esa-sceva/satcom-chunk-collection huggingface dataset:
# How Environmental Factors Affect Forest Fire Occurrence in Yunnan Forest Region
###### Abstract
However, these studies did not consider daily meteorological data and combustible vegetation of local areas, nor did they consider multiple natural and social factors, and their related studies did not explore the mechanisms by which spatial and temporal variation in the occurrence of forest fires is associated with certain critical environmental changes. The increasingly significant characteristics of global climate change have brought great challenges to the accurate prediction of forest fires. Therefore, it is necessary to analyze the main influencing factors in forest fire occurrence based on the background characteristics of the study area and then revise or reconstruct the original forest fire prediction model on this basis to overcome this challenge. This study considers fire points in Yunnan monitored by satellite from 2005 to 2019 and the environmental conditions at the outset of forest fires, including daily weather data, combustible substance type, terrain, and social factors, to thoroughly investigate the occurrence of forest fires in Yunnan. We also derive quantitative expressions to understand important environmental factors that affect the occurrence of forest fires and the mechanisms that cause forest fires. Based on the above reasons, this study will consider two points to fully explore forest fires in Yunnan: First, we comprehensively consider various environmental conditions when the forest fire occurs and use quantitative expressions to reveal the important environmental factors of forest fire occurrence. The main consideration is the threshold division of environmental factors; the second is to find out the main environmental factor variables by exploring the temporal and spatial trend of forest fires so as to determine the main environmental variables of forest fires in Yunnan. The study is divided into the following sections: (1) key environmental factors and their thresholds, (2) interannual trends of forest fires, (3) intra-annual trends of forest fires, and (4) trends of spatially heterogeneous forest fires. The results of the study provide suggestions for fire prevention.
## 1 Introduction
The coloring property of this process noise can be troublesome for subsequent applications as it creates spurious peaks for FRF measurement when the H\({}_{1}\) estimator is used. Another difficulty in obtaining accurate DIC-based FRFs is that the length of acquired data (e.g., number of images) can be quite limited due to the finite onboard memory capacity of high-speed cameras even though the DIC technique is able to provide detailed spatial measurements. Usually, only a few thousands of images can be obtained with affordable high-speed cameras, and the number of time domain data used for estimating point-wise FRFs is much smaller compared to that used for spectral FRF estimation with an accelerometer. This can cause significant estimation uncertainty and bias error for the spectral estimation of DIC-based FRFs in the presence of optical sensor noise and process disturbance. In summary, the way that the FRFs are derived from DIC measurements needs to be improved. This issue is addressed in the present work from a Bayesian perspective by considering the intrinsic properties of the FRF and local AOI data (subset displacements). In the fields of system and control, the impulse response function, which is the time domain counterpart of the FRF, is modeled as a Gaussian process (GP) characterized by a reproducing kernel of the Hilbert space. The impulse response function estimation is obtained as a solution of Tikhonov regularization in the reproducing kernel Hilbert space, which outperforms classical system identification techniques when the training data are limited [17; 18]. This GP-based nonparametric modeling method seems optimal for deriving the FRF from limited time domain DIC data, achieving an optimal bias-variance trade-off (i.e., the smallest mean square error). Subset displacements within a local AOI at the desired location are all utilized by considering the spatial variation in FRFs. The idea is that the mode shape can be well approximated by a low-order multivariate polynomial within a local AOI. Thus, a spatial-frequency model of the point-wise FRF is proposed, which not only models the FRF's properties by a complex-valued GP, but also averages the local AOI data in an optimal manner. A Bayesian approach is developed for the statistical inference of the spatial-frequency model, where a numerically stable and efficient procedure is implemented to tune the hyperparameters and obtain the maximum a posteriori (MAP) of the FRF with the associated covariance matrix. The proposed method is finally justified with a simulated example and an experimental study.
## 3 Results and Discussion
### Evaluation of Memory Effect  
#### 3.2.1 Immediate Memory Effect Evaluation  
According to the test results, the experiment divides the different scores into \"excellent\" (9-10 points), \"good\" (6-8 points), \"qualified\" (3-5 points) and \"unqualified\" (1-2 points). The average score of the control group was higher than that of the experimental group by 18%. The data compilation showed that the hierarchical structure of the control group was better than the experimental group, as shown in Figure 4a.  
The performance of each group of students in various types of questions is different, as shown in Figure 4b. In the first six questions related to the construction principle of coiled waterproof roofing, the control group scored twice as much as the experimental group. There is no significant difference in the scores between the two groups of the seventh question about construction safety details and the eighth and ninth questions on construction structure. In the map recognition questions involving specific process operation, the experimental group were 96% correct, and the scoring rate was twice that of the control group. Therefore, we can learn from the students' feedback compared with the traditional teaching method that VR teaching has no obvious advantage for the mastery of theoretical knowledge in instantaneous memory, but VR teaching is more effective when it involves process practice memory.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cronbach’s Alpha** & **KMO Value** & **[PERSON]’s Sphericity Test Sig Value** \\ \hline
0.712 & 0.775 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reliability and validity analysis.
## 3 Background of SDN
### Application Plane  
The top layer in the SDN is called the application plane, consisting of business applications. Business applications provide management and optimization of business services. These applications implement the control logic based on the network state information received from controller NBIs to modify the network behavior. In the study, [57] the solutions of SDN for traffic engineering are discussed. In paper [58], a security-based survey study is conducted. [PERSON] et al. [59] proposed analysis of distributed denial of service (DDoS) attacks in SDN networks in a cloud computing environment. A survey on fault management issues in SDN and its solutions is presented in [60]. SDN is deployed in real-time scenarios including transport network, wireless networks [61; 62], optical networks [63], wide area networks (WAN) [64], IoT, EC [65], and cloud computing [66].
# Towards Client Selection in Satellite Federated Learning
## 4 Algorithm
### Client Selection
#### 4.2.3 Data Quality  
Ensuring data quality poses challenges in FL because of the diverse range of client devices. Randomly selecting clients as a strategy could result in global model deterioration as a result of the inclusion of poor data samples, leading to sluggish convergence or even failure to converge.  
We propose a method for measuring the degree of anomaly in local models and determine the affinity of clients to assess the quality of data maintained by them. In doing so, we consider the impact of local data when designing the CS mechanism.  
This method determines whether the client's data have deteriorated the global model by comparing the distance from the updated local model to the global model, and other clients' models. We define the abnormality of the model as \(Ab_{k}\) for client \(k\) in global round \(t\):  
\[Ab_{k}^{t}=\sigma\|\omega_{t}-\omega_{t+1}^{k}\|_{2}+\frac{(1-\sigma)}{|K|-1} \sum_{j\in\mathcal{K}\setminus\{k\}}\|\omega_{t+1}^{k}-\omega_{t+1}^{j}\|_{2} \tag{16}\]  
where \(\sigma\) is the proportionality factor, a hyper-parameter that can adjusted according to need.  
We evaluate the quality of the client's data according to its historical performance, and determine the client affinity \(Af\) to indicate the impact of the client's data on the global model during the training process. For a selected client \(k\), its \(Af_{k}\) can be calculated as follows:  
\[Af_{k}=\sum_{x=0}^{x=t}\frac{e^{x-t}}{Ab_{k}^{x}} \tag{17}\]
## 7 Conclusions  
The AAOISE realizes the field control, data collection, operation monitoring, data storage, and remote control functions of the Antarctic equipment. The system is designed to enhance the reliability of the system through redundancy design. The system is operated by system diagnosis, scheduling, and other technologies. The system is operated by means of independent operation combined with remote intervention.  
The AAOISE can work in a low-temperature and low barometric pressure environment to provide instruments with heat-preserved foundation support and continuous electric power. During the field inspection period, AAOISE mainly worked in self-control mode and its remote monitor function passed the test too. It showed good independent control stability and flexible remote handling. The field inspection indicated that the power supply and heat preservation were successful.  
The AAOISE's design tried to reduce the impact on the environment and dependence on distant control. It is an independent completely controlled intelligent equipment, which also supports remote real-time online monitoring. Its site master control, SMC, is set up based on the field bus technology in an independent control way. Redundant technology is applied widely to satisfy the demands for power supply and temperature maintenance as low energy consumption as possible.  
After 50 days of operation of AAOISE in 2011, it worked fluently in Kunlun Station. Then, in 2019, the capacity of output power was improved and served the science observation instruments in an even better way in Taishan Station.  
Investigation, X.C.; Methodology, S.F.; Visualization, J.G.; Writing--review & editing, K.Z. and H.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China under Grant 2018 YFB1500800 and in part by the National Natural Science Foundation of China under Grant 61973083.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Powder Preparation and Fabrication  
IN718 powder was supplied from EOS GmbH (Krailling, Germany). The powder was sieved based on ASTM B214 [54] to avoid inhomogeneity in the distribution of particle size during fabrication. A scanning electron microscope (SEM) image of the fresh powder is presented in Figure 2a. The SEM analysis revealed that the powder had a spherical shape (the average circularity of 0.71), acceptable flowability and packing density, low impurity content, and excellent transformation ability. Moreover, the image was used for further analysis of the distribution of particle size using ImageJ software [57]. Figure 2b represents a histogram for the particle-size distributions of fresh IN718 powders.  
An EOS M290 metal 3D printer (EOS GmbH Electro Optical Systems, Germany) equipped with a 400 W Ytterbium fiber laser was used to fabricate the designed parts with three repetitions. The laser processing parameter set used for the fabrication were laser power (\(P\)) of 285 W, scanning speed (\(v\)) of 960 mm/s, hatch spacing (\(h\)) of 110 \(\upmu\)m, and layer thickness (\(t\)) of 40 \(\upmu\)m, with an energy density (\(E\)) of 67 J/mm3, as calculated from the Equation (1) [58; 59; 60]. A stripe scanning strategy with a 67\({}^{\circ}\) rotation angle in each consecutive layer was employed for all the parts.  
\[E=\frac{P}{h.p.t} \tag{1}\]  
Figure 1: Computer-aided designs of the three different support structures: (**a**) angled-type (AT53G80), (**b**) cone-type (CT53G80), and (**c**) pin-type (PT53G80). (**d**) The thickness (T) and gap (G) of a support wall are presented from the top view. The cross-section view shows the contact area between the main sample and supports for (**e**) angled-type (AT53G80), (**f**) cone-type (CT53G80), and (**g**) pin-type (PT53G80).
# Re-Optimized Design for the ADR-Based Dual-Loop Controller of 100 m Class Aperture Radio Telescope
## 4 Simulation Results and Analysis
### Single-Velocity-Loop Performance Analysis  
In the open-position-loop case, the motor's velocity and input voltage responses of the system are shown in Figure 5. The mean, maximum, median, and root mean square values of the tracking errors due to disturbances and response times are shown in Table 1.  
It is clear from Figure 5a that both LADRC and DLADRC achieve fast and overshoot-free tracking responses to the speed command relative to RPID. When the system is subjected to external wind disturbances, LADRC and DLADRC can also quickly achieve anti-disturbance; the maximum response amplitudes to the disturbance are reduced by 3.18 times and 3.87 times compared with RPID, and their disturbance response times are reduced by 1 time compared to RPID; in addition, the disturbance response is smoother than that of RPID. Compared with LADRC, DLADRC has smoother initial velocity responses curves, a 20% reduction in the peak disturbance response amplitude, and smoother steady-state responses, indicating the effectiveness and superiority of the re-optimized method. The relatively smoother input voltage responses of the motor controlled by DLADRC in Figure 5b show that it is easier for it to obtain excellent control performance than the traditional LADRC.
## 1 Introduction  
In the last few decades, a significant increase in nosocomial fungal infections has been observed, and _Candida_ species are by far the most common cause of invasive fungemia in humans [1; 2]. Among _Candida_ species, _Candida albicans_ is the main etiological agent of invasive candidiasis [3; 4], being associated to high mortality rates [4]. Together with its virulence traits [5; 6], its ability to acquire drug resistance [7; 8; 9] makes this opportunistic pathogen a severe threat.  
Only three classes of antifungal drugs are licensed to treat _Candida_ infections (azoles, echinocandins, and amphotericin B), and only some azoles and echinocandins are recommended as first-line agents [10]. Currently, there has been a rise in the frequency of multidrug-resistant clinical isolates, and therapeutic options are running low. This is true for _C. albicans_, but even more so for other emerging non-albicans _Candida_ species, such as _C. glabrata_, _C. krusei_ and _C. auris_. For example, in recent studies, almost 40% of the _Candida glabrata_ isolates shown to be resistant to at least one echinocandin were also resistant to fluconazole [11; 12]. In non-_albicans_ pathogenic _Candida_ species, the scenario is even more frightening, as several of them display, either intrinsic or easily acquired resistance to several of the available antifungal agents. For example, in a recent case, _Candida auris_ isolates were identified as resistant to the three classes of available antifungal drugs, further raising public concern on the future efficacy of current antifungal therapeutic options [13]. The identification of new drug targets and new drugs is crucial to overcome the increase in therapeutic failure.  
Genome-scale metabolic models have the potential to provide a holistic view of cell metabolism. Historically, these global mathematical descriptions of cell metabolism have mostly been linked to metabolic engineering of microbial cell factories, given their potential to simulate global metabolic behavior and provide hints to guide experimental optimization of such organisms for the production of added-value compounds [14]. However, recent examples have shown the potential of these models in the quest for novel drug targets in pathogenic organisms [15; 16; 17; 18; 19]. For example, [PERSON] et al. in 2018, described the reconstruction of genome-scale metabolic models for five life cycle stages of _Plasmodium falciparum_, enabling the identification of potential drug targets that could be used as both, anti-malarial drugs and transmission-blocking agents [20].  
Here, we present the first validated in silico genome-scale metabolic reconstruction of _C. albicans_, the iRV781. This model is provided in the well-established SBML format and can easily be read in most metabolic engineering platforms such as OptFlux [21] and COBRA [22]. The model validation procedure is detailed, and evaluation of the potential of this model for research is advanced for new drug targets in this fungal pathogen.
## 5 Conclusions
By implementing these policies, OECD countries can effectively balance economic growth with the imperative to reduce carbon emissions and combat climate change. These integrated approaches are essential for achieving long-term sustainability goals and ensuring a resilient and environmentally sound future. Of course, several research limitations exist in this paper, which also suggest the potential paths for future investigation. Firstly, this study relies on available data from OECD countries, which may contain gaps or inconsistencies that could affect the robustness of the results. Future research should aim to incorporate more comprehensive and higher-resolution datasets, potentially including non-OECD countries to provide a broader comparative analysis. Additionally, the use of more recent and frequent data collection could enhance the accuracy and reliability of the findings. Secondly, although this study considers several key variables, such as renewable energy, environmental taxes, industrialization, urbanization, foreign direct investment, and economic growth, other potentially influential factors, such as technological innovation, political stability, and public awareness, are not included. Future research should expand the scope of variables to include additional factors that could influence CO2 emissions. Incorporating variables such as technological advancements, governance quality, and social awareness could provide a more holistic understanding of the determinants of emissions. Thirdly, the CS-ARDL approach, while robust, may have limitations in capturing the dynamic complexities of CO2 emissions due to potential non-linearities and interactions between variables. Future research should explore the application of advanced econometric techniques, such as machine learning models or non-linear dynamic models, to better capture these complexities and interactions. This could provide deeper insights into the relationships and help identify more effective policy interventions. Finally, this study may not fully account for temporal and spatial variations across OECD countries, potentially overlooking regional differences and temporal dynamics that influence CO2 emissions. Future studies should incorporate spatial econometric models to account for regional heterogeneity and spatial dependencies. Additionally, temporal analysis could be enhanced by considering time-series models that capture the evolution of relationships over different periods, thereby providing a more nuanced understanding of how these relationships change over time. By addressing these limitations, future research can build on the findings of this study to provide more detailed and comprehensive insights into the factors influencing CO2 emissions and the effectiveness of various policy measures. This will aid policymakers in designing more targeted and effective strategies for achieving environmental sustainability. This research received no external funding. Not applicable. The data presented in this study are available from the authors upon request. The author declares no conflicts of interest.
# Application of a Hybrid Model in Landslide Susceptibility Evaluation of the Western Tibet Plateau
## 3 Results  
### Landslide Susceptibility Mapping by the IVM Model  
The relationships between the factors calculated by the information model and disasters are presented in Table 4. The change in the calculated weight of each factor at each stage is shown in Figure 4 by the IVM model. With respect to slope and aspect, the most sensitive range of slope is 20-30\({}^{\circ}\) (0.28) and the west direction has the largest weight at 0.98, indicating that these two classes are most susceptible to landslide occurrence. The most affected lithology is hard rock (2.1). The elevation class from 4800 to 5100 m has the highest weight factor (0.43). Among the lithology units, hard rock has a value of 2.11. When the distance to faults is \(>\)2100 m, the probability of disaster occurrence is the largest, with the weight of 0.88. The class between 900 and 1200 m shows the highest susceptibility (1.28) for distance to roads. At 900-1200 m, the factor of being away from the rivers has the maximum weight (0.42). Finally, the risk assessment map is divided into five categories based on the natural discontinuity method: very low, low, moderate, high, very high (Figure 5). The proportion of disaster area of each hazard level is calculated by statistical analysis. From very low to very high, the percentages of disaster area are 0.39%, 3.03%, 7.87%, 34.22%, and 54.49%, respectively (Table 5).
## 3 Simulation Experiments and Statistical Analyses of Results  
### Design of Simulation Experimental Area and Experimental Data  
To verify the accuracy and stability of the inversion parameters of the IGGIII-BFGS algorithm, a simulation experiment area is established to discuss the performance of the inversion of the parameters of each method after adding gross errors to the simulated observations.  
Firstly, the geological mining conditions and probability integral parameters of the mining face are designed, with mining thickness in the vertical tangential direction m = 4 m, the dip angle of coal seam \(\alpha\) = 6\({}^{\circ}\), the working face tendency length D\({}_{1}\) = 300 m, strike length D\({}_{3}\) = 500 m, and mining depth H = 300 m. The values of the probability integral parameters are as follows: subsidence coefficient q = 0.85, horizontal displacement factor b = 0.25, tangent of major influence angle tan \(\beta\) = 2.15, propagation angle of extraction \(\theta_{0}\) = 86\({}^{\circ}\), and offset distance of inflection points s\({}_{1}\) = s\({}_{2}\) = s\({}_{3}\) = s\({}_{4}\) = 50 m. There are 13 monitoring points on the strike line of the designed working face (E1 to E13) and 13 monitoring points on the  
Figure 1: Flowchart of IGGIII-BFGS algorithm.  
tendency line (N1 to N13), totaling 26 monitoring points. The locations of the simulated working face and monitoring points are shown in Figure 2.  
According to the designed parameter values in the probabilistic integral model, the subsidence and horizontal movement values of the above 26 monitoring points were calculated as the true values. Afterward, some gross errors are artificially added to them to compare the anti-interference ability of parameter inversion of each method.