esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation Modelling of Silicon Gated Field Emitter Based Electronic Circuits
## 2 I-V Characterization Experiment  
To establish a simulation model, I-V characterization experiments were carried out using a 1000 \(\times\) 1000 silicon-gated field emitter array (Si-GFEAs) [14]. The gap between each tip is 1 \(\upmu\)m, and the total number of tips are 1 million. These GFEAs include self-aligned polysilicon gates (gate aperture \(\approx\) 300 nm), and each tip is supported by silicon nanowires at the bottom, which have a diameter of 150 nm and a height of 10 \(\upmu\)m. These nanowires act as ballast resistors. Detailed fabrication methods and device descriptions can be found elsewhere [6]. The 1000 \(\times\) 1000 arrays were able to produce a current density greater than 100 A/cm\({}^{2}\)[14], which made these a clear choice for the experiments. I-V characterization experiments were executed in a high vacuum environment created in a stainless-steel test chamber. The high vacuum chamber is fitted with electrical feedthroughs and a three-axis manipulator probe arm, which was used to connect to the gate pads of the GFEAs. A Keysight B2902A source measurement unit was used to carry out the experiments. Elaborated test chamber setup and experimental details can be found in our previous works [7; 19].  
Figure 1 shows the experimental schematic structure. For the output characteristics, the V\({}_{\text{C}}\) was swept from 0 to 200 V, and for the transfer characteristics, the V\({}_{\text{G}}\) was swept from 0 to 40 V. For all the tests, V\({}_{\text{E}}\) was kept at the ground. The results were used to develop an appropriate VT model, and the details can be found in our previous work [17], where we developed, simulated, and experimentally validated a 152 kHz Colpitts oscillator. This validation provides confidence in our circuit design.  
Figure 2a shows the transfer characteristics comparison data of experiment and model for a collector voltage of 200 V, and Figure 2b shows the output characteristics of experiment and LTspice model for a fixed gate voltage of 30 V. From Figure 2a, it can be observed that the device turns on at a gate voltage of approximately V\({}_{\text{G, ON}}\) = 24 V and reaches > 0.225 mA  
Figure 1: Experimental schematic model showing emitter (E), gate (G), and collector (C).  
of current at V\({}_{\text{G}}\) = 30 V. From Figure 2a, it can be observed that experiment and model agree well. Figure 2a (inset) shows the Fowler-Nordheim (F-N) [11] plot for the gate sweep of 0 to 30 V. From Figure 2b, it can clearly be observed that the data of the experiment and model results match well. Please note that, for the model development, a gate voltage of 40 V was used, and the details can be found in our previous work [17].
## 2 X-Ray Detection of Weld Defect Features and Dataset Construction  
Weld defects in pipeline joints can lead to local thickness variations, causing differences in X-ray intensity transmission across different areas. This results in varying levels of blackness in the film or Digital Radiography (DR) image, allowing the X-ray detection system to identify whether defects exist at the weld joint. By analyzing the difference in Blackness Difference between a small region and its adjacent area, the presence of a defect at the weld can be determined. The X-ray weld defect images studied in this paper were obtained through on-site captures by the author and provided by the Guangxi Special Equipment Inspection and Research Institute. The dataset contains 1234 images, which include typical defects such as circular, linear, non-fusion, non-penetration, and crack defects, with some images containing multiple types of defects. The X-ray inspection equipment used for capturing weld defect images is shown in Figure 1.  
Different defect types exhibit distinct characteristics in X-ray detection images, as shown in Figure 2: (1) circular defects appear as circular or elliptical dark spots, with an aspect ratio of less than or equal to 3; (2) linear defects appear as irregularly shaped dark strips or blocks, with an aspect ratio greater than 3; (3) non-fusion manifests as black lines or strips of varying width, usually located away from the center of the weld; (4) non-penetration appears as discontinuous or continuous black lines with neat contours on both sides, typically located in the middle of the weld; (5) cracks appear as sharp-contoured black lines or threads with fine serrations, generally thinner at the ends and wider at the center. The original image dataset comprises 572 images of circular defects, 396 images of linear defects, 200 images of non-fusion, 142 images of non-penetration, and 135 images of cracks. It was subsequently split into training, validation, and test subsets in a ratio of 8:1:1. To enhance the robustness of the model, each image in the divided dataset was augmented using three local data augmentation techniques: horizontal and vertical flipping, random rotation, and random scaling. After augmentation, the dataset consisted of 4936 defect-containing images. In addition, during model training, the Mosaic online  
Figure 1: X-ray inspection of the scene: (**a**) weld appearance image; (**b**) X-ray equipment.  
data augmentation technique was employed. This method combines four images into one through stitching and blending, incorporating random rotation and scaling, which effectively increases the diversity of a single image and improves training efficiency. Defect types in all images were annotated using the MakeSense tool, with the labeling results independently validated by non-destructive testing (NDT) engineers from the Guangxi Institute of Special Equipment Inspection and Research.
## 2 Materials and Methods
### Statistical Analyses  
The relative abundance of BPH, WBPH, or GLH and of the free-living stages of egg parasitoids, as well as parasitism rates estimated using sentinel plants with different herbivore eggs or under different honeydew treatments were analyzed using Generalized Linear Mixed Models (GLMM) with Template Model Builder (TMB) or Generalized Linear Mixed-Effects Models (GLMER) adequate for the split-plot field design. In cases of overdispersion, the beta-binomial distribution was fitted to the GLMMTMB models. After checking for overdispersion and randomness of the residuals, the most suitable models were selected according to the value of the Akaike Information Criterion (AIC) for each model. Parasitism rates were estimated as the number of parasitized eggs divided by the total numbers of emerged nymphs and the total number of parasitized eggs (including all parasitoids). This eliminated from the calculations possible non-viable eggs, or eggs consumed by predators. Because parasitism by different species is contemporaneous, we assessed the effects of calculating parasitism using only available eggs for corresponding species (e.g., emerged planthoppers and eggs parasitized by _Anagrus_ spp. as the denominator for the calculation of _Anagrus_ spp. parasitism rates). Calculations based on available eggs increased calculated parasitism rates, but did not affect patterns across varieties or nitrogen levels; therefore, we used viable eggs as the denominator in calculations. This assumes that parasitism by conspecific or congeneric parasitoids did not limit parasitism by any species. This also allowed us to sum parasitism rates from different parasitoid species to estimate total mortality of eggs from all parasitoids combined. We also checked for multicollinearity of parasitism rates by _Oligosita_ spp. and _Anagrus_ spp., this examination of the data also found no correlations; therefore, egg parasitism by these two species was analyzed separately. Total honeybee production and the proportions of xylem-derived honeydew were examined using Generalized Linear Models (GLM) with a Gaussian and Binomial distribution, respectively. All data were analyzed using R v4.0.2 [56].
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.19 Dynamic TOS  
Dynamic TinyOS [73] is an extension to TinyOS that allows TinyOS components (grouped into ELF objects) to be downloaded individually, and dynamically linked into a TinyOS application. Modifications to the _nesc_ compiler maintain the component boundaries, and at run-time a new Tiny Manager components dynamically links in replacement objects--and the components they contain. A file system is provided for downloaded ELF objects, and a global symbol table is maintained to resolve dependencies. An _Interrupt Router_ allows for interrupt service routines (ISRs) to be replaced. The results show significant energy benefits over a monolithic TinyOS update using Deluge, with a minimal run-time performance impact.  
Dynamic TOS provides no additional features related to autonomic support when compared with TOS, but the increased granularity of updates, and performance improvement, may be a useful feature in this regards.
###### Abstract  
The accurate identification of forest tree species is important for forest resource management and investigation. Using single remote sensing data for tree species identification cannot quantify both vertical and horizontal structural characteristics of tree species, so the classification accuracy is limited. Therefore, this study explores the application value of combining airborne high-resolution multispectral imagery and LiDAR data to classify tree species in study areas of different altitudes. Three study areas with different altitudes in Muyu Town, Shennongjia Forest Area were selected. Based on the object-oriented method for image segmentation, multi-source remote sensing feature extraction was performed. The recursive feature elimination algorithm was used to filter out the feature variables that were optimal for classifying tree species in each altitude study area. Four machine learning algorithms, SVM, KNN, RF, and XGBoost, were combined to classify tree species at each altitude and evaluate the accuracy. The results show that the diversity of tree layers decreased with the altitude in the different study areas. The texture features and height features extracted from LiDAR data responded better to the forest community structure in the different study areas. Coniferous species showed better classification than broad-leaved species within the same study areas. The XGBoost classification algorithm showed the highest accuracy of 87.63% (kappa coefficient of 0.85), 88.24% (kappa coefficient of 0.86), and 84.03% (kappa coefficient of 0.81) for the three altitude study areas, respectively. The combination of multi-source remote sensing numbers with the feature filtering algorithm and the XGBoost algorithm enabled accurate forest tree species classification.  
Keywords:different altitudes; multispectral image; LiDAR; machine learning; tree species classification
## 3 Results and Discussion
### Analysis of Surface Microtexture and Roughness
#### 3.1.1 Analysis of Cu-SiC Surfaces Processed by Abrasive Materials
It is extremely difficult, and sometimes impossible, to control such characteristics over a surface with a heterogeneous elemental composition. The wetting properties depend on the elemental composition, and the wetting determines the three-phase contact line (TPCL) movement and the dynamic contact angles [53; 54]. On the surface of mechanically processed Cu-SiC composite materials, local sites containing mainly either Si and C or Cu will randomly replace each other along the trajectory of TPCL movement. The surfaces of SiC and Cu have different wetting properties. It is concluded that under conditions of liquid spreading over mechanically processed Cu-SiC surfaces with a heterogeneous elemental composition, it is impossible to control the spreading characteristics.
## 4 Application Example
### Motion-Based Design of Passive Damping Devices under Uncertainty Conditions
The optimization process can be summarized in the following steps: (i) an initial random population of parameter vectors is generated; (ii) the objective function is evaluated for all the individuals; (iii) a new population is created using three mechanisms (selection, crossover, and mutation); (iv) the objective function is evaluated for the individuals of the new population; (v) the steps (iii) and (iv) are repeated until some convergence criterion is met. The following parameters were considered for the considered genetic algorithms: (i) an initial population of 5 individuals; (ii) a crossover fraction of 0.4; (iii) a mutation fraction of 0.9; and (iv) a total number of iterations equal to 6. As result of the optimization process, a Pareto front was obtained. Subsequently, a decision-making problem should be solved, the selection of the best solution among the different elements of the Pareto front. In order to address this problem, an additional condition was included. Among thedifferent elements of the Pareto front, the element of the Pareto front with a lower value of the damping coefficient was selected as best solution. The commercial software Ansys [57] and Matlab [64] were used to solve this design problem. The results of the optimization problem are summarized in the next sub-sections.
# Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms
## 3 Hierarchical Task Assignment and Path Finding
### HTAPF: Principles and Methods
#### 3.2.1 World Model  
As previously stated, we employ a world model that consists of a hierarchical description of the discretized space, which is represented by a generic \(k\)-d tree. Such a hierarchical description is general enough to be automatically constructed, without requiring knowledge about the structure of the work area in terms of rooms, corridors, and connecting spaces. In the following, we consider \(k=4\), that is, a quad-tree where each leaf node relates to a small area where task may require execution, and the root node corresponds to a region representing the whole \(N\times N\) environment. Leaf and non-root nodes are assigned following the classical quad-tree generation as illustrated in Figure 2 (left and  
where the term \(C^{\prime}(T)\rightarrow[0,1]\) is the normalized cost for robot \(r\) to reach task \(T\), as estimated by the motion planner, computed as the number of actions needed to move from the robots' current location to its target location, normalized by the maximum lower bound cost for the given environment. The term \(H_{FW}(r_{o},T)\rightarrow[0,1]\) is a heuristic function that estimates the normalized lower bound cost for any other robot \(r_{o}\) to reach task \(T\) or its associated area, computed using the Floyd-Warshall algorithm (also see Section 3.2.3). If, for any reason, Equation (3) is not defined, we set \(U^{\prime}(a_{j}^{n})=0\). In other words, the utility of an area \(a_{j}^{n}\) is higher the more tasks are present, the lower the cost for reaching them (as estimated by the motion planner), and the lower the number of other robots that can potentially execute the same tasks. In case of non-perfect communication, \(r\) might not have knowledge of the full range of tasks and other robots, in which case the utility function only considers the set of known robots and tasks.
# Situational Awareness Classification Based on EEG Signals and Spiking Neural Network
## 4 Experimental Results  
### Dataset  
The dataset used in this study comprised a total of 25 h of EEG recordings collected from five healthy volunteer participants, all of whom were students, engaged in a low-intensity control task [24]. This task involved controlling a computer-simulated train using the \"Microsoft Train Simulator\" program. Each experiment required the participants to control the train for 35-55 min along a primarily featureless route. The study focused on three mental states: focused but passive attention, unfocused or detached but awake, and drowsy.  
The first mental state, \"focused\", involved participants passively supervising the train while maintaining continuous concentration and focus without active engagement. The second state, \"unfocused\", was characterized by participants being awake but not paying attention to the screen, representing a potentially dangerous state which should trigger an alert. This state, which is difficult to detect through external cues such as video monitoring, requires sophisticated discrimination methods. The third state involved explicit drowsiness [24].  
Each participant controlled the simulated train for three distinct 10 min phases during each experiment. Initially, the participants focused closely on the simulator's controls. In the second phase, they stopped providing control inputs and ceased paying attention to the screen while remaining awake. In the final phase, the participants were allowed to relax, close their eyes, and doze off. Each participant completed seven experiments, with a maximum of one per day. The first two experiments were for habituation, and data collection was conducted during the last five trials. To facilitate the transition to the drowsy state, all experiments were conducted between 7:00 p.m. and 9:00 p.m. The raw data from these experiments are available to researchers on the Kaggle website ([[https://www.kaggle.com/datasets/inancigdem/eeg-data-for-mental-attention-state-detection](https://www.kaggle.com/datasets/inancigdem/eeg-data-for-mental-attention-state-detection)]([https://www.kaggle.com/datasets/inancigdem/eeg-data-for-mental-attention-state-detection](https://www.kaggle.com/datasets/inancigdem/eeg-data-for-mental-attention-state-detection))).
## 4 Performance Evaluation
### Cluster Stability  
The stability of IVC links determines the protocol performance at dynamic mobility. Otherwise, the MDMs could not be disseminated successfully. The number of CHs and CMs switching to adjacent clusters shows the stability of a cluster. Only three CH and five CM switches were found at the maximum speed of 60 m/s, as depicted in Figure 8. Besides this, the CH and CM switches are very low at the mobility of less than 50 m/s, which shows the creation of a stable cluster formation.  
Figure 8: (**a**) UGAVs-MDVR mobility versus the number of CH switches; (**b**) UGAVs-MDVR mobility versus the number of CM switches.  
Figure 7: (**a**) UGAVs-MDVR delay with respect to mobility; (**b**) UGAVs-MDVR delay with respect to the number of clusters.  
The cluster's stability has been found by varying the mobility of the vehicles. The CH switching and CM switching are less, which illustrates the strong inter-vehicular communication links that result in efficient MDM dissemination among vehicles. The stability of the clusters and the consistent links of the nodes are directly dependent upon the quick organization of the network and the instant updating of the node's records. Therefore, the network remains maintained and consistent for efficient dissemination of MDMs, irrespective of the dynamic mobility of the nodes. Hence, the proposed protocol has achieved a stable and organized network by keeping the rapid update of cluster records, reducing the CH and CM switching.
# Research on Multiple-Axis Contour Error Suppression Method Based on Composite Layered Control
## 2 Design of a Single-Axis Controller Based on Sliding-Mode Disturbance Observer  
This section analyzes and studies the stability and tracking error of single-axis servo control and proposes a single-axis controller based on an improved S-type function sliding-mode disturbance observer. It can effectively improve the dynamic response performance,steady-state performance, robustness, and chatter phenomenon generated during the control process of the servo control system, thereby reducing the motion error of each servo axis and improving motion stability.
## 3 Augmented Reality in Industry
These systems comprise visualization devices that can be classified into smart glasses or head-mounted displays (HMD), all-silicon microdisplay [10], smartphones, projectors, tablets, and TVs or monitors. According to [11], graphical elements such as images, icons, animations, and other visual resources used in augmented reality interfaces to provide information about an object or process, guide the user during a task, or provide real-time visual feedback, have certain limitations for industrial use. For instance, the occlusion of graphical elements can limit situational awareness and operator safety. Additionally, the choice of these elements can be influenced by the type of display device used. Conversely, these authors recommend using such elements to enhance efficiency and safety in industrial environments. In this context, the data presented in Figure 5 correspond to the number of publications on AR and visualization devices (smart glasses/HMD, smartphones, projectors, tablets, and TVs) associated with the terms augmented reality and industry. In this sense, the sample revealed that the use of smart glasses/HMD and smartphones is equally frequent in augmented reality applications in industry, with 70 occurrences each. Tablets come in third place with 57 events, followed by projectors with 20, and TVs with only 8 occurrences. These results suggest that most augmented reality applications in industry are being developed for mobile devices such as smartphones, tablets, and smart glasses/HMDs. This can be explained by the fact that these devices offer greater portability and flexibility for use in different environments and situations. There is also an inference of greater availability of smart glasses and decreased costs. Moreover, it is interesting to note that the use of projectors and TVs in augmented reality applications is relatively low. This may be due to the mobility limitations of these devices and the need for a physically larger space for their use. Figure 4: Countries with the highest number of publications. Considering the publications involving AR in industry, [12] revealed that some of the challenges associated with AR implementation in the sector are low adoption rates, safety and operability challenges, and usability and interface issues. However, despite these challenges, augmented reality technology emerges as one of the nine enabling technologies that will drive the transformation supported by the Industry 4.0 initiative and is regarded as a critical technology for the development of intelligent manufacturing. The use of augmented reality in industry can bring various benefits, such as improving worker efficiency and productivity, reducing errors and rework, enhancing workplace safety, reducing training time for new employees, improving equipment maintenance and repair, and increasing customer satisfaction, among others [11].