esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Average Revisit Time  
The revisit time is the time when the satellite passes over the targeted regions. In lower inclination orbits, areas with latitudes closer to the inclination angle chosen have the best (least) revisit time. Figure 15 shows the satellite path with a 24-degree inclination angle (low inclinations), while Figure 16 displays a visual representation of the revisit time of the proposed orbit on a 3D plane.  
The green regions in the figures show an average revisit time as low as 4 h, with a maximum revisit time of 18 h at the extremities of the near-equatorial region defined. Moreover, in Figure 17, the average revisit times are plotted over a 7-day period for the SSO and lower inclination orbits. The x-axis shows the near-equatorial latitude selected in the mission definition, from 0\({}^{\circ}\) to \(\pm\)27\({}^{\circ}\). The green and black lines highlight the lower inclination orbit and the SSO average revisit time, respectively. It is clear that in the near-equatorial regions, the lower inclination orbit is preferred, as the average revisit time is lower. For example, at the 10\({}^{\circ}\) N point, the average revisit time for the lower inclination orbit is 19 h, as opposed to 105 h for the SSO.  
Figure 14: Number\(-\)of\(-\)access for SSO and lower inclination orbit as a function of latitude.  
Figure 15: Satellite path with a 24-degree inclination angle (low-inclined).  
Lastly, in Figure 18, the lower inclined orbit has a lower gap duration than that for SSO. Additionally, for the same duration, the lower inclined orbit has a higher access percentage than the SSO. For instance, for a duration of 25 h, the lower inclined orbit has an access of 92% while SSO has only 80%.
# NPS6D200--A Long Range Nanopositioning Stage with 6D Closed Loop Control
###### Abstract  
This work presents the new development of a nanopositioning machine for a large operating range. The machine, called NPS6D200, provides \(\diameter 200\,\mathrm{mm}\) planar and \(25\,\mathrm{mm}\) vertical travel range and applies a 6D closed loop control with all drive forces applied directly to the same moving part. The stage architecture evolves from an integrated planar direct drive which is extended by high precision vertical positioning capability. The setup of the machine and the characteristics of the different subsystems are presented together with investigations into the positioning performance that is achieved with the NPS6D200. In constant setpoint operation as well as in synchronized multiaxial motion tasks over three orders of magnitude, the system shows servo errors only in the low nanometer range and proves suitable as positioning platform for nanoscience applications.  
nanopositioning stage; sub-10 nm; multi-coordinate direct drive; 6D-control +
Footnote †: journal: _Applied Sciences_  
This covers the technical specifications, the functional subsystems, underlying principles, key components utilized, as well as the positioning performance in closed loop control. Following the introduction, Section 2 presents the state of the art in the field of nanopositioning devices. Section 3 and its subsections describe the general stage concept, the key characteristics of the realized device and give insights into the planar and vertical drive as well as into the control system. Subsequently Section 4 presents the achieved positioning performance for different motion tasks and in Section 5 conclusions and the outlook to future research activities with this setup are given.
# Analytic Form Fitting in Poor Triangular Meshes
## 3 Methodology
### Primitive Fitting from Triangle Set
#### 3.2.1 Cylinder Fitting  
The geometrical parameters to define the cylinder are:  
1. \(p_{0}\): axis pivot point.
2. \(\hat{\vartheta}\): axis direction.
3. \(R\): radius.  
Our algorithm takes advantage of the fact that the input triangle set provides information on the vectors locally normal to the cylinder. This information enables the calculation of \(p_{0}\) and \(\hat{\vartheta}\).  
**Axis point \(p_{0}\)**  
The axis pivot point is calculated as follows:  
1. Create a set of lines \(L_{n}=\{\ell_{1},\ell_{2},...,\ell_{n}\}\), where \(\ell_{i}\) is a line generated by the baricenter \(b_{i}\) and the normal \(n_{i}\) of the triangle \(t_{i}\).
2. Obtain \(C_{p}\): as the set containing the points of the minimum distance between each pair of lines from \(L_{n}\). Every line \(\ell_{i}\) is approximately perpendicular to, and nearly intersects, the cylinder axis \([p_{0},\hat{\vartheta}]\). Therefore, \(C_{p}\) is a point sample of the axis \([p_{0},\hat{\vartheta}]\).
3. Compute \(p_{0}\) as the geometric center of gravity of \(C_{p}\).  
Figure 7 presents an axial view of a cylindrical triangle set. Lines \([b_{i},n_{i}]\) ideally intersect, and are perpendicular to, the cylinder axis \([p_{0},\hat{\vartheta}]\). Figure 8 displays an actual example set \(C_{p}\) and cylinder axis identification.  
Figure 6: Result of border identification in the triangle set. (**a**) and (**b**) present the identified border of the triangle set after weaving the B-Rep.  
**Cylinder Axis direction \(\vartheta\)**  
To calculate the cylinder axis, the method relies on the ability to:  
1. Translate the unitary vectors \(n_{i}\) normal to the triangles from their supposed pivot point (triangle baricenter \(b_{i}\)) to the origin.
2. Collect the tips of the origin-based vectors resulting from step (1). These tip points form a tilted flat disk \(d\) centered at the origin (Figure 9).
3. Obtain (via Principal Component Analysis (PCA)) the cylinder axis direction \(\vartheta\) as the one of smallest dispersions of the points of disk \(d\) (Figure 9).  
Figure 8: Set of points \(C_{p}\) approximating the cylinder axis.  
Figure 7: \(p_{0}\) calculation with the intersection of lines \([b_{i},n_{i}]\) whose directions are triangle normal vectors \(n_{i}\) and pass through triangle baricenters \(b_{i}\).  
Figure 9: Cylinder fitting. Coplanar circular set of tips of cylinder normal unitary vectors, applied at origin. \(\vartheta\): direction of minimal dispersion of disk \(d\), cylinder axis direction.  
Knowing the cylinder axis \([p_{0},\vartheta]\), the radius \(R\) of the cylinder is determined as follows:  
1. Rigidly move the triangle set \(S\) so its axis \([p_{0},\vartheta]\) maps to the \(Y_{w}\) World axis and the geometric center of \(S\) maps to the origin \(O_{w}\).
2. Project the point set resulting from (1) onto the \(X_{w}Z_{w}\) plane, resulting in a flat circular point set.
3. Compute the radius of such a circular point set.
## 3 The FAURAS Framework
### Request Overwrite  
#### 3.2.1 Overwriting Strategy  
The Request Overwrite module is responsible for proactively rewriting the bitrate of the segment request to the one matching with the fair bandwidth if it detects that the client is demanding a higher value. The operation is performed on-the-fly and no additional communication link is required for both the server and the client side. A safe and aggressive strategy is to immediately overwrite the client's request once the bitrate exceeds the fair video rate. This effectively reduces the adaptation delay, hence maintaining the buffer at a high level and definitely preventing the rebuffering from happening. However, such an approach results in a large down-switching amplitude \(\Delta r\) (i.e., the bitrate difference between the current bitrate decision and one right before it), which has been proven to have negative impact on the user's QoE [36]. Therefore, it is necessary to develop an overwriting strategy that can minimize the bitrate difference while ensuring a smooth video playback.  
In the FAURAS framework, we propose a QoE-aware strategy for overwriting the bitrate request. The video will not be rebuffered if there is at least one segment in the buffer [25]. Considering the case of the HTTP/2 server push, during a push cycle, the client fetches \(k\) segments with the same bitrate which cannot be changed in-between. To this manner, for the k-push strategy, it is optimal to maintain \(k\) segments in the buffer at all time. Thus, the Request Overwrite module will correct the client's bitrate decision once the following condition is met:  
\[\begin{cases}r_{x,t}>R_{t}^{f}\\ B_{t^{\prime}}^{e}<k*L\end{cases} \tag{4}\]  
When the client sends a request for segment \(S_{i}^{n}\), it will notify the proxy of its current buffer condition \(B_{t}\). Such information is delivered via an explicit HTTP header included to the request so that no extra connection is created, thus limiting unnecessary overheads [35]. If the requested bitrate \(r_{x,t}\) exceeds the fair value, the proxy extracts the buffer information from the request headers to estimate the buffer \(B_{t^{\prime}}^{e}\) after finishing the download of the push cycle. Once \(B_{t^{\prime}}^{e}\) is expected to drop under the duration of \(k\) segments, the proxy will overwrite the bitrate decision to \(R_{t}^{f}\).
# On the Properties of WC/SiC Multilayers
###### Abstract  
A study of the materials properties of WC/SiC multilayer coatings is presented. We investigated the dependence of interface and surface roughness, intrinsic stress, microstructure, chemical composition, and stoichiometry as a function of multilayer period and in some cases compared these to W/SiC multilayer systems. The WC/SiC material pair forms multilayers with extremely smooth and sharp interfaces and both materials remain amorphous over a wide range of thicknesses. These properties are desirable for multilayer-based high-resolution diffractive x-ray optics, such as multilayer Laue lenses (MLLs), which require very thick films in which the layer spacing varies considerably. Thermal and structural stability studies show that WC/SiC multilayers have exceptional thermal stability, making this an extremely robust and favorable material pair for MLLs and other multilayer-based X-ray optical elements.  
Keywords:multilayers; X-ray optics; thermal stability; multilayer Laue lenses; tungsten carbide; silicon carbide +
Footnote †: journal: Applied Physics Letters
# Substring Position Search over Encrypted Cloud Data Supporting Efficient Multi-User Setup +
## 1 Introduction
We present a construction that is very efficient and does not require large ciphertext space. Our encryption takes \(O(kn)\) time, and the ciphertext is of size \(O(kn)\), where \(k\) is the security parameter and \(n\) is the size of stored data. The search protocol takes \(O(m^{2}+occ)\) time and three rounds of communication, where \(m\) is the length of the queried substring and \(occ\) is the number of occurrences of the substring in the document collection. We perform a thorough experimental evaluation of our solution on a real-world genomic dataset. 4. We consider a natural extension of the SSP-SSE scheme, where an arbitrary group of data users can submit substring queries to search the encrypted collection. We design a scheme support distributed setup, where data users choose their own secret key rather than receive the key from a trusted authority. We formally define a Multi-User Substring Position Searchable Symmetric Encryption (MSSP-SSE) and present an efficient construction. We organize the rest of the paper as follows: Section 2 gives an outline of the most recent related work. In Section 3, we give an overview of the system and threat models, notations and preliminaries. In Section 4, we present algorithms and data structures that allow a substring search on the plaintext data. We give a brief overview of each data structure and later present a discussion on choosing the right data structure to enable substring search in an untrusted cloud environment. In Section 5, we provide the details of the SSP-SSE scheme and define the security definitions and requirements. Section 6 is devoted to security and performance analysis. The extension of our solution towards an arbitrary group of users is presented in Section 7. Lastly, we conclude in Section 8.
# A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm
## 3 Methodology
### Proprietary Internet Video Traffic Dataset Generation
```
1:Input: Each flow sample from segmented CSV files
2:Output: YVAIL, YVATL, YVALL, YVAP, FVAIL, FVATL, FVALL, FVAD, TVAIL, TVATL, TVATL, TVAP dataset
3:for each CSV file in segmented CSV files do
4:for each flow sample in CSV file do
5: Read each PCAP file's prior knowledge
6: Update each flow sample label
7:endfor
8: Keep all remaining flow samples in the CSV file
9:endfor
10: Update each CSV file label according to different platforms
11: Merger each segment CSV file into three platform categories CSV files
12:for Each CSV file in platform categorized CSV files do
13:for Each flow sample in CSV file do
14:if Flow sample application name major protocol \(\
eq\) \"TLS\", \"QUIC\", or \"Unknown\" then
15: Delete the flow sample
16:endif
17:if Flow sample requested server name \(\
eq\) \"Blanks\", application name = \"TLS\" and application category name = \"Web\" then
18: Delete the flow sample
19:endif
20:if Flow sample bidirectional duration or bidirectional packets = 0 then
21: Delete the flow sample
22:endif
23:endfor
24: Save all remaining flow samples
25:for Each flow sample in remaining samples do
26:if Flow sample application category name \(\
eq\) \"Cloud\", \"Media\"; \"SocialNetwork\", \"Video\", \"Unspecified\", or \"Web\" then
27: Delete the flow sample
28:endif
29:if Flow sample application name minor protocol \(\
eq\) \"AmazonAWS\", \"AmazonVideo\", \"Azure\", \"Twitch\", \"Facebook\", or \"YouTube\" then
30: Delete the flow sample
31:endif
32:endfor
33: Save all the remaining samples
34: Create a new video quality category column
35: Read the updated flow sample label
36:endfor
37: Save each updated CSV file
38: Output YVAIL, YVATL, YVALL, YVAP, FVAIL, FVATL, FVALL, FVAD, TVAIL, TVATL, TVATL, TVATL, TVAP dataset
```  
**Algorithm 2** Pseudo-code for nDPI-detected YouTube, Facebook, and Twitch video Quality Category dataset generation (nYFTQC)
# Modeling and Optimizing the System Reliability Using Bounded Geometric Programming Approach
## 4 Computational Study  
The proposed bounded solution method for the geometric programming problem under varying parameters was implemented in different real-life applications. The following two examples were adopted from engineering problems. Furthermore, it was also applied to the system reliability optimization problem. All the numerical illustrations were coded in AMPL and solved using the optimizing solver CONOPT through the NEOS server version 5.0 on-line facility provided by Wisconsin Institutes for Discovery at the University of Wisconsin in Madison for solving optimization problems; see (Server [28]).  
**Example 1** ([4]).: _A cofferdam is an engineering design optimization problem. It is a prominent structure to attach a trivial submerged area to permit building a permanent structure on an allocated site. The cofferdam function is elicited in a random environment by transitions in surrounding water levels. The architecture designs a dam of height \(x_{1}\), length \(x_{2}\) breadth \(x_{3}\), and total required perimeter \(x_{4}\) and intends to estimate the most promising total cost for making decisions. The RHS parameters can be of any simplex dimensions such as area, volume, etc., which is not quite certain. These are no longer crisp or deterministic, but the allowable lower and upper bounds over each area/volume are determined in the closed interval. Thus, the use of varying parameters is quite worthwhile and the decision under such variation will be helpful in determining the range of optimal outcomes. Figure 1 depicts an illustrative example of a cofferdam._Thus, the equivalent mathematical programming problem with varying parameters is given as follows (27):  
\[\begin{array}{ll}\text{F}=\underset{x}{\text{Minimize}}&2x_{1}^{-0.9}x_{2}^{-1.5}x_{3}^{-1}x_{4}^{-1.6}+4x_{1}^{-1}x_{2}^{-1}x_{3}^{-0.1}x_{4}^{-1}\\ \text{s.\,x.}&\\ &2x_{1}^{-2}x_{2}^{-1}x_{3}^{2}x_{4}+1.6x_{1}x_{3}x_{4}^{2}\leq\tilde{B}_{1} \\ &1.9x_{1}^{2}x_{1}^{1}x_{3}x_{4}+3.1x_{1}^{2}x_{4}\leq\tilde{B}_{2}\\ &x_{1},x_{2},x_{3},x_{4}\geq 0.\end{array} \tag{27}\]  
where \(\tilde{B}_{1}\in(3,3.2)\) and \(\tilde{B}_{2}\in(2,2.4)\) are the varying parameters. Since all the parameters are crisp except the RHS, then Problems (23) and (26) can be utilized to obtain the upper and lower bounds of the objective values in Problem (27). According to Problems (23) and (26), the formulations of the upper and lower bounds for Problem (27) can be presented as follows:  
**Model A**  
\[\begin{array}{ll}\text{s.\,x.}&\\ &y_{01}+y_{02}=1,\\ &y_{01}+y_{02}-2y_{11}+y_{12}+2y_{21}+2.2y_{22}=0,\\ &y_{01}-y_{02}-y_{11}+1.4y_{21}=0,\\ &-y_{01}+y_{02}+2y_{11}+y_{12}+y_{21}=0,\\ &y_{01}-y_{02}+y_{11}+2y_{12}+y_{21}+y_{22}=0,\\ &y_{01},y_{02},y_{11},y_{21},y_{22}\geq 0\\ &3\leq\tilde{B}_{1}\leq 3.2,\ \ 2\leq\tilde{B}_{2}\leq 2.4,\ \ \forall\ i=1,2,\ldots,m.\end{array} \tag{28}\]  
and  
Figure 1: Illustrative figure of a cofferdam.
# Can IMU Provide an Accurate Vertical Jump Height Estimate?
## 2 Materials and Methods
### Measurement Procedure  
The testing of VJ height was performed within a single testing session. The session took place between 9 and 11:30 AM in the postseason period of the yearly training cycle. Prior to the testing, all participants performed a 15 min individual warm-up supervised by the team strength and conditioning coach. Measurement of the VJ height was performed for the two exemplary jumping tasks, SQJ and CMJ [11]. The subjects started both tasks standing on the FP shoulder-width apart with their hands on their hips. For the SQJ subjects were instructed to flex their knees to a self-selected position (90-120\({}^{\circ}\) knee flexion) which was maintained for 2 s (Figure 3--left). Afterward, subjects performed a jump without  
Figure 2: Flight time with the effect of different signal filters with detected events and optimal trigger.  
a counter-movement. For the CMJ the subjects were instructed to flex their knees to a self-selected position as quickly as possible which was followed by an immediate jump. For both jumps, it was recommended that at take-off the subjects leave the floor with the knees and ankles extended and land in a similarly extended position [13] (Figure 3--middle). The recovery period between the individual trials was set to 30 s while the pause between the jump modalities was set to a minimum of 3 min [32]. All subjects were instructed to jump for maximum height. The IMU was placed on the upper distal area of the metatarsal part of the foot of the participant, more precisely, at the level of 2 nd to 3 rd metatarsal bone as shown in Figure 3--right. The placement of the sensor close to the endpoint of the kinetic chain simplifies the detection of takeoff and landing instances and was previously used by [17; 26].
# An Intelligent Healthcare System for Residential Aged Care during the COVID-19 Pandemic
## 5 Experiment Design
A single subsample provides validation data for testing, and the remaining nine subsamples are training data. All observations can be used for both training and testing, and each observation for validation occurs exactly once. These algorithms are chosen because they are popular machine learning algorithms and have been widely used [51]. Figure 3: The Prototype System. The accuracy of classification algorithms is shown in Figure 4. For the baseline only, the accuracy of random forest is 73%, SVMs is 70.9%, logistic is 73.6%, and MLP is 77.7%. After adding IO, the accuracy of random forest is 75, SVMs is 79.1%, logistic is 79.1%, and MLP is 78.4%. If SL is continuously increased to the risk factor list, the accuracy of random forest is 79.8, SVMs is 82.4%, logistic is 83.1%, and MLP is 81.1%. As for IHAS, the accuracy of random forest is 79.1, SVMs is 84.5%, logistic is 83.8%, and MLP is 81.1%. To test the first hypothesis, ANOVA was used to assess whether or not the effect of risk factors reveals significant differences between using the availability data on the fall detection model (including PIM, CF, IO, SL, and RM) and not using the availability data. The main effects of fall prediction (F (3, 15) = 11.75, \(p\) < 0.01) are significant. Therefore, the first hypothesis is supported. The LSD test was carried out for multiple comparisons. IHAS has significantly higher accuracy than the baseline (mean difference = 8.33, \(p\) < 0.05) and Treatment 2 (mean difference = 4.23, \(p\) < 0.05). Therefore, the second and third hypothesis are all supported. As for Treatment 3 (mean difference = 0.53, \(p\) > 0.05), the fourth hypothesis is not supported. To give a comprehensive discussion of such results, regarding the relevant risk factors of availability based on sensor information, the results of the evaluation indicate that the risk factors had significant impacts on fall detection. IHAS (including all risk factors) performs better in fall detection than the existing methods using only PIM and CF (Treatment 1). There is a difference in the results between IHAS and other treatments. For example, Treatment 3 has an insignificant impact on increasing fall detection.
## 2 Materials and Methods  
### Dataset  
A total of 81 MRI studies of the thoracic region were collected from patients treated for lung cancer at the CGFL in Dijon, France. The MRIdian 0.35 T MR-Linac (Viewray Inc., Oakwood Village, OH, USA) was used for image acquisition and for the treatments. A balanced steady-state free precession (bSSFP) sequence producing T2/T1-weighted images was used for the MRI acquisitions with dedicated thoracic coils. In order to limit movement artifacts, all acquisitions were performed with the patient lying supine in a reproducible breath-hold position without any contrast media or patient-specific conditioning. This bSSFP sequence had an echo-time of 1.27 ms, a repetition-time of 3.00 ms, and a total acquisition time of 25 s. The full field of view had the following dimensions: 465 \(\times\) 540 \(\times\) 429 mm\({}^{3}\), with a voxel size of 1.5 \(\times\) 1.5 \(\times\) 3.0 mm\({}^{3}\) (matrix of 310 \(\times\) 360 \(\times\) 143). Subsequent to each MRI exam, the radiation therapy structure set (RT structure) was retrieved. This set encompassed all the segmented targets and the OARs created by experts using the treatment planning software of the MR-Linac machine. Both the MRI exams and the RT structure were stored as DICOM files.  
Figure 1 presents a sample of the original DICOM images along with the corresponding structures segmented by medical experts for five organs: the left lung, the right lung, the esophagus, the spinal cord, and the heart. For each patient, there were up to 144 slices, creating 6852 images in our dataset. Generally, having a larger dataset can contribute to better generalization and performance of the deep learning model. In the context of multiorgan segmentation, where nuances and variations can be critical, a dataset of 6852 images is considered relatively large and should provide a solid foundation for training our multiorgan segmentation model, potentially leading to more accurate and reliable results.
## 3 Database Collection and Processing
### Preparation of the Data for AI Modeling  
#### 3.2.1 Normalization  
In a database, each input or output variable has a specific unit. The data normalization function can eliminate the weight of the units, reduce network errors, and increase training speed, by normalizing the data to a value between zero and one. The linear normalization function utilized in this study is outlined below.  
\[X_{norm}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{6}\]  
The four terms in this equation are \(X_{max}\), \(X_{min}\), \(X\), and \(X_{norm}\), which correspond to the maximum, minimum, actual, and normalized values, respectively.