esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Values of Inclinations Calculated Using [PERSON]'s Model  
The theoretical values of the inclinations were calculated using [PERSON]'s model which was implemented in the EDN-OPN computer program. The EDN-OPN program includes a package of programs enabling the calculation of the values of any mining area deformation indicators at any number of points, located at any distance from the edges of any number of mining fields, and at any time of exploitation. The results were obtained in tabular form using the EDBJ1 program and as surface maps of the distribution of the deformation indicator values using the EDBJ2 program. Moreover, the TGB1 program also made it possible to determine the parameter values for the selected forecasting model based on the subsidence values measured at points representing the profile of a static, full subsidence trough formed after the end of a given stage of exploitation. For proper operation, the EDN-OPN program requires four types of files with data on the following:  
* The geometry and location of the mining fields (a file with the.eks extension);
* The location of the calculation points where the values of the deformation indicators are calculated (a file with the.pkt extension);
* The geological structure of the rock mass and the thickness of the overburden over the hard coal seams (a file with the.ndk extension);
* Connecting the abovementioned files into one file and controlling the EDBJ1 and EDBJ2 programs (files with the.st1 and.st2 extensions, respectively). The theoretical inclination values were calculated for the middle points of the measurement sections numbered from 1 to 33, after the end of the exploitation in the 1/II longwall of the 405/1 hard coal seam (inclination values for the period from 4 March 2019 to 17 August 2019) using [PERSON]'s model with the following values for its parameters:  
* The \(a\) exploitation coefficient (roof rocks' deflection coefficient): \(a=0.976\); \begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Category of Mining Area**} & **Number of Measured** & **Percent of Total Number** \\  & **Inclinations** & **Measured Inclinations** \\  & **\(\textbf{N}_{\textbf{T}_{\textbf{U}\textbf{U}\textbf{U}\textbf{U}\textbf{U}}}\) & **\(\textbf{P}_{\textbf{Z}\textbf{N}_{\textbf{T}_{\textbf{U}\textbf{U}\textbf{U} \textbf{U}}}}\) [\%]** \\ \hline
0 & 12 & 36.36 \\
1 st & 13 & 39.39 \\
2 nd & 3 & 9.10 \\
3 rd & 2 & 6.06 \\
4 th & 2 & 6.06 \\
5 th & 1 & 3.03 \\ Total & 33 & 100.00 \\ \hline \end{tabular}
\end{table} 
Table 6: Determined categories of a mining area due to a number of \(T_{\textbf{U}+1}\) observed inclinations (source: own study). \begin{table}
\begin{tabular}{c c} \hline \multirow{2}{*}{**Category of Mining Area**} & **Inclination** \\  & **\(\textbf{T}\textbf{[}\frac{\textbf{mm}}{\textbf{m}}\textbf{]}\)** \\ \hline
0 & \(|T|\leq 0.5\) \\
1 st & \(0.5<|T|\leq 2.5\) \\
2 nd & \(2.5<|T|\leq 5.0\) \\
3 rd & \(5.0<|T|\leq 10.0\) \\
4 th & \(10.0<|T|\leq 15.0\) \\
5 th & \(|T|>15.0\) \\ \hline \end{tabular}
\end{table} 
Table 5: Categories of the mining area based on the inclination values (source: [3; 5]).
# A Network Architecture and Routing Protocol for the MEDIcal WARNING System
## 5 The MultiPath Routing Protocol for MEDIWARN (MP-RPM)  
Here, we consider a network made up of three different node types, i.e., end nodes, relay nodes and the sink. The end nodes are unaware of the network routing logic, so they broadcast the data acquired by the sensors without any path (or route) selection. The relay nodes are the core of MP-RPM, as they take care of message forwarding in the network, from the source to the destination. The sink, i.e., the MEDIWARN Virtual Biosensor in Figure 1, is the network collector, i.e., the destination node of all the sensor data sent by the end nodes.  
The MP-RPM works over the medium access control (MAC) layer, uses standard IEEE 802.11 frames and the EUI-48 address format. The relay nodes are set to receive all frames in promiscuous mode (regardless of their destination address). Finally, the end nodes and their applications are totally unaware of the network topology and the routing protocol used, i.e., they simply send messages with the sink address as destination. The relay nodes are in charge of forwarding these messages to the sink. This way, as the end nodes do not take part in the routing decision, any node can be supported, regardless of the high-level application that runs on it.  
The MP-RPM can be split into two phases, i.e., initialization (init) and data exchange, as described below.
## 3 Current Measurement Techniques
### Snow Property Determination Techniques
#### 3.2.3 Radar Measurements  
Measurements of SWE using various types of radar have been attempted for many years, with varying results, yet no system has ever become successful operationally. The three factors of prime importance to radar measurement error are the liquid water content of the snowpack, the configuration of wet/dry snow interfaces, and the homogeneity in the depth of the snowpack [42]. Variations in these parameters can cause large errors, even in simple one-layer or two-layer snowpack scenarios.  
Perhaps the most promising advances in this field have been made in Ground Penetrating radar (GPR), where SWE measurements can be made using a towed sledge, or from an aircraft. GPR also has the added and very significant advantage of being able to measure the internal vertical variation in snow density. Apart from radar, the only way of determining this currently is by manual measurement or modeling. Errors in GPR measurements of SWE are also dependent on the specific snow conditions. A comprehensive definition of the errors involved in GPR is provided by [42].
## 2 Methodology
### Feature Selection
Once the score is obtained for all the features by using Equation (11), the next step is to select a few good features with a high score. While this can be performed arbitrarily, it is achieved in a more objective manner by applying the formula based on the risk priority number (RPN) threshold estimation [21]. The idea is to divide the whole features into negligible parts, characterized by a gradual change, and the significant part, with a steep increase in score. It was originally developed to select failure modes with highest RPN in the failure mode, effect, and criticality analysis (FMECA), but the authors believe that it is applicable here too. The process begins with setting a unique score and calculating the frequency of each score. The weighted finite difference is defined by dividing the finite difference by the frequency of the unique score as follows:  
\[Diff_{i}=\frac{score_{i+1}-score_{i}}{frequency_{i+1}} \tag{12}\]  
where \(i\) is the index of the unique score. It indicates that the smaller the frequency of the unique score and the larger the finite difference, the larger the weighted finite difference and the greater the variation between the two consecutive values. Then, the peak, namely the local maxima, is obtained to indicate a significant increase in two consecutive unique scores, which is obtained by the following equations:  
\[PP_{i}=\frac{Peak_{i-1}+Peak_{i-2}+Peak_{i-3}}{3} \tag{13}\]  
\[\Delta Peak_{i}=100\times\frac{Peak_{i}-PP_{i}}{PP_{i}}_{\%} \tag{14}\]  
In the final step, maximum peak is identified and the corresponding score value is used as the threshold to identify the most significant features. Further details will be addressed in the subsequent case study. \[FDR=V_{W}{}^{-1}V_{B} \tag{6}\]0 Features with different _corr_ and _FDR_: (**a**) case with low _corr_ and high _FDR_, (**b**) case with high _corr_ and low _FDR_, and (**c**) case with high _corr_ and high _FDR_ (_LV\({}_{i}\)_ in the figures indicates the fault severity level). Sometimes, the selected features are dependent on each other, that is, show a similar trend in terms of fault severity. An example is that of variance and standard deviation. In this case, the two are highly redundant and just one is enough to conduct the diagnosis. To this end, Pearson correlation is computed for pairs of features combination. If the value is close to 1, remove either one in the resulting features. Finally, one obtains the selection of features for each component that fulfills all the criteria: consistent trend, separability, and independency.
# Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix
## 3 Experimental Results and Discussion
### Object-Oriented Change Detection
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment Name** & \begin{tabular}{c} **Missing Detection** \\ **Rate** \\ \end{tabular} & \
\begin{tabular}{c} **False Detection** \\ **Rate** \\ \end{tabular} &
\begin{tabular}{c} **Overall Accuracy** \\ **Rate** \\ \end{tabular} & **Kappa Coefficient** \\ \hline Comparison 1– & 48.15\% & 39.13\% & 60.00\% & 0.20 \\ Spectral-based & 48.15\% & 39.13\% & 60.00\% & 0.20 \\ Comparison 2– & 6.25\% & 9.09\% & 90.74\% & 0.81 \\ GLCM 3– & 2.22\% & 6.38\% & 94.96\% & 0.89 \\ LETRIST & 7.45\% & 1.10\% & 94.62\% & 0.88 \\ Comparison 5– & 3.33\% & 1.60\% & 97.83\% & 0.95 \\ OMTLBP & 6.67\% & 3.45\% & 94.44\% & 0.89 \\ GLCM-SBS & 0.00\% & 3.33\% & 98.15\% & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Change detection accuracy of Sentinel-2 in different experiments. Figure 11: Change detection results of comparisons and the proposed algorithms of Skysat: (**a**) Spectral-based, (**b**) GLCM, (**c**) LETRIST, (**d**) LDZP, (**e**) OMTLBP, (**f**) GLCM-SBS, (**g**) the proposed algorithm, and (**h**) the standard change detection result. In order to verify quantitatively the effectiveness of the proposed method in different data sets, the missing detection rate, false detection rate, overall accuracy rate, and Kappa coefficient are calculated and are listed in Table 4. It can be seen that with the increase in spatial resolution, the accuracy of detection has generally improved; for example, the accuracy of the spectral-based method is 92.45%. However, at the same time, the pixels among the superpixels increased dramatically, the texture features described by LETRIST and LDZP have difficulty representing global texture features, and the accuracies are relatively low, only 88.68% and 89.15%, respectively. The OMTLBP uses a multi-scale approach to describe the whole texture characteristics, resulting in significant improvements. As  
Figure 12: Enlarged images of Skysat in Areas 1 and 2: (**a1,b1**) Spectral-based, (**a2,b2**) GLCM, (**a3,b3**) LETRIST, (**a4,b4**) LDZP, (**a5,b5**) OMTLBP, (**a6,b6**) GLCM-SBS, (**a7,b7**) the proposed algorithm, and (**a8,b8**) the standard change detection result. a whole, the LBP family performs equal to or worse than the GLCM-based method, and the traditional GLCM-based methods perform worse than the proposed algorithm that describes the texture characteristics by considering the band correlation.
## 5 Conclusions  
Based on remote sensing products, in this study, the changes in the vegetation LAI and GPP in the terrestrial ecosystems from 2000-2018 were analyzed from a region-wide to zonal perspective. In addition, the PML-V2 GPP and data from eight flux stations were compared, and it was found that they were of good quality in these regions. The analysis results revealed that, in general, the LAI and GPP increased significantly during 2000-2018, and the contributions of SC and EC were relatively high.  
Although grassland accounted for the largest proportion of the total area, forest and cropland were the main vegetation types leading to LAI and GPP changes. Under the premise of ensuring biodiversity and ecological balance, expanding the area of forest land and arable land is the key to improving ecological benefits and greening the Earth. Due to the high altitude, cold climate, and lack of plant diversity in SW, the urbanization process has had a negative impact on carbon sequestration. Strengthening the management of urban greening and planting trees with strong environmental adaptability may improve this situation. The strong correlation between GPP and LAI suggests it is wise to change the vegetation structure to grow terrestrial carbon sinks through ecological engineering. The results of this study provide a theoretical basis for quantitative analysis of the effects of LUCCs on the LAI and GPP in China and the current regional ecological situation. They also provide new data and a reference for regional ecological improvement planning and other land use change plans.  
Conceptualization, Z.W. and E.G.; methodology, Z.W. and Q.H.; software, J.Z.; validation, W.L. and F.F.; formal analysis, E.G. and J.Z.; investigation, Q.H.; resources, Q.H. and Z.W.; data curation, E.G. and Z.W.; writing--original draft preparation, E.G.; writing--review and editing, Z.W.; visualization, E.G. and J.Z.; supervision, W.L. and F.F.; project administration, Q.H. and Z.W.; funding acquisition, Q.H. Z.W. and F.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Joint Funds of the Key Science and Technology Project of the Ministry of Water Resources (SKR-202017), the National Natural Science Foundation of China(Nos.42277478), the Joint Funds of the National Natural Science Foundation of China (No. U21A20109), and Special Research Fund of the YRIHR (HKY-JBYW-2022-13).  
Not applicable.  
The data is available on request from the corresponding author.  
We gratefully thank the anonymous reviewers for their useful comments and constructive suggestions on the manuscript.  
The authors declare no conflict of interest.
# Plasmonics in the Ultraviolet with Aluminum, Gallium, Magnesium and Rhodium
###### Abstract  
Ultraviolet plasmonics (UV) has become an active topic of research due to the new challenges arising in fields such as biosensing, chemistry or spectroscopy. Recent studies have pointed out aluminum, gallium, magnesium and rhodium as promising candidates for plasmonics in the UV range. Aluminum and magnesium present a high oxidation tendency that has a critical effect in their plasmonic performance. Nevertheless, gallium and rhodium have drawn a lot of attention because of their low tendency of oxidation and, at the same time, good plasmonic response in the UV and excellent photocatalytic properties. Here, we present a short overview of the current state of UV plasmonics with the latest findings in the plasmonic response and applications of aluminum, gallium, magnesium and rhodium nanoparticles.  
ultraviolet; plasmonics; UV plasmonics; aluminum; gallium; magnesium; rhodium 1
## 5 Methodology and Experiments
### RNN Model  
The second model tested in the scope of this work was an RNN model. RNN models can successfully deal with sequential data, because RNN units are designed to memorize previous states [42]. This memory property was used in the computation of the current state. Thus, RNN units accept two inputs: previous output and current input value. The working mechanisms behind RNN units can be formulated as (1).  
\[y(t)=f(x(t)+y(t-1)). \tag{1}\]  
where \(x(t)\) is the current input, \(y(t)\) is the current output, and \(y(t-1)\) is the past output. The function that models this relation is represented by \(f(x)\). In the case of RNN, the function \(f(x)\) is a simple hyperbolic tangent function that introduces nonlinearity since it has a tendency to have unstable gradients. Using a non-saturating activation function such as the rectified linear unit function causes gradients to grow arbitrarily large. The RNN model is implemented using the \"SimpleRNN\" function inside the tensorflow.keras.layers API. In this model, two recurrent layers and one dense layer were used, as shown in Figure 8.  
The recurrent layers accept a batch size of 128. Each recurrent layer has 100 units, and there are 30 time steps for each. The dense layer accepts the recurrent layers' outputs and produces a single value as the predicted number of questions. For each window, RNN runs with an initial state value of zero. Then, the state value is updated at each time step until RNN makes its prediction. If not inferred otherwise, SimpleRNN function in tensorflow.keras API clears the state value after a prediction is made and does not keep the state value for the next iterations. This configuration is known as the stateless RNN. In the stateless model, batches can randomly be chosen from the datasets, and they can even overlap each other. On the other hand, in a stateful model, batches should be chosen consecutively, and they should not overlap.  
Figure 8: RNN model structure.  
Figure 7: Forecasting performance of the dense model.  
Another configuration is known as the stateful model. In this architecture, RNN units keep the state information and transfer it to the next iterations. Stateful RNN models perform better on longer sequences, but they require a longer training time, and sometimes they perform poorly because consecutive batches are highly correlated. Forecasting performance of the stateless and stateful versions are presented in Figures 9 and 10, where orange lines are the predicted values and blue lines are the real numbers. The stateless RNN model performed better than the stateful RNN, but both performed worse than the dense model. The forecasting performance in terms of MAE was 6.679 for stateless RNN and 7.039 for stateful RNN.
# Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm
## 2 Preliminaries and Notation
### Minimum Base Attitude Disturbance  
During the FFSR2 maneuver, minimum attitude disturbance acting on the free-floating base is required, which is expressed as:  
\[\min F_{2}: =\sqrt{\alpha_{0}^{2}+\beta_{0}^{2}+\gamma_{0}^{2}} \tag{8}\]  
In Equation (8), \(\alpha_{0}\), \(\beta_{0}\) and \(\gamma_{0}\) are employed to depict the base attitude of FFSR2, denoting the Euler angles around the \(X\)-, \(Y\)-, and \(Z\)-axis of \(\Sigma_{0}\), respectively. The order of rotation around the axes is \(Z-Y-X\).  
**Remark 1**.: _The MTTP aims at searching for the optimal sequence of waypoints and the optimal joint movements. If not consider the optimal joint movements, the MTTP is the Traveling Salesman Problem (TSP) [18; 20]. The TSP denotes that a salesman is required to visit a set of predefined cities with minimum time (or path length), while the time employed to stay at each city is not considered. This is because the time spent at each city has no influence on the optimal sequence of the cities, which also works in the MTTP. Therefore, the time period during which the end effector executes specific tasks at each waypoint is omitted._
## 3 Test Design
### Model Materials and Design
#### 3.2.2 Model Soil  
The soil profile at the prototype caisson is multilayered, with the shear wave velocity (\(v_{s}\)) increasing with depth (\(z\)). Although simulating a detailed layered soil profile is not the focus of this paper, it is important to ensure the distribution of soil stiffness along the depth. To model the \(v_{s}(z)\) distribution, the prototype soil profile is idealized as a single-layer model soil, where \(v_{s}\) increases parabolically with depth \(z\). The tests were conducted using a model soil consisting of a mixture of dry sand and sawdust, where the mass ratio of sand to sawdust was 2.5:1. The grading curves of dry sand, mixed model soil, and sawdust are shown in Figure 3. The results of the resonance column tests on synthetic model soils [25] indicated a nonlinear increase in the shear modulus \(G_{0}\) with confining pressure \(\sigma_{0}^{\prime}\), following the relationship:  
\[G_{0}=1251.63\times({\sigma_{0}^{\prime}}^{)}^{0.684}\;(\mathrm{kPa}) \tag{1}\]  
The key mechanical parameters of the model soil are determined through the direct shear test. The effective internal friction angle \(\varphi\) is 30.7\({}^{\circ}\), and the effective cohesive strength \(c\) is 0.54 kPa.  
In the free-field system mode test, a physical model with a depth of 1.68 m was constructed layer by layer, with each layer measuring 11.2 cm in thickness. The density \(\rho\) of the model soil was controlled at 910 kg/m\({}^{3}\), which meant that the mass of the model soil needed for each layer was 556.5 kg. Manual tamping and mechanical vibration were used to compact each soil layer until the target thickness was achieved.  
In the soil-caisson system model test, the model soil was built up to the caisson base elevation, and then the caisson foundation was placed in the pre-designed position.  
Figure 2: Dimensions and physical view of the caisson foundation model: (**a**) Dimensions (units: mm); (**b**) Physical view.  
Subsequently, each layer of model soil above the caisson base was constructed until the target thickness was reached.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 3 NP Properties and Characterization Methods
### Physical Properties and Their Characterization  
Precisely determining the properties of MNPs is extremely important. The physical properties of MNPs are highly dependent on their size, bearing in mind that as the size decreases to nanoscale, various physical properties can change significantly [87].  
The size and shape of MNPs has a direct impact on their behavior and functionality, influencing features such as magnetic response, biocompatibility, and cellular uptake. As MNPs decrease in size, they may transition from multi-domain to superparamagnetic behavior, exhibiting negligible magnetic remanence and coercivity. Smaller MNPs often reach magnetic saturation more quickly under an external magnetic field as compared to larger particles. This property is crucial for applications such as magnetic storage and sensing [88].  
For example, nanoparticles with a size less than 25 nm, such as magnetite, display a superparamagnetic behavior [89], which is an essential characteristic for applications like targeted medication administration and MRI.  
Size can also influence the blocking temperature at which MNPs become magnetically blocked. Smaller particles typically have lower blocking temperatures [90].  
Moreover, smaller MNPs have a higher surface area-to-volume ratio, making them more reactive and suitable for drug delivery or catalysis applications. This particularity allows for more effective functionalization by attaching targeting ligands or other molecules to achieve higher biocompatibility [88].  
On the other hand, smaller nanoparticles are more prone to agglomeration due to increased surface energy [91].  
The characterization of magnetic nanoparticles (MNPs), particularly for biomedical applications, encompasses a range of procedures that primarily examine MNPs' inorganic crystalline core and organic shell, which is required to inhibit the formation of MNPs aggregates and minimize the likelihood of an immune system reaction [92; 93]. The main techniques capable of providing crucial insights into the physical characteristics of these NPs are transmission electron microscopy, dynamic light scattering and magnetometry methods.