esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Verification
### Case Study I: Depending on Different Fault Components
#### 4.1.3 The Results of the Diagnosis  
The first 20% of the sample was selected in the SAE section, and after the dimension reduction, the first 10% of the sample was selected in the SF section. Using the same training dataset to train the three models, the diagnostic accuracy of the data of the three parts is shown in Figure 11. It can be seen that SAFC, which contained both methods, was more accurate and stable than SAE or SF alone. In the process of diagnosis, the diagnostic accuracy of the three methods for gear fault was the lowest among the three parts, so took gear as an example. The average accuracy and time of the three methods were calculated, the results of which showed that the SAFC accuracy was above 99.13%, and the standard deviation was no more than 0.11 while the mean accuracy rate of SAE and SF was only 86.37% and 93.80%, and the standard deviation exceeded 3.10 and 1.05. This shows that the proposed method had higher accuracy and stability. The average training time of the sample was also calculated, and the specific calculation results are shown in Table 2. It can be seen that the proposed method SAFC is not only more accurate than SF, but also faster in diagnosis, which benefits from the rapid speed of SAE in training data and its dimension reduction of the data. It can also be seen that although SAFC is not as fast as SAE in diagnostic speed, its accuracy is far better than SAE, which is due to the powerful feature extraction capability of  
In order to prove the classification effect of the proposed method better, the three methods were used to extract features from the data of three parts, and the t-SNE dimension reduction algorithm was used to transform the obtained feature vectors into two-dimensional views.  
The results of feature mapping of the three methods are respectively shown in Figures 12-14 [24]. It can be seen that in the proposed method, characteristic samples of the same health status were clustered in the corresponding clusters, and test samples of different health status were well separated. Only one sample of Sun 3 was divided into the cluster of planet 2 in gear failure. However, in SAE, not only in the bearing data, a sample of inner ring 2 was not well aggregated, but also in the shaft data, three groups of crack characteristics on the left side of the larger rotor were not very well separated. In addition, in the process of gear fault diagnosis, four groups of characteristics were staggered, while in the SF, although the characteristics of different health conditions were generally separated, uniform health conditions at different speeds were also separated, which obviously did not meet the classification requirements.  
Figure 12: Signal characteristic dimension reduction of shaft crack using the three methods.  
Figure 13: Signal characteristic dimension reduction of bearing fault using the three methods.
# Frequency Range Optimization for Continuous Wave Terahertz Imaging
## 5 Frequency Range Optimization
as \(k=m\cdot n\), where \(m\) is the number of columns, and \(n\) is the number of rows in the AOSM. The data in the first row of the AOSM represent the first \(m\) frequencies in the spectrum, and the data in the second AOSM's row represent the first \(m\cdot 2\) frequencies. Following this principle, the last row represents the full spectrum, dilated with dilation factor \(k=n\). The resulting OSF is shown in Figure 11. The OSF shows which frequencies are the most important for distinguishing between the three different materials in the observed sample. The local maximums in the OSF were detected and the optimal frequency range was selected in the third step of the proposed FRO algorithm. Local maximums represent the frequencies at which the prediction obtained with the CNN would be the most sensitive (i.e., which frequencies in the observed spectrum were the most important in the prediction). The optimal frequency range can be determined around the detected local maximums using the following criteria:  
\[v_{0}\to O(v_{0})=O(v_{peak}\cdot 0.99),v_{0}\leq v_{peak} \tag{15}\]  
\[v_{1}\to O(v_{1})=O(v_{peak}\cdot 0.99),v_{1}\geq v_{peak} \tag{16}\]  
where \(v_{0}\) is the starting frequency and \(v_{1}\) is the end frequency. Based on the proposed criterion, three frequency ranges were detected, and are shown in Table 2. The second optimized frequency range was wider between the determined frequency ranges. Therefore, the image acquisition speed improvement would be lower, but the acquired image could have a higher contrast. As it is, the most improvement in acquisition speed could be provided with the last frequency range, which was the narrower among the three selected ranges.
# Energy Reduction Multipath Routing Protocol for MANET Using Recoil Technique
###### Abstract  
In Mobile Ad-hoc networks (MANET), power conservation and utilization is an acute problem and has received significant attention from academics and industry in recent years. Nodes in MANET function on battery power, which is a rare and limited energy resource. Hence, its conservation and utilization should be done judiciously for the effective functioning of the network. In this paper, a novel protocol namely Energy Reduction Multipath Routing Protocol for MANET using Recoil Technique (AOMDV-ER) is proposed, which conserves the energy along with optimal network lifetime, routing overhead, packet delivery ratio and throughput. It performs better than any other AODV based algorithms, as in AOMDV-ER the nodes transmit packets to their destination smartly by using a varying recoil off time technique based on their geographical location. This concept reduces the number of transmissions, which results in the improvement of network lifetime. In addition, the local level route maintenance reduces the additional routing overhead. Lastly, the prediction based link lifetime of each node is estimated which helps in reducing the packet loss in the network. This protocol has three subparts: an optimal route discovery algorithm amalgamation with the residual energy and distance mechanism; a coordinated recoiled nodes algorithm which eliminates the number of transmissions in order to reduces the data redundancy, traffic redundant, routing overhead, end to end delay and enhance the network lifetime; and a last link reckoning and route maintenance algorithm to improve the packet delivery ratio and link stability in the network. The experimental results show that the AOMDV-ER protocol save at least 16% energy consumption, 12% reduction in routing overhead, significant achievement in network lifetime and packet delivery ratio than Ad hoc on demand multipath distance vector routing protocol (AOMDV), Ad hoc on demand multipath distance vector routing protocol life maximization (AOMR-LM) and Source routing-based multicast protocol (SRMP) algorithms. Hence, the AOMDV-ER algorithm performs better than these recently developed algorithms.  
link error; mobile ad hoc network; network lifetime; on demand routing; residual energy
## 3 Hardware Implementation
### Resources Utilisation and Power Consumption
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**CODEC** & **RTAX2000** & \multicolumn{2}{c}{**RTG4**} & \multicolumn{2}{c}{**Zynq-7000**} & \multicolumn{2}{c}{**Virtex 5**} \\ \hline  & LUT & util\% & LUT & util\% & LUT & util\% & LUT & util\% \\ \hline SpaceFibre & 5830 & 27.11\% & 4632 & 3,98\% & 2800 & 1.28\% & 2919 & 3.56\% \\ R-SpaceFibre & 3479 & 16.18\% & 2561 & 2,07\% & 1675 & 0.73\% & 1682 & 2.05\% \\ \% Reduction & 2351 & 40.33\% & 2071 & 44,71\% & 1125 & 40.18\% & 1237 & 42.39\% \\ \hline  & DFF & util\% & DFF & util\% & DFF & util\% & DFF & util\% \\ \hline SpaceFibre & 2605 & 24.23\% & 1994 & 2,24\% & 2441 & 0.56\% & 1702 & 2.08\% \\ R-SpaceFibre & 1530 & 14.23\% & 1227 & 1,19\% & 1791 & 0.4\% & 1103 & 1.35\% \\ \% Reduction & 1075 & 41.27\% & 767 & 38,46\% & 650 & 26.63\% & 599 & 35.20\% \\ \hline  & RAM & util\% & RAM & util\% & RAM & util\% & RAM & util\% \\ \hline SpaceFibre & 12 & 18.75\% & 39 & 1,10\% & 5.5 & 1.28\% & 7 & 2.35\% \\ R-SpaceFibre & 4 & 6.25\% & 16 & 0,45\% & 1.5 & 0.28\% & 2 & 0.67\% \\ \% Reduction & 8 & 66.66\% & 23 & 58,97\% & 4 & 72.72\% & 5 & 71.42\% \\ \hline \end{tabular}
\end{table} 
Table 2: SpaceFibre vs. R-SpaceFibre resources utilisation on Microsemi RTAX2000 and RTG4. \begin{table}
\begin{tabular}{c c c c} \hline
**CODEC** & **Dynamic Power (mW)** & **Static Power (mW)** & **Total Power (mW)** \\ \hline R-SpaceFibre & 49 & 199 & 248 \\ SpaceFibre & 55 & 199 & 254 \\ \hline \end{tabular}
\end{table} 
Table 3: R-SpaceFibre and SpaceFibre power consumption. communication link, those mechanism are rarely activated. Thus, the small difference between the two power consumption can be attributed to the fact that SpaceFibre CODEC computes the CRC code on received data frames.
# Thermal Hydrolysis of Sewage Sludge: A Case Study of a WWTP in Burgos, Spain
## 2 Materials and Methods  
### Description of the Burgos WWTP  
The WWTP collects domestic wastewater from the surrounding area through the general and industrial collectors. After treatment, the wastewater is returned to the Arlanzon river once the discharge parameters are within the stipulated values. The treatment capacity is 118,000 m\({}^{3}\)/d, which serves a total of 833,000 equivalent inhabitants (e.i.). There are two treatment lines called General and Villalonquez collectors, which are completely independent, having two well-differentiated discharge points.  
The plant is composed of the following treatment stages: (1) pretreatment, which is intended for the removal of bigger-sized objects, small particles, sand and grease (using pre-filter wells, screeners, sieves, grit and oil removal traps), (2) primary treatment, where the removal of settable and floating matter is carried out using primary decanters, and (3) secondary treatment for the removal of dissolved and colloidal organic matter by microbial degradation and subsequent settlement of biomass material, which is purged as excess sludge, a fraction of which is recycled back to the aerobic basins.  
The primary sludge from primary decanters has an independent treatment line for each collector, with each having its own gravity thickener, summing up to two units for the whole treatment plant. By contrast, the treatment line for WAS has two common air flotation thickeners. The sludge treatment lines are composed of thickening, digestion, conditioning and dewatering sections.  
The digestion section has three digesters equipped with nine sludge recirculation pumps with a flow of 75 m\({}^{3}\)/h each. Conditioning is performed using three polyelectrolyte dosing compact plants with a throughout flow capacity of 3200 L/h each. The dewatering section has three centrifugal pumps with a flow capacity of 35 m\({}^{3}\)/h each, providing 30% solid content as the maximum dewatering value. Devatered sludge is transported to the drying area and then to a composting plant adjacent to the WWTP.  
The biogas line contains all elements associated with gasometers for biogas storage and engines. There are three rotary vane biogas compressors for digester mixing, with a flow capacity of 448 m\({}^{3}\)/h each (measured at standard temperature and pressure conditions), six sludge recirculation exchangers (power of 400 kW/each, two units per digester), one common torch of 2000 m\({}^{3}\)/h, three gasometers with a capacity of 703 m\({}^{3}\)/each and four biogas engines.
## 3 Methodology
### Data Analysis
#### 3.3.2 Identification of Sensitive Wavebands and Indices to C Fluxes  
A PLSR statistical method based on Principal Component Analysis (PCA) was used to determine sensitive wavebands of Hyperion images to estimate C fluxes. PLSR can analyze data with strong colinearity, noise, and numerous x-variables, and can also simultaneously process several response variables, Y [45]. In the PLSR models, the importance of every x-variable both to explain x and to correlate to y was calculated in the PCA as the VIP (Variable Importance for a PLS Projection) value. VIP values larger than 1 indicate \"important\" x-variables, and values lower than 0.5 indicate \"unimportant\" x-variables. The interval between 1 and 0.5 is a gray-zone, where the importance level depends on the size of the data set. Thus, the waveband's important to GPP and Re can be considered as a selection criterion for remote sensing bio-indicators.  
Similarly, sensitive bio-indicators also were selected by analyzing the VIP value for GPP and Re. The values were calculated for each \(\mathrm{x_{k}}\) by summing the squares of the PLS-weights, \(\mathrm{w_{ak}}\), weighted by the amount of y-explained in each model component, a.  
Figure 2: The study flowchart. (White rectangles illustrate process modules, grey rectangles illustrate multi-source data integration modules, and ellipses illustrate the start and the result modules).
# Optical accelerometers for Detecting Low-Frequency Micro-Vibrations
## 2 Principle and Requirements
### Requirements of Low Frequency Accelerometers
#### 2.2.3 Vibration Isolation Systems  
In microscale and nanoscale precision measurement systems and ultra-precision machining systems, the effects of vibration on measurement precision and machining accuracy is devastating, and the vibration is a major factor that limits the improvement of measurement and machining accuracy [12; 13]. Vibration isolation is divided into active and passive vibration isolations; passive vibration isolation techniques experience difficulty in isolating low-frequency vibrations of 0.5-5 Hz [14], while active vibration isolation can suppress low-frequency vibrations more efficiently. In an active vibration control system, accurate access to real-time vibration information is the key. Therefore, high-precision real-time detection of low-frequency micro-vibrations must be inevitably achieved. Optical accelerometers demonstrate advantages in measurement accuracy and response speed. They have potential applications in the field of precision active vibration isolation.
# A Review of Properties of Nanocellulose, Its Synthesis, and Potential in Biomedical Applications
## 3 Chemical Modification and Functionalization of Nanocellulose
### Cellulose Graft Copolymerization  
This is an interesting and adaptable way of adding several functional groups to the polymer to change the physical and chemical characteristics [72]. The graft copolymerization method enables the effective qualities of two polymeric units to be combined in a single physical unit [73].  
The graft copolymerization of cellulose is commonly accomplished by grafting a polymeric branch to the cellulose material, which induces certain qualities without compromising the natural features of the substrate. The method of grafting monomers to the cellulose and its derivatives usually occurs by a range of techniques that are divided into (a) free radical polymerization, (b) ionic and ring-opening polymerization, and (c) live radical polymerization [74].  
\"Grafting-to\", \"grafting-from\", and \"grafting-through\" are the three main approaches on which the method of graft copolymerization relies [75]. In the \"grafting-to\" method, peptides or polymers are linked to the cellulose backbone, which involves fusing the polymer's reactive terminal end group to the hydroxyl group of the cellulose. Polystyrene, poly(caprolactone), and polypropylene are polymers that can be produced and connected to cellulose. The \"grafting-from\" approach involves first functionalizing the cellulose with an initiator and afterward polymerizing monomers straight from the surface. Comparedto the \"grafting-to approach\", this approach obtains increased polymer densities. In the \"grafting-through\" method, the functionalization of cellulose or its derivatives occurs with polymerizable vinyl-containing monomers. After that, the functionalized cellulose is combined with a co-monomer, followed by the polymerization initiation [74; 76; 77].  
Dimensional stability, wrinkle recovery, elasticity ion exchange capabilities, thermal responsiveness, and microbiological invasion resistance can all be achieved depending on the polymer used for the grafting [78; 79; 80; 81; 82].
# A Survey of Machine Learning-Based System Performance Optimization Techniques
## 2 Background
### Machine Learning Based Sort
#### 3.3.1 NN-Sort (Neural Network Sort)  
In paper [10], a neural network-based sorting algorithm called NN-sort [10] is proposed. NN-sort trains the model about past data and classifies incoming data in the future. The overall structure of NN-sort is divided into three phases as shown in Figure 7: Input Phase, Sorting Phase, and Polish Phase.  
Input Phase serves to convert data into a vector so that NN-sort can learn the various type of data.  
Sorting Phase serves to convert unsorted data into roughly sorted data by repeatedly executing the proposed model. If different input data result in the same position, NN-sort stores the input data in the conflicting array c. On the other hand, the non-conflicting key is stored in the array \(o_{k}\). In this case, c is used as input of model f at the next iteration. If c is below the threshold after one iteration, \(c\) is not supplied from model f again, but \(c\) is sorted by the existing algorithm.  
At Polish Phase, all arrays of \(\{o_{1},o_{2},\ldots,o_{k},\ldots,o_{t}\}\)(\(0\ <\ t\ <\ \epsilon\), \(\epsilon\) is the maximum number of iterations) are scanned and merged with \(w\). If the position of an element in \(o_{i}\) is incorrect, NN-sort inserts the element in the correct position and if the position of the element is correct, NN-sort appends the element in the result array. Compared to the real-world dataset, NN-sort shows a sorting rate of 5950 data points per second, which is 2.72 times of std::sort [22], 7.34 times of Redis sort [23], and 58% faster than std::heap [22].
# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 1 Introduction
However, these VI combinations are insensitive to the vegetation population and not useful for CCC retrieval. Most studies to date have used a combination of VIs for LCC estimation and focused on eliminating the influence of the LAI on the CHL-VI. However, only a few have proposed VI combinations for estimating CCC. One such study estimated grassland CCC using a combined VI based on two single VIs calculated from Landsat data, but it did not include a red-edge index such as the MTCI [32]. The most common statistics-based retrieval algorithm is linear regression (LR), which reflects empirical relationships between CCC and VIs [33]. However, the empirical formulae are occasionally incapable of representing nonlinear relationships in complex environmental conditions [34; 35]. Machine learning has been widely applied to retrieve vegetation parameters by training spectral reflectance data based on simulations or field measurements, which has shown robustness and improved prediction accuracy [36; 37]. In machine learning, the random forest is a classifier containing multiple decision trees and is used for classification and regression [38]. Past research has demonstrated that random forest regression (RFR) is a strong predictor for retrieving the biochemical components of vegetation. The method is widely used due to its accuracy, ease of use, and favorable stability [39; 40]. [PERSON] et al. [41] used random forest regression training with several VIs for retrieving the leaf chlorophyll content in wheat, showing good performance (RMSE = 3.62-3.91 \(\mathrm{\SIUnitSymbolMicro m}^{-2}\)). RFR has significant advantages for estimating biochemical components, but it has seldom been used to predict CCC. The purpose of this research was to introduce a VI combination approach for retrieving the CCC in crops with fewer uncertainties relative to the use of single VIs. This study combined the MTCI and LAI-VIs as a univariate variable, and two corresponding single VIs were included as binary variables. LR and RFR were used to determine the relationships between CCC and the two variables. The feasibility of the VI combination approach was verified through simulations with the PROSAIL-D model, and its reliability was validated using field canopy spectra and MERIS satellite data.
## 4 Results
### Performance Index Analysis of UAV Platform and Electro-Optical Payload  
This section uses Sobol sensitivity analysis to access the mission time sensitivity relative to three specific indices. The defined ranges for the UAV climbing and descending angle \(\alpha\), the photoelectric load viewing distance \(L_{gd}\), and the photoelectric load viewing angle \(\theta_{gd}\) are set to [0, 45] (\({}^{\circ}\)), [1, 3] (km), and [0, 60] (\({}^{\circ}\)), respectively, with the parameter sample space established at 10,000. The results are presented in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Algorithm** & **[D, \(\Delta d\)]** & **Function** & \(N\) & **Mean[\(L_{i}\)]** & **Max[\(L_{i}\)]** & \(\sigma_{1}\) \\ \hline PSO & [22.44, 12.44] & 156.14 & 23 & 64.34 & 85.86 & 11.61 \\ \hline SCO & [24.44, 13.79] & 156.14 & 23 & 64.34 & 85.86 & 11.61 \\ \hline \end{tabular}
\end{table}
Table 2: Relevant data of UAVs’ path plan after optimization.  
Figure 5: Optimization process. (**a**) Fitness. (**b**) Number of UAVs. (**c**) Mean length. (**d**) Max length. (**e**) Standard deviation. (**f**) Result. The green circle represents the target, and the pink star represents the start point.  
From the analysis results, the UAV's climb and descent angle \(\alpha\) exhibits a global sensitivity and first\(-\)order sensitivity of 0.73 and 0.97, respectively. It has the most direct impact on the task time. On the other hand, the global sensitivity and first\(-\)order sensitivity of the photoelectric payload visible distance \(L_{gd}\) and the photoelectric payload visible angle \(\theta_{gd}\) 0.02 and 0.18 and 0.02 and 0.17, respectively. While their impact on task time is comparatively small, it cannot be ignored. They should be considered during metric optimization verification.