esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Observation of Canopy Solar-Induced Chlorophyll Fluorescence  
The SIF observation was conducted by a Top-of-Canopy spectrometer (AutoSIF-1, Bergsun Inc., Beijing, China), which was an automatic long-term system fixed on the stationary ground-based platform 10-12 m above the closed canopy. The observation system adopts a single high-resolution spectrometer (QE65 Pro, Ocean Optics Inc., Dunedin, FL, USA) with a sampling interval of 0.155 nm, spectral resolution of 0.31 nm, SNR of 1000:1 and a spectral range of 650-800 nm. A vertically upward CC-3 cosine corrector (Ocean Optics Inc., Dunedin, FL, USA) was installed at the end of upward fiber used to collect the downward solar irradiance spectrum, and a bare fiber (Ocean Optics Inc., Dunedin, FL, USA) with \(\text{VAZ}=25^{\circ}\) inclined downward was used to measure the upward canopy radiance reflection spectrum. In order to minimum the measurements errors caused by meteorological conditions (such as clouds), the measurement adopts a \"sandwich\" mode [21], in which the spectrometer first collects solar radiation, then quickly switches the shutter to measure canopy radiation, and finally switches to measure solar radiation, with all measurements recorded at the same time. Before each data acquisition, the instrument optimizes the integration time automatically according to the light intensity at that time. Therefore, the period of each measurement depends on the light conditions at the time, and it will be shorter at midday than at sunrise or sunset [47]. During the growing season, the canopy solar-induced chlorophyll fluorescence is continuously monitored, and the instrument is calibrated regularly.  
The retrieval of SIF signal are generally based on the principle of FLD. We applied the spectral fitting method (SFM) approach to gain the SIF signals [48] in this study; the SFM method assumes that both the reflectance and fluorescence as a function of wavelength are in accordance with Taylor polynomials. Three bands of spectral data were needed, including one in the absorption band and two on both sides of the absorption band [49]. All original SIF signals were resampled into 30 min intervals by Python 3.7.6 (Python Software Foundation, Beaverton, OR, USA). Research have demonstrated that SIF in the red band may involve more PSII-related information. However, due to the more complex impacts of canopy structure and direction, and angle of observation instrument on red SIF than on far-red SIF [5], we only considered the relationship between canopy far-red SIF and GPP in this study for the purpose of minimizing the influence of re-scatting and the re-absorption on SIF signals.
# Practical Approach for Smart and Circular Cities: Chatbots Used in Waste Recycling
## 4 An Analysis of Five Representative Chatbots That Support the CE
### Litterbot  
Litterbot is an advanced interactive system developed to help users identify items suitable for recycling. Its core mission is to simplify recycling by providing accurate and relevant information on local waste management regulations. The chatbot is based on sophisticated data analysis and natural language processing technologies, providing personalized and up-to-date answers in real time that are tailored to specific user requirements [20].  
Litterbot's mission is to simplify and facilitate the recycling process for people by providing accurate and relevant recycling information according to local regulations. Through technology, Litterbot aims to educate users about the environmental impact of recycling and encourage them to adopt more sustainable waste management practices.  
What makes Litterbot so unique is its accessible and intuitive approach to providing recycling information. Using data analytics and natural language processing technologies, this chatbot offers fast, accurate answers tailored to users' local needs and regulations. Typically, the chatbot asks the user to find out the material of which the product to be recycled is made, followed by a recycling number found on the product packaging. The purpose of these questions is to be able to advise the user whether they can recycle it or not (see Figure 3).  
A distinctive aspect of Litterbot is its accessible and intuitive approach, which promotes understanding of the recycling process and encourages the adoption of sustainable waste management practices. Through its interactive communication, the system helps to raise awareness of the importance of recycling and promote more environmentally responsible behavior.  
Figure 3: The logic underlying Litterbot to guide users.  
Implementing advanced technologies such as data analytics and natural language processing enables Litterbot to provide accurate and tailored responses in line with local requirements and regulations. Thus, Litterbot represents an innovative and efficient solution in waste management, helping to promote a recycling culture and protect the environment in an academic and accessible way.  
In conclusion, Litterbot represents a significant innovative tool in waste management, providing a simple and effective solution to educate and engage people in recycling. Through its accessible approach and the advanced technologies that it uses, Litterbot contributes to promoting a recycling culture and protecting the environment.
## 2 Materials and Methods
### Modification of EGS with APTES and Goethite  
The modification steps were performed by treatment of etched glass spheres surface with amino-silane coupling agent (APTES) followed by iron oxyhydroxide (GT) precipitation (Figure 1). The freshly cleaned EGS material (10 g) was submerged in non-polar solvent, xylene, with simultaneous introduction of N\({}_{2}\) for 30 min. After that, APTES (10% \(w/w\) in xylene) was added drop-wise for 120 min. Reaction was carried out for 20 h at 25 \({}^{\circ}\)C in N\({}_{2}\) atmosphere, followed by dropping of 5 mL of 96% ethanol ten times in the course of last period of the modification procedure. Centrifugation and thorough rinsing with ethanol several times and then water was applied. The obtained material, denoted as EGS@APTES, was stored wet prior to impregnation with goethite (35 wt.% water content).  
Final steps of hybrid adsorbent preparation was EGS@APTES modification via GT precipitation analogously to the procedure described elsewhere [36; 37]. It involved three identical steps in order to distribute oxide as evenly as possible through porous EGS@APTES material without pores clogging. The first step of GT precipitation was carried out by following procedure: EGS@APTES material (50 g) was submerged in xylene. The reaction was conducted continuously with simultaneous introduction of up-flow N\({}_{2}\) stream to obtain a fine distribution of the iron solution over the spheres inner, i.e., interior porous structure, and outer surface for 30 min in suspension by drop-wise addition of 20 mL Fe\({}_{5}\)O\({}_{4}\)-7H\({}_{2}\)O saturated solution (2.0 mol L\({}^{-1}\)) for 30 min and simultaneous bubbling of N\({}_{2}\). The oxidation was performed by changing the nitrogen with air introduction and neutralizing reaction by addition of 10 mL NaHCO\({}_{3}\) saturated solution (2.6 mol L\({}^{-1}\)) for 30 min, which caused the precipitation of the oxide in goethite form. The reaction was conducted for 48 h under air flow until the green-blue color of the material changed to ocher shade. The synthesized material was rinsed three times with water and stored wet. The procedure for the precipitation of GT was repeated in an analogous manner (the second and the third step) to obtain \(\sim\)19 wt.% of GT in relation to the EGS@APTES carrier. The final material, denoted as EGS@APTES-GT, was used wet (42 wt.% water content). Onthe Figure 1 shows (step 3) differences of GT content in EGS@APTES-GT after the first, second, and third precipitation procedure step, where color changes from light brown to brown-red.
# Mechanical Design and Gait Optimization of Hydraulic Hexapod Robot Based on Energy Conservation
## 5 Simulation and Result Discussion  
The HHR performance is tested via the co-simulation of MATLAB/Simulink and ADAMS, which simulate the hydraulic system and robot motion separately, as shown in Figure 13. The mechanical model is established based on the optimization results of AMGA in Table 5, as shown in Figure 14.  
The servo valve model is approximated by following second-order linear differential equations. \(\omega_{\rm n}\) is the natural frequency of the servo valve, \(\xi_{\rm n}\) is the damping ratio, and \(x_{i,j}\)\({}^{*}\) is the servo valve's spool displacement of the joint \(j\) of leg \(i\).  
\[\vec{x}_{i,j}^{*}+2\omega_{n}\xi_{n}\dot{x}_{i,j}^{*}+\omega_{n}^{2}=\omega_{n }^{2}u_{i,j} \tag{45}\]  
Figure 14: The mechanical model of HHR in ADAMS.  
Figure 12: Design sensitivity for objective function: (**a**) the overall objective function; (**b**) flow rate; (**c**) maximum pressure; (**d**) stability margin.  
Figure 13: Overview of the virtual prototype.  
The quantity of flow through the servo valve is established. Q\({}_{i,j,1}\)* and Q\({}_{i,j,2}\)* are the flow rate of the piston chamber and rod chamber. P\({}_{i,j,1}\)* and P\({}_{i,j,2}\)* are the pressure of the piston chamber and rod chamber. f\({}_{i,j}\)* is the output force of the cylinder. c\({}_{i,j}\)* is the length of the cylinder.  
\[\begin{split}\text{Q}_{i,j,1}^{*}&=k_{q}x_{i,j}^{ *}\sqrt{\Delta P_{1}^{*}}\\ \text{Q}_{i,j,2}^{*}&=k_{q}x_{i,j}^{*}\sqrt{\Delta P _{2}^{*}}\end{split} \tag{46}\]  
where  
\[\begin{split}\Delta P_{1}^{*}&=\left\{\begin{array}{ ll}P_{s}-P_{i,j,1}^{*}&u>0\\ P_{i,j,1}^{*}-P_{t}&u<0\\ P_{i,j,2}^{*}-P_{t}&u>0\\ P_{s}-P_{i,j,2}^{*}&u<0\end{array}\right.\end{split}\]  
The pressure of the cylinders' chambers is modeled as  
\[\begin{split}\dot{P}_{i,j,1}^{*}&=\dot{\psi}_{1}\bigg{(}-A_{ 1}\dot{c}_{i,j}^{*}+\text{Q}_{i,j,1}^{*}+C_{ip}\bigg{(}P_{i,j,1}^{*}-P_{i,j,2}^ {*}\bigg{)}+C_{ep}P_{i,j,1}^{*}\bigg{)}\\ \dot{P}_{i,j,2}^{*}&=\dot{\psi}_{2}\bigg{(}A_{2}\dot{c}_{i,j}^{*}- \text{Q}_{i,j,2}^{*}+C_{ip}\bigg{(}P_{i,j,2}^{*}-P_{i,j,1}^{*}\bigg{)}+C_{ep}P _{i,j,2}^{*}\bigg{)}\end{split} \tag{47}\]  
where the volume of the chambers is  
\[\begin{split} V_{1}&=A_{1}\bigg{(}c_{i,j}^{*}-c_{ \text{min}}\bigg{)}+V_{pl}\\ V_{2}&=A_{2}\bigg{(}c_{\text{max}}-c_{i,j}^{*} \bigg{)}+V_{pl}\end{split} \tag{48}\]  
The output force of the cylinders transferred to the ADAMS model is  
\[f_{i,j}^{*}=A_{1}P_{i,j,1}^{*}-A_{2}P_{i,j,2}^{*}-B\dot{c}_{i,j}^{*}-F_{f} sign(\dot{c}_{i,j}^{*}) \tag{49}\]  
The servo valves are controlled by PID (Proportion-Integral-Derivative) controller with the parameter set as \(k_{p}=40\), \(k_{d}=100\), and \(k_{i}=0.3\), as shown in Equation (50).  
\[u_{i,j}=k_{p}\Big{(}c_{i,j}-c_{i,j}^{*}\Big{)}+k_{d}\Big{(}\dot{c}_{i,j}-\dot {c}_{i,j}^{*}\Big{)}+k_{i}\int\Big{(}c_{i,j}-c_{i,j}^{*}\Big{)}dt \tag{50}\]  
The simulation parameters of the HHR in MATLAB/Simulink and ADAMS are shown in Tables 6 and 7.
## 2 Method
### Testing with Dense Point Cloud
#### 3.2.2 Test Results  
The filtering accuracy evaluation results are shown in Table 5. Test 1 is a flat terrain with a total filtering error of 3.46%. Type I and II errors are very small and evenly distributed. Test 2 has a relatively steep terrain with a total filtering error of 11.50%. The type I error is large, and the misclassification rate of ground points is high. Test 3 is a steep slope terrain including ravines. The total filtering error is 9.91%, and the type I and II errors are relatively close. The results are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Number of Point Clouds** & **Feature** & **Dataset Short Name** \\ \hline Test 1 & 10,339,269 & Flat & AZ17\_Donager \\ Test 2 & 7,797,662 & slope & CA14\_Guo \\ Test 3 & 8,942,695 & high and steep slopes & CA13\_Guo \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristics of testing datasets.  
Figure 7: Cross-validation results.  
The total error represents the feasibility of using the filtering method. From the perspective of the total filtering error of three different terrains, the filtering effect of the proposed method is relatively good and has feasibility.
# Reduction of Schottky Barrier Height at Graphene/Germanium Interface with Surface Passivation
## 2 Materials and Methods  
Intrinsic Ge (001) samples were degreased in ethanol for 30 min, then successively dipped in a dilute HCl bath (HCl:H\({}_{2}\)O, 1:4) for 30 s and a H\({}_{2}\)O\({}_{2}\) bath (H\({}_{2}\)O\({}_{2}\):H\({}_{2}\)O, 1:10) for 60 s, three times each. The surface oxide was then etched one last time in HCl followed by a final oxidizing step in NH\({}_{4}\)OH:H\({}_{2}\)O\({}_{2}\):H\({}_{2}\)O (1:2:10). For the realization of graphene/germanium contacts, monolayer CVD-graphene grown on Cu was purchased from Graphene(r). A 200 nm polymethyl methacrylate (PMMA) layer was spin-coated on top of the G/Cu to serve as a mechanical support layer during transfer. The Cu was then etched in a FeCl\({}_{3}\) solution until no Cu could be seen and the floating PMMA/G was rinsed in several water and diluted HCl baths in order to eliminate metallic residues. After rinsing, the PMMA/G was transferred onto the Ge wafer and left to dry overnight. After transfer and drying, the PMMA resist was cleaned in acetone and the sample was introduced under ultra-high vacuum (UHV) with a base pressure of 10\({}^{-10}\) Torr for a 3 h annealing at 300 \({}^{\circ}\)C in order to remove resist residues. After annealing, the sample was taken out of UHV for a wet passivation step either in HF (10%) for 10 min for hydrogen passivation (hereafter named Ge:H) or in dilute 7.5% HCl for 30 s for Cl passivation (hereafter named Ge:Cl).  
We used X-ray photoemission spectroscopy (XPS) to study the physico-chemistry of the G/Ge contacts with different Ge surface treatments before and after gold deposition. Measurements were realized at normal emission and room temperature using a MgK\(\alpha\) (1253.6 eV) X-ray source. The kinetic energy of the emitted electrons was measured by employing a hemispherical analyzer (Omicron EA125) with a five-channel detection system. The total energy resolution was 0.80 eV. Before photoemission measurement, the analyzer was calibrated with a thick gold sample. We assume an energy difference of 84.0 eV between the Au4f7/2 peak and the Fermi level [28].
# The Spotting Distribution of Wildfires
## Appendix. Ember Release, Burning, Flying and Fuel Ignition
### The Launching Distribution \(\phi(z,m)\)
** We assume that each firebrand of mass \(m\) is lofted to a unique height \(z=z(m)\), as for example in Equation (A5). We can then define  
\[\phi(z,m)=\delta(z-z(m))\mu(m),\] (A6)  
where \(\mu(m)\) is a given mass distribution, and \(\delta\) represents the Dirac delta functional. **Model L2. ** Instead of assuming that each mass is lofted to a unique height, we might instead suppose that it is launched randomly about the standard lofting height \(z(m)\). For example, if heights are normally distributed about the lofting height \(z(m)\), we can write \(\phi(z,m)=\mathbb{N}(z(m),\sigma)\mu(m)\), a one-sided normal distribution where \(\mathbb{N}\) has mean \(z(m)\) and variance \(\sigma\), with  
\[\mathbb{N}(z(m),\sigma):=\frac{A}{\sigma\sqrt{2\pi}}\exp\left(-\frac{(z-z(m)) ^{2}}{2\sigma^{2}}\right),\] (A7)  
where the constant \(A\) is chosen so that the distribution is normalized. We allow values of \(z\) to lie in \([H,z_{max})\), where \(H\) is the canopy height and \(z_{max}\) is the maximum lofting height predicted by the Baum and McCaffery plume. **Model L3. ** Finally, we consider the case where the launching heights \(z\), and masses \(m\), are independent of each other, so we can write  
\[\phi(z,m)=\mathbb{Z}(z)\mu(m),\] (A8)  
where \(\mathbb{Z}(z)\) is a probability distribution which describes how firebrands are distributed with height \(z\), and \(\mu(m)\) is the mass distribution. A similar sort of model was employed by [PERSON] in the context of firebrand transport by line thermals [13], where \(\mathbb{Z}\) is a uniform distribution. \[\mathbb{Z}(z)=U[z_{min},z_{max}].\] (A9)
# Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization
## 5 Experimental Evaluation
### Clustering Techniques
#### 5.4.2 Self-Organizing Maps Clustering Technique  
The mechanism in which the Self-Organizing Maps (SOM) work is discussed in Section 3.1. For the purpose of our experiment, we use the implementation of SOM that is available in the MiniSom Python library [55]. Clustering using SOM requires setting six parameters:  
* \(dim_{x}\): the \(x\) dimension of the map.
* \(dim_{y}\): the \(y\) dimension of the map.
* \(D\): the number of features in the dataset.
* \(sig\): the sigma value representing the radius of the different neighbors in the SOM representing the different categories.
* \(n(t)\): the learning rate value.
* \(t\): the number of iterations (epochs).  
For the purpose of tuning the parameters of the SOM, the dataset was divided into three parts, training, validation, and testing, with 60%, 20%, and 20%, respectively. The first two parts were used to tune the parameters while the last part was used to generate the final clustering results.  
The first two parameters, \(dim_{x}\) and \(dim_{y}\), define the dimension of the map. There are two methods to set the SOM's dimension. The first method is considered to be a rule of thumb where the dimension is obtained from the number of features in the dataset using Equation (3).  
\[\sqrt{5\times\sqrt{D}} \tag{3}\]  
On the other hand, the second method requires tuning the SOM to obtain the least consistent QE value. We used both methods and concluded that the first method where the dimension is obtained from Equation (3) provided better clustering results. The third parameter is the number of features in the dataset which is equivalent to the number of columns in the dataset's representation version. The sigma and learning rate have default values of 1 and 0.5 consequently. The final parameter represents the number of epochs for training the clustering model. For each dataset, the number of training epochs was tuned while fixing the other five parameters. The numbers of tested epochs were 10, 50, 100, 150, 200, 300, 400, 500, 1000, 2000, 5000, 7000, 8000, 9000, and 10,000. For each epoch, we observed the relation between the SOM's dimension and the QE. Eventually, the number of epochs is selected when the QE reaches the lowest consistent value. Figure 5 shows the results for fine-tuning the number of epochs parameter.
## 4 Results and Discussion
### RUL Prediction Based on ARIMA-LSTM
This study predicted the RUL of an HSV using the ARIMA-LSTM model. When the prediction cycle was set as the start of the degradation state, _MSE_ and _RMSE_ were 0.8421 and 0.9177, respectively. Figure 12 is the predicted and original value of the health index for the HSV. The results showed that the ARIMA-LSTM performed well in predicting the performance degradation trend of the HSV. Figure 13 shows the predicted curve after it was filtered. At HSV failure, the _EOL_ was 15,000. The _EOP_ was 15,096 when _Kp_ was at the start of the degradation state. Therefore, the _RUL(Kp)_, _RUL(ae)_, and _RUL(re)_ were 6096, 96, and 1.6%, respectively. Moreover, the predicted trajectory of the ARIMA-LSTM was consistent with the original trajectory. In summary, the relative error of RUL prediction of the hybrid model is less than 1.6%. The model can be used for guiding engineers in RUL forecasting.
## 2 Risk Modelling
### Manoeuvrability Restriction Modelling  
The minimum turning radius of a vessel represents the turning ability of the vessel when following a predefined path. Actually, this ability is restricted to the manoeuvrability of the vessel. If the restriction of turning radius is not set, the generated path could not be followed by the vessel and the risk of collision and stranding could even happen.  
Assume that \(P_{i-1}\), \(P_{i}\), \(P_{i+1}\) are the continuous nodes of a path, which is shown in Figure 5.  
Figure 4: The berthing path generated by traditional A-Star algorithm.  
Figure 5: The restriction of turning radius.  
\(\theta\in[0,\pi)\) is the included angle between line \(P_{i-1}P_{i}\) and line \(P_{i}P_{i+1}\). \(l\) is the shorter length between the length of \(P_{i-1}P_{i}\) and the length of \(P_{i}P_{i+1}\). A circle is built through node \(P_{i-1}\), which is tangent to line \(P_{i-1}P_{i}\) and line \(P_{i}P_{i+1}\). Then the radius of the circle, \(R_{i}\), is obtained:  
\[R_{i}=l\tan\left(\frac{\theta}{2}\right). \tag{6}\]  
If the minimum turning radius of the vessel \(R_{0}\) is greater than \(R_{i}\), it means that the continuous nodes \(P_{i-1}\), \(P_{i}\), \(P_{i+1}\) is not available for this vessel, and vice versa. Hence, a risk model for manoeuvrability restriction is defined as follows:  
\[r_{m}\left[m\right]=\begin{cases}b\left(1+\frac{R_{i}}{R_{0}}\right),R_{0}>R_ {i}\\ 0,R_{0}\leq R_{i}\end{cases}, \tag{7}\]  
where \(b>0\) is a constant coefficient.
# RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System
## 2 Materials and Methods
### RSIAPI: Uniform Python Interfaces for Images Storage, Access and Retrieval  
The uniform APIs provided by RSIAPI hide the differences of remote sensing image with various structures, making it very simple and straightforward for data access and management. For data administrators, RSIAPI provides a set of management APIs, such as products registration, remote sensing images ingestion, metadata update, etc. For common data users, RSIAPI provides a set of uniform access APIs, such as remote sensing image retrieval, read, write, etc. These APIs are shown as Table 2.  
Figure 20: Detailed structure of /vsirados/ implementation.
## 2 Materials and Methods
### Classification  
#### 2.6.1 K-Means  
Classification with K-means clustering is widely used because of its simplicity to understand and apply. We initialize how many clusters we want to have (k clusters), and the algorithm starts with random k clusters. Then, k centers are chosen from the clusters that are the centroids of them. New clusters are created according to the distance of the data from the centers. This procedure is repeated until the centers do not change [49; 50]. Many researchers have applied clustering techniques to solve medical image segmentation problems. K-means is a well-known clustering algorithm that is widely applied for segmenting and classifying medical images. The training data create the centers that the testing data use to classify the masses [51; 52; 53]. MATLAB's command \"kmeans\" was applied to create clusters and \"pdist2\" for the test data [37]. Two groups are created (\"G1\" and \"G2\" in Figure 6) with labels \"1\" and \"2\", without knowing which is malignant or benign. Two truth tables are created, the first using the labeled data with \"1\" as malignant and the second truth table as \"2\" as malignant. Two accuracies are extracted (\"Acc1\" and \"Acc2\" in Figure 6). We use the truth table of the accuracy that is greater than the other.  
Figure 6: Classification using decision tree based on K-means.