esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
## 3 Results and Discussions
### Performance Aspects
#### 3.4.3 Global Hybrid Accelerometer Design  
Several architectures could be considered, with more or less complex designs, and technological risks vs. global performances of the hybrid accelerometer. The more risky architecture would be the one where the atomic cloud is at the center of gravity of the proof-mass with a common vacuum chamber, to have the two accelerometers at the center of gravity of the spacecraft. The main interest is to remove gravity gradient/angular acceleration effects between the CAI and EA, but this architecture would have a complex impact on the EA design. This solution would require a dedicated study to analyze its feasibility. To limit technological risks, the global architecture of the hybrid accelerometer was designed to allow the nominal work of the EA, regardless of the operation conditions of the CAI. The center of gravity of the proof-mass and the one of the atomic cloud are at a distance of a few centimeters, but the EA is completely independent of the CAI.  
We present in Figure 30 the global architecture of the hybrid instrument.  
Figure 29: (a) HybridSTAR accelerometer performance on the along-track and cross-track axis. (b) HybridSTAR accelerometer performance on the radial axis.  
This architecture is composed of the following sub-assemblies:  
* The interface with the spacecraft;
* The EA, fully functionally independent;
* The support of the glass cell of the CAI, with the pumping system and dispenser;
* The support of the optics of atom detectors and the coils for the magnetic field;
* The support of MOT fibers and optics;
* The support of the fiber and optics of the Raman laser;
* The two magnetic shields, at the CAI level and the overall instrument.  
All those sub-assemblies are mounted on the spacecraft interface.  
The MOT is positioned at 240 mm from the center of gravity of the proof-mass (217.8 mm to the mirror face PM 44.4 \(\times\) 44.4 \(\times\) 44.4 mm). The main structural parts of the CAI will be manufactured in titanium alloys, as much as possible, for the magnetic and weight aspects. If necessary for thermal stability, the Invar alloy will be used (as the EA with magnetic thermal treatment). The spacecraft interface could be manufactured in a honeycomb sandwich with carbon panel.
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
## 4 Proposed Method
### Stacked-LSTM  
In order to effectively extract the deep features of the communication signal and improve the separation accuracy, this paper proposes a Stacked-LSTM signal separation network. The network structure of Stacked-LSTM is shown in Figure 4, which consists of three parts: an encoding part, a separation part and a decoding part. The encoding part performs feature representation on the mixed signal. The separation part is trained through the stacked LSTM block to obtain the source signal mask, and the decoding part is used to restore the waveform. Accurate separation of sequence signals requires longer time window information, that is, long-term dependence. The superior performance of LSTM in sequence signal modeling and processing can meet the long-term dependence modeling of sequence signals such as voice signals and communication signals. The structure of the LSTM unit block is shown in Figure 3.  
Figure 3: Structure of the LSTM Block.
## 2 Modeling, Fabrication, and Experimental Characterization of THz FSSs
This difference in stopband performance can be explained by the larger amount of conductive material in the case of the 3D-printed sample as compared to the PE sample. In terms of peak frequency transmission, predicted filter responses for both types of filters were fairly comparable to those obtained from simulations and experiments. However, there are some apparent differences among simulated, calculated, and measured results in terms of bandwidth (BW). Particularly in the case of PE-based THz filters, a significant difference in BW was found. One reason for this discrepancy could be related to the absorption characteristics of THz waves in the PET substrate, which were not taken into account in the numerical modeling. The comparison between the PE-based and 3D-printed THz FSS in terms of several design and output parameters is presented in Table 1.
## 2 Materials and Methods
### Correcting Seasonal Effects in PRI  
Because reflectance at 531 nm is responsive to variations in the xanthophyll pigments' epoxidation state, the PRI exploits the changes in reflectance at 531 nm and a reference wavelength at 570 nm to represent the dynamics of the epoxidation status of xanthophyll pigments [27; 30; 47].  
However, the application of PRI to estimate instantaneous photosynthesis is limited by changes in the ratio of chlorophylls to carotenoids and the canopy structure. It has previously been discovered that at a 20\({}^{\circ}\) view zenith angle, the influence on canopy reflectance in the visible range is insignificant [31; 34]. Therefore, in order to accurately quantify the \"departure from steady/xanthophyll cycle deoxidation\", we calculated the 'corrected' \(\Delta PRI\)that took the seasonal variations in pigment composition into consideration [34]. At the diurnal timescale, the \(\Delta PRI\) could be expressed as follows:  
\[\Delta\text{PRI}_{\text{diurnal}}=\text{PRI}_{\text{t}}-\text{PRI}_{\text{o}} \tag{7}\]  
where \(\text{PRI}_{\text{o}}\) represents the light-acclimated PRI, which could be computed by taking the early morning PRI during the day (prior to xanthophyll de-epoxidation); \(\text{PRI}_{\text{t}}\) represents the PRI at a particular time of day.  
At the seasonal timescale, the \(\Delta\text{PRI}\) could be calculated by deducting the \(\text{PRI}_{\text{o}}\) for a given day from the minimum PRI, which reflected the maximal xanthophyll cycle de-epoxidation (\(\text{PRI}_{\text{mid}}\), generally at midday):  
\[\Delta\text{PRI}_{\text{seasonal}}=\text{PRI}_{\text{mid}}-\text{PRI}_{ \text{o}} \tag{8}\]
## 4 Development and Validation of the Platform
### Application Performance
#### 4.4.1 QoS Metrics and Measurement Goals
Measuring these KPIs under real-world network conditions, where latency can be affected by bandwidth availability and network congestion, provides a realistic picture of how responsive the system would be in practice. Early results show that, on average, the Command-to-Reception delay is only 50 ms, which aligns with industry standards [58; 59] and empirical benchmarks [60] commonly used in latency-sensitive applications. However, the Command-to-Execution increases up to 350 ms, whereas the end-to-end (E2E) latency (i.e., Command-to-Execution plus Video Streaming Latency) increases up to 650 ms; these values exceed those that can be considered real-time according to standards and are due to the complexity and experimental character of the prototype. Finally, latency thresholds for haptic feedback (i.e., under 30 ms) are based on studies [61] indicating that delays above this level reduce the user's perception of real-time response, diminishing the immersive quality essential for applications like remote driving.
# A Novel Radio Network Information Service (RNIS) to MEC Framework in B5G Networks
## 3 Our Proposed RNIS as a Service to MEC Framework in B5G Networks
### RNIS as a Service Specification
* PLMN information: Retrieves the current state of Public Land Mobile Network (PLMN) information, which refers to a public mobile communication network;
* S1 Bearer information: Retrieves the current state of S1 Bearer information, which is a communication interface between different parts of a mobile communication network;
* Layer 2 measurements: Retrieves the current state of Layer 2 measurement information, which includes data about the data link layer in communication;
* All subscriptions for a subscriber: Retrieves a list of active subscriptions for a specific subscriber;
* POST Create a new subscription: Creates a new subscription for a subscriber;
* Existing subscription: Retrieves information about a specific subscription using the subscription ID;
* PUT Modify existing subscription by sending a new data structure: Modifies an existing subscription by sending a new data structure;
* DELETE Cancel the existing subscription: Cancels the existing subscription;  
Figure 4: RNIS as a service specification workflow. * Notification callback: Sends a notification using the callback reference provided by the client.
# Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review
## 2 Materials and Methods
### Literature Search  
#### 2.1.1 Search Strategy  
A literature search was conducted in PubMed and Scopus on 29 January 2024, covering publications from 2018 onwards. This decision was made due to the substantial growth of deep learning applications in medical contexts observed only in more recent years, especially in tasks related to image generation [21; 26]. The search strategy was formulated to encompass three primary concepts: \"Quality enhancement\", \"Ultrasound\", and \"Deep learning\". It should be noted that even though this review focuses on the POCUS applications, the search included methods developed for general ultrasound imaging to ensure a comprehensive coverage of relevant algorithms. Synonyms and related keywords for each concept were identified and included in the search string, such as specific aspects of quality enhancement like denoising or specific types of deep learning networks like a convolutional neural network. The complete search strings for both databases are reported in Appendix A. Additionally, a snowballing search was conducted to identify related articles, and duplicates were removed during the screening process.
## 2 Node Mobility Model
Given that \(x(\mathrm{t})\) and \(y(\mathrm{t})\) are uncorrelated, the joint probability density of the node is as follows:  
\[\mathrm{f}_{x,y}(x,y)=\frac{1}{\pi\alpha}\exp\left[-\frac{\left(x^{2}+y^{2} \right)}{\alpha}\right]. \tag{1}\]  
Assume nodes M and N move according to mobility parameters \(\left(\lambda_{\text{M}},\ \mu_{\text{M}},\ \sigma_{\text{M}}^{2}\right)\) and \(\left(\lambda_{\text{N}},\ \mu_{\text{N}},\ \sigma_{\text{N}}^{2}\right)\), respectively. As the movements of the two nodes are independent and uniform, the joint motion distribution is investigated by considering node N as stationary and the movement of node N as the opposite movement of node M. In Figure 2, after time t, nodes M and N reach positions M\({}^{\prime}\) and N\({}^{\prime}\), respectively, and the distance between them is denoted as D. Figure 3 is equivalent to Figure 2, where the random mobility vector of node M relative to node N is derived by fixing node N. This is represented as \(s_{M,\ N}(\mathrm{t})=s_{M}(\mathrm{t})-s_{N}(\mathrm{t})\), and its amplitude approximately follows a Rayleigh distribution with parameter \(\alpha_{M,\ N}=\alpha_{M}+\alpha_{N}\). \[\alpha_{M,\ N}=\ (\frac{2\mathrm{t}}{\lambda_{\text{M}}})(\mu_{\text{M}}^{2}+ \sigma_{\text{M}}^{2})+(\frac{2\mathrm{t}}{\lambda_{\text{N}}})(\mu_{\text{ N}}^{2}+\sigma_{\text{N}}^{2}). \tag{2}\]  
Figure 2: Joint mobility transformation.
# A Wearable Sonification System to Improve Movement Awareness: A Feasibility Study
## 3 Materials and Methods
### Design of the Wearable Device  
The wearable device, WT901 BLECL, is a commercially available IMU sensor from WitMotion(r), WitMotion Shenzhen Co., Ltd., Shenzhen, China (Figure 2), which offers wireless data transmission via its built-in Bluetooth chip nRF52832 and has a built-in 260 mAh rechargeable battery. The model WT901 BLECL was selected because of the reasonable performance of its embedded motion sensor MPU9250 at measuring rotation, angular velocity, and acceleration in x, y, and z directions, as well as its compact form factor and light weight, making it easy to attach to participants' bodies with Velcro-style strapping, eliminated the need for cables, enhancing user comfort and mobility. When the IMU unit was attached to a participant's body, its x-axis rotation was about the human anatomical sagittal plane, while its z-axis rotation was about the frontal plane. The IMU also has built-in Kalman filtering that removes noise from the raw data it records, thereby enhancing  
Figure 1: An overview of the design and the signal flow diagram of the wearable auditory feedback system.  
the accuracy of the results. As illustrated in Figure 1, the IMU captures the participant's movement data. Then, the digitalized signal is transmitted over Bluetooth to an ordinary laptop computer. MATLAB (R2022b) was used on the computer to ingest the data and extract the features to trigger the sonification process. Once a trigger is received, the Max(r) (version 8) software by Cycling74 will generate a sonified signal. This signal can be played back to the participant either through the computer's speakers or transmitted via Bluetooth to headphones.  
After several trials, we found that positioning the IMU on the sternum offered the most uniform location for all participants, ensuring consistent and reliable torso measurement. Before each experiment, a calibration process was conducted while the participant remained stationary. The process involves the collection of 50 angular rotation samples at the participant's sternum, from which an average is computed to establish a reference point. The actual angular rotation values, i.e., angular displacement, are determined during the experiment by subtracting this reference value from the sensor's raw data. This method ensures an accurate estimation of the sternum's position and orientation.
## 3 Statistical Model-Based Ultrasound Envelope Statistics Parametric Imaging Techniques
### Ultratrasound Homodyne-K Imaging  
The PDF of the HK model is defined by [37]  
\[f_{\rm H}(r)=r\int\limits_{0}^{\infty}xJ_{0}(sx)J_{0}(rx)\Bigl{(}1+(x^{2} \sigma^{2})/(2\mu)\Bigr{)}^{-\mu}dx, \tag{3}\]  
where \(J_{0}(\cdot)\) is the zeroth-order Bessel function of the first kind; \(s^{2}\) is the coherent signal energy; \(\sigma^{2}\) denotes the diffuse signal energy; \(\mu\) denotes the effective number of scatterers per resolution cell; the derived parameter \(k=s/\sigma\) represents the ratio of the coherent to diffuse signal.  
The HK distribution is considered as a statistical model of ultrasound backscattered signals whose parameters have a physical meaning [10; 38]. However, the application of the HK model has been limited due to the analytical complexity in estimating the parameters. Currently, there are moment-based estimators [37; 39; 40; 41] and numerical methods [42] for estimating HK parameters. Moment-based estimators include (i) the estimator that uses the signal-to-noise ratio (SNR) and the skewness based on the first three integer moments of the intensity, which is the square of the envelope (this method is termed \"FTM\") [37; 39], (ii) a level-curve method that uses the SNR, skewness, and kurtosis based on the fractional moments of the envelope (this method is termed \"RSK\") [40], and (iii)an estimation method based on the first moment of the intensity and two log-moments, namely X- and U-statistics (this method is termed \"XU\") [41].  
The HK model has been applied to characterizing cell pellet biophantoms [43], tissue phantom heated by focused ultrasound [44], reperfused infarcted porcine myocardium in vivo [45], mice breast cancer in vivo [46], human breast lesions in vivo [47; 48], response of advanced human breast cancer to neoadjuvant chemotherapy in vivo [49], cancerous human lymph nodes ex vivo [50], porcine red blood cell aggregation ex vivo [51], human carotid artery plaque in vivo [52], human skin ulcer ex vivo [53], nonalcoholic steatohepatitis of rats in vivo [54], and hepatic steatosis of rabbit livers ex vivo [55] and rat livers in vivo [20].  
Using a rat model, [PERSON] et al. [55] demonstrated that there is a significant increase in the HK \(\mu\) parameter with increasing fat content in the liver samples. Using a rat model, [PERSON] et al. [20] reported that the \(\mu\) parameter increased monotonically as the steatosis stage increased. The area under the receiver operating characteristic curve (AUC) was 0.947, 0.914, and 0.813 for \(\geq\)mild, \(\geq\)moderate, and \(\geq\)severe, respectively. However, the feasibility of the HK model in detecting human hepatic steatosis in vivo remains unknown.
## 5 Fuel-Consumption Assessment Results
### FPGA Implementation
#### 6.2.2 Timing Performance  
Before the implementation, the maximum operational frequency was calculated. For that purpose, the architecture was implemented with a minimum clock period of 10 ns, obtaining a slack of 2.292 ns. Thus, the maximum operational frequency of the design can be calculated, as follows:  
\[f_{max}=\frac{1}{T_{imp}-d_{slack}}=\frac{1}{10\text{ ns}-2.292\text{ ns}}=129.74\text{ MHz}. \tag{10}\]  
With the maximal clock frequency of Equation (10), the designed HW implementation could be used as an AXI4 peripheral dependent on an AXI4 bus clock frequency of 100 MHz. With this operational frequency, and applying Equation (9) with \(N=4\) features and \(M=121\) neurons, this design delayed 12 clock cycles (0.12 us at \(\text{F}_{\text{CLK}}=100\) MHz) to return a valid result with a power consumption of 0.3 W. These results outperformed the timing that was obtained for a full-software PC-based MATLAB model design (20-core Intel Xeon E5-2630 v4 CPU 2.20 GHz with 32 GB of DDR4 RAM), with a timing performance of 128.03 \(\pm\) 12.01 us to compute the same SOM, as well as a PC-based, C-coded prototype that achieved timing marks of 1.34 \(\pm\) 0.28 us.  
The obtained timing performance was better than in other FPGA-based SOM applications, such as the work by the authors of [63], where the highest operational frequency obtained was 101.54 MHz for a considerably smaller SOM network. On the other hand, in [64], an absolutely novel architecture is presented with the aim of improving the general performance of the minimum's finding procedure  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Resource** & **Utilization** & **Available** & **\% Used** \\ \hline LUT & 21 107 & 218 600 & 9.66 \\ Flip-flops & 13 337 & 437 200 & 3.05 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Post-implementation resources report (Xilinx XC7Z045-2 FFG900).  
by sidelining the use of compaers. This architecture, despite operating at even lower frequencies (a maximum of 19.6 MHz), achieves very high throughput at the cost of drastically increasing complexity. In contrast, in [65], outstanding frequencies of 188.9 MHz are achieved for a bigger SOM implementation by carrying out some simplifications that might compromise the accuracy of smaller networks.  
Consequently, we can assert that the HW partition developed in this work is an appropriate solution between conventional SW-based approaches and novel FPGA-based, extreme performance architectures, which provides an adequate trade-off between complexity, performance, and development time.
###### Abstract  
This paper proposes a framework for the wireless sensor data acquisition using a team of Unmanned Aerial Vehicles (UAVs). Scattered over a terrain, the sensors detect information about their surroundings and can transmit this information wirelessly over a short range. With no access to a terrestrial or satellite communication network to relay the information to, UAVs are used to visit the sensors and collect the data. The proposed framework uses an iterative k-means algorithm to group the sensors into clusters and to identify Download Points (DPs) where the UAVs hover to download the data. A Single-Source-Shortest-Path algorithm (SSSP) is used to compute optimal paths between every pair of DPs with a constraint to reduce the number of turns. A genetic algorithm supplemented with a 2-opt local search heuristic is used to solve the multi-travelling salesperson problem and to find optimized tours for each UAVs. Finally, a collision avoidance strategy is implemented to guarantee collision-free trajectories. Concerned with the overall runtime of the framework, the SSSP algorithm is implemented in parallel on a graphics processing unit. The proposed framework is tested in simulation using three UAVs and realistic 3D maps with up to 100 sensors and runs in just 20.7 s, a 33.3\(\times\) speed-up compared to a sequential execution on CPU. The results show that the proposed method is efficient at calculating optimized trajectories for the UAVs for data acquisition from wireless sensors. The results also show the significant advantage of the parallel implementation on GPU.  
data acquisition; genetic algorithm; graphics processing units; path planning; parallel computing; unmanned aerial vehicle; wireless sensors +
Footnote †: journal: Sensors  
1
Footnote 1: [[https://doi.org/10.3390/s21206851](https://doi.org/10.3390/s21206851)]([https://doi.org/10.3390/s21206851](https://doi.org/10.3390/s21206851))
## 3 Current Research and Applications
### Highway Infrastructure Management  
The application of UAV technology in transportation involves managing highway infrastructures. The two most extensively investigated areas are bridge inspection and pavement distress recognition. [PERSON] et al. [88] performed a bridge inspection using a DJI Phantom 4 drone and identified different structural damage. [PERSON] et al. [89] proposed an algorithm to classify different types of fractures in the preprocessing images to characterize a cracked area. [PERSON] et al. [90] used UAV images and deep learning models to assess bridge and pavement conditions. UAVs are also used in pavement distress recognition and road distress monitoring. [PERSON] et al. [91] used 3D mapping data from UAVs to develop an automated method for road condition assessment. [PERSON] [92] used the DJI Mavic 2 Pro drone to obtain UAV videos, which were then used to detect potholes on the road.