequence of concentration data. On the contrary, the experimental standard deviation, brought as a comparison to the model, is far from instantaneousness. This is the result of a not-instantaneous data sampling time (10 s) and the non-ideality of the measurement tool. For this reason, it suffers two kind of data losses: low frequency losses due to the finite mediation time and high frequency losses due to the analyzer's response time. The measured standard deviation of the concentration is forced to decrease. The overestimation of the model presented is therefore someway expected.  
Despite these \"Signal theory\" dissertations, considering the simplicity of the model, the modelled data are considered acceptable.  
Figure 4: Scatter plot of observed against modelled \(\sigma_{c}\) (\(\mu\)g/m\({}^{3}\)). Moreover, 1:1, 1:0.5, and 1:2 and trend lines are reported.
## 2 Results and Discussion  
A preliminary study in which TBAF/DMSO was directly used as elution solvent was performed with various TBAF concentrations (2.5, 5 and 10 mg/mL). However, the column pressure limit (30 bars) was rapidly reached, indicating column saturation. The higher the TBAF concentration was, the faster the saturation occurred. TBAF molecules seem to interact with PS-DVB co-polymer composing the stationary phase of the column. Consequently, DMSO alone was used as elution solvent and cellulose and pullulan samples were dissolved in TBAF/DMSO solutions, before SEC analysis. Detection was performed only with a refractive index detector using pullulans calibration.
## 2 Methods
### Measurement Model for Odometer Observation with Vehicular-Motion Constraint
If an unsatisfactory state estimation is detected and excluded by (23) or a GNSS outage occurs,  
Figure 2: Relationship between the vehicle frame and the global frame in the horizontal plane. the information, including odometer-based velocity and smoothed-heading from the last epoch can provide the DR solution. Although this solution provides only a 2-D solution with a constant height state, which is vulnerable to variations in height and heading, such design can bridge the gap in positioning and maintain the estimation accuracy over a short time. Another additional aid from an odometer is the zero-velocity detection. When a vehicle is stationary, the velocity state estimation is nearly zero. However, in the densely packed urban areas, NLOS/multipath-contaminated reception becomes more severe for the duration that the vehicle is stationary. Such continuous faulty-signal reception not only degrades the velocity estimation but also misleads the position state due to the path delay. These measurements falsely update the EKF during the static motion, causing the EKF to diverge. To address this situation, the use of an odometer can provide precise constraints by assigning the velocity to be zero in any direction, thereby preventing error growth. This zero-velocity constraint is expressed as,  
\[\mathbf{H}_{\alpha\omega}=\left[ \begin{array}{cccccc}0_{3\times 3}&\mathbf{I}&0_{3 \times 3}&0_{3\times 1}&0_{3\times 1}&0_{3\times 12}\end{array} \right]_{3 \times 12} \tag{24}\]  
\[\delta\mathbf{z}_{\alpha\omega}=[0_{3\times 1}-\mathbf{v}_{u}^{e}]_{3\times 1} \tag{25}\]  
where the identity matrix \(\mathbf{I}\) corresponds to each component of the velocity state in (1). Moreover, during the activation of zero-velocity constraint, GNSS measurements will not update EKF and fix the position state and heading output, thereby preventing an erroneous position change. This position-fix design can be formulated in (26), where \(v_{thr}\) denote the velocity threshold of determining the stationary motion. This mechanism can maintain stability for the state and its covariance matrix of EKF, and the accuracy of the solution. The flowchart depicting the utilization of zero-velocity constraints is presented next (Figure 3):  
\[\mathbf{r}_{u}^{e}(t_{k}) \begin{cases}fix\text{ position equal to }\mathbf{r}_{u}^{e}(t_{k-1}),&v(t_{k})\leq v_{thr}\\ position\text{ state estimation},&v(t_{k})>v_{thr}\end{cases}  \tag{26}\]
## 8 Summary of Key Findings
### Comparison of Energy-Harvesting Techniques  
Each energy collecting approach has distinct advantages and limits, making it appropriate for a variety of applications [142; 143]. The choice of energy-harvesting technology is determined by factors such as energy demand, ambient conditions, and deployment limits. Table 4 outlines the major characteristics of each technique: restrictions and solutions. The comparison of energy-harvesting techniques in Table 5 presents a summary of various energy harvesting methods, including solar, thermal, kinetic, and RF, highlighting their relative effectiveness and suitability for different applications.
# Nitrate-Polluted Waterbodies Remediation: Global Insights into Treatments for Compliance
## 4 Nitrate Removal Technologies in Waterbodies
### Transformation-Based Technologies
#### 4.2.4 Photocatalytic Denitrification
The commercial implementation of photocatalytic denitrification is still restricted by the unavailability of a cost-effective, visible-light active catalyst along with a relatively limited comprehension of photoreactor designs.