esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Given the pervasive use of satellite and over the horizon wireless communication technology in modern society, ionospheric disturbances that can disrupt such services are a crucial consideration. Ionospheric irregularities, plasma bubbles and other phenomena can have a great impact on satellite navigation and communications, impacting other systems reliant on such technologies. The Ionospheric Dynamics and Attitude Subsystem Satellite (IDEASSat) was a 3U developed by National Central University (NCU) to measure irregularities in the ionosphere, as well as to establish spacecraft engineering and operations capacity at NCU. The onboard Compact Ionospheric Probe (CIP) could measure high-resolution plasma parameters, which can be used for identifying ionospheric irregularities that can cause scintillation in satellite navigation and communications signals. Part of the spacecraft sub-systems were independently designed and developed by students, who were also responsible for integration, testing, and operations. IDEASSat was successfully launched into low Earth orbit on 24 January 2021, and then began mission operations. The spacecraft successfully demonstrated three-axis attitude stabilization and control, tracking, telemetry and command (TT&C), as well as flight software and ground systems that could support autonomous operation. The spacecraft experienced a critical anomaly 22 days after launch, followed by a 1.5-month communications blackout. The spacecraft briefly recovered from the blackout for long enough to replay flight data, which allowed for the cause of the blackout to be determined as an inability of the electrical power subsystem reset circuit to recover from an ionizing radiation induced single event latch-up. Although the mission was not completed, flight data obtained during the mission will help to improve the designs of future spacecraft in development at NCU. This paper will introduce IDEASSat's final flight model design and implementation, integration, testing, environmental verification, and failure analysis, and will review the performance of the spacecraft during on-orbit operations. The results and experiences encountered in implementation and operations of the IDEASSat mission are presented here as a reference for other university small satellite teams.  
**Keywords:** CubeSat; ionosphere; IDEASSat; small satellite; integration; testing; operations; capacity building; anomaly; lessons learned
## 5 Simulation Results
The reason is that the PSO-based method iteratively updates the global and local particles' positions to search for the optimal solution, instead of systematically evaluating all possibilities to find its optimal solution, as the exhaustive search method does. Figures 5 and 6 show that the proposed CR-NOMA scheme outperforms the baseline OMA scheme, and this finding confirms the outcomes obtained in the simulation results seen in Figure 3. From Figure 5, we can see that the SEE decreases when increasing the minimum required harvested energy at User 3. This is because the transmitter needs to send more power to satisfy the requirement of the EH user, and this fact requires \(p_{3}\) to increase, which is inversely proportional to the maximization of the objective function (11a). Moreover, as the values of the data rate requirement increase, the SEE performance decreases, since more power must be delivered by the transmitter and the relay in order to satisfy the data rate requirements, which makes it easier for the eavesdropper to catch the messages. Figure 6 shows a comparison of SEE performance between our proposal using NOMA versus the minimum energy harvested by User 3 when the minimum data rates at the secondary users are \(\phi_{1}=\phi_{2}=0.5,1,1.5\) (bit/s/Hz), when the maximum available transmission power at the transmitter and the relay is \(P_{T}=P_{R}=30\) dBm. From Figure 6, we verify that the performance under NOMA is superior to OMA, and we can see that when the values of energy harvested and for data rate requirements increase, the values of the SEE start to decay. Figure 4: SSE performance comparison between the exhaustive search method, CR-NOMA with cooperative relaying, and CR-OMA with cooperative relaying, based on the minimum data rate requirements for the secondary users. Figure 5: SSE performance comparison between CR-NOMA and CR-OMA according to the minimum harvested energy at User 3 and the minimum rates at secondary User 1 (\(\phi_{1}\)) and secondary User 2 (\(\phi_{2}\)).
# Coronary Centerline Extraction from CCTA Using 3D-UNet
## 4 Experimental Results and Discussion
### Training the Network  
The training of the proposed U-NET architecture was performed with different configurations for the following parameters:  
Input patch size;  
Layer reduction;  
Batch size;  
Feeding or not the network patches with no single voxel of ground truth.  
The hard constraint put on the parameters is given by the limited amount of VRAM of the video card. As an example constraint, if the input path size was increased, the batch size or the number of convolutional kernels in the layers needs to be reduced.  
The input patch size is described in voxel dimensions as an (X, Y, Z) pair. The tested patch sizes were 128 \(\times\) 128 \(\times\) 96, 128 \(\times\) 128 \(\times\) 128, 256 \(\times\) 256 \(\times\) 128, 320 \(\times\) 320 \(\times\) 64, and 384 \(\times\) 384 \(\times\) 48.  
The layer reduction is the divisor of the number of convolution kernels (the number of output filters in the convolution) for each convolutional layer. Batch size is the number of input volumes fed to the network before backpropagating the error.  
When the ground truth is very sparsely distributed along the volume, many of the patches will not have any parts of the segmentation. As measured by experiment, this tends to make the network fall into a local optimum and to always say that there is never anything to segment in the input volume. To prevent this, for patches of a smaller size, the network is fed only inputs that contain at least one segmented ground truth voxel.  
A difficult challenge in biomedical image segmentation is the small number of images available for training neural networks. A way to overcome this problem is to use augmentation.  
The _Batchgenerators_ framework [37] was used because it provided a wide range of transforms and included spatial augmentation suitable for the 3D input data. The framework allows single or multithreaded computation of the augmented data. An alternative framework that uses the GPU to compute some of the transformations faster is available in [27].  
The following spatial transformations were used: random cropping, elastic deformations, rotation on all axes, scaling, and mirroring on all axes. The following color transformations were added: brightness transformation, gamma transformation, and reverse gamma transformation. Noise transformations were also utilized, including Gaussian noise and Gaussian blur. All the transformations are applied with a probability per input sample.  
Only the spatial transformations are applied, with the same parameters, to the ground truth. This is conducted in order to keep the annotation in sync with the input volume. Without augmentations, the dataset was too small for the training to converge.  
An alternative to the data augmentation presented above will be the use of synthetic data generated using generative adversarial neural networks (GANs), which were applied for medical imaging with success in increasing the accuracy for liver lesion classification [38].
## 5 Conclusions  
This paper presents a new optimization algorithm based on elephant herding behavior called Elephant Herding Optimization (EHO) and three improved variants called \(\alpha\)EHO, CEHO, and BIEHO. The EHO and its three variants are developed to estimate single, double, and three-diode solar cell models. The 57 mm diameter RTC Company of France commercial silicon solar cell with 26 points of measured data was chosen to present single and double models' problem under one irradiance level (25 \({}^{\circ}\)C and 1000 W/m\({}^{2}\)). The EHO variants results are compared with two good algorithms (ABSO, HS). For presenting the three-diode model multi-crystalline PV solar module CS6P-240P under four irradiance levels (109.2, 246.65, 347.8, and 580.3 W/m\({}^{2}\)) at temperature (37.32, 40.05, 347.8, and 51.91 \({}^{\circ}\)C) respectively. The EHO algorithms are compared with another three algorithms (MFO, FBA, and DEIM). The superiority of the four EHO algorithms is proven in the results. Cultural-based algorithms outperformed all algorithms used in the double- and three-diode models and ABSO, HS, and Biased in the single-diode model. Finally, it can be concluded from the results that EHO algorithms are very suitable for solving parameters extraction of solar cell problems for variant models.  
Among the drawbacks of conventional EHO is its scale factor alpha being a constant value. Additionally, the behavior of EHO requires more attention to the solutions. Therefore, it would be helpful to employ more hybrid solutions, as this study recommends. Moreover, due to the practical nature of elephant herding, there are more processes involved than clan updating and separating. Thus, more models should be developed and incorporated into the EHO method that models elephant behavior. Finally, the main EHO was designed for solving continuous problems, so it must be validated for continuous and discrete problems [58].  
Future work will include extracting parameters for more complex models for more accurate parameter extraction. In addition, the adaptive scaling factor is more promising than being a constant value in the range [0, 1]. Moreover, due to the superiority of the CEHO algorithm, we can do more enhancements to the CEHO algorithm to get more accurate results for more complex optimization problems. In addition, more behavior characteristics are recommended to investigate an advanced version of EHO accomplished with new hybrid algorithms.  
Conceptualization, Y.I.R. and M.A.E., R.A.E.-S.; methodology, A.M., A.A.M.; software, A.M., A.A.M.; validation, A.M., A.A.M.; formal analysis, M.A.E., R.A.E.-S.; investigation, A.M., A.A.M.; resources, A.M., A.A.M.; data curation, Y.I.R.; writing--original draft preparation, Y.I.R.; writing--review and editing, M.A.E., R.A.E.-S. and A.M.; visualization, A.M., A.A.M.; supervision, M.A.E., R.A.E.-S.; project administration, A.M., A.A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 10: Measured current vs. calculated for three-diode model by CEHO.
# Construction of a Geological Fault Corpus and Named Entity Recognition
## 2 Related Work
### Geological Named Entity Recognition
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Representative** & \multirow{2}{*}{**Vantage**} & \multirow{2}{*}{**Disadvantage**} \\  & **Technology** & & \\ \hline \multirow{4}{*}{Early Methods} & Rule-based method, Dictionary-based method & No need to annotate a large amount of data, better in specific domains & Poor flexibility and portability, need to redefine rules for different texts, long construction cycle \\ \cline{2-4}  & Maximum entropy models, & Good scalability and flexibility for large-scale datasets, no need to manually & Large dependency on labelled data \\ \cline{2-4}  & Random Fields, Hidden Markov Models & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} Deep learning-based methods \\ \end{tabular} } & \begin{tabular}{c} Maximum entropy models, \\ \end{tabular} & \begin{tabular}{c} Good scalability \\ and flexibility for \\ large-scale datasets, \\ no need to \\ manually \\ write rules \\ \end{tabular} & \\ \hline \multirow{4}{*}{\begin{tabular}{c} Deep learning-based methods \\ \end{tabular} } & \begin{tabular}{c} Convolutional \\ Neural Networks, \\ Recurrent Neural Networks, Hybrid \\ Neural Networks \\ based on the \\ Transformer model \\ \end{tabular} & \
\begin{tabular}{c} Automatically learn \\ text features, \\ reducing human \\ intervention \\ \end{tabular} &
\begin{tabular}{c} Requires large amount of labelled \\ corpus, weak \\ migration of \\ domain entities \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Different named entity recognition models and their advantage and disadvantages. develop named entity recognition methods specifically tailored to the textual characteristics of fault geological survey reports.
# Novel Dual Beam Cascaded Schemes for 346 GHz Harmonic-Enhanced TWTs
###### Abstract  
The applications of terahertz (THz) devices in communication, imaging, and plasma diagnostic are limited by the lack of high-power, miniature, and low-cost THz sources. To develop high-power THz source, the high-harmonic traveling wave tube (HHTWT) is introduced, which is based on the theory that electron beam modulated by electromagnetic (EM) waves can generate high harmonic signals. The principal analysis and simulation results prove that amplifying high harmonic signal is a promising method to realize high-power THz source. For further improvement of power and bandwidth, two novel dual-beam schemes for high-power 346 GHz TWTs are proposed. The first TWT is comprised of two cascaded slow wave structures (SWSs), among which one SWS can generate a THz signal by importing a millimeter-wave signal and the other one can amplify THz signal of interest. The simulation results show that the output power exceeds 400 mW from 340 GHz to 348 GHz when the input power is 200 mW from 85 GHz to 87 GHz. The peak power of 1100 mW is predicted at 346 GHz. The second TWT is implemented by connecting a pre-amplification section to the input port of the HHTWT. The power of 600 mW is achieved from 338 GHz to 350 GHz. The 3-dB bandwidth is 16.5 GHz. In brief, two novel schemes have advantages in peak power and bandwidth, respectively. These two dual-beam integrated schemes, constituted respectively by two TWTs, also feature rugged structure, reliable performance, and low costs, and can be considered as promising high-power THz sources.  
terahertz; traveling-wave tube; folded waveguide (FWG); slow wave system; high harmonic traveling wave tube +
Footnote †: journal: Electronics Letters
## 4 Biological Applications
### Therapeutic Applications
#### 4.2.1 Thermal Treatments  
Thermal treatment, also called hyperthermia, is an emerging technique for therapeutic cancer treatment. This technique increases the temperature of the medium surrounding tumor cells. Research has established that tumor cells are sensitive and can be destroyed at temperatures above 42 \({}^{\circ}\)C [179]. The heat generated in hyperthermia treatments can originate from ultrasonication, radiofrequency, or other types of irradiation [180]. These methods, however, can sometimes cause severe damage to surrounding healthy cells [10; 42]. In efforts to develop a more targeted approach, researchers have described the use of metallic nanoparticles that can locally heat the tumor cells without damaging the surrounding healthy tissue [181; 182]. To provide specificity, the nanoparticles can be conjugated with antibodies that preferentially bind to tumor cells, and upon NIR irradiation, the absorbed photolytic energy causes the nanoparticles to warm, leading to the destruction of the tumor tissues [183; 184]. Gold nanoparticles have been reported for use in photothermal therapies based on the optical properties and biocompatibility. As previously mentioned, bimetallic nanomaterials, especially gold-based bimetallic nanoparticles, can be designed for activation at visible and NIR wavelengths, making them suitable for use in photothermal cancer therapy. For example, bimetallic AgAu nanocages have been evaluated as potential materials for photothermal therapy [69; 185]. The surface plasmon resonance of these types of nanocages can be tuned to the NIR region (800 to 2500 nm), making them compatible with the wavelength of laser irradiation used in many photothermal therapies [38]. Under laser irradiation, gold nanocages generate extremely high local temperatures that can provide a destructive effect on cancer cells without destroying the particles themselves [167]. Recent simulation studies have explored the process by which thermal damage of the lipid bilayer occurs by this process and the subsequent increased permeability of the membrane [186]. Moreover, due to their strong and tunable surface plasmon resonance, gold nanocages can also be used as a potential contrast enhancement agent for optical coherence tomography (OCT) and spectroscopic optical coherence tomography (SOCT) for the imaging of biological tissues for early cancer detection [72]. Furthermore, in addition to plasmonic materials, magnetic particles have also been employed for photothermal therapy using magnetic FePt nanoparticles [104]. [PERSON] et al. demonstrated the capability of FePt nanoparticles to heat to 100 \({}^{\circ}\)C in picoseconds via femtosecond-laser irradiation. Under the localized energy irradiation, FePt nanoparticles can trigger photothermolysis and then effectively kill cancerous cells [104]. In addition to photothermal treatments, another class of hyperthermia treatment is magnetic hyperthermia--a therapy that depends on magnetic nanomaterials that can be selectively positioned near target cells and generate localized heat when exposed to an alternating external magnetic field.
# Optimizing Ambiance: Intelligent RGB Lighting Control in Structures Using Fuzzy Logic
## 2 Related Work
Within the AAL and AMI context, adaptive lighting holds even broader applications. For instance, in healthcare environments, adaptive lighting can be used to enhance patient comfort and aid in recovery, while in active aging environments, it can improve safety and independence for elders. Adaptive lighting can also have a significant impact on health and well-being. Using the proper rules, it allows the body's internal cycles that determine sleep and wake patterns to be influenced by light. By adjusting the intensity and color temperature of light throughout the day, adaptive lighting can help synchronize these circadian rhythms, which can improve sleep quality and daytime performance. The study evaluated human reactions to different TC window tints, finding that blue-tinted windows increased alertness but reduced acceptability, while bronze-tinted windows provided more natural and acceptable lighting conditions. Visual Tasks Performance: Subjects performed visual tasks under various TC conditions, with significant effects observed in color naming tasks. Energy Efficiency vs. Occupant Comfort: The research highlights the importance of balancing energy conservation goals with the visual and comfort needs of building occupants [26]. Driven by sensor technology, adaptive lighting has the potential to transform our living and working environments. As the technology advances, it is possible that we witness more innovative applications in the future. However, it is also important to consider potential implications regarding privacy and security. Our research is grounded in ethical and responsible implementation practices. This study distinguishes itself from others in terms of the integration of AIoT for real-time decision making. Other studies may use different approaches, such as open-loop systems, sensor-based systems, or centralized control. The correlation between environmental and emotional variables to determine lighting conditions is key to create an environment that contributes to activation or relaxation in a real setting. Conducting a pilot test using an EEG sensor by changing the stimuli and measuring specific deviations. These results indicate the relation between the direct influence of the proposed system on perception and behavioral contribution. Our research proposal uses fuzzy rules and membership functions in neuro-fuzzy control, which is a novel approach for adjusting artificial lighting conditions. Furthermore, the proposed system results in a reduction in energy consumption through efficient lighting. Lastly, the self-contained system can operate both online and offline, offering advantages in privacy management or in structures lacking connectivity.
## Materials and Methods
### BC Structural Properties Analysis
#### 2.7.2 X-ray Diffraction Analysis (XRD)  
The crystal structure of the BC was analysed by using a high-resolution X-Ray diffractometer (Aeris, Malvern Panalytical, Kassel, Germany) with CuK\(\alpha\) radiation generated at 1.5406 \(\AA\) wavelength, operated at 40 kV and 8 mA. The crystallinity index (\(CrI\)) of BC microfibrils was calculated using Equation (5)  
\[CrI(\%)=\frac{(I_{200-Iam})}{I_{200}}\times 100\%, \tag{5}\]  
where \(I_{200}\) is the maximum intensity of the peak corresponding to the plane in the sample with the Miller indices \(I_{200}\) at a \(2\theta\) angle between 21.5 and 24 degrees; \(I_{am}\) is the intensity of diffraction of the amorphous material at 18.3 \(2\theta\)[27]. The crystallite size was calculated using the Scherrer Equation (6).  
\[L=\frac{\text{K}\cdot\lambda}{\beta\cdot\cos\theta}, \tag{6}\]  
where \(L\) is particle size (nm); K is the Scherrer constant (0.9); \(\lambda\) is the X-ray wavelength; \(\theta\) is the Bragg angle; \(\beta\) is the full weight at half maximum (FWHM) of the peak from the fitting procedure in radians. The diffractograms were analysed using OriginPro2021 software.  
2.7.3. Scanning Electron Microscope Analysis (SEM)  
The micromorphology of BC was assessed using a Hitachi SU3500 scanning electron microscope. Prior to analysis, the purified, wet BC samples were dried overnight by lyophilisation in CHRIST Alpha 1-2 LD plus freeze dryer. Then, samples were sputtered with Cr using a SEM Quorum Q150T ES, a turbomolecular-pumped coater.
## 1 Introduction
In order to estimate the six degrees of freedom (DoF) parameters for the transformation, the coordinates of at least three non-collinear points extracted from the two scan stations for the coarse registration are required [24]. As a result, at least three sets of coordinates of spherical target centers from each station are needed for the coarse registration. Moreover, in most cases, errors that adhere to the estimation of the sphere centers will propagate into the registration errors [25]. When the number of scans becomes large, setting up multiple spherical targets becomes very time-consuming and sometimes challenging, especially in harsh environments with many obstacles and interferences. For example, multiple spherical targets were in  
Figure 1: Scanning being performed at a tunnel segment factory in the Panyu District, Guangzhou, China: (**a**) scanning of a mold for the segment production; (**b**) scanning of the segment. stalled in a limited space with confined distribution at a tunnel segment factory (Figure 1). Therefore, a new target-based registration method that requires the minimum number of target primitives is desired. In this paper, we propose a method that only requires the setup of a single low-cost cubical target (e.g., a die-like target) to replace multiple survey-grade targets for point cloud registration. The method includes simulations of two local coordinate systems (object spaces) with errors (axis non-orthogonality and translational errors). These errors are then compensated by two proposed algorithms based on the projection geometry and the cubical constraints. The method can be performed readily as the dice can be placed on the ground without using any tripod or support. The only requirement is that three facets should be scanned from each station, with only pips from two facets being recognized. There is no strict requirement for scanning overlapping facets of the die target.