esa-sceva/satcom-chunk-collection huggingface dataset:
# SS3 DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
## 2 Related Work
### Conventional Techniques  
Conventional 3D reconstruction methods rely on geometric principles like triangulation and epipolar geometry to estimate depth from multiple images [7; 8]. The triangulation process estimates the depth of a point in space by calculating the intersection of the lines projected from different camera views. This process needs to know the relative location/pose of the cameras. Epipolar geometry makes it easy for this case because a point in one image corresponds to a line (the epipolar line) in the other image. Although these techniques yield accurate results in ideal conditions, they are computationally intensive and require thorough camera setup calibration [1].
## 3 Simulation Results and Analyses  
To verify and test the performance of the proposed calibration technique, the simulations of a four-channel and an eight-channel TIADC are presented in this section, respectively. We used MATLAB to model our proposed algorithm and acquired non-calibrated data. In order to verify the effectiveness of the algorithm, we also provide many different simulation cases. All the simulations were performed in MATLAB with the optimization of the fixed-point coding and all the data were processed with rounding. We also adopted the ASIC design flow to evaluate the hardware resource cost.
# Analysis of Nanoindentation Test Results of Asphalt Mixture with Different Gradations
## 3 Results and Discussion  
### Analysis of the Three Gradations  
Figure 6 shows the scanned images of the three asphalt mixtures in cross-section. The red circles indicate the selected test interface. As can be seen from Figure 6, the continuous gradation asphalt mixture contains a large number of fine aggregate particles. Its aggregate-asphal mastic interface is more complex and unclear, which causes difficulties in selecting the measurement area and subsequent tests. Moreover, the gap-gradation asphalt mixture has a clear and flat aggregate-asphal mastic interface suitable for nanoindentation. Although the open-graded asphalt mixture has a clear and flat aggregate-mastic interface, there are more holes at the interface, which affects the selection of the test area.  
Figure 4: Typical aggregate-asphal mastic interface under optical microscope.  
Figure 5: Microscopic images of the nanoindentation test area of asphalt mastic samples.  
The load-depth curves obtained for the nanoindentation specimens of the three kinds of asphalt mixture are shown in Figure 7. The results of the nanoindentation modulus after removing the outliers are shown in Table 3. As shown in Figure 7 and Table 3, the load-depth curves of continuously graded asphalt mixture have some dispersion in the maximum depth and curve form, and the red part of the curve is the abnormal curve. After removing the outliers, the mean values of moduli have high coefficients of variation, which may be due to the distribution of some of the measurement points near the fine aggregate particles. The maximum depth and modulus values of the load-depth curves of gap-graded and open-graded asphalt mixtures are less discrete. The maximum depth dispersion of gap-graded asphalt mixtures is less than that of open-graded asphalt mixtures.  
It is worth mentioning that the nanoindentation modulus of dense-graded asphalt mixture is much higher than that of gap-graded and open-graded asphalt mixtures. This may be attributed to the dense-graded asphalt mixture contains a large amount of fine aggregate and mineral filler particles, which produce a stronger interaction (adhesion) with asphalt. Another possible reason could be that there are fine aggregate particles below the asphalt music phase, which affects the nanoindentation test results.  
In conclusion, gap gradation and open gradation are significantly more suitable for nanoindentation test studies on asphalt mixtures, both in test operation and characterization of the mechanical properties of the asphalt music.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gradation** & **Modulus/GPa** & **Coefficient of Variation/\%** \\ \hline AC-13 & 0.576 & 31.8\% \\ SMA-13 & 0.028 & 16.3\% \\ OGFC & 0.014 & 11.03\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Nanoindentation modulus of different gradation asphalt mixtures.  
Figure 6: Asphal mixture pictures.  
Figure 7: Nanoindentation load–depth curves of asphalt mixture with different gradations.
# Feature-Interaction-Enhanced Sequential Transformer for Click-Through Rate Prediction
## 4 Experiments
### Hyperparameter Tuning
#### 4.4.2 Effect of Feature Interaction Depth  
Feature interaction stacks multiple layers to change the layer depth and learn high-order feature interactions. In our proposed model, we focus on how the input feature interaction layer depth and the target item feature interaction layer depth affect the performance.  
The experiment results are concluded in Table 11. We observe that the model performs the best in terms of both the AUC and Logloss when both the input feature interaction layer depth and the target item feature interaction layer depth are two. Therefore, both the input feature interaction layers and the target item feature interaction layers are necessary. When the interacting layer depth is lower than two, the feature interaction can only extract low-order simple feature interactions, and the model performance is limited. When the interacting layer depth is three, the feature interaction can extract as high as eight-order fea  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Datasets** & **Evaluation** & **FESeq** & **FESeq w/o Scale** \\ \hline Ele.me & AUC & **0.6127** & 0.5926 \\  & Logloss & **0.0918** & 0.0926 \\  & AUC & **0.8912** & 0.8874 \\  & Logloss & 0.0117 & **0.0109** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Ablation studies of models with and without scale factor in sequence pooling layer. The bold number denotes the best value in the column.  
Figure 4: Effect of the embedding dimension on AUC and Logloss.  
ture interactions, but the recommendation performance tends to decrease, which indicates that extremely high-order feature combinations are not useful for CTR prediction.
## 2 Materials and Methods
### Model
#### 2.2.1 Encoder with Multidimensional Spatial Attentions  
The encoder is essentially an RNN that encodes the input sequences into a feature representation in machine translation [19]. An RNN is a neural network consisting of a hidden state \(h\) and an optional output which operates on a variable-length sequence \(X=(x_{1},x_{2},\ldots x_{T})\)_with_\(x_{t}\in R^{n}\), where \(n\) is the number of other features. At each time step \(t\), the hidden state \(h_{t}\) of the RNN is updated by  
\[h_{t}=\ f_{1}(h_{t-1},x_{t}), \tag{4}\]  
where \(f_{1}\) is a non-linear activation function, \(f_{1}\) can be an element-wise logistic sigmoid function and also can be a long short-term memory (LSTM) unit [10]. In this paper, we use an LSTM unit as \(f_{1}\) to catch long-term dependencies.  
One important property of human perception is that one does not tend to process a whole scene in its entirety at once [20]. Instead humans selectively focus attention on different parts of the visual space to acquire information at the required time and place. Inspired by human beings focusing on the selected parts of attention to obtain the information what they need, we propose a multidimensional spatial attention-based encoder that can adaptively select the relevant features time series. Given the \(k\)-th input feature time series \(x^{k}=(x^{k}_{1},x^{k}_{2},\ldots x^{k}_{T})^{T}\in R^{T}\), we employ the multidimensional spatial attention mechanism to adaptively capture the correlation between the Chl-a time series and other features input sequences with:  
\[e^{k}_{t}=\ v^{T}_{l}tanh(W_{l}[h_{t-1};c_{t-1}]+U_{l}x^{k}), \tag{5}\]  
\[\alpha^{k}_{t}=\frac{exp(e^{k}_{t})}{\sum_{i=1}^{n}exp(e^{i}_{t})}, \tag{6}\]  
where [.;.] is a concentration operation, \(v_{l}\in R^{T}\), \(W_{l}\in R^{T\times 2m}\), and \(U_{l}\in R^{T\times T}\) are parameters to learn, \(h_{t-1}\) is the previous hidden state and \(c_{t-1}\) is the memory cell state in the encoder LSTM unit. The spatial attention weight can be calculated by Equations (5) and (6) [16]. \(\alpha^{k}_{t}\) is the attention weight measuring the importance of the \(k\)-th input feature at time step \(t\). Once we obtain the attention weights, we can update the input feature sequences and the hidden state at time \(t\) as the following:  
Figure 1: DA-RNN networks architecture.  
\[\widetilde{x}_{t}=(\alpha_{t}^{1}x_{t}^{1},\alpha_{t}^{2}x_{t}^{2},\ldots, \alpha_{t}^{n}x_{t}^{n})^{T}, \tag{7}\]  
\[h_{t}=\ f_{1}(h_{t-1},\widetilde{x}_{t}), \tag{8}\]  
where \(f_{1}\) is still an LSTM unit, and \(\widetilde{x}_{t}\) is the newly input feature sequences. With this spatial attention mechanism, the encoder can selectively capture the dynamic correlation between the Chl-a and other features rather than treating all the input features equally.
# Entropy and Statistical Complexity in Bioelectrical Signals: A Literature Review
## 3 Results
### Complexity
The magnitude of information required to characterize a system defines its complexity, reflecting its level of disorder, and increases as the system exhibits greater uncertainty [10; 14]. Complexity manifests in the description of a system or model's behavior, where multiple components interact in diverse ways, producing intricate patterns that are challenging to explain due to their high complexity and detail [60]. This concept also encompasses the system's adaptability and functionality in changing environments, representing the significant structural richness that enables it to adjust to variable conditions [65]. The degree of organization, memory, structure, and symmetry within a system determines its complexity [1]. Complex patterns are often challenging to identify by examining individual components, highlighting the overall complexity of the system. The complexity of a system can manifest in specific regimes [60]. A system is considered complex if it exhibits nonlinear, non-stationary, time-irreversible dynamics with fluctuations across multiple time scales [14]. On the other hand, the complexity of a system can be understood as a balance between order and disorder or between regularity and irregularity [26]. A system typically exhibits lower complexity when its entropy is either very high or very low, indicating random order and regular order, respectively. However, an intermediate system demonstrates higher complexity by contrast. Complexity measures are used not only to evaluate the level of randomness in a system but also to assess its degrees of freedom; even in a nonlinear system, complex behavior may arise with a limited number of degrees of freedom [66]. From the perspective of the disorder function, which considers the system's distance from its equilibrium state, complexity increases monotonically with the degree of disorder [10]. In contrast, regular signals are more predictable and uniform and contain less information, making them easier to describe [14; 63]. On the other hand, irregularity, characterized by a less predictable and more varied structure, can lead to high entropy, representing pure chaos. However, despite high irregularity, the essential information required to characterize the investigated system is relatively low, resulting in reduced complexity [26; 63]. This perspective emphasizes that both complete order and pure chaos decrease a system's complexity, as both states are easier to describe [10; 14].
## 3 Results and Discussion
### Combinations of TPZs with Conventional Antibiotics
coli_ strain (with FICI of \(\sim\)0.56; Figure 2D1). The less pronounced partial synergistic effects were defined for di-\(N\)-oxides in combination with the 1/2 MIC concentration of Cipro (FICI of 0.56-0.74), with the highest (16-fold) reduction in MIC of triapazamine (**1**) against the _E. coli_ strain (FICI = 0.56; Figure 2A1) and the lower (eight-fold) reduction in MICs of TPZ (**1**) against _S. enterica_ and _S. aureus_ (FICI \(\sim\) 0.62-0.63; Figure 2A2,A3), and the same (eight-fold) decrease in MICs of the lead compound (**2**) against the _S. aureus_ strain. Meanwhile, the same combination assay carried out against the _S. enterica_ strain over-expressing AcrAB-TolC revealed only additive and non-interactive effects (FICI of 1.0-1.5) (Table 3); the additive effects have been observed for the lead compound (**2**) coupled with the 1/2 MIC concentrations of either Cipro or NFT (FICI \(\sim\) 1; Figure 2B4,D4). Overall, the results obtained from the combined _in vitro_ trial provided some evidence that di-N-oxides coupled with the commonly utilized antibiotics Cipro and NFT could generate enhanced (partial and close to complete synergistic) and additive effects. These findings suggest that TPZs may become valuable antibiotic complements and could be considered as a new choice for the treatment of infectious diseases. The strongest effects were achieved for TPZs coupled with NFT. One of the plausible modes of their action is highly likely to be related to their reductive activation, i.e., single-electron reduction to the free anion radical species accompanied by ROS generation; NFT may undergo a further reduction process through the sequential single- and/or two-electron reduction pathway to its highly reactive nitroso- and/or hydroxylamine intermediate species ([52; 53; 55], and refs. therein), whilst the anion radical species of TPZs may experience rapid intra-molecular transformation into highly reactive benzotriazinyl radical species ([37], and refs. therein). Hence, on the whole, the production of the reductive intermediates of the compounds along with the amplified ROS generation could be one of the main causes of the multi-factorial antibacterial effects involving multiple cellular targets. Meanwhile, the TPZs coupled with Cipro may exert their inhibitory potencies through at least two major mechanisms, i.e., the inhibition of DNA synthesis by interactions of Cipro with topoisomerase II (DNA gyrase) and topoisomerase IV ([33; 54], and refs. therein), along with the reductive activation of TPZs with ROS generation. In addition, one may note that the antibacterial action of Cipro can also induce the production of ROS ([33; 60], and refs. therein). A more detailed study needs to be done in order to examine the mode of actions underlying the interactions between the TPZs and antibiotics identified in the current work, which will be one of the subjects of our upcoming research.
# Solar Hybrid Micro Gas Turbine Based on Turbocharger
## 4 Discussion
As this configuration has not related operational cost to consumed fuel, but the cost of electricity is mainly affected by the investment cost, the breakdown of LEC value is performed in order to point out the components that have the highest effect on LEC. Cost components of LEC are shown in Figure 14. The solar part, specifically the mirrors, is the main cost contributor. As the turbocharger is a low cost component, it represents a minute contribution to the overall cost (this should be compared with other estimates where power generation turbines have been used and the contribution to the total cost could be as high as 50% [25]). The dominating role of solar components on LEC, for a solar MGT, is also mentioned in the literature [17]. Configurations running solely on solar energy are capital cost intensive, the solar components being the main cost contributors [36]. As long as the cost of such components remains high, the low cost T/C is not a significant cost contributor, even though it is of lower cost than micro gas turbine. If the solar components cost reduces, however, then the relative importance of the turbine cost will be higher and the low cost T/C solution is expected to offer an advantage over more expensive micro gas turbine arrangements. A reduction of the cost of solar parts would have a strong effect on the overall LEC. This reduction could be achieved when such configurations are in commercial stage and the needed components are mass-produced. In order to estimate the effect of investment cost reduction on LEC, the value of LEC is computed for various reduction rates of investment cost of solar components (mirrors and receiver). The decrease of LEC in function of the reduction of the solar components cost reduction, for the case of TIT = 800 \({}^{\circ}\)C, is shown in Figure 9:0 Mirror cost reduction has a more pronounced effect on LEC, as it is its main cost component. Significant reduction of solar components cost, such as those that the photovoltaics market has experienced, could make this option competitive to conventional electricity sources. Figure 9:1 Influence of changes in size of the heliostat field on electricity cost.
## 4 Multiple Non-Linear Regression Analysis of Strength  
### Strength Modeling  
Through the principle of least squares, the regression analysis can provide insight into the interactions between the 3 d, 7 d, and 28 d compressive strengths and multiple factors to better understand and predict these data. For this purpose, these data can be analyzed using the non-linear regression function in MATLAB to better predict and analyze the results. The strength model was developed by fitting a multiple non-linear regression with the slurry mass fraction and cement content as independent variables and the UCS at different days as the objective function (S).  
Based on the analysis of the influencing factors of strength, the factors affecting the strength of the cemented backfill are the cement-sand ratio, the water-cement ratio, and the concentration of the slurry. Therefore, according to the strength variation law due to the influencing factors, combined with the previous research results, the following model of the strength of the colloidal body is proposed:  
\[S=\mathrm{b}_{0}+\mathrm{b}_{1}e^{\mathrm{b}_{2}x_{1}}+\mathrm{b}_{3}x_{2}{}^ {-\mathrm{b}_{4}}+\mathrm{b}_{5}\sum_{i=1\atop j=1}^{2}a_{i}x_{i}x_{j} \tag{1}\]  
where  
\(S\): Compressive strength of the cement (MPa);  
\(\mathrm{x}_{1}\): Concentration of the slurry (%);  
\(\mathrm{x}_{2}\): Water-cement ratio;  
\(\mathrm{b}_{\mathrm{k}}\): Regression coefficient, \(k\) = (0, 1, 2, 3, 4, 5);  
\(\mathrm{a}_{\mathrm{i}}\): Polynomial coefficient to be determined (i = 1, 2).  
This model enables a comprehensive assessment of the effect of materials, water, and other factors on the strength of the structure, allowing for better regression analysis.
## Materials and Methods
### Variational Mode Decomposition (VMD)
#### 2.1.3 VMD Algorithm Flow  
* **Step 1**: Initialize parameters \(\{u_{k}^{1}\}\), \(\{\omega_{k}^{1}\}\), \(\hat{\theta}^{1}\) and \(n\). Set the number of iterations to be \(n\) to \(1\).
* **Step 2**: Update \(u_{k}\) and \(\omega_{k}\) according to Equations (7) and (8).  
* **Step 3**: Update \(\theta\) via \[\hat{\theta}^{n+1}(\omega)\leftarrow\hat{\theta}^{n}(\omega)+\tau\Bigg{[}\hat{f} (\omega)-\sum\limits_{k=1}^{K}\hat{u}_{k}^{n+1}(\omega)\Bigg{]}.\] (9)
* **Step 4**: If the discrimination precision \(e>0\) and \(\sum\limits_{k=1}^{K}\frac{\|\hat{u}_{k}^{n+1}-\hat{u}_{k}^{n}\|_{2}^{2}}{\| \hat{u}_{k}^{n}\|_{2}^{2}}<e\) are satisfied, the iteration ends and **Step 2** is returned.
* **Step 5**: Obtain the corresponding modal subsequences based on the given mode number.
###### Abstract
It groups with different social relationship strength to aggregate each group member's location preference. However, more relevant information should be taken into account to further improve the performance of recommendation. The existing group recommendation method fails to generate top-N POIs due to poor precision and the relevancy [12]. In the research of group recommendation, the question of how to obtain a better recommendation performance has attracted widespread attention. Most of the previous methods used aggregation strategy to fuse individual recommendation results. However, due to social environment or personal disposition factors, a user's behavior in a group is easily affected by other members of the group, the group recommendation results obtained by aggregating method do not consider the differences of members, so it is difficult to obtain satisfactory results. The group recommendation results are not related to individual preferences and it is often very different from personal location preference. To solve this problem, we take the whole group as a virtual user, and construct the preference model of the group, considering the divergence of group members at the same time. The divergence is the difference among relevance of locations to different group members. Therefore, this paper proposes a collaborative filtering based algorithm with intragroup divergence (called CFID) for POI group recommendation. Therefore, the key contribution of paper is presented as follows. (1) A POI group recommendation algorithm based on intragroup divergence with collaborative filtering model is proposed. The category of locations and temporal features are fully taken into account in this paper. It utilizes users' check-in behaviors to construct the user preferences in visiting category and access time. (2) The group is regarded as a user, and the group preference model is established by aggregating each group member's preference. Meanwhile, intragroup divergence is also an important factor affecting group recommendation results, and we develop a novel group divergence computation method for candidate POIs by analyzing the preference probability of all members in the group and their friends. (3) Experimental evaluations of the proposed methods are conducted on two LBSN datasets. The experimental results demonstrate that the proposed method obtains much higher precision and recall value than the other three recommendation methods, and is superior to other POI recommendation methods. The rest of the paper is organized as follows. The related work of group recommendation is reviewed in Section 2, the details of the proposed algorithm is introduced in Section 3. The comparative experiments on two LBSN datasets are analyzed in Section 4. Finally, the conclusion of the paper and future work directions are shown in Section 5.