esa-sceva/satcom-chunk-collection huggingface dataset:
# User Acceptance of Information Feed Advertising: A Hybrid Method Based on SEM and QCA
## 6 Conclusions
### Research Implications  
The results of the study can offer some insightful implications for future research on information feed advertising. Although previous studies have studied information feed advertising [5; 14; 35], a few of them focused on the advertising effect of information feed in the mobile Internet environment. In this study, we defined a new variable set based on mobile Internet and information feed advertising features, which can reflect the consistency, accuracy, informativeness, sociality, advertising reward, and perceived advertising clustering of the information feed advertising effect. Regarding the research model, this study's unique contribution is that we propose to combine SEM and fsQCA to measure the impact of various factors on user acceptance of information feed advertising. Such a model can utilize the advantages of quantitative analysis and qualitative analysis, considering the impact of a single factor and revealing the impact of selective multiple factors. We believe that this idea can also be applied to other fields to deliver more convincing results.  
A critical finding of this study is that it is unnecessary to consider all influencing factors to advance information feed advertising. Through the fsQCA analysis, we also noted that any combination of the designed factors could emphasize some aspects but neglect other ones. To this end, enterprises can choose appropriate constructs according to their real demands and environments. For example, even when some factors have a low impact on a specific construct, users may also be willing to accept information feed advertising. In addition, although there is a significant negative influence factor of perceived advertising clustering, users will also have the willingness to accept information feed advertising when other influencing factors have a high impact. This is much important for enterprises to develop information feed advertising because not all of them can develop infrastructures or systems to emphasize every factor.
## Appendix C Transfer Matrix Coefficient for Piles in the Active Segment  
Rewriting Equation (28) into differential equation form, which reads  
\[\begin{split}\left\{\begin{array}{l}\frac{d\
u_{ai}}{d\omega_{ ai}}=\varphi_{ai}\\ \frac{d\varphi_{ai}}{d\omega_{ai}}=\frac{M_{ai}}{E\mathrm{I}_{a}}-\frac{b_{1}}{ \kappa A_{\rm c}\mathrm{C}_{\rm c}}\overline{k}_{ai}y_{ai}\\ \frac{dM_{ai}}{d\omega_{ai}}=\Big{(}1+\frac{\overline{R}_{ui}}{\kappa A_{\rm c }\mathrm{C}_{\rm c}}\Big{)}Q_{ai}-\left(\overline{N}_{ai}+\overline{R}_{vai} \right)\varphi_{ai}+\overline{N}_{ai}\varphi_{IN}\\ \frac{d\Omega_{ai}}{d\omega_{ai}}=-b_{1}\overline{k}_{ai}y_{ai}\end{array}\right.\end{split}\] (A34)  
where \(y_{ai}\), \(\varphi_{ai}\), \(M_{ai}\), \(Q_{ai}\), \(N_{ai}\), \(z_{ai}\) are, respectively, the horizontal deformation, axis rotation angle, bending moment, shear force and depth at the bottom of the \(i\)th discrete active segment for the pile shaft.  
Equation (A34) can be simplified as the following matrix function:  
\[\frac{dS_{ai}}{dz_{ai}}=A_{ai}S_{ai}+f_{ai}\] (A35)  
In which \(S_{ai}\), \(A_{ai}\) and \(f_{ai}\) are matrix variables as follows:  
\[S_{ai}=\begin{bmatrix}y_{ai}&\varphi_{ai}&M_{ai}&Q_{ai}\end{bmatrix}^{T}\] (A36)  
\[A_{pi}=\begin{bmatrix}0&1&0&0\\ -\frac{b_{1}\overline{k}_{ai}}{\kappa A_{\rm c}\mathrm{C}_{\rm c}}&0&1/EI_{a}&0\\ 0&-\big{(}\overline{N}_{ai}+\overline{R}_{vai}\big{)}&0&1+\frac{\overline{R}_{ vai}}{\kappa A_{\rm c}\mathrm{C}_{\rm c}}\\ -b_{1}\overline{k}_{ai}&0&0&0\end{bmatrix}\] (A37)  
\[f_{ai}=\begin{bmatrix}0&0&\overline{N}_{ai}\varphi_{IN}&0\end{bmatrix}^{T}\] (A38)  
By assuming \(L[S_{ai}(z_{ai})]=F_{ai}(s_{ai})\) and \(L[f_{ai}(z_{ai})]=g_{ai}(s_{ai})\) (where the symbol \(L\) represents the Laplace transformation; \(F_{ai}\) and \(g_{ai}\) are the Laplace functions; and \(s_{ai}\) is the corresponding variable of Laplace function), Equation (A46) can be converted to a Laplace function expressed as:  
\[F_{ai}(z_{ai})=(I\times s_{ai}-A_{ai})^{-1}S_{a(i-1)}+(I\times s_{ai}-A_{ai})^{- 1}g_{ai}(s_{ai})\] (A39)  
In which \(g_{ai}(s_{ai})\) is given by  
\[F_{ai}(z_{ai})=(I\times s_{ai}-A_{ai})^{-1}S_{a(i-1)}+(I\times s_{ai}-A_{ai})^{- 1}g_{ai}(s_{ai})\] (A40)  
\[g_{ai}(s_{ai})=\begin{bmatrix}0&0&\frac{\overline{N}_{ai}\varphi_{IN}}{s_{ai}}&0 \end{bmatrix}^{T}\] (A41)The Laplace inverse transformation of Equation (A40) is given by  
\[S_{ai}(z_{ai})=L^{-1}\Big{[}(I\times s_{ai}-A_{ai})^{-1}\Big{]}S_{a(i-1)}+L^{-1} \Big{[}(I\times s_{ai}-A_{ai})^{-1}g_{ai}(s_{ai})\Big{]}\] (A42)  
Assuming \(T_{ai}=\overline{N}_{ai}-\overline{R}_{\text{vai}}\), \(D_{ai}=T_{ai}+\Big{(}b_{1}\overline{k}_{ai}EI_{a}/\kappa A_{c}G_{c}\Big{)}\)
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 5 Protocol Stack
### Link Layer
#### 5.2.2 Wakeup Patterns  
A wakeup pattern is a set of instructions generated by the GW that define the wakeup plan of its associated STAs over time periods. With the goal of minimizing the time STAs remain active (and, consequently, their energy consumption), two different wakeup patterns controlled by the GW are proposed according to the network's traffic flow [37].  
The _periodic wakeup pattern_ is suitable for listening to broadcast downlink communications from the GW, as it makes all STAs wake up at the same time. On the other hand, uplink communications follow a _staggered wakeup pattern_, which allocates different active periods (also called _ring slots_, with  
Figure 4: HARE beaconing system consisting of _network association primary beacons_, data primary beacons_, and _secondary beacons_.  
Figure 3: MAC sublayer consisting of TDMA slots and CSMA/CA technique inside them.  
\(T_{r}\) duration) to nodes belonging to adjacent rings with partial overlapping (as shown in Figure 5). Apart from reducing time STAs are awake during uplink communications, this method facilitates the implementation of data aggregation mechanisms.  
Even though STAs have predetermined active periods, they can go to sleep even earlier in the transmitting (TX) time period if their parent has acknowledged all their data, or in the receiving (RX) time period after having received all data from their children.
# An Initial Case Study of the Computational Modeling of Cord-Rubber Segments
## 2 Materials and Methods
### Theoretical Formulations
#### 2.1.2 Hyperelastic Materials Description  
An elastomer is categorized as a hyperelastic material. These materials exhibit substantial, reversible deformations equating to percentages in the double digits, even when subjected to low loads [24]. As a result, their stress-strain behavior can no longer be described by the linear Hooke's law. A material classified as hyperelastic is characterized by an elastic potential function \(W\) (or strain-energy density function) that depends solely on one of the strain or deformation tensors. The derivative of this function to the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Material** \\ \hline Bushing & Low-alloyed steel \\ Rubber & Natural rubber NR65 \\ Cord & Polyamide 6.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Materials of the cord–rubber segment components.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Material & Polyamide 6.6 \\ Construction & 940 \(\times\) 1 \(\times\) 2 4 tex \\ Number of filaments & 140 \\ Yamt twist & Z 350 t/m \\ Maximum tensile force (MTF) & 150 N \\ Elongation at MTF & 23\% \\ Diameter & 0.57 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the cord.  
Figure 2: Dimensions of the cord–rubber segment.  
associated strain component determines the relevant stress response. This relationship is mathematically expressed as follows:  
\[S_{ij}=\frac{\partial W}{\partial E_{ij}}\equiv 2\frac{\partial W}{\partial C_{ij}} \tag{1}\]  
where \(S_{ij}\)--components of the second Piola-Kirchhoff stress tensor, \(W\)--strain-energy density function, \(E_{ij}\)--components of the Lagrangian strain tensor, \(C_{ij}\)--components of the right Cauchy-Green deformation tensor.  
According to what constitutes their basis, there are two types of constitutive hyperelastic models. The first type--structural models--are based on micromechanical models of the internal structure of the elastomer, and their material parameters have a clearly defined physical meaning. Their representatives include Neo-Hookean and Arruda-Boyce models. The second type--phenomenological models--are based on a mathematical description of the stress-strain behavior of the elastomer. The material parameters of these models generally do not have a clearly defined physical meaning. These include models such as polynomial, Mooney-Rivlin, Yeoh and Ogden models. Two of the models used in this work are expressed as follows:
## 3 Results  
### The Temporal and Spatial Variation Trend of GPP  
Linear regression analysis of GPP trends in Xinjiang during 2000-2020 revealed distinct spatiotemporal variations in GPP across the region (Figure 2a). Spatially, a predominant portion of the area (comprising 58.75%) experienced a significant GPP increase. Areas exhibiting an extremely significant increase (making up 41.21%) were primarily clustered in the mid-section of the northern Tianshan Mountains slopes and the western foothills of the Kunlun Mountains. Conversely, regions showing a decline in GPP (constituting 6.38%) were sporadically distributed within the Iii River Valley and parts of the Junggar Basin.  
On an interannual scale, the mean GPP for Xinjiang demonstrated a statistically highly significant increasing trend over the 2000-2020 period (\(p<0.001\), Figure 2b). Specifically, the period from 2000 to 2010 showed relatively moderate fluctuations, whereas after 2010, the rate of GPP increase accelerated and exhibited greater interannual variability, with GPP peaking in 2016 (325.70 gC/m\({}^{2}\)/year).
## 2 System Model
### Receiver  
For the receiving end, each user is equipped with a PD, which converts the detected photons into photo-electrons through the photoelectric effect and then triggers the built-in counter to count these photo-electrons. This process is described by the PCP. Within the symbol duration \(\tau\), the electron counts received by user \(k\), denoted as \(y_{k}\), provide probabilistic measurements of the received photon counts [37]. The PDF of \(y_{k}\) is given by  
\[\Pr(y_{k}=r|\mathbf{u})=\frac{(\lambda_{k})^{y_{k}}}{y_{k}!}\exp(-\lambda_{k}), \tag{15}\]  
where \(r=0,1,\cdots,\infty\) is a non-negative integer representing the possible values of the received count, and \(\lambda_{k}\) denotes the expected number of signal photons received by the \(k\)-th user, as given by  
\[\lambda_{k}=\mathbb{E}(y_{k}|\mathbf{u})=\eta\mathbf{g}_{k}^{T}(\mathbf{W} \mathbf{u}+\mathbf{D})+n_{b}. \tag{16}\]  
Here, \(\eta\) is the PD efficiency and \(n_{b}=\eta\frac{p_{b}\tau}{hw}\) represents the average number of photons generated via background radiation during each symbol duration, where \(P_{b}\) denotes the incident background power. The expected number of received photons can be divided into two parts:  
\[\mathbb{E}(y_{k}|\mathbf{u})=\underbrace{\eta\mathbf{g}_{k}^{T}\mathbf{W} \mathbf{u}}_{\text{expected signal of user $k$}}+\underbrace{\eta\mathbf{g}_{k}^{T}\mathbf{D}+n_{b}}_{\text{ expected extraneous counts for user $k$}}. \tag{17}\]For convenience in subsequent representations, we define \(\gamma_{k}=\eta\mathbf{g}_{k}^{\mathsf{T}}\mathbf{D}+n_{b}\) as the extraneous counts.
## 2 Materials and Methods
### Genotypic Characterization of Amoeba Strain  
For the extraction of nuclear DNA, trophozoites were harvested by centrifugation at 2500 rpm for 5 min. After centrifugation, the supernatant was aspirated, and the pellet was resuspended twice in 25 mL of phosphate-buffered saline. Subsequently, the pellet was resuspended in lysis buffer (10 mM Tris HCl, pH 8.5, 5 mM EDTA, 200 mM NaCl, 0.2% SDS) and incubated at 60 \({}^{\circ}\)C for 5 min. Then, 2.5 \(\upmu\)L of proteinase K and 5 mL of RNASE (1 mg/mL) were added and incubated at 60 \({}^{\circ}\)C for 1 h. After incubation, 250 \(\upmu\)L of NaCl (5 M) was added and incubated on ice for 5 min then centrifuged at 2500 rpm for 15 min. The supernatant was transferred into a new tube and an equivalent volume of isopropanol was added. Following incubation at \(-\)20 \({}^{\circ}\)C for 60 min, the DNA pellet was washed with 500 \(\upmu\)L of ethanol (70%). Ethanol was carefully removed using a micropipette, and after the pellet was dried, 200 \(\upmu\)L tris-EDTA (TE) buffer (pH 8.0) was added (all reagents were purchased from Sigma-Aldrich, Milan, Italy).  
DNA was subjected to PCR aiming for the specific recognition of 18S rDNA from amoebae of the genus _Acanthamoeba_ using the primers JDP1 (5\({}^{\prime}\)-GGC CCA GAT CGT TTA CCG TGA A-3\({}^{\prime}\)) and JDP2 (5\({}^{\prime}\)-TCT CAC AAG CTG CTA GGG GAG TCA-3\({}^{\prime}\)).  
The following DNA thermal cycler was performed for 42 cycles: 95 \({}^{\circ}\)C for 7 min, then 95 \({}^{\circ}\)C for 1 min, 55 \({}^{\circ}\)C for 1 min, and 72 \({}^{\circ}\)C for 2 min, followed by 40 cycles of 72 \({}^{\circ}\)C for 10 min, as described earlier [25].  
The DNA extraction product was evaluated by electrophoresis on a 1% agarose gel, to determine the presence of DNA.  
The QIAquick PCR Purification Kit (Qiagen, Milan, Italy) was employed to purify positive amplicons from PCR, to recover approximately 95% of clean DNA up to 10 kb (as per the manufacturer's instructions). After purification, the amplicons were sequenced by the ABI PRISM 3130 XL Genetic Analyzer (Applied Biosystem, Milan, Italy) using amplification primers. The BLAST (version 1.30) program of the National Center for Biotechnology Information (www.ncbi.nlm.nih.gov), available online, was used to perform sequence alignment and analysis.  
A matrix-assisted laser desorption ionization (MALDI) time-of-flight mass spectrometer (TOF/MS) (bioMerieux, Craponne, France) was employed to obtain further confirmation of the strain's affiliation to the _Acanthamoeba_ genus [26]. The MALDI-TOF MS compares spectra produced by intact cells against a database of unique, conserved peaks that are used to identify the species.
## 4 Convolutional Neural Network (CNN): Modeling and Training
### U-Net: Fully Convolutional Neural Network  
Convolutional neural networks have existed for a long time and typically are used in developing classification tasks. They have a single class as the output. However, in many visual tasks, such as biomedical ones and those for autonomous vehicles, the output must be an image, where each pixel is classified in a particular class.  
Thus, to modify and extend the architecture of the convolutional network to work with a smaller number of images and to perform more accurate image segmentation, U-Net was developed, as shown in Figure 4.  
Figure 4: U-Net: fully convolutional network.  
Figure 3: Overview of antenna placement optimization with the machine learning model U-Net to predict the radiation pattern.  
U-Net has a simple architecture and good performance. It is trained using only convolutions, which can be separated into two paths. The first (left side) is the contraction path (also called encoder) that is used to capture the context of the image. The encoder is made up of convolution layers with a 3 \(\times\) 3 kernel filter, followed by the application of the rectified linear unit (ReLU) activation function, and at the end of each step, a 2 \(\times\) 2 max pooling operation with stride = 2 is performed to reduce by half the size of the image.  
Convolution operations may vary depending on the padding and configuration received. The'stride' is the number of pixels (steps) the kernel advances and is defined as two. As the the resulting image of a convolution can be smaller than the input image size, a padding ='same' is used to fill the original input before wrapping it so that the output's size is the same as that of the input. Padding can be seen as incrementing the number of pixels added to the edges of the image, as to not lose edge detail.  
The second (right side) path is the symmetrical expansion path (also called decoder), which is usually used to find the precise location of an object by using transposed convolutions. It consists of four stages with two types of operations. The first is upsampling, which doubles the size of the image, and the second is upconvolution, which reduces the size of the feature channels by half, the opposite process of convolution.  
Due to the loss of information of the border pixels in each layer, a copy of the feature maps is made from left to right. This copy is concatenated with the feature maps resulting from the up-convolution and then undergoes two 3 \(\times\) 3 convolutions, followed by the ReLU activation function and then an up-sampling. Finally, to assign each characteristic vector to the desired number of classes, a 1 \(\times\) 1 convolution is performed on the last layer.  
Therefore, U-Net is a fully convolutional network end to end, containing only convolutional layers and no dense layer, which is why it is able to accept images of any size.
## 3 Materials and Methods
### Software Features and Algorithm
#### 3.1.5 Post-Processing  
In post-processing (Figure 1; step 1.5), morphological operations are applied to eliminate residues. First, dilation is used to fill parts that belong to the same individual but are detected separately. Second, the gradient is calculated and subtracted from the expanded frame to eliminate undesirable edges. Finally, erosion is applied to eliminate any noise erroneously detected as individuals.
# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
Estimating and using the degradation representations in this way has two advantages: (1) there is no need to model the complex degradation process in the real blurred image; and (2) the degradation representations estimated in a learning way can be adapted to the blurring in different images. * In terms of network structure, in order to fully utilize the degradation representations, we designed a multi-scale degradation representation fusion module, which is integrated into the reblurring subnetwork and deblurring subnetwork, and is used both for training and testing. We also conduct an ablation study to demonstrate the effectiveness of implicit representation estimation. Our results show that our network achieves stable and efficient outcomes on multiple datasets.
## 2 Modeling Theory and Methods
### Model Evaluation  
The evaluation of the model is mainly based on the stability and reliability of the model [49]. In this paper, the evaluation indices of prediction accuracy including the absolute average relative deviation (\(AARD\)) and the root mean square error of prediction (\(RMSEP\)) are defined as follows:  
\[AARD=\frac{1}{N}\sum_{i=1}^{N}\frac{|\overline{y}_{i}-y_{i}|}{y_{i}} \tag{6}\]  
\[RMSEP=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\overline{y}_{i})^{2}} \tag{7}\]  
The squared correlation coefficient (\(R^{2}\)) reflects the correlation between predicted values and experimental values and is defined as follows:\[R^{2}=\frac{\left[\sum\limits_{i=1}^{N}(y_{i}-y_{ave})(\overline{y}_{i}-\overline{ y}_{ave})\right]^{2}}{\sum\limits_{i=1}^{N}(y_{i}-y_{ave})^{2}\sum\limits_{i=1}^{N}( \overline{y}_{i}-\overline{y}_{ave})^{2}} \tag{8}\]  
In these formulas, \(N\) is the number of samples; \(y_{i}\) is the predicted or calculated value of the model; \(y_{i}\) is the actual value obtained in experiments; \(y_{ave}\) is the average of actual values of the samples; and \(\overline{y}_{ave}\) is the average of predicted values.
## 2 Proposed Method
### The Proposed Model
#### 2.2.2 Objective Function  
A fake image generated from the input vector \(z_{1},\ z_{2}\) can be expressed as \(G(z_{1},\ z_{2})\). The goal of the discriminator is to distinguish real image \(x\) from fake image \(G(z_{1},\ z_{2})\). Therefore, the discriminator attempts to reconstruct \(x\) only, not \(G(z_{1},\ z_{2})\). On the other hand, the generator attempts to produce an image that can be reconstructed well by the discriminator. As a result of the adversarial training of the generator and the discriminator, the output images from the generator become more realistic to deceive the discriminator. In other words, the generator is trained to reduce the Wasserstein distance between the loss distributions of real and fake samples in the auto-encoder. The adversarial loss of the generator and discriminator can be expressed as  
\[\begin{split} L_{D}=L(x;\theta_{D})-k_{l}L(G(z_{D};\theta_{G}); \theta_{D}),\\ k_{l+1}=k_{l}-\lambda_{k}(\gamma L(x)-L(G(z_{G}))\ \text{for each step }t, \end{split} \tag{3}\]  
and  
\[L_{G}=L(G(z_{D};\theta_{G});\theta_{D}), \tag{4}\]  
Figure 1: Overview of the proposed model, consisting of two networks; generator and discriminator. The generator takes two input vectors and generates a fake image. The discriminator takes a real or fake image and it is learned to reconstruct only real sample (Top). There are AdaIN layers which stylize feature maps with transformed input vector after convolutional layer in each block of the generator (Left bottom). The encoder down-samples input image to two latent vectors with convolutional layers and down sampling layers (Right bottom).  
where \(L(\cdot)\) denotes the \(L_{1}\) loss from the auto-encoder, and \(k_{t}\) is the parameter that controls the proportion of generator and discriminator loss introduced in BEGAN. This is required because the discriminator cannot achieve a suitable reconstruction quality at the beginning of training. At this time, \(k_{t}\) has a value close to zero and gradually increases as training progresses.  
As mentioned in Section 2.2.1, \(z_{1}\) and \(z_{2}\) are both involved in the generation of different scale-specific areas. To apply this principle to the decoder, we add a novel constraint on the encoded latent vectors, referred to as double-constraint loss. It includes the difference between the input vector and the encoded vector as defined by  
\[L_{dc}=\|z_{1}-z_{1}^{*}\|_{1}+\|z_{2}-z_{2}^{*}\|_{1}, \tag{5}\] \[[z_{1}^{*}z_{2}^{*}]=Enc(G(z_{1},\,z_{2})),\]  
where \(Enc(\cdot)\) denotes the output of the encoder. The double-constraint loss is designed to stabilize training because the inputs of the generator and decoder would be similar. It can also be extended to the synthesis of existing images because real samples are mapped to a space similar to the latent space of the input. Hence, the generator loss can be modified as  
\[L_{G}=L(G(z_{D};\theta_{G});\theta_{D})+\alpha\cdot L_{dc}, \tag{6}\]  
where hyperparameter \(\alpha\) represents a weighting factor for the double-constraint loss.