esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions

Table 2: First five natural frequencies of a symmetric three-layered beam with the different end conditions. where \(S_{f}\) is the effect of the face strain energy on the natural frequency, and \(S_{f,a}\) and \(S_{f,b}\) are the axial and bending strain energies, respectively. \[S_{f}=S_{f,a}+S_{f,b}=\sum_{i=0}^{n}\frac{E_{f}A_{f}\Big{(}\frac{\mathrm{d}U(x_{ i})}{\mathrm{d}x}\Big{)}^{2}}{m_{f}\Big{\{}\big{(}U(x_{i})\big{)}^{2}+\big{(}W(x_{ i})\big{)}^{2}\Big{\}}}+\sum_{i=0}^{n}\frac{E_{f}I_{f}\Big{(}\frac{\mathrm{d}^{2}W(x_{ i})}{\mathrm{d}x^{2}}\Big{)}^{2}}{m_{f}\Big{\{}\big{(}U(x_{i})\big{)}^{2}+\big{(}W(x_{ i})\big{)}^{2}\Big{\}}} \tag{37}\]  
and \(S_{c}\) is the effect of the core strain energy on the natural frequency,  
\[S_{c}=\sum_{i=0}^{n}\frac{\frac{1}{2}k_{c}A_{c}G_{c}\Big{\{}\frac{2}{k_{c}}U(x _{i})+\Big{(}1+\frac{h_{f}}{k_{c}}\Big{)}\frac{\mathrm{d}W(x_{i})}{\mathrm{d} x}\Big{\}}^{2}}{m_{f}\Big{\{}\big{(}U(x_{i})\big{)}^{2}+\big{(}W(x_{i})\big{)}^{2} \Big{\}}} \tag{38}\]  
Therefore, the effect of the strain energies of the core and faces can be deduced from Equation (36):  
\[C_{f}\Big{(}=C_{f,a}+C_{f,b}\Big{)}+C_{c}=1 \tag{39}\]  
where \(C_{f}\Big{(}=S_{f}/\omega^{2}\Big{)}\) is the effect of the normal strain energy of the face layers on the natural frequency of free vibrations of a symmetric three-layered beam, and \(C_{c}\big{(}=S_{c}/\omega^{2}\big{)}\) is the effect of the strain energy of the core layer. In addition, \(C_{f,a}\Big{(}=S_{f,a}/\omega^{2}\Big{)}\) and \(C_{f,b}\Big{(}=S_{f,b}/\omega^{2}\Big{)}\) are the effects of the axial and bending strain energies in the face layer, respectively. By using Equation (39), the effects of the axial, bending, and shear strain energies on the natural frequencies can be analyzed. The effect of variations in these strain energies on the natural frequencies of three-layered beams with four end conditions are presented in Tables 4-11. TMM and Equation (36), as shown in Tables 4-11, are the natural frequencies computed using the transfer matrix method and Equation (36), respectively. The effect of the strain energies on the first natural frequency of three-layered beams with different end conditions, depending on the variation in \(L/h\), is presented in Table 4. The effects of the strain energies on the natural frequencies of the three-layered beams with the C-F end condition, depending on the variation in \(L/h\), are listed in Tables 5 and 6. The effects of the strain energies on the natural frequencies of the three-layered beams with the C-F end  
Figure 2: First five mode shapes of the symmetric three-layered beam with four classical end conditions.
## 2 Secure Network Encryption Protocol  
SPINS has been designed to provide security for two types of communications among the base station and the nodes:  
* Unicast communication: Node to base station (e.g., sensor readings) and base station to node (e.g., specific requests) are kinds of unicast communication between the base station and a specific node. SNEP has been designed to secure all of these unicast communications.
* Multicast communication: Base station to all nodes (e.g., routing beacons, queries or reprogramming of the entire network) needs one to many multicast communication. Note that the TESLA protocol [9] was originally developed to secure multicast data transmission. However, TESLA is not designed for the limited computing environments commonly found in WSNs. To secure multicast communications in WSNs, a tiny and stripped version of the TESLA protocol has been proposed, which is referred to as \(\upmu\)TESLA.  
Note that in this paper, we model and validate the security properties of SNEP only. Table 1 presents the notations that are being used in SNEP specification. To secure the communication from the direction \(A\) to \(B\), two different keys are used for encryption (_i.e._, symmetric key \(K_{AB}\)) and message authentication code (MAC) generation (_i.e._, shared key \(K^{\prime}_{AB}\)). Similarly, two different keys are used to secure the communication from the direction \(B\) to \(A\). Hence, \(A\) and \(B\) need two pairs of keys (four keys in total) to secure bidirectional communication between them. These keys are either derived from the master secret, \(\chi_{AB}\), shared between \(A\) and \(B\), or distributed by a trusted server. SNEP is composed of a number of sub-protocols, which are explained in the following.
## 3 Experimental Setup and Observations
### M-Shaped Jumper Flow Pattern Information Collection  
Based on the multiphase flow laboratory and the signal measurement device shown in Figure 11, the information acquisition of the M-type jumper internal flow pattern is performed. The calibration parameter of the ECT system is set to 2, and the continuous measurement mode is used to acquire ECT images of different flow patterns.  
The multiphase flow experiment bench control system controls the gas-liquid velocity by adjusting the gas-liquid flow rate independently or in parallel. For laboratory safety reasons, the gas-phase transition velocity is in the range of 0-5 m/s and the liquid-phase transition velocity is in the range of 0.1-4 m/s. The gas-liquid flow rate is changed while being monitored and controlled in order to achieve the desired flow pattern. Stratified flow, slug flow, annular flow, and bubbly flow are the most common flow patterns to be observed. With thorough mixing of the gas-liquid two phases in the fully developed section, as well as stability checking of the experimental tube section's flow pattern, the vibration signal was collected using the piezoelectric acceleration sensor as shown in Figure 11b, and data of each flow pattern and each set were collected for a duration of 10 s. Meanwhile, the ECT system (shown as Figure 11a) is used to acquire image data while 1000 sets of signal data and image data are recorded for each flow pattern. In addition, a high-speed camera captures the flow pattern videos for further analysis and verification.
## 4 Experimental Evaluation  
### Datasets  
Because CMU-MOSI and CMU-MOSEI datasets are widely used in sentiment analysis, this study selects CMU-MOSI and CMU-MOSEI datasets to verify the feasibility of the proposed model. The designed experiments are verified from the perspective of word aligned and unaligned.  
CMU-MOSI: As a data set widely used in the area of sentiment analysis, CMU-MOSI is edited from 93 videos posted by 89 different narrators on YouTube, including a total of 2199 discourse videos. Among them, 1284 discourse video segments are used as training samples, 229 discourse video segments are used as verification samples, and 686 discourse video segments are used as test samples. Video features are extracted with a sampling rate of 15 Hz, while text modality is segmented by word and represented as discrete word embedding.  
CMU-MOSEI: As a data set also used in the area of sentiment analysis, CMU-MOSEI is much larger than CMU-MOSI in terms of data volume. CMU-MOSEI is edited from 3837 videos posted by 1000 different narrators on YouTube, including a total of 22,856 discourse videos. In the training process of the model, 16,326 discourse video segments are selected as training samples, 1871 discourse video segments are selected as verification samples, and 4659 discourse video segments are selected as test samples. Video features are extracted with a sampling rate of 15 Hz. The datasets are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **CMU-MOSI** & **CMU-MOSEI** \\ \hline Train set & 1284 & 16,326 \\ Valid set & 229 & 1871 \\ Test set & 686 & 4659 \\ Data set summation & 2199 & 22,856 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data set statistics.
## 2 Materials and Methods
### Data Analysis  
For each selected study, only the data including the sample size, mean, and standard deviation of mineral loss (\(\Delta\)Z) (vol% \(\times\) mm) were extracted to be analyzed in the meta-analysis. For studies that evaluated several laser parameters, the single sample size, mean, and standard deviation for each condition were extracted. Considering the clear presence of heterogeneity between the methodologies used by the authors, the meta-analysis was conducted using a random-effect model based on the standardized mean difference in mineral-loss-related data to estimate the pooled effect of each intervention. Statistical significance was set at 0.05. Statistical heterogeneity among studies was assessed using the Cochrane Q statistic and I\({}^{2}\) test (0-40%: considered not important; 30% to 60%: may represent moderate heterogeneity; 50% to 90%: may represent substantial heterogeneity; 75% to 100%: represents considerable heterogeneity) [33]. When I\({}^{2}\) > 50%, subgroups were created and a separate analysis was carried out considering the distinct measurement for evaluating the demineralization of caries (mineral loss measured by cross-sectional microhardness, or mineral loss measured by traverse microradiography). The analysis was performed using the software Review Manager (RevMan Version 5.3, The Nordic Cochrane Centre, The Cochrane Collaboration, Copenhagen, Denmark, 2014) and the results were graphically presented by means of forest plots.
# Echelle Grating Spectroscopic Technology for High-Resolution and Broadband Spectral Measurement
## 3 Optical Layout  
### Littrow Mode  
The light usually incidents on the echelle by the Littrow mode [27], which provides a greater resolving power, as shown the illustration above. However, in the strict Littrow mode, the incident rays overlap with the diffracted rays of the blazed wavelength, which limits the layout of optics. Therefore, it is necessary to introduce a certain angle to separate the two beams to form a quasi-Littrow mode. There are two ways--one is the In-plane Quasi-Littrow mode, and the other is the Off-plane Quasi-Littrow mode [28], as shown in Figure 5.  
Figure 4: The diffraction efficiency schematic of the echelle in a broadband spectral range.  
Figure 3: The wavelength distribution at each order.  
For IQLM (Figure 5a), the incident rays and diffraction rays are all in the principal plane. In OQLM (Figure 5b), the incident and diffraction rays deviate from the principal plane by small angles. Instead of the coincidence with Na in the strict Littrow mode, the incident rays deviate from Na by a small angle, both in IQLM and OQLM, which makes the optical layout easier. However, the diffraction efficiencies of these two modes have some differences. When \(i_{d}=0\), it is the strict Littrow mode whose diffraction efficiency is maximum. As \(i_{d}\) increases in IQLM, the peak diffraction efficiency decreases rapidly, which indicates that there is a trade-off between the efficiency and the optical layout. A small \(i_{d}\) is beneficial for diffraction efficiency, but it is easy to obstruct other optics. While in OQLM, the projections of the incident and diffraction rays on the principal plane satisfy the Littrow condition. The diffraction efficiency is \(\cos\gamma\) times that of the strict Littrow mode. \(\cos\gamma\) has little effect on diffraction efficiency, even when \(\gamma\) increased to \(10^{\circ}\). This indicates that OQLM maintains the characteristics of the full-spectrum blaze better, taking full advantage of the light flux [29]. So, OQLM is widely accepted by most instruments.
## 5 Discussion and Conclusions  
The main motivation behind the use of the MSCE algorithm was to sufficiently utilize the knowledge obtained from the solutions of neighborhood scenarios to improve the overall search efficiency of solving all scenarios. The computational results for the two practical cases (H-95 and C-668) with 100 scenarios confirmed that the proposed algorithm could obtain better solutions in a much shorter time when compared to the LAH algorithm and GA. The algorithm was suitable for solving problems with a large number of scenarios, as more similar scenarios would be found in a large set of scenarios. If there are a small number of scenarios that are quite different to each other in the problem, there would not be many similarities between these optimal solutions, that is, the MSCE algorithm cannot find sufficient useful knowledge. In this situation, the traditional approximate metaheuristics may be more suitable.  
Figure 4: The relative objective value obtained by the GA compared to that of the multi-scenario cooperative evolutionary (MSCE). (**a**) H-95 with \(p=10\); (**b**) C-668 with \(p=10\).  
Figure 3: The statistical results of the total computational time for all 100 scenarios by the genetic algorithm (GA). (**a**) H-95 with \(p=10\); (**b**) C-668 with \(p=10\).  
In this work, six instances with different sizes generated from two practical cases were tested and compared in the experiment. In future research, more experiments for different cases should be conducted to further test the performance of the proposed algorithm. The proposed algorithm can also be extended to solve many other optimization problems under uncertain scenarios, for example, the path planning problem in [27], the portfolio optimization problem in [28], and the vehicle routing problem with uncertain demand [29]. Aside from the LAH and GA, other metaheuristics can be compared with the MSCE algorithm. Three neighborhood knowledge utilizing operators are designed to draw and use the helpful knowledge in solutions of neighborhood scenarios. It would be a valuable direction to study new learning strategies for utilizing the similarities among scenarios, then, a more efficient algorithm can be developed.  
Methodology, J.S. and R.W.; Validation, [PERSON]; Formal analysis, [PERSON] and X.Z.; Investigation, [PERSON]; Data curation, [PERSON]; Writing--original draft preparation, [PERSON]; Writing--review and editing, [PERSON]; Visualization, [PERSON]; Supervision, [PERSON]; Funding Acquisition, [PERSON].  
This research was supported by the National Natural Science Foundation of China (grant nos. 71771215, 61773390), the Natural Science Fund of Distinguished Young Scholars in Hunan (grant no. 2018)J1035), the key project of National University of Defense Technology (ZK18-02-09), and a project from the Department of Education of Guangdong Province (Grant no. 2018 KTSCX245).  
The authors declare no conflict of interest. The funders 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.
## 2 Related Works
### Segmented Images
#### 2.2.1 Training a Customized CNN with Segmented Images  
[PERSON] et al. trained a CNN with 3D Ray tracing simulated data at 28 GHz to predict path loss exponent for 1 km\({}^{2}\) area based on an image input resulting from the combination of two images. The first image is a segmented image that presents the heights of buildings with respect to the height of the tallest building as shades of red, and the second image is a height above sea level difference between the transmitting antenna and ground level presented in shades of green [10]. However, building height information is available for only a few environments, making the model useless where building height information is unavailable. Though the model is for an urban environment, the effect of vegetation that is present in most urban environments is omitted, especially for a millimeter wave of 28 GHz, which is easily degraded and blocked. Satellite images should have been used to capture such effects, and the model is made versatile. In [9], images containing gray shadings based on building heights between transmitter and receiver were used in training a CNN for path loss prediction at 900 MHz, in which the training data were obtained using 3D Ray Tracing. The performance was comparable to that of a two hidden layer MLP trained with 23 features describing the path from the transmitter to the receiver. The model, however, has to be modified for use at frequencies other than 900 MHz.  
[PERSON] et al. trained an Attention-Enhanced CNN to predict path loss within a suburban environment at 28 GHz. The model's input is an image segmented based on the Distance Embedded Local Area Multi-scanning (DE-LAMS) algorithm and has an accuracy better than that of a normal CNN, dilated CNN, and a Global Context (GC) CNN [27]. [PERSON] et al. developed a 5G millimeter wave signal strength prediction model based on a 3D CNN, whose inputs are 3D images generated by the 3D-LAMS algorithm based on building and terrain information. Pooling layers in the CNN were avoided so as to prevent information loss. The model, however, neglects the effect of vegetation, which is a very important determinant of path loss/received signal strength and the millimeter frequency/frequencies considered during model development were not specified [28]. [PERSON] et al. developed a path loss prediction model with numerical input characterizing network parameters and image-based inputs characterizing the environment. The images were developed based on grayscale encoding such that each type of land cover between the transmitter and receiver had its own gray level/shade. A CNN was used for image feature extraction, and a fully connected neural network was used for the numeric data. The outputs of the two networks are fused together and passed onto fully connected layers, the output of which is the predicted path loss. The model's accuracy was better than when a combination of vector-based environment characterization and numeric inputs or when the three input types were combined [29]. The encoding method used does not capture micro variations within each type of environment.
## 7 Specific Absorption Ratio (SAR) Analysis  
SAR analysis is mandatory when a MIMO antenna is placed on a human body as the side or back lobes' electromagnetic radiation of the antenna may get absorbed in the body. The constant excessive exposure to EM wave radiation might affect the human tissues and causes health issues [27; 28]. Therefore, to ensure safety from such EM wave-radiating devices, IEEE C95.1-2019 and the International Commission on Non-Ionizing Radiation Protection (ICNIRP) [29] have standardized the peak average SAR \(<\) 1.6 and 2.0 W/kg over 1 and 10 g of tissues, respectively. To evaluate the SAR values for the proposed MIMO array design, a cuboid multilayer phantom of 150 \(\times\) 150 mm\({}^{2}\) with muscle, fat, and skin of thickness 25, 8, and 4 mm [30; 31], respectively, is used in CST Microwave Studio simulator, as shown in Figure 20. The electrical properties of three layers of tissue (muscle, fat, and skin) are presented in Table 2. The average SAR (ASAR) at 1 and 10 gm (in W/kg) tissues are estimated (Figure 21), and the corresponding values are depicted in Table 3 at an input power of 2.5 mW. The maximum ASAR and the acceptable power at 28.0 GHz for 1/10 gm values are 0.0125/0.0079 W/kg and 320.00/632.91 mW, respectively, whereas, at 38.0 GHz, the maximum ASAR and corresponding power values are 0.0189/0.0094 W/kg and 211.64/531.91 mW, respectively. From Table 3, it can be observed that the calculated ASAR is less than the acceptable limit, and hence, the 1 \(\times\) 4 linear MIMO array antenna is appropriate for mm-wave and wireless body area network (WBAN) applications.  
Figure 19: Realized gains and total radiation efficiencies at different bending conditions.  
Figure 21: SAR values of \(1\times 4\) MIMO array antenna.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**28.0 GHz**} & \multicolumn{2}{c}{**38.0 GHz**} \\ \cline{2-5}  & **Permititivity (\(\epsilon_{r}\))** & **Conductivity (\(\sigma\))** & **Permititivity (\(\epsilon_{r}\))** & **Conductivity (\(\sigma\))** \\  & **in S/m** & **in S/m** \\ \hline
**Muscle** & 24.4 & 33.6 & 19.1 & 41.8 \\ \hline
**Fat** & 6.09 & 5.04 & 5.33 & 6.36 \\ \hline
**Skin** & 16.6 & 25.8 & 12.3 & 31.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Electrical properties of body tissues [32].  
Figure 20: Proposed \(1\times 4\) MIMO antenna on cuboid phantom for SAR calculation.
## 1 Introduction
### Contribution of the Work  
1. We propose an RDH scheme in encrypted images that is completely reversible with a peak signal-to-noise ratio of \(\infty\) and a structural similarity index of 1. The scheme also has an improved payload of 0.0625 bits per pixel.
2. The use of a Fibonacci transform algorithm for data hiding helps to retain the pixel intensities of the encrypted image during the data-hiding process, thus, preserving the same entropy and histogram of the encrypted image and the image after hiding the data. This helps to support the encryption efficiency and security established via the encryption algorithm.
3. An efficient framework to achieve complete reversibility is presented. The framework employs ensemble models, whose voting recovers the cover image without any distortion and, simultaneously, the secret message as well.
4. Our RDH scheme is compatible with any image encryption algorithm and the data recovery depends on the recovery of the cover image. Hence, the embedded secret message is as secure as the cover image.  
The rest of this paper is organized as follows: Related studies are discussed in Section 2. The proposed RDH scheme is explained in Section 3. The experimental results are presented in Section 4. The conclusions of the paper, with discussion of future research directions and opportunities, are provided in Section 5.
# Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments
## 4 Experiments and Results
### Evaluation of Global Path Planning  
In Figure 7a, subgoals planned by A* are not placed at the exit of local minima due to ignoring the map topology, such as for example the local minimum caused by the concave canyon between subgoal 1 and subgoal 2. In Figure 7b, the number of subgoals of A* is over two times that of JPS-IA3C. Although the subgoals generated by A* cover all of the exits of local minima, they also include many unnecessary subgoals, which will hinder the ability to avoid moving obstacles. In conclusion, the subgoals planned by JPS-IA3C are essential waypoints that can help the motion controller handle the problem of local minima and simultaneously give full play to capabilities of the motion controller.  
To avoid randomness, A* and JPS-IA3C are tested 100 times on large-scale maps (Figure 8), and the inflection points of optimal paths are regarded as subgoals of A*. First-move lags of A* include the time of planning optimal paths and finding subgoals by checking inflection points, while JPS-IA3C can directly finding subgoals without generating optimal paths. In Table 4, the first-move lags of A* are 271, 1309, and 860 times those of JPS-IA3C on these tested environments. Obviously, compared with A*, JPS-IA3C can more efficiently generate subgoals by the cached information about map topology and the canonical ordering way of search in JPS+ (P). Besides, the first-move lag of JPS-IA3C is below 1 millisecond on large-scale environments, which proves that the proposed approach can plan subgoals at low time costs to provide users with excellent experiences.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Environment 1 (ms)** & **Environment 2 (ms)** & **Environment 3 (ms)** \\ \hline JPS-IA3C & 0.097 & 0.134 & 0.230 \\ A* & 26.287 & 175.36 & 197.71 \\ \hline \hline \end{tabular}  
* The first-move lag denotes the amount of planning time the robot takes before deciding on the first move (i.e., time of finding subgoals).  
\end{table}
Table 4: First-Move Lag of A* and JPS-IA3C.  
Figure 7: (**a–b**) Orange lines denote paths planned by A*, and purple lines denote paths generated by the path planner in JPS-IA3C. Small red circles denote subgoals planned by JPS-IA3C. Yellow regions denote warning areas. (**a**) A* find subgoals (i.e., green crosses) by sampling optimal paths at fixed intervals. (**b**) A* plans subgoals (i.e., green crosses) through taking the inflection points of optimal paths.