esa-sceva/satcom-chunk-collection huggingface dataset:
# Sulfate Resistance in Cements Bearing Bottom Ash from Biomass-Fired Electric Power Plants
## 4 Conclusions  
The following conclusions can be drawn from this study:  
* BA-bearing paste cement resistance to sulfate attack rises with the replacement ratio. In the 180 days materials containing 20 wt% of the addition, resistance was 6.7% higher than in the OPC of the same age.
* Because the cement pastes studied, irrespective of replacement ratio, exhibited a 56 days Koch-Steinegger corrosion index of \(>\)0.70, they may be deemed sulfate resistant at the concentrations and other experimental conditions established in this study.
* The weight and volume gains induced by sulfate soaking were lower in OPC + 20 BA than in OPC pastes, the former by 20.5% and the latter by 28.5%.
* The microcracking observed in the pastes analyzed is attributable to the expansive properties of the products of sulfate attack.
* Sulfate and sodium ingress into the paste microstructure translates primarily into inside-pore ettringit formation and gypsum plate precipitation, densifying the cementitious matrix.
* Gypsum and ettringit form primarily within the pore system, inducing its refinement.  
Conceptualization, J.M.M., M.I.S.d.R. and C.M.; methodology, J.M.M., I.F.S.d.B. and M.F.; formal analysis, J.M.M. and I.F.S.d.B.; resources, M.I.S.d.R., M.F. and C.M.; writing--original draft preparation, J.M.M. and I.F.S.d.B.; writing--review and editing, M.F., M.I.S.d.R. and C.M.; supervision, I.F.S.d.B.; project administration, M.I.S.d.R. and C.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Ministry of Economy and Competitiveness (BIA2016-76643-C3-1-R) (MINECO/ERDF), as well as by the Government of Extremadura and the European Regional Development Fund (ERDF) under grant GR 18122 awarded to the MATERIALS research group.  
The cement supplied by the Lafarge Group plant Villaluenga de la Sagra, Toledo, Spain, is gratefully acknowledged.  
The authors declare no conflict of interest.
## 2 Methods and Materials
### AEOM Extraction and Separation  
We followed [PERSON] et al. [32] where bulk AEOM was extracted with an alkaline solution. Briefly, 5 g air dry sediment was added to 0.1 N NaOH 100 mL solution. The sediment suspension was shaken for 24 h and centrifuged at 4500 rpm for 30 min. Four-liter bulk AEOM and DOM solutions (<0.45 \(\upmu\)m) were separated into the five molecular weight AEOM and DOM fractions. A cross-flow ultrafiltration system sequence equipped with 10, 3, 1, and 0.3 kDa nominal molecular weight cutoff ceramic membrane cartridges (Filtanium, France) was used to separate the five molecular weight AEOM/DOM solutions: MW-A (10 kDa-0.45 \(\upmu\)m), MW-B (3-10 kDa), MW-C (1-3 kDa), MW-D (0.3-1.0 kDa), and MW-E (<0.3 kDa). The feed flow rate was 1.7-2.0 L/min. In the concentration process, the volume concentration factor (C\({}_{l}\)) was kept at 10, the ratio of the sum volume of the retentate and permeate to retentate volume. In each separation process, the retentate flow was sent back to the feed flow container and the permeate flow was collected in another container. The mass balances (\(R\%\)) of the DOC and Ni were calculated by Equation (1). The mass percentages (\(M_{i}\)) of organic carbon and Ni in each fractioned DOM/POM were calculated following Equation (2).  
\[R(\%)=\frac{\sum(C_{i}\times C_{i})}{C_{bulk}\times V_{bulk}}\times 100 \tag{1}\]  
\[M_{i}(\%)=\frac{C_{i}\times V_{i}}{\sum(C_{i}\times V_{i})}\times 100 \tag{2}\]  
where \(R(\%)\) is percentage of mass balances for DOC, and Ni. \(M_{i}(\%)\) is mass percentages for individual size-fractionated DOC and Ni. \(C_{i}\) and \(V_{i}\) were the concentrations and volumes for each size-fraction in the separation processes for DOC and Ni. The volume ratios for the study were 1.0, 0.1, 0.09, 0.081, 0.0729, and 0.6561 for bulk and the five size-fractionated solutions: MW-A, MW-B, MW-C, MW-D, and MW-E, respectively. In the separated process the membranes were carefully cleaned following the suggestion by the manufacture.
## 5 Case Studies  
After determining a nominally optimal SWIPE prediction model, we have deployed it to provide sudden convective storm tracking and warning using H08/AHI data in NRT since 1 April 2018 at NSMC/CMA. For the H08/AHI data within a 10 min interval and 2 km spatial resolution, the averaged time cost of this SWIPE algorithm for tracking and warning sudden local convective storms over the East Asian area mentioned before is about 4 minutes, which can meet the latency requirement for operational nowcasting applications. Two typical sudden local convective storm cases tracked by the SWIPE are illustrated in detail as follows for demonstration purposes.
## 3 Data Preparation
### Road Segmentation for Data Collection  
The segmentation of road sections is the process of classifying large and complex datasets into homogeneous groups with similar characteristics [29]. Considering that traffic accident data are heterogeneous, the data segmentation is generally regarded as the first and most important step in the prediction of accident events and the severity of accidents [30].  
It has been reported that using an appropriate segmentation method can significantly reduce false positives and negatives, while determining the best sub-tree [31]. A representative segmentation method divides sections that are judged to have the same geometric factors regardless of the length [32; 33]. Although the segmentation into homogeneous sections is a common approach that is used in several accident prediction studies [34; 35; 36], because this study aims to estimate accident severity, modified homogeneity determination criteria from the existing methods were applied. As shown in Figure 2, the homogeneous section was defined as a section that is located 100 m in the front and back of the location of the RORC. In Korea, the interval between intersections of national roads is at least 500 m. In order to minimize the effect of intersections, the length of the homogeneous section was reduced to 100 m in the front and rear [37].  
In the segmentation method of the homogeneous section, the length of the section is increased by including both longitudinal and planar curve coincidence sections, and changes in other factors within the section occur significantly. This is because the characteristic is averaged over all \"fixed objects\" in the interval. However, the segmentation method of this study can minimize the factor change for \"fixed objects\" while reflecting the characteristics of the homogeneous section. In addition, within the same section, further classification was made according to factors such as the horizontal alignment (whether straight/curved), the radius of the curve and the vertical slope, and the combination status of reverse curve/continuous curve/compound curve, plane curve, and the vertical slope.
## 3 Results and Discussion  
### Fermentation Characteristics of Exogenous Strains  
_L. plantarum_ demonstrated a rapid increase in acid production in MRS medium, rising from 0.25 g/100 mL at 6 h to 1.95 g/100 mL at 24 h. After 24 h of fermentation, the total acid content remained relatively stable, showing minimal fluctuations (Figure 1a). The trend in acid production by _L. plantarum_ closely mirrored its growth pattern, primarily because lactic acid is a key metabolite for lactic acid microbes [33]. In the fermentation process, the production of organic acids, including lactic acid and acetic acid, by _L. plantarum_ enhanced the flavor and taste of baijiu. Additionally, it provided ample precursors for the composition of ester compounds [34].  
_P. anomalous_ exhibited a notable increase in total ester content in PDA medium from 6 h to 24 h, reaching a maximum of 2.61 g/100 mL at 24 h, highlighting its strong ester-producing capability (Figure 1b). The ester content gradually decreased from 24 h to 36 h and stabilized thereafter. The decrease in ester content might have been because of hydrolysis of esters by lipase from _P. anomalous_[35]. Ester compounds play a significant role in contributing to the characteristic flavor profile of CFB [8; 30].  
The tolerance of _L. plantarum_ and _P. anomalous_ to various ethanol concentrations was as depicted in Figure 1c. After 48 h of cultivation, both _L. plantarum_ and _P. anomalous_ showed robust growth in ethanol solutions with concentrations of up to 8%. Although growth was inhibited at an ethanol concentration of 12%, there was still some increase in cell density. During the brewing process of CFB, the maximum alcohol concentration in the mash reached 12%vol [31], indicating that both _L. plantarum_ and _P. anomalous_ can tolerate this level of alcohol. This tolerance allows for continued growth metabolism and the accumulation of additional flavor compounds, thereby enhancing the quality of the baijiu.
# Investigating Anti-Evasion Malware Triggers Using Automated Sandbox Reconfiguration Techniques
## 6 Comparison between MORRIGU and Cuckoo
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Malware Sample** & **Cuckoo Score (Win10)** & **Cuckoo Score (Win7)** \\ \hline
**Adposel\_60 de (Adware)** & 0.2 & 3.4 \\
**Andromeda\_240a (Downloader)** & 0.4 & 6.4 \\
**Andromeda\_928a (Downloader)** & 0.0 & 9.8 (9.2) \\
**Andromeda\_ea1e (Downloader)** & 0.4 & 6.4 \\
**Azorult\_c4e2 (Infostealer)** & 1.8 & 1.8 \\
**Bayrob\_8972 (Infostealer)** & 0.6 & 0.6 \\
**Bayrob\_ab9b (Infostealer)** & 0.6 & 0.8 \\
**Bayrob\_b12e (Infostealer)** & 0.4 & 0.4 \\
**Bayrob\_b6c7 (Infostealer)** & 0.4 & 0.0 \\
**BitCoinMiner\_8554 (Cryptocurrency Miner)** & 0.6 & 0.6 \\
**Blocker\_2c1c (Ransomware)** & 0.0 & 0.6 \\
**Blocker\_7 fe1 (Ransomware)** & 0.0 & 4.2 (1.0) \\
**Dridex\_58 fb (Banking Trojan)** & 4.4 & 3.6 \\
**Dridex\_d684 (Banking Trojan)** & 4.8 (5.2) & 4.4 \\
**Dridex\_f236 (Banking Trojan)** & 4.0 (4.4) & 3.6 \\
**Gamarue\_f7d2 (Downloader)** & 0.8 & 1.8 \\
**Gozi\_3130 (DGA)** & 0.2 & 1.0 \\
**Gozi\_8e8b (DGA)** & 0.8 & 2.0 (1.2) \\
**IcedID\_04 fb (Banking Trojan)** & 2.2 & 2.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cuckoo Sandbox scoring results for 40 evasive malware samples, using the default cuckoo settings (Windows 10: Mean = 1.2, Median = 0.6; Windows 7: Mean = 3.66, Median = 2.2). Scores in brackets show where results differ when the “human” module is disabled. Figure 6: Distribution of Cuckoo scoring for the 40 evasive malware samples (Windows 10 (Orange): Mean = 1.2, Median = 0.6; Windows 7 (Blue): Mean = 3.66, Median = 2.2).
## Appendix A Proof of Theorems 1 and 2
### B.1. Proof of Theorem 3
#### b.1.2 \(H(A|Z)\leq I(U;Y)-I(U;Z)\)  
In this case, we need to prove that any rate-equivocation triple \((R_{1},R_{2},R_{e})\) satisfying the following constraints are achievable.  
\[R_{1} \leq H(B)\] \[R_{1}+R_{2}\leq I(U;Y)\] \[R_{e} \leq R_{1}+R_{2}\] \[R_{e} \leq H(A|Z)\]  
It is sufficient to show that \((R_{1},R_{2},R_{e}=H(A|Z))\) are achievable. The coding scheme is as follows.  
_Codebook generation and encoding_: Let \(R_{1}=H(B)-\theta_{1}^{\prime}\) and \(R_{1}+R_{2}=I(U;Y)-\theta_{2}^{\prime}\), where \(\theta_{1}^{\prime}\), \(\theta_{2}^{\prime}\) are fixed positive numbers. For each \(m_{1}\in\{1,2,...,2^{NR_{1}}\}\), an independent and identically distributed (i.i.d) codeword, \(b^{N}(m_{1})\), is generated according to \(p(b^{N})=\prod\limits_{i=1}^{N}p(b_{i})\). Then, the \(2^{NR_{2}}\) action sequences\(a^{N}(m_{1},m_{2})\) are i.i.d generated for each \(b^{N}(m_{1})\) according to \(p(a^{N}(m_{1},m_{2})|b^{N}(m_{1}))=\prod\limits_{i=1}^{N}p(a_{i}|b_{i})\), where \(m_{2}\in\{1,2,...,2^{NR_{2}}\}\). For each \(a^{N}(m_{1},m_{2})\), a corresponding codeword, \(u^{N}(m_{1},m_{2})\), is generated according to \(p(u^{N}(m_{1},m_{2})|a^{N}(m_{1},m_{2}))=\prod\limits_{i=1}^{N}p(u_{i}|a_{i})\). To send the message \((m_{1},m_{2})\) with the action sequence, \(a^{N}(m_{1},m_{2})\), and corresponding state sequence \(s^{N}\), the encoder selects the codeword, \(u^{N}(m_{1},m_{2})\). Then, the input sequence of the channel is generated by \(p(x^{N}|u^{N}(m_{1},m_{2}),s^{N})=\prod\limits_{i=1}^{N}p(x_{i}|u_{i},s_{i})\).  
_Decoding and error probability analysis_: This step follows similarly from Case \(A\) in Section IV.  
_Equivocation analysis_: We need to prove \(\lim\limits_{N\to\infty}\triangle=\lim\limits_{N\to\infty}\frac{H(M_{1},M_{2} |Z^{N})}{N}\geq R_{e}\). The methods in [18] are utilized.  
\[\lim\limits_{N\to\infty}\frac{H(M_{1},M_{2}|Z^{N})}{N} =\lim\limits_{N\to\infty}\frac{H(A^{N}(M_{1},M_{2})|Z^{N})}{N} \tag{75}\] \[=\lim\limits_{N\to\infty}\frac{NH(A|Z)}{N}\] (76) \[=H(A|Z)\] \[\geq R_{e}\]  
where Equation (75) is from that \(A^{N}\) is a function of \((M_{1},M_{2})\), and Equation (76) is from that the sequences, \(A^{N}\) and \(X^{N}\), are i.i.d generated and that the channels are discrete memoryless.  
We complete the proof of Theorem 3.
## 4 Deep Learning Methods for HDR Reconstruction
### HDR Video Reconstruction
#### 4.5.2 Attention-Based Approaches  
Seeking to tackle limitations from flow estimation errors, which cause ghosting in frames with complex motion or extreme saturation, some attention-based HDR video reconstruction methods have emerged, including SoTA approaches such as the network by [PERSON] et al. [33] named LAN-HDR. This network aligns frames by computing a luminance-based attention score to register the motion between adjacent LDR frames and the reference frame. [PERSON] et al. [34] recently proposed an HDR video reconstruction model using diffusion, which is illustrated in Figure 12. The architecture is composed of a latent HDR diffusion model that learns the prior distribution of individual HDR frames, a module for  
Figure 11: HDRFlow architecture.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**2-Exposure**} & \multicolumn{4}{c}{**3-Exposure**} \\ \cline{2-9}  & **PSNR\({}_{T}\)** & **SSIM\({}_{T}\)** & **HDR-VDP-2** & **Time (ms)** & **PSNR\({}_{T}\)** & **SSIM\({}_{T}\)** & **HDR-VDP-2** & **Time (ms)** \\ \hline Kalantari19 [30] & 39.91 & 0.9329 & 71.11 & 200 & 38.78 & 0.9331 & 65.73 & 220 \\ \hline Chen [31] & 42.48 & **0.9620** & 74.80 & 522 & 39.44 & 0.9569 & 67.76 & 540 \\ \hline LAN-HDR [33] & 41.59 & 0.9472 & 71.34 & 415 & 40.48 & 0.9504 & 68.61 & 525 \\ \hline HDRFlow [32] & **43.25** & 0.9520 & **77.29** & 35 & 40.56 & 0.9535 & **72.42** & 50 \\ \hline HDR-V-Diff [34] & 42.07 & 0.9604 & 70.88 & – & **40.82** & **0.9581** & 68.16 & – \\ \hline \hline \end{tabular}  
* **Bold**: best performance; Underlined: second best.  
\end{table}
Table 8: Performance of HDR video reconstruction methods on the DeepHDRVideo dataset.  
temporally consistent alignment that learns multi-scale feature alignment among frames, and a reconstruction module that, with the help of a Zero-init Cross-Attention (ZiCA) block, effectively integrates the learned information.  
Table 9 summarizes the main advantages and disadvantages of HDR video reconstruction methods discussed in the previous subsections.
## 3 Experiment  
### Data Used in the Experiment  
To assess the validity and robustness of the model, we evaluated it in three publicly available datasets containing retinal data from 61 independent subjects with normal and diseased conditions obtained from different OCT instruments and provided by different clinical center institutions.  
These datasets were from the A2A SD-OCT program registered with the U.S. Clinical Trails Registry ([[https://clinicaltrials.gov/](https://clinicaltrials.gov/)]([https://clinicaltrials.gov/](https://clinicaltrials.gov/))) and provided by the AREDS2 Collaborating Institutions. The first dataset, named Data-1 [40], contains 17 noisy retinal images. The noisy images were acquired by an 840 nm wavelength Biopitgen SDOCT imaging system, which is from Biopitzen Inc. (Research Triangle Park, NC, USA) and had an axial resolution of \(\sim\)4.5 \(\upmu\)m per pixel in tissue. The clean images were obtained by capturing multiple B-scans from the same location, aligning these B-scans using an image alignment technique, and then averaging them. First, 6 image pairs with limited quality were removed from this dataset due to excessive smoothing of some images during registration and averaging, and details were lost, affecting the authenticity of the clean images. The structural features and details were not clearly displayed, which had a significant impact on the training process. The remaining 11 pairs of images were used to conduct the experiments. We randomly selected 8 pairs of images as the training set and 3 pairs of images as the test set. Since the GAN model requires a large amount of data for training and each pair of images did not have the same size, all the images in the training set were slid through each training sample with a resolution of 500 \(\times\) 950 (height \(\times\) width), a window size of 256 \(\times\) 256, and a stride of 30, generating 3888 blocks of images for training. In the preprocessing stage, the input image was converted into a single-channel grayscale map adapted to the single-channel nature of the retinal OCT image, while the image was normalized to the [0, 1] range. The remaining 3 pairs of images were fed into the trained generator for comparison with other methods used for OCT denoising.  
To further evaluate the generalization ability of the model, the second dataset, named Data-2 [41], contains 24 pairs of retinal images of the same size, normal eyes, and retinas with non-neovascular age-related macular degeneration. The noisy images were acquired by the same Biopitgen SDOCT imaging system. The third dataset, Data-3 [42], contains 20 noisy images of size 1000 \(\times\) 512, with no corresponding clean images. The noisy images were from real low-quality clinical scan images without any artificial noise synthesis. To assess the generalization ability of the model, the trained model of dataset Data-1 was used in this experiment to process the images in datasets Data-2 and Data-3. The denoised images and performance metrics of all the methods will be shown in the results.
## 3 Experimental Results and Simulation Analysis
### Experimental and Simulation Analysis  
The data between simulation and experiment was compared, and the overall variations in temperature, relative humidity, and velocity among the six monitoring points were analyzed. Tables 3-5 compare the temperature, relative humidity, and velocity data for each layout between the experimental and simulation monitoring locations. Firstly, the initial layout (Layout 1) is analyzed, and detailed information on other layouts will be mentioned below. The maximum relative errors between experimental and simulation monitoring points for Layout 1 are only 3.21%, 7.14%, and 7.14%, which are within an acceptable range. At the same time, after the simulated electric heating wire reaches a steady state, the average temperature of the electric heating wire in the experiment is 75 \({}^{\circ}\)C, and the simulated average temperature is 73 \({}^{\circ}\)C, with a relative error of 2.7%. On this basis, further research will be conducted on the distribution of multiple physical fields in cable rooms under different layouts. Then, each layout was simulated, the same boundary condition parameters at the entrance and exit positions as Layout 1 were set, and experimental verification was conducted, repeating the previous experimental process of Layout 1. According to Tables 3-5, the maximum relative error (RE) between the temperature of the corresponding monitoring points in each layout experiment and simulation is only 3.61%, the maximum RE between the relative humidity of the experimental and simulation monitoring points is only 7.14%, and the maximum RE between the speed of the experimental and simulation monitoring points is only 7.69%. Additionally, the average absolute percentage error (MAPE) of each layout and data did not exceed 5.87%. The error between the simulation and experiment is within an acceptable range, which further verifies the reliability of the simulation. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Points**} & \multicolumn{4}{c}{**Layout 1**} & \multicolumn{4}{c}{**Layout 2**} & \multicolumn{4}{c}{**Layout 3**} & \multicolumn{4}{c}{**Layout 4**} \\ \cline{2-13}  & \multicolumn{4}{c}{**Temperature**} & \multicolumn{4}{c}{**Temperature**} & \multicolumn{4}{c}{**Temperature**} & \multicolumn{4}{c}{**Temperature**} & \multicolumn{4}{c}{**Temperature**} \\ \cline{2-13}  & **EXP** & **CFD** & **RE** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\%)** & **MAPE** & **EXP** & **CFD** & **RE** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)**  
Simultaneously, a similar trend between the experiment and simulation was discovered in each layout by comparing the total data of each measurement point. Figure 7a,b display the relative humidity and dew point temperature trend charts for each configuration. The data distribution map is divided into three sections by two vertical dashed lines.
## 1 Introduction
### Background
Moreover, the use of a variety of levels of EMP shielding effectiveness and shielding materials is very advantageous for the military, as they facilitate flexibility in military operations. In this study, we differentiated EMP protection levels using a method of optimizing the military operation environment and available assets as compared to the conventional EMP protection method, and we developed decision-making factors for determining protection levels. Based on this, we modeled brigade-level, underground EMP protection facilities, simulated their EMP-shielding performance to develop an EMP shielding method suitable for strategic and tactical troops, and applied it to national defense policies.
## 2 Materials and Methods  
### Pedicle Screw Design  
Six types of pedicle screws were made through the same materials and processes based on three different shape (cylindrical, conical, and proximal-conical) and two thread profiles (single-thread and dual-thread). The screws were grouped as follows: single-thread and cylindrical shape (S-Cy), single-thread and conical shape (S-Co), single-thread and proximal-conical shape (S-PCo), dual-thread and cylindrical shape (D-Cy), dual-thread and conical shape (D-Co), and dual-thread and proximal-conical shape (D-Pco). Cylindrical, conical, and proximal-conical screws differed mainly in the outer diameter from the hub to the tip. Cylindrical screws maintained a constant outer diameter (6.5 mm) from hub to tip, conical screws had an outer diameter (6.5 mm) that tapered from the hub to the tip by 30% (to an outer diameter 4.5 mm), and proximal-conical screws had an outer diameter (6.5 mm) from hub and tapered by 30% to an outer diameter (4.5 mm) at 20 mm from hub, then maintained the outer diameter (45 mm) to the tip. The proximal high slope design of the proximal-conical screws theoretically preserves more pedicle bone stock than traditional cylindrical or conical screw. For the single-thread screw, coarse threads covered the entire length; for the dual-thread screw, fine threads covered the region 20 mm from the hub, followed by coarse threads to the tip. The thread pitch and depth of the coarse thread were designed as 4.0 mm and 1.1 mm, respectively, and 2.0 mm and 1.1 mm of the fine thread, respectively. The six groups of pedicle screws with various designed geometries are illustrated in Figure 1.