esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Catalytic Conversion of Fructose on POPs
Acid-base titration showed that SPPTPA-1 had strong acidity, which could be used as a solid acid catalyst for the preparation of HMF by the dehydration of fructose [85]. The fructose was completely transformed in an organic solvent system at 140 \({}^{\circ}\)C for 20 min, and HMF with a yield of 94.6% was obtained. The activity of the catalyst did not decrease significantly after four catalytic cycles. In addition, [PERSON] et al. formed a solid acid catalyst, SPPTPA, by adding chlorosulfonic acid [86]. In order to further facilitate recycling, they used Fe\({}_{3}\)O\({}_{4}\) nanoparticles to synthesize the catalyst FeSPPTPA for the fructose preparation of HMF, achieving a higher specific surface area than other catalysts and reaching an HMF yield of 95% at 100 \({}^{\circ}\)C for 20 min. In the process of separation and recovery, magnetic characteristics could be effectively used for intelligent processing, and the activity of the catalyst did not decrease obviously in four reaction cycles, which also had a certain reference for subsequent research and innovation. Sulfonic acid-functionalized materials are widely used in the field of catalysis [87]. In the dehydration reaction of the fructose preparation of HMF, a POP catalyst was almost sulfonated in the synthesis process and contained sulfonic acid sites, which proved able to improve the reaction efficiency. To broaden the research direction, [PERSON] et al. [88] used phosphate-functionalized POPs for biomass conversion into HMF for the first time. They synthesized the HCP-structured catalyst (B-POP) by replacing the sulfonic acid site with a phosphoric acid site. The reaction was carried out in DMSO at 160 \({}^{\circ}\)C for 30 min to obtain a 100% fructose conversion and 85% HMF production. The catalytic effect was further used to convert HMF into the oxidation product DFF and the ring-opening product Levulinic acid (LA). In addition, the fructose conversion rate was only reduced by less than 5% after 10 cycles, and the HMF yield did not change visibly. Certainly, in the application of POPs as catalysts in the conversion of fructose, there are still some aspects to be investigated, such as avoiding side reactions, reducing the use of raw materials, and making the reaction conditions more moderate, etc. Here, we mainly discuss the influence of the solvent system on the reaction. Table 1 summarizes the catalytic system of fructose conversion into HMF with POPs mentioned in this section as the catalysts. The data show that most of the systems worked under atmospheric pressure, and the working conditions were relatively mild. In about half an hour, they achieved raw material conversion rates of more than 95%, and the product yield was between 70% and 98%. Furthermore, it was not difficult to find that in the conversion of fructose to HMF, the solvent used was mostly DMSO, and in other biomass conversion reactions, it was also commonly used as a solvent [89]. From a green point of view, it is undoubtedly better to reduce the harm of DMSO or not use DMSO in the reaction.
# Frequency Response Extension Method of MET Vector Hydrophone Based on Dynamic Feedback Network
## 2 Principles and Background
### Open-Loop System Analysis of MET  
Figure 4 shows the block diagram of the open-loop system of MET, where \(H\) represents the total transfer function of the system, and \(W_{\text{s}}\) and \(W_{\text{f}}\) are the inherent transfer function of the MET reaction chamber and the transfer function of the preamplifier circuit, respectively, which can be represented by Equation (8).  
\[H=W_{\text{s}}W_{\text{f}} \tag{8}\]  
The inherent transfer function \(W_{\text{s}}\) of the electrochemical vector hydrophone is affected by its physical and chemical characteristics, and varies due to changes in its internal design parameters and external environment.
## 2 Fault Type and Fault Feature Information of the Protection System
### Classification of Protection System Fault Types
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Fault Types** & **Fault Feature Information** \\ \hline \hline \(16\) & BO1\_GOOSE-A network storm of intelligent terminal & BO1\_GOOSE-A network storm alarm of intelligent terminal \\ \hline \(17\) & BO1\_GOOSE configuration error of intelligent terminal & BO1\_GOOSE configuration error of intelligent terminal \\ \hline \(18\) & BO2\_A network link N of intelligent terminal disconnected & BO2\_A network link N of intelligent terminal disconnected \\ \hline \(19\) & GPS timing signal of intelligent terminal not accessed & B01 synchronous anomaly alarm of intelligent terminal \\ \hline \(20\) & Error in HTM data exchange between intelligent terminal device boards & B01 HTM error of intelligent terminal \\ \hline \(21\) & Device stall fault of intelligent terminal & BO1 report BO2 stall error of intelligent terminal \\ \hline \(22\) & The power supply of the intelligent terminal & BO3 sampling power supply anomaly of intelligent terminal \\ \hline \(23\) & B09 input power loss of intelligent terminal & B09 optocoupler power loss alarm of intelligent terminal \\ \hline \(24\) & Abnormal opening of intelligent terminal & Bus QD Signal Anomaly \\  & outlet power supply & \\ \hline \(25\) & GOOSE text configuration error of intelligent terminal & Link N configuration error \\ \hline \(26\) & GPS input signal of intelligent terminal is lost or abnormal & GPS clock abnormal \\ \hline \(27\) & Abnormal timing of the protection device & Abnormal timing of the protection device \\ \hline \(28\) & Spring device of protection device does not store energy & No energy storage alarm for spring device of protection device \\ \hline \(29\) & CT disconnection of protection device & CT disconnection alarm \\ \hline \(30\) & PT disconnection of protection device & PT disconnection alarm \\ \hline \(31\) & Synchronous voltage circuit disconnection & Abnormal synchronous voltage \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Set the complex fault set as Formula (1):  
\[F=\{f_{1},f_{2},\ldots,f_{12}\} \tag{1}\]  
In Formula (1), \(f_{1}-f_{12}\), respectively, represent the 12 faults in Table 2.  
For the simple faults in Table 1, the expert system can be used for fault diagnosis according to the fault feature information summarized in Table 1. For space reasons, the performance of the method proposed in this paper is explored based on the faults in the complex fault set \(F\). In addition, with the development of intelligent substations and the improvement in field complexity, the fault set \(F\) will be further expanded, and the method proposed in this paper is still applicable to subsequent faults.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 6 Real Shipping Movement Model
### Node Types  
There are two types of nodes in the model: fixed and mobile. A small number of fixed nodes, identified under AIS as base stations with antennae, comprise the coastguard vessel traffic service (VTS) stations in the area, located at Snaefell, Isle of Man (node 1, N54.26\({}^{\circ}\), W4.461\({}^{\circ}\)), South Stack, Anglesey (node 2, N53.31\({}^{\circ}\), W4.69\({}^{\circ}\)), and Crosby (node 3, N53.50\({}^{\circ}\), W3.058\({}^{\circ}\); see Figure 11). From these antennae locations, the fixed infrastructure connects to the relevant coastguard stations, which act as internet access points (Maritime and Coastguard Agency, nd [196]). The remaining nodes in Figure 11 are classed as vessels by AIS. However, a significant number of them are permanently in one location. For example, the Elsam Traffic (node 0), classified as a manned VTS by the Maritime and Coastguard Agency (MCGA), is physically a ship but stays in one place for periods of months at a time and has access to fixed, land-based infrastructure. As far as the simulation is concerned, this is considered a base station. All other nodes are vessels at sea and, at any time, can be moving or stationary. Ships can remain at anchor or moored for the duration of the simulation. These vessels do not have infrastructure data connections and could move at any time; therefore, they are mobile nodes as far as MANET modelling is concerned. Obviously, ships that are moving during the simulation period are regarded as mobile nodes.
# Sparse Weighting for Pyramid Pooling-Based SAR Image Target Recognition
## 3 Contrast Experiment Results and Analysis
### Experiment 2: Comparison of SWAPP with Related Algorithms  
In order to show a more rounded analysis, the presented approach was compared with the following methods: the sparse representation classification (SRC) method [11], the PCA feature [23], and the LDA feature [24]. Table 3 gives the average classification results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline _dim_ & **Data** & **ScSPM** & **LLC-SPM** & **SWSPP** \\ \hline \multirow{4}{*}{100} & BMP2 & 77.27 & 76.17 & 80.99 \\  & BTR70 & 92.35 & 95.77 & 97.04 \\  & T72 & 85.36 & 88.24 & 85.02 \\  & av & 84.99 & 86.72 & 87.68 \\ \hline \multirow{4}{*}{200} & BMP2 & 79.66 & 80.82 & 83.79 \\  & BTR70 & 94.13 & 95.36 & 98.03 \\  & T72 & 86.17 & 88.09 & 87.29 \\  & av & 86.65 & 88.09 & 89.70 \\ \hline \multirow{4}{*}{400} & BMP2 & 83.65 & 83.48 & 87.67 \\  & BTR70 & 96.73 & 96.73 & 99.39 \\  & T72 & 86.87 & 87.79 & 88.14 \\  & av & 89.08 & 89.33 & 91.73 \\ \hline \multirow{4}{*}{600} & BMP2 & 84.16 & 85.33 & 89.27 \\  & BTR70 & 97.19 & 97.86 & 100 \\  & T72 & 87.63 & 88.36 & 89.00 \\  & av & 89.66 & 90.52 & 92.76 \\ \hline \multirow{4}{*}{800} & BMP2 & 84.67 & 86.51 & 91.31 \\  & BTR70 & 98.16 & 97.81 & 100 \\  & T72 & 88.38 & 88.95 & 90.03 \\  & av & 90.41 & 91.09 & 93.78 \\ \hline \multirow{4}{*}{1024} & BMP2 & 87.05 & 86.46 & 90.46 \\  & BTR70 & 96.94 & 98.78 & 100 \\ \cline{1-1}  & T72 & 89.18 & 89.78 & 92.27 \\ \cline{1-1}  & av & 91.06 & 91.67 & 94.24 \\ \hline \hline \end{tabular}
\end{table}
Table 2: MSTAR classification results (%).  
Figure 4: Curves of classification results of different codebook dimensions of the three different methods.  
According to this table, we can see that the presented method had advantages over all the other methods. The average classification result of our proposed method was 2% higher than the other approaches. On the basis of the results, we can see that the classification accuracy of the presented method was better; in particular, the correct rate of the BTR70 was 100%. The result validates the effectiveness of the proposed dependability weighted feature learning method for SAR image recognition.
# Analytical Investigation of a Novel System for Combined Dew Point Cooling and Water Recovery
## 3 Results
### Performance of the DPC-WR System under Different Climatic Conditions  
The analytical model of the DPC-WR system is used to calculate different performance indicators. Calculations are made for a single DPC-WR module. Geometrical parameters of the module are listed in Table 2. The mass flow of air supplied to the module is \(G_{1}=1.5\) kg/s and the NTU\({}_{1}\) of the module is 10. The ratio of air mass flow rate in the 1 st stage is \(G_{2}/G_{1}=0.45\) and in the 2 nd stage \(G_{4}/G_{3}=1.22\). The idea of the DPC-WR system is to utilize the renewable energy delivered to the system with ambient air. This means that the DPC-WR system is highly dependent on the parameters of the air supplied to the system; therefore, system performance should be studied for different climatic conditions. Different performance indicators are calculated for \begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **1 st Stage** & **2 nd Stage** \\ \hline Length, m & 0.50 & 0.50 \\ Width, m & 2.30 & 1.00 \\ Height, m & 2.30 & 1.25 \\ Channel height, m & 0.004 & 0.004 \\ Wall thickness, m & 0.0003 & 0.0003 \\ Heat exchange surface, m\({}^{2}\) & 581 & 145 \\ Number of primary channels & 253 & 145 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Geometrical parameters of the DPC-WR module. various climatic zones according to the Koppen-Geiger climate classification system [25] (climatic zones that include low-population areas were excluded from this study). The temperature (\(t\)) and humidity ratio (\(x\)) of the ambient air for selected locations in each climatic zone are defined by design conditions given by ASHRAE [26] and listed in Table 3. The given ambient air parameters are used as the temperature and humidity ratio of air that enters the DPC-WR system (\(t=t_{1i}\), \(x=x_{1i}\)). The performance indicator COP of the DPC-WR system is calculated for different climatic conditions and presented in Figure 4. It can be seen that the COP varies in the range from 138 to 768. High values of COP result from the fact that the cooling process in the DPC-WR system is run by the renewable psychrometric energy related to the latent heat of water evaporation. This renewable energy is delivered to the system with ambient air, which would be supplied to the system anyway. Therefore, the only external energy taken for COP calculation is the electric power input. It can be further seen that the highest COP is noted in semi-arid, hot desert, and subtropical climatic zones. These climatic zones are characterized either by high air temperature or by low humidity ratio, or by both. On
## 5 Discussion  
### Identification and Results Analysis  
Table 2 shows the probability of correct rejection of the algorithms. Since there are many possible comparatives between bird species, the main objective of this table is to show the algorithm's capability to reject individuals from impostor species. Note that the described performance (over 80% in almost all the identification comparatives) correspond to the automatic algorithm. The semi-automatic performance varies depending on the capability of the user to determine different species by analyzing the spectrogram.  
Table 3 shows the evaluation of the semi-automatic algorithm, where the number of comparatives correspond to the \(TA+FA+TR+FR\) matchings. The variability of comparatives is due to the variability of audio data available for each species (see Table 1). Since it is a combinational process, the number of comparatives carried out using the semi-automatic algorithm was only a random sampling. On the other hand, Table 4 shows the same information as in Table 3 but using the automatic algorithm. In this case, the number of comparatives was the total of the combinational process.  
The research hypothesis states that the BLPOC function can be used to identify individuals. Table 5 shows the precision results (based on previous tables). Notice that the results differ depending on the bird species and that, although the average stated is 86.53%, the results are given by specifying the individual species performance. The difference between both algorithms' precisions is minimum for noncomplex vocalizations. On the other hand, the automatic algorithm shows a better precision (in comparison with the semi-automatic algorithm) for complex vocalizations. A possible explanation that supports such results is based on the observer identification of the information in the spectrogram. Since this is a subjective selection, the user may omit important information by unselecting this information in the spectrogram (see Figure 5). However, a trained and experienced observer may distinguish the primal spectrogram information.  
Thus, the obtained results represent a proof-of-concept and investigating more complex acoustic scenarios is needed to obtain the reliability of the algorithm.
## 2 Mathematical Model Formulation
### The MIP Model  
The MIP decision variables, objective function and the constraints are given in the following equations. The MIP formulation of PFSP, which were proposed by [PERSON] [34], is used.  
**Decision Variables:**  
\(C_{max}\): Makespan  
\(C_{i,k}\): Completion time of job \(i\) on machine \(k\)  
\(D_{i,j}\) : Binary variable: 1 if job \(i\) is scheduled before job \(j\); 0, otherwise; \(i<j\)  
**MIP Model: Objective and Constraints:**  
\[Min\ C_{max} \tag{6}\]  
\[st: \tag{7}\]  
\[C_{max}\geq C_{i,m}\forall i=1,\ldots,n \tag{8}\]  
\[C_{i,k}-C_{i,k-1}\geq p_{i,k}\forall i=1,\ldots,n,\ \forall k=2,\ldots,m \tag{9}\]  
\[C_{i,k}-C_{j,k}+MD_{i,j}\geq p_{i,k}\forall i=1\leq i<j\leq n,\ \forall k=1,\ldots,m \tag{10}\]  
\[C_{i,k}-C_{j,k}+MD_{i,j}\leq M-p_{j,k}\forall i=1\leq i<j\leq n,\ \forall k=1, \ldots,m \tag{11}\]  
\[D_{i,j}\in(0,1). \tag{12}\]  
The objective function (6) minimizes the makespan while Constraint (7) calculates the maximum completion time of all jobs on the last machine. In PFSP, all jobs follow the same route through the machines so that their final processes will be done on the last machine. Constraint (8) computes the completion time of each job on machine 1 ensuring that they cannot occur earlier than the duration of their processing time on machine 1 which is the starting machine for all jobs. Constraint (9) ensures that the completion time of each job on each machine cannot be processed before their completion time on the previous machine. Constraints (10) and (11) specify the relationship between the processing of two consecutive jobs on the same machine. Constraint (11) starts that if job \(i\) precedes job \(j\) in the permutation, then job \(i\) should be completed before job \(j\) on each machine. Otherwise, job \(j\) should precede job \(i\) on each machine which is shown by Constraint (10).
# Fast Localization and High Accuracy Recognition of Tire Surface Embossed Characters Based on CNN
## 3 Machine Visual Processing
### Template Matching
#### 3.4.3 Gaussian Pyramid-Based Template Matching Acceleration  
The normalized correlation function algorithm (NCC) is used to match with high accuracy but is computationally intensive. To improve the processing speed of the algorithm, NCC is combined with the idea of image Gaussian pyramid hierarchical matching to increase the matching rate.  
Gaussian pyramids are sampled images using Gaussian smoothing and subpixel sampling [23; 24]; that is, i + 1 layer Gaussian images can be obtained by smoothing and subsampling the i-th layer Gaussian pyramid. The Gaussian pyramid is constructed by performing the following steps:  
Step 1. The bottom layer of the image pyramid is the original image and is represented by G0.  
Step 2. Convolution of G0 with a Gaussian kernel, using a 5 \(\times\) 5 Gaussian kernel, as follows:  
\[w(m,n)=\frac{1}{256}\begin{bmatrix}1&4&6&4&14\\ 4&16&24&16&4\\ 6&24&36&24&6\\ 4&16&24  
\[G_{i+1}(x,y)=\sum_{m=-2}^{2}\sum_{n=-2}^{2}w(m,n)G_{i}(2x+m,2y+n) \tag{9}\]  
Step 4. Repeat Step 3, and obtain the whole image pyramid by iterating the input image Gi as shown in the figure, the size of the iterated image \(G_{i+1}\). The size of the iterated image is 1/4 of Gi, and the resolution is 1/2 of Gi, which significantly reduces the computational effort when analyzing it. The Gauss Pyramid Significance is in Figure 19.  
Step 5. The pyramid was taken to 5 layers, and the NCC algorithm was used on the top G4 image to find the position with the greatest similarity, then the inverse transformation was passed to the bottom original image to find the best matching position, and the matching result was obtained as shown in the Figure 20.
## 2 Methodology
### Evolutionary Algorithms for Optimization
#### 2.3.1 Particle Swarm Optimization--PSO  
PSO was introduced by [19] and several variants that extend the standard PSO have been developed since then. One of the most noteworthy improvements is the use of an inertia function, which is equivalent to introduce a virtual mass to stabilize the search and improve the convergence of the algorithm. PSO with inertia function was proposed in [20] and further studied in [21]; such a variant is used in this research and its description is the following.  
PSO searches in the space of an objective function by adjusting the trajectories of individual agents called particles. The movement of each particle is composed by two main components: a stochastic and a deterministic component, in which every particle is attracted to both: the position of the current global best \(\boldsymbol{g^{*}}\) and to its own best position in history \(\boldsymbol{x_{i}^{*}}\), while at the same time it has a tendency to move randomly. The set or array of particles is known as a swarm. Now, we analyze the algorithm itself.  
Let \(\boldsymbol{x_{i}}(t)\in\mathbb{R}^{d}\) i.e., \(\boldsymbol{x_{i}}(t)=[\begin{array}{cccc}x_{i1}&\cdots&x_{id}\end{array}]^{T}\), be the position vector and \(\boldsymbol{v_{i}}(t)\in\mathbb{R}^{d}\) i.e., \(\boldsymbol{v_{i}}(t)=[\begin{array}{cccc}v_{i1}&\cdots&v_{id}\end{array}]^{T}\) the velocity vector of a particle \(i\) respectively, where \(d\) is the dimension of the search space. Then, the new velocity is determined by  
\[\boldsymbol{v_{i}}(t+1)=\theta_{M}\boldsymbol{v_{i}}(t)+\alpha_{P}\epsilon_{1}( \boldsymbol{g^{*}}-\boldsymbol{x_{i}}(t))+\beta_{P}\epsilon_{2}(\boldsymbol{x _{i}^{*}}-\boldsymbol{x_{i}}(t)) \tag{11}\]where, \(\epsilon_{1}\) and \(\epsilon_{2}\) are two random numbers that take values between 0 and 1. Parameters \(\alpha_{P}\) and \(\beta_{P}\) are the learning parameters or acceleration constants which typically take approximate values of 2, for example, \(\alpha_{P}\approx\beta_{P}\approx 2\). \(\theta_{M}(t)\) is the inertia function and takes values between 0 and 1. In the most simple case the inertia function can be considered as a constant, i.e., \(\theta_{M}(t)=\theta_{M}\), and typically \(\theta_{M}\approx\) 0.5\(\sim\)0.9 is used.  
On the other hand, the new position of particle \(i\) is updated by  
\[\mathbf{x_{i}}(t+1)=\mathbf{x_{i}}(t)+\mathbf{v_{i}}(t+1) \tag{12}\]  
The fundamental steps of the PSO algorithm can be summarized as it is shown in Figure 2.