esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Processing Method of Acoustic Focusing Cavities Based on the Temperature Gradient
## 2 Mathematical Model and Methods
### Modeling and Boundary Condition Setting  
Plane wave focusing with a discrete temperature gradient is a method combining an acoustic metasurface [31] with discrete temperature. The length of the metasurface was set to 1 cm, the frequency was set to 20 kHz, and the speed of sound was set to 343 m/s, yielding a wavelength of approximately 2 cm, thus meeting the design requirement of the metasurface. It can be seen from Figure 2 that the two linear metasurfaces and boundaries 4 and 5 formed a rectangular transmission unit. By setting different constant temperatures in the transmission unit, using the temperature to change the speed of sound and air density, the acoustic impedance in the transmission unit could be changed, such that the acoustic wave was abnormally transmitted. Hence, the temperature could be used to control the transmission angle and the phase of the acoustic wave, thereby focusing the transmitted acoustic wave. Firstly, the propagation model of the plane wave in a single transmission unit was established, as shown in Figure 2.  
The length of the three rectangular areas was 10 cm and the width was 1 cm. The plane wave propagated along the \(x\)-direction, transmitted into the middle constant-temperature transmission unit, and then transmitted out of the constant-temperature transmission unit. Boundaries 1 and 8 were set as plane wave radiation, and an incident pressure field with a size of 1 Pa was added to boundary 1, representing the sound pressure amplitude of incident sound waves. The materials were all air. Temperatures in the transmission unit were set to 430 K, 530 K, 650 K, 760 K, 850 K, 970 K, and 1190 K.
# SA-GAN: Stain Aclimation Generative Adversarial Network for Histopathology Image Analysis
## 4 Experimental Results
### Inter Datasets Color Constancy Analysis  
In this section, influence of proposed SA-GAN algorithm on the stain color differences between different datasets originated from different laboratories has been observed. Stain color variations across the datasets are checked in terms of color consistency using normalized median intensity (_NMI_) [38] metric. _NMI_ is used to measure stain color intensities of nuclear and eosin regions. _NMI_ is defined as:  
\[NMI(I)=\frac{median\{I(i)\}}{P_{95}\{I(i)\}} \tag{10}\]  
where \(I(i)\) is the mean of R, G & B channels of image \(I\) for pixel \(i\) and denominator denotes 95 th percentile However, _NMI_ specifies the image's intensity information more than the color contents. So, to measure variability in hue color across the datasets, we quantified a specific color consistency metric, i.e., normalized median hue (_NMH_). _NMH_ is a novel color metric recently proposed in [39]. _NMH_ is defined as:  
\[NMH(h)=\frac{median\{H(h)\}}{P_{95}\{H(h)\}} \tag{11}\]  
Figure 4: Violin plots of prior proposed methods show the variation of SSIM metric due to the improper selection of target images (’1’, ’2’, ’3’ in \(x\)-axis represents the three different target images). The results are evaluated on ICIAR 2018 dataset [34].  
where the numerator is median of hue channel of HSV image and denominator is 95 th percentile of hue channel for pixel \(h\).  
The goal of these metrics is to determine the variation of color distributions across a population of images. We also computed population variability metric, i.e., coefficient of variation (CV) across train and test sets of each dataset. The CV (standard deviation divided by mean) is computed separately for NMI and NMH metrics from each dataset. The low value of CV-NMI and CV-NMH indicates that fewer image intensity variations and low color variability exist within an image population. The CV of NMI and NMH for the proposed SA-GAN algorithm are computed and compared amongst the normalized image sets to un normalized sets and plotted the results in Figure 5a,b. The polar plane results show the optimal values of CV-NMI and CV-NMH for normalized datasets in comparison to un normalized datasets. These optimal values indicate that after the stain transfer, the processed images have less population variability. In polar plots, the clustering effect of our SA-GAN algorithm for normalized datasets can also be confirmed. Moreover, comparison of measured metrics values for various normalization algorithms [16; 17; 20; 23; 26] on all tested datasets are given in Table 3. Low values of CVs (Table 3) represent the proxy measurement of quality of proposed SA-GAN method, compared to other normalization methods. Minimization of coefficient of variation of NMI and NMH in normalized image datasets is the indication of color consistency across the datasets which improves the classification performance of deep CNN models.  
Figure 5: Cont.
## 3 The Hybrid Distributed Topology Control Scheme (HDTC)
### Phase I: Forming the Communication Group
#### 3.1.3 Hybrid Scheme  
The vehicle \(u\) (green node) uses the above procedures to find the nodes belonging to Group 1 and Group 2. As shown in Figure 5 (left), the nodes (red nodes), which are marked in both Group 1 and Group 2, are candidates of the group members. Therefore, we have:  
\[G_{1}=\{p^{{}^{\prime}}|p^{{}^{\prime}}\in N_{u},\cos X_{(p^{\prime})}\leq\cos\theta\} \tag{3}\]  
Figure 4: The timer implementation in the modified cone-based scheme: Vehicle \(w\) moves slower than vehicle \(u\) (**Left**); vehicle \(w\) moves faster than vehicle \(u\) (**Right**).  
Figure 3: The cone-based scheme (**Left**); the time-slot scheme (**Right**).  
\[G_{2}=\{p^{{}^{\prime}}|p^{{}^{\prime}}\in N_{u},D_{up^{{}^{\prime}}}(t_{0})\leq d _{0}\} \tag{4}\]  
\[G_{u}=\{p^{{}^{\prime}}|p^{{}^{\prime}}\in(N_{u}\bigcap G_{1}\bigcap G_{2}\} \tag{5}\]  
where \(N_{u}\) is the set of neighboring vehicles of vehicle \(u\). \(D_{up^{{}^{\prime}}}\) is the distance between vehicle \(p^{{}^{\prime}}\) and vehicle \(u\). Accordingly, the vehicle \(u\) sends a message \"join\" to its neighboring member nodes. When the nodes, which belong to the set \(G_{u}\), receive this message, they join the group of vehicle \(u\) and form the communication group of vehicle \(u\).  
Figure 5 (right) shows a traffic scenario. The right-hand side is an up vector for representing the moving direction of vehicles, and the left-hand side is a down vector. Note that the purple node is the observation node (say, node _u_), and red nodes are the members that are found by the purple node after performing the proposed method. Figure 6 depicts the flow diagram of the proposed scheme. First, we use the cone-based method to check the nodes. Secondly, we use the time-slot scheme to test the distance information. Third, group those nodes into members, and send the message to them. Finally, when receiving a message from other nodes, the node becomes a group member associated with the message with the earliest time stamp.
## 4 Encoding and Decoding Design
### Encoding  
In this study, the solution to the problem is represented using an operation-based encoding method. The operations of a job are indicated by the number of the job, and the number of times that the job number appears represents which operation it is for that job. Due to the design order requirements of the products considered in this paper, and the possibility of identical jobs existing among different products, product information needs to be read sequentially to determine the required jobs for the product, and each job is assigned a unique code for identification. The pseudocode for the encoding is shown in Algorithm 1.  
To better illustrate the encoding process, we use an example where the demand for product 1 is 2, and the demand for product 2 is 1. Product 1 consists of components 1 and 2, while product 2 consists of components 3 and 4. Component 1 is composed of job 1; component 2 is composed of jobs 2 and 3; component 3 is composed of jobs 2 and 4; and component 4 is composed of jobs 5, 6, and 7. Each job is processed through corresponding operations. For example, job 1 includes operation 1, operation 2, and operation 3. The bottom encoding sequence represents the order of operations for each job, such as [1 1 1 2 3 1 1 1 2 3 2 4 4 5 6 6 7], where [1 1 1] denotes the three operations of job 1. The specific encoding method for this example is shown in Figure 2.  
```
Input: order information; empty set \(\mathit{chrom}\) Output: individual encoding \(\mathit{chrom}\)  Read order information, including product types \(N_{p}\), number of jobs required for each product \(N_{p}\), product composition \(P_{\_part}\), and product demand \(P_{\_need}\). For\(i=1\) to \(N_{p}\)do For\(j=1\) to \(N_{p}\)do \(\mathit{job}\gets P_{\_}{\_}{\_}{\_}{\_}{\mathit{part}}\) For\(k=1\) to \(P_{\_}{\_}{\mathit{part}}\)do \(\mathit{chrom}\gets\mathit{chrom}\bigcup\mathit{job}\) End For End For End For
```  
**Algorithm 1** Encoding Method
# JITScanner: Just-in-Time Executable Page Check in the Linux Operating System +
## 4 Assessment
### Effectiveness
Such short or failed executions can lead not to intercept actual activities that the malware can (at least potentially) carry out via WX pages that include code signatures--which would have been observed by JITScanner. However, at the same time, this is the scenario where the malware becomes essentially idle and does not lead to real security problems. To assess point 2, we compared the traces extracted from the plain malware to those obtained from the packed samples. It is clear that all signatures are retained if the pages extracted from the plain sample constitute a subset of those extracted from the packed sample. The packed samples will obviously execute over more pages, as they need to decrypt the payload from memory. The details of the experiments are summarized in Table 4. As observed, there exists a substantial difference between the effectiveness of ClamAV and JITScanner when dealing with packed malware. ClamAV lacks a mechanism for detecting signatures at runtime. This makes it not capable to effectively detect packed samples once their signature is unpacked in memory for its actual usage. Conversely, JITScanner is able to use the same signatures for the most of all samples examined, recognizing 75.91% of them as malware. Furthermore, some malwares are no longer recognized because either the unpacker did not successfully lead to the execution of the malware code (and hence, JITScanner could not intercept the fetch of the malware instructions from memory) or the unpacking led to different access permissions for a few pages. As for the latter aspect, we found that some pages that are set up with read-execute permission with a regular loading of the ELF are instead setup as read-only by the unpacker. In this case, JITScanner does not perform any snapshot of the read-only page for a final check of its content when it is materialized--such a snapshot is instead performed for read-execute pages, which gives rise to a different signature characterizing the application. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Family** & **Detected Plain** & **Total Samples** & **Signature Flexibility** \\ \hline JITScanner & Emotet & 2 & 2 & **100\%** \\ ClamAV & Emotet & 1 & 2 & 50\% \\ \hline JITScanner & Tsunami & 57 & 71 & **80.2\%** \\ ClamAV & Tsunami & 33 & 71 & 46.47\% \\ \hline JITScanner & XMRIG\_Miner & 13 & 21 & **61.90\%** \\ ClamAV & XMRIG\_Miner & 8 & 21 & 38.09\% \\ \hline JITScanner & Gafgyt & 76 & 320 & 23.75\% \\ ClamAV & Gafgyt & 238 & 320 & **74.75\%** \\ \hline JITScanner & Mirai & 15 & 52 & **28.84\%** \\ ClamAV & Mirai & 8 & 52 & 15.38\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Signature flexibility of JITScanner and ClamAV.
## 3 Results and Discussion
Figure 9 shows the density profiles of the PVDF system composed of the PVDF surface, water molecules, and ions at 5 and 100 ns of simulations. The density profile of the PP system is similar and for this reason not shown here. The analysis of the atomic fraction distribution indicated at 100 ns an increase of the ions' density profile (Figure 9b) at about 2 nm away from the PVDF surface resulting in an increase in the crystal density. In parallel, there is a partial reduction of the density profile relative to water in correspondence of the ions' increase. This is an indication of the removal of water molecules and ion-ion aggregation, which preludes the crystal formation as shown in Figure 8. Due to the hydrophobicity of the membrane surface, water molecules are \"shifted\" far away from the surface and ions are physically constrained in a smaller region wherein attractive ion-ion Coulomb interactions take place. This effect could be related to a kind of \"extra supersaturation\" induced by hydrophobic surfaces that catalyze ion aggregation through reinforcement of Coulomb interactions when compared with the model of bulk NaCl. Figure 8: g(r) of water-Na\({}^{+}\) ion interaction in the PP system. These findings are in agreement with the results of [PERSON] and [PERSON] [33] at high concentrations and with recent fallouts coming from polymeric hydrophobic matrices with complementary functions such as bismuth selenide confined inside PVDF that produce a quicker and controlled growth rate of NaCl salts [67]. These preliminary simulations shed light on their application capability to predict crystallization with different complex polymeric matrices.
# Enhanced Sensitivity of FeGa Thin-Film Coated SAW Current Sensor
## 4 Sensor Experiments and Discussions
### Sensor Performance Evaluation  
#### 4.2.1 Sensitivity Evaluation  
First, the sensitivity of the prepared sensor was evaluated by measuring the sensing response towards increasing current values from 0 to 10 A at room temperature (25 \({}^{\circ}\)C) using the experimental setup described in Figure 6b. The effect from FeGa film thickness on sensitivity was demonstrated by measuring the sensitivity of developed sensors with various FeGa film thicknesses (300 nm, 500 nm, and 700 nm), as shown in Figure 7a. Obviously, the sensitivity increases with increasing thickness of FeGa film. This can be explained by the following formula [29]:  
\[\frac{\Delta V}{V}=-fh{\sum\limits_{i=1}^{3}c_{i}\left(\rho-\frac{E^{(i)}}{V^{ 2}}\right)} \tag{7}\]  
where \(f\), \(h\), \(c_{i}\) and \(E^{(i)}\) are the operation frequency of SAW device, FeGa film thickness, coupling parameter relating to the piezoelectric substrate, and [PERSON]'s modulus of the FeGa film, respectively. It can be seen that the acoustic velocity is proportional to the film thickness when other parameters are fixed. Therefore, increasing the FeGa film thickness will improve the sensitivity. However, a turning point occurred when thicker FeGa film over 700 nm was applied. This is because the magnetic domain wall gradually changed from Neel wall to Bloch wall with the increase in the film thickness, resulting in the degradation of the film soft magnetic properties. The result indicates that too thick of a FeGa film will reduce the sensitivity. Hence, 500 nm FeGa film was chosen to construct the sensing devices in this work.  
Figure 7b shows the linear response of the proposed sensor coated with 500 nm patterned FeGa thin film. The concluded sensitivities of 37.9 kHz/A, 32.2 kHz/A, 23.1 kHz/A were obtained from the sensing devices coated with dotted, grating, and membranous FeGa thin films, respectively. The sensitivity of membranous FeGa thin film coated device approximately agreed with the predicted value. The largest sensitivity was achieved from the dotted-FeGa thin film coated device, which is the result of enlarged magnetostrictive properties and reduced coercivity in the dot-film structure [9].  
Figure 7: (**a**) The effects of film thickness on the sensor sensitivity; (**b**) the frequency response of current sensor with a 500 nm thick film.
## 7 Data Analysis Based on Field Instrumentation-Recorded Data and Generated Numerical Data  
After completing each phase of HWE performance monitoring, a large quantity of data was gathered and explained. A substantial amount of data on the soil's physical and mechanical properties was collected based on various numerical analyses for different possible scenarios based on the local projected rainfall patterns (rainfall being the primary climate change impact in this region). Similarly, field instrumentation data provided sufficient datasets for approximately three years of soil-water physical property monitoring.  
Due to the vast amount of available data, data analysis employing machine learning was used to develop multiple predictive models for various outputs based on different inputs. The combination of input parameters and output parameters is summarized in Table 3, which illustrates the defining of all model development structures, including preliminary HWE evaluation, prediction of soil-water physical properties based on available data in the field in natural conditions (HWE slope stability is dependent on SMC and SMS), evaluation of crack propagation, and evaluation of safety based on the HWE FS. In addition, the SMC of the soil affects the soil shear strength in the unsaturated zone, and SMS is an additional indicator of the soil shear strength, which is an essential factor to consider.  
For prediction, machine learning produces highly accurate predictive models. During this research, hybrid intelligent prediction models for various variables (Table 3) were created. Utilizing an artificial neural network (ANN) with a Bayesian regularization backpropagation training algorithm (ANN-BR) (Figure 13), two intelligent systems, particle swarm optimization (PSO) and the butterfly optimization algorithm (BOA) (Figure 13), were developed to optimize the ANN-BR algorithm for predicting each variable. The model's development, structure, architecture, and processes for both single and hybrid predictive algorithms based on collected and recorded data from the field and numerical analysis are studied and reported in [2; 33; 35; 36; 37].
## 4 Results
### Cost Analysis
#### 4.2.5 Scenario 5  
The S5 or OFP-RECT scenario used 3 DCP for off-site construction of identical rectangular houses. S5 was like S4, only differing from it in the way the houses were constructed off-site. The cost components for S5 were labor, material, machinery, and transportation costs, representing 41.6%, 25.3%, 17.5%, and 15.6% of the total cost, respectively. S5 was 35.2% more expensive than S4 because of the higher machinery and transportation costs and the slightly higher labor costs. This finding suggests that 3 DCP is more cost-efficient when performed on-site for large projects such as the one studied for this article. When comparing S5 with S1, we found that S5 was 71.4% more expensive per constructed unit. This proved the cost savings from the use of AM design, which enables houses to be manufactured on-site with less labor and less equipment required for transportation and assembly. For more detail refer to Table A6.
# Time-Domain Sound Field Reproduction with Pressure and Particle Velocity Jointly Controlled
## 5 Conclusions  
We have proposed a time-domain sound field reproduction method with sound pressure and particle velocity jointly controlled. The control was formulated using a Lagrangian cost function with a tuning parameter to adjust the control weights, which gives the flexibility to achieve the optimal control at different loudspeaker array layouts. While most existing works implement particle velocity or sound intensity assisted sound field reproduction in frequency domain, the present work focused on time-domain reproduction and adopted the conjugate gradient method to reduce computational complexity. The proposed method was evaluated on both a regular loudspeaker array layout and an irregular loudspeaker array layout. We demonstrated that the proposed method improves both sound pressure and sound intensity reproduction with reduced computational complexity. Given that the reproduction system of controlling the particle velocity is especially suitable to a non-uniformly spaced loudspeaker array with reduced number of loudspeakers and control points required, the present work has the potential in real-time sound field reproduction applications when the reproduction environment is time varying, such as in-car audio systems.  
Conceptualization, W.Z. and X.H.; methodology, X.H.; validation, X.H. and J.W.; writing--original draft preparation, X.H. and J.W.; writing--review and editing, W.Z. and L.Z.; supervision, L.Z.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61671380.  
Not applicable.  
Figure 12: The NMSE of the reproduced intensity using the direct inverse and the adopted CG method. \(I=100,200,400,800\) is the iteration number of CG. The reverberation time \(RT60=200\) ms and the control filter length \(J=400\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Method** & **Inverse** & **\(I=100\)** & **\(I=200\)** & **\(I=400\)** & **\(I=800\)** \\ \hline
**Time**(s) & 203.97 & 25.53 & 29.90 & 39.02 & 70.75 \\ \hline \end{tabular}
\end{table}
Table 3: The processing time of the direct inverse operation and the CG method, where the corresponding computational complexity is \(\mathcal{O}((LJ)^{3})\) and \(\mathcal{O}(I(LJ)^{2})\), respectively. \(I\) is the iteration number of the CG method. The reverberation time \(RT60=200\) ms and the control filter length \(J=400\).