esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain Technology for Secure Communication and Formation Control in Smart Drone Swarms
## 4 Materials and Methods
### Proposed Scheme
Next, this exact location will be occupied by the swarm member who selects it. Otherwise, if the desired position is not available, the next nearest position is selected, and so on. In the illustration above (Figure 6), the leader's coordinates are \((0,0)\). In the V formation, the leader will, therefore, be in position 0. Consider a follower (\(i\)) with the coordinates \((x_{j},y_{j})=(2.5,8)\). Based on Equations (1) and (2), the follower determines all \(r_{jp}\) Euclidean distances for positions 1, 2, 3, 4, 5, and 6. From these, he selects the position that is nearest to him, in this case, position 4, and moves there. Repeating this procedure allows each follower in the swarm to communicate its position to the blockchain, where it is visible to all other followers. If this position is already taken, the next one closest to it is chosen (in this case, position 5). This strategy (submitting chosen positions to the blockchain and determining their availability) avoids situations where the same position is chosen by two (or more) separate drones, which may result in collisions. Figure 6: Position selection. Each number corresponds to a specific position in the vee formation. Position 0 has been occupied by the leader. The \(i_{th}\) follower selects position 4 in the formation. If position 4 is taken, it selects position 5, and so on. One can see the position submission utilized by the drones to disclose their positions through the smart contract in the aforementioned function (Listing 7). Also, as previously stated, specific measures have been taken in order to ensure that the position submitted to the blockchain is valid and not occupied. In any other case, the transaction will be reverted. A typical flowchart for follower functionalities is shown below (Figure 7). Although the suggested smart contract is only defined for use in the military domain, it can still be updated and applied to other sectors as well. The basic concept behind this model is that each smart contract will only be effective for a set amount of time before becoming obsolete and needing to be reprogrammed in accordance with the new task that the drones must complete. In the matter of unplanned situations during a mission where the drones must operate differently, the deactivateMission function can be used in order to abort the pre-defined mission.
### Research Method  
The research objectives were met, as mentioned above, through a case study and panel mock-up tests. A real-world free-form building called Haewoojae, a two-story toilet museum with 418 m\({}^{2}\) gross floor area built in Suwon-City, Korea in 2007, was identified as a case study subject to measure the difference of the as-built from the as-designed curved panels [12].  
Two-staged reverse engineering (RE) applications were implemented to get the as-designed and the as-built data of the facility. The first stage of RE was performed to obtain the as-designed data by use of Digital Project (DP hereafter), the 3D free form-oriented BIM tool developed by Gehry Technology [6]. Since the facility was initially designed in 2007 with 2D AutoCad and Maya program, the 3D engineering information and the curvatures coordinates of the facade panels of the facility have not been available. Through the RE process, Haewoojae was programmed as a 3D BIM model and the curvature types of the facade panels, i.e., planar, single-curved, and double-curved, and their locations and numbers were identified.  
The second stage of RE was facilitated with 3D laser scanning technology to obtain the as-built data of the facility. The as-built data was then overlapped with the as-designed data by applying the data superimposition method to measure the magnitude of deviation between the as-designed and the as-built models. Given the superimposed results, the author analyzed what panel types showed bigger discrepancy and how big the discrepancy was along with the panel types.  
In order to extend the findings from the case study to more population, a total of eight mock-up panels with planar, single-curved, and double-curved shapes were designed and fabricated, and once again the 3D laser scanning technology was applied to gauge the difference between the as-built and the as-designed mock-up panels.  
The author used two materials in forming the mock-up panels, i.e., aluminum and GFRC, to test which material was more capable in lessening discrepancy when used for free-form panels. Four out of eight mock-up panels were made of aluminum while the other four were GFRC. The mock-up panels were fabricated with the support of two companies, which had been engaged in fabricating metal sheet panels and in producing GFRC or fiber-reinforced plastic (FRP) panels by means of the casting technology respectively in the Korean construction market. The author interviewed the managers and engineers involved in the study to figure out the practice and to come up with certain approaches to enhance free form facade quality.
# Evaluation of Antioxidant and Anticorrosive Activities of _Ceriops_ _tagal_ Plant Extract
## 3 Results and Discussion
### Corrosion Inhibition
#### 3.3.6. Corrosion Mechanism  
The most relevant mechanism behind the inhibition of corrosion by plant extract against mild steel in 1 M HCl is represented in Figure 8.  
They are a few possible ways that components in any plant extract interact and interrupt corrosion. The active metabolites adsorbed on the surface of metal are affected by the structure of inhibitors, the metal's surface and nature, and the type of corrosive medium used [67]. Organic compounds such as phenols, flavonoids, amino acids, dye, etc. are known green inhibitors as they are non-toxic, go easy, biodegradable, and cost effective. Experiments on metals in aggressive solutes such as HCl and H\({}_{2}\)SO\({}_{4}\) have successfully found several new plant-based materials as corrosion inhibitors, as these compounds pose heteroatoms such as O, S, N, and P which are oxidized on the metal surface or adsorbed in the metal forming protective layer [60]. Apparently, the inhibition mechanism is strongly influenced by the choice of electrolyte, nature of metal, structure and concentration of inhibitors, temperature, and pH. In the case of electrolyte, variations in inhibitor behavior (lawsonia extract) were observed against corrosion of nickel and C-steel  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Elements**} & \multicolumn{2}{c}{**Composition (Atomic\%)**} \\ \cline{2-3}  & **Blank** & **Inhibited** \\ \hline C & 4.34 & 6.37 \\ Fe & 71.54 & 84.21 \\ O & 22.10 & 9.01 \\ S & 0.12 & 0.11 \\ N & 0.18 & 0.12 \\ Mn & 1.51 & 0.07 \\ Mg & 0.21 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Percentage compositions of elements present on the mild steel surface.  
Figure 6: EDX spectra of mild steel dipped in: (**a**) 1 M HCl; (**b**) 1 M HCl with 600 ppm of inhibitor.  
in neutral, acid, and alkaline mediums. On the one hand, nitrogen and sulfur containing compounds, alkaloids, and aldehydes influence acid media; on the other hand, chromate, nitrite, and phosphate act on neutral media [12; 33]. The green inhibitors are heterocyclic with functional polar groups (-OH, -CH3, -OCH3, -NO2, and -COOH). The functional polar groups along conjugated \(\pi\)-electrons with double/triple bonds aid in adsorption, thereby, blocking cathodic and anodic reaction results in corrosion inhibition [56; 68; 69; 70; 71].  
Figure 8: An illustration of possible corrosion mechanism of _C. tagal_ extract.  
Figure 7: 2D images of mild steel: (**a**) 1 M HCl; (**b**) 1 M HCl with 600 ppm of inhibitor.
## 3 Results
In the third improvement procedure, a confusion matrix was created for Model 6 to evaluate the accuracy of each class (Figure 11a). The low prediction accuracy results of Melos can be explained if we take into account that an extremely imbalanced dataset was used: 132 entries for Methana, 104 for Melos, 906 for Santorini, and 2263 for Nisyros (Table 2). For this reason, SMOTE was applied to augment Dataset5. One new dataset was created, labeled Dataset 5--balanced 900. The confusion matrix (Figure 11b) showed an improvement. The final model was devised by using the optimized XGB algorithm and Dataset 5--balanced 900 created by this imputation. Accuracy improved to 93.07% and the standard deviation was calculated as 1.21%. This final model was tested with the 40 handpicked entries left out of the training and testing data. In this validation sample, 90% accuracy was achieved. The Methana class scored 100% and the Melos class scored 80%. One sample was misclassified as Santorini and one as Nisyros. The Santorini class scored 90%, with only one sample being misclassified as Melos. The Nisyros class scored 90%, one sample being misclassified as Santorini.
## 2 Materials and Methods  
### DE Deposit Used  
The DE deposit was obtained from a single mine located in the Prefecture of Thessaly in Central Greece, in the area of Elassona (Latitude: 39.894293, Longitude: 22.184054). DE sentiments of this mine are characterized as clay diatoms, due to their high clay content, having a discoid and cylindrical structure [32]. Geochemical analysis of these DE sediments was carried out by CTL Group, Construction Technology Laboratories, Skokie, IL, USA. DE components that were qualitatively and quantitatively determined by this analytical procedure were SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), Fe\({}_{2}\)O\({}_{3}\), K\({}_{2}\)O, MgO, CaO, SO\({}_{3}\), Na\({}_{2}\)O, TiO\({}_{2}\), P\({}_{2}\)O\({}_{5}\), Mn\({}_{2}\)O\({}_{3}\), SrO, Cr\({}_{2}\)O\({}_{3}\) and ZnO. The sentiments were found to contain 69.49% SiO\({}_{2}\), 13.12% Al\({}_{2}\)O\({}_{3}\), 4.88% Fe\({}_{2}\)O\({}_{3}\), 1.85% K\({}_{2}\)O and 1.52% MgO; all the other elements found were below 1%. Data about the insecticidal activity of DE deposits of the same wider area are available in the literature [23; 26; 27]. Regarding the formulations used in these bioassays, a basic process of milling and drying of the heterogeneous rocks firstly occurred, leading to the creation of the first DE powder formulation (DE3) with 90% of particles smaller than 20 \(\upmu\)m and 40-60% of totally fractured diatom content (Figure 1).  
In an attempt to evaluate the magnitude of the powder granulometry, the existence of fractured or intact diatoms and their percentage in the powder in the insecticidal activity of a given DE--a process of separating the diatoms from other elements--was followed, using air separation methods. Therefore, four new DE powders, enriched in diatoms and with variations in their particle size were created. Particularly, DE1 refers to a DE powder with 76% physically intact diatom content and 20% of particles larger than 100 \(\upmu\)m (Figure 2a); DE2 with 64% semifractured diatom content and 3.5% of particles larger than 100 \(\upmu\)m (Figure 2b); DE5 with 70% of semifractured diatoms and 80% of particles smaller than 45 \(\upmu\)m (Figure 2c); DE6 with 68% of totally fractured diatoms and 99% of particles smaller than 45 \(\upmu\)m (Figure 2d). The DE formulations were stored in the laboratory at ambient conditions, until the beginning of the experiments. The examination of the formulations under SEM (visually) was conducted by the Centre of Research and Technology Hellas (CERTH), Themi, Thessaloniki, Greece (Figures 1 and 2). X-ray powder diffraction (XRPD) measurements were carried out by Faculty of Geology and Geoenvironment, School of Science, National and Kapodistrian University of Athens, Greece.
## 4 Experiment  
### Preparations  
#### 4.1.1 Datasets  
A multimodal dataset collects information from different modalities, such as text, speech, and vision, providing researchers with opportunities to gain a deeper understanding and analysis of sentiment expression. Three publicly available datasets are used in this article, including CMU-MOSI, CMU-MOSEI, and CH-SIMS. Figure 5 illustrates some samples from the CMU-MOSI and CMU-MOSEI datasets, and Figure 6 illustrates the CH-SIMS dataset.  
Figure 5: Examples in the CMU-MOSI and CMU-MOSEI datasets.  
Figure 6: Examples in the CH-SIMS dataset. The green box in the image captures the speaker’s facial expression.  
CMU-MOSI [46] (Multimodal Opinion Level Sentiment Intensity) is a multimodal dataset with character subjective sentiment and sentiment intensity annotations. It contains 2199 multimodal samples from 93 YouTube videos, with each video ranging from 2-5 min and featuring 89 different speakers. Each video is annotated with sentiment intensity, ranging from strong positive to strong negative on a scale from \(-3\) to 3.  
Another dataset is CMU-MOSEI [21] (CMU Multimodal Opinion Sentiment and Emotion Intensity), an upgraded version of the CMU-MOSI dataset and one of the largest sentiment analysis datasets covering multiple fields, including sentiment recognition. CMU-MOSEI contains 23,453 manually annotated video clips from 5000 videos on YouTube, including 1000 different speakers and 250 different topics, covering almost all topics in daily life. CMU-MOSEI uses the same annotation method as CMU-MOSI.  
In addition, considering the research on multimodal sentiment analysis in the Chinese community, we also used CH-SIMS [47], a refined Chinese multimodal dataset. It contains 2281 samples from 60 videos collected from movies, TV shows, and variety shows. Compared to the first two datasets, it not only includes multimodal sentiment labels but also provides independent fine-grained single-modality sentiment labels for each sample. Each label in this dataset is manually annotated from \(-1\) (strongly positive) to \(1\) (strongly negative). The statistical information of these three datasets is shown in Table 2.
# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 7 Conclusions  
Human mobility is one of the essential human needs, and traffic congestion is a negative side effect of it. Traffic engineers use various approaches to solve or reduce this negative side effect. To achieve the best results, they need to accurately estimate the traffic flow performance or the level of congestion. For accurate estimation of the traffic flow performance, one of the approaches is to use data collected from multiple sensors and find a clever way to fusion this data. In this review, an overview of papers, where authors tried to determine the traffic state or predict congestion using multisource DF approach, is given. Additionally, a representation of different DF methods and techniques used for traffic congestion estimation is provided. This review aimed at making a basic analysis of recently used approaches in data fusion and providing guidelines to researchers in this field by helping them determine which approaches are most likely to provide good results. Dominant data fusion approaches, used from 2010 to 2020, are statistical analysis, and in recent approaches the most dominant are DNNs. It can be concluded that estimation of the traffic flow, generally, should be done using data from multiple different sources to provide resistance to possible biases some data collection technologies can impose. Another beneficial tool in the development of prediction methods, like data mining utilization, would be a standardized testing dataset from various multisource data, which would provide actual numerical evidence of how successful an approach is, given the available data.  
The conceptualization of the study was done by [PERSON], [PERSON], [PERSON], and [PERSON], who also did the funding acquisition. The writing of the original draft and preparation of the paper was done by [PERSON], and [PERSON] All authors contributed to the writing review and final editing. The supervision was done by [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work has been financially supported by the University of Zagreb and Faculty of Transport and Traffic Sciences.  
[PERSON] acknowledges the support the European Regional Development Fund under the grant KK.01.1.1.01.0009 (DATACROSS).  
The authors declare no conflict of interest. The funding institutions had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Pulse-Shape Discrimination of SiPM Array-Coupled CLYC Detector Using Convolutional Neural Network
###### Abstract
used a pole-zero cancellation network to offset the influence of large capacitance on the signal [14], and [PERSON] et al. used the signal-differentiation algorithm to amplify the difference between the shape of a neutron and the gamma signal, and then used the charge comparison method to discriminate the signal [9]. Subsequently, the results showed that the charge comparison method had excellent _PSD_ performance in the thermal neutron-equivalent electron luminescence energy region. However, due to the decrease in signal amplitude and signal-to-noise ratio in the lower equivalent electron luminescence energy region, the effect of neutron and gamma signal discrimination was poor, and particle discrimination was impossible. In addition, the _PSD_ performance of the charge comparison method heavily depended on the selection of window parameters [2]; these window parameters needed to be traversed and optimized in advance to improve the calculation time of _PSD_. Therefore, the SiPM-coupled CLYC detector urgently needs an efficient calculation method that can improve the _PSD_ discrimination ability in the low equivalent electron luminescence energy region. Artificial neural networks, one of the main methods in nonlinear science and computational intelligence science, were first applied to \(n/\gamma\) particle discrimination in 1998 by [PERSON] et al., who found that the use of an artificial neural network could improve the effect of _PSD_ discrimination in low-energy regions [15]. Subsequently, [PERSON] [16], [PERSON] [17],  
Figure 1: Schematic diagram of time window selection for charge comparison method. (**a**) is the original diagram; (**b**) is the enlargement of the dotted box area in (**a**). and [PERSON] et al. [18] used artificial neural networks to achieve \(n/\gamma\) particle discrimination in different measurement conditions and detectors. However, in these studies, the artificial neural network structure was based on the traditional backpropagation neural network (BPNN). Traditional BPNNs have the following main defects: (1) the learning speed is slow; and (2) the network generalization ability is poor [19], which leads to problems such as a high time consumption and low accuracy in _PSD_ discrimination. The Convolutional Neural Network (CNN) also uses the backpropagation framework for learning in supervised learning [20], but it contains convolution and pooling layers, with local connections, weight sharing, and rough descriptions [21]. Compared with traditional BPNNs, CNNs have a better learning rate and generalization ability [22]. Therefore, a CNN was designed in this study and the input signal in the two-dimensional matrix format was selected to be used in the _PSD_ of the CLYC detector. This study aimed to discriminate the collected neutron and gamma signals and analyze them in comparison with the result of the charge comparison method.
# Effects of Movement for High Time-Bandwidths in Batched Pulse Compression Range-Doppler Radar
## 5 Influence of Linear Movement within the Signal Bandwidth due to Doppler
### Higher-Order Motion Aspects  
The actual target motion might further not be strictly linear so that the bistatic velocity changes within the covered timespan \(T_{\rm cov}\) (Section 2.4). In this case, the influence of higher-order motion aspects has to be considered additionally [31]. Hereby, effective acceleration (\(a(t)={\rm d}\,v_{\rm eff}\ /\ {\rm d}\ t\)) and jerk influences could be caused by a complex motion or by the relative position change with respect to the geometry (Section 6.4). In the case  
Figure 12: Fluctuating return of the previously shown approaching target with \(v\approx 225\,\rm km/h\), shortly after Figure 11 with a deviated target return in \(B_{\rm eff}=7.61\,\rm MHz\) over \(T_{\rm int}=1.79\,\rm s\) and \(T_{\rm cov}=2.24\,\rm s\).  
of involved motion, a trace as shown in Figure 13 is likely--this is a strong deviation from a rectangular shape. It further extends over the analysed Doppler frequency \(f_{\mathrm{d}}\) by a deceleration of its effective velocity within the covered timespan. In this example, a simple linear velocity based compensation is certainly counter-productive since it would be based on the rectangular shape in the range-Doppler domain (Section 3). A worse example obtained from quite a weak, spread and decelerated return is shown in Figure 14. Probably due to fluctuations and noise overlap, four track heads were generated by an uncompensated processing so that the track was frequently lost. The contributions of acceleration might be important and considerable beside the velocity-induced implications. Every non-predictable contribution will however open another search dimension in addition to bistatic range and effective velocity for search processing. This will certainly restrain practical implementations to previously covered or presumed target tracks.  
Figure 14: Target previously shown in Figure 5 tracked with a bistatic velocity \(v_{\mathrm{eff}}\approx 270\,\mathrm{km}/\mathrm{h}\) over \(T_{\mathrm{cov}}=896\,\mathrm{ms}\) with \(T_{\mathrm{int}}=717\,\mathrm{ms}\), decelerated and noise overlapped that gave multiple track heads.  
Figure 13: Moderate deceleration in addition to the Doppler frequency spread of a bistatic velocity of \(v_{\mathrm{eff}}\approx 270\,\mathrm{km}/\mathrm{h}\) with track history indicating the change using \(T_{\mathrm{int}}=717\,\mathrm{ms}\) and \(T_{\mathrm{cov}}=896\,\mathrm{ms}\).
## 3 Results and Discussion
### Hydrophilicity Test  
Measuring the contact angle is a method of evaluating the hydrophilicity and hydrophobicity of a material surface and can be used to observe the effect of the material surface on cell adhesion. Therefore, the hydrophilicity and hydrophobicity of the material surface are two of the most important biological parameters of implants [28]. The literature points out that increasing the surface wettability of a material can improve its biological activity. In addition, a hydrophilic surface is better than a hydrophobic surface for the adhesion of bone cells in the early stage of implantation [29]. Hydrophilic and hydrophobic test results of titanium sheets after different surface treatments are shown in Figure 4. [PERSON] et al. showed that TNTs produced after anodization are hydrophilic and hydrophobic. However, annealing treatment will produce highly crystalline anatase that expresses hydrophilic properties [30]. The titanium sheet had a relatively hydrophilic surface after alkali treatment (Figure 4A). These results are similar to those in the literature such as alkali treatment increasing the hydrophilicity of a metal surface [31]. [PERSON] et al. used NaOH to alkali-treat titanium flakes and showed that titanium flakes had better hydrophilicity as the concentration increased [20]. Results of acid treatment and anodic oxidation were similar as both were hydrophobic, which may have been caused by the same crystalline phase being formed.