esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Proton and Neutron Irradiations
#### 3.1.1 Radiation Induced Absorption  
During the proton irradiation, the Fibos OGA was employed as a tool to measure the spectral characteristics of the FBG as well as track the wavelength of the sensors during the test, see Figure 4. Radiation induced absorption (RIA) was assessed as a comparison between the maximum FBG reflectivity immediately prior to the first dose and that measured immediately after each dose. The analysis of test results concluded that there were no significant changes to the reflectivity of the FBGs during any of the proton irradiations. Any changes to reflectivity over the course of the experiment were found to be within the expected range observed during disconnecting and reconnecting of the fiber optic connectors (\(\pm\)1 dB). Neither the UV, FS nor RAD test specimens demonstrated any statistically significant relationship between dose or dosage rate and RIA. This result was in agreement with the observations of [PERSON] et al. who reported \(\sim\)0.1 dB/m of RIA with 50 times higher dose rates and \(\sim\)8.5 times higher accumulated dose. With the presented test specimens only exposing 1 m of fiber to radiation, a 0.1 dB loss in reflectivity would have been not observable given the repeatability error of the system.
## 2 CFD Simulation and FMT Validation
### CFD Simulation  
Three-dimensional CFD simulation was carried out with ANSYS 17.1, ANSYS Inc. [26]. The meshing model was established with the real scale. The total mesh number of the geometry was about 5.9 million. Due to the limitation of the hardware, the grid convergence analysis was only carried out on the meshing geometry of the AIG and the hybrid grid. The corresponding results are shown in Appendix A. Figure 2 shows the meshing geometry of the representative area.  
The model is considered to be adiabatic and the chemical reactions are not considered here. A standard \(k\)-\(\varepsilon\) model was used to deal with the turbulence and a SIMPLE (Semi-Implicit Method for Pressure-Linked Equation) algorithm was used to solve the Navier-Stokes equation. Moreover, a porous media model was used as a substitute for catalyst layers and the permeability term was eliminated considering the honeycomb structure of the catalyst layers. The corresponding boundary conditions are given below:  
1. The flow distribution of inlet was set to be uniform as the sufficient resistance provided by the economizer. The velocity was 14.75 m/s under 100% BMCR condition;
2. The NH\({}_{3}\) concentration was diluted to 5% and the corresponding average injection velocity was set to 20 m/s based on the NH\({}_{3}\)/NO\({}_{x}\) molar ratio of 1:1;
3. An atmospheric pressure was set at the pressure outlet and the total pressure loss of two catalyst layers was set to 420 Pa;
4. An observation plane was located 0.2 m above the porous media. As the overall height of the tower type SCR-deNO\({}_{x}\) facility is about 100 m, the flue gas velocity and NH\({}_{3}\) concentration distributions at the observation plane can be regarded as those of the catalyst entrance.  
Iteration continued until the residuals had converged below 10\({}^{-6}\). Contours of gas velocity and NH\({}_{3}\) concentration at the observation plane were generated respectively. Thereafter, the corresponding RSDs and the total pressure loss were calculated.
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 2 Low-Resource Language Speech Recognition
### Methods of Low-Resource Language Speech Recognition  
As mentioned above, low-resource language speech recognition encounters three issues: data scarcity, uncertainty in writing and pronunciation, and the individuality of each language. Most of the current research focuses on the data scarcity. The intuitive way to solve this issue is to collect more data. Recently, crowdsourcing has become a popular way to collect a large volume of data at a low cost [1; 38]. Despite the clear importance of data collection, in this review paper, we focus on technical approaches to the low-resource language problem and summarize several representative methods, as shown in Table 1.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Method** & **Summary** \\ \hline Data augmentation & Various data augmentation approaches to simulate complex behavioral and/or environmental variety. \\ \hline Phoneme mapping & Construct correlations between phonemes of different languages, so that one language can borrow models from other languages. This may construct a system for a new language even without any training data. \\ \hline Feature sharing & Resorting to the commonality of human pronunciation, employ multilingual data to train generic acoustic feature extractor, so that the data required for training the acoustic model for a new language can be significantly reduced. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Representative methods of low-resource ASR.
## 2 Materials and Methods  
### Algal Strain and Experimental Setup  
The experiments were performed with the green alga _Monoraphidium pusillum_ (Chlorophyceae, Sphaeropleales) available in the Algal Culture Collection of the Department of Hydrobiology, University of Debrecen (ACCDH-UD0911).  
The algae were cultured in a hybrid-type photobioreactor with 2800 mL full volume and a 4000 mL Erlenmeyer flask in the same final volume as the hybrid photobioreactor (2800 mL).  
The applied hybrid-type photobioreactor was the combination of a fermenter and a helical-tubular reactor. The central part was a conventional fermenter-type reactor body made of glass (1 L in volume). A glass spiral (i.e. a helical tubular reactor) with 20 mm inner and 24 mm outer diameter and \(\sim\)2 L in volume was placed around the central reactor body (Figure 1). Mixing was provided by a sparger in the central reactor body, responsible for gas supply. The stirring motor was not installed. The whole culture was circulated in the glass spiral by a peristaltic pump (ISMATEC ECMINE VC-380, Cole Parmer GmbH, Wertheim, Germany). Illumination was provided by a removable illumination unit: 8 fluorescent lamps (18 W) with 1200 lm luminous flux per lamp in the unit, which was mounted between the central fermenter body and the glass spiral. (Figure 1). The default settings allowed only continuous illumination. The hybrid reactor had a built-in turbidity measuring system. The sensor used light ranging from 840 to 910 nm. The measured values are provided as Absorbance Units (AU). The measurement range is 0-6 AU. The default settings automatically increased the irradiation (given in % of lamp performance) in parallel with the increasing turbidity. The culture temperature was 24 \({}^{\circ}\)C.  
The Erlenmeyer flask was considered a column or tank-type photobioreactor, and the culture was bubbled with sterile air (it served as carbon source and mixing) and kept under continuous irradiation (40 \(\upmu\)mol photons m\({}^{-2}\) s\({}^{-1}\); Osram Lumilux T8 L 36W/865 Cool Daylight and 36 W/830 Warm White fluorescent lamps) at 24 \({}^{\circ}\)C.  
Cultures were maintained in Jaworski's medium [41]. Biomass and lipid production was studied in control and in nitrogen depleted cultures (with 75, 50, and 25% nitrate contents considering 100% the nitrate content of the control culture) in both reactor types. The duration for each experiment was 7 days.
## 2 Related Research Work
The PSNR measures the signal power by dividing the maximum power by the distorted signal power, whereas, the SNR is the ratio of signal power to signal noise power. The SSIM and HSSIM measure the structural similarity after data compression. The Effectiveness column explains the level of significance of the explored study. We categorized the significance into three levels, Significant (Sig. ), Not Significant (Not Sig. ), and Mixed. The detail of these levels is explained in the caption of Table 1.  
In this paper, we proposed an ITE framework that dynamically selects the haptic codec scheme corresponding to the level of congestion in the network while attaining the desired QoE performance under variable network resources and required QoS.
# Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS
## 2 Materials and Methods
### Operation of the Training Environment  
Each participant interacts through a personalized avatar. These avatars represent users in the virtual environment and are the primary means of interacting with the system, simulation scenarios, and other participants. Through their avatars, users immerse themselves in various simulated scenarios that replicate real-world cybersecurity situations. These can range from managing a phishing attack to responding to a large-scale security breach. Using OpenAI, the environment simulates realistic interactions and challenges [17]. Avatars can interact with elements of the environment, such as accessing virtual systems, analyzing security data, and making decisions to mitigate threats. The system uses artificial intelligence algorithms to adapt scenarios to users' skills and experience levels. This allows each training session to be relevant and challenging for the individual, regardless of their skill level [18].  
In some scenarios, avatars can work as a team, allowing user collaboration. This is essential to simulate incident response in a real team environment, where communication and collaboration are crucial. The system provides real-time feedback as users interact with the environment through their avatars. This includes evaluations of decisions made and advice on improving responses to threats. Using AWS infrastructure, the environment can scale to accommodate multiple users and complex scenarios while maintaining data security and integrity.  
After each training session, a detailed analysis of the user's performance is conducted. This analysis helps identify areas for improvement and adjust future training scenarios to address these deficiencies [19]. The environment is regularly updated with new scenarios, reflecting the latest trends and threats in cybersecurity. This ensures that training remains relevant and challenging [20].  
Figure 1 presents the various stages involved in the cybersecurity training environment that we developed, integrating OpenAI artificial intelligence with the AWS infrastructure. Each step represents a critical stage, beginning with immersion through avatars and moving through several critical processes such as simulation scenarios, realistic interaction, and adaptive learning [21]. Arrows indicate how the process flows from one stage to another, highlighting collaboration and role-playing, real-time feedback, and integration with AWS. The diagram culminates with the post-training analysis and scenario update, ensuring a dynamic and updated approach to preparing cybersecurity professionals.
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 2 Related Work
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Platform** & **Advantages** & \multicolumn{2}{c}{**Disadvantages**} \\ \hline Unmanned ground & \(\bullet\) & Low operational risk & \(\bullet\) & Slow process \\ vehicle & \(\bullet\) & Continuous operation & \(\bullet\) & Requires good terrain accessibility \\ \hline \multirow{4}{*}{Fixed-wing drone} & \(\bullet\) & Fast speed operation & \(\bullet\) & High cost \\  & \(\bullet\) & Cover long distances & \(\bullet\) & Inability to hover in one spot \\  & \(\bullet\) & High payload capacity & \(\bullet\) & Requires a safe and flat landing area \\  & \(\bullet\) & Longer flight duration & & \\ \hline \multirow{4}{*}{Multi-rotor drone} & \(\bullet\) & Ability to hover in one spot & \(\bullet\) & Less power-efficient \\  & \(\bullet\) & High-resolution imagery & \(\bullet\) & Short flight duration within each charging cycle \\  & \(\bullet\) & High maneuverability & & \\  & \(\bullet\) & A high degree of controllable & & \\ \hline \multirow{2}{*}{Robotic blimp} & \(\bullet\) & Ability to hover in one spot & \(\bullet\) & Difficult to control autonomously \\  & \(\bullet\) & Requires no energy for maintaining altitude & \(\bullet\) & Low payload capacity \\  & \(\bullet\) & High operational cost & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of common unmanned inspection platforms. To accomplish inspection missions efficiently, drone swarms need to communicate with each other and coordinate the allocation of tasks. By using multiple drones with coordination and cooperation capabilities, drones can inspect different parts of the infrastructure simultaneously, thus saving time on the inspection [28; 29]. Today, drones are typically applied as piloted single-drone systems without internet connectivity and access to cloud services. Therefore, the swarm operation is limited to centralized coordination with no inter-drone coordination. Comparing our study to related studies in the literature, we find that our proposed UAS aligns well with widely accepted concepts. To complement existing reports, we propose a system design and advance the existing knowledge base by providing specific technology choices with proof-of-concept design and validation of swarm-based UASs. Our literature survey did not reveal papers that discuss UAS design holistically as in our study. A holistic approach to UAS system design is important to circumvent unnecessary sub-optimization and to provide better system performance as a whole taking into account the concept of operation, the design of individual components (e.g., hardware and software), and their interplay.
# The Method of Multi-Camera Layout in Motion Capture System for Diverse Small Animals
## 3 Experimental Verification  
### Materials and Methods  
#### 3.1.1 Experimental Animals  
One to two years old adult geckos taken from Guangxi Autonomous Region, China (_G. gecko, Linnaeus_) (53.3 \(\pm\) 1.1 g mass, mean \(\pm\) s.d., snout-vent length: 146.6 \(\pm\) 3.8 mm, \(N\) = 3) and adult spiders from Hainan tarantula (_Haplopelma hainanum, Ornithoctoninae_) (11 \(\pm\) 1.8 g mass, body length: 90 \(\pm\) 10 mm, \(N\) = 3) were used to do the experimental verification. These animals were kept in a special room which simulates the natural environment of gecko habitat, maintaining an indoor temperature of 25 \(\pm\) 2 \({}^{\circ}\)C and humidity of 70-85%.  
Before the experiments, the reflective sticker with uniform shape (circular shape), size (diameter of 5 mm) and material was used as markers, which were pasted on the back of the gecko and spider's body and the joints of the legs (Figure 5a). Since the size, the motion range and the exercise frequency of the gecko body was pretty different that of the legs, we selected two sets of sample markers for statistical analysis and one set of the markers were on the head (snout-M\({}_{\text{g1}}\) and head vertex-M\({}_{\text{g2}}\)), another set of markers were on the leg (hip joint-M\({}_{\text{g3}}\) and wrist joint-M\({}_{\text{g4}}\)) [33]. The distances between the two markers on the head and on the leg were \(L_{h}\) and \(L_{l}\) respectively. Spiders are a typical kind of exoskeleton animals with multiple joints on the feet and three markers on the right rear leg (M\({}_{\text{s1}}\), M\({}_{\text{s2}}\), M\({}_{\text{s3}}\)) on the tibia, the metatarasus and the tips of the legs [26] were selected to measure two kinds of lengths (\(L_{T}\) and \(L_{PT}\)).  
Figure 5: Experimental animals and setup (**a**) Experimental animals with markers on the body and legs (above: a spider; lower: gecko;) and the markers defined need data processing. for data processing; (**b**) Experimental equipment (including multi-camera unit modules), object motion plane, 3D motion reaction test platform, camera fixed frame and data transmission and processor); (**c**) Partial enlargement of the multi-camera unit module (including 4 cameras: C1, C2, C3 and C4) and a standard calibration bar (A black link device with two markers P\({}_{\text{b1}}\), P\({}_{\text{b2}}\) fixed) and an L-shaped calibration block (Installed with 4 vertical markers P\({}_{\text{L1}}\), P\({}_{\text{L2}}\), P\({}_{\text{L3}}\), P\({}_{\text{L4}}\)) used as the calibration tools in the middle of the picture.
## 2 Electric Field Characteristics of Insulators under Complex Operating Conditions
### Space Electric Field Distribution of Insulators under Path Tilt  
As shown in Figure 7, in the practical application scenario of the insulator strings of overhead transmission lines, due to comprehensive stress, installation mode, and other factors, insulator strings may be subject to \"sagging\" similar to the conductor, while the traditional detection path of UAV is mostly straight, so it is necessary to analyze the influence of insulator string \"sagging\" on the detection results.  
In the Figure 8, it is assumed that the detection distance is 300 mm, the red line represents the actual detection path of the detection device, and the blue line represents the \"sagging\" of the insulator string due to stress and the actual electric field detection path to be measured due to the natural sagging of the insulators. This situation is bound to have a certain influence on the electric field measurement results. This paper sets \"sag angle\" \(\delta\) to \(165^{\circ}\) and proposes a method of adding path interest points at the bottom of the arc to reduce the influence. The simulation results are as follows Figures 9 and 10.  
Figure 8: Traditional detection path.  
Figure 7: “Sag” phenomenon of insulator string in operation state.  
Figure 9: Optimized detection path.  
It can be known through a simulation study that under the detection distance of 300 mm, when the \"sag angle\" \(\delta\) is set to 165 and the detection path is straight without considering sag, the electric field strength of the first to tenth insulator strings is higher than of the detection path considering sag, and natural sag in the middle of insulator strings has significant influences on the electric field distribution of the insulator string, with a maximum deviation of about 4.8%. Therefore, in the process of electric field detection with contactless devices, it is necessary to improve detection means, thus avoiding the natural drop in the middle of insulator strings affecting electric field detection results. Combined with the simulation results, it can be seen that the maximum error of the measured electric field distribution is reduced to 0.4% after optimizing the path. Therefore, the error caused by sag can be effectively reduced by increasing path interest points.
# Experimental Study on Femtosecond Laser Processing Performance of Single-Crystal Silicon Carbide
###### Abstract  
Femtosecond laser processing technology offers a promising technique for the preparation of micro and nanostructures of single-crystal silicon carbide (SiC), thanks to its high precision and non-destructive processing. However, further research is needed to optimize processing parameters, as well as improve efficiency and quality of the process. This study conducts experiments to explore the effects of femtosecond laser ablation on single-crystal SiC. The influence and significance of parameters, such as fluence (F), repetition rate, scan speed (S), multipass scanning (c) and numerical aperture on the performance of grooves, including groove depth, groove width, heat-affected zone (HAZ) width, material removal rate (MRR) and side wall inclination angle, were studied. The results show that the influence of fluence and numerical aperture on groove depth, groove width, HAZ width, MRR and side wall inclination angle is very significant. The scan speed has a very significant effect on the groove depth, groove width, HAZ width and side wall inclination angle but has insignificant effect on the MRR. Repetition rate and multipass scanning have a very significant effect on groove depth, HAZ width, MRR and side wall inclination angle and a moderately significant effect on groove width. The experimental methods of increasing the aspect ratio and reducing the HAZ width were studied, and a significance analysis was carried out. Fluence, multipass scanning and \(z\)-layer feed have significant effects on groove depth, groove width, aspect ratio, HAZ width and MRR. The influence of polarization angle on groove depth, groove width, aspect ratio and MRR is insignificant.  
femtosecond laser; silicon carbide; processing performance
###### Abstract  
Aspect-level sentiment analysis aims to identify the sentiment polarity of specific aspects appearing in a given sentence or review. The model based on graph structure uses a dependency tree to link the aspect word with its corresponding opinion word and achieves significant results. However, for some sentences with ambiguous syntactic structure, it is difficult for the dependency tree to accurately parse the dependencies, which introduces noise and degrades the performance of the model. Based on this, we propose a syntactic and semantic enhanced multi-layer graph attention network (SSEMGAT), which introduces constituent trees in syntactic features to compensate for dependent trees at the clause level, exploiting aspect-aware attention in semantic features to assign the attention weight of specific aspects between contexts. The enhanced syntactic and semantic features are then used to classify specific aspects of sentiment through a multi-layer graph attention network. Accuracy and Macro-F1 are used as evaluation indexes in the SemEval-2014 Task 4 Restaurant and Laptop dataset and the Twitter dataset to compare the proposed model with the baseline model and the latest model, achieving competitive results.  
aspect-level sentiment analysis; graph attention network; feature extract Article