esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Definitions, Representation and Assumptions
### Assumptions  
Since SDSE evaluates data security provided by encryption mechanisms, key management schemes and intrusion detection systems (details are in Section 3.2), we assume that the networks in which SDSE will be installed already have these three security mechanisms properly installed and configured. In this case, when SDSE is installed, it will communicate with the underlying operating system of the WSN in order to automatically recognize the security mechanisms installed and will then communicate with these mechanisms whenever it needs to retrieve their configuration parameters required for calculating the security metrics (details are in Section 3.1).  
However, in case a sensor network does not have all three security mechanisms, one of the three following approaches can be executed, depending on the choice of the network administrator:  
1. SDSE installs the missing security mechanisms in the network. This is possible because SDSE will have these mechanisms previously embedded in itself;
2. SDSE ignores the missing mechanisms and calculates the security level based solely on the mechanisms present in the WSN. In this case, if network data present a high security level, for example, this will indicate that the data are secure in relation to the attacks addressed by the present mechanisms, but it will not be possible to know whether the data are secure regarding the attacks addressed by the missing mechanisms, since these mechanisms could not be evaluated by SDSE;
3. SDSE simply assigns the value of zero to the security level, since the lack of any one of the three main security mechanisms can make the network totally insecure.  
The calculations of SDSE estimates are performed in the base station, which does not require any additional energy consumption from the sensor nodes. To make this possible, SDSE requires security mechanisms that have modules running on the base station. Such a type of mechanism is very common in WSNs [10], given that base stations are much more powerful nodes that can perform computations that cannot be run by sensor nodes. In this case, SDSE can communicate with these core modules located at the base station in order to obtain the parameters necessary for calculating SDSE estimates. These modules running on the base station typically manage the operation of the distributed modules that run in each sensor node of the network.  
In addition to these parameters obtained in real time from the security mechanisms, in order to properly compute its estimations, SDSE also needs some \"static\" information about the mechanisms that cannot be obtained during runtime (for example, the security strength of cryptographic keys; see Section 3.2.1). For that reason, we assume that SDSE will keep a database to store that type of \"static\" information for several existing WSN security mechanisms. As we discuss in Section 3.2, this type of information required by SDSE is publicly available; therefore, such a database can be easily created and distributed along with SDSE.
## 2 Principles of Improved Harmonic Detection Methods  
The Mallat algorithm uses the characteristics of multi-frequency analysis to decompose the signal and divides the signal into low frequency and high frequency parts. Add Bartlett-Hann window function interpolation DFT algorithm to realize the steady-state signal restoration and the existence of steady-state signals at high frequency. The DWT algorithm implements the reconstruction of non-steady signals. The principles of the DFT algorithm and the DWT algorithm are relatively common. The following mainly introduces the principles of the Mallat algorithm and the Bartlett-Hann window algorithm.
## 3 Background
### Motivation  
As discussed in Section 3.1, achieving satisfactory results in pose estimation and tracking using off-the-shelf image-level approaches is challenging due to various factors such as motion blur, person occlusions, and unusual poses. However, videos contain richer information about the same person instance, allowing for higher confidence when utilizing nearby frames. In the person detection stage, it is natural to extract useful feature information from neighboring frames to maximize the accuracy of person instance detection in a given frame. By doing so, the performance of human pose estimation and tracking can experience a substantial boost. Additionally, the analytical framework, which achieved 2 nd place in the keypoint detection task of COCO 2018, has demonstrated good performance in still images. The challenge at hand is how to effectively incorporate instance-level information from nearby frames to address the problem of frame degeneration. Although the analytical framework performs well, there is still room for improving the accuracy of person detection in the person detection stage. This motivates the design of a module that can effectively exploit temporal information for specific instances between adjacent video frames. To maintain consistency with the current top-down pipeline, we propose a framework, which includes an additional module that learns to sample and aggregate features across temporal correspondences to enhance person detections over adjacent video frames.  
Therefore, addressing two important blocks is crucial for proper feature sampling and aggregation: (1) determining how to sample useful instance-level features from nearby frames to a given frame, and (2) finding an appropriate approach to aggregate the features across multiple frames. These two blocks will be further elaborated below.
# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 3 Security Challenges and Mechanisms
### IoT on the Ground
#### 3.2.7 Extensions with Applications in Entertainment  
Internet of Media Things has been studied in some recent research works [178].  
Some variants of the Internet of Media Things are studied below:  
* Internet of Video Things The Internet of Video Things connects things with visual sensors [179]. Objectives such as scalability [180] have been considered in the design of the Internet of Video Things.
* Internet of Audio Things Internet of Audio Things refers to a network connecting physical objects called Audio Things with computing devices embedded inside them aiming at the production, transmission, and analysis of audio in distributed environments. There is a survey in this regard studied in Section 2.1.2 of Section 2[25].  
The Internet of Media Things has not been adequately studied from a security perspective.  
Internet of Multimedia Things The Internet of Multimedia Things is a recent extension of the IoT [181].  
Researchers have considered the following security challenges and mechanisms in the design of this extension:  
* Security Challenges
* Confidentiality Low-cost compression while maintaining the confidentiality of the Internet of Multimedia Things would allow for major advancements in entertainment. The authors of [182] propose a low-cost and confidentiality-preserving multi-image compressed acquisition model. The scheme evenly groups images together using the sigmoid sequence, and each group is randomly assigned compressive sensing. The groups are then combined into a larger image and then encrypted to increase confidentiality. The image is sent to the cloud and run through a decryption algorithm. The cloud is able to use the designated reconstruction algorithm to recreate the image. Through simulations, the scheme has proven to be effective in confidentiality and cost in transmission of the images.
* Security Mechanisms
* Authentication The Internet of Multimedia Things has many applications in other fields, including healthcare, surveillance, and the automotive industry. Each of these industries transmits large amounts of multimedia data. The authentication of edge devices on a network while protecting the privacy of user information is a large focus of the industry. In [183] the authors propose a multi-layer framework based on edge computing to solve the problem. The scheme provides security through a four-way handshake that happens between pre-made clusters. To keep privacy, the framework uses aggregation based on frame matching and a Label Distribution protocol-based technique that adds noise to the aggregation. When compared to existing privacy-preserving schemes, the proposed framework outperforms them based on a lower rate of error and data load.
## 2 Materials and Methods
### LC-LTQ/Orbitrap MS Conditions  
Chromotographic separations were performed on an Accela LC system (Thermo Fisher Scientific, Inc. GmbH, Bremen, Germany) consisted of an Accela AS autosampler model 2.1.1 and an Accela quaternary gradient LC-pump. The LC system was coupled to an LTQ-FT Orbitrap XL 2.5.5 SPI mass spectrometer (Thermo Fisher Scientific, Inc. GmbH, Bremen, Germany). The linear ion trap (LTQ) part of the hybrid MS system was equipped with an Ion Max Electrospray Ionization (ESI) probe, operating in negative ionization mode while LC-MS instrument control and data processing were carried out by Xcalibur 2.1 software (Thermo Electron, San Jose, CA, USA).  
The target EDCs were separated on a reversed phase analytical column Hypersil GOLD PFP (50 mm \(\times\) 2.1 mm i.d., 1.7 \(\upmu\)m particle size, Thermo Fisher Scientific, Inc. GmbH, Bremen, Germany) maintained at 20 \({}^{\circ}\)C. Chromatographic analyses were carried out using a gradient mobile phase consisting of water and methanol both containing 0.1% ammonia at a stable flow rate of 0.3 mL min\({}^{-1}\). Gradient elution was performed by increasing the percentage of methanol in water from 30 to 95% over the first 4.5 min and then to 100% over a 2 min period. This composition was maintained for 1 min, after which the initial solvent conditions were restored. The column was equilibrated for another 2 min before the next sample injection. The optimum injection volume was found to be 20 \(\upmu\)L. BPA, OP and NP isomers were adequately separated in 9 min and were detected as [M-H]\({}^{-}\) pseudo-molecular ions. The mass range selected for BPA and APs full-scan spectrum acquisition was within m/z 150-250 amu. The instrument main operational parameters were optimized either at the instrument tuning sections or via experimental design in terms of the maximum signal-to-noise ratio (S/N values). Quantification wasperformed post-acquisition using an isolation window of \(\pm\)2 mmu. The ESI source values and the MS parameters after optimization were as follows: spray voltage 3.5 V, sheath gas 40, aux gas 10 and sweep gas 0 arbitrary units, capillary temperature 320 \({}^{\circ}\)C, capillary voltage \(-\)35 V, tube lens \(-\)90 V as well as AGC target 1 \(\times\) 10\({}^{6}\) at resolution 60,000.
## 2 Related Work
### Security Level  
When comparing different cryptographic algorithms, key size (i.e., the number of bits of the key) cannot be used directly as a measure of the strength provided by an algorithm. A better option is to use the security level, which is a value that quantifies the required effort to break a cryptographic primitive [29]. If the effort is \(2^{k}\), it is said to offer k-bit security and, therefore, it provides a security level \(k\). Different types of cryptographic algorithms (e.g., symmetric, asymmetric, and hash functions) present different relationships between the key size and the security level they provide. For example, considering a \(k-bit\) key size, symmetric algorithms provide a \(k-bit\) security level while hash algorithms provide \(k/2-bit\) security level. Asymmetric key algorithms present different relationships between security level and key size. The National Institute of Standards and Technology (NIST) recommendation for key management [30] provides the reference security levels of symmetric algorithms when compared with two asymmetric algorithms, Elliptic Curve Digital Signature Algorithm (ECDSA) and RSA. These values are summarized in Table 1. For example, a 128-bit security level is achieved by either using 3072-bit RSA or just a 256-bit key size of an ECC curve. For this reason, the concept of security level is used throughout this paper to present a fair comparison among the tested algorithms. The interested reader can find in [31] a comprehensive analysis of the relationship between cryptographic key lengths and security.
# Effect of Chemical Composition of Bio- and Petroleum-Based Modifiers on Asphalt Binder Rheology
## 3 Results
### Chemical Characterization of Modifiers
#### 3.1.4 Thin Layer Chromatography Flame Ionization Detection (TLC-FID)  
Table 4 shows the SARA composition and the unfiltered residue for different modifiers. Following are the observations from SARA analysis:  
* Modifiers A, C, D, and E are primarily composed of resins.
* Modifiers A and D consist of some aromatics (A = 19.7% and D = 10.5%), limited asphaltenes (A = 4.6% and D = 7.5%), and some saturates (A = 8.3% and D = 1.1%).
* Modifiers C and E show limited or no presence of aromatics and saturates. They are primarily composed of resins (C = 70.1% and E = 83.4%) and asphaltenes (C = 23.2% and E = 13%).
* Modifier K has a significant proportion of saturates (59.3%) and exceptionally high residue content (26%) compared to other modifiers.  
Modifier G possesses unique characteristics which are different from other modifiers. It is a water-soluble modifier and did not dissolve in the solvent used for other modifiers. Modifier G is also toluene insoluble which suggests there is no presence of asphaltic materials. Hence, the SARA approach seems inappropriate to characterize the chemical composition of modifier G. Furthermore, solubility of G in water can have moisture durability issues in AC designed from this modified binder which is not in the scope of this study. Therefore, AC properties to evaluate the effect of moisture should be investigated.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample ID**} & **Saturates** & **Aromatics** & **Resins** & **Asphaltenes** & **Resolute** & **Total** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline A & 8.3 & 19.7 & 62.5 & 4.6 & 4.9 & 100.0 \\ C & 0.0 & 0.0 & 70.1 & 23.3 & 6.6 & 100.0 \\ D & 1.2 & 10.5 & 78.5 & 7.5 & 2.3 & 100.0 \\ E & 0.0 & 0.1 & 83.4 & 13.0 & 3.5 & 100.0 \\ K & 59.3 & 0.4 & 10.3 & 4.0 & 26.0 & 100.0 \\ \hline \hline \end{tabular} Note: Results for modifier G cannot be determined using TLC-FID.  
\end{table}
Table 4: Percentage composition of saturates, aromatics, resins, asphaltenes and residue for modifiers.  
Figure 3: Signal intensity vs. retention time for molecular size determination of modifiers.
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
### Key Contributions  
1. We introduce a novel hybrid algorithm rooted in deep learning principles. It initiates with raw traffic data, utilizing autoencoder for dimensionality reduction.
2. Our hybrid algorithm employs a one-class SVM and a deep autoencoder neural network to distinguish between non-attack and attack data. Identified attacks undergo processing using DBSCAN to form precise attack clusters.
3. Upon detection of attacks, the MITRE ATT&CK-based prevention system is activated to respond accordingly.
4. Validation entails assessing enhanced metrics such as accuracy, precision, specificity, recall, F-measure, false negative rate (FNR), prevention rate, priority-based blocking rate, and success rate on two datasets, namely, CSECIC-IDS2018 and CIC-IDS2017.  
This research article unfolds as follows: Section 1 covers intrusion detection basics and prerequisites. Section 2 explores the current literature. Section 3 describes the datasets utilized, Section 4 details our novel method, while Section 5 scrutinizes the results as well as the comparative analysis with state-of-art methods. Finally, Section 6 concludes our paper, offering insights and suggesting future research directions.
## 3 Related Work
### [PERSON]--Client Selection for FL  
As predescribed, the existing FL works optimize for better training accuracy with fewer training rounds (the so-called statistical model efficiency) or shorter average time duration per round (the so-called system efficiency), in which the participating clients are randomly selected for ease of deployment. Nevertheless, as pointed out by [17], a random selection of participants may lead to poor performances, biased testing sets, and loss of confidence in FL results.  
Unlike previous works that address specific challenges, [PERSON] [17] proposes a client selection framework (located inside the coordinator of an FL framework and interacting with the driver of an FL execution) to select high-quality clients for effective participation in the training job. It utilizes a utility function to measure the clients' priority based on three dimensions: data distribution, computing resources, and communication bandwidth. The system architecture of Oort is depicted in Figure 1.  
To make the extra cost paid for selection affordable, the authors of [17] (Section 4.2) introduced an effective statistical utility measure for each client, which can capture the heterogeneous data utility across and within categories and samples for various tasks. In other words, Oort's selection framework can also consider the non-iid client data issue by defining an appropriate utility measure. Moreover, [19] presented theoretical proof of the effectiveness of the adopted utility function in [17] over random sampling and empirically justified its performance in practice.  
However, even if the Oort framework selects preferable clients, there may still be a significant training time gap among the selected clients. According to our experimental results, as the green numbers in Figure 2 show, the fastest client needed to wait for the slowest client for nearly 1400 s to complete the whole training, which significantly burdened the overall training performance. This surprising observation inspired us to investigate ways to enhance the overall efficacy of FML.  
Figure 1: The system architecture of Oort (the indicated numbers represent the sequence order of the Oort’s execution).  
Figure 2: The overall training time gap among selected clients using Oort (in which the experiment conducted followed the environment settings presented in Section 5.1).
## 2 Materials and Methods
### Qualitative Assessment and Comparison with Previous Cocoa Classification Studies  
The last yet integral part of our analysis involved assessing the quality of the cocoa map generated by the best-performing random forest. For this purpose, we conducted visual inspections, which enabled us to critically reflect on the model's generalisation capacity. Through this process, we could identify cases of successful cocoa classification and misclassification, which were findings that quantitative accuracy measures alone could not reveal.  
However, the extensive size of our study area (17.4 Mha) made it impractical to conduct a thorough visual inspection across the entire classified area. Documenting every possible visual example in our study was also unfeasible. To address these challenges, we opted to report on a series of illustrative cases of successful and unsuccessful cocoa classification in Cote d'Ivoire and Ghana. Example sites were selected in areas with no training polygons in the proximity, i.e., at least 20 km away. Another requirement for selecting these illustrative examples was that full-sun cocoa trees and other land-use/cover types were visually distinguishable. To this end, we overlaid our classified map on top of very high-spatial-resolution Google Earth images (i.e., usually at submeter resolution), which served as a reliable reference for the actual land type. Only Google Earth images captured in 2021 or in January 2022 at the latest were used to align with our study time frame. We employed the Google Earth Pro desktop version for this purpose [115]. QGIS software version 3.14.1 was utilised for other mapping tasks [116].  
To provide a more comprehensive understanding of the potentialities and limitations of our cocoa classification, we visually compared our findings with those of two previous cocoa classification studies carried out by [PERSON] et al. [45] and [PERSON] et al. [34]. Both [PERSON] et al. and [PERSON] et al. performed binary pixel-based cocoa classification in Cote d'Ivoire and Ghana, though neither of them specified the type of cocoa cultivation they classified, i.e., full-sun cocoa and/or cocoa agroforestry [34; 45]. [PERSON] et al. focused on an annual classification for 2019 using a random forest and fused Sentinel-1 and Sentinel-2 data [45]. [PERSON] et al. conducted a three-year classification from 2019 to 2021 by employing a convolutional neural network (CNN) and Sentinel-2 imagery [34]. We selected these studies due to their relevance to our study area extent, the similar time frame, the use of optical data, the identical number of classes and the matching spatial resolution. The open access to their classification maps also facilitated our comparative analysis.
## 3 Results
### Daily Nutrient Intake by the Serum 25(OH)D Concentration  
The daily nutrient intake is listed in Table 2 according to the serum 25(OH)D concentration range. The purpose of this study was to investigate the association between vitamin D status and MetS. Thus, the basic adjustment (age, education level, occupation, residential area, smoking, alcohol consumption, physical activity, dietary supplement, and season of blood draw) as well as the intake of energy, dietary fiber, SFA, PUFA, and body mass index (BMI) were additionally adjusted as these variables may influence metabolic profiles such as blood pressure.  
Individuals with a blood 25(OH)D concentration of more than 20 ng/mL ingested the most protein and SFA daily. Lower serum 25(OH)D levels (\(<\)10 ng/mL) were related with higher daily consumptions of fat, MUFA, and PUFA. The serum 25(OH)D levels were strongly correlated with protein (\(p\) = 0.019), fat (\(p\) = 0.037), SFA (\(p\) = 0.008), MUFA (\(p\) = 0.010), and PUFA (\(p\) = 0.003) consumption. Similar trends were observed in men. The serum 25(OH)D level was significantly correlated with protein (\(p\) = 0.032), fat (\(p\) = 0.027), SFA (\(p\) = 0.006), MUFA (\(p\) = 0.005), and PUFA (\(p\) = 0.003) intake. Protein and SFA correlated positively with the serum 25(OH)D level, whereas fat, MUFA, and PUFA correlated negatively with the serum 25(OH)D level. However, in women, there was no connection between the nutrient intake and the serum 25(OH)D levels. Only the cholesterol consumption was slightly higher in those with higher serum 25(OH)D concentrations (\(p\) = 0.05).  
Note that there was no correlation between the dietary vitamin D intake and the serum 25(OH)D categories. Likewise, there was no significant difference in the consumption of vitamin D-rich food groups of the participants contributing to vitamin D intake by the serum 25(OH)D level and gender (Supplement Table S1).