esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration of Deep Learning and Collaborative Robot for Assembly Tasks
## 3 Implementation
### Hand-Tracking System  
The objective of the hand-tracking subsystem is to determine the location of the hand in space and facilitate human-machine interaction for part delivery and retrieval within the assembly space. The Metacarpophalangeal Joint (MCP) in the middle finger was used as the reference point of the hand (Figure 7a). The system consists of three stages: hand capture, hand detection processing, and coordinate array generation.  
A volume was defined inside the workspace of the robot where human-robot collaboration was permitted, as shown in Figure 7b. An Intel(r) RealSense Depth Camera D435 (Santa Clara, CA, USA) was necessary to accurately capture the depth, ensuring the end effector could reach any desired point. Hand recognition was performed using the MediaPipe library, which employs a pre-trained model for real-time image processing and the extraction of information about the hand position of the operator. The MediaPipe process consists of two stages: The first one receives the whole RGB image as an input and locates hands with oriented bounding boxes. Then, another model is applied to the hand bounding box region and outputs a matrix with landmarks for the detected palm. One of these landmarks is located at the Metacarpophalangeal Joint, which is the target of the hand recognition system [48].  
The robot is commanded to move to the position of the MCP joint as long as it is detected within the allowed volume. It uses the Inverse Geometric Model (IGM) of the robot to calculate the joint-based movements. Figure 8 exemplifies the application of the hand-tracking subsystem, in Figure 8a for bringing a part and in Figure 8b for taking and storing the part.  
Figure 8: Use example of hand tracking. (**a**) The robot brings a part to the user for assembly. (**b**) The user gives a part to the cobot for storage.  
Figure 7: (**a**) The Metacarpophalangeal Joint (MCP), located in the hand of the user, can be located to allow for hand tracking. (**b**) Robot–Human collaboration/hand-tracking zone.
# Covert Channel Based on Quasi-Orthogonal Coding
## 4 Simulation Tests
### Simulation No. 2  
In order to reduce cross-correlations (from Simulation No. 1), the sorting was changed (the number of covert samples for the block remains the same as in Figure 5). The spectrum in Figure 6b) was obtained by means of two successive repetitions of sorting (according to Formulas (13) and (14)) of the originally sorted \(x_{1\_sorted}\).  
\[i_{1}=\text{paralel\_to\_serial}\left\{\begin{bmatrix}1:2:\text{N}\\ 2:2:\text{N}\end{bmatrix}^{\text{T}}\right\} \tag{13}\]  
\[i_{2}=f(i_{1})=\left[1:\text{k}:\frac{\text{N}}{2},\text{N}:-1:\frac{\text{N }}{2}+1\right] \tag{14}\]  
As a result of the sorting, the cross-correlation for the symbol \"7\" is the same before and after the orthogonalization process. Nevertheless, the variance of the cross-correlation is twice as small. Hence, a much lower BER was obtained for the sorted cover than in the previous case (Figure 5f) vs. (Figure 6f).  
Figure 5: Testing the impact of cover orthogonalization on the non-ideal SIC process (**a**) Averaged FFT spectrum of unsorted cover and 8 FSK (**b**) Averaged FFT spectrum of sorted cover and 8 FSK (**c**) Averaged cross-correlation value of unsorted cover and 8 FSK (**d**) Histogram of the cross-correlation value of the unsorted cover and 8 FSK (**e**) Histogram of the cross-correlation value of the sorted cover and 8 FSK (**f**) Reception of covert information (8 FSK) for the unsorted and sorted cover (non-ideal SIC).
## 4 Results
### Factor and Correlation Analysis  
#### 4.2.1 Pre-Factor Analysis  
The pre-factor analysis of four indicators was conducted using the SPPS statistical software (version 26). It was oriented to investigate an internal consistency of questionnaire items, a level of questionnaire reliability, sampling adequacy, and usefulness of factor analysis. Results of the pre-factor analysis are presented in Table 4.  
As per Table 4, the Cronbach \(\alpha\) indicator has a value of 0.875, which shows that the internal consistency of questionnaire items is relatively high and acceptable. Test reliability and acceptance to perform data reduction using EFA and CFA techniques were confirmed by the Spearman-Brown and Kaiser-Meyer-Olkin coefficients. The value of KMO was 0.839, exceeding the suggested cut-off value of 0.6. Moreover, it indicates a meritorious selection of variables. These results indicate that the sampling was adequate and supports the application of factor analysis [61]. Finally, the [PERSON]'s test of sphericity (a test of at least one significant correlation between two of the items studied) was significant: \(\chi^{2}\) (153) = 1140.42, \(p<0.05\). Here, the p-value is smaller than the significance level (\(\alpha=0.05\)); therefore, it confirms that the dataset is suitable to continue investigation within procedures of EFA and CFA.
## 2 Materials and Methods
### Cavitation Model  
The Rayleigh-Plesset equation and its variations are commonly employed to simulate mass transfer in the cavitation phenomena. Within the Ansys Fluent software package, three prevailing cavitation models are available, namely, the Singhal, Schnerr and Sauer, and Zwart-Gerber-Belamri models. Among these models, the Singhal model is the most comprehensive, considering the effects of phase transition, bubble motion, turbulent pressure fluctuations, and non-condensable gases on vapor phase formation and condensation. However, its numerical stability is inferior to that of the Schnerr and Sauer model and the Zwart-Gerber-Belamri model. In addition, prior studies [21; 28; 29] have utilized the Schnerr and Sauer model to analyze Venturi cavitation, making it a suitable choice for simulating vapor-liquid mass transfer in Venturi tubes. The rate of evaporation (\(R\)e) and the rate of condensation (\(R\)c) of vapor in the Schnerr and Sauer models can be expressed as follows:  
When \(P\leq P_{V}\)  
\[R_{\mathrm{e}}=\mathrm{F_{vap}}\frac{\rho_{\mathrm{v}}\rho_{l}}{\rho_{\mathrm{ m}}}\alpha_{\mathrm{v}}(1-\alpha_{\mathrm{v}})\frac{3}{3\Re_{\mathrm{B}}} \sqrt{\frac{2}{3}\frac{(P_{V}-P)}{\rho_{l}}} \tag{6}\]  
When \(P\geq P_{V}\)  
\[R_{\mathrm{c}}=\mathrm{F_{cond}}\frac{\rho_{\mathrm{v}}\rho_{l}}{\rho_{\mathrm{ m}}}\alpha_{\mathrm{v}}(1-\alpha_{\mathrm{in order to investigate the impact of the lengths of converging and diverging sections on Venturi cavitation characteristics, a series of 15 Venturi models were constructed under varying operational conditions, as outlined in Table 1. The models maintained a consistent throat diameter (\(\rm L_{t}=D_{t}=10\) mm), convergence angle (\(\rm\alpha=45^{\circ}\)), and divergence angle (\(\rm\beta=12^{\circ}\)). In addition, the dimensionless parameter cavitation number is also included in Table 1, which reflects the cavitation flow state inside the Venturi tube, which is obtained from the following equation:  
\[\rm\sigma=\frac{\it P_{out}-\it P_{V}}{\frac{1}{2}\it\rho_{l}\it{U_{th}}^{2}} \tag{9}\]  
where \(\it P_{out}\) is the outlet pressure, and \(\it U_{th}\) is the average velocity at the throat of the Venturi tube.
## 4 Evaluation
### Evaluation Methodology  
We select CCAF, an existing fair routing solution which represents a most recent improvement and can also be easily incorporated into an existing encounter-based routing protocol. CCAF and our proposed FLARoute are each incorporated into PRoPHET. Then the results of both solutions are compared with network performance when running on PRoPHET alone. This allows us to observe the impact of our approach on addressing routing fairness, as well as gain insight on the performance of existing approaches. We also experiment with Bubble Rap, dLife, HERO and SnF, in order to observe the performance of existing routing techniques under high degrees of spatial locality.  
For configuration settings, SnF uses binary spraying, and the value of \(L\) is taken as 8, which yielded best overall performance from several trials. Community formation and centrality computation for Bubble Rap is in accordance with the K-Clique and cumulative window algorithms given by [PERSON] et al. [45], while dLife considers 24 daily samples (i.e., 1 h each) as mentioned by [PERSON] et al. [57]. HERO collects a location sample every hour, and maintains up to 7 days record. Based on results from several trials with FLARoute, \(B_{th}\), the overload threshold, is taken as 40, \(\gamma\) is taken as 20%, while \(\Delta Tb_{i}\) and \(\Delta Tg_{i}\) are taken as 3 h and 6 h, respectively.  
Since the performance of the routing proposals is evaluated through simulations, the mobility of nodes needs to portray realistic human movement properties. Although real-world traces represent actual movement scenarios, existing publicly available datasets are less suitable for evaluating fair routing for OppNets in Smart City scenarios due to: (i) the lack of realistic node density and encounter frequencies portrayed in city-wide scenarios; and (ii) the inability to change network properties such as node population and geographical area without interfering with the encounter opportunities between nodes. Hence, we resort to using WDM, which provides the desired level of flexibility and is also able to reproduce realistic properties of human movement.  
For the remainder of this section, Section 4.1.1 presents the simulation set-up in the ONE simulator while Section 4.1.2 presents the metrics for evaluating the performance of FLARoute and the selected routing proposals.
## 5 Conclusions  
In this paper, firstly we combine the JSCC technique with Turbo equalization and investigate this system over OD-ISI MR channels. Moreover, by means of the M-JPEXIT algorithm, a re-design scheme for the channel codes in the proposed system is put forward to improve the error performance. Both the M-JPEXIT analysis and the BER simulation results show the performance improvement of the enhanced channel codes, especially in the water-fall region.  
In future work, the optimization of the JSC decoder for the JSCC system over OD-ISI magnetic recording channels will be investigated.  
Conceptualization, C.C.; Data curation, Y.S.; Formal analysis, Q.C.; Funding acquisition, C.C. and Q.C.; Investigation, Y.S.; Methodology, C.C.; Project administration, C.C.; Resources, S.L.; Software, Y.S.; Supervision, C.C.; Validation, S.L.; Writing--original draft, Y.S.; Writing--review & editing, C.C. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research received National Natural Science Foundation of China: 61901182; National Natural Science Foundation of China: 62101195; Science Foundation of the Fujian Province, China: 2020J05056; Scientific Research Funds of Huaqiao University under Grant: 19 BS206; the Scientific Research Funds of Huaqiao University under Grant: 21 BS118.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 4: Simulated BER results of the enhanced and the benchmark codes at \(p=0.06\).
## 6 High-Level Abstract-Feature Extraction
### Wide Residual Network (Wide ResNet)  
Numerous variants of ResNet have been developed; one of the most popular of these is Wide ResNet. ResNet [17] focused on the effect of NN depth on accuracy, whereas Wide ResNet [18] determines the effect of NN width on accuracy.  
In [18], the researchers indicated that in an overly deep NN, some residual blocks were unable to provide feature data or only some residual blocks could learn key feature data. In other words, a large number of residual blocks in an overly deep network had no actual function but rather caused redundant computation. Wide ResNet reduced the depth of networks and widened the number (width) of convolution kernels in residual blocks for verification. The ResNet and Wide ResNet designs [18] are compared in Figure 6 which was redrawn based on the information in [18].  
ResNet contains more than 50 layers of models. To reduce the number of model parameters, a bottleneck design was employed to replace two 3 \(\times\) 3 convolution kernels with a 3 \(\times\) 3 convolution kernel that had two 1 \(\times\) 1 convolution kernels clipped onto it. However, to test the effect of width on this design [18], as shown in Table 1 that adopted the data in [18], the researchers still employed two 3 \(\times\) 3 convolution kernels. In addition, to impede overfitting as the number of parameters increased, a dropout layer was added between any two consecutive layers. Based on this structure, two parameters were proposed, namely the number of residual blocks (N) and the number of times that convolution kernels are widened (K).  
In [18], experiments on residual blocks composed of multiple combinations of convolution kernels were conducted and optimal results were obtained from the basic B(3,3) framework. Adding more layers or incorporating the bottleneck structure yielded testing outcomes with higher error occurrence or no performance improvement; by contrast, adding a dropout layer engendered notable effects. For more information about Wide ResNet, refer to [18].
## 1 Introduction
### Major Contributions  
Skip layer pooling fusion: It is observed that shallow layers possess more information of higher resolution about location and detail but offer less semantic understanding. On the contrary, deeper layers provide richer semantic information but may lack the perception of fine details. Therefore, the fusion of deep and shallow features is vital for improving the performance of mask-wearing classification models. Based on this idea, we introduce an innovative feature fusion technique named skip layer pooling fusion (SLPF) to fuse deep and shallow features in a neural network. In comparison to other feature fusion methods, this architecture effectively improves accuracy while reducing the complexity (number of parameters) associated with feature fusion.  
Layer attention: We observe that the features extracted from each convolutional layer of the neural network contribute differently to the final prediction results. Some convolutional layers extract more important features, while others extract features that have a less noticeable impact on the prediction results. Based on this observation, we propose an attention mechanism called layer attention (LA) for feature fusion, aiming to prioritize the former whilst neglecting the latter.  
Spatial-frequency feature fusion: The frequency domain features mainly include high-frequency and low-frequency components, which correspond to different features of the image. The features that reveal part of the nose and mouth are considered detail features of an image and produce abrupt changes in grey values. They correspond to high-frequency components in the frequency domain and are easier to analyze. To maximize the utilization of these features, a spatial-frequency feature fusion module was proposed.
###### Abstract  
Injectable resin composites offer flowability while providing enhanced mechanical properties, making them suitable for both posterior and anterior restorations. However, their susceptibility to discodization and the efficacy of toothpaste in removing stains have not been extensively studied. This study aimed to evaluate the color stability of injectable (G-enial Universal Injectable), flowable (3M\({}^{\text{TM}}\) Filek\({}^{\text{TM}}\) Supreme Flowable, 3M, St. Paul, MN, USA), and conventional composite (3M\({}^{\text{TM}}\) Filek\({}^{\text{TM}}\) Z350 XT Universal Resatrative, 3M, MN, USA) after red wine exposure and to compare the stain removal efficacy of whitening (Colgate\({}^{\text{\textregistered}}\) Optic White\({}^{\text{TM}}\) Volcanic Mineral Whitening Toothpaste, Colgate-Palmolive, Chonbur, Thailand) versus daily (Colgate\({}^{\text{\textregistered}}\) Cavity Protection Toothpaste, Colgate-Palmolive, Chonburi, Thailand) dendrimific. Disc-shaped (7 mm in diameter and 2 mm in height) samples (\(n=30\)) in A3 shade were prepared using injectable, flowable, and conventional composites. The samples were polymerized for 40 s on each side using a curing device set at 1500 mW/cm\({}^{2}\) and immersed in red wine for 7 days. After staining, they were brushed with either whitening or daily denittice using 4950 strokes. Color measurements were taken at baseline (T1), after staining (T2), and after brushing (T3), using a spectrophotometer. Statistical analysis was performed using ANOVA. Results showed that conventional composites exhibited the highest color change (\(\Delta\)ET1-T2 = 5.50) compared to injectable (\(\Delta\)ET1-T2 = 3.60) and flowable composites (\(\Delta\)ET1-T2 = 3.36). While whitening dendrimific facilitated a greater recovery of discodization in stained samples (\(\Delta\)ET1-T3 = 1.38-3.67) compared to daily denittice (\(\Delta\)ET1-T3 = 2.01-4.28), the observed difference was not statistically significant. In conclusion, all composite types are prone to staining from red wine, and both whitening and daily denittifrices show no difference in efficacy in reducing discodization.  
njectable resin composite; color stability; daily denittice +
Footnote †: journal: applied sciences
###### Abstract
We also show that it is not only a theoretical proposition but was practically validated, as described in Section 5. The work described in this document has been implemented as an IoT network created for the management and monitoring of an indoor greenhouse. The solution involves a substantial number of actuators and sensors, and the main objective of the project was to ensure the reliability and productivity of the solution, as measured by its overall outcome. To gather the outcome data, we prompted the operator of the farm to provide feedback through a mobile application. By collecting a significant amount of data over time, we were able to build analytical models that were applied to the graph representation of the solution. For more information about the implementation, please refer to Section 5. We wish to underscore the fact that the system crafted to facilitate auto-configuration of the IoT network is not a predefined monolithic entity. Rather, it comprises a network of easily configurable devices within a dynamic architecture. This rationale is precisely why we opted to implement the logic of IoT devices through microservices, following the standards supported by The Open Group. Another noticeable aspect relates to defining capabilities as a collection of desired properties essential to the system. The synopsis details instances where these predefined sets of capabilities [7] were impacted due to both internal (e.g., failures) and external (e.g., configuration changes) factors. Importantly, the system autonomously managed to restore these specified capabilities in both scenarios without needing manual intervention from support personnel. In this context, the measure of effectiveness is not solely gauged by the overall system performance. Rather, it is rooted in assessing the cost implications associated with introducing changes aimed at reinstating the set of capabilities. The increased crop efficiency, as depicted in the example in Section 5, should be understood as a side effect rather than a fundamental criterion for measuring the effectiveness of the presented solution.
## 2 Research Design and Data Processing
### Data Processing  
First, the obtained data were preprocessed. After removing the data that had nothing to do with the subject and affected text analysis, various advertisements, and unannotated records, regular expressions were used to filter the noise data. A total of 13,958 comments were available after deleting expressions, such as emojis and website addresses.  
The data after noise reduction processing cannot be directly subjected to sentiment analysis. This research used the open-access Chinese word segmentation tool (i.e., Jieba) in Python to process the text, and then we scored the data for word segmentation based on the emotion dictionary. Finally, 50% of the scored entries were randomly selected for a second manual review to improve the accuracy of the emotion dictionary analysis results.
# DDS: A Delay-Based Differentiated Service Virtual Network Embedding Algorithm
## 3 Virtual Network Embedding Problem Formulation  
### Network Model  
When modeling the VNE problem, the previous approaches either do not consider link propagation delay or only consider node CPU resources, which is insufficient to a certain extent. In order to be closer to the actual network environment, this paper comprehensively considers node computing resources, storage resources, link bandwidth resources, and link propagation delay constraints in order to model the substrate network and virtual network.
# Enhanced Steganography for High Dynamic Range Images with Improved Security and Capacity
## 2 Preliminary
### Security by Statistical Analysis
Thus, it can be inferred that the majority of almost embeddable pixels possess an HV of 2. As only \"_promising_\" pixels are selected for embedding secret messages in steganography, it is important to note that these pixels possess certain characteristics, namely HV = 2 and \(\max(R,G,B)=254\) or \(\max(R,G,B)=127\). In order to examine homogeneity, the following analysis is presented. With the exception where \(\max(R,G,B)=254\) or \(\max(R,G,B)=127\), the other four values contain at least two even numbers, which can be combined to generate another element through a division by 2 or multiplication by 2 of another element. The remaining two numbers have a 50% probability of being even. In summary, in the three color channels of embeddable pixels, the likelihood of an arbitrary number's being even is 75%, while the likelihood of it's being odd is 25%. Prior to message embedding, the probability that all three color channels are even in the cover image is 25% (i.e., \(\frac{1}{2}\times\frac{1}{2}\times 100\%\)). However, after embedding the secret message, approximately half of the even numbers will become odd due to multiplication and division, resulting in a probability of 12.5% that all three color channels are even in the stego-images, illustrated in Figure 1.  
**Observation 2**. : _The number of embeddable \"irregular\" pixels noticeably increases following the embedding procedure in Ref. [13]._  
Based on the analysis of the HDR image database in the study by [PERSON] et al., the number of embeddable \"_irregular_\" pixels is typically negligible. However, following the embedding process, the number of embeddable \"_irregular_\" pixels noticeably increases. This operation disrupts the typical distribution, as roughly half of the \"_promising_\" pixels in each HDR image are transformed into embeddable \"_irregular_\" pixels. As shown in Figure 2, these non-normal distributions of pixel counts can easily trigger suspicion upon a specific analysis of the stego-image. The above analysis is presented in Section 4. Figure 1: The probability histograms of 101 HDR images, wherein the three color channels of each pixel possess even values: (**a**) 0.25 of the original cover images and (**b**) 0.125 of the embedded images, as per [PERSON] et al.’s scheme. As the eavesdropper acquires an increasing number of HDR images, the vulnerability of the security scheme to specific forms of analysis becomes increasingly evident.