esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
## 3 Principles and Methods
### Contextual Information Integration  
In the field of change detection, with the increasing spatial and temporal resolution of remote sensing images, the changing characteristics of complex terrain areas, such as coastal zones, exhibit multi-scale and multi-level properties. Coastal zone change detection often involves various types of changes at different scales, including ocean, land, vegetation, and man-made structures, with these changes being highly heterogeneous in both space and time. For example, coastal erosion, sea-level rise, and anthropogenic development activities can lead to topographic and geomorphological changes at different scales, resulting in both local and global variations in change across the coastal zone. These changes exhibit multi-scale characteristics in space and may also follow multi-stage, variable-frequency patterns over time. Therefore, effectively extracting and integrating change information  
Figure 4: Schematic diagram of the isotropic Sobel operators in eight directions.  
across different scales and levels is crucial for improving the accuracy of coastal zone change detection.  
Although the high-frequency attention module can enhance the model's ability to capture edge information, this information is often scattered across feature maps at various scales, and effectively integrating it remains a challenge for improving detection accuracy. Therefore, by integrating features from different scales, a richer feature representation is provided to the model, helping it to better identify and capture change areas at different scales in the coastal zone, especially in complex geographic environments, offering a significant advantage. To this end, we introduced the spatio-temporal difference attention module (SDAM) to enhance the performance in coastal zone change detection tasks. Additionally, feature fusion of same-branch feature maps was performed prior to inputting them into the SDAM module. For complex coastal zone areas, changes often exist at multiple scales, and by fusing high-frequency features at various scales, the model's ability to perceive multi-scale changes is enhanced, making it more adaptable to complex terrain and diverse change types.
## 4 Examples of Application
### Valve Abnormality Alarm and Fault Diagnosis
Only eight exhaust valve clearance faults were misdiagnosed as other faults, and the accuracy reached 93.33%. The situation of both valve clearance faults was the same as the exhaust valve clearance fault. The recognition rate of the overall valve fault reached 93.06% and satisfactory results were obtained under different speed conditions. In order to highlight the advantages of the fault diagnosis method based on VMD and RF, this method was compared with the other two methods, as shown in Table 8.  
As could be seen from Table 8, the combination method of statistical characteristics in the time and frequency domain and KNN was poor in the performance of the fault identification rate for the increase of exhaust valve clearance. Although the performance of EMD and the decision tree algorithm in the recognition accuracy of three faults was not bad, the average recognition rate was only 84.76%. The combined method of VMD and RF presented in this paper performed well in the recognition rate of three valve clearance faults, and the average recognition rate was obviously higher than the other two methods.
## 2 Results and Discussion
### Fluorescence Response  
The photophysical responses of six compounds (**1**, **2**, **3**, **6**, **9** and **11**) toward the addition of anions tested were also investigated. Just as Figure 3 showed, the fluorescence spectra of compound **1** exhibited a very weak response. Upon the addition of acetate anion (0-50 equiv.) to the solution of compound **1**, the fluorescence centered at 430 nm was enhanced. To account for such fluorescence enhancement, the PET mechanism was exploited [26, 27, 28]. In the case of compound **1**, the excited state of the fluorophore was not, or only to a minor extent, enhanced by electron transfer (ET) from the receptor to the fluorophore and the receptor-anion interaction. The addition of F\({}^{-}\) and H\({}_{2}\)PO\({}_{4}\)\({}^{-}\) induced similar fluorescence enhancement (the spectra are listed in the supplementary information Figure S2). Nevertheless, the fluorescence emission of compound **1** was insensitive to the additions of excess equivalents of Cl\({}^{-}\), Br\({}^{-}\), and \(\Gamma\) ions, which indicated the weak interaction.  
In view of the fluorescence titration spectra of other compounds (**2**, **3**, **6**, **9**, and **11**), the fluorescence intensity was enhanced for all of them due to the increase in acetate anion concentration (Figure 3), which indicated that the above compounds interacted with the acetate anion. The addition of H\({}_{2}\)PO\({}^{4}\)\({}^{-}\) and F\({}^{-}\) induced similar fluorescence enhancement for compounds **2**, **3**, and **6** (the spectra are listed in the supplementary information Figure S2). Compound **9** showed different binding ability for AcO\({}^{-}\) and H\({}_{2}\)PO\({}^{4}\)\({}^{-}\), while, compound **11** only showed binding ability for AcO\({}^{-}\). The addition of Cl\({}^{-}\), Br\({}^{-}\), and I\({}^{-}\) aroused very weak fluorescence enhancement and the interaction of host and guest could be ignored.  
Figure 3: _Cont._
## 5 Experimental Evaluation
Figure 7: Execution time of MIR and parallel microwave image reconstruction (PMIR) algorithm on Eddie and Google Cloud Platform (GCP). For validation purpose, the obtained results are compared with Reference [30]. We chose this paper because they use PySpark for the implementation of functional magnetic resonance imaging (FMRI) pipeline for brain extraction application (BEA). There are other relevant literature, as described in the Related Work section, however they used Hadoop and Spark instead of PySpark. The method was tested on standalone system using PySpark and standard Python. In terms of processing time, the proposed pipeline is four times faster than the one developed in Python. Although the testing was performed on a single node, it was reported that the performance will increase by a factor of 10 to 20 times using a cluster. As shown in Figure 8, PySpark improves the performance in terms of processing time for both MIR and BEA. The aim of this comparison is to determine the effectiveness of using PySpark for parallel implementation of imaging algorithm, rather than comparing the performance of MIR and BEA. In order to check if the algorithm can be generalized, it is deployed on GCP using the Google storage bucket to store data and final output images. Three virtual machine instances were created with a master and two worker nodes, as shown in Figure 9. The master node reads input data and the antennas' location forms the Google storage bucket and runs the algorithm similar to Eddie. The results take slightly more time due to network saturation, the number of cores, and memory. The algorithm faces high latencies due to data transfers between the parallel file system and computation nodes. However, the PMIR algorithm outperforms the MIR algorithm on both Eddie and GCP. The experiment design on GCP does not aim to compare the performance with Eddie, rather evaluate the generality of the PMIR algorithm. The goal is to verify the behavior of the PMIR algorithm that maximizes the localization of data retrieval, processing, and storage for a job. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Stand-Alone** & **Eddie** & **GCP** \\ \hline MIR & 14.91 & 8.14 & 13.3 \\ PMIR & 8.04 & 0.285 & 7.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Processing time (in seconds) of MIR and PMIR on stand-alone, Eddie, and GCP. Figure 8: Execution time of MIR and brain extraction application (BEA) [30] on stand-alone system. From the analysis on Eddie and GCP, it can be noticed that serializations overhead and network congestion are negligible for large medical image data. However, Spark provides very flexible solutions for large, fast image processing tasks. Although the use of big data technologies for large-scale image processing has become crucial for both research and clinical practice, very few modern big data approaches are considered in clinical practices. Hence, creating robust algorithms for large-scale image data processing becomes an enormous effort, particularly in the context of HPC and cloud platforms.
## 5 Conclusions  
The pattern-coupled SBL model with nine different coupling structures was applied to the time-domain reconstruction of RIRs. The reconstruction performance was evaluated using two experimental datasets. The results indicate that CPC-ST (Centered Pattern-Coupled with Spatio-Temporal Coupling) outperforms other coupling structures, particularly in the extrapolation of the sound field. Furthermore, for scenarios requiring reduced com  
Figure 2: NMSE and PCC over time across different coupling structures for the UTS dataset: (**a**) large meeting room, (**b**) medium meeting room, (**c**) small meeting room, and (**d**) shoebox room.  
putational cost, CPC-S (Centered Pattern-Coupled with Spatial Coupling) is also worth considering due to its balanced performance and reduced complexity.  
Further exploration of the balance between algorithm richness and overfitting risk is crucial for identifying the optimal model parameters and structures, especially when working with large, high-dimensional datasets. This would enhance the model's ability to generalize, ensuring its effectiveness across a wide range of acoustic environments.  
Conceptualization, S.C. and X.F.; methodology, S.C.; software, S.C. and X.F.; validation, S.G. and Y.Z.; formal analysis, S.C. and S.G.; investigation, S.C. and Y.Z.; data curation, S.C.; writing--original draft preparation, S.C.; writing--review and editing, S.G. and X.F.; supervision, X.F. and Y.S.; project administration, X.F. and Y.S.; funding acquisition, Y.S. 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 (Grant No. 12374446).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Adaptive Vector Quantization for Lossy Compression of Image Sequences +
## 2 The Adaptive Vector Quantization (AVQ) Algorithm
### Compression and Decompression Phases
In detail, each block visually indicates the dimensions of the corresponding block, identified in the dictionary \(D\). In Figure 6b, we graphically show the progress of the decompression phase (Algorithm 2). The graph in Figure 7 shows the number of access (on the \(y\)-axis) to a specific index of the dictionary \(D\) (on the \(x\)-axis), during the compression phase.
## 2 Materials and Methods
### Moderate Resolution Imaging Spectroradiometer (MODIS) Data Products  
We obtained the MODIS reflectance product (MCD43A4, V6) and leaf area index (LAI) product (MCD15A2H, V6) from the Land Processes Distributed Active Archive Center (LP DAAC) for the study period. MCD43A4 offers 500 m-resolution spectral reflectance adjusted by the nadir bidirectional reflectance distribution function (BRDF) based on reflectance data from both Terra and Aqua at the daily timescale, and the BRDF correction can minimize the effects of varying illumination and observation conditions on surface reflectance [54]. The MCD15A2H LAI product was retrieved from both Terra and Aqua with 8-day intervals. For each MODIS product, we extracted the pixel values for the area (2.5 km \(\times\) 2.5 km) surrounding each flux tower. For each 8-day interval, we averaged the values with good quality based on the quality assurance (QA) flags. Since the vegetation indices are widely used to monitor vegetation greenness and have significant relationships with vegetation GPP [7; 8; 55], we also derived the widely-used vegetation index, enhanced vegetation index (EVI), and examined its relationship with GPP to illustrate the performance of SIF.  
Figure 1: Orbiting Carbon Observatory-2 (OCO-2) solar-induced chlorophyll fluorescence (SIF) observations surrounding the Park Falls (US-PFa) and Willow Creek (US-WCr) sites on 1 July 2015. The base map is the MODIS land cover product (MCD12Q1) at 500 m spatial resolution.
# Advances in Genome Sequencing and Natural Rubber Biosynthesis in Rubber-Producing Plants
## 3 Natural Rubber Biosynthesis  
### Biosynthesis of the Building Block IPP and Initiator Molecules  
IPP is a precursor for the biosynthesis of NR and various isoprenoid compounds. Rubber-producing plants utilize two distinct pathways to synthesize the key rubber monomer, isopentenyl pyrophosphate (IPP): the mevalonate (MVA) pathway in the cytosol and the 2-C-methyl-D-erythritol-4-phosphate (MEP) pathway in plastids (Figure 1) [6, 29]. Acetyl-CoA is enzymatically converted into IPP through a series of reactions, which involves six major steps catalyzed by specific enzymes in the MVA pathway [30, 31, 32, 33, 34]. These enzymes include acetyl-CoA acetyltransferase (ACAT), 3-hydroxy-3-methyl-glutaryl-coenzyme synthase (HMGS), HMG-CoA reductase (HMGR), mevalonate kinase (MVK), phosphomevalonate kinase (PMK), and mevalonate diphosphate decarboxylase (MVD). Among them, HMGS and HMGR have been identified as key rate-limiting enzymes. The MEP pathway employs seven consecutive enzymes to convert pyruvate and D-glyceraldehyde-3-phosphate into IPP [35]. The initial step of the MEP pathway, catalyzed by 1-deoxy-D-xylulose 5-phosphate synthase (DXS), is rate-limiting. This step converts the precursors pyruvate and glyceraldehyde 3-phosphate into 1-deoxy-D-xylulose 5-phosphate (DXP). Afterward, the formation of the final IPP is catalyzed by six enzyme relays, including 1-deoxy-D-xylulose 5-phosphate reductio isomerase (DXR), 2-C-methyl-D-erythritol 4-phosphate cytidyltransferase (CMS), 4-diphosphocytidyl-2-C-methyl-D-erythritol kinase (CMK), 2-C-methyl-Derythritol 2,4-cyclodextrin diphosphate synthase (MCS), 1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate synthase (HDS), and 1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate reductase (HDR). Studies have shown that metabolites can flow between the cytoplasmic MVA pathway and the plastid MEP pathway [21, 36, 37]. Therefore, it is speculated that the MEP pathway can serve as an alternative pathway for supplying IPP during natural rubber biosynthesis (Figure 1).  
Subsequently, isopentenyl diphosphate isomerase (IPPI) catalyzes the conversion of a portion of IPP into DMAPP. The synthesis of initiator molecules such as geranyl pyrophosphate (GPP), farnesyl pyrophosphate (FPP), and geranylgeranyl pyrophosphate (GGPP) is generated through the consecutive condensation of IPP and DMAPP [8]. This condensation is catalyzed by a group of prenyl pyrophosphate synthase enzymes, including geranyl diphosphate synthase (GPS), farnesyl diphosphate synthase (FPS), and geranylgeranyl pyrophosphate synthase (GGPS). Finally, by sequentially condensing 15,000-30,000 IIPPs on the initial molecules, a substantial chain of natural rubber with a high molecular weight is generated (Figure 1).
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## 1 Introduction
Then, another question is whether we can extend the scheme for the AWGN RC with AWGN feedback to the MISO fading relay channel (FRC) with noisy feedback. To this end, we further extend the proposed scheme to the MISO fading relay channel (FRC) with noisy feedback. In this extension scheme, to deal with the complex signals in the fading channel, we introduce the two-dimensional complex plane mapping method. Furthermore, the pre-coding strategy is used to transform the feedforward MISO channel into a complex-valued SISO channel, and the beamforming strategy is employed to convert the feedback single-input, multi-output (SIMO) channel into a complex-valued SISO channel. Then, following the scheme for the AWGN RC with AWGN feedback, the scheme for the MISO FRC with noisy feedback is obtained. Finally, we show that our extended scheme may achieve the PLS requirement for some cases. The remainder of this paper is organized as follows. A formal definition of the RC with noisy feedback is introduced in Section 2. The proposed SK-type scheme for the AWGN RC with AWGN feedback is shown in Section 3. The extended SK-type scheme for the MISO fading RC with noisy feedback and its secrecy analysis are shown in Section 4. Numerical results are given in Section 5. Section 6 summarizes all results in this paper and discusses future works.
## 3 Results and Discussion
### Microhardness Measurement Results  
To evaluate the mechanical performance of the coatings, the hardness was measured with a constant load of 0.05 kgf and a corresponding holding time of 30 s. Each Vickers hardness value was obtained by averaging five indentation tests on the HEA coatings on stainless steel substrate. The values obtained are shown in Table 5.  
The Vickers hardness results, presented in Table 5, demonstrate the mechanical performance of the Cu-enhanced high-entropy alloy (HEA) coatings applied to stainless steel 304L. The uncoated stainless steel 304L exhibited a hardness of 318.8 HV. When coated with I8-6, the hardness increased to 331.8 HV, while the I8-7 coating showed a slightly lower hardness of 331.3 HV. These results suggest that the introduction of Cu into the HEA coatings improves the hardness of the stainless steel substrate, with I8-6 coating providing a modest improvement over the uncoated material. The slight reduction in hardness observed for the I8-7 coating may indicate a change in the coating's microstructure or phase composition, which requires further investigation. Nevertheless, both Cu-enhanced coatings provide improved hardness compared to the uncoated substrate, suggesting better wear resistance and mechanical durability, which are critical for applications where surface performance is crucial.
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 3 Fabrication of Hydrogels
### Bulk Hydrogel vs. Hydrogel Particles  
Hydrogels are conventionally synthesized via the formation of a continuous network of polymers through physical or chemical crosslinking in a single step. This process can create large-scale structures like scaffolds, membranes, or injectable hydrogels. Bulk hydrogels tend to exhibit uniform properties across the structure, because the functionalities of bulk hydrogel mostly originated from its building block. For example, the powerful antioxidant activity of chitosan hydrogel is based on the strong tendency of protonation of amino groups present in the chitosan molecule [11,73,78].  
Hydrogel particles conventionally refer to a discrete phase of hydrogel at micro- and nanoscales within a liquid medium, also known as a colloidal system [115,144,145,146]. Particle synthesis using hydrogel material focuses on creating micro- or nanoscale hydrogels, whose surface-to-volume ratio becomes high enough to exhibit surface-oriented functionalities. Also, smaller-scale hydrogels exhibit much faster diffusional mass transfer and become more eligible for intravenous injections. Combined with exceptional biocompatibility and biodegradability and a large surface area for functionalization, hydrogel particle systems construct a highly convincing new approach for advanced drug delivery systems [115,147,148,149,150].
###### Abstract  
Protein is an essential nutrient for laying hens, playing a crucial role in egg production and supporting their overall health. An 8-week feeding trial was conducted on 120 Lohmann Brown laying hens (aged 38 weeks). The layers were assigned randomly to three groups and housed in cages (twenty replicates \(\times\) two birds/cage). All groups were fed a corn-soybean meal basal diet (2750 kcal/kg metabolizable energy (ME) with 17.8% crude protein (CP)). In contrast to conventional diet (CON), the experimental groups were supplemented primarily at the expense of soybean meal with 2.0% _Chlorella vulgaris_ (CV2%) and 2.0% _Spirulina platensis_ (SP2%). Their high concentrations of chlorophyll \(a\) (5.56; 9.06 mg/g), chlorophyll \(b\) (0.88; 1.34 mg/g), and antioxidant activity expressed as 2,2-diphenyl-1-picrylhydraz (73.29; 81.27 DPPH% inhibition) improved egg yolk quality. At the end of the trial, eighteen eggs/group (six yolk samples/group, three eggs/sample) were collected to determine the egg quality and nutritional parameters (fatty acids profile, cholesterol, \(\beta\)-carotene, yolk color, and antioxidant capacity). To determine the activity of antioxidant enzymes, including superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH), and total antioxidant capacity (TAC), blood samples were collected at the end of the period. Microalgae inclusion increased (\(p<0.05\)) the fatty acid content, \(\beta\)-carotene concentration (\(p<0.001\)), antioxidant capacity (\(p<0.0001\)), and yolk color intensity (\(p<0.001\)) significantly, especially the yolk redness a* color parameter, but without any significant results concerning cholesterol concentration. Boiling the eggs for 10 min significantly (\(p<0.001\)) increased the b* color parameter on microalgae treatments. The supplementation of laying hens' diet with microalgae positively influenced egg quality and nutritional properties.  
Keywords:antioxidant capacity; blood antioxidant enzymes; cholesterol; fatty acids; laying hens; microalgae; yolk color +
Footnote †: journal: Applied Sciences
# A Review on Microfluidic Platforms Applied to Nerve Regeneration
## 2 Design of Microfluidic Devices
### Structures  
The geometry of the microenvironment varies a lot in nerve regeneration experiments with different conditions. As shown in Figure 1, the basic structure of a microfluidic device for neural regeneration contains at least two compartments for cell soma and distal axon storage and several micro-channels connecting the two compartments, in which the neural axons can grow. Cell somas are attached to the flat surface in the somal compartments, where they can adhere and allow the axon to grow in micro-channels.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Fabrication** & **Categories** & **Themo-Stability** & **Surface** & **Optical** & **Gas** & **Bicompatibility** & **Hydrobilicity** & **Cost** \\ \hline Silicon & Inorganic & High & High & No & No & \(\mathbf{x}\) & Hydrophilic & High \\ Glass & Inorganic & High & High & High & No & \(\mathbf{x}\) & Hydrophilic & High \\ Ceramic & Inorganic & High & High & No & No & \(\mathbf{x}\) & Hydrophilic & Low \\ Thermoplastic & polymeric & High & Medium & High & Low & ✓ & Hydrophobic & Low \\ PDMS & polymeric & Medium & Low & High & High & ✓ & Hydrophobic & Low \\ Hydrogel & Hydrogel & Low & N/A & Low & No & ✓ & Hydrophobic/ & Low \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of different materials for microfluidic devices applied in neurobiology.