esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A hybrid fixation method, using a combination of verteboplasty and cement-augmented screws, has been demonstrated as a useful technique for securing osteoporortic burst fractures. The purpose of this study was to assess changes in the range of motion (ROM) and stress in the spine after treating a lumbar burst fracture with this hybrid method. Five finite element models were developed: (a) intact lumbar spine (INT), (b) INT with verteboplasty at L3 (AwC), (c) two-segment fixation of AwC (AwC-TSF), (d) AwC-TSF model with cement-augmented screws (AwC-TSF-S), and (e) INT with an L3 burst fracture treated with two-segment fixation (TSF). After loading, the models were evaluated in terms of the ROM of each motion segment, stiffness of fusion segments, and stresses on the endplates and screws. The results showed that the TSF model has a larger ROM at the instrumented segments than both the AwC-TSF and AwC-TSF-S models. The stiffness at L2-L4 under extension and lateral bending in AwC-TSF, AwC-TSF-S and TSF was approximately nine times greater than the INT model. In conclusion, the hybrid fixation method (AwC-TSF-S) results in a stiffer construct and lower ROM at instrumented segments, which may also reduce the risk of fracture of adjacent vertebrae.  
finite element analysis; verteboplasty; cement-augmented screws; lumbar burst fractures; two-segment fixation +
Footnote †: journal: Applied Sciences
## 5 Simulation Experiment Results
### Results and Analysis
The simplified EUTC integration system comprises large frequency error detectors and refined frequency processors, but uses the same conventional channel prefilters as the CUTC integration. The frequency tracking errors are shown in Figure 14. The results show that the conventional prefilter cannot track the large changes in frequency errors in time. Referring to Figure 10a, the adaptive prefilter significantly reduces the hysteresis of large frequency error estimation. The attitude, velocity and position errors of the simplified EUTC integration are shown in Figure 15. Evidently, the convergences of navigation errors are significantly slower than the EUTC integration system using adaptive prefilters. The navigation errors are obviously larger than the navigation errors of the EUTC integration shown in Figure 11. In other words, the adaptive prefilter can help to reduce navigation errors caused by large frequency errors. Table 2.0 Frequency tracking errors of the simplified EUTC integration. Table 2.1 Comparison of \(C/N_{0}\) estimates calculated by the NWPR method.
## 2 Materials and Methods
### Tensile Experiments  
Quasi-static tensile tests were performed using a Zwicko Z100 tensile machine according to DIN EN ISO 6892-1, but using flat samples with a gauge length of 25 mm and a width of 6.25 mm. The thickness of the plate was 1 mm. Tests were conducted in the parallel and perpendicular directions to the rolling direction on the sheet plane, at a strain rate of \(4\cdot 10^{-5}\) s\({}^{-1}\) in the elastic region and \(8\cdot 10^{-3}\) s\({}^{-1}\) after yielding. The stopping criterion was defined by final fracture. Two repetitions of each test were performed, such that, in total, four quasi-static tests were performed with each of the materials.  
High strain rate tensile tests were performed using the Split Hopkinson Tensile Bar (SHTB) setup at Ghent University [46]. Incident, reflected and transmitted signals captured with strain gauges were translated to engineering tensile curves using the equations by [PERSON] [47]. The experiments were performed at strain rates of approximately 650 s\({}^{-1}\) using the specimen geometry described in [48], with a gauge length of 5 mm. All the experiments were performed such that the longitudinal direction of the specimen coincideswith the rolling direction of the sheet. Three experiments were performed under the same conditions for each of the materials.
# An Improved Channel Estimation Technique for IEEE 802.11p Standard in Vehicular Communications
## 3 Overview of Current Channel Estimation Schemes
### Scenario 2: Remain the Structure of the IEEE 802.11p
#### 3.2.2 Generalized Discrete Prolate Spheroidal (GDPS) Sequences Based Scheme  
Another iterative approach is followed by [15], that estimates the channel based on the generalized DPS sequences. Theoretically, the DPS sequences can tighten the design of subspace and the iterative estimation mechanism provides the pilot information. This concept was investigated in [35]. The DPS sequences minimize the MSE by tightening the subspace. The generalized DPS sequences can simplify the channel estimation and it only requires DSP coefficients. The iterative algorithm requires higher computational complexity and in order to deal with such problem, an enhanced pilot symbol pattern was proposed as well in [35]. The overall kernel is postamble appended after the Data section. However, such reduction in computational complexity will lead to a decrease in the efficiency and incompatibility with the standard.
## 4 Experiments
### Ablation Study
#### 4.3.1 Multi-Level Supervision  
To verify the effectiveness of the progressive super-resolution and multi-level supervision network structure of our method, the super-resolution output results for different levels were compared. First, the number of components in our method's network was reduced to one, setting the super-resolution scale factor of the component to four. The classical one-step super-resolution method was conducted on our network. Second, the number of components in our network was increased to two, setting the super-resolution multiplier to two for both. Third, the number of components in our network was increased to three, setting the super-resolution multiplier to two for both. Third, the number of components in our network was increased to three and the super-resolution scale factor of each component was set to \(\sqrt{2}\), \(\sqrt{2}\) and two. Finally, the number of components was increased to four and the super-resolution scale factor for each component was set to \(\sqrt{2}\). In the process of increasing the number of components, supervision was applied at the output of each component.  
The experimental results corresponding to different numbers of components are shown in Table 3. As the number of components in the network increased, the PSNR and SSIM of the super-resolution results improved. This indicated that as the number of components in the network increased, the number of supervision levels increased and more supervision images were introduced into the network training process. The network training received more constraints, and the ill-posedness of the super resolution was reduced. This benefited the reconstruction accuracy of the super-resolution results.
## 2 Materials and Methods
### Obstacle Cost Map Construction
#### 2.2.3 Obstacle Cost Map Generation  
For the heuristic algorithm, the map cost value directly affects the accuracy of the estimation function, thus affecting the quality of the final path. Voronoi diagram is a geometry-based route planning method for robot path planning [36; 37], which divides the space into several regional units according to the nearest neighbor attributes of the elements in the site collection. [PERSON] [30], the inventor of the Hybrid A\({}^{*}\) algorithm, used the generalized Voronoi diagram to generate the planning map, which made the planning path can pass through the narrow area and without collision. The generation steps for the obstacle cost map are as follows:  
1. Building the two-dimensional Voronoi diagram from the binary map. The 1-value coordinate points in the binary map of obstacles are regarded as the scatter points. Then, calculate the 2-D Voronoi diagram of all scatter points. The red dot is the Voronoi station, and the blue line is the Voronoi edge, as shown in Figure 6a.
2. Cutting off the Voronoi edges which passing through the obstacle polygon, and the remaining edges are the edges of GVD. The black square is the obstacle polygon, and the red line is the GVD edges, as shown in Figure 6b.
3. Converting the GVD into grid form by sampling method, as shown in Figure 6c. Then, calculating each grid cost by Equation (5) to construct the obstacle cost map. The grid cost reaches its maximum within obstacles, and the color is close to black. It reaches its minimum on the GVD edges. The color is close to white, as shown in Figure 6d.  
\[gridCost(x,y)=\left\{\begin{array}{ll}(\frac{a}{\alpha+d_{o}(x,y)})(\frac{d_ {o}(x,y)}{d_{o}(x,y)+d_{o}(x,y)})(\frac{(d_{o}-d_{o}^{max})^{2}}{(d_{o}^{max} )^{2}}),\;d_{o}<d_{o}^{max}\\ 0,\;others\end{array}\right. \tag{5}\]  
where \(gridCost(x,y)\) is the cost of each grid. It is continuously distributed in the range of 0 to 1, only takes 1 in the obstacles, and takes 0 on the Voronoi edge. \(\alpha\) is the parameter to control the descent rate of cost, \(\alpha>0\). \(d_{o}^{max}\) is the maximum range of potential value distribution, \(d_{o}^{max}>0\). For the grid points that exceed the \(d_{o}^{max}\), the grid cost is 0. \(d_{o}\) is the distance from the grid point to the nearest obstacle edge. \(d_{v}\) is the distance from the grid point to the nearest Voronoi edge.  
Using this method to process the obstacle binary map of the cutting zone, we get the corresponding obstacle cost map, as shown in Figure 7. However, due to the high grid precision and the maximum range of potential value distribution, only the area at the edge of the obstacle (black area) has an available cost (gray area). For the rest of the open space area (far away from the obstacle), the grid cost is 0 (white area). Therefore, to make the algorithm consider the cost in the open area, we construct the roughness cost map to complement this area.  
Figure 5: Obstacle binary map of cutting zone. The black area is the obstacle area, which retains the typical details of the four-direction scanning binary maps.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 5 HARRF LNA Design
### K/Ka-Band LNA Design Procedure
#### 5.2.1 Stage-by-Stage Design Considerations  
* **Stage one:** The first stage uses a 2 \(\times\) 50 um pHEMT with inductive source feedback to achieve the optimum noise match (\(\Gamma_{\text{opt}}\)) for the target frequency. The inter-stage matching network transforms the 50 \(\Omega\) input source impedance to the required noise match impedance.
* **Stage two:** The second stage also uses a 2 \(\times\) 50 um pHEMT. The inter-stage matching network ensures that the output impedance of stage one is properly matched to the input impedance of stage two to maintain maximum stable gain.
* **Stage three:** A 4 \(\times\) 50 um pHEMT is employed in the final stage to provide higher output power. Parallel feedback is introduced to enhance gain flatness across the entire band of operation.  
Figure 13 shows the complete schematic of the three-stage K/Ka-Band LNA. Key aspects include:  
* C1 and C11 serve as DC blocking capacitors, preventing DC currents from flowing along the RF path.
* C2, C4, C5, C7, C8, and C10 are power supply decoupling capacitors to isolate RF signals from the bias lines.
* R1, R3, and R5 are large-valued resistors that help in reducing RF coupling into the DC supply.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref.** & **Process** & **Application** & **Freq. (GHz)** & **Gain (dB)** & **Noise (dB)** & **Power (mW)** \\ \hline
[19] & 0.7 μm GaAs mHEMT & SATCOM & 7–11 & 30 & 1 & 62.2 \\
[20] & 0.65 μm CMOS & SATCOM & 9.35–10.65 & 27.8 & 1.8 & 4.68 \\
[21] & 0.13 μm SiGe BiCMOS & SATCOM & 6–12 & 21 & 1.7 & 100 \\
[22] & 0.65 μm CMOS & SATCOM & 8–12 & 15 & 8.4 & 110 \\
[23] & 0.18 μm CMOS & SATCOM & 6.4–7.4 & 12.5 & 3 & 19 \\
[24] & 0.1 μm GaAs mHEMT & SATCOM & 4–12 & 31.5 & 1.31 & 8 \\
[25] & 0.1 μm InPEMT & SATCOM & 0.3–14 & 40 & 2.73 & 12 \\
[15] & 0.15 μm InGaAs pHEMT & SATCOM & 8–12 & 40 & 0.9 & 43 \\ \hline \end{tabular}
\end{table}
Table 1: State-of-the-art LNAs operating at the target X frequency band.  
Figure 12: Three-stage K/Ka-Band LNA topology ([18]).  
The third stage employs a series RC feedback network (C9 and R6) to achieve the desired gain flatness.
* M1, M2, and M3 denote the pHEMT devices in stages one, two, and three, respectively.
## 3 Candidate Universal Decoders for URLLC
### Belief Propagation Decoding (BPD)  
The BP algorithm was first developed by [PERSON], and, later, [PERSON] proposed a Tanner graph representation of it. As opposed to decoders based on successive cancellation, belief propagation (BP) decoding has low latency and high parallelism, making it a suitable choice for hardware implementation. BP decoding is based on message transmission between check nodes and variable nodes positioned on the right and left edges of the factor graph. A factor graph is defined as a bipartite graph with a vertex set \(V\cup F\), where \(V\) represents the variable nodes and \(F\) is the set of factors. Polar code decoding using belief propagation (BP) offers the benefits of high parallelism and low latency, which makes it particularly scalable for URLLC applications. The given Algorithm 5 is developed based on the article [31].  
```
1 Variable declaration
2\(y\): received signal vector
3\((i,j)\): edge between variable node \(i\) and check node \(j\)
4\(L_{i}^{(t)}\): LLR value of variable node \(i\) at \(t\)-th iteration
5\(N(i)\): set of check nodes connected to the variable node \(i\)
6\(N(j)\): set of variable nodes connected to the check node \(j\)
7\(M_{ij}\): message from each variable node \(i\) to check node \(j\)
8\(M_{ji}^{(t)}\): message from check node \(j\) to variable node \(i\) at \(t\)-th iteration
9\(M_{kj}\): message from each variable node \(k\) to check node \(i\)
10 Step I Initialization based on channel observations
11 Input:\(y\)
12 Compute \(L_{i}\) for each variable node
13 Each variable node sends \(L_{i}\) to connected check nodes
14 Step II Iterative message passing
15 Compute \(M_{ij}=L_{i}+\Sigma\ M_{ki}\), \(k\in N(i)\) excluding \(j\)
16 Compute \(M_{jl}^{t}=2\ tanh^{-1}(\ \Pi tanh(M_{lj}^{t-1}/2))\), \(l\in N(j)\) over all variable nodes excluding variable node \(i\)
17 Step III Message update
18 Compute updated belief \(L_{i}^{t}=L_{i}+\Sigma M_{jl}\), \(j\in N(i)\)
19 step IV Hard decision
20 Input:\(L_{i}^{t}\)
21 Output:\(\hat{u}_{i}\)
22 Compute \(\hat{u}_{i}=\begin{cases}0,if\ L_{i^{\prime}}>0\\ 1,if\ L_{i^{\prime}}<0\end{cases}\)
```  
**Algorithm 5**Belief propagation decoding (BPD)
# PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment
## 5 Results  
To verify the effectiveness of the proposed visual inertial SLAM algorithm based on the fusion point and line features in indoor environments, experiments were carried out using EuRoC datasets. The dataset was collected by a micro aerial vehicle (UAV) at two different scales, industrial factory and indoor room. There are 11 sequences, including binocular stereo (752\({}^{\circ}\)480) images, 200 Hz synchronous IMU information, trajectory truth, and calibration files for external and internal parameters of different sensors. These sequences are classified into different levels based on lighting, texture, dynamic motion, or motion blur.  
The experimental platform was configured as Intel I7-7700 HQ (8 cores @ 2.8 ghz) CPU, 16 GB memory, no GPU acceleration, and 64-bit Ubuntu 18 operating system.  
Firstly, this paper verifies the effectiveness of the proposed improved LSD algorithm in screening invalid line segments in indoor environments, especially in the efficiency of line segment extraction and matching. Then, the root mean square error (RMSE) of absolute trajectory error (ATE) is used to evaluate the effect of the improved LSD algorithm on improving the accuracy of camera pose tracking, and the effect of the nonlinear optimization algorithm with point and line residuals on the accuracy of camera motion trajectory.
# Detection of Frozen Soil Using Sentinel-1 SAR Data
###### Abstract  
The objective of this paper is to evaluate the potential of Sentinel-1 Synthetic Aperture Radar \"SAR\" data (C-band) for monitoring agricultural frozen soils. First, investigations were conducted from simulated radar signal data using a SAR backscattering model combined with a dielectric mixing model. Then, Sentinel-1 images acquired at a study site near Paris, France were analyzed using temperature data to investigate the potential of the new Sentinel-1 SAR sensor for frozen soil mapping. The results show that the SAR backscattering coefficient decreases when the soil temperature drops below 0 \({}^{\circ}\)C. This decrease in signal is the most important for temperatures that ranges between 0 and \(-\)5 \({}^{\circ}\)C. A difference of at least 2 dB is observed between unfrozen soils and frozen soils. This difference increases under freezing condition when the temperature at the image acquisition date decreases. In addition, results show that the potential of the C-band radar signal for the discrimination of frozen soils slightly decreases when the soil moisture decreases (simulated data were used with soil moisture contents of 20 and 30 vol\(\%\)). The difference between the backscattering coefficient of unfrozen soil and the backscattering coefficient of frozen soil decreases by approximately 1 dB when the soil moisture decreases from 30 to 20 vol\(\%\)). Finally, the results show that both VV and VH allow a good detection of frozen soils but the sensitivity of VH is higher by approximately 1.5 dB. In conclusion, this study shows that the difference between a reference image acquired without freezing and an image acquired under freezing conditions is a good tool for detecting frozen soils.  
Keywords:frozen soils; dielectric constant; IEM; SAR; C-band; Sentinel-1 +
Footnote †: journal: Remote Sensing, 10, 1182; doi:10.3390/rs10081182
# Improving Air Pollution Prediction System through Multimodal Deep Learning Model Optimization
## 3 Dataset  
In our previous work [2], we forecast fine dust after 8 h using only numerical data, and the best performance was obtained when up to 5 h prior data were used as an input value. In this paper, we construct a multimodal dataset by merging satellite image data with the same numerical dataset in [2] to improve performance further.
## Alternative Organic Solvents in RP-HPLC
### Others Solvents  
#### Acetone  
Acetone is another greener substitute to ACN in RP-HPLC applications due to its low toxicity and its biodegradability properties. Acetone shares similar physicochemical characteristics to ACN in terms of solubility and miscibility properties with other solvents, including water. Indeed, acetone is in the same group as ACN in [PERSON] et al.'s [2] classification. In addition, they present similar viscosities (0.37 mPa\(\cdot\)s for ACN and 0.33 mPa\(\cdot\)s for acetone at 20 \({}^{\circ}\)C) [26]. The main drawback of acetone compared to ACN is its high UV cut-off, which reaches 340 nm, limiting its use with UV detection [9]. Moreover, acetone is highly volatile, and therefore difficult to pump. However, the fastpopolarization of MS-based detectors in LC and the new generation of aerosol-based LC detectors offer new opportunities for the usage of acetone in RP-HPLC.  
The possibility of effectively substituting ACN with acetone was investigated by [PERSON] et al. [65]. In this study, a RP-HPLC method with double detection UV and corona-charged aerosol detector (CAD) was developed to fingerprint a complex plant extract. Based on the design of experiments, the methods using ACN or acetone were successfully optimized and compared. The same performance of separation and detection was noticed with both solvents using a CAD detector. The number of detected peaks and peak capacity were proved to be statistically similar. However, the superiority of acetone was evidenced when parameters relating to method greenness were taken into account. Moreover, there are some reports of satisfactory replacement of ACN with acetone in RP-HPLC employing mass spectrometer detectors in the analysis of peptides [66; 67]. However, as UV remains the most widespread detector in pharmaceutical analysis, acetone is clearly not the favorite green solvent to replace ACN in RP-HPLC.
# Ionospheric Science: An Example of the Importance of Diversity in Approaches to Scientific Research
## 3 Where Are We Today?
This is now being addressed through the development of \"Research-to-Operations\" (R20) programmes that bring researchers together with service providers to promote the development of high-quality modelling software and to support its validation in simulated operational environments. It is essential that such software be able to detect and smoothly deal with operational problems such as missing or poor-quality data, to alert operations teams to major problems, and to be secure against the hostile actions of those who might wish to disrupt operational services of all kinds. This need for dedicated actions to transition research into operations should also be complemented by feedback from operations teams to research teams. This \"Operations-to-Research\" (O2R) dialogue can be a great stimulus to researchers, triggering ideas for future research projects, both curiosity-driven and targeted. The support from operators can also help to build the case for funding that research. Thus, as discussed by [PERSON] et al. [37], links between researchers and service operators can form an iterative loop (R202R) that can advance both high-quality science and high-quality services. In summary, ionospheric science is now a mature field similar to many other environmental sciences. Whilst we have a good understanding of the basic science, there is a wealth of detail that needs to be explored and that has links to other disciplines including atmospheric science, plasma physics, magnetospheric physics, and planetary and exoplanetary science. There is much scope for curiosity-driven research as shown by a range of ionospheric effects that have been discovered over the past 20 years, e.g., the longitudinal modulation of the equatorial ionisation anomaly by energy flows from the troposphere (e.g., see [38]), the curious auroral structures known as STEVE (e.g., see [39]), and many others. However, as with other environmental sciences, there is also much scope for targeted research and to build strong links between researchers and service providers, both vital to focus research on strategic goals and then transition research outputs into operational use.