esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Beamforming Damage Imaging of Lamb Wave Based on CNN
## 4 Discussion and Analysis of Results  
### Finite Element Simulation Model  
The proposed method was validated based on a COMSOL 5.6 finite element numerical simulation as shown in Figure 3. The specimen was an aluminum plate with dimensions of 400 \(\times\) 400 \(\times\) 2 mm\({}^{3}\). The density of the aluminum plate was 2700 kg/m\({}^{3}\), the modulus of elasticity was 70 GPa, and the Poisson's ratio was 0.33. In order to avoid the influence of the reflected waves at the boundaries, a Rayleigh damping absorption condition with a width of 50 mm was set up at the surrounding boundaries of the model. A total of nine array elements were set up; among them, the middle one excited the Lamb wave signal with coordinates of (0, 50) mm and the other eight were used to receive the Lamb wave signal. The longitudinal coordinates of all the array element positions were set to 50 mm, and the transverse coordinates were set from \(-\)140 mm at 40 mm intervals to 140 mm. Antisymmetric displacement loads along the thickness direction were applied to excite the A0 mode Lamb wave. Damage was simulated to a through-hole of 5 mm in diameter, with the exact location adjusted according to the research needs. In the finite element simulation model, the meshes near the damage and the transducer unit were refined, with a minimum mesh size of 0.01 mm and a maximum mesh size of 0.15 mm; the initial computational step size was 0.1 us, and the step size was automatically adjusted with the advancement of the computational process [28]. The excitation signal was a sinusoidal pulse signal modulated by a five-cycle Hanning window with a center frequency of 200 kHz.
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 3 Methodology
### Impacts of Single Satellite on the Accuracy and Integrity of Subset
#### 3.1.3 Integrated Performance Impact of Single Satellite  
As discussed in the above paragraphs, a larger slope value of a single satellite means a greater accuracy impact it has, but a too-large slope value is disadvantageous for the integrity of the subset and a new satellite may be required to reduce it. As the purpose of this study is to find a relatively small subset that satisfies the accuracy and integrity requirements, the gap between the required performance and the performance of the current subset can be employed to help decide which satellite is more valuable.  
A cost function is constructed to represent the integrated performance impact of a newly added satellite as:  
\[\delta P_{i}=\frac{2*Slope_{i,i}}{\sqrt{2 drms_{0}^{2}-2 drms_{req}^{2}}}+ \frac{\delta Slope_{i}}{Slope_{\max,0}-Slope_{\max,req}}=\frac{2*Slope_{i,i} }{\sqrt{2 drms_{0}^{2}-2 drms_{req}^{2}}}+\frac{Slope_{\max,0}-Slope_{\max,req}} \tag{17}\]  
The \(\delta P_{i}\) in Equation (17) is the integrated performance impact of the new satellite. The \(2 drms_{req}\) is the accuracy requirement and the \(Slope_{\max,req}\) is the max slope requirement derived from the required HPL. The \(Slope_{i,i}\) is the slope of the new satellite after its introduction. It is easy to derive from Equation (17) that, if the performance requirement is given, the integrated performance impact of a single satellite mainly depends on its slope in the new subset and the max slope of the new subset after its introduction. When a new satellite owns a larger slope value and more greatly reduce the max slope of the subset, it makes a greater integrated performance impact and thus more suitable for the current subset to efficiently achieves the required performance.  
If the navigation system is not aware of the performance requirement, the value of \(2 drms_{req}\) and \(Slope_{\max,req}\) in Equation (17) can be replaced by zero. Then the \(\delta P_{i}\) turns to the sum of the slope reduction rate and accuracy reduction rate of the new satellite. Nevertheless, it can still be a reference to the integrated performance impact of the satellite.
## 2 Materials and Methods
### Characterization of Hydrochar and Activated Carbons  
The proximate analyses were performed by the ASTM methods D3173-11 (moisture), D3174-11 (ash) and D3175-11 (volatile matter (VM)) using a Mettler Toledo apparatus (TGA/SDTA851e). The elemental analyses were carried out in a LECO Model CHNS-932 elemental analyzer. The higher heating value (HHV) of hydrochar samples was determined using an IKA Calorimeter System C2000 according to the ASTM D5865 procedure.  
The porous texture of the AC samples was characterized by 77 K N\({}_{2}\) adsorption-desorption in a Micromeritics apparatus (Tristar 3020, Micromeritics, Narcross, GA, USA). The samples were previously degassed (150 \({}^{\circ}\)C for 6 h) in a Micromeritics VacPrep 061 device (Micromeritics, Narcross, GA, USA). The surface area was calculated by the BET equation within the 0.05-0.30 relative pressure range and the micropore volume (V\({}_{\text{micro}}\)) was obtained by the t-method. The difference between the volume of N\({}_{2}\) adsorbed at 0.95 relative pressure (as liquid) and the micropore volume was taken as the mesopore volume (V\({}_{\text{meso}}\)). The external or non-micropore surface area (A\({}_{\text{ext}}\)) was also obtained from the t method. Surface area and micropore volume of the samples were also determined by CO\({}_{2}\) adsorption at 273 K in the same equipment. The corresponding surface area (S\({}_{\text{DA}}\)) and the micropore volume (V\({}_{\text{micro-DA}}\)) values were calculated by the Dubinin-Astakhov equation [48].  
The surface composition of the hydrochars and activated carbons was analyzed by FTIR spectroscopy using a FTIR Bruker IFS66v spectrophotometer (Bruker Corporation, Billerica, MA, USA), with a KBr disc, having a resolution of 4 cm\({}^{-1}\) from 4000 to 550 cm\({}^{-1}\) and 250 scans. Temperature programmed desorption (TPD) was also used to assess the amount of oxygen surface groups. The samples (0.1 g) were heated up to 900 \({}^{\circ}\)C at 10 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) in a vertical quartz tube under continuous N\({}_{2}\) flow of 1 NL-min\({}^{-1}\). The amounts of CO\({}_{2}\) and CO evolved were analyzed by non-dispersive infrared absorption in a Siemens model Ultramat 22 equipment (Siemens Aktiengesellschoff, Munich, Germany).  
The morphologies of hydrochars and activated carbon samples were studied by scanning electron microscopy (SEM) with a Hitachi S-3000N apparatus (Hitachi Ltd, Tokyo, Japan). The samples were metalized with gold using a Sputter Coater SC502 (Quorum, East Sussex, UK). Images were obtained in the high vacuum mode under an accelerating voltage of 20 kV, using secondary and backscattered electrons.  
The pH slurry of the carbons was determined measuring the pH (pH-meter, Crison) of an aqueous suspension of the sample (1 g) in distilled water (10 mL) [49].
# Modeling Photovoltaic Potential for Bus Shelters on a City-Scale: A Case Study in Lisbon
## 3 Results and Discussion
### The City's Solar Bus Shelters
Figure 5: Map of the Global Radiation for the 21 st December (**a**), and a neighborhood close-up (**b**). Figure 6: Misalignment between the bus shelter (**a**) and the solar map (**b**). The PV potential was then compared with the energy required by some typified applications (Table 2). Figure 8: Low (**a**,**b**) and high (**c**,**d**) insolation bus shelters’ spatial distribution in Lisbon (**e**). Figure 7: The fraction of bus shelters as a function of a minimum Direct Irradiance Fraction. The solar bus shelters were rated based on local energy production and the possibility of receiving the mentioned applications. Every shelter could accommodate a remote small-scale sensor, but fewer would be suitable for a small info display to deliver pertinent information, and even fewer would be suitable for a ticket vending machine. Higher-energy-consumption applications, such as coffee vending machines, were only possible in 23 shelters. Among these, only eight shelters, due to their large dimensions, would be suitable for more demanding applications. The solar bus shelters were then mapped according to the level of energy required for each application (Figure 9). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Application Example** & **Power (kW)** &
\begin{tabular}{c} **Daily Energy** \\ **Required (kWh/day)** \\ \end{tabular} & **Bus Shelters** \\ \hline L1: Remote small-scale sensors & \(<\)0.01 & \(<\)0.24 & 612 & 100\% \\ L2: Non-refrigerated vending machine & 0.01 \(<\) P \(<\) 0.05 & 0.24 \(<\) E \(<\) 1.2 & 430 & 70\% \\ L2: Small-scale info display up to 20 to 40\({}^{\prime\prime}\) & 0.05 \(<\) P \(<\) 0.1 & 1.2 \(<\) E \(<\) 2.4 & 159 & 26\% \\ L3: Ticket machine & 0.1 \(<\) P \(<\) 0.5 & 2.4 \(<\) E \(<\) 12 & 23 & 4\% \\ L4: Refrigerated vending machine & 0.1 \(<\) P \(<\) 0.5 & 2.4 \(<\) E \(<\) 12 & 23 & 4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Solar bus shelters suitable for PV-typified applications. Figure 9: Solar bus shelters with typified PV applications (**a**), and an interface station example (**b**). Furthermore, the illumination capability was also investigated, considering the required energy density (i.e., kWh/m\({}^{2}\)/day) necessary to feed LED lamps, functioning 16 h in a day (winter conditions) (Figure 10). We can conclude that all the solar bus shelters would be suitable for up to 7 W/m\({}^{2}\) LED lamps, but only 40% would be suitable for 10 W/m\({}^{2}\) LED lamps. Higher-energy-consumption LED lamps would not be possible to install.
## 2 Real-Time BDS-3/BDS-2 PPP Model
\tag{5}\]  
where \(\phi_{IF}\) and \(p_{IF}\) denote the OMC (observed-minus-computed) phase and code observables of IF combination, respectively, \(\mu\) denotes the unit vector in line-of-sight direction, \(X\) denotes the three-dimensional (3D) receiver positions, \(c\dd{t_{r}}\) denotes the receiver clock estimates including code hardware delays at the receiver, \(N_{IF}\) is the phase ambiguity estimates grouped with phase and code hardware delays at both satellite and receiver ends, \(Z\) is the tropospheric zenith wet delay (ZWD), \(m\) is the wet mapping function, and \(\varepsilon_{p_{IF}}\) and \(\varepsilon_{\phi_{IF}}\) are the noise terms of \(p_{IF}\) and \(\phi_{IF}\), respectively. Although we do not display the relativistic effect, phase windup effect, station displacement, phase center variation (PCV) and phase center offset (PCO) in Equation (4), the corresponding models or products should be used to mitigate them [19; 33]. The estimated parameters cover ZWD, phase ambiguity, receiver clocks, and receiver positions, that is:  
\[S=\left[ \begin{matrix}\cdot&\cdot\\ X,c\dd{t_{r}},Z,N_{IF}\end{matrix} \right]^{\text{T}} \tag{6}\]  
where \(S\) denotes the vector of estimates. In this study, for the parameter estimation, we employ a Kalman filter. Both static receiver positions and phase ambiguity parameters are estimated as constants, while white noise processes are adopted for kinematic receiver positions and receiver clock parameters. As for the ZWD, it is modeled as random-walk processes. In this study, the open-source software RTKLIB [34] is used, but with many improvements, such as the stochastic model. As the observations at low elevations are vulnerable to the measurement noises and multipath errors, the elevation-dependent weighting schemes are employed for real-time BDS-3/BDS-2 PPP regarding the stochastic model, that is:  
\[\sigma^{2}(ele)=w^{2}\cdot\left(a^{2}+\frac{b^{2}}{(\sin ele)^{2}}\right) \tag{7}\]  
where \(\sigma^{2}(ele)\) is the variance of code or phase observations with an elevation angle \(ele\), and \(a\) and \(b\) are two constants, both of which can be taken as 0.003 and 0.3 m for the phase and code measurements, respectively. \(w\) is the weight factor to consider the different effects of the accuracy of the precise clock and orbit corrections of satellites in the real-time situation among different satellite constellations. Following [PERSON] et al. [35], a good positioning performance can be achieved when the weight factor is proportional to the numerical value of the signal-in-space ranging error (SISRE). The weight factor is roughly set to 1.0, 1.0, 1.5 and 6.0 for BDS-2 MEO, BDS-3 MEO, BDS-2 IGSO and BDS-2 GEO satellites, respectively, according to the results of accuracy evaluation for the real-time precise products in this study.
## 3 Results
### Barepodometric Platform
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **Preop** & **Postop** & & **Preop** & **Postop** & **\(p\)-Value** \\  & & **Operated** & **Operated** & **Operated** & **Non-Operated** & **Non-Operated** & **\(p\)-Value** \\  & & **Foot** & **Foot** & & **Foot** & **Foot** & **Foot** & **\(p\)-Value** \\ \hline \multirow{8}{*}{STATIC} & Load distribution (\%) & 50.2 \(\pm\) 6.1 & 49.9 \(\pm\) 2.8 & _0.889_ & 49.8 \(\pm\) 6.1 & 50.2 \(\pm\) 2.7 & _0.889_ \\ \cline{2-8}  & Mean pressure (kPa) & 35.8 \(\pm\) 5.1 & 34.1 \(\pm\) 3.2 & _0.386_ & 33.1 \(\pm\) 4.8 & 31.5 \(\pm\) 4.5 & _0.421_ \\ \cline{2-8}  & Peak pressure (kPa) & 108.0 \(\pm\) 28.9 & 115.0 \(\pm\) 63.9 & _0.770_ & & & \\ \cline{2-8}  & Foot barycenter (mm\({}^{2}\)) & 21.8 \(\pm\)17.1 & 17.2 \(\pm\) 13.9 & _0.232_ & & & \\ \cline{2-8}  & Body barycenter (mm\({}^{2}\)) & 86.4 \(\pm\) 78.6 & 42.3 \(\pm\) 20.2 & _0.191_ & & & \\ \cline{2-8}  & C.O.P. distance (mm) & 169.9 \(\pm\) 86.1 & 126.4 \(\pm\) 30.9 & _0.282_ & & & \\ \hline \multirow{8}{*}{DYNAMIC} & Mean pressure (kPa) & 64.3 \(\pm\) 7.2 & 68.9 \(\pm\) 11.1 & _0.301_ & 60.4 \(\pm\) 6.1 & 70.6 \(\pm\) 7.2 & _0.036_* \\ \cline{2-8}  & Peak pressure in & 145.3 \(\pm\) 23.7 & 165.5 \(\pm\) 29.1 & _0.211_ & & & \\ \cline{1-1} \cline{2-8}  & Peak pressure in & 143.5 \(\pm\) 9.5 & 139.4 \(\pm\) 30.2 & _0.728_ & & & \\ \cline{1-1} \cline{2-8}  & Peak pressure in & 181.9 \(\pm\) 49.2 & 169.2 \(\pm\) 59.7 & _0.658_ & & & \\ \hline \multicolumn{8}{c}{\({}^{*}\)**Bold**\(p\)-value indicates reaching statistical significance.} \\ \end{tabular}
\end{table} 
Table 5: Baropodometric measurement outcomes. pressure was localized in 25% of cases, respectively, at the I, II, III and IV metatarsal head, and at the hallux in the remaining 12.5% of cases before surgery. On the other hand, after surgery, the peak pressure was localized in 25% of cases, respectively, at the I, II, III and V metatarsal head, at the hallux in 12.5% of cases, and at the calcaneus in the remaining 12.5% of cases.
## 2 Materials and Methods
### Correctness of the VLES Method  
Figure 5 exhibits the \(Fr\) distribution of the computational domain at \(A/D=1\) (ratio of the excitation amplitude to the cylinder diameter) and \(f_{c}\)/\(f_{s}=0.75\). According to the principle of the VLES method, the \(Fr\) value determines the computational model in the computation domain. When \(Fr\) approximates 1.0 near the wall, the computational model is recalled into the RANS model to effectively solve the flow near the boundary of the oscillating cylinder. However, when \(Fr\) locates between 0 and 1.0, the turbulence is modeled as the VLES model, considered to be a \"coarse LES\" model, to capture the large-scale vortices located in the regions away from the wall. The \(Fr\) contour and the \(Fr\) distribution, in Figure 5, show that the \(Fr\) values of most of the regions are within the range 0-0.1, meaning these regions adopt the VLES model to solve the Navier-Stokes equations. In addition, regions behind the  
Figure 4: Velocity profiles at four cross-sections for constant x-position as compared with the experimental results. Phase position (a) 180\({}^{\circ}\); (b) 210\({}^{\circ}\); and (c) 330\({}^{\circ}\).  
cylinder have \(Fr\) values from 0.1 to 0.4, due to the larger turbulent kinetic energy and moderate grids, and also employ the VLES model. However, the \(Fr\) value around the boundary of the cylinder was basically maintained between 0.75 and 1.0, indicating that the RANS model is recalled. The above distribution law of the \(Fr\) values is in good agreement with the principle of the VLES method. It can also be seen in Figure 5 that the distribution of \(Fr\) is continuous in the computational domain.  
In addition, the VLES method had a clear explanation on the y+ distribution that \(Fr\) tended to 1 in the premise of \(y+<3.12\), which means that the RANS method has been successfully recalled near the wall [18]. As can be seen in Figure 6, y+ on the cylinder surface is guaranteed to be within 2.0 and its average value is around 1.0, which meets the above requirements.
# Metabolic Engineering _Escherichia coli_ for the Production of Lycopene
## 2 Metabolic Engineering of Two Major Pathways in _E. coli_ for Lycopene Production
### The Primary Biosynthetic Pathways for Lycopene Production
G3P, glyceraldehyde 3-phosphate; DXP, 1-deoxy-d-xylulose-5-phosphate; MEP, methylerythitol phosphate; CDP-ME, 4-diphosphocytidyl-2C-methyl-d-erythitol-cDP-ME, 4-diphosphocytidyl-2C-methyl-d-erythitol-2-phosphate; MEC, 2C-methyl-d-erythitol-2-4-cyclo-diphosphate; HMBPP, 4-hydroxy- 3-methyl-2-(E)-butenyl-4-diphosphate; HMG-CoA, 3-hydroxy-3-methylglutaryl-CoA; MVAP, mexolonate-5-diphosphate; IP, isopentenyl diphosphate; DMAPP, dimethylallyl diphosphate; GPP, geranyl diphosphate; FPP, farnesyl pyrophosphate; GGPP, geranylgeranyl diphosphate; DXS, DXP synthase; DXR, DXP reductoisomerase; IspD, CDP-ME cytidyltransferase; IspE, CDP-ME kinase; IspF, MEC synthase; IspG, HMBPP synthase; IspH, HMBPP reductase; ACCT, acetoacetyl-CoA thiolase; HmgS, HMG-CoA synthase; HmgR, HMG-CoA reductase; MK, mexolonate kinase; PMK, MVAP kinase; MDD, MVAPP decarboxylase; MPD, MVAP decarboxylase; IPK, IP kinase; IDI, isopentenyldiphosphate isomerase; IspA, FPP synthase; CrtE, GGPP synthase; CrtB, phytoene synthase; CrtI, phytoene desaturase. and Crtl [15; 27; 28; 29; 30] were the primary biosynthetic route and strategy in the metabolic engineering of _E. coli_ for lycopene production. Based on this, further work on the modification of the two major pathways was conducted. Lycopene production via various metabolic engineering optimization strategies in _E. coli_ are summarized and listed in Table 1.
# Analysis of the Scalability of UWB Indoor Localization Solutions for High User Densities
## 5 Scalability Model  
In the previous sections, we provided mathematical formulas of the different PHY, MAC and localization approaches. In this section, we combine these formulas to create a full-system scalability model. This is used to derive the maximum user density in an UWB network with the aforementioned characteristic. In Figure 4, an overview of what is taken into account when studying the scalability is outlined. Each input impacts the overall scalability of the UWB localization system. With this framework, we want to simplify the analysis of a network in which as many users as possible can be localized/tracked. Therefore, the steps to compute the maximum user density are kept as general as possible. Some relevant examples are also provided in Section 6 to validate the scalability model using the characteristics of the commercial Decawave DW1000 chip. In the following paragraph, a list of the steps in order to evaluate the achievable user density is presented.
# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 3 Methods
### Multimodal Two-Step Enhanced Fusion Method
To achieve this, we employ an attention mechanism to perform self-enhancement on the item representation of each modality, thereby emphasizing the distinctive features of individual modalities and adaptively adjusting the importance of each modality to facilitate a more accurate item representation of in subsequent embedding learning. The definition of self-enhancement and the fusion process for multimodal item representations is as follows:  
\[\varepsilon_{i}^{m}=softmax(q_{i,m}^{T}\tanh(W_{i,m}\tilde{h}_{i,m}+b_{i,m})) \tag{16}\]  
\[\tilde{h}_{i}=\sum_{m\in M}\varepsilon_{i}^{m}\tilde{h}_{i,m} \tag{17}\]  
where \(W_{i,m}\in\mathbb{R}^{d\times d}\) and \(b_{i,m}\in\mathbb{R}^{d}\) denote the transformation matrix and bias, respectively, \(q_{m}\in\mathbb{R}^{d}\) is the attention vector, and \(\varepsilon_{i}^{m}\) denotes attention weight for modality \(m\), which reflects the importance level of modality \(m\) in item \(i\) as derived from the multimodal representation. We employ Equation (17) along with this attention weight to perform a weighted fusion of the modality representation \(\tilde{h}_{i,m}\) for the item. Following the acquisition of the self-enhanced multimodal item representation \(\tilde{h}_{i}\) (Equation (17)) and the enhanced item behavior representation \(\overline{e}_{i}\) (Equation (14)), the subsequent step involves fusing these representations to produce an initial item representation \(\tilde{e}_{i}\) used for embedding learning. The fusion process is defined as follows:  
\[\tilde{e}_{i}=\lambda_{i}\overline{e}_{i}+(1-\lambda_{i})\tilde{h}_{i} \tag{18}\]where \(\lambda_{i}\) represents the fusion weight, serving as a hyperparameter within UPGCN, and its value determines the balance between \(\overline{\epsilon}_{i}\) and \(\tilde{h}_{i}\) during the final fusion process.
## 2 Materials and Methods
### Experimental Design  
Confluent myotube cells were separated into different conditions, with at least 3 flasks in each condition. Those 70%-90% confluent myotubes (around 2 weeks in culture) (at least n = 3 flasks) were serum-starved (Ham-F 10 medium alone) for one week. Then, cells (Figure 2) were treated for one hour with vehicle (0.1% DMSO), NECA, CGS21680, NECA+PSB 603 (cells were pretreated with PSB 603 for 10 minutes before addition of NECA), or PSB 603 and for 10 minutes with 100 nM insulin (cells were pre-treated with NECA for one hour prior to the addition of insulin). After that, cells were washed with ice cold PBS, then lysed with TRIzol (Invitrogen product name) (2 mL per flask).  
Figure 1: Representative myoblasts derived from passage number 7 myoblasts taken after 1 day seeding into 25 cm\({}^{2}\) (10\(\times\)).  
Figure 2: Representative myotubes derived from passage number 7; myotubes taken at (Ham-F10, 1 % P/S) after 7 days starvation (10\(\times\)).  
The question of whether BCL2 mRNA gene expression in rat L6 skeletal muscle cells could be induced by stimulating adenosine A2 receptors was addressed by relative quantification by qRT-PCR of NECA's effects using a non-selective adenosine receptor agonist. mRNA gene expression of BCL2 was quantified after starved skeletal muscle cells had been incubated for one hour with NECA (10 uM), while the question whether BCL2 mRNA gene expression could be induced in rat L6 skeletal muscle cells by stimulating adenosine A2A receptors was addressed by relative quantification by qRT-PCR of the effect of the selective adenosine receptor agonist CGS21680. mRNA gene expression of BCL2 was quantified after incubation of starved skeletal muscle cells with CGS21680 (100 nM) for one hour. Of note, in the absence of a selective agonist, a selective antagonist to adenosine A2B receptors was used in the form of PSB 603. Its effect was investigated by incubating rat L6 skeletal muscle cells with a combination of PSB 603 and 10 uM NECA, the PSB 603 having been added to the cells 10 minutes before the NECA was added. Two separate PSB 603 concentrations were used: 100 nM and 1 uM.