esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Traditional Digital Image Processing Techniques
### Comparison and Summary of Traditional Digital Image Processing Techniques  
Table 1 compares the research results of traditional digital image processing techniques in some references, from which the following four conclusions can be drawn. (a) The recognition method based on color features, although easily affected by lighting conditions and susceptible to disturbance in natural environments, is more suitable for working in artificially structured environments. (b) Although the recognition method based on geometric features avoids the influence of lighting conditions, the recognition performance is poor in the case of occlusion and overlapping, so it is more vulnerable to interference in the natural environment. (c) Since the fruit surface is relatively smooth, the use of texture features can effectively achieve the recognition function, but the method is easily affected by environmental noise and offers poor performance in complex orchard environments. (d) The feature fusion-based method can effectively avoid the limitations of individual features and improve the performance of the visual recognition system of picking robots, but its performance still cannot meet the requirements of operation in a natural environment. Therefore, it is difficult to extend fruit recognition technology based on traditional digital image processing techniques to practical applications, and a more effective recognition method should be found.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Traditional Digital Image Processing Techniques** & **Application to Crops** & \begin{tabular}{c} **Technical Characteristics** \\ **and Performance** \\ **Indicators** \\ \end{tabular} & **Limitations** & **References** \\ \hline Color feature-based & Pineapple, apple & \
\begin{tabular}{c} Can significantly segment \\ the fruit from the \\ background, with a \\ combined recognition \\ success rate of 80\% \\ \end{tabular} & Easily affected by lighting conditions & [11; 12; 13] \\ \hline Geometric feature-based & Tomatoes, oranges &
\begin{tabular}{c} Capable of acquiring fruit \\ outline information, with a \\ combined recognition \\ success rate of 90\% \\ \end{tabular} & Vulnerable to fruit & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of research results of traditional digital image processing techniques.
## 2 Related Work
### Encrypted Traffic Classification and Feature Engineering
#### 2.2.4 Encrypted Traffic Feature Engineering  
Although the deep learning method can automatically select features, it does not mean that the selected features are optimal. In addition, these features are not interpretable. Therefore, some researchers start from feature engineering to optimize deep learning in encrypted traffic classification as the automatically selected features cannot effectively extract complex high-dimensional Markov features.  
In time series features, there are FlowPic [46] and the non-zero load packet FlowPic [47]. Then, [PERSON] et al. [48] transformed the time-related features and took the transformation as classification input.  
Some researchers attempt to make use of residual plaintext features, which could only be useful in some particular scenes, such as simultaneous queries of multiple hostnames on Android mobile terminals [49] and plaintext DNS traffic with handshake certificate [50].  
Many studies have shown that the length features are strongly relevant to the transmission data. The representative studies include the SoB method [27] using the length of the certificate in TLS handshake and the first TLS application data size, FS-Net [21] based on the multi-layer bidirectional GRU model with representation learning, TFNN model [51] based on LSTM and composite deep learning model architecture LSCDL [19] with PDU length sequence as the input and N-gram length sequence as the features.  
Both feature optimization and model iteration improve classification performance in the case of fixed datasets. However, datasets and features determine the upper bound of classification, and models and algorithms approximate it. Therefore, it is necessary to realize automated labeled sample construction and efficiently continuously sample updating, as the A3C proposed in this paper.
# Dual-Polarization Observations of Slowly Varying Solar Emissions from a Mobile X-Band Radar
## Radio Emission Properties of the Sun
### Transforming the 10.7 cm DRAO Solar Flux Measurements to the Corresponding X-Band Values  
In order to be correct and rigorous, we should collect dedicated absolute measurements of the solar flux at the 3.2 cm wavelength rather than transforming the reference S-band values into the X-band. However, a transformation equation that makes use of the remarkable stability of the blackbody spectrum of the slowly varying component of the solar activity can be used if one is ready to accept some degree of uncertainty in the calibration procedure. The equation is a simple linear transformation:  
\[F_{\lambda}=p_{\lambda}(F_{10.7}-q_{10.7})+P_{\lambda}, \tag{1}\]  
where \(p_{\lambda}\) represents a scaling factor and \(q_{\lambda}\) represents the quiet sun flux density of the desired wavelength and the \(q_{10.7}\) value is the quiet solar flux density at the 10.7 cm reference wavelength. The rationale behind this equation is described in detail in Section 5 of [17]. Using the values listed in the table shown in Section 5 of [17], it is possible to derive the solar flux at the \(\lambda=3.2\) cm wavelength, \(F_{3.2}\).  
From a radiometric viewpoint, the flux densities observed in radio astronomy are spectral irradiances, dimensions for which are power per unit bandwidth and per unit equivalent area. These values are typically very small if expressed using the SI units, hence they are expressed in the solar flux units (sfu), where 1 sfu = 10\({}^{-22}\) W\(\cdot\)m\({}^{-2}\cdot\)Hz\({}^{-1}\). According to [17], the quiet solar flux density at the reference frequency is \(q_{10.7}=64\) sfu, as shown inin Equation (2).  
\[F_{3.2}=0.69\cdot(F_{10.7}-64)+255. \tag{2}\]
# Probing Collisional Plasmas with MCRS: Opportunities and Challenges
## 3 Results and Discussion
### Penetration of the Microwave Field into the Plasma
These points are indicated by a red circle when the coaxial plasma column was located at an antinode and by a black circle when the plasma was positioned at a node. These points show a similar dependence on the plasma radius as the skin depth. It appears, depending on the selection of resonant mode and collision frequency, that the limit \(r_{\rm p}<\delta\) is in most cases too optimistic in limiting the range of plasma parameters that can be probed. This is not surprising as the number of passes of the EM-field in a cavity is large--the quality factor is a good estimate for the number of passes--and therefore the waves should be able to penetrate the plasma without much attenuation and reflection per pass. An improvement of the limit would be \(r_{\rm p}\ll\delta\). As this limit is in good agreement with the results from the previous section and is very intuitive, it is a good candidate for a practical limit for the MCRS diagnostic approach when studying collisional plasmas. However, this limit is in some cases too stringent in indicating the range of plasma parameters which can be probed by MCRS.
## 2 Basic Ideas: Random Walk Process Model of the Demagnetization Steps
### The Principal Component Analysis in Application to Zijderveld Plot  
Here, we recall the construction in a Cartesian coordinate system according to the description in [2] (see the basic idea in [3]). We start from Zijderveld plot data and then apply versions (named _PC_ and _aPC_) of principal component analysis to it.  
Figure 1: Scheme: \(p=0.99\)-confidence regions of five specimens and the corresponding intersection where the true direction supposed to lie.  
Let the Zijderveld plot consist of \(n\) data points, each data point \(k\) is defined by its Cartesian coordinates \((x_{1k},x_{2k},x_{3k})\). Compute the \"center of mass\" of these points, of coordinates \((\bar{x}_{1},\bar{x}_{2},\bar{x}_{3})\), where \(\bar{x}_{j}=\frac{1}{n}\sum_{k=1}^{n}x_{jk}\), and the new coordinates \((x_{1k}^{\prime},x_{2k}^{\prime},x_{3k}^{\prime})=(x_{1k}-\bar{x}_{1},x_{2k}- \bar{x}_{2},x_{3k}-\bar{x}_{3})\) with respect to this center of mass. The following matrix is known as the _orientation tensor_\(T\)[2]  
\[T=\begin{pmatrix}\sum x_{1k}^{\prime}x_{1k}^{\prime}&\sum x_{1k}^{\prime}x_{2k }^{\prime}&\sum x_{1k}^{\prime}x_{3k}^{\prime}\\ \sum x_{2k}^{\prime}x_{jk}&\sum x_{2k}^{\prime}x_{2k}^{\prime}&\sum x_{2k}^{ \prime}x_{3k}^{\prime}\\ \sum x_{3k}^{\prime}x_{1k}&\sum x_{3k}^{\prime}x_{2k}^{\prime}&\sum x_{3k}^{ \prime}x_{3k}^{\prime}\\ \end{pmatrix}, \tag{3}\]  
where all sums are from \(k=1\) to \(n\). Additionally, a slightly different version of tensor \(T\) reads  
\[T=\begin{pmatrix}\sum x_{1k}x_{1k}&\sum x_{1k}x_{2k}&\sum x_{1k}x_{3k}\\ \sum x_{2k}x_{1k}&\sum x_{2k}x_{2k}&\sum x_{2k}x_{3k}\\ \sum x_{3k}x_{1k}&\sum x_{3k}x_{2k}&\sum x_{3k}x_{3k}\\ \end{pmatrix}, \tag{4}\]  
we will refer to it as the \"anchored\" version _aPC_[8; 10]. This differs from initial version _PC_ due to a special requirement: the best fit to the data points strictly go through the origin of the Zijderveld plot.  
By construction, the matrix \(T\) is positive and self-adjoint, and according to the principal components method, the obtained demagnetization direction acts as a normalized eigenvector of this matrix. Because of the Gaussian nature of the three-dimensional random walk model, we have a three-dimensional Gaussian distribution for arbitrary \((x_{1k},x_{2k},x_{3k})\), and the distribution of eigenvalues is a much more subtle issue, see [5].
# Data Privacy in the Internet of Things: A Perspective of Personal Data Store-Based Approaches
## 3 Data Privacy in the Internet of Things
### Information Privacy Protection  
Information privacy protection is an essential user right [33] and a complex challenge that has gained significant attention, eliciting concerns among individuals, enterprises, and governments over the years [23; 34; 35]. Addressing this challenge and moving towards a protected privacy environment requires multifaceted solutions, encompassing raising awareness of privacy risks, employing technologies, and implementing regulatory laws. [PERSON] et al. [23] proposed the ART approach encompassing these three complementary factors to face privacy concerns:  
Raising awareness. The basic idea is to provide means for users to be aware of privacy violations and the privacy risks involved in each operation performed by them. Awareness has the potential to change the user's perception of privacy risks. Therefore, the more awareness, the lower the level of trust, increasing the demand for control over data disclosure. Consequently, appropriate data control can empower people to protect their data and match their risk perception;  
Regulation aims to empower users with more control over their data and safeguard them against violation by regulating data processing. The regulation laws determine what data can be accessed, stored, transmitted, and manipulated and under what conditions. Unfortunately, they have limitations because they define the boundaries of data processing, but the penalties for noncompliance are applied after the privacy violation occurs. Besides that, the penalties for each violation depend on the judge's interpretation;  
Technology allows users to deal with privacy risks. As users increase their perception and become more aware of privacy risks, technologies help to protect them. For instance, anti-virus software, anti-spyware, firewalls, and spam comprise conventional technologies that give some protection. Similarly, privacy-enhancing technologies (PETs) give users control over their information. PETs enhance user power by giving them a choice about what information they will release and may consent or not to the acquisition of personal information [36].  
An integrated approach is essential to effectively address privacy issues. Raising awareness alone addresses only part of the problem, imparting understanding about threats to individuals. However, it is imperative to equip individuals with tools to mitigate threats and enact regulations to safeguard them against violations. Combining diverse perspectives is indispensable in confronting these concerns. The ART approach emphasizes raising awareness among individuals and motivating them to familiarize themselves with regulatory laws and pertinent technologies for privacy protection.
## 3 Results and Discussion
### Headspace SPME-GC/MS Analysis  
The flavour changes of the pretreated bass fillets under storage conditions were investigated by HS-SPME GC/MS. A total of 43 volatile compounds including alcohol (10), aldehyde (10), ketone (3) hydrocarbons (17) and others (3) were detected in the sample. As shown in Table 2, the most important compounds were alcohols and aldehydes, but alcohols were not the main contributors to the overall aroma because of their high odour threshold [32]. The odour threshold of nonanal, hexanal and other aldehydes was low. They come from the degradation of fatty acids and triglycerides, which can have a great impact on the overall aroma of marine products [33]. A few compounds such as valeraldehyde, trans-2-heptenal, cyclobutanol and 1-nonanol appeared occasionally  
Figure 2: The effect of different treatments on sensory evaluations: springiness, hardness, cohesiveness and chewiness of largemouth bass during storage. A–D: Differences between different treatment groups; a–f: Differences in treatment groups during storage. during storage. According to the treatment and storage conditions of bass meat, most compounds can be divided into three categories. The first group included four compounds that were detected throughout the storage of bass meat. The second group included many compounds that were detected only in the middle and/or end stage, and at the end of treatment and storage; the initially detected compounds decreased or disappeared. In the first group of compounds, hexanal, heptaldehyde, octanal and nonanal exhibited a tendency to reduce gradually in HHP groups throughout the whole storage, whereas they tended to increase in HHP+SV groups. The relative content of the second group of compounds in fish fillets was low. The species and peak areas of aldehydes were significantly reduced after HHP and UHP+SV treatment. Only the HHP-treated samples did not detect benzaldehyde, indicating that the HHP-only treatment caused the samples to lose their nutty aroma, while the HHP+SV treatment avoided this phenomenon and maintained the original flavour characteristics. After treatment and storage of the samples, trans-2-heptenal and 1-nonanol compounds disappeared. In some cases, metabolites produced during corruption may be further metabolized by microorganisms [34]. These aspects probably explain the above VOC changes.
## 3 Model Development and Evaluation
### Model Development  
The Wide and Deep neural network model is an integrated machine learning framework first proposed by Google [26]. The core idea of this model is to combine a wide model (wide) with a deep neural network (deep) to simultaneously capture both linear and nonlinear relationships among features.  
The neural network structure consists of two components: The wide part and the deep part. The wide part is a linear model, typically used for inputting explicit features. The deep part, on the other hand, is a deep neural network that excels at capturing complex and high-order nonlinear relationships between features.  
In the context of cement bond quality prediction, key fluid parameters such as the density of the preflush, spacer fluid, and cement slurry, as well as operational parameters including displacement rate, and centralizer position, directly influence the final cement bond quality. Therefore, in the neural network architecture design, these parameters are input into the wide component and directly connected to the output layer to enhance the model's analytical capability for direct influencing factors. Meanwhile, formation characteristics such as well depth, deviation angle, which interact and are interdependent, are input into the deep component to effectively capture their complex nonlinear relationships, thereby reflecting the difficulty of the current cementing environment. The wide and deep components interact synergistically to optimize the model's predictive performance and  
Figure 4: Neuron schematic diagram.  
ultimately output the cement bond quality. The overall structure of the neural network model is illustrated in Figure 5.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 4 Results  
### Minimum Noise Fraction (MNF) Transformation  
After the MNF method was applied to the OLI and ASTER subset data, a substantial decline in eigenvalue magnitude vs. MNF band number occurred between 1 and 7 for OLI and between 1 and 9 for ASTER. In order to enhance the outcomes of the subsequent spectral processing, MNF components with eigenvalues less than 1 are frequently eliminated from the data as noise [87]. As a result, for further data processing, all nine bands of the ASTER data and the seven bands of the OLI data were kept. The greatest information is often carried by the first few MNF bands, whereas the noise level in later bands increases. It was clear from a visual examination of the MNF bands that a heterogeneous surface composition was possible in the research region.
## 4 Challenges and Future Research Directions
### Suitable Physical Layer Security  
Securing a UDN consisting of diverse node types becomes a prime concern in which to achieve flexible quantum-safe security measures for 6G. Due to these considerations, a bottom-up method is advocated, thereby aiming to harness all available security measures across the generic communication stack. A pivotal technology candidate in this endeavor are the potential lattice-based signature schemes [134].  
Moreover, various techniques such as secure beamforming, physical layer encryption, and authentication mechanisms have been proposed in previous studies to protect smart grid communication from eavesdropping and unauthorized access [135]. As 6G aims to offer a comprehensive security approach, it is imperative to not only focus on security by design, but to also address privacy by design. Both aspects are essential for providing a robust and trustworthy 6G wireless communication system for smart grid monitoring and operations.
## 5 Case Studies
### Regulation Requirements of Distributed Energy Storage  
The regulation requirements of DES for rapid ramping, intra-day load balancing, and peak supply assurance have been analyzed in the context of modern power systems with widespread renewable energy integration.  
In Figure 5, the annual PV curve with an installed capacity of 100 MW for the demonstration county is presented. During this period, the intra-day hourly-level flexible ramping demand of the power system can reach up to 21.84 MW. In Figure 1, the 2023 net load curve for the demonstration county is shown, with a maximum daily load peak-to-valley difference of approximately 183.8 MW, accounting for 62.35% of the daily maximum load (referred to as the daily maximum peak-to-valley difference rate). For the year 2023, the maximum load of the county is projected to be 449.8 MW. Under the constraint of a 13% system reserve margin, the installed capacity of wind power in the demonstration county is assumed to be 750 MW, and the installed capacity of PV is assumed to be 1142.6 MW. Using assumptions of an effective capacity factor of 35% for wind power and 20% for PV, the power supply assurance demand during peak periods in 2023 is estimated to be approximately 17.254 MW.
# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 5 Challenges with Solutions
### Latency  
Fog networking is a key paradigm to provide solutions to latency-sensitive future IIoT applications. Many researchers have contributed to cater latency minimization problem. Online Fog Network Formation Algorithm was proposed by [PERSON] et al. in [87] for minimizing the overall maximum latency (communication and processing) of a fog network. The objective problem constitutes of the sum of two types of delays i.e., fog network formation and task distribution. A joint energy and latency optimization (JELO) scheme for F-RAN was proposed in [88]. The joint optimization of energy consumption and latency was formulated as an integer-programming (IP) problem subject to user association, capacity and latency threshold. The proposed complex problem was divided into two sub-problems of knapsack and semi-assignment problem. The proposed algorithm gives better results as compared to the existing techniques in the literature. A latency minimization problem was formulated for IoT-fog network under user association, workload and latency threshold constraints. The authors have proposed a matching-game theory for the proposed resource management problem for network latency minimization [89].  
Over the last few years, the Internet of Vehicles (IoV) has been a matter of growing interest. Cloud computing provides high performing IoT services to IoV, still, there are many shortcomings when it comes to mobility support, latency and location awareness. [PERSON] et al. [90] have integrated fog computing into SDN. While fog Computing helps with the latency of the network, SDN provides flexibility in the centralized control and providing the complete global knowledge of the network. Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I) and Vehicle to Base station (V2B) communication can be supported by a weighted undirected graph having Road Side Units (RSUs). The simulation results given by the aforementioned authors decrease the latency and achieve a higher QoS. The scheme proposed by [PERSON] et al. [91] tackles an optimization problem, providing a solution that there is no trade-off between communication and using resources and energy for computation. The applied system model demonstrates that in optimization scenarios, reduced average round-trip time (RTT) and delays up to 39 percent can be achieved using fog. The scheme also considers an independent cooperative number of working nodes.  
For the smart city, Fairness Cooperation Algorithm (FCA) was proposed by [PERSON] et al. [92], for the joint optimization problem. The authors have formulated a convex-non-linear programming problem of minimizing the total system cost (delay and energy consumption), subject to power, workload and computation capacity threshold constraints. QoE and fairness of users under FCA was compared with baseline algorithm (BA) and distributed optimization algorithm (DOA).