esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Determinant Factors of Obesity
### Obesity as a Symptom
#### 6.2.2 Monogenic Causes  
The discovery of several diseases that involve a single gene has aided in better understanding both the normal function of weight regulation, as well as some of the genetic factors involved in weight excess. Most of the mutations affect elements within the leptin/melanocortin-hypothalamic axis involved in appetite regulation. Mutations in the genes that code for leptin or the leptin receptor are pertinent examples [190].  
Congenital leptin deficiency is a recessive autosomal disease caused by a series of possible mutations of its coding gene. It is characterized by the presence of severe obesity, hyperplasia, and a series of metabolic, neuroendocrine, and immune dysfunctions. It typically responds well to the parenteral supplementation of leptin, with significant weight reduction after treatment and a decrease in voluntary food ingestion. Mutations involving the hypothalamic receptor for leptin have a similar clinical presentation but do not respond to treatment involving leptin [191].  
Alteration of POMC production (including mutations of the enzymes responsible for the cleavage of POMC and production of aMSH) or loss of function of MC4R receptors fit into this category as well. Due to the pigmentation effects of MSH, patients with POMC deficit or abnormal MC4R function will frequently associate obesity with red hair and pale complexion. Some studies have shown a prevalence of up to 5% of these types of mutations in morbidly obese children [192; 193; 194].  
Another example concerns the activity of PPAR-gamma, a transcription factor involved in adipocyte differentiation. Patients with mutations affecting the receptor for PPAR-gamma will invariably be severely obese [195].
# Heterologous Production of Acrylic Acid: Current Challenges and Perspectives
## 1 Introduction
However, although heterologous production of 3-HP followed by chemical conversion to AA is a better option than using propene, it is still not ideal as it requires a chemical catalyst, high amounts of energy and high production costs to purify 3-HP [32]. Hence, the design of a biosynthetic pathway for AA production from renewable resources is of utmost importance. Since 2015, several pathways for AA production from simple carbon sources have been developed [27; 28; 29; 30; 31; 32; 33; 34]. Some of the pathways produce 3-HP as an intermediary or do a bypass having several steps in common (Figure 2). Nevertheless, as far as it is known, industry has not implemented a biosynthetic pathway to produce AA from carbon sources, since the concentrations obtained until now are low (up to 237 mg/L of AA from glucose in _Escherichia coli_[34]). Figure 1: Acrylic acid and its derivatives’ chemical structures. State-of-the-art synthetic biology will be of upmost importance to overcome barriers in AA biosynthesis. In this paper, the advances in AA heterologous production are reviewed for the first time. The different biosynthetic routes, key enzymes and hosts' potential are explored. Although the main focus of this paper is the heterologous production of AA, 3-HP biosynthesis is also described as this compound is often produced as an intermediary in AA biosynthesis, and even when it is not produced, the pathways share several steps. Additionally, the challenges that need to be addressed so that AA heterologous production can become a viable alternative to its production from 3-HP chemical conversion are highlighted.
## 2 Computational Simulation for Oblique Incidence of SV Waves
### Numerical Verification  
An FE model of 400 \(\times\) 400 \(\times\) 400 m\({}^{3}\) was established to verify the precision of the proposed oblique incidence simulation technology. The solid cube element with a side length of 8 m which satisfies the FE precision was used [35]. Meanwhile, the timestep length of the FE equation is 0.001 s and the cutoff frequency is 15 Hz. The mass density of the half-space medium is 2000 kg/m\({}^{3}\), the Poisson's ratio is 0.3, and the elastic modulus is 1 GPa. Figure 4 shows the waveform of the incident SV wave with a displacement amplitude of 1 m. The displacement equation is expressed in Equation (11).  
\[u(t)=\frac{1}{2}[1-\cos(8\pi t)], \tag{11}\]  
where \(0\leq t\leq 0.25\) s.  
The model sets the SV wave as obliquely incident into the cube area at 30\({}^{\circ}\), and the SV wave front passes through the \(y\)-axis at time zero. Figure 5 shows the cloud map of the displacement field in the horizontal direction of the cube. After 0.46 s of propagation, the wave front of incident SV wave can be well displayed in the cloud image, and the angle between the wave front and the horizontal plane is 30\({}^{\circ}\); when \(t=1.13\) s, the SV wave propagates to the ground surface. At this time, three wave fronts appear in the cloud image. In addition to the wave front of incident SV wave, there are also wave fronts of the reflected SV wave and reflected P wave, in which included angles with the horizontal plane are 30\({}^{\circ}\) and 69.3\({}^{\circ}\), respectively; When \(t=1.45\) s, only the wave fronts of the reflected SV wave and reflected P wave are left in the cloud image, indicating that the action area of the incident SV wave has exceeded the truncated cube area.  
Figure 4: Plane SV wave pulse of oblique incidence: (**a**) displacement; (**b**) velocity.  
Figure 6 is a horizontal displacement time history tie course of the center point of the surface corresponding to the incident angle. It can be seen that the analytical solution is in good agreement with the numerical solution. The traveling wave effect and waveform superposition are well shown in the diagram. In summary, the numerical simulation for oblique incidence of SV wave has good accuracy [36,37].
# Measuring the Lense-Thirring Orbital Precession and the Neutron Star Moment of Inertia with Pulsars
## 5 Psr J0514\(-\)4002E, a Massive Binary with a Companion in the Mass Gap  
A recent study featured an interesting binary pulsar system discovered by the [PERSON] telescope in the globular cluster NGC 1851, PSR J0514\(-\)4002E. The pulsar is in a highly eccentric orbit (\(e=0.71\)) with a massive compact companion. Based on the measurement of \(\dot{\omega}\), the total mass of this system is measured to be \(3.8870\pm 0.0045\,\rm M_{\odot}\), which is heavier  
Figure 9: Kinematic contributions of Galactic acceleration (blue) and the Shklovskii effect (orange) on the observed orbital period derivative \(\dot{P}_{\rm b}\) with respect to the distance of PSR J1946\(+\)2052. The total contribution is plotted in green. The solid curves correspond to a moderate proper motion of \(\mu_{a}=\mu_{\delta}=3\) mas yr\({}^{-1}\), whereas the dashed curves correspond to a proper motion of \(\mu_{a}=\mu_{\delta}=6\) mas yr\({}^{-1}\). Vertical dashed lines indicate distances of 3.5 kpc and 4.2 kpc derived from the electron-density models NE2001 and YMW16, respectively.  
than any known DNS systems known in the Galaxy [104] and in LIGO events, such as GW190425 [105]. The mass of the companion is between 2.09 and 2.71 M\({}_{\odot}\) (95% confidence) [86], which is larger than any known NS, while being well below the minimum mass observed for BHs in X-ray binaries (\(\sim\)5 M\({}_{\odot}\)). The nature of the companion can be a very massive NS or a low-mass BH [86], or possibly a primordial BH formed during the first-order phase transition in the early universe [106].  
If the companion is a massive NS, its mass measurement from future timing can possibly help us identify the EoS of NS or challenge the current models and provide new insights. If the companion is a light BH, its unusually small mass compared to Galactic stellar-mass BHs indicates it could have been formed from the merger of a DNS, such as the case of the GW170817 merger event [3]. During such a merger, the BH is likely to acquire a spin angular momentum of \(0.6<\chi_{\rm c}<0.875\), which would induce LT precession in \(\dot{x}\lesssim 1.7\times 10^{-13}\)[86]. In addition, a pulsar with a stellar-mass BH (SBH) companion will allow new tests of gravity theories, such as [PERSON]'s cosmic censorship hypothesis and no-hair theorem. It has been shown that, with new-generation radio telescopes such as FAST and SKA, a few years of observations on sufficiently compact pulsar-SBH systems will allow precision measurements of the BH mass and spin (1% precision) [107], as well as significantly improve the constraint on scalar-tensor theories such as Damour-Esposito-Farese gravity [108; 109].
# Transformative Technology for FLASH Radiation Therapy
## 7 High-Current Electron Linear Accelerator for X-ray FLASH-RT Developed by RadiaBeam and UCLA
### X-ray FLASH-RT with the PHASER  
The inherent inefficiency of producing therapeutic X-rays through bremsstrahlung radiation from an electron beam hitting a target contributes to the challenge of achieving FLASH dose rates with conventional photon radiotherapy. Another critical factor constraining the treatment time in a conventional radiotherapy device is the mechanical motion of the gantry and multi-leaf collimator. The PHASER program for pluridirectional high-energy agile scanning electronic radiotherapy, led by the SLAC National Accelerator Laboratory and Stanford Medical School, seeks to eliminate gantry and collimator motion while achieving the FLASH dose rate through a system of 16 linacs arrayed around the patient, as shown in Figure 15, and electronic scanning of the bremsstrahlung source coupled with a beamlet-based collimator array [141]. This design enables multiple angles of approach, as needed for intensity-modulated conformal radiation therapy. The PHASER program is intended to increase the therapeutic index of radiotherapy through highly conformal image-guided FLASH treatments and improve the accessibility of state-of-the-art FLASH-capable medical equipment through the implementation of a compact and economical design.  
PHASER seeks to achieve FLASH capability by producing a \(>\)400-fold increase in the average beam current compared to conventional 10 MV photon therapy systems with a typical dose rate of 10 Gy/min. The linac structure will rely on a distributed coupling topology to improve the power efficiency [142], allowing the system to take advantage of a network of 16 compact \"klystrinos\", each producing a peak power of around 330 kW. The R&D from the initial PHASER program has laid the groundwork for extending the design  
Figure 15: Conceptual diagram of the PHASER architecture with 16 linacs arrayed around the patient for highly conformal FLASH photon therapy. Reproduced with permission from Ref. [141].  
concept to a very-high-energy electron (VHEE) therapy system, treating directly with \(\geq\)100 MeV electrons without X-ray conversion [143], as well as eventually with protons.
## 2 Wind Power, Photovoltaic Power and Load Power Forecasting Model
### Stacking Ensemble Learning Model
#### 2.2.6 LR and RR  
LR is a statistical analysis method to determine the quantitative relationship between two or more variables. The general form of the linear regression equation [32] is  
\[y=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\cdots+\beta_{k}x_{k}+\varepsilon \tag{13}\]  
Write (13) in terms of matrices as follows:  
\[y=W^{T}X+\varepsilon \tag{14}\]where \(W=[\beta_{0},\beta_{1},\ldots,\beta_{k}]^{T}\) is the regression coefficient matrix; \(X=[1,x_{1},x_{2},\ldots,x_{k}]^{T}\) is the independent variable matrix; \(y\) represents the dependent variable; and \(\varepsilon\) is white noise. The least square method is used to obtain the solution of \(W\) as follows:  
\[W=(X^{T}X)^{-1}X^{T}y \tag{15}\]  
Linear regression modeling is simple and has strong interpretability but is only suitable for linear problems. It is sensitive to outliers and multicollinearity and has limited fitting and predicting performance. Therefore, LR is more suitable as a meta-learner to integrate the information learned by base learners, rather than as a base learner to extract the features of original data.  
RR is an improved method of least squares estimation. It obtains regression coefficients that are more realistic and reliable at the cost of losing some information and reducing accuracy. Specifically, the ridge regression adds a matrix \(\lambda I\) in (15), so the result of ridge estimation is  
\[W=(X^{T}X+\lambda I)^{-1}X^{T}y \tag{16}\]  
Ridge regression prevents overfitting by shrinking the regression coefficients, and is suitable for multicollinearity problems. But there is also a risk of underfitting the model if the parameters are not selected properly.
# Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks
## 5 Conclusions  
We discussed the research direction of energy efficiency in cellular communication and summarized a concrete method for each direction. Then, Section 2 established a system model based on the random access process combined with the wireless resource block. Based on this model, with the cooperative relaying idea as the foundation, this paper proposed a distributed dynamic cluster-head selection and clustering scheme based on an improved K-means algorithm, and built a system simulation platform to simulate and analyze the proposed algorithm. The simulation results show that the proposed algorithm can significantly improve the efficiency of the system. At present, we have mainly considered the impact of the LTE-M cellular network used in out-of-cluster communication on system energy efficiency. In the next phase, we will determine the impact of the technology used in intra-cluster communication on system energy efficiency. Also, the clustering preprocessing algorithm used in the adaptive clustering algorithm is a DBSCAN algorithm used for the non-uniformity of device distribution density. However, as the number of devices further increases, the number of devices activated each time gradually increases, and the density tends to be uniform. At this point, the advantages of the algorithm are no longer reflected. Therefore, it is necessary to explore new algorithms to cope with the challenge of a larger number of devices in the future.  
All the authors contributed to the conception, design, and performance of the experiment, the analysis of the data and the writing of the paper. [PERSON], [PERSON] and [PERSON] initiated and discussed the research problem; [PERSON] and [PERSON] conceived and designed the scheme; [PERSON], [PERSON], and [PERSON] performed the experiments and constructed the figures. [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] wrote the paper.  
The work presented in this paper was partially supported by the 2018 National Natural Science Foundation of China (Grant number 61871339).  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Impact of Disruption on Ship Emissions in Port: Case of Pandemic in Long Beach
## 3 Model and Methodology
### Ship Emission Estimation Model  
#### 3.3.1 Motion-Based Formulae for Calculating Ship Emissions  
The top-down method, for instance, relies on ship fuel consumption and emission variables for various fuel sources [22; 37]. This method is determined by the amount of fuel utilised. The disadvantage is that it cannot be evaluated using the ship's real-time dynamic data. The operational status of ships fluctuates over time, as do their emissions. Therefore, calculations should be done for various navigational conditions. The main engine, auxiliary engines, and boilers are utilised when the ship is at sea. However, internal combustion engines are used differently at anchor and in port. In addition, the ship's heading and speed will vary, due to the meteorological conditions' impact, such as waves, wind speed, wind direction, and other forces acting on the vessel.  
The bottom-up method is a real-time, dynamic analysis of ship emissions. The operational state, load factor, fuel type, ship speed, and vessel position are considered to analyse and estimate ship emissions at each time-point. Thus, this strategy is more precise than the top-down approach. This study's calculation is based on this methodology, which improves upon the method presented by [PERSON] et al. [25] and [PERSON] [26]. The analysis of ship emissions for different ships can be formulated as follows:  
\[E_{i,j,k,l}=P_{j}\times LF_{j,l}\times T_{j,k,l}\times EF_{i,j,k}/10^{6} \tag{3}\]  
where \(i\) represents the air pollution type; \(j\) represents the engine type; \(k\) represents the fuel type; \(l\) represents the type of operating mode; \(E_{i,j,k,l}\) denotes the quantity of pollutant emissions of a particular type (\(i\)) of pollution generated by each ship, using a different type (\(j\)) of the engine, by using a specific fuel (\(k\)), in a particular operation mode (\(l\)); \(P_{j}\) denotes the engine power (\(j\)) of the ship (main engine, auxiliary engine, boiler); \(LF_{j,l}\) indicates the load factor of the ship by using different engine types (\(j\)) for different types of operating modes (\(l\)); \(T_{j,k,l}\) indicates the operating time for a certain way of operation (\(l\)), with a particular fuel (\(k\)), under a specific engine type (\(j\)); \(EF_{i,j,k}\) indicates the emission factor of a substance (\(i\)) emitted by a ship with a particular engine type (\(j\)) is used, and a specific fuel (\(k\)) is used.
# Integrated Magnetic Management of Stored Angular Momentum in Autonomous Attitude Control Systems
## 1 Problem Statement and Spacecraft Dynamics
Indeed, the term \(\mathbf{R}\mathbf{h}_{R}\) is the cumulative angular momentum of all the reaction wheels expressed in body frame, and the overall spacecraft's angular momentum results in:  
\[\mathbf{h}^{B}=\mathbf{I}\boldsymbol{\omega}+\mathbf{h}_{R}^{B}=\mathbf{I} \boldsymbol{\omega}+\mathbf{R}\mathbf{h}_{R}. \tag{2}\]  
Formally, the configuration matrix \(\mathbf{R}\) is a \(3\times 4\) matrix, whose columns represent the direction of the axis of rotation of each reaction wheel in \(B\):  
\[\mathbf{R}=[\mathbf{\hat{r}}_{1},\mathbf{\hat{r}}_{2},\mathbf{\hat{r}}_{3}, \mathbf{\hat{r}}_{4}], \tag{3}\]  
Figure 1: Problem setting of a spacecraft orbiting in LEO with four reaction wheels and three magnetic torquers. and the vector of reaction wheels' angular momenta \(\mathbf{h}_{R}\) is a column vector with 4 elements representing the angular momentum of each rotor around its spin axis:  
\[\mathbf{h}_{R}=I_{R}\big{[}\omega_{R_{1}},\omega_{R_{2}},\omega_{R_{3}},\omega_{R _{4}}\big{]}^{T}, \tag{4}\]  
where \(I_{R}\) is the rotor's inertia, which is equal for the 4 reaction wheels. The quaternions, used to represent the spacecraft's attitude, lead to the following representation for the attitude kinematics:  
\[\mathbf{\dot{q}}=\frac{1}{2}\mathbf{W}(\mathbf{\omega})\mathbf{q}, \tag{5}\]  
where \(\mathbf{q}=[q_{1},q_{2},q_{3},q_{4}]\) is the unit norm quaternion vector and the angular velocity matrix is expressed as:  
\[\mathbf{W}(\mathbf{\omega})= \begin{bmatrix}0&\omega_{3}&-\omega_{2}&\omega_{1}\\ -\omega_{3}&0&\omega_{1}&\omega_{2}\\ \omega_{2}&-\omega_{1}&0&\omega_{3}\\ -\omega_{1}&-\omega_{2}&-\omega_{3}&0\end{bmatrix} . \tag{6}\]  
The environmental disturbance torques taken into account to model the spacecraft dynamics are due to both external and internal effects. They are modeled according to the international standards for system simulation and verification [26; 27]. In particular, gravity, magnetic, atmospheric drag, and radiation pressure torques are considered, as well as internal micro-vibrations-induced torques and electro-magnetic interaction disturbances.
## 2 Results and Discussion
### Comparison of Extraction Effect of SPME and SAFE  
Eighty compounds in EVOOs were identified by SPME-GC-MS/O and 54 by SAFE-GC-MS/O method (Table 1). Forty-four compounds were detected by both methods. Two extraction methods (SPME and SAFE) had different extraction efficiency for different kinds of volatile compounds (Figure 1).  
It can be seen from Figure 1 that more alcohols, aldehydes, ketones, esters, terpenes, and others were detected by SPME compared to SAFE. The same numbers of acids and aromatics were extracted by these two methods. The reasons can be the following: formyl groups of aldehydes and carbonyl groups of ketones were unstable, which could get oxidized or reduced in organic solvent. Alcohols in EVOOs are derived from the reduction of aldehydes by dehydrogenase, for example, from (\(E\))-2-pentenal to 1-pentene-3-ol, from (\(E\))-2-hexenal to (\(E\))-2-hexenal, from octanal to octanol, which resulted in the more alcohols by SPME [31]. This result was in accordance with that of watermelon juice [32]. It indicated that SPME had better extraction effect on aldehydes, ketones, and alcohols just because of the different systems of EVOOs and watermelon juice.  
Similarly, SPME could extract more esters than that by SAFE, while the result was just opposite in natto [33]. In natto, all the esters were ethyl esters, and there were more complicated constitutes of esters in EVOOs including ethyl, methyl, hexyl, and linalyl ester, which might result in the different extraction effect. SPME also had better extraction effect to terpenes. The previous studies showed that SPME with the fiber of DVB/CAR/PDMS (divinylbenzene/carbon/polydimethylsiloxane) had better extraction effect to terpenes compared to the other fibers (100 \(\upmu\)m PDMS, 85 \(\upmu\)m PA (polyacrylate), and 7 \(\upmu\)m PDMS) or method (hydrodistillation), which confirmed the result in this study to some extent [34; 35]. In conclusion, SPME had better extraction efficiency to the aroma compounds in EVOOs.  
However, nine compounds were only extracted by SAFE, including (\(E\))-3-hexenol, undecanol, octanal, (\(Z\))-4-decenal, (\(E\))-2-dodecenal, 1-octen-3-one, 2-nonanone, butyl acetate, and 1,2,4-trimethylbenzene. In particular, undecanol (mandarin), (\(Z\))-4-decenal (green, must), (\(E\))-2-dodecenal (green, fat, sweet), and 2-nonanone (hot milk, soap) were found in EVOOs for the first time to the best of our knowledge. All these compounds had a significant aroma property, and 2-nonanone was the key aroma compounds identified by AEDA. (\(E\))-3-Hexenol and 1-octen-3-one reported in the previous studies were also the potent aroma compounds in this study.  
Figure 1: Number of different groups of flavor compounds extracted by SPME and SAFE.