esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Types of Catalysts Used in Transesterification
### Acid-Base Catalyst
#### 4.3.1 Zeolite-Based Catalysts  
Both natural as well as synthetic zeolites are promising catalysts. Naturally, zeolites are found as aluminosilicates that are crystalline, interlinked by Oxygen and microporous and their versatile catalytic properties are related to their pore size (equal molecular pores or channels), ion-exchange characteristics and chemical composition. Negative ions are produced within the catalytic structure and, as a result, a basic catalyst emerges. As electropositivity of exchange cation increases, the base strength is increased. Their electro-neutrality maintenance by cation-polar interaction and shape selectivity makes them unique catalysts. Small pore diameter and weak basic strength are demerits associated with basic zeolites [38]. The castor oil biodiesel was synthesized by NaY zeolite-supported La\({}_{2}\)O\({}_{3}\) reported by [PERSON] et al., 2018. S-La\({}_{2}\)O\({}_{3}\)/NaY-800 an optimized catalyst produced by mixing of La\({}_{2}\)O\({}_{3}\), Kaolin, Sodiumcarboxymethyl cellulose (CMC) and zeolite NaY and the mass ratio was 70:20:10:2.5 for NaY/kaolin/La\({}_{2}\)O\({}_{3}\)/CMC and 4 wt. % surfactant was added and calcination was conducted at 800 \({}^{\circ}\)C. FAEE yield (84.6%) was achieved with 10 wt. % catalyst loading, 1:15 M oil to ethanol ratio at 70 \({}^{\circ}\)C for 50 min [49]. [PERSON] et al. (2019) reported FAEE from castor oil (98.6 %) using 3 wt. % Li/NaY zeolite as catalyst. Catalyst, with 1:1 M (Li\({}_{2}\)O\({}_{3}\):NaY), synthesized via co-precipitation method followed by calcination at 750 \({}^{\circ}\)C for 4 h from fly ash. The optimized conditions were 1:18 M oil/ethanol ratio, 3 wt. % catalyst in 2 h.
## 5 Results
### Visualization Experiments
while significantly reducing the number of model parameters and the computational load, achieves a comprehensive improvement in the detection precision and recall rates for solar panels, antenna, and the main body, with the most notable improvement in recall rates. For a straightforward and intuitive depiction of the detection effect of the proposed LTSCD-YOLO model, different satellite images were selected for visualization experiments, providing a direct comparison between YOLOv8s and LTSCD-YOLO, with some results shown in Figure 16. Figure 16a represents the Ground Truth, Figure 16b represents the YOLOv8s detection results, and Figure 16c represents the LTSCD-YOLO detection results, where red boxes detect solar panels, green boxes detect antennas, blue boxes detect the main body, yellow boxes represent false detections, and orange boxes represent missed detections. The results indicate that YOLOv8s had one false detection and two missed detections, while LTSCD-YOLO did not have issues with false or missed detections and also demonstrated improved detection accuracy, further proving the effectiveness of the improved LTSCD-YOLO model and its reliable support for typical satellite component detection tasks. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Typical Satellite** & \multirow{2}{*}{**P/\%**} & **R/\%** & **Params/10\({}^{6}\)** & **GFLOPs/G** & **Number of** \\  & **Components** & & & & **Images** \\ \hline YOLOv8 & Solar panel & 95.8 & 91.9 & & \\ (Baseline) & Antenna & 86.9 & 78.9 & 11.14 & 28.8 & 360 \\  & Main body & 96.0 & 93.7 & & & \\ \hline LTSCD-YOLO & Solar panel & 96.0 & 93.1 & & & \\ (Ours) & Antenna & 88.8 & 83.2 & 2.40 & 9.8 & 360 \\  & Main body & 96.4 & 96.8 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of detection performance for solar panel, antenna, and main body. Figure 16: Detection results image: (**a**) Ground Truth; (**b**) YOLOv8s algorithm; (**c**) and ours.
# Fast and Efficient Lunar Finite Element Gravity Model
## 3 Representations of Lunar Potential Using Orthogonal Finite Elements
### Sampling: Radii and Shells
#### 3.1.1 Radial Sampling  
At an arbitrary radius, \(r\) in the range of \(R_{m}\leq r\leq 1.11R_{m}\), let the gravity data \(U(r,\lambda,\phi)\) be provided at a constant radius, \(r\), on a specific spherical shell. These data on this shell should be transformed into \(U(\zeta,\zeta,\eta)\), where \(-1\leq\zeta\leq+1\) and \(-1\leq\eta\leq+1\); we can retrieve an accurate gravity modeling representation of \(U(\zeta,\eta)\) on \"sufficiently-dense\" sets of concentric spherical surfaces. However, sampling radial coordinates is key when considering this unique variation.  
To determine a proper radial sample, two different sampling methods are considered: uniform sampling, approximating the gravity at every radius between the lunar surface to 200 km above in 10 km increments, and cosine sampling. For cosine sampling, consider the transformed position \(r\) in which the position follows a \"cosine-like\" transformation as a function of the previously mentioned variable \(\zeta\) as the following:  
\[r=r_{min}+(r_{max}-r_{min})\Big{[}1-cos\Big{(}\frac{\pi}{4}(1+\zeta)\Big{)} \Big{]} \tag{28}\]where  
\[\xi=\frac{4}{\pi}cos^{-1}\left(1-\frac{r-r_{min}}{r_{max}-r_{min}}\right)-1 \tag{29}\]  
where \(-1\leq\xi\leq+1\) and \(r_{min}\leq r\leq r_{max}\), \(r_{min}=R_{m}\) and \(r_{max}=1.11R_{m}\). At higher radii above the lunar surface, gravity anomalies decline rapidly. With this fact, we seek to format an efficient way to sample surface gravity that allows memory allocation. With cosine sampling, this transformation \"naturally\" constrains dense measurements at smaller radii, ensuring that gravity perturbations are maximized and mimic how gravity would naturally act. This means that no matter the number of points \(N\) at lower radii, these nodes then bunch up in comparison to larger radii. This idea is illustrated in Figure 5 for 10 and 21 sample points; compare this method to uniform sampling. To compact this progression, the transformed radius variable \(\xi\) was created to help treat \(r\) as an independent variable. It is again important to note that \(\xi\) is sampled non-uniformly using another cosine transformation in order to satisfy the Chebyshev orthogonality conditions:  
\[\xi_{i}=-cos(i\pi/2M),\ i=0,1,2,...,M \tag{30}\]  
Not only does cosine sampling simulate a natural progression in gravity, but this sampling method also produces the error magnitude on the order of (\(10^{-9}\)) while taking the same amount of computational power as uniform sampling. For these reasons, we continue using cosine sampling in this analysis at a latitude and longitude of \(\lambda=\phi=0^{\circ}\) degrees on the Moon.
## 3 Methodology
### Implementation of Rock Bridge Failure  
Depending on the investigated rock mass scenario, a discrete fracture network consists of the pre-existing fractures but also of fractures that are newly formed due to rock bridge failure during the failure process.  
In this study, approximately 600 different fracture network scenarios where rock bridges may fail and coalesce with two pre-existing fracture sets were simulated. In nature, fracture coalescence between two pre-existing fractures is characterized by tensile (i.e., wing cracks, en echelon) and/or shear (secondary cracks) failure modes, according to the stress state in the rock mass (e.g., [51]). Thus, the newly formed fractures do not simply grow as single fractures aligned in the direction of the shortest distance. In order to avoid too much complexity and the short fracture coalescence length assumed herein, a simplified approach based on fracture propagation in the direction of the shortest distance between two pre-existing fractures is chosen. As an assumption and simplification, a new fracture will be formed between two pre-existing fractures if the shortest rock bridge thickness is below a pre-defined threshold. For this simulation the threshold value of the rock bridge was set to (i) 0.05 m, (ii) 0.1 m, (iii) 0.2 m, (iv) 0.3 m, and (v) 0.4 m, respectively. The presented approach does not consider the complexity of fracture coalescence due to stress heterogeneities or anisotropies and does not consider the impact of non-planarity of fractures on the block area distribution.  
Considering the new, due to the intact rock bridge failure formed fractures, the shortest distance between the two fractures is calculated according to the MATLAB function _DistBetween2 Segment_[52]. This function uses a geometric approach to find the unique vector, which is characterized as the smallest possible length between the two pre-existing fractures [53]. In the case that the distance between two fractures is smaller than the threshold value of the rock bridges (i.e., here specified as 0.05 m, 0.1 m, 0.2 m, 0.3 m, or 0.4 m), a new fracture representing rock bridge failure is generated along the line representing the shortest measured length (red dotted lines in Figure 3). Restrictions, where rock bridge failure is not possible, are summarized in Figure 3 and comprise: (1) a failure distance that is larger than the threshold length, (2) a new coalescing fracture crossing a different fracture in between, and (3) two existing fractures, which are already intersecting. Concerning the example of Figure 3, three closed polygons are formed (green, magenta, and turquoise background colors). It should be noted that, for example, a merged polygon consisting of the magenta, turquoise, and green polygons, is not considered to be a valid polygon in this study. The block areas are then calculated by using a method similar to the method described above.
## 2 Methods and Design
### Design of the Fundus Imaging System
#### 2.2.1 Eye Model  
The human eye is a very sophisticated and complex optical imaging system [15], and the design of the fundus optical system needed be carried out on the basis of conforming to the optical characteristics of human eyes. The optical system of the eye mainly includes  
Figure 2: Structure diagram of new fundus optical imaging system.  
the cornea, anterior chamber, lens, vitreous, and retina, and external light is imaged on the retina through the previous optical element in turn [16]. The power of light is provided by the cornea and the lens, so, when building the eye model, these two parts were mainly simulated. The cornea had a higher diopter and resembled a convex lens in an optical lens, and the lens was a biconvex structure that resembled a biconvex optical lens. Figure 3a is the standard eye model. The refractive index of the lens used in the standard eye model was uniform. The axial length was simplified to 24.0 mm, the retinal radius was simplified to 11.0 mm, and the anterior surface of the relaxed lens was simplified to a spherical surface with a radius of 10.0 mm [17].  
However, the posterior surface of the lens was not flat and was approximately a parabola. This was a key factor in controlling off-axis aberration. Figure 3b is a more accurate model of the eye. Fine-tuning the conic coefficient on the posterior surface of the lens instead of the real-world refractive index gradient could compensate for the lower refractive index in the midline of the eye and reduce the time for ray tracing in optimized and non-sequential modes. The cone coefficient of the lens was set to 3 and the cone coefficient of the cornea was set to \(-0.3\). The simulation in ZEMAX and the specific parameters and materials are shown in Table 1. The accurate human eye model brought the design of the imaging system proposed in this paper more in line with the requirements of the real environment and improved the accuracy of the imaging system.
# An Efficient Mobility Model for Improving Transmissions in Multi-UAVs Enabled WSNs
## 5 Performance Evaluations
The overall comparative analysis of the proposed approach against the standard discussed approaches is presented in Table 4. The statistical variations among the proposed and compared approaches are presented in Figure 4:0  
Figure 4:1 End-to-End Delivery comparison among the Proposed Approach, Random Waypoint, Random Walk, Fixed Manuevers and Gauss Markov. Figure 4:2 Packet Drop comparison among the Proposed Approach, Random Waypoint, Random Walk, Fixed Manuevers and Gauss Markov. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Proposed** & **Random** & **Random** & **Gauss** & **Fixed** \\
**Metrics** & **Approach** & **Way-Point** & **Walk** & **Markov** & **Maneuvers** \\ \hline Coverage & 98\% & 91\% & 90\% & 55\% & 10\% \\ Throughput & 82\% & 74\% & 72\% & 55.7\% & 7.1\% \\ Latency & 8.3\% & 12.5\% & 12.5\% & 16\% & 86\% \\ Delay & 0.625\% & 11.25\% & 35.625\% & 5\% & 0.625\% \\ Jitter & 2\% & 9\% & 6\% & 5\% & 2\% \\ PDR & 99\% & 91\% & 90\% & 60\% & 12\% \\ Data Transferred & 91\% & 74\% & 72\% & 49\% & 7\% \\ EED & 0.8\% & 12.5\% & 24.1\% & 3.3\% & 0.8\% \\ Packet Drop & 0.8\% & 4.1\% & 4.1\% & 35\% & 83\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative Analysis of the Proposed Approach against the featured techniques.
## 2 WTIPM Solutions for LiGaS\({}_{2}\)-Based OPA
### PM Design and Manipulation
As the refractive index of the nonlinear crystal is also a function of temperature, the PM condition becomes worse when there is a significant temperature rise in the nonlinear crystal under high-power operation. Similar to Equation (2), we can expand \(\Delta k\) against the crystal temperature \(T\):  
\[\Delta k=\Delta k_{0}+\left.\frac{\partial\Delta k}{\partial T}\right|_{T=T_{0 }}\Delta T+O\Big{[}(\Delta T)^{2}\Big{]}, \tag{4}\]  
where \(\Delta T=T-T_{0}\) is the temperature deviation from the preset temperature \(T_{0}\). The first-order TIPM condition can be expressed as,  
\[\left.\frac{\partial\Delta k}{\partial T}\right|_{T=T_{0}}=0. \tag{5}\]  
In a collinear PM configuration, this condition may be fortunately satisfied under a specific wavelength in a specific crystal. Inspired by the noncollinear WIPM, some works have tried to realize TIPM using a noncollinear configuration in the near-IR OPA [26; 27; 28]. Furthermore, the WTIPM condition can be achieved if the first-order derivative terms of \(\Delta k\) in Equations (3) and (5) can both satisfy the following:  
\[\left.\frac{\partial\Delta k}{\partial\lambda_{\mathrm{s}}}\right|_{\lambda= \lambda_{\mathrm{s}0}}=\left.\frac{\partial\Delta k}{\partial T}\right|_{T=T_{0 }}=0. \tag{6}\]  
In this situation, a good PM condition can be ensured, even under large deviations of wavelength and temperature. However, the conditions given in Equation (6) are difficult to fulfill in a collinear configuration. Even in a noncollinear configuration, the noncollinear angles corresponding to the WIPM (Equation (3)) and TIPM (Equation (5)) are usually different from each other, so it is difficult to fulfill WTIPM by using single noncollinear angle \(\alpha\). To this end, more control degrees of freedom must be introduced, which means that the WTIPM may exist under appropriate combinations of multiple PM parameters, such as the noncollinear angle \(\alpha\), PM angle \(\theta\) (or \(\varphi\)), pump wavelength \(\lambda_{\mathrm{p}}\), signal wavelength \(\lambda_{\mathrm{s}}\), and preset temperature \(T_{0}\). For \(\lambda_{\mathrm{p}}\) between 1 \(\upmu\)m and 2 \(\upmu\)m, we evaluated several widely used mid-IR crystals (e.g., AgGaS\({}_{2}\), AgGaSe\({}_{2}\), BaGa\({}_{4}\)S\({}_{7}\), BaGa\({}_{4}\)Se\({}_{7}\), ZnGeP\({}_{2}\), and LiGaS\({}_{2}\)) and found the WTIPM solution only in the LiGaS\({}_{2}\) crystal.
## 5 Experiments and Results
### Quality Assessment Metrics
#### 5.2.1 Coefficient of Determination (\(R^{2}\))  
The \(R^{2}\) score evaluates the proportion of variance in the dependent variable that is predictable from the independent variables. It is defined as  
\[R^{2}=1-\frac{\sum_{i=1}^{N}(Y_{i}-\hat{Y}_{i})^{2}}{\sum_{i=1}^{N}(Y_{i}-\bar {Y})^{2}}, \tag{10}\]  
where \(Y_{i}\) denotes the actual value, \(\hat{Y}_{i}\) is the predicted value, and \(\bar{Y}\) is the mean of the actual values. An \(R^{2}\) score close to one indicates that the model explains a high proportion of the variance, suggesting good predictive capability.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 6 Analysis and Validation of Statistical Model for AMQP IoT Attack Detection
### Summary of the Statistical Model Performance
#### 6.5.5 Practical Implications  
The model's ability to maintain high accuracy while minimizing false positives ensures its applicability in real-world IoT network environments. Key strengths include the following:  
* Reliable differentiation between normal and malicious traffic.
* Effective detection of distinct attack signatures, supporting targeted threat mitigation.
* Robust performance across varied traffic scenarios, providing flexibility for deployment in dynamic IoT environments.  
This analytical summary highlights the statistical model's strengths, identifies areas for further refinement, and reinforces its value as a tool for securing AMQP IoT protocols.
## 2 Method and Approach  
Essentially, the goal now is to obtain digital twins for route descriptions directly from existing scanned 2D and 3D maps. It is assumed that the environment to be traversed can be scanned in such a way that both the areas to be traversed and the position of the individual tasks are available. Such a map is directly acquired or generated, for example, by vacuum cleaner robots via a so-called simultaneous localization and mapping (SLAM) or the manual scan of a work plan. The map with different events was encoded in RGB. Thus, the map consists of the obstacles, the individual tasks with different colors as well as the initial position of the individual robots. Based on this map a digital twin is created by considering seven steps. Figure 1 shows the different steps generating a digital twin from the RGB map, which leads to a subset of a labeled graph with edges and nodes.  
First, the map with the tasks is saved as an RGB image, where the individual tasks, routes and obstacles are coded in the appropriate color. Then the separated events in the image are extracted as markers. A distinction can be made between \"passable\", \"not passable\", \"position of the robot\", and \"tasks to be passed\" in the marker extraction Section 2.1. Via a shrinkage also referred as constrained skeletonization in Section 2.1 the task markers are used as constrained conditions to calculate the skeleton in the passable area. This skeleton links the individual tasks in the traversable domain. The skeleton can be decomposed into individual path segments using a topological segmentation in 2.2. Since the paths are still in the form of pixel chains, they must be reduced to simple edges and nodes to a graph using a collapsing procedure in 2.2. This graph is then enriched with the local passage width in 2.3. The digital twin can now be obtained directly from the paths as well as from additional information in 2.4. Finally, based on the digital twin, the ability to reposition the robots must be integrated in 2.5, which have left the path with some uncertainty in 2.5. In the following, the individual concepts are explained schematically  
Figure 1: Digital twin generation: marker extraction and conditional shrinkage in 2.1, topological segmentation and polyline fitting in 2.2, clearance width estimation in 2.3, digital twin generation in 2.4, and robot position updating in 2.5.  
using a simple example. For each chapter section, a corresponding result of a more detailed use case can be found in Sections 3.1-3.5. Furthermore, several additional use cases of complex 2D maps in 3.6, of 3D maps in 3.7 and of real world maps in 3.8 are considered.
# In-Process Tool Condition Forecasting of Drilling CFRP/Ti Stacks Based on ResNet and LSTM Network
## 2 Methodologies
### ResNet-Based In-Process TCM Model  
The TCM model is needed to predict current tool wear using measured real-time signals because it is nearly impossible to measure tool-wear values in the CFRP/Ti drilling process. The ResNet can use multi-channel signals as input, with the ability to extract multi-domain sensitive signals features, which helps to overcome the low efficiency of manually selecting signal features [35]. Otherwise, ResNet increases the performance of the network with a large number of layers by handling the vanishing gradient problem [5]. Therefore, it is chosen in this paper due to its ability to improve accuracy and robustness.  
The framework of the ResNet-based model is shown in Figure 5. The inputs are four-dimensional time series signal segments as \(X_{n}=[p_{n},v_{n1},v_{n2},v_{n3}]\), including moving average spindle power, \(X\) cutting vibration MARMS, \(Y\) cutting vibration MARMS and \(Z\) cutting vibration MARMS signals, which are acquired by the proposed signal preprocessing method.  
As depicted in Figure 5, this TCM model is used to learn a mapping from the input time series signal segments \(X_{n}\) to an output value \(\widetilde{y_{m}}\), the tool-wear values at drilling hole numbers.  
The inputs are preprocessed by a de-noising block into a post-activation residual block followed by the pre-activation residual blocks. The structure of post-activation residual block starts with a convolutional layer (Conv), followed by batch normalization layer (BN), rectifier linear unit (ReLu), dropout layer in sequence, and ends with Conv. Otherwise, a max-out pooling layer is utilized as the shortcut of residual learning framework to reduce and eliminate redundant features.  
Every pre-activation residual block starts with a BN, followed by Relu, Dropout, Conv, repeating in sequence. In addition, a number of pre-activation residual blocks are used in TCM model. Dense is a fully-connected layer, which compiles the extracted data of previous layers to form the final output. It is used, finally, to produce a tool-wear value.
# Energy-Efficient Hosting Rich Content from Mobile Platforms with Relative Proximity Sensing
## 7 Evaluation of _T-Wing_
### Performance Evaluation  
We compare the performance of _T-Wing_ against two alternative implementations, one that only uses Wi-Fi network and other that only uses cellular network. We contrast our approach using two metrics: energy and response time.  
* _Energy Consumption_: To quantify the energy consumption of _T-Wing_, we measured the power consumption of the embedded platform. For this measurement, we assumed that 10 web page requests arrived per hour, and every user sends 10 location-based query and web page requests per hour. We assumed the size of every web page is 500 KB. Table 2 illustrates the power consumption of each state. The mobile platform expends severe battery power to keep its Wi-Fi interface on when it is broadcasting and communicating. Even when the Wi-Fi radio is connected, the device consumes more than about 10 times the battery power than in cellular network interface on. These numbers indicate that the total lifetime of a mobile platform can be significantly increased if the Wi-Fi radio is turned off most of the time. Figure 10 plots the total communication energy consumption for one hour and the battery life time for the various communication mode. It shows the energy consumed in the wireless interface (Wi-Fi and Cellular network), compared to the energy consumption when utilizing _T-Wing_. Our base comparison is using the Wi-Fi in always on mode for the mobile platform. We save up to 95% of the energy consumption compared to always Wi-Fi or the periodic Wi-Fi mode.  
Figure 8: Performance of the Wi-Fi connectivity prediction algorithm for different metrics and values of \(\mathcal{N}\).  
* _Response Time_: We compare the response time of _T-Wing_ when serving requests compared to an alternative implementation in which the web content is hosted in the cloud, and downloaded using the cellular network. Figure 9 illustrates the response time when varying the number of concurrent requests per second. There is not much difference when the content is small. However, the time to download content over cellular networks is significantly more for larger sized content.