esa-sceva/satcom-chunk-collection huggingface dataset:
# Fractal Cardioid Slot Antenna for Super Wideband Applications
## 1 Introduction
The antenna in [23] is designed on FR-4 substrate with a standard thickness of 1.58 mm for frequencies up to 18 GHz achieving a bandwidth ratio of 13:1 at 10 dB return loss, with an electrical area of 0.17\(\lambda\)\(\times\) 0.37\(\lambda\). It is well known that the FR4 substrate has been commonly used for low frequency applications, but it has been recently applied successfully in the design of low-cost antennas at frequencies over 30 GHz, Refs. [24; 25; 26]. The antenna proposed in [25] uses the FR-4 substrate, although it is intended for operations up to 37 GHz, with an operating range of 11:1. However, the electrical area of the antenna is pretty large ( 0.32\(\lambda\)\(\times\) 0.34\(\lambda\)). The simplest method for increasing the antenna's bandwidth without increasing the antenna size is to modify the geometry of the antenna by using fractals [27]. The self-similarity property of fractal geometries causes multiband or broadband behaviors. A comparative study of these antennas can be found in [28]. Fractal antennas with good performances can be found in [29; 30], but these antennas do not cover the GSM and UMTS bands, in which a significant amount of EM energy is emitted. Considering the antenna size, the proposed antenna is more than three times smaller in terms of electrical dimensions than the antenna reported in [26]. This paper proposes a novel design of a slot antenna based on nested fractal geometry that exhibits ultra-wideband characteristics. The antenna is designed on inexpensive FR-4 substrate and is fed by coplanar waveguide (CPW) transmission line, which makes it uniplanar, very easy for fabrication and integration with MMICs. The use of fractals in the form of cardioids made it possible to cover all commercial frequency bands from 1.8 to 30 GHz while preserving the small size of the antenna because of the space filling property of the fractals. The proposed antenna can be used in energy harvesting systems, as well as in IoT, WLAN, mobile MIMO and satellite communication systems and radars.
## 2 Ontology Alignment Techniques
### Evaluation of Alignment Techniques  
Ontology Alignment Evaluation Initiative (OAEI) [20] is a yearly event, held since 2004, for the evaluation of ontology alignment systems. The main goal of OAEI is to provide a platform facilitating the evaluation of alignment systems, assessing and comparing the performance of automatic methods, and fostering collaboration between researchers developing alignment techniques. Evaluation consists of several problems (11 in 2009), including alignment of various ontologies, dictionaries, and thesauri, matching of ontologies with divergent vocabularies, and even matching of cross-lingual resources. Reports on evaluation results for years 2004 to 2009 [21] are available publicly. Although submitted results comprise only a small fraction of available alignment systems (16 systems were submitted in 2009), OAEI provides valuable insights into the performance of various automatic alignment systems in different circumstances and in different domains. Since the evaluation contest has already been running for six years, it is also possible to follow the improvement gains on a yearly basis. While tangible improvements can indeed be observed, apparently, it is also true that the improvements are diminishing from year to year despite the fact that the techniques are becoming significantly more sophisticated and more complex.
# Failure Analysis of Gantry Crane Slewing Bearing Based on Gear Position Accuracy Error
## 3 Analysis of Results
### Analysis of Experimental Results  
#### 3.2.1 Analysis of Displacement Results  
In this test, since the influence of other vibration factors such as noise during the test was considered, the acceleration of the object was digitally filtered. The velocity parameter was obtained after one integration, and the corresponding displacement parameter was obtained after another integration.  
From the displacement waveform after secondary integration (Figure 19), it can be seen that the maximum horizontal displacement value is 8.313 mm at the large gear ring of the slewing ring (channel 1-1 in the time course curve) and 11.215 mm at the small gear (channel 1-2 in the time course curve.) During the actual operation of the gantry crane, the displacement produced by the small gear and the large gear ring of the slewing ring is not consistent. Comparing the displacement test finite element displacement analysis results (Table 3), it is shown that the deviation between the displacement test value and the finite element analysis value is small and the deviation between the large gear ring and the small gear is essentially consistent. Thus, it can be considered that the test results are essentially reasonable. From the test results, it can be found that the displacement deviation value of the two test parts is 2.902 mm. On this basis, it can be understood that the pinion gear and the large gear ring produced an abnormal meshing relationship. Owing to the horizontal displacement at the pinion gear being larger, the extrusion of the upper part of the gear teeth of the large gear ring is produced.
# FD-LTDA-MAC: Full-Duplex Unsynchronised Scheduling in Linear Underwater Acoustic Chain Networks
## 3 Simulation Scenarios
### Linear UWA Chain Full-Duplex Network
#### 3.1.2 Medium Scale Scenarios  
Similarly, a medium scale scenarios are denoted by Medium_L_H, where L ranges from 50 to 100 km and H ranges from 25 to 100 hops. In the case presented here, pipeline lengths of 50 km and 100 km configured with 25, 50 and 100 hops are considered.
# A Review of Parallel Heterogeneous Computing Algorithms in Power Systems
## 3 PHC in Power Systems
### CPU Clusters, Fog and Cloud Computing, and FPGA
#### 3.2.2 Contingency Analysis  
Following transient stability analysis, contingency analysis stands as the second application where PHC technologies are the most used ref. [109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123]. The authors in ref. [121; 117] showed how the master-slave asymmetric communication model was used to analyze thousands of contingencies in large-scale power systems. The master schedules the algorithm tasks among slaves to evaluate all contingencies using proactive task scheduling and stealing methodologies to optimize the load balancing of workers (slaves). This methodology allows for the start of a contingency evaluation as soon as one work is finished without waiting for any instruction from the master. The communication between master and workers is achieved using MPI, where workers run in multi-threads in different processors.
# Revising the Observation Satellite Scheduling Problem Based on Deep Reinforcement Learning
## 3 Solving Method
### DRL-Based Method for Optimization
#### 3.2.2 Optimization with DDPG  
In 2015, [PERSON] et al. [44] proposed the first successful deep Q-network (DQN) frame in Atari games, with the main idea of mapping the state to action-value function by deep networks. However, the action taken is given by \(a_{t}=argmaxQ(s_{t},a)\), which means the DQN could handle discrete action space problems. In the optimization research domain, in  
Figure 4: Markov decision process in EOSSP.  
terms of the TSP model or VRP model problems, the agent is designed to make sequential discrete decisions, and the DQN has been applied successfully [27; 28; 29].  
COntrary to the DQN, the main idea of the DDPG is mapping state to policy (specific actions taken) directly. Therefore the DDPG could make continuous decision variables and has advantages in large-dimensional problems [45]. In this paper, the EOSSP is modeled as a time-continuous resource allocation problem, where the VTW is the crucial resource. The policy made is to decide a specific time for each observation task, and the action space is typically continuous, so it is suitable for the DDPG solution.  
The DDPG algorithm conforms to the actor-critic framework, which includes the actor network and critic network. The actor network is represented by parameters \(\theta_{\mu}\), which offers a strategy action distribution according to the current state. The critic network evaluates the current strategy by calculating the value function and its parameters are denoted by \(\theta_{q}\).  
Figure 5 illustrates the DDPG algorithm. The actor network outputs an action from a continuous action space, which converts the state space into the action \(a=\mu(s)\). In the critic network, the output \(Q(s,\mu(s))\) is learned by using the Bellman equation, which represents the approximation of the discounted total reward. In every step of the optimization training process, the actor network is improved by computing the gradient of the \(Q(s,\mu(s))\) function, which could be calculated by applying the chain rule [45]:  
\[\
abla Q(s,\mu(s))\approx\
abla_{a}Q(s,a)\
abla_{\theta_{\mu}}\mu(s) \tag{21}\]  
In order to ensure the scheduling network convergence, target networks are adopted to update parameters periodically. Correspondingly, \(\theta^{\prime}_{\mu}\) and \(\theta^{\prime}_{q}\) are defined to represent parameters of the target actor network and the target critic network.
# ScienceIoT: Evolution of the Wireless Infrastructure of KREONET
## 4 Current Services of ScienceLoRa
### Campus IoT Service for Research and Education  
ScienceLoRa provides network infrastructure not only for the IoT R&D in science applications but also for educational purposes for students. Currently, ScienceLoRa is collecting environmental data such as temperature, humidity, fine dust, harmful gas, and solar radiation, which are widely used for agriculture, energy, and environmental studies in cooperation with universities. There are three use cases of the ScienceLoRa-based campus IoT infrastructure. First, researchers can directly use data for their research projects. Second, researchers can build any customized research system, such as research facility monitoring, research environment management, and event alarm systems. Third, IT courses can use the ScienceLoRa infrastructure to develop devices in an embedded system class, analyze big environmental data in data science classes, and configure communication parameters in computer communication classes. The bottom right images in Figure 9 show the deployed ScienceLoRa in campuses including KISTI, Chonnam National University, and Kumoh National Institute of Technology.  
Researchers can build their services based on ScienceLoRa. Figure 15 shows two typical use cases. First, the real-time monitoring service visualizes the collected data so that researchers can observe real-time information. This type of service may be used in research facility monitoring and environmental monitoring. Second, the rule-based alarm service sends urgent messages to the user's smartphone, email, and web application when a critical event occurs. Researchers might use this alarm service in accident detection (e.g., fire, gas leakage, explosion) and invasion detection (e.g., sensitive research facilities and security equipment). Furthermore, we provide a data-sharing service for exchanging multi-sourced research data among researchers for developing cooperative research projects. The data-sharing system was developed based on Nextcloud [22], which is open-source client-server software that provides secure and convenient data file sharing, as shown in Figure 16.  
Figure 15: Examples of services using ScienceLoRa data. (**a**) Real-time data monitoring and (**b**) rule-based alarm service.
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 5 Rockburst Prediction Methods
### Machine Learning Methods  
Machine learning methods are utilized in some studies for rockburst prediction, taking advantage of machine learning's ability to handle nonlinear situations. [PERSON] et al. applied fuzzy mathematics and neural networks to create a rockburst prediction model trained with enhanced BP algorithm using typical rockburst data [172]. [PERSON] et al. proposed a method for predicting rockbursts based on a swarm optimization algorithm and general regression neural network (GRNN) [74]. To anticipate rockbursts, [PERSON] [173] utilized a radial basis function (RBF) neural network improved by a genetic algorithm. [PERSON] used a support vector machine (SVM) to classify long-term rockbursts for underground holes [4]. [PERSON] employed principal component analysis and a fuzzy comprehensive evaluation model to evaluate the possibility of a coal burst [174]. [PERSON] compared the prediction accuracy of 12 machine learning algorithms, including artificial neural network (ANN), distance discriminant analysis (DDA), support vector machine (SVM), Bays discriminant analysis (BDA), and Fisher linear discriminant analysis (LDA), among others [175], in long-term rockburst prediction using different rockburst indicators as input features and varying training sample sizes. The accuracy of 12 machine learning algorithms is shown in Figure 12a.  
Machine learning algorithms have been widely used in rockburst prediction, specifically for long-term prediction of potential bursts. This approach utilizes various algorithms, such as artificial neural networks, distance discriminant analysis, support vector machines, and more, to predict the occurrence of rockbursts based on rockburst indicators as input features. On the other hand, some researchers have employed machine learning in microseismic monitoring, a method that predicts rockbursts in the near future. The development of various machine learning algorithms for rockburst classification and prediction is illustrated in Figure 12b.
# An Observer-Based Active Fault Tolerant Controller for Vehicle Suspension System
###### Abstract  
This paper proposes an observer-based active fault-tolerant controller for a half-vehicle active suspension system subjected to the actuator fault as the nonzero offset fault. By constructing the augmented fault suspension system model, an \(H_{\infty}\) weighted output feedback controller was developed to improve the vehicle dynamics performances under a fault-free condition. Moreover, a robust observer was designed to make an accurate estimation of the fault information with the auxiliary diagnosis system and further to develop an \(H_{\infty}\) fault compensation controller, such that the closed-loop control system can eliminate the negative effects of the actuator faults on vehicle suspension performances. Finally, a numerical simulation investigation was provided to verify the effectiveness of the proposed controller under the random and bump road disturbances.  
active suspension system; fault estimation; fault compensation; fault tolerant control
## 2 Model Construction and Simulation Parameters
### Simulation Program  
The Grand Canonical Ensemble Monte Carlo (GCMC) method was used to simulate the adsorption of CH\({}_{4}\) at different temperatures, water contents, and mutual coupling. The Sorption module of Materials Studio was used, with Adsorption Isotherm selected for the task item, COMPASS selected for the force field, and pressures ranging from 0.01 to 10 MPa.  
(1) In the study of temperature effects, the simulation temperatures were set to 223 K, 243 K, 263 K, 303 K, 313 K, 343 K, and 363 K, and the adsorbent was a dry coal model with 0% water content.  
(2) In the simulation of different water content, the temperature was fixed at 303 K, and the adsorbent was set as a coal structure model with water content of 1.10%, 4.27%, and 8.31%, respectively.  
(3) In analyzing the coupling process adsorption, orthogonal tests were set up for different temperatures and water contents, and the primary and secondary order of the influencing factors were determined by polar analysis.  
The number of gas molecules adsorbed in a single crystal cell can be obtained by GCMC simulation. Formula (1) is replaced by the amount of adsorption commonly used in the experiment:  
\[V=1000\frac{N}{N_{A}\cdot M}, \tag{1}\]  
In the equation: \(V\) is the adsorption capacity, mmol/g; \(N\) is the number of gas molecules adsorbed in a single unit cell, moleculars/u.c; \(N_{A}\) is the Avogadro constant, 6.02 \(\times\) 10\({}^{23}\); \(M\) is the unit cell mass, g.  
Figure 1: Coal model construction: (**a**) Coal model; (**b**) lowest energy configuration; (**c**) coal structure unit cell.  
The isothermal data were fitted by the Langmuir Equation (2):  
\[V=\frac{abp}{1+bp}, \tag{2}\]  
In the equation, \(V\) is the adsorption capacity, mmol/g; \(a\) is the saturated adsorption parameter, mmol/g; \(b\) is the adsorption equilibrium parameter, MPa\({}^{-1}\); \(p\) is pressure, MPa.
# Image Analysis of Dynamic Brain Activity Based on Gray Distance Compensation
## 1 Introduction  
Functional brain imaging provides a means to reveal the relationship between activity in discrete neuronal and glial cell populations and animal behavior. With the development of genetically encoded indicators that change their fluorescence in response to cytosolic concentration of signaling intermediates, such as calcium, pH and cAMP (cyclic adenosine monophosphate), there is an unprecedented opportunity for studying cellular activity in a variety of physiological and pathological contexts. By assessing intensity changes in serial images acquired during different behaviors, activity patterns in the brain accompanying specific behaviors can be defined [1; 2; 3]. However, most studies have been performed on animals that are restrained beneath a microscope, limiting the extent of behaviors that can be assessed. Fiber-optic technology [4] has many advantages for in vivo studies of brain activity: it can be miniaturized, it is flexible, and it can carry information over long distances with minimal signal loss, allowing high resolution information to be obtained from the brains of genetically modified animal models engaged in specific behaviors. We recently reported the development of a fiber bundle-based fluorescence microscope that can image cellular activity in freely moving animals for long periods of time [5]. Dynamic changes in intracellular calcium levels in astrocytes, indicative of neuromodulatory signaling [3; 6; 7], are typically assessed by measuring the intensity change over time in user-defined regions of interest. Although this cursory analysis providesgeneral information about activity patterns, it can lead to the inaccurate detection of active regions, it is time consuming to select regions, and is susceptible to user bias.  
To increase throughput and improve reproducibility, we developed an automated analysis method for detection of fluorescence intensity changes in serial images. This approach involves three discrete steps to detect and assess intensity profiles for individual cells: (a) The weak emission from the activated cells are enhanced; then (b) the regions of maximum intensity are detected and the activity regions in serial frames are matched; and finally (c) intensity changes of serial frames are calculated and mapped to the active regions. We show that this approach greatly facilitates the measurement of cellular activity acquired in vivo using a fiber optic imaging device.
###### Abstract  
_Valsa pyri_-induced pear Valsa canker is among the most prevalent diseases to impact pear quality and yields. Biocontrol strategies to control plant disease represent an attractive alternative to the application of fungicides. In this study, the potential utility of _Bacillus atrophaeus_ strain HF1 was assessed as a biocontrol agent against pear Valsa canker. Strain HF1 suppressed _V. pyri_ mycelium growth by 61.20% and induced the development of malformed hyphae. Both culture filtrate and volatile organic compounds (VOCs) derived from strain HF1 were able to antagonize _V. pyri_ growth. Treatment with strain HF1-derived culture filtrate or VOCs also induced the destruction of hyphal cell membranes. Headspace mixtures prepared from strain HF1 were analyzed, leading to the identification of 27 potential VOCs. Of the thirteen pure chemicals tested, ibervern, hexanoic acid, and 2-methylvaleraldehyde exhibited the strongest antifungal effects on _V. pyri_, with respective EC\({}_{50}\) values of 0.30, 6.65, and 74.07 \(\mu\)L L\({}^{-1}\). Fumigation treatment of pear twigs with each of these three compounds was also sufficient to prevent the development of pear Valsa canker. As such, these results demonstrate that _B. atrophaeus_ strain HF1 and the volatile compounds isterperin, hexanoic acid, and 2-methylvaleraldehyde exhibit promise as novel candidate biocontrol agents against pear Valsa canker.  
pear Valsa canker; _Valsa pyri_; antagonistic biocontrol; _Bacillus atrophaeus_; volatile organic compounds (VOCs)
## 1 Introduction
The type and content of flavor compounds are important indicators of beer quality, and the use of gas chromatography-mass spectrometry (GC-MS) facilitates the simultaneous measurement of a variety of volatile compounds such as alcohols, esters, acids, aldehydes, phenols, and heterocyclic compounds in conjunction with an effective sample pretreatment method [21], and the main extraction techniques include liquid-liquid extraction (LLE) [22], solid-phase microextraction (SPME) [23], and stir bar sorptive extraction (SBSE) [24]. Beer yeast is one of the important raw materials used for brewing beer, and it is responsible for converting the saccharides in worn into alcohol and carbon dioxide through fermentation. In this process, beer yeast will also produce esters, higher alcohols, and acids, giving beer a unique aroma and taste, such as fruity or floral. Volatile components in beer play an important role in shaping the taste of beer [25]. The flavor of beer is primarily derived from the synergistic effect of compounds, including alcohols, esters, acids, and aldehydes, which are generally produced during the fermentation stage and consist of intermediate metabolites or yeast by-products [26]. The main types of beer include lager and ale. In their study, [PERSON] and colleagues discovered evidently different metabolites between lager and ale using GC/MS metabolomics [27]. Yeast used in beer brewing is generally divided into two categories: top-fermenting beer yeast and bottom-fermenting beer yeast, which are used to produce ale and lager, respectively. The top-fermenting beer yeast is fermented at a higher temperature (16-25 \({}^{\circ}\)C) to produce ale, and the bottom-fermenting beer yeast is fermented at a low temperature (8-15 \({}^{\circ}\)C), with floc forming at the bottom of the container after fermentation, which is widely used in the production of industrialized lager. In addition, beer is generally pasteurized to maintain quality stability, and pasteurization can induce slight increases in beer protein content. Generally speaking, ripe beer is pasteurized beer, whereas draft beer is packaged beer that has not been subjected to pasteurization. Draft beer is incredibly popular among consumers because it retains its original flavor and nutritional value [28]. In previous studies, researchers have primarily focused on the types and contents of nutrients in different types of beer [29, 30]. Therefore, in this study, we aimed to mainly compare different functional compounds in lager beer and ale beer, draft beer, and ripe beer, including amino acid derivatives (GABA and GSH), \(\beta\)-glucan, phenolic acid, vitamin B, and volatile substances, and to explore the changes in functional compounds in beer. The research results presented herein lay a foundation for the development of functional beer in the future.