esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Torsional Shear Testing of
## 3 The Mechanical Theory of Torsional Shear Tests
### Failure Criteria of Asphalt Mixture  
The compressive strength and the tensile strength are important mechanical properties of asphalt mixtures. The compressive strength mainly relates to the aggregate skeleton and is measured in a uniaxial compression test, and the tensile strength mostly reflects the strength of the binder and is measured in a traditional test as a direct tensile test or a bending test. However, the shear strength can better represent the strength of the asphalt mixture of aggregate materials and binder. Shearing forces or stresses have to overcome both the interlocking of the aggregate structure and the cohesive forces of the binder [8].  
The Mohr-Coulomb criterion is the failure criterion most used in a shear failure test to represent asphalt mixtures. The Mohr-Coulomb criterion can be express as follows:  
\[\tau_{f}=c+\sigma\text{tan}\,\varphi \tag{10}\]  
where \(\tau_{f}\) is the critical shear stress, \(\sigma\) is the normal stress on the failure plane, \(c\) is the cohesion, and \(\varphi\) is the angle of internal friction. These parameters are illustrated in Figure 5. The cohesion \(c\) indicates that even if the normal stress is zero, a certain shear stress is necessary to produce shear failure [18].
## 2 Research Region and Data
### Material and Processing
validation were MOD17A3 HGF images obtained from [[https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)]([https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)) (accessed on 30 October 2022) and maintained by the NASA EOSDIS Land Processes Distributed Activity Archive Center (LP DAAC) at the USGS Earth Resources Observation and Science (EROS) Center ([[https://doi.org/10.5067/MODIS/MOD17A3](https://doi.org/10.5067/MODIS/MOD17A3) HGF.061]([https://doi.org/10.5067/MODIS/MOD17A3](https://doi.org/10.5067/MODIS/MOD17A3) HGF.061), accessed on 30 October 2022). Chinese NPP data for 2001-2010 were provided by the Resource and Environmental Science and Data Centre (acronym \"GLO-PEM\") ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)), accessed on 30 October 2022), and the Global Change Science Research Data Publishing ([[http://www.geodoi.ac.cn/](http://www.geodoi.ac.cn/)]([http://www.geodoi.ac.cn/](http://www.geodoi.ac.cn/)), accessed on 30 October 2022) ChinaNPP_1985_2015 data were provided by the Global Change Science Research Data Publishing System (abbreviated as \"Chinanpp\"). (4) Additional data  
Additional data considered for the study comprised administrative boundary data, soil data, socio-economic data, and topographical data. Data on administrative divisions were taken from the National Centre for Basic Geographical Information ([[http://ngcc.sbsm.gov.cn/](http://ngcc.sbsm.gov.cn/)]([http://ngcc.sbsm.gov.cn/](http://ngcc.sbsm.gov.cn/)), accessed on 15 June 2022). GDP and population data were taken from the Resource and Environmental Science and Data Centre ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)), accessed on 19 September 2022). Soil texture data were acquired from the Harmonized world soil data base v1.2 of the Geographical FAO database of the Chinese Academy of Sciences. The 30 m resolution DEM data were downloaded from the Geospatial Data Cloud ([[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/)), accessed on 15 June 2022) and slope data were extracted on the basis of these data (Table 3).
## 4 Application
### Simulation of Alpha-Stable Random Variables
The estimated distribution parameters show the dispersion in each system through the delta parameter and the impulsivity through the alpha parameter. This system, given the value of alpha, is the closest to a behavior of a Gaussian distribution \((\alpha=2)\). Figure 3: IEEE-24 bus system simulations. Figure 4 shows the simulation of instances based on the IEEE-118 bus system, with the alpha stable parameters \(S(\alpha=1.5,\beta=1.0,\gamma=13.9,\delta=35.9)\) and the histogram of these instances. This system shows impulsiveness and significant dispersion. The fitting of the data to the stable distribution and the generation of instances, according to the parameters, show the presence of heavy tails and impulsivity in the generated data. The alpha parameter measures the impulsivity of the series and defines how heavy are the tails of the distribution of probability. Figure 5 shows different scenarios of impulsivity parameter generating differences in the tails of the distribution of the IEE-104 simulated system. The example of this system was used since it was the one with the most impulsiveness. It is relevant to know the effect of the alpha parameter on the impulsivity of the series. The impulsivity of the series differs depending on the architecture of the system. Therefore, having parameters from different systems will allow us to generate extreme scenarios to evaluate in the optimization model used. Once the stable alpha random values for the energy demand per test system have been generated, it is necessary to consider the seasonal effect of the demand. For this, the estimation of a GAM will be carried out using spline functions. This will be made using the previously generated random demand data as inputs so that there will be enough data to make the right fit.
## 3 Problem Formulation  
We define the problem of learning cleaning robot navigation as calculating optimal parameters \(\phi\) for policy \(\pi\). Here, the policy is expressed as taken action \(a\) conditioned on state observation \(s\) as \(\pi(a=A_{t}|s=S_{t})\). This problem can be modeled as a Markov decision process (MDP) and can be expressed as a tuple \((S,A,R,\gamma)\), where \(S\) is the set of states, \(A\) is the set of actions, the set of rewards is \(R\) and \(\gamma\) is the discount factor. To learn the optimal policy \(\pi^{*}\) for an agent and condition it not only on the observations from the agent itself but also other surrounding agents, the state \(S\) needs to contain the observation of the ego agent and other agent features. Therefore, we need to restructure the state observation \(S\) as follows. The set of states can be expressed as a tuple \((S_{e},S_{o}^{e})\), where \(S_{e}\) is the set of states of the ego agent and \(S_{o}^{e}\) is the set of state features of other agents expressed in the ego agent's frame of reference. The \(S_{o}^{e}\) itself is a tuple of dynamic number \(n\) of state features of other agents \((S_{o}^{e},S_{1}^{e},...,S_{n}^{e})\). Since \(n\) is dynamic, the algorithm solving the MDP is required to be able to deal with a dynamic number of inputs. Therefore, the problem of solving the MDP for the optimal parameters requires dealing with the following issues:* Obtain and express translation and rotation-invariant features of other agents in the ego agent frame for interaction modeling.
* Create a neural network architecture capable of handling an unknown number of \(n\) other agents in any given scene. The goal of this paper is to solve these issues to obtain optimal policy \(\pi^{*}\).
## 5 Results: Assisted Service Composition
### Summary  
The assisted service composition hypotheses posited at the beginning of this article were reviewed in light of the evidences; see Table 14. Statistical analysis showed that the non-programmers prefer assisted composition over manual composition, which is an expected result (i.e., supporting H2b). Moreover, thematic analysis of user comments showed a more positive perception of assisted composition by the non-programmers than the programmers. However, the other results (i.e., satisfaction scores) were generally similar across the two groups; thus, partially supporting H2a.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Assisted Composition Hypothesis** & **Differences between the Groups** & **Verdict** \\ \hline  & -Statistical differences in number of positive feedback of the assisted-composition approach (\(t\)-tests)—Table 10 & \\ H2a Non-programmers will hold a more positive perception about assisted composition compared to programmers. & -Statistical differences in number of perceived usability problems of the assisted-composition approach (\(t\)-tests)—Table 10 & Partially supported \\  & -Percentage of conceptual problems (thematic analysis)—Table 11 & \\ \hline H2b Non-programmers will favour assisted composition than manual composition compared to programmers. & -Statistical differences in satisfaction scores between assisted composition and manual composition (\(t\)-tests) & \\ \hline \hline \end{tabular}
\end{table}
Table 14: Review of the assisted-composition hypotheses (H2a and H2b).  
Figure 6: Average usability rating of assisted service composition by non-programmers and programmers; no statistical differences were detected.  
In addition to the hypotheses listed above, various interesting findings emerged with respect to assisted service composition. Non-programmers were found to spend less time selecting the services of the design template compared to programmers (\(t\)-tests), which signifies a high reliance and trust in the recommendations of the tool by the non-programmers. Moreover, programmers complained about the simplicity and inability to express complex logic by the assisted composition, as shown by the high percentage of conceptual problems concerning this issue.
# A New Click-Through Rates Prediction Model Based on Deep&Cross Network
## 2 Preliminaries
### Optimization for DNN
#### 2.3.2 Dropout  
Neural networks usually are easy to be overfitting [34]. In statistical machine learning, model ensemble can be used to deal with the problem, while, in DNN, dropout can effectively alleviate overfitting and plays the role of regularization.  
In the training of feed-forward neural networks, dropout [27] is simply to make the activation value of neurons in the hidden layer be 0 with a certain probability \(p\), so that the learning of neurons is more flexible, the model can learn more effective features, and the generalization of the model can be stronger.  
The function of averaging is applied after dropout, since different network structures can produce different overfitting and averaging can make them cancel each other. Actually, dropout can be seen as a powerful ensemble of models, leading to better generalization and avoiding overfitting to a certain extent.
###### Abstract  
Molecular communication (MC) represents a paradigm shift in communication technologies, extending beyond traditional electromagnetic methods by incorporating advances in nanotechnology, biotechnology, and communication theory. This innovative approach holds promise for groundbreaking applications in diverse fields such as medicine, military operations, and environmental monitoring. MC employs molecules to carry and transmit data. In concentration-encoded molecular communication (CEMC), information is represented through the concentration levels of the transmitted molecules. In this study, we introduce a novel method named time-hopping concentration-position shift keying (TH-CPSK), designed to facilitate multiple access within MC networks. The TH-CPSK method encodes information based on the position of the transmitted molecular concentration, significantly enhancing the accuracy of data transmission by reducing interference in MC channels. Numerical simulations reveal that an increase in the number of users adversely affects communication performance. Furthermore, our findings indicate that augmenting the number of hops improves communication performance at transmitter-receiver distances of 1 cm and 2 cm. Conversely, at a distance of 3 cm, performance degradation is observed, attributed to the increased complexity. Therefore, it is important to carefully select the number of hops considering the molecular channel's characteristics. Overall, TH-CPSK can enhance the efficiency and reliability of CEMC systems, offering a significant step forward in the realization of MC's potential applications.  
m _Applied_  
acciences  
1
## 3 Results  
### Estimation of LUE Using Multi-Angle Observed PRI  
The observed PRI (\(\text{PRI}_{\text{obs}}\)) changed with the viewing azimuth angles and its angular pattern differed with the solar position at a varied time on DOY 204 (a sunny day) (Figure 2). Within 30 min, solar radiation and solar position were near constant and the variations of PRI were a result of the different parts of the maize canopy viewed at different angles. We found that \(\text{PRI}_{\text{obs}}\) obtained from 14:30 to 15:00 were much higher than that obtained closer to noon (11:30 to 12:00). \(\text{PRI}_{\text{obs}}\) values in hotspot directions appeared lower than those when observer and the sun were far apart. These differences were more obvious in the afternoon hours than that at noon. The hotspot direction was in the same direction as the sun, resulting in more sunlit leaves being observed in the hotspot direction.  
Using the multi-angle observations, we compared the observed PRI in the single view angle (\(\text{PRI}_{\text{s}}\), \(\text{VAA}=180^{\circ}\)) and the canopy PRI with multi-angle mean values (\(\text{PRI}_{\text{can}}\)) with \(\text{LUE}_{\text{EC}}\) (Figure 3). Both \(\text{PRI}_{\text{s}}\) and \(\text{PRI}_{\text{can}}\) showed similar diurnal variations to \(\text{LUE}_{\text{EC}}\). \(\text{LUE}_{\text{EC}}\), \(\text{PRI}_{\text{s}}\), and \(\text{PRI}_{\text{can}}\) decreased from sunrise and reached their minimum at noon than increased until the sunset. \(\text{PRI}_{\text{can}}\) (R\({}^{2}\) = 0.49, \(p\) < 0.01) had a stronger correlation with \(\text{LUE}_{\text{EC}}\) than \(\text{PRI}_{\text{s}}\) (R\({}^{2}\) = 0.41, \(p\) < 0.01). The \(\text{PRI}_{\text{can}}\), which was the mean value of the multi-angle \(\text{PRI}_{\text{obs}}\), performed better in estimating LUE with tens of measurements evenly distributed at different view angles.  
Figure 2: Photochemical reflectance index (PRI) observations in a polar coordinate system. The observed PRI (\(\text{PRI}_{\text{obs}}\)) were obtained at different view angles at a different time on Day of Year (DOY) 204 (23 July, 2018). The red pentagram represents the average solar position within the 30 min in the polar coordinate system delineated by solar zenith angle and solar azimuth angle. Azimuth angles are defined from geodetic north.
# How Should the Transition from Underwater to Surface Swimming Be Performed by Competitive Swimmers?
###### Abstract  
Despite the increasing importance of the underwater segment of start and turns in competition and its positive influence on the subsequent surface swimming, there is no evidence on how the transition from underwater to surface swimming should be performed. Therefore, the aim of the present study was to examine the role of segmental, kinematic and coordinative parameters on the swimming velocity during the pre-transition and transition phases. A total of 30 national male swimmers performed 4 \(\times\) 25 m (one each stroke) from a push start at maximum velocity while recorded from a lateral view by two sequential cameras (50 Hz), and their kinematic and coordinative swimming parameters were calculated by means of two-dimensional direct linear transformation (DLT) algorithms. Unlike pre-transition, backward regression analysis of transition significantly predicted swimming velocity in all strokes except breaststroke (R\({}^{2}\) ranging from 0.263 in front crawl to 0.364 in butterfly). The inter-limb coordination was a predictor in butterfly stroke (\(p=0.006\)), whereas the body depth and inclination were predictors in the alternate strokes (front crawl (\(p=0.05\)) and backstroke (\(p=0.04\))). These results suggest that the body position and coordinative swimming parameters (apart from kicking or stroking rate and length) have an important influence on the transition performance, which depends on the swimming strokes.  
kinematics; inter-limb coordination; performance; direct linear transformation +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Floral Volatile Component Analysis by HS-SPME-GC-MS  
To extract volatile compounds from the fresh flower sample, 2 g of fresh flower sample from all the _Aster_ species' plants was placed in a 20 mL glass vial and sealed with a silicone/PTEE septum. A headspace-solid phase micro extraction (HS-SPME) was carried out using a fiber coated with 80 \(\upmu\)m divinylbenzene/carbon wide range/polydimethylsiloxane (DVB/C-WR/PDMS) (PAL SYSTEM, Switzerland) on the automatic HS-SPME autosampler (PAL RSI, PAL System). The conditions of sample incubation were 40 \({}^{\circ}\)C and 10 min, and the fiber exposure time was 20 min. The fiber was directly desorbed for 1 min. HP-5 MS (Agilent Technologies, Inc., 30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m) was used as a capillary column and helium (1 mL/min) was used as the carrier gas and the inlet was operated in splitless mode. The oven temperature was initially maintained at 40 \({}^{\circ}\)C for 5 min, then raised to 220 \({}^{\circ}\)C at the rate of 3 \({}^{\circ}\)C/min, and the final temperature of 220 \({}^{\circ}\)C was maintained for 5 min. The electron energy was 70 eV, and the mass scan range was 30-500 (3.1 scans/s). NIST 14 (National Institute of Standards and Technology, Gaithersburg, MD, USA) library was used for the mass spectra library, \(\leq\)80 for the match factor, and Mass Hunter Qualitative Analysis Workflows (Agilent) for the software. The relative amounts of the volatile compound were calculated using the area of the individual peak relative to the total areas. The results of volatile perfume analysis using HS-SPME-GC-MS were compared with the n-alkane, NIST 14 mass spectrum library to identify compounds, and their retention index (RI) was compared with literature values, and volatile compounds compared to standards were confirmed.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 4 Edge Node Deployment Algorithm Design
### Improved Sand Cat Swarm Optimization Algorithm
#### 4.2.1 Logistic Chaotic Sequence  
Chaotic sequences possess the attributes of randomness, nonlinearity, and ergodicity. They are instrumental in sustaining population diversity, subsequently enhancing the algorithm's convergence speed and accuracy. In this study, logistic chaos mapping is employed to initialize the population, with the improved formula provided as follows:  
\[X_{i+1}=\gamma x_{i+1}+(1-x_{i}) \tag{20}\]  
\[Y_{i+1}=l_{min}+x_{id}(l_{max}-l+min) \tag{21}\]  
where \(\gamma\) serves as the control parameter, with \(\gamma>1\), the value range of \(x_{i}\) falls within \(0<x_{i}<1\), \(x_{id}\) signifies the chaotic sequence generated by Equation (19), \(Y_{i}\) represents the position of the ith individual, and \(l_{max}\) and \(l_{min}\) denote the search range of the population.
## 4 Proposed Methodology
### Solution Based on the Recommendation System
#### 4.2.3 Collaborative Filtering  
In this step, once the data preprocessing phase is completed and all users' requirement vectors are constructed, the CF system evaluates the similarity between the target user \(u_{t}\) and each other user \(u_{j}\) in the system using cosine similarity.  
Let  
* \(R_{t}=[\text{CPU}_{t},\text{RAM}_{t},\text{Storage}_{t},\text{Delay}_{t}]\) be the requirement vector of the target user \(u_{t}\).
* \(R_{j}=[\text{CPU}_{j},\text{RAM}_{j},\text{Storage}_{j},\text{Delay}_{j}]\) be the requirement vector of another user \(u_{j}\).
* \(n\) be the number of considered features.  
The cosine similarity between users \(u_{t}\) and \(u_{j}\) is computed as  
\[\text{Similarity}(u_{t},u_{j})=\frac{\sum_{i=1}^{n}R_{t,i}\cdot R_{j,i}}{ \sqrt{\sum_{i=1}^{n}R_{t,i}^{2}}\cdot\sqrt{\sum_{i=1}^{n}R_{j,i}^{2}}} \tag{2}\]  
After computing similarity scores, the system ranks all users and selects the top \(K\) most similar ones. The parameter \(K\) is set to 5, and this value was empirically chosen to achieve the responsiveness of the system.  
The node selection phase is executed as follows:  
1. For each of the top \(K\) users, the system retrieves the node they previously used to deploy the same or a similar service.
2. If the node is still available (i.e., it satisfies the current resource constraints), it is immediately selected.
3. If not, the system proceeds to the next most similar user, continuing this process until a suitable node is found or the \(K\)-list is exhausted.  
The pseudo-code Algorithm 2 illustrates this.
## 4 Simulation Analysis of the Designed Probes
### Analysis of Probe Scanning Modes
In this case, the eddy currents in the sample are concentrated in the layers where the fiber direction is parallel to the y-axis, i.e., the 90\({}^{\circ}\) layer, as shown in Figure 5.  
To gain a clearer understanding of the detection of delamination under two scanning modes of the probes, the eddy current distributions in the orthogonal laminated CFRP specimen shown in Figure 5 were simulated for the two aforementioned modes. Figure 10 shows the current density models in the x-z plane of the sample under both scanning modes. It is evident from Figure 10 that layers aligned with the excitation current direction exhibit higher current density models, consistent with the conclusions from Figure 8. Based on this, the impact of delamination on the eddy currents within the CFRP sample under different scanning modes was simulated. Figures 11 and 12 illustrate the effects of delamination on the eddy currents in the CFRP sample under the two scanning modes. As shown in Figures 11a and 12a, different probe placements result in different induced electromagnetic fields within the CFRP sample, leading to varying eddy current distributions. In Figures 11b and 12b, a distinct area of almost zero current can be observed, indicating that delamination significantly hinders the eddy currents in the CFRP under both scanning modes, causing a redistribution of the original magnetic field. By comparing the numerical values of the current density models with and without delamination in the CFRP sample, it can be observed that the presence of delamination causes a decrease in the  
Figure 9: Probe scanning modes: (**a**) scanning mode 1; (**b**) scanning mode 2. From the detection results in Table 6, it can be seen that when using scanning mode 1 for eddy current testing, when the excitation current direction is aligned with the fiber direction (0\({}^{\circ}\) layer), \(C\) is relatively high. For example, the maximum \(C\) value is 0.03847 for the third layer (0\({}^{\circ}\) layer). Similarly, when using scanning mode 2 for eddy current testing, when the excitation current direction is aligned with the fiber direction (90\({}^{\circ}\) layer), \(C\) is also relatively high. For example, the maximum \(C\) value is 0.05480 for the second layer (90\({}^{\circ}\) layer). This result validates the effectiveness of the probe scanning modes illustrated in Figure 9.