esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study explored a series of Co-Fe mixed oxides supported on ZSM-5, as synthesized using an ultrasound-assisted impregnation method for the abatement of Hg\({}^{0}\). The conclusions of this study are presented as follows:  
1. Compared with other samples, Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5 performed relatively better, with a roughly 96.6% Hg\({}^{0}\) removal efficiency and a roughly 901.63 ug/g Hg\({}^{0}\) adsorption capacity achieved at 120 \({}^{\circ}\)C. The interaction between CoO\({}_{x}\) and FeO\({}_{x}\) enhanced the reducibility of oxygen species, which promoted the oxidation of Hg\({}^{0}\).
2. Among various gas components, O\({}_{2}\) and NO had a positive impact the removal of Hg\({}^{0}\), whereas SO2 inhibited the adsorption of Hg\({}^{0}\) to a certain extent.
3. The Mars-Masson mechanism dominated the Hg\({}^{0}\) adsorption process over Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5. Co\({}^{3+}\) and Fe\({}^{3+}\) provided the active sites required to oxidize Hg\({}^{0}\) into Hg\({}^{0}\) for the subsequent deposition onto the adsorbent surface.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12083769/s1](https://www.mdpi.com/article/10.3390/app12083769/s1)]([https://www.mdpi.com/article/10.3390/app12083769/s1](https://www.mdpi.com/article/10.3390/app12083769/s1)), Figure S1: (a) simulated adsorption capacity of Co4 Fe1-ZSM-5, Co-ZSM-5 and Fe-ZSM-5; (b) breakthrough curve of Co4 Fe1-ZSM-5, Co-ZSM-5 and Fe-ZSM-5 under N\({}_{2}\) + 5%O\({}_{2}\) + 200 ug/m\({}^{3}\), t = 540 min; Figure S2: (a) N\({}_{2}\) adsorption-desorption isotherms and (b) pore distributions of the fresh and regeneration samples; (c) XRD pattern of the fresh and regeneration sample; Table S1: Comparison of the Hg0 removal performances onto various adsorbents; Table S2: The content of metal in samples obtained by ICP-AES; Table S3: N\({}_{2}\) adsorption results of the fresh and regeneration samples.  
Conceptualization, W.M. and D.Y.; methodology, W.M. and D.Y.; validation, W.M., D.Y. and H.W.; formal analysis, W.M.; investigation, W.M.; resources, H.W.; data curation, W.M.; writ-ing--original draft preparation, W.M.; writing--review and editing, D.Y.; visualization, W.M.; supervision, H.W.; project administration, H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by the National Natural Science Foundation of China (51568024).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Proposed System Technology, Architecture, and Operation
### Design and Construction of Dr. What-Info System with CEOntoIAS  
The tourism-related ontology (Figure 1) technology can help users quickly, accurately, and effectively obtain appropriate and timely cloud information and use the OntoIAS for allowing us to accomplish the research purpose of expanding the information agent shell of EOntoIAS to CEOntoIAS (refer to Figure 4 for details). The support includes: (1) driving the intelligent cloud information data mining agent system OntoDMA [8] to utilize the classification and clustering technology of cloud data mining; extracting the relevant cloud information operation knowledge or rules according to the relevant support and confidence, as provided in advance by the system cloud operation mode; and actively providing the system with suitable, real-time, and fast cloud information prediction service; (2) driving the intelligent cloud information case reasoning agent system OntoCBRA [31] to match the cloud information ontology service and similarity calculation between the corresponding cases, introducing a case-based reasoning mechanism, actively learning the relevant system knowledge, and expanding the cloud system knowledge and operation mode, and thus enhancing system-related cloud information processing robustness; (3) when neither of the aforementioned factors can provide an appropriate cloud information solution, the system triggers OntoIAS [10] through the solution finder to search for the appropriate cloud information solution from outside the Internet directly through information search, information retrieval, information classification, information presentation or sorting, and other technologies. This solution is passed to the front- and back-related cloud information processing systems via Ubi-IA (ubiquitous interface agent) with a canonical user request representation language (CURRL) [10] in JSON format through the solution finder, becoming the basic cloud information material for OntoDMA and OntoCBRA to further expand learning prediction and case-based reasoning, which completes the learning cycle of the whole system in response to the information query and enhances the robustness of system cloud information processing and decision-making support. The CURRL is a frame-based command representation that makes it easy to map query command intentions, objects, and goals into corresponding frame slots. Table 2 shows the frame slots and their descriptions, whereas Table 3 illustrates some examples of query commands. Finally, through the cloud information processing of OntoDMA, OntoCBRA, and OntoIAS three-stage intelligent decision-making, as well as with the support of the cloud intelligent multi-agent system shell CEOntoIAS (which supports the processing, exchange, communication, operation, integration, analysis, and decision-making of cloud information), the research goal of autonomously searching and recommending the best cloud information solution can easily be reached.  
Figure 4: [PERSON] system architecture.
## 2 Materials and Methods
### Four Time Function Models and Parametric Analysis
#### 2.2.2 Weibull Time Function Model  
The Weibull model is a continuous probability distribution model introduced by [PERSON] in 1951 [48]. This model can effectively utilize known sample data to achieve good predictive results and finds wide applications in material fatigue strength analysis and lifespan prediction [49,50]. Based on the Weibull function model, a dynamic subsidence prediction model for surface deformation can be established. The expression for this model is:  
\[W(t)=W_{m}*(1-e^{-at^{b}}) \tag{2}\]  
where \(a\) and \(b\) are model parameters related to the nature of the overlying rock formation.  
When analyzing the impact of parameter \(a\) on the predictive model, assuming \(W_{m}=-3560\) mm and \(b=3\), as shown in Figure 5, an increase in the parameter \(a\) causes the three curves to become steeper. The time to reach peak values for each curve decreases. The initial and active phases of surface deformation were shortened, while the decline phase is advanced and lengthened, and the maximum values of subsidence velocity and acceleration increase.  
When analyzing the impact of parameter \(b\) on the predictive model, we take \(Wm=-3560\) mm and \(a=6\times 10^{-7}\), as shown in Figure 6, increasing the parameter \(b\) leads to shorter durations for the initial and active phases of surface deformation, while  
Figure 4: (**a**) Effect of parameter \(c\) on surface subsidence; (**b**) effect of parameter \(c\) on surface subsidence velocity; (**c**) effect of parameter \(c\) on surface subsidence acceleration.  
Figure 5: (**a**) Effect of parameter \(a\) on surface subsidence; (**b**) effect of parameter \(a\) on surface subsidence velocity; (**c**) effect of parameter \(a\) on surface subsidence acceleration.  
the decline phase is prolonged, and the time to reach the peak subsidence values is advanced. The impact of parameter \(b\) is similar to that of parameter \(a\). These two parameters have a synergistic effect on surface deformation, and when both \(a\) and \(b\) increase, the maximum values of subsidence velocity and acceleration also increase, and the time for surface deformation advances.
#### Article
###### Abstract  
On the brink of sophisticated generations of mobile starting with the fifth-generation (5G) and moving on to the future mobile technologies, the necessity for developing the wireless telecommunications waveform is extremely required. The main reason beyond this is to support the future digital lifestyle that tends principally to maximize wireless channel capacity and number of connected users. In this paper, the upgraded design of the multi-carrier orthogonal generalized frequency division multiplexing (OGFDM) that aims to enlarge the number of mobile subscribers yet sustaining each one with a high transmission capacity is presented, explored, and evaluated. The expanded multi-carrier OGFDM can improve the performance of the future wireless network that targets equally the broad sharing operation (scalability) and elevated transmission rate. From a spectrum perspective, the upgraded OGFDM can manipulate the side effect of the increased number of network subscribers on the transmission bit-rate for each frequency subcarrier. This primarily can be achieved by utilizing the developed OGFDM features, like acceleration ability, filter orthogonality, interference avoidance, subcarrier scalability, and flexible bit loading. Consequently, the introduced OGFDM can supply lower latency, better BW efficiency, higher robustness, wider sharing, and more resilient bit loading than the current waveform. To highlight the main advantages of the proposed OGFDM, the system performance is compared with the initial design of the multicarrier OGFDM side by side with the 5G waveform generalized frequency division multiplexing (GFDM). The experimented results show that by moving from both the conventional OGFDM and GFDM with 4 GHz to the advanced OGFDM with 6 GHz, the gained channel capacity is improved. Because of the efficient use of Hilbert filters and improved rate of sampling acceleration, the upgraded system can gain about 3 dB and 1.5 dB increments in relative to the OGFDM and GFDM respectively. This, as a result, can maximize mainly the overall channel capacity of the enhanced OGFDM, which in turn can raise the bit-rate of each user in the mobile network. In addition, by employing the OGFDM with the dual oversampling, the achieved channel capacity in worst transmission condition is increased to around six and twelve times relative to the OGFDM and GFDM with the normal oversampling. Furthermore, applying the promoted OGFDM with the adaptive modulation comes up with maximizing the overall channel capacity up to around 1.66 dB and 3.32 dB compared to the initial OGFDM and GFDM respectively. A MATLAB simulation is applied to evaluate the transmission performance in terms of the channel capacity and the bit error rate (BER) in an electrical back-to-back wireless transmission system.  
orthogonal generalized frequency division multiplexing; generalized frequency division multiplexing; Hilbert filter; oversampling factor; adaptive modulation; future mobile waveforms; wireless networks; physical layer; bit error rate +
Footnote †: journal: Information
# The Development of Key Technologies in Applications of Vessels Connected to the Internet
## 2 Internet of Vessels Architecture
### The Technical Architecture of the Application Layer  
The main function of the application layer is providing service for the administration, enterprises, crews, ship owners and so on. The customers select the service from the client service list and then the service request schedules the corresponding resources through the management system [41]. Lastly, the web applications are deployed.  
For example, based on the Yangtze River Delta inland water transportation public information service platform, Dai [41] established a kind of application layer which is shown in Figure 8. It can be seen that in the application layer is a core layer in the IoV system. With the data resource layer's support, variety of the application systems is developed in this layer. In order to meet the requirement of the business management and public information service, the functions including the shipping company and crew management, traffic assessment information, meteorological information, policies services, logistics information, dangerous goods declaration and channel congestion prediction are developed and integrated into several multi-platforms or systems. Based on the data analysis results, each platform could help the administrators make a good decision.
# Stochastic Approach to Hosting Limit of Transmission System and Improving Method Utilizing HVDC
###### Abstract
Therefore, this study, proposes a method that evaluates hosting capacity in a transmission system, extended from that in the distribution system with considered options of what has been mainly performed in the distribution system to the transmission system evaluation, by reflecting the matters to be considered in the large-scale transmission system. In this way, the proposed method not only considers the probabilistic characteristics of DERs but also suggests an optimization problem of where to install the HVDC system by the implementation of large-scale renewable energy and which operational strategy should be taken. Different optimization problems were applied to the calculation of the installation location and operating point of HVDC, and the power flow sensitivity analysis was utilized in this process. In the simulations, the simulations were performed in various scenarios. e.g., a change in generation system that occurs as penetration level increases, the large DERs integration, which is concentrated on a specific region because the hosting limit is highly scenario dependent. This study can help operators to evaluate the hosting capability of the current transmission system and to establish an effective transmission expansion plan that could maximize the hosting limit. The remainder of this paper is organized as follows. Section 2 introduces the stochastic approaches of DERs, their utilization to estimate the hosting limit of the system, and sensitivity knowledge for the proposed method is introduced. Moreover, a method to evaluate the hosting limit of the transmission system and a method to find the optimal operating point and construction points to improve the hosting capacity are introduced. In Section 3, the simulation scenarios description and results are presented with discussion on the IEEE 39 bus test system. Finally, in Section 4, the conclusions of this study and future works are presented.
## 3 System Model and Problem Statement
### Problem Formulation  
In the grid system described above, we assume that a large renewable energy generation station is to be connected, which might incur oscillating power input to the grid. In order to consume the extra power generated from renewable sources, multiple datacenters can be established at several different locations and connected to the grid system as controllable loads. The problem here is to determine how much power each datacenter should consume in the case of multiple datacenters and make adjustment accordingly in a real-time manner.  
Assume there are \(n\) datacenters deployed in the grid. Denote the solar power generated as \(P_{S}\), the load of the \(i\)-th datacenter as \(L_{i}\). Then, the total power losses will be impacted by them, denoted as a function Loss (\(P_{S}\), \(L_{1}\), \(L_{2}\),..., \(L_{n}\)). Then, the problem we need to address can be defined as:  
\[\text{minimize Loss }(P_{S},L_{1},L_{2},\ldots,L_{n}) \tag{3}\]  
\[P_{S}=\sum_{i=1}^{n}L_{i} \tag{4}\]  
\[U_{M}(1-\varepsilon_{1})\leq U_{i}\leq U_{M}(1+\varepsilon_{2}),\ 1\leq i\leq n \tag{5}\]  
\[|P_{ij}|\leq P_{ij}^{MAX},\ 1\leq i,j\leq n \tag{6}\]  
wherein \(U_{M}\) is the system nominal voltage; \(\varepsilon_{1}\) and \(\varepsilon_{2}\) are the allowable deviation rates specified internationally; \(P_{ij}\) is the branch power flowing from bus node \(i\) to bus node \(j\), and \(P_{ij}^{MAX}\) is the maximum power allowed to pass through the branch (i.e., the branch capacity limit value). As shown, Equations (5) and (6) are both constraints to ensure the stability of the grid by avoiding any over-limit violations across all of the buses and branches. Especially, Equation (5) is the constraint for the bus voltage of each node and Equation (6) gives the limitation of the power flow on each branch.
# BEAC-Net: Boundary-Enhanced Adaptive Context Network for Optic Disk and Optic Cup Segmentation
## 3 Methods
### Loss Function  
We observed that the fundus image in the dataset has some common characteristics; the background region (the area is black in the fundus image) and the foreground region (the OD and OC area) are imbalanced. Only one small part of the OC region is occupied. This problem affects the robustness and stability of the training model and the oscillations and anomalies of each evaluation metric. To improve the performance of the Dice coefficient and IoU, we conduct both multi-class cross-entropy and Dice Loss in the loss function.  
(1) Multi-class cross-entropy: the multi-class cross-entropy is defined as  
\[L_{ce}=\frac{1}{N}{\sum_{i}{L_{i}}}-\frac{1}{N}{\sum_{i}{\sum_{i}^{M}{y_{ic} log(p_{ic})}}} \tag{6}\]where \(M\) is the class number, \(p_{ic}=\{0,1\}\), and \(p_{ic}\) is the prediction when sample \(i\) belongs to \(c\).  
(2) Dice coefficient: Dice Loss training focuses more on mining the foreground area, which can alleviate the negative impact of the foreground-background area imbalance in the sample, which means that most of the area in the image does not contain the target. Furthermore, only a small part of the area contains the target, which is formulated as follows:  
\[L_{Dice}=1-\frac{2\sum_{n=1}^{N}p_{n}y_{n}+\alpha}{\sum_{n=1}^{N}p_{n}^{2}+ \sum_{n=1}^{N}y_{n}^{2}+\alpha} \tag{7}\]  
where \(p\) denotes the prediction; \(y\) denotes the ground truth, which is usually used in semantic image segmentation; and \(\alpha\) is set to a fixed value to achieve a non-zero denominator. We let the loss function calculate normally. We can also assign the maximum value of \(\alpha\) in the denominator to avoid overfitting problems in the trained model.  
Finally, we define the sum of the cross-entropy loss and Dice Loss together as follows:  
\[L_{loss}=w_{ce}L_{ce}+w_{dice}L_{dice} \tag{8}\]  
where \(w_{ce}\) is the weight of the cross-entropy loss, and \(w_{dice}\) is the weight of the Dice Loss. In this study, we use \(w_{ce}=0.4\) and \(w_{dice}=0.6\) in the training phase.
## 4 Experimental Results and Analysis
### Data Preprocessing  
Processing the images before fitting them into the learning model is essential to attain more accurate results. We extract the RGB channels from each image to provide better spatial resolution. The original U-net model deals with only one channel from each input image. Color images contain more information that provides better classification results and high-resolution feature maps [40]. Each image is then resized into 256 \(\times\) 256 pixels using the bi-cubic interpolation algorithm to facilitate the batch learning process and reduce the computational complexity.
# A Novel Coaxial Balance Mechanism for Reciprocating Piston Engines
###### Abstract  
Vibration is an important issue faced by reciprocating piston engines, and is caused by reciprocating inertia forces of the piston set. To reduce the vibration without changing the main structure and size of the original engine, we proposed a novel coaxial balance mechanism design based on a compact unit body. By introducing a second-order balance mass, this mechanism can significantly increase the efficiency of vibration reduction. The proposed mechanism can effectively balance the first-order and second-order inertia forces with the potential of balancing high-order inertia forces. To accurately determine the second-order balance mass, a closed-form method was developed. Simulation results with a single-cylinder piston DK32 engine demonstrate the effectiveness and advantage of the proposed mechanism. At a crankshaft speed of 2350 r/min, compared with the first-order balance device, the average root mean square velocity of the test points on the engine's cylinder was reduced by 97.31%, and the support reaction force was reduced by 96.54%.  
piston engine; vibration reduction; coaxial balance mechanism; compact unit body; closed-form +
Footnote †: journal: Applied Sciences
## Materials and Methods
### HPLC-MS\({}^{n}\) Analysis  
The HPLC-MS\({}^{n}\) method was used for the identification of secondary metabolites in the crude methanolic extract of _P. spinosa_. Before analysis, the dry sample was dissolved in methanol at a concentration of 5 mg/mL and filtered through a 0.45 \(\upmu\)m Millipore filter. The HPLC system was composed of a Varian 212 binary pump equipped with a Varian Prostar 430 autosampler coupled to a Varian 500 Ion Trap mass detector (MS). An Agilent Eclipse plus C18 column (2.1 \(\times\) 150 mm, 3.5 \(\upmu\)m) was used as the stationary phase. A gradient of acetonitrile (A) and 0.1% \(v/v\) formic acid in water (B) was used as the mobile phase. The gradient was set as follows: 0 min, 10% A; 20 min, 54% A; 23 min, 100% A; isocratic up to 32 min. Re-equilibration time was 8 min. The flow rate was 0.2 mL/min. MS data were acquired in the negative ion mode [ESI (-)], and the operating parameters of the spectrometer were as follows: needle voltage, 4500 V; capillary voltage, 70 V; RF loading, 100%; nebulizing gas pressure, 20 psi (nitrogen); dry in gas pressure, 15 psi; dry in gas temperature, 350 \({}^{\circ}\)C. Mass spectra were recorded in the range of \(m/z\) 50-2000. The turbo detection data scanning (TDDS) function of the instrument was used to investigate the fragmentation pattern of the eluted compounds, setting n = 3 levels of fragmentation. Fragmentation data, together with information on the molecular weight of the compounds,were used to perform the tentative identification of secondary metabolites from _P. spinosa_. As a comparison, literature data were evaluated.  
Identified phenolic compounds were quantified using linear calibration curves. These latter were calculated by analyzing standard solutions of rutin (for flavonoids) and chlorogenic acid (for phenolic acids) and integrating the corresponding peaks to obtain the area under the curve (AUC) values. Concentration ranges were 12-120 \(\mu\)g/mL and 10.4-104 \(\mu\)g/mL for rutin and chlorogenic acid, respectively. The equations were y = 7342x \(-\) 19,707 (R\({}^{2}\) = 0.9999) for rutin, and y = 7504.8x \(-\) 3890.2 (R\({}^{2}\) = 1) for chlorogenic acid. The analysis was performed in triplicate, and the results were expressed as mean \(\pm\) standard deviation (S.D.).
## 6 Results and Discussion
### Permutation Feature Importance  
The correlation used to explain the model is, in fact, a methodology to explain the relationship between each input variable and output variable before model development; however, it is slightly insufficient to comprehensively explain the influence of a specific input variable on the prediction of the ML model [85, 86]. Permutation feature importance (PFI) is used as a method to comprehensively determine the importance of variables in a model. To determine the strength of the relationship between the feature and the target value, the error increase in the model prediction is measured after the features are randomly removed. If the model error increases when randomly removing one feature, it is a \"significant\" feature because it indicates that the model depends on that feature when making predictions. Conversely, if there is no difference in error, the feature is said to be \"non-significant\" [87].  
Figure 16 shows that \(S_{\text{max}}\) and f are very important input variables in the gradient boosting model. It also shows that \(f_{c}\) and \(R\) are the next most important features, and shape and \(h\)/\(w\) are features with very weak influence on the gradient boosting model.