esa-sceva/satcom-chunk-collection huggingface dataset:
However, its limitations on low spatial resolution and poorer data quality compared to later instruments have limited its applications. In recent years, with advances in computational power and artificial intelligence, deep learning methods for enhancing low-resolution images to high-resolution ones have become a research focus. Currently, deep learning-based super-resolution (SR) reconstruction algorithms for remote sensing images primarily include convolutional neural network (CNN), residual network (ResNet), generative adversarial network (GAN), and combinations of various network modules, and the deep learning-based methods have thrived and shown very impressive capability for SR. Furthermore, some studies have applied super-resolution methods to reconstruct long time series AVHRR data [16; 17; 18], but these datasets do not meet the requirements for fine resolution. For example, [PERSON] developed 1 km land cover maps from 5 km AVHRR data using a super-resolution method [18]. This study used CNN-LSTM networks to map quantitative remote sensing products to land cover classification products. The CNN network requires bicubic convolution interpolation of the low-resolution 0.05\({}^{\circ}\) quantitative remote sensing products, which increases computational complexity. Moreover, training the LSTM network necessitates a long time series of data (1982-2015), making data collection challenging. [PERSON] used a change detection algorithm combined with AVHRR, moderate resolution imaging spectroradiometer (MODIS), and Landsat time series data to detect the exact timing of land cover changes and update land cover datasets from 1980 to 2015 [19]. However, this method directly resampled AVHRR data to 30 m resolution using the nearest neighbor approach, which can cause abrupt changes in the data, resulting in a loss of spatial information and image quality, thereby affecting change detection results and reducing the accuracy of the produced land cover products. To address these issues, we reconstructed the AVHRR time series data to 250 m based on GAN to enhance the details, and the reconstructed dataset was subsequently used to generate 30 m annual land cover products for 1980-1990 using change detection methods; specifically in this study, the proposed method was applied to the Qilian Mountains for testing and verification. First, GAN models were established between the AVHRR normalized difference vegetation index (NDVI) data and moderate spatial resolution MODIS NDVI data to enhance the resolution of early AVHRR NDVI time series data with low spatial resolution (5 km) and low quality. Second, the breaks for additive seasonal and trend (BFAST) algorithm was employed to detect changes from the super-resolved NDVI time series data to capture the changes in land cover types. Third, the detected changes from the second step were employed to produce the annual land cover datasets by using the China multi-period land use land cover dataset (CNLUCC) at 1980 and 1990 as the references. Finally, a comprehensive analysis and validation were conducted for evaluation.
## 4 Experimental Results and Analysis
### Tool Formal Security Validation
#### 4.4.2 AVISPA-Based Security Analysis  
AVISPA is a model checker that verifies the security of cryptographic protocols using the Dolev-Yao attacker model. We modeled our ECC-based key exchange protocol using the High-Level Protocol Specification Language (HLPSL) and analyzed its security under the OFMC (On-the-Fly Model Checker) and CL-AtSe (Constraint Logic-based Attack Searcher) backends. It is designed to validate the security of cryptographic protocols within adversarial settings. It operates under the Dolev-Yao attacker model, which assumes that an attacker has complete control over the communication channel, being able to intercept, modify, and replay messages. Our work involved modeling the ECC-based key exchange protocol within AVISPA using the High-Level Protocol Specification Language (HLPSL), ensuring a comprehensive analysis of its security guarantees.  
Given the advanced nature of our protocol, which includes parameter contraction techniques (\(\alpha,\beta\)) alongside modular arithmetic and factorization, it was necessary to extend the traditional HLPSL model to accurately reflect these cryptographic operations. Our AVISPA model defines the role of each participant (device \(A\) and device \(B\)) and specifies the structured exchange of messages during key agreement. The security properties verified included:  
* Secrecy of the Shared Key (\(K_{AB}\))--Ensuring that no adversary can derive the final session key.
* Authentication--Confirming that only legitimate devices participate in the key exchange.
* Replay Attack Resistance--Validating that old messages cannot be used maliciously.
* Man-in-the-Middle Attack Resistance--Ensuring that attackers cannot alter the exchanged keys without detection.  
It was used with OFMC (On-the-Fly Model Checker) and CL-AtSe (Constraint Logic-based Attack Searcher) backends parameterizations. The results confirmed that our ECC-based model remains secure against all known logical attacks, thereby validating its robustness in real-world IoT environments where computational constraints and adversarial threats coexist.
## 2 Materials and Methods
### Data Analysis  
First, the normality of the variables was checked by the Kolmogorov-Smirnov test, concluding that they all followed normal distribution. A sample size calculation was performed. For the analysis of within- (pre vs. post) and between-group (FG vs. CG) changes in anthropometry and physical condition variables, two-way ANCOVA was performed with a repeated measurement (time \(\times\) group). Height and weight were included as covariates due to their influence on bone parameters. For the analysis of physical demands in absolute terms between the three positions (DF, MF, and FW) and the sessions (MD-4, MD-2, and MD), two-way ANOVA was used. Furthermore, two-way ANOVA was also used to compare the physical performance variables in relative terms between the three positions and the age categories. A Bonferroni post-hoc test was used for all pairwise comparisons. In addition, the 95% confidence interval and effect size (ES; Cohen's d) were included. The effect size (ES) was calculated and categorized: trivial (0-0.2), small (0.2-0.5), moderate (0.5-0.8), and high (>0.8). A \(p\)-value of less than 0.05 was considered statistically significant. Data analyses were carried out using the SPSS 25.0 statistical package (SPSS Inc., Chicago, IL, USA).
## 4 Discussion
### Regulation of the Amount of RNA by Degradation  
sRNAs often modulate mRNA targets stability, notably by recruitment of RNases [49]. This indicates that degradation is a regulatory process, by which bacteria adjust the translation of certain transcripts in response to changing environments. However, turnover of Csr/Rsm sRNAs has not been well-reported in the literature, with the exception of a study reported by [PERSON] et al. [50]. These authors identified a regulatory protein (CsrD) that targets the global regulatory RNAs CsrB and CsrC for degradation by RNase E. Investigation of Rsm sRNAs degradation in _P. brassicacarum_ indicates that the half-lives of the four Rsm sRNAs increase in the stationary phase and when nutrients are limited. The rate of turnover for Rsm sRNAs thus seems to be related to growth conditions and cell physiology. Furthermore, the four Rsm sRNAs are not equally stable, with RsmZ appearing to be extremely stable even during the exponential phase (Table 2).  
Regulation of RNA amount by degradation is achieved by ribonucleases (RNases), whose activity is dependent on the sequence and/or the structural elements of the RNA molecule. In _Salmonella enterica_ serovar Typhimurium, the endoribonuclease RNase III hasbeen shown to regulate MicA (an sRNA involved in porin regulation) in a target-coupled way, whereas RNase E is responsible for the control of free MicA levels in the cell [51].  
We hypothesize that the observed differences in Rsm sRNAs are mainly due to the sequence and structural elements of each Rsm sRNA. In _P. protegens_ CHA0, the estimated half-lives of RsmY and RsmZ are >20 min in the wild-type and <10 min in the _rsmA rsmE_ double mutant [52]. RsmA/E appears to stabilize Rsm sRNAs in vivo probably by protecting them from degradation by RNases. In _Erwinia carotivova_ subsp. _carotivova_, RsmA also increases the half-life of the RsmB riboregulator [53]. Higher levels of RsmB RNA in the _rsmA_+ strain than in the _rsmA_- strain were shown to be due to the increase in RsmB stability and not to an increase in transcription.  
It has been suggested that RNase E and Hfq have similar (AU-rich) target sequences that should permit Hfq to protect sRNA from RNase E attack [54]. In _P. aeruginosa_, RsmY has been described as stabilized by Hfq, which sequesters RNase E cleavage sites [55], and thus enhances RsmA sequestration by RsmY [56]. In _P. brassicacearum_, an interaction of Hfq with RsmY and RsmZ may explain the extreme stability of these two sRNAs. Nevertheless, in _P. aeruginosa_, Hfq doesn't affect RsmZ stability, whereas in _P. brassicacearum_, RsmZ exhibits an A-U rich loop, which could interact with Hfq.
###### Abstract  
Recently, bioassay techniques have been gaining prominence in assessing water toxicity, offering comprehensive evaluations without identifying the individual chemical component. However, microscopic observation is a crucial component in microbioassays to know the critical features of the targeted microorganisms. However, as the microorganism's size becomes smaller, observation becomes more difficult due to the narrower focal depth of the imaging system. To address this challenge, we propose a novel laser biospeckle non-imaging technique utilizing biospeckle patterns generated by microorganisms, enabling non-imaging assessments of their swimming ability. Paramecium and Euglena were used as microorganisms. Paramecium and Euglena were subjected to varying concentrations of heavy metal pollutants (Zn(NO\({}_{3}\))\({}_{2}\):6H\({}_{2}\)O and FeSO\({}_{4}\)-7H\({}_{2}\)O), and their swimming activity was quantified using a dynamic biospeckle analysis. The results show a concentration-dependent effect of Zn on both species, leading to decreased swimming ability at increased concentration. Conversely, Fe exhibited varying effects on Paramecia and Euglena, with the latter displaying tolerance at lower concentrations but a notable response at higher concentrations. The advantage of the method is that owing to the non-imaging system, an enormous number of microorganisms can be processed. Moreover, the method allows for an immediate and statistically significant estimation of their swimming ability in response to environmental pollution.  
microbioassay; biospeckle; microorganisms; Euglena; Paramecia; water toxicity; heavy metals; Zn; Fe environmental pollution; swimming ability
###### Abstract  
This paper addresses the problem of time difference of arrival (TDOA) based passive target localizational proposes an improved chaos-driven hybrid differential evolution (DE) algorithm and butterfly optimization algorithm (BOA), named ICDEBOA, to solve this complex optimization problem. The proposed algorithm consists of a new mutation strategy with the mechanisms of the BOA algorithm incorporated into the DE algorithm. To boost optimization effectiveness, chaos theory is employed to adjust the control parameter values. The considered localization problem is formulated using the maximum likelihood estimator. To perform the accuracy comparison, the convex constrained weighting least squares algorithm is applied to the considered localization problem as the widely used method in literature. The statistical analysis shows that the proposed modifications to the ICDEBOA algorithm improve its optimization performance, as demonstrated by the improved performance on the CEC2014 benchmark problems. The ICDEBOA algorithm is also shown to be more robust than existing algorithms in noisy environments. Numerical simulation results show that the proposed ICDEBOA algorithm meets the CRLB and achieves better performance than the CWLS, DE, and BOA algorithms.  
localization; time difference of arrival; butterfly optimization algorithm; hybrid optimization; differential evolution; Cramer-Rao lower bound Article An Improved Chaos Driven Hybrid Differential Evolution and Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements.  
[PERSON], [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] An Improved Chaos Driven Hybrid Differential Evolution and Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements.  
684[[https://doi.org/10.3390/app13020684](https://doi.org/10.3390/app13020684)]([https://doi.org/10.3390/app13020684](https://doi.org/10.3390/app13020684))
# An Improved CNN Model for Within-Project Software Defect Prediction
## 2 Background
### Word Embedding  
The key idea behind word embedding is distributed representation of words. When a one-hot encoder is used, each word is encoded into a vector in the shape of \(\{0\dots 0,1,0\dots 0\}\). The length of the vector is equal to the vocabulary size N, and words are independent of each other. On the contrary, distributed representation regards words as related items, and a word is encoded into a shorter vector. The length of the vector is far less than the vocabulary size N, and each item is a decimal number between 0 and 1. After such word embeddings, the distance of words can be calculated easily.  
In our improved CNN model, we built an embedding layer as the first layer, which was based on the skip-gram model. Detailed information on the mathematical formulations of the skip-gram model is given in [36]. In Figure 3 we illustrate the very basics of a skip-gram model. First, each word in the corpus is selected, and its surrounding words (define by window size) are picked to form tuples with two elements. A skip-gram model focuses on one word (\"new\" for example) as input, which is represented as a one-hot encoder. The word vector for the input word is learned in the hidden layer, and the length of the word vector is far less than the word vocabulary size. In the output layer, the model predicts the neighboring words of the input word using softmax classifier. The weights of the hidden layer are updated by comparing the model prediction result (neighboring word within word vocabulary) with the neighboring word extracted from the original corpus (\"york\" for example). After training, the learned word vectors can be used to represent input words, and they are extracted as output of the embedding layer.  
In software defect prediction, input vectors representing source code ASTs information are embedded, which can be measured by distance and features a grid-like property. CNN models work perfectly on grid-like data, and that is why the CNN model can be utilized for defect prediction after word embedding.
# A Triple Deep Image Prior Model for Image Denoising Based on Mixed Priors and Noise Learning
## 4 Experiments
### Experimental Results
#### 4.3.2 Visual Comparisons  
Visual quality is a crucial criterion for assessing model quality in image tasks. To compare the denoising performance of various methods subjectively, in Figure 7, we list the different denoising methods' PSNR values on the Boat image from the Set12 dataset with a noise level of 25. The area containing the mast in each images is magnified in the lower right corner for detailed comparison. As shown in Figure 5, DnCNN, as a classic supervised algorithm, almost entirely lost the rope details in the position indicated by the red arrow, which demonstrates that the supervised method may have defects in smoothing the image's local details. The unsupervised model N2V, retains more information about the rope in the indication but performs inadequately on the PSNR result. This is consistent with our inference that the unsupervised model can retain more local details according to the internal prior of a single image, while compared to the supervised model, it is challenging to repair the overall content due to the lack of external prior. For the Transformer-based model, although Restormer preserves texture details well, its PSNR value is inferior to that of our proposed model by 0.68 dB. As for the TripleDIP model, it can be noted from the enlarged part of the image that the over-smoothing is suppressed, and the discernibility of the rope edge is enhanced. Meanwhile, the denoised image generated by TripleDIP achieves the highest PSNR values (i.e., 31.41 dB) compared to the listed methods and is the closest to the original image from a global perspective. The main reason for this is that the internal and external mixed priors and two-branch noise constraint learning proposed in this study can complementarily recover high-frequency details and low-frequency information.  
Figure 7: Visual denoising results of various methods on Boat image.
## 4 Remote Sensing Platforms Used in Peatland Research
### UAV-Based Remote Sensing
#### 4.3.2 Monitoring of Peatland Hydrology  
UAVs with thermal and multispectral sensors have shown promise in monitoring peatland hydrological dynamics [117]. By capturing images at different wavelengths, these sensors can detect fluctuations in soil moisture content and the levels of the water table. Such information is crucial for assessing peatland health, understanding hydrological processes, and informing land management decisions.  
Hydroological processes in peatlands influence carbon cycling and overall ecosystem health. Studies by [PERSON] et al. (2020) and [PERSON] et al. (2022) focus on monitoring hydrological impacts in peatland ecosystems using UAV remote sensing [118; 119]. [PERSON] et al. (2020) investigated vertical mixing processes and boundary layer methane isotopy using UAV-based sampling to study spatial variability [119]. [PERSON] et al. (2022) analysed elevation changes and flow paths in peatland restoration, highlighting the ability of UAV mapping to assess wetness and hydrological impacts. Therefore, they reveal the effectiveness of UAV remote sensing in studying spatial variability and capturing methane emissions [118].  
Additionally, [PERSON] et al. (2022) studied UAV-based methane emission mapping in Arctic terrestrial ecosystems and demonstrated the potential of UAVs in capturing spatial variability and enhancing the research on methane dynamics in peatlands [120]. [PERSON] et al. (2022) focus on monitoring the hydrological impacts of peatland restoration using UAV and LiDAR data. They demonstrate that UAV mapping can assess elevation changes, flow paths, and wetness, providing valuable insights into hydrological processes [118].
# Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane
## 2 RA Diagnosis Methods: Modernism vs. Traditionalism
### The Physician Method
#### 2.3.6 MRI Findings  
MRI is a sensitive imaging modality that allows detailed assessment of inflammation as well as structural damage in RA. Compared to a physical examination, MRI is a more sensitive tool for the identification of tissue damages because of its direct visualization of synovitis, cartilage destruction, bone erosion, bone marrow edema, tenosynovitis, and surrounding soft tissue structures [40].  
Synovitis (inflammation of the synovia) and tenosynovitis (inflammation of the tendons) at MCP (metacarpophalangeal joints), wrist and MTP (metatarsophalangeal joints) were independently associated with clinical swelling. MRI could detect inflammation in 54-64% of joints with no clinical swelling [42].  
In order to quantify synovovitis, bone marrow edema and bone erosions detected with MRI, an OMERACT RA MRI score (RAMPIS) has been developed and is often used for hands and wrists.  
Overall, MRI is often used in the clinical praxis when there is diagnostic doubt in patients with clinically suspected arthralgia (CSA) because it can detect early findings of RA in the preclinical phase, thus offering the physician a window of opportunity of early treatment, which is the key for a good prognosis in RA patients.
## 4 Challenges and Opportunities in Marine Biotechnology
### Downstream Processing and Biorefinery Models  
Downstream processing presents another significant challenge in the commercialisation of microalgae-derived compounds. The extraction and purification of target molecules such as peptides, polysaccharides, and carotenoids often rely on energy-intensive, solvent-based procedures that can compromise bioactivity and reduce overall yields. To address these limitations, several emerging technologies are being explored. Supercritical CO\({}_{2}\) extraction offers high selectivity and produces solvent-free products, making it particularly suitable for high-value bioactives like carotenoids and fatty acids. However, this method is constrained by high capital investment and energy demands, which limit its scalability and render it more appropriate for premium, small-batch applications. In contrast, membrane filtration presents a more scalable and energy-efficient alternative, enabling continuous processing and selective separation based on molecular size or charge. Despite its advantages, this technique can be hampered by membrane fouling, lower specificity for certain compounds, and significant maintenance requirements when processing complex microalgal matrices. To improve the economic viability of microalgae bioproducts, biorefinery model approaches, where multiple compounds are sequentially recovered from a single biomass stream, are increasingly being adopted. These models enhance resource efficiency and profitability, and their success will depend on continued process optimisation and comprehensive techno-economic evaluations. Recent studies have demonstrated the valorisation of residual biomass after lipid extraction for biofuel production or bioplastics, aligning with circular economy principles. For example, defatted _Nannochloropsis_ biomass has been successfully converted into bioethanol or used as feedstock for thermochemical conversion processes. Such multiproduct strategies not only increase resource efficiency but also reduce waste and improve overall process sustainability [23].