esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points
## 3 Error Analysis Model and Influencing Factors of Positioning Algorithm  
### Simulation Analysis Method and Evaluation Index of Positioning Algorithm  
Carrying out simulation analysis, which establishes models to assess different algorithms' performances in most situations, is crucial before the actual flight test. In this section, the total differential method and Monte Carlo method will be discussed as the error analysis methods [74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Positioning Algorithm Basics** & **Algorithm Advantages** & **Algorithmic Disadvantages** \\ \hline \multirow{2}{*}{Earth Ellipsoid Model} & Single image long-distance positioning & \multirow{2}{*}{Elevation information required} \\  & Real-time processing capabilities & \\  & Well extensible and easy to modify & \\ \multirow{2}{*}{Digital elevation model} & Ability to calculate target elevation & \\  & & Unstable calculation results \\ \multirow{2}{*}{LRF or SAR} & High positioning accuracy & Limited working distance of LRF \\  & Influence of angle error is small & \\ \multirow{2}{*}{Multipoint intersection measurement} & Reduce terrain error & \\  & High theoretical positioning accuracy & \\ \multirow{2}{*}{wFiltering algorithm} & long-distance positioning & \\  & High positioning accuracy & Only applicable to target tracking \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary table of typical target localization algorithms.
# Research on the Impact of a Fluid Field on an Acoustic Field in Herschel-Quincke Tube
## 2 Governing Equations and Solution Method
### Governing Equations
#### 2.1.3 The Equation for Flow Field  
Since the 1960s, computational fluid dynamics (CFD) has had a significant impact in many other applications, such as biomedical, mineral processing, aerodynamics, aeroacoustic [25; 26], and the entire processing industry. This widespread application has led to a significant development of models and multi-physics concepts, which have been implemented on various simulation platforms [27]. CFD is widely used in the aerospace industry and has significant applications in industrial operations, including turbulence models, high-order numerical algorithms, output-based mesh adaptation, and geometric centrality in numerical design optimization [28].  
CFD is a subject that uses numerical methods to solve mathematical equations by computer, revealing the physical laws of fluid motion, and studying the spatial physical characteristics of steady flow motion and the spatiotemporal physical characteristics of unsteady flow motion [29]. The basic idea can be summarized as follows: replace the  
Figure 1: The artificial boundary \(\Gamma\) truncate the unbounded region \(R\) into two different domains: the computational domain \(\Omega\) and the external domain \(D\).  
fields of continuous physical quantities in the time and space domains, such as velocity and pressure fields, with a set of variable values at a finite number of discrete points; establish an algebraic equation system through certain principles and methods regarding the relationship between the field variables at these discrete points; and then solve the algebraic equation system to obtain an approximate value of the field variables.  
The governing equations are the Navier-Stokes equations, including the continuity equation and the momentum equation, as written below:  
\[\frac{\partial u_{i}}{\partial x_{i}}=0 \tag{9}\]  
\[\frac{\partial u_{i}u_{j}}{\partial x_{i}}=-\frac{1}{\rho}\frac{\partial p}{ \partial x_{i}}+\
u\
abla^{2}u_{i} \tag{10}\]  
where \(u_{i}\) is the velocity component in the \(i\) direction, \(x_{i}\) is the coordinate component in the \(i\) direction, \(\rho\) is the fluid density, \(\
u\) is its dynamic viscosity coefficient and \(p\) is the pressure.  
Turbulence is modeled with the Reynolds averaging method, and the velocity is decomposed into the time-averaged and fluctuating components. The Reynolds stress model is adopted to close the steady Reynolds-averaged Navier-Stokes (RANS) equations, and the Reynolds stress tensor is written as  
\[\overline{u^{\prime}_{i}u^{\prime}_{j}}=\
u_{t}(\frac{\partial u_{i}}{\partial x _{j}}+\frac{\partial u_{j}}{\partial x_{i}})+\frac{2}{3}k\delta_{ij} \tag{11}\]  
where \(u^{\prime}_{i}\) is the fluctuation velocity component in the \(i\) direction, \(\
u_{t}\) is the turbulent viscosity, \(k\) is the turbulent energy and \(\delta_{ij}\) is the Kronecker delta.
### Multi-Path Routing  
The protocols for single-path routing are designed to find and use a single path between a source and a sink. On the other hand, multi-path routing provides numerous ways between the source and the sink. The benefits and drawbacks of multi-path routing are briefly described in the following sections.
###### Abstract  
Fiber optic sensors (FOSs) have transformed industrial applications with their high sensitivity and precision, especially in real-time monitoring. This study presents a fiber optic sensor based on multimodal interference (MMI) applied to detect honey adulteration. The sensor is built using a non-core multimode fiber (NC-MMF) segment spliced between two standard single-mode fibers (SMFs). We focus on reporting the detection of two main adulterants in honey that modify its refractive index (RI): the presence of glucose and moisture content. Detailed testing was performed with two commercially approved honey brands, named A and B. The sensor successfully detected glucose concentrations from 1% to 5% and moisture content from 0% to 20% for both brands. For glucose detection, we obtained sensitivity values \(-0.55457\) nm/% for brand A and \(-2.61257\) nm/% for brand B. In terms of moisture content in honey, we observed a sensitivity around \(-0.3154\) nm/% and \(-0.3394\) nm/% for brands A and B, respectively. Additionally, temperature tests were performed, showing that the sensor works optimally up to 30 \({}^{\circ}\)C. The results were validated using a conventional refracometer, showing a close agreement with the data obtained and confirming the reliability and accuracy of the proposed sensor. Compared to other refracometers, the MMI sensor offers advantages such as real-time monitoring, ease of assembly, cost-effectiveness, and minimal maintenance. Furthermore, the sensor represents an alternative tool to guarantee the quality and authenticity of honey, overcoming the limitations of conventional measurement techniques.  
fiber optic sensor; honey adulteration; multimodal interference  
[PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Multimodal Interference-Based Fiber Optic Sensors for Glucose and Moisture Content Detection in Honey. _Appl. Sci._**2024**, _14_, 7914. [[https://doi.org/10.3390/app14177914](https://doi.org/10.3390/app14177914)]([https://doi.org/10.3390/app14177914](https://doi.org/10.3390/app14177914))
## 3 Materials and Methods
Firstly, the full set of survey questions was evaluated by volunteer participants on the survey platform prior to the launch of the survey to avoid ambiguous question wording and to provide general feedback. Secondly, the surveyed student population was selected to be large enough to create a diverse sample regarding experiences but also demographic and non-demographic factors. Thirdly, participants were recruited from two interdisciplinary information systems lecture courses from two different study programs, which increases the diversity of the surveyed population; however, all participants had some connection to IT-related study subjects. Fourthly, our survey data collection is affected by the inherent limitations of self-reporting. Lastly, participating students completed the survey online. Paper-based survey completion was not a viable option during the pandemic, and the online survey ensured the anonymity of all participants. Survey completion was incentivized by being part of a number of voluntary tasks to become eligible for a grade bonus on the final exam. The survey did not stand out in terms of time commitment or complexity of the task. Most of the participants (85.8%) were students within the 18-25 age range, while 10.9% were in the 26-29 age category, with only the remaining 3.3% either below the age of 18 or above the age of 29 (see Table 1). There was a higher proportion of male students (\(n\) = 464, 58.0%), a smaller proportion of female students (\(n\) = 323, 40.5%), and the remaining students (\(n\) = 11, 1.5%) chose not to disclose their gender details. Survey participants were classified as either work experience students (\(n\) = 448) or students without any work experience (\(n\) = 350). Table 2 summarizes the self-reported duration of work experience. Most of the students (\(n\) = 594) were residents in Germany, while the remainder (\(n\) = 204) self-reported to normally reside outside Germany or outside Europe. The regular place of remote work and remote study was the home location or the student dormitory location, and only a small number of students selected other alternative locations. We used statistical analysis to substantiate cyber-security awareness differences between the two subgroups of students (students with and without work experience). Primarily, we conducted Pearson's chi-square tests to assess whether the frequency distributions of the comparable survey questions (students with and without work experience) are independent of each other. When any other statistical method or analysis was used, the details are highlighted in the results part of this paper. We defined survey questions to be as comparable as possible for the two subgroups of students (students with and without work experience); the only difference was typically in the survey question wording itself (question either included \"remote work\" or \"remote study\"). We carefully pretested the survey with various colleagues and integrated any feedback received. Sample questions for the different topic categories and for the two subgroups of students are available in Appendix C.
## 3 Materials and Methods  
### Remote Sensing  
The Sentinel-2 mission provides the continuity of the Landsat and SPOT missions to acquire land cover classification maps with a short revisit cycle (five days for Sentinel-2A and B) and has been found very useful for geological applications [34; 35]. Two images of Sentinel-2B, in conjunction with the previous mapping [27; 28], were analyzed in detail for geological mapping and the structural interpretation of the Manzai Ranges. The multi-spectral instrument (MSI) of Sentinel-2B has thirteen spectral bands with a spatial resolution of 10, 20, and 60 m in the Visible Near Infra-Red (VNIR), Near Infra-Red (NIR), and Short-Wave Infra-Red (SWIR) bandwidth, respectively [36]. The study area has very little to no vegetation, and the scenes analyzed were acquired during the winter of November 2019 with 0% cloud cover. The images have the product type L2A, which is geometrically, radiometrically, and atmospherically corrected by the European Space Agency (ESA) using the Sentinel-2 toolbox. Additionally, the product L2A shows the bottom of atmosphere reflectance. Finally, both scenes were stitched together seamlessly to cover the extent of the study area.  
The VNIR (2, 3, 4, 8), NIR (5, 6, 7), and SWIR (11, 12) bands were used in this study. Spectral characteristics were enhanced by band ratios by dividing one spectral band by another. Band ratios 12/7-12/3-12/11 (Figure 3a), 11/12-12/7-8/11 (Figure 3b), and 8/11-11/12-12/7 (Figure 3c) were found useful to differentiate the different types of sedimentary rocks and alluvial deposits. Geological mapping by [PERSON] [27] was helpful as a reference map in our study area. High-resolution (12 m) Tandem-X Digital Elevation Model (DEM) data were obtained from the German Aerospace Center. DEM and Hillshade images were used to extract the stream network, drainage anomalies/deflection (Figure 3d), river offsets, and fault scarps.
## 3 Fuzzy Logic Q-Learning Routing Scheme with Asymmetric Link Aware  
### System Model and Assumption  
The network model simulates a typical multi-hop urban environment composed of a large number of mobile nodes N. The topology of a network is a directed graph \(G(V,E)\), where \(V\) is the set of all mobile  
Figure 1: Examples of MANETs communication applications.  
nodes and \(E\) is the set of wireless links in the network. Among these mobile nodes, few are equipped with long-range \((N_{L})\) transmission capabilities, and others use the common short-range \((N_{S})\). Each node is equipped with a wireless radio transceiver. A wireless link exists between any two nodes \(A\) and \(B\), if and only if \(B\) locates within the transmission range of \(A\). A link is bidirectional if \(A\to B\in E\) and unidirectional if \(B\to A\
otin E\). Each node is equipped with a GPS device that provides information about its position and speed. In addition, every node can obtain the destination position when needed using a location service such as Region-based Location Service Management Protocol (RLSMP) [30] and Mobile Group based Location Service Management (MG-LSM) [31]. Network nodes obtain neighbors information through broadcasting a Hello message periodically at predefined intervals \((\tau+jitter)\), to control traffic storm, and to avoid potential collisions. Table 1 shows the format of a Hello message used in FQ-AGO, which includes the sender's ID, which identifies each node in the network, a unique sequence number that reflects the freshness of the neighbors' information, the current position, and node velocity also included in the Hello messages. Each network node maintains a local view of its neighbors by constructing a neighbors' table to manage neighbor's information obtained from exchanging Hello packets. We define the set of neighbor's nodes from which forwarder node can select next-hop to relay its packet as the Candidates Set denoted by \(C_{n}\). A candidate node from \(C_{n}\) is selected to relay the forwarder packet to its corresponding destination according to the neighbor evaluation strategy of our proposed fuzzy logic-based scheme. To avoid situations where a selected candidate might be out of the forwarder communication range, Hello packet transmission interval can be tuned based on the network environment requirements. However, the Hello packet transmission interval should be small, to get the current location information about \(C_{n}\). Accordingly, there is a trade-off between the routing protocol performance and the network overhead. After conducting a set of initial simulation experiments, we found that setting the Hello message transmission interval in our scheme to 1 s is sufficient in most cases because the mobile node relative movement during this amount of time is much smaller than the transmission range [25].
## 3 Results
### Physical and Chemical Parameters of Cultivation  
Generally, an overall increase in the chlorophyll concentration for every treatment was registered (Figure 5). In the SE assay, the control was the treatment in which _C. vulgaris_ exhibited the highest content of chlorophyll (day 12; 2301.67 \(\mu\)g/ L), throughout the experiment (Figure 5a). Nevertheless, from day 4 until the end of the experiment, it was possible to establish two statistical different groups, namely the group that exhibits higher--control, 25% and 50% SE--and lower chlorophyll content--75% and 100% SE (Figure 5). However, the cultivation of _C. vulgaris_ in 100% SR significantly enhanced the chlorophyll synthesis comparatively with control (Figure 5b). After day 6, it was possible to observe differences (_p_-value < 0.05) between the SR treatments. On day 6, the highest chlorophyll concentration was found for 75% SR (866.67 \(\mu\)g/L), and was statistically different from all the other treatments. From day 8 until day 12, 100% SR stood out positively from all the other treatments (_p_-value < 0.05), achieving a maximum chlorophyll content of 237,834 \(\mu\)g/L (day 12). In both assays (Figure 6a,b), it was observed an increase of pH values in the control and 25% treatment, ranging between 7 and 9.5. Despite the slight oscillation in pH values for 50%, 75% and 100% treatments, pH seems to stabilize around 8. Figure 4: _C. vulgaris_ specific growth rate in the different concentrations of the secondary effluent (SE) and sludge run-off (SR) (mean \(\pm\) standard deviation; n = 3). * _p_-value < 0.05, when compared with the 75% SE. Statistically significant differences found between the two types of effluent (SE and SR), comparatively with the control, are expressed by different letters. Figure 5: Chlorophyll concentration of _C. vulgaris_ grown in the different concentrations of (**a**) secondary effluent (SE) and (**b**) sludge run-off (SR) for 12 days (mean \(\pm\) standard deviation; n = 3). The symbol * indicates statistical differences among data (_p_-value < 0.05). Regarding nutrients availability, in the SE assay (Table 3) a tendentious increase of N-NH3 concentration was observed in control, 50% and 100% treatments. Concurrently, total P concentration decreased, significantly (_p_-value < 0.05) in the 50% and 100% SE concentrations, achieving removal rates of 45.4% and 94.8%, respectively. For the SR experiment (Table 4), the content of N-NH3 increased in control, whereas it significantly decreased in the other treatments, removal rates of 78.7% and 95% being achieved, respectively, for 50% and 100% SR concentrations. Furthermore, a decrease in total N concentration were found for all treatments. Control, 50% and 100% SR exhibited removal rates of 52.1%, 73.9% and 65.9%, respectively. For total P, a reduction of total P concentration was registered in control (14.7%) and 50% SR (31.2%), but an increase of this nutrient in the 100% SR was observed.
# Performance Estimation of Intensity Accumulation Display by Computer-Generated Holograms
## 3 Simulation  
We performed simulations to confirm the noise model. The simulation specifications are as follows. The wavelength and the pixel pitch are 0.65 \(\upmu\)m and 7.56 \(\upmu\)m, respectively. The computing workspace is \(2000\times 2000\) pixels, while both the object size and the hologram size are \(512\times 512\) pixels. The object distance is 190 mm. First, we used the RP method to generate 100 binary CGHs and calculated the PSNR of the reconstructed image with different accumulation numbers. The ground truth image (object) applied in the simulation is shown in Figure 2a. Figure 2b shows a typical reconstructed image, which is from the intensity accumulation of images reconstructed by 100 RP binary CGHs. The resolution of the reconstructed image is apparently less than the ground truth image because of the natural lowpass filtering of the limited size of the hologram (Figure 1). The PSNR of the reconstructed image is calculated in the region of interest (the same size as the object) at every number of intensity accumulation. As we obtain the PSNR curve from simulations, we can fit the simulation curve using Equations (8) and (9) to find the values of \(S\) and \(K\). Both the \(PSNR\) from simulations (in circle) and the fitting \(PSNR_{C}\) (in solid line) are shown in Figure 3. Apparently, the proposed noise formula matches the simulation very well. In this case, the values of the parameters \((S,K)\) are found to be 2.04 and 17.36, respectively. The values can be also regarded as a reference of comparison. The same procedures for MIFA CGHs (five iterations and ten iterations), the LRDS CGHS, and the LRDS+AIA CGHs were repeated. The PSNR data, together with the fitting curves, are shown in Figures 4-7, respectively. The model fit well most types of CGHs, except for the AIA method. These simulation results are discussed in the next section.  
Figure 3: PSNR of the reconstructed image by RP binary CGHs as a function of accumulation number. The values of the parameters \((S,K)\) of the fitting curve are (2.04, 17.36).  
Figure 2: The ground truth image (**a**) and the reconstructed image (**b**) by 100 intensity accumulation of reconstructed images of RP binary CGHS. The PSNR of (**b**) is 17.86 dB.  
Figure 4: PSNR of the reconstructed image by MIFA binary CGHs (iteration number is 5) as a function of accumulation number. The values of the parameters \((S,K)\) of the fitting curve are (3.11, 11.40).  
Figure 5: PSNR of the reconstructed image by MIFA binary CGHs (iteration number is 10) as a function of accumulation number. The values of the parameters \((S,K)\) of the fitting curve are (3.27, 9.55).
## 4 Experimental Section
### Clinical Experiments of Tremor Quantification
#### 4.2.2 Experiment Setup  
A total of seven patients with Parkinsonian tremor were tested by the tremor assessment system. Their tremor severities (UPDRS tremor scores: \(D\)) ranged from 1 to 3. The measurements of these patients' tremors were performed after their medication had been stopped for more than 24 h.  
Rest, postural, and action tremor assessment tasks were sequentially performed. However, for each patient, only the side with the more severe tremor was assessed. Each tremor assessment task lasted for ten seconds.  
Time-frequency analyses and statistical analyses were carried out based on the IMU signals [42]. The valid state detection algorithm was executed for each assessment task.
# Enhanced Conjunction Assessment in LEO: A Hybrid Monte Carlo and Spline-Based Method Using TLE Data
## 4 Conclusions
Future improvements may also involve integrating empirical atmospheric density models, such as NRLMSISE-00 or JB2008, to enable force-aware orbit propagation. This would allow the framework to better account for variations in orbital decay due to solar activity and enhance the robustness of collision predictions under dynamic space weather conditions. Conceptualization: S.K.T., A.M.A. and J.L. ; methodology, S.K.T., I.E.M., A.M.A. and J.S. ; software, S.K.T. and X.Y; formal analysis, J.L. ; data curation, S.K.T. and J.S. ; writing--original draft preparation, S.K.T., A.M.A., I.E.M. and J.L. ; writing--review and editing, S.K.T., A.M.A., X.Y. and J.L. All authors have read and agreed to the published version of the manuscript. This research received funding from the Research Program for Space Debris and Near-Earth Asteroid Defense (Grant No. KJSP2020010203), supported by China National Space Administration. Data Availability Statement: Data are contained within the article. This paper is based upon work supported by the Science, Technology & Innovation Funding Authority (STDF), The Egyptian Ministry of Higher Education and Scientific Research, under grant number 48102. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} LEO & Low Earth Orbit \\ TLE & Two-Line Element \\ TCA & Time of Closest Approach \\ SGP4 & Simplified General Perturbations Model 4 \\ SSA & Space Situational Awareness \\ SSO & Sun-Synchronous Orbit \\ PDF & Probability Density Function \\ RTN & Radial-Transverse-Normal \\ ROI & Region of Interest \\ ECI & Earth-Centered Inertial \\ DAMC & Differential Algebra Monte Carlo \\ ICRE & Improved Critical Region Estimation \\ MC & Monte Carlo \\ \end{tabular}