esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 GPR Investigations on Songino Fault
### GPR Observation at Transstensionnal Part of the Fault (SE Part of Songino Fault)  
The GPR profile with a 250 MHz antenna across the transstensional section of the fault (see red arrow in Figure 2) exhibits a strong reflection, which is offset (see white arrows). This vertical apparent displacement of about 1.3 m (Figure 4) probably does not correspond to the real vertical displacement as the fault is also associated with a strike-slip component. Indeed, when non-horizontal layers are affected by a pure strike-slip, a vertical shift (up or down) of these layers appears in a perpendicular section or trench. This process could account for a part of the vertical apparent displacement. In addition, the thickness of the sediment (composed of sand and silt) over this strong reflection is much greater uphill (left side of Figure 4) than downhill (on the right).It can be either due to a lateral shift (thicker sediments are moved laterally) or to deposits against a natural dam built by a vertical component on the fault (uphill, north) and lower deposits or even erosion (downhill, south). The GPR image shows a normal subsurface deformation that is enhanced by competent layer structure under the scarp (see white arrows). A change in GPR reflections is observed in the fault zone below the concave surface topography, indicating a filling zone with a different sedimentation and without visible stratification. The GPR reflections in this zone are less continuous compared to the reflections on both sides of the fault (uphill and downhill).  
Another GPR profile at about 150 m east of the previous one (red arrow in Figure 2) was conducted with a 250 MHz antenna in September 2012. The black arrow at around 48 m horizontally indicates the fault zone (Figure 5b).  
On the left (north part) of the fault zone (black arrow at 48 m in Figure 5b), we observe reflections with a good continuity down to 1 m and a clear continuous reflection located between 20 and 40 m horizontally (white arrow of Figure 5b), whereas on the right of the fault zone (south part), the reflections are irregular and might correspond to the weathered bedrock. As in the case of Figure 4, the thickness of the sediment on the left of the scarp is more important uphill (left side in Figure 5) than downhill (on the right), showing a jump in the sedimentation process when crossing the fault zone. The height of the fault scarp in Figures 4 and 5 is around 0.5 m. On both GPR profiles, the uphill has been lowering, suggesting a counter-slope of the fault.  
Figure 4: GPR image of the profile at the southeastern part of the Songino fault (transstensional fault zone, see red arrow of Figure 2) obtained with a 250 MHz antenna. The topographic correction is performed using a velocity of 0.1 m/ns. The GPR image shows a strong reflection (white arrows) that is affected and shifted by recent deformation. The black arrows indicate the apparent vertical movement along the fault zone.
## 2 Numerical Methods
### Aim of the Numerical Method  
The main aim of the method is to determine if a minimal reserve capacity can be guaranteed by evaluating the Load Rating Factor \(V\)[24] for a particular railway load, given only the span \(L\), rise-to-span ratio \(f/L\), and design loads.  
The Load Rating Factor \(V\) is associated with the load multiplier and provides an estimate of the ratio between capacity \(C\) and demand \(D\).  
In general, for an arch which bears the permanent load \(g\) and the live load \(q\), the total load \(F\) can be defined as in Equation (1):  
\[F=g+\alpha\cdot q \tag{1}\]  
where \(\alpha\) is the load multiplier. The multiplier allows quantification of the live load increase in comparison to the reference value \(q\). For \(\alpha=1\), the live load is the exact reference load, whereas for \(\alpha>1\) the live load is greater than the reference load. An evaluation of the critical load multiplier \(\alpha_{cr}\) is carried out by increasing the load multiplier \(\alpha\) and verifying the arch subjected to the load \(F\) for each and every increment. When the arch is not verified, it means that the load \(F\) is excessively high for the given arch, and any further increment of \(\alpha\) would be pointless. The last, or rather the maximum, load multiplier \(\alpha\) for which the arch is verified is the critical load multiplier \(\alpha_{cr}\).  
In the case of moving loads, the procedure for the evaluation of the critical load multiplier is to be repeated for every position \(k\) of the load. For instance, given a single load, the load would be applied, in turn, at every coordinate \(x_{k}\). Instead, given a two-axial load, with each axis at a distance \(a\), the position of the two axes would be \(x_{k}\) and \(x_{k}+a\). The critical load multiplier of the whole structure would be the minimum of the critical load multipliers \(\alpha_{cr,k}\) found for every position of the load, as in Equation (2).  
\[\alpha_{cr}=min(\alpha_{cr,1}(x_{1}),\alpha_{cr,2}(x_{2}),\alpha_{cr,3}(x_{3}),\ldots) \tag{2}\]  
Finally, the Load Rating Factor \(V\) can be defined as the minimum of the critical load multipliers \(\alpha_{cr}\), as shown in Equation (3):  
\[V=\frac{C-g}{q}=\frac{(g+\alpha_{cr}\cdot q)-g}{q}=\alpha_{cr} \tag{3}\]
# Mesh Twisting Technique for Swirl Induced Laminar Flow Used to Determine a Desired Blade Shape
## 2 Materials and Methods  
### Governing Equations  
The flow is governed by the Navier-Stokes equations. Furthermore, assuming incompressible flow of Newtonian fluid with constant viscosity and no gravity effects, the governing equations become  
\[\
abla\cdot\mathbf{V}=0, \tag{1}\] \[\frac{\partial\mathbf{V}}{\partial t}+\left(\mathbf{V}\cdot \
abla\right)\mathbf{V}+\frac{1}{\rho}\
abla p=\
u\
abla^{2}\mathbf{V}, \tag{2}\]  
where \(\mathbf{V}\) is the velocity vector, \(\rho\) is density, \(\
u\) is kinematic viscosity and \(p\) is pressure. Equation (2) is the traditional representation of the Navier-Stokes equation but the advection term, \(\left(\mathbf{V}\cdot\
abla\right)\mathbf{V}\), is a pseudovector expression making it variant under coordinate transformation. In pipe flow, cylindrical coordinates are a natural choice (it should be noted that OpenFoam will nonetheless solve the equationsin Cartesian coordinates) so an invariant coordinate representation of the Navier-Stokes equation is preferred [20], i.e.,  
\[\frac{\partial\mathbf{V}}{\partial t}+\
abla\left(\frac{\mathbf{V}\cdot\mathbf{V} }{2}\right)-\mathbf{V}\times\left(\
abla\times\mathbf{V}\right)+\frac{1}{ \rho}\
abla p=-\
u\
abla\times\left(\
abla\times\mathbf{V}\right). \tag{3}\]  
When choosing the desired guidance blade shape to be inserted to induce swirling flow, both the resulting velocity field and pressure drop are of interest. Specifically, the axial and tangential velocity components, \(v_{z}\) and \(v_{\theta}\), respectively, describe the resulting flow as shown in Figure 1. Note that the axial velocity distribution in fully developed laminar flow is represented by a perfectly parabolic profile,  
\[v_{z}=2\bar{v}\left(1-\frac{r^{2}}{R^{2}}\right), \tag{4}\]  
where \(R\) is the radius of the pipe, \(r\) is the radial coordinate and \(\bar{v}\) is the average velocity in the pipe.  
The domain under consideration consists of an intake (inlet), an exit (outlet) located 25 diameters from the inlet, and surrounding fixed walls (pipe). The boundary conditions for \(\mathbf{V}\) and the kinematic pressure \(\frac{p}{\rho}\) used in the study are following:  
* Velocity is purely axial, with a velocity profile specified in accordance with Equation (4) with an average velocity of 1 m/s (corresponding to \(\mathrm{Re}=100\)). The normal gradient of pressure is set to zero, thus \(\frac{\partial p}{\partial n}=0\).
* The normal gradient of all velocity components is set to zero. Pressure normal gradient is still zero, but with a fixed average value of 0 which sets a reference pressure for the whole system.
* No slip is allowed for the velocity, effectively setting \(\mathbf{V}=\mathbf{0}\), and the normal gradient of pressure is zero.
Future Internet_**2013**, \(5\), 190-204; doi:10.3390/fi5020190  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
Digital Differentiation in Young People's Internet Use--Eliminating or Reproducing Disability Stereotypes  
**[PERSON]**  
Program for Physiotherapy Education, Department of Health and Social Work, Sor-Trondelag  
University College, P.O. 2320, Trondheim NO-7004, Norway; E-Mail: [EMAIL_ADDRESS]; Tel.: +47-9243-2888  
_Received: 12 March 2013; in revised form: 8 April 2013 / Accepted: 15 April 2013 / Published: 7 May 2013_  
**Abstract:** Norwegian authorities' policy aims at securing an information society for all, emphasizing the importance of accessible and usable Information and Communication Technology (ICT) for everyone. While the body of research on young people's use of ICT is quite comprehensive, research addressing digital differentiation in young people with disabilities' use of ICT is still in its early days. This article investigates how young people with disabilities' use, or non-use, of assistive ICT creates digital differentiations. The investigation elaborates on how the anticipations and stereotypes of disability establish an authoritative definition of assistive ICT, and the consequence this creates for the use of the Web by young people with disabilities. The object of the article is to provide enhanced insight into the field of technology and disability by illuminating how assistive ICT sometimes eliminates and sometimes reproduces stereotypes and digital differentiations. The investigation draws on a qualitative interview study with 23 young Norwegians with disabilities, aged 15-20 years. I draw on a theoretical perspective to analyze the findings of the study, which employs the concept of identity multiplicity. The article's closing discussion expands on technology's significance in young people's negotiations of impairment and of perceptions of disability.  
**Keywords:** assistive technology; digital differentiation; disability; identity
## 4 Results and Discussion
### Robustness Complementarity  
This study not only designed two zero-watermarking schemes that are not conflicting and show strong robustness against certain attacks, but also show complementary robustness. The dual zero-watermarking scheme combines the advantages of the two individual zero-watermarking schemes and improves the robustness as reflected in the compound attack experiment, including geometric attacks, vertex attacks and object attacks, where the dual zero-watermarking scheme performs better than the two independent schemes. For example, in a geometric attack, the coordinate transformation attack does not impact on the non-feature vertex watermark such as that the \(NC\) values remain to be \(1\). By comparison, the coordinate transformation attack could influence the feature vertex watermark greatly as the \(NC\) values distribute between 0.83 and 0.86. For the compression attack, the feature vertex watermarks could be well detected but the non-feature vertex watermarks could not be extracted if the compression strength exceeds a certain threshold of watermark construction. For both vertex addition and deletion attacks, the non-feature vertex watermarks perform better than the feature vertex watermarks. The non-feature vertex watermark performs better than the feature vertex watermark under object addition attacks, but worse under object deletion attacks. In all these cases, the feature vertex and non-feature vertex watermarking schemes show to be independent and complement, reflecting the superiority of the dual zero-watermarking scheme.  
Figure 8: The scattered distributions of the \(NC\) values for the feature vertex watermarks, the non-feature vertex watermarks, and the dual watermarks under compound attacks in (**a**) ZC and (**b**) SZ.  
Figure 9: The ratio distribution of watermarks for \(NC\) higher than 0.8 under different numbers of compound attack in (**a**) ZC and (**b**) SZ.
## 2 Materials and Methods
### Data  
#### 2.2.1 Sentinel-3 OLCI Land Product  
Sentinel-3 OLCI is an inheritor of the Medium Resolution Imaging Spectrometer (MERIS) and has many enhancements [51; 57]. There are three main kinds of OLCI products available to the public (i.e., level-1B products, level-2 land products, and level-2 water products). The level-2 land product provides land and atmospheric geophysical products at full and reduced resolution, where full resolution is approximately 300 m on the ground and reduced resolution is approximately 1200 m on the ground. An OLCI Level-2 Land Full Resolution (OL_2_LFR) product includes an OGVI band and OTCI band. OGVI describes FAPAR in the plant canopy and OTCI represents the terrestrial chlorophyll index. Sentinel-3 OLCI products applied in this study covered January 2017 to December 2018 at the US-WCr site, US-Ton site, US-PFa site and US-Var site, respectively. However, due to the lack of site data, Sentinel-3 OLCI products from January 2017 to September 2018 at the US-Rls site and US-Rws site, January 2017 to March 2018 at the US-GLE site and April 2017 to December 2018 at the US-Bi2 site were used.  
The Sentinel-sat module offers a powerful Python API for searching and downloading the metadata of Sentinel satellite images from the Copernicus Open Access Hub ([[https://scihub.copernicus.eu/dhus](https://scihub.copernicus.eu/dhus)]([https://scihub.copernicus.eu/dhus](https://scihub.copernicus.eu/dhus)) (accessed on 13 January 2021)) through executing condition and spatial queries (a region of interest can be created with a polygon on the GeoJSON website) and was used to batch download the OLCI online and offline products. The filtered OLCI products were automatically downloaded and stored locally as compressed files, and a total of about 5200 scene data were downloaded in this work. Then, we developed a complete set of OLCI product preprocessing processes, including mosaic, reprojection, resampling, and subset processes. The snappy module developed by the European Space Agency (ESA) and a custom script written in Python was applied to complete these data preprocessing operations. The traditional data processing software (i.e., SNAP and ENVI) either cannot batch process or takes a long time. Our python script can dramatically improve the efficiency of data processing through fully automatic data retrieval, batch processing and multithreading technology.
## 2 Materials and Methods
### Test Case  
The problem of multivariate function optimization is used to test the optimization ability of CMCO. The model of the problem is shown in Formula (22):  
\[\begin{split} minF&=x_{2}^{2}+x_{3}+x_{4}+e^{-y_{2}} \\ s.t.&\ g_{i}\leq 0,\ i=1\ \sim 4\\ g_{1}:&\ x_{4}=x_{1}^{2}+x_{2}+x_{3}-0.2x_{5}\\ g_{2}:&\ x_{5}=\sqrt{x_{4}}+x_{1}+x_{3}\\ g_{3}:&\frac{x_{4}}{8}-1\geq 0\\ g_{4}:&\ 1-\frac{x_{5}}{10}\geq 0\\ -10&\leq x_{1}\leq 10\\ 0&\leq x_{2},\ x_{3}\leq 10\\ \end{split} \tag{22}\]  
According to the principles of CMCO, the model is transformed into one system-level model and two discipline-level models. These models are shown in (23)-(25).  
The model of the system level:  
\[\begin{split} minF&=z_{2}^{2}+z_{3}+z_{4}+e^{-z_{5} }\\ s.t.&\ J_{1}=|z_{1}-x_{1,1}|+|z_{2}-x_{1,2}|+|z_{3}-x_{1,3}|+|z_{ 4}-x_{1,4}|+|z_{5}-x_{1,5}|\leq\varepsilon\\ J_{2}&=|z_{1}-x_{2,1}|+|z_{3}-x_{2,3}|+|z_{4}-x_{2,4}|+| z_{5}-x_{2,5}|\leq\varepsilon\\ \end{split} \tag{23}\]  
Figure 1: Detailed steps of CMCO.  
The model of discipline level 1:  
\[\begin{split} minI_{1}=(z_{1}-x_{1,1})^{2}+(z_{2}-x_{1,2})^{2}+(z_{3}- x_{1,3})^{2}+(z_{4}-x_{1,4})^{2}+(z_{5}-x_{1,5})^{2}\\ s.t.\ \ g_{1},g_{3},g_{4}\end{split} \tag{24}\]  
The model of discipline level 2:  
\[\begin{split} minI_{2}=(z_{1}-x_{2,1})^{2}+(z_{3}-x_{2,3})^{2}+(z_{ 4}-x_{2,4})^{2}+(z_{5}-x_{2,5})^{2}\\ s.t.\ \ g_{2},g_{3},g_{4}\end{split} \tag{25}\]  
The simulation results are presented in Table 1 and Figure 2. Four initial points are chosen randomly to test whether CMCO is sensitive to the initial points. A, B, C, and D are four initial points that have been chosen randomly for the optimization process of CMCO. The initial point A is (1,0,0,0,0), the initial point B is (\(-\)1,2,0,10,5), the initial point C is (1,0,1,2,1), and the initial point D is (2,1,0,3,1), all of which are the input data of the algorithm. Table 1 shows a comparison of the optimization abilities of CO, FCO, and CMCO under different initial points, while Figure 1 shows the detailed iteration process. In order to test whether CMCO is sensitive to initial points, ten initial points are chosen randomly, and the optimal results are shown in Figures 2 and 3.  
Table 1 and Figure 2 show that FCO can find global optimums, but it will need too many iterations. Additionally, the CMCO has fewer iterations while also enhancing the effectiveness of optimization compared with other algorithms. Figure 3 shows that CMCO is not sensitive to initial values compared with CO and FCO. The simulation results in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Initial Points** & **Optimal Value** & **Iteration** & **Relaxation Factor** \\ \hline \multirow{5}{*}{CO} & A & 15.54 & 52 & 0 \\  & B & 22.53 & 35 & 0 \\  & C & 21.37 & 50 & 0 \\  & D & 31.12 & 31 & 0 \\  & A & 8.005 & 446 & 0.1 \\  & B & 8.005 & 542 & 0.1 \\  & C & 8.005  
this section mean that this algorithm has a sufficient ability to solve easy mathematical problems. However, its ability to solve practical problems still needs to be researched.
# Indoor Occupancy Sensing via Networked Nodes (2012-2022): A Review
## 2 Comparison with Contemporary Review Articles  
It may be helpful to elaborate here on the contributions of this review. This review offers the following unique advantages. 1. The review's primary objective is to guide a method selection process for occupancy sensing via a decision-making process that relies on quantifiable parameters called ADPs. A flowchart that illustrates the method selection process based on ADPs is presented in Figure 1.
2. The review limits its focus by only considering methods employing networked sensor nodes and making it mandatory to use PIR technology in combination with other underlying sensing technologies. PIR was explicitly chosen to judge the algorithmic performance of occupancy sensing methods; these need to perform on a standard modality. PIR is the most frequently used occupancy sensing modality [14]. 3. The review comments on the conformity of reviewed articles to the claimed application areas based on the conformance criteria attached to ADPs which is detailed in Section 4. Moreover, to further establish the novelty of this review, the contributions of contemporary review studies related to occupancy sensing, along with their corresponding gaps are listed in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Review Study Title** & \multicolumn{2}{c}{**Study Gaps**} \\ \hline  & & a. & Although occupancy models from the literature are compared under three broad categories. the models’ accuracies are not standardized and thus no meaningful comparison is made. \\
[15] & Review on occupancy detection and prediction in building simulation & b. & Details about the presence and deployment of networked sensor nodes are not provided. \\  & & c. & Suitability of methods to applications is not made using any quantifiable parameters. \\ \hline  & & a. & The study compares the performance, the occupancy resolution, the type of sensors used, the type of buildings, and the energy-saving potential of occupancy sensing algorithms, yet it does not focus on algorithms specifically designed for networked sensor nodes. \\
[10] & A comprehensive review of approaches to building occupancy detection & b. & The performance comparison lists the accuracy without considering the spatial complexity of the observed environment. Temporal resolution information is missing from the analysis. \\  & & c. & Application suitability for the algorithms is not discussed. \\ \hline \end{tabular}
\end{table} 
Table 1: Contribution and gaps for contemporary occupancy sensing review studies involving networked sensor nodes. Figure 1: A decision tree that illustrates the expected use of ADPs (in diamond shaped boxes) to determine the suitable application area for the reviewed method. The same illustration is used to perform suitability analysis in Section 6. Sensor failure rate ADP is not used in the decision-making process as some reviewed methods did not specify the sensor details.
# On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning
## Appendix A Detailed Calculations
### Copa  
As mentioned, the first task is to set a target rate and achieve it, which can be expressed as  
\[\lambda=\frac{1}{\delta d_{q}} \tag{114}\]  
where \(\lambda\) is the sending rate, \(d_{q}\) is the measured queuing delay in seconds, and \(\frac{1}{\delta}\) is the unit of packet size. The second is the window update rule that encourages the sender to move toward the target rate. Finally, the TCP competitive strategy is to rival buffer-filling flows.  
\[\lambda=\frac{W}{\tau} \tag{115}\]  
where \(\tau\) is the smallest observed RTT in the current window, where \(\tau=srtt/2\)[16; 59]. \(\tau\) is considered as RTTstanding at [16]. \(\tau\) also refers to the RTT corresponding to a standing queue as it is the smallest observed RTT in the recent window, and srtt refers to the value of the standard smoothed RTT estimation.  
Copa demands the sender to track the target rate, and they do so by using Equation (114). The sender also estimates the queuing delay with the following equation:  
\[d_{q}=\tau-T_{m} \tag{116}\]  
where \(T_{m}\) is the minimum RTT measured over a long period.  
Using Equations (114)-(116) with a fixed-point approach along the lines of [18], we obtain the equilibrium congestion window \(W^{\star}\) as  
\[W^{\star}=\frac{\tau^{\star}}{\delta(\tau^{\star}-T_{m})} \tag{117}\]  
We consider cases to explain the causes of unfairness and starvation.  
Case i: Considering two Copa flows with different RTTs, \(\tau_{1}\) and \(\tau_{2}\), and experiencing the same loss, \(p\), we can evaluate how the congestion windows \(W_{1}^{\star}\) and \(W_{2}^{\star}\) evolve as  
\[W_{1}^{\star}=\frac{\tau_{1}}{\delta(\tau_{1}-T_{m})};\ W_{2}^{\star}=\frac{ \tau_{2}}{\delta(\tau_{2}-T_{m})}\]  
\[\frac{W_{1}^{\star}}{W_{2}^{\star}}=\frac{\tau_{1}}{\tau_{2}}\Big{(}\frac{\tau _{2}-T_{m}}{\tau_{1}-T_{m}}\Big{)} \tag{118}\]  
If \(\tau_{1}=2\tau_{2}\), then \(\frac{W_{1}^{\star}}{W_{2}^{\star}}=\frac{2\tau_{2}}{\tau_{2}}\Big{(}\frac{ \tau_{2}-T_{m}}{2\tau_{2}-T_{m}}\Big{)}\).  
Case ii: Considering Copa flows with same RTTs, \(\tau\), and experiencing different losses, \(p_{1}\) and \(p_{2}\), we can evaluate how the congestion windows \(W_{1}^{\star}\) and \(W_{2}^{\star}\) evolve:  
\[W_{1}^{\star}=\frac{\tau_{1}}{\delta(\tau_{1}-T_{m})};\ W_{2}^{\star}=\frac{ \tau_{2}}{\delta(\tau_{2}-T_{m})}\]\[\frac{W_{1}^{*}}{W_{2}^{*}}=\frac{(\tau-T_{m})(1-p_{2})}{(\tau-T_{m})(1-p_{1})}= \frac{1-p_{2}}{1-p_{1}} \tag{119}\]  
If \(p_{1}=2p_{2}\), then \(\frac{W_{1}^{*}}{W_{2}^{*}}=\frac{1-p_{2}}{1-2p_{2}}\).