esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Mask Extraction of Grassland, Cropland, and Forest in China  
Moderate Resolution Imaging Spectroradiometer (MODIS) Global Vegetation Classification product (MODIS Collection5 MOD12Q1, Land Cover Data) provides globally categorized data classified by the International Geosphere-Biosphere Program (IGBP) Land Classification Rules. This dataset (500 m, annual) comprises 17 land cover categories, including 11 natural vegetation types, 3 categories for developed and urbanized areas, and 3 categories for non-vegetated land. Predominantly, these categories encompass grassland, cropland, and forest. For this study, we chose three land-use categories from the dataset: grassland, cropland, and forest. These categories were defined based on the IGBP land classification standard, with grassland represented by code 10, cropland by 12, and forest by codes 1, 2, 3, 4, 5, and 8.  
Before evaluating the productivity of grassland, cropland, and forest in China, we generated 0.5\({}^{\circ}\) and 0.05\({}^{\circ}\) masks for Chinese grassland, cropland, and forest. Initially, utilizing the IGBP land classification system of the MOD12Q1 dataset spanning from 2001 to 2018, we systematically extracted image elements corresponding to land-use types across China at a 500 m resolution annually. The components were then integrated into 0.05\({}^{\circ}\) resolution images utilizing ArcGIS 10.8. Subsequently, the extracted elements from these images were spatially superimposed across multiple time periods. Elements present in at least two-thirds of the 18-year period (i.e., 12 years) were designated as 0.05\({}^{\circ}\) masks representing grassland, cropland, and forest [9].  
To align with the 0.5\({}^{\circ}\) resolution data of the model GPP dataset, we constructed a 0.5\({}^{\circ}\) resolution grid of land use types for the Chinese region. The approach involved tallying the proportion of grassland, cropland, and forest image elements (0.05\({}^{\circ}\)) within each 0.5\({}^{\circ}\) grid cell. Subsequently, image elements corresponding to land use types exceeding 60%within each grid cell were filtered and designated as 0.5\({}^{\circ}\) grassland, cropland, and forest masks. The processing details for this step are shown in Figure 1.  
The following mask images of China's terrestrial ecosystems was created based on the above two methods (Figure 2).  
The above approach was taken mainly because the grassland, cropland, and forest images have been converted to other land use types in the past 18 years in China. To facilitate a more accurate study of the spatial and temporal dynamics of China's terrestrial ecosystems, it is imperative to disregard image elements exhibiting conspicuous alterations in land use types. The 0.05\({}^{\circ}\) and 0.5\({}^{\circ}\) masks generated in this study were employed to extract additional GPP and hydrometeorological data pertaining to China's terrestrial ecosystems [10; 11].
## 3 Manufacturing System Bottleneck Research
### Bottleneck Management
#### 3.3.1 Proactive Bottleneck Prediction  
Bottleneck prediction holds significant strategic importance in the optimization of manufacturing systems. By accurately identifying potential bottlenecks in the production process, companies can proactively formulate improvement measures, thereby enhancing overall production efficiency and resource utilization. Bottleneck prediction is essentially a process of preemptively identifying potential bottlenecks, aiming to recognize parts of the system that have not yet become bottlenecks but may develop into them in the future.  
[PERSON] and his group studied the effects of product type, processing time, machine utilization, and buffer capacity on bottlenecks, and proposed a method based on adaptive network fuzzy inference to predict bottleneck issues in the system [92]. [PERSON] utilized system buffer data, combining active state methods and buffer data to predict production bottlenecks [93]. [PERSON] developed a bottleneck index based on the workload and variability of wafer manufacturing systems. He proposed a new method for predicting bottleneck machines during system transient processes [94]. Those above bottleneck prediction methods still have certain limitations, and most models rely on static analysis assumptions, failing to adequately account for the changes in and evolution of bottlenecks in dynamic environments. Consequently, these prediction methods lack flexibility and accuracy when applied to dynamic models and fail to predict bottlenecks promptly.  
However, the prediction of bottlenecks based on dynamic models enables a more accurate and timely identification of potential bottlenecks within a system. [PERSON] utilized an online monitoring system to collect and process real-time production data from manufacturing enterprises for bottleneck prediction, aiming to achieve production line balance [26]. [PERSON] proposed a data-driven algorithm for bottleneck activity cycles, which has the advantage of utilizing real-time production data for forecasting [95]. [PERSON] and his team members used real-time data captured by Arena software and employed a data-driven approach to predict bottlenecks under multivariable uncertainty in remanufacturing systems [96]. Compared to static bottleneck prediction methods that apply only to long-term stable systems, this data-driven approach can predict system bottlenecks in real-time and with higher flexibility. However, the accuracy of this method often depends on the availability of large volumes of real-time data and the performance of the software.
## 2 Study Area and Dataset
### Dataset  
The first kind of LULC data adopted in this study come from the Climate Change Initiative Land Cover (CCI LC) dataset of the European Space Agency (ESA, Paris, France); this product offers LULC data with some of the highest temporal and spatial resolutions available at present. These data are acquired by using a multiyear and multisensory strategy to maximize the consistency of the product with a spatial resolution of 300 m ([[http://maps.eile.ucl.ac.be/CCI/viewer/index.php](http://maps.eile.ucl.ac.be/CCI/viewer/index.php)]([http://maps.eile.ucl.ac.be/CCI/viewer/index.php](http://maps.eile.ucl.ac.be/CCI/viewer/index.php)), accessed on 20 December 2021). The other kind of LULC data are from the Resources and Environmental Science and Data Center (RES, Beijing, China, [[http://www.resdc.cn/](http://www.resdc.cn/)]([http://www.resdc.cn/](http://www.resdc.cn/)), accessed on 20 December 2021), whose data are produced based on Landsat TM/ETM remote sensing images of each period as the main data source [35] and generated through manual visual interpretation with a spatial resolution of 30 m. The two LULC datasets selected in this paper both cover the year 2010.
###### Abstract  
Field emission is an important work mode for electron sources, and carbon nanotubes (CNTs) have been extensively studied for their good emission properties. It is well known that the parameters of the grid deeply influence the field emission performance of CNTs, a relationship that requires further elucidation. Therefore, in this study, the relationship between the grid aperture ratio and electron transmittance was studied through simulations and experiments. This study's results indicate that the electron transmittance improved as the grid aperture ratio increased. Meanwhile, electron beam spot simulations and imaging experiments indicate that an increased grid aperture ratio will expand the cathode electron divergence, leading to a larger electron beam spot size. These results demonstrate that there is a trade-off in maintaining the grid aperture ratio between high electron transmittance and relatively small electron beam spot size, and the optimum grid aperture ratio is between 75% and 85%. These results will provide a reference for the design and optimization of X-ray tubes and other electron sources.  
field emission; CNTs; grid aperture ratio; electron transmittance; electron beam spot
## 6 Experiments
### Results
#### 6.4.3 Bird's Eye View of the Capture Points  
Figures 13-15 show a bird's eye view of the capture points of the three datasets in different points of the proposed incremental clustering process. These capture points are shown with different shapes and colors, depending on the cluster they belong to. Figures 13j, 14g and 15j show the result after completing all the proposed incremental mapping method, and the subfigures show how the clusters are being created while the map is updated. Observing the pairs of subfigures: Figure 13c,d,f,g or Figure 15e,f it is possible to see how node merging (Section 4.6) works. Initially, there are several clusters and after including some new images and creating new clusters or making a specific cluster larger, it is possible to obtain similar clusters, so the merging process is launched and it results in a lower number of clusters. Finally, Figure 14b,e shows how transition between rooms resulted in an increased number of clusters. After 399 images, the process has detected 7 clusters but from moment  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**HOG** & & & \\ \hline
**Dataset** & **Number of Clusters** & **Proposed Method** & **Batch Spectral Clustering** \\ \hline Route 1 INNOVA & 6 & 0.3973 & 0.13 \\ Route 2 Saarbrücken & 16 & 0.2756 & 0.15 \\ Route 3 Freiburg & 30 & \(-0.1526\) & 0.13 \\ \hline
**Gist** & & & \\ \hline
**Dataset** & **Number of Clusters** & **Proposed Method** & **Batch Spectral Clustering** \\ \hline Route 1 INNOVA & 6 & 0.2556 & 0.38 \\ Route 2 Saarbrücken & 19 & 0.1262 & 0.25 \\ Route 3 Freiburg & 34 & \(-0.1449\) & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between the maximum silhouette obtained with the proposed method and the silhouette obtained with a batch spectral clustering with the same number of clusters.  
Figure 14e to the end of the process the robot has moved across a long corridor and only changes the room once, for that reason only 2 clusters are created on that process. Others characteristics such as loop closure can be observed in the figures.  
Figure 13: Maps obtained during the process using HOG \(y=2.25\), \(\Omega=1.7\) with INNOVA dataset. The subfigures show different steps of the process. In the end, the process detects 6 nodes and the final average silhouette is 0.3973. The pairs of **c,d,f,g** show how the node merging process works.  
Figure 14: Maps obtained during the process using HOG \(y=1.25\), \(\Omega=1.85\) with the Saarbrücken dataset. The subfigures show different steps of the process. In the end, the process detects 16 nodes and the final average silhouette is 0.2756.
# A Novel Dual-Band Implantable Antenna for Pancreas Telemetry Sensor Applications
## 2 Methods and Results
### Antenna Simulation in Anatomical Models  
Furthermore, we studied the electromagnetic behavior of the proposed antenna when it was placed in an anatomical model that simulated the human body with all the electrical properties at the frequencies of interest and the corresponding tissue dimensions, depending on the age and sex of the anatomical model. The radiation from the antenna inside the human body and the spatial loss index, which determines the safety of the patient, were examined and characterized. The software of the company ZMT Zurich MedTech AG,  
Figure 19: Electric field density of the proposed antenna at 2.45 GHz displayed in two- dimensional (**a**) and three-dimensional space (**b**).  
Figure 18: Electric field distribution of the modified antenna at 2.45 GHz with no structure modifications (**a**), after inserting the sorting pin (**b**), after inserting the first U-slot (**c**), and after inserting the second U-slot (**d**).  
Sim4 Life [18] was used for the implementation of the simulation. We selected two typical anatomical models from the models ViP3.1 virtual population, as shown in Figure 20.  
The pancreas is located in the terrestrial area, as illustrated in Figure 21; hence, the behavior of the antenna is mainly affected by the surrounding organs and tissues. In the simulation, there were 75 different tissues that were taken into account. Their electrical properties per frequency were obtained from the IT'IS Foundation [18].
## 4 Components and System
### Coding Filters
#### 4.1.2 Simulation and Measurement  
To perform the radar measurements, two notch filters with stop-band frequencies of about 8.45 GHz and 11.33 GHz were designed. Figure 6 shows the simulation model as well as the fabricated filter structure of the 8.5 GHz filter. Rogers RO4350B with a thickness of 0.254 mm and \(\tan\delta=0.0037\) was used as substrate. It should be mentioned that the dielectric losses are 4 times later than in the substrate used in [27]. As it turned out, additional losses due to surface roughness were assumed to be too low in the initial filter design. This resulted in a smaller stopband attenuation of the realized filters. The simulation model was later adjusted to match the observed filter characteristic. In further optimization steps, a stop-band attenuation of up to 20 dB could be achieved. Simulated and measured S-parameters of the realized filters are shown in Figure 7.  
The overall shape of the transmission factor agrees well with the simulation. The losses outside the stop-band are moderate with 0.7 dB and are to be expected due to the frequencies involved and the choice of substrate. The stop-bands are located at 8.49 GHz with 7.4 dB attenuation and 11.44 GHz with 7.1 dB attenuation. The response is very smooth and the notch is symmetrical. Both filters show a small shift of the resonance towards higher frequencies. This can be explained by manufacturing tolerances. Under etching in the magnitude of the copper thickness, i.e., tens of micrometers, is rarely avoidable and is already sufficient to shift the resonant frequency in the observed scale. This offset could be taken into account in the simulation model and thus be corrected, as long as the undercut remains constant in production. The line widths are also affected by these tolerances, and so are the gap widths. These parameters have an influence on the matching of the filter structure. Simulation and measurement therefore deviate slightly from each other. However, the average matching is still achieved and is very good with \(S_{11}<-20\) dB for the 8.49 GHz filter and \(S_{11}<-25\) dB for the 11.44 GHz filter.
## 5 Conclusions  
In this study, the FEM-CZM method that was developed was proposed to reveal the mechanical and cracking mechanism of rock masses with two holes, and the loading conditions (including the shear rate and normal stress) were mainly considered. The main conclusions are briefly described as follows:  
1. The results exhibit that the shear process in this research could be determined and identified as the elastic, strengthening, plastic, and residual stress stages, respectively. The cracks' initiation at the beginning of the crack strengthening stage gradually gathered and penetrated, mainly at the rock bridge.
2. It was observed that the crack initiation stress and the peak shear strength were significantly affected by the shearing rate and normal stress. The shear rate and normal stress were positively related to the peak shear strength. When the shear rate was 0.02 mm/s and the normal stress was 2 MPa, the crack initiation stress was the smallest, and it was easier than it was in the other conditions to bring about the crack initiation. This point should be considered in engineering practice.
3. The shear rate and normal stress significantly influenced rock masses' cracking behavior and the mechanical properties of those with holes. Specifically, the maximum shear dilatancy was positively related to the shear rate, but negatively related to normal  
Figure 12: Stress distribution and crack propagation process of rock-like materials under different normal stresses and shear rates of 0.02 mm/s. **(a1)–a(3)**). The stage of crack initiation; **(b(1)–b(3))**. The stage of crack propagation; **(c1)–c(3)**). The stage of crack coalescence.  
stress. The rock bridge played a reinforcing role in the load transfer process, as the vertical displacement near the load end was larger than that far from the load side. Hence, the displacement field of the specimen was non-centrosymmetric.
4. In this paper, a detailed analysis of the crack initiation, propagation, and coalescence behavior was carried out, and it was found that the shear rate and normal stress have significant effects on crack initiation, propagation, and coalescence. Under the load, the cohesive element at the end of the artificial crack in the edge-notched cracks were more likely to reach the damage evolution stage. The crack always started from the edge-notched, artificial crack tip, near the shear load end, and the stress concentration was more pronounced at the tip of the crack.  
Conceptualization, Y.Z. and Y.W.; methodology, Y.Z. and H.C.; software, A.L.; validation, Y.W. and X.Z.; formal analysis, Y.W.; resources, Y.Z.; data curation, Y.Z.; writing--original draft preparation, Y.W.; writing--review and editing, Y.Z. and Y.W.; supervision, Y.W.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the National Natural Science Foundation of China (Grant No. 11902136).  
Data sharing not applicable.  
The authors declare that there is no conflict of interest regarding the publication of this paper.
## 5 Discussion and Conclusions
### Discussion
The overall accuracy was 95.66%, which can guarantee the accuracy of LULC datasets of other time periods in a relatively high level. Another thing that should be noted is that the gradation result of the ecological level for every LULC type in this research (Table 2) cannot be popularized at the national scale. For example, in food production areas, farmland, with a high supply service, should be graded with the highest ecological level. Therefore, the definition of the ecological levels of different LULC types in this research is only suited for the TRHR. When popularizing these to other areas, researchers should adjust or consider other LULC types according to the main ecological services of the specific research areas.
## 3 Results
### Subjective Assessment of Mental Workload  
Considering Figure 3, the NASA-TLX questionnaire stands out as the most widely used subjective workload measure, appearing in 61% of the studies. In addition to NASA-TLX, the Bedford Workload Scale--another NASA-developed scale--was also utilized [51]. Unlike multi-dimensional rating scales such as NASA-TLX, the Bedford scale is uni-dimensional and specifically designed to assess an operator's spare mental capacity.  
Beyond those measures, several researchers have employed other subjective metrics to evaluate factors associated with mental workload. For instance, refs. [4, 43] gathered qualitative insights through unstructured feedback. Emotional responses were analyzed by [4] using the Self-Assessment Manikin (SAM). Meanwhile, ref. [40] combined NASA-TLX with task-specific scales, such as the Technology Acceptance Model (TAM), along with customized well-being and work experience surveys.
## 5 Discussion
### Evaluation of the Effectiveness and Validity of the Proposed Approach  
To evaluate the effectiveness of the proposed LSTM structure, a comparative evaluation was carried out with a convolutional neural network (CNN) (Figure 32). CNN can reduce the data preprocessing time through dimensionality reduction operations. In order to take advantage of CNNs, it is necessary to transform the dimensionality of the network data into 2D spatially correlated data. This is conducted to represent each network packet as a vector. Thus, instead of convolving in 2D, convolution is performed in 1D in the time dimension, but with a 2D kernel. The training of a CNN model is shown in Figures 33 and 34.  
Figure 32: Convolutional neural network model.  
As can be seen from the figures, the CNN is inferior to LSTM. The graphs are non-uniform, saw-toothed and the training accuracy reached 0.92 and stopped growing after the 25 th epoch.  
Figures 35 and 36 compare the accuracy of the CNN with LSTM, as well as the loss function in the training neural network process.  
It can be seen from the figures that in CNN, the learning engines take more time because they have to memorize rather large sequences. On the contrary, the learning mechanisms of the LSTM network are more optimized and remember less information.  
The validity of the proposed approach is ensured as follows. Firstly, this approach uses well-proven methods, which are the TTSN method, the theory of Markov processes and deep learning technology using LSTM networks. In other words, we can discuss ensuring the constructive validity of the approach. Secondly, as mentioned above, each experiment on the simulation stand was carried out several times. The values of probabilistic temporal characteristics and resilience probabilities presented in the graphs should be considered as average values. This provided internal validity. At the same time, it should be noted that the proposed approach has some limitations that affect its external validity. They mainly concern the nature of the impact of computer attacks on the SDN. In the approach we have considered, it is assumed that, firstly, the attacks are single, and, secondly, they have a known implementation algorithm. If the SDN is affected by multiple or combined attacks,  
Figure 34: Graph of precision and recall of CNN training.  
Figure 33: Graph of increasing accuracy and decreasing loss function in the CNN training.  
then the approach should be slightly modified by building two-level stochastic networks. At the bottom level of these networks, the stages of implementation of individual attacks will be displayed, and the relationship of attacks will be reflected at the top. If the attack is unknown, then the proposed approach allows it to be detected using the LSTM network. However, to apply the stochastic network and the Markov attack model, additional expert judgments are needed.
# Uses of Nanoclays and Adsorbents for Dye Recovery: A Textile Industry Review
## 3 Reused Waste Water Dyes
### Pigment  
One of the options that has been applied to the reuse these colorants is to employ them as pigments [76; 229; 262; 301; 302; 303; 304]. Once trapped inside a substrate such as clays, they can remain in this element to be later utilized during stamping processes.  
In order to be used as pigments, their stability must be good, and it is necessary to ensure that the colorant does not undergo any desorption processes. Therefore, after introducing the colorant into clay, tests are carried out, during which the colorant's degree of fixation is analyzed. Only in the event of having high fixation results can its use as a pigment with no discoloration risk due to loss of colorant be guaranteed.  
These pigments are very frequently used in industries, such as textiles, plastics, printing, or industrial coatings, although they have a series of properties that confer them with low stability, poor weather resistance, and poor dispersion, which mean their use is limited [305; 306]. Thus, in order to improve the described properties and defects, it is necessary to prepare stabler hybrids with colorants in clay mineral matrices [307]. By way of example, some works exist where hybrids with montmorillonite and methylene blue have been prepared, where thermal stability, photostability, and covering power have improved [308; 309].  
Other works have compared the color and stability properties of dye-clay hybrids. These studies, based on a CIELAB 1976 analysis [310], conclude that the color stability of the hybrids formed by halloysite were superior to others, such as MMT and sepiolite [311], which can be attributed to each nanoclay's individual structure and chemical properties.