esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 UWB Positioning Scheme
### Multiple-Station ADS-TWR Technique  
Figure 3 shows the unilateral ADS-TWR of multiple stations for a single tag. It can be seen from the figure that only five messages, Poll, RespA, RespB, RespC, and Final, will be able to complete a single tag corresponding to the three base stations. Anchor A and tag can calculate the corresponding time \(T_{prorA}\) (s), which is then multiplied by the speed of light \(c\) (m/s) to obtain the distance \(D_{A}\) (m) between the two devices:  
\[T_{pror}=\frac{\left(T_{round1A}\times T_{round2A}-T_{reply1A}\times T_{reply2A }\right)}{\left(T_{round1A}+\ T_{round2A}\ +\ T_{reply1A}+T_{reply2A}\right)}, \tag{3}\]  
\[D_{A}=T_{prorA}\times c \tag{4}\]  
Similarly, between anchor B, anchor C and tag can calculate the corresponding time \(T_{prorB}\) (s) and \(T_{prorC}\) (s), which is then multiplied by the speed of light \(c\) (m/s) to obtain the distance of \(D_{B}\) (m) and \(D_{C}\) (m) between the two devices.
## 5 Hybrid Network  
Table 1 shows the significant differences in design parameters which are imposed when comparing stationary nodes to MAV nodes.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Metric** & **Stationary network** & **MAV nodes** \\ \hline Duration & 10\({}^{6}\) seconds & 10\({}^{3}\) seconds \\ Data rates & 10\({}^{2}\) bits/second & 10\({}^{5}\) bits/ second \\ Latency & 10\({}^{2}\) seconds & 10\({}^{-2}\) seconds \\ Network routing & stable & dynamic \\ \hline \hline \end{tabular}
\end{table}
Table 1: Differences in design parameters between stationary and mobile nodes in a hybrid sensor network.  
Figure 3: Time synchronized channel hopping in WSNs: a node synchronizes to its time parent by listening to advertisement (ADV) packets. Packets are sent a predetermined time into a slot, permitting for efficient resynchronization. Once synchronized, a node is assigned a slot and channel offset, and only communicates with neighbors according to this schedule.  
Rather than building a single network architecture that compromises on all these metrics, we propose instead to have a dual-mode network. The deployed stationary network operates by default as a standard TDMA-based mesh. As new nodes are dropped off by the MAV, they join the mesh and carry on as described by TSMP standards. A modification is added to facilitate communications with the MAV; this communication is carried out on an independent set of channels. In an IEEE 802.15.4 network, 16 channels are typically available for transmission. In the proposed architecture, a subset of these channels is reserved for low throughput stationary mesh communications, while the remainder is only used when required by MAV agents.
## 2 Materials and Methods
### Clinical Protocol  
A previously standardized dentist performed the oral examinations of the patients using a [PERSON] probe (Hu-Friedy) and a flat dental mirror. The first clinical exam recorded the degree of periodontal involvement, assessed by the Gingival Index (GI), Papilla Bleeding Index (PBI), degree of tooth mobility probing depth (PD), and clinical attachment loss (CAL). The evaluation of pathologic tooth mobility was based on the usual scoring system [2]: 0--no mobility, 1--horizontal mobility less than 1 mm, 2--horizontal tooth mobility exceeding 1 mm, and 3--axial tooth mobility. Periodontal probing was carried out with a [PERSON] probe (Hu-Friedy), in 6 points for each tooth. Maximum PD was recorded as the distance from the gingival margin to the deepest site reached by the blunt end of the periodontal probe. Bleeding on probing, expressed by PBI, was recorded simultaneously with PD measurements. The scores for PBI [2] were: 0--no bleeding, 1--single bleeding point, 2--multiple bleeding points, 3--bleeding filling interdental space, and 4--profuse bleeding or blood drop. GI ([PERSON] 1963) scores were [2]: 0--normal aspect, 1--slight change in color and texture of the gingiva, no bleeding, 2--inflammation and bleeding on probing, 3--advanced inflammation and severe bleeding on probing. A periodontitis case was defined according to the new classification proposed by consensus in the last World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions in 2017 [19]. A periodontitis case was considered when: there was interproximal clinical attachment loss (CAL) in \(\geq\)2 non-adjacent teeth, or \(\geq\)2 teeth with buccal or palatal site affected by CAL \(\geq\) 3 mm and PD \(>\) 3 mm (in the same site) were detected, and PD \(\geq\) 4 mm [19].
## 3 Results  
### Genome-Wide Associations  
A total of 35 significant (according to the Bonferroni corrected genome-wide significance and less conservative threshold) SNPs for the main effect were detected on 14 different chromosomes for the traits PMN, sN, bN, MAJOR, MINOR, cultural negative, AER, and CNS (Tables 1 and 2). A subset including eight SNPs surpassed the Bonferroni significance threshold, and 27 SNPs were significant according to the less conservative threshold. The chromosome including the largest number of significantly (Bonferroni corrected genome-wide significance and less conservative threshold) associated SNPs was chromosome 14. For the cell fractions PMN and sN, the same significant SNPs when considering the less conservative threshold were detected on chromosome 6. One significant (less conservative threshold) SNP on chromosome 28 was significantly associated with bN. For MAJOR, 18 significant (Bonferroni corrected genome-wide significance and less conservative threshold) SNPs were located on chromosomes 1, 2, 4, 5, 11, 15, 17, 18, 22, and 26. Five SNPs exceeded the Bonferroni significance threshold, and 13 exceeded the less conservative threshold. For MINOR, four significantly associated SNPs (according to the less conservative threshold) were detected, and three of them were located on chromosome 14. In addition, for cultural negative, all three significant SNPs (two passed the less conservative threshold and one passed the Bonferroni genome-wide significance) were located on chromosome 14. Four SNPs were identified on chromosomes 4, 5, and 15 for AER. For CNS, three SNPs were detected on chromosome 8. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Trait** & **SNP** & **Chromosome** & **Position** & \(p\)**-Value SNP** & **Gene Name** \\ \hline PMN & BTA-110591-no-rs & 6 & 16024226 & 0.00001080976 & _COL25A1_ \\ sN & BTA-110591-no-rs & 6 & 16024226 & 0.000005703675 & _COL25A1_ \\ bN & BTB-00944057 & 28 & 22970021 & 0.000002966474 & _CTNNA3_ \\ \hline PMN, polymorphonuclear neutrophils; sN, segmented neutrophils; bN, banded neutrophils; \(p\)-value: All SNP passed the less conservative threshold (\(p_{\text{avg}}=0.05/4479=1.12\times 10^{-5}\)). & & & \\ \end{tabular}
\end{table} 
Table 1: Genome-wide significant SNPs for the main effect and potential candidate genes associated with the differential somatic cell fractions.
## 2 Protocols for Synchrotron X-Ray Experiments
### XAFS
#### 2.2.1 Preparation of Measurement Conditions  
Effective XAFS measurements require precise adjustments of the optical system and meticulous sample preparation. The uniformity of the samples and optimal tuning of the optical system are critical for reliable data acquisition on both the BL14B2 and BL36 XU beamlines.  
During optical system adjustment, mirror alignment and energy calibration are performed in collaboration with the beamline staff. Mirrors are used to eliminate the higher-order harmonic light from the insertion device (ID) and focus the beam. For example, the mirror angles are set to 4.5 mrad during the experiments targeting the Pt L\({}_{3}\)-edge or Co K-edge at BL36 XU. Energy calibration is performed using a standard sample such as a metal foil corresponding to the absorption edge of interest to correct for the monochromator angle.  
The samples are uniformly packed into quartz glass capillaries with a diameter of 1 mm. This step is critical because the sample uniformity significantly affects the measurement reproducibility. Additionally, the optimal gas compositions for the ionization chambers are determined based on the absorption edge to enhance measurement accuracy. For instance, when measuring the Pt L\({}_{3}\)-edge at BL36 XU using 31 cm ionization chambers, a gas mixture of N\({}_{2}\):95% and Ar:5% for I0 and N\({}_{2}\):50% and Ar:50% for I1 with an applied voltage of 1000 V is used.
# A Survey on Shortest Unique Substring Queries
## 6 SUPS Queries
### Optimal Approaches  
In 2018, [PERSON] et al. [4] introduced Problem 5, which is a version of \(\mathsf{SUS}\) queries focusing on palindromes. They propose an optimal algorithm which takes \(\mathcal{O}(n)\) time to preprocess the string \(\mathcal{S}\) and can return all \(\mathsf{SUPS}\) for any interval query in \(\mathcal{O}(occ+1)\) time, where \(occ\) is the number of outputs. For any interval \([i,j]\), checking whether \(\mathcal{S}[i,j]\) is a palindromic substring or not takes \(\mathcal{O}(n)\) preprocessing time and constant query time using \(\mathcal{O}(n)\) space. Similar to the approach for solving position/interval \(\mathsf{SUS}\), they define _Minimal Unique Palindromic substring(MUPS)_. _MUPS_\(\mathcal{S}[i,j]\) is a unique palindrome substring in \(\mathcal{S}\) such that \(\mathcal{S}[i+1,j-1]\) is a repeat in \(\mathcal{S}\) or \(1\leq|\mathcal{S}[i,j]|\leq 2\). Similar to \(\mathsf{MUS}\)s, _MUPS_ cannot contain each others. In addition, for each \(\mathsf{SUPS}\)\(\mathcal{S}[i,j]\) for some interval, there exists exactly one _MUPS_ that is contained in \([i,j]\) with the same center as \(\mathcal{S}[i,j]\) (\(\frac{i+j}{2}\)). Using this fact, they could preprocess \(\mathcal{S}\) in linear time to report all \(\mathsf{SUPS}\)s.  
In addition, they provided some bounds on the number of \(\mathsf{SUPS}\)s. They showed the maximum number of point and interval \(\mathsf{SUPS}\) in \(\mathcal{S}\), which is a string from an arbitrary size alphabet, is at most \(n\). For a binary alphabet, they gave a lower bound for the number of point \(\mathsf{SUPS}\). Let \(\mathcal{PS}_{\mathcal{S}}\) and \(\mathcal{IS}_{\mathcal{S}}\) be the set of substrings of \(\mathcal{S}\) where each of the substrings in \(\mathcal{PS}_{\mathcal{S}}\) is an \(\mathsf{SUPS}\) for some point and each of the substrings in \(\mathcal{IS}_{\mathcal{S}}\) is an \(\mathsf{SUPS}\) for some interval.  
**Theorem 9** ([4]).: _There exists a binary string \(\mathcal{S}_{k}\) of length \(6k+8\) such that \(\mathcal{PS}_{\mathcal{S}_{k}}=\mathcal{IS}_{\mathcal{S}_{k}}=4k+6\) for any \(k\geq 0\). Thus, \(\mathcal{S}_{k}\) contains at least \(\frac{2}{3}n\) point and interval \(\mathsf{SUPS}\)s where \(n=|\mathcal{S}_{k}|\)_  
In the end, they discuss the number of point and interval \(\mathsf{SUPS}\)s on palindromic rich strings. A string is a palindromic rich if it has \(n+1\) distinct palindromic substring including an empty string. They consider the palindromic rich string \(R_{k}=a^{1}b^{1}\cdots a^{k}b^{k}\) and prove two following theorems.  
**Theorem 10** ([4]).: _There exists a binary string of length \(n\) such that the number of interval \(\mathsf{SUPS}\)s is \(n-\sqrt{1+4n}+3\)._  
**Theorem 11** ([4]).: _There exists a binary string \(R_{k}\) of size \(k(k+1)\) s.t \(\mathcal{IS}_{R_{k}}=k(k-1)+2\) for any \(k\geq 1\). Thus, \(R_{k}\) contains \(n-\sqrt{1+4|R_{k}|}+3\) interval \(\mathsf{SUPS}\)._
## 1 Introduction
### The Mission  
The main goal of the GEROS-ISS (GNSS rEfficometry, Radio Occultation and Scatterometry onboard International Space Station) [1] is to demonstrate the capabilities of GNSS remote sensing to derive geophysical parameters of ocean, ice and land surfaces. \"The main mission objectives of GEROS are (in order of priority):* To measure and map altimetric sea surface height of the ocean with an accuracy of 30 cm or better (goal: 20 cm) using reflected GNSS signals to allow methodology demonstration, establishment of error budget and resolutions and comparison/synergy with results of satellite based nadir-pointing altimeters. This includes Precise Orbit Determination of the GEROS payload.
* To retrieve scalar ocean surface mean square slope (MSS), which is related to sea roughness, wind speed, with a GNSS spaceborne receiver to allow methodology testing, establishment of error budget and resolutions. MSS accuracy should be equivalent to a wind accuracy of 10% or 2 m/s whichever is greater. In addition, 2D MSS (directional MSS, related to wind direction) would be desirable.
* To assess the potential of GNSS scatterometry for land applications and in particular to develop products such as soil moisture, vegetation biomass, and mid-latitudes snow/ice properties and to further explore the potential of GNSS radio occultation data (vertical profiles of atmospheric bending angle, refractivity, temperature, pressure, humidity and electron density), particularly in the Tropics, to detect changes in atmospheric temperature and climate relevant parameters (e.g., tropopause height) and to provide additional information for the analysis of the reflectometry data from GEROS\" (from [2]).  
The GEROS-ISS payload will be attached to the Upper Limb Balcony of the Columbus External Payload Facility part of the International Space Station as shown in Figure 1.
# Approximate Triangulations of Grassmann Manifolds
## 2 Materials and Methods
### Persistent Homology  
Suppose we are given a finite nested sequence of finite simplicial complexes  
\[K_{R_{1}}\subset K_{R_{2}}\subset\cdots\subset K_{R_{p}},\]  
where the \(R_{i}\) are real numbers \(R_{1}<R_{2}<\cdots<R_{p}\). For each homological degree \(\ell\geq 0\), we then obtain a sequence of homology groups and induced linear transformations (homology with \(\mathbb{Z}/2\)-coefficients for simplicity)  
\[H_{\ell}(K_{R_{1}})\to H_{\ell}(K_{R_{2}})\to\cdots\to H_{\ell}(K_{R_{p}}).\]  
Since the complexes are finite, each \(H_{\ell}(K_{R_{i}})\) is a finite-dimensional vector space. Thus, there are only finitely many distinct homology classes. A particular class \(z\) may come into existence in \(H_{\ell}(K_{R_{s}})\), and then one of two things happens. Either \(z\) maps to \(0\) (i.e., the cycle representing \(z\) gets filled in) in some \(H_{\ell}(K_{R_{t}})\), \(R_{s}<R_{t}\), or \(z\) maps to a nontrivial element in \(H_{\ell}(K_{R_{p}})\). This yields a _barcode_, a collection of interval graphs lying above an axis parametrized by \(R\). An interval of the form \([R_{s},R_{t}]\) corresponds to a class that appears at \(R_{s}\) and dies at \(R_{t}\). Classes that live to \(K_{R_{p}}\) are usually represented by the infinite interval \([R_{s},\infty)\) to indicate that such classes are real features of the full complex \(K_{R_{p}}\).  
As an example, consider the boundary of the tetrahedron \(T\) with filtration  
\[T_{0}\subset T_{1}\subset T_{2}\subset T_{3}\subset T_{4}\subset T_{5}=T\]  
defined by \(T_{0}=\{v_{0},v_{1},v_{2},v_{3}\}\), \(T_{1}=T_{0}\cup\{\text{all edges}\}\), \(T_{2}=T_{1}\cup[v_{0}v_{1}v_{2}]\), \(T_{3}=T_{2}\cup[v_{0}v_{1}v_{3}]\), \(T_{4}=T_{3}\cup[v_{0}v_{2}v_{3}]\), and \(T_{5}=T\) (this is topologically a 2-sphere). The barcodes for this filtration are shown in Figure 1. Note that, initially, there are four components (\(\beta_{0}=4\)), which get connected in \(T_{1}\), when three independent 1-cycles are born (\(\beta_{1}=3\)). These three 1-cycles die successively as triangles get added in \(T_{2}\), \(T_{3}\), and \(T_{4}\). The addition of the final triangle in \(T_{5}\) creates a 2-cycle (\(\beta_{2}=1\)).  
For analyzing point cloud data, one needs a simplicial complex modeling the underlying space. Since it is impossible to know a priori if a complex is \"correct\", one builds a nested family of complexes approximating the data cloud, computes the persistent homology of the resulting filtration, and looks for homology classes that exist in long sections of the filtration. We discuss two popular methods for doing this in the next subsection.  
Figure 1: The barcodes for a filtration of the boundary of the tetrahedron.
## 4 Results
### Simulated Point Targets  
There are 14 scattering points distributed from depth 25-60 mm and lateral direction \(-\)7 mm to 7 mm. Moreover, the echo images of point targets processed by various algorithms are shown in Figure 3. Figure 4 shows the lateral variation of the point at the depth of 35 mm and 45 mm. According to Figure 4, we can analyze the full width at half maximum (FWHM) and the peak sidelobe level (PSL) of each algorithm, which represents the lateral resolution and sidelobe energy, respectively. Moreover, Table 1 lists the calculation results of FWHM and PSL of each algorithm at the depth z = 40 mm. Observing the FWHM, GSC-CROSS can increase the FWHM by 76.9%, 68.8%, and 17.1% compared with DS, SA, and GSC. As for ultrasound imaging, the lower PSL is, the better the echo image will be. Compared with DS, SA, and GSC, PSL with GSC-CROSS is reduced by 75.64, 61.96, and 43.44 dB, respectively. Therefore, the sidelobe noise in the images processed by DS, SA, and GSC is obvious, while it is basically not observed in GSC-CROSS. The point targets simulation experiment shows that the GSC-CROSS algorithm can improve the lateral resolution of the echo image while suppressing sidelobe energy.
## 3 Results
### Second Step Validation
#### 3.2.1 Set Up Configuration  
Scenario  
In order to achieve the main objective of the current work, it is necessary to carry out a test in which data can be obtained from a fixed point of view on the infrastructure. This configuration is relevant for traffic supervision tasks, crossing monitoring, etc. In this way, our methodology is intended to validate with this new configuration widely used in the field of mobility. The choice of location in Figure 6 is particularly important, as it has to be ensured that data acquisition is from a point of view as radically different from the traditional one (sensors placed on the roof of a vehicle).  
Sensors  
The sensors used in this experiment were a normal video camera and a LiDAR. The camera used is the Aukey PC-LM1E webcam characterized by providing a 640 \(\times\) 480 resolution RGB image compressed in MJPG format at 30 fps. Meanwhile, the LiDAR used is the OS1-64 manufactured in San Francisco, USA by Ouster. It is characterized mainly by its 64 laser layers, a scope of 120 m, vertical field view of 45\({}^{\circ}\) and a frequency of 10 Hz.  
Data Processing  
In order to carry out this experiment, DL models, particularly YOLOv5, had to be trained and data obtained directly from the sensors had to be captured and processed. All data have been processed on a NVIDIA Jetson AGX Xavier with 32 GB memory manufactured in Santa Clara, California, USA by NVIDIA and Ubuntu 20.04 installed.  
Figure 6: Scenario satellite image.  
Figure 7: Sensors set up.  
As the Nvidia Jetson series is an ARM64 architecture, it is not possible to install the OpenPCDet project, as the authors explain in their installation guide, due to dependencies between the packages and the system. To overcome this problem, the docker image l4t-pytorch provided by NVIDIA has been installed as a container to run the OpenPCDet models.
# Soft Fuzzy Reinforcement Neural Network Proportional-Derivative Controller
## 4 Results and Discussion
### Complexity Analysis
#### 4.3.2 Time Complexity Analysis  
The time complexity of each layer in the model is as follows:  
* In the input layer, applying the \(\tanh\) function to each input element has a time complexity of \(O(1)\). For \(n\) input features, the overall time complexity is \(O(n)\).
* In the fuzzification layer, we compute \(j\) fuzzy membership values for each input \(x_{i}\), and each computation takes \(O(1)\) time. Therefore, for \(i\) inputs and \(j\) memberships, the total time complexity is \(O(i\cdot j)\).
* The multiplication layer (fuzzy inference) calculates the fuzzy inference result \(\alpha\) for each rule by multiplying \(n\) fuzzy memberships. The time complexity for each rule is \(O(n)\), and for \(m\) rules, the total time complexity is \(O(n\cdot m)\).
* The normalization layer involves summing \(m\) fuzzy inference results and normalizing each \(\alpha_{j}\), which takes \(O(m)\) time.
* The defuzzification layer computes the output \(y_{i}\), with a time complexity of \(O(m\cdot r)\), as there are \(r\) outputs requiring summation of \(m\) values.  
Thus, the overall time complexity is:  
\[O(n)+O(i\cdot j)+O(n\cdot m)+O(m)+O(m\cdot r) \tag{28}\]Simplified, this becomes:  
\[O(n\cdot m+i\cdot j+m\cdot r) \tag{29}\]  
When increasing the number of fuzzy membership functions \(j\), the number of fuzzy rules \(m\) increases, which leads to an exponential increase in time complexity. For high-dimensional systems, this can result in a significant increase in computational time per step, which could pose a challenge for real-time implementation. Thus, adding more input variables or membership functions could drastically increase the computational load, impacting system performance.
# Effect of Structural Materials on Monopropellant Thruster Propulsion Performance in Micro Scale
## 3 Numerical Comparative Performance Analysis
### Geometry and Computational Domain  
The computational geometry was modeled with dimensions identical to those of the experimentally tested chamber and nozzle geometry. The cuboid chamber had a length of 5 mm in an axial direction, with a cross-sectional geometry of 4.35 \(\times\) 3.56 mm. The dimension of the nozzle inlet was 0.35 \(\times\) 3.56 mm, the dimension of the nozzle throat was 0.35 \(\times\) 0.23 mm, and the dimension of the nozzle exit was 0.35 \(\times\) 0.35 mm. The converging section half angle was 45 degrees, and the diverging section half angle was 12 degrees. The total length of the nozzle was 2.05 mm. A rectangular channel with vertical and axial lengths of 1 and 0.5 mm, respectively, was attached to the upper and lower chamber surface where the chamber meets the nozzle. This channel was used to install a metal screen, employed to hold the catalyst bed in the chamber during the experiments, the details of which can be found in [13]. The void geometry of the domain implied that the gas mixture entered this channel and the flow was retained, as the channel was located away from the midplane and long in a perpendicular direction to the free stream.  
The majority of the heat transfer occurs at the contact region between the fluid and the solid, and thus the contact area is a major factor in estimating where there is the most heat transfer. A 3D geometry was created for the simulation, which took account of the heat loss from the chamber wall by using a large interface area to enable heat transfer. Assuming a fully decomposed flow entering the chamber, the geometry of the catalyst bed was neglected in the modeling. A higher thrust level than recorded in the experiments was expected in the simulation, as decomposition efficiency and the pressure loss of the gas flowing through the catalyst bed were ignored. The computational 3D geometry is shown in Figure 2. Geometrically, a quarter of the experimentally tested microthruster was considered in the simulation to reduce computational cost.  
In order to lower the computational cost even further, and also to obtain a higher solution accuracy, hexahedral elements were used in the fluid domain. The boundary layer was resolved in a finely spaced grid placed in a normal direction, to capture the viscous effects at the near-wall regions accurately. The value of y+ of the grid was kept below 3 for the entire flow field. Tetrahedral elements were used in the solid domain to compensate for the computational costs incurred by the smaller time scale of the heat transfer process in the solid domain. A non-conformal mesh interface was also used since the dense grid created by the conformal meshing at the near-contact region in the solid domain increased the computational costs dramatically. Figure 3 illustrates the fluid domain with hexahedral elements and the solid domain with tetrahedral elements. The total number of mesh elements in each domain was 936,400 in the fluid domain and 116,419 in the solid domain.