esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions and Future Works
It would be advisable to use special hardware systems equipped with ad hoc software that guides the workers during maintenance activities to correctly fill the report. Dropdown menus or other easy-to-use techniques that can facilitate the acquisition phase of the data should be adopted. For example, these menus should contain standard words that the operator can easily choose to speed up the writing process and reduce errors when entering information. In this way, the history of these activities will be clean and complete. This would improve the forecast performances, and the better the forecast is, the lower the costs will be, as the used components will be exploited throughout their life. This will further avoid the waste of unnecessary resources, the costs related to the replacement or repair of some components, and the loss of production due to the unexpected shutdown of the plant. At this time, the forecasts obtained are subject to particularly high average errors, and only in cases where samples were large enough, we obtained acceptable predictions. On the other hand, potential limitations of our approach are related to the peculiarity of the used data: the methodology is applicable to other domains, but each use case will be different, and a human component must be always employed to identify the features of the data that need to be completed. As future directions, we would like to improve the prediction procedure by integrating sensor data. This is important because the proposed procedure does not take into account the working conditions of the individual component. The sensors provide useful quantities to understand the operating status of each component. Vibrations are, in fact, one of the physical quantities that can be analyzed for this purpose by indicating the state of health of a plant component by signaling anomalies when the values go out of the predetermined ranges. Moreover, the generated data structure could also be automated with an IoT system and data storage in the Cloud with automatic procedures. In this way, it will be possible to train the machine learning models and make predictions in real time, and this will allow us to intervene promptly in the event of an imminent failure. Thanks to the help of these models, it will therefore be possible to intervene when strictly necessary to optimize the number of maintenance interventions and exploit the underlying component as much as possible. Conceptualization, P.F.O., G.M. and S.M. ; methodology, P.F.O. and D.R.R. ; software, R.P. and G.M. ; validation, R.P. and D.R.R. ; formal analysis, D.R.R. and S.A.; Investigation, P.F.O. and S.A.; Resources, G.M. and S.M. ; data curation, R.P., G.M. and S.M. ; writing original draft preparation, R.P. and D.R.R. ; writing review and editing, D.R.R., S.A.; supervision, P.F.O. and D.R.R. ; project administration, P.F.O. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors declare
## 3 Experimental Results and Analysis
### Details in Training  
In the experiment, our pretrained ResNet152 would extract visual features of dimension \(2048\times L\), where the value of L would vary by image size. For instance, \(L=49\) when image size was \(224\times 224\). BERTBASE would extract word-level text features of dimension \(768\times M\) before generating 2048-dimensional statement-level text features via a two-way GRU network. This paper used the Adam optimizer to train the network over 80 epochs for the training rounds. The minibatch size was 64, and the learning rate dropped by 90% every other 10 epochs, starting from the initial value of 0.0001.
# Effects of Different Variables on the Formation of Mesopores in Y Zeolite by the Action of CTA+
## 3 Results
### Influence of Surfactant Concentration
In the absence of addition of CTA\({}^{+}\), the volume of micro- and mesopores was almost zero because the crystalline structure had been completely destroyed, as shown previously. Addition of the CTA\({}^{+}\) surfactant at different concentrations resulted in an almost constant micropore volume, in agreement with the effect on the relative crystallinity, described previously. Figure 4: Micropore and mesopore volumes, as a function of surfactant molar ratio. A and B represent the volume of micropores and mesopores in the original USY zeolite, respectively. Figure 3: N\({}_{2}\) physisorption isotherms for the original USY zeolite and the modified zeolites produced using different surfactant concentrations. The original sample possessed a small quantity of pores with diameters around 3.8 nm, derived from the hydrothermal treatment applied to the Y zeolite to increase its catalytic stability (Figure 5). Modification of the USY zeolite using the lowest surfactant concentration (y = 0.02) maintained part of the original pores but led to the formation of smaller mesopores around 3 nm in size. The use of higher surfactant concentrations led to the disappearance of pores of around 3.8 nm, but increased the amount of mesopores around 3.0 nm in size. Given that the diameters of CTA\({}^{+}\) cation micelles are in the range 3 to 4 nm [7], the formation of mesopores in this same size range could be attributed to the role of the surfactant as a template in the formation of mesopores.
## 2 Intelligent Processing on Image and Optical Information
### Compression, Completion, and Correction  
The paper entitled 'Error Resilience for Block Compressed Sensing with Multiple-Channel Transmission' by [PERSON], [PERSON], and [PERSON] [17] proposed an error resilient transmission scheme for block compressed sensing. In their paper, the compressed information is transmitted over the multiple independent and lossy channels. By introducing correlation to multiple description coding, errors induced in the lossy channel are effectively alleviated.  
The incomplete images can contain many missing pixels distributed over the entire image. In the paper entitled 'Image Completion with Hybrid Interpolation in Tensor Representation', [PERSON] and [PERSON] [18] proposes an interpolation algorithm for a wide spectrum of image-completion problems. Their algorithm outperforms other conventional methods with a considerably shorter computational runtime.  
In the paper entitled 'A Correction Method for HeatWave Distortion in Digital Image Correlation Measurements Based on Background-Oriented Schlieren', [PERSON], [PERSON], [PERSON], and [PERSON] [19] proposed a correction method based on the background-oriented schlieren technique. The method can correct the distortion caused by heat waves in measuring image correlation.
# Influence of Acoustic Streams on the Efficiency of Ultrasonic Particle Agglomeration
## 3 Results
### Determination of Fractional Efficiency  
To confirm the efficiency of capturing particles of different sizes, experimental studies were carried out using a mixture of microtalc of different grades (Jetfine T1 CA d50 = 1 \(\upmu\)m, Luzenac St30 d50 = 6 \(\upmu\)m, Luzenac H50 d50 = 9 \(\upmu\)m) which had polydisperse composition. Comparative experiments were carried out at initial particle concentration \(N_{inlet}\) = 2 \(\times\) 10\({}^{-2}\) g/m\({}^{3}\), at three different modes of agglomeration chamber operation: exposure with a flat radiator; exposure with a step-function radiator; without ultrasonic exposure. As a result, the fractional efficiency data shown in Figure 11A were obtained. Figure 11B shows the dependence of the particle size at the outlet of the agglomeration chamber on the particle size at the inlet of the coagulation chamber.  
The analysis of the obtained curves shows the following:  
1. Particle capture efficiency without ultrasonic impact (curve 3, Figure 11A) is significantly lower than with exposure to it.
2. When using a step-function radiator (curve 2, Figure 11A), when a standing wave is generated, the particles hardly interact with each other within the node areas, and at very low concentration the agglomeration efficiency is reduced to zero.
3. The use of a flat radiator (curve 1, Figure 11A) enables generation of vortex flows between adjacent regions oscillating in counter-phase, which significantly increases the interaction of particles located in nodal zones.
4. The dependences presented in Figure 11B allow us to conclude that when exposed to a flat radiator, the maximum increase in agglomeration efficiency is achieved on the smallest particles  
Thus, the results of experimental studies have confirmed that increasing the efficiency of ultrasonic agglomeration can be reached owing to generation of acoustic streaming in the form of vortex flows. In this case, the efficiency of capturing particles of 2.5 \(\upmu\)m in size increases by 6%--from 89% to 95%; particles of 1.5 \(\upmu\)m in size by 7%--from 85% to 92%; and particles of 0.5 \(\upmu\)m in size by 9%--from 76% to 85%.  
Figure 11: Fractional efficiency (**A**) and the dependence of the particle size at the outlet of the agglomeration chamber on the particle size at the inlet of coagulation chamber (**B**): 1—flat radiator; 2—step radiator; 3—without ultrasonic influence.
# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## Materials and Methods
### Methods
#### 3.2.4 Tree Crown Parameter Extraction  
Four tree crown parameters (location, height, longest crown spread and longest crown cross-spread) are extracted for the segmented trees following each of the investigated processing variants. The height can be simply taken from the highest point [26]. Tree crown locations will be extracted from the segmented points to evaluate the segmentation step.  
There are two main strategies to identify tree crown locations. The first is simply taking the location of the highest point as the location of a tree, which is based on the assumption that the top of the tree is where the tree is located. This is generally true when a tree has a clear peak and is straight upright. The second strategy is to fit a geometric shape to the points on the crown, either in 2D or 3D, which is supposed to be more robust to outliers. To identify the crown location and spreads, the crown base was firstly determined by computing a convex hull around all the tree points in 2D. The average height of the points on the convex hull can be considered as the crown height [27]. Then the crown location, longest spread and longest cross-spread can be determined by fitting an ellipse to these points, where the ellipse center is the crown location, and the two semi-axes represent the two crown spreads. These two strategies will be assessed in this paper to investigate the effects of crown parameter extraction on segmentation validation.
## 2 Single Conversion Standard Delay Calculation Method  
In this paper, we refer to the vehicle delay calculation method in the \"Phase Difference Model of Arterial coordinating Control and Its Optimization Method\" [18], which considersthe discrete phenomenon of fleet from upstream intersections to downstream intersections for the adjacent intersections under arterial coordination control, to analyze the arrival and departure at downstream intersections of fleets in the same traveling direction. It classifies the cases of fleet generating delays into six categories and calculates the total fleet delay separately.  
In mixed bus and car traffic, the two types of vehicles affect each other, thus the delays of the two types cannot be calculated directly. The existing single conversion standard delay calculation method converts buses into cars, first calculates the total delay of all vehicles, and then finds the delay of the two types of vehicles. The following will introduce the input parameters of delay calculation and the specific steps of the single commutation standard delay calculation method, respectively. Considering that the acceleration, speed, and stopping characteristics of the vehicle all influence the delay, we will also analyze the difference in the characteristics of the two vehicles.
## 2 Mathematical Preliminaries
### Projection-Based Combined Reduction  
Projection-based combined state and parameter reduction is based on (bi-)orthogonal truncated projections for the state- and parameter-space respectively. The state-space trajectory \(x(t)\), not too far from a steady-state \(\bar{x}\in\mathbb{R}^{N}\), \(\bar{u}\in\mathbb{R}^{M}\), \(f(t,\bar{x},\bar{u},\theta)=0\ \ \forall t\), is approximated affinely using truncated reducing and reconstructing projections \(U_{1}\in\mathbb{R}^{N\times n}\) and \(V_{1}\in\mathbb{R}^{N\times n}\), with \(V_{1}^{\mathsf{T}}U_{1}=\mathbb{1}_{n}\):  
\[x_{r}(t):=V_{1}^{\mathsf{T}}(x(t)-\bar{x})\Rightarrow x(t)\approx\bar{x}+U_{1 }x_{r}(t).\]  
The relevant parameter-space volume is also approximated by truncated reducing and reconstructing projections \(\Pi_{1}\in\mathbb{R}^{p\times p}\) and \(\Lambda_{1}\in\mathbb{R}^{P\times p}\), with \(\Lambda_{1}^{\mathsf{T}}\Pi_{1}=\mathbb{1}_{p}\):  
\[\theta_{r}:=\Lambda_{1}^{\mathsf{T}}(\theta-\bar{\theta})\Rightarrow\theta \approx\bar{\theta}+\Pi_{1}\theta_{r},\]  
relative to a nominal parameter \(\bar{\theta}\). Given these truncated projections, a projection-based reduced order model is then obtained by:  
\[\dot{x}_{r}(t) =V_{1}^{\mathsf{T}}f(t,\bar{x}+U_{1}x_{r}(t),u(t),\bar{\theta}+ \Pi_{1}\theta_{r}), \tag{2}\] \[\ddot{g}(t) =\phantom{-}g(t,\bar{x}+U_{1}x_{r}(t),u(t),\bar{\theta}+\Pi_{1} \theta_{r}),\] \[x_{r}(0) =V_{1}^{\mathsf{T}}(x_{0}-\bar{x}),\] \[\theta_{r} =\Lambda_{1}^{\mathsf{T}}(\theta-\bar{\theta}).\]  
Thus, to obtain a projection-based reduced order model with respect to the state- and parameter-space, the overall task is determining the truncated projections \(U_{1}\), \(V_{1}\), \(\Lambda_{1}\) and \(\Pi_{1}\).  
It should be noted that this approach produces globally reduced order models, meaning \(U_{1}\), \(V_{1}\), \(\Lambda_{1}\), \(\Pi_{1}\) are valid over the whole operating region, which is an application-specific subspace of the Cartesian product of the full order state- and parameter-space \(\mathbb{R}^{N}\times\mathbb{R}^{P}\).
## 3 Experimental Results
### Fourier-Transform Infrared Spectroscopy (FTIR)
#### 3.3.2 Non-Encapsulated and Encapsulated MAPbI\({}_{3}\) Samples  
Non-encapsulated and encapsulated MAPbI\({}_{3}\) samples were prepared and subjected to the degradation process. Fourier-transform infrared spectroscopy analysis was performed to the samples before (pristine) and after the degradation process.  
By comparing the FTIR spectra of the perovskite samples before and after subjecting them to the degradation process, it is possible to observe the existence of vibration modes clearly defined in the pristine perovskite, which disappear after the degradation process takes places. These are at 3181 cm\({}^{-1}\) (N-H stretch), 3124 cm\({}^{-1}\) (N-H stretch) and 1465 cm\({}^{-1}\) (symmetric NH\({}_{3}\)\({}^{+}\) bend) [73]. In fact, having exposed the samples to full solar and UV lamp irradiations, a slight decrease in these peaks happened after 50 min degradation, and they completely disappeared after 70 min degradation, as can be seen in Figure 8A. Since this phenomenon turned out to be the same in both cases of degradation, it is possible to infer that the UV range of the spectra was the agent responsible for said degradation. This hypothesis was also supported by the FTIR spectra performed to the encapsulated samples that were subjected to 350 h of full solar irradiation, where the observed vibration modes remained stable due to the UV-cut properties of the EVA material applied (see Figure 8B).  
Figure 7: (**A**) FTIR spectrum of MAPbI\({}_{3}\) versus DMF and DMSO solvents and (**B**) FTIR spectrum of MAPbI\({}_{3}\) versus PbI\({}_{2}\) and CH\({}_{3}\)NH\({}_{3}\)I. (* is used to appoint the maximum peaks).  
Finally, it is important to notice that the intermediate complex sensitive to hydration, which lies at 1660 cm\({}^{-1}\)[75], was not observed in any of the samples. Therefore, no degradation due to humidity was apparent, despite performing all the tests in ambient conditions.
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 3 Experiments and Analyses
### Experimental Results  
All parameters in Tables 5 and 6 were chosen to verify the neural-network-based multi-granularity negotiation method. We collected and analyzed the profits and costs based on negotiation results. The profit shown in Figure 9 and the cost-effectiveness ratio shown in Figure 10 are most relevant.  
Figure 8: Training convergence process of the best neural network under the two-layer structure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Network ID**} & \multicolumn{2}{c}{**First Layer**} & \multicolumn{2}{c}{**Second Layer**} & \multicolumn{2}{c}{**Third Layer**} \\ \cline{2-7}  & **Activation** & \multirow{2}{*}{**Nodes Number**} & **Activation** & \multirow{2}{*}{**Nodes Number**} & **Activation** & \multirow{2}{*}{**Nodes Number**} \\  & **Function** & & & & & \\ \hline
1 & satlin & 8 & softmax & 16 & purelin & 3 \\
2 & satlin & 128 & radbas & 16 & purelin & 3 \\
3 & satlin & 16 & radbas & 64 & purelin & 3 \\
4 & satlin & 16 & softmax & 64 & purelin & 3 \\
5 & satlin & 64 & softmax & 256 & purelin & 3 \\
6 & softmax & 32 & radbas & 64 & purelin & 3 \\
7 & softmax & 32 & satlin & 16 & purelin & 3 \\
8 & satlin & 32 & satlin & 32 & purelin & 3 \\
9 & satlin & 64 & softmax & 128 & purelin & 3 \\
10 & softmax & 64 & softmax & 256 & purelin & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Parameters of the best ten neural networks under the two-layer structure.  
Figure 7: The error distributions of the best ten neural networks under the two-layer structure.
# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 3 Materials and Methods
### Proposed Methodology
#### 3.2.2 Patch Extraction  
This stage generates a set of valid patches for the 2D-CNN and RNN of size \(32\times 32\times n\) and \(8\times 8\times n\), respectively, centered on each pixel to classify the central pixel based on neighborhood frequency information, where \(n\) is the number of bands of each sequence. In order to obtain a suitable trade-off among computational cost, context capturing, and localization accuracy, a small sample size is used. At this stage, each patch is examined, and the frequency of pixels with similar class values to the pixel of interest (central pixel) is determined. When the frequency is greater than 512 pixels, the patch is selected as a valid patch, otherwise the patch is discarded. Then, for each class, 1K valid patches are randomly selected. The outcome of this stage is a training set of 10K for sequences 1 and 9K for sequence 2. Since corn, sorghum, bean, and soil are scarce in February and July, the number of patches is less than 1K. Therefore, to handle this issue, synthetic fields for these crops are developed using geometric transformations such as horizontal (\(T_{x}\)) and vertical (\(T_{y}\)) translations with \(1\ cm\) displacements on the available data. Such geometric transformations are benchmarking data augmentation methods that have effectively augmented the original data in some DL applications [47].  
The geometric transformations applied are given by Equation (1):  
\[\begin{bmatrix}x^{\prime}\\ y^{\prime}\\ 1\end{bmatrix}=\begin{bmatrix}1&0&T_{x}\\ 0&1&T_{y}\\ 0&0&1\end{bmatrix}\begin{bmatrix}x\\ y\\ 1\end{bmatrix}, \tag{1}\]  
where \(x^{\prime}\) and \(y^{\prime}\) are the transformed coordinates and \(x\) and \(y\) are the initial coordinates of the pixels; \(T_{x}\) and \(T_{y}\) are the values of the horizontal and vertical translations. Table 1 summarizes the amount of synthetic patches generated for each class used in this work.
## 3 PCAWF- and ARPCAWF-Based OMA Methods and Their Limitations
### PCAWF-Based OMA for LTV Structures  
We now construct a PCAWF-based OMA method for LTV structures using the principle of the exponential forgetting algorithm (Equation (5)) combined with PCA-based OMA for LTI structures. The principles of modal parameter recognition are as follows: (1) Data from the first \(i\) samples are selected to initialize the PCA model and the exponential forgetting factor is set to \(\mu\). Different weights are assigned to the columns of the initial data matrix. (2) The PCA-based OMA method for LTI structures is used to find the modal parameters (instantaneous modal frequency and modal shape) at time step \(i\). (3) When the time step \((i+1)\) vibration response vector is added, the matrix of the previous \((i+1)\) samples vibration response matrix is reassigned with a different weight. The PCA model is reconstructed to determine the instantaneous modal parameters. (4) The instantaneous modal parameters identified at each time are connected using a curve fitting technique, allowing the modal parameters to be identified and the LTV structure to be tracked. The procedure of PCAWF-based OMA for LTV structures is illustrated in Figure 3.
## 2 Mathematical Modelling
### Flat-Plate Geometry  
The model was first developed to describe photobioreactors with flat-plate geometry and different reactor depths, enlightened from one side only and covered with a black panel on the opposite side, as depicted in Figure 1.  
Figure 1: General scheme of the flat reactors employed in this work.  
The experiments were conducted in batch mode. This means that illumination inside the PBR decreased over time as biomass concentration increased. The mass balance on the microalgal biomass inside the flat-plate reactor with volume \(V\), concentration \(X(t)\), enlightened surface \(A\) and depth \(L\), in the transitory period can be expressed as:  
\[V\frac{dX}{dt}=\int_{0}^{L}r_{X}Adz \tag{4}\]  
where \(r_{X}\) is the net biomass generation rate on volume unit. By expressing the growth rate \(\mu\) by means of a Monod-like equation (Equation (3)), \(r_{X}\) may be given by the following expression:  
\[r_{X}=\left(\mu_{max}\frac{I}{I+I_{K}}-k_{d}\right)X \tag{5}\]  
where \(k_{d}\) is the constant of cellular death. By substituting for \(I\) the Lambert-Beer law (Equation (1)) and by combining the Equation (4) with Equation (5) one obtains:  
\[V\frac{dX}{dt}=\int_{0}^{L}\left(\mu_{max}\frac{I_{0}e^{-k_{d}Xz}}{I_{0}e^{-k_ {d}Xz}+I_{K}}-k_{d}\right)XAdz \tag{6}\]  
And by integrating and dividing by \(V\), one obtains:  
\[\frac{dX}{dt}=\mu_{max}X-\frac{\mu_{max}}{k_{d}L}\ln\left(\frac{I_{K}}{I_{0}} e^{k_{d}XL}+1\right)+\frac{\mu_{max}}{k_{d}L}\ln\left(\frac{I_{K}}{I_{0}}+1 \right)-k_{d}X \tag{7}\]  
The obtained differential equation can be numerically integrated to return the variation of \(X\) in time.