esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methods
### Feature Selection
Moreover, the PM\({}_{2.5}\) time series in our multi-year dataset also has a yearly periodic trend. Figure 3b shows the PM\({}_{2.5}\) yearly periodic trend within 2008 and 2016. The low monthly PM\({}_{2.5}\) is seen in every summer, and the high monthly PM\({}_{2.5}\) in a year commonly appears in winter. With the observations from daily and yearly trends of PM\({}_{2.5}\) variations, we propose periodic variables based on sine and cosine values of daily and yearly ordinal hour to capture these trends. (2) _Meteorological variables_. The meteorological feature variables are commonly used in the PM\({}_{2.5}\) forecasting literature. We include in the initial feature set the broadly used variables as follows: temperature (Temp), relative humidity (RH), precipitation (Prep), wind speed (WS), and direction sine (sin_w) and cosine (cos_w). (3) _Short-term history meteorological variables_. In addition to the meteorological condition within the current hour, the PM\({}_{2.5}\) concentration is strongly related to its recent weather status. For example, a constantly blowing strong wind for a couple of hours would significantly mitigate the PM\({}_{2.5}\) concentrations. Therefore, we record short-term history meteorological variables observed in a time window of prior six hours. To explore more effective features, we propose new compound feature variables as follows. As the wind speed (WS) and wind direction sine (sin_w) and cosine (cos_w) have cohesive meaning as a whole, some important information may be missing if these features are used independently. We propose the compound features _sinw_WS and _cosw_WS by calculating the sum of product of WS and sin_w and of WS and cos_w, respectively. Moreover, a complex feature combining _sinw_WS and _cosw_WS is proposed. The feature ST_WB, defined as the root of the sum of square _sinw_WS and square _cosw_WS, is found to have higher prediction capability than _sinw_WS and _cosw_WS, as will be noted. (4) _Short-term history autoregression variables_. The PM\({}_{2.5}\) series has strong autocorrelation characteristic. Figure 4 shows the autocorrelation of the PM\({}_{2.5}\) as a function of the time lag in hours. It is seen that the autocorrelation has a daily periodic trend and decays with the time lag length. Thus, we devise three autoregression variables within 48-h time lag.
# Investigations on Interface Shear Fatigue of Semi-Precast Slabs with Lattice Girders
## 3 Small Size Fatigue Tests to Determine S-\(N\) Curves for Lattice Girders
### Results of Small-Size Fatigue Tests  
The lattice girder diagonals subjected to monotonic tensile stress failed with the fracture being located between load application and the welded node (Figure 7a). Failure in the immediate region between the load application and welded node with a distance \(\leq 4\cdot\mathcal{O}_{\text{dia}}\) did not occur. The yield strength of the static test was determined as \(f_{\text{y}}=564\)-\(573\) N/mm\({}^{2}\) for the specimens with diagonals and lower chords and between \(f_{\text{y}}=558\)-\(567\) N/mm\({}^{2}\) for diagonals and upper chords.  
Failure of the diagonal in the fatigue tests for both the lower and upper chord nodes occurred at the welding point. In this context, Figure 7b shows the fractured diagonal of a lower chord node that was subjected to a stress range of \(\Delta\sigma=375\) N/mm\({}^{2}\) and resisted \(N=162\),\(177\) load cycles. Figure 7c gives a detailed view of the fatigue fracture.  
Figure 7: Fracture pattern of small size specimen under monotonic loading (**a**), cyclic loading (**b**), and fatigue fracture of welded node (red marking) (**c**) with definition of fatigue zone (orange) and final rupture zone (blue) (**d**).  
The test results of the static and fatigue tests of lower and upper chords are shown in Figure 8. For comparison, the test results from fatigue tests from literature [40; 52] (Section 2.2) were added to the diagram. The comparison of the applied stress range \(\Delta\sigma\) and reached number of load cycles \(N\) for the lower chords (black points) and upper chords (grey points) shows a similar distribution of test results. For most cases, the upper chord tests bore fewer load cycles for high stress ranges but with less scatter compared to the lower chords. The required stress range according to the technical approval of the lattice girders with \(\Delta\sigma_{\text{Rsk}}=92\) N/mm\({}^{2}\) for \(N=2.0\) million load cycles was satisfied for all tests.  
The comparison of the test results from literature shows the extended range of the fatigue-strength relation, especially for high stress ranges and low number of load cycles. In most cases, the own investigations resisted a higher number of load cycles within the range of small stress amplitudes, which can be explained by improved welding processes, since the tests from the literature date back more than 25 years.
# Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer
## 2 AI Techniques for Drug Repurposing and _De Novo_ Drug Design
### Machine and Deep Learning Techniques  
Some of the most common machine learning algorithms used for drug repurposing include k-nearest neighbor (kNN), random forest (RF), or support vector machine (SVM). Furthermore, with the development of deep learning, and thanks to its ability to automatically extract features from raw data, the impact of AI-based approaches in drug repurposing has increased significantly. The use of deep learning algorithms such as artificial neural networks (ANNs), CNNs, and long short-term memory (LSTM) has revolutionized the field. One of the best examples in this field is AlphaFold2, a commonly known, neural network-based model technology that predicts the 3D conformation of proteins, even those without known structures, and therefore offers emerging opportunities for structure-based drug discovery [12].  
General machine learning approaches in drug repurposing can be classified according to the type of datasets used. As an example, structural information related to proteins or small molecules has been used for the development of structure-based virtual screening methods, such as molecular docking [21] or, together with binding activity data, ligand-based chemometric modeling methods such as quantitative structure-activity relationship (QSAR) modeling [22]. Other types of data commonly used in machine learning-based drug repurposing approaches include cell phenotype, employed for example through the CellProfiler software (v.4.2.8) [23]. Also, the use of transcriptomic data allows us to uncover novel drug mechanisms. Among this, the use of the L1000 assay data, an improvement over the old Connectivity Map (CMap), stands out, covering information on the variation in gene expression in response to more than 42,000 perturbing agents (including drugs) [24]. Finally, data related to electronic health records, and even social media, are also very helpful to develop deep learning approaches based on natural language processing (NLP) and other algorithms [25].
This approach is shown to correctly identify the problematic zones of the underlying area and propose cell azimuth reconfiguration actions in a completely automated manner. The algorithm uses the spatial component to identify clusters of human densities in the zone and then applies filtering on the centroid values of each cluster to narrow down to true traffic hot-zones (i.e., where user terminal activity is high). To quantify the performance benefits of the proposed scheme against the baseline approaches, system-level simulation of an indicative 5G network deployment was performed. The simulation also included a validated, complex mobility model that will increase the realism of the simulation and better showcase the effects of each reconfiguration scheme. Analysis of the experimental results produced in this research validates the hypothesis that the proposed algorithm outperforms the baseline approaches and can be an important component of an automated radio access network planning tool. Table 1 lists all abbreviations used in this document, along with the term they describe. The rest of this paper is organized as follows. State-of-the-art analysis and related work related work are presented in Section 2. Section 3 introduces the problem statement of the current work. In Section 4, there is an extended description of the solution methodology and the different methods implemented for the experiments. Section 5 presents the performance evaluation procedure, which includes a detailed overview of the simulation-based evaluation platform as well as detailed performance evaluation results for each algorithm,along with the statistical distribution analysis of network KPIs and the comparison of improvements compared to the initial scenario. Section 6 presents a number of discussion topics based on this work. Finally, the paper is concluded in Section 7.
## 3 Experimental Results
### Feature Comparison  
In addition to validating the security performance of our proposed method, we also conducted experiments comparing it with other similar schemes [12; 13; 14; 15; 16] in the field of image embedding, to delve into the strengths and weaknesses of each approach. We not only compared the performance of the proposed method with other schemes in terms of PSNR and the embedding rate but also conducted difference comparisons based on [12]. These metrics provide a more comprehensive assessment to determine the advantages of our method. To comprehensively evaluate the performance of our proposed method, comparative experiments were conducted with five similar methods. Fifty images were randomly selected from the Bossbase image database, and the experimental results are shown in Table 1.  
By comparing the embedding rates, which refer to the amount of data embedded per pixel, we found that our bpp is still slightly higher than that of [PERSON] et al. [14], maintaining approximately 1.60 more dB compared to their results. Moreover, our proposed method places more emphasis on data concealment and security. This is because the experimental data all use unencrypted PSNR, and our method does not involve LSB or the most significant bit (MSB) replacement. Based on the data results, our method shows a significantly higher PSNR than other similar methods. To ensure data security, the proposed method replaces the pixel values after embedding data, deliberately reducing the quality of PSNR. Decreasing PSNR means the ability to blur or hide details in the image, which is a useful technique in privacy or copyright protection. For instance, in scenarios where identity needs protection or sensitive information needs to be handled, blurring the image can help reduce identifiable information. While maintaining data fidelity and preventing data overflow, our method still provides a higher embedding rate, achieving the effects of reversible data extraction and image recovery. These are significant advantages of our method. Therefore, our research not only focuses on data concealment and protection but also considers how to achieve a high embedding rate through these means, comprehensively evaluating the performance of our proposed method. This comprehensive analysis gives our method a significant competitive advantage in the field of image processing.
## 3 Results
### Colorimetry of the Three Wood Species Using CIEL*a*b* Space
#### 3.3.2 Results of the Color Analysis of the Three Species  
The comparative color analysis of the three species is highlighted in Figure 13, indicating a higher luminance for balsa (Figure 10), a degree of red that is accentuated negatively (35.28-37.62) for all analyzed wood species, and a very low degree of yellowish (3.04-3.66).  
Figure 11: Degree of green–red (a*) of paulownia timber before and after planning.  
Figure 12: Degree of yellow–blue (b*) of paulownia timber before and after planning.  
Figure 13: Values of the CIELab color space for luminance (L*) (**a**), for the degree of red–green (**a*) (**b**), and for the degree of blue–yellow (**b*) (**c**).  
It could be observed that both luminance (L*), red-green contrast (a*), and blue-yellow contrast (b*) had some very close values, which means that from a coloristic point of view, the three analyzed species are appropriate and homogeneous (Figure 13). Furthermore, when the \"a*\" and \"b*\" values of the three species were plotted in a two-dimensional plane (Figure 14), it could be noticed that all three points are located in the fourth quadrant of the plane, and the points are very close to each other. Thus, from a color point of view, the three species are very similar.
## 3 Results and Discussion
### Field-Emission Scanning Electron Microscopy (FESEM) Analysis  
In order to evaluate how the cellulose source and hydrolysis method affected the size and characteristics of the CNCs sample, a morphological analysis was required. This was investigated using scanning electron microscopy characterization tests, the outcomes of which are displayed in the following (Figure 8).  
Figure 8: FESEM micrographs of CNCs (**d–f**) with reference of standard MCC (**a–c**).  
The SEM image of the MCC sample shown in (Figure 8a-c) reveals strongly agglomerated and rough surfaces. (Figure 8d-f), displays the particle size reduction and surface morphological changes that occurred in the CNCs sample after acid hydrolysis. The produced particles had an average diameter of 7.65 \(\pm\) 2.56 nm. The loss of the amorphous phase after hydrolysis made these morphological adjustments conceivable [43]. This finding corroborates the nanoparticle generation of CNCs and agrees with the XRD results. This agrees with what we saw in our TEM investigation, demonstrating that hydrolysis results in significantly smaller particles due to surface erosion [44]. Strong hydrogen bonding and hydrophilic contact were observed between the CNCs, showing that they were well bonded [45].
# A P2P Scheme for Debating and Voting with Unconditional Flexibility
## 6 Conclusions  
The following is a summary of the most relevant achievements and key aspects of this document. In order to clarify them, it is necessary to explain the starting point. In the situation prior to this work, flexible and secure debating in a decentralized manner was not possible. In fact, for simple decentralized voting there were very few published systems. The proposed scheme broadens the range of boardroom e-voting systems. It provides unconditional flexibility because no constraints are imposed on the vote format; as a consequence this scheme is independent of any debate software. Moreover, other interactions, such as proposals and opinions, can also be protected, maintaining privacy, eligibility, verifiability, and the rest of features. This makes it suitable for other uses like debates or gathering criticism from a group anonymously.  
Regarding BFTs systems, in the case of our scheme, it is feasible to use them in phase 2, when anonymity problem is solved. Thus, any of the leaderless (P2P) BFT systems like DBFT [46], Honey Badger [47], or BEAT [15] could be selected instead of the alias signature used to achieve consensus. However, it implies the loss of verifiability, which is a desirable feature in e-voting systems.  
The scheme does not include any step that requires the involvement of all the participants, so it is robust against adversaries inside the group, and they cannot block the process.  
A flexible debate can be compared to previous voting systems from two points of view:  
1. Each proposal is an independent voting. Then, the user who sends the proposal is the one who defines the ballot frame. In a debate there are many proposals; therefore, there will be many polls. Thus, a comparison between our voting system and others results in the advantage of making multiple polls with the computational cost of just one.
2. All the proposals are in the same voting. Then, we are considering a unique ballot that contains the votes for each proposal. This ballot can be huge and extremely complex, a problem for some schemes as was explained in Section 1. Even more, for other e-voting systems, this ballot must be defined by an initiator. In other words, users have not a secure way to choose what to vote.  
The experiments and analysis show the ratio between group size, key size, and process times. These results are based on two specific study cases; therefore, they cannot be generalized. However, they do illustrate an actual example of debates.  
The main constraint of this scheme, as in any boardroom system, is the group size. However, this alias approach suggests that a hierarchical structure of nets is possible. Thus, future work will be based on this idea to improve scalability.  
Conceptualization, D.A.L.-G.; Methodology, D.A.L.-G.; Literature review, D.A.L.-G., J.P.T., D.V. and M.S.-R.; Software, D.A.L.-G., J.P.T., D.V. and M.S.-R.; Vialidation, J.P.T., D.V. and M.S.-R.; Writing--original draft preparation, D.A.L.-G.; writing--review and editing, D.A.L.-G., J.P.T., D.V. and M
## 3 Results and Discussion  
### Mechanical Characterization of TPMS Structures  
#### 3.1.1 Deformation Mechanism  
The high-resolution video camera records the whole experiment process in order to observe the deformation behavior of the sample in the experiment. The quasi-static compression deformation characteristics of the test samples and finite element simulation at different compression times are shown in Figure 9. The colored part represents the von Mises stress distribution of the porous material after compression.  
Figure 9: Cont.  
During quasi-static compression, the small deformation and mechanical changes caused by a small amount of partially molten AlSi10 Mg material blocking the porous structure of TPMS are ignored. Gyroid, Diamond, Fischer-Koch S, and PMY samples undergo apparent shear failure along with the extension of the curved surface. Different angles between the shear band and the loading direction lead to different stress conditions and deformation forms. The shear bands of Gyroid, Fischer-Koch S, and PMY samples have larger angles with the loading direction, and their deformation is mainly manifested as layered collapse. However, the Diamond sample first exhibited 45\({}^{\circ}\) shear deformation during loading. I-WP, Neovius, Primitive, and F-RD samples show that the middle and upper parts of the samples are damaged first. The deformation extends to the top and bottom parts of the sample. The deformation extends to the top and bottom parts of the sample.  
bottom of the sample in the form of a drum-shaped crack in the middle. As the compression process progresses further, the porous TPMS structure samples experience internal buckling deformation with local fracture failure around the surface hole until densification.  
When the deformation of various porous TPMS structures reaches 20-30%, shear bands with different angles to the compression direction appear at different positions. As the compressive displacement increases, the shear bands and compressive directions change in various TPMS porous structures. The deformation mechanism of buckling collapse ensures uniform stress distribution when the TPMS porous structure is loaded.
# A Machine Learning Approach to Predict Fluid Viscosity Based on Droplet Dynamics Features
## 3 Results and Discussion
### Droplet Dynamics Analysis
The theoretical value is closest to the experimental value in the case of \(\partial h=0.79\). As the number of \(\partial h\) increases, the experiment shows the same trend as the theory. Figure 4c illustrates the regime of inertial-viscous contraction in the final stage of contraction of G-W liquid necks for five viscosities. The combined effect of viscosity and inertia in this contraction regime leads to the fracture of the liquid neck. The critical \(h_{min}\) for the emergence of this regime was shown to vary with \(\partial h\) in [PERSON]'s study, with smaller \(\partial h\) numbers leading to smaller \(h_{min}\). When \(\partial h=0.23\), the emergence of regime IV was found to be at \(h_{min}=0.01\), and the experimental results can be seen in Figure 4c. Figure 4d illustrates the evolution of the liquid neck contraction state during the droplet falling process. When \(=0.15\) to \(0.79\), the contraction of the liquid neck is initially dominated by inertia (regime I--inertia), then the influence of viscosity gradually increases (regime V--viscosity), and ultimately rupture occurs under the combined action of viscosity and inertia (regime IV--viscosity-inertia). For the \(99\%\) G-W solution, when \(\partial h>1\), the hindrance of viscosity to flow starts earlier and is manifested in the initial stage of liquid neck contraction, followed by entry into the inertia contraction region with an exponent of \(2/3\) (regime I--inertia). The contraction process is characterised by a transition from regime V to regime I, ultimately leading to rupture under the combined effects of viscosity and inertia (regime IV--viscosity-inertia). However, in the study by [PERSON], the range of action of the inertia region (regime I) is within the interval where \(h_{min}/R\) is greater than \(10^{-2}\) and less than \(10^{-1}\). In our study, the action range of the inertia region (regime I) is around \(10^{1}\). Furthermore, in the experimental data, it is observed that there is a region with a contraction exponent of \(3/2\) between the inertia region (regime I) and the viscosity region (regime IV) during the liquid neck contraction process when \(\partial h>0.48\), as indicated by the black line in Figure 4d. [PERSON] et al. [29] referred to this as the transitory regime (TR). In the article by [PERSON] et al. [16], experimental data at \(\partial h=1.81\) also show a contraction process  
Figure 3: Sequence of different viscosity fluids dripping in the air from the same needle (R = 0.8 mm, Q = 0.2 mL/min). All images in the figure share the scale. (**a**) \(\partial h=0.18\); (**b**) \(\partial h=0.25\); (**c**) \(\partial h=0.48\); (**d**) \(\partial h=0.79\); (**e**) \(\partial h=3.12\). with an exponent greater than 1 following the inertia region (regime I), a phenomenon commonly observed in other experimental studies [21, 23, 27, 28, 30]. However, there is currently no comprehensive theoretical explanation for this contraction process.
## 3 Results and Discussion  
The 2011 Mw 9.0 Tohoku-Oki earthquake (11 March 2011, 05:46:24 UTC) in Japan and the 2010 Mw 7.2 El Mayor-Cucapah earthquake (4 April 2010, 22:40:42 UTC) in Mexico were well recorded not only by strong motion stations, but also by high-rate GPS receivers. They are good examples to evaluate the performance of integrated displacements for which abundant high-rate GPS and strong motion records are available [1,27].  
We firstly processed 1 Hz data of about 90 globally distributed real-time IGS stations using the EPOS-RT software of GFZ [28] in simulated real-time mode for providing GPS orbits, clocks and UPD corrections at 5 s sampling interval. Based on these corrections, we process the GPS and strong motion data collected at about thirty collocated stations during the Tohoku-Oki and El Mayor-Cucapah earthquakes. As PPP can be performed with a single GPS receiver, the integrated displacements are estimated on a pair-by-pair basis for each collocated GPS and strong motion pair.  
For the 2011 Tohoku-Oki earthquake, the 1 Hz GPS data is collected at the GPS Earth Observation Network System (GEONET) stations operated by the Geospatial Information Authority (GSI) of Japan. One hundred Hz accelerometer data is collected from strong motion stations of the K-Net and Hi-Net. For the 2010 El Mayor-Cucapah earthquake, 5 Hz GPS data is collected from the California Real-Time Network (CRTN) and Plate Boundary Observatory (PBO). Two hundred Hz accelerometer data is collected from strong motion stations of the Southern California Seismic Network (SCSN) operated by the USGS (U.S. Geological Survey) and Caltech.