esa-sceva/satcom-chunk-collection huggingface dataset:
# ALReg: Registration of 3D Point Clouds Using Active Learning
## 3 Method  
### Problem Definition  
Given two point clouds \(X=\{x_{1},...,x_{i},...,x_{N}\}\in R^{N\times 3}\) and \(Y=\{y_{1},...,y_{i},...,y_{N}\}\in R^{N\times 3}\), our aim is to find the rigid transformation \([R,t]\) with six degrees of freedom where \(R\in SO(3)\) is the rotation matrix and \(t\in R^{3}\) is the translation vector according to the error function:  
\[\underset{R\in SO(3),t\in R^{3}}{\text{argmin}}\sum_{i}^{N}||Rx_{i}+t-y_{i}||. \tag{1}\]  
The baseline studies have different architectures and methods to minimize error. However, they use all points from a training sample. To train the network using an active learning approach, every sample from the training set \(D=\{X_{0},...X_{i},...,X_{N}\}\) should be decomposed into superpoints \(X_{i}=\{S_{i0},...,S_{ij},...,S_{iM}\}=S_{L}(X_{i})\cup S_{U}(X_{i})\) where \(M\) represents superpoint count. \(S_{L}\) and \(S_{U}\) represent sets of labeled and unlabeled superpoints respectively. In ALReg, an active learning approach is followed to efficiently select superpoints from \(S_{U}\) to be used in the training process.
# A Computational Study on the Aeroacoustics of a Multi-Rotor Unmanned Aerial System
## 4 Conclusions  
The aeroacoustics of a quadrotor biplane tailsitter (CRC-3) were studied numerically for various conditions with different pitch angles, propeller rotating velocities and vehicle speeds. Comparison of the sound pressure level around CRC-3 with an isolated propeller case showed significantly larger noise for CRC-3, which was found to be altered by pitch angle, unlike the isolated propeller case. This results in a 20% increase in noise from pitch angle \(\theta=0^{\circ}\) to \(\theta=75^{\circ}\) at both BPF and OASPL. The studies showed that the major source of CRC-3 noise at \(\theta=0^{\circ}\) is the noise generated by the four propellers and their interactions. The increase of pitch adds noise due to the interaction of the propellers and the CRC-3 structure. The rate of increase in SPL with \(\theta\) decreases as \(\theta\) grows. Doubling the RPM of CRC-3 results in about a 14% increase in the noise at BPF and 18% increase in overall noise, which is smaller than the increase due to changing the pitch angle from \(\theta=0^{\circ}\) to \(\theta=75^{\circ}\). Additionally, the noise around CRC-3 was found to alter irregularly with increase in inflow velocity, due to changes in balance between steady and unsteady loading effects with propellers' advance ratio.  
Studying unsteadiness on CRC-3 by calculating the pressure fluctuation on the propeller and the structure indicated a two-way mechanism through which the propellers and the structure impose unsteadiness on each other. The unsteadiness on the propellers was found to have a more dependence on the pitch angle rather than RPM. In contrast, the trend of pressure fluctuation on the structure showed that it is more affected by RPM.  
Conceptualization, R.S. and H.S.; methodology, H.S.; software, M.H. and H.S.; validation, H.S. and M.H.; formal analysis, H.S. and M.H.; investigation, R.S., H.S. and M.H.;resources, H.S.; data curation, H.S. and M.H.; writing--original draft preparation, M.H. and H.S.; writing--review and editing, H.S., R.S. and M.H.; visualization, M.H.; supervision, H.S.; project administration, H.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin and University of North Texas High-Performance Computing Services for providing HPC resources that have contributed to the research results reported within this paper.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
The volcanic eruption forecasting algorithm (VEFA) was tested against unrest episodes occurring at four volcanoes; Grimsvotn, Okmok, Yellowstone National Park, and Mount Saint Helens. A detailed discussion of forecasts generated for the Icelandic example is presented, since it best illustrates the mechanics of our process. Results from all four examples are compared to forecasts generated by the Bayesian Event Tree Eruption Forecasting (BETEF) 2.0 application [9]. It should be noted that in each of the examples below, algorithm and episode day represent the number of days since initiation of the algorithm and the transition of the trigger state from 0 to 1.  
Figure 9: Internal functionality of the forecasting algorithm, where gray indicates processes internal to the algorithm, blue represents external data sources, and green identifies products.
## 5 Numerical Results and Discussion  
Since the pipe with assembly parts was most prone to crack initiation and final fracture near the sleeve end (as shown in Figure 2), further consideration and subsequent discussion of the ratcheting behavior of the pipe with assembly parts in that area was needed to facilitate failure analysis and improve maintenance.  
In order to examine the ratcheting response of the pipe with assembly parts near the sleeve end, three positions in the longitudinal section of the pipe were selected in this area for comparative analysis, as exhibited in Figure 5. Position 1 was a node on the outer wall of the pipe and inside the sleeve, Position 2 was a node on the outer wall of the pipe and closest to the sleeve end, and Position 3 was a node on the outer wall of the pipe and outside the sleeve end. In Figure 5b, Position 1', Position 2', and Position 3' are on the pipe without assembly parts, and correspond with Position 1, Position 2, and Position 3 on the pipe with assembly parts.  
Detailed analysis sections are presented as follows. Firstly, the ratcheting behavior of the pipes with and without assembly parts were compared at the locations set out in Figure 5. The ratcheting behavior of the pipe with assembly parts is then further discussed to explore the assembly contact effect on the ratcheting response. The effect of the clearance between the pipe and the sleeve on the ratcheting behavior at the positions of interest is also examined. It is worth noting that hoop ratcheting strain was the main concern of this paper, as ratcheting strain mainly occurs in the hoop direction [13; 35].  
Figure 4: Comparison of the maximum axial strain from the numerical simulation and experiment under different internal pressures: (**a**) 0 MPa, (**b**) 10 MPa, (**c**) 21 MPa, and (**d**) 28 MPa.
## 1 Introduction  
The accuracy of performance evaluation on satellite-to-aircraft (S2A) communication is important since there might be critical safety-of-life applications. Moreover, the accuracy of system performance evaluation is to some extend determined by precise modeling of the channel. Existing techniques for modeling of the S2A channel are not sufficiently precise [1], although some literatures such as [1; 2; 3; 4] have proposed methodologies on S2A channel modeling or have presented measurements to improve the channel models. Fortunately, there are some preliminary studies on the channel modeling of air-to-ground (A2G) communications, air-to-air (A2A) communications, and vehicle-to-vehicle (V2V) communications that are worth learning. For example, the methods of developing regular shaped geometry-based stochastic models (RS-GBSMs) were presented in [5; 6; 7], and non-stationary characteristics of mobile channels were discussed in [8]. With the methods of developing non-stationary RS-GBSM and introducing the ground scattering that must be considered in S2A channel modeling, we are able to develop and present an analytical non-stationary RS-GBSM based S2A model in this paper.
## 3 Materials and Methods
### Experimental Setup  
The custom simulator (Figure 2) used in this study consists of a Stewart motion platform with six degrees of freedom (MOOG 6 DOF 2000E) [53; 54] and a driving seat with a Logitech steering wheel and pedals. The motion platform offers a realistic force feedback which enhances the immersion in the virtual environment. The dynamic model of the simulator is based on the Motion Cueing Algorithm [55] and details about the implementation were presented in a previous study [56].  
The autonomous vehicle driving behavior was implemented with the tools provided by the open-source software platform, CARLA [57]. The 0.9.13 release of CARLA was used on a VR-ready desktop computer with a RTX 3090 graphics card, an AMD Ryzen 9 5950X processor, and 32 GB of RAM. The basic autopilot function integrated in CARLA was sufficient for the purpose of this study.  
In this study, the participants visualized the immersive mixed reality environment through a Microsoft HoloLens 2 headset. The mixed reality experience offered by HoloLens 2 is possible thanks to its see-through holographic lenses with a 2k resolution, a second-generation holographic processing unit (HPU), a Qualcomm Snapdragon 850 processor, an advanced set of cameras (light camera, infrared cameras, and depth sensor), a 5-channel microphone array, and built-in spatial sounds. The main features of HoloLens 2 are hand tracking, real-time eye tracking, and iris recognition, as well as voice command and control.  
The Microsoft Mesh [58] preview application was used to virtually bring people together and facilitate collaborative mixed reality experiences. Mesh contains a collection of technologies and tools designed to enable rich, immersive, 3D collaboration experiences between people. Mesh allows users to have 3D interactions on content by capturing, storing, and displaying 3D objects. Moreover, Mesh users can move and look around in the digital  
Figure 2: Driving simulator.  
space, play, rotate, manipulate, and point to 3D objects as if they were in the same physical space. Mesh also supports spatial audio that plays real-time sounds at the point where you look or listen, so the user's eyes and ears determine where a sound is coming from.  
Microsoft Mesh provides a set of out-of-the-box cartoon avatars that can be personalized by the user. Each participant was represented in the 3D digital space as a custom cartoon avatar in an immersed way. The avatar can move and display facial expressions consistent with the user's actions acquired through HoloLens 2 sensors. The participants can have a unique perspective in the multi-user scenario, can bring in 3D models and interact with them, as well as create annotations using a pinch gesture.
## 3 Results and Discussion
### Influence of Validation Data on the Performance of ANN Model  
To evaluate the influence of validation data on the performance of the ANN model with eight neurons, the MSE and maximum difference of ANN models A and B were calculated and compared, as listed in Table 2. As depicted in Figure 7, the validation data for model B were mainly selected from the nonlinear ranges of the training data  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**Maximum Difference (\%)**} \\ \cline{2-5}  & **Training Data** & **Validation Data** & **Training Data** & **Validation Data** \\ \hline
6 & 0.00034 & 0.10270 & 0.77 & 5.81 \\
7 & 0.00037 & 0.07500 & 0.58 & 5.07 \\
8 & 0.00002 & 0.00510 & 0.53 & 2.04 \\
9 & 0.00013 & 0.01580 & 0.25 & 6.70 \\
10 & 0.00024 & 0.01810 & 0.35 & 9.14 \\
11 & 0.00036 & 0.02780 & 0.46 & 11.65 \\
12 & 0.00021 & 0.05700 & 0.32 & 13.40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Influence of number of neurons in sub-layer on the performance of ANN model.  
Figure 8: Relationship between mean squared error and epoch of ANN model A with 8 neurons.  
(consisting of nine validation data points), whereas the validation data for model A were mainly selected from the linear ranges of the training data (consisting of 12 validation data points). Since predicting the linear behavior of validation data is easier, model A exhibits a lower MSE and a smaller maximum difference in validation data, i.e., 0.00510 and 2.04%, respectively.  
On the other hand, when both models were applied to predict 25 testing data points (i.e., randomly selected as illustrated in Figure 7), model B yielded a lower MSE and a smaller maximum difference in testing data, i.e., 0.00309 and 2.16%, respectively. This suggests that selecting nonlinear validation data (as performed in model B) enhances the performance of the ANN model in predicting testing data. Consequently, model B is considered as appropriate choice for predicting the \(K_{\text{I}}\) of a pressurized pipe with an infinitely long internal surface crack in the longitudinal direction.
## 4 Security Threats
### Negotiation
#### 4.2.3 Spectrum Requests Falsification  
One more problem, which arises when exchanging requests and other operational details between nodes and the spectrum sharing mechanism, is that an attacker may try to falsify the requests sent by its competitors [28]. Such an attack belongs to the class of _man-in-the-middle_ ones, where an attacking node captures the packets in question (carrying negotiation-related information) and injects them back after manipulating their contents in a way advantageous to itself. The most appropriate way to cope with this attack would be to apply techniques able to assure the integrity of the exchanged messages.
## 4 Bionanosystems
### Bionanosystems for RA  
As discussed in the previous paragraphs, RA is a phenolic molecule with numerous possible applications in the food and pharmaceutical industries due to its antioxidant, antimicrobial, and anti-inflammatory properties. However, RA has low lipophilicity reflected in reduced transmembrane penetration and poor oral availability [89; 90]. Furthermore, it is unstable, poorly soluble in water, and its low partition coefficient limits its transport across biological barriers, as well as its inclusion in a cosmetic formulation and efficacy as an antioxidant [91]. In addition, the acidic environment and enzymes present in the human digestive tract or in many food products impact its stability resulting in low bioavailability and reduced biological activities [92]. To increase the RA stability, as well as its permeability and thus bioavailability, a viable method is its incorporation into micro- or nanosystems.  
Many different carriers have been tested so far for the encapsulation of polyphenols, including a wide variety of natural and synthetic polymers. The most commonly used encapsulating materials include polysaccharides (i.e., chitosan, cyclodext, alginate gel), lipids (waxes, paraffin, oils), bio-based polyesters (i.e., poly(lactic acid), poly(lactic co-glycolic acid)), acrylic polymers, polycaprolactone, and also inorganic materials (i.e., anionic and cationic clays, halloysite, silica nanoparticles) [15; 93; 94].  
Selected examples are discussed below about the encapsulation/immobilization of RA into different types of carriers, organic and inorganic, aiming to improve its stability, permeability, and bioavailability.
## 2 Related Work
### WSN Architecture  
According to function and logical organization, there are three types of network architecture: flat [19], hierarchical [20, 21] and hybrid [22]. A flat network is known as a non-hierarchical network, of which the property and function are the same for all sensors, such as RIS [23], MSNL [24], LADS [25]and ASCEMT [26]. A hierarchical network is a network in which all sensor nodes are clustered through the cluster technique according to property and function [27,28]. Every cluster is composed of at least one sensor, and each sensor in a cluster has a corresponding cluster head, which collects data from the sensors in the cluster and transmits the data to a data sink. Hierarchical networks facilitate equalized power consumption. In addition, the sensors in a cluster are located close to one another. The data collected from sensors in the same cluster are similar. Data aggregation will reduce data transmissions, thereby reducing power consumption and prolonging network lifetime. Concepts based on the hierarchical network to save and reduce power and equalize power consumption by sensors include LEACH [29], LDS [30] and HEED [31].  
In hierarchical networks, Low Energy Adaptive Clustering (LEACH) is the most well-known distribution cluster algorithm. In this approach, the cluster heads alternate, and periodicity is selected through threshold adjustment to equalize power consumption. Initially, each sensor has two probability and threshold values between zero and one. The sensor will be a cluster head if the probability is less than a threshold. The threshold will be zero if the sensor is a cluster head in the present period. This cluster head node will not be selected as a cluster head in the next period. A threshold of sensor will be raised to increase the possibility of being selected as a cluster head. When a node has never been a cluster head, the node will be forced to become a cluster head by setting its threshold to zero. LEACH produces a large number of broadcast packages and repetitive information, which can cause broadcast storms and packet collisions. Initially, the cluster will be destroyed after each period to increase the cost of reconstructing the network and affect network stability and, indirectly, power consumption.
# Agrochemical Contamination and Ageing Effects on Greenhouse Plastic Film for Recycling
## 2 The Effect of Agrochemicals on Greenhouse Plastic Film for Recycling
Specifically, by means of laboratory and field tests, all of these studies have investigated the effects of ageing on the distinct technical performances of plastic films used for greenhouses, i.e., radiometric properties and mechanical strength. To date, no research has been conducted that assesses the influence of non-natural ageing factors, such as the contact of the plastic film with agrochemicals under UV radiation [21], on these materials, from a reusability and recyclability perspective. An agricultural plastic material may be considered as having completed its working life when the main mechanical properties--principally, the percentage elongation at break, in the case of a plastic film--has reduced below 50% of the value it had in its virgin state. Therefore, the indicator of suitability for mechanical recycling is the presence of sufficient mechanical characteristics compared to the plastic waste material when it was new [10]. The majority of agricultural plastic waste in Europe consists of PE-LD, PE-LD copolymers and, in some cases, linear PE-LD (PE-LLD) films from protected cultivation, shrink films and silage applications; thicknesses range from 20 to 250 \(\upmu\)m [22]. All of these products may contain a significant amount of additives (up to 15%). Greenhouse films are usually polystromatic and multilayered, containing ethylene-vinyl acetate (EVAC) or ethylene butyl acrylate copolymer (EBAC), while most films contain colorants and additives for UV protection (such as mulching films, irrigation pipes and tapes, containing carbon black). The mechanical properties of the recycled material may also be influenced by the presence of other factors, e.g., the addition of starch to PE-LD blends increases the tensile strength and the elongation at break, while it reduces the MFI values [23,24]. This paper contributes to expanding the knowledge on the degradation effects of films' ageing in contact with agrochemicals. In this context, the results of laboratory tests on agricultural plastic films, which were artificially aged for different periods, and after being contaminated with two different agrochemicals, are presented.
## 4 Finite Element Analysis
### Modal Analysis  
The double-layer filter-wheel component designed has a high lightweight rate. Further modal analysis is required to verify the sufficient local and overall stiffness of the structure. Table 5 shows the first three modes of the component.  
The analysis reveals that the first-order modal of the double-layer filter-wheel assembly is at 470 Hz, with a vibration direction distinct from the motor's rotation. The motor's rated speed of 10,000 rpm corresponds to an angular frequency of 167 Hz, which is significantly lower than the first modal, thus reducing the risk of resonance during operation.
## 4 Experimental Setup
### Power Electronics
#### 4.2.2 DC-DC Boost Converter  
A modular Insulated Gate Bipolar Transistor (IGBT) power stack (model MTM-1/2B2 IC0225F12 HB, manufactured by GUASCH, Girona, Spain) is utilized to implement  
Figure 6: Wind turbine model (Rutland FM910-4) used in the experimental setup.  
Figure 7: Diode bridge rectifier and ripple capacitor.  
the boost converter stage. The converter employs a half-bridge topology with two IGBTs: one (T1) remains open, acting as a freewheeling diode, while the other (T2) is driven by a control signal via PWM from the Arduino(r). This configuration allows precise control of the output voltage by adjusting the duty cycle of the PWM signal, providing real-time regulation of the turbine's electrical operating point.  
The converter operates at a switching frequency of 10 kHz, enabling efficient voltage regulation. The power stage is designed to support various control strategies, including MPPT and power curtailment. By dynamically adjusting the duty cycle, the boost converter efficiently manages power transfer from the turbine to the storage system, maintaining optimal operating conditions despite variations in wind speed. Figure 7(a) shows the appearance of the modular IGBT power stack, providing an overview of the physical setup. Figure 7(b) presents the electrical schematic, illustrating the connections and components within the boost converter stage, such as the two IGBTs, inductor, and capacitor.  
In the converter's design, key components such as the inductor, capacitor, and the IGBTs work together to facilitate smooth power conversion, minimizing switching losses and ensuring stable operation under different load conditions. The dynamics of this circuit are governed by the voltages across the inductor and capacitor, as well as the current flowing through the system, following the relationships derived in the dynamic modeling of the boost converter (as seen in Equation (9)).