esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Membrane Performance Testing
#### 2.6.1 Antifouling Tests with BSA (Bovine Serum Albumin) Solutions  
The membrane resistance to organic fouling was evaluated during filtration of 0.1 wt. % BSA solutions. The pure water flux before (\(J_{0}\)) and after BSA filtration (\(J_{r}\)) were calculated. The normalized flux is simply the \(J_{r}\)/\(J_{0}\) ratio, which is indicative of the water flux recovery. The total fouling rate (\(R_{t}\)) and reversible (\(R_{r}\)) and irreversible (\(R_{ir}\)) fouling rates were also calculated from the normalized flux data using Equations (4)-(6). The flux recovery ratio (\(FRR\)) was calculated using Equation (7).  
\[R_{r}\ (\%)=\left(\frac{J_{w2}-J_{BSA}}{J_{w1}}\right)\times 100 \tag{4}\]  
\[R_{ir}=\left(\frac{J_{w1}-J_{w2}}{J_{W1}}\right)\times 100 \tag{5}\]  
\[R_{t}=R_{r}+R_{ir} \tag{6}\]  
\[FRR=\left(J_{w2}/J_{w1}\right)\times 100 \tag{7}\]  
where \(J_{w1}\) is the initial flux with DW, \(J_{BSA}\) is the flux for BSA solution and \(J_{w2}\) is the recovered DW flux after BSA filtration. BSA rejection (\(R\)) was calculated using Equation (8). A Shimadzu spectrophotometer (Japan) at 258 nm was used to measure the optical density (OD) of the permeate and feed solutions.  
\[R=\left(1-C_{p}/C_{f}\right)\cdot 100 \tag{8}\]  
where \(C_{p}\) and \(C_{f}\) are the concentration of BSA in the permeate and in the feed solution, respectively.
## 3 Test Results and Analysis
### Stress Wave Propagation Characteristics of Bolt Tension Process
#### 3.2.1 Stress Wave Signal Filtering Based on CEEMD Modal Decomposition Method
Decomposition results in 7 Intrinsic Mode Functions and 1 residual term, calculates the correlation number and energy of each IMF, takes the correlation coefficient greater than 0.2, and adds the IMFs with higher energy to attain the reconstructed signal. Figure 8 shows the CEEMD decomposition results of mg1-0. Table 2 shows the cross-correlation coefficients and energy ratios of each IMF component with the original data. Figure 7: Flow chart of CEEMD decomposition. The cross-correlation coefficient and energy ratios of each IMF component to the original signal are calculated (Table 2). The cross-correlation coefficient is calculated from the cross-correlation function and is a statistical measure used to indicate the degree of correlation between two signals, which can express the correlation between the IMF component and the original signal. The energy ratio indicates the weight of the IMF component in the original signal. It is obvious from the data in Table 2 that the number of cross-correlation coefficients between IMF2 and IMF3 is significantly greater than the other components, and energy summation accounts for 96.55% of the total energy. It can be observed that IMF2 and IMF3 basically contain the main features of the original signal and can be reconstructed as the feature components of the original signal to obtain the reconstructed signal.
_Appl. Sci._**2013**, \(3\), 457-468; doi:10.3390/app3020457  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Ultrasonics Promoted Synthesis of 5-(Pyrazol-4-yl)-4,5-Dihydropyrazoles Derivatives**  
**[PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 3 and [PERSON] 3,* *  
antitumor [7, 8, 9, 10], antifungal [11, 12, 13], antiviral and antibacterial [12, 14, 15], as well as fluorescent properties [16, 17, 18, 19, 20, 21]. In addition to these effects, in the last decade, pyrazolines and substituted pyrazolines have emerged as promising anti-depressant and anti-convulsant agents [22, 23, 24, 25]. Of all the synthesized pyrazoline derivatives, the 1,3,5-_tri_-substituted derivatives are of particular importance. So, it is important to find simple and convenient procedures for pyrazole and pyrazoline preparations with different substituent in their moiety, with the aim of obtaining some novel heterocyclic compounds with potentially enhanced properties.  
The development of new, rapid and clean synthetic routes toward focused libraries of nitrogen-containing heterocycles is of great importance to both synthetic and medicinal chemists. They have been reported in literature procedures for the design and development of new heterocycles (pyrazole and pyrazoline derivatives) by means of multistep reactions [26, 27, 28], metal-catalyzed synthesis [29, 30], domino reaction of 2-acylaziridines with the Huisgen zwitterions [31] and 1,3-dipolar cycloaddition reactions [32] to access important heterobiaryls.  
The first synthesis of the pyrazoline framework by the reaction of an \(\alpha\),\(\beta\)-enone with a hydrazine derivative was published by [PERSON] and [PERSON] [33]. Then, the reaction of \(\alpha\),\(\beta\)-unsaturated aldehydes and ketones with hydrazine derivatives became one of the most popular methods for the synthesis of pyrazolines [34, 35, 36, 37].  
Cyclization of chalcones, leading to pyridine, pyrimidine and pyrazoline derivatives, has been a developing field within the realm of heterocyclic chemistry for the past several years, because of their ready accessibility and the broad spectrum of biological activity of the products [38, 39, 40, 41, 42, 43, 44]. These observations led us to synthesize chalcones and its corresponding pyrazoline, exploring simple procedures.  
Sonochemistry is attracting considerable research activity within the synthetic chemistry community, because it offers a new approach to the preparation of organic compounds. In the last two decades, sonochemical methods have become widely used in organic synthesis [45, 46, 47]. Nowadays, the ultrasonic irradiation technique has been employed, not only to decrease reaction times, but also to improve yields in a large variety of polyfunctionalized heterocycles. Compared with traditional methods, this method is more convenient and easily controlled. A large number of organic reactions can be carried out in a higher yield shorter reaction time and milder conditions under ultrasound [48, 49, 50, 51, 52].
# Difference DV_Distance Localization Algorithm Using Correction Coefficients of Unknown Nodes
## 5 Simulations and Performance Comparison
### Results and Analysis
#### 5.2.2 The Influence Analysis of Anchor Node Density  
We can see that the average localization error and the corresponding mean square deviation decrease with the increase of anchors density. For the Figure 7, the localization accuracy of basic DV_Distance algorithm is still worst. Methods in references [23; 24; 25] are the improved localization algorithms using distance difference, and their localization accuracy is significantly better than that of [PERSON]'s method. Besides that, the localization accuracy of [PERSON]'s method and [PERSON]'s method is also slightly better than that of [PERSON]'s method, and the main reason is that both [PERSON] and [PERSON] make the distance difference equally distributed to each hop. The localization accuracy of [PERSON]'s method is similar to that of [PERSON]'s method and [PERSON]'s method in the situation of low anchors density, but is slightly worse than them with the increase of anchors density. The localization accuracy of the proposed algorithm is obviously better than that of other algorithms, that reason is mainly that the proposed algorithm not only uses the distance difference to correct, but also takes into account of the difference of correction coefficients from unknown nodes. For Figure 8, the localization stability of the proposed algorithm is better that of other algorithms, and the localization stability of methods in references [23; 24; 25; 26] is better  
Figure 5: The change curve of average localization error with noise intensity.  
Figure 6: The change curve of mean square deviation with noise intensity.  
than that of basic DV_Distance algorithm and [PERSON]'s method. Moreover, the localization stability of methods in references [23; 24; 25; 26] will be consistent gradually with the increase of anchors density.
## 3 Results
### Identification Samples with High Phenolics and Flavonoid Contents
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Accession ID**} & **Phenolic** & **Phenolic** & **Flavonoid** & **Flavonoid** & & & \\  & **Median** & **(mg** & **Median** & **(mg** & **Cut** & **Code** & **Location** \\  & **Score** & **GAE/gDW**) & **Score** & **Rutin/gDW**) & & & \\ \hline
3-PS\_Tray\_090-OC063 & 0.99555278 & 33.1466 & 0.98281601 & 38.9544 & 3 & OC063 & Chai Badan, Lop Buri \\
3-PS\_Tray\_111-OC194 & 0.99313232 & 30.122 & 0.99997377 & 36.2052 & 3 & OC194 & Udon Thani \\
3-PS\_Tray\_094-OC064 & 0.99262559 & 31.6475 & 0.99993515 & 31.8276 & 3 & OC064 & Roi Et \\
3-PS\_Tray\_082-OC057 & 0.99036166 & 30.0168 & 0.98824856 & 32.5889 & 3 & OC057 & Damoon saduak, \\  & & & & & & & \\
3-PS\_Tray\_200-Green & 0.98963133 & 36.5657 & 0.99991351 & 32.9061 & 3 & Green & Chia Tai Co. Ltd., \\  & & & & & & & & Bangkok, Thailand \\
3-PS\_Tray\_097-OC072 & 0.98840198 & 28.4914 & 0.99991798 & 26.7521 & 3 & OC072 & Wat Phleng, \\  & & & & & & & & \\
3-PS\_Tray\_108-OC135 & 0.98801568 & 29.1489 & 0.9999814 & 26.6675 & 3 & OC135 & Mueang Phitsanulok, \\  & & & & & & & & Phitsanulok \\
3-PS\_Tray\_115-OC195 & 0.98785043 & 26.4926 & 0.99940136 & 26.9636 & 3 & OC195 & Kumphawapi, Udon \\
3-PS\_Tray\_091-OC063 & 0.98740476 & 29.9905 & 0.99934819 & 34.5557 & 3 & OC063 & Chai Badan, Lop Buri \\
3-PS\_Tray\_193-OC148 & 0.98612973 & 34.3301 & 0.9999848 & 22.8397 & 3 & OC148 & Mae Lao, Chiang Rai \\
3-PS\_Tray\_107-OC113 & 0.97709599 & 31.2793 & 0.9953292 & 37.4741 & 3 & OC113 & Mueang Rayong, \\  & & & & & & & Rayong \\
3-PS\_Tray\_194-OC148 & 0.97607613 & 30.2725 & 0.99943241 & 17.9715 & 3 & OC148 & Mae Lao, Chiang Rai \\
2-PS\_Tray\_211-Green & 0.97229585 & 26.1395 & 0.99513295 & 16.7679 & 2 & Green & Chia Tai Co. Ltd., \\  & & & & & & & Bangkok, Thailand \\
3-PS\_Tray\_180-OC141 & 0.96748805 & 27.5972 & 0.99998224 & 17.7262 & 3 & OC141 & Mueang Phrae, Phrae \\
2-PS\_Tray\_175-OC141 & 0.95617777 & 27.8602 & 0.99375805 & 21.021 & 2 & OC141 & Mueang Phrae, Phrae \\
3-PS\_Tray\_104-OC081 & 0.95401239 & 33.5937 & 0.99527285 & 36.6705 & 3 & OC081 & Bang Klam, Songkhla \\
3-PS\_Tray\_196-OC148 & 0.95355386 & 30.5954 & 0.99998945 & 19.3926 & 3 & OC148 & Mae Lao, Chiang Rai \\
3-PS\_Tray\_113-OC194 & 0.95003963 & 30.122 & 0.9999494 & 36.9242 & 3 & OC194 & Udon Thani \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of samples with high prediction scores (\(\geq\)0.95) for both phenolic and flavonoid contents, showing their median scores, measured contents, and collection locations across Thailand.
# Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT
## 3 Proposed Method
### Classification of ECG Images
#### 3.2.2 Lightweight CNN
If a potential heart disease is detected, the disease alert module is triggered. 3. The disease alert module sends alerts to relevant parties (medical personnel, patients) for immediate medical care. 5. Route Discovery (DSR Protocol): 1. When a source node (S) wants to send data to a destination node (D) and does not have a route in its cache: 2. S broadcasts a route request message to its neighboring nodes. 3. Each neighbor receiving the request checks its own cache for a route to D. 4. If a route is found, the neighbor sends a route reply message back to S with the route information. \begin{table}
\begin{tabular}{l l} \hline \hline
**Name** & **Description** \\ \hline Batch size & 16 \\ Optimizer & Adam \\ Training & 80\% \\ Loss Function & Categorical cross-entropy \\ Epochs & 70 \\ Learning rate & 0.001 \\ Testing & 20\% \\ Activation & SoftMax \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Proposed parameters for lightweight CNN. * If no route is found, the neighbor forwards the request to its own neighbors. * Route Maintenance (DSR Protocol): 1. When a route reply message is received by S, it records the route in its cache for future use. * The route is also saved in the route caches of all nodes that helped find it. * Route Selection (REL protocol): 1. REL protocol uses residual energy and link quality to find routes for improved QoS reliability. * Links are selected based on LQE values (e.g., LQI, RSSI) and residual energy. * Classification of ECG Images 2. Dataset Preparation: 1. Load the \"ECG Images dataset of Cardiac Patients\", consisting of 12-lead-based ECG images and four classes (normal, myocardial infarction, previous history of MI, abnormal heartbeat). * Split the dataset. * Lightweight CNN Model: 1. Input layer for processing the ECG data. * Multiple convolutional layers with pooling and activation functions to capture important patterns. * Contextual encoding layer: discover contextual connections in input data for identifying dependencies in sequences. * Depthwise separable convolution: reduce model parameters for efficiency. * Flattening: flatten the improved features into a one-dimensional vector. * SoftMax classifier: determine the probability of each identity category based on the extracted ECG data.
## 1 Introduction  
Space is a critical domain for safeguarding national security and conducting international competition and cooperation. Since the 21 st century, major space-faring nations have been racing to develop their space capabilities, and space has gradually become the pinnacle of great power rivalry and strategic competition [1]. With the continuous advancement of aerospace technology, the future battlefield landscape will extend from traditional land, sea, and air domains to outer space [2], and space operations will emerge as the new frontier in military competition. In particular, the deployment of new-generation remote sensing and communication satellites has rapidly increased global demand for spatial data and information services, which now exhibit characteristics of diversity and high timeliness [3, 4]. Against this backdrop, effectively deploying satellite missions under conditions of limited resources and ever-changing objectives has become a pivotal challenge in future space operations.  
Satellite mission planning is a complex systems engineering task that involves multiple constraints, including orbital maneuvering [5], mission scheduling [6], and resource allocation [7]. To address the multiple challenges of target timeliness, dynamic environmental changes, and multi-satellite coordination, traditional single-objective or single-satellite mission planning methods can no longer meet the demands of modern aerospace systematic operations and efficient resource utilization [8]. Therefore, current satellite mission planning urgently requires comprehensive optimization under multi-objective, multi-constraint, and dynamic environments, utilizing advanced algorithms and theoretical tools to enhance the execution efficiency and reliability of satellite missions. With the continuous development of technology, satellite mission planning will play a crucial role in future space operations, becoming a key component in ensuring national space security and effective operational capabilities.
# A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring
## 2 Parallel Auto-Encoder Framework
### The Proposed Approach  
In contrast to general sequential approaches (e.g., AutoDNet and SAF) which treat the mode shapes and frequencies together [3], we first separate the modal data into several subsets based on their physical meaning and magnitude scale and then fed them into the proposed framework in parallel.  
In order to achieve our objective of damage detection/identification under the above difficulties, two major components shown in Figure 1 are developed in the proposed framework as below.  
1. Dimensionality reduction component for: 1. Scale-invariant, correlated and noise-robust features extraction for reduction from modal information; 2. Scale-invariant, correlated and noise-robust features extraction for reduction from frequency information;
2. Relationship learning component for: 3. Learning the relationship between extracted features and output (elementary stiffness parameters); In the following section, each component of this work will be discussed in detail.
# CERRT: A Mobile Robot Path Planning Algorithm Based on RRT in Complex Environments
## 4 Simulation and Experiment
### Path Planning Simulation
#### 4.1.2 Maze Environment  
The maze environment was designed to evaluate the performance of algorithms in complex environments without passages. Figure 10 illustrates the planning situations of the four algorithms, indicating that the utilization rates of tree nodes were low for RRV, RRT, and Fast-RRT, and there were numerous repeated exploration points on the left side of the map. In contrast, CERRT's tree nodes were evenly distributed, enabling the exploration of a broader area with fewer points.  
Table 2 presents the algorithms' performance in the maze environment. RRT's time consumption was 2.7 times that of CERRT, Fast-RRT's time consumption was 1.7 times that of CERRT, and RRV's time consumption was 15 times that of CERRT. CERRT had the shortest running time and the smallest number of tree nodes. Furthermore, CERRT's time standard deviation was significantly lower than those of RRT and RRV, further indicating that this algorithm was most stable in the Maze environment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Algorithm** & **Avg. Time (ms)** & **Min Time (ms)** & **Max Time (ms)** & **Std** & **Avg. Nodes** & **Success Rate** \\ \hline RRT & 9.08 & 5.61 & 14.48 & 2.04 & 263 & 1.00 \\ RRV & 37.04 & 7.33 & 71.00 & 10.90 & 281 & 1.00 \\ Fast-RRT & 13.15 & 6.58 & 32.45 & 3.94 & 355 & 1.00 \\ CERRT & 15.86 & 9.28 & 24.63 & 2.81 & 286 & 1.00 \\ \hline \end{tabular}
\end{table}
Table 1: Results for planning in a simple environment.  
Figure 10: Performance comparison of four algorithms in maze environment. (**a**) RRT; (**b**) RRV; (**c**) Fast-RRT; (**d**) CERRT.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Algorithm** & **Avg. Time (ms)** & **Min Time (ms)** & **Max Time (ms)** & **Std** & **Avg. Nodes** & **Success Rate** \\ \hline RRT & 108.29 & 83.66 & 153.99 & 13.97 & 2284 & 1.00 \\ RRV & 603.24 & 438.86 & 1001.21 & 120.67 & 3042 & 1.00 \\ Fast-RRT & 68.93 & 48.75 & 135.38 & 11.58 & 1368 & 1.00 \\ CERRT & 39.73 & 34.59 & 112.35 & 8.66 & 610 & 1.00 \\ \hline \end{tabular}
\end{table}
Table 2: Results for planning in a maze environment.  
Based on the above, it can be concluded that the new CERRT algorithm outperformed the RRT, RRV, and Fast-RRT algorithms in various aspects in complex non-navigable environments. The main reason for this is the improved vertex expansion strategy, which effectively improved the utilization rate of vertices by pre-allocating them for expansion. This strategy reduced repeated exploration of the same region and introduced a vertex death mechanism to eliminate useless vertices, thus reducing the time cost of selecting the optimal neighboring vertex.
## 3 Methodology
### TRFM Trajectory Prediction Model
#### 3.3.3 Decoder  
Similar to the encoder, the core structure of the decoder is the TRFM unit. To better generate the predicted sequence, a portion of the input sequence is used as prior information to guide the decoding. That is, the input sequence of the decoder consists of the latter part of the model's input sequence and a portion of the initialized normal distribution input sequence. This is then combined with the encoding of temporal positions to form the input of the decoder. The overall length of the input sequence of the decoder is consistent with the output sequence length of the encoder.  
Notably, the TRFM unit in the decoder uses a cross-multi-head attention mechanism. The features of the output sequence from the encoder are replicated twice to form the \(K\) and \(V\) matrices. The input of the decoder is only used to compute the Q matrix. As a result, the output of the TRFM layer in the decoder also yields a sequence feature of the same length as the input. By repetitively stacking the TRFM layers of the encoder, the output undergoes a fully connected layer, and the resulting output is the final prediction of the model.  
The output of the trajectory prediction model is a sequence of differential latitude and longitude values. To obtain the true predicted trajectory coordinates, they need to be calculated using the absolute regression of latitude and longitude values. Thus, the pseudo-code for the trajectory prediction process in this paper is provided in Algorithm 2.
## 3 Results and Discussion
### Printing Pattern and Its Application  
Experiments were carried out to make patterns through the electrohydrodynamic (EHD) jet printing process with the micro-ring dispersion ink (Figure 5a). First, we tried to print a line pattern containing the micro-rings through the dispensing method by heating the substrate to induce an evaporation rate of the toluene solvent. Figure 5b demonstrates the detailed pattern with the micro-ring array along the printing direction. Due to the coffee ring effect originating from the capillary flow induced by the differential evaporation rates across the drop [17], the micro-rings were concentrated at the edge of the pattern.  
SERS substrates can be offered as one of the biological applications with these Ag nanowire micro-rings. The substrates are used to determine the minimum quantity of biomolecules and allow us to detect biological species in biofluids. Therefore, this technique can enhance the Raman scattering by molecules adsorbed onto metallic structures. We measured the Raman spectrum of the Ag nanowire micro-ring patterned substrate and bare Si wafer at 532 nm wavelength under the same measurement conditions, and clearly observed enhanced total surface background intensity values (Figure 5c). The shape and size of the metallic nanostructures affect the enhancement strength due to the factors influencing the ratio of absorption and scattering [18]. Moreover, self-assembled metallic nanoparticles such as Ag and Au nanostructures can actively enhance the Raman scattering. These data suggest that our Ag nanowire micro-ring structure could be utilized in SERS substrate as the Ag nanowire micro-rings have nanoscale widths (\(<\)25 nm), thus offering great printability on surfaces.  
Figure 3: (**a**) A schematic of the pinched flow fractionation microchannel with a particle. Trajectory of 10 \(\upmu\)m of hollow glass particles in different flow rate ratios of (**b**) 1:1, (**c**) 1:4, and (**d**) 1:10 (\(\upmu\)L/min).  
Figure 4: (**a**) A microscopic image of the sorting process in the pinched flow fractionation (PFF) microchannel, (**b**) Ag nanowire rings in dispersed solution from outlet 1, and (**c**) droplet diameters of the sorted droplets in outlets 1 and 2.  
Figure 5: (**a**) Patterning with the micro-ring solution using electrohydrodynamic (EHD) jet printing, (**b**) a microscopic image of the micro-ring distribution, and (**c**) diagram of the Raman spectra for the Ag nanowire micro-ring structure and bare wafer.