esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Processing polyethylene terephthalulate (PET) into functional materials has both sustainable and economic significance. Therefore, this study aims to prepare functional nanofibers using PET, combining electrospun nanofibers with metal-organic frameworks (MOFs), which is an effective solution to increase the added value of functional nanofiltration membranes (NFMs). The surface morphology of PET fibers is successfully controlled by electrospinninging parameters and post-treatment. The formation of a uniform coating of CuBTC crystals on the PET surface is induced by a simple and low-cost in situ growth technique. CuBTC@PET was treated to prepare superhydrophobic CuBTC@PET (SCP), thus improving the stability of CuBTC in water and expanding its potential applications. Through a series of optical and thermal characterizations, the porous morphology formation mechanism and MOF in situ growth mechanism of SCP fibers were discussed. Then, the air filtration performance and bacteriostatic properties of SCP nanofiltration membranes were investigated. The as-prepared SCP showed a high water contact angle (146.4\({}^{\circ}\)), low-pressure drop (39.7 Pa), and high filtration efficiency (95.3%, 3 \(\upmu\)m NaCl), as well as unique, broad-spectrum antibiosis potency against _Staphylococcus aureus_ (_S. aureus_) and _Escherichia coli_ (_E. coli_). This study shows that SCP nanofiltration membranes can be practically applied in high-performance antibacterial filtration membranes.  
electrospinning; porous fiber; metal-organic frameworks; antibacterial; air filtration +
Footnote †: journal: Applied sciences
## 4 Discussion and Analysis  
Utilizing the methodologies described, the landslide-susceptibility distribution map for the research area was generated. A comparative verification of the study's outcomes was undertaken to ascertain the two coupled models that best fit the characteristics of the study area. This paper predominantly adopts a statistical approach, featuring an analysis with the ROC curve. The comparative outcomes are detailed below:  
The ROC curve is a pivotal tool in statistical classification analyses, particularly for datasets of substantial size. It graphically represents the model's performance by plotting sensitivity against specificity. A ROC curve that extends towards the upper-left corner of the graph signifies a model with superior predictive accuracy, suggesting a near-perfect predictive capability.  
Furthermore, the efficacy of the landslide-susceptibility models is assessed through a quantitative measure known as the Area Under the ROC Curve (AUC). A higher AUC value, nearing the threshold of 1, indicates a higher degree of precision in the model's predictive performance.  
A ROC analysis was conducted on the predictions from the two coupled models, with both yielding ROC curves that nearly hugged the upper left corner, indicative of high predictive accuracy, and AUC values surpassing 0.86. Collectively, the ROC validation curves for these models were robust, with AUC values above 0.860 (refer to Figure 5), confirming that both the DNN-I-SVM coupled model (AUC of 0.860) and the DNN-I-LR coupled model (AUC of 0.875) are apt for the landslide-susceptibility assessment within the research area.  
Given that in the article a comparison of the zoning capabilities among various models is already present, with an assessment against actual conditions, it is redundant to further illustrate the ROC curves for the standalone models. The objective of crafting a coupled model is to increase the precision of individual models. Furthermore, drawing from an abundance of literature both domestically and internationally, it is observed that the aggregate precision of individual models typically does not surpass that of the coupled models, provided that there is a foundation of rationality.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Landslide-** \\ **Susceptibility** \\ **Zoning** \\ \end{tabular} & \begin{tabular}{c} **Low-Risk** \\ **Areas** \\ \end{tabular} & \begin{tabular}{c} **Lower-Risk** \\ **Areas** \\ \end{tabular} & \begin{tabular}{c} **Moderately** \\ **Prone Areas** \\ \end{tabular} & \
\begin{tabular}{c} **Higher-Risk** \\ **Areas** \\ \end{tabular} &
\begin{tabular}{c} **High-Risk** \\ **Areas** \\ \end{tabular} \\ \hline LR/km\({}^{2}\) & 10.0953 & 4.0275 & 2.9808 & 1.8306 & 1.2456 \\ SVM/km\({}^{2}\) & 10.467 & 5.8239 & 1.9755 & 2.25 & 1.6155 \\ percentage-LR & 50.03\% & 19.96\% & 14.77\% & 9.07\% & 6.17\% \\ percentage-SVM & 47.29\% & 26.31\% & 8.93\% & 10.17\% & 7.30\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis table for susceptibility zones across models.
## 2 Applications for VANETs  
Many different convenience and commercial applications have been proposed for VANETs by researchers in [6, 7, 8] and safety applications have been proposed by the US Department of Transportation in [9]. With so many vehicles on the roads/highways having so much computing power and sensors, it is obvious and logical to utilize all this data to form an array of scattered sensor networks or highly mobile _ad hoc_ networks. Some of the applications that have been recommended for VANETs include:  
Figure 1: Vehicular Internet environment.  
**i. Safety applications:** Notifications for crashes, hazards on the roads (slippery or wet road conditions), traffic violation warnings, curve speed warnings, emergency electronics brake light, pre-crash sensing, co-operative forward collision warnings, _etc._ This could also include generating warning messages that inform drivers of approaching emergency vehicles.
**ii. Convenience applications:** Navigation, personal routing _etc._, Congestion advice, toll collection, parking availability information, _etc._ Also, in disaster situations, the critical things are power failure and network breakdown. The connected vehicles can play a very significant role in such situations as they have on-board batteries and many sensors including cameras, _etc._, thusproviding valuable images and SOS calls. The vehicular network can become the emergency communication mechanism. Similarly, road and weather conditions can be monitored by sharing the data from on-board vehicle sensors [9].
**iii. Commercial applications:** Vehicle diagnostics exchanges for avoiding possible car problems, location-based services such as advertisements and entertainment, _i.e._, data/video relay, social networking updates, _etc._  
In the applications discussed above, the vehicles are sharing information in V2V and V2I communications that includes their position information and also information of road conditions. Furthermore, the driver may need to make life-saving decisions based on the information received (e.g., emergency brake light, forward collision warning). It is therefore necessary that the privacy, reliability, and integrity of the messages are ensured so that actions can be taken in a split second after the information is received due to the speeds involved in the case of vehicles.  
**3. VANET and the Cloud**  
The large amount of unused storage space and computing power coupled with on-board sensors provide a unique opportunity to utilize vehicles as a wireless sensor network. The vehicles will be equipped with not only sensors to measure temperature, humidity, wind, _etc._, but also with cameras. This together with high speed (4G/LTE) connectivity can be a great advantage for a variety of applications. VANETs can be a part of the cloud in the following three possible ways (Figure 2).  
Figure 2: VANETs and cloud (left), cloud applications (right).
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 2 Proposed Chaos-Based Cryptosystem  
The architecture of the proposed chaos-based cryptosystem is shown in Figure 1, for the encryption process and in Figure 2, for the decryption process. This structure achieves high confusion-diffusion effects. On the encryption side, the confusion is achieved by a circular substitution layer based on a proposed strong S-box, which is the non-linear element of the cryptosystem as in the Advanced Encryption Standard (AES) algorithm. The diffusion process consists of three steps: a permutation layer based on the modified 2-D-Cat map, a horizontal addition diffusion (HAD), and a vertical addition diffusion (VAD). The confusion and diffusion layers use the proposed PRNG-CS. On the decryption side, apart from the PRNG-CS, reverse diffusion and reverse substitution are achieved by reverse processes of those used in encryption, as indicated in Figure 2.  
The block cipher operation can be repeated \(r\) times, if necessary, to obtain the final secure encrypted image.  
Each component of the proposed cryptosystem is described in depth in the following sections.
# Image-Based Scratch Detection by Fuzzy Clustering and Morphological Features
## 5 Conclusions  
In this paper, an algorithm is proposed for the detection of scratch defects on a piezoelectric ceramic plate surface based on a combination of the FCM method and morphological characteristics. The method proposed in this paper has invariance of rotation and translation for the detection results. Through collecting 20 images from different angles and positions of the same ceramic chip and processing them, it was found that the scratch detection results were consistent. The surface of the piezoelectric ceramic chip has the characteristics of an uneven texture and a random gray value, which affected the accuracy of scratch detection. Experiments show that the algorithm can accurately detect the scratch defects from images with an uneven texture and a random gray value. In order to verify the accuracy of the proposed method for scratch detection, image acquisition and processing were carried out on 30 piezoelectric ceramic slices in 10 positions and directions, respectively. The results show that the proposed method achieves a 100% detection rate and a false alarm rate of 5.63%.  
Figure 10: Scratch detection results. (**a**) Detection result for piezoelectric ceramic plate images with wide scratches. (**b**) Detection result for piezoelectric ceramic plate images with narrow scratches.  
Figure 9: Approximate size of each region.  
Future directions of this work could lead to the development of more types of defect detection. Furthermore, the efficiency of the algorithm will be improved by parallel processing of morphological features extraction.  
Methodology, Z.T.; software, Y.J.; validation, Z.F., X.X. and B.Z.; investigation, Z.T. and X.X.; resources, Z.T.; data curation, Y.J.; writing--original draft preparation, Z.T. and Y.J.; writing--review and editing, Z.F. and X.X.; visualization, B.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors acknowledge the support of the Fundamental Research Funds for the Central Universities (2018B03914), the Changzhou Science and Technology Program (Grant No. CJ20190044), and the Jiangsu Key R&D Program (BE2018004).  
The authors declare no conflict of interest.
## 4 Discussion
Whilst these preliminary results are promising, there are limitations to the study. The ground truth was based on serial ultrasound measurements, which despite being the standard-of-care investigation used for screening and follow-up for AAAs, are operator dependent and unidimensional. [18F]FDG PET-CT is not routinely used to assess AAAs unless there is concern for an infective or inflammatory process. The cohort in this study was derived from patients with an AAA who were undergoing diagnostic evaluation for suspected malignancy which may not reflect the general population with AAAs. Although cases with tumours overlapping the aorta were excluded, the potential for systemic malignancy to subtly influence aneurysm wall tracer uptake cannot be entirely ruled out. A future study could incorporate patient demographics, morphometric features, automatic data-driven feature extraction from volumetric data and biomechanical finite element analysis to enhance growth rate prediction accuracy and facilitate evaluation of risk of aneurysm rupture. The sample size of 153 and retrospective nature limits the power of the study. For instance, accuracy would typically increase to a certain degree, with the training dataset size, while external validation with a larger multi-centre dataset would improve confidence in the prediction accuracy of the proposed approach, on unseen test cases. In our study, the feature-to-sample ratio after dimensionality reduction resulted in approximately 5.3 samples per feature. While this may be seen as a limitation under the traditional 'One in Ten Rule' for mitigating overfitting, more recent literature suggests that this rule may be overly conservative for advanced machine learning models [59]. The application of ten-fold cross-validation and external validation in our study likely reduced the risk of overfitting, but future studies with larger datasets are needed to fully address this issue. Data augmentation was not employed in this study due to its retrospective nature, which limited the feasibility of introducing synthetic or augmented data. The focus was on analysing radiomic features directly extracted from existing PET-CT datasets. While this approach ensured the integrity of the dataset, the absence of data augmentation may have limited the model's ability to generalise further. Future prospective studies incorporating data augmentation techniques could help to mitigate overfitting and improve model performance. Furthermore, the absence of harmonisation techniques to address potential variability between PET/CT scanners was not performed in this study. While consistent imaging protocols were used, inter-scanner variability cannot be entirely ruled out. Future studies incorporating harmonisation strategies, such as phantom calibration or scanner-specific corrections, may enhance reproducibility and standardisation. Finally, manual segmentation of the AAA and aortic blood pool is time consuming and not suitable for routine integration into clinical reporting workflow. An automatic segmentation algorithm is under development, and this should aid further validation and potential clinical translation.
## 2 Materials and Methods
### Correlation Analysis  
In the following study, after preprocessing the Landsat-8 remote sensing images, the band reflectance data of the monitoring points were extracted. In order to explore the relationship between the remote sensing data and water quality parameters, Pearson correlation analysis was performed, and the results are shown in Figure 3. The results  
Figure 2: PCWA-ResCNN model structure.  
show that there was a strong correlation between TUB and B5. CODMn showed a strong correlation with both B4 and B5; however, the correlation with B5 was more prominent. NH3-N is strongly correlated with B1 to B4, especially with B2. DO is strongly correlated with B3, B6, and B7, and the correlation with B7 is the greatest.  
Based on the above findings, we selected all parameters, B1-B7, of the pre-processed Landsat-8 remote sensing images as input features for the construction and training of three models, PCWA-ResCNN, CNN, and LSTM, all of which adopted the structural design of seven inputs and single outputs. From the collected data, a total of 170 sample points were extracted, of which 136 (80%) were used for training the model and the remaining 34 (20%) were used for validating the model.
# Bioactive Compounds and Nanodelivery Perspectives for Treatment of Cardiovascular Diseases
## 4 Therapeutic Effect of Bioactive Compounds on Cardiovascular System
### Polyphenols (Anthocyanins)  
Berries are high in polyphenols, especially anthocyanins, as well as micronutrients and fiber. The consumption of berries results in an improvement in heart health. Chokeberries, cranberries, blueberries, and strawberries contain distilled anthocyanin derivatives. They showed substantial progress in LDL oxidation, lipid peroxidation, total plasma antioxidant potential, dyslipidemia, and glucose metabolism. It causes the activation of endothelial nitric oxide synthase, the lowered activity of carbohydrate digestive enzymes, and reduced oxidative stress by increasing the endothelial functions and the plasma lipid profiles. This led to a reduction of abnormal platelet aggregation. The research recommends berries (anthocyanins) as an important fruit group in a heart-healthy diet [117].  
Pomegranate juice contains anthocyanins, catechins, quercetin, rutin, and ellagitannins, and it results in reducing high blood pressure, which is a result of the ACE activity antioxidant activity due to the radical scavenging effect of anthocyanins and hydrolyzable tannins [118; 119; 120; 121].
## 2 Materials and Methods
### Definition of Simplicial and Temporal Filtration
Given the problem settings, one can either set a reasonable time delay limit \(\tau\) given the domain knowledge or tune \(\tau\) from \(0\) to \(\infty\), similar to the filtration process with the spatial filtration parameter \(\epsilon\). The latter approach can potentially extract temporally invariant topological summary statistics. Figure 2 is an intuitive example of the criterion of edge forming in the temporal filtration. In the example, we have seven data points, which are marked by their time stamps (when they were measured). The node marked 1 indicates it is collected at time step 1. There is a one-time step difference between each data point of consecutive numbers. To illustrate the differences between the conventional and temporal filtration, the schematic is a snapshot of the full filtration process frozen at a set of filtration thresholds. In all four cases, we consider the case where the spatial threshold \(\epsilon\) of the nerve ball around each data point is one (which, in our case, only contains every data point's nearest neighbor and not the second-nearest neighbor). If we only performed spatial filtration, we would consider them all to be connected. However, that would not match the temporal skeleton. Instead, we could set a temporal constraint \(\tau\) such that only if two data points that were spatially (in the feature space) proximal to each other were also measured to be temporally close to each other would their edge be included. If \(\tau\) was small (e.g., one time step apart), then we would have a fine resolution temporal skeleton which separated the data points into three main phases. If \(\tau\) was medium-sized (e.g., two time steps apart), then we would have a relatively crude-resolution temporal skeleton which separated the data points into two main phases. If \(\tau\) was big (e.g., three time steps part), then we would have a crude temporal skeleton which grouped them all into connected components. This also demonstrates the possibility of using \(\tau\) as a hierarchical mechanism to parse the persistent features of different temporal resolutions.
## 7 Open Research Areas
### Network Management Algorithms for Different Traffic Patterns  
WirelessHART, WIA-PA (in the mesh level) and ISA100.11a use centralized network management techniques for communication scheduling and managing routes. However, those standards do not specify the specific optimization algorithms that can be used by the system manager to allocate resources. In [68, 69, 70] the authors have proposed the centralized scheduling algorithm in WirelessHART for convergecast by considering linear and tree networks models. ISA100.11a standard supports peer-to-peer communication, in addition to uplink and downlink traffics. This feature makes the communication scheduling and route managing algorithm more complicated than WirelessHART, in which the main concern is forwarding the traffic toward the gateway and _vice versa_. To the best of our knowledge, there are currently no algorithms indicating strategies to be followed for communication scheduling and route formation in ISA100.11a.
# Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals
## 5 Conclusions  
Tool wear estimation is studied in this paper based on the modal analysis and curve fitting. An algorithm is developed for real-time estimation of tool wear through monitoring and decomposing sensor signals. The vibration signals have a larger correlation with tool wear, and under the condition that the actual vibration signals are easy to obtain, so the estimation method based on vibration signals is more practical. EEMD can decompose signals to multiple modes and reduce the mode mixing problem, which helps to improve the accuracy in estimation by feature extraction. The Gaussian fitting function provides the best fit to describe the trend that the tool wear changes with the features, and root mean square error (RMSE) is only 0.0093. The viability of the method is tested with the tool  
\begin{table}
\begin{tabular}{c c c c} \hline
**Time (s)** & **Estimated Tool Wear (mm)** & **Actual Tool Wear (mm)** & **Absolute Error (mm)** \\ \hline
376 & 0.0918 & 0.0874 & 0.0044 \\
816 & 0.1303 & 0.1065 & 0.0238 \\
1256 & 0.1413 & 0.1583 & 0.017 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of estimated and actual tool wear.  
Figure 9: The tool wear real-time monitoring graph.  
milling experiments and absolute error is controlled within 0.025 mm. In further work, the method can be used to real-time estimate tool wear. The paper contributes in a modal way to our understanding of tool wear and provides a basis for wear estimation of tool by modal analysis. Notwithstanding the relatively limited sample, this work offers valuable insights into the works for tool wear, and the tool modal can be illustrated through the physical mechanism. The issue of the tool modes is an intriguing one which could be usefully explored and applied in manufacturing industry in further research.  
[PERSON] devised and validated the model; [PERSON] conceived and designed the signal processing methods; [PERSON] and [PERSON] analyzed the data. [PERSON] contributed to optimizing the programming.  
This work was supported by National Science and Technology Major Project, China (No.2017 ZX04008001), and National Defense Fundamental Research Foundation, China (No. JCKY2016601C006).  
The authors declare no conflict of interest.