esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Dynamic Modeling and Transition Flight Strategy of VTOL UAV
## 3 Mathematical Model of Longitudinal Motion
### Full Mechanical Model  
[PERSON]'s second law is used to express the force and moment as follows  
\[m(\delta\mathbf{V}/\delta t+\boldsymbol{\omega}\times\mathbf{V})=\mathbf{F}, \tag{47}\]  
\[\delta\mathbf{h}/\delta t+\boldsymbol{\omega}\times\mathbf{h}=\mathbf{M}, \tag{48}\]  
where \(\mathbf{F}\) represents the resultant force; \(\mathbf{M}\) represents the resultant moment; \(\mathbf{m}\) represents mass; \(\mathbf{V}\) is the velocity of motion; \(\mathbf{h}\) represents the moment of momentum.  
According to the assumption mentioned before, the above equation is expanded into the force and moment equations of the movement of the vertical take-off and landing UAV. The force consists of gravity, main wing aerodynamics, and induced wing aerodynamics, lift fan thrust, and lift duct thrust. The resultant moment consists of the aerodynamic moment of the main wing, the aerodynamic moment of the induced wing, the thrust moment of the lift fan and the thrust moment of the lift duct.  
Then the six-degree of freedom motion equation of UAV can be expressed as follows  
Dynamic translational equation of the center of mass  
\[\begin{array}{c}\mathbf{m}\Big{(}dV_{x}/dt+V_{z}\omega_{y}-V_{y}\omega_{z} \Big{)}=F_{Xb,g}+F_{Xb,w}+F_{Xb,iw}+F_{Xb,f}+F_{Xb,d}\\ \mathbf{m}\Big{(}dV_{y}/dt+V_{x}\omega_{z}-V_{z}\omega_{x}\Big{)}=F_{Yb,g}+F_{ Yb,w}+F_{Yb,iw}+F_{Yb,f}+F_{Yb,d}\\ \mathbf{m}\Big{(}dV_{z}/dt+V_{y}\omega_{x}-V_{x}\omega_{y}\Big{)}=F_{Zb,g}+F_{ Zb,w}+F_{Zb,iw}+F_{Zb,f}+F_{Zb,d}\end{array}\Bigg{\}}, \tag{49}\]  
Dynamic rotational equations of the center of mass  
\[\begin{array}{c}I_{xx}\cdot dp/dt+\Big{(}I_{zz}-I_{yy}\Big{)}qr-I_{xz}(pq+ dr/dt)=M_{Xb,w}+M_{Xb,iw}+M_{Xb,f}+M_{Xb,d}\\ I_{yy}\cdot dq/dt+(I_{xx}-I_{zz})pr+I_{xz}\Big{(}p^{2}+r^{2}\Big{)}=M_{Yb,w}+M_ {Yb,iw}+M_{Yb,f}+M_{Yb,d}\\ I_{zz}\cdot dr/dt+\Big{(}I_{yy}-I_{xx}\Big{)}pq+I_{xz}(qr-dp/dt)=M_{Zb,w}+M_{ Zb,iw}+M_{Zb,f}+M_{Zb,d}\end{array}\Bigg{\}}, \tag{50}\]  
From the formation process of the coordinate system of the body, the projection of the rotation angular velocity on the coordinate system of the body can be listed as follows:  
\[\left[\begin{array}{c}p\\ q\\ r\end{array}\right]=\left[\begin{array}{c}\dot{\phi}-\dot{\psi}sin\theta\\ \dot{\theta}cos\phi+\dot{\psi}sin\phi cos\theta\\ -\dot{\theta}sin\phi+\dot{\psi}cos\phi cos\theta\end{array}\right], \tag{51}\]  
where, \(\phi,\theta,\psi\) are three Euler angles.  
The position information in the earth coordinate can be obtained by the following formula:  
\[\begin{array}{c}\dot{x}_{E}=V_{x}cos\theta cos\psi+V_{y}(sin\phi sin\theta cos \psi-cos\phi cos\psi)+V_{z}(cos\phi sin\theta cos\psi+sin\phi sin\psi)\\ \dot{y}_{E}=V_{x}cos\theta sin\psi+V_{y}(sin\phi sin\theta sin\psi+cos\phi cos \psi)+V_{z}(cos\phi sin\theta sin\psi-sin\phi sin\psi)\\ \dot{z}_{E}=-V_{x}sin\theta+V_{y}sin\phi cos\theta+V_{z}cos\phi cos\theta\end{array} \Bigg{\}}. \tag{52}\]
## 2 Comparison of SIFT Descriptors and YOLO Neural Network
### YOLO Neural Network  
YOLO is a real-time object detection system that uses deep learning to identify and locate objects within images. It is known for its speed and efficiency, making it suitable for real-time applications [18]. The use of neural networks in object detection was introduced and improved with the development of R-CNN and its subsequent enhancements [19; 20]. Other detectors, such as SSD, further improved efficiency [21]. However, the emergence of the YOLO detector, with unified detection in a single step, revolutionized real-time detection [18]. Several versions of YOLO, such as YOLOv2, YOLOv4, and YOLOv8, were developed and informally released by Ultralytics, incorporating advancements like the enhanced CSPDarkNet, improving efficiency and accuracy for real-time applications [22]. The applications of YOLO benefit research in a wide range of fields [23; 24; 25].  
YOLO offers several strengths. First, it provides superior speed, making it ideal for real-time object detection [18]. Second, it can detect multiple objects simultaneously and is adaptable to various imaging tasks, including object detection, segmentation, and classification [18]. Third, recent versions like YOLOv5, YOLOv8, and YOLOv10 have introduced optimizations that reduce computational overhead while maintaining high accuracy [26].  
However, YOLO also has some weaknesses. It requires significant computational resources and may be less efficient on low-end hardware compared to SIFT [18]. Additionally, YOLO models need extensive training on large datasets to achieve high accuracy, which can be a limitation in resource-constrained environments [18].  
In the context of MRI, YOLO is used for detecting and segmenting tumors and other structures in MRI images, aiding in diagnosis and treatment planning [18]. It can also provide real-time feedback on the position and movement of organs in MRI-guided procedures, enhancing the precision of interventions [18].
## 4 Experimental Results
### Experimental Results 3  
In the third set of experimental results, the performance of various combinations of loss functions and optimizers was evaluated on an adapted UNETR architecture. UNETR, a Transformer-based neural network designed for computer vision tasks such as segmentation, was only used with input images of 256 \(\times\) 256 due to hardware limitations on the GPU.  
For this set of experimental results, four commonly used loss functions were selected: binary cross-entropy with logit loss (BCEWithLogitLoss), mean squared error loss (MSELoss), Huber loss, and L1 loss. These functions, in combination with the optimizers SGD and Adam, were evaluated using the same number of epochs as in previous sets of experimental results.  
To adapt the output of the UNETR model to the specific characteristics of each loss function, the necessary modifications were made to the output layer of the network. In this way, stable and efficient training of the model was ensured.  
The results obtained from this second experiment are presented in Table 3.  
Figure 8: Loss function curve for 50 epoch and input of 512 px (UNET).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Epochs**} & \multicolumn{5}{c}{**Jaccard Index**} & \multirow{2}{*}{**Optimizer**} \\ \cline{2-2} \cline{4-6}  & **MSELoss** & **BCEWithLogitsLoss** & **HuberLoss** & **L1 Loss** & \\ \hline \multirow{2}{*}{5} & 0.250473 & 0.251709 & 0.253736 & 0.251475 & SGD \\ \cline{2-6}  & 0.250589 & 0.256445 & 0.254074 & 0.245669 & Adam \\ \hline \multirow{2}{*}{10} & 0.249062 & 0.248323 & 0.25057 & 0.2555 & SGD \\ \cline{2-6}  & 0.249169 & 0.251482 & 0.250568 & 0.25397 & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 3: Jaccard index with UNETR.  
Figure 9: Segmented image for 50 epoch and input of 512 px (UNET).  
The loss function curve for every sample of 50 epochs is shown in Figure 10.  
An example of a segmented image is shown in Figure 11.
# Towards a Glass New World: The Role of Ion-Exchange in Modern Technology
## 2 Glass Strengthening: The Ion-Exchange Contribution
### Ion-Exchanged Noble Metal Nanoparticles in Glasses for Sers Applications
#### 3.2.2 Thermal Annealing in Air (O\({}_{2}\) Atmosphere)  
The most common and simple strategy is represented by a thermal annealing treatment, carried out in air (O\({}_{2}\)) in a suitable furnace, at a temperature close to the transition temperature T\({}_{\text{g}}\) of the glass. The mechanism that regulates the formation of silver NPs inside an ion-exchanged soda-line glass by means of this thermal post-process is well explained in the work of [PERSON]. [PERSON] et al. and outlined in Figure 4 below [106].  
After the ion-exchange process, only Ag+ ions are present within the glass. By increasing the annealing time and temperature (providing that the temperature remains below the critical threshold of about 400 \({}^{\circ}\)C), there is an increasingly large formation of silver clusters, which occur in the status of dimers Ag\({}_{2}\) (diameter size d < 1 nm). These small clusters are stable at elevated temperatures and remain within the glass matrix. The presence of these non-plasmonic nanostructures is the first responsible for the photoluminescence enhancement of the ion-exchanged sample. Thermal treatment performed beyond the aforementioned threshold temperature, which corresponds to the decomposition temperature of silver oxide, leads to the formation and the further growth of metallic silver NPs as the exchange time increases and the process temperature approaches Tg. In fact, in this  
Figure 4: Outline of the mechanism regulating the formation of silver clusters and particle growth in soda-line ion-exchanged glass under thermal annealing in air at different time and temperature. Reprinted with permission from [106]. Copyright © 2012, American Chemical Society.  
temperature range, the number of reducing agents--the non-bridging oxygens (NBOs)--increases inside the glass matrix while the number of silver clusters remains unchanged. Consequently, the existing clusters begin to grow through the progressive addition of silver atoms Ag0 (monomers) towards the formation of ever larger plasmonic metal NPs, while the overall concentration of the particles remains constant.  
Therefore, for a given temperature, the final size of these metal nanostructures is determined by the concentration of the dimers Ag\({}_{2}\) and the duration of the heat treatment. Typically, the formation of these particles is limited to the layers immediately below the glass surface, at a depth of a few tens/hundreds of nanometres, due to temperature-induced diffusion effects. Differently from the silver cluster case, the presence of these plasmonic metal nanoparticles produces a quenching effect on the photoluminescence intensity of the ion-exchanged specimen, especially when their size reaches several tens of nanometres or, at most, a few hundred nanometres [106].
## 2 CNN-LSTM Based Automatic Multi-Class Classification Network
### CNN-LSTM Network Structure  
Our proposed CNN-LSTM network consists of two main components: the feature extractor and the one-vs-rest combinational classifier. The feature extractor utilizes the CNN and LSTM modules to extract more discriminative and generalizable feature vectors. The one-vs-rest combinational classifier is then used to efficiently perform multi-class classification on the obtained feature vector.
## 4 Case Study--Analysis of Disruptions  
In the analyzed case study, real-world research was conducted to monitor the transportation conditions of fresh and frozen products in 40 containers over a period 2 months. The scope of the analysis includes the following:  
* Number of containers/sensors: 40 units;
* Measurements: Temperature, light;
* Measurement interval: 10 min;
* Device memory: Up to 8 days of measurements;
* Number of gateway devices: 3 units;
* Analysis period: From 6 May 2022 to 6 July 2022 (2 months);
* Basis for data analysis: Logistic cycle detection algorithm;
* Transported goods: \(\bigcirc\)  Fresh (from 2 \({}^{\circ}\)C to 6 \({}^{\circ}\)C);
* Frozen (below \(-12\)\({}^{\circ}\)C)
* Range: Distribution center (DC) plus approximately 150 km.
## 4 Enhancements to ShinyAnonymizer Hashing Techniques
### Implementation of Advanced Techniques such as Hashing
#### 4.1.3 CRC32  
* Description: CRC32 is also an example of a non-cryptographic hash algorithm that is 32-bit large. It stands between the most commonly utilized algorithms for error detection in transmission and retention and for checking accidental changes in raw data.
* Application: CRC32 is utilized to guarantee integrity during file transfer and in any other action involving data storage. Though it is useful in detecting errors, it is not safe for cryptographic purposes, because of its low collision tolerance and loss of security against beneficial tampering
# AOA-Based Three-Dimensional Positioning and Tracking Using the Factor Graph Technique
## 4 Fg-Ekf
### Prediction of the State  
At the beginning, state prediction of the current time index is conducted based on the FG-EKF outputs at the previous time index. According to Figure 3, the message \(\mu_{c}(\mathbf{s}_{k|k-1})\) indicating the state prediction is given by:  
\[\begin{split}&\mu_{c}(\mathbf{s}_{k|k-1})\\ =&\sum_{\mathbf{s}_{k-1}}\sum_{\mathbf{v}_{k-1}}f( \mathbf{s}_{k|k-1}|\mathbf{s}_{k-1},\mathbf{v}_{k-1})\mu_{a}(\mathbf{s}_{k-1} )\mu_{b}(\mathbf{v}_{k-1}),\end{split} \tag{58}\]  
where the message flows \(\mu_{a}(\mathbf{s}_{k-1})\) and \(\mu_{b}(\mathbf{v}_{k-1})\) are taken from the FG-EKF outputs at time index \(k-1\), where the function \(f(\mathbf{s}_{k|k-1}|\mathbf{s}_{k-1},\mathbf{v}_{k-1})=\mathbf{s}_{k-1}+ \mathbf{v}_{k-1}\) holds.
## 3 Results and Discussion
### Arc Voltage and Arc Length
This is caused by the high vaporization rate of the electrode tip, which cools down the plasma at this region decreasing its electrical conductivity, and subsequently increases the actual arc length, contributing to the increase in arc voltage. Additionally, the increase in the diameter of the arc core due to an increase in the fraction of iron metal vapor represents an increase in the cathode area, consequently increasing the overall voltage fall in the cathode. Therefore, it is possible that the proportionality between arc length and voltage only holds when considering the different welding transfer modes in constant voltage GMAW (e.g., short-circuit, globular, and spray), due to the fact that those transfer modes occur at increasing values of current and the average arc length over time is larger. However, due to sudden change in welding current in pulsed GMAW welding, the arc dynamics are altered and arc voltage is not proportional to arc length,  
Figure 7: Variation of welding voltage with arc length for current values of **(a)** 50 A and **(b)** 380 A.  
and the present study involving a nearly ideal square wave pulse exemplifies one of the cases where the estimation of arc length (as defined in prior work [2; 3]) does not hold consistent.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 4 Edge Node Deployment Algorithm Design  
Building on the proposed edge node deployment models for the two distinct scenarios, we have developed specific placement algorithms aimed at achieving optimal deployment objectives [26]. For stationary equipment, we propose an enhanced dual-population genetic algorithm, while for mobile equipment scenarios, we introduce an improved sand cat swarm optimization algorithm. These algorithms are designed to address the unique challenges posed by each scenario, ensuring efficient and effective edge node placement.
## 3 Results
### Seasonal Analysis  
Figure 11 shows the average seasonal precipitation (mm) for the dry and wet seasons and for IMERG and gauge data. The average is calculated using all meteorological stations and the dry season  
Figure 10: (**a**) False alarm ratio (_FAR_) map; (**b**) probability of detection (_POD_) map; (**c**) critical success index (_CSI_) map; (**f**) accuracy (_ACC_) map; and (**c**), (**d**), (**g**) and (**h**) are _FAR_, _POD_, _CSI_ and _ACC_, respectively shown as a function of meteorological stations elevations (red lines); the black lines show the trends and the elevation of the meteorological stations is represented by the gray, shaded area. IMERG estimates and gauge daily data from 1 April 2014 to 31 October 2015 have been used for these calculations.  
is considered to be from November to April. Here, it can be noted that during the dry season the average precipitation is close to 200 mm and during wet season that amount is tripled. During the dry season IMERG quantifies the average precipitation quite well and overestimates the precipitation observed during the wet season by approximately 100 mm.  
Table 4 shows continuous statistics results for the seasonal precipitation as registered (or estimated) at the meteorological stations using gauge (or IMERG) data. Results here confirm that the IMERG represents the overall precipitation for the dry season better than for the wet season. For instance, the correlation is close to 0.7 for the dry season and close to 0.5 for the wet season. The bias is significantly better for the dry season (approximately 1 mm) than for the wet season (approximately 96 mm). For the _RMSE_, results are not favorable, yet the dry season is still favored.
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
## 3 Design Methodology
### Orbital Library Preprocessing  
The first step involves preprocessing the operational orbit library. Each orbital family is sorted in ascending order of their periods, and the minimum and maximum periods for each orbital family are recorded. Assuming there are \(N\) orbital families, a characteristic period matrix \(\vec{P}\) of size \(N\times 3\) is defined, where each row vector corresponds to the following:  
\[\vec{p}_{i}=\left[\begin{array}{cc}\min(\vec{P}_{i})&\max(\vec{P}_{i})&I_{i} \end{array}\right] \tag{16}\]  
Here, \(\vec{P}_{i}\) denotes the period vector of the \(i\)-th orbital family, and \(I_{i}\) represents its identifier. The matrix \(\vec{P}\) is then sorted by the first column (minimum periods) in ascending order. The largest element in the second column of \(\vec{P}\), denoted as \(P_{m}\), indicates the maximum period across the entire orbit library. This process establishes the period ranges for all orbital families, completing the preparatory phase for the resonant constellation generation algorithm.  
Figure 3: Earth–Moon resonant constellation generation methodology flowchart.
# Review on Quality Control Methods in Metal Additive Manufacturing
## 2 Metal AM and Welding
### Metal AM  
Metal AM is popular in the aerospace and biomedical industries, which require final products of complex shapes [4]. Two types of metal AM processes are commonly used, depending on the deposition method: PBF and DED [5]. The PBF process spreads metal powder as a thin layer, typically under 100 \(\upmu\)m, and then selectively deposits the powders as a 2D cross-sectional shape on a single layer. Metal powders are repeatedly spread and deposited on top of the existing layers, and the deposited 2D cross-sections are also embedded within each other to form a final 3D shape. The DED process works on a similar depositing principle but directly melts and positions the powder or wire-type metal material layer by layer using nozzles, instead of spreading metal powders in a layer [6].  
Figure 1 shows a schematic diagram of a typical PBF process, which consists of the following steps: a 3D digital model is introduced into the PBF software, and the process parameters are determined in advance, for example, the layer thickness to separate the 3D digital model into layers, with each layer being a 2D cross-section of the model; when the process starts, a powder-leveling roller uniformly spreads metal powders onto the build platform; the scanning mirror controls the laser or electron beam to selectively deposit the spread powders following the 2D cross-sections of the digital model for each layer; when the deposition for one layer is completed, the build platform is lowered and the feed platform is elevated; the powder-leveling roller spreads the next powder layer upon the deposited layer, and the deposition and spreading of powders for each layer are repeated until the entire process is completed. During the process, the deposited 2D cross-sections of the top layers are also partially remelted and solidified together with new layers, eventually forming a 3D shape.  
Figure 2 presents a schematic of a typical DED process. The materials are supplied by a coaxial or individual nozzle. The materials used for the DED process are in the form of a metal powder or wire to be melted quickly [7]. The DED process proceeds as follows. A 3D digital model of the product is introduced into the DED software, and the process parameters such as energy source power, nozzle speed, material feeding rate, and nozzle path settings are determined before starting. The nozzle moves through multiaxis motions, following the deposition path settings, and deposits the layers until the process is completed. During the process, the top of the predeposited part is remelted and solidified together with the newly fed molten metal, eventually forming a 3D shape.  
Figure 1: Schematic diagram of the PBF process; (**a**) a schematic model and (**b**) a schematic side view of PBF process.
# A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks
## 4 Performance Analysis
### Combined RL and UAV(s) Trajectory Optimization
The UAV moves alongside the building and traverses the same path on its way back from the end of the building. We have considered variable energy for the nodes varying between 0 and 1 J. The bars in Figure 17a show that all the schemes achieve their maximum throughput after 250 s. The TUOAO records the highest throughput and provide 2%, 15%, and 20% gain when compared with TUOAS, TUSA, and SU, respectively. Figure 17b presents the number of DNs for each scheme. The difference between the schemes TUOAO, TUSA, and SU was minor and only TUOAS showed 7% lesser DNs compared to the other three schemes. Figure 17c shows the residual energy after 250 s. TUOAO has the most residual energy whereas TUOAS was second with 6% lower residual energy, and SU and TUSA are third and fourth, respectively. Figure 17d shows EE. All the schemes present a higher performance compared to the SU case. TUOAO presents the highest gain in EE, whereas the EE of TUOAS is 10% lower. The EE of TUSA and SU is 23% and 27% lower when compared to TUOAO. Figure 18 compares the number of control packets sent for different trajectory schemes. The scheme with a single UAV sends the least amount of control packets, 14% lower than that of TUOAS. Figure 19 shows the number of CH changes against time. The schemes with two UAVs have a higher number of CH changes. This is mainly due to the routing overhead induced by the movement of the UAVs, which result from frequent changes in routing paths. Intuitively, the SU scheme in comparison has 50% fewer CH changes than the TUOAS. Figure 13:0 Results after 250 s.