esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Sound-to-Image Translator
### Network Architecture  
As depicted in Figure 2, the audio autoencoder consists of a mirrored architecture of 26 convolutional layers, each one followed by batch normalization (BN). The inner layers of both the encoder and decoder are activated by rectified linear units (ReLU), while the last layer employs a hyperbolic tangent function (TanH). As for the decoder part, the network applies dropout regularization to each inner layer to prevent overfitting.  
Regarding the generator architecture, a notable improvement over the baseline S2I translator was achieved by employing a deeper 25-layer architecture, especially when applying \"skip connections\" to make a dense generator. This final architecture draws inspiration from DenseNet [59] (Figure 3). Compared to the initial 13-layer sequential generator, which resembles the audio decoder in structure, the new deeper and denser architecture allows for a substantial improvement in the quality of the generated images. The convolutional layers of the generator are followed by BN and ReLU activation, except for the output layer, which employs TanH. Additionally, we include dropout regularization between each dense block to improve model generalization, as well as to prevent deterministic inference through its application also at test time, as utilized by [PERSON] [60] and [PERSON] et al. [13].  
As for the discriminator architecture (Figure 4), it closely resembles the structure of the audio encoder, differing primarily in the input and the shape of the feature maps. Additionally, the last convolutional layer of the discriminator is conditioned with the embedding extracted from the audio encoder. Moreover, dropout regularization is applied after each inner layer. The last convolutional layer directly outputs a scalar corresponding to the RC-score.  
Figure 4: Discriminator architecture.  
Figure 2: Audio autoencoder architecture.
# Evaluation of Ultrasonic Bonding Strength with Optoacoustic Methods
## 6 Conclusions  
We applied the optoacoustic method to evaluate the bonding strength in a non-destructive and non-contact fashion. The experiment showed that the bonding strength of an ultrasonically bonded joint increased with the enlargement of the bonded area. The increase in the bonded area led to the higher resonant frequency of the joint in the vibration test: the resonant frequency increased exponentially with the bonding strength. It was indicated that the joint strength is correlated with the resonant frequency of the joint better than with the vibration time. In addition, the resonant frequency and the vibration modes were visualized by ESPI. The exponential correlation between the bonded area and the resonant frequency was confirmed by FEA. The vibration modes obtained by FEA were consistent with the experimental results in ESPI.  
This study indicated that it is possible to evaluate the ultrasonic bonding strength by obtaining the correlation curve beforehand between the bonding strength measured by the tensile shear test and the resonant frequency with a non-destructive method.  
Conceptualization: S.Y., Funding acquisition: T.S., Investigation: T.K. and T.S., Project administration: T.S., Writing, original draft: T.K., Writing, review and editing: S.Y. and T.S.  
This research received no external funding.  
The authors declare no conflict of interest.
# Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion
## 3 Framework for System Design and Experimentation
### Thermal Camera  
Thermal camera is a device that measures the temperature variations using the infrared light. Every pixel on a camera image sensor is an infrared temperature sensor and gets a temperature of all points at the same time. The images are generated according to temperature format and displays images in the form of RGB. Unlike normal imaging cameras, thermal camera is not constrained by dark surroundings and can work with any environment regardless of its shape and texture [37]. Seek Thermal Camera, used in this work, is a compact thermal camera consisting of 206 \(\times\) 156 Thermal Sensor, a 36-degree field of view, measurement of temperature range \(-\)40 \({}^{\circ}\)C to 330 \({}^{\circ}\)C, framerate <9 Hz, and 32,136 Thermal Pixels to be able to see a thermal image easily.  
The gas sensors and thermal camera are used simultaneously to collect data for training and testing of the developed fusion model. The next part of the paper describes the data collection and its preprocessing in detail.
## 2 Results and Discussion  
### Transgenic P. berghei Lines Expressing Tagged Baits  
The aim of this study was to identify PP1c partners in order to better unravel its biological roles and functions in _Plasmodium_ asexual and sexual stages. To address the analysis of its interactions and its formed complexes, we conducted an IP-MS based study using tagged proteins in _P. berghei_. Our selected protein baits were PP1c and its I2 regulatory subunit. The generation of a _P. berghei_ line expressing mCherry-tagged PP1c has previously been reported [14]. As for PbI2, a parasite line expressing mCherry-tagged I2 from its endogenous locus was generated in the same strain used for PbP1c. Genotyping analysis showed the correct integration of the tagged PbI2 and the expression of mCherry-PbI2 was checked by immunoblot using anti-RFP antibody (Supplementary Materials Figure S1). It is important to point out that the _P. berghei_ ANKA strain used throughout this study produced gametocytes with expected ookinetic conversion rates (data not shown).  
For proteomic experiments, a schematic illustration of the methodology used is shown in Figure 1. Schizonts and gametocytes were purified, and IP experiments were performed with proteins extracted from five independent biological replicates using beads coupled to anti-RFP antibody (see Section 3 for details). Prior to MS proteomic analysis, the presence of tagged baits in each IP was checked by immunoblot (illustrated in Figure 1). As control experiments, we performed IP on proteins extracted from the same parasite stages of the parental wild-type strain. In all experiments, the eluted PbPP1c and PbI2-interacting complexes were analyzed by LC-MS/MS.
## 4 Experimental Results
### Case Studies
#### 4.2.2 Typhoon Noru (1705)  
In Figure 9, we also noted that our method tended to underestimate the maximum wind speeds when the best track data was about 40 m/s. Figure 11 shows the SSMIS and HY-2A images of Super Typhoon Noru (1705) at 30 July 2017 (the mature stage). The maximum wind speed estimated by the regression model was 45.98 m/s, but the maximum wind speed in the CMA best-track data was 52 m/s. In the regression model, SSW_MAX_C250 was 28.54 m/s. At the same intensity, we found that in the previous image, SSW_MAX_C250 value was 38.5 m/s. Meanwhile, we analyzed that the value of the Advanced Scatterometer (ASCAT) wind field data on this day was 28.23 m/s. Also, the precipitation rate obtained by the GPM was above 450 mm/day. Therefore, we inferred that this error was probably due to the wind speed retrieval error caused by heavy rainfall.  
Figure 10: Images of Super Typhoon Noru (1705) at 20 July 2017, during the early stages of development,(**a**) SSMIS, (**b**) HY-2A. The estimated maximum wind speed was 8.1 m/s, but the CMA best-track data was 13.6 m/s. The cross symbol is the center of Noru. The radii of the circles from inside to outside are 0.5\({}^{\circ}\), 0.75\({}^{\circ}\), 1\({}^{\circ}\), and 1.25\({}^{\circ}\), respectively.  
Figure 9: Time series plot of the maximum wind speed estimated by the regression model, and the best track data for (**a**) Typhoon Tembin (1214) and (**b**) Super Typhoon Noru (1705).
## 3 Results and Discussion
### Validation of Various Methods on Independent Datasets  
To further demonstrate the generalization ability of our predictor for identifying 4 mC sites, we validated them on the independent datasets built by [PERSON] et al. Through applying the XGB4 mcPred predictor to the six independent datasets, the four metrics were calculated. The detailed comparison results can be found in Table 4. Since many of the previous predictor webservers are not accessible, we compared it with the six state-of-the-art predictors, including 4 mCPred [19], 4 mcPred-SVM [20], Meta-4 mCpred [21], 4 mcPred-IFL [22], Deep4 mC [24], and DeepTorrent [25]. Among them, the DeepTorrent is the most advanced predictor available using deep learning techniques. The detailed  
Figure 8: Performances of the proposed XGB4 mcPred and five state-of-the-art predictors on six benchmark datasets from different species. comparison results can be found in Table 4. As shown in Table 3, compared with the previous state-of-the-art predictor built by the traditional machine learning methods, our test results show that our proposed XGB4 mcPred predictor outperformed the previous state-of-the-art machine learning predictors on the four datasets of _A. thaliana_, _C. elegans_, _E. coli_, _D. melanogaster_. Especially on the _C. elegans_, the ACC value of our independent test set breaks through to the value of 90% for the first time. Compared to current state-of-the-art machine learning predictors, the accuracy of the _D. melanogaster_ dataset also reached 91.8%, which was 1.1-3.2% increased. On the _E. coli_ dataset, our predictor significantly improved by 3.7-7.8% compared with the previous state-of-the-art machine learning predictors. For the _A. thaliana_ dataset, we also got a slight improvement. However, on the _G. subterruneus_ and _G. pickergini_ datasets, our model's generalization was slightly inferior to the Meta-4 mcPred predictor. However, compared with other state-of-the-art machine learning predictors, our predictors are still better than them.
# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 6 Epigenetics in Cancer: From Biomarkers to Therapeutic Targets
### A New Frontier: Epigenome Editing and Precision Oncology  
With advances in CRISPR-based tools, targeted epigenome editing is now practical, allowing for the activation or repression of specific gene loci without inducing DNA  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Epigenetic Target** & **Type of Modification** & **Representative Inhibitors** & **Cancer Types** & **Mechanism/Clinical Use** \\ \hline DNMTs (DNMT1, _DNMT3A_) & DNA methylation & Azacitidine, decitabine & AML, MDS & Reactivates silenced tumor suppressor genes \\ HDACs (HDAC/1/2) & Histone deacetylation & Vorinostat, romidepsin, & T-cell lymphoma, breast, & Promotes gene re-expression; \\ EZH2 & Histone methylation & Tazemetostat & Follicular lymphoma, & Inhibits gene repression \\  & (H3K2/me3) & & epithelial sarcoma & under clinical application \\ BRD4 & acetylation reader & JQ1 (prelinical) & TNBC, prostate cancer & Disrupts oncogenic \\  & (bromodomain) & & TNNC, prostate cancer & Transcription programs \\ Lactylation enzymes & Histone lucid & Under preclinical & Hypoxic tumors & May regulate stemness, \\  & & investigation & Hypoxic tumors & angiogenesis, immune \\  & & & evasion \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overview of key epigenetic targets in cancer, their associated modifications, representative therapeutic agents, and mechanisms of action. Several of these targets are currently under clinical evaluation, while others represent emerging opportunities in epigenetic therapy.  
breaks. This approach is being used in models to restore tumor suppressor expression, modulate immune gene expression, and even sensitize tumors to chemotherapy [66]. In summary, epigenetic mechanisms play a multifaceted role in cancer biology as drivers of transformation, biomarkers for early detection, and targets for novel therapies. Their reversible nature offers a unique therapeutic window, and their integration into multi-omics oncology strategies is expected to advance personalized, dynamic, and mechanism-driven cancer care.
# Numerical Analysis of Ultrasonic Multiple Scattering for Fine Dust Number Density Estimation
## 5 Results and Discussion
### Effects of Particle Shapes  
When computing a total scattering cross-section for a circular fine dust particle, the analytical solution for the case of a rigid circular cavity is used. This is because the scattering cross-section for a circular fine dust particle is similar to that for a rigid circular cavity (see Figure 6) and the rigid cavity case is computationally comprehensive. However, the shape of fine dust particles can vary in real-world applications.  
The effects of varying particle shapes on the total scattering cross-section were investigated by considering six different particle shapes, as shown in Figure 9a. The upper three particle shapes (standard, ellipse, and two aggregated circles) were artificially designed by the authors, and the lower three particles (SEM 1 to 3) were obtained by image segmentation from scanning electron miscopy (SEM) images of actual fine dust particles.  
Figure 8: Number density estimation results (**left**) and estimation error (**right**) for different scatterer volume fractions: (**a**) 2%, (**b**) 5%, (**c**) 10%, and (**d**) 15%.  
The total scattering cross-sections computed for the six particle shapes are depicted in Figure 9b along with those computed from the analytical solution (Equation (13)) with a cavity diameter of 2 \(\upmu\)m. Figure 9b indicates that the scattering cross-section values vary significantly for the six particle shapes and are different from the analytical solution, especially at frequencies higher than 4 MHz. These results reveal that varying particle shapes can be sources of uncertainty when estimating the number density in real-world applications. Modeling the scattering cross-section as a point estimate can cause an erroneous number density estimation. Rather, we suggest the scattering cross-section as a probability distribution as an output across possible values. This approach can provide a probability distribution that enables not only the estimation of the number density but also the uncertainty quantification of the estimate.
## 3 Results
### Results of Statistical Correlation between Brain-Volume and pASL
#### 3.2.2 Patients with MCI  
Of the 18 brain regions, one displayed a good positive linear correlation between significant brain atrophy and a reduced rCBF of the pASL data. The left inferior fronto-occipital fasciculus (IFO) demonstrated a good correlation between rCBF and brain volume (Spearman-Rho = 0.786, \(p\) = 0.036).
## 3 Results and Discussion
### Activity of the Enzymatic Preparations Used in Our Experiments  
Since we used commercial enzyme preparations, it was deemed useful to determine the actual pectinase, cellulase, and xylanase activity values in the commercial batches of Viscozyme and UltrafloMax. The results are presented in Table 2.  
The enzymatic activities we determined for Viscozyme correlate with those presented by [PERSON] et al. [25], who reported cellulase, xylanase and pectinase activities of 20.9, 25.9 and 312.6 U/mL. [PERSON] et al. [56] also reported similar activities of the same enzymes as ours (13.9, 25.1 and 412 U/mL). Similarly, the results presented by us for the UltrafloMax preparation are confirmed in the publication by [PERSON] et al. [25], who reported the following values: 32.7, 64.7 and 21.2 U/mL for cellulase, xylanase and pectinase activity, respectively. For the same preparation, [PERSON] et al. [56] reported similar cellulase and pectinase activities (27.8 and 24.2 U/mL). In comparison, the xylanase activity they reported (127.4 U/mL) is more than twice as high as the one presented in Table 2 (58.29 U/mL), as well as the value provided by [PERSON] and colleagues [25]. This difference may result from using preparations from different production batches, which, in the case of multi-enzyme industrial preparations, may differ in the activity of individual enzymes. Similarly, other transport and storage conditions may also have affected the activity of the preparations analysed.  
The determined cellulase activity was used to establish the dose of both enzyme preparations at 0.05 mL/g dry weight. Based on literature reports [25; 26; 57] and our previous work in this field [36; 37; 58], we assumed that the cellulase dose should be in the range of 20-30 FPU/g cellulose. The enzyme dose we adopted (0.05 mL/g DM) corresponded to approximately 27 FPU/g of cellulose contained in banana peels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Enzyme** & **Celluse** & **Pectinase** & **Xylanase** \\ \hline  & & **Activity [U/mL]** & \\ \hline Viscozyme & 23.26 \(\pm\) 0.76 & 287.06 \(\pm\) 8.2 & 27.41 \(\pm\) 1.18 \\ UltrafloMax & 30.3 \(\pm\) 0.88 & 23.94 \(\pm\) 0.91 & 58.29 \(\pm\) 3.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Activity of selected enzymes in Viscozyme and UltrafloMax preparations.
# Moving Mesh Strategies of Adaptive Methods for Solving Nonlinear Partial Differential Equations
## 2 The Improved Moving Mesh PDEs
### The Proposed Moving Mesh Equations  
According to the discretization forms of the moving mesh equations of [PERSON] and [PERSON], one can observe that [PERSON]'s MMPDEs and [PERSON]'s moving mesh equations are complementary. In order to accelerate the mesh's convergence to the EP, we combine the location-based and velocity-based method and construct the new moving mesh PDE that moves the mesh by two steps: Firstly, the approximation to the EP on fixed time, then the movement along the original PDE to keep the distribution.  
For the original PDE (1), the proposed moving mesh PDE takes the form:  
\[x_{t}=-\frac{1}{\tau}\frac{E_{1}(x,t)}{M(x,t)}-L(u). \tag{17}\]  
To interpret the proposed moving mesh Equation (17) more precisely, discreting Equation (17) applying the explicit Euler discretization on time, one can get:  
\[x(\xi,t_{k+1})=x(\xi,t_{k})-\frac{\Delta t_{k}}{\tau}\frac{E_{1}(x(\xi,t_{k}), t_{k})}{E^{\prime}_{1x}(x(\xi,t_{k}),t_{k})}-\triangle t_{k}L(u(x(\xi,t_{k}),t_{k})),\]  
divide the above into two steps:  
\[x^{(1)}(\xi,t_{k})=x(\xi,t_{k})-\frac{\Delta t_{k}}{\tau}\frac{E_{1}(x(\xi,t_ {k}),t_{k})}{E^{\prime}_{1x}(x(\xi,t_{k}),t_{k})}, \tag{18a}\] \[x(\xi,t_{k+1})=x^{(1)}(\xi,t_{k})-\triangle t_{k}L(u(x(\xi,t_{k}),t_{k})), \tag{18b}\]  
where \(x^{(1)}(\xi,t_{k})\) is an intermediate term.  
**Remark 1**: _For arbitrary initial mesh, if the mesh is already equidistributed, \(E_{1}=0\), (18a) is no longer in force and (18b) keeps the equidistribution when moved to the next time iteration. If the mesh is not equidistributed, then \(E_{1}\
eq 0\), (18a) could draw the mesh to be more equidistributed, then (18b) could keep the distribution when moved to the next time iteration. For arbitrary initial mesh, the new moving mesh PDE is able to draw the mesh to equidistribution efficiently and stably._  
**Remark 2**: _Compared with the semi-discretization of moving mesh PDEs (11) proposed by [PERSON], (16) proposed by [PERSON], and the present (18), one can observe that the proposed moving mesh equation includes both the advantages of the MMPDEs proposed by [PERSON] and [PERSON]. The convergence speed of the mesh to the EP are accelerated and the mesh equidistribution process is more continuous, hence, the probability of the mesh crossing during time iterations is reduced, and the moving mesh process becomes more stable._
## 3 Results and Discussion
### Corrosion Protection Evaluation
#### 3.2.2 RAMAN Spectroscopy  
The adsorption of ATT onto mild steel was also evaluated by Raman spectroscopy. Figure 8 illustrates the corresponding spectra of mild steel panels after exposure to a corrosive environment for 144 h, in the presence or absence of Ce\({}_{\mathrm{x}}\)Ti\({}_{\mathrm{y}}\)O\({}_{\mathrm{z}}\) nanocontainers loaded with ATT. The identification of the corresponding Raman spectra peaks is tabulated in Table 4. Considering the Mild Steel-3.5%NaCl, (Figure 8a) the characteristic peaks of iron oxides due to corrosion process can be clearly observed. The compounds hematite (\(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)) [45], leptolocroide (\(\gamma\)-FeOOH) [46], goethite (\(\beta\)-FeOOH) [47], magnetite (Fe\({}_{3}\)O\({}_{4}\)) [48], feroxyhte (\(\delta\)-FeOOH) [45], are clearly seen in the region 154-393 cm\({}^{-1}\). The presence of maghemite(\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)) is confirmed by the peaks at 715 and 776 cm\({}^{-1}\)[48]. The peak at 683 cm\({}^{-1}\) is assigned to goethite [45, 49], whereas the peaks at 1362 and 1613 cm\({}^{-1}\) are attributed to ferrihydrite (Fe\({}_{5}\)HO\({}_{8}\)-4H\({}_{2}\)O) [45, 50]. Finally, the peak at 154 cm\({}^{-1}\) is ascribed to the symmetric stretch of interlayer chloride ions in iron oxide (green rust) [51].  
Figure 7: (**a–c**) SEM images and the corresponding (**d–i**) EDS analyzes of mild steel immersed into 3.5 wt.% NaCl for 144 h in the presence of Ce\({}_{\mathrm{x}}\)Ti\({}_{\mathrm{y}}\)O\({}_{\mathrm{z}}\) nanocontainers loaded with ATT (Mild Steel-3.5%NaCl-ATT).  
Regarding the Mild Steel-3.5%NaCl-ATT, several peaks confirm the adsorption of ATT onto the mild steel surface (Figure 8b). The peaks at 341 and 469 cm\({}^{-1}\) are considered to be caused by the Fe-S bond vibration created by the chemisorption of ATT [52, 53]. The peak at 406 cm\({}^{-1}\) is attributed to the NCS of the ring vibration due to ATT [54]. The peak at 788 cm\({}^{-1}\) is assigned to the N-H out of plane stretching due to ATT [55]. Furthermore, the N-N vibration due to ATT appears at the peak 1037 cm\({}^{-1}\). In addition, the peaks at 1146 and 1391 cm\({}^{-1}\) are ascribed to the C-N vibration due to ATT [54]. Finally, the peak at 185 cm\({}^{-1}\) is attributed to the symmetric stretch of interlayer chloride ions in iron oxide (green rust) [51].