esa-sceva/satcom-chunk-collection huggingface dataset:
# Marine Bioactives and Their Application in the Food Industry: A Review
## 2 Marine Sources of Bioactive Molecules
### Marine Invertebrates
#### 2.3.1 Sponges  
Sponges, belonging to the phylum Porifera, are the oldest and the most primitive of the metazoans, with a global distribution in all aquatic habitats. They are more abundant (area coverage/biomass/volume) in benthic communities compared to other benthic marine organisms. Sponges are the simplest form of multicellular animals with gelatinous material, mesophyll, and a hollow pitcher-like body that consists of spicules and spongia, as well as often some species associated with dense and complex microbiota [4]. They are ecologically important due to their active involvement in marine nutrient cycling and, in the meantime, play a pioneering role in the discovery of bioactive substances from marine organisms. The very first bioactive compounds from marine sources were the serendipitous isolation of C-nucleosides, spongouridine, and spongothymidine from the Caribbean sponge _Cryptothecan crypt_ in the early 1950s. Since then, numerous chemically diverse biotechnology relevant natural products such as peptides, sterols, alkaloids, terpenoids, polyketides, macrolides, and polyphenolic compounds have been isolated from these sponges [91; 94] (Table 4).
## 2 Methods
### Electron Dynamics Simulation for Periodic Systems  
First, we briefly describe the first-principles electron dynamics simulation based on TDDFT [30]. Details of the simulation are described elsewhere [32]. In the framework of TDDFT, electron dynamics in a periodic system is described by the following time-dependent Kohn-Sham equation,  
\[i\hbar\frac{\partial}{\partial t}u_{b\vec{k}}(\vec{r},t) = \left[\frac{1}{2m_{e}}\left\{\vec{p}+\hbar\vec{k}+\frac{e}{c} \vec{A}(t)\right\}^{2}+\vartheta_{ion}+v_{H}(\vec{r},t)+v_{XC}(\vec{r},t) \right]u_{b\vec{k}}(\vec{r},t) \tag{1}\] \[= H_{KS\vec{k}}(t)u_{b\vec{k}}(\vec{r},t),\]  
where \(u_{b\vec{k}}(\vec{r},t)\) is the Kohn-Sham Bloch wavefunction, \(b\) is a band index, and \(\vec{k}\) is the Bloch wave vector. The electron-ion interaction is described by the ionic potential \(\vartheta_{ion}\), while the electron-electron interaction is described by the combination of the Hartree potential \(v_{H}(\vec{r},t)\) and the exchange-correlation potential \(v_{XC}(\vec{r},t)\). The Hartree potential satisfies the Poisson equation, \(\
abla^{2}v_{H}(\vec{r},t)=4e^{2}\pi\rho(\vec{r},t)\), where the electron density \(\rho(\vec{r},t)\) is evaluated as  
\[\rho(\vec{r},t)=\sum_{b\vec{k}}n_{b\vec{k}}\left|u_{b\vec{k}}(\vec{r},t)\right| ^{2}, \tag{2}\]  
with occupation factors, \(n_{b\vec{k}}\).  
The exchange-correlation potential \(v_{XC}(\vec{r},t)\) is a functional of the electron density. Because its exact form is unknown, one has to approximate it. In this work, we employ the adiabatic local-density approximation [33] (ALDA), where the exchange-correlation potential depends on the electron density locally in space and time. External laser fields are described by spatially-uniform time-varying vector potentials \(\vec{A}(t)\) in the dipole approximation.  
To describe monolayer \(h\)-BN, we employ three-dimensional periodic boundary conditions with a hexagonal lattice. The real-space atomic configuration in the plane is shown in Figure 1a. In this work, the \(y\)-axis is taken to be parallel to a B-N bond, while the \(x\)-axis is taken to be perpendicular to it. The lattice constant of the hexagonal lattice is set to \(a=4.76\) a.u. The out-of-plane direction is assigned to the \(z\)-axis, and the period of that direction is set to \(L=50\) a.u., which is large enough to avoid spurious inter-layer interactions.  
We discretize the hexagonal lattice into three-dimensional real-space grid points with a grid spacing about 0.36 a.u. We further discretize the two-dimensional first Brillouin zone of \(h\)-BN into \(32\times 32\)\(k\)-points.  
In this work, dynamics of core electrons in the \(1s\) shell of Boron and Nitrogen atoms are not expected to be significant for transient absorption. Therefore, we treat explicitly only valence electrons while the core electrons are frozen. For this purpose, we employ the Hartwigsen-Goedecker-Hutter pseudopotentials [34] to describe interactions between valence electrons and ionic cores.  
All the density functional theory (DFT) and TDDFT calculations in this work are carried out with the _Octopus_ code [35].
## 6 Performance Analysis
### Computational Complexity Analysis
#### 6.1.3 Computational Complexity in Roaming Authentication  
In the process of roaming authentication, we use symmetric encryption to ensure the efficiency of the scheme. The AES CBC algorithm was tested using OpenSSL library in the same environment. We tested the symmetric encryption and decryption algorithms 10,000 times and took their average values: the encryption algorithm costs 0.000112 ms, and the decryption algorithm costs 0.000110 ms. Assuming that the times required for symmetric encryption of the AES algorithm are \(T_{senc}\) and \(T_{sdec}\), and compared with the overheads in Table 2, they are negligible. In order to be consistent with the anonymous authentication phase, roaming authentication uses the same session key generation method. The proposed roaming authentication protocol involves \(2T_{Exp}\) operations to negotiate the session key. It was assumed that the time required for the encryption algorithm in asymmetric encryption is \(T_{aenc}\) and the time required for the decryption algorithm is \(T_{adec}\). After 10,000 calculations using the OpenSSL library, the average value was obtained: \(T_{aenc}=0.015079\) ms, \(T_{adec}=0.028344\) ms. Then the computational cost required for the UE to send a message to LEO was \(T_{Exp}=0.148430\) ms. The computational cost for the LEO to send a message to the UE was \(T_{aenc}+T_{Exp}=0.176774\) ms. It was shown that the total roaming authentication calculation cost is far less than the cost of re-authentication.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 3 Results
### Data Inputs  
Researchers considered various data sources to inform estimations of demand flexibility. The most common data sources include weather and climate data, technology characteristics, demand data, and building data, with demand data being the most prominent.
## 3 Hybrid Boundary-Type Meshless Method for Modeling Heat Conduction Problems
At the interface, the boundary collocation points on the left and right sides coincide with each other. The basic concept of the DDM for modeling composite materials is that the subdomains intersecting at the interface must satisfy flux conservation and continuity of the temperature for the problem, so that flux and temperature at the interface between two consecutive layers remain the same. For instance, we consider a rectangular domain \(\Omega\), which can be divided into two smaller subdomains, \(\Omega_{1}\) and \(\Omega_{2}\) as shown in Figure 1. In order to simulate the heat conduction problem, the rectangular domain is divided into \(\Gamma_{1}\), \(\Gamma_{2}\),..., \(\Gamma_{8}\) sub-boundaries. At \(\Omega_{1}\) subdomains, the sub-boundaries include \(\Gamma_{1}\), \(\Gamma_{2}\), \(\Gamma_{3}\) and \(\Gamma_{4}\); At \(\Omega_{2}\) subdomains, the sub-boundaries include \(\Gamma_{5}\), \(\Gamma_{6}\), \(\Gamma_{7}\) and \(\Gamma_{8}\). As noted above, the sub-boundary at the interface should satisfy the flux conservation and continuity of the temperature between two consecutive materials. The additional boundary conditions at the interface can be expressed as  
\[u\big{|}_{\Gamma_{2}}=\left.u\right|_{\Gamma_{6}},\ \ \frac{\partial u}{ \partial n}\bigg{|}_{\Gamma_{2}}=\left.\frac{\partial u}{\partial n}\right| _{\Gamma_{6}} \tag{21}\]  
Matching the Dirichlet and Neumann boundary conditions on boundary collocation points, we may obtain the following simultaneous linear equations  
\[\mathbf{A}_{\mathrm{D}}\mathbf{\alpha}_{\mathrm{D}}=\mathbf{b}_{\mathrm{D}} \tag{22}\]  
\[\mathbf{A}_{\mathrm{D}}=\left[ \begin{array}{cc}\mathbf{A}_{\Omega_{1}}& \mathbf{0}_{\Omega_{2}}\\ \mathbf{A}_{\mathrm{I}}\big{|}_{\Gamma_{2}}&\mathbf{A}_{\mathrm{I}}\big{|}_{ \Gamma_{6}}\\ \mathbf{0}_{\Omega_{1}}&\mathbf{A}_{\Omega_{2}}\end{array} \right],\ \mathbf{\alpha}_{\mathrm{D}}=\left[ \begin{array}{c}\mathbf{\alpha}_{\Omega_{ 1}}\\ \mathbf{\alpha}_{\Omega_{2}}\end{array} \right],\ \mathbf{b}_{\mathrm{D}}=\left[ \begin{array}{c}\mathbf{b}_{\Omega_{1}}\\ \mathbf{b}_{\mathrm{I}}\\ \mathbf{b}_{\Omega_{2}}\end{array} \right] \tag{23}\]  
where \(\mathbf{A}_{\Omega_{1}}\) with the size of \(l_{1}\times P_{1}\) and \(\mathbf{A}_{\Omega_{2}}\) with the size of \(l_{2}\times P_{2}\) are the \(\mathbf{A}\) matrix shown in Equation (18) for \(\Omega_{1}\) and \(\Omega_{2}\), respectively. \(l_{1}\) and \(l_{2
## 2 Methods
### Model Selection Criteria  
To determine the accuracy of the models and to select the appropriate model, the four statistical metrics of the root-mean-squared error (\(RMSE\)), \(R\) squared (\(R^{2}\)), adjusted \(R^{2}\) (\(R^{2}_{ndj}\)), and Akaike's information criterion (\(AIC\)) were calculated [27; 28].  
Firstly, the \(RMSE\) is one of the standard measures used to assess the goodness of fit of a model. It is the average squared difference between the observed values and the predicted values, and it is defined by  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}\Big{(}y_{i}-\widehat{y}_{i}\Big{)}^{2} }{n-p}} \tag{6}\]  
Figure 1: The three compartments of the susceptible–infectious–recovered (SIR) model.  
where \(y_{i}\) denotes the observed values, \(\hat{y}_{i}\) indicates the predicted values, \(n\) is the number of observations, and \(p\) is the number of parameters in the model.  
Additionally, \(R^{2}\) statistics measure the predictive accuracy of a fitted model. It represents the amount of the variation that is accounted for by the fitted model and is defined by  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}\left(y_{i}-\widehat{y}_{i}\right)^{2}}{ \sum\limits_{i=1}^{n}\left(y_{i}-\overline{y}_{i}\right)^{2}} \tag{7}\]  
where \(y_{i}\), \(\hat{y}_{i}\), and \(\overline{y}\) denote the observed values, the predicted values, and the mean of the observed values, respectively.  
Furthermore, the adjusted \(R^{2}_{adj}\) adds a small penalty for adding more variables in the model compared to the \(R^{2}\) with variability in the data [29] and is defined by  
\[R^{2}_{adj}=1-\left(1-R^{2}\right)\left(\frac{n-1}{n-p}\right) \tag{8}\]  
Lastly, the \(AIC\) is one of the most common tools used to measure the goodness of fit of a model and is calculated by  
\[AIC=n\log\frac{\sum\limits_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{n}+2p \tag{9}\]
# RF Energy Harvesting Wireless Communications:
## 2 Urban and Semi-Urban RF Environment  
The ambient RF energy is a perpetual resource to RF energy harvesting devices. In this section, we investigate the features of ambient RF energy and introduce the urban and semi-urban RF environments.
# Heart Failure and Diabetes Mellitus: Biomarkers in Risk Stratification and Prognostication
## 3 Biomarkers in Diabetics with Known HF
### Biomarkers of Inflammation and Fibrosis
#### 3.2.1 Growth Differential Factor-15  
Growth differential factor-15 (GDF-15) is a stress-induced multifactorial cytokine, which is markedly expressed in cardiac myocytes, endothelial cells, vascular smooth muscle cells, adipocytes, macrophages, and in normal and pathological conditions including HFpEF/HFrEF and T2 DM [89]. The main biological function of GDF-15 is to improve glucose metabolism, attenuate energy homoeostasis and weight loss, as well as to protect various tissues, such as the myocardium, the vasculature, and the adipose tissue against injury by the inhibition of JNK, Bcl-2-associated death promoter (Bad), and epidermal growth factor receptor (EGFR) and activating Smad/eNOS, PI3K/AKT signaling pathways [89].  
The XENDOS (XENical in the prevention of Diabetes in Obese subjects) trial has shown that the levels of GDF-15 are related to body weight, BMI, waist-to-hip ratio, and insulin resistance in non-T2 DM obese individuals [90]. The elevation of circulating levels of GDF-15 strongly predicted the development of T2 DM and HF and was associated with an adverse prognosis for both diseases [91]. Using a database derived from 3792 participants from the ARIC (Atherosclerosis Risk in Communities) Study, [PERSON] et al. (2021) [92] concluded that higher GDF-15 levels (highest vs. lowest quartile) were positively associated with T2 DM (adjusted odds ratio (aOR) = 2.48, 95% CI = 1.89, 3.26), atherosclerotic CVD (aOR = 1.57, 95% CI = 1.16, 2.11), increased hs-cTnT (aOR = 2.27, 95% CI = 1.54, 3.34), increased NT-proBNP (aOR = 1.98, 95% CI = 1.46, 2.70), and HF (aOR = 3.22, 95% CI: 2.13, 4.85). Importantly, GDF-15 added prognostic information to NYHA functional class, LVEF, and serum levels of NT-proBNP in patients with HF, regardless of age, body mass index, HF etiology, concomitant medical therapy, and renal function [93]. Although elevated serum levels of GDF-15 are closely related to mortality rate and the risk of HF occurrence, the exact mechanism of the interrelationship remains uncertain [94]. The prognostic utility of GDF-15 requires further evaluation in large clinical trials in a head-to-head comparison with traditional biomarkers.
# Design and Optimization of Lattice Structures: A Review
###### Abstract  
Cellular structures consist of foams, honeycombs, and lattices. Lattices have many outstanding properties over foams and honeycombs, such as lightweight, high strength, absorbing energy, and reducing vibration, which has been extensively studied and concerned. Because of excellent properties, lattice structures have been widely used in aviation, bio-engineering, automation, and other industrial fields. In particular, the application of additive manufacturing (AM) technology used for fabricating lattice structures has pushed the development of designing lattice structures to a new stage and made a breakthrough progress. By searching a large number of research literature, the primary work of this paper reviews the lattice structures. First, based on the introductions about lattices of literature, the definition and classification of lattice structures are concluded. Lattice structures are divided into two general categories in this paper: uniform and non-uniform. Second, the performance and application of lattice structures are introduced in detail. In addition, the fabricating methods of lattice structures, i.e., traditional processing and additive manufacturing, are evaluated. Third, for uniform lattice structures, the main concern during design is to develop highly functional unit cells, which in this paper is summarized as three different methods, i.e., geometric unit cell based, mathematical algorithm generated, and topology optimization. Forth, non-uniform lattice structures are reviewed from two aspects of gradient and topology optimization. These methods include Voronoi-tessellation, size gradient method (SGM), size matching and scaling (SMS), and homogenization, optimization, and construction (HOC). Finally, the future development of lattice structures is prospected from different aspects.  
unit cell; uniform lattice structures; non-uniform lattice structures; topology optimization; additive manufacturing +
Footnote †: journal: Applied Sciences
# Meniscal Tear Management Associated with ACL Reconstruction
## 4 Biological Augmentation of Meniscus Healing  
Augmenting the healing process after the meniscal suture has been of great interest in recent years, with a lot of research being carried out [1, 109, 110].  
It has been shown that the meniscal repair concomitant with ACL reconstruction positively affects meniscus healing [1, 28, 111, 112]. This may be explained by biological factors (platelet-derived growth factor, insulin-like growth factor and vascular endothelial growth factor) released while drilling the bone tunnels [62, 113, 114].  
The use of platelet-rich plasma (PRP) [115, 116], fibrin clots, marrow stimulation, and mesenchymal stem cells (MSC's) have shown preclinical success. However, the clinical efficacy and safety of biologic augmentation for arthroscopic meniscal repair, although promising, remain controversial [117, 109, 118].  
The current level I evidence does not support PRP augmentation when performing arthroscopic meniscal repair [110, 117, 119]. Furthermore, it seems that PRP does not reduce the risk of meniscal repair failure in the setting of concomitant ACL reconstruction [120], suggesting that bleeding after bone tunnel drilling provides sufficient growth factors [121] and that additional administration of PRP is unnecessary [1].  
Although several fibrin clot augmentation techniques were described in the last decade [122, 123], they do not have sufficient high-quality evidence to support their routine use at this time [124, 125] and therefore further confirmatory studies are needed to support this procedure.  
Mesenchymal stem cells (MSCs) are used to enhance the ability of healing the meniscus [110] since they have a high proliferative [126] and chondrogenic potential [127]. Therefore, they are attractive for meniscal repair [128]. There is no evidence that they can form meniscal similar to primitive human meniscus in the human body [129]. Therefore, more in-depth research is needed.  
There are no studies to back up the use of fibrin glue to enhance meniscus healing. Applying it locally may be considered but this is not currently recommended (especially if concomitant ACL reconstruction is performed) [1].  
Although patient reports after repair with biological augmentation show some improvements in functional outcomes scores, the benefit over standard repair remains questionable [109].
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 2 PULSAR Unmanned Aerial System (UAS) Overview
### PULSAR Integrated System High-Level Architecture  
The proposed implementation introduces a fully customized UAV designed to accommodate a range of hardware components tailored for advanced smart algorithms, incorporating sensors and cameras for comprehensive data acquisition. The UAV stands out in on-board image processing through the application of artificial intelligence (AI) techniques, particularly deep neural networks (DNNs) and convolutional neural networks (CNNs). These neural networks enable sophisticated image and video processing capabilities, facilitating tasks such as object detection, tracking, and monitoring. By leveraging DNNs and CNNs, the UAV is capable of analysing captured imagery, allowing rapid identification of objects of interest and enabling quick decision making. This advanced on-board capability not only enhances the UAV's performance in various applications but also improves the overall operational efficiency. Furthermore, the geolocation is ensured  
Figure 1: PULSAR UAV Conceptual design: (**a**) octocopter configuration. (**b**) quadcopter co-axial configuration. (**c**) quadcopter configuration.  
through a global navigation satellite system (GNSS), providing precise spatial awareness. Detailed information about GNSS can be found in Section 4.5.  
The computational heavy lifting is efficiently handled by the on-board NVIDIA JET-SON AGX Xavier supercomputer (NVIDIA, Santa Clara, CA, USA), known for its enhanced AI performance within a compact form factor, making it ideal for mobile robotic applications. This feature makes it exceptionally well suited for mobile robotic applications, ensuring powerful processing capabilities for real-time decision making and autonomous navigation. The graphical processing unit (GPU) plays a pivotal role in processing the output from the various sensors, enabling the UAV to derive valuable insights and make informed real-time decisions.  
The piloiting module takes charge of the general controller, mission preparations, stability monitoring, mission adaptation, and tool actuators, ensuring a comprehensive approach to UAV control and mission execution. Simultaneously, the mission monitoring module is responsible for system status, mission status, and reporting, including anomaly reports. This dual-module approach enhances the UAV's overall functionality, providing a robust framework for both piloiting and mission monitoring. Together, these components create a high-level architecture of the UAV system capable of executing complex tasks with precision and adaptability (Figure 2).
## 3 Results
### Ionospheric Investigation
The four scalar magnetic field anomalous tracks of _Swarm_ Bravo 80 days before the eruption (i.e., 1 July 2021) are due to an issue on the mission pointed out by ESA in the operational mission news ([[https://earth.esa.int/eogateway/news/swarm-bravo-scalar-magnetic-field-data-unavailable-from-1-to-4-july-2021](https://earth.esa.int/eogateway/news/swarm-bravo-scalar-magnetic-field-data-unavailable-from-1-to-4-july-2021)]([https://earth.esa.int/eogateway/news/swarm-bravo-scalar-magnetic-field-data-unavailable-from-1-to-4-july-2021](https://earth.esa.int/eogateway/news/swarm-bravo-scalar-magnetic-field-data-unavailable-from-1-to-4-july-2021)), accessed on 26 July 2022) of the mission. Therefore, they need to be disregarded for this study as they are not due to a geophysical phenomenon but due to post-processing of the satellite data. Figure 24: (**A**) Normalized _TEC_ time series in the year of “La Palma” volcano eruption (2021) compared with mean and standard deviation computed on historical values from 2000 to 2020. The geomagnetically disturbed time has been marked with transparent red bars and has been defined on Dst and ap, whose trend is shown in the lower panel (**B**). Figure 25: The cumulative number of _Swarm_ magnetic anomalies was extracted with a threshold of k\({}_{t}\) = 2.5 within a radius of 380 km from the volcano. Subpanels (**A–C**) represent the analysis of X-North, Y-East, and Z-Centre (vertical) components of the geomagnetic field. The analysis of the total intensity of the geomagnetic field is shown in (**D**), even if only five tracks are extracted as anomalous.
# Optimal Harmonic Mitigation in Distribution Systems with Inverter Based Distributed Generation
## 2 Problem Formulation
### Constraints  
This optimization problem constitutes the following constraints:  
1. Voltage limits: \[V_{\min}\leq V_{i}\leq\ V_{\max}\ \,\] (7) where, \[|V_{i}|=\sqrt{\left|V_{i}^{(1)}\right|^{2}+\sum_{h=3}^{h_{\max}}\left|V_{i}^{ (h)}\right|^{2}}\] (8)
2. Power balance constraints: \[\sum_{i=1}^{NG}PG_{i}-P_{L}=P_{d}\sum_{i=1}^{NG}QG_{i}-Q_{L}=Q_{d}\] (9)  
\(P_{L}\) and \(Q_{L}\) are active and reactive power losses, respectively. \(P_{d}\) and \(Q_{d}\) are load demand active and reactive powers. \(PG_{i}\) and \(QG_{i}\) are active and reactive power at \(i\)th DG unit, respectively.
3. Generation operating limits: \[PG_{i}^{\min}\leq PG_{i}\leq PG_{i}^{\max},\,QG_{i}^{\min}\leq QG_{i}\leq QG_{i} ^{\max}\] (10)
4. Harmonic distortion limits: \[THD_{i}(x)\leq THD_{\max}\] (11) \[IHD_{i,h}(x)\leq IHD_{\max}\] (12)  
where \(THD_{\max}\) and \(IHD_{\max}\) are the maximum allowable total harmonic distortion and individual harmonic distortion at each bus, which have values equal to (5% and 3%), respectively, according to the IEEE-519's standard [3]. Note that the THD as low as possible can be considered as a preventive action to protect the system from any future increase in harmonic sources. If we design the filters to only meet the standard, it needs to redesign the filters with each increase in the harmonic emissions. Added to that, we considered two conflicting objectives (Minimizing the THD and the filter investment cost) to guarantee the optimal economic solution.