esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Experiment of Grain Litter for Sorghum Harvester
## 4 Field Experiment and Analysis
### Experimental Design and Method
#### 4.2.2 Test Methods  
According to the test method of GB8231-87 [26] sorghum harvester and GB/T8097-2008 [27] harvester, 3 test areas were randomly selected in the test field [28]; each test was divided into preparation area, test area, and parking area, in which the preparation area was 10 m long, the test area was 50 m long, the parking area was 5 m long, and the width of the test area was the full working width of the cutting table, the comparison test and three-factor and three-level orthogonal test were carried out, and the response values of ear loss rate and missing loss rate were selected as assessment indexes. The leakage loss and ear loss  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Project** & **Unit** & **Minimum Value** & **Maximum Value** & **Mean Value** \\ \hline Plant height & cm & 206.23 & 231.44 & 224.06 \(\pm\) 9.28 \\ Blade length & cm & 28.82 & 38.46 & 33.72 \(\pm\) 5.60 \\ Ear length & cm & 41.2 & 68.6 & 58.66 \(\pm\) 25.60 \\ Ear weight & g & 42.63 & 105.98 & 62.78 \(\pm\) 17.10 \\ Stem moisture content & \% & 34.86 & 52.63 & 45.15 \(\pm\) 4.63 \\ Grain water content & \% & 17.71 & 27.83 & 22.11 \(\pm\) 2.76 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical characteristics of sorghum plants (Variety: Liang Nuo No 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Levels** & \begin{tabular}{c} **Forward Speed/v\({}_{\text{m}}\)** \\ **(ms\({}^{-1}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Inclination of Grain** \\ **Lifter/0 (\({}^{\circ}\))** \\ \end{tabular} &
\begin{tabular}{c} **Rotation Speed of the** \\ **Bottress/n (rm\({}^{-1}\))** \\ \end{tabular} \\ \hline \(-1\) & 0.8 & 20 & 300 \\ \(0\) & 1.0 & 28 & 330 \\ \(1\) & 1.2 & 36 & 360 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Factors and levels of test.  
Figure 3: Sorghum harvester with grain lifter.  
of each test area were calculated separately, and the average value of the three test areas was taken.  
The loss rate of ear shedding is the percentage of the weight of ear shedding grains caused by plant break during the lifting process and the yield of sorghum in the test section, namely:  
\[R_{\text{wd}}=\frac{w_{d}}{w_{l}+w_{d}+w_{h}}\times 100\% \tag{19}\]  
The loss rate of leakage is the percentage of the weight of sorghum grains that the plant falls to the surface without lifting and the yield of sorghum in the test section, namely:  
\[R_{\text{wl}}=\frac{w_{l}}{w_{l}+w_{d}+w_{h}}\times 100\% \tag{20}\]  
Here,  
\(w_{l}\)--Weight of sorghum grains that lie on the ground without lifting, kg:  
\(w_{d}\)--The weight of sorghum grains whose ears fell off due to plant break, kg;  
\(w_{h}\)--Grain quantity of the grain bin of the harvester, kg.
## 3 System Model, Trust Model and Design Goals
### Trust Model  
We have made the following assumptions regarding the trustful roles of the entities in VIoT.  
**TA**: A TA, which a central agent of the whole system, is under strong physical protection and not affected by adversaries.  
**RSU**: RSUs are connected to the server through a secure communication channel. They will tackle to adversaries and never disclose internal information. However, it might be possible that some strong adversaries can hack this or even deploy bogus RSU. Nevertheless, The TA can inspect and recover RSU at a high level, if detected to be compromised.  
**Requesting Vehicle**: It is assumed that the requesting vehicle is honest when computing the trust of a target vehicle for relaying information. Meanwhile, it also concerns about the privacy of the target vehicle.  
**Target Vehicle**: Because vehicles are driven by humans, there is a chance to be selfish for saving its resources and to drop messages deliberately. To minimize this effect, an opinion table is built on each vehicle which is maintained and updated by the TA.
###### Abstract  
No-reference image quality assessment is one of the most demanding areas of image analysis for many applications where the results of the analysis should be strongly correlated with the quality of an input image and the corresponding reference image is unavailable. One of the examples might be remote sensing since the transmission of such obtained images often requires the use of lossy compression and they are often distorted, e.g., by the presence of noise and blur. Since the practical usefulness of acquired and/or preprocessed images is directly related to their quality, there is a need for the development of reliable and adequate no-reference metrics that do not need any reference images. As the performance and universality of many existing metrics are quite limited, one of the possible solutions is the design and application of combined metrics. Several possible approaches to their composition have been previously proposed and successfully used for full-reference metrics. In the paper, three possible approaches to the development and optimization of no-reference combined metrics are investigated and verified for the dataset of images containing distortions typical for remote sensing. The proposed approach leads to good results, significantly improving the correlation of the obtained results with subjective quality scores.  
image quality assessment; visual quality metrics; neural networks; combined metrics +
Footnote †: journal: Applied Sciences
## 2 Method
### Wavelet Decomposition on Gravity Anomalies  
In order to study the tectonic features in a limited depth range such as the crustal lithosphere, it is necessary to decompose the total gravity anomaly. The wavelet multiscale decomposition method could decompose the ground total gravity anomaly into the wavelet approximations \(A_{K}\) and the corresponding multiorder wavelet details \(D_{k}\)\((k=1,2,3,...,K)\) as shown in Equation (1).  
\[\Delta g(x,y)=A_{K}+\sum_{k=1}^{K}D_{k} \tag{1}\]  
where the wavelet approximations \(A_{K}\) are the low-frequency background gravity anomalies and the wavelet details \(D_{k}(k=1,2,3,...,K)\) are the high-frequency regional gravity anomalies. The former are generated by the geological bodies in the deep Earth, while the latter are generated by the shallower geological bodies.  
The main advantage of wavelet multiscale decomposition in decomposing the Earth's gravity field is that it has a low-order invariance criterion [30]. As the order of the decomposition increases, the results of the individual lower-order wavelet details do not change. The decomposition result is simply a new pair of wavelet details and wavelet approximations added to the original. Therefore, when we use wavelet multiscale separation to study the Earth's gravity field, we can simply increase the order of the decomposition calculation according to our needs. Any existing wavelet details \(D_{k}\) can be further decomposed to obtain the advanced order wavelet approximation \(D_{k}A_{p}\) and wavelet details \(D_{k}D_{t}(t=1,2,3,...,p)\), as shown in Equation (2).  
\[D_{k}=D_{k}A_{p}+\sum_{t=1}^{p}D_{k}D_{t} \tag{2}\]  
where \(D_{k}D_{t}\) can be further decomposed until its corresponding field source depth matches our research objective. The source depths of gravity anomalies could be calculated by radial power spectrum analysis of the bit-field spectrum theory [30].
## 3 Proposed Solution
### HI Curve Construction  
The second step is to create a curve with the history of values of the HI. It is necessary to normalize all extracted features before constructing the HI. The amplitudes of the bins are normalized using min-max normalization, as shown in Equation (3). This normalizes  
Figure 1: Proposed Solution: feature extraction, HI curve construction, health stage division, and RUL prediction.  
data between 0 and 1 in order to represent the degradation of the electric motor, where 0 means no failure and 1 full degradation. For testing, when the top end of the scale of the time-domain feature is not yet known, the maximum value used to normalize is the maximum value for each input feature of the set used for training. This is done in order to determine a common full scale for normalization.  
\[x_{norm}=\frac{x-min(x)}{max(x)-min(x)} \tag{3}\]  
Once the features are normalized, they are fused and denoised using a stacked autoencoder (SAE). Autoencoders (AEs) are unsupervised machine learning algorithms based on encoders, which convert the high-dimensional input into a low-dimensional output, and a decoder, which reconstructs the input data from the low-dimensional output generated by the encoder. An SAE is an AE with a deeper neural network and symmetric dimensions of the encoding and decoding sets. The ability of SAEs to denoise signals has been widely used in many domains, such as image processing [63] or fault detection [64] to name a few, as it is able to learn a compressed representation that contains only the essential information of each feature.  
The SAE network is built using TensorFlow in the Keras framework with 3-2-1-2-3 structure, where 3 corresponds to the 3 normalized input features, 2 is the number of nodes at the first and third hidden layers, and 1 is the dimension of the feature obtained from the first hidden layer, which is the health indicator. The hyperparameters used by the SAE network are included in Table 1. A schematic diagram of the SAE model structure is shown in Figure 2. The three normalized and one-dimensional input features correspond to the normalized values of the amplitudes of bins 2X, 3X, and 4X BPFO gathered during the lifetime of the electric motor. The encoder fuses these input features into a low-dimensional feature, which is called HI value. The purpose of the decoder is to reconstruct the original input from the encoded representation (HI value) produced by the encoder. It takes the encoded representation as input and generates an output that should be as similar as possible to the original input data. This reconstruction process allows the SAE to learn a compact and informative representation of the input data, containing only the most important information.  
An exponentially weighted moving average function (EWMA) is then used to smooth the obtained HI. EWMA functions give higher weights to the most recent values. The Pandas ewm function is used to smooth the obtained HI, with a center of mass of 5. The smoothed HI is finally normalized using min-max normalization.
# Intelligent Neural Network Schemes for Multi-Class Classification
## 3 Dimensionality Reduction
### Feature Selection
#### 3.1.1 Mutual Information  
The mutual information [34] between two attributes \(u\) and \(v\), denoted as MI(\(u\),\(v\)), measures the information that \(u\) and \(v\) share. That is, it measures how much knowing the values of one attribute reduces the uncertainty about the values of the other. If MI(\(u\),\(v\)) is large, there is likely some strong connection between \(u\) and \(v\). One favored property of mutual information is that it can measure non-linearity relationship between \(u\) and \(v\).  
We develop a feature selection technique based on MI. Let there be \(q\) attributes \(x_{1}\), \(x_{2}\),..., \(x_{q}\), and \(y\) be the target. We calculate mutual information between \(x_{i}\) and \(y\), MI(\(x_{i},y\)), \(1\leq i\leq q\). Let MI\(\left(x_{d_{1}},y\right)\) be the largest, indicating that \(x_{d_{1}}\) is most relevant to \(y\). Therefore, \(x_{d_{1}}\). is selected. Next, we calculate MI\(\left(\left\{x_{d_{1}},x_{i}\right\},y\right)\), \(1\leq i\leq q\), and \(i\
eq d_{1}\). Let MI\(\left(\left\{x_{d_{1}},x_{d_{2}}\right\},y\right)\) be the largest. Then, \(x_{d_{2}}\) is also selected. Then, we calculate MI\(\left(\left\{x_{d_{1}},x_{d_{2}},x_{i}\right\},y\right)\), \(1\leq i\leq q\), \(i\
eq d_{1}\) and \(i\
eq d_{2}\), etc., until some criterion is achieved. In this way, the attributes that are most relevant to \(y\) are determined [35; 36].  
For multi-label data, two transformations, binary relevance (BR) and label powerset (LP), are adopted to deal with the target vectors for multi-label classification [37; 38; 39]. BR transforms the training target vectors into \(m\) vectors \(\mathbf{y}_{1}\), \(\mathbf{y}_{2}\),..., and \(\mathbf{y}_{m}\). For each training instance \(\mathbf{x}_{i}\), if \(y_{j,i}=+1(-1)\), the \(i\)th element of \(\mathbf{y}_{j}\) is set to \(+1(-1)\) for \(1\leq j\leq m\). The discriminative power of the attributes with respect to each vector is evaluated. LP considers each unique set of categories that exists in a multi-label training set as one new category. This may result in a large number of new categories.
# A Study of Current Controlled Discharge in a Nitrogen Filled Tube
## 2 Materials and Methods
### Tube Discharge Dynamics Modelling
#### 2.1.1 NPINCH Code  
The code NPINCH is a time dependent one-dimensional code, modelling the time evolution of the radial distributions of plasma parameters. The 1D approximation is considered owing to the large length-to-radius ratio. Approximation of two-temperature (ion and electron) one-fluid magnetohydrodynamics is used. The plasma is considered as highly collisional, with typical mean free paths of particles being much less than discharge diameter. Thus, magnetohydrodynamic approximation to describe plasma dynamics is applicable. The dissipative processes of electron thermal conductivity, Joule heating, Nernst and Ettinghausen effects, the radiation losses and ion viscosity are taken into account by the code [6,15]. The expressions for the dissipative coefficients are taken from [16], where they were obtained for the case of a plasma with an arbitrary mean value of the ion charge and the possible considerable difference between the Coulomb logarithms for electron-electron and electron-ion collisions. The local thermodynamic equilibrium (LTE) approximation is assumed to calculate ionization, pressure and specific internal energy of plasma. At low temperatures, as long as the mean ion charge is less than unity, there  
Figure 1: Schema of the experiment (**a**) and measured pre-ionization current (**b**).  
Figure 2: Measured current passing through the tube (orange line), interpolated current used as input for the ZSTAR code (green line) and the current fitted by straight lines connecting points \(1,2,3,4\) for NPINCH.  
is a noticeable fraction of neutral particles in the plasma. We re-normalize the electron-ion collision frequency by taking into account a contribution of neutral particles to the electron scattering. Code NPINCH was used previously in [1; 16; 17; 18; 19; 20; 21; 22] for simulations of dense Z-pinches as well as capillary discharges. In the considered discharge the plasma pressure is much higher than magnetic field pressure. Thus, the discharge will expand, if it would be not bounded by a dielectric tube. The dielectric tube bounding the discharge is considered as a passive motionless wall, implemented in the model by the two boundary conditions. Radial plasma velocity at the boundary vanishes, whereas its temperature is much less than typical temperature of the discharge plasma. More sophisticated description of plasma-wall interaction in these conditions was considered for example in [6; 18] and in the references therein.
# Combining Optimization and Simulation for Designing a Robust Short-Sea Feeder Network
## 4 Optimization and Simulation Model
### Simulation Model
Hence, route compositions that often incur duration or synchronization violations are receiving a high penalty cost and are less likely to be chosen by the master problem in subsequent iterations.
# A Self-Scrutinized Backoff Mechanism for IEEE 802.11 ax in 5G Unlicensed Networks
## 3 A Self-Scrutinized Backoff Mechanism for HEW
### Channel Observation-Based Scaled Backoff (COSB)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Operating Frequency & 5 GHz & Physical rate of channel & 54 Mbps \\ Bandwidth & 20 MHz & MAC header & 24 Bytes \\ MAC payload & 1024 Bytes & PHY header & 20 \(\upmu\)s \\ Acknowledgement (ACK) & 14 Bytes + PHY header & Transmission range & 10 meters \\ \(W_{min}\) & 32 & \(W_{max}\) & 1024 \\ \(\omega\) & 32 & \(\sigma\) & 9 \(\upmu\)s \\ SIFS & 16 \(\upmu\)s & DIFS & 60 \(\upmu\)s \\ Propagation delay (\(\delta\)) & 1 \(\upmu\)s & Max. backoff stages (m) & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Medium access control (MAC) layer parameters used in simulation and analysis. in scaling-up or scaling-down of the current contention window, respectively. The scaling-up and scaling-down of the contention window operates as follows:  
\[W_{b_{i+1}}= \begin{cases}min\left[2^{b_{i+1}}\times W_{min}\times\omega^{P_{obs}},\ W_{max}\right],\ \text{collision at $i^{th}$ transmit}\\ max\left[2^{b_{i+1}}\times W_{min}\times\omega^{P_{obs}},\ W_{min}\right],\ \text{ success at $i^{th}$ transmit}\end{cases} , \tag{3}\]  
where \(\omega\) is a constant design parameter to control the optimal size of the contention window and is expressed as \(\omega=W_{min}\). Figure 4: Backoff stage after collision/successful transmission; (**a**) backoff stage increment/reset in binary exponential backoff (BEB); and (**b**) backoff stage increment/decrement in COSB. Figure 3: Channel observation mechanism of channel observation-based scaled backoff (COSB) during the backoff procedure.
## 4 Enterprise Network Functionality
### Cloud and Multi-Cloud Integration  
In order to provide access to applications and services, enterprise networks frequently interact with both public and private cloud environments [37; 72]. Secure application programming interface (API) gateways, dedicated links, or VPNs are commonly used to accomplish this task. With the help of multi-cloud approaches, or cloud systems, organizations can use services from several cloud providers to create a scalable and robust environment. A typical example is when a user or an organization has data stored on a private cloud, performs document sharing on, say, Google Cloud platform, and executea data analysis on another cloud application.  
However, as businesses streamline their operations in multi-cloud environments, the need to ensure adequate security and resource management for optimal performance may arise. Thus, cloud providers can address security challenges by taking into account several factors. They include implementing identity and access management (IAM), controlling threats and vulnerabilities, and conducting comprehensive audit processes. These functions are embedded in federated access control, which hinges on creating relationships of trust between cloud and identity providers [73]. With federated access control, enterprises can be assured of adequate security without compromising network performance.
## 3 Proposed Methods
### Improved Grey Wolf-Harris Hawks Hybrid Algorithm
#### 3.3.1 Tent Chaotic Mapping for Initial Population  
At the initial stage of the algorithm, the Tent chaotic map is used to initialize the population, enhancing diversity and spatial coverage. As a representative chaotic mapping method, the Tent map exhibits strong traversal ability, uniform distribution, and dynamic complexity. These properties help avoid the clustering and sparsity issues often associated with random initialization, thereby improving the algorithm's global search performance [46]. The mathematical expression of the Tent map used to generate the chaotic sequence is given in Equation (40).  
\[z_{n+1}=\left\{\begin{array}{ll}\frac{z_{n}}{u}&,\mbox{ }0\leq z_{n}<\frac{1}{2},\\ \frac{1-z_{n}}{1-u}&\frac{1}{2}\leq z_{n}\leq 1,\end{array}\right. \tag{40}\]where \(z_{n}\in[0,1]\) denotes the chaotic sequence of the \(n\)th generation. The initial value \(z_{o}\) is typically generated randomly within the interval (0, 1). When \(u=\frac{1}{2}\), the Tent map exhibits its most representative chaotic behavior.  
Figure 4 compares the population distribution resulting from random initialization and Tent map-based initialization. The Tent mapping generates a more uniformly distributed sequence in the solution space, effectively reducing blind search regions and enhancing algorithmic robustness. This initialization approach provides a more favorable starting point for subsequent optimization.  
The initial chaotic sequence \(z_{n}\), generated via the Tent map, is subsequently mapped into the problem domain \([X_{min},X_{max}]\) to construct the initial population \(X_{i}^{0}\), as presented in Equation (41).  
\[X_{i}^{0}=X_{min}+(X_{max}-X_{min})\times z_{n}. \tag{41}\]
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 4 Threats and Vulnerabilities
### Smart Grid Threats
#### 4.1.2 Safety-Based Threats  
This type of threat can cause a severe problem, especially regarding user and equipment safety. Several solutions, such as regularly maintaining and upgrading physical  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Threat Classification** & **Type** & **Description** & **Enhancements** \\ \hline \multirow{4}{*}{Security-based} & \multirow{4}{*}{Advanced Persistent Threats} & Has sophisticated zero-day nature that would disrupt critical systems & Updating systems/operating systems software batches and security measures up to date \\ \cline{2-4}  & & Compromises its components where a malicious code can be injected into the smart grid network, halting its operational service & Incident response training, incident response planning \\ \cline{2-4}  & & The objective is to manipulate employees to divulge sensitive information or exploit their access privileges to compromise the smart grid’s security & Non-disclosure agreements, accountability and training, contracts and agreements, secure communications \\ \hline \multirow{4}{*}{Safety-based Security-  
infrastructure and developing effective emergency response plans, can be useful to ensure the smart grid's safety and reliability [75]. Equipment failures and malfunctions pose a significant safety risk to smart grids as they can lead to electrical hazards, fires, or explosions that could harm individuals and property assets. Natural catastrophes like hurricanes, earthquakes, and floods can also harm infrastructure, posing a risk to public safety and causing long-lasting power outages and blackouts. These threats will surely make the smart grid integration into IoT prone to various vulnerabilities. These vulnerabilities will be presented next.
# Mission-Oriented Real-Time Health Assessments of Microsatellite Swarm Orbits
## 2 Swarm Health State Model
### Determination of Health Levels  
To describe the health state intuitively and reduce the computational complexity, the health state is divided into three health classes: Health (H), Sub-Health (SH), and Fault (F). The determination of the health classes belongs to the clustering problem by nature,for which three experts are invited to contribute to this paper, and the gray clustering method [13] is used to ultimately obtain the health state classes, as shown in Table 1.