esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Total Plate Count (TPC)  
The results of TPC (Figure 1) show that the TPC of high-pressure-treated samples is significantly lower than HIGs (\(p<0.05\)). TPC of untreated raw pangasius minced muscle was 5.24 \(\pm\) 0.21 log CFU/g. All treatments resulted in a reduction in microbial load from 3.02 to 4 logs CFU/g, depending on the treatment conditions. Studies on the effects of high pressure on microorganisms have shown that, at pressure levels of 300 MPa or higher for a few minutes and room temperature (RT), it generally inactivates viable microorganisms in various food products [29]. HPP was known to effectively inactivate heat-resistant fungal spores, such as _Talaromyces flavus_ and _Byssochlamys nivea_, which cansurvive conventional thermal processing [30]. Moreover, the combination of HPP and cooking has been shown to improve the inactivation of spore-forming bacteria such as _Bacillus cereus_ and _Bacillus subtilis_[31]. Thus, these findings are in agreement with previous research on the antimicrobial effects of high-pressure processing. The lowest TPC was found at a pressure level of 500 MPa, ranging from 1.38 to 1.45 log/mL, and significantly lower than samples treated at 300 MPa and 400 MPa (_p_ < 0.05). This result is also similar to the findings of [PERSON] et al. [32], in which TPC decreased as the pressure level increased. TPC results show that high-pressure processing effectively reduces microbial numbers in pangasius gel, particularly at 500 MPa.
# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 3 Discussion
### Limitations of Current Platforms  
Despite their successes, there are several limitations and gaps that remain in current multi-omics platforms [33]. One of the most pressing issues is the need for greater data integration. While many platforms support multiple omics types, the integration of additional layers--such as epigenomics, spatial omics, and microbiomics--is still lacking in several tools [34]. This restricts the ability to capture the full complexity of biological systems. Moreover, interoperability between platforms remains a challenge. The lack of standardized data formats and integration protocols can hinder the seamless exchange of data between different tools, reducing their overall utility in multi-disciplinary research settings.  
Another critical limitation is the reliance on static datasets [35]. Many platforms operate on fixed datasets that may not be frequently updated or connected to real-time data sources. This is particularly problematic in clinical settings, where dynamic and real-time data integration is essential for timely decision-making and personalized treatment strategies. For example, clinical applications require platforms that can continuously update data based on patient outcomes and incorporate new discoveries from ongoing research. Additionally, while some platforms have integrated machine learning (ML) and AI-based tools, the full potential of these technologies has not yet been realized [36]. Many platforms still rely on traditional statistical approaches, which may not be sufficient to handle the growing volume and complexity of multi-omics data. The incorporation of more advanced AI tools, including deep learning models and smart algorithms, is essential for extracting actionable insights from multi-omics datasets.  
While platforms such as multiSLIDE offer user-friendly, web-based interfaces that reduce barriers to entry for researchers without advanced programming skills, the learning curve for new users remains a consideration. Features like keyword-driven queries, real-time data filtering, and interactive heatmap-based visualization simplify multi-omics data exploration without requiring coding expertise. Additionally, accessible resources such as GitHub repositories, example datasets, and platform-specific documentation can support less experienced users in becoming proficient. However, formal training or familiarity with omics data analysis is still beneficial for fully leveraging these tools, particularly in interdisciplinary research settings.
## 2 Overview of InSAR Methodology
### InSAR Technique for the Estimation of Height Topography
Accordingly, the larger the perpendicular baseline, the more accurate the estimate of the topography, as clarified by the following Equation (12) that relates the standard deviation of the phase measurement (i.e., the error of the InSAR phase measurement)--namely \(\sigma_{\varphi}\)--to the standard deviation of the InSAR-driven height measurements, namely \(\sigma_{z}\):  
\[\hat{\gamma}_{2}=\gamma_{2}\exp\left[-j\frac{4\pi}{\lambda}(r+\delta r)\right] \tag{3}\]2  
By using the parameters of the C-band ASAR RADAR instrument mounted on board the ENVISAT satellite of the European Space Agency (ESA), we have evaluated the accuracy of the height measurement in a real context. Note that the ENVISAT/ASAR instrument operated from 2002 to 2010 with a wavelength of 5.6 cm, the scenes were imaged with an average side-looking angle of about 23\({}^{\circ}\) and the sensor-to-target distance, namely \(r\), was approximately 800 km. Figures 3 and 4 depict the plot of the standard deviation of the height measurement (as calculated by Equation (12)) vs. the perpendicular baseline and the standard deviation of the interferometric phase, respectively. The accuracy of InSAR phase, however, depends on the perpendicular baseline of the interferogram. Unfortunately, an increase in the perpendicular baseline, which would be beneficial for reducing the standard deviation of the height measurement by Equation (12), is responsible for increasingly severe decorrelation noise artefacts [41; 43; 44; 45] (see Section 2.2 for details) in the generated map of the interferometric phase. Accordingly, the use of very large baseline interferograms limits the efficacy of InSAR-based height measurement. For this reason, in practice, a compromise arises for establishing a criterion for the selection of the optimal baseline separation between orbits for InSAR applications. Height accuracy can also be calculated with respect to the components of the baseline vector. To do that, we observe that, by using Equations (7) and (11) (neglecting the flat-Earth term contribution), the topography can be expressed as a function of the parallel baseline component as:  
\[\hat{\gamma}_{2}=\gamma_{2}\exp\left[-j\frac{4\pi}{\lambda}(r+\delta r)\right] \tag{3}\]3  
and, making use of Equations (1), we get:  
\[\hat{\gamma}_{2}=\gamma_{2}\exp\left[-j\frac{4\pi}{\lambda}(r+\delta r)\right] \tag{3}\]4  
Therefore, the standard deviation of the topography can also be expressed as follows:  
\[ \begin{split}\sigma_{z}=\frac{r\sin^{2}\vartheta}{b_{\perp}} \sigma_{b_{h}}\\ \sigma_{z}=\frac{r\sin(2\vartheta)}{2b_{\perp}}\sigma_{b_{v}} \end{split}  \tag{15}\]  
Finally, another frequently-used measure of the interferometric performance for the estimation of height topography is the so-called height ambiguity, which represents the amount of height change leading to a \(2\pi\)-change of the interferometric phase, which can be easily derived from Equation (12) by imposing \(\sigma_{\varphi}=2\pi\), as:  
\[z_{2\pi}=\left|-\frac{\lambda r\sin\vartheta}{2b_{\perp}}\right| \tag{16}\]
Remote Sens._**2014**, \(6\), 2664-2681; doi:10.3390/rs6042664  
_remote sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
The Estimation of Regional Crop Yield Using Ensemble-Based Four-Dimensional Variational Data Assimilation  
**[PERSON] 1,2,3, [PERSON] 2,3,*, [PERSON] 1, [PERSON] 2,3, [PERSON] 2,3 and [PERSON] 2,3**  
1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China; E-Mails: zhiweijt(@163.com (Z.J.); [EMAIL_ADDRESS] (J.C.)
2 Key Laboratory of Agri-Informatics, Ministry of Agriculture, Beijing 100081, China; E-Mails: [EMAIL_ADDRESS] (J.R.); [EMAIL_ADDRESS] (Z.L.); [EMAIL_ADDRESS] (L.S.)
3 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-10-8210-9615; Fax: +86-10-8210-8684.  
_Received: 6 January 2014; in revised form: 3 March 2014 / Accepted: 17 March 2014 / Published: 25 March 2014_  
To improve crop model performance for regional crop yield estimates, a new four-dimensional variational algorithm (POD4 DVar) merging the Monte Carlo and proper orthogonal decomposition techniques was introduced to develop a data assimilation strategy using the Crop Environment Resource Synthesis (CERES)-Wheat model. Two winter wheat yield estimation procedures were conducted on a field plot and regional scale to test the feasibility and potential of the POD4 DVar-based strategy. Winter wheat yield forecasts for the field plots showed a coefficient of determination (\(R^{2}\)) of 0.73, a root mean square error (RMSE) of 319 kg/ha, and a relative error (RE) of 3.49%. An acceptable yield at the regional scale was estimated with an \(R^{2}\) of 0.997, RMSE of 7346 tons, and RE of 3.81%. The POD4 DVar-based strategy was more accurate and efficient than the EnKF-based strategy. In addition to crop yield, other critical crop variables such as the biomass, harvest index, evapotranspiration, and soil organic carbon may also be estimated. The present study thus introduces a promising approach for operationally monitoring regional crop growth and predicting yield. Successful application of this assimilation model at regional scales must focus on uncertainties derived from the crop model, model inputs, data assimilation algorithm, and assimilated observations.  
keywords: four-dimensional variation; crop model; data assimilation; yield estimation; leaf area index; remote sensing
## 4 Hydrocarbon Potential of formations in Malaysia
### Organic Geochemical Characteristics of Rock Formations in Sabah Region
#### 4.2.4 Generic Potential (GP)  
The GP values of the formations in Sabah ranged from 0.12 to 279.69 mg HC/g with an average of 55.19 mg HC/g. The Pinangah formation had the highest value of 143.07, with the Temburong having the lowest value of 0.12 mg HC/g (Table 12). According to the [PERSON] [75] classification of GP, the Pinangah, Ganduman, Tanjong, Kapilit and Sandakan formations had very good GP while that of West Crocker and Sebahat were fair (Table 12). The GP of Kalabakan, Meligan, Beliat, and Setap formations are all poor, according to the [PERSON] [75] classification (Table 12). The low GP values of these formations could be attributed to their low maturity and low organic richness. As the thermal maturity of the sediment increases, the S2 reduces, thereby decreasing the GP. Low thermal maturity and high organic matter could be attributed to the high GP values of the Pinangah, Ganduman, Tanjong, Kapilit, and Sandakan formations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Formation** & **Generic Potential (GP) S1 + S2 (mg HC/g)** & **([PERSON], 1995), GP Interpretation** & **Reference(s)** \\ \hline
**1** & Ganduman & 21.37 & Very good & [58] \\ \hline
**2** & Sebahat & 3.57 & Fair & [58] \\ \hline
**3** & Tarjong & 181.29 & Very good & [55,59,60] \\ \hline
**4** & Sandakan & 18.84 & Very good & [61] \\ \hline
**5** & Temburong & 0.12 & Poor & [15,62] \\ \hline
**6** & Pinangah & 279.69 & Very good & [90] \\ \hline
**7** & Beliat & 1.75 & Poor & [15,64] \\ \hline
**8** & Meligan & 0.92 & Poor & [15] \\ \hline
**9** & West Crocker & 4.85 & Fair & [15] \\ \hline
**10** & Setap & 0.56 & Poor & [64] \\ \hline
**11** & Kapilit & 148.9 & Very good & [60,65] \\ \hline
**12** & Kalabakan & 0.45 & Poor & [60] \\ \hline \hline \end{tabular}
\end{table}
Table 12: Interpretation of average GP values of formations in Sabah according to [PERSON] [75].  
Figure 13: Tmax versus HI plot showing the level of kerogen maturity of formations in Sabah.
## 5 Analytical Validation and Comparison with Existing Systems  
The proposed framework for the 5G-satellite integrated network was validated through analytical comparison with established models and performance benchmarking relative to existing systems. To validate the proposed model, its outputs were compared against established theoretical frameworks, specifically the ITU-R P.676 and ITU-R P.838 recommendations for atmospheric and rain attenuation, respectively [35; 36; 37; 38]. These recommendations provide tabulated data and equations widely adopted in the telecommunications industry for high-frequency signal propagation.
# Empirical Evaluation on Utilizing CNN-Features for Seismic Patch Classification
## 2 Related Work
### Seismic Fault Detection  
In geophysics, current fault detection methods can be broadly categorized into two classes: traditional methods [17; 18; 19], and machine-learning-based algorithms [1; 20; 21; 3; 4; 5; 22; 23]. In general, traditional methods work by detecting the local discontinuity in seismic images on the basis of some of the seismic features, such as semblance, variance, etc. For example, [PERSON] and [PERSON] [19] propose a combination of the Hough transform and the tracking vector to extract faults from coherence maps. Although these methods can produce satisfactory results in early studies, they are time-consuming, and they also require professional background knowledge to calculate the informative attributes.  
With the great success of various machine-learning and deep-learning techniques in many fields, scholars have developed many automatic fault detection procedures to alleviate the shortcomings of traditional ones. For instance, SVMs [20] and the multilayer perceptron technique [21] are separately applied to analyze the attributes (including the detected edges, and the geometric and texture features) extracted from different seismic images, and, finally, to classify whether there are faults or not. In [5], [PERSON] et al. provide a review of the use of image-processing and machine-learning methods to identify faults. Considering that the performance of machine-learning algorithms strongly relies on the calculated attributes, it is popular, in recent years, to employ deep-learning approaches to achieve automatic fault detection. Among them, the CNN is the most commonly used technique because of its powerful ability to extract useful features. Recently, it has been combined with transfer learning [1; 2; 3] in order to further enhance its performance while alleviating the difficulty in obtaining sufficient labeled data.
## 3 Implementation and Experimentation
### Model Specification  
The following criteria explain the specification of our model and also evaluate its performance.  
1. The total time consumed by the computer for both training and validation in our experiment was 3 h and 21 min.
2. The memory capacity required for the parameters was nearly 200 MB. The data memory used was near 400 MB. However, this can vary and depends mainly on the batch size.  
Figure 6 illustrates the full details of the designed architecture, which is inspired by [PERSON]'s work [31].
# Influence of Burner Nozzle Parameters Analysis on the Aluminium Melting Process
## 2 Experimental Research  
### Experimental Device  
The aim of the research was to analyse heat transfer and aluminium melting processes in the experimental device, in relation to the change of the combustion mixture flow rate from the burner. The experimental measurements were carried out with the oxidizer oxygen concentration ranging from 21% to 50%. The experiments were conducted using a tilting rotary furnace (Figure 1) with an inner diameter of 305 mm and a length of 607 mm. The experimental model inclination angle was set to 7\({}^{\circ}\) during the measurement.  
The furnace was equipped with a 16 K (NiCr-NiAl) PTTK-TKb-60-2-SP thermocouples (MERATEX, s.r.o., Kosice, Slovakia), for which the manufacturer states a measurement uncertainty at the level of \(\pm 2.5\)\({}^{\circ}\)C within the temperature range up to 1200 \({}^{\circ}\)C. The thermocouples were used to continually sense the temperature distribution changes in the combustion area, lining, stack, and charge. As the thermocouples were placed on the casing, the experimental model did not rotate during the experiment. The flue gas composition was analysed by a TESTO 350 XL flue gas analyser (K-TEST, s.r.o., Kosice, Slovakia). Other parameters relevant for the furnace operation were recorded by the furnace control system (Figure 2).  
The charge was composed of aluminium ingots with the size of \(5\times 10\times 2.5\) cm. The total charge weight was 15 kg. To guarantee comparability of individual measurements, the process of experimental measurement was terminated when the charge reached the temperature of 740 \({}^{\circ}\)C.  
Figure 1: Experimental model of the tilting rotary furnace.
# Deep-Learning-Based Recovery of Frequency-Hopping Sequences for Anti-Jamming Applications
## 2 System Model  
In traditional frequency-hopping communication systems, the transmitter and the receiver use the predetermined frequency-hopping sequence for communication. The jammer intercepts and analyzes the frequency-hopping signal of the transmitter and uses different types of jamming signals to interfere with the communication process [29]. The system model of an intelligent anti-jamming frequency-hopping communication system is shown in Figure 1. The upper part of Figure 1 is the transmission system. An anti-jamming decision-making module is added on the basis of the traditional transmission system. This module makes decisions on the optimal frequency-hopping pattern (i.e., frequency-hopping sequence) according to the perception of the current interference environment and generates frequency-hopping signals for transmission. The lower part is the receiving system. The FH sequence estimation module recovers the frequency-hopping sequence from the received signal, which is then used to de-hop the signal and thus realize intelligent signal reception. An intelligent estimation module based on CNN-GRU is designed in this paper, which will be discussed in detail in the next section. Therefore, the transmitter can intelligently generate the frequency-hopping sequence through an anti-jamming decision, and the receiver can recover the frequency-hopping sequence through the proposed CNN-GRU module to achieve reliable and intelligent anti-jamming communication.  
The mathematical model of the frequency-hopping signal is represented as  
\[\left\{\begin{array}{l}X(t)=\sum\limits_{l=0}^{N-1}x_{i}(t)=\sqrt{2E}\sum \limits_{i=0}^{N-1}rect(t-iT_{FH})\cos(2\pi f_{i}(t-iT_{FH})+\phi),0\leq t\leq NT _{FH}\\ rect(t)=\left\{\begin{array}{l}1,t\in[0,T_{FH}]\\ 0,\mathit{others}\end{array}\right.\end{array}\right. \tag{1}\]  
where \(x_{i}(t)\) represents the \(i\)-th frequency-hopping signal component, \(rect(\cdot)\) represents a rectangular window with a width of \(T_{FH}\), \(E\) represents the signal power, \(T_{FH}\) represents the frequency-hopping period, \(f_{i}\) represents the hopping frequency, and \(N\) represents the number of hopping frequencies.
# Bionic Design of a Winding Roller and Experiments for Cleaning Long Foreign Matter from Raw Cotton
## 2 Materials and Methods
### Bionic Design of a Winding Roller  
Structural bionics is an innovative design method distinct from traditional design methods. In terms of cottons, most LFM is mixed with raw cotton after picking, and the length is usually greater than 3 cm. As stated above, the LFM must be removed prior to pinning process, otherwise it is broken into pieces, degrading the lint. To a certain extent, fish are characterized by densely spaced, elongated, comb-like gill rakes, which simultaneously eliminate plankton or other particles when water passes through the teeth. From the point view of structural bionics, the cotton layer is similar to the stream of water during the feeding process. Therefore, the clearing principle of LFM is similar to the principle by which the gills clear plankton or other particles. In this case, LFM can be easily winded and separated before separating the cotton seed from the lint due to their length and different properties from lint. Therefore, LFM can be easily cleaned by a similar  
Figure 1: Gill morphology in carp (cyprinus carpio): left, photo of a carp gill; right, close-up (GR: gill rakes; GA: gill arches; GF: gill filaments).  
structure to the comb-like gill rakes. Moreover, the design of the bionic structure minimizes the fiber damage to the lint and seed as it passes through the comb-like process.  
In terms of the design of the bionics structure, elaborating the design of the geometry and dimensions of the winding roller is the key technology for high LFM cleaning efficiency. Here, two factors should be considered. First, in the design of a single winding roller mimicking the comb-like gill rakes, the shaft of the winding roller is used to imitate the gill arch of a carp and the teeth on the winding roller are used to imitate the comb structure of the gill rake. Because the gill rake of a carp is usually arranged in one to two rows on each gill arch, the teeth on the shaft of the winding roller are designed in multiple rows, four rows in this design. The second factor is the design of the multi-roller configuration based on the single winding roller; different multi-roller configurations may lead to differences in cleaning efficiency. As shown in Figure 2, the roller is designed considering manufacturing convenience and feasibility, which includes equally-spaced winding teeth welded onto a shaft and placed vertically in four rows along the axial line. The interlaced arrangement of the teeth can interact with other rollers, and can increase cleaning efficiency in the designed multi-roller configuration.  
Each roller is a simply supported beam with two bearings, and is driven by a chain. According to the productivity of a plant, the width of the cleaning device room is designed as 2000 mm. The key design parameters of the teeth are D (diameter), L (length), and S (spacing). The teeth must be designed and optimized to enhance the cleaning efficiency.