esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting the Mine Friction Coefficient Using the GSCV-RF Hybrid Approach
## 3 Example Analysis
### Prediction Result Comparison  
By comparing Figures 8 and 11, we determined that the GSCV-RF model and the RF model had the same prediction tendency for the datasets. Their main difference was the accuracy of their prediction results. Table 8 displays the quantitative evaluation results of the RF and GSCV-RF models. With respect to the prediction results of the passive support training sets provided by the GSCV-RF model, the value of _MAE_ decreased by 5.26%, the value of _RMSE_ decreased by 13.79% and the value of \(R^{2}\) increased by 0.13%. With respect to the prediction results of the passive support test sets provided by the GSCV-RF model, the value of _MAE_ remained unchanged, the value of _RMSE_ decreased by 1.26% and the value of \(R^{2}\) increased by 0.35%. With respect to the prediction results of the active support training sets provided by the GSCV-RF model, the value of _MAE_ increased by 40%, the value of _RMSE_ increased by 26.67% and the value of \(R^{2}\) decreased by 1.37%. Even though the rate of the increase in _MAE_ and _RMSE_ was greater, their respective indicator magnitude order was lower, and the error was still at a lower level. With respect to the prediction results of the active support test sets provided by the GSCV-RF model, the value of _MAE_ decreased by 11.11%, the value of _RMSE_ decreased by 9.68% and the value of \(R^{2}\) increased by 1.41%. Consequently, we realized that, compared to the RF model, the GSCV-RF model is superior in its prediction ability, yielding more accurate and reliable data.  
Comparing the results in Figures 10 and 13 and combining them with the quantitative evaluation results of the models in Table 8, we determined that the BP model has a larger prediction error and lower accuracy for the datasets. With respect to the prediction results of the passive support training sets provided by the BP model, the value of _MAE_ increased by 516  
support training sets provided by the BP model, the value of \(MAE\) increased by 171.43%, the value of \(RMSE\) increased by 163.16% and the value of \(R^{2}\) decreased by 21.86%. With respect to the prediction results of the active support test sets provided by the BP model, the value of \(MAE\) increased by 70.83%, the value of \(RMSE\) increased by 100% and the value of \(R^{2}\) decreased by 22.15%.  
The RF model and GSCV-RF model offer the best prediction effects among the three models presented in this research, whereas the GSCV-RF model provides the most accurate \(\alpha\) prediction. While the BP model has more substantial error samples and a lower \(\alpha\) prediction accuracy. For the purpose of \(\alpha\) prediction, the GSCV-RF model is the best of the three models, followed by the RF model and the BP model.
# Whole Organ Engineering: Approaches, Challenges, and Future Directions
## 4 Approaches to Engineering Whole Organs
### Organoids  
Organoids are a tissue engineering technology that seeks to replicate native organ function in miniature fetal or adult organ-like structures. They have been used for more accurate disease modelling, toxicology and drug studies due to their greater similarity to native organs than more basic in vitro cultures, as well as tissue/organ replacement [94; 95]. Organoids are also well suited to these applications as they can be developed from patient-derived cells, improving the accuracy of disease models or drug studies and mitigating the concern of an immunological response of tissue replacement. Patient-specific toxicology studies have been demonstrated to be a particularly useful application of organoids, as native organ physiology can be replicated and tested in vitro if the organoids are engineered using patient cells [94; 96]. [PERSON] et al., developed human colon organoids derived from both healthy and cancerous sections of colon from patients with colorectal cancer [94]. Using proteomic analysis of the organoids, [PERSON] and colleagues were able to identify specific phenotypes in patients that could dictate their disease progression and ultimate clinical outcome, thus allowing a personalized treatment to be developed thanks to the organoids [94]. In 2016, [PERSON] and colleagues demonstrated that organoids formed from pancreatic cells derived from human embryonic stem cells were capable of performing pancreatic endocrine functions in vitro and in vivo [95]. In vitro glucose stimulation of the pancreatic organoids induced release of insulin-containing vesicles, and implantation of the organoids into diabetic mice resulted in a decrease of blood glucose levels and insulin was detected in the transplant sites [95]. These results demonstrate the ability of organoids to replicate and even replace organ function, doing so autonomously in response to native physiological cues.  
Organoid technology can potentially be utilized to advance whole organ research. Both areas of tissue engineering are concerned with addressing organ failure and regeneration, and both have similar challenges. The main technical challenge in the way of organoid development is engineering an organoid environment that provides the same structural support biochemical cues as the native environment. As the native ECM is intrinsically designed for such a purpose, utilizing native ECM components to engineer the organoid microenvironment is an approach that shows promise in addressing this challenge.
## 2 Background
### Multi-Channel Operation  
In the initial rollout phase, the organizations opted to equip the V2X systems with a single transmitter and receiver installation. However, thanks to the rapid development, the single-channel solution will not be sufficient, as it can become easily congested due to the larger, more complex packets sent by the increasing number of facilities protocols of Day 2 and beyond systems [16]. Since the penetration of C-ITS systems is constantly growing, the efficiency of radio resource usage needs to be enhanced. One way to do this is to use more than one channel [15]. For this reason, vehicle communication organizations in both Europe and the US have been working for years on a solution that uses more channels instead of one to allocate resources and avoid external distractions such as interference. In fact, for future Day 2 applications, a requirement has already been introduced to increase the number of active channels to a minimum of two, thus helping the deployment and evolution of MCO. V2X applications such as Platooning are already dedicated to such an environment to ensure a fail-safe operation [17; 18].  
In V2X systems, the Channel Busy Ratio (CBR) is used to measure the proportion of time that a channel is occupied with message transmissions compared to the total available time. Therefore, the CBR is calculated by dividing the time the channel is busy transmitting data by the total observation time (Equation (1)). The result is a ratio, which indicates the saturation of the channel. In a multi-channel environment, monitoring the CBR is crucial  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Message** & **CAM** & **DENM** & **CPM** & **VAM** & **MCM** \\ \hline
**Transmission type** & Periodic & Event-driven & Periodic & Periodic & Event-driven \\ \hline
**Release** & Release 1 & Release 1 & Release 2 & Release 2 & Release 2 \\ \hline
**Transmission** & & & & & \\
**Rate** & & & & & \\ \hline
**Hop Count** & Single Hop & Multi-hop & Single Hop & Single Hop & Single Hop \\ GeoBroadcast & GeoBroadcast & GeoBroadcast & GeoBroadcast & GeoBroadcast & GeoBroadcast \\ \hline
**Data** & Vehicle’s position, & Relevance area, & Detected object’s & Pedestrian’s and & Trajectories \\ speed, etc. & & cause description & description & cyclist’s position, & Speed, heading \\ \hline
**Importance** & Basic status and attribute information & Safety-related situations (e.g., hazardous locations) & Advertise nearby object’s information & Information about vulnerable road users & Planned trajectory to nearby vehicles \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of selected Day 1–2 facilities protocol messages.  
for efficient communication and resource management. It determines when the service should change its channel since it can identify congested channels.  
\[CBR=\frac{\text{T}_{\text{busy}}}{100\ ms} \tag{1}\]
## 3 Results and Discussion
### Antibacterial Persistence and Reusability of PVA/BC/C-Ag Composite Membranes  
The number of _E. coli_ colonies in water after continuous treatment of the composite membrane is shown in Supplementary Table S2. The PVA/BC/C-Ag composite membrane maintained an antibacterial rate of approximately 80% after 30 days of continuous bacteriostasis according to calculations, indicating that it had excellent antibacterial durability (Figure 7a). Some residual bacteria may have adhered to the surface of the composite membrane during the continuous bacteriostatic process, decreasing the bacteriostatic effect. The antibacterial rate of the composite membrane decreased as the number of immersion cycles increased, because the residual deposition of dead bacteria on the surface or internal of the membrane hindered the release of silver ions (Figure 8b). The number of _E. coli_ colonies in water after repeated treatment of the composite membrane is shown in Supplementary Table S3. The antibacterial rate was still calculated to be above 85% after repeated use five times, indicating that the composite membrane has satisfactory reusability in practical applications. After each reuse, we measured the concentration of silver in the solution, and in every case the silver concentration was less than 3 \(\mu\)g L\({}^{-1}\) (Figure 6), below the national drinking water standard (GB5749-2006, China). Silver loss from the PVA/BC/C-Ag composite during the bacteriostatic process was extremely small, and the composite appears to be very safe for drinking water applications.  
Figure 6: Silver loss from PVA/BC/C-Ag composite membranes for continuous (**a**) and repeated use (**b**).  
Figure 7: Bacteriostatic persistence (**a**) and reusability (**b**) of PVA/BC/C-Ag composite membranes for _Escherichia coli_.
# Voice Communication Systems Impact on Navigating Officers
## 1 Introduction
For that purpose, a series of interviews and an extensive survey were conducted. In order to estimate the officer's workload, the collected data were analyzed by three main indicators. The first one was the frequency of incoming calls in total and divided by communication system type. The second one was the process time, which represented the time spent on a conversation. The process time was estimated per call, system type and in total per each standard watch (4 h). The third indicator was the estimation of how many calls were perceived as low-priority calls, i.e., as distractors, disturbing the officers during watch keeping. A call management system that could contribute to decrease the number of unnecessary distractions of an OOW and thus improve the safety of a ship during navigation is discussed after the analysis.
###### Abstract  
As adaptive multi-rate (AMR) speech applications become increasingly widespread, AMR-based steganography presents growing security risks. Conventional steganalysis methods often assume known embedding rates, limiting their practicality in real-world scenarios where embedding rates are unknown. To overcome this limitation, we introduce a novel framework that integrates a multi-scale transformer architecture with multi-task learning for joint classification and regression. The classification task effectively distinguishes between cover and stego samples, while the regression task enhances feature representation by predicting continuous embedding values, providing deeper insights into embedding behaviors. This joint optimization strategy improves model adaptability to diverse embedding conditions and captures the underlying relationships between discrete embedding classes and their continuous distributions. The experimental results demonstrate that our approach achieves higher accuracy and robustness than existing steganalysis methods across varying embedding rates.  
steganography; steganalysis; unknown embedding rates; multi-task learning; multi-scale transformer Article
# Acquisition of Weak BDS Signal in the Lunar Environment
## 3 Results
### Observation Results
#### 3.1.4 Gdop  
As shown in Figure 12, the GDOP values are given using different GNSS combinations for the circumlunar orbit. Obviously, more GNSS satellites can significantly improve the GDOP. In the circumlunar orbit mission, GDOP values can reach an average of about 9.3 when considering four GNSSs at a reception threshold of 15 dB-Hz. Therefore, a GNSS-based PNT service for lunar exploration still has challenges. With the receiver threshold reduced to 10 dB-Hz, the GDP reaches an average of about 2.2; however, using BDS-3 or GPS alone, the GDOP values are 8.8 and 7.3, respectively. Therefore, a much more significant improvement in the receiver sensitivity is achieved by using advanced algorithms. This will result in the lunar mission achieving a high precision orbit determination as well as navigation.  
Figure 11: Received signal Doppler shift using a quad GNSS system with a reception threshold of 10 dB-Hz.  
Figure 12: GDOP values of different GNSS system combinations at the \(C/N_{0}\) thresholds of 15 (**left**) and 10 (**right**) dB-Hz.
## 4 Dual-Polarization Multipath Mitigation Algorithm
The RMSEs of \(\hat{t_{0}}\) estimated by the DP-SIMLE algorithm in different received signal cases defined by Table 2 are depicted in Figure 7 for theoretical performance evaluation when one constructive (lines above zero) or destructive (lines below zero) multipath is present. From the results of MC simulations in Figure 7, we can conclude that:
* Comparing the solid line in red with that in magenta, the DP-SIMLE algorithm improves a lot in mitigating multipath whose relative delay to the LOS signal is from 7.5 to 25 m (constructive multipath scenario). * The increasing RMSE of DP-SMILE algorithm represented by blue lines over that in red indicates that the short delay multipath mitigation performance decreases as the power of the MP received by LHCP antenna decreases. * The difference between the RMSEs of DP-SIMLE illustrated by solid lines and dashed lines are caused by different powers of the LOS signal in the LHCP channel. Ignoring the received LOS  
\[residual_{\text{LHCP}}(t)=r_{\text{LHCP}}(t)-\sum_{i=1}^{PathNum-1}B_{i}e^{ \text{i}\theta_{i}^{\text{i}}}R_{\text{ss}}(t-t_{i})\]7 The RMSEs of the TOA estimation of the LOS signal using DP-SIMLE algorithm in different received signal cases defined by \[residual_{\text{LHCP}}(t)=r_{\text{LHCP}}(t)-\sum_{i=1}^{PathNum-1}B_{i}e^{ \text{i}\theta_{i}^{\text{i}}}R_{\text{ss}}(t-t_{i})\]8 The lines above zero are the results when constructive multipath is present, and the lines below zeros are that of the destructive multipath. The simulation parameters are the same with that in Section 3. In RHCP channel, the power of the MP signal is 6 dB less than the LOS signal; the CNR is 42 dB-Hz and the integration time is 100 ms.  
signal by the LHCP antenna does affect the performance of the DP-SIMLE algorithm, and the lower the received power of the MP signal in the LHCP channel is, the stronger the impact.
## 3 Results and Discussion
### Practicality Analysis  
The dataset used in practicality analysis was acquired by a RIEGL VMX-450 mobile mapping system, as shown in Figure 6. The imaging unit of this mobile mapper was composed of two RIEGL VQ-450 laser scanners and four VMX-450-CS6 cameras. The navigation unit of the mobile mapping  
Figure 5: Relationship between random error and the reconstruction result. (**a**) Relationship between the deviation of random error and the curve parameter. (**b**) Relationship between the deviation of random error and the main stake point coordinates.  
system included an integrated INS/ GNSS system. Table 4 presents the detailed specifications of this mobile mapping system.  
MLS was mounted on a train and placed on an open-top railcar operating at 120 km per hour to avoid interference with the regular train schedule. The collected dataset contained more than 100 million points, covering about 3500 m of railroads from Xi'an to Baoji in China and containing three units of track curve. The planimetric size of the area is 3648 \(\times\) 322 m, with a 69-m height variation. Figure 7 demonstrates a close-up view of a portion of the dataset. The dataset contains spatial information, i.e., 3D coordinates of the points and images of the integrated digital cameras. It covers points on the railroad infrastructure.  
As is shown in Table 4, the MLS's high range precision (5 mm) and high positional accuracy (5 cm) will produce high-quality data in which the noise level of the dataset is quite minimal. Robustness  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Measurement rate & Up to 1.1 million points per second \\ Range precision & 5 mm \\ Positional accuracy & 5 mm \\ Maximum range & 300 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specifications of RIEGL VMX-450 used to collect data in this research.  
Figure 6: RIEGL VMX-450 mobile laser system.  
Figure 7: A close-up view of the railroad infrastructure.  
analysis (Section 3.3) has proved that the accuracy of the results met practical applications when the error of data was less than 14 mm. Thus, the dataset is not cleaned for noise removal and the entire acquired dataset is employed for processing. The achieved results are assessed by relative error of the curve parameters. Table 5 demonstrates the results at the point cloud data level in terms of relative error, which is computed as in Equation (16).  
\[\delta=\frac{B}{L}\times 100\% \tag{16}\]  
where \(\delta\) is the relative error of the curve parameters, \(B\) is the deviation between the measured value and the calculated value, and \(L\) is the measured value.  
The achieved results depict that an overall average of 2.3% relative error was obtained. While the second transition curve had the highest relative error (greater than 5.4%), the second tangent had the lowest relative error (0.8%) among all the railroad elements. In addition, the full curve reached 1.6% relative error. The above results show that the proposed method can meet actual requirements.
# Median Filter Aided CNN Based Image Denoising:
## 5 Conclusions  
In this paper, we have proposed an ensemble of three deep learning models after suitable customizations for the purpose of image denoising. In the case of the first model, we use an ADNet with an increased dilation rate, along with median filter layers. In the case of the second model, we use a DnCNN with median filter layers and, for the third model, we use an IRCNN model with dilated convolutional layers and a dilation rate of 6 along with median filter layers. The final output image is a weighted average of the individual model's output images. When comparing the performance of the proposed model to other state-of-the-art models, we observe that our model outperforms others when applied on the BSD500 dataset and, in the case of the Set12 dataset, our model outperforms most of the existing denoising models. In future, we would like to use our model for the denoising of images containing other types of noise, like salt-and-pepper noise and Poisson noise.  
Conceptualization,software, S.D.; methodology,formal analysis, S.D., R.B.; writing--original draft preparation, S.D., R.B., R.S.; validation, R.B., R.S. and F.S.; supervision, R.S., F.S.; project administration, F.S.; funding acquisition, F.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 4: Denoised images using non-deep learning models and Proposed ensemble method.
# Microwave Photonic Frequency Conversion Based on a Wavelength Swept Laser
###### Abstract  
Microwave photonic frequency conversion, with flexible tunability and a simple structure based on a wavelength swept laser (WSL), is proposed and experimentally demonstrated. In the proposed frequency conversion system, a broadband WSL was used to generate a frequency-chirped optical carrier, which was sent to a Mach Zehnder modulator (MZM). A microwave signal, with its frequency to be converted, was applied to the MZM. The modulated signal was then sent to a dispersive device, where the waveform was compressed or expanded depending on the dispersion of the dispersive device. After photodetection, a frequency up-converted or down-converted microwave signal was generated. The tuning of the conversion frequency was achieved by adjusting the chirp rate of the optical carrier from the WSL or the dispersion of the dispersive device. The proposed approach was experimentally demonstrated. A microwave signal, with its frequency at 5 GHz, was up-converted to 5.577 GHz and down-converted to 4.936 GHz. The quality of the frequency converted microwave signal was also evaluated. The conversion performance could be further improved by introducing a WSL with a high duty cycle.  
frequency converter; microwave photonics; wavelength swept laser +
Footnote †: journal: Applied Physics Letters
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 3 Methods
### Starting Position  
A perhaps unremarkable but important design decision for the model is the initial guess of the model. We denote the different strategies we used and provide a small justification for our decision before delving deeper into the nuances in the Experiments section. The easiest strategy and the strategy followed by [PERSON] et al. is to start with all positional variables set to 0. As such, we will call this strategy _center_. One important aspect of how the model works is that during the projection step, the model projects only to pixels in front of it, and as such, it is unable to look backwards. Because of that, an improved strategy is to start at the minimum possible longitudinal value, which we will simply denote as _lon - 1_. A summary of the starting position strategies we used is given in Table 1. Further strategies that were explored but that did not show consistent improvements included either randomizing certain or all positional variables and training the model to guess an initial position close to the ground truth before starting the LM optimization process.
# Pentaband Dual-Polarized Antenna for Multiservice Wireless Applications
## 2 Proposed Antenna Configuration and Description  
Figure 1 provides a systematic representation of the various stages involved in the design process of the dual-polarized pentaband antenna. It highlights the various stages that were followed, including the initial design and simulation phase, followed by the fabrication and testing of the antenna. The design process involved various stages, including the selection of the antenna type, defining the operating frequency range, antenna geometry optimization, simulation, and prototyping. Furthermore, the subsequent subsection elaborates on the design process and results of a multiband antenna geometry with a single port. It provides detailed insights into the optimization process and performance characteristics of the antenna. Following this, the next sub-section discusses the geometry and characteristics of the quad-port configuration, highlighting the diversity performance of the proposed MIMO antenna.