esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Novel Vehicle Terminal Based on Acoustic Communication Technology
### Acoustic Demodulation Module
#### 3.2.3 Noise Reduction Algorithm  
In order to further improve the anti-noise ability, it is necessary to use the acoustic enhancement algorithm to suppress the noise component in the acoustic wave to achieve the purpose and requirement of noise suppression. The traditional single-microphone acoustic enhancement algorithm cannot make full use of the spatial information of the acoustic signal, so that its performance is limited. Thus, the microphone array associated with the double-microphone acoustic enhancement algorithm appears [30]. Through receiving space acoustic wave signal from multi-microphone at the same time and analyzing the position, it could accurately extract the target acoustic wave signal so as to achieve the purpose of improving acoustic wave enhancement performance.  
The acoustic wave signals from two microphones have a strong correlation, in which there is a linear relationship between the phases, that is, the two-channel acoustic wave signal is a coherent signal. However, the noise signals in the two microphones are irrelevant and their phases do not exist directly. Double-acoustic wave enhancement algorithm is dealt by calculating the correlation function. The signal with stronger coherence can be passed, and the signal with weak coherence will be filtered out.  
Specifically, if the amplitude of the correlation function is equal to or close to \"1\", it means that the signal is a speech signal, which can be passed through filter design so that the correlation function is close to \"1\". On the other hand, if the amplitude of the obtained correlation function is close to \"0\", it indicates that the signal is a noise signal, and the noise signal can be filtered by the filter design. The flow chart based on the correlation function filter shown in Figure 6 can be obtained by the above analysis [31].  
The two input signals, namely \(y_{1}(m)\) and \(y_{2}(m)\), are converted into the relevant signals \(Y_{1}(w,k)\) and \(Y_{2}(w,k)\) respectively in the frequency domain after passing through a filter and after the windowing Fourier transform process (Windowing + FFT). Then, the complex correlation function \(H(w,k)\) in the frequency domain needs to be calculated, and the necessary function is obtained by the correlation function device, so the input of the two signals through this filter can filter out the noise signal, so as to get the required original signal \(\hat{x}(m)\) in the frequency domain and \(x(m)\) in the time domain by inverse fast Fourier transform (IFFT), and to a large extent to eliminate the noise of the communication noise source.  
Figure 6: Correlation filter.
## 2 Data Collection
### Model Selection and Algorithm Design
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{3}{c}{(**a**)} \\ \cline{2-4}
**Training Features** & **KNN** & **Random Forest** & **XGBoost** \\ \cline{2-4}  & **(\% Accuracy)** & **(\% Accuracy)** & **(\% Accuracy)** \\ \hline Acceleration (X, Y, Z) & 65 & 67 & 60 \\ \hline Gyro meter (X, Y, Z) & 74 & 75 & 64 \\ \hline Combined Acceleration & & & \\ and Gyro meter (X, Y, Z) & 84 & 86 & 75 \\ \hline \hline
**ML Algorithm** & & **Percentage Accuracies of Individual Features for Predicting MFC Height** \\ \hline  & **AccX (\%)** & **AccY (\%)** & **AccZ (\%)** & **GyroX (\%)** & **GyroZ (\%)** \\ \hline KNN & 41 & 45 & 55 & 56 & 51 & 55 \\ \hline Random Forest & 40 & 43 & 51 & 51 & 46 & 48 \\ \hline XGBoost & 46 & 53 & 56 & 58 & 56 & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: (**a**) Comparison of prediction accuracies using linear acceleration and angular velocity as separate features on KNN, Random Forest, and XGBoost. (**b**) Comparison of individual axial features (acceleration and angular velocity) on prediction with KNN, Random Forest, and XGBoost.
## 2 Materials and Methods
### Experimental Data and Remote Sensing Imagery
#### 2.2.1 Synchronous Field Spectral Data  
Based on the above-surface spectra measurement method [40], the ASD Field Spec Pro portable spectrometer was used to collect the water surface spectral information, mainly including the water surface radiance and the total irradiance of the water surface incident. The water surface remote sensing reflectance is calculated as Equation (1):  
\[R_{rs}=\frac{L_{w}}{E_{d}(\sigma^{+})} \tag{1}\]  
where \(R_{rs}\) represents the remote sensing reflectance calculated by Equation (1) (units: sr\({}^{-1}\)), \(L_{w}\) is the water surface radiance (units: w \(\cdot\) m\({}^{-2}\cdot\) sr\({}^{-1}\)) and \(E_{d}(\sigma^{+})\) is the total irradiance of the water surface incident (units: w \(\cdot\) m\({}^{-2}\)); both \(L_{w}\) and \(E_{d}(\sigma^{+})\) are measured by ASD spectrometer equipment.
# New Compact Wearable Metamaterials Circular Patch Antennas for IoT, Medical and 5G Applications
###### Abstract  
The development of compact passive and active wearable circular patch metamaterials antennas for communication, Internet of Things (IoT) and biomedical systems is presented in this paper. Development of compact efficient low-cost wearable antennas are one of the most significant challenges in development of wearable communication, IoT and medical systems. Moreover, the advantage of an integrated compact low-cost feed network is attained by integrating the antenna feed network with the antennas on the same printed board. The efficiency of communication systems may be increased by using efficient passive and active antennas. The system dynamic range may be improved by connecting amplifiers to the printed antenna feed line. Design, design considerations, computed and measured results of wearable circular patch meta-materials antennas with high efficiency for 5G, IoT and biomedical applications are presented in this paper. The circular patch antennas electrical parameters on the human body were analyzed by using commercial full-wave software. The circular patch metamaterial wearable antennas are compact and flexible. The directivity and gain of the antennas with Circular Split-Ring Resonators (CSRR) is higher by 2.5 dB to 3 dB than the antennas without CSRR. The resonant frequency of the antennas without CSRR is higher by 6% to 9% than the antennas with CSRR. The computed and measured bandwidth of the stacked circular patch wearable antenna with CSRR for IoT and medical applications is around 12%, for S11 lower than \(-6\) dB. The gain of the circular patch wearable antenna with CSRR is around 8 dBi.  
circular patch antennas; 5G; IoT; medical systems; metamaterial antennas; active antennas +
Footnote †: journal:
# Leveraging Urban Water Distribution Systems with Smart Sensors for Sustainable Cities
## 5 Experimental Results
### Lugo Scenario
The variation in resource savings across nodes directly results from differing water demand patterns and network complexity within Lugo. For example, Node 28, with the highest savings, is located in a high-demand area, which likely serves a larger residential or commercial district. This node benefits the most from the optimization algorithm, achieving substantial water savings of 648,000 L/day. In contrast, Node 8, which sees far fewer savings (42,744.65 L/day), is likely located in a lower-demand zone, such as a peripheral residential area. These savings illustrate the importance of targeted optimization, where the algorithm can identify and prioritize nodes with the greatest potential for efficiency improvements. The highest-saving node (Node 28) achieves substantial water conservation, driven by its higher-than-average base demand, suggesting this node serves a high-demand area. Conversely, the lower savings at Node 8 reflect its smaller local demand, typical of less dense or peripheral urban areas. The cumulative savings across the system indicate substantial efficiency gains, further emphasizing the importance of targeted node optimization in maximizing water conservation.
## 1 Introduction
### Vehicle-Highway Bridge Coupling System Model  
A four-degree-of-freedom half-vehicle model and a simply supported beam model are used to illustrate the mechanical mechanism of vehicle-highway bridge coupling vibration analysis, as shown in Figure 1. In this model, the vehicle body and wheelsets are treated as rigid bodies [19; 32]. The suspension and dampers are positioned between the vehicle body and the wheelsets. The tires are located between the wheelset centers of mass and the highway bridge pavement. The interactions between the rigid bodies are established using springs and dampers. The connection between the wheelset centers of mass and the highway bridge pavement is represented by the tires, which are also modeled using springs and dampers, as shown in Figure 2. The motion of the vehicle model can be expressed by the dynamic differential equation, as shown in Equation (1).  
\[\mathbf{M}_{\rm v}\ddot{\mathbf{Y}}_{\rm v}+\mathbf{C}_{\rm v}\dot{\mathbf{Y}}_{\rm v}+\mathbf{K} _{\rm v}\mathbf{Y}_{\rm v}=\mathbf{G}_{\rm v}+\mathbf{F}_{\rm vb} \tag{1}\]  
where \(\mathbf{M}_{\rm v}\) is the vehicle mass matrix; \(\mathbf{C}_{\rm v}\) is the vehicle damping matrix; \(\mathbf{K}_{\rm v}\) is the vehicle stiffness matrix; \(\mathbf{G}_{\rm v}\) is the vehicle gravitational load vector; \(\mathbf{F}_{\rm vb}\) is the instantaneous coupling force vector at the vehicle-bridge contact point; and \(\dot{\mathbf{Y}}_{\rm v}\), \(\mathbf{Y}_{\rm v}\) and \(\mathbf{Y}_{\rm v}\) are the acceleration, velocity, and displacement vectors of the vehicle model, respectively.  
For the bridge model, its motion can also be described by its dynamic differential equation, as shown in Equation (2).  
\[\mathbf{M}_{b}\ddot{\mathbf{Y}}_{b}+\mathbf{C}_{b}\dot{\mathbf{Y}}_{b}+\mathbf{K}_{b}\mathbf{Y}_{b}=\mathbf{F }_{\text{bg}}+\mathbf{F}_{\text{bv}} \tag{2}\]  
where \(\mathbf{M}_{b}\) is the bridge mass matrix; \(\mathbf{C}_{b}\) is the bridge damping matrix; \(\mathbf{K}_{b}\) is the bridge stiffness matrix; \(\mathbf{F}_{\text{bg}}\) is the load vector applied to the bridge, unrelated to the vehicle-bridge coupling; \(\mathbf{F}_{\text{bv}}\) is the wheel force vector applied to the bridge nodes, which is the action-reaction pair to \(\mathbf{F}_{\text{vb}}\); and \(\ddot{\mathbf{Y}}_{b}\), \(\dot{\mathbf{Y}}_{b}\), and \(\mathbf{Y}_{b}\) are the acceleration, velocity, and displacement vectors of the bridge nodes, respectively.
## 4 Control Model
### Inferring Control Models from Network Interaction Logs
The basic idea of [PERSON] et al. [44] is to infer the service's state models to enhance the playback in virtualised services. It has three phases as analysis, model inference and run-time. It defines an event as a specific/unique request-response message type pair and event series as the sequences of events related to specific service at the analysis phase. Then, at the model inference phase, it infers the state model of each service from its event sequences using k-tail (K = 0). The run-time phase, upon receiving a request, uses the inferred state model and its current state of the service to specify the response type. Then, it synthesises response message by dynamic substitution of the nearest matching request type among the interactions. However, this research has improved state of the art of current service virtualisation (opaque SV), in considering the contextual information in generating the type of the response message, but it does not consider the contextual information in generating response content. [PERSON] et al. [45] proposed a technique for extracting contextual information in network logs by integrating a graph-based causal inference algorithm and a pruning method on the basis of both the layer-2 and layer-3 network topology domain knowledge along with log events' functional layers. The proposed method applied to a set of log data illustrated that the pruning method reduces processing time and detects more relevant troubleshooting information compared to the available methods. Table 3 shows a summary of the introduced methods that infer control model from network logs. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Specification** & **Remarks** \\ \hline \multirow{2}{*}{ScriptGen [26]} & \multirow{2}{*}{Generating a FSM includes all the messages of the traces} & Using macro and micro-clustering to identify semantically similar messages. Tested on SMB, IM \\ \hline \multirow{4}{*}{[PERSON] et al. [37]} & Using several instances of the same attack, to automatically retrieve the semantically important fields from intra-protocol and inter-protocol dependencies vital for the client and server’s conversation \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \multirow{2}{*}{[PERSON] [24]} & \multirow{2}{*}{Automatically extract a protocol specification as state machines} & Tested on Agobot, SMTP and SMB, SIP \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Different approaches of inferring control model from network logs.
# Client-Aware Negotiation for Secure and Efficient Data Transmission
## 1 Introduction  
As a security layer protocol, the Secure Socket Layer (SSL) and the subsequent Transport Layer Security (TLS) are widely used in TCP/IP networks. SSL/TLS is often combined with the Hypertext Transfer Protocol (HTTP) to ensure the security of data transmission; in this case, HTTP is also known as Hypertext Transfer Protocol Secure (HTTPS). Compared to the traditional transmission mode, data transmission based on SSL/TLS needs to introduce additional security control overhead. The efficiency and security are closely related. Usually, more efficiency means less security; conversely, more security means less efficiency and, more broadly, higher energy consumption. In practical applications, different control strategies can be set for data transmission with different security requirements.
## 1 Introduction
In [21], a single LED positioning system based on circle projection using single image sensor is proposed and demonstrated. The LED image is no longer treated as a point in the actual scene, and the expressions for determining the orientation and position of the receiver are derived from the geometric parameters of the LED circle projection in the image. However, this method can only apply in a very short scope. According to our experiment, when the perpendicular distance between camera and LED increases (especially in a large indoor environment), the LED image on the image sensor plane can only be seen as a point. Otherwise, the geometric parameters of the LED circle projection will deliver a large percentage of error and cause a larger error in the positioning. Moreover, the marginal marker point mentioned in the paper can only be applied in a short range. Once the distance increases, it would be difficult to recognize and lead to a large error. Moreover, the algorithm in [21] is not suitable for practical application because of its complexity and large amount of computation. Finally, all above-mentioned studies (except Reference [15]) do not pay much attention to real-time ability, but only focus on static positioning and the positioning accuracy. However, both accuracy and real-time ability are crucial for indoor positioning systems. The contribution of our work can be listed as follows. In this paper, firstly we propose an image-sensor-based double-light positioning system and set up relevant experiments to test the performance of the proposed system. Although the authors of [14; 15] used a double-light positioning algorithm, the work in [14] does not consider the LED-ID recognition problem. If the LED-ID recognition is unsuccessful, the positioning would fail. In [15], there is a LED-ID recognition defect. If the LED-ID recognition is deviated, the positioning algorithm is difficult to exert a normal effect. This paper considers the problem of LED-ID recognition. Under the premise of accurate identification of LED-ID, the double-light positioning algorithm is used for accurate positioning. Accurate LED-IDrecognition is the basis for the success of the positioning algorithm. If the LED-ID is not recognized correctly, the positioning would fail and the positioning algorithm would be meaningless. Secondly, we utilize the rolling shutter mechanism of the CMOS image sensor and combine machine learning algorithm to identify the LED-ID. Thirdly, in the proposed double-light positioning algorithm with low complexity, we take the symmetry problem of angle into consideration, which has not been considered before. Finally, the proposed double-light positioning system delivers satisfactory performance in terms of the real-time ability and the accuracy of positioning, and experimental results show that the proposed system could achieve an accuracy of 3.85 cm with an average computing time of 56.28 ms. The rest of the paper is organized as follows. Section 2 presents system principle and the double-light positioning algorithm. Section 3 shows the experiments and results of the proposed system. Section 4 provides concluding remarks.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 2 Overview of V2X Protocols
### Lte-V2x  
Moreover, 3 GPP's study of LTE-V2X began with the publication of Release 14 and was further refined in Release 15. LTE-V2X is designed to support autonomous driving applications such as safety and traffic management. LTE-V2X also supports services for V2X communications: Intelligent Transport Systems--Generation 5 (ITS-G5) and dedicated short-range communication (DSRC) [24; 25].  
The LTE physical layer can operate in different channel bandwidths, including 1.4 MHz, 3 MHz, 5 MHz, 10 MHz, 15 MHz, and 20 MHz. In the case of LTE-V2X, the channel bandwidth can be configured as necessary to meet the channel requirements. The channel is divided into 180 kHz bandwidth resource blocks (RBs), which correspond to 12 OFDM subcarriers spaced 15 kHz.  
For V2X communications, LTE-V2X defines two resource allocation modes: mode 3 and mode 4. In mode 3, the Evolved Node B (eNB) manages V2X communications, with the function of configuring and selecting communication resources; in this mode, vehicles need to be inside the network coverage. In mode 4, the vehicles select and configure the subchannels autonomously, without the need to use the cellular infrastructure, i.e., they can operate outside network coverage. For subchannel selection in mode 3, there is dynamic scaling, in which the vehicle asks the eNB for subchannels for each transport block (TB). In semi-persistent scheduling (SPS), the eNB reserves subchannels so that a vehicle can transmit several TBs. Mode 3 can outperform mode 4 since the scheduling of transmissions is centralized at the eNB. However, it requires operation in network coverage and introduces cellular uplink (UL) and downlink (DL) signaling overhead [26; 27]. In mode 4, vehicles select their communication channels using semi-persistent programming (SPS) [28].
# Development of a CALPHAD Thermodynamic Database for Pu-U-Fe-Ga Alloys
## 3 Literature Review
### The Uranium-Iron-Gallium Phase Diagram  
There have been six experimentally reported intermetallic compounds in the U-Fe-Ga phase space. They are UFeGa [77], UFeGa\({}_{5}\), [78; 79; 80; 81] UFe\({}_{6}\)Ga\({}_{6}\)[82], UFe\({}_{5}\)Ga\({}_{7}\)[83], U\({}_{2}\)FeGa\({}_{8}\)[84] and U\({}_{4}\)FeGa\({}_{12}\)[85]. The crystal structures of these phases have been investigated and low temperature experiments (up to 25 \({}^{\circ}\)C), including heat capacity, magnetic susceptibility and electrical resistivity have been performed [77; 78; 79; 80; 81; 82; 83; 84; 85]. There is no known information on phase stability above room temperature and a thermodynamic assessment with the given information is not possible.
## 4 Results and Discussion
The induced electric field, magnetic field, and surface current are strongly centered on the octagonal ring as well as on the metal strip (dumbbell-shaped tuning fork) of the unit cell. With the increase of the frequency, the electromagnetic resonance shifted to the top layer of the unit cell. It is observed that the octagonal ring is sensible for the absorption peak at 6.45 GHz while absorption peak at 14.89 GHz is seen due to tuning fork along with dumbbell shape. It is seen no doubtfully, the surface electric flow is mostly centralized along the octagonal ring shape and tuning fork resonator, creating a strong magnetic field. In addition, the induced E-field is opposite to the H-field. Therefore, the design simulates an equivalent LC architecture, the resonance of which can be adjusted using control parameters. Figure 4: Absorption graph at different incident angle. Figure 5: Graph at (**a**) effective permittivity (**b**) effective permeability. Figure 3: Absorption graph at different polarization angle. Finally, a comparison between the performance of the proposed PMA and different published PMA is shown in Table 2. Some factors, for example absorption rate, absorption band application, metamaterial structure, and dimension have been inspected. Figure 8: Surface current distribution (**a**) 6.45 GHz (**b**) 14.89 GHz. Figure 6: Electric field distribution (**a**) 6.45 GHz (**b**) 14.89 GHz. \[A_{\text{avg}}=\frac{1}{\lambda\mathcal{U}-\lambda L}\int_{\lambda L}^{ \lambda\mathcal{U}}A\;(\lambda)d\lambda \tag{5}\]0 Magnetic field distribution (**a**) 6.45 GHz (**b**) 14.89 GHz.
# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 6 Conclusions  
We presented a novel application of Basemap and Planet Fusion analysis ready datasets to monitor sub-weekly OFRs surface area changes. We tested both datasets to monitor 340 OFRs of different sizes, and we found that these datasets can be employed to monitor OFRs with uncertainties \(<10\%\) when compared to an independent validation dataset and with uncertainties \(<5\%\) when compared to PlanetScope imagery. While Basemap had higher surface area variability and it was more susceptible to the presence of cloud shadows and haze, Planet Fusion had a smoother time series with less variability and fewer abrupt changes throughout the year. Given that the surface area classification can be impacted by the OFR environmental conditions (e.g., presence of vegetation inside the OFR), therefore limiting the use of these datasets, we recommend assessing the OFRs' surface area time series before employing them for monitoring purposes. As the number of OFRs is expected to increase globally, the use of these datasets is of great importance to understanding OFR sub-weekly, seasonal and inter-annual surface area changes, and to improving freshwater management by allowing better assessment and management of OFRs.  
**Author Contributions:** Conceptualization, V.P., S.R., J.K., T.H. and M.G.T.; methodology, V.P., S.R. and J.K.; software, V.P., S.R. and J.K.; validation, V.P.; formal analysis, V.P.; investigation, V.P.; resources, V.P., S.R. and J.K.; data curation, V.P., S.R., J.K., N.S., T.B., M.R. and M.A.Y.; writing--V.P.; writing--review and editing, V.P., S.R., J.K., T.H., N.S., T.B., M.R. and M.A.Y.; visualization, V.P.; supervision, T.H.; project administration, T.H.; funding acquisition, V.P., T.H. and M.G.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The first author is supported by NASA through the Future Investigators in NASA Earth and Space Science and Technology fellowship. The first author acknowledges the support provided by Planet Labs throughout his internship at the company, which included access to SkySat and PlanetScope imagery and on-demand generation of Basemap and Planet Fusion specifically for this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.