esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Modeling and Methodology
### Validation of the FEM
This concordance bolsters the credibility of the calibrated material models and corroborates that the numerical methodology can simulate not only the overarching response but also the desired damage mitigation mechanism integrated within the ACPH design. Cyclic plastic deformation of the energy-dissipating steel plates, especially the 3 mm-thick Q235-grade fuse components located at the top and bottom corners of the hinge, primarily explains the energy dissipation behavior of the ACPH system. These plates  
Figure 6: Comparison of the hysteretic curves of the FEM and the reference frame. experience steady hysteretic energy dissipation during seismic stimulation by means of alternating tensile and compressive loads. This method preserves the elastic behavior of the surrounding concrete and reinforcement by efficiently concentrating inelastic demands in a limited area. The arrangement works similarly to a metal damper purposefully placed within the structural joint. In both the referenced experimental study by [PERSON] et al. [15] and the calibrated numerical simulations conducted herein, the energy dissipation capacity was characterized by the equivalent viscous damping ratio, calculated using the relationship commonly employed in seismic performance evaluations, as outlined by [PERSON] [36] and [PERSON] [38]:  
\[\xi_{eq}=\frac{1}{2\pi}\frac{E_{d}}{E_{s}}\]  
where \(E_{d}\) symbolizes the energy wasted in one cycle (enclosed region of the force-displacement loop), and \(E_{s}\) represents the strain energy stored under elastic loading. Exceeding the usual 5-6% range seen in traditional monolithic RC systems, the ACPH-1-300 specimen evaluated under reversed cyclic loading showed a stable damping ratio of almost 10%. The energy dissipation characteristics were replicated in the finite element simulations through the application of an elasto-plastic material model tailored for the steel plates, with parameters carefully calibrated to align with the observed experimental hysteresis data. The subsequent cyclic response exhibited a high degree of correspondence regarding the shape of the hysteresis loops, the amount of energy absorbed, and the lack of notable pinching or performance degradation. These results substantiate the conclusion that the ACPH system, as originally developed by [PERSON] et al. [15], proficiently converts seismic input energy into managed plastic deformation within a replaceable steel element.
## 2 Methodology
### Energy Analysis  
For all components of the ORC, the mass balance of Equation (1) and the energy balance of Equation (2) where applied, where \(\dot{m}\) is the mass flow of the working fluid, \(h\) the specific enthalpy of the working fluid, \(\dot{Q}\) is the heat transferred, and \(\dot{W}\) the work involved. The energy balance of the three-phase heat exchanger is given by Equations (3)-(5). The efficiency and the work generated by the turbine are given by Equations (6) and (7). Equations (8) and (9) show the energy balance for the two-phase condenser (cooling and condensing). The equations that model the thermal process of Pump 2 are described below in Equations (10) and (11). Finally, the energy balance in the regenerator is given in Equation (12).  
\[\sum\dot{m}_{in}-\sum\dot{m}_{out}=0 \tag{1}\]  
\[\sum\dot{m}_{in}\cdot h_{in}-\sum\dot{m}_{out}\cdot h_{out}+\sum\dot{Q}-\sum \dot{W}=0 \tag{2}\]  
\[\dot{Q}_{a}=\dot{m}_{AT}\left(h_{ATf}-h_{2 AT}\right)=\dot{m}_{ORC}\left(h_{6 /ORC}-h_{6 ORC}\right) \tag{3}\]  
\[\dot{Q}_{b}=\dot{m}_{AT}\cdot\left(h_{ATg}-h_{ATf}\right)=\dot{m}_{b}\cdot \left(h_{fgORC}\right) \tag{4}\]  
\[\dot{Q}_{c}=\dot{m}_{AT}\left(h_{1 AT}-h_{ATg}\right)=\dot{m}_{ORC}\left(h_{1 ORC }-h_{6 ORC}\right) \tag{5}\]  
\[\eta_{T1}=\frac{h_{1 ORC}-h_{2 ORC}}{h_{1 ORC}-h_{2 ORC}} \tag{6}\]  
\[\dot{W}_{T1}=\dot{m}_{ORC}\cdot\left(h_{1 ORC}-h_{2 ORC}\right) \tag{7}\]  
\[\dot{Q}_{3b}=\dot{m}_{ORC}\cdot\left(h_{3 ORC}-h_{3 ORC}\right)=\dot{m}_{1A} \cdot\left(h_{2A}-h_{1 gA}\right) \tag{8}\]  
\[\dot{Q}_{3a}=\dot{m}_{3 ba}\cdot\left(h_{3 ORC}-h_{4 ORC}\right)=\dot{m}_{1A} \cdot\left(h_{1 gA}-h_{1A}\right) \tag{9}\]  
\[\eta_{P2}=\frac{v_{4 ORC}\cdot\left(P_{5 ORC}-P_{4 ORC}\right)}{h_{5 ORC}-h_{4 ORC}} \tag{10}\]  
\[\dot{W}_{P2}=\dot{m}_{ORC}\cdot\left(h_{5 ORC}-h_{4 ORC}\right) \tag{11}\]  
\[\dot{Q}_{RC}=\dot{m}_{ORC}\cdot\left(h_{6}-h_{5}\right)=\dot{m}_{ORC}\cdot \left(h_{2}-h_{3}\right) \tag{12}\]
# FSRFNet: Feature-selective and Spatial Receptive Fields Networks
## 6 Conclusions  
In this paper, inspired by the additive effect of feature-selective and spatial attention on the receptive field sizes of the visual cortex neurons, we constructed the Feature-selective and Spatial Receptive (FSRF) block and inserted it into existing convolutional architecture to form the FSRFNet architecture. The FSRF block is implemented via three operations: Multi-branch Convolution, Fuse, and Interactions between Feature-selective and Spatial Attention. Fuse combines the results of multiple branches with different kernel sizes, and constructs attention building blocks (average and max channel; and average and max spatial), on which SoftMax and sigmoid operators are applied. Numerous experiments have demonstrated the effectiveness of FSRFNet from large models to small models and from large datasets to small datasets, including various benchmarks.  
[PERSON] contributed to the paper in conceptualization, methodology, formal analysis, software, visualization, data curation and review and editing. [PERSON] contributed to the paper in conceptualization, methodology, investigation and original draft preparation. [PERSON] contributed to the paper in conceptualization, methodology, investigation and original draft preparation.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Models** & **\#P** & **CIFAR-10** & **CIFAR-100** \\ \hline ResNeXt\(-29,16\times 32\)d(our impl.) & 24.5M & 5.74 & 28.75 \\ ResNeXt\(-29,8\times 64\)d(our impl.) & 32.9M & 5.58 & 28.37 \\ ResNeXt\(-29,16\times 64\)d(our impl.) & 66.7M & 5.47 & 27.99 \\ SENet \(-29\)(our impl.) & 34.1M & 5.63 & 28.42 \\ SKNet \(-29\)(our impl.) & 26.8M & 5.42 & 27.68 \\ FSRFNet \(-29\)(ours) & 29.6M & 5.26 & 27.51 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Top-1 errors (%) on CIFAR, with SENet-29, SKNet-29, and FSRFNet-29 all based on ResNeXt-29, \(16\times 32\)d.  
Figure 8: The Grad-CAM visualization results, using SKNet-50 and our proposed FSRFNet-50 as backbone networks.  
Funding: This work is supported by Shanghai Science and Technology Development Foundation, Grant No. 18511103900.  
The authors declare no conflicts of interest.
# AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing
## 4 Image Privacy Recommendations
### Textual Feature Extraction
#### 4.2.1 Weighted Graph  
Weighted graphs were used in [30] for classifying the images using associated tags (keywords) that describe their content. Since images could have tags in common, a graph could be built to highlight the relationships between words and the associated privacy policies. Previous research showed that the image tags tend to be noisy [31]. Hence, two different but semantically similar words which could provide meaningful insight into building a context tend to be separated in the graph. In order to avoid this problem, we apply stemming to the tags.  
After stemming the words, we built the following graph. Vertices are represented by the stemmed tags, while edges represent the occurrence of two tags in the tag list for a given image. Edges are non-oriented and weighted. For each occurrence of two tags in the same list for a given image, the weight of the edge connecting them is incremented by 1. The weight is useful to aggregate the vertices and find groups of semantically close tags.  
A group of words might provide insight into the decision of a privacy policy. Hence, we aggregated the vertices in different clusters using the Louvain method [30; 32]. Each tag has a privacy policy associated to the image where it was first found in. For a given group of vertices, the most frequent privacy policy was chosen as the representative.  
For each tag, we used a relevance measure, similarly to what was proposed by [PERSON] et al. in [30]:  
\[r_{G}(t)=\begin{cases}0&\text{if }|C_{t}|\leq 1,\\ \frac{deg_{C_{t}}(v_{t})}{|C_{t}|\cdot(|C_{t}|-1)\cdot\frac{1}{2}}&\text{ otherwise.}\end{cases} \tag{4}\]  
Here, \(deg_{C_{t}}(v_{t})\) represents the number of edges in the cluster \(C_{t}\) incident to the vertex \(v_{t}\in C_{t}\) associated to a tag \(t\), and \(|C_{t}|\) is the number of vertices in the cluster for a given \(v\) associated to \(t\).  
The above equation provides a measure for the relevance of a word in evaluating the appropriate privacy policy. It represents the ratio of existing connections inside a group for a given tag (vertex) with respect to the maximum number of links (edges) inside the group. The equation can be interpreted as follows:If the stemmed tag does not belong to any group, it cannot be evaluated;
* If the group is made up by the stemmed tag itself, it might have no meaning and it should not be considered;
* If the stemmed tag (vertex) is linked to at least one other stemmed tag inside the group, the relevance measure can be calculated.  
Based on the equation, it is easy to consider all the tags representing an image and their associated (weighted) privacy policy values in order to compute the privacy policy (i.e., public or private) of the image based on a weighted sum.
## 3 Software Platform and Simulation Tools
### Platform  
In the present study, we use the Tmote-Sky platform as the network nodes with MSP430 microcontroller for network simulation. Contiki OS considers the value of 32,768 Hz as tick per the second parameter in the RTIMER_SECOND variable based on the MSP430F1611 basic clock module [33]. The frequency of a real-time clock varies with the application. The frequency 32,768 Hz (32.768 kHz) is used because it is a power of 2 (\(2^{15}\)) value. Also, one can get a precise 1 s period (1 Hz frequency) by using a 15 stage binary counter. RTIMER_SECOND variable has a key role in calculating radio activity times [34].  
The radio used in the Tmote-Sky Platform is CC2420. The CC2420 is an IEEE 802.15.4 compliant RF transceiver 2.4 GHz designed for low-power and low-voltage WSN applications. CC2420 is controlled via SPI port, and a series of digital output and input, as well as interrupts by MSP430. The CC2420 driver in Contiki OS provides two types of radio check, CCA and RSSI. CCA is based on the measured RSSI value and a programmable threshold [35]. The RSSI value is averaged over eight symbol periods (128 \(\upmu\)s), in accordance with [26]. MCU SPI pin takes strobe commands registers and sends them to CSn Radio pin for calibration. There are types of registers, status bits, and pins for radio control in CC2420 driver in Contiki OS. Table 1 lists the ones which are used in three ON (), RSSI (), and OFF () functions in the course of an idle listening channel check by RSSI according to CC2420.c file available in Contiki OS.
# A Model-Assisted Combined Machine Learning Method for Ionospheric TEC Prediction
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & & **MAE/TECU** & **MRE/\%** & **RMSE/TECU** & \(\rho\) \\ \hline High Solar Activity & daytime & 2.45 & 19.75 & 3.65 & 0.82 \\ Year (2015) & night & 1.34 & 10.21 & 2.35 & 0.94 \\ \hline Low Solar Activity & daytime & 0.37 & 5.42 & 1.04 & 0.90 \\ Year (2020) & night & 0.19 & 2.13 & 0.63 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance indices of MMAapGA-BP-NN during daytime and night in a high solar activity year (2015) and low solar activity year (2020). Figure 11: The deviation of TEC forecast data in daytime and night from MMAapGA-BP-NN in a high solar activity year (2015) and low solar activity year (2020): (**a**) Prediction error stem of daytime and night in a high solar activity year (2015); (**b**) Prediction error stem of daytime and night in a low solar activity year (2020). Geomagnetic storms can cause the disturbance of TEC. Multiple storm periods occurred during the years 2015 and 2020 [42]. For example, a geomagnetic storm occurred on 17 March 2015 [43]. The impact of geomagnetic storm intensity on TEC prediction errors is analyzed next. And the Ap index can also characterize the geomagnetic storm intensity. The qualitative expression of geomagnetic storm intensity is shown in Table 3.  
Figure 12: (**a**–**d**) are the performance indicators of MMAdapGA-BP-NN during daytime and night in a high solar activity year (2015) (average of 50 run results); (**e**–**h**) are the performance indicators curve of MMAdapGA-BP-NN during daytime and night in a low solar activity year (2020) (average of 50 run results). Furthermore, the qualitative analysis yields the number of \(A_{p}>15\) days and the number of \(A_{p}~{}\geq~{}30\) days in high solar activity years (2015) and low solar activity years (2020), as shown in Figure 13. It can be found that there is no obvious correlation between the absolute error of predicted TECs and the geomagnetic storm days. In addition, the correlation coefficient \(\rho\) between the \(A_{p}\) index, and the absolute error is calculated quantitatively and is \(-0.36\) and \(0.35\), respectively. It can be found that the correlation coefficient between the \(A_{p}\) index and the absolute error is small.
## 3 Results and Discussion
Our previous study showed that an effective volume ratio \(\beta\) can be introduced as \(\beta=V_{\text{effective}}/V_{\text{active}}\) in the efficiency equation function to understand the effect of reduced \(V_{\text{effective}}\) on the IQE droop due to the InGaN clustering and carrier localization [25]. The equilibrium carrier density \(n\) in the QW can be calculated by  
\[n=\sqrt{\text{IQE}\cdot I}/\sqrt{\text{B}qV_{\text{effective}}} \tag{1}\]  
Hence, we can revise it by introducing \(\beta\)  
\[n=\sqrt{\text{IQE}\cdot I}/\sqrt{\text{B}qV_{\text{active}}\beta}=n\prime/ \sqrt{\beta} \tag{2}\]  
Which assumes that the carriers have distributed uniformly in the InGaN QW. Therefore, IQE can be calculated by [25]  
\[\text{IQE}=\frac{\text{B}(n\prime/\sqrt{\beta})^{2}}{\text{A}(n\prime/\sqrt{ \beta})+\text{B}(n\prime/\sqrt{\beta})^{2}+\text{C}(n\prime/\sqrt{\beta})^{3}} \tag{3}\]  
The external quantum efficiency (EQE) is calculated by, where \(P_{\text{output}}\) and \(Inv\) are the output power and photonic energy. In our experiment, the light extraction efficiency (LEE) is assumed to be 0.8 for the high-efficiency InGaN LEDs grown on PSS and it has been used in our previous study [24]. Therefore, the IQE can be achieved by considering the LEE as IQE = EQE/LEE. By fitting the experimental data through Equation (3), accurate-fitting results can be achieved. The fitting IQE versus carrier density for InGaN LEDs is plotted in Figure 4. It is worth pointing out that a large value of the Auger coefficient was not introduced and the extracted Auger coefficient with a value of \(2\times 10^{-30}\) cm\({}^{6}\) s\({}^{-1}\) is more reasonable, which agrees well with the expectations by the experimental measurements and the theoretical calculations [5; 6]. The relationship between the carrier density and the driven current with consideration to the reduced effective volumes for the three LEDs are shown in Figure 4a. For a given current, the carrier density of the InGaN LED with a wide InGaN LQW is lower than that of the blue LED. Moreover, the IQE versus carrier density for InGaN LEDs are plotted in Figure 4b. The InGaN LED with 5.2 nm thick LQW has shown a significant alleviation of efficiency droop. Therefore, the larger onset-droop current  
Figure 3: (**a**) The measured absolute and (**b**) Normalized external quantum efficiency (EQE) of the LEDs with different LQWs of 3.0 nm, 4.5 nm and 5.2 nm. and the alleviated droop ratio of LED with a wide InGaN well can be well interpreted by the reduction of the carrier density, which leads to a reduction of Auger recombination and carrier leakage [20].
## 2 Testing of Dynamic and Static Elastic Parameters
### Dynamic Elastic Parameter Acquisition Method  
There are two methods for determining the dynamic elastic parameters. One is to use logging equipment to conduct on-site sonic logging under geo-stress conditions, then convert the rock acoustic wave data (P-wave and S-wave time differences) and rock bulk density to obtain the elastic parameters of the rock formations continuously along the well depth. The calculation method is shown in Equations (1) and (2).  
\[E_{\text{d}}=\frac{\rho_{bulk}\Big{(}3\Delta t_{\text{S}}^{2}-4\Delta t_{\text{ P}}^{2}\Big{)}}{\Delta t_{\text{S}}^{2}\Big{(}\Delta t_{\text{S}}^{2}-\Delta t_{ \text{P}}^{2}\Big{)}} \tag{1}\]  
\[\mu_{\text{d}}=\frac{\Delta t_{\text{S}}^{2}-2\Delta t_{\text{P}}^{2}}{2\Big{(} \Delta t_{\text{S}}^{2}-\Delta t_{\text{P}}^{2}\Big{)}} \tag{2}\]  
where \(E_{\text{d}}\) is the dynamic elastic modulus, GPa; \(\rho_{bulk}\) is the bulk density, g/cm\({}^{3}\); \(\Delta t_{\text{S}}\) is the P-wave time difference, \(\mu\)s/m; \(\Delta t_{\text{P}}\) is the S-wave time difference, \(\mu\)s/m; \(\mu_{\text{d}}\) is the dynamic Poisson's ratio, dimensionless.  
The second method is to convert the speed of ultrasonic propagation in rock specimens through laboratory tests. Using the ultrasonic pulse transmission method to measure the P-wave and S-wave velocity of rock samples, the experimental principle is shown in Figure 2. First, connect the two probes and observe whether the jumping time of the received waveform on the oscilloscope is zero. If the starting point time does not return to zero, adjust the knob of the pulse position in the pulse source to set the starting point time to zero, and the oscilloscope can directly detect the time when the first wave arrives [36]. Based on the theory of elastic waves, Equations (3) and (4) can be used to determine the dynamic elastic modulus (\(E_{\text{d}}\)) and Poisson's ratio (\(\mu_{\text{d}}\)) of rocks [34].  
\[E_{\text{d}}=\frac{\rho_{bulk}V_{\text{s}}^{2}\Big{(}3V_{\text{P}}^{2}-4V_{ \text{s}}^{2}\Big{)}}{V_{\text{P}}^{2}-V_{\text{s}}^{2}} \tag{3}\]  
\[\mu_{\text{d}}=\frac{V_{\text{P}}^{2}-2V_{\text{s}}^{2}}{2\Big{(}V_{\text{P}}^ {2}-V_{\text{s}}^{2}\Big{)}} \tag{4}\]  
where \(\rho_{bulk}\) is the bulk density of the sample, the unit is g/cm\({}^{3}\); \(V_{\text{P}}\) is the P-wave velocity, the unit is m/s; \(V_{\text{s}}\) is the S-wave velocity, the unit is m/s.  
Figure 1: (**a**) is the tangent elastic modulus, (**b**) is the average elastic modulus, (**c**) is the secant elastic modulus [39].  
However, acquiring S-wave data in the actual well-logging data is complex and expensive. Thus, conventional well-logging lacks S-wave velocity data [40]. Therefore, the first dynamic testing method is generally used in practical applications.
## 3 Results and Discussion  
The schematic diagram of the AFAM and the image acquisition have been clearly presented in Ref. [1], where AFAM was used to successfully and accurately image the morphology and internal structures of cells in a facile and non-invasive manner. Sixteen groups of morphology and phase images were used from 2 scans in Ref. [1] for testing, which were provided by the Medical Ultrasonic Laboratory of Huazhong University of Science and Technology, Wuhan, China. From Figure 4b,d, it is obvious that the backgrounds in the phase images are complicated. To show the cells more clearly, we segmented the cell image from their corresponding morphology images and ignored the background.  
Figure 4: Different scan results of atomic force acoustic microscopy (AFAM) in our experiments. (**a**) morphology image of scan 1; (**b**) phase image of scan 1; (**c**) morphology image of scan 2; (**d**) phase image of scan 2.  
Figure 3: Segmentation result: (**a**) Histogram and segmentation threshold of the morphological image; (**b**) segmentation boundaries. The green, blue, and red lines are the first, second, and final segmentation result boundaries, respectively.  
Our fusion method is compared with five different fusion methods, including Laplacian pyramid (LP) [17], curvelet transform (CVT) [18], NSST-VGG [19], gradient transfer fusion (GFT) [20], and FusionGAN [21]. In the experiments, for the sake of fairness, the resolution level samples in LP, CVT, and NSST-VGG are set as 3, which are the same as our proposed method. The rest of the parameters were set to produce the best results of the experimental results of their methods.
## 3 Our Approach
### Applying Tensor Decomposition with Different Rank Methods
#### 3.3.3. Tensor Train Decomposition  
Tensor Train decomposition decomposes a tensor of order n into a chain of product tensors of order-two or order-three tensors. Tensor Train decomposition is a type of non-recursive tensor decomposition that, unlike Tucker decomposition, does not suffer from the curse of dimensionality [49]. An nth-order tensor that decomposes to second- or third-order tensors, where r is the rank, is formally defined as follows:  
\[\mathcal{X}(u_{1},...,u_{d})=G_{1}[u_{1}]G_{2}[u_{2}]...G_{d}[u_{d}]. \tag{4}\]  
where \(G_{d}\) is a core tensor and can be of order two or three.  
All tensors (\(G_{d}[u_{d}]\)) related to the same dimension (d) must be of the same size (\(r_{d-1}\times r_{d}\)), and \(r_{0},r_{d}=1\). The chain of \(\{r_{d}\}_{d=0}^{d}\) is the rank of the Tensor Train format.  
Tensor Train Singular Value Decomposition (TT-SVD), Tensor Train Alternative least square (TT-ALS), TT-Rounding, and other algorithms are used to compute the Tensor Train decomposition. In this paper, we use recursive TT-SVD Algorithm 4 on the tensors of fully connected layers and adopt the algorithm proposed by [PERSON] et al. [53] to decompose the convolutional layers.  
Identifying the optimal Tensor Train decomposition for a given tensor is an NP-hard problem. This study employs a methodology akin to prior decomposition for rank determination, with an enhanced focus on layer sensitivity due to its encouraging outcomes, as detailed in subsequent sections. This approach is applied to both convolutional and fully connected layers using Tensor Train decomposition.
## 3 Methods  
### Kruskal-Wallis and Mann-Whitney U  
The Kruskal-Wallis test is used for comparing the median values of data obtained from different groups [77]. This non-parametric statistical method is preferred, especially when the data are not normally distributed or when sample sizes are small [78]. The Kruskal-Wallis test investigates the hypothesis that K groups of samples originate from a single statistical population, or from comparable statistical populations, with respect to their means [79].  
After the Kruskal-Wallis test identifies that at least one group is different, the Mann-Whitney U test can be applied for pairwise comparisons between groups. (This test is also referred to as Wilcoxon's rank sum [80].) The Mann-Whitney U (MWU) test combines the data from two groups to form an overall ranking [81] to jointly assess data from both groups and calculate rank sums. The test statistic is then determined based on the rank sums of both groups and the number of observations in each group and used to test whether there is a significant difference between the group medians.