esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Patent Retrieval and Data Collection  
Patents belong to DJI were gathered from the SIPO using the search query \"Inventor = (Shenzhen Dajiang Innovation) AND DESC = (\"unmanned aerial vehicle\" OR \"UAV\" OR \"camera drone\") AND Invention_Type = (Invention) AND Application_  
Date \(\leq\) (20220622)\" (date: 23 June 2022), and 720 patents were collected.  
Figure 10 illustrates the number of inventions about UAV DJI applied for patents for from 2010 to 2022. As presented in the graph, the number of applications for UAV inventions from 2010 to 2013 was only 1, 0, 3, and 3, respectively. However, there was a substantial growth in the number of inventions DJI applied for; 50 in 2014, and 56 in 2015. The change in the number of inventions in 2013 and 2014 matches the fact that DJI first launched a drone named \"Phantom\" in early 2013 and \"Inspire\" in 2014, followed by the \"Spark\" series and \"Mavic\" series in the following years. It is clear from the data that the number of annual applications remained high for the next two years (197 inventions in 2016 and 250 inventions in 2017). In other words, DJI continued to maintain and even enhance its innovation capabilities. In addition, the number of filed patents in 2021 and 2022 shown in the figure are zero, which does not mean that DJI applied for no patents in these two years. It is more likely that the patents filed by DJI in 2021 and 2022 have not been granted yet, as it takes about 2 years for Chinese patents to be granted after the initial disclosure. Likewise, the downward trend in the number of patents shown in the graph from 2018 to 2020 does not reflect a recent lack of innovation at DJI, as there may have been patents filed during this period not yet granted.  
Finally, 720 inventions for UAVs constitute a patent set. Their application dates, IPC symbols, and description sections were extracted.
# Prediction of the Tensile Load of Drilled CFRP by Artificial Neural Network
###### Abstract  
The application areas of carbon fiber reinforced plastics (CFRP) have been increasing day by day. The machining of CFRP with incorrect machining parameters leads in huge loss cost and time. Therefore, it is very important that the composite materials are machined with correct machining parameters. The aim of this paper is to examine the influence of drilling parameters on tensile load after drilling of CFRP. The drilling operations were carried out on Computer Numerical Control (CNC) by Tungsten Carbide (WC), High Speed Steel (HSS) and Brad Spur type drill bits with spindle speeds of 1000, 3000 and 5000 rpm and feed rates of 0.05, 0.10 and 0.15 mm/rev. The results indicate that the surface roughness, delamination and thrust force, were affected by drilling parameters therefore tensile load was also affected by the same parameters. It was observed that increase in surface roughness, delamination and thrust force all lead to the decrease of tensile load of CFRP. If the correct drilling parameters are selected; the decrease in tensile load of CFRP can be saved up to 25%. Furthermore, an artificial neural network (ANN) model has been used to predict of tensile load. The results of the ANN model are in close agreement with the experimental results.  
CFRP; drilling; surface roughness tensile load Article
This approach improves the management of distributed trust; the hierarchical model naturally aligns with the multi-level communication and control structures found in modern distributed systems, such as intelligent vehicle surveillance networks. It facilitates more efficient key management and trust delegation among diverse stakeholders, ensuring that security is maintained even in complex, dynamic environments. Collectively, these advantages position the HMPS scheme as a highly effective solution for real-time IoT applications, delivering superior computational efficiency, scalability, and robustness compared to traditional approaches that merely append signatures. The use case described in this paper is collaborative issues detection in the surveillance of autonomous vehicle systems [5, 6].
## 3 The Proposed Framework for Testing
### High Speed Serial Interface Testing  
Emulators have long been used by hardware and software engineers to emulate the behavior of complicated circuit building blocks and enable hardware and software diagnostics and debugging (shown in Figure 8). An emulator is a hardware device or software program that has been used to capture real-time data in a proposed framework.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Test Case ID** & **Test Scenario** & **Test Case** & \multicolumn{2}{c}{**Pre-Condition(s)**} & \multicolumn{2}{c}{**Test Steps**} & \multicolumn{2}{c}{**Test Data**} & \multicolumn{2}{c}{**Expected Test**} & \multicolumn{2}{c}{**Post-Condition(s)**} & \multicolumn{2}{c}{**Actual Test**} & \multicolumn{2}{c}{**Status**} \\  & & & & & & & & & & & \\ \hline \multirow{4}{*}{TC\_TX-001-1} & Transmitting & \multirow{4}{*}{\begin{tabular}{c} Transmitting \\ data from the \\ Tx \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Transmitting \\ data from the \\ Tx \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Tx does not \\ in place. \\ 2. Rx due to some \\ \end{tabular} } & 1. Tx should be & \begin{tabular}{c} 1. Tx does not transmit the \\ data. \\ 2. If data are not received at the \\ Rx end then \\ \end{tabular} & \
\begin{tabular}{c} \(<\)Tx does not \\ transmit the \\ data. \\ \end{tabular} & \multirow{4}{*}{
\begin{tabular}{c} Transmitting \\ data. \\ \end{tabular} } \\  & & & & & & & \\ \cline{4-10}  & Transmitting & & & & & & & \\ \cline{4-10}  & Transmitting & & & & & & & \\  
In the proposed approach, each emulator was connected to a processor using a different computer. Several emulators were used with interfaces that captured real-time data. A ramp signal was sent as the input from one processor to another. The ramp signal was the best signal to test these interfaces. It contained values that provided visual indications of any bug that may arise during inter-processor communication and to understand dynamic system behavior through the velocity factor. Real-time data of every PEs were captured using emulators, shown in Figure 9. This identified the incorrect ramp waveform and then compared it with a knowledge base. In this way, the complete system was emulated at the same time.  
In this case, the expected results of different PEs interfaces did not match the actual results; then, the proposed framework compares the actual results to the stored results of the knowledge base and computes the similarities between them. The most closely related correlation is then matched to the specific use case. As a result, the proposed framework extracts and localizes the bugs automatically.
# Swarm Intelligence Algorithms for Feature Selection: A Review
## 4 Methodology  
In this section, strategies for searching the relevant literature for this survey are described in details. Firstly, we have selected the most appropriate keywords and built a search string. Selected keywords were \"swarm intelligence\" and \"feature selection\", while the search string was: \"swarm intelligence\" + \"feature selection\". Search was limited on the major databases that mostly cover computer science related content: ACM Digital Library, Google Scholar, IEEE Xplore, ScienceDirect, SpringerLink, and Web of Science. Results obtained from those databases are presented in Table 1.  
The next stage involved pre-screening the abstracts of returned results. The main aim of pre-screening was to remove redundant data (some papers were returned in more than one database) along with inappropriate results. Inappropriate results covered some papers, where authors claimed they used SI concept, but our study revealed that there were only bio-inspired algorithms without SI concept.  
Altogether, we selected 64 papers that were studied in deep details. Findings of this study are presented in the next sections.  
Papers comprising SI and FS have had almost exponential growth over the past 17 years. The growth is visible in Figure 4, where 1170 papers were written in 2017 on this topic alone. Nonetheless, the biggest increase is noted in 2017, when 206 more papers were written than the year before. The statistics were obtained from Google Scholar in June 2018.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Academic Online Database** & **Hits** \\ \hline ACM Digital Library & 61 \\ IEEE Xplore & 52 \\ ScienceDirect & 409 \\ SpringerLink & 603 \\ Web of Science & 98 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hits for “swarm intelligence” AND “feature selection” in online academic databases from 2007 to 2017.
# Imogolite Nanotubes: A Flexible Nanoplatform with Multipurpose Applications
## 3 Surface Properties and Modifications  
### Colloidal Behavior  
The structure of INTs with inner and outer hydroxyl groups makes them excellent candidates for colloidal dispersions. [PERSON] moved forward with a unique intrinsic polarization, the isolated -SiOH groups on the inner cavity developing a negative charge, whereas the -AlOHAI groups along the external tube walls bear a weak positive charge [56]. This model has been subsequently corroborated by numerical simulations [24], showing no dependence on the substitution ratio [Si]/([Si] + [Ge]) [25]. Thanks to the surface charge, INTs are easily dispersed in polar solvents to form stable colloidal suspensions [57; 58]. Furthermore, the possibility to varying both nanotube aspect ratio (through the synthesis conditions) and electrostatic interactions (pH, ionic strength,... ) allows controlling, directly in aqueous phase, the self-organization of imogolite nanotubes from a liquid sol to arrested phases [59; 60; 61], making such nanotubes suitable for a wide range of solution processes. Similarly, it is also possible to control the nanotube arrangement in the solid state during the drying process. For instance, powders of Ge-SWINT tend to form large bundles on a 2D hexagonal lattice if they are processed from suspensions prepared at an ionic strength higher than \(\sim\)10\({}^{-3}\) mol\(\cdot\)L\({}^{-1}\)[61]. Interestingly, this kind of organization also induces significant radial deformations of the cross section, INTs having a regular hexagonal shape instead of a cylindrical one [62]. Another unique feature of INTs is the high surface density of hydroxyl groups (\(\sim\)18 OH/nm\({}^{2}\)), considering the atomic structure reported in [19], which offers multiple binding environments and renders the surface very hydrophilic with high water retention capacity [63; 64; 65]. INTs were recognized as good adsorbents very early on, especially in environmental contexts where they interact strongly with ions [38; 66; 67; 68; 69; 70].  
Despite these benefits, the presence of water inside INTs might hinder or block the diffusion of other interest molecules (CO\({}_{2}\), CH\({}_{4}\),... ). Similarly, in the field of nanocomposites, the effective dispersion of nanoparticles must be achieved only by modifying their surface to make it compatible  
Figure 3: Inner diameter size for the different endmembers of (OH)\({}_{3}\)Al\({}_{2}\)O\({}_{3}\)Si\({}_{x}\)Ge\({}_{1-x}\)(OH) imogolite nanotubes. (**a**) Si-SWINT; (**b**) Ge-SWINT; (**c**) Ge-DWINT. Color code: aluminum (blue); silicon (yellow); germanium (orange); oxygen (red); hydrogen (white).  
with the organic matrix. Hence, significant efforts have been undertaken in order to make INTs suitable with the intended applications.
## 2 Deep Learning-Based Method for Wind Turbine Operation and Power Forecasting
### Attention Mechanisms  
Attention is essentially a tool to filter and focus important information from a large number of available processing resources, while ignoring non-important information [31; 32]. It is usually combined with threshold functions, such as softmax and sigmoid, or sequential techniques [33; 34]. In both computer vision and sequence tasks, it has shown superior performance [35; 36]. In these applications, the attention mechanism usually acts on one or more top layers to further reshape the characteristics of the higher level. Attention mechanisms have provided a lot of benefits in many applications, e.g., image classification [37], object detection [38], multi-modal task [39], few-show learning [40], and machine translation [21].  
The more common attentions are channel attention [37; 41], spatial attention [37; 42], temporal attention [43], and branch attention [44]. Channel attention adaptively recalibrates the weight of each channel and can be viewed as an object selection process, thus determining what to pay attention to. [PERSON] et al. [41] introduced a lightweight attention operation with a Squeeze-and-Excitation block to model channel-wise relationships. Spatial attention can be seen as an adaptive spatial region selection mechanism for determining where to pay attention to. [PERSON] et al. [42] proposed deformable convolutional networks (deformable ConvNets) to be invariant to geometric transformations, but they paid attention to the important regions in a different manner. Self attention [21] is also used as a spatial attention mechanism to capture global information. Temporal attention is a dynamic time-selection mechanism. [PERSON] et al. [43] proposed a global-local temporal representation (GLTR) to exploit multi-scale temporal cues in a video sequence. In a multi-branch structure, branch attention is used for branch selection. Reference [44] proposed an automatic selection operation called selective kernel (SK) convolution implemented using three operations: split, fuse, and select.  
The above-mentioned attention methods are often combined in application. [PERSON] et al. [45] dynamically modulated the sentence generation context in multi-layer feature maps using encoding channel attention and spatial attention. Reference [46] identified spatial saliency associated with image pixels and executed temporal intensity filtering and predictive coding to filter spatiotemporal redundancies from images.  
On the basis of the above overview, we propose a feature fusion method based on time series channel attention, aiming to enhance the model's long-term forecasting ability in wind power forecasting.
## 4 Data Transmission
### Frame Organization  
In the design of the modulations, synchronization has to be taken into account as a key process in the demodulation. The frame organization, while we perform tests to finish the design of the entire physical layer is the shown in Figure 9.  
A synchronization sequence set (usually, three m sequences of length 2047 and oversampled with 10 samples per chip) is settled in the beginning of each data block [32]. The data block can contain either one test (with one bandwidth and symbol period) or several tests if these are shorter. However, the entire data block is mandatory to be shorter than a coherence time of the multipath ionospheric channel. The reason of this limitation is that with this criterion the receiver has information about the evolution of the channel performance. The bitrate depends on the modulation used, and varies from a range from 10 bps to 2 kbps. The dynamic margin of bitrates available are shown in Section 5.
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 2 Nanofluid
### The Mechanisms of Enhancement in Nanofluids
#### 2.3.1 Shuttle (Grazing) Effect  
The shuttle effect was proposed by [PERSON] et al., who discussed the rate enhancement of gas absorption in liquid in a gas-liquid-solid three-phase system when solid particles are present, using a theoretical model [74]. The grazing effect states that the gas absorption from liquid can be enhanced by using particles that can penetrate the gas-liquid mass transfer membranes to absorb a specific amount of gas [75].  
Also, the shuttle effect considers that particles could enter the gas-liquid mass transfer membrane and absorb a certain amount of gas. And due to the concentration difference, particles carrying absorbed gas return to the bulk liquid and then desorb [76]. Owing to the strong adsorption of the diffusing gas phase component in the dispersed phase particles, the concentration of this gas-phase reactant in the liquid phase near the interface will be decreased, resulting in an increment in the absorption rate [77].
## 2 Materials and Methods
### Model Inputs, Calibration, and Validation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **SWAT Input File** & **Descriptions** & **Fitted Value** & **Min. Value** & **Max. Value** & **Rank** \\ \hline r\_CN2 &.mgt & \multicolumn{2}{c}{SCS runoff curve number} & \multicolumn{1}{c}{} & 0.16 & \(-\)20 & 20 & 18 \\ v\_BIOMIX &.mgt & \multicolumn{2}{c}{Biological mixing efficiency} & 0.76 & 0 & 1 & 19 \\ v\_CO2 &.sub & \multicolumn{2}{c}{Carbon dioxide} & \multicolumn{1}{c}{} & 285 & 0 & 800 & 4 \\  & & \multicolumn{2}{c}{Decomposition response to} & \multicolumn{1}{c}{} & & & \\ r\_CF &.hru & soil temperature and moisture & 0.07 & 0.5 & 1 & 13 \\  & & \multicolumn{2}{c}{moisture} & & & & \\  & & Undisturbed soil turnover & & & & \\ r\_CFDEC &.hru & rate under optimum soil & 0.052 & 0.045 & 0.065 & 11 \\  & & \multicolumn{2}{c}{water and temperature} & & & & \\ v\_SOL\_Z &.sol & \multicolumn{2}{c}{Depth from soil surface to} & \multicolumn{1}{c}{} & 145 & 0 & 3500 & 5 \\ r\_RSDCO &.bsn & \multicolumn{2}{c}{Residue decomposition} & \multicolumn{1}{c}{} & 0.06 & 0.02 & 0.1 & 14 \\ v\_SOL\_CBN &.sol & \multicolumn{2}{c}{Organic carbon content} & \multicolumn{1}{c}{} & 2.7 & 0.05 & 10 & 15 \\ v\_R8 &.gw & \multicolumn{2}{c}{Lag coefficient for soil temperature} & \multicolumn{1}{c}{} & 0.77 & 0 & 0.95 & 7 \\ v\_SOL\_K &.sol & \multicolumn{2}{c}{Soil hydraulic conductivity} & \multicolumn{1}{c}{} & 0.45 & 0 & 5 & 17 \\ v\_SUB\_SFTMP &.sno & \multicolumn{2}{c}{Snowfall temperature (\({}^{\circ}\)C)} & \multicolumn{1}{c}{} & 0.73 & \(-\)7 & 7 & 6 \\ v\_SUB\_SMTMP &.sno & \multicolumn{2}{c}{Snowfall melt base} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\  & & \multicolumn{2}{c}{temperature (\({}^{\circ}\)C)} & \multicolumn{1}{c}{} & & & \\  & & \multicolumn{2}{c}{Maximum melt rate for} & \multicolumn{1}{c}{} & & & \\ v\_SUB\_SMTMX &.sno & \multicolumn{2}{c}{snow during the year} & \multicolumn{1}{c}{} & 0.72 & 0 & 7.15 & 2 \\  & & \multicolumn{2}{c}{Minimum melt rate for snow} & \multicolumn{1}{c}{} & & & \\ v\_SUB\_SMFMN &.sno & \multicolumn{2}{c}{during the year} & \multicolumn{1}{c}{} & 0.25 & 0 & 10 & 3 \\  & & \multicolumn{2}{c}{(mm/\({}^{\circ}\)C-day)} & & & & \\ v\_SUB\_TIMP &.sno & \multicolumn{2}{c}{Snowpack temperature lag} & \multicolumn{1}{c}{} & 0.21 & 0.2 & 1 & 8 \\ v\_SOL\_AWC &.sol & \multicolumn{2}{c}{factor} & \multicolumn{1}{c}{} & 0.35 & 0.2 & 0.4 & 12 \\ v\_SOL\_ALB &.sol & \multicolumn{2}{c}{Moist soil albedo} & \multicolumn{1}{c}{} & 0.05 & 0 & 0.25 & 16 \\ v\_ESCO &.hru & \multicolumn{2}{c}{Soil evaporation} & \multicolumn{1}{c}{} & 0.09 & 0 & 1 & 10 \\  & & \multicolumn{2}{c}{Snow water equivalent that} & \multicolumn{1}{c}{} & & & \\ v\_SNO50 COV &.bsn & \multicolumn{2}{c}{corresponds to 50\%} & \multicolumn{1}{c}{} & 0.65 & 0 & 1 & 9 \\  & & \multicolumn{2}{c}{snow cover} & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SWAT model parameter calibration and sensitivity analysis for the Athabasca River Basin.
## 3 Results and Discussion
### Profile and Topography Measurement Results
The smallest profile peak material fractions were observed in S2 rings (21.365%) machined with the XCEL tool, and P1 rings (22.497%) machined with the WIPER tool. These rings may be expected to have a longer service life compared with the others due to the least number of peaks causing slower wear. Additionally, this feature has a beneficial effect on the functionality of the part. However, the disadvantage is that it can have less tightness due to a larger ratio of the surface core and greater contact stress. Interestingly, both the WIPER and XCEL tools produced the largest \(Rmr1\) values. The largest material share had ring Q2 (\(Rmr1\) = 32.269%) made with the STANDARD tool, but also the S1 sample (\(Rmr1\)  
Figure 8: Average results of profile height \(\overline{Rz}\) and respective topography height \(\overline{Sz}\) measurement of the samples. 31.303%) fabricated with the XCEL tool and the O2 sample (\(Rmr1=31.214\%\)) machined with the WIPER tool. These rings can be expected to have the greatest tightness but the worst lubricating ability. In all cases of measured rings, there was a greater variance concerning the \(Ra\) and \(Rz\) parameters, which may be a result of cutting tool traces on the surface. The acceptable rate according to the drawings was \(Rmr1\geq 10\%\) and this requirement was fulfilled by all the examined cutting tools. The largest values of spatial parameter \(Smr1\) found in Figure 9 were for thin-walled bearing rings Q2 (31.543%) and O2 (27.149%) machined with the WIPER tool, and for the R2 sample (27.156%) machined with the STANDARD tool. This parameter indicated that WIPER geometry of the CBN tool might have contributed to the slower wear of the surface, longer service life, and better functionality of the bearing ring. In this case, the smallest measured parameters \(Smr1\) were obtained for the samples S2 (15.684%) and S1 (18.985%), both machined with the XCEL tool. This may result in a shortened life of the bearing rings due to the rapid wear of the sliding surface. As would be expected, there is no single profile or surface parameter that unequivocally ensures a long service time of the bearing ring, and there is no single cutting tool that may be considered 'the best' one. Therefore, apart from geometrical surface features, residual stresses in the upper layer should be taken into consideration.
# Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms
## 3 Methodology and Problem Definition
### Genetic Algorithm
#### 3.3.2 Fitness Function
\(w_{1}\) (distance exceeding penalty): Distance-exceeding routes were penalized while maintaining diversity by reducing fitness. \(w_{2}\) (waiting time penalty): Adjusted to prioritize routes with shorter charging times, thereby deterring heavily congested stations. \(w_{3}\) (distance factor): Incentivizes shorter routes while balancing feasibility and waiting times. It is possible to adjust the weights according to the user's priorities (e.g., shortest distance versus shortest wait). Algorithm 3 is a pseudocode of the penalty calculations related to the fitness function. ```
1 Input: Initial control points
2 Output: total penalty
3\(total\_penalty=total\_distance+queueing\_time_penalty+exceeded\_km\)
4 Compute the total penalty
5 if\(total\_penalty>0\)then
6\(1/total\_penalty\)
7 else
8\(1*e^{-10}\)
9 end;
```  
**Algorithm 3** Compute the total penalty. By optimizing these parameters, the fitness function ensures that the genetic algorithm develops efficient routes, complies with EV capacity constraints, and minimizes travel time and penalties. This results in a robust solution for the EV routing problem, enhancing the overall efficiency of the electric vehicle network.