esa-sceva/satcom-chunk-collection huggingface dataset:
# A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
## 3 Deep-Space Telemetry
### Galileo
\begin{split} G^{(1)}(D)&=1+D^{2}+D^{3}+D^{8}+D^{12} \\ G^{(2)}(D)&=1+D+D^{2}+D^{3}+D^{5}+D^{7}+D^{8}+D^{9}+D^{10 }+D^{11}+D^{13}\\ G^{(3)}(D)&=1+D^{2}+D^{4}+D^{5}+D^{5}+D^{7}+D^{10 }+D^{11}+D^{13}\\ G^{(4)}(D)&=D+D^{2}+D^{3}+D^{5}+D^{5}+D^{7}+D^{9}+D^ {11}+D^{13}.\end{split}  \tag{12}\]  
Figure 15: A block diagram of the modified Galileo orbiter system to support the “Galileo S-band mission”. Simplified from [61]. Compression reduced the number of information bits to be stored on the orbiter and transmitted to Earth. This was an important development for making the transmission of image data viable. A description of the compression algorithm is given in [61]. At the ground station, an innovative decoding strategy was adopted. The decoding algorithm is described and analyzed in [68]. Decoding was accomplished in four stages. * The soft outputs of the PCM/PSK demodulator were applied convolutional decoder based on the VA. The VA outputs were deinterleaved. The first RS codeword, from the (255,161) RS code, was decoded. The (255,161) RS code, capable of correcting \(t=47\) errors, is the most powerful of the four codes used for the outer code. * The detected symbols from Stage 1 were fed back to the VA for a second application of the VA assisted by the RS decoding decisions; called \"redecoding\" in [68]. The VA outputs were deinterleaved. The fifth RS codeword, from the (255,195) RS code, was decoded. The (255,195) RS code, capable of correcting \(t=30\) errors, is the second most powerful of the four codes for the outer code. * The detected symbols from Stage 2 were fed back to the VA for a third application of the VA assisted by the previous RS decoding outputs. The VA outputs were deinterleaved. The third and seventh RS codewords, based on the (255,225) RS code, were decoded. The (255,225) code, capable of correcting \(t=15\) errors, is the third most powerful of the four codes used for the outer code. * The detected symbols from Stage 3 were fed back to the VA for a fourth and final round of VA assisted by the previous RS decoding outputs. The VA outputs were deinterleaved. The second, fourth, sixth, and eighth RS codewords, based on the (255,245) RS code, were decoded. The (255,245) RS code, capable of correcting \(t=5\) errors, is the least powerful of the four codes used for the outer code. Despite all the failures, 70% of the mission objectives were met. The delayed launch date and extended voyage time created some science opportunities that would not have existed with the original plan. _Galileo_ images two asteroids in transit to Jupiter: Gaspra (October 1991) and Ida (August 1993). The images of Ida showed it had a small moon, the first asteroid-moon system discovered. About 18 months before Galileo reached Jupiter, the spacecraft found itself uniquely positioned to image the impact of Comet Shoemaker-Levy 9 on the upper atmosphere of Jupiter in July 1994.
# Calibrating of a Simulation Model to Predict the Flexural Capacity of Pre-Stressed Concrete Beams
## 3 Results and Discussion
### Experimental Tests  
Table 2 shows the results of the experimental tests on the flexural joists. The values of force, deformation and moment in the two situations: cracking and rupture are shown here. In addition, the measured crack spacing is indicated as well. Figure 6 shows the force-deformation relationship measured in the experimental tests of joists T18-1 and T35-5, having performed 3 repetitions of each joist. These results have been used to calibrate and validate the simulation model.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & **T13-1** & **T13-2** & **T18-1** & **T18-3** & **T35-3** & **T35-4** & **T35-5** & **T35-6** \\ \hline \multirow{3}{*}{Cracking} & Force (kN) & 5.40 & 8.00 & 9.65 & 13.73 & 45.85 & 108.77 & 79.40 & 79.99 \\  & Deformation (mm) & 2.00 & 3.10 & 1.30 & 2.00 & 3.75 & 1.16 & 4.45 & 2.76 \\  & Moment (kN\(\cdot\)m) & 2.60 & 3.80 & 4.51 & 6.46 & 37.60 & 43.78 & 63.65 & 65.58 \\ \hline \multirow{3}{*}{Ultimate} & Force (kN) & 11.20 & 15.90 & 17.35 & 26.90 & 81.05 & 177.77 & 117.10 & 137.90 \\  & Deformation (mm) & 29.50 & 22.80 & 25.27 & 17.08 & 23.83 & 4.73 & 16.45 & 13.94 \\  & Moment (kN\(\cdot\)m) & 5.30 & 7.50 & 8.15 & 12.64 & 66.45 & 71.57 & 96.45 & 113.10 \\ \hline \multicolumn{2}{c}{ Crack spacing (mm)} & 172 & 122 & 202 & 182 & 175 & 233 & 194 & 204 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental test results for flexural joists.
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 4 Experimental Setup
### Indicator Extraction
#### 4.4.5 EEG Data  
The raw EEG signals were preprocessed using the EEGLAB toolbox in MATLAB. The specific workflow is illustrated in Figure 5.  
Figure 4: The preprocessing of ECG data.  
Figure 3: The division of areas of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Eye Movement Indicators** & **Explanation** \\ \hline Average Fixation Duration & The mean duration of each fixation within a time window (seconds). \\ Number of & The number of fixations, saccades, and blinks within a time window. \\ Average Saccade Amplitude & The mean Euclidean distance between the start and end coordinates of each saccade within a time window. \\ Left (Right) Pupil Diameter & The average diameter of the left (right) pupil at each timestamp within a time window (meters). \\ Fixation Duration & The average duration of each fixation within area of interest A (B) in a time window (seconds). \\ Number of Fixations & The number of fixations within area of interest A (B) in a time window. \\ in Area of Interest A (B) & a time window. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Eye movement indicators and their explanations.  
Following the approach of [PERSON] et al. [61], this study used the average power of the alpha band every 10 s from four EEG channels--FP1 and FP2 in the frontal lobe and T7 and T8 in the temporal lobe--as the characteristic indicators of workload. To eliminate variations between individuals, these values were normalized within each participant. The EEG workload characteristic value for the \(i\)th participant during the \(j\)th task is denoted as \(E_{ij}\). Statistical tests demonstrated that \(E_{ij}\) can significantly distinguish between the levels of workload across different flight task difficulties (\(p<0.05\)), and it showed a Pearson correlation coefficient greater than 0.85 with subjective scales of NASA-TLX, indicating a strong positive correlation. Therefore, it may be concluded that \(E_{ij}\) can represent the magnitude of workload.
## Materials and Methods  
### About the CT Images  
Computed tomography (CT) is a widely used method for liver imaging [30]. The basic idea of CT is that the body is scanned by X-rays from multiple angles, and from these images the attenuation quotient for every pixel is computed. From these data, a 3D-map of the examined voxel can be built. Inside the voxel, the tissues can be differentiated by their different attenuation quotient, which is normalized to the attenuation of water. This normalized scale is the Hounsfield scale, and its units are Hounsfield units (HU) [31]; in which, 0 HU is the attenuation (density) of the water and \(-1000\) HU is the density of the air. Soft tissues such as liver and tumor tissue are around 20-60 HU on non-enhanced images, depending on their fat content. The computer gives every Hounsfield unit a shade of the color gray. It can be seen that, if we used the whole scale of the HU levels, the gray shades of the soft tissues would be indistinguishable from each other [32]. Therefore, a special medical image format called DICOM (digital imaging and communications in medicine) is used for medical images [33]. This has the capacity to handle a large density range and has the capability to tailor it to the needs of the operator. Using the DICOM file format, a range can be set, in which the computer divides the visible 30-60 shades of gray, to give a better contrast. This is called windowing. This is why it is important to use original DICOM files and not conventional image formats, because with the latter a huge amount of information could be lost. The DICOM file format contains not only the image data, but the patient personal data are also embedded. These are strictly protected by European and American law (GDPR in EU [34], HIPAA in USA [35]). To remove them, the first step of data gathering should be anonymization. This can be a rather time-consuming process with large amounts of data. The strict EU and USA laws regarding patient rights are the main reason why no huge databases of medical data are accessible. Another reason could be that many of the diseases are rare or, especially in benign lesions, do not need further imaging after they are discovered. In these scenarios, the large database that a proper and accurate deep learning system would demand is not collectable. Our solution to this problem is that we started to develop a system which can work with rather small amounts of medical data. We want to use expert knowledge processed on the basis of fuzzy signatures to complement the image processing systems, for a better and medically acceptable result.
# Closed-Form Method for Atmospheric Correction (CMAC) of SmallSat Data Using Scene Statistics
## 2 Materials and Methods
### Experimental Design: Testing CMAC against State-of-the-Art Sen2 Cor
Though the six QIAs are in the same region, measured Atm-I varied among the QIAs even on the same day.
## 4 Results
### Improved Performance on the Argoverse Dataset
#### 4.2.1 Qualitative Evaluation  
To complement our quantitative gains (see below), we present specific, qualitative improvements. Firstly, the LiDAR-enhanced novel method produces a _Semantic Mesh Model_ which fits the LiDAR point cloud more tightly. This means that the reconstructed shape more accurately captures the 3D locations of the semantic vertices on the target vehicle. This in turn will lead to a more accurate camera-based pose estimation. To show the qualitative improvements, we selected three test trajectories on the Argoverse dataset.  
Figure 5 shows the shape reconstruction improvements. It can be observed that although the camera-based method captures the shape closely, there can be a slight scale error in the reconstruction, which is eliminated by the LiDAR-assisted pipeline. The corresponding improvement in the pose estimation can be observed in Figure 6. Note how the novel method improves the scale correctness significantly.  
There are cases where the reconstruction fails completely in the camera-only reconstruction in [15]. This usually is the result of faulty initial pose estimates and erroneous keypoint detections misleading the optimization. Figure 7 shows a prime example of this. Through the use of the novel method, the error is corrected.  
Figure 5: This figure shows the improvements over [15] with regard to shape reconstruction. In blue, the semantic vertices reconstructed by [15], in green the current results, and in black the reference LiDAR shape.  
Figure 6: In this figure, the improvements of MM-VSM (green) over BAAM [13] (red) and Semantic [15] (blue) estimation can be observed on three demonstration trajectories from the Argoverse dataset. These are the qualitative pose estimates in the world coordinate system with the LiDAR points as backgrounds. Semantic and MM-VSM use their respective previously reconstructed _Semantic Vertex Model_ and _Semantic Mesh Model_ for single-image-based pose estimation.  
The more accurate scale leads to significantly improved single image-based pose estimation. This qualitatively is visible in Figure 6, which contains the result of the single-image-based pose estimates using the reconstructed models from Figure 5. Figure 6 contains reference LiDAR point clouds, the original camera-only method's results and the outputs of BAAM [13] for visual comparison.
# Study on the Performance of Optical Lenses under High Fluxes of Solar Radiation
## 2 Materials and Methods
### Vibrational Spectral Analyses of Lenses
#### 2.3.1 Ultraviolet (UV)-Visible Spectral Analysis  
UV-visible absorbance/transmission spectra were obtained at room temperature with a Spectronic Helios Alpha UV-visible spectrophotometer (Thermo Electron Corporation, Waltham, MA, USA) in the 190-1100 nm wavelength range.
## 2 Pipe-Soil Collapse Analysis Model  
Buried pipelines are laid under the surface, so the pipe-soil collapse analysis model is greatly affected by the surrounding soil. How to establish a reasonable pipe-soil interaction analysis model is particularly important. There are three main types so far: the elastic foundation beam model, the soil spring model, and the nonlinear contact model. The most famous elastic foundation beam model is [PERSON]'s local elastic foundation model. The principle is to simplify the foundation into many independent springs on a rigid base. The biggest disadvantage of this model is that it is mainly suitable for hand calculation and analysis of simple structures and is not suitable for large and complex structures. The core content of the soil spring model theory is to equate the soil medium around the pipeline into a series of springs, referred to as equivalent elastic-plastic springs. The degree of freedom of the spring depends on the movement form of the collapsed soil, and the stiffness of the spring depends on the soil quality of the soil covering the pipeline. The advantage of this model is that it is more computationally efficient than complex soil models. The disadvantage is that nonlinear behavior is ignored. Soil spring models are usually based on elastic behavior and therefore ignore the nonlinear behavior of soil, such as plastic deformation, friction effects, etc. In some cases, this may result in models with limited accuracy for soil behavior. The nonlinear contact model can truly describe the nonlinear friction process between pipe and soil. Existing numerical simulation research methods for soil collapse mainly use the method of applying displacement loads, and only some studies use unit life and death technology. This technology can more realistically simulate soil loss during the gradual development of collapse. Therefore, the pipe-soil nonlinear coupling model of buried pipelines passing through the collapse area constructed in this paper based on unit life and death technology is more in line with the applied research on collapse geological hazard problems.
## 3 Results and Discussion
### Adsorption Efficiency of LPD Biomass  
#### 3.2.1 Effect of Contact Time  
Figure 2 shows the adsorption profile of Pb\({}^{2+}\) onto LPD adsorbent over the adsorption period of 0-180 min. It is obvious that, under the applied adsorption conditions, two phases of sorption behavior are present: a relatively fast, initial one from 0 to 90 min, followed by a slow, final phase up to 180 min, the pseudo-equilibrium, with experimental adsorption capacity (\(q_{e}\)) of 4.12 mg/g and removal percentage (\(R_{e}\)) of 32%. The rapid adsorption in the first stage of adsorption can be attributed to the high availability of vacant sites on the adsorbent surface for the heavy metal ions occupation. By reaching the last stage of adsorption, the adsorbed ions cause increased repulsive forces, making the remaining sites difficult to be accessed, resulting in reduced adsorption rate [27]. Similar findings were reported, using banana peels and _Ultra lactuca_ for the removal of Pb(II) and Cd(II) [28; 29], and _Schoenoplectus californicus_ plant for the removal of Cr(III) and Pb(II) [30].
## 5 Conclusions  
This study has proposed a wearable assistive system to help the visually impaired walk safely on the sidewalk or crosswalk without hitting obstacles. The semantic segmentation model Fast-SCNN was trained to recognize the user's surrounding environment, and the depth map created by the ZED 2 was used to measure the object's distance in front of the user. Combining the above two environmental information, we have developed a walking guide strategy for the visually impaired. Moreover, the object detection model YOLOv5s was trained to detect and identify obstacles. With the aid of the proposed assistive system, the experiment has shown that a visually impaired user can walk on the sidewalk and crosswalk safely without hitting any obstacles. However, we have to admit that if an object appears suddenly in the camera's blind spot within 0.2-1 m in front of the user, they may not have time to avoid it. In this situation, the white cane is still helpful for the visually impaired person. Therefore, we suggest that the visually impaired person still uses the white cane to maximize safety when using the assistive system. In addition, the study on visually impaired people traversing an intersection with traffic lights is still in progress. The traffic light platform \"Insignal\" can provide the traffic light status and timing for each intersection in northern Taiwan. This platform will be used in our consequent experiment. We will publish the study result if we have a more stable experiment result in the future. Furthermore, we will continue experimenting with more visually impaired users to verify the effectiveness of our proposed system. For the limited testers, we have completed different experiments many times, and the performance is almost consistent.  
Conceptualization, W.-J.W.; Formal analysis, W.-J.W.; Funding acquisition, W.-J.W.; Investigation, W.-J.W.; Methodology, I.-H.H., W.-J.W., and H.-C.C. (Hsiao-Chu Cheng); Resources, H.-C.C. ([PERSON]); Software, I.-H.H. and H.-C.C. ([PERSON]); Validation, W.-J.W. and H.-H.K.; Visualization, I.-H.H., H.-C.C. ([PERSON]), and H.-H.K.; Writing--original draft, I.-H.H., H.-C.C. ([PERSON]), and H.-H.K.; Writing--review and editing, I.-H.H., W.-J.W., H.-C.C. ([PERSON]), H.-H.K., and H.-C.C. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry of Science and Technology of Taiwan with the grant number 110-2634-F-008-005. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available in [[https://github.com/kev72806/TW-NCU-ICIP-Lab-dataset](https://github.com/kev72806/TW-NCU-ICIP-Lab-dataset)]([https://github.com/kev72806/TW-NCU-ICIP-Lab-dataset](https://github.com/kev72806/TW-NCU-ICIP-Lab-dataset)) (accessed on 24 October 2021). Conflicts of Interest: The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Materials and Methods
### Instrument Setup and Data Collection
#### 2.2.1 Gas Data  
Gas concentrations both outside and within the barn were measured using sample lines, polytetrafluoroethylene (PTFE) tubes with an inner diameter of 6 mm equipped with capillary traps every 8-10 m to ensure constant volume flow over the whole length (Figure 2). The sampling lines were positioned according to Figure 2, with two lines outside the barn (at the southern (s-out) and the northern openings (n-out), and four lines inside the barn (n-in, m-in, m-in-up, and s-in) ). The gas concentrations were measured with a FTIR spectrometer (Gasmet CX4000, Gasmet Technologies Inc., Karlsruhe, Germany) capable of switching between sample lines using a manifold. Each of the six lines was measured for ten minutes (4 min spent flushing the measuring cell and 6 min of measurement). Using this procedure, all lines could be measured sequentially each hour.
# Structural Behavior of Floor Systems Made by Floor Plates--Mechanical Model Based on Test Results
## 3 Results
### Strains  
#### 3.3.1 Horizontal Strains  
The main values of the horizontal strains measured on both sides of the specimen at five levels (see Section 2.3) are used to evaluate the deformation of the cross-section at midspan. The diagrams in Figure 7 show that for some specimens the evolution of this deformation is a function of the applied moment (including the dead load--see Equation (4),further on) and are used to evaluate following parameters: (a) presence of voiding elements, (b) the position of the lattice girder, and (c) type of finishing of the floor plate.  
Figure 6: Deformation shape along the longitudinal axis: (**a**) solid slab C02; (**b**) solid slab C04; (**c**) solid slab with Plexi C05 and free rods at the start; (**d**) solid slab with Plexi C06 with fitted rods.  
Comparing the results of A02 (Airdeck(r)) and A10 (solid) gives insight into the effect of the voiding elements. In A10 the strains at the lower level are smaller than in slab A02, for the same load level. However, the strains in the topping are larger, indicating a more monolithic behavior of the solid composite floor. The position of the lattice girder to the joint has a clear influence on the deformations of the slabs as a whole. In slab C03, the lattice  
Figure 7: Deformation of the cross-section at midspan at different levels of the total applied moment for (**a**) A-series (A02 and A10); (**b**) C-series (C03 and C04); (**c**) D-series (D01 and D02).  
girder is positioned 95 mm from the joint, resulting in a permanent monolithic behavior of the floor plate and the topping, and validity of [PERSON]'s hypothesis over the entire slab depth. In C04 the lattice girder is placed 400 mm and a clear shift in strain in the floor plate and the topping is observed. However, it can be seen that the strain at the lower level in the cross-section is very similar in both slabs. In series D, two types of finishing of the floor plate were tested: a smooth (D01) and an intended surface (D02). Only a small difference in behavior (slightly larger strains in D01) can be observed between the two slabs.  
A supplementary vertical crack passed the measurement basis at the highest load level of the D02-test (245 kNm), which causes an extra deviation of the observed strains.