esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Representation of Multirotor UAV Configurations  
Multirotor UAV can be viewed as a rigid body with six DOF since it operates in a 3D space. Considering that a rigid body is an idealized form of invariant volume and shape, in which the mutual position of the particles is unchanging, the motion of a multirotor UAV can be described as the motion of a particle located in the aircraft center of gravity (COG). Therefore, the motion of a multirotor can be described by the translation and rotation of a particle with respect to a base station which is represented with the inertial coordinate system (often called the Earth frame, \(\mathcal{F}^{\text{E}},\{\text{O}_{\text{E}},\text{ X}_{\text{E}},\text{ Y}_{\text{E}},\text{ Z}_{\text{E}}\}\)). The Earth frame is a right-handed Cartesian coordinate system fixed to the considered stationary point (on the Earth's surface), where the Z axis has a positive direction defined as upwards from the ground level. Fixed-pitch propellers mounted on the motor rotors are the only moving parts, therefore multirotor UAV dynamics directly depend on the rotors' angular velocities, where the stiffness of the propeller is assumed. Furthermore, the model is simplified through the assumption that the origin of the aircraft reference frame coincides with the COG and that the frame axes coincide with the multirotor main axes of inertia.  
Common to all multirotor configurations is that they consist of N rotors. Their configuration is defined by the geometric arrangement of the rotors and by the characteristics of rotors (e.g., power). The design of multirotor configuration affects the performance and energy consumption of the system as a whole. Conventional configurations consist of an even number of rotors located in one or more parallel planes (so-called planar configurations) which are symmetrically arranged. In order to cancel the reactive moment around the vertical body axis, an equal number of rotors rotate in a clockwise (CW) and counterclockwise (CCW) direction. In addition to the most commonly used quadrotor, conventional configurations with six rotors (hexarotor) [22], and eight rotors (octorotor) [23] are often found in research and applications, where possible embodiments are shown in Figure 1. When more power is required in a compact aircraft, configurations with so-called coaxial rotor arrangements [24] or overlapping propulsion arrangements are used [25].  
Apart from the aforementioned conventional configurations with a planar rotor arrangement, there are also many studies which deal with configurations with a non-planar geometric arrangement. In [26], a novel and more efficient multirotor configuration with four rotors is presented. There is a whole range of research on passively tilted hexarotors [14; 15; 16; 17; 18; 19] which are the most common representative of fully actuated configurations.
## 5 Calibration Experiment and Algorithm Comparison  
### Data Acquisition for Calibration  
To assess the optimization effects of the designed LM-D algorithm, data collection is the initial step. The calibration experimental setup, depicted in Figure 8, has been established. The ABB IRB 6660-1.95 robot model is employed in the experiment. A standard sphere with a diameter of 38.05 mm is utilized, and the probe model is the RENISHAW OMP40-2, with a nominal repeatability of 1 \(\upmu\)m, aligning with the calibration accuracy requirements of the robot. The probe is affixed to the corresponding tool holder, and the robotic end effector is equipped with a spindle. Upon automatically retrieving the tool holder from the tool library, the robot initiates the TCP calibration program, automatically deploys the probe, and traverses to various positions and orientations concerning the standard sphere through the program. When the ruby probe makes contact with the standard sphere, it triggers a signal, and the current robot pose is promptly recorded through the robot controller, precisely executing the sphere-to-sphere process. Subsequently, the robot returns to the Home position and commences the next point measurement, repeating this cycle until all predefined measurement points are covered. A total of 10 data sets were amassed through the TCP calibration experiment. The specific data, illustrated in data 1, 2, and 3 in Tables 1-2, for three sets of data, encompass the tool position and quaternion parameters for each measurement point.  
In the analysis of the data sets from data 2 in Table 1, within a designated hyperspace following space discretization, we compute the optimal solution for each discrete point. Given that hyperspace cannot be visually represented in a three-dimensional graph, we present a schematic depiction of the general relationship between TCP parameters X, Y, and the function value in Figure 9. The purpose is to visualize the situations of local and  
Figure 7: The LM-D hybrid algorithm flowchart.  
global optima in the function. From the graph. From the graph, it is discernible that a local minimum is situated in the vicinity of point P1 at (615, \(-\)30) in the function values X, Y, while the global minimum is approximate to point P at (585, \(-\)5). This observation aligns with the characteristics of the calibration function for the probe's TCP, as discussed earlier, suggesting its classification within the non-convex function category. Consequently, jumping out of local optima and pursuing the global optimum becomes imperative in optimizing the calibration process. We provide this Figure 9 to visually illustrate the issue of multiple local minima in the data. In practical algorithm use, it is not necessary to draw it.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 9 Manufacturing of Hollow-Core Fibers
### Stack-and-Draw  
This is the standard method for making HCFs and other microstructured fibers. The central part of fiber preform (the future photonic cladding) is assembled from glass capillaries with a 1-3 mm diameter and thin rods, drawn previously from larger, commercially available tubes, rods or plates, and consolidated via heating. This assemblage is drawn into a \"cane\", and jacketed with another glass tube. Complete preform is heated in electric furnace and drawn into the fiber, with polymer protective coating(s) applied [94].  
Preforms for drawing NANFs and similar fibers have few parts, all of which are tubes: 7-26 for fibers with 5-8 single, nested or double-nested tubes, and one or two jacketing tubes. For comparison, the number of parts required to assemble a preform of HC-PBGF fiber like OFS AccuCore [28] or NKT HC-1550-02 [35] (Figure 6) is close to 300, resulting in substantial labor costs. The situation is similar for Kagome fibers (Figures 2 and 11).  
A big problem in the fabrication of fibers including hollow cavities is the tendency of hot, softened glass to close such voids due to surface tension, with smaller voids shrinking faster and closing first. Non-circular tubes in some recently proposed ARFs with reduced confinement loss (Section 5.3, Figure 15) will likely lose their original shape.  
During the consolidation of HC-PBGF preform the same phenomenon re-shapes the original assembly of round tubes into a network of (mostly) hexagonal cells constituting the photonic bandgap structure in an HC-PBGF fiber.  
To prevent the deformation or collapse of tubes during the consolidation of preform and drawing of fiber, or sticking of tubes in ARF fibers, they must be selectively pressurized. The typical overpressure inside holes during drawing of a \(\approx\)200 \(\upmu\)m fused silica NANF with a relatively large tension of 4 N (increased tension reduces distortions of fiber geometry) is 15 kPa, or 15% of sea level atmospheric pressure [95], while the tolerance of this overpressure is \(\leq\)0.1 kPa [89]. A serious problem in drawing of NANFs is mid-draw contact between the tubes in the zone where the softened preform just begins to reduce its diameter, and the gas pressure dominates over the surface tension. As a result, the tubes expand too much, permanently sticking to each other and losing their circular shape when an attempt to make a fiber with closely spaced tubes (and reduced confinement loss) is made. The prevention of the deformation or sticking of tubes is described in [95].  
The stack-and-draw process, while flexible, is slow due to the careful manual assembly of a preform followed by multi-stage consolidation and drawing. Low manufacturing rates, high costs, and variability in the dimensions are the result. These issues can be addressed by automation, in particular, stacking using a robot, and the application of artificial intelligence to identify irregularities via image analysis and perform corrective actions.
## 5 AI-Empowered Communication with Perceptual Constraint
### AI-Empowered Semantic Communication
#### 5.2.4 Generative Modeling in Core Network  
The core network may include multiple types of encoders and decoders to handle data from different protocol stack layers, such as voice encoding and video encoding, to meet diverse service requirements and quality of service (QoS) standards. In [114], two models, 5 GCSeq2 Seq and 5 GC-former, are proposed based on the vanilla Seq2 Seq model and the transformer, respectively, to accurately replicate the principal functionalities of the 5G core network control plane, including registration, authentication, handover, and DPU sessions.
## 3 Results
### DTW Validation Study
For coach 1, the beginners' score was 54.6 \(\pm\) 20.96, while the intermediates' score was 81.61 \(\pm\) 10.54, with a \(p\) value of <0.001. For coach 2, the beginners' score was 50.8 \(\pm\) 17.06, while the intermediates' score was 80.71 \(\pm\) 8.01, with a \(p\) value of <0.001. However, individual differences still existed. For example, player 25 in the intermediate group received a score of 50 from coach 1 and 70 from coach 2. Therefore, our analysis suggests that the beginner group and intermediate groups could be statistically different in terms of skill level, and the coaches' rating can, on average, give a reliable measure of skill level. We then turned to analyze the raw DTW values of the two groups. The results are shown in Figure 9. The two groups had different DTW values, with higher DTW values for the beginners (48.14 \(\pm\) 10.81) vs. intermediates (40.17 \(\pm\) 7.34), with a \(p\) value of 0.004. [PERSON]'s d was also calculated to be 0.86, meaning that a large difference was observed for the beginners versus the intermediates. Thus, the DTW value could be used to determine the statistical difference in player stroke effectiveness between beginners and intermediates, showing the usefulness of the DTW function. The raw data for the coaches' scoring and DTW are summarized in Tables 2 and 3. Figure 8: Comparison of coaches’ scoring of players of different levels. (**a**) Coach 1’s and (**b**) Coach 2’s score (**c**) Beginner and (**d**) Intermediate players’ score comparison between two coaches. Figure 9: Raw DTW comparison for players of different levels. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Player Number** & **Coach 1** & **Coach 2** & **DTW** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 2: Coaches’ ratings and DTW for players in beginner group. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Player Number** & **Coach 1** & **Coach 2** & **DTW** \\ \hline
1 & 70 & 85 & 32.1214 \\ \hline
2 & 80 & 75 & 33.3529 \\ \hline
3 & 85 & 75 & 45.51 \\ \hline
4 & 75 & 75 & 39.6811 \\ \hline
5 & 85 & 90 & 55.2254 \\ \hline
6 & 75 & 80 & 35.6166 \\ \hline
7 & 75 & 80 & 41.3269 \\ \hline
8 & 75 & 70 & 36.8432 \\ \hline
9 & 100 & 90 & 44.3954 \\ \hline
10 & 80 & 80 & 36.5016 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Coaches’ ratings and DTW for players in intermediate group.
###### Abstract  
This paper describes, for the first time, the application of an Elastodynamic Boundary Element Method (BEM) in Laplace Domain for the Structural Health Monitoring (SHM) of poly-crystalline materials. The study focuses on Ultrasonic Guided Wave (UGW) propagation and investigates the wave-material interactions at micro-scale. The study aims to investigate the interaction of UGWs with assessing micro-structural features such as grain size, morphology, degradation, and flaws. Numerical simulations of the most common micro-structural features demonstrate the accuracy and validity of the proposed method. Particular attention is paid to the study of porosity and its influence on material macro-properties. Different crystal morphologies such as cubic, rhombic, and truncated octahedral are considered. The detection of voids based on the changes in the amplitude and Time of Arrival (ToA) of the backscattered signal is investigated. The study also considers inter-granular cracks, which cause laceration, and examines flaw position/orientation, length, and distance from a specific reference. Furthermore, a framework is proposed for generating Probability of Detection (PoD) curves using numerical simulations. Experimental tests in pristine conditions are shown to be in good agreement with the numerical simulations in terms of ToA, signal amplitude, and wave velocity. The numerical simulations provide insights into wave propagation and wave-material interactions, including different types of defects at the micro-scale. Overall, the BEM and UGW methods are shown to be effective tools for better understanding micro-structural features and their influence on the macro-structural properties of poly-crystalline materials.  
poly-crystalline materials; Boundary Element Method; Structural Health Monitoring; Non-Destructive Testing; defects; Ultrasonic Guided Waves; micro-scale; Probability of Detection
## 3 Results and Discussion  
### Identification of the Volatile Profile  
Figure 1 shows a chromatogram and the identification of orange juice's main volatile substances.  
Table 1 presents the identified volatile compounds and their respective retention times (RTs), Kovats indices, odor thresholds in water, and primary odor descriptions. A total of 24 substances were identified in the chromatograms, accounting for 98 \(\pm\) 1% of the total peak area. Seven other substances (2 \(\pm\) 1% of the total peak area) were detected in trace amounts; thus, it was not possible to identify them with accuracy.  
Nine compounds (ethyl butanoate, \(\alpha\)-pinene, \(\beta\)-pinene, 3-carene, terpionolene, p-cymenene, limonene, \(\gamma\)-terpinene, and eremophila-1(10),11-diene) accounted for 94 \(\pm\) 1% of the total peak area, being the main compounds found in pasteurized orange juice. The main group of substances found in the pasteurized orange juice and in the plasma-treated juice were terpenoids, mainly limonene. The juice also presented small amounts of alcohols (including terpenyl alcohols), aldehydes, esters, organic acids, sesquiterpenes, and terpenyl hydroperoxides. The compounds found in this work were similar to the volatile profiles published by other authors [9; 24; 25].  
Figure 1: Representative chromatogram of pasteurized orange juice: (a) ethyl butanoate, (b) \(\alpha\)-pinene, (c) 3-carene, (d) terpionolene, (e) limonene, (f) \(\gamma\)-terpinene, and (g) eremophila-1(10),11-diene.
###### Abstract  
Alkaline treatment has been extensively implemented in the extraction process of hydroxyapatite (HAp) extraction from various kinds of bio-materials, such as animal bone and scales. The main purpose of such treatment is to remove proteinaceous substances from raw materials. The influence of the alkaline treatment that could alter not only the organic contents but also chemical composition--specifically the Ca/P mole ratios of bio-calcium, HAp, and the biphasic apatite powders derived from salmon bone, a by-product from the salmon industry--was investigated. Both HAp and biphasic apatite powders were obtained from the calcination of bio-calcium powders with and without alkaline treatment, respectively. An X-ray diffraction analysis confirmed the presence of hydroxyapatite and \(\beta\)-tricalcium phosphate (\(\beta\)-TCP) in the calcined bone powder without alkaline treatment while only a single phase of hydroxyapatite was observed in the alkaline-treated sample. Calcium and phosphorus contents were measured by an inductively coupled plasma optical emission spectrometer (ICP-OES). A variation of Ca/P ratios was observed among all samples, depending on the chemical and heat treatment conditions. Organic molecules, such as protein, fat, hydroxyproline, and TBARS, were significantly lowered in bio-calcium powders with the alkaline treatment. This work represents important research on chemical treatment prior to the raw material conversion process, which significantly influences chemical and phase compositions of the bio-calcium and hydroxyapatite powder derived from salmon bone waste.  
bio-calcium powder; calcined powder; hydroxyapatite; salmon bone
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 6 Conclusive Observations
### Dynamic Modelling  
Over the years, several distribution models have been used aiming to achieve effective distribution. More often, those models have considered static information to generate the distribution schedule. However, the presented disaster scenarios are highly uncertain and information is not static. Therefore, the static RID models are less suitable in disaster scenarios. In practice, the dynamic models are the best-suited models as the dynamic model adopts the changing information over time and generates a distribution plan accordingly. The major advantage of the dynamic RID model is the generation of the distribution schedule with the updated information on available vehicles, other resources, road conditions and disaster region priority at any particular time slot. Combining multiple components in a single model has reflected a closer realistic condition for relief item distribution under disaster conditions. The inclusion of dynamicity in the distribution task is best suited for decision-makers in disaster relief management operations.
## 4 Results and Discussion  
### Overall Performance of the Statistical Approaches  
#### 4.1.1 Multi-Incidence Estimates of X-Band Backscattering Coefficients  
The values of backscattering coefficients estimated in HH polarization using the MLR or RF algorithm are compared to those derived from the TerraSAR-X images (Figure 7). The independent subsets of samples used for the training and validation phases are distinguished (in gray and black, respectively) and associated with their corresponding statistical indices. Regarding MLR, the estimates of the backscatter coefficients are based on the incidence angle and the topsoil moisture, which are the only explanatory variables among those tested to present p-values lower than 0.05. All surface and satellite descriptors were retained for the RF algorithm. Higher accuracy is observed for estimates based on RF compared to the performance obtained with MLR, as highlighted by the values of correlation of 0.86 and 0.77 (values obtained with the validation subset of samples). A more in-depth analysis of the validation subset shows that backscattering coefficients are estimated without any bias (values close to 0), with a high level of correlation (values greater than 0.75) and with an acceptable level of error (values of RMSE less than 1.3 dB), regardless of the considered statistical algorithm. Such a level of performance surpasses the results obtained in the same study site using [PERSON] and Dubois' semi-empirical models [10; 11; 12; 30]. As detailed in the following Section 4.1.3, these performance metrics are also superior to those obtained using a dataset collected at other study sites used to test or to develop modified versions of [PERSON] and Dubois' models.
## 4 Simulation Results and Analysis
### Computational Complexity and Runtime Evaluation
Based on the simulation settings described in Section 4.1, the average runtime of each algorithm is as follows: Algorithm A requires approximately 1.76 s, Algorithm 1 requires approximately 0.003 s, and Algorithm C requires approximately 5 s.
# Adaptive Emergency Call Service for Disaster Management
## 5 Simulation Results
### Performance Analysis
#### 5.2.4 Impact of Emergency Data Size
In terms of residual energy, FINDER has a higher energy consumption compared to 5G-SOS and M-HELP, as seen in Figure 17. Furthermore, CHs in FINDER consume higher energy than cluster member devices. Moreover, 5G-SOS consumes a higher energy compared to M-HELP. The 5G-SOS buffers the multiple simultaneous calls received while M-HELP ignores such calls. The computation of the waiting time \(T_{r}\) in (4) offers a dynamic  
Figure 16: Comparison of the number of messages per node against the ratio of the number of emergency calls (NEC) to the number of UEs (NoU) under 5G-SOS, M-HELP, and FINDER. Parameters: NoU: NEC: 1% of NoU to 80% of NoU, NoU: 15,000 UEs, ETI: 1800 s (30 min). Figure 15: Comparison of end–end latency against the ratio of the number of emergency calls (NEC) to the number of UEs (NoU) under 5G-SOS, M-HELP, and FINDER. Parameters: NEC: 1% of NoU to 80% of NoU, NoU: 15,000 UEs, ETI: 1800 s (30 min). and distributed way to select the stronger relay devices and limit the maximum \(T_{r}\) based on congestion detected in the local neighborhood; this avoids high data congestion at a particular relay device, disperses the traffic among the network devices, and conserves the energy of the intermediate relay devices. Furthermore, the use of the \(RS_{\text{th}}\) parameter prevents overloading the network with multiple copies of the same requests in a dense environment. Moreover, the lightness of the protocol adding less weight on the relay devices and utilizing the stronger devices to transmit emergency data are the major contributions of 5G-SOS and M-HELP compared to FINDER. All in all, 5G-SOS and M-HELP have improved performance compared to FINDER in terms of transmission success rate, \(EEL\), energy consumption, and network congestion control. The overall summary of the performance of 5G-SOS over M-HELP and FINDER is given in Table 6. The average improvement by 5G-SOS over all the scenarios considered is approximately 24.9% than M-HELP and 73.9% than FINDER in terms of success rate. Further, the reduction in average end-end latency is 20.8% compared to M-HELP and 61.7% compared to FINDER. Moreover, 5G-SOS enabled a reduction in the average energy consumption by 79.2% compared to FINDER. In contrast, 5G-SOS has a higher energy consumption than M-HELP by around 29.1%. On top of that, the average messages per node in 5G-SOS is lower than FINDER by around 81.3%, but higher than M-HELP by 6.2%.