esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Evolution of SDN Control Architecture for VANETs
### Centralized Controller Model  
This mode is comprised of a solitary controller that is utilized to deal with the whole system. This indicates that the SDN is boosted by OpenFlow rules convention, which is utilized and incorporated by the controllers to control and deal with the whole system [13]. Thus, on the grounds that a solitary controller is utilized it must have a global visualization of the whole system. The controllers correspond through the gadget that is straightforwardly associated with the system to distinguish the deficiency and the attacks that happen on the whole system. These legitimately associated gadgets forward data to the controller. By utilizing the single controller, it is anything but difficult to deal with the usefulness of the whole system. Notwithstanding, a solitary controller has a few impediments. It needs to refresh the entire system more often than the customary system does on the grounds that when the stream changes, the stream table must be refreshed so as to look after productivity. This procedure delivers a high over-burden in light of the fact that the makers should be completed by the controller, and this maker builds the handling cost of the controller. A model is grouping streams with various needs into numerous classifications. Expanding the usefulness in a solitary hub requires higher measures of intensity in handling, information stockpiling limit, and throughput to convey the information [13]. Figure 4A represents the centralized model.  
After some time, new rules and streams are acquainted with encouraged correspondence, yet the memory of the controller must be extended over and over [19]. The third problem in the unified system is that paying little attention to where the main pack of the new stream is presented, it is first sent to the incorporated controller for assessment. The controller decides the way for the stream and stretches out this data to every one of the gadgets for the whole system. The smash utilized through the controllers to characterize the guidelines can be over-burdened by the huge measure of essential information to be handled. In this circumstance, some disappointments in the controller may cause dissatisfaction of the whole network [11]. After some time, the SDN organized framework turns out to be progressively mind boggling due to new prerequisites of the job of traffic. They are intended to help variable correspondence with the expansion of the security, loading, adjusting, and firewalls [7]. Various administrations are intended to arrange through the control plane to accomplish their objective. In utilizing these conflicting steering topologies, it is hard for controllers to accomplish ideal execution [20].
## 2 Distributed Fair Rate Allocation Algorithm
### Existing Work  
The problem we have formulated in Problem (1) is known in the literature as the Network Utility Maximization (NUM) problem [22] and several authors have employed classical decompositions, _i.e._, primal decomposition [17] (Section 6.4.2) and dual decomposition [17] (Section 6.4.1) to solve it. Dual decomposition is usually preferred since it naturally provides a full distributed solution [23, 24]. Indeed, the Active Queue Management (AQM) policy employed in the Transport Control Protocol (TCP) can be viewed as a convex optimization problem that is distributedly solved using a dual decomposition approach. Notwithstanding, optimally solving Problem (1) in a distributed manner is now time and signalling consuming due to the fact that the algorithms are based in a projected subgradient approach [17] (Section 2.3). In general, the convergence of the algorithms is slow and furthermore, it depends on a user-adjusted step-size, which is not necessarily optimally fixed.  
In order to overcome these drawbacks, we have proposed the Coupled-Decompositions Method (CDM) [11, 12]. It simultaneously intertwines primal and dual decomposition techniques in a single approach, converges much faster to the optimal solution and avoids the choice of a user-defined step-size. Note that there exist other primal-dual convex methods in the literature that are different to our approach. Some of them do not take into account the separability of the problem and thus are not true decomposition-based techniques [25, 26]. Other works use primal and dual domains as well as the separability of the problem but they concatenate primal and dual domains in their algorithms instead of really mixing them [22, 27, 28], as we do in our CDM. In the following, we briefly review the classical decomposition methods. Afterwards, we extend the formulation of the CDM to rate allocation problems that exhibit a tree topology thus obtaining a novel distributed version of the method.
# Long-Wavelength VCSELs: Status and Prospects
###### Abstract  
Single-mode long-wavelength (LM) vertical-cavity surface-emitting lasers (VCSELs) present an inexpensive alternative to DFB-lasers for data communication in next-generation giga data centers, where optical links with large transmission distances are required. Narrow wavelength-division multiplexing systems demand large bit rates and single longitudinal and transverse modes. Spatial division multiplexing transmission through multicore fibers using LW VCSELs is enabling still larger-scale data center networks. This review discusses the requirements for achieving high-speed modulation, as well as the state-of-the-art. The hybrid short-cavity concept allows for the realization of \(f_{\rm 3 dB}\) frequencies of 17 GHz and 22 GHz for 1300 nm and 1550 nm range VCSELs, respectively. Wafer-fusion (WF) concepts allow the realization of long-time reliable LW VCSELs with a record single-mode output power of more than 6 mW, 13 GHz 3 dB cut-off frequency, and data rates of 37 Gbit/s for non-return-to-zero (NRZ) modulation at 1550 nm.  
vertical-cavity surface-emitting lasers (VCSELs); wafer bonding; superlattices; optical modulation; long-wavelength; short-cavity; 1300 nm; 1550 nm; MBE; MOVPE
# Multiscale Content-Independent Feature Fusion Network for Source Camera Identification
## 2 Methodology  
Although deep learning has achieved excellent performance in computer-vision-related tasks, this does not mean that a traditional CNN can be directly applied to the field of image forensics. In contrast to visual tasks, the key features of image forensics are the noise artifacts left in the image during the image acquisition process, not the image content. By contrast, the image content is the largest interference factor affecting source camera identification. Therefore, to successfully apply the existing CNN to the field of image forensics, we must suppress the image-content-related features as much as possible. In this paper, a multiscale content-independent feature fusion network (MCIFFN) is proposed to solve the problem of source camera identification. In order to capture more comprehensive information of the images, three branch networks are paralleled together to construct the MCIFFN. The three branch networks are used to extract different types of image features by adding different preprocessing modules. The design of the three preprocessing modules is different from each other, which are used to filter different types of image content and extract the noise features. The preprocessing modules of the first two branches are composed of two adaptive filters with different scales, which are used to remove the image content information and extract the multiscale content-independent noise features related to the camera attributes. In order to retain the information related to the image color [13], this paper does not preprocess the third branch network, but directly sends the image data to CNN, so the preprocessing module of the third branch is set to be empty. The image data are first sent to the preprocessing module of each branch to remove the image content features, and then sent to the corresponding CNN feature extraction network. Finally, the CNN features of the three branches are fused, and the fused features are used for image source classification. The CNN in the MCIFFN structure is a shallow network with a squeeze and exception (SE) structure. The structure of MCIFFN is shown in Figure 2.
## 3 System Requirements for BVLOS Flight Operations
### RAVEN-Proposed Small UAS DAA System
#### 3.5.2 Ground Portable Radar for Non-Cooperative Traffic  
Since 2014, the RAVEN project has also worked with Seamatica Aerospace (SMA) for the use of their Zeus ground portable radar. As shown in Figure 9b, the Zeus radar is based on OEM hardware originally designed for airborne weather detection. It operates at X-band (permitting physically small and light-weight RF hardware to be employed) and makes exclusive use of solid state technology. In addition, the Zeus radar uses long pulses with linear frequency modulation (LFM) pulse compression to increase the energy on target while maintaining a tight range resolution [16].
## 1 Introduction  
A synthetic aperture radar (SAR) system is a measurement system that acquires measurement data for earth-observation applications. Many applications like soil moisture [1] or forest biomass estimation [2] require absolutely radiometrically calibrated data products. The absolute radiometric calibration ensures that systematic effects have been characterized and are compensated so that the derived radar ground reflectivity appears to be independent of the measurement system. Not until then can the data be used for physical parameter inversion modeling.  
The necessary radiometric calibration is a two-step process and is divided into relative and absolute calibration [3,4]. During relative calibration, the system transfer function, antenna pattern, instrument drift (due to temperature and time), antenna mis-pointing and mis-polarization, SAR mode (Stripmap, ScanSAR, _etc._), atmosphere, and processor effects are characterized and compensated. After relative calibration, the mapped reflectivities are still only given as system indications, _i.e._, as digital numbers derived during processing. The digital numbers are then related to the definition of a physical unit through absolute calibration. Absolute calibration is, for practical reasons and an expected higher calibration quality, executed as an _external_ calibration for space-borne SAR systems [3]. Reference point targets (typically corner reflectors or active radar calibrators [3, 4, 5, 6]) with known radar reflectivities are distributed within an imaged scene, and their backscatters are related to the indications from the SAR system through a proportionality constant, often called calibration factor \(K\). Usually the external calibration is repeated for a number of acquisitions, each using a number of reference point targets, in order to reduce random effects through averaging.
## 2 Results
### VOCs
The different drying treatments resulted in a drastic reduction in terpenes, mainly in the samples subjected to OD and FD. Oxygenated terpenes and sesquiterpene hydrocarbons were also found in trace amounts. The drying treatments could be assumed to have an impact on the levels of these compounds, resulting in their lower levels, based on the higher amounts generally detected in fresh sea fennel. No esters were detected in the fresh samples or in the microwave-treated samples, while low amounts of esters were found in the samples subjected to RTD, OD, and FD. Aldehydes and acids were found in trace amounts in all analyzed samples, with acetic acid being the only organic acid detected. Regarding alcohols, fresh sea fennel was characterized by very low amounts of these compounds, whereas the dried sea fennel samples were characterized by higher amounts. In detail, alcohols represented about 1% of volatile compounds in fresh sea fennel and 4, 6, 15, and 26% of volatile compounds in sea fennel dried via MD, FD, RTD, and OD, respectively. Ethyl alcohol was the most representative alcohol. Traces of phenyl proponoids (dillapiole) were found in all the analyzed samples. To better understand the differences between the analyzed samples, a PCA of the 53 VOCs detected was carried out, and the results are shown in Figure 4. The two principal components (PCs) explained ca. 67.82% of the total variance of the data. Several compounds loaded positively on PC1, including aldehydes, alcohols, esters, and acids, while numerous other substances, such as terpene hydrocarbons, oxygenated terpenes, and sesquiterpene, loaded negatively on PC1. Based on the main class of volatile compounds detected, the samples of fresh sea fennel were clearly separated from those dried at room temperature and even more so from those subjected to OD, FD, and MD. The drying process significantly influenced the aroma pattern of the analyzed samples. As shown in the score plot, the analyzed samples are clearly clustered based on their treatment (no drying, FD, RTD, OD, and MD). In fact, according to the first PC (explaining 47.19% of the total variance), the sea fennel samples treated using MD, FD, and OD are distributed opposite to the fresh and room-temperature-dried sea fennel samples. According to the second PC (explaining 20.63% of the total variance), the fresh and microwave-dried sea fennel samples are separated from the other samples. In detail, the underd samples are in the third quadrant, the room-temperature-dried samples are in the second quadrant, the microwave-dried samples are in the fourth quadrant, and the oven-dried and freeze-dried samples are in the first quadrant of the score plot. The loading plot clearly shows how strongly VOCs influence the clustering of the samples, e.g., high levels of terpenes contribute to the grouping of fresh sea fennel, high levels of furfurals contribute to that of the microwave-dried samples, and high levels of alcohols contribute to that of the oven-dried samples.
## 3 Measurement Results and Discussion
### Experiments under Cloudy Weather Conditions
#### 3.3.3 Analysis Results and Discussion  
In order to perform the same analysis as in the summer and winter experiments, we classified the objects in the images into four categories. The heat load on the human body for each structures was calculated as shown in Figure 14.  
In the visible and near-infrared wavelengths, it was shown that there was little sunlight reaching ground, but the heat load reaching the human body from the sky increased. Due to the clouds blocking the sunlight, only scattered radiation reached the ground, and the heat load on the human body due to reflected radiation from the ground became small. Clouds absorb sunlight in the near-infrared wavelengths well. This is because the main component of clouds is water molecules. As a result, the reflected energy in near-infrared wavelengths was smaller than that in the visible. This cloudy experiment (Oct.) was performed in the season between summer (Aug.) and winter (Feb.). The environmental condition of this cloudy experiment (Table 3) was close to winter temperatures, as can be seen Tables 1 and 2 for the comparison, and the heat load to the human body at far-infrared wavelengths had negative values for all structures. Furthermore, these negative heat load values were smaller than those of the winter experiments. From these experiments, we can say that there is a good link between the temperature data and the infrared radiation energy data; thus, there is possibly a relationship between the air temperature and the far-infrared radiation energy.  
Figure 14: Calculated radiation energy to human body in cloudy weather experiment at the Imperial Palace.  
Figure 13: Measured image in cloudy weather experiment at the Imperial Palace with (**I**) visible, (**II**) near-infrared, and (**III**) far-infrared light camera.
# Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model
## 3 Artificial Neural Network (ANN)  
Over the past few decades, the term \"ANN\" has become notable and has been adopted by a wide variety of applications, including atmospheric science, renewable energy systems, civil engineering [46; 47], microwave computer-aided design, medicine and treatment, and natural resources research [48; 49; 50; 51; 52; 53; 54]. It is a branch of the AI family inspired by human brain biological neurons. The underlying architecture of ANN consists of input layers, hidden layer(s), and an output layer [55; 56; 57]. Here, layers are developed by fundamental elements of ANN well known as many highly interconnected neurons (nodes or processing elements) along with activation function [58; 59]. The primary purpose of the input layer is to communicate the output layer via the hidden layer(s) using weight values and activation function. Usually, the network's weights are adjusted (changes) and, accordingly, processed to obtain the desired response. Moreover, the ANN system with single hidden layer is adequate to compute the specified problem [60; 61]. Whereas, ANN with two or more hidden layers pattern may create complex glitches [12]. The input elements (nodes) has been received the feature variables from raw environmental information and forwarded to the hidden layer. In this layer, it obtains knowledge of previous nodes and performs computations using encoded by weights and yield net input. Subsequently, the net input has been processed to the output layer through an activation function [62]. In the output layer, it has received the previous process information from the hidden layer(s) and computes the desired output with less error and more accurate. In the current study, the parameters include W, R, B, P, S, and FR and are considered as inputs and output, respectively.  
Figure 3: Matrix of the attributes used in the fly-rock database.  
In ordered to predict blast-induced fly-rock efficiently, the following ANN architecture model was adopted and as depicted in Figure 4.
## 1 Introduction
However, further exploration is needed to understand the activation of specific signaling pathways and the composition of small-molecule compounds. At present, various mainstream sequencing analysis technologies, including genomics, transcriptomics, proteomics, and metabolomics, play pivotal roles in research [14]. The integration of multiple analysis techniques is a crucial aspect of omics. By leveraging genomic information as a backdrop, correlations among genes with varying expression levels in different life activities can be discerned [15; 16]. Simultaneously, enrichment analysis using the GO [17], KEGG [18], and HMDB [19; 20] databases helps to refine the screening of candidate genes, offering reliable data models to support accurate gene localization and function prediction for experiments related to gene function validation. If a gene exhibits a consistent expression trend in all omics contexts, it is highly likely to be a candidate as a housekeeping gene. Conversely, if the expression trend fluctuates between up- and down-regulation across different omics techniques, it is probable that the gene is associated with regulatory functions [21; 22]. A multi-omics approach can be used to effectively screen target genes and metabolic pathways. If the genome is used as background information to annotate other omics, it is possible to use gene IDs to represent products at different omics levels to determine their correlation with genes whose information can be looked up in databases such as the NCBI. In a laboratory setting, the mycelium undergoes gradual degeneration and subsequent demise during mycorrhizal passages, resulting in a shortened transgression time and diminished survival. This phenomenon could be attributed to the absence of specific nutrients and signaling molecules that typically stimulate relevant regulatory genes, or to metabolic dysfunctions arising from the simplified culture environment compared to the field environment. We categorized the life history of _T. ganbajun_ into three pivotal periods: the mycelium, the primordium, and the fruiting body (see Figure 1). Subsequently, we conducted proteomic and metabolomic analyses of samples from these stages. We then carried out a comprehensive joint analysis with the transcriptome, exploring gene expression and the corresponding downstream metabolites. The aim of this approach was to uncover the process by which _T. ganbajun_ produces substances and engages metabolic pathways crucial for ectomycorrhizal and fruiting body formation during its growth. At the same time, delving into the different components of _T. ganbajun_ offers insights into the genes or products associated with nutritionally defective phenotypes, enhancing our understanding and providing a valuable reference for the harvesting and protection of strains in the field.
## 2 System Model
### Generalized Pointing Errors Model with Non-Zero Boresight  
Principally, pointing errors include the random beam offset and the fixed displacement between the beam and detector--i.e., the jitter and the boresight component--respectively [41]. Hence, when a Gaussian beam propagates through distance \(\delta_{m}\) from the transmitter to a circular detector with aperture radius \(r_{m}\), the fraction of the collected power at receiver can be approximated as [42]  
\[h_{p}(R_{m},\delta_{m})\approx A_{0,m}\exp\left(-\frac{2R_{m}^{2}}{w_{eq,m}^{ 2}}\right),\quad R_{m}\geq 0, \tag{5}\]  
where \(w_{eq,m}\) represents the equivalent beam radius in the \(m\)-\(th\) receiver aperture which is given as \(w_{eq,m}=\left[\sqrt{\pi}\text{erf}(v_{m})w_{\delta,m}^{2}/2v_{m}\exp(-v_{m}^ {2})\right]^{1/2}\), with \(v_{m}=\sqrt{\pi}r_{m}/\sqrt{2}w_{\delta,m}\) and \(\text{erf}(. )\) denotes the error function [59], (8.250.1). Additionally, in TOW links the parameter \(w_{\delta,m}=\delta_{m}\tan(\theta_{m}/2)\), stands for the \(m\)-\(th\) beam waist on the receiver plane at distance \(\delta_{m}\) from the transmitter [36; 37; 38; 39], with \(\theta_{m}\) being the corresponding divergence angle. Furthermore, \(A_{0,m}=\text{erf}^{2}(v_{m})\) is the fraction of the collected power at \(r_{m}=0\)[7; 42]. Moreover, \(R_{m}\) is the radial displacement at the \(m\)-\(th\) receiver that is expressed as \(R_{m}=\left|\overset{\rightarrow}{R}_{m}\right|=\sqrt{R_{x,m}^{2}+R_{y,m}^{2}}\) where \(\overset{\rightarrow}{R}_{m}=\left[R_{x,m},R_{y,m}\right]^{T}\) is the radial displacement vector with \(R_{x,m}\), \(R_{y,m}\), representing the displacements located along the horizontal and elevation axes at the detector plane, respectively. These variables are considered as nonzero mean Gaussian distributed random variables, i.e., \(R_{x,m}\sim N\left(\mu_{x,m},\sigma_{x,m}^{2}\right)\), \(R_{y,m}\sim N\left(\mu_{y,m},\sigma_{y,m}^{2}\right)\) where \(\mu_{x,m}\), \(\mu_{y,m}\), denote their mean values and \(\sigma_{x,m}\), \(\sigma_{y,m}\), the jitters for horizontal and elevation displacements, respectively [42]. Here, it should be clearly outlined that the approximation in (5) is accurate enough for \(w_{0,m}/r_{m}>6\), which is satisfied in typical FSO systems. Hence, a generalized statistical model which can accurately emulate the pointing errors effect considering the beam width, the detector size, the different jitter for the elevation and the horizontal displacements and the NZB error, is [PERSON]'s model with the following probability density function (PDF) [58],  
\[ \begin{array}{ll}f_{R,m}(R_{m})&=\frac{R_{m}}{2\pi\sigma_{x,m}\sigma_{y,m}} \\ &\times\int\limits_{0}^{2\pi}\exp\left(-\frac{\left(R_{m}\cos\varphi_{m}-\mu_{ x,m}\right)^{2}}{2\sigma_{x,m}^{2}}-\frac{\left(R\sin\varphi_{m}-\mu_{y} \right)^{2}}{2\sigma_{y}^{2}}\right)d\varphi_{m},\end{array}  \tag{6}\]  
where \(\varphi_{m}\) stands for the transmit divergence angle, which denotes the increase in beam radius with distance from the transmitter [42].