esa-sceva/satcom-chunk-collection huggingface dataset:
# A Parallel Two-Stage Iteration Method for Solving Continuous Sylvester Equations
## 4 Convergence Analysis  
The convergence property above two-stage iteration method is mentioned here.  
**Lemma 1** (see [35]).: _Let \(H\) be a positive definite matrix, and \(H=M-N\) be a splitting, with \(M=(H+H^{\rm T})/2\), and \(N=(H^{\rm T}-H)/2\). Then, \(\rho(M^{-1}N)<1\) if for all \(y\in C^{n}\), it holds that \(y^{\rm H}My>|y^{\rm H}Ny|\)._  
Based on the above lemma, we obtain the following conclusion.  
**Theorem 1**.: _Assume the positive definite matrix \(H\in R^{n\times n}\) is split according to Lemma 1. If for all \(y\in C^{n}\), we have that \(|Re(y^{\rm H}Hy)|>|Im(y^{\rm H}Hy)|\), then \(\rho(M^{-1}N)<1\) holds._  
Proof of Theorem 1.: For all \(y\in C^{n}\), we can get  
\[y^{\rm H}My=(y^{\rm H}Hy+y^{\rm H}H^{\rm H}y)/2=Re(y^{\rm H}Hy) \tag{12}\]  
and  
\[y^{\rm H}My=(-y^{\rm H}Hy+y^{\rm H}H^{\rm H}y)/2=Im(y^{\rm H}Hy). \tag{13}\]  
According to the Lemma 1, we can obtain \(|Re(y^{\rm H}Hy)|>|Im(y^{\rm H}Hy)|\). Therefore \(\rho(M^{-1}N)<1\).  
The above theorem is generalized to the continuous Sylvester equation. The convergence theorem of the matrix equation can be obtained as follows:  
**Theorem 2**.: _Let \(A,B\in R^{n\times n}\) be positive definite matrices in the Sylvester Equation (1) and suppose that they are split as in the two-stage iterative format (5). If for all \(Y\in C^{n\times n}\), we have_  
\[|Re[Y,AY+YB]|>|Im[Y,AY+YB]|, \tag{14}\]  
_then_  
\[\rho((M_{1}\otimes I+I\otimes M_{2}^{\rm T})^{-1}(N_{1}\otimes I+I\otimes N_{ 2}^{\rm T}))<1 \tag{15}\]  
_holds._  
Proof of Theorem 2.: By using the Kronecker product, we can transform Equation (1) into the matrix-vector form:  
\[(A\otimes I+I\otimes B^{\rm T})x=f, \tag{16}\]  
where \(x=\overline{vec}(X)\), \(f=\overline{vec}(F)\). When the coefficient matrices \(A\) and \(B\) are positive definite matrices, we split them as in Section 2.1. Then, Equation (3) can be rewritten equivalently as  
\[(M_{1}\otimes I+I\otimes M_{2}^{\rm T})x=(N_{1}\otimes I+I\otimes N_{2}^{\rm T })x+f. \tag{17}\]  
According to Theorem 1, we yield  
\[|Re(y^{\rm H}(A\otimes I+I\otimes B^{\rm T})y)|>|Im(y^{\rm H}(A\otimes I+I \otimes B^{\rm T})y)|. \tag{18}\]  
On the other hand, it holds that  
\[y^{\rm H}(A\otimes I+I\otimes B^{\rm T})y=tr(Y^{\rm H}(AY+YB))=[Y,AY+YB]. \tag{19}\]  
Therefore,  
\[|Re[Y,AY+YB]|>|Im[Y,AY+YB]|. \tag{20}\]
# SNR-Based Receiver-Type Decision Using Deep Learning for Multiple-Input Multiple-Output Detection
## 4 Simulation Results
This implies that the proposed method can achieve the main goal, reducing the computational complexity of the ML receiver while maintaining the error performance successfully. Note that the \(2\times 2\) MIMO system is more effective for reducing computational complexity than the \(4\times 4\) MIMO system because it has the higher probabilities of \(f_{R}=1\) than the \(4\times 4\) MIMO system. Summarizing the above results, we can notice that it is important to determine the threshold in (13) and (15) according to the system parameters. Figure 11: The number of candidate searches during the demodulation process in the inference phase for a \(2\times 2\) MIMO system with \(4-\)QAM. Figure 10: The probabilities of \(f_{R}=1\) in the inference phase for a \(4\times 4\) MIMO system with \(16-\)QAM. Figure 14: The number of candidate searches during the demodulation process in the inference phase for a \(4\times 4\) MIMO system with 16—QAM. Figure 5:0 The number of candidate searches during the demodulation process in the inference phase for a \(2\times 2\) MIMO system with 16—QAM. Figure 5:1 The number of candidate searches during the demodulation process in the inference phase for a \(4\times 4\) MIMO system with 4—QAM.
## 1 Introduction
During the enzymatic transesterification for the production of biodiesel, one of the products formed is glycerol, which can be deposited on the surface of the enzyme, forming an outer layer preventing contact between the substrate and the enzyme active site, reducing the catalytic activity of the enzyme [1; 40]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Substrate** & **Alcohol** & **Conversion/Experimental Conditions** & **References** \\ \hline Sweet basal seed oil & Methanol & 94.58\%/68 h at 47 \({}^{\circ}\)C & [41] \\ Fish oil & Ethanol & 52.11\%/8 h at 35 \({}^{\circ}\)C & [42] \\ Jatropha oil & Methanol & 66.80\%/24 h at 40 \({}^{\circ}\)C & [43] \\ Microalgal oil & Methanol & 88.00\%/4 h at 40 \({}^{\circ}\)C & [44] \\ _Botryococcus_ sp. & & & \\ Oils from microalgae & Methanol & 19.30\%/6 h at 35 \({}^{\circ}\)C & [45] \\ Corn oil & Methanol & 91.00\%/18 h at 60 \({}^{\circ}\)C & [46] \\ Soybean oil & Methanol & 96.00\%/4 h at 40 \({}^{\circ}\)C & [22] \\ Palm oil & Methanol & 91.00\%–92.00\%/10, 20, 24 h at 40 \({}^{\circ}\)C & [47] \\ Frying oil & Methanol & 89.10\%/4 h at 50 \({}^{\circ}\)C & [48] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Application of Novozym 435 in the transesterification reaction for biodiesel synthesis. Remarkably, cost reduction in biodiesel production has been a major challenge for more than 20 years [49]. In this sense, efforts have been made to enable the use of a wide range of raw materials, to optimize the processes of production and to investigate more efficient catalysts. As a result, low-cost raw materials from non-edible resources have been investigated for the production of biodiesel [50], once the cost of the raw material is one factors that directly affects the economic viability of biodiesel production are. In fact, the cost of the raw material is one of the main and most important parameters in the production of biodiesel, as it corresponds to 80% of the total production cost [11]. According to [PERSON] and [PERSON] (2016) [51], the raw material and the type of catalyst are the most important factors for an efficient production of biodiesel. In this regard, heterogeneous catalysts have been used as an effective substitute for reactions using homogeneous catalysts [52]. As a matter of fact, heterogeneous catalysts such as immobilized enzymes are used as a new method in the purification and production of biodiesel, in the quest to obtain greater conversions, to reuse the catalyst in consecutive cycles and to produce products with high added value and to achieve higher yields, when compared to the processes catalyzed by chemicals catalysts [53; 54]. Thus, the aim of this work is to present a route for industrial production of biodiesel, using as raw material the residual babassu oil, obtained from the extraction of damaged almonds, a waste that is discarded in the environment, adding value to this product; furthermore, it seeks to analyze the influence of temperature, molar ratio (FFAs/alcohol), biocatalyst content (Novozym(r) 435) and time in the production of biodiesel.
# Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments
###### Abstract  
LoRaWAN is a popular internet of things (IoT) solution over the unlicensed radio band. It sustains low-cost, durable, and long range IoT wireless communications. Nonetheless, with over 24 billion connected IoT devices being expected by the end of the year, and over 50 billion by 2025, the concurrent and legacy approaches to spreading factor and channel assignment in LoRaWAN networks can no longer keep up. This is exacerbated with the growing densification of IoT device deployments and, with the increasing requirements for better throughput and packet delivery ratios. In this paper, we propose a proportional fair-based joint optimal formulation for spreading factor and channel assignment in multi-operator LoRaWAN deployments. The objective of this problem is to maximize the total sum of the logarithmic normalized throughput. We split the problem into two subproblems, and propose a game theoretic approach to solving them. We prove that our games converge towards a pure Nash equilibrium and, afterwards, solve the optimization problems using both semi-distributed and completely distributed algorithms. Via simulations, we show that our algorithms greatly improve the total normalized throughput for LoRaWAN as well as the packet success rate, in comparison to the legacy approaches.  
LoRaWAN; game theory; spreading factor; channel selection +
Footnote †: journal: _Sensors_
## 4 Proposed mmWave Assisted CR-IoV
### Performance Measures  
(1) Throughput  
Throughput is defined as the sum of all data rates delivered for all SUs in the network. In simple, throughput measures the successful data delivery over the network at a given time slot. It can be given as,  
\[Throughput=\frac{Data\ Size}{Transmission\ Time} \tag{39}\]  
Throughput is in general computed in terms of bits/second or kilobits/second.  
(2) Delay  
Delay is defined as the time taken by the data to reach a destination from the source. The delay computation includes propagation (\(Dl_{P}\)), queuing (\(Dl_{Q}\)) and transmission time (\(Dl_{T}\)). The delay can be given as,  
\[Delay=[Dl_{P}+Dl_{Q}+Dl_{T}] \tag{40}\]  
(3) Packet Delivery Ratio (PDR)  
PDR is defined as the ratio between the number of packets transmitted (\(\#TP\)) by the source node, and the number of packets received successfully (\(\#RP\)) by the destination. The PDR is defined as,  
\[PDR=\frac{\#TP}{\#RP} \tag{41}\]  
(4) Bit Error Rate (BER)  
BER is defined as the ratio between a number of bit errors (\(\#BE\)) and the total number of bits transmitted (\(\#BT\)). The BER is computed as,  
\[BER=\frac{\#BE}{\#BT} \tag{42}\]  
Figure 7: Connected Self-Driving Cars Scenario.
## 1 Introduction
### Contribution of the Research  
In this paper, 1 \(\times\) 3 elements of the UWB-MIMO antenna along with notches at WiMax, WLAN, and X-band for satellite communication were presented using only two notching elements. The contributions of the paper are as follows:  
1. A third notch for the X-band was successfully created with the help of mutual coupling between already placed notching elements for WiMax and WLAN. To the best of the authors' knowledge, the element's structure is based on the novel shape of the Ottoman Empire's KAYI-shape flag with a common KITE ground plane.
2. The UWB-MIMO antenna has a size of 30 \(\times\) 30 \(\times\) 1.6 mm\({}^{3}\). The radiating elements are placed with a symmetrical phase of 120\({}^{\circ}\) from each other. This tri port symmetry is beneficial in many ways and help to avoid various constraints of the PCB design, whereby the antenna and other electronic components are placed in a small space and compact geometry.
3. The space on PCB is sometimes not inline to creating 1 \(\times\) 4 or 2 \(\times\) 2 MIMO configurations. Therefore, there is a need for a circular configuration to have three MIMO antennas with a symmetric phase of 120\({}^{\circ}\). The symmetric phase difference between the antenna elements produced at least 33% lower mutual coupling in the MIMO system as compared to a 1 \(\times\) 4 MIMO configuration on the same scale.
4. The parasitic strip are loaded with slits having inverted an L-shaped and inverted U-shaped slots, which are used for the notching characteristics of WiMax and WLAN bands, respectively.
5. The simulated and fabricated KAYI-KITE-shaped UWB-MIMO antenna have a small size, good impedance matching, high mutual coupling between the antenna elements, good diversity performance, and triple-notched band characteristics using only two notching elements. The mutual coupling between the notching elements of the antenna are used to take advantage of creating the third notch band.
## 3 AI Techniques and Their Applications
### Deep Learning Techniques
#### 3.2.4 Temporal Modeling with RNNs and LSTMs  
While CNNs have proven highly effective in spatial analysis, certain GPR applications require attention to temporal dependencies, especially in scenarios involving sequential scans or multi-pass surveys. In such cases, recurrent neural networks and their advanced variant, LSTM networks, offer unique advantages by capturing evolving patterns over time. These temporal models have demonstrated strong performance in key GPR interpretation challenges. The primary application is monitoring changes in subsurface conditions--vital for structural health monitoring, detecting environmental shifts, and observing subsurface movements during repeated surveys. LSTMs have also shown effectiveness in time-lapse GPR studies, where consistent feature detection is essential despite variations in noise and scanning parameters. By learning and leveraging temporal correlations, these models are capable of highlighting subtle shifts that static spatial models may miss [51,52].  
A comparison of CNN (classification), U-Net (segmentation), and YOLO (object detection) adapted for GPR is set out in Figure 4. Beyond change detection, temporal models are valuable in addressing the persistent issue of transient noise and motion artifacts in GPR data. These disturbances can obscure true subsurface reflections and degrade interpretation accuracy, particularly in dynamic or mobile survey contexts. RNN-based architectureshelp mitigate this by modeling the expected temporal continuity of genuine subsurface signals, allowing them to distinguish between structural features and non-stationary noise. Incorporating such temporal modeling techniques into AI-driven GPR workflows enhances robustness and interpretability in time-sensitive applications. While not yet mainstream, these approaches hold considerable potential for tasks such as infrastructure degradation monitoring and the early detection of dynamic subsurface events such as sinkhole formation. Their integration paves the way for more advanced spatiotemporal analyses, pushing the boundaries of conventional deep learning in GPR interpretation.
## 5 Experimental Results
### Quantitative Results  
#### 5.3.1 Basic Settings of VarDA System  
Firstly, the numerical model settings follow the protocol detailed in Section 4.1. The synthetic observations are obtained from the 'true' state by adding the Gaussian noise of the zero mean and covariance matrix **R**, where observation errors are supposed to be spatially independent and uncorrelated--that is, the **R**-matrix is proportional to an identity matrix (\(\textbf{R}=\sigma_{\varphi^{a}}^{2}\textbf{I}\)), where the standard deviations of observation errors \(\sigma_{\varphi^{o}}\) are set to 0.5, 1.0, 1.5, 2.0, and 2.5, respectively. The observation interval is the same as the integration time step of the model.
## 2 System Model
### Detection of Covert Communications by [PERSON]  
In this paper, we assumed that [PERSON] knows the transmit power of the UAV, the channel gain of the UAV to [PERSON], and the noise distribution at [PERSON]. In this case, the signal received by [PERSON] can be expressed as a binary hypothesis test  
\[y_{w}[i]=\left\{\begin{array}{ll}\sqrt{P_{u_{1}}}h_{w}x_{1}[i]+n_{w}[i],& \mathcal{H}_{0}\\ \left(\sqrt{P_{u_{1}}}x_{1}[i]+\sqrt{P_{u_{2}}}x_{2}[i]\right)h_{w}+n_{w}[i],& \mathcal{H}_{1}\end{array}\right., \tag{9}\]  
where \(\mathcal{H}_{0}\) is the hypothesis that the UAV is not communicating with the covert user [PERSON] and the hypothesis that the UAV is communicating with [PERSON]. \(n_{w}[i]\) is [PERSON]'s received Gaussian noise with the variance 0. According to [9; 27; 28], the optimal decision criterion for [PERSON]'s total detection error probability is  
\[T_{w}\overset{\Delta}{=}\frac{1}{N}\sum_{i=1}^{N}|y_{w}[i]|^{2}\underset{< \,p_{0}}{\geq}\tau, \tag{10}\]  
where \(T_{w}\) denotes the received power of [PERSON], \(\tau\) is defined as [PERSON]'s detection threshold, and \(D_{0}\) and \(D_{1}\) are the binary decisions supporting \(\mathcal{H}_{0}\) and \(\mathcal{H}_{1}\), respectively.  
Note that the transmit power between the UAV and the user is considered to be fixed within each time slot. However, the transmit power of the UAV is assumed to change in different time slots for the reason of being undetectable.  
Assuming that the channels are infinitely many, i.e., \(N\to 0\), combining Equations (6) and (7), we can derive the following expression:  
\[T_{w}=\left\{\begin{array}{ll}P_{u_{1}}|h_{w}|^{2}+\sigma_{w^{\prime}}^{2},& \mathcal{H}_{0}\\ (P_{u_{1}}+P_{u_{2}})|h_{w}|^{2}+\sigma_{w}^{2},&\mathcal{H}_{1}\end{array}\right., \tag{11}\]  
Assuming that the initial probabilities of \(\mathcal{H}_{0}\) and \(\mathcal{H}_{1}\) are equal, [PERSON]'s detection performance is measured by its DEP, which is defined as  
\[\xi=P_{F}+P_{M}, \tag{12}\]  
where \(P_{F}\overset{\Delta}{=}\Pr\{\mathcal{D}_{1}\mid\mathcal{H}_{0}\}\) is the false alarm probability (FAP) and \(P_{M}\overset{\Delta}{=}\Pr\{\mathcal{D}_{0}\mid\mathcal{H}_{1}\}\) is the missed detection probability (MDP).  
According to Equations (10) and (11), we can derive the total detection error probability \(\xi\), which contains an incomplete gamma function [29]. In this paper, we use the Kullback-Leibler (KL) scatter as a lower bound \(\xi^{*}\) for the minimum detection error rate, expressed as  
\[\xi^{*}\geq 1-\sqrt{\frac{1}{2}\mathcal{D}(P_{F},P_{M})}, \tag{13}\]  
where \(\xi^{*}\geq 1-\varepsilon\) is defined as the covertness constraint and \(\varepsilon\) is the covertness constraint limiting constant. According to Equation (13), it holds that  
\[\mathcal{D}(P_{F},P_{M})\leq 2\varepsilon^{2}. \tag{14}\]  
In the following sections, Equation (14) is used as the covertness constraint.
# Impact of Environmental Factors on Stilbene Biosynthesis
###### Abstract
Due to their potential use in the nutraceutical, cosmeceutical, and pharmaceutical fields, great interest is directed at the methods for large-scale production of stilbenes. For instance, it has been estimated that the global market for _trans_-resveratrol will almost double in the next 6 years, from 58 million USD in 2020 to 99.4 million USD by 2026 [37]. Methods for obtaining stilbenes can be grouped into three categories: direct extraction from plants, chemical synthesis, and the use of biotechnologies. The chemical synthesis of stilbenes has been reported, but this method is not economically feasible, in addition to being difficult in terms of stereospecific synthesis [38; 39]. Considerable efforts have been devoted to the development of biotechnological methods for stilbene production, which broadly include tissue culture techniques [40], biotransformation [41], and metabolic engineering [42]. Nevertheless, the major way of supplying stilbenes is the direct extraction from plants such as _P. cuspidatum_ and _V. vinifera_[43]. The stilbene content and profile in stilbene-producing plants vary strongly in response to a variety of environmental factors. In recent years, a considerable body of knowledge regarding the stilbene biosynthetic pathway and the impact of environmental conditions on the production of these valuable metabolites has accumulated. This review presents the recent knowledge of the stilbene biosynthetic pathway and the impact of different environmental factors on stilbene production.
## 4 EMS Optimization
### Load  
Load (Figure 8) depicts the load variation over a 24 h period, with a time scale ranging from 0 to 25 h. The power scale spans from 34.8 to 35.5 kW. The load starts at 34.9 kW, gradually rising to 35.5 kW by the 12 th h. After reaching this peak, the load decreases slowly to 35.1 kW by the 17 th h. There is a slight increase to 35.22 kW by the 20 th h, and the load finally stabilizes at 35 kW by the 24 th h.  
Figure 8 shows the power output associated with the load (in kW) over a 24 h period, highlighting the dynamic management by the energy management system (EMS). The horizontal axis represents time from 0 to 24 h, while the vertical axis denotes power output, ranging from 35 kW to 35.4 kW.  
* Daytime Period (6:00 AM-6:00 PM, 6 to 18 h): During the daytime, the load power output gradually increases, reaching a peak around midday (approximately 10 h). This increase reflects higher energy demand during the day, as the EMS manages to supply power to meet this demand. After the peak, the load begins to decrease, showing the response to the fluctuating energy requirements as the day progresses.
* Nighttime Period (6:00 PM-6:00 AM, 18 to 6 h): At night, the load power output exhibits smaller fluctuations compared to the daytime, reflecting a generally lower and more stable energy demand. The EMS's role during this period is to maintain a  
Figure 8: Load.  
steady supply of power, ensuring that energy needs are met efficiently during periods of lower demand, with minimal variation in power output.  
Figure 8 emphasizes the EMS's critical role in managing load power to ensure the stability of the system, with distinct patterns of power fluctuation corresponding to day and night cycles. Recognizing these patterns is essential for optimizing load management and maintaining efficient energy operation throughout the day.  
This figure provides an overview of the load process, highlighting how the system adjusts power load throughout the day. The load management strategy is designed to balance demand and supply efficiently, ensuring optimal grid operation. Understanding these variations aids in optimizing load distribution, reducing peak demand stress, and maintaining grid stability.
# A Novel Method for Ocean Wind Speed Detection Based on Energy Distribution of Beidou Reflections
## 4 Results, Analysis and Discussion  
To investigate the performance of the proposed method, we processed the reflected signals from the Beidou GEO4 satellite from August 3 to August 5 and from August 13 to August 14 in 2013. In the second period, the Typhoon UTOR approached the coast of Yangjiang of Guangdong province.
## 3 The Gut-Brain Axis (GBA) and Gut Microbiome
### Enteric Nervous System  
A crucial constituent of the ANS is the ENS situated in the GI tract. It is made of 200 to 600 million neurons. The ENS regulates gut activity, secretion and absorption, immune defense, and motor activity and preserves gut equilibrium and communication with the gut microbiota [5]. The ENS interacts with the CNS via the SNS (prevertebral ganglia) and the PNS (the vagus nerve) [14; 15]. The gut microbiota controls the functions and development of the ENS by stimulating toll-like receptors (TLRs), which can identify microbial peptidoglycan, LPS, and viral RNA. Furthermore, gut microbiota controls the ENS by producing metabolites and neurotransmitters such as SCFAs, catecholamines, GABA, and acetylcholine [3; 15]. On the other hand, the ENS exhibits an influence on the gut microbiota [15].