esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
## 5 Conclusions  
In this paper, we introduced a new routing protocol (ELR-W) for the purpose of achieving energy efficiency in WBANs. We introduced a novel path cost function contingent on residual energy, link efficiency, hop counts, and distance to the BNC for selection of the next-hop to transmit the data packets. We performed a series of experiments in NS-2 to analyze the performance of ELR-W for different criteria which included network lifetime, throughput, and energy consumption. The experimental results revealed less energy consumption and packet loss by ELR-W protocol which yielded high throughput and network lifetime in contrast to the state-of-the-art M-ATTEMPT and iM-SIMPLE protocols. Furthermore, this work can be further extended towards integration with Internet of Things (IoT) for monitoring of multiple WBANs in a hospital environment.  
M.A. makes substantial contributions to conception and design of proposed protocol its implementation in simulation and analysis and interpretation of simulation results with existing solutions. A.H.A. and A.A. provide guideline to organize the paper and help to provide technical assistance. K.N.Q., F.M., and M.F. participate in drafting the article or revising it critically for important intellectual content and provide technical assistant to complete the paper and also help to improve the paper writing. Y.C. and R.K. give final technical assistance, guideline in revisions and approval of the version to be submitted in journal.  
This research received no external funding.  
The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for funding this research. The authors would also like to extend their appreciation to the Deanship and support by Ministry of Higher Education Malaysia in collaboration with Research Management Center at Universiti Teknologi Malaysia.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### PAH Extraction  
Samples were extracted using an accelerated solvent extraction ASE350 system (Dionex, Ltd., Camberley, UK). A total of 30 \(\mu\)L of surrogate PAHs solution containing 500 \(\mu\)g mL\({}^{-1}\) of naphthalene-d8, fluorene-d10 and pyrene-d10 was spiked onto filter prior to the extraction to determine the extraction efficiency. All ASE packing agents were baked at 450 \({}^{\circ}\)C for 8 h before use. Silica gel was deactivated with 10% _w/w_ deionized water. A filter sample was cut into small pieces and packed into the extraction cell with approximately 3 g of silica gel and 3 g of anhydrous sodium sulphate as in-cell cleanup agents while the remaining cell volume was filled with pelleized diatomaceous earth. Samples were extracted in three cycles with a solvent mixture of toluene-hexane, volume ratio 4:1. The ASE extraction temperature was at 110 \({}^{\circ}\)C with 6 min static time and 1500 psi pressure. Extracts were dried with anhydrous sodium sulphate and then evaporated without further purification under vacuum using a Buchi Syncore(r) Analyst (Buchi, Ltd., Newmarket, UK). The final volume of each sample was adjusted to 1.5 mL with toluene and spiked with 30 \(\mu\)L of 1000 \(\mu\)g mL\({}^{-1}\) internal standard solution (acenaphthene-d10, phenanthrene-d10, chrysene-d12) prior to the GC-MS analysis. The 16 US EPA priority PAHs were quantified by a quadrupole gas chromatograph-mass spectrometer (Thermo Scientific, Inc.).
# Understanding Olive Oil Stability Using Filtration and High Hydrostatic Pressure
## 2 Results
### Quality Evolution During Storage  
#### 2.2.1 European Legal Requirements  
All of the olive oil samples were compliant with the EU legal chemical limits [1] during the storage tests (Supplementary Material Table S1); no significant variations occurred between either the CON, HHP, FIL and F-HHP samples or the different storage times (i.e after 15 days, 1 month and 6 months of storage). The oil samples had low acidity values (approx. 0.21% oleic acid), low peroxide values (approx. 4.9 meq O\({}_{2}\) kg\({}^{-1}\)) and low UV index values (approx. 1.67 and 0.13 for K\({}_{232}\) and K\({}_{270}\), respectively). A color shift from green to yellow was visually noticed in all samples regardless the treatment, probably due to the light effect on samples.  
Instead, the filtration and HHP treatments had a significant effect on the legal sensory attributes of the olive oil (Table 2). The positive \"fruity\" attribute showed a significant change as a function of filtration and storage time. The filtered oil (FIL and F-HHP samples) was perceived as fruitier (\(p<0.001\)) than the veiled oil (CON and HHP samples). Furthermore, in all of the olive oil samples the fruitiness attribute significantly decreased during storage (\(p<0.001\)). The positive bitterness attribute changed significantly as a function of filtration (\(p<0.01\)), storage time (\(p<0.001\)) and their interaction (\(p<0.05\)). The filtered oil was bitterer than the veiled oil after 15 days of storage, while no significant differences occurred after 1 and 6 months of storage; the bitterness significantly decreased in intensity during storage. The behaviour of the positive pungency attribute during storage was also consistent with the bitterness attribute.  
\"Fusty\" and \"rancid\" sensory defects occurred in some olive oil samples, causing the oil to be downgraded from EVOO to virgin or lampante olive oil. The negative \"fusty\" attribute was related to filtration (\(p<0.001\)), HHP treatment (\(p<0.05\)) and their interaction (\(p<0.01\)). The CON oil samples were the only ones with the \"fusty\" defect. The negative \"rancid\" attribute was significantly related to the treatments, storage time and all of their interactions. The \"rancid\" defect was not present in the filtered oil samples during storage, but it was perceived in the veiled oil samples after 1 month of storage. The intensity of the rancidity attribute was high and increased with storage time in the CON oil samples.
# Metabolomic Insights into the Potential of Chestnut Biochar as a Functional Feed Ingredient
## 3 Results
### Effects of Chestnut Agro-Residue Biochar Extract on E. coli Growth  
The growth of _E. coli_ strains F4+ and F18+ was tested in the presence of different concentrations (0, 25, 50, and 100 \(\upmu\)L/mL) of CB extract, which inhibited _E. coli_ growth. Both strains entered the logarithmic phase after one hour, with inhibitory activity observed after 2 h, which persisted for up to 4 h. Pathotype F4+ was more sensitive than F18+. The maximum inhibition rates at the highest concentration (100 \(\upmu\)L/mL) were 28.6% for F4+ and 15.8% for F18+ after 3 h of incubation with the CB extract (Figure 3).  
Figure 3: Growth inhibition of CB extract against _E. coli._ (**a**) Growth inhibition of _E. coli_ F4+. (**b**) Growth inhibition of _E. coli_ F18+. Data are shown as the means and standard deviations. Different superscript letters indicate significant differences at \(p\) < 0.05 among different concentrations within the same time point.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Formula** & **Area** & **Retention Time** & **Adduct** \\ \hline
4-Hydroxybenzoic acid & C\({}_{7}\)H\({}_{6}\)O\({}_{3}\) & 108,484 & 8.35 & [M-H]\({}^{-}\) \\ \hline Azelaic acid & C\({}_{9}\)H\({}_{16}\)O\({}_{4}\) & 1,830,729 & 8.02 & [M-H]\({}^{-}\) \\ \hline Succinic acid & C\({}_{4}\)H\({}_{6}\)O\({}_{4}\) & 365,375 & 1.67 & [M-H]\({}^{-}\) \\ \hline
6-Methylciumarin & C\({}_{10}\)H\({}_{8}\)O\({}_{2}\) & 20,094 & 8.15 & [M-H]\({}^{+}\) \\ \hline
7-Methoxycumarin & C\({}_{10}\)H\({}_{8}\)O\({}_{3}\) & 118,168 & 7.97 & [M-H]\({}^{+}\) \\ \hline
7-Hydroxycumarin & C\({}_{9}\)H\({}_{6}\)O\({}_{3}\) & 337,625 & 6.98 & [M-H]\({}^{+}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Metabolomic profile of CB extract.
## 7 Simulation and Results
### Increasing Number of Hops
#### 7.1.2 Average End to End Delay  
We have shown average end-to-end delay against the number of hops between the source and the sink. As shown in Figure 19 Average end-to-end delay is much less in the case of hotline assisted routing as compared to all wireless routing. We get a huge advantage in terms of less end-to-end delay in the case of hotline assisted routing due to gateway-to-gateway communication (except for few cases as covered by SBR). As the number of hops increases, the delay increases as well. Hotlines avoid these overheads and reduce the overall end-to-end delay as compared to the all wireless routing. These simulation results are very encouraging and pave the way for the deployment of the proposed topology for mission-critical applications encountered in healthcare and military.
## 5 Evaluation
### Comparison with Related Works  
The weed detection accuracy of the bagged UNet++ model in this study is lower than that reported in related studies. However, direct comparison is not straightforward due to differences in the datasets used for training. In related studies, the images were captured from a top-down perspective. In contrast, the dataset used in this study consists of images taken from the deck of an aquatic drone at a near-horizontal angle, where weeds tend to blend into the background, such as rice plants and water surfaces. This difference suggests that weed detection from a horizontal viewpoint is inherently more challenging than detection from a vertical perspective.
# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 5 Discussion
### Insufficient Research  
Although the results of this study are reasonable and interpretable, there are still areas that can be improved. The fusion of SAR and optical images effectively improves the monitoring accuracy, but how to optimize the fusion of different data sources to give full play to the advantages of each is still a challenge in practical applications. In this study, pixel-level fusion is used, and feature-level fusion using deep learning techniques can be explored in the future to further improve the fusion effect. In addition, the proposed method relies on high-quality multi-source imagery, and cropland monitoring requires frequent observations. However, in regions characterized by frequent cloud cover and heavy rainfall, optical imagery is often limited, which may affect the timeliness of monitoring. Therefore, in such cloud-prone and rainy climates, it is crucial to explore SAR-based change detection methods, as they hold greater potential for practical applications. Moreover, the method proposed in this study can be used to construct non-agriculturalization monitoring datasets to provide support for deep learning applications. By quickly and accurately extracting change patches over a large area as a sample database for the deep learning model, and because these patches exhibit strong change signals in both optical and SAR imagery, they can be used for both change monitoring within the same data source and change detection to a different data source, which can help to solve the challenges of non-agriculturalization monitoring.
## 3 Results and Discussion
### Experimental Results  
#### 3.2.1 Surface Residual Stress  
Table 3 shows the average value of the residual stress on the surface of the T-type rib after different processes. The cold pressing process plays a significant role in the reduction of the residual stress. The compressive residual stress on the stiffened surface decreases from \(-200\) MPa in the quenching process to around \(-160\) MPa in the stepwise cold pressing, thus resulting in a stress reduction of \(20\%\). The residual stress on the stiffened surface further decreases to around \(-100\) MPa with artificial aging, as a result, a stress reduction of \(50\%\) is achieved by the combination of cold pressing and artificial aging. It can be observed that the stress reduction on the smooth surface in the cold pressing is larger than that on the stiffened surface, indicating that the cold pressing is more effective in stress reduction of the smooth surface of the T-type rib. A total stress reduction of \(50\%\) is also obtained on the smooth surface after cold pressing followed by artificial aging.
# Semi-Global Stereo Matching Algorithm Based on Multi-Scale Information Fusion
## 2 SGM-MSIF Algorithm
### Improved Cross-Scale Multi-Path Cost Aggregation
Setting \(\widetilde{V}=\left[\widetilde{C}^{0}(i^{0},l^{0}),\widetilde{C}^{1}(i^{1},l ^{1}),\ldots,\widetilde{C}^{S}(i^{S},l^{S})\right]^{T}\), then the \(S+1\) elements in vector \(\widetilde{V}\) represent the cost value of multi-path cost aggregation at each scale. Through the above process, \(S+1\) linear equations can be obtained and expressed in matrix form as follows:  
Figure 3.1  
In Equation (16), the inverse of \(A\) always exists since \(A\) is a diagonal matrix with dimension \((S+1)\times(S+1)\) and its diagonal elements are not equal to \(0\). Thus,  
Figure 3.2  
In the proposed algorithm, the cost volumes \(\widetilde{C}^{0}(p^{0},d^{0}),\widetilde{C}^{1}(p^{1},d^{1}),\ldots,\widetilde {C}^{S}(p^{S},d^{S})\) of pixel \(p\) after multi-path cost aggregation at all scales can be obtained through Equation (7). By substituting them into Equation (17), we can get the final cost volume that fuses multi-scale information. To obtain a disparity map with the same resolution as the original image, the cost volume \(\tilde{C}^{0}\) on scale \(0\) is selected for the next disparity computation.
# Probabilistic Models for Competence Assessment in Education
## 3 Results  
### Probabilistic Models of Peer Assessment  
In the last decade, the boom of massive online courses (MOOCs) has propitiated that some platforms that provide online educational resources direct efforts towards implementing technologies that somehow automate the assessment process. The goal is to help teachers evaluate usually large numbers of assignments in this teaching modality.  
The variability of answers to open questions and their challenges to natural language processing (NLP) techniques make automatic assessment a limited tool to deal with this task. The more unique or creative an assignment is, the less appropriate it is to rely on purely computer-based assessment methodologies [11]. Many authors have proposed peer assessment techniques as a promising alternative to speed up evaluation in online courses. Moreover, according to these voices, this methodology may provide other potential benefits such as helping students see the task from an assessor's perspective and boosting self-reflection, as well as providing valuable feedback [3,12].  
The three models of peer assessment that were studied in this paper are presented in this section. All of them are probabilistic and focus on an educational context and they all address a common question: How can we generate an automatic quality assessment of a submission not yet evaluated by the teacher in charge?
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 1 Introduction
### Early Detection Value Proposition  
This paper introduces the Polymorphic Ransomware Deep Learning (PRDL) framework, a novel and sophisticated solution designed to significantly improve the early detection of evasive, shapeshifting ransomware during its brief pre-encryption phase, a critical window before any data is locked or ransom demands are made. Traditional cybersecurity methods often fail in this phase due to limited observable behavior, constant code mutations, and a lack of model adaptability. To overcome these challenges, the PRDL framework integrates a GAN-augmented DBN and introduces three key innovations:  
* Realistic Ransomware Pattern Generation: A novel Bi-Gradual Minimax Loss function uses a gradual up-weighting coefficient to enable the GAN to produce highly realistic artificial ransomware attack patterns.
* Dynamic Feature Adaptation: An Incremental Mutual Feature Selection (IMIS) technique allows the model to continuously adapt to evolving ransomware behaviors by reassessing feature relevance.
* Optimized Training for Timely Detection: The Uncertainty-Aware Dynamic Early Stopping (UA-DES) technique, based on Bayesian approximation, precisely determines the optimal training epochs for the DBN, ensuring efficient and early detection. Beyond technical enhancements, the PRDL framework contributes strategically to ransomware defense by providing
* First-of-its-Kind Taxonomy of Ransomware Attack Success Factors: This work introduces a groundbreaking categorization of factors contributing to successful ransomware attacks, particularly within industrial SCADA environments. This offers essential insights for risk mitigation and targeted defense [11; 14].
* Comprehensive Ransomware Attack Model for Industrial Systems: We have developed a detailed model illustrating ransomware's operational mechanisms in industrial systems, crucial for designing effective prevention and mitigation strategies.
* Advanced Situational Awareness for Prediction: The framework enhances the ability to predict ransomware attacks by integrating diverse evidence, enabling proactive intervention. It also establishes a benchmark for evaluating future ransomware prediction methodologies in Industrial Control Systems (ICSs) [10].
## 3 Methods  
For proper comparison of temperatures obtained from different data sources, they should be reduced to one parameter and synchronized. Integral airglow intensity observed with a ground-based instrument cannot be transformed to height profile; therefore, to compare airglow intensity from SABER data and simulation results with that from ground-based instruments, it should be reduced to arbitrary units--Rayleighs. Temperature profiles obtained with simulation and the satellite instrument should also be averaged with the weights equal to airglow intensity in order to obtain integral characteristic--effective temperature [16; 19]--which shall be further compared with the interferometer observation results. Finally, due to the fact that observations with ground-based instruments are only possible in the night, daylight hours should be excluded from time series of satellite and model data. The resultant data series shall be brought to a common form. Gaps in data series due to inappropriate observational conditions or technical problems, data dispersion due to weather conditions and equipment noise impede comparison, e.g., of the studied parameters' annual dynamics. Hence, we compared time series of nighttime means and monthly means of the studied parameters. The time series were preliminarily averaged over 2017-2021.
# FCN-Based 3D Reconstruction with Multi-Source Photometric Stereo
###### Abstract  
As a classical method widely used in 3D reconstruction tasks, the multi-source Photometric Stereo can obtain more accurate 3D reconstruction results compared with the basic Photometric Stereo, but its complex calibration and solution process reduces the efficiency of this algorithm. In this paper, we propose a multi-source Photometric Stereo 3D reconstruction method based on the fully convolutional network (FCN). We first represent the 3D shape of the object as a depth value corresponding to each pixel as the optimized object. After training in an end-to-end manner, our network can efficiently obtain 3D information on the object surface. In addition, we added two regularization constraints to the general loss function, which can effectively help the network to optimize. Under the same light source configuration, our method can obtain a higher accuracy than the classic multi-source Photometric Stereo. At the same time, our new loss function can help the deep learning method to get a more realistic 3D reconstruction result. We have also used our own real dataset to experimentally verify our method. The experimental results show that our method has a good effect on solving the main problems faced by the classical method.  
Photometric Stereo (PS); 3D reconstruction; fully convolutional network (FCN) +
Footnote †: journal: Applied Physics Letters
# Image-Based Malicious Network Traffic Detection Framework: Data-Centric Approach
## 3 Proposed Detection Framework
### Data Preprocessing and Feature Engineering  
As shown in Figure 1, the data preprocessing and feature engineering module is divided into two main components: _data preprocessing_, which converts raw network traffic data into normalized numeric feature vectors, and _feature engineering_, which refines these vectors by selecting relevant features and analyzing their relationships. These processes are essential to reduce data dimensionality, mitigate biases from varying feature scales, and ultimately generate meaningful image representations for the CNN model. In the data preprocessing component, we first perform data transformation to encode categorical (non-numeric) features into numeric values. Then, data normalization is applied to rescale all feature values to a consistent range. In the feature engineering component, we employ feature selection based on information gain to remove features with low importance, thereby reducing dimensional complexity. Finally, feature correlation analysis examines the relationships between the selected features to determine an optimal pixel mapping strategy for image generation. In the following subsections, each step is explained in detail.
## 4 Medium Access and Network Layer Protocols
### Network Layer Protocols
#### 4.2.1 Flooding
The algorithm operates as follows: Data are transmitted from node A to node F. When a packet arrives at node A, it is forwarded to nodes D, C, and B. Node B then relays this packet to nodes E and C. Node C further sends the packet to nodes F and D. Additionally, node D forwards the packet to nodes F and C. Finally, node E transmits a packet directly to node F.
## 2 Methods
### Tuning Parameter Selection
It fits one Gaussian Mixture Model (GMM), \(l(x)\), to the set of hyperparameter values with the best loss function values, and another GMM, \(g(x)\), to the remaining hyperparameter values. The TPE algorithm maintains sorted lists of observed variables and chooses the hyperparameter value \(x\) that maximizes the ratio \(l(x)/g(x)\)[27]. The different methods being tested have different tuning parameters. ATPRK has 6 tuning parameters, Range_min, Sill_min, L_range, L_sill, rate, and H. These parameters define a search space internal to the method used to select semivariogram functions for kriging. SupReME has a single tuning parameter, the regularization strength \(\lambda\). For S2 Sharp, \(p\), the number of dimensions of the subspace \(\mathbf{U}\), can be tuned, as well as the regularization strength for each of the subspace dimensions, \(\lambda_{j},j\in\{1,\dots,p\}\). MuSA and SSSS both have regularization tuning parameters, \(\lambda\) and \(\mu\), due to using similar implementations of SALSA. For S2 SSC, learning rate and batch size and scale reduction are tuned. Parameter tuning was not performed for DSen2 and Sen2 res. DSen2 is provided as a pre-trained neural network and can not be effectively tuned without extensive retraining. Sen2 res does expose some tuning parameters, but they do not lend themselves well to optimization and offer minimal improvement possibilities without prohibitive growth in running times.