esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Viridiflorol Could Serve as a Defense Compound against Other Plant-Associated Fungi  
It has been shown repeatedly that volatile organic compounds, including volatile terpenoids, mediate communication between plants and microorganisms [60]. Several plant terpenoids, such as \(\alpha\)- and \(\beta\)-pinene or abiotic acid, have been considered to be positively involved in the development of ectomycorrhizal associations, mainly during the pre-symbiotic phase [61; 62]. On the other hand, little is known about fungal terpenoids and their role in endophytism and to our knowledge, there is no direct evidence that they facilitate establishment of plant-endophytic interactions. Terpenoids produced by fungi are considered to be implicated mostly in defense against antagonists or in signaling with other microorganisms [60; 63]. Therefore, we redirected the focus of our study into exploring the potential of viridiflorol as a defense compound. The in vitro growth assay showed that high concentrations of viridiflorol (>100 ug/mL) could successfully inhibit the germination and consequently the growth of the phytopathogenic fungus _C. truncatum_. Previous studies have also shown that viridiflorol has weak to moderate anti-inflammatory and antimicrobial activity [49; 64; 65; 66], suggesting that the ability to produce this compound could provide_S. indica_ with an advantage when growing in a more competitive environment, where several rhizospheral microorganisms are fighting for a niche within the plant tissue.  
In our case, antagonism assays and co-cultures including _S. indica_ wild type or mutants and other plant-associated microorganisms might further support our hypothesis about viridiflorol being implicated in defense. Additionally, _in planta_ experiments with knockout _S. indica_ mutants and other microbial competitors could provide an even more realistic image regarding the actual role of SiTPS and sesquiterpenes during a tripartite association between a plant, a pathogen, and a beneficial symbiont. Identifying the role of endophytic terpen during symbiotic associations will provide a more in-depth understanding of these complex interactions and their effect in the surrounding environment, opening up new opportunities for their application in agriculture.
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
## 4 Performance Evaluation of Overload Mitigation Strategies
### Grid RoCoF Disturbance Test  
To evaluate the overload mitigation and recovery capability of these strategies, the GFM inverter is tested against frequency excursions.  
Initially, the GFM inverter is operating at its rated power (1 pu), and both the grid voltage and frequency are at their rated values. At t = 1 s, the grid frequency drops from 50 to 49.5 Hz at a RoCoF of 2 Hz/s. Under this frequency excursion, the GFM inverter must increase its active power output, activating the overload mitigation strategy. At t = 3 s, the grid frequency is increased from 49.5 Hz to 50.25 Hz using the same RoCoF. As grid frequency increases, the GFM inverter returns to normal operation, exiting the overload mitigation strategy in a fast and seamless manner.  
The grid frequency profile is shown in Figure 13a, whereas the active power response of the different overload mitigation strategies is shown in Figure 13b. For comparison, the responses of a GFM with VP strategy and without overload mitigation strategy are included.
## 3 Results  
### Recognition Results and Performance Analysis  
#### 3.1.1 The Effect of Initial Parameters on Network Performance  
In this paper, the PyCharm framework based on Python is used to build the network model; the platform is configured in the computer with Inteli5-6500 CPU, 8 GB of memory, and win10 64-bit operating system. Based on the PRPD images measured by the transformer simulation experiment, the model is trained according to the steps in Section 2.5; finally, the model construction is completed.  
The initial network parameters such as learning rate (the method for adjusting input weights of neural networks) and epoch (the process of training all training samples once) will have a certain impact on the classification performance of the model. To prevent the step size from jumping out of the optimal area in the later stage of training, the learning rate is dynamically adjusted during the training process. The decay factor of the weight is set to 1 \(\times\) 10\({}^{5}\), the momentum of the optimizer is set to 0.9, the learning rate is reduced in the specified iterative period. When the number of the training cycle is 100 and the learning rate is set to be reduced by 10 times in the 30 th, 60 th, and 90 th cycles, the comprehensive performance of the model is optimal. When the training period is greater than 100, the accuracy is slightly improved, but the generalization ability of the model will be reduced due to overfitting. Therefore, the training cycle of the model is set to 100, and different initial learning rates are adjusted in turn to obtain the variation trend of classification performance under the corresponding conditions, as shown in Figure 7.  
From the analysis of the above results, it can be concluded that when the learning rate is greater than 0.2, each training process will cross the optimal area due to the large step, resulting in the poor overall performance of the model and even non-convergence. Because the learning rate will be dynamically adjusted, the too-small initial value will also reduce the accuracy of recognition to some extent, and with the decrease of the learning rate, the training time will also increase greatly. For the MCNN using PD data for training designed in this paper, when the initial learning rate is set to 0.15 and the number of iterations is 100, the model achieves the best classification effect and has the strongest generalization ability.
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 2 Overview of Feature Detectors and Descriptors
### Akaze  
The authors of [57] enhanced the computation cost of KAZE and proposed AKAZE, a faster and multi-scale feature detection and description algorithm. Earlier developed methods for detecting and describing features in nonlinear scale space take more time because constructing the nonlinear scale space is computationally costly. AKAZE employs the Fast Explicit Diffusion (FED) scheme, which is incorporated in a pyramidal structure and speeds up feature detection in nonlinear scale space. By employing FED techniques, nonlinear scale space may be created quicker than any other discretization technique. FED schemes also outperform AOS schemes in terms of ease of implementation and accuracy. AKAZE implements the Modified-Local-Difference Binary (M-LDB) descriptor, which is efficient, scale- and rotation-invariant and uses less memory.
# HalluciNet-_ing_ Spatiotemporal Representations Using a 2D-CNN
## 2 Related Work  
Our work is related to predicting features, developing efficient/light-weight spatiotemporal network approaches, and distilling knowledge. Next, we briefly compare and contrast our approach to the most closely related works in the literature.  
Capturing information in future frames: Many works have focused on capturing information in future frames [16, 17, 18, 22, 23, 24, 25, 26, 27, 28, 29, 30]. Generating future frames is a difficult and complicated task, and usually requires the disentangling of background, foreground, low-level and high-level details, and modeling them separately. Our approach to predicting features is much simpler. Moreover, our goal is not to a predict a pixel-perfect future, but rather to make predictions at the semantic level.  
Instead of explicitly generating future frames, works such as [16, 17, 18, 19] focused on learning to predict the optical flow (very short-term motion information). These approaches, by design, require the use of an encoder and a decoder. Our approach does not require a decoder, which reduces the computational load. Moreover, our approach learns to hallucinate features corresponding to 16 frames, as compared to motion information in two frames. Experiments confirm the benefits of our method over optical flow prediction.  
[PERSON] et al. [29] introduced a novel, compact representation of a video called a \"dynamic image\", which can be thought of as a summary of full videos in a single image. However, computing a dynamic image requires access to all the corresponding frames, whereas HalluciNet requires processing just a single image.  
Predicting features: Other works [27, 31, 32] proposed predicting features. Our work is closest to [32], where the authors proposed hallucinating depth using the RGB input, whereas we propose hallucinating the spatiotemporal information. Reasoning about depth information is different from reasoning about spatiotemporal evolution.  
Efficient Spatiotemporal Feature Computation: Numerous works have developed approaches to make video processing more efficient, either by reducing the required input evidence [33, 34, 35, 36], or explicitly, through more efficient processing [12, 37, 38, 39, 40, 41, 42].  
While these works aim to address either reducing the visual evidence or developing a more efficient architecture design, our solution to hallucinate (without explicitly computing) spatiotemporal representations using a 2D-CNN from a single image aims to solve both the problems, while also providing stronger supervision. In fact, our approach, which focuses on improving the backbone CNN, is complementary to some of these developments [43, 42].
## 2 Materials and Methods
### GC-IMS Analysis  
The volatile fingerprints at different manufacturing stages of SFABT were executed using a state-of-the-art GC-IMS apparatus (Flavourspec(r), G.A.S, Dortmund, Germany). The analysis conditions referred to our previous studies [23]. Briefly, tea samples (1.0 g) were loaded into a 20 mL headspace vial. The samples were subsequently subjected to incubation at a consistent temperature of 60 \({}^{\circ}\)C for a duration of 15 min, with a constant agitation speed of 500 rpm. Post-incubation, 500 \(\upmu\)L of the headspace samples was transferred to the injection port through a preheated syringe maintained at 85 \({}^{\circ}\)C. Volatile constituents were isolated utilizing an MXT-5 capillary column (15 m \(\times\) 0.53 mm \(\times\) 1 \(\upmu\)m; Restek, Beijing, China). High-purity nitrogen (99.999%) served as the carrier gas with a programmed procedure: 0-2 min, 2 mL/min, and 2-20 min, 100 mL/min. The flow velocity of the drift gas (nitrogen) within the drift tube was adjusted to 150 mL/min. The column temperature was regulated at 60 \({}^{\circ}\)C, while the drift tube temperature was kept at 45 \({}^{\circ}\)C. All analyses were performed in triplicate.  
The retention index (RI) of the volatile compounds was determined utilizing \(n\)-ketones C4-C9 (Sinopharm Chemical Reagent Beijing Co., Ltd., Beijing, China). The volatile compounds were qualitatively evaluated through a comparative analysis of RI values and the drift time against the GC-IMS library. The obtained IMS data were analyzed by using built-in software, such as VOCal 0.4.03, which was used to visually display the analytical spectrum. The Reporter plug-in 1.4.00 functioned to generate two-dimensional and three-dimensional diagrams, respectively. The Gallery Plot plug-in 1.2.8 performed visual fingerprint comparisons between different samples.
# The RHO Family GTPases: Mechanisms of Regulation and Signaling
## 3 Regulation of RHO Family GTPases
### GTPase-Activating Proteins (GAPs)
#### 3.4.3. The Mechanism by Which the GAP Domain Mediates GTP Hydrolysis  
Crystallographic studies of RHOGAP domains in complex with CDC42 bound to GppNHp, RHOA/CDC42 bound to GDP\(\cdot\)AlF\({}_{4}\)[76; 77; 95] and RHOA bound to GDP\(\cdot\)MgF\({}_{3}\)[132] have provided insights into the catalytic mechanism of GTP hydrolysis upon stimulation. The GTPase reaction, as part of the switch mechanism, leads to changes in the conformation of the GTPase, especially in flexible and mobile loops known as switch regions. RHOGAP interacts with the switch I and II regions [3; 133] and the P-loop of the RHO protein. The GAP domain accelerates the intrinsic GTP hydrolysis by RHO proteins in two ways. First, it directly contributes to catalysis by inserting catalytic arginine in the GAP domain into the active site of the RHO protein. This establishes contacts with the main-chain carbonyl of Gly12 (RAC1 numbering) and helps stabilize the GTP-hydrolysis transition state [134]. Second, this interaction stabilizes the negative charges formed during the transition state of GTP hydrolysis and positions the catalytic glutamine residue (Gln61 RAC1 numbering) of the RHO protein to enable its coordination with nucleophilic water molecules [129; 135]. RHOGAP also stabilizes the switch regions of the RHO protein by interacting with residues associated with its intrinsic GTPase activity [113]. ARHGAP36, CNTD1, DEP1, DEP2, FAM13B, INPPSP [136], and OCRL1 lack an arginine finger, which makes them catalyticallyinactive (Figure 4) [114]. ARHGAP36 is involved in GLI transcription factor activation, but this function proceeds independent of its GAP domain. CNTD1 lacks RHOGAP activity and acts as an ARF6 GAP. DEP1 and DEP2 coordinate cell cycle progression and interfere with RHOA action and signaling even though they lack RHOGAP activity. OCRL1 has been shown to interact with GTP-bound RAC1 without stimulating hydrolysis. p85\(\upalpha\) and p85\(\upbeta\) (85-kDa regulatory subunits of phosphoinositide 3-kinases) are also RHOGAP-like proteins (Figure 4), as they do not show any detectable GAP activity toward different RHO proteins [28]. A prerequisite of GAP function is that the GAP domain position its catalytic residue Arg282 (p50 numbering); therefore, GAPs include a number of amino acids that are critical for binding and stabilizing the protein complex. Both p85 isoforms lack most of binding determinants (e.g., Arg323, Asn391, Val394, and Pro398) as well as the conserved amino acids around the arginine finger [114].
## 3 Path-following Control Based on Improved LOS
### Improved LOS
#### 3.2.3 Design of the State-Feedback-following Controller
 \tag{16}\]  
The control quantity designed based on the state feedback is denoted by \(u\):  
\[u=-Ke \tag{17}\]  
where \(K\) represents the feedback gain matrix. As a result, the equation for the closed-loop system is  
\[\dot{e}=(A-BK)e \tag{18}\]  
In the above equation, the characteristic polynomial of the system's closed-loop equation is  
\[|sI-A+BK|=(s-p_{1})(s-p_{2}) \tag{19}\]  
During the pole configuration process, \(p_{1}\) and \(p_{2}\) are chosen as negative roots with conjugate complex values to determine the damping ratio and control time of the system. Since the conjugate complex roots, \(p_{1}\) and \(p_{2}\), are situated on the left half of the s-plane, the closed-loop following system is stable, which serves to prove the overall stability of the system.
## 1 Introduction
[36] performed the optimization of the fluidized bed drying of a sesame seed to find the least change in color and texture using the response surface method and genetic algorithm. The coefficients of Response Surface Methodology (RSM) models were optimized using the genetic algorithm (GA). GA-optimized models had better fitness than RSM models. [36]. [PERSON] et al. [37] used the integration of RSM and GA to develop an ANN to optimize the FBD of the carrot. The results showed that this approach (RSM with GA) is a useful tool to find the optimal topology of ANN for predicting energy and exergy in FBD [37]. [PERSON] et al. [38] predicted optimal drying conditions of pistachio for effective moisture diffusion, shrinkage, drying time, specific energy consumption, and color change as a function of fluidized bed drying conditions with RSM [38]. [PERSON] et al. [39] used the Taguchi method to optimize the drying of bird's eye chilli in an FBD. They also obtained similar results when using the one factor at a time (OFAT) method [39]. Various methods such as mathematical modelling, regression analysis and ANN have been used to predict the drying rate. However, few studies have been conducted on the modelling of product quality parameters. The tested product: _E. amoenum_, as mentioned, is a medicinal plant and the use of inappropriate parameters for drying have negative effects on the therapeutic properties of pharmaceutical dried products. It also requires, as with the case of any product, an appropriate colour of dried material. Drying is a very energy-consuming process, so it seems advisable to carry out this process with low energy expenditure. However, in the case of drying _E. amoenum_ petals, where the quality directly affects the price of the dried petals (and poor-quality product is worthless), the drying process should be carried out with the lowest possible energy expenditure. The presented study investigated the effect of drying process variables (air temperature (\(T_{d}\)) and air velocity and (\(v_{d}\))) on the following quality parameters of _E. amoenum_ petals: the color difference and the bioactive compounds as loss of total antioxidant capacity, loss of total phenolic content, loss of total flavonoids content, anthocyanin loss and energy consumption for fluidized drying. The study focuses on multi-objective optimization (MOO) of the process variables. The goal in an MOO problem is to optimize the several objective functions simultaneously and thus finding the best parameters for _E. amoenum_ petals drying process.
# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 1 Introduction  
Wireless sensor networks (WSNs) have recently evolved as a very vigorous research area in the field of advanced network communication. Moreover, it also plays an important role in terms of rapid technological progress, emerging practical development, and application activities [1]. The rapid growth of WSNs between 2012 and 2022 has been reported as increasing from 0.45 billion to 2 billion [2]. The major advantages of WSNs are self-organized and configured within the specified time interval, which is fixed by developments in manufacturing. Furthermore, the high impact of WSNs is enhanced in the capacity, integrity, and reliability of the network. Mostly, the WSNs are composed of many tiny and scattered sensor nodes, which have less battery capacity, thereby raising the issue of power and energy consumption. The fundamental purpose of WSNs is the collection of data by the collaboration of intermediate sensor nodes via the wireless connection. Due to any changes that occur in the environment such as temperature, humanity, velocity, speed of the wind, and light, data need to be updated according to the application requirements.  
The area's including building monitoring, smart agriculture, and healthcare are easy to deploy, while the environmental [3, 4], glacier [5], habitat and traffic monitoring [6], hazardous chemical detection, earthquake [7], volcano eruptions and disaster management [8], and weather forecasting and flood detection [9] are not considered as an easydeployment due to their hostile environment. Moreover, in these environments, the sensor nodes are distributed randomly by dropping through the uniform airdrop deployment method (UAD).  
The contributions of this survey article are mentioned as follows:  
* The WSNs types in different groups are classified according to their protocols, applications, and current issues.
* The data-driven models for WSNs categorize into four groups such as query-driven, event-driven, time-driven, and hybrid-driven.
* These data models are described according to the related issue and their proposed solution.
* Last, this survey also highlights each data model's limitations and challenges for helping new researchers to work on new enhancements and modification in the field of data-driven WSNs.  
The rest of this paper is organized as follows: Section 2 consists of a summary of the earlier existing studies, surveys, and reviews from the last six years on WSNs data-driven models. Section 3 describes in detail the WSNs' architecture overview. Section 4 presents a detailed taxonomy of WSN types in different groups according to their protocols, applications, and current issues. In Section 5, the detailed description of WSNs data-driven models is presented related to data management for energy efficiency. Section 6 highlights each data-driven model's limitations and challenges for helping new researchers, and the conclusion is presented in Section 7.
## 3 Research Framework and Main Contributions  
The research framework of this study is illustrated in Figure 1. Using a specific building cluster area in Shanghai, China, as an example, the study analyzes the carbon emission behavior of the RIES under the goal for achieving carbon peaking from both macro and micro perspectives. At the macro level, the study predicts the carbon-peaking implementation pathway for Shanghai during the time window of 2020-2040. At the micro level, employing multi-objective optimization and considering the carbon-peaking implementation pathway as a constraint on carbon emissions, this study analyzes the carbon emission behaviors of three typical RIES configurations. Additionally, it examines the cross-RIES carbon emission behavior under the carbon-trading and green-certificate-trading mechanisms.
# Design and Analysis on Decoupling Techniques for MIMO Wireless Systems in 5G Applications
## 3 Evaluation Parameters of Mutual Coupling  
There are many parameters to judge the behavior of MIMO systems, including performance parameters, such as operating band, matched impedance, channel capacity, etc., and diversity performance parameters, like envelop correlation coefficient (\(ECC\)), diversity gain (DG), total active reflection coefficient and mean effective gain (MEG). The evaluation of the degree of mutual coupling between antenna elements is usually characterized by the \(S\)-parameter and \(ECC\). These two parameters are briefly introduced in the following section to set the stage for the analysis of MIMO decoupling techniques later.