esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Secondary Optical Design
### TIR Lens Design  
A total internal reflection (TIR) lens is a special free-form lens whose main function is designed to collect the light emitted from the LED light source as much as possible, while at the same time reducing the divergence angle of the light beam and reducing the numerical aperture, in order to achieve high brightness illumination of the lighting device. Facilitating the design of subsequent optical systems and improving the light energy utilization rate are also improved by reducing the numerical aperture to achieve high-brightness illumination of the illumination device and the subsequent optical system design [17]. Since free-form TIR lenses obtain the surface shape of the lens by tracing the travel path of each light ray, it is difficult to obtain the structure of the lens as a function of the illumination requirements. Therefore, the initial structure of the lens surface is obtained by the theoretical calculation of each light path. The working principle is shown in Figure 5.  
Figure 4: 3-D visualization of the inference rules.  
Since the LED has rotational symmetry, the lens surface can be composed of a \(360^{\circ}\) rotation of the cross section around the \(y\)-axis, and the LED light source is represented by the \(O\) point. When the angle of incidence \(\theta\) (\(0\leq\theta\leq\theta_{1}\)), the light of the LED is only transmitted by the surface \(AB\) to become parallel light. When the angle of incidence \(\theta\) (\(\theta_{1}\leq\theta\leq 90^{\circ}\)), the LED light is refracted by the rotating surfaces \(AC\) and \(BE\) and then reflected by the rotating surfaces \(CD\) and \(EF\) to become parallel light. Therefore, the TIR lens is designed in two aspects according to the magnitude of the incidence angle.
## 3 Intelligent Congestion Control Model Based on DRL in Heterogeneous V2X
### Design of State Space
#### 3.2.1 Size of The Congestion Window  
Assuming that the link speed between two nodes in the V2X network is \(C\) (Kbps) and the packet size is \(\eta\) (Kbits), and assuming that the round-trip delay \(RTT\) (S) of the link is constant, the maximum size of the congestion window is defined as Formula (2)  
\[cwnd_{max}=\frac{BDP}{\eta} \tag{2}\]  
where \(BDP\) is the link delay bandwidth product, and \(BDP=C\times RTT\).  
Buffer size plays an important role in expanding link capacity. First, if the size of the send window is 100 packets, that means the sender will allow up to 100 packets to be sent. Moreover, assume that the link buffer size is 20 packets, which means that the maximum number of packets allowed to be transmitted is 120. Therefore, Formula (2) can be modified into Formula (3)  
\[cwnd_{max}\approx\frac{BDP}{\eta}+b \tag{3}\]  
where \(b\) represents the link buffer size, and \(b\) is constant.
###### Abstract  
Partial discharge (PD) detection plays an important role in online condition monitoring of electrical equipment and power cables. However, the noise of PD measurement will significantly reduce the performance of the detection algorithm. In this paper, we focus on the study of a PD noise reduction algorithm based on improved singular value decomposition (SVD) and multivariate variational mode decomposition (MVMD) for white Gaussian noise (WGN) and periodic narrowband interference signal noise. The specific noise reduction algorithm is divided into three noise reduction processes: The first noise reduction completes the suppression of narrowband interference in the noisy PD signal by the SVD algorithm with the guidance signal. The guidance signal is composed of a sinusoidal signal of the accurately estimated narrowband interference frequency component, and the amplitude is twice the maximum amplitude of the noisy PD signal. The second noise reduction decomposes the noisy PD signal after filtering the narrowband interference signal into \(k\) optimal intrinsic mode function by the MVMD after parameter optimization. By calculating the kurtosis value of each intrinsic mode function, it is determined whether it is the PD dominant component or the noise dominant component, and the noise dominant component is subjected to \(3\sigma\) filtering to obtain the reconstructed PD signal. The third noise reduction uses a new wavelet threshold algorithm to denoise the reconstructed PD signal to obtain the denoised PD signal. The overall noise reduction algorithm proposed in this paper is compared with some existing methods. The results show that this method has a good effect on reducing the noise of PD signals measured in simulation and field.  
Keywords:partial discharge; noise reduction; multivariate variational mode decomposition; marine predators algorithm; singular value decomposition Article
# Environmental Attention-Guided Branchy Neural Network for Speech Enhancement
## 3 Experiments and Results
### Performance Evaluation of Branchy Neural Network  
#### 3.2.1 Classification Accuracy Evaluation  
In our proposed DNN framework, the classification network plays an important role in the process of back propagation and forward propagation. Assuming that the proposed approach works in an application scenario with four known noise interferences (babble, factory1, destroyer engine, and destroyer operation noises), the classification network needs to have sufficient accuracy to lead the denoising network to learn specific branches in the training stage and distinguish the noise types in the test stage. We performed experiments to evaluate its classification accuracy in the generated training and validation datasets, as shown in Table 1. The \"Noisy LPS\" denotes that the classification network only used the noisy log power spectrum (LPS) feature, and \"Noisy LPS + Noise LPS\" represents that the estimated noise log power spectrum features were concatenated with the noisy LPS for noise classification. Furthermore, the evaluation of classification accuracy in the validation dataset reflects the generalization performance of the model. The experimental results in Table 1 show that the presented classifier with the estimated noise LPS features contributed to better performance in noise classification and model generalization. The classification accuracy in the training and validation datasets is more than 99%, which means that the proposed classification module has sufficient ability to guide the training and testing of the denoising module.
# Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models
## 2 Materials and Methods
### Classification Model
#### 2.4.2 Hyperparameters  
The classification model used in this study is linear, polynomial(2 nd, 3 rd), and Gaussian kernel support vector machine. Each model is designed to find an optimization model through the regulation of hyperparameters:  
* Hyperparameters of the linear and polynomial models (2d, 3d): The value of regulation terms C has adjusted. C was 0.001, 0.01, 0.1, 1, 10, 100, and 1000.
* Hyperparameters of the Gaussian kernel model: The values of regulation terms C and gamma were adjusted. Gamma is a parameter that regulates the dispersion of the Gaussian kernel. C was adjusted to 0.001, 0.01, 0.1, 1, 10, and 100. Gamma was adjusted to auto, 0.001, 0.01, 0.1, 1, 10, and 100. Here, if gamma is set to auto, the reciprocal of the number of predictors is the input.
* Grid search: Grid search methods were used to find the optimal numerical value among adjustable hyperparameters efficiently. Grid search performs pre-learning for all cases for a group of hyperparameter candidates and derives optimal hyperparameters through cross-validation. Here, the test data should not be involved in the grid search. Therefore, the entire data were divided into three types of data: training, validation, and test.
# Heart Diseases Diagnose via Mobile Application
## 2 Materials and Methods  
### Data Set  
For training and validation of the algorithm, the PhysioNet 2016 database which consists of 2430 labeled heart sounds was used. This dataset is a combination of nine heart sound databases collected by independent research groups at different times and with different devices. Since this database consists of nearly all of the available databases, it is widely used and accepted as the reference set for heart sound classification tasks [15; 16; 17; 18]. All of the recordings are labeled as normal and abnormal, and the quality of the sounds, recording position, and patient profile are different. Since each database was evaluated by different medical staff and digital stethoscope devices, like all big datasets, the PhysioNet dataset is biased and includes personal judgment.  
During the verification tests at Ankara City Hospital, the mobile application was used for 162 individuals. This verification dataset consists of 88 normal and 74 abnormal heart sound samples. All participants were between 15-70 years old and ThinkLabs One digital stethoscope was used by focusing multiple recording positions on the chest near the heart.
# Dual-Modality Cross-Interaction-Based Hybrid Full-Frame Video Stabilization
## 3 Proposed Method: Cross-Interaction-Based Video Stabilization  
### Methodology  
To address the performance issues caused by the major problems identified, we propose a combination of three technologies. This holistic approach integrates hardware and software solutions to significantly enhance the quality of video stabilization, aiming for a comprehensive improvement over existing methods. Firstly, considering the necessity for IMU sensors to perform motion compensation from shaky input data, we sought to improve motion estimation performance. It was essential to establish a portable connection between sensor data and video data, necessitating the integration of a developing board, an IMU sensor, and a camera, which operates within the given power consumption. The data could be collected once the communication between the IMU sensor and the developing board was established. To achieve our research objectives, we developed a technique that began with synchronizing sensor data with video footage using a Raspberry Pi to communicate with an IMU sensor. This synchronization enabled precise motion compensation in the preprocessing stage for each frame of the input video. To enhance the accuracy of the sensor data, the VQF method was applied alongside a Low-Pass Filter (LPF) to correct the errors in sensor data, followed by the utilization of the AKAZE optical flow. Motion compensation was executed using the collected IMU sensor data with Gyroflow (v1.5.4) [21]. Secondly, to address cropping, we utilized PCA-flow-based video stabilization [8]. This method could also improve the slight cropping caused by motion compensation in our approach. However, applying PCA-flow-based video stabilization [8] has the disadvantage of a low visual quality due to increased distortion. Lastly, to further mitigate distortion that arose during the full-frame video creation process, we applied neural rendering [5], culminating in the production of the stabilized frames. Therefore, we achieved a final full-frame stabilization video with improved stability and distortion. This comprehensive approach aimed at addressing the key challenges in video stabilization, significantly improving the quality and usability of the output video footage. Our method represents a novel approach, utilizing sensor data for initial motion compensation followed by a deep learning-based full-frame video stabilization process, a strategy not previously explored in existing research. Additionally, our technique offers the advantage of being applicable to videos of any type, providing a versatile solution to video stabilization challenges. This comprehensive method integrates hardware and software to refine the stabilization process, ensuring the output video maintains high fidelity to the original scene dynamics while correcting for unwanted motion artifacts.
## 2 Materials and Methods
### Animals and Housing Conditions  
Six sows, aged over 8 months and weighing \(215\pm 15\) kg, were used. They were divided into two groups: PFS and CTL, each with three respiratory chambers. One sow was placed in each chamber with ad libitum access to feed and water. Chambers (\(1\times 2.4\times 1.5\) m) had feeding pipes, waste removal doors, plastic slat flooring, and ventilation via a ring blower (HB-129s, Hae Sung Techologies, Seoul, Republic of Korea) providing 300 L min-\({}^{-1}\) airflow. Airflow was monitored using a mass flow meter (HFM 200 LFE, Teledyne Hastings, Hampton, VA, USA). Chambers maintained a temperature-controlled environment (24 \({}^{\circ}\)C) using strategically placed thermocouples (T-type). Lighting mimicked natural patterns (6 a.m. to 8 p.m.). This lighting schedule not only contributed to the animals' natural rhythms but also facilitated meticulous observation and management of the sows to guarantee their well-being and adherence to the experimental conditions. The animal protocol used in this study has been reviewed by the Chungnam National University-Institutional Animal Care and Use Committee (CNU-IACUC) for ethical procedures and scientific management.  
All sows in both groups (conventional diet and diet supplemented with 3.0% peat moss) underwent a feed adaptation period of five days. Following the adaptation period, excreted pig slurry from sows was collected for three days. The collected pig slurry (30 L of each group) was thoroughly mixed and immediately refrigerated at 5 \({}^{\circ}\)C. Conventional diet consisted of 12.9% crude protein, 2.6% crude fat, 0.7% calcium, 0.4% phosphorus, 17.2% neutral detergent fiber (NDF), 8.1% acid detergent fiber (ADF), and 0.7% lysine. The feed additive peat moss employed in this study was formulated with the following constituents on a dry matter basis: 0.73% crude fat, 0.51% calcium, 0.07% phosphorus, 43.7% NDF, and 37.4% ADF.  
During the experiment (56 days), pig slurry was sampled from the vessel five times every two weeks. Total solid (TS) of raw pig slurry obtained from CTL and PFS was 127.5 and 133.5 g\(\cdot\)kg\({}^{-1}\), respectively. Volatile solid (VS) of CTL and PFS was 103.4 and 105.5 g\(\cdot\)kg\({}^{-1}\), respectively (Table 1). When the stored pig slurry was put into the vessel, distilled water (waste water from waters and washing water), equivalent to about 20% of the weight of the pig slurry, was evenly mixed with the pig slurry [38]. The initial moisture content of raw slurry was 87.3% for CTL and 86.6% for PFS. After adding water, the moisture content of CTL and PFS increased to 89.4% and 88.9%, respectively.  
Figure 1: Schematic diagram of the pig slurry pit simulator for measuring CH\({}_{4}\) and CO\({}_{2}\) emissions. PFS and CTL represent peat moss fed slurry and control, respectively.
# Nanoarchitectures in Management of Fungal Diseases: An Overview
## 4 Nanocarriers in Management of Fungal Infections
### Nanoparticles
#### 4.2.2 Metal Nanoparticles  
[PERSON] et al. fabricated zinc oxide (ZnO)-loaded NPs and found that a concentration of 9 mmol/L for the sample restricted the _E. salmonicolor_ growth, reflecting more significant antifungal activity [58]. [PERSON] and [PERSON] evaluated the antifungal activities of calcium oxide (CaO), magnesium oxide (MgO), and ZnO powders against _C. albicans_, _Rtizopus stolonifer_, _A. niger_, and _Saccharomyces cerevisiae_. The results showed that the CaO and MgO powders unveiled the antimicrobial properties against all fungi presented in the study [59].  
[PERSON] and coworkers reported the advantages of reverse micelles to fabricate <5-nm silver, gold, and gold-silver NPs with antifungal activity against five Candida strains accountable for several infections/diseases. Developed highly reactive and monodispersed NPs with <5-nm sizes showed great antifungal action against C. _krusei_, _C. parapsilosis_, _C. glabrata_, _C. albicans_, and _C. guillermondii_ with MIC\({}_{50}\) < 0.7 ppm. Thus, NPs are considered promising nanoplatforms to form alternative therapies to treat fungal diseases/infections [60].  
[PERSON] et al. characterized and explored the potential of AgNPs against _A. oryzae_ and _Penicillium chrysogenum_. The antifungal drug terbinafine revealed lower MIC values (0.0063-0.0251 g/mL) for the experimental strains. Chemically fabricated AgNPs showed antifungal action against all _T. rubrum_ strains, whereas biologically fabricated AgNPs showed more activity than FLZ but less than ITZ, terbinafine, and chemically synthesized AgNPs. Hence, the outcomes showed that biologically developed AgNPs might be utilized as antifungal agents to treat dermatophytes [61]. Recently, [PERSON] et al. determined the antifungal potential of biogenic AgNPs produced by simvastatin and fungi (alone or in combination) against three toxic species relating to the genera Aspergillus section Flavi (_A. nomius_, _A. parasiticus_, and _A. flavus_) and Circumdati section (_A. melleus_ and _A. ochraccus_). These results showed significant advantages of combined AgNPs and simvastatin regarding the inhibition of fungal growth [62].  
[PERSON] et al. revealed the antifungal property of AgNPs on _C. albicans_ and _S. cerevisiae_. The study indicated that AgNPs have extensive antifungal activity compared to other antifungal agents, worth further research for clinical use [63].  
[PERSON] et al. introduced AgNPs in which ribose and sodium dodecyl sulfate were used as the reducing agent and stabilizer, respectively. While the outcome showed that stable NPs with 12.5 \(\pm\) 4.9-nm sizes exhibited more antifungal action against _Candidida_ spp. [64]. [PERSON] et al. researched the effective dose and shape of AgNPs needed to treat fungi present in the wastewater. The authors found that rod-shaped NPs resulted in the least fungi growth compared with cube-shaped NPs [65]. AgNPs are promising nanocarriers for drug delivery [66].
## 5 Experiments and Results
### Comparison Results
#### 5.3.2 Results of Comparison of Policy-Based Algorithms  
(1) _Easy: We test all algorithms on the original asymmetric heterogeneous map MMM2._ In Figure 6a, G-MAPPO and MAPPO converged to _WR_ 0.9 at about 7M training steps. COMA acquires a little _WR_ before the first 3M steps and then fails to learn effective policy. HetGPPO reaches _WR_ 0.6 with the largest variance. HAPPO reaches _WR_ 0.1 at the end of training, while MAT fails to learn any policy.  
(2) _Medium: We scale up ally units and increase enemy units for balancing._ In Figure 6b-e, G-MAPPO impressively outperforms all other baseline methods with high _WR_ and low variance. MAT becomes the second method in Figure 6b,c, but still fails in Figure 6d,e, indicating that the sequential decision-making architecture requires modification in heterogeneous and large-scale MARL problems. MAPPO is capable of acquiring a _WR_ of 0.2 to 0.4 with a relatively large variance. HAPPO completely fails in Figure 6c but has similar performance with MAPPO in Figure 6b,d,e. The performance of HetGPPO is unstable, which may be caused by the DTDE scheme. In Figure 6b, HetGPPO performs worse than MAPPO and HAPPO. In Figure 6c, HetGPPO performs better than MAPPO and MAT but still worse than G-MAPPO. In Figure 6d, HetGPPO is close to MAPPO and HAPPO. In Figure 6e, HetGPPO converged to local optimality with a large variance. COMA fails on all of the four maps.  
(3) _Hard: We double all units of both sides simultaneously._ Although most of the baseline algorithms achieve good performance in Figure 6b, only G-MAPPO can reach _WR_ 0.4 with low variance. As we have mentioned before, Figure 6f is the most complex and an extremely hard map that combines the complexity of heterogeneity and scalability. The outstanding performance of G-MAPPO indicates the effectiveness of the GAS and CGP loss. The _WR_ of HetGPPO is still unstable with large variance.  
In summary, G-MAPPO outperforms other baseline algorithms, also because G-MAPPO increases the cooperation between agents and solves the action semantic conflict problem. G-MAPPO increases the cooperation of agents better than MAPPO and HetGPPO with the help of CGP loss and the GAS architecture.
## 2 Materials and Methods
### Determination of Total Antioxidant Capacity  
The ABTS assay was performed following the method described by [PERSON] et al. [27]. ABTS radical cation was generated by reacting 25 mL of ABTS (7 mM) with 440 \(\upmu\)L of potassium persulfate (140 mM). The mixture was incubated in dark conditions for 12-16 h at room temperature. Prior to assay, ABTS radical cation was diluted with ethanol 1:100 to obtain an absorbance of 0.70 (\(\pm\)0.02) at 734 nm. The standard curve of prepared Trolox (5 mM) was constructed at different concentrations (0, 50, 100, 150, 200, 250, 300 \(\upmu\)M) employing ethanol. The assay was performed with 2 mL of ABTSTM working solution as the initial point of reaction (A\({}_{0}\)). Then, 0.1 mL of diluted sample extracts or Trolox standards were added and the absorbance was determined as (A\({}_{\mathrm{f}}\)). The initial absorbance (A\({}_{0}\)) and the final absorbance (A\({}_{\mathrm{f}}\)) (after 3 min) were read using spectrophotometry at 734 nm in a Perkin-Elmer UV/Vis Lambda 2 spectrophotometer (Perkin-Elmer, Rodgau-Jugesheim, Germany). The percentage of inhibition was calculated as:  
\[\%\,\mathrm{inhibition}=(1-(\mathrm{A}_{\mathrm{f}}/\mathrm{A}_{0}))\times 100 \tag{1}\]  
The antioxidant activity was determined using the Trolox standard curve and expressed as \(\upmu\)M trolox equivalents (TE).  
Oxygen radical absorbance capacity (ORAC) was determined according to the method previously detailed by [PERSON] et al. [28], with some modifications. The reaction was carried out in 75 mM phosphate buffer (pH 7), for a final reaction volume of 125 \(\upmu\)L. Fifty microliters of sample, loaded onto a 96-well microplate, were mixed with 50 \(\upmu\)L of fluorescein, and the mixture was preincubated at 37 \({}^{\circ}\)C for 10 min. Then, 25 \(\upmu\)L of AAPH solution was added rapidly, using micropipette multimode. The plates were immediatelyplaced in the reader Multilabel Plate Counter VICTOR3 1420 (Perkin-Elmer, Turku, Finland) and the fluorescence recorded every minute for 60 min with an excitation wavelength of 485 nm and emission wavelength of 528 nm. The phosphate buffer (as blank) and the Trolox (as standard) were used in this assay. Each extract was analyzed in five replicates, and the differences in areas under the fluorescein decay curve (AUC) between the blank and the sample were used to calculate the antioxidant activity.