esa-sceva/satcom-chunk-collection huggingface dataset:
# STN-Homography: Direct Estimation of Homography Parameters for Image Pairs
## 6 Conclusions  
In this study, we showed that after pixel coordinate normalization of the homography matrix, we can apply a direct regression of the normalized homography matrix values with the proposed STN-homography model rather than estimate the alternative four-point homography. The mean corner error of the single STN-Homography model was 4.85 pixels, which was smaller than the state-of-the-art, one-stage, CNN-based four-point homography estimation methods. We also showed that with the hierarchical method, we could decrease the homography estimation error. Accordingly, the mean corner error of our three-stage hierarchical STN-Homography model was 1.57 pixels, which was superior to the state-of-the-art homography estimation outcomes. We also proposed a sequence STN-Homography model which could be trained in an end-to-end manner, and obtained superior results than the hierarchical STN-Homography model. The mean corner error of three-stage sequence STN-Homography model was only 1.21 pixels. The average processing times of our proposed three-stage hierarchical STN-homography model and three-stage sequence STN-Homography model on a GPU were 17.85 ms and 13.85 ms respectively, and they both satisfied the real-time processing requirements imposed in most potential applications.  
conceptualization, Q.Z. and X.L.; methodology, Q.Z.; software, Q.Z.; validation, Q.Z.; formal analysis, Q.Z.; investigation, Q.Z.; resources, X.L.; data curation, Q.Z.; writing--original draft preparation, Q.Z.; writing--review and editing, X.L.  
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (No. U1613203, 51975514), Shenzhen Science and Technology Plan (No. JCYJ20170816172938761), and the Fundamental Research Funds for the Central Universities (No. 51221004).  
The authors declare no conflict of interest.  
Figure 8: Prediction results of our three-stage sequence STN-Homography model.
## 4 Future Directions
### Build Auto-Recharge Power Optimization Model  
A seawater-based auto-recharging mechanism was developed in [13; 14]. These are also known as sodium-seawater batteries (Na-SWB). [PERSON] et al. [39] also proposed a battery model that can be recharged using seawater in UIoT devices, it is also known as the seawater battery (SWB). Finally, [PERSON] et al. [40] proposed a novel power optimization scheme for surface water buoys using seawater batteries. Therefore, it is necessary to apply an auto-recharging mechanism for underwater nodes in the future, or it is essential to build a power optimization model in the surface gateway and remotely operated underwater vehicle.
# Geotechnical Evaluation of Diesel Contaminated Clayey Soil
## 3 Results and Discussion
### Influence of Diesel Contamination on the Soil's Texture  
Diesel contamination of natural soil caused a change in its texture classification from silty clay to silty loam. This change, from a fine texture to a ridged one after soil contamination, was due to the agglomeration of soil particles as a response to soil acidification and the reduction of the dielectric constant of the pore fluid.  
A decrease in pH promoted the exchange of calcium ions by hydrogen ions, which resulted in clay particle agglomeration/floculation due to its surface charge alteration [36]. Furthermore, partial replacement of water contained in the pores of soil by the contaminant changed the dielectric constant value of the pore fluid. Hence, the attractive forces prevailed over repulsive ones, resulting in soil agglomeration/floculation [37].  
The van der Waals attraction force developed between two parallel plates can be calculated as shown in Equations (4) and (5), while the repulsion force can be obtained as indicated in Equations (6) and (7) [37].  
\[Att=\frac{H}{6\pi d^{3}}, \tag{4}\]  
where \(d\) is the distance between plates and \(H\) is the Hamaker constant obtained as follows [37]:  
\[H=\frac{3}{4}kT\bigg{(}\frac{\varepsilon_{1}-\varepsilon_{2}}{\varepsilon_{1} +\varepsilon_{2}}\bigg{)}^{2}+\frac{3\hbar v_{e}}{16\sqrt{2}}\frac{\big{(}m_{1 }^{2}-m_{2}^{2}\big{)}^{3/2}}{\big{(}m_{1}^{2}+m_{2}^{2}\big{)}^{3/2}}, \tag{5}\]  
where \(k\) is the Boltzmann constant, \(T\) is the absolute temperature, \(\varepsilon_{i}\) denotes the dielectric constant of the medium, \(h\) is the constant of Planck, \(v_{e}\) corresponds to the main electronic adsorption frequency in ultraviolet light, and \(m_{i}\) is the reflective index of the medium in visible light.  
\[Rep\propto\frac{1}{K_{1}\beta}, \tag{6}\]where \(K_{1}\) is a constant that depends on the valence of the ions and the ionic concentration and \(\beta\) can be obtained as follows [37]:  
\[\beta=\frac{K_{1}}{\varepsilon kTK_{3}}, \tag{7}\]  
with \(K_{1}\) and \(K_{3}\) being constants that depend on the ionic concentration.  
It can be appreciated from Equations (4) and (6) that both attractive and repulsive forces depend on the dielectric constant value of the pore fluid. However, the variation of the dielectric constant value of the pore fluid has a higher effect over the repulsive forces [37]. Thus, a decrease on the value of the dielectric constant of the pore fluid reduced both attractive and repulsive forces, but the decrease in repulsive forces was bigger than the decrease in the attractive forces. Hence, attractive forces prevail over repulsive ones (the net effect was attractive) and gave as result the agglomeration of the soil particles.
## 1 Introduction  
As the demand for flexible and stretchable electronic devices increases, flexible and stretchable gas barrier films are in great need to prevent moisture from entering devices to increase the lifetime and stability of electronics and display devices [1, 2, 3]. However, conventional gas barrier films are manufactured using an inorganic film, where cracks or pinholes are generated when the gas barrier film is bent or stretched, and water can penetrate through these defects [4]. Additionally, inorganic layers have been fabricated by vacuum processes. However, vacuum processing has the disadvantages of low production efficiency and high production costs [5]. To solve this problem, we fabricated a stretchable gas barrier film using nano-materials and polymers in place of inorganic layers. Instead of a vacuum process for depositing inorganic layers [6, 7, 8], a layer-by-layer (LBL) assembly method based on solution processing via electrostatic attraction was used to deposit the passivation layer [9, 10, 11]. As nano-materials to lengthen the moisture permeation path of water, graphene oxide (GO) and montmorillonite (MMT) were used. Since GO exhibits a large aspect ratio and MMT is a plate-like material, they were suitable for gas barrier films [12, 13, 14, 15]. In addition, the transmittance is an important factor in our gas barrier film because a high transmittance is essential for the film to be applied to construct a display [16]. Therefore, in our paper, GO and MMT were used to form a flexible gas barrier film for improved transmittance and a decrease in the water vapor transmission rate. Furthermore, polydiallydimethylammonium (PDDA) and poly (vinyl alcohol) (PVA) were used as suitable polymers to be mixed with GO and MMT.  
Polydimethylsiloxane (PDMS) and the Eooflex series (here, Ecoflex 00-30) are commonly used stretchable substrates. However, each has specific strengths and weaknesses.  
PDMS exhibits low stretchability and high transmittance, rendering it suitable for transparent applications, whereas Ecoflex exhibits high stretchability and opaque characteristics. Both materials are types of silicone rubber, which means that they are miscible [17; 18; 19]. Thus, we attempted to mix PDMS and Ecoflex as a stretchable gas barrier film substrate. When a passivation layer was laminated on the stretchable substrate, pre-strain was applied and laminated to eliminate spaces between the nano-clays when the substrate was stretched. The water vapor transmission rate (WVTR) characteristics and surfaces of the substrates fabricated after applying 10% and 30% pre-strain and without applying pre-strain were compared. The oxygen transmission rate (OTR) was measured via MOCON, and the WVTR of the stretchable gas barrier film was measured via the Ca test method using the electrical properties of calcium [20; 21]. The fabricated stretchable gas barrier film was confirmed to be suitable for use in stretchable electronic devices.
## 4 Application of Moisture Identification and Algorithm Comparison
### Comparison of Selection Algorithms
#### 4.2.2 Principal Component Analysis  
Principal component analysis (PCA) is a widely used dimensionality reduction method, in which the basic idea is to transform the original data into a new coordinate system by using linear transformation and obtaining the principal components [36]. Dimensionality reduction was conducted on the six original feature parameters by PCA, and the coefficient matrix is shown in Table 4.  
The components in the first four columns of the coefficient matrix were used as the projection matrix to ensure the consistency of the comparative experiment. Assume the new characteristic parameters \(\overline{X}=X\times D\), where \(X\) is the original sample matrix after subtracting the average value, and \(D\) is the projection matrix of the first \(s\) principal components, where \(s\) is valued at \(4\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pearson Correlation** & **Energy** & **Duration** & **Counts** & **Rise Time** & **Arrival** & **Peak** \\
**Coefficients** & **Energy** & **Duration** & **Number** & **Rise Time** & **Time** & **Amplitude** \\ \hline Energy & \(1.0000\) & \(0.8999\) & \(0.9129\) & \(0.6856\) & \(-0.0350\) & \(0.8657\) \\ Duration & \(0.8999\) & \(1.0000\) & \(0.9028\) & \(0.8140\) & \(-0.1541\) & \(0.8447\) \\ Counts Number & \(0.9129\) & \(0.9028\) & \(1.0000\) & \(0.7464\) & \(-0.1123\) & \(0.8152\) \\ Rise Time & \(0.6856\) & \(0.8140\) & \(0.7464\) & \(1.0000\) & \(-0.1400\) & \(0.6372\) \\ Arrival Time & \(-0.0350\) & \(-0.1541\) & \(-0.1123\) & \(-0.1400\) & \(1.0000\) & \(-0.0455\) \\ Peak Amplitude & \(0.8657\) & \(0.8447\) & \(0.8152\) & \(0.6372\) & \(-0.0455\) & \(1.0000\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pearson correlation coefficient matrix.
## 4 Discussion on the Proposed Technique and Results Obtained during the Experiments
### Discussion on the Efficiency of the Applied Decomposition Technique  
Let us now discuss the applied optimization technique in detail. The proposed method is based on the decomposition of logic functions represented by the BDD form and extensively searching for common subexpressions. In order to evaluate the efficiency of the presented optimization idea, let us compare it with the others that are already known in the literature: Decomp [51], ABC [53], DAOmap [54], DDBDD [57], BDS-PGA 2.0 [58], and MultiDec [66].  
The Decomp system uses a classic decomposition theory by [PERSON], effectively coping with multi-output functions and incompletely specified functions. The ABC technique is based on the And-Inverter Graph representation of circuit and structural transformations. The DAOmap method also cuts AIGs but under mapping depth constraints. The three remaining tools apply BDDs to represent the logic functions. The DDBDD technique uses a technique called linear expansion for BDD decomposition, which, in turn, enables a dynamic programming algorithm to efficiently search through the optimization space. The BDS-PGA method heuristically manipulates BDDs with resynthesis elements. The MultiDec tool is also based on cutting BDDs but uses multiple cutting to achieve parallel decomposition and the results are evaluated with triangle tables. Finally, the MultiDec tool and the PdekBDD are able to use blocks which could be configured to five inputs and one output or four inputs and two outputs.  
Table 4 shows the results of experiments where the proposed decomposition method is compared to the existing ones. The set of benchmarks is taken from [96]. The first three columns show the name and the number of inputs and outputs of the benchmark circuit. The remaining columns show the number of five-input blocks. There are gaps in the table since not all results are available. At the bottom of Table 1 is the sum of the number of blocks. In the last row, the ratio of the sum of blocks by number for a given tool to the corresponding sum of blocks for PdekBDD is presented. For all tools except BDS-PGA, the ratio is greater than 1, which means that PdekBDD gives better results. In one extreme case (ex1010 benchmark), PdekBDD gives eight times better results than DDBDD and  
seven times better results than DAOmap, but these tools are delay constrained. Only for BDS-PGA is the total result of PdekBDD worse by about 9%. This is mainly due to the alu4 benchmark where BDS-PGA achieves 190 blocks and PdekBDD 324. The authors' tool gives a very low number of blocks for symmetric functions (9 sym) or multi-output functions with symmetric functions (rd73 and rd84). PdekBDD achieves good results when it comes to minimizing chip area occupancy. The main disadvantage of PdekBDD is that it does not support the do not care state and the delay in the chip can only indirectly be reduced by reducing the number of blocks--there is no way to impose restrictions on the propagation time in the target system.
# Improving Robotic Bin-Picking Performances through Human-Robot Collaboration
## 4 Requirements of a Collaborative Bin-Picking Robotic Cell
### Hardware Requirements  
* The human operator and the robot must be able to work in close proximity without safety fences: therefore, a collaborative robot must be used, since it is designed to interact with operators in the same working environment [24]. Safety is guaranteed by torque measurements in each joint that allow the implementation of collaborative features, i.e., impedance control algorithms [25]. Many modern cobots also offer the possibility to manually hand guide the robot end effector to a certain position [26], which can be particularly useful for some collaborative bin-picking applications;
* A 3D vision sensor is needed for 3D visual data acquisition of the bin [27]. The vision sensor technology must be chosen in accordance with the objects that need to be picked and its 3D point cloud resolution must be sufficient to detect the objects inside the bin;
* The presence of a human worker inside the workcell must be detected, and his/her position must be tracked, for example, in order to avoid collisions. A vision sensor can be used for this purpose. This could be a 2D- or 3D-camera and could even be the same one used for object recognition, as experimented in Section 5 of this paper. It is worth noting that using a collaborative robot already ensures the operator's safety, since if collisions occur, they are not dangerous. However, a collision usually results in an undesirable fault state of the robot and for this reason should be avoided in advance;
* Robot end effectors must ensure the operator safety: collaborative or soft robotic end effectors must be used [18];
* The robot and the operator must be able to communicate: in particular, the robot must be able to ask for help when it detects a potential failure. This might be carried out, for example, by turning on one or more LEDs or by exploiting a human-machine interface (HMI). The operator must also be able to notice the robot when the fault has been cleared and bin-picking task execution can be restored. For this purpose, the operator can use some buttons on the robot arm or on the HMI panel. However, to inform the robot that the fault has been cleared, the operator can also simply touch the robot, since cobots are able to detect contacts.
## Appendix A Sample Design and Preparation  
As shown in Figure 10, thin-walled longitudinal direction tube (LDT) geometries were designed to maintain nearly uniform stress in the test section while ensuring the ability to yield the specimen without torsional buckling. Machining of the tubular specimens was performed using the internal diameter as a reference to ensure minimum wall eccentricity in the test section (measured eccentricity is +/\(-\)0.001 mm). The inner and outer diameters of the sample were measured using (a) micro-computed tomography (XCT), and (b) a T-gage (Telescoping gage, amazon.com). For torsion experiments, each LDT specimen is 144.78 mm long with a uniform central section that is 24.13 mm long with 1.27 mm wall thickness; measurements show a variance of +/\(-\)0.001 mm for the wall thickness.  
For uniaxial tension loading, Figure 10,d show the two types of dog-bone specimens that were manufactured. The dog bone specimens are prepared using advanced pre-programmed software and code-controlled computerized numerical control (CNC) machining techniques. The tensile axis of the 50 mm long Longitudinal Direction Dog-bone (LDD) specimen shown in Figure 10 is oriented along the longitudinal direction of the MB bars. The tensile axes of the Radial Direction Dog-bone (RDD) specimens shown in Figure 10 are oriented radially at angles of \(0^{\circ}\), \(-45^{\circ}\), and \(90^{\circ}\) with respect to the arbitrary x-direction; the specimens are designated RDD0, RDD45 and RDD90, respectively. Due to size limitations imposed by the extruded bar diameter, the RDD specimens are relatively small, with a gage length of 10 mm.
# Data Embedding in SHVC Video Using Threshold-Controlled Block Splitting
## 4 Experimental Results
### Comparison with Conventional Methods
All in all, in comparison to AVC-based and HEVC-based methods, the achievable embedding capacity in scalable coded video by the proposed method is encouraging because the syntax elements in all coded layers can be exploited for data embedding purposes. Furthermore, some existing techniques can be combined with our work to achieve better trade-off among embedding capacity, quality, and bit rate. For example, the PB embedding technique can be applied to BL while the MVD, coefficient and merge mode can be applied to all layers to achieve a higher combined embedding capacity. This trade-off will be further investigated as part of our future work.
# Multi-Fusion Approach for Wood Microscopic Images Identification Based on Deep Transfer Learning
## 3 Construction of Wood Microscopic Images Identification Model
### Model Architecture  
Based on the traditional Faster RCNN, we use the above-mentioned receptive field SPP module and multi-scale fusion FPN module to replace the C5 and backbone modules in the traditional model. The specific construction process is shown in Figure 5:  
**Step1:** Resize a given input image to a size of \(600*600\), the purpose is to facilitate small batch training and Stochastic Gradient Descent (SGD) optimization;  
Figure 4: The Structure of Res-Net FPN.  
Figure 5: The framework diagram of the proposed approach.  
**Step2:** Extract image features at different levels through the backbone network ResNet from bottom to up;  
**Step3:** The feature map C5 extracted from the convolution block at the end passes through the receptive field enhancement module;  
**Step4:** The high-level feature map is sampled and merged with the horizontally connected low-level feature map, from top to down;  
**Step5:** After fusion, each level feature undergoes \(3*3\) convolution for information integration and transition, and input to the RPN network;  
**Step6:** Perform \(1*1\) convolution operation on the two branches to obtain the positive and negative category information of the anchor frame and rough regression;  
**Step7:** Perform Non-Maximum Suppression (NMS) according to classification confidence and eliminate redundant frames;  
**Step8:** Obtain the characteristics of the region of interest through ROI pooling;  
**Step9:** Perform candidate frame classification and fine prediction, after the two fully connected layers integrate the features.  
Where, _proposal_, _im_info_, _bbox_pred_, _cls_prob_ are the structure of Faster-RCNN where, _Proposal_ is the proposal regional suggestion Module, _im_info_ is the image information, _bbox_pred_ is the prediction box result, _cls_prob_ is the predict classification results  
A Channel Attention Module (CAM) is a module for channel-based attention mechanism in convolutional neural networks [18]. We produce a channel attention map by using the inter-channel relationship of features. As each channel of a feature map is considered as a feature detector, channel attention focuses on what is meaningful given an input image. To compute the channel attention, we squeeze the spatial dimension of the input feature map. We first aggregate spatial information of a feature map by using both average-pooling and max-pooling operations, generating two different spatial context descriptors (average-pooled features and max-pooled features). Both descriptors are then forwarded to a shared network to produce our channel attention map \(\text{M}_{\text{c}}\). Finally, a spatial attention module is used to refine the features.
###### Abstract  
Release-type communication is a common way to guide the public to reduce food waste. It is of great significance to clarify the intervention effect of release-type communication on residents' waste behavior and reveal the time evolution law of public response to reduce food waste. This study obtains 13,958 comments on food waste through big data mining and explores characteristics of public response to food waste behavior in different time and space from two dimensions of support intention and implementation intention. Through statistical analysis, empirical mode decomposition method, and cross-analysis, we find that: Public concern about release-type communication to stop food waste has experienced five stages: Incubation period, outbreak period, recession period, second outbreak period, and fading period. Overall, the support intention presents a down-up-down trend, the implementation intention rises and then descends, both appear inflection point in recession period. The trend term of empirical mode decomposition shows that the support intention goes down, the implementation intention goes up. Besides, the support intention and implementation intention of release-type communication to stop food waste are generally higher in western China. In particular, the support intention is significantly reduced in Beijing, Guangdong, and Fujian. The implementation intention is significantly increased in Shanghai, while it's reduced notably in Shandong. This study provides important implications for guiding the public to reduce food waste, the government should formulate differentiated governance strategies to guide the public to reduce food waste according to the time change characteristics and spatial territorial differences of the public on release-type communication.  
food waste; release-type communication; public response; spatiotemporal difference; Empirical Mode Decomposition (EMD) method +
Footnote †: journal: Applied Sciences