esa-sceva/satcom-chunk-collection huggingface dataset:
# Framework Based on Simulation of Real-World Message Streams to Evaluate Classification Solutions
###### Abstract
This aims to reduce the network costs and latency associated with distributed environments. The framework incorporates test datasets from various sources and includes features for feature space simplification and textual data preprocessing. It also integrates basic stream classification algorithms, provides minimal evaluation and monitoring functionality, and is compatible with the Spark analytics engine or the Python sci-kit-learn library [18]. MOA [15] is derived from WEKA, the Waikato Environment for Knowledge Analysis framework, written in Java and accessible via a graphical user interface (GUI) or command line. Originally developed to assess stream classification performance and manage algorithm speed, memory usage and accuracy MOA includes numerous datasets, preprocessing approaches, stream-classification-related algorithms and evaluation methods. Advanced applications include extensions for tweet collection, sentiment analysis and data reduction techniques in evolving streams. Both frameworks do not allow adequate modelling of environments where multiple message stream generators are simultaneously configured to emulate the behaviour of spammers and other message sources that may exist and contribute to the main message stream (limited to what is reflected in the messages of the corpus used). This research hypothesises that the modelling of the sources of message emission to the main stream as profiles with configurable behaviours, and the modelling of the stream according to simulation techniques and queueing theory, will provide a message stream to evaluate message processing and/or classification solutions closer to the real one, thus allowing to detect in advance possible problems of processors and/or classifiers and to avoid degradation of their performance during their operation. This paper presents a framework that simulates real-world message streams to evaluate classification solutions. The framework consists of four modules: message stream simulation, processing, classification and evaluation. The first module uses simulation techniques and queueing theory to replicate real-world message streams. The next module enhances the input messages for effective classification. The third module applies existing classification solutions to categorise the generated stream. Finally, the fourth module evaluates the performance of the classification solutions by measuring various metrics. The rest of the paper is structured as follows: Section 2 describes the details of the proposed framework. Section 3 develops a case study in the spam detection environment to evaluate the proposed framework. Finally, Section 5 summarises the main conclusions.
# Enhanced Curvature-Based Fabric Defect Detection:
## 2 HIL (Hardware in the Loop) Implementation
### Prepare Data-Set  
Preparing a dataset for fabric defect detection is essential to ensure that the model can effectively learn and generalize from the data. The image database will be used for learning algorithms, as well as spectral or statistical methods. Therefore, the captured images must be segmented into 64 \(\times\) 64 pixel sections. These pixel dimensions are utilized in detection algorithms employing CNNs and Gabor filters. The curvature algorithm, which does not require a pre-existing database, enables the processing of images at varying scales. This approach is adopted to accurately determine the defect location.  
To build a robust dataset, it is important to gather a diverse set of fabric images that represent the full range of defects the model needs to detect. The dataset should include various defect types, severity levels, and orientations. Each defect type is organized into separate folders, each labeled with its corresponding defect code. The faulty fabric images contained within the dataset are depicted in Figure 2.  
Augmenting the training dataset by applying various transformations to the images is crucial for helping the model generalize effectively and reducing overfitting. Common augmentations include rotation, flipping, scaling, brightness adjustment, and cropping. During the process of dividing images into 64 \(\times\) 64 pixels, data augmentation can be applied by overlapping certain areas. Defects within the images are shifted and re-selected. Additionally, the dataset was further augmented by slightly altering the image scale by approximately 10%.  
In many vision systems, particularly those employing machine learning algorithms, a large and diverse dataset is required for training. Data acquisition ensures that the system has access to a comprehensive set of examples from which it can learn, thus enhancing its ability to generalize and make accurate predictions.  
The model's performance on the validation set should be continuously evaluated, with adjustments made as new data are obtained from the machines. This may involve adding more fabric images or updating the data augmentation strategy. Once a well-prepared dataset for fabric defect detection is established, it can be used to train the model or fine-tune the parameters of other algorithms.
# Erosion Resistant Hydrophobic Coatings for Passive Ice Protection of Aircraft
###### Abstract  
Novel polymeric coatings, namely slippery polyurethane (SPU) coatings, with high surface hydrophobicity and superior erosion resistance against high speed solid particles and water droplets were successfully developed to protect the leading edge of fast moving aerodynamic structures, such as aircraft wings and rotor blades, against ice accretion. The coatings comprise newly synthesized surface-modifying polymers (SMPs) bearing fluorinated and polydimethylsiloxane branches at a loading level of 1-5 wt.%, based on the total resin solid, which showed good compatibility with the erosion-resistant polyurethane matrix (PU-R) and rendered effective surface hydrophobicity and slipperiness to the coatings, as evidenced by the high water contact angles of 100-115\({}^{\circ}\). The coatings can be easily be sprayed or solution cast and cured at ambient temperature to provide highly durable thin coating films. X-ray photoelectron spectroscopy (XPS) investigation showed concentration of fluorine on the surface. The presence of 1-5 wt.% of SMPs in the polyurethane matrix slightly reduced the tensile modulus but had no significant impact on the tensile strength. All coating films exhibited good thermal stability with no material softening or degradation after heating at 121 \({}^{\circ}\)C for 24 h. DSC heating scans revealed no thermal transitions in the temperature range of \(-80\)\({}^{\circ}\)C to 200 \({}^{\circ}\)C. Ice adhesion strength (IAS) tests using a static push rig in a cold room of \(-14\)\({}^{\circ}\)C showed IAS as low as 220 kPa for the SPU coatings, which is much lower than that of PU-R (i.e., about 620 kPa). Sand erosion tests using 50 \(\upmu\)m angular alumina particles at an impinging speed of 150 m/s and an impinging angle of 30\({}^{\circ}\) revealed very low erosion rates of ca. 100 \(\upmu\)g/g sand for the coatings. Water droplet erosion tests at 175 m/s using 463 \(\upmu\)m droplets with 42,000 impingements every minute showed no significant coating removal after 20 min of testing. The combination of the high surface hydrophobicity, low ice adhesion strength and superior erosion resistance makes the SPU coatings attractive for ice protection of aircraft structures, where the coatings' erosion durability is of paramount importance.  
Accepted: 16 September 2022  
Published: 24 September 2022  
Dept. of the 2022  
Submitted: 24 September 2022  
[[http://doi.org/10.3390/app1299589](http://doi.org/10.3390/app1299589)]([http://doi.org/10.3390/app1299589](http://doi.org/10.3390/app1299589))
## 2 Methods and Steps
### Explosion Surface Fitting Based on Data Fusion Method
#### 2.3.2 Data Fusion Algorithm Process  
1. Sampling principle of the heaped-up bulk data  
After construction of the heaped-up bulk 3D model is completed using 3D laser scanning technology, profile cutting is made along the strike direction of the heaped-up bulk model at every interval distance a, and then the control point position is taken along the inclination direction of the heaped-up bulk model at every interval distance b on the profile (the schematic diagram of 2D and 3D heaped-up bulk models is shown in Figures 3 and 4). It is assumed that m sections are cut along the strike direction of the heaped-up bulk model in total, and that they are marked as vectors \(Z_{1}\), \(Z_{2}\), \(Z_{3}\), \(\cdots\), \(Z_{m}\). If there are n control points in the inclination direction of the section of the \(i\)th heaped-up bulk model, then they denote \(h_{i1}\), \(h_{i2}\), \(h_{i3}\), \(\cdots\), and \(h_{in}\), respectively. If this is such, then the existing relationship can be expressed as follows:  
\[Z_{i}= \begin{bmatrix}h_{i1}&h_{i2}&h_{i3}&\cdots&h_{in}\end{bmatrix}  \tag{3}\]If Matrix \(A\) is used to describe the morphological characteristics of the 3D model of the entire heaped-up bulk, then the relation of \(A\) can be expressed as follows:  
\[A_{i}= \begin{bmatrix}Z_{1}&Z_{2}&Z_{3}&\cdots&Z_{m}\end{bmatrix} ^{T}= \begin{bmatrix} h_{11}&h_{12}&h_{13}&\cdots&h_{1n}\\ h_{21}&h_{22}&h_{23}&\cdots&h_{2n}\\ \vdots&&\cdots&&\vdots\\ h_{m1}&h_{m2}&h_{m3}&\cdots&h_{mn}\end{bmatrix}  \tag{4}\]  
The assumption of the 3D model's m section elevation observation data measurement error of the equation, \(\sigma_{1}^{2},\sigma_{2}^{2},\sigma_{3}^{2}\cdots\sigma_{m}^{2}\), involves the use of a little \(P\), and its real value is the \(h_{2p}\) elevation. If the elevation value at point \(P\) on the heaped-up bulk simulation curve is \(h_{2p}\) after processing with the data fusion method, then in order to determine the accuracy of the elevation value after data fusion, the relationship of the research and judgment method can be expressed as follows:  
\[H_{2p}=\sum_{i=1}^{m}\omega_{i}h_{ip} \tag{5}\]  
\[\sum_{i=1}^{m}\omega_{i}=1 \tag{6}\]  
Figure 4: Schematic diagram of the heaped-up bulk profile along the tendency direction. Figure 3: Schematic diagram of the heaped-up bulk cutting surface along the strike direction.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy (\%)** & **Specificity (\%)** & **Sensitivity (\%)** & **AUC** \\ \hline WL & 86.7 (78.1–95.3) & 86.7 (78.1–95.3) & 86.7 (78.1–95.3) & 0.94 (0.86–0.99) \\ BLI & 93.3 (87.0–99.6) & 93.3 (87.0–99.6) & 93.3 (87.0–99.6) & 0.96 (0.88–1.00) \\ LCI & 90.0 (82.4–97.6) & 80.0 (69.9–90.1) & 93.3 (83.2–100) & 0.89 (0.79–0.97) \\ Combined & 95.0 (89.5–100) & 93.3 (87.0–99.6) & 95.6 (89.2–100) & 0.97 (0.92–1.00) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Individual modality results of our CADx system based on EfficientNetB4. The combined results are obtained after averaging the CNN output probabilities for each modality. The results are presented together with confidence intervals at 95%. AUC—Area under the curve. region, which focuses on the intestinal tract instead of the CRP. Therefore, the combined information of BLI and LCI allows for a correct decision of malignancy, facilitating endoscopists with a better diagnosis during colonoscopy. Figure 3: Output generated from the CADx system where an image of a colorectal polyp (CRP) is received from the endoscope. Two outputs are presented, (1) a prediction of whether the polyp is benign (green) or pre-malignant (red), and (2) a GRAD-CAM map that allows the endoscopists to judge the decisions of the CADx. From left to right, the three modalities employed in this study are shown: White Light, Blue Light Imaging, and Linked Color Imaging. In the first row, a benign polyp is predicted from the three modalities with the CADx system decision shown in the following row. In the third and fourth rows, a pre-malignant polyp is predicted from the combined modalities.
# Enhancing the 3 GPP V2X Architecture with Information-Centric Networking
## 3 NDN for V2X Communications
### NDN-Based Content Delivery for V2X: An Overview
** Although the above-mentioned schemes can be very effective in limiting packet collisions, the percentage of packet losses in vehicular environments can still be huge, due to adverse propagation effects and mobility. It is therefore crucial to design robust packet retransmission schemes to quickly recover from losses. Being a content usually composed of multiple Data packets, the authors of [27] proposed to extend the Interest header with a bitmap field indicating the missing packets of a content chunk. A retransmission time-out (RTO), estimated as a moving average of round-trip-time samples, is maintained by the consumer. When the RTO expires, the Interest is retransmitted with the bitmap set to request selected (missing) Data packets. Bouk et al. [28] observed that frequent Interest/Data loss over the wireless channel may cause adverse effects on the PIT size. Indeed, each Interest is maintained in the PIT until it is consumed by the Data or the PIT Entry Lifetime (PEL) expires. A PEL of 4 s is usually considered in the vanilla NDN implementation, but such a static value can be too high in the vehicular scenario. The authors of [28], instead, proposed a hop limit based adaptive PIT Entry Lifetime (LAPEL) scheme, which adaptively estimates the PEL at each Interest forwarding vehicle. By doing so, the PIT scalability and a more reliable transport service are achieved. **Prioritized transmissions. ** Vehicular applications may have different requirements in terms of latency, throughput and reliability. These requirements can be codified into well-defined name prefixes and translated into differentiated treatments at the NDN layer. For instance, the work in [29] uses two main name prefixes, /high and /low, to identify the content priority and to set accordingly the logic for the defer time calculation before (re-)broadcasting the packet. Similarly, in [30], content priorities are used by vehicles to compute the defer time, but they are codified with integer values and included as an additional header field in the Interest packet. For instance, value 1 is assigned to road conditions information that have the highest priority, value 2 is assigned to vehicle status information, value 10 to social media information and so on. **Hybrid cellular-IEEE 802.11 forwarding. ** To improve the delivery performance and reduce the network congestion, the work in [15] proposes to forward Interest and Data packets over different network interfaces. Instead of using IEEE 802.11 to convey all messages, signaling information (i.e., Interest packets) are transmitted over the cellular network, while Data are transmitted over ad hoc contacts. This solution offloads the cellular network of Data exchange, and guarantees that content requests reach the interested nodes with a high probability. In [16], instead, vehicles can use the cellular and the IEEE 802.11 interfaces in parallel, if the application requires high reliability and low latency.
## 2 The Smart Rainfall System (SRS)
### High Level Post-Processing  
In order to provide more comprehensive information, the SRS system can incorporate measurements taken by traditional sensors (RGs, water gauges, anemometers, CCTV cameras, infrared systems, etc.), making the most of existing investments.  
Based on the information analyzed, SRS creates real-time interactive and high-resolution rainfall maps (resolution of the order of 100 m can be expected) of the entire monitored area following the methodology described in [PERSON] et al. [10] and makes them available to its users, via an online service.  
SRS is currently provided as a tool for Decision Support System (DSS) for hydro-meteorological risks mitigation, allowing for faster and more efficient hazard analysis compared to traditional monitoring techniques. Decision-makers can predict the consequences of rainfall by monitoring whether critical rainfall levels have been exceeded in the catchment and applying hydrologic/hydraulic models for short-term runoff forecasting (e.g., by adopting semi-distributed or distributed continuous hydrologic algorithms).  
SRS also stands out for its low implementation requirements: as a matter of fact, SRS measurement stations use already active infrastructures (satellite telecommunications, internet, and mobile networks) and are made up of off-the-shelf, low-cost components. In addition, each SRS sensor has been designed to optimize power consumption: a set of four sensors co-located can be operated autonomously by a 50 W solar panel with proper battery storage.  
For SRS, the Italian patent was recognized in 2014 [26], and it has been extended at the European level in 2019 [27].
## 2 Methodological Approach
### Proposed Final Solution  
With all the methods explored, a final solution was proposed for automatic gauge reading, as shown in Figure 15. The algorithm begins with a trained CNN object detection to localize different gauges in an image. These RoIs are cropped, and a specific QR code is identified to read the needed information. The next step is the dial segmentation, which begins with the grayscale transformation and the bilateral filter application. Then, the circle Hough transform is applied. Since there are a lot of variations among gauges, the following steps help to uniformize gauge images. First the tell-tale pointer, if existent, is segmented using a mask in HSV color space with the red and orange colors. Then, to correct black dials, the RoI and its inverted colors image are binarized using the adaptive threshold, but only the image with the highest number of white pixels inside the dial is used. With this binarized image, the pointer angle is determined by first performing a polar coordinate transformation, followed by a pixel projection. In the end, the gauge indicating value is calculated using the angle method.  
Figure 15: Proposed solution for automatic gauge reading.
# Phytomicrobiones: A Potential Approach for Sustainable Pesticide Biodegradation
## 4 Omics Approaches in Pesticide Biodegradation
### Proteomics  
In complex pesticide biodegradation, proteomics stands as an important tool, unraveling the patterns, functions, and interactions of proteins [157]. This omics approach not only unravels the identity of potential enzymes and metabolic pathways linked to pesticide degradation but also uncovers the intricate protein pathways activated in response to pesticide-induced damage. [PERSON] et al. (2003), in a pioneering proteomic study, scrutinized the degradation of methylcarbamate pesticides by _Burkholderia_ spp. C3 [158]. Their quest explored protein expressions triggered by exposure to carbamyl compounds, with _Burkholderia_ spp. C3 unveiling a preference for N-methylcarbamate as its exclusive carbon source. Within their proteomic approach, 60 out of 867 detected proteins played pivotal roles in pesticide metabolism, an insight that helped in understanding the protein's involvement in biodegradation. Turning to the anaerobic realm, [PERSON] et al. (2014) used proteomic experiments to investigate the differential expression of proteins amidst the degradation of hexachlorobenzene by _Dehalococcus mccartyi_ CBDB1 [159]. Their meticulous efforts led to the creation of a comprehensive protein profile, unmasking the molecular mechanism that governs this intricate process. Their proteome characterization ventured to cover 70% of the 1458 annotated protein-coding sequences, offering a comprehensive glimpse into the proteins engaged in this biodegradation. [PERSON] et al. (2018) unveiled the potent role of proteomics in understanding _Fischerella_ spp.'s response to methyl parathion (MP) exposure [160]. Through proteomics, they discovered that even after 8 days of incubation with MP, the growth of _Fischerella_ spp. remained unharmed. Instead, proteins, including antioxidative enzymes, signaling proteins, and chaperones, were invigorated, underscoring the resilience and adaptive capabilities of these microorganisms in the face of pesticide-induced stress. Proteomics is a critical player in the quest to comprehend pesticide biodegradation, unraveling the roles of proteins in this intricate ecological happening.
# Rentable CDN Using Blockchain and Proof of Provenance
## 1 Introduction
The communication within the network is passed from a peer to a peer, thus it is designed to allow anyone with the hardware to participate, making the network easily scalable. It also means that searching requires more efforts than server client model-based networks because there is no centralized server that keeps the locations of the contents. The most obvious method to search a content in p2p network is called flooding. This is where the search request is sent out to all the connected peers and the requests are relayed to their peers until the content is found [14]. More advanced version of flooding improves upon how the contents and peers are indexed. Improvements in content indexing reduces the search space while the improvements in peer indexing allows for more efficient way to keep track of which peers have which contents. The most famous one is called Kademlia DHT system. In this system, pieces of contents and the peer associated with having that piece is stored as a keyword and a string which resembles the hash of the keyword, respectively [15]. Thus, searching for content becomes faster and the network overhead is reduced greatly. Although it has many benefits, p2p based CDN lacks many global features, such as caching, content moderation, load balancing, financial incentive, and reliability. Although lack of reliability is a serious issue, since users can shutdown their hardware at any time and bring down the CDN, more important issue is that there is no payment system that can financially reward users for their content delivery efforts. Without the financial incentive, users may not have good reasons to participate in p2p based CDN for prolong period. With the rise in the development of technologies related to Blockchain, a new type of p2p based CDN became available: we refer to it as \"rentable CDN\". In rentable CDN, the content is delivered using p2p communications and the content delivers are paid using decentralized payment system based on Blockchain. It provides affordable localized content delivery network for small and medium-sized content distributing companies without the expensive initial investment and risks from continued operation. It also provides income for consumers with what they already have, making the market fair, and promote fair pricing. The detailed explanation of existing work on rentable CDN is expanded in the next section. The rest of the paper is described using existing Blockchain terminology and p2p protocols to aid with understanding, but the proposed method is not bound by a specific Blockchain or p2p software.
# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 2 Existing Surveys
### Surveys Focusing on a Single IoT Extension
#### 2.1.3 Extensions Deployed in the Sea  
Some extensions of the IoT are deployed under the sea, e.g., the Internet of Underwater Things, or on the sea, e.g., the Internet of Ships (IoS). These extensions have been studied in a few surveys, which are discussed below.  
The Internet of Underwater Things has been studied from different perspectives. For example, the survey presented in [39] discusses current advances, challenges, and open issues in this IoT extension, with a focus on applications in smart oceans. Moreover, a five-layer system architecture for the Internet of Underwater Things has been proposed in this survey. The proposed architecture consists of a sensing layer, communication layer, networking layer, fusion layer, and application layer. Another relevant survey has studied the Internet of Underwater Things from a big data analytics perspective [40].  
Internet of Ships  
A survey on the IoS has been presented in [41], along with related architectures and elements. This survey also discusses emerging applications of the IoS. Moreover, some potential future opportunities, such as satellite communications, as well as some potential challenges, such as data collection, management, and analytics, have been studied in this survey.