esa-sceva/satcom-chunk-collection huggingface dataset:
# Frequency-Domain and Spatial-Domain MLMVN-Based Convolutional Neural Networks
## 4 MLMVN as a Frequency-Domain CNN and the Frequency-Domain Pooling  
### MLMVN as a Frequency-Domain CNN  
In regular convolutional neural networks, the convolution operation is performed in the spatial domain, but there is another way to perform it--in the frequency domain. According to the Convolution Theorem, the convolution of two signals in the spatial domain is equal to the inverse Fourier transform of the product of their Fourier transforms:  
\[f*g=\mathcal{F}^{-1}(F\cdot G), \tag{32}\]  
where \(f\) and \(g\) are two signals or images, \(F\) and \(G\) are the Fourier transforms of \(f\) and \(g\), respectively, the \(*\) sign is the convolution operator, and the \"\(\cdot\)\" sign denotes the element-wise multiplication.  
It is important to mention that the coefficients of the Fourier transform and the weights of MVN are both complex-valued. Thus, if we employ a Fourier transform of an image as an MLMVN input, it is possible to interpret the weighting of the input as a component-wise multiplication of the Fourier transform of the image with a frequency-domain convolutional kernel (which should in turn be interpreted as a Fourier transform of the respective spatial-domain convolutional kernel). We can therefore associate this operation with the Convolution Theorem. Thus, if the Fourier transform of an image to be classified is used as MLMVN input, all neurons in the first hidden layer of MLMVN can be treated as the convolutional kernels performing a convolution in the frequency domain followed by the processing of its results.  
This implies some important distinctions when compared to regular CNNs. The first distinction is that the result of a convolution performed by each neuron in the first hidden layer is not a feature map as in regular CNNs in the spatial domain, but it is a sum of the frequency-domain feature map coefficients, i.e., the sum of the coefficients which should be interpreted as those of the Fourier transform of a respective convolution. Therefore, the second convolutional layer cannot be used in this case. The second distinction is that we cannot set the size of the kernel, and therefore we cannot control the size of the features being extracted. The size of the frequency-domain kernels formed in the first layer is the same as the input image size. However, what is very important in this case is that MLMVN independently determines the features it needs to recognize images. This process becomes self-organized. It is quite interesting to discover what exactly MLMVN needs to extract to recognize images and how it self-organizes this process. We will consider this in Section 5.
# Unlocking Efficiency in Fine-Grained Compositional Image Synthesis: A Single-Generator Approach
## 1 Introduction
The limitations of the discussed models can be summarized based on three criteria: whether they are semantic-aware, compositional and efficient (see Table 1). It is evident that those models proposed in the existing literature either lack semantic awareness, compositionality or efficiency. In this paper, we introduce SSSGAN to address these limitations. The primary contribution of our work is a novel method that addresses the challenging task of generating all parts using a single local generator. This innovation is crucial, due to the inherent difficulties posed by strong entanglement and mode collapse problems associated with the generation of semantic parts using a shared generator. These challenges are further elaborated on in Section 3. The resulting model is both semantic-aware and compositional, while maintaining high efficiency. We conducted a comprehensive comparative analysis between SSSGAN and a state-of-the-art model, clearly demonstrating that SSSGAN excels in efficiency while making only minimal compromises in compositional performance. Our contributions are summarized as follows:  
1. We propose a novel method that enables the generation of all semantic parts using a single local generator. This method effectively addresses the efficiency challenge encountered in large-scale compositional image-synthesis tasks. 2. We introduce SSSGAN, a model that is semantic-aware, compositional and efficient. It addresses the gap in existing works by meeting all three of these criteria simultaneously. 3. We conduct extensive experiments to evaluate the performance of SSSGAN, examining its compositional properties and the characteristics of its latent space. 4. We investigate the entanglement mechanism between semantic parts and devise a method to control the intensity of this entanglement. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Semantic-Aware** & **Compositional** & **Efficient** \\ \hline StyleGAN [10] & ✗ & ✗ & ✓ \\ SPADE [11] & ✓ & ✗ & ✓ \\ SemanticStyleGAN [12] & ✓ & ✓ & ✗ \\ Ours & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of representative models with the proposed model. In the following sections, we delve deeper into the topic of compositional image generation. Section 2 provides a comprehensive review of the related literature, highlighting the existing approaches and their limitations. In Section 3, we present our proposed model, SSSGAN, detailing its architecture and key components. The experimental setup and results are presented in Section 4, and we evaluate the performance and efficiency of SSSGAN against a state-of-the-art model. Finally, in Section 5, we conclude the paper by summarizing our contributions, discussing the limitations faced and outlining future research directions in this field.
## 4 Results
### Additional Biochemical Traits Detected
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Biochemical** & **Spectral Range and** & **Dimensionality Reduction Techniques** & **Accuracy and Technique** & **Number of Samples** & **Ref.** \\
**Trails and Plant** & **Instrument** & Harvest 1 (four weeks) & \\  & & Prediction of Sulfur P1 SR Models PLS1 (single & \\  & & Nutrient output: & \\  & & RP\({}^{2}\) = 0.6 RMSE = 0.04 & \\  & & PLS2 (multiple nutrient output): & \\  & & RP\({}^{2}\) = 0.6 RMSE = 0.04 & \\  & & Classification of sulfur deficiency (F1, Precision, \\  & & Recall) (PLSI, PLS2, PLSDA, MLP) & \\  & & Best results: Threshold 1: & \\  & & MLP & \\  & & & \(\rm F1\) = 0.84 Precision = 0.72 Recall = 1 & \\  & & Threshold 2: & \\  & & P1S1 & \(\rm F1\) = 0.83 Recall = 0.63 & \\  & & PLSDA & \\  & & PLSDA & \\  & & & \(\rm F1\) = 0.71 Precision = 0.83 Recall = 0.63 & \\  & & identify the predictor variables & & \\  & & Prediction of Sulfur P1 SR Models & \\  & & PLS1 (single nutrient output): RP\({}^{2}\) = 0.27 & \\  & & RMSE = 0.04 & \\  & & PLS2 (multiple nutrient output): RP\({}^{2}\) = 0.33 & \\  & & RMSE = 0.04 & \\  & & Classification of magnesium deficiency (F1, \\  & & Precision, Recall) & \\  & & Best results: Threshold 1: & \\  & & MLP & \\  & & & \(\rm F1\) = 0.84 Precision = 0.73 Recall = 1 & \\  & & Threshold 2: & \\  & & MLP & \\  & & & \(\rm F1\) = 0.75 Precision = 0.75 Recall = 0.75 & \\ \hline \hline \end{tabular}
\end{table} 
Table 25: Additional Biochemical Traits. The detection of a wider array of biochemicals in lettuce, spinach, and basil using spectral instruments can give a cheaper alternative to the traditional chemical analysis for advancing plant management, optimizing health, quality assessment, and nutrient optimization. As can be seen in the table below, all biochemical traits have good accuracies presented by the studies. Some of them, like potassium, phosphorus, and calcium, have many entries in the table, which shows the trend in research in recent years. Studies including biochemicals such as vitamin C, magnesium, and sulfur can give an edge to the grower in monitoring plant defense and enhancing nutritional value. Further studies should explore the potential of spectral imaging, focusing on the expansion of detectable biochemical traits.
## 3 Trichome Biology in _Cannabis sativa_ L.  
Trichomes are hair-like epidermal appendages that occur on various aerial surfaces of plants [15; 21]. In C. _sativa_, two broad categories exist: non-glandular (cystolithic) and glandular. While non-glandular trichomes often serve mechanical or thermoregulatory functions, the glandular trichomes in cannabis are of primary economic and medicinal importance due to their capacity for cannabinoid secretion. Within each glandular head, specialized secretory disk cells synthesize and secrete cannabinoids, terpenes, and other compounds into a subcuticular storage cavity [22; 23]. This compartmentalization enables the plant to safely accumulate high levels of bioactive chemicals without disrupting normal physiological processes. There are three main morphotypes of glandular trichomes on female flowers:  
* Bulbous Trichomes:
* The smallest glandular form, often difficult to visualize without magnification.
* Less studied, but still capable of secreting small amounts of resinous metabolites [24].
* Capitate-Sessile Trichomes:
* Characterized by a sesquiterpene and cannabinoid-rich head directly attached to the epidermal surface.
* Typically contain eight secretory disk cells [25].
* Commonly found on bracts and sugar leaves, but less productive than capitate-stalked types.
* Capitate-Stalked Trichomes:
* The largest and most abundant trichomes on mature female infloreses, often featuring 12-16 secretory cells [24, 26].
* Responsible for the vast majority of cannabinoid production.
* Highly visible due to their elongated stalk and large, bulbous gland head, making them a key target for horticulture/agronomic manipulation and breeding.  
High trichome density in cannabis provides multiple defensive advantages. Dense coverage can help to trap or deter insect pests, reduce water loss under arid conditions, and mitigate harm from intense sunlight or UV radiation [27]. Thus, trichomes are integral to both chemical and physical aspects of the plant's defense strategy [28]. In the context of cannabinoid production, trichome abundance may be correlated with cannabinoid yield [16]. Cultivators seeking maximum potency or medicinal efficacy often prioritize methods that increase glandular trichome development. These can include manipulating light intensity, nutrient availability, or hormonal signals to encourage the plant to produce more resin. Furthermore, a higher trichome density is a desirable trait often associated with quality and potency by consumers.  
One of the most compelling strategies to enhance trichome density involves the use of jasmonates, especially MeJA [19, 20, 29]. By activating jasmonate signaling pathways, MeJA can stimulate glandular trichome initiation, lengthen trichome stalks, and potentially upregulate key enzymes in cannabinoid biosynthesis [30]. For _C. sativa_, this represents an exciting avenue to selectively boost desirable phytochemicals while harnessing the plant's innate defense mechanisms.
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 1 Introduction  
A Wireless Sensor Network (WSN) is a group of spatially dispersed sensor nodes, which are interconnected by using wireless communication [1]. As seen in Figure 1, a sensor node, also called mote, is an electronic device which consists of a processor along with a storage unit, a transceiver module, a single sensor or multiple sensors, along with an analog-to-digital converter (ADC), and a power source, which normally is a battery. It may optionally include a positioning unit and/or a mobilization unit.  
A sensor node uses its sensor(s) in order to measure the fluctuation of current conditions in its adjacent environment. These measurements are converted, via the ADC unit, into relative electric signals which are processed via the node's processor. Via its transceiver, the node can wirelessly transmit the data produced by its processor to other nodes or/and to a selected sink point, referred to as the Base Station.  
As illustrated in Figure 2, the Base Station, by using the data transmitted to itself, is able to both perform supervisory control over the WSN it belongs to and transmit the related information to human users or/and other networks [2].  
The collaborative use of a sufficient quantity of such sensor nodes, enables a WSN to perform simultaneous data acquirement of ambient information at several points of interest positioned over wide areas. The inexpensive production of sensor nodes of this kind, which despite their relatively small size, have exceptionally advanced sensing, processing, and communication abilities has become feasible due to continuous technological advances. For this reason, although WSNs were initially used mainly for military purposes, nowadays they support an ever-growing range of applications of different types [3].  
The aim of this paper is to study characteristic examples of different existing applications of WSNs, both widely used and novel ones, and examine various issues of interest related to this topic. Specifically, in Section 2 applications of WSNs are classified according to their nature into corresponding categories and their specific features are examined, through the presentation of indicative examples. Specific concerns arising from this study are discussed in Section 3. Finally, concluding remarks are drawn in Section 4.
# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 5 Result and Analysis  
For checking the effectiveness of the proposed scheme EE-FDAS, extensive simulation is performed under NS 2.35. The sensor nodes are placed in the network area of 3000 \(\times\) 3000 m. The transmission range of these nodes is considered 30 m and it remains the same for all types of attributes. The sensor nodes produce data packets after every 30 s. The total time allotted for simulation is 2500 s. The tool command language (TCL) file contains comprehensive information about configuring and deploying nodes in the network. For implementing the functionality of sending and receiving data packets, separate classes are built by using C language. Afterward, to extract processed data result files, AWK files are considered. In each simulation scenario, the nodes are deployed in a Gaussian distribution model to deploy the patients where the different types of sensor nodes are attached to each patient as per the monitoring of required healthcare parameters. The result of the proposed scheme is compared with some existing robust schemes including FDAS, EHDA, DICA, and DICA_EXTENSION. The list of parameters is presented in Table 2.  
The attribute represents the sensor nodes, when there is only one attribute, it shows a homogeneous network where all sensors have the same type. For instance, all sensors take temperature or heart rate readings. When the number of attributes increases, the network starts becoming heterogeneous, demonstrating the existence of more sophisticated sensors. When there are two types of attributes, it indicates that both are taking different readings. For example, the first type of sensor could be monitoring sugar levels, and the other could be a blood pressure monitor. When the number of attributes is four, it shows the existence of four different sensors that are taking values of blood glucose, heart rate monitors, blood pressure, and oxygen saturation. As the number of attributes remains increasing it ultimately boost the complexity, and makes the environment highly heterogeneous. It enhances the ability to monitor and gather patient data thoroughly via numerous sensors. We have considered the emergency scenario to share full readings and the threshold based scenario to send message with complete readings after the adjusted threshold as 10 or above. In this scenario, a little communication cost and energy consumption will be increased to send a full message of larger bit size containing all the original values of all attributes after a certain threshold.  
The evaluation parameters that are considered more crucial and suitable for de-duplication and data aggregation are chosen. The key metrics, including energy consumption, average aggregation, needed transmission slots, packet loss ratio, and storage cost, are analyzed to assess the performance of the EE-FDAS scheme. The proposed scheme is simulated under a different number of attributes in a heterogeneous environment to check its efficiency from a simple to a complex environment.
## 4 Transparent Vivaldi Antenna Fabrication  
Three transparent mesh antennas have been created using the process described in [28], which involves depositing a 1.35 um thick continuous silver film and a 5 nm thick titanium adhesion film onto a substrate using RF sputtering. Photolithographic wet etching is then used to create the antenna patterns with appropriate photomasks (Figure 6a). The silver film was made to be three times thicker than the skin depth value (0.45 um at 26 GHz) using standard photolithographic wet-etching processes and appropriate photomasks. During the photolithography process, the careful alignment of the photomasks and the substrate is crucial to ensure the accuracy of the antenna. The resulting antenna is optically transparent, making it suitable for use in applications where visibility is important, such as in smart windows or heads-up displays. The distance between the antenna elements (pitch) affects the level of optical transparency, with a wider pitch resulting in higher transparency levels (ranging from 66% to 89%). This transparency is measured using a UV-visible spectrophotometer. The process of creating a transparent antenna, illustrated in Figure 6, involves creating square apertures in the metal layers, with a specific pitch and metal strip width. In this case, the pitch is 150 um and the metal strip width is close to 15 um. The mesh antenna underwent an optical transparency test using a UV-visible spectrophotometer. The results showed a transparency of 76% across the visible light spectrum of 400-800 nm. This confirms the high optical transparency of the mesh antenna, as shown in Figure 6b. The transparent antenna's ability to transmit visible light with minimal absorption ensures its suitability for optical applications.
## 2 HGV Tracking with Modified Aerodynamic Model
### Radar Measurement Model  
Assuming that, in the spherical coordinate system, the coordinates of the origin of the radar measurement coordinate system are \(\left[\begin{array}{cc}r_{0}&\lambda_{0}&\phi_{0}\end{array}\right]^{T}\), the coordinates of HGV centroid are \(\left[\begin{array}{cc}r&\lambda&\phi\end{array}\right]^{T}\) in the spherical coordinate system and \(\left[\begin{array}{cc}x&y&z\end{array}\right]^{T}\) in the radar measurement coordinate system. According to coordinate transforming relations, the transforming equations can be denoted as  
\[\left[\begin{array}{c}r\text{cos}\phi\text{cos}\lambda\\ r\text{cos}\phi\text{sin}\lambda\\ r\text{sin}\phi\end{array}\right]=\left[\begin{array}{c}r_{o}\text{cos}\phi_ {o}\text{cos}\lambda_{o}\\ r_{o}\text{cos}\phi_{o}\text{sin}\lambda_{o}\\ r_{o}\text{sin}\phi_{o}\end{array}\right]+F\left[\begin{array}{c}x\\ y\\ z\end{array}\right] \tag{9}\]  
where \(F\) is the transform matrix of the radar measurement coordinate system to the geocentric rectangular coordinate system and can be denoted as  
\[F=R_{Z}\left(90^{\circ}-\lambda_{o}\right)R_{X}\left(-\phi_{o}\right)R_{Y} \left(90^{\circ}\right) \tag{10}\]  
where \(R_{Z}\), \(R_{X}\) and \(R_{Y}\) is the rotation matrixes around the \(Z\)-axis, \(X\)-axis and \(Y\)-axis, respectively.  
Assuming the HGV coordinate in radar measurement coordinate system is \(\left[\begin{array}{ccc}x&y&z\end{array}\right]^{T}\), based on the radar principle [26]\(RAE\) can be expressed as:\[\left\{\begin{array}{c}R_{0}=\sqrt{x^{2}+y^{2}+z^{2}}\\ \arctan\left(\frac{z}{x}\right)+\pi,x<0\\ \arctan\left(\frac{z}{x}\right),x>0,z>0\\ \arctan\left(\frac{z}{x}\right)+2\pi,x>0,z<0\\ E=\arcsin\left(y/R_{0}\right)\end{array}\right. \tag{11}\]  
In the HGV tracking, we assume the radar measurement noises are known independent white noises, so the radar measurement equations can be expressed as  
\[\left\{\begin{array}{c}\widetilde{R_{0}}=R_{0}+\varepsilon_{R}\\ \widetilde{A}=A+\varepsilon_{A}\\ \widetilde{E}=E+\varepsilon_{E}\end{array}\right. \tag{12}\]  
Define \(Y=\left[\begin{array}{cc}\widetilde{R_{0}}&\widetilde{A}&\widetilde{E}\\ \end{array}\right]^{T}\) and \(v_{R}=\left[\begin{array}{cc}\varepsilon_{R}&\varepsilon_{A}&\varepsilon_{ E}\\ \end{array}\right]^{T}\), then the radar measurement equation can be expressed as  
\[Y=h\left(X\right)+v_{R} \tag{13}\]  
The statistical characteristics of radar measurement noises \(v_{R}\) can be expressed as  
\[R=\left[\begin{array}{ccc}\sigma_{R}^{2}&0&0\\ 0&\sigma_{A}^{2}&0\\ 0&0&\sigma_{E}^{2}\\ \end{array}\right] \tag{14}\]  
where \(\sigma_{R}^{2}\), \(\sigma_{A}^{2}\) and \(\sigma_{E}^{2}\) are measurement error deviations of range, azimuth and elevate, respectively.
## 2 Materials and Methods
### Numerical Study
#### 2.3.7 Variables Analyzed  
The results for several relevant variables are examined using (as customary in earthquake engineering) their peak unsigned values throughout time history as representative. For energy variables the values at the end of motion are used. Because added damping systems are included as protective systems, emphasis is placed on those variables closely related to damage measure: interstorey drift ratio \(\theta\) (or drift \(\Delta\)); residual interstorey drift ratio \(\theta_{r}\) (or residual drift \(\Delta_{r}\)); absolute storey acceleration \(a_{t}\); plastic strain energy at beams (\(W_{beam}\)), columns (\(W_{column}\)) or frames (\(W_{frame}\)); relative input energy \(E_{I}\)[10]; energy dissipated by added damping \(W_{damper}\). The following variables are also examined, but only summarized results are provided here for brevity: beam peak rotation \(\theta_{b}\); column peak rotation \(\theta_{c}\); structural shear \(V_{s}\); damper shear \(V_{d}\); total shear \(V_{t}\); column moment \(M_{c}\); beam moment \(M_{b}\); column unsigned peak axial force \(N_{col,min}\). Results for strain energy in structural elements have been obtained by numerical integration of generalized internal forces over generalized internal displacements. Other energy variables have also been similarly obtained by numerical integration [14]. Storey shear \(V_{s}\) excludes damper shear \(V_{d}\). Total shear \(V_{t}\) includes damper shear \(V_{d}\) and storey shear \(V_{s}\). Due to uncoupling \(V_{t}\) is not the addition of \(V_{d}\) and \(V_{s}\) for individual motions.