esa-sceva/satcom-chunk-collection huggingface dataset:
# Control of Porosity in Parts Produced by a Direct Laser Melting Process
## 1 Introduction
As demonstrated, previous studies have mainly focused on porosity formation mechanisms and mechanical effects based on specific alloy systems. The size and morphology of powders must be strictly controlled due to their influence on packing behavior, material deposition, and subsequent laser consolidation. In the DLM, when the laser beam radiates towards the powder layer, laser beam energy is dissipated by means of absorption and scattering. In order to investigate the effect of powder size distributions on the scattering and absorption characteristics of the metal powder layer, ray tracing (RT) models have been proposed for modeling the interaction between laser radiation and powder particles more accurately [16; 17]. This study presents the effects of powder morphologies and process parameters on porosity formation during DLM. The effects of powder morphologies on porosity generation are analyzed by means of the scattering characteristics of test powders, while the effects of laser processing parameters are investigated using powder consolidation kinetics [18; 19; 20]. As DLM holds significant potential to become a robust technology, it is also critical to understand the complex physical phenomena taking place inside and around the melt pool, and how these may affect porosity generation. Furthermore, despite ongoing research efforts focusing on the characterization of porosity in the laser additive processes, practical studies on quantitative porosity control have not been sufficiently represented and are generally lacking. Therefore, this quantitative study may provide a basis for active porosity control in laser processing. To this end, functionally-graded parts with variable porosity densities were fabricated to demonstrate the active porosity control during the DLM process.
## 4 Routing Strategies
### MEC Path Selection Strategy  
This strategy is based on the idea that blocking may occur at any link traversed by a burst along the path. Therefore, paths for source-destination pairs should be selected so that demands have the smallest probability of contending with other demands at every link from source to destination, minimizing the end-to-end blocking. This is achieved by the following ILP optimization problem, where \(\varphi_{MAX}\) denotes the value of the path having the highest number of contends.  
\[\text{Minimize }\varphi_{MAX} \tag{5}\]  
Subject to  
\[\sum_{v\in\mathcal{V}_{s,d}}\sigma^{v}=1,\ \forall s,d\in\mathcal{N} \tag{6}\]  
\[\eta^{v,v^{\prime}}\geq(\sigma^{v}+\sigma^{v^{\prime}}-1)\times q^{v,v^{ \prime}},\ \forall v\in\mathcal{V},\forall v^{\prime}\in\mathcal{V}\setminus \mathcal{V}_{s(v),d(v)} \tag{7}\]  
\[t_{s,d}+\sum_{v\in\mathcal{V}_{s,d}}\sum_{v^{\prime}\in\mathcal{V}\setminus \mathcal{V}_{s,d}}\eta^{v,v^{\prime}}\times t_{s(v^{\prime}),d(v^{\prime})} \leq\varphi_{MAX},\forall s,d\in\mathcal{N} \tag{8}\]  
\[\sigma^{v},\eta^{v,v^{\prime}}\in\{0,1\};\text{non-negative integer: }\varphi_{MAX} \tag{9}\]  
where \(\sigma^{v}\) is a binary variable that indicates if \(v\) is used to carry bursts from node \(s(v)\) to node \(d(v)\), and \(\eta^{v,v^{\prime}}\) is a binary variable that indicates if \(v\) and \(v^{\prime}\) have both been selected to carry bursts and share at least one link. Similarly to the previous strategy, the constraint expressed on Equation (6) states that one path must be found for each pair of nodes. Equation (7) forces \(\eta^{v,v^{\prime}}\) to be 1 if \(v\) and \(v^{\prime}\) share a link and  
Figure 9: A small illustrative example of SP, MCL, and MEC.  
have both been selected to carry bursts. Otherwise, and due to the minimizing nature of the objective function, \(\eta^{v,v^{\prime}}\) will be 0. Equation (8) states that the contending value of a source-destination pair must not exceed \(\varphi_{MAX}\).  
Still considering the same network scenario, we can observe in Figure 9(c) that the longest path 2\(\rightarrow\)3 in Figure 9(b) no longer exists and that this algorithm also tries to keep the traffic in a distributed manner.
## 6 System Development
### Crdt
#### 6.3.3 Client-Side Conflict Resolution  
CRDT (Conflict-free Replicated Data Type) is a type of data structure that allows multiple copies of a data object to be modified concurrently without the need for coordination between the copies, while still guaranteeing eventual consistency [62].  
CRDTs are designed to handle distributed systems and enable collaborative editing in scenarios where multiple users can make changes to a shared document or resource simultaneously. CRDT can be applied also on the server side, but it also implies the same scalability issues as the Operational Transformations approach.  
Implementing CRDT in a simplified manner represents defining a special data structure that can hold all the data and metadata of interest while being able to apply a conflict resolution strategy. Based on the application type, CRDT can be configured to treat conflicts in various ways. There are multiple strategies to solve conflicts and select winners: last write wins, first write wins, majority vote, minority vote, etc. Each one of those strategies has a purpose and the main decision factor is the dimension over which the changes are propagated. In a document editing scenario, the dimension over which the changes are dispatched is the time: the last write of a resource section (the last write win strategy) will outperform the previously dispatched changes. CRDT can function in two ways: operation-based CRDT [63] and state-based CRDT [64].  
Operation-based CRDT is built around the idea that peers will exchange with each other the operations applied to the text resource. This approach is still dependable on a centralized service that should replicate the common resources the user interacts with.  
State-based CRDT does not require a centralized system since the peers will exchange their state and each local CRDT datatype will be able to decide how to adapt the incoming changes to the local copy of the state. The biggest advantage of the state transaction is the server that becomes just a communication and side effects layer that will not need an application state to deliver the base functionality. Furthermore, the communication layer can be replaced by any transportation channel that can move data from one system to another, even a local network.  
On the other hand, it creates another problem on its own: state modeling. The communication channel (WebSocket) has limitations in terms of the frame size transportable over the network and it is very sensitive to the network speeds. To maintain a consistent rate of transfer, the payload exchanged over the network must not exceed a certain size. This can be achieved by modeling how the document content will look from a point of view of content organization. This degree of flexibility was the main reason for choosing a non-relational database system.
###### Abstract  
In this study, response surface methodology (RSM) was employed for the optimization of the antioxidant potentials of red pitaya peels using independent variables: temperature (45-65 \({}^{\circ}\)C), ethanol concentration (70-90%, \(v/v\)) and time (80-120 min) through its responses, which were DPPH scavenging activity, ferric ion reducing antioxidant power (FRAP), and beta-carotene bleaching (BCB) inhibition, respectively. In Vitro anti-tyrosinase and vitamin C content assays were carried out spectrophotometrically to determine the skin whitening efficacy of the optimized red pitaya peel extract. A gas chromatography-mass spectrometry (GC-MS) analysis was employed to identify the chemical constituents present in the optimized extract. The optimized conditions were ethanol 82% (\(v/v\)) for 103 min at 56 \({}^{\circ}\)C with values of 75.98% for DPPH, 7.45 mM Fe\({}^{2+}\)/g dry weight for FRAP and 93.29% for BCB respectively. The in vitro anti-tyrosinase and vitamin C content evaluation of the optimized extract showed a good tyrosinase inhibition of 66.29% with IC\({}_{50}\) of 24.06 \(\mathrm{\SIUnitSymbolMicro mL}\) while the vitamin C content was 5.45 mg/g of the extract. The GC-MS analysis revealed the presence of thirty phytoconstituents with L-(+)-ascorbic acid 2,6-dihexadecanoate being the most abundant with a peak area of 14.66%. This study evidently suggests the potential of red pitaya peels to be exploited as a natural skin whitening agent in the cosmeceutical and pharmaceutical formulations.  
red pitaya; anti-tyrosinase; vitamin C; antioxidant properties; response surface methodology; optimization +
Footnote †: journal: Applied Soft. 2018, 8, 1516; doi:10.3390/app8091516
## 3 Application
### Artificial Intelligence Model Results
#### 3.2.2 Artificial Neural Network Results  
For an accurate ANN model development, the structure of the hidden layer including the number of hidden layers and nodes in hidden layers should be determined first. Given the study of [PERSON] et al. [38] and [PERSON] et al. [40], the application of only a hidden layer also has a nice performance in solving the regression problems. Meanwhile, studies of [PERSON] [76; 77] also show that the ANN model with two layers is often used for more complex problems. Hence, a parameter investigation was carried out to find out the optimal number of nodes and the optimal number of hidden layers for BRZ prediction. In this investigation, various ANN models with the number of nodes from 1 to 9 and the number of hidden layers for 1 to 3 were developed, the 10-fold cross-validation method was applied, each model was run five times, and the optimal ANN models with different structure will be compared. After comparing, 2 was found to be the optimal number of hidden layers, 7 and 4 were respectively found to be the optimal number of nodes in the first hidden layer and the second hidden layer. Then, the predictive performance with _VAF_ of 90.7759, \(R^{2}\) of 0.9086 and _RMSE_ of 0.1779 for training datasets and _VAF_ of 88.4466, \(R^{2}\) of 0.8993 and _RMSE_ of 0.1630 for testing datasets was achieved (see Figure 5).
# Coordinated Multi-Platooning Planning for Resolving Sudden Congestion on Multi-Lane Freeways
## 1 Introduction
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Symbol** & **Description** & **Symbol** & **Description** \\ \hline \(k\) & The number of lanes & \(v_{j}\) & The longitudinal velocity of vehicle \(j\) in the platoon \\ \(\alpha\) & The number of lanes blocked due to the accident & \(v_{j}T\) & The desired distance \\ \(X\) & The road segment & \(a_{j}\) & The longitudinal acceleration of vehicle \(j\) in the platoon \\ \(S\) & The set of incoming vehicles in the segment before the accident & \(a_{j-1}\) & The acceleration of the preceding vehicle \(j-1\) in the platoon \\ \(i\) & The index of the vehicle & \(\varepsilon_{j}\) & The relative velocity between vehicle \(j\) and \(j-1\) in the platoon \\ \(x_{i}\) & The position of vehicle \(i\) & \(\delta_{j}\) & The distance error between the actual distance \\ \(v_{i}\) & The longitudinal velocity of vehicle \(i\) & \(x_{j}-x_{j-1}+L_{j-1}\) and the desired distance \(v_{j}T\) \\ \(v_{i}\) & The longitudinal velocity of vehicle \(i\) & \(L_{j-1}\) & The length of the preceding vehicle \(j-1\) in the platoon \\ \(v_{mxx}\) & The maximum allowed vehicle velocity on this road segment & \(a_{cnn}\) & The acceleration \\ \(a_{i}\) & The longitudinal acceleration of vehicle \(i\) & \(p_{n,j}\) & The vehicle index \(j\) of platoon \(P_{n}\) \\ \(a_{mxx}\) & The maximum allowed vehicle acceleration on this road segment & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notation used in this work. Figure 1: The illustration of traffic congestion caused by a vehicle accident on a multi-lane freeway, where there are many approaching vehicles (behind the accident spot). These vehicles need to negotiate to form platoons (bounded by lines) via 5G V2V communications. The grouped vehicles then move, in order, through the accident spot on the exit lanes (Lane 1 to Lane 3).
## 3 ACDM-Based VLP System by Using OPRC
### System Principle of OPRC-VLP
\begin{split}& CV_{ik}=\frac{1}{L_{S}}\sum\limits_{n=1}^{L_{S}} \boldsymbol{R}(n)\boldsymbol{S}_{i}\langle n+\tau_{k}\rangle\\ &=\frac{1}{L_{S}}\beta\sum\limits_{j=1}^{N_{LED}}\sum\limits_{n= 1}^{L_{S}}\delta_{j}\boldsymbol{T}_{j}\langle n+\tau_{j}\rangle\boldsymbol{S }_{i}\langle n+\tau_{k}\rangle+\frac{1}{L_{S}}\sum\limits_{n=1}^{L_{S}} \boldsymbol{S}_{i}\langle n+\tau_{k}\rangle N_{\text{AWGN}}(n)\\ &=\alpha\beta P_{k}\sum\limits_{j=1}^{N_{LED}}\left[\delta_{j}C_ {S_{j}\boldsymbol{S}_{i}}(|\tau_{j}-\tau_{k}|)+\sum\limits_{n=1}^{L_{S}}\delta _{j}\boldsymbol{S}_{i}\langle n+\tau_{k}\rangle\right]+\frac{1}{L_{S}}\sum \limits_{n=1}^{L_{S}}\boldsymbol{S}_{i}\langle n+\tau_{k}\rangle N_{\text{ AWGN}}\\ &=\alpha\beta P_{i}\delta_{i}C_{S_{i}\boldsymbol{S}_{i}}(|\tau_{i }-\tau_{k}|)+N_{\text{MAI}}+\widetilde{N}_{\text{AWGN}},\\ &\text{where }N_{\text{MAI}}=\alpha\beta P_{k}\sum\limits_{j=1}^{N_{LED}} \delta_{j}C_{S_{j}\boldsymbol{S}_{i}}(|\tau_{j}-\tau_{k}|),\\ &\text{and }\widetilde{N}_{\text{AWGN}}=\frac{1}{L_{S}}\sum \limits_{n=1}^{L_{S}}\boldsymbol{S}_{i}\langle n+\tau_{k}\rangle N_{\text{ AWGN}}.\end{split}  \tag{10}\]  
Here, \(N_{\text{MAI}}\) represents MAI caused by the interference of different OPRCs, and \(\widetilde{N}_{\text{AWGN}}\) represents AWGN reduced by processing gain of OPRC. According to Equation (5), the correlation functions between different OPRCs are zero; thus, Equation (10) can be written by:  
\[CV_{ik}=\alpha\beta P_{i}\delta_{i}C_{S_{i}\boldsymbol{S}_{i}}(|\tau_{i}-\tau _{k}|)+\widetilde{N}_{\text{AWGN}}, \tag{11}\]  
where \(\widetilde{N}_{\text{AWGN}}\) represents the AWGN reduced by the coding gain of OPRC. Then, the RSS of the \(i\)-th LED is equal to the maximum of \(CV_{ik}\), written as:  
\[RSS_{i}=\max(CV_{ik})=\alpha\beta P_{i}\delta_{i}+\widetilde{N}_{\text{AWGN }}. \tag{12}\]  
By substituting Equation (9) into the above equation and performing a straightforward transformation, we can derive the signal transmission distance from the \(i\)-th LED transmitter to the PD receiver from:  
\[D_{i}=\left[\frac{(m_{l}+1)A_{r}h^{m_{l}+1}g_{f}g_{c}\alpha\beta P_{i}}{2\pi \left(RSS_{i}-\widetilde{N}_{\text{AWGN}}\right)}\right]^{1/(m_{l}+3)}. \tag{13}\]  
Finally, after obtaining the signal transmission distance from each LED transmitter to the PD receiver, we can apply the trilateration algorithm to determine the receiver's position. This process allows us to achieve VLP without MAI in the asynchronous system.
# Cardiac Diffusion Tensor Biomarkers of Chronic Infarction Based on In Vivo Data
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Native T1 (ms)**} & \multicolumn{3}{c}{**ECV**} \\ \cline{2-7}  & **Infact** & **Border** & **Remote** & **Infact** & **Border** & **Remote** \\ \hline Mean & 1546 & 1434 & 1344 & 0.49 & 0.39 & 0.32 \\ Median & 1554 & 1389 & 1282 & 0.47 & 0.38 & 0.31 \\ Q1, Q3 & 1363, 1700 & 1274, 1553 & 1210, 1442 & 0.36, 0.62 & 0.31, 0.46 & 0.27, 0.37 \\ \hline  & \multicolumn{3}{c}{**FA**} & \multicolumn{3}{c}{**MD (1 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s)**} \\ \cline{2-7}  & **Infact** & **Border** & **Remote** & **Infact** & **Border** & **Remote** \\ \hline Mean & 0.25 & 0.28 & 0.29 & 1.81 & 1.69 & 1.46 \\ Median & 0.23 & 0.27 & 0.29 & 1.84 & 1.68 & 1.46 \\ Q1, Q3 & 0.18, 0.30 & 0.21, 0.33 & 0.23, 0.35 & 1.52, 2.12 & 1.45, 1.92 & 1.32, 1.60 \\ \hline  & \multicolumn{3}{c}{**e\({}_{1}\) (1 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s)**} & \multicolumn{3}{c}{**e\({}_{2}\) (1 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s)**} \\ \cline{2-7}  & **Infact** & **Border** & **Remote** & **Infact** & **Border** & **Remote** \\ \hline Mean & 2.27 & 2.17 & 1.91 & 1.80 & 1.65 & 1.40 \\ Median & 2.28 & 2.14 & 1.94 & 1.83 & 1.65 & 1.38 \\ Q1, Q3 & 1.98, 2.55 & 1.90, 2.39 & 1.74, 2.10 & 1.48, 2.11 & 1.37, 1.92 & 1.22, 1.58 \\ \hline  & \multicolumn{3}{c}{**e\({}_{3}\) (1 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s)**} & \multicolumn{3}{c}{**RD (1 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s)**} \\ \cline{2-7}  & **Infact** & **Border** & **Remote** & **Infact** & **Border** & **Remote** \\ \hline Mean & 1.38 & 1.25 & 1.06 & 1.59 & 1.45 & 1.23 \\ Median & 1.42 & 1.24 & 1.06 & 1.62 & 1.45 & 1.22 \\ Q1, Q3 & 1.09, 1.71 & 1.01, 1.48 & 0.91, 1.23 & 1.29, 1.90 & 1.20, 1.69 & 1.08, 1.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall mean, median, and Q1–Q3 computed across all subjects for native T1, ECV, FA, MD, diffusion tensor eigenvalues, and RD in the infarct, border, and remote regions. Figure 5: Raincloud plots [28] overlaid to the corresponding box plots for FA, MD, eigenvalues e\({}_{1}\), e\({}_{2}\), and e\({}_{3}\), radial diffusivity (RD), extracellular volume fraction (ECV), and native TI in the infarct, border, and remote myocardial regions.
## 2 Mathematical Model
### Objective Function
#### 2.1.2 Output Power of the EV Parking Lot  
The output power of the EV parking lot is equal to  
\[P_{parkch}=P_{v}*n \tag{3}\]  
where \(P_{v}\) represents the power rate at which the EV is charged, \(n\) represents the number of available vehicles in the parking lot, and \(P_{parkch}\) denotes the total electrical power that the parking lot can supply to the connected vehicles. This capacity is determined by the parking lot's charging capacity and the number of charging connectors it possesses. The charging capacity indicates the maximum power that the charging station can deliver at any given moment. This capacity may vary depending on factors such as the type of charging station (AC or DC fast charging), the capabilities of the charging infrastructure, and constraints within the local electrical grid.  
For example, if you have a DC fast charging station with a charging capacity of 150 kW and it has two charging connectors, the total output power of the station would be  
Output Power = 150 kW \(\times\) 2 = 300 kW  
This means that the station can deliver a maximum of 300 kilowatts of power to the connected electric vehicles simultaneously.  
It is important to consider that although the charging station has a specific output power capacity, individual vehicles may not always draw the maximum available power. The charging rate of each EV is influenced by factors such as the capabilities of the vehicle's onboard charger, the battery's state of charge, and the power supply of the charging station. As a result, the actual charging rate of each vehicle may be lower than the station's maximum output power.
###### Abstract
The browning phase (Figure 2) begins with a decrease in soil moisture and increase in both surface temperature and vapor pressure deficit, which triggers the closure of the stomata to prevent water loss through transpiration. The closure of the stomata prohibits the flow of CO\({}_{2}\) into the leaf and thus reduces GPP. Figure 1: The location of the sites and the dominant land cover classes in Sahel. The Sahelian borders are based on the 150- and 700-mm annual rainfall isohyets. Dominant land cover types are according to the Food and Agriculture Organization’s 2014 Global Land Cover-SHARE database. Grassland and croplands together cover more than 67% of the total surface area of the Sahel. Most studies covering the Sahel base their results on a handful of sites, due to the scarcity of data on C fluxes over the region's 3.3 million km\({}^{2}\) of surface area [27]. Thus, Earth observation is an important tool for large-scale studies of the GPP of Sahelian ecosystems. The only global model of satellite-driven GPP estimates is the MOD17 production efficiency model [28], which is based on the light-use efficiency concept [29]. However, this dataset has been shown to considerably underestimate GPP over semi-arid Sahelian ecosystems [21; 30]. Recent work by [PERSON] et al. [31] found that the shortwave infrared spectral range of 2105-2155 nm, corresponding to band 7 in the moderate-resolution imaging spectroradiometer (MODIS) satellite sensor, provides optimal sensitivity to variations in soil moisture. Furthermore, aerosol effects are negligible because most aerosol particulates are smaller than that wavelength range [32]. The drying and wetting processes that influence vegetative growth could be captured by satellite sensors that cover this spectral range because soil moisture at the top 1-2 cm of soil significantly influences soil surface reflectance at these wavelengths [33; 34]. Another recent study by [PERSON] and [PERSON] [35] found that shortwave infrared radiation within the spectral range of 1850-2130 nm was sensitive to soil volumetric water content. Consequently, there is potential utility of this spectral range in C cycle studies that focus on Sahelian settings. This study has two objectives: (1) To identify the relative strengths of biophysical limitations that reduce CO\({}_{2}\) uptake in the Sahel; and (2) evaluate empirical relationships between Earth observation-derived spectral products that can account for these limitations to better explain the variability in eddy covariance (EC) GPP using Earth observation data.