esa-sceva/satcom-chunk-collection huggingface dataset:
# The Relationship between Radon and Geology: Sources, Transport and Indoor Accumulation
## 4 Risk Assessment and Mitigation
### Geographical and Seasonal Variations in Radon Concentration
#### 4.2.4 Influence of Climate and Topography  
Other factors that can influence radon concentration in an area are climate and topography [190]. For instance, areas with higher precipitation can have wetter soils, which may reduce soil permeability and the mobility of radon [191]. In addition, topography can affect air circulation and radon dispersion, with areas in depressions or valleys having a higher potential for radon accumulation [192]. Temperature variations due to climate and topography can also affect soil pressure, which can impact radon mobility. In their seminal work, [PERSON] and [PERSON] (1996) explored the interplay between geologic, pedologic, and climatic factors in determining the radon emanation coefficient [193]. They discovered that a soil's radon emitting power and radon transport characteristics depend on a variety of variables, including its radium concentration, grain size, the position of radon parent atoms inside or on grain coatings, and the soil's moisture levels. It was interesting to see that soils from comparable parent rocks in different places had dramatically varying emanation coefficients, mostly as a result of the influence of climatic conditions on these soil properties. The authors also stressed the use of ground-based and aerial gamma radioactivity measurements as instruments for determining the power of radon sources. Gamma spectrometry measurements of the soil's radium content and soil gas or indoor radon concentrations were shown to be correlated regionally, indicating the impact of climatic and geologic conditions on intrinsic permeability and radon emanation coefficients. When paired with information on emanation coefficients, the gathered data on soil radium concentration, permeability, and moisture content were suggested as the foundation for creating quantitative prediction models for radon production in rocks and soils.
# Polar Code Parameter Recognition Algorithm Based on Dual Space
## 2 Preliminaries
### Polar Code Construction in Gaussian Channel  
The calculation of the exact value of the Bhattacharyya parameter is only applicable to the polar code construction of the BEC. It is difficult to obtain the specific value of the Bhattacharyya parameter for other channels. Usually, a GA construction algorithm with low complexity and high precision is used in AWGN; its basic idea is to approximate the log likelihood ratio (LLR) of all sub-channels to a Gaussian distribution with the mean half of the variance, gradually calculate the LLR of each sub-channel through the LLR mean of the total channel, and then sort the reliabilities of subchannels from smallest to largest. The LLR expressions for the channel transition probabilities are  
\[\begin{split} L_{2n}^{(2i-1)}(y_{1}^{2n},u_{1}^{2i-2})=\ln\frac{ W_{2n}^{(2i-1)}(y_{1}^{2i},u_{1}^{2i-2}|u_{2i-1}=0)}{W_{2n}^{(2i-1)}(y_{1}^{2i},u_{1} ^{2i-2}|u_{  
The error probability for the \(i^{\text{th}}\) sub-channel is given approximately as [20]\(Q(\sqrt{E[L_{2n}^{(i)}]/2})\), \(1\leq i\leq 2n\).
# Insights into Membrane Damage by \(\alpha\)-Helical and \(\beta\)-Sheet Peptides
###### Abstract  
Peptide-induced disruption of lipid membranes is central to both amyloid diseases and the activity of antimicrobial peptides. Here, we combine all-atom molecular dynamics simulations with biophysical experiments to investigate how four amphipathic peptides interact with lipid bilayers. All peptides adsorb on the membrane surface. Peptide M01 [Ac-(FKFE)\({}_{2}\)-NH\({}_{2}\)] self-assembles into \(\beta\)-sheet nanofibrils that span both leaflets of the membrane, creating water-permeable channels. The other three peptides adopt \(\alpha\)-helical structures at the water-lipid interface. Peptide M02 [Ac-FFKKFFEE-NH\({}_{2}\)], a sequence isomer of M01, does not form \(\beta\)-sheet aggregates and is too short to span the bilayer, resulting in no observable water permeation across the membrane. Peptides M03 and M04 are \(\alpha\)-helical isomers long enough to span the bilayer, with a polar face that allows the penetration of water deep inside the membrane. For the M03 peptide [Ac-(FFKKFFEE)\({}_{2}\)-NH\({}_{2}\)], insertion into the bilayer starts with the nonpolar N-terminal amino acids penetrating the hydrophobic core of the bilayer, while electrostatic interactions hold negative residues at the C-terminus on the membrane surface. The M04 peptide, [Ac-FFKKFFEEKKFFEE-NH\({}_{2}\)], is made by relocating a single nonpolar residue from the central region of M03 to the C-terminus. This nonpolar residue becomes unfavorably exposed to the solvent upon insertion of the N-terminal region of the peptide into the membrane. Consequently, higher concentrations of M04 peptides are required to induce water permeation compared to M03. Overall, our comparative analysis reveals how subtle rearrangements of polar and nonpolar residues modulate peptide-induced water permeation. This provides mechanistic insights relevant to amyloid pathology and antimicrobial peptide design.  
peptides; lipids; amyloids; antimicrobial; self-assembly; pore-like structure; water permeation 2020
# Performance Analysis of Hybrid PDM-SAC-OCDMA-Enabled FSO Transmission Using ZCC Codes
## 1 Introduction
OCDMA-enabled FSO system in Section 4. Performance analysis and results are presented, respectively, in Sections 5 and 6. Finally, conclusion is given in Section 7.
## 2 The Experimental Data
As an example, let us consider the records from sound source #2290 of line ISEM-2073-108 (see the location in Figure 1). Figure 4a,b present a depth-migrated reflection section of line ISEM-2073-108, showing the seafloor in the study area, sloping gently (\(\sim\)0.01\({}^{\circ}\)) westwards between water depths of \(\sim\)1200-1300 m. The top of the Messinian salt layer was imaged at a depth of \(\sim\)2200 m and a relatively mild structure was observed in the sedimentary overburden below the seafloor footprint of source #2290. This image was used to constrain the travel-time modeling of travel times recorded from this source. Figure 4c depicts a reduced time vs offset display of the source #2290 records, overlaid with the picked phases, including the seafloor reflection, top-salt reflection, and bending rays' refraction. Figure 3: A frequency spectrum of the direct arrival in ISEM-2073-108 source #2290 records (red solid line). The frequency band (as defined by the half-amplitude taper \(A_{1/2}\); blue dashed lines) is 20 to 70 Hz, with the peak frequency at 35 Hz.
# Plant Disease Diagnosis in the Visible Spectrum
## 3 Materials and Methods  
A methodology is proposed to carry out a diagnosis of plant diseases by estimating the percentage of color associated with the diseases considered: chlorosis, necrosis, or white spots. The proposal is carried out in two steps,  
* Segmentation of the plant using GMM or PS.
* Color identification and quantification of the portion of the plant that has pixels with colors related to any of the considered diseases using HSV thresholds.  
A diagram to visualize the overall strategy is presented in Figure 1. The segmentation of the plant is performed using two methods.  
A GMM method is used in the first place because it is faster and has better segmentation in the case of images with low noise; if a high-noise background is detected, a subsequent segmentation using the PS method is performed, as that method is more robust.  
For this methodology, we analyzed a total of 4977 images; a special set of 23 images, which were selected because of the challenging background, complement the 4954 images of the public dataset PlantVillage, which is used to compare the performance of the proposal to a benchmark. The analysis of the 23 images has the purpose of evaluating the performance when subject to challenging backgrounds, and the analysis of the PlantVillage dataset has the purpose of comparing the effectiveness of the proposal against previous works.
# Impact Analysis of Satellite Geometry Variation on ARAIM Integrity Risk over Exposure Interval
## 4 Experiment and Analysis  
In order to examine the integrity performance of the ARAIM, we conducted \(P_{\text{HMI}}\) simulations based on the proposed method. First, we analyzed the bounding tightness of the evaluated \(P_{\text{HMI,actual}}\) to the reference \(P_{\text{HMI,ref}}\). Second, a worldwide ARAIM integrity performance analysis based on a combination of GPS and Galileo constellation was carried out using the proposed method. The baseline simulation conditions, which are specified in some earlier works [13; 26; 27; 28], were applied to the analysis, and those conditions are also shown in Table 1. Based on the open-source software MAAST for ARAIM Version 0.4 of Stanford University ([[https://gps.stanford.edu/resources/software-tools/maast](https://gps.stanford.edu/resources/software-tools/maast)]([https://gps.stanford.edu/resources/software-tools/maast](https://gps.stanford.edu/resources/software-tools/maast)), accessed on 4 June 2023), the GPS and Galileo dual constellations were used for the global simulation experiment. The GPS and Galileo almanac files contained in MAAST were used. The global simulation parameters came from ARAIM Technical Subgroup [1] and are listed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Satellite clock and orbits correction error \(\sigma_{\text{URA}}\) & V-ARAIM: 1.5 [m] \\  (integrity) & H-ARAIM: 2.4 [m] \\ Satellite clock and orbits correction error \(\sigma_{\text{URE}}\) & (2/3) \(\sigma_{\text{URA}}\) [m] \\  (accuracy and continuity) & V-ARAIM: \(9.8\times 10^{-8}/150\) s \\  \(P_{\text{HMI,REQ}}\) & H-ARAIM: \(1.0\times 10^{-7}/\)h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters setting [1].
# Adaptive TCP Transmission Adjustment for UAV Network Infrastructure
## 3 Asran
### Design for ASRAN  
The main concept of _ASRAN_ is adjusting TCP parameter of _ssthresh_ to decide CWND increase rate according to the main reason of congestion. If the congestion is due to heavy traffic, CWND is reduced, and then it should increase slowly, like original TCP, not to incur frequent congestion later on. On the other hand, CWND should increase fast in order to fully use the wireless link which was momentarily unstable. For this purpose, _ASRAN_ adjusts _ssthresh_ value as shown in Algorithm 1.  
```
1:while segment_loss = true do
2:function\(ssthresh\_calculator(current\_max\_cwnd)\)
3:\(original\_ssthresh=current\_max\_cwnd/2\)
4:\(recalculated\_ssthresh=\max(original\_ssthresh,last\_max\_cwnd)\)
5:\(last\_max\_cwnd=current\_max\_cwnd\)
6:return\(recalculated\_ssthresh\)
7:endfunction
8:endwhile
```  
**Algorithm 1** Modified _ssthresh_ calculator.  
When a segment loss occurs, the data sender receives duplicated ACKs (Retransmission timeout may also occur, but this means that a more serious problem occurred with the network, so _ASRAN_ behaves the same as before). In this case, a typical TCP (TCP-NewReno) reduces the CWND size in half. Also, the _ssthresh_ value is updated to half of the CWND size. If CWND size is larger than _ssthresh_ value, TCP switches from Slow-Start phase to congestion avoidance phase. In other words, after segment loss, TCP automatically activates congestion avoidance phase. Using the TCP feature of switching phase according to _ssthresh_, the _ASRAN_ adjusts the _ssthresh_ value to induce Slow-Start rather than congestion avoidance when a segment loss occurs due to transient link failure. To choose appropriate _ssthresh_ value, _ASRAN_ uses the latest CWND size prior to the previous segment loss event, named _last_max_cwnd_. Because _last_max_cwnd_ is the CWND size of successful transmission until the segment loss occurs, _ASRAN_ exploits the presumption that _last_max_cwnd_ reflects the available capacity of the network for transmission. When a segment loss occurs, _last_max_cwnd_ is compared  
Figure 1: Throughput measurement result of multi-hop UAV network.  
to the originally calculated _ssthresh_ value, _original_ssthresh_. The bigger value will be the new _ssthresh_ value, named _recalculated_ssthresh_. After that, _last_max_cvnd_ will be updated to the latest CWND size prior to the current segment loss for _ssthresh_ decision of future segment loss. Also, as shown in Algorithm 1, every segment loss event updates the _last_max_cvnd_ value which means that _ASRAN_ always reflects fluctuation of network environment.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 5 Experiment Results
### Summary  
In conclusion, the proposed HADRM method outperformed the comparative algorithms in multiple key performance indicators. HADRM performs particularly well in large-scale task scenarios, with significantly improved resource utilization and significantly better objective function values and task execution times than IDCE-GA, TS-SVMPP, MILP-STUDIO, HEFT-LA, RA-CNP, and VNE-AC. RA-CNP and VNE-AC significantly lag behind. HADRM also consistently outperforms the other algorithms in terms of CC, especially in large-scale environments. Compared to IDCE-GA, TS-SVMPP, and MILP-STUDIO, HADRM achieves a significant reduction in communication cost. It is worth noting that MILP-STUDIO has demonstrated solution accuracy and efficiency on a small task scale that is not inferior to the algorithm proposed in this paper. However, in large-scale models, our method outperforms MILP-STUDIO in terms of resource utilization and execution time. In contrast, IDCE-GA has slower convergence speed and is prone to becoming stuck in local optima due to the limitations of genetic algorithms. When the workload of TS-SVMPP surges, the search space expands rapidly and the computational load increases rapidly. RA-CNP adopts a static network model, which cannot easily adapt to environmental changes. The fixed scheduling mechanism of VNE-AC also limits its response to dynamic changes in task dependencies. Although HEFT-LA and PA-LBIMM perform well in certain evaluation metrics such as resource utilization, HEFT-LA's local optimal decision-making is enhanced by introducing forward-looking information from task sub-nodes, but still lacks adaptability to dynamic environmental changes. The load balancing mechanism of PA-LBIMM also limits its flexible response to changes in task priority. It is precisely these specific shortcomings that have led to a significant decline in the performance of comparative algorithms in large-scale complex task environments, while HADRM has achieved superior performance due to its real-time dynamic optimization mechanism.
# Quality of Experience Experimentation Prediction Framework Through Programmable Network Management
## 3 Preliminaries and Methodology
### Subjective Evaluation of DASH  
In principle, the aim is to evaluate the relevant QoE parameters and variables that take into account the kinetic properties of the video. Common objective indicators of a subject's performance to regularly determine their relevance to human perception are essential for evaluation. There is no subjective evaluation of DASH adaptive streaming for justifying longer video patterns, which are enough to explain the bit rate switching for the dataset that acquires the longer segment videos for various network condition sequences. Estimating the end user's real-time quality of experience (QOE) online by exploring the apparent influence of delay, diverse packet loss rates, unstable bandwidth, and the apparent quality of using the altered size of a DASH video stream segment over a video streaming assembly under multiple video arrangements is quite possible with this paper's approach. The performance and potential of the system and prospects depend upon the mean opinion score. The subjective evaluation of DASH gives an overview of impairments with various networks and video segments on different systems. For the test setup and procedure, we used the most recent ITU-T recommendation as general guidelines. A screen with 4K resolution was used to passively stream the content and record its segmentation, which was used to compile a separate video and present to the user to eliminate virtual environment computational power limitations from affecting the video and its MOS rating. We recommended sitting at a distance of approximately four times the height of the screen. The applied test protocol was as follows: Firstly we started with the welcome text-based information, briefing and informed consent. Then we moved on to the explanation and recommendations of the setup as recommended by the ITU-T, screening and demographic information. Following that, we showed our content, 5 video samples based on different networking runs. Our evaluation stage was next, which was a collection of 3 QoE-related questionnaires for each video sample. We ended with a debriefing which entitled the feedback and remarks from the end user. This way, we have the MOSes from the users and the recorded network data of their experiment to use for our prediction mechanisms later. Equation (1) defines MOS where R represents the user's ratings for the given question and the question's representation of the video's stimulus, where R is the individual ratings for a given stimulus by N subjects. The MOS ratings were defined into 5 different classes before uploading the rating data to the training process as shown in Table 3.  
\[MOS=\frac{\sum_{n=1}^{N}R_{n}}{N} \tag{1}\]  
where \(R\) is the individual ratings for a given stimulus by \(N\) subjects.
# Simple and Efficient Non-Contact Method for Measuring the Surface of a Large Aspheric Mirror
## 2 Methodology
### Preload and Pressure  
To increase the stiffness of the air-bearing and maintain a constant air gap during the scanning, the non-contact probe should be preloaded. Using a weight much heavier than the expected variation in the loading of the bearing preloads the air-bearing, so it rides at a smaller air gap and makes it less prone to surface slope variations (usually 10 degrees for large mirrors) and traction force.  
Figure 3 shows the relationship between load and lift of a \(\O 25\) mm pad under a working pressure of 4.1 Bar (data are from the pad's specification). It can be concluded that the air gap change is more sensitive to the light load. However, the heavy load will increase the moving mass. For large mirrors with active support, the heavy load would lead to rigid-body motion of the mirror and introduce measurement error.  
The air supply should be clean and regulated to constant pressure. Typical operating pressures range from 1 to 6 bar depending on the stiffness, load capacity, and air gap. Figure 4 shows the experiment result of the relationship between pressure and lift for a \(\O 25\) mm pad with a \(0.4\) kg load, and the maximum repeatability of lift is \(0.5\) \(\upmu\)m.  
Figure 2: The relationship between the radius of curvature of the measured surface and the average sag for a \(\O 25\) mm and a \(\O 50\) mm pad.  
The co-ordinate measurement also needs probe 'radius' compensation. The probe 'radius' consists of three parts, as shown in Figure 5:  
1. L1, the distance between the cube corner retroreflector and the bearing's floating surface. This length is a constant value and can be calibrated by measuring a plane with an SMR and non-contact probe (without air supply) respectively and calculating the distance between the fitting planes.
2. L2, floating height, usually a few microns, depends on the pad stiffness, which varies with air pressure and surface condition; it can be measured with the laser tracker or indicator by turning the air supply on or off.
3. L3, the sag related to the surface geometry, which can be directly calculated.  
Figure 4: Relationship between pressure and lift for a \(\mathcal{O}\) 25 mm pad (0.4 kg load).  
Figure 5: Schematic representation of the composition of probe ‘radius’.  
Figure 3: Relations between load and lift for a \(\mathcal{O}\)25 mm pad.
# Research on Crack Width Measurement Based on Binocular Vision and Improved DeeplabV3+
## 3 Visual Method for Measuring Crack Width in Concrete  
### Binocular Vision Spatial Coordinate Acquisition Algorithm  
Binocular vision obtains the spatial information of objects by matching the left and right camera images. Global matching and semi-global matching algorithms are commonly used, but these methods will produce a large number of mismatching regions in the matching process, resulting in holes. In this paper, an algorithm based on only three matching feature points to establish the space equation for crack measurement is designed for the case that the cracks of concrete are mostly in the plane region.  
The measurement principle of binocular vision is based on the parallax theory, that is, if \(P\) is a point in space, the spatial coordinates are \((X,Y,Z)\), \(p_{L}\) and \(p_{R}\) are the imaging points of the target \(P\) on the left and right cameras, and the image coordinates are \((x_{l},y_{l})\), \((x_{r},y_{r})\) respectively, then the calculation method of the spatial coordinates of \(P\) is as follows.  
\[\left\{\begin{array}{l}X=d\cdot x_{l}/x_{l}-x_{r}\\ Y=d\cdot y_{l}/x_{l}-x_{r}\\ Z=d\cdot f/x_{l}-x_{r}\\ \end{array}\right. \tag{10}\]  
where \(f\) is the focal length of the camera and \(d\) is the baseline length of the binocular camera.  
Since the SIFT feature point matching algorithm is robust to noise and illumination, this paper relies on it to match the feature points of the left and right crack images. As shown in Figure 7, it can be seen that the matching accuracy of the left and right image feature points of this method is high.  
The three points with the highest matching similarity of feature points are selected as the concrete plane space equation, as shown in Figure 8, \(O\) represents the optical center of the left camera, \(O_{L}X_{L}Y_{L}\) is the imaging plane of the left camera, \(O_{w}X_{w}Y_{w}\) is the spatial structure plane, \(p\) is a point on the crack edge of the spatial plane represented by the blue curve. Suppose that the coordinates of three non-collinear spatial points are \(p_{1}(X_{1},Y_{1},Z_{1})\), \(p_{2}(X_{2},Y_{2},Z_{2})\), and \(p_{3}(X_{3},Y_{3},Z_{3})\).  
Figure 8: Crack edge space projection.  
Figure 7: Feature point matching of left and right crack images.  
Therefore, the normal vector \(n\) of the structural plane where the crack is located can be solved by the following equation:  
\[\left\{\begin{array}{l}p_{1}^{\rightarrow}p_{2}=(X_{2}-X_{1},X_{2}-Y_{1},Z_{2} -Z_{1})\\ p_{1}^{\rightarrow}p_{3}=(X_{3}-X_{1},Y_{3}-Y_{1},Z_{3}-Z_{1})\\ \overrightarrow{n}=p_{1}^{\rightarrow}p_{2}\times p_{1}^{\rightarrow}p_{3} \end{array}\right. \tag{11}\]  
Then the structural plane equation is expressed as follows.  
\[(X-X_{1},Y-Y_{1},Z-Z_{1})\cdot\overrightarrow{n}=0 \tag{12}\]  
Combined with Equation (10), the corresponding relationship between each pixel in the left image and the spatial coordinates can be finally obtained.