esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Experimental Results
### Original BEE
#### 7.1.5. Network Coverage  
The cluster radius in both BEE and HEED is set to be 25 m. The sensing radius is set to be 10 m. To simplify our problem, we partition the network area into grids of the size 10 m \(\times\) 10 m rather than circles. Through this way, the sensing field is partitioned into 100 cells. In each cell, if there is a sensor still alive, we assume this area can be monitored by system. To guarantee the network coverage, a clustering algorithm should be able to cover the cells maximally. Since the sensors are randomly deployed in the network, the initial coverage with 300 sensors is 93 cells, as shown in Figure 7. The coverage of the network after each round is shown in Figure 8.  
We can see from Figure 6c that nodes started to die around 100 round for BEE. However, in Figure 8, BEE started to lose coverage around 180 round. It means that the sensors started to die from 100 round till 180 round did not have any impact on the sensing coverage of the network. For long term, BEE can provide better coverage than HEED and LEACH.  
Figure 6. Comparison between HEED and BEE. (**a**) CH percentage; (**b**) The iterations required in each round; (**c**) Number of sensors still alive.  
Figure 7. Sensing grid of the network at the initial state.
## 6 Empirical Experiments
### Generative Task
With the imposed dependence-minimisation constraint, that is, the higher \(\alpha\) values (5, 10, and 15), the generative processes of two-component VAEs are exhibited in two different ways. \(\alpha=10\) serving as an example, the base VAE unfolds the generation process through digit \"8\", whereas the scaffolding VAE unfolds its generation through digit \"6\". Moreover, besides the observed differentiated generative processes of two-component VAEs in EVAE, with further elevated \(\alpha\) values (20 and 25), both generative processes of two-component VAEs are heavily influenced by the start and end signal (digits). Both generative processes focused on producing start digit \"2\" and end digit \"9\" in various scales and orientations. However, with an elevated \(\alpha\), the generated digits appeared to be obscure and less crisp in comparison with those small \(\alpha\) cases (0.1 and 1). For comparative purposes, we also included the \(\alpha=0\) case to degrade EVAE to two independent VAEs. Reflecting on the actual generation processes of two VAEs, two linearly interpolated digits were drastically different. Unfortunately, further increment on \(\alpha\) leads to a case of collapsed generation. A demonstration of this collapsed generation is given in Appendix A. Armed with these generated (interpolated) sequences of images on digit transformation, we ran a small-scale behaviour experiment, inviting 10 individuals to assess and rate the similarity of generated digits between base and scaffolding VAEs. All 10 raters were blind to the value of \(\alpha\) to give subjective similarity ratings on two linearly interpolated sequences of images on generated digits independently for each \(\alpha\). The rating statistics were collected with a five-level Likert scale, with \"1\" indicating the most similar generation pattern. The \(\alpha\)-wise similarity rating statistics are reported in Table 2. From Table 2, we observe the trend that, with an elevated \(\alpha\), generated digits from two-component VAEs are more likely to be judged as different generations. As \(\alpha\) was set to either 0.1 or 1, the generated digits from two-component VAEs were judged to be nearly identical with each other. We further ran a one-way analysis-of-variance (ANOVA) test to assess whether these \(\alpha\)-wise similarity ratings differed statistically. The computed F-statistic was 15.31 (\(p<0.001\)), suggesting that imposing the dependence-minimisation constraint, that is, tuning \(\alpha\) to a large value on EVAE, ensures differentiated generative processes for two-component VAEs in EVAE, which improves the generative modelling performance of an EVAE.
## 4 Experiments and Results
### Experimental Environment  
All of the experiments were conducted on a PC cluster connected with 100M Ethernet. Each PC was equipped with a 3.20 GHz Intel Core i5 and 4 GB main memory, running the Centos6.6 operating system. The version of the platform is Hadoop 2.6.2. The system configuration is shown in Figure 4. The synthetic data set used in this paper was generated by tree generator using the method in [29]. Parameters of the synthetic dataset are set as follows: f = 10 (f represents fan-out), d = 10 (d represents the depth of the tree), n = 100 (n represents the number of labels), m = 100 (m represents the number of tree nodes), and t (t represents the number of trees). While t = 50,000, the data set is denoted as D5; t = 100,000, denoted as D10; t = 200,000, denoted as D20; t = 500,000, denoted as D50; t = 1,000,000, denoted as D100; t = 2,000,000, denoted as D200. The real data set was obtained from CSLOGS data set, which is from a month-log of the data of the Rensselaer Polytechnic Institute's web site. CSLOGS10 contains 100,000 trees and CSLOGS100 contains 1,000,000 trees. The support thresholds for D5, D10, D20 and CSLOGS10 are 0.01 and 0.05. For D50, D100, D200 and CSLOGS100, the support thresholds are 0.01 and 0.001.
## 2 Design of Bow-Tie Microstrip Antennas
### Antenna Design by Using RT/Duroid 5880 LZ Material  
In the second stage, two different antennas were designed and fabricated using the same materials but different thicknesses. This material has a lower relative permittivity and thicknesses than that used in Section 2.1. Again, to avoid confusion during comparison and analysis of the results, the first antenna is named antenna A while the second one is antenna B.  
The purpose of using this material is to investigate the impact of relative permittivity and substrate thicknesses on the antenna performance. Furthermore, design and fabrication of antennas with different relative permittivity and substrate thicknesses gives a practical experience to choose the best material for antenna design and hence achieving more accurate results.  
The geometry of antenna A and antenna B are shown in Figure 2. The antenna A established on a 0.508 mm substrate having a relative permittivity of 1.96. The antenna is symmetric and all antenna dimensions are presented in Table 3 (all the dimensions are in millimeters.)  
Antenna B was built on a 1.27-mm-thick substrate with a relative permittivity of 1.96. Table 4 presents the required dimensions of antenna B.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Antenna Parameters** & **Dimensions (mm)** \\ \hline patch length PL & 35.5 \\ Base length BL & 30.5 \\ Gap & 3.7 \\ Feed line width Wf & 0.5 \\ Apex length AL & 5.5 \\ Substrate length L & 42 \\ Substrate width W & 85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Antenna B Parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Antenna Parameters** & **Dimensions (mm)** \\ \hline Patch length pL & 36 \\ Base length BL & 34 \\ Gap & 4 \\ Feed line width Wf & 0.5 \\ Apex length AL & 5.5 \\ Substrate length L & 45 \\ Substrate width W & 87 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Antenna A parameters.  
Figure 2: Design of the geometry of antenna A and B with same substrate RT/Duroid 5880 LZ material and different thickness.
## 3 Results and Discussion
### Comparison of Different Networks  
In order to verify the performance of the proposed classifier, we further compared the Transfer OAM network designed in this paper with traditional CNN networks and the network in reference [14] and named them Net1 and Net2 for convenience. Table 3 shows the detection accuracy under different turbulence intensities and the required number of datasets for each mode. From Table 3, it can be seen that the detection accuracy of the three networks remains at 100% under weak turbulence; Under moderate turbulence, the detection accuracy of Net2 and Transfer OAM networks can maintain 100%, while the accuracy of Net1 drops to 95.62%; When the turbulence strengths further increase to strong turbulence, the detection accuracies of Net1, Net2, and Transfer OAM are 74.89%, 81.34%, and 90.74%, respectively, with Transfer OAM performing the best. In addition, compared with the other two networks, the Transfer OAM modal classifier reduces the number of datasets required for each modality by 10 times. This indicates that the Transfer OAM modal classifier proposed in this paper significantly reduces the required dataset size while improving detection accuracy.
# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
## 4 Simulation Results and Evaluations
### Simulation Results
#### 4.2.2 Active Nodes  
Another significant factor that directly affects the network lifetime is active nodes. Due to non-uniform events, the node energy involved in the disaster environments reaches zero after a while. The greater number of live nodes--or the reduction of the number of dead nodes--demonstrates that a better division of labor has taken place in terms of energy, and the CHs have optimally transferred the gathered data to the sink. In other words, the number of nodes increases the network lifetime that must be achieved to ensure network coverage. As shown in Figure 10, the proposed method in terms of active nodes outperforms PSO-HSA by about 16% to 36% and TPSO-CR by about 19% to 39% for the different sink locations. It can also be seen in Figure 10 that the LEACH algorithm also suffers as a result of the randomly chosen CHs in this application.  
Figure 9: The average residual energy along with various sink locations.
# Subarctic Vegetation under the Mixed Warming and Air Pollution Influence
## 2 Materials and Methods
### Field Studies
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**TP**} & \multirow{2}{*}{**Coordinates**} & \multirow{2}{*}{**Elevation**} & \multicolumn{3}{c}{**The Distance of**} & \multirow{2}{*}{**Species**} & \multirow{2}{*}{**Number of**} & \multirow{2}{*}{**Mean Tree**} & \multirow{2}{*}{**Mean DBH**} & \multirow{2}{*}{**Mean Tree**} \\  & & & **(m)** & & & & & & \\  & & & **(km)** & & & & & \\ \hline
10 & \(88.36^{\circ}\) E, & \multirow{2}{*}{37} & \multirow{2}{*}{6.9} & _Larix sibrica_ (alive) & 16 & \multirow{2}{*}{11.4} & \multirow{2}{*}{29.2} & 200 \\  & \(69.38^{\circ}\) N & & & _Larix sibrica_ (dead) & 10 & & & 225 \\
11 & \(88.33^{\circ}\) E, & \multirow{2}{*}{43} & \multirow{2}{*}{7.1} & _Larix sibrica_ (alive) & 8 & \multirow{2}{*}{13.9} & \multirow{2}{*}{32.8} & 190 \\  & \(69.39^{\circ}\) N & & & _Larix sibrica_ (dead) & 9 & & & 150 \\ \hline \hline \multirow{2}{*}{12} & \multirow{2}{*}{\(88.40^{\circ}\) E, \(69.46^{\circ}\) N} & \multirow{2}{*}{54} & \multirow{2}{*}{14.9} & _Larix sibrica_ & 18 & 12.8 & 30.5 & 190 \\  & \(69.46^{\circ}\) N & & & _Pica obovata_ & 15 & 11.8 & 26 & 160 \\  & \(69.45^{\circ}\) N & & & _Bettula_ sp. & 14 & 9.5 & 13.3 & 80 \\  & \(68.37^{\circ}\) E, & & & _Larix sibrica_ & 20 & 14.6 & 35.6 & 150 \\  & \(69.45^{\circ}\) N & & & _Bettula_ sp. & 16 & 9.3 & 17.8 & 170 \\  & \(69.45^{\circ}\) N & & & _Bettula_ sp. & 13 & 10.4 & 15.3 & 90 \\  & \(69.45^{\circ}\) N & & & _Larix sibrica_ & 19 & 16 & 40 & 205 \\
14 & \(88.34^{\circ}\) E, & \multirow{2}{*}{38} & \multirow{2}{*}{12.1} & _Pica obovata_ & 14 & 11.6 & 23.6 & 125 \\  & \(69.44^{\circ}\) N & & & _Bettula_ sp. & 15 & 9.5 & 15 & 85 \\ \hline \hline \multirow{2}{*}{15} & \multirow{2}{*}{\(88.04^{\circ}\) E, \(69.66^{\circ}\) N} & \multirow{2}{*}{41} & \multirow{2}{*}{35.6} & _Larix sibrica_ & 25 & 8.8 & 21.8 & 110 \\  & \(68.03^{\circ}\) E, & & & & & & \\ \(69.67^{\circ}\) N & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)  
Figure 3: (a) Summer (June–August) and annual (January–December) air temperature anomalies. (b) Summer soil temperature. (c) Summer soil moisture. The dashed line indicates the trend of local minimum values. (d) Terrestrial water storage (EWTA, June and annual data). Trends are significant at \(p\) < 0.05 (a,b) and \(p\) < 0.1 (c,d).
## 1 Introduction
### Response Time  
Response time is defined as the time to respond to significant events that may otherwise lead to an operational crisis. The required time strongly depends on the detected critical event. If the reaction is set earlier, it gives a higher chance of managing any crisis that develops, although early intervention may also be associated with more uncertainty as to how an event could develop and unfold. Thus, it would be not enough to answer the question for the timing of the response, as it should be the minimum possible. Nevertheless, it can be stated that the overall magnitude of a response time is in the range of seconds to minutes. For example, for a drill kick, the response time strongly depends on the parameters of the subsurface formation, such as permeability, fluid properties, as well as pressure conditions [4]--therefore, an extension in the magnitude of minutes may be required for the response time. Furthermore, often ream and wash operations are applied to prevent a stuck pipe, usually after a stand is drilled and before connecting a new stand to the drill string. Thus, waiting for a decision to recommend or not such an operation makes no sense if the response time is in the magnitude of the duration of that operation. Each decision support function has to meet estimated response time requirements, as shown in Table 1.  
During drilling, the driller needs continuous knowledge about the borehole stability and, even more urgently, about any significant borehole instability. The actual state of the borehole is used for any immediate or any eventual urgently required counter-actions or to make any revisions to improve the drilling plan. Its state is typically evaluated using a number of different information sources and is, as far as possible, incorporated into a geological model. Such information sources usually supply real-time data, measurements at regular intervals, and drilling reports [5].  
Real-time data typically originate from sensors mounted at the surface, as well as, in some special cases, from down hole; the data are sampled and provided with an interval in the magnitude of seconds. Real-time data are, in general, used also by third-party data providers, such as mud-loggers and directional drillers, to supply an additional set of information in real time. Other measurements, such as mud properties, are provided in intervals in the magnitude of hours, and drilling reports are available on daily basis. Drilling reports contain much more information about the drilling process, wellbore geometry parameters such as diameter and depth, mud properties, wellbore trajectory data, and descriptions of normal and abnormal situations and corresponding counter-actions.
# A New Concept Compliant Platform with Spatial Mobility and Remote Actuation
## 5 Discussion  
The aim of this paper was to present a new concept high accuracy platform with spatial mobility, remotely actuated, and equipped with selective compliance joints. The system can be built on a unique material block or with a reduced number of components, which reduces the costs and time of production. It can be also built by means of 3D printing.  
The platform was mounted on three arms, and it was remotely controlled by six wires. The system included a mobile plane and a connection base fixed to an endoscopic instrument. The instruments was accurate and maneuverable with continuous motion granted by the interaction between the cable tensions and the elastic energy stored in the elastic media. Based on selective compliance, the principle of design gave rise to optimal configurations where parasitic motions, friction, and wear were minimized. No lubrication was needed.  
The invention is expected to drive surgical operations in laparoscopic, endogastric, or endarterial environments, but other applications are not excluded, such as in aerospace, automotive, appliances, and microelectronics. However, it is worth noting that the platform fabrication and its actual implementation for real operations deserve further refinements, and therefore, they will be treated in future contributions. Among the important subjects that still represent open problems, the following will be briefly addressed.  
The first problem is the selection of an optimal material for the elastic parallel mechanism. This choice will depend on the overall size of the device and on the consequent preferred method of machining. In any case, some desired characteristics of a material that could be conveniently employed to build a compliant mechanism block were proposed in [57], among which are good performances at variable temperatures, biocompatibility, low susceptibility to creep, resistance to wear and fatigue, and the predictability of such properties. However, the most important characteristic is a high value of the ratio of the yield strength to Young's modulus. According to Table 2.1. presented in [57], there are several materials that have an acceptable ratio, among which are nylon, e-glass, Kevlar, and polyethylene.  
The actual fabrication of the device will depend both on size and material. Unfortunately, the geometry is rather complicated, and so, many of the classical machining operations would be problematic.  
The identification of a proper PRBM, as proposed in the present contribution, could be helpful to complete typical tasks in robotics, such as the study of the critical configurations. Furthermore, the PRBM will be useful to optimize the platform and the legs' geometry, in order to suit the required workspace, mobility, and mechanical advantage.  
Finally, an efficient control algorithm would be required for each specific application.  
Figure 7: A view of an end-effector mounted in the platform: catheter (**a**); base link (**b**); legs (**c**); platform (**d**); on-board hardware (**e**); on-board end-effectors (**f**) and (**g**).
## 3 Results
### Study of a Frame-Type Structure
#### 3.2.1 Case Study 1--Optimization of a Frame-Type Structure with Constant Square Cross-Section  
The first case study is about the structure presented in Figure 4 considering an equal square cross-section in all elements. Two behavioral constraints were initially considered; the maximum allowable displacement was set to correspond to the length of the member suffering the greatest displacement divided by 200. It was also considered a minimum displacement, to avoid unnecessary over-dimensioning of the structure, defined as the length of the member suffering the greatest displacement divided by 400. It is also assumed that no buckling occurs under the circumstances in which the structures are analyzed.  
The results achieved in the 10 runs performed, considering in each run 100 iterations and populations of 30 individuals, are presented in Table 9. The number of runs was limited to 10 as a balance solution between computational resources and solutions' representativity.  
The results obtained show a concentration of results at the lower limit defined for the maximum resulting displacement of the structure, corresponding to a cross-section with an edge of approximately 74.75 mm.  
Figure 4: Schematic representation of geometrical characteristics for the illustrative frame structure studied. Loads and boundary conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Run**} & **Best Solution (b)** & **Displacement** & **Fundamental** & \\  & **[mm]** & **[mm]** & **Frequency [Hz]** & **Mass [kg]** \\ \hline
1 & 74.7529 & 15.0000 & 57.2724 & 1202.1071 \\
2 & 74.7529 & 15.0000 & 57.2723 & 1202.1056 \\
3 & 74.7528 & 15.0001 & 57.2722 & 1202.1029 \\
4 & 74.7529 & 15.0001 & 57.2723 & 1202.1060 \\
5 & 74.7528 & 15.0001 & 57.2722 & 1202.1024 \\
6 & 74.7529 & 15.0000 & 57.2723 & 1202.1061 \\
7 & 74.7496 & 15.0027 & 57.2697 & 1201.9992 \\
8 & 74.7529 & 15.0001 & 57.2723 & 1202.1042 \\
9 & 74.7463 & 15.0054 & 57.2672 & 1201.8921 \\
10 & 74.7521 & 15.0007 & 57.2716 & 1202.0795 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Optimization of the structure with equal constant square cross-section in all members.
# Multi-Step Traffic Speed Prediction Based on Ensemble Learning on an Urban Road Network
## 3 Prediction Methodology
### Ensemble Learning  
As aforementioned, the ensemble learning can perform well in dealing with regression and classification tasks. Bagging, boosting, and stacking are the three conventional ensemble learning algorithms to integrate weak models into a strong one for applications in different fields [37]. The final prediction of bagging algorithm is equal to the average of all base learners or underlying models. The common boost model is Adaboost. Boosting is an ensemble meta-algorithm that builds a model by iteratively training a new model to emphasize the misclassified training samples from the previous model.  
Depending on the combination structure, the stacking regression is an ensemble learning technique to combine multiple regression models via a meta-regressor, which was first introduced by [PERSON] in 1992 [38]. Firstly, each individual prediction model is trained based on the complete training set. Then, the meta-regressor is fitted based on the outputs-meta-features-of the individual predictor. Thus, it can be found that stacking algorithm depends on meta-regressor learning mechanism to combine all underlying predictors, and goes beyond simple weighting mechanisms with the comparison of boosting and bagging.
## 5 Surface Engineering of SnO\({}_{2}\) ETL
### Interfacial Chemical Interaction
#### 5.2.3 Hybridization
In comparison to the 31.24\({}^{\circ}\) contact angle of pristine SnO\({}_{2}\), E-SnO\({}_{2}\) exhibits the lowest contact angle (20.67\({}^{\circ}\)), creating a wettability interface for the perovskite. As a result, the perovskite coating on the E-SnO\({}_{2}\) displays improved the crystallinity and complete surface coverage. In addition, the substrate's small contact angle results in low surface energy, which causes the perovskite film's grain size to increase during the growth of the networked structure by three times when it is deposited on E-SnO\({}_{2}\) as opposed to pristine SnO\({}_{2}\). The composition of E-SnO\({}_{2}\) was compared with that of pure SnO\({}_{2}\). A larger grain size can successfully reduce the moisture permeability and enhance the environmental stability of PSCs based on the E-SnO\({}_{2}\) ETLs at the grain boundaries. Typically, PSC hysteresis is attributed to the interface capacitance created by the ion-generated charge buildup at the contact migration, resulting in many traps and uneven charge transport inside the perovskite device. Figure 16: Perovskite films coated on (**a**) SnO\({}_{2}\) and (**b**) SnO\({}_{2}\)-CPCN substrates. Reprinted with permission from [PERSON] et al. [158] (**c**) EDTA, (**d**) SnO\({}_{2}\), and (**e**) E-SnO\({}_{2}\) substrates covered with perovskite films, as seen in top-view SEM images. (**f**) The distribution of perovskite’s grain sizes when it is deposited on different substrates. Reprinted with permission from [PERSON] et al. [124]. The surface modification used for the SnO\({}_{2}\) ETL with its main contributions to improving the properties of SnO\({}_{2}\) are summarized in Table 2. The role of surface engineering was to improve PSC performance, which is not trivial.