esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 3 Materials and Methods
### Estimation Using Elevation of Arrival (EOA)  
For estimation using AOA, the elevation angle (EOA: elevation of angle) is utilized in addition to the azimuth angle. Figure 11 shows the training data for EOA, while Figure 12 presents the target data. Compared to RSSI and AOA, Figure 13 reveals that the target data do not significantly overlap with the training data. Possible reasons for this include the fact that the normal distribution model used for RSSI and AOA is not suitable for EOA, or that the target data distribution differs from the training data due to multipath effects. Additionally, in the ray-tracing simulation, the altitude was set up to 150 m, which may have made the elevation angle more susceptible to multipath effects.  
Figure 11: Elevation of arrival (EOA) distribution obtained during the training phase. This angle represents the vertical direction of signal arrival.  
Figure 10: Overlay comparison between AOA training and target data. This illustrates their consistency, validating the modeling approach for AOA-based localization.  
When estimating with AOA, weights are assigned to AOA and EOA. In this study, AOA and EOA are weighted equally at 0.5 each. In the HYBRID model described later, the weighting coefficients for AOA and EOA are determined using linear interpolation. Therefore, for simplification, the estimation using AOA assigns equal weights of 0.5 to both RSSI and AOA.
###### Abstract
The proposed method was developed for a multi-sensor scenario. As these studies showed, it is challenging to obtain representative data from multiple positions and their possible displacements. For example, pending devices (pendants) make the task difficult because of the countless movement combinations that could occur. In this context, we hypothesized that data augmentation techniques could help in synthesizing different sensors' configurations to artificially explore a wide range of possible scenarios. Nowadays, data augmentation is standard practice when dealing with ML applied to images, to obtain additional information for the ML models and to avoid overfitting [15]. Typically applied image-augmentation techniques include geometric transformations, filtering, mixing images, random erasing, and feature space augmentation [16]. Wearable sensor data augmentation represents a less common approach field; however, it was shown to positively affect time-series based computation and to provide potential improvements in data-driven tasks such as HAR. The review from [PERSON] et al. [11] states that high quality data augmentation techniques are necessary for the growth of HAR research. Augmentation of wearable sensors data was firstly addressed by [PERSON] et al. [17], proposing an augmentation strategy that considers the physical constraints of the arms applied to a multi-sensor scenario, including an accelerometer, a gyroscope, and an electromyography sensor. [PERSON] et al. [18] proposed an ensemble data augmentation to the spectral feature space to improve activity recognition performances among only three classes (sitting, standing, and walking), reaching an accuracy of 88.87%. The study by [PERSON] et al. [19] proposed a method to classify the motor state of Parkinson's Disease patients by using data augmentation, where applying measurements rotation improved the performance compared to other techniques. [PERSON] et al. [20] stated how HAR sensor data annotation represents a challenging task. To tackle it, they applied resampling augmentation of accelerometer data within a contrastive learning framework. This newly proposed approach learns representations by contrasting positive pairs, corresponding to the same sample augmentations, against negative pairs, or unrelated separated samples, helpful when few training data are available [21; 22]. Device orientation is an important determinant when a three-axial acceleration solution for HAR is considered. Accordingly, the aim of this research was to investigate the impact of changes in sensor orientation on a deep-learning (DL) HAR algorithm targeted on patient-like activities, such as slow and aided walking and wheelchair. Ultimately, we propose an orientation-independent HAR model that leverages data augmentation, and that is trained with acceleration measurements recorded from five sensor locations on the participant's trunk.
# Analytic Form Fitting in Poor Triangular Meshes
## 3 Methodology
### Primitive Fitting from Triangle Set
#### 3.2.3 Sphere Fitting  
The geometrical parameters to define the sphere are:  
1. \(p_{0}\): sphere center.
2. \(R\): radius.  
**Sphere center \(p_{0}\)**  
We consider a sphere which is faceted by a manifold set \(S\) of triangles \(t\). The straight line \([b_{1},n_{i}]\) normal to triangle \(t_{i}\) passes through the triangle baricenter \(b_{i}\). This line has direction \(n_{i}\). The points at which all lines \([b_{i},n_{i}]\) are nearest to (or intersect) each other are the elements of set \(C_{p}\) (Figure 7) and probabilistically lie at the sphere center \(p_{0}\). The center of the sphere \(p_{0}\) is estimated to be the center of gravity of \(C_{p}\).  
**Sphere Radius \(R\)**  
The radius of the sphere is calculated by computing the mean distance from \(p_{0}\) to all the vertices of the triangle subset.  
\[R=\frac{1}{m}\sum_{i}^{m}||p_{i}-p_{0}||. \tag{4}\]
# Polar Code Parameter Recognition Algorithm Based on Dual Space
## 4 Numerical Simulation
### Algorithm-Validity Verification  
Algorithm validity was evaluated in two cases: (1) with polar code length 256, code rate 1/2, and SNR = 30 dB, and (2) with polar code length 256, code rate 2/3, and number of transmitted codewords 2000. The results of the average likelihood ratio (LLR) and threshold are presented in Figures 4 and 5.  
As shown in both figures, the abscissa \"Oder\" corresponds to \(j\) in Algorithm 1, so the code length and rate corresponding to each abscissa can be calculated according to  
Figure 4: Algorithm-validity verification with 5 dB.  
Figure 5: Algorithm-validity verification with 30 dB.  
Section 3.4. For example, when the abscissa is 38, the average LLR exceeds the threshold for the third time and the total number of code rates is 8. It is easy to calculate that \(38\div 8=4\cdots\cdots 6\), so the corresponding code rate is the sixth \(R^{\prime\prime}\), \(\frac{3}{4}\), and the code length is the \(4+1\), or fifth, \(n^{\prime\prime}\), 256. It can be seen from the figures that when the code length and rate are not lower than the real code length and rate, respectively, the code length and rate will be mistakenly identified as the real polar code parameters. This is due to the channel polarization effect, which guarantees a certain structural relationship between a polar code and polar codes in which the code length and rate are not lower than this code. Therefore, we selected the minimum code length and rate corresponding to the maximum \(\eta\) as the final result.
## 3 Effect of Surfactants on Mechanico-Thermal Properties
### Effect of Anionic Surfactants
Beycostat A B09 could modify organic nanofillers like CNCs and modified CNCs have been utilized for the preparation of CNC/PLA nanocomposites [18]. The modification of CNCs improved the mechanical properties of the nanocomposites, such as tensile strength and tensile modulus. The tensile strength and tensile modulus increased by up to 63% and 50%, respectively, which was attributed to the Beycostat A B09 efficiently dispersing CNCs in the PLA matrix [18]. However, the decrease in the elongation at break of the nanocomposites was due to the CNCs initiating considerable local stress concentrations and then failure at lowered strain values. Moreover, the decrease in crystallization temperature was because of the presence of Beycostat A B09 on the CNC surface, which caused a better dispersion of the CNCs in the PLA matrix, which certainly increased the nucleation effect on the nanocomposites [18]. Nonetheless, the other thermal properties, for instance, the glass transition temperature and melting temperature, decreased insignificantly compared to the nanocomposite without Beycostat A B09. On the other hand, SDS could also modify inorganic nanofillers like G NPs and modified G NPs have been applied for the preparation of G NP/PVA nanocomposites [21]. The modification of G NPs improved the mechanical properties of the nanocomposites, such as tensile strength and tensile modulus, by up to 75% and 154%, respectively, compared to neat PVA. The substantial increases were due to the SDS aiding the dispersion of G NPs, which maximized the load transfer from PVA to the G NPs. Moreover, the elongation at break of the nanocomposites also increased by up to 53% because of the slippage of the intercalated state of G NP dispersion in the PVA matrix during tensile testing [21]. Additionally, the degradation temperature and crystallization temperature of the nanocomposites also increased by up to 0.8% and 1.6%, respectively, even at a low content of SDS-modified G NPs. This due to a heterogenous nucleating effect, which was caused by the adsorption of SDS into the G NPs [21]. Nevertheless, the melting temperature of the nanocomposites decreased, which was related to the decrease in their degree of crystallinity. Table 7 clearly displays that the modification of inorganic and organic nanofillers by anionic surfactants improved the mechanico-thermal properties of the prepared polymer nanocomposites, regardless of the type of anionic surfactant.
## 5 Results and Evaluations  
### Experimental Environment  
In general, the data utilized in our research are derived from the international 1000 Genomes Project, which commenced in January 2008. We specifically selected the human genome (ID: HS37D5) as our reference sequence, which can reach a size of up to three gigabytes. For one of our natural short-read datasets, we employed the SRR766060 paired-end genome, with each segment approximately measuring 8.7 GB in size. Furthermore, we incorporated four widely recognized benchmark datasets in their latest version for alignment quality evaluation purposes--BALIBASE, OXBENCH, SABRE, and PREFAB. Additionally, for speedup evaluation and sequence complexity experiments, we utilized specified sequence datasets obtained from several widely implemented read simulation tools in bioinformatics, such as randDNA and wgsim. The utilization of these compelling datasets has significantly contributed to obtaining reliable outcomes throughout the experiment we conducted.  
The overview information on the experimental environment and configurations is shown in Table 3. Several additional widely used FM algorithms--SOAP2, BOWTIE2, BWA, and GEM3--were used in this research to comprehensively compare several significant issues. The experiments were also conducted on two different NUMA nodes, Intel Xeon and AMD Opteron, which are also commonly used in heterogeneous systems. These NUMAnodes have the capacity to support four processors with 32 cores, 64 threads, and 128 GB of memory. Although each processor has four NUMA nodes, they are interconnected differently among the cores. AMD Opteron 6376 utilizes a core network with full ties, while Intel Xeon E5 4620 employs a ring structure with circular connections among its cores, as shown in Figure 1.
# Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model
## 1 Introduction
Importantly, in order to approximate the nonlinear sequences, for feed-forward neural networks it is usual to increase layer depth. Unfortunately, this will increase the amount of computation exponentially, which is unacceptable for real-time systems. Differently, Elman neural networks have the advantages of self-learning, nonlinear mapping, parallel distributed processing, and high prediction accuracy. In order to achieve the purpose of memory, a support layer is added, as a one-step delay operator, in the hidden layer of the feed-forward network. Thus, the system has the ability to adapt to time-varying characteristics, and can directly reflect the dynamic process. Therefore, in this paper, the random gyro drift error model is studied by use of an Elman neural network. In addition, it usually takes a long time to determine the initial value of the neural network model, using the trial and error method. There is still some distance between the obtained initial weight values of the neural network and the optimal value of the final convergence. This will slow down the convergence speed of the network, and even may cause the network to fall into minimum traps during the convergence process. Under more serious circumstances, due to improper selection of initial values, the system may diverge. In fact, the initial value problem can be transformed into a parameter optimization problem under certain conditions. At present, in this field, the most widely used method is based on the intelligent optimization algorithm. Currently, there are many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithms (GA), evolutionary strategy (ES), and so on. However, most of them have the shortcomings of premature convergence, slow convergence speed, and many uncertain parameters. Therefore, the paper will use a differential evolution algorithm to obtain the initial parameters of the network, which can greatly improve the training time and ensure the stability of the network with a high probability. The paper is structured as follows: The first part provides the current research status of compensation methods for fiber-optic gyro error. Then, the second part introduces some background knowledge, including the architecture of photoelectric stabilization and the track platform, the components of the gyro output signal, and ALLAN variance [4]. Some relative knowledge, including the ARIMA model, Elman model, lift-wavelet filter model, and the DE algorithm, is illustrated in the third part, and relative work results are also shown. Next, the hybrid ARIMA-Elman model, the flow chart of the whole error compensation process, and experimental results are provided in the fourth part. Meanwhile, the comparison results with other commonly used methods and the generalization ability test of the hybrid model are also analyzed. Finally, the paper is concluded in part five.
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
## 4 Results  
In this paper, the case study is based on the CubeSat 1U FloripaSat-I, whose orbit is nearly-circular and Sun-synchronous, and has an altitude of 620 km [36]. For these reasons, the orbit's inclination is simplified to 90\({}^{\circ}\) and perfectly circular. It is a condition that entails solar eclipse caused by the earth, so the unit vector inside \([M_{i}]\) is \(\vec{u}_{i}=[0,1,0]\) and \(\theta_{i}=90^{\circ}\).  
Figure 8: Integrated simulation model fluxogram.  
The parameters to perform this inclination and the translation of the orbit's satellite are summarized in Table 4.  
It will be assumed that the orientation results from a passive attitude control based on magnets, which aligns the satellite with Earth's magnetic field. Therefore, the matrix \([M_{a}]\) synchronizes one axis of the CubeSat with the Earth's magnetic field, simplified as a dipole pattern. This axes is still free to rotate around itself, so a second matrix \([M_{b}]\) is used. The attitude scenario to be tested is summarized in Table 5. The CubeSat rotates twice per orbit around one axis to align with the Earth's magnetic field, and the free spin around the secondary axis has the arbitrary value of 10 rotations/per orbit, representative of a spin condition without any damping.  
We will also define the energy modes (\(L\)), in which the satellite will work, as presented in Table 6. The energy levels are related to CubeSat's maximum power consumption according to the battery's state of charge. The energy levels presented, which are based on the CubeSat FloripaSat-1 [36], are related only to the available energy level (SoC). However, they can be used, for example, to trigger energy-saving states, such as decreasing the energy usage in eclipse regions.  
A simple control algorithm was employed to illustrate the case scenarios. The process defines the satellite's maximum energy consumption according to the battery charge status and applies an on/off controller to the heater according to the defined temperature setpoint. To illustrate, a heater with \(R_{H}=10\)\(\Omega\) was used in the simulations. The timestep of the simulation was set to 1 s and the total time of the simulation was equivalent to 12 orbits.
# The Link between Purine Metabolism and Production of Antibiotics in _Streptomyces_
## 3 Secondary Metabolism in _Streptomyces_  
Members of this genus produce numerous antibiotics or secondary metabolites that are commercially or clinically relevant. These natural products possess, for example, antibacterial, antiviral, anti-tumor, and anti-fungal properties [4]. Secondary metabolites are produced in response to environmental conditions, including starvation or growth of other nearby organisms that may belong to the same or different bacterial genera, or to different species such as plants or fungi; such compounds are therefore thought to furnish the producer with a competitive advantage [34]. Under laboratory conditions, many of the gene clusters that encode proteins involved in generation of natural products are silent. Efforts to elicit their expression by co-culturing _Streptomyces_ spp. with various other microbes or by addition of natural and synthetic chemicals have been implemented, and this has resulted in the identification of numerous antibiotics [35]. Combining drug resistant mutations also resulted in activation of antibiotic gene clusters [36; 37]. Inducers of secondary metabolism are likely to act indirectly through intracellular signaling, including the generation of alarmones. Since many gene clusters have resisted efforts to elicit their expression during laboratory conditions, _Streptomyces_ spp. (and other bacterial species known for production of bioactive secondary metabolites, such as _Burkholderia_ spp. [38]) are predicted to constitute a valuable resource for discovery of novel biologically active compounds.  
Genes linked to biosynthesis of secondary metabolites generally form very large clusters, often comprising multiple operons, and analysis of genome sequences suggests that the majority are species specific [39]. For instance, _S. coelicolor_ strain A3(2) produces several compounds, including the red-pigmented undecylprodigiosin (RED, a member of the prodiginine family of bacterial alkaloids), the deep blue polyketide actinorhodin (ACT), and methylenomycin (Figure 2; reviewed in [40]), while _S. griseus_ produces streptomycin, _S. virginia_ produces virginiaymcin, _S. natalensis_ produces pimaricin, and _S. avernitilis_ produces avermectin [40]. The antibiotic biosynthetic gene clusters often include regulatory genes referred to as cluster-situated regulators (CSR) [40; 41]. The CSRs may directly regulate the transcription of the corresponding antibiotic gene cluster or they may control antibiotic production indirectly through other mechanisms [40]. Activation of antibiotic synthesis is complex and may involve multiple regulators, including factors that affect both antibiotic production and differentiation such as BldD, and it may require a variety of signals, including accumulation of (p)ppGpp and c-di-GMP.  
Figure 2: Examples of secondary metabolites produced by _S. coelicolor_. Structures were obtained from the PubChem database and rendered using PyMol (C, light blue; N, dark blue; O, red). Actinorhodin, CID 441143; undecylprodigiosin, CID 135515151; methylenomycin A, CID 122733.
# Influence of Rice Variety and Freezing on Flour Properties
## 1 Introduction  
Rice flour is an important raw material used in many foods due to the functional properties. Thus, it is considered to be the most suitable cereal flour for the production of gluten-free breads, but it is also used for obtaining breakfast cereals, different snack foods, noodles, puddings, and salad dressings [1, 2]. The quality of these food products highly depends on the physicochemical and thermo-mechanical properties of the rice flour.  
The rice cultivar [3, 4, 5, 6, 7] and freezing treatment [8, 9, 10] have an important impact on physicochemical properties, due to their influence on starch behavior during processing. Considering the amylose content, rice can be categorized as low, medium, and high amylose content cultivars [6]. Different behavior is expected for different rice cultivars, because the amylose content has a high influence on gelatinization, retrogradation, and pasting properties of flour, and further on end use products [11, 12]. Previous studies performed on cooked rice or polysaccharides gels subjected to freezing treatment, regardless of temperature (freezing at low or ultra-low temperature) and time of storage at freezing temperature, revealed important changes in the mechanical properties of the gel and starch retrogradation [8, 9, 10].  
The type of grinder used for rice milling might also exert a high influence on the technological functionality of the flour. Different grinding methods are available to be used for producing rice flour, such as wet grinding, semidry grinding, and dry grinding. The most used grinding machines are roller mills, hammer mills, disc mills, and pin mills [2]. Due to the differences in the mechanical action on the rice kernels, the methods and type of grinding machines influence the particle size distribution and starch damage, which are nominated in the literature to be the essential parameters that influence the physicochemical properties and end use of the rice flour for different categories of foods [1, 2, 13]. Theflours with high amounts of damaged starch are susceptible to rapid water absorption and hydrolysis by amylase. In addition, the swelling power of the flours increases with the increase of the amount of fine particles. The tests performed by [PERSON] and [PERSON] [2] on three types of mills revealed that freezing the rice samples prior to grinding ensures obtaining flour with fine particle size and lower amounts of damaged starch compared to the conventional dry grinding processes. To the best of our knowledge, there is no study available in the literature on the influence of freezing the grains prior to grinding on the technological functionality of the resulting flours and rheological properties of the dough.  
The objectives of this study were to evaluate the impact of freezing at low temperature on the milling properties of two rice varieties, and on the functional and thermo-mechanical properties of the rice flours. In addition, the impact of freezing at low temperature on the starch properties and gel texture properties were examined.
## 3 Experiments
### Datasets  
#### 3.1.1 BreakHis Dataset  
The BreakHis dataset is a publicly available dataset widely used for breast cancer histopathological image analysis. It contains 7909 breast cancer tissue slice images from 82 patients, with images captured at four magnification levels including \(40\times\), \(100\times\), \(200\times\), and \(400\times\). All images are divided into two main categories, benign and malignant, with further subdivisions into specific subtypes. The benign tumors include adenosis (A), fibroadenoma (F), tubular adenoma (TA), and phyllodes tumor (PT). The malignant tumors include ductal carcinoma (DC), lobular carcinoma (LC), mucinous carcinoma (MC), and papillary carcinoma (PC). Some typical histopathological images in breakHis at \(400\times\) magnification are given in Figure 4.  
Figure 4: Typical images in the BreakHis dataset. Images of benign and malignant tissues under a \(400\times\) microscope.