esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress in the Knowledge, Application and Influence of Extremely Low Frequency Signals
## 3 Results and Discussion
### Author Analysis, Affiliations, and Countries of Investigation in ELF  
In total there are 28,875 authors investigating different areas of ELF waves in 27 subjects in 114 countries. The twenty countries with the highest concentration of researchers contributing to their scientific publications on ELF waves have been analyzed and are represented in the Figure 13. The main contribution is made by the United States with 6023 researchers (20.86%), followed by China and Japan with 3072 (10.64%) and 2088 (7.23%) researchers, respectively. The following countries in the ranking belong to the European Union where Italy is the European country with the highest contribution of researchers with 1984 scientists (6.87%), followed closely by Germany with 1706 researchers (5.91%). The rest of the European countries have a contribution of scientists around 1512 from France (5.24%) and 1369 (4.74%) from the United Kingdom. The rest of the countries contribute less than 3% of the total of the authors, as is the case of Spain with 791 researchers (2.74%). In Figure 14 the distribution of authors' participation can be observed according to the country of origin, according to a degradation of colors, the darkest being the one with the greatest contribution of researchers. There are 15.4% of other countries with a low-percentage contribution of less than 1% of the global number, such as Austria, Brazil, Taiwan, and Finland, among others. The results show that both the United States and the Chinese states actively collaborate in the progress by establishing collaboration ties with other countries in order to move forward--this can be seen from the intertwining of the communities (Figure 2). Table 1 shows a list of the 20 authors with the highest H-index according to Scopus in the ELF theme. If this table is compared with the countries with the highest contributions of authors, Figure 15, it is observed that there is no direct correspondence between the two, except for the United States. The first two authors with the highest H-index are from the United States, thus corresponding to the country with the highest contributions in authors in the search topic, with H-index values of 150 and 139 respectively. No Chinese author is among the 20 with the highest H-index, the first being in the 21 st place, with a value of 93. The first Japanese author occupies the 16 th position of the table with an H-index of 97. The Italian authors are found in positions 10, 12, and 18, with an H-index of 102, 99, and 95 respectively. Figure 14: Distribution of authors per country. Figure 13: Representation of the 20 countries with the most participating authors.
# Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
## 5 Dataset
### Generation of Time-Series Data  
In this paper, we explore the temporal features of Twitter data for timely detection of rumors in social media. Specifically, we generated time-series data by transforming Twitter conversations, where each conversation contains a list of tweets, into times-series vectors that contain reaction counts as features, and fed them as input to deep learning models. We transformed each of the Twitter conversation sample present in the PHEME seven events dataset into time-series vector for each time interval \(T\), where \(T=\{2,5,10,30,60\}\) min. After successful transformation of all conversations into time-series data, each vector represents one whole conversation and each of its values are the total reaction counts with respect to \(T\).  
Denote \(E=\{e_{i}\}\) the set that contains data of seven events present in the dataset, then for each event data \(e_{i}\), \(c_{ij}\) is a conversation sample related to that event. As the dataset has conversations separated by event, we iterated over all the events one-by-one. In each iteration, for every conversation sample present in them, we extracted timestamp of its source-tweet \(timeSource\) (starting point of the conversation) and its \(timeReactions=\{tr_{1},tr_{2},\cdots,tr_{n}\}\), which is a set of timestamps of all the reactions corresponding to that source-tweet. For a conversation sample, its length \(N\left(c\right)\) is determined by,  
\[N\left(c\right)=\lceil\frac{\max\left(timeReactions\right)-timeSource}{T}\rceil \tag{6}\]  
Assume \(c\) represents a conversation sample, if \(\left(a,b\right]\) is the time interval limit for \(k\)-\(th\) interval, where \(k=1,2,\cdots,N\left(c\right)\) then the total reactions count for that time interval is given by,  
\[count_{k}=\mathbf{card}(Q) \tag{7}\]  
where \(Q\subset timeReactions\) and \(Q=\left\{x\quad|\quad x>a\wedge x\leq b\right\}\), here \(x\) is the timestamp of a reaction (tweet) and cardinality is the measure of the size of set \(Q\), and the transformed vector representation is as follows:  
\[V\left(c\right)=\left[count_{k}\quad count_{k+1}\quad\cdots\quad count_{N}\right] \tag{8}\]  
The final vector representation of all conversation samples for each event is given by,  
\[e_{i}=\begin{bmatrix}V(c_{1})\\ V(c_{2})\\ \vdots\\ V(c_{n})\end{bmatrix} \tag{9}\]  
The flow chart of transforming Twitter conversations into time-series vectors for all combinations of \(E\) and \(T\) is given in Figure 4.
###### Abstract  
Positive Displacement Motors (PDM) are extensively used in the oilfield, either in drilling or in coiled tubing (CT) operations. They provide a higher rate of penetration and the possibility of drilling horizontal wells. For coiled tubing operations, PDMs can mill through obstructions and enable shut-in wells to work again. One of the major challenges while using these tools is the motor stalling, which can lead to serious damage to the PDM and lost time events in drilling and workover rigs. These events result in total losses of hundreds of thousands of dollars, and their avoidance mostly depends on trained and fully aware equipment operators. If a PDM starts to stall, the pumping needs to be halted immediately or the tool may fail. This paper describes the use of a Fuzzy Inference System (FIS) to detect the stalling events as they start to happen using the acquisition data from the coiled tubing unit, the output of the FIS could then trigger an alarm for the operator to take the proper action or remotely stop the pump. The FIS was implemented in Python and tested with real CT milling acquisition data. When tested using real data, the system analyzed 68,458 acquisition points and detected 94% of the stalling events across this data during its first seconds, whereas, during the real job, a CT operator could take longer to notice this event and take the proper action, or even take no action. If the FIS was applied on a real coiled tubing acquisition system, it could reduce PDMs over-pressurization due to stalling, leading to an increase on its useful life and decrease on premature failure. As of now there is no similar system in the market or published and this kind of operation is fully performed using human supervision only.  
cited tubing; positive displacement motor; fuzzy logic; oilfield +
Footnote †: journal: Applied Sciences  
Acticle  
A Fuzzy Inference System for Detection of Positive Displacement Motor (PDM) Stalls during Coiled Tubing Operations
## 3 Results and Discussion
### Cell Viability Analysis  
The cell viability of MG-63 cells attached to untreated/heat-treated Ti64 specimens after 4, 24, and 72 h of incubation was examined using an MTT assay (Figure 7). Compared with untreated specimens, MG-63 cells attached to the heat-treated surface showed a statistically significant difference in viability after 4 and 24 h of incubation, which implied the heat-treated surface promoted rapid cell attachment. After 72 h of incubation, the proliferation for MG-63 cells attached on the heat-treated surface was still high, but the difference was not statistically significant (\(p\) = 0.08). These results indicating that heating process could enhance the cell proliferation due to the promotion of attachment of MG-63 cells especially occurred in the initial 4 h of cell attachment.
# Microsphere Polymers in Molecular Imprinting: Current and Future Perspectives
## 3 Preparation of Molecularly Imprinted Microspheres
### Suspension Polymerisation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Polymerisation Technique** & **Advantages** & **Diodvantages** \\ \hline \multirow{3}{*}{Precipitation Polymerisation} & \(\bullet\) & Easy to operate. & \(\bullet\) \\  & \(\bullet\) & Requires no stabiliser or surfactant. & \(\bullet\) \\  & \(\bullet\) & Good control of particle sizes and morphology. & \(\bullet\) \\ \hline \multirow{6}{*}{Controlled/living radical precipitation} & \(\bullet\) & Requires no stabiliser or surfactant. & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\  & \(\bullet\) & High control of polymerisation rate, composition, & \\  & \(\bullet\) & High control of polymerisation rate, composition, & \\  & \(\bullet\) & and molecular weight. & \\ \cline{1-1}  & polymerisation (CRPP) 2 & \(\bullet\) & Able to do advanced surface modification because the & \\ \cline{1-1}  & \(\bullet\) & presents of living’ groups on the polymer surface. & \\ \cline{1-1}  & \(\bullet\) & Can performed under mild reaction conditions & \\ \hline \multirow{3}{*}{Atom Transfer Radical Precipitation Polymerisation (ATRP)} & \(\bullet\) & Wide scope of monomer, initiator and & \(\bullet\) \\ \cline{1-1}  & catalyst utilization. & \(\bullet\) \\ \cline{1-1}  & \(\bullet\) & The obtained MIMs are generally end-capped with a reactive halogen groups. & \(\bullet\) \\ \cline{1-1}  & \(\bullet\) & Relatively low-cost. & \\ \cline{1-1} \cline{1-1}  & \(\bullet\) & Compatible with many molecular imprinting systems & \\ \cline{1-1}  & \(\bullet\) & The obtained MIMs are generally end-capped with an & \\ \cline{1-1}  & \(\bullet\) & infierter groups. & \\ \hline \multirow{3}{*}{Reversible Addition-Fragmentation Chain Transfer Precipitation Polymerisation (RAFTP)} & \(\bullet\) & Wide scope of monomers (nearly all monomers) & \(\bullet\) \\ \cline{1-1}  & \(\bullet\) & The obtained MIMs are generally end-capped with a & \\ \cline{1-1}  & \(\bullet\) & diffinoseur groups. & \\ \cline{1-1}  & \(\bullet\) & Well suited for the preparation of high molecular & \\ \cline{1-1}  & \(\bullet\) & weight polymers. & \\ \cline{1-1} \cline{1-1}  & \(\bullet\) & The addition of BPA can produce & \(\bullet\) \\ \cline{1-1}  & \(\bullet\) & multi-hollow microsphere & \\ \cline{1-1}  & \(\bullet\) & Require use of surfactants & \\ \hline \multirow{3}{*}{Suspension Polymerisation} & \(\bullet\) & Dispersing agent must be inert with the template, & \(\bullet\) \\ \cline{1-1}  & \(\bullet\) & monomer, crosslinker and initiator & \\ \cline{1-1}  & \(\bullet\) & Required use of special dispersing phases & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Advantages and disadvantages of different polymerisation technique in preparation of molecularly imprinted microspheres.
## 2 Ge Growth Techniques  
Tracing back in history, the first Ge on Si detector was reported in 1984 by [PERSON] _et al._[5]. The demonstrated detector showed 41% quantum efficiency at a wavelength of 1.45 \(\upmu\)m, where an MBE-grown 1,800 A n + Ge\({}_{\mathrm{x}}\)Si\({}_{\mathrm{1-x}}\) alloy (graded in ten steps from x = 0 to x = 1) acted as a buffer layer for the heteroepitaxy of Ge on Si. Since then, various techniques with their own pros and cons have been pursued for the growth of Ge films on Si surfaces. The main quality criterion of the Ge layer can be categorized as: procedure complexity, material cost, growth temperature, and the resulting Ge layer's dislocation density and strain.
# Multi-Objective Optimization of Spatially Truss Structures Based on Node Movement
## 3 Numerical Method Verification
### Space Tower Optimization  
The form for a tower structure is shown in Figure 13, whose internode length of horizontal and vertical lengths are both 3 m. There is a 10 kN horizontal load on left side node (as shown in Figure 14). Under this load, the structure was optimized and analyzed to minimize the total structure weight and the maximum structural displacement. There were three boundary conditions. (a) The four angular points of each part of the tower were defined as the control nodes, which allowed the displacement value to move in the horizontal direction from 0 to 1.5m for intra-movement; moreover, the maximum displacement distance was half the side length. After control nodes were located, other nodes were obtained by interpolating, then connected to the vertical members. (b) The coordinate of the base position did not change.  
Figure 12: Suggested selection point.  
Figure 13: Initial tower model.  
As shown by the continuous evolution diagram of the tower frame, all the nodes were constantly moving during the early stage of the optimization calculation, but the movement was more chaotic (as shown in Figure 15). When optimal computation accessed 30 steps, the nodes close to the base are moved away from the section center, meaning the tower was sturdier. However, the closer the node was to the top, the closer to the section center, meaning the tower was thinner, which is also conformed to the load-bearing characteristics of the cantilever. When the optimal computation achieved 50 steps, the tower weight was 10,000 kg at the maximum displacement of 35 mm, compared with the first generation for the same displacement, which lost a weight of 4%. In addition, the overall configuration showed that the points between the nodes were smoother.  
Figure 14: Wind load effect.  
Figure 16 shows the Pareto frontier curve drawn using the optimization step corresponding to Figure 15. With continuous deepening of evolution, the data points became smoother and more uniform, and the optimization effect became more obvious; thus, the method is feasible, and the results are credible.  
Figure 15: Tower evolution diagrams: (**a**) Step-5 evolution diagram; (**b**) Step-16 evolution diagram; (**c**) Step-25 evolution diagram; (**d**) Step-31 evolution diagram; (**e**) Step-42 evolution diagram; (**f**) Step-50 evolution diagram.
## 3 Design and Result Analysis of the Battery Cycle Life Test
### Experimental Procedure  
Cycle life and OCV tests [42] of the batteries were carried out at room temperature, in which the cycle life test, also called Test 1, is the main experiment and after 80-100 cycle tests, an OCV test, also called Test 2, was performed to obtain the OCV-SOC curves for different degrees of aging of the batteries.  
Test 1: Cycle life test, the steps of one cycle life test are as follows:  
(1) The battery rests for 30 min before the cycle life test.  
(2) The battery is charged in the standard constant current constant voltage (CCCV) mode. CCCV means that the battery is charged with a CC until the charging cutoff voltage and a constant  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nominal Capacity** & **Nominal Voltage** & **Charging Cutoff Voltage** & **Discharging Cutoff Voltage** \\ \hline
1500 mAh & 3.7 V & 4.2 V & 2.5 V \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main specifications of the tested battery.  
Figure 1: Experiment platform.  
voltage (CV) is used to charge until the current is reduced to a predefined current. The CC is 750 mA, the charging cutoff voltage is 4.2 V and the charging is ended when the current is reduced to 20 mA under the cutoff voltage.  
(3) The battery has rests for 30 min.  
(4) The battery is discharged in the CC mode until the discharging cutoff voltage, at which the CC is 750 mA and the discharging cutoff voltage is 2.5 V.  
(5) The battery rests for 30 min.  
Test 2: OCV test to obtain the OCV-SOC curve. A flowchart of the experiment is showed in Figure 2 and the steps of one cycle life test are as follows:  
(1) The battery is charged to fully state in the standard constant current constant voltage (CCCV) mode just like test 1 and then it will rest for 2 h.  
(2) The battery continues 20 cycles, that is, the battery is discharged in CC mode for 324 s and then rests for 2 h.  
(3) The battery continues 19 cycles, that is, the battery is charged in CC mode for 324s and then rests for 2 h.  
(4) The battery is charged to fully state in the standard constant current constant voltage (CCCV) mode just like test 1 and then it will rest for 2 h.  
In test 2, CC and CCCV have the same meaning with the test 1.
# A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
###### Abstract  
In this work, we introduce a novel approach to model the rain and fog effect on the light detection and ranging (LiDAR) sensor performance for the simulation-based testing of LiDAR systems. The proposed methodology allows for the simulation of the rain and fog effect using the rigorous applications of the Mie scattering theory on the time domain for transient and point cloud levels for spatial analyses. The time domain analysis permits us to benchmark the virtual LiDAR signal attenuation and signal-to-noise ratio (SNR) caused by rain and fog droplets. In addition, the detection rate (DR), false detection rate (FDR), and distance error \(d_{error}\) of the virtual LiDAR sensor due to rain and fog droplets are evaluated on the point cloud level. The mean absolute percentage error (MAPE) is used to quantify the simulation and real measurement results on the time domain and point cloud levels for the rain and fog droplets. The results of the simulation and real measurements match well on the time domain and point cloud levels if the simulated and real rain distributions are the same. The real and virtual LiDAR sensor performance degrades more under the influence of fog droplets than in rain.  
LiDAR sensor; rain; fog; sunlight; advanced driver-assistance system; backscattering; Mie theory; open simulation interface; functional mock-up interface; functional mock-up unit +
Footnote †: journal: Sensors  
Article  
A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
## 3 Materials and Methods
### Hardware and Software Resources  
We conducted this study using a local workstation with macOS Sonoma 14.2, featuring an 8-Core Intel Core i9 2.3 GHz central processing unit (CPU) (Intel Corporation, Santa Clara, CA, USA), and 16 GB of random access memory (RAM). Our computational environment was enriched with a suite of open-source tools, including Keras (v2.15.0) for DL; Programming Language: Python (v3.11.11); Conda management system (v24.11.1); PyCharmEdu (v2022.1.3) integrated development environment; synthetic data vault (SDV), a Python library creating tabular synthetic data; and Gaussian Copula Synthesizer (2023) (GCS).
# The Application of Improved YOLO V3 in Multi-Scale Target Detection
## 2 Brief Introduction to YOLO V3  
YOLO (You Only Look Once) is a one stage target detection algorithm, which has been developed into the third generation called YOLO V3. YOLO V3 is end-to-end target detection algorithm. It can predict the category and the location of the targets directly, so the detection speed of the YOLO V3 algorithm is fast.  
To perform feature extraction, YOLO V3 uses successive \(3\times 3\) and \(1\times 1\) convolutional layers and draws on the idea of the Residual Networks [21]. There are 5 residual blocks in YOLO V3. Each residual block is composed of multiple residual units. The residual unit is shown in Figure 1. With the residual unit, the depth of the network can be deeper and the gradient fading can be avoided.  
The input image is downsampled by five times. YOLO V3 predicts the targets in the last 3 downsampled layers. There are 3 scales for YOLO V3 to detect the targets. At scale 3, the feature map downsampled by 8\(\times\) is used to detect small targets. At scale 2, the feature map downsampled by 16\(\times\)is used to detect medium-sized targets. At scale 1, the feature map downsampled by 32\(\times\) is used to detect big targets. Feature fusion is used to detect targets because small feature map provides deep semantic information and large feature map provides more finer-grained information of the targets. To perform feature fusion, YOLO V3 resizes the feature maps of the deeper layer by upsampling. Then the feature maps at different scales will have the same size. YOLO V3 merges the features from the earlier layer with the features from the deeper layer by concatenation. So YOLO V3 has good performance to detect both large and small targets. The network of YOLO V3 is shown in Figure 2.  
Figure 1: The structure of the Residual unit.  
Figure 2: The network of YOLO V3.
# Isoline Tracking in Particle-Based Fluids Using Level-Set Learning Representation
## 3 Proposed Framework
### Learning Representation for Isoline Tracking  
The proposed method aims to efficiently represent isolines in 2D by learning the level-set values needed for surface reconstruction through network training. To achieve this, the model structure is designed to be as simple as possible to reduce memory usage and ensure operability in general test environments. It should also track and reconstruct surfaces with accuracy comparable to traditional methods. To reduce wasted resources, it is essential to analyze the correlation of input data used for level-set inference to prevent unnecessary learning, and experiment with various approaches to find the optimal layer structure and parameters in the neural network for effective model training.  
Figure 2 represents the network structure of the model proposed in this paper. By learning and inferring values derived from traditional algorithms when tracking the fluid surface with the proposed network structure, the computational process for level-set values is eliminated, enabling real-time surface tracking. As the scale of the simulation increases, the time and resources consumed can be significantly reduced compared to traditional approaches. The physical data used as input are processed during the preprocessing phase  
Figure 1: [PERSON]’s method [27] represents the outer curve (red), while [PERSON] and [PERSON]’s method [10] represents the inner curve (black).  
of the simulation, allowing the fluid surface to be tracked during runtime without additional resource consumption. Furthermore, the simplicity of our network structure occupies less than 30 kb of storage space, making the model's initial loading into memory faster than models like CNN and LSTM, and thus suitable for use in middleware.