esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed EP-MAC Protocol Design
### Contention Window Size for Priority Fixing  
Priority can be set by using different contention window size for sensor nodes. Sensor nodes with a larger number of waiting message in the queue can be set as a high priority and with small number of message can be dealt as a low priority. A node with a high transmission priority, picks a random time uniformly over contention interval [1, \(CW_{hp}\)], while nodes with low priority picks a random time uniformly within [1, C\(W_{hp}\)]. The average window size observed by a node with high priority would be (\(1+CW_{hp}\))/2 and for the node with low priority would be C\(W_{own}\) + (\(1+CW_{hp}\))/2. The node with high priority will have the smaller contention window. So if the node has a data to send it will take hold of the channel every time. Since sensor nodes works for a common goal so that it is more logical to allocate the transmission channel to the node who has a large number of message waiting in the queue in order to avoid message loss by queuing overflow. For both high priority and low priority transmission nodes, RTS transmission in EP-MAC always stars by waiting and listening for a random time within the contention interval.
# YOLOv8n-CBAM-EfficientNetV2 Model for Aircraft Wake Recognition
## 3 The Proposed Method  
The YOLO model has gained considerable popularity in the field of target detection in recent years. The core concept of the YOLO model is to transform the target detection problem into a regression problem, thereby enabling the localisation and classification of the target in the image through a single forward propagation [16; 17; 18; 19; 20; 29]. YOLOv8 represents a recent iteration of the YOLO model. It exhibits enhanced detection accuracy and a more rapid inference speed, which contributes to its extensive deployment in a diverse array of target detection applications, including automated driving, security monitoring, and medical image analysis.  
YOLOv8n represents a lightweight variant of the YOLOv8 model. It offers a rapid recognition speed, high detection accuracy, and minimal memory occupation. Moreover, it can be deployed on hardware platforms, such as central processing units (CPUs) and graphics processing units (GPUs). Nevertheless, experimental findings have revealed that there are certain limitations associated with the deployment of YOLOv8n for aircraft wake detection purposes. For example, the dissipation of the wake renders it challenging for the model to capture the characteristics of the wake. The YOLOv8n model may exhibit false or missed detections in complex backgrounds.  
Therefore, based on the YOLOv8n model, we proposed the YOLOv8n-CBAM-Efficient-NetV2 model for aircraft wake recognition. The specific improvements include the introduction of the MBConv module and the Fused-MBConv module (referred to as the EfficientNetV2 submodule in this study) in the EfficientNetV2 model as well as the CBAM. Figure 3 shows the structure of the YOLOv8n model. The backbone part constitutes the backbone network of YOLOv8n, which is primarily responsible for the extraction of features from the input image. In the backbone network, the stem module can realise the initial feature extraction of the input image. The Conv indicates the convolution module. The C2f represents the feature extraction module, which is capable of processing information extracted in the convolutional layer. SPPF, Spatial Pyramid Pooling-Fast, is a multiscale information capture module. The Concat module is able to concatenate the output after the jump connection with the output of the convolutional module. The neck part represents the neck network, which is capable of further processing and fusing the features extracted by the backbone. The Concat module can concatenate the output after the jump connection with the output of the convolutional module. The linked feature information is then output to the next module. The head part, which is also called the detecting head, is responsible for classifying and locating the target according to the features passed by the neck. The detecting head outputs the category probability and bounding frame coordinates for each detection frame.  
Figure 2: LiDAR RHI scanning pattern.
## 2 Data and Methods
### Ephemerides and Earth Orientation Parameters  
To establish the precise geometric relationship between the Moon and the Earth, this paper utilizes two types of data: ephemeris and Earth orientation parameters.  
The Development Ephemerides (DE) series, developed by NASA's Jet Propulsion Laboratory, is one of the most widely used ephemerides in the international astronomical community. These ephemerides provide information on the positions, velocities, and accelerations of celestial bodies in the Solar System [21]. The coordinate and time systems used for DE430 are the International Celestial Reference System (ICRS) and Barycentric Dynamical Time (TDB), respectively. Since the 1960s, JPL has successively released DE200, DE403, DE421, DE430, DE432, DE436, DE438, and other versions of ephemeris products. DE430, which was released in 2013, is a general-purpose ephemeris generated by fitting numerically integrated orbits of the Moon and planets to observations, and it inherits the features of DE421 and those of its predecessor DE405. It provides the Moon's state vectors from 1960 AD to 2650 AD, including the position, velocity, and orientation, stored as Chebyshev polynomial coefficients. Since more precise lunar nutation and vibration are considered in DE430, it was chosen for use in this paper.  
The Earth Orientation Parameters are the parameters representing the rotational part of the transformation between the ICRS and the International Terrestrial Reference System (ITRS). It uses the UT1 time system as the reference time, which is based on the Earth's rotation rate. Due to the gravitational influence of celestial bodies such as the Sun and the Moon, the rotation rate and spatial orientation of the Earth's rotation axis experience irregularities, resulting in a variety of long-period and short-period changes, such as precession, nutation, and polar shift. To accurately transform the Geocentric Celestial Reference System (GCRS) into the ITRS, the EOPs are utilized to define these irregularities in the Earth's rotation.
## 2 Materials and Methods
### Transcriptomic Analysis  
To retrieve clean reads from the raw data, the FastQC tool ([[http://www.bioinformatics.babraham.ac.uk/projects/fastqc/](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/)]([http://www.bioinformatics.babraham.ac.uk/projects/fastqc/](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/))) (accessed on 13 November 2021) was used to remove reads containing adapters, poly-N, and low-quality reads. Transcriptome assembly was carried out using Trinity (v2.11.0) by employing the paired-end method, and Corset ([[https://code.google.com/p/corset-project/](https://code.google.com/p/corset-project/)]([https://code.google.com/p/corset-project/](https://code.google.com/p/corset-project/))) (accessed on 22 November 2021) was used to realign the relevant transcripts into unigenes. Then, the assembled unigenes were functionally annotated with various databases, such as Nr (NCBI non- redundant protein sequences), Swiss-Prot (A manually annotated and reviewed protein sequence database), Trembl (a variety of new documentation files and the creation of TrEMBL), KEGG (Kyoto Encyclopedia of Genes and Genomes), GO (Gene Ontology), KOG/COG (COG: Clusters of Orthologous Groups of proteins; KOG: Karyotic Ortholog Groups), and Pfam (Protein Family). Using RSEM (RNA-Seq via Expectation Maximization) to estimate the gene expression levels, and calculating the FPKM (fragments per kilobase of transcript per million mapped reads) of each gene based on the gene length. DEG (differentially expressed gene) analysis was performed using the DESeq R package (DESeq2 v1.22.1), with an adjusted \(p\)-value of <0.05. The DEGs were then subjected to enrichment analysis of GO functions and KEGG pathways, as described in our previous study [34].
## 2 Experimental Model Considering CSI Effect
### CSI Effect Analysis
#### 2.3.2 Considering Reduced-Order CSI Effect  
In this case, the differential term of the control force is neglecting, namely \(-(L_{a}/R_{a})\dot{u}=0\). Then, the relationship between the input and output of the DC motor is:  
\[u=\frac{-K_{b}K_{i}K_{g}^{2}}{R_{a}r_{m}^{2}}\dot{x}_{A}+\frac{K_{i}K_{g}}{R_{ a}r_{m}}V \tag{16}\]  
The state equation of this system is:  
Figure 6: Block diagram of three CSI models.  
\[\dot{Z} =\left[\begin{array}{cc}0&I\\ -\frac{K}{M}&-\frac{C}{M}\end{array}\right]Z+\left[\begin{array}{c}0\\ \frac{B_{s}}{M}\end{array}\right]\left(-\frac{K_{b}K_{i}K_{g}^{2}}{R_{a}r_{m}^{2 }}\right)\dot{x}_{A}+\left[\begin{array}{c}0\\ \frac{B_{s}}{M}\end{array}\right]\frac{K_{i}K_{g}}{R_{a}r_{m}}V+\left[ \begin{array}{c}0\\ \frac{B_{gs}}{M}\end{array}\right]\ddot{x}_{g} \tag{17}\] \[=\left(\left[\begin{array}{cc}0&I\\ -\frac{K}{M}&-\frac{C}{M}\end{array}\right]+\frac{K_{b}K_{i}K_{g}^{2}}{R_{a}r_{ m}^{2}}\left[\begin{array}{cc}0\\ \frac{B_{s}}{M}\end{array}\right]\left[\begin{array}{cc}0&B_{s}^{T}\end{array} \right]\right)Z+\left[\begin{array}{c}0\\ \frac{B_{s}}{M}\end{array}\right]\frac{K_{i}K_{g}}{R_{a}r_{m}}V+\left[ \begin{array}{c}0\\ \frac{B_{gs}}{M}\end{array}\right]\ddot{x}_{g}\]  
The observation equation is:  
\[Y=\left(\left[\begin{array}{cc}I&0\\ 0&I\\ -\frac{K}{M}&-\frac{C}{M}\\ 0&0\end{array}\right]+\frac{K_{b}K_{i}K_{g}^{2}}{R_{a}r_{m}^{2}}\left[ \begin{array}{c}0\\ 0\\ \frac{B_{s}}{M}\\ 1\end{array}\right]\left[\begin{array}{cc}0&B_{s}^{T}\end{array}\right] \right)Z+\left[\begin{array}{c}0&0\\ 0&0\\ \frac{B_{gs}}{M}&\frac{K_{i}K_{g}}{R_{a}r_{m}}M\\ 0&\frac{K_{i}K_{g}}{R_{a}r_{m}}\end{array}\right]\left[\begin{array}{c}\ddot {x}_{g}\\ V\end{array}\right] \tag{18}\]  
From Figure 7, with the increase of frequency, the magnitude of the transfer function becomes larger, before tending to a definite value. The phase transforms from a minus value to zero; a minus phase indicates that a control force lags behind control voltage. Therefore, the time-delay of the control voltage to the control force gradually reduces to zero, with the increasing frequency of the control voltage. The nonlinear relationship between the control force and control voltage is influenced by the changing frequency of the control voltage (or feedback signal), which is consistent with Equation (17). Additionally, as the frequency of the control voltage is consistent with the natural frequency of the experimental frame, a sudden change in the transfer function is generated.
## 3 Experimental Results and Analysis
### Experimental Analysis
#### 3.3.1 Impact of the Number of Transformer Components in the Model  
In the dual-channel transformer non-cooperative space object pose estimation algorithm, the transformer component is the core of the model, and due to the complexity of the transformer model, it brings challenges to model training and inference efficiency. This section mainly analyzes the influence of the number of transformer components on the pose estimation accuracy and inference speed, and selects combination A to complete relevant experiments. For the dual-channel transformer model structure, the model can be built according to the different number of transformer components, and N and M are used to represent the number of transformer components in the translation transformer and orientation transformer.  
A. Relationship between the number of transformer components and model accuracy transformer  
This section mainly analyzes the influence of the number of transformer components on the model accuracy. In order to analyze the coupling relationship between the model accuracy and the number of transformer components, different numbers of transformer components are selected in the translation transformer and orientation transformer channels, whose ranges are \(3\leq\text{N}\leq 9\) and \(3\leq\text{M}\leq\)9. The experimental results are shown in Table 3, reflecting the pose estimation accuracy \(E_{R}\) and \(E_{T}\) for different numbers of transformer components.  
The results in Table 3 show that when N is constant, the mean values of \(E_{R}\) are 1.5301, 1.5448, 1.5398, 1.5179, 1.5214, 1.5191, and 1.5440, respectively. When M is constant, the mean values of \(E_{T}\) are 0.0423, 0.0422, 0.0423, 0.0422, 0.0422, 0.0421, 0.0422, and 0.0420, respectively. The accuracy range fluctuates, and the fluctuation range is very negligible, which verifies that the dual-channel network design successfully decouples the spatial translation information and orientation information of the satellite. There is very little interaction between the two channels. When N is constant, the mean values of \(E_{T}\) are 0.0439, 0.0430, 0.0423, 0.0418, 0.0416, 0.0413, and 0.0415, respectively. When M is constant, the mean values of \(E_{R}\) are 1.6556, 1.6071, 1.5424, 1.5311, 1.5112, 1.4583, and 1.4113, respectively, and the accuracy shows an upward trend.  
The results show that the inference speed slows down as the number of transformer components increases. When N = 4 and M = 6, the inference time increases significantly. To balance the inference speed and pose estimation accuracy, N = 4 and M = 6 can be used in subsequent experiments for related research. By analyzing the size of model params and the number of transformer components, it can be seen that the size of model params increases by 16 M for each additional transformer component. As shown in Figure 6, the model inference speed is positively correlated with the size of the model params.
## 3 Feature Extraction Algorithm for Industrial Control Protocol
### Protocol Payload Segmentation Based on N-Gram Model  
Protocol data are transmitted in binary on the network, which can be recognized as machine language. The N-Gram model [23] is a natural language processing model. This paper applies the N-Gram algorithm to the extraction of features for industrial control protocol. The protocol data are regarded as a corpus, and each protocol packet is regarded as a text for segmentation.  
\begin{table}
\begin{tabular}{c} document content\textbackslash{} \\ \hline ['01’, '04’, '02’, '03', 'F1’, '0C', '0C', '02', '00’, '00’, '02', '19', '4C’, '29', '21', '20’, '4B', \(\ldots\) ] \\ ['01’, '00’, '30’, 'F1’, '0C', '0C', '02’, '00’, '00', '02', '19', '4C’, '29', '21’, '3E', 'C4', '01’, \(\ldots\) ] \\ ['01’, '04’, '02', '03', 'F2', '0C', '0C', '02', '00', '00', '02', '19', '4C’, '29', '22', '08', '87', \(\ldots\) ] \\ ['01’, '00’, '30’, 'F2’, '0C', '0C', '02', '00’, '00', '02', '19', '4C', '29', '22’, '3E', 'C4', '01', \(\ldots\) ] \\ \end{tabular}
\end{table}
Table 2: Data format after preprocessing.  
Figure 1: Process of industrial control protocol feature extraction algorithm.  
The basic idea of N-Gram is using a sliding window of length N to start from the first character of the text and move backward one character in turn. The N characters contained in the window are an N-Gram data unit. Figure 2 is a schematic diagram of segmentation when N = 2.  
In the N-Gram model, the value of N is related to the validity and integrity of the segmentation. The larger the value of N, the better the integrity of the segmented data, but the large range of words after segmentation results in low effectiveness. The smaller the value of N, the more likely the keywords are to be divided, so that the word segmentation segment cannot contain complete lexical information. In this paper, we choose the appropriate value of N through [PERSON]'s law [24]. According to [PERSON]'s law, setting the frequency of a certain data unit in the protocol data to be \(p\) and the frequency ranking to be \(r\), then Equation (1) can be obtained.  
\[pr=C \tag{1}\]  
where \(C\) is a constant, take the logarithm to obtain Equation (2)  
\[ln(p)+ln(r)=ln(C) \tag{2}\]  
Taking \(ln(p)\) and \(ln(r)\) as the horizontal and vertical coordinates, respectively, we can finally obtain a straight line with a slope of \(-1\), then take N as 1, 2, and 3 to segment the protocol data. If an approximate straight line is obtained, it is considered that [PERSON]'s law is satisfied.
# Role of Melatonin to Enhance Phytoremediation Capacity
###### Abstract  
Phytoremediation is a green technology that aims to take up pollutants from soil or water. Metals are one of the targets of these techniques due to their high toxicity in biological systems, including plants and animals. Their elimination or, at least, decrease will help keep them from being incorporated in the trophic chain and thus reaching animal and human food. The metal removal efficiency of plants is closely related to their growth rate, tolerance, and their adaptability to different environments. Melatonin (\(N\)-acetyl-5-methoxyryptamine) is a ubiquitous molecule present in animals, plants, fungi, and bacteria. In plants, it plays an important role related to antioxidant activity, but also as an important redox network regulator. Thus, melatonin has been defined as a biostimulator of plant growth, especially under environmental stress conditions, whether abiotic (water deficit and waterlogging, extreme temperature, UV radiation, salinity, alkalinity, specific mineral deficit/excess, metals and other toxic compounds, etc.) or biotic (bacteria, fungi, and viruses). Exogenous melatonin treated plants have been seen to have a high tolerance to stressors, minimizing possible harmful effects through the control of reactive oxygen species (ROS) levels and activating antioxidative responses. Furthermore, important gene expression changes in stress specific transcription factors have been demonstrated. Melatonin is capable of mobilizing toxic metals, through phytochelatins, transporting this, while sequestration adds to the biostimulator effect of melatonin on plants, improving plant tolerance against toxic pollutants. Furthermore, melatonin improves the uptake of nitrogen (N), phosphorus (P), and sulfur (S) in stress situations, enhancing cell metabolism. In light of the above, the application of melatonin seems to be a useful option for clearing toxic pollutants from the environment by improving phytoremediation. Interestingly, a variety of stressors induce melatonin biosynthesis in plants, and the study of this endogenous response in hyperaccumulator plants may be even more interesting as a natural response of the phytoremediation of diverse plants.  
abiotic stress; biostimulators; cadmium; cobalt; copper; heavy metals; lead; nickel; plant growth promoters; zinc +
Footnote †: journal: Applied Sciences
## 3 Numerical Examples
### The Advantages of the Proposed MRRM as Compared to the Transfer Matrix Method (TMM)
Figure 7: Phase velocity-related dispersion curves of bending waves in the bi-coupled periodic beam with Ternary-I unit cell: (**a**) the phase velocity-wavenumber curves; (**b**) the phase velocity-wavelength curves; (**c**) the phase velocity-frequency curves. Figure 8: Frequency-related dispersion curves of bending waves in the bi-coupled periodic beam with Ternary-I unit cell within frequency range 500–505 kHz: **(a)** the phase constant and the attenuation constant spectra; **(b)** the wavelength spectra; **(c)** the phase velocity spectra.
## 1 Introduction  
Soil stabilization through addition of a hydraulic binder is used extensively to improve physical and mechanical properties of soft soils to achieve desired strength properties. Additives like cement and lime are typically used as stabilizers. More recently, industrial by-products, such as fly ash, cement kiln dust, blast furnace slags and other slags have been used. The chemical reaction between the soil and the stabilizer alters the physical and engineering properties of the soil and thus the desired strength and durability are obtained. For soil stabilization application and during the design process, a laboratory investigation is conducted first to obtain the enhancement in soil strength and stiffness. Usually, the specimens of the soil-binder mixture are prepared in a laboratory according to a standard procedure, which in principle should simulate the field conditions. These procedures vary between different countries; in addition, there are variations between different testing companies [1; 2; 3; 4; 5; 6]. For instance, in Sweden, the specimens of soil-binder mixture are prepared according to the common procedure described by the Swedish geotechnical society report [4] and the design guide for soft soil stabilization [3]. In Japan, the specimens are prepared according to the Japanese geotechnical society standard [7; 8]. These variations are related to differences in the soil type, type and procedure of soil stabilization in the field and the differences in traditional laboratory testing in general [5]. The uniform distribution of binder around soil particles represents the most common concern for obtaining a uniform soil binder mixture. Disaggregating the natural soil prior to treatment has many effects such as homogenizing the soil, reducing the variation in water content and obtaining smaller chunks by separating the agglomerated particles. This process could assist in obtaining a uniform distribution for the cementitious binders around soil particles. Table 1 summarizes the most common procedures used to homogenize natural soil prior to treatment. The most common step in all procedures is that the natural soil has to be disaggregated and homogenized before adding the stabilizer. However, it is noticeable that most of the standards do not specify the required time for the disintegration and homogenization process [3; 4; 5; 9; 10]. Consequently, there is little knowledge regarding the time required to disaggregate and homogenize the soil before treatment with a cementitious binder. For these reasons, an experimental program was initiated in this study to determine the effects of various disaggregation times on the uniformity and homogeneity of soil.
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
## 3 Transmitter Design
### No CSI-Based Precoder  
The transmitter here has no access to the CSI, which means that the analog precoder vector of the \(u\)th UT is generated randomly accordingly to  
\[\mathbf{f}_{a,u}=\left[e^{\left|2\pi\phi_{n}^{u}\right|}\right]_{1\leq n\leq N _{tx}} \tag{8}\]  
noting that \(\phi_{n}^{u}\in[0,1]\), where \(n\in\{1,\dots,N_{tx}\}\) and \(u\in\{1,\dots,U\}\), are i.i.d. uniform random variables.
# Transitioning Broadcast to Cloud
## 2 Processing Chains in Broadcast and Cloud-Based Online Video Systems
### Distribution Formats
The preferred way of carriage depends on player capabilities, and may vary for different platforms. The SCTE-35 information is allowed to be carried only at manifest level in HLS, by either manifest of in-band events in MPEG-DASH, and only in-band in MSS [7; 45; 75]. To manage such broad diversity of formats, DRMs, and metadata representations, online video platforms are commonly deploying so-called dynamic or just-in-time (JIT) packaging mechanisms [23]. This is illustrated by an architecture shown in Figure 2. Instead of proactively generating and storing all possible permutations of packaged streams on origin server, such system stores all VOD content in a single intermediate representation, that allows fast transmux to all desired formats. The origin server works as a cache/proxy, invoking JIT transmuxers to produce each version of content only if there is a client device that requests it. Such logic is commonly accompanied by dynamic manifest generation, matching the choices of formats, DRMs, and metadata representation to capabilities of devices requesting them. This reduces amount of cloud storage needed and also increases the efficiency of use of CDNs when handing multiple content representations [23]. As easily observed, delivery formats and their support system in case of OTT/streaming is completely different as compared to broadcast.