esa-sceva/satcom-chunk-collection huggingface dataset:
# Nutrition in Pediatric Intensive Care: A Narrative Review
## 5 Macronutrients
### Carbohydrates  
Carbohydrates contain 4 kcal/1 g and should represent between 40 and 60% of E intake. In enteral feeding, the carbohydrate can be delivered as monosaccharides (glucose, fructose), oligosaccharides (lactose, mannose, dextrins) and polysaccharides (starch). Rapid glycolysis after carbohydrate delivery leads to a breakdown of oligomers and polymers to monomers, where glucose is the main final carbohydrate E substrate transported to the tissues. In the case of parenteral nutrition, the carbohydrates are delivered in the form of 5%, 10%, 20%, 40% or even 50% glucose (dextrose) solutions. The preferred glucose concentration is limited by the route of delivery (a peripheral intravenous line can be used up to 850 \(\mathrm{m}\mathrm{m}\mathrm{o}\mathrm{m}\mathrm{o}\mathrm{l}\)/L \(\approx\) 12.5% glucose solution or lower) and fluid intake (higher concentration preferred in patients with limited daily fluid intake). The carbohydrate/glucose delivery should meet the metabolic/energy demands but should not lead to overfeeding, which might cause hyperglycemia and excessive CO2 production. Glucose/carbohydrate delivery aiming to meet the basal E needs should be between 0.5 and 10 mg/kg/min (0.7-14 g/kg/day) [36], with a lower threshold in older children and the acute phase of critical illness, higher in younger patients and later (recovery) in the course of ICU stay. The so-called tight glucose control (aiming to maintain the laboratory glycemia reference levels) is associated with worse outcomes, and it is not recommended [37]; however, hypoglycemia (<2.5 \(\mathrm{m}\mathrm{o}\mathrm{m}\mathrm{l}\)/L) and/or hyperglycemia (>10 \(\mathrm{m}\mathrm{m}\mathrm{o}\mathrm{l}\)/L) should be aggressively treated in all patients [36]. Blood glucose can be monitored by validated point-of-care (POCT) glucometers from peripheral blood or capillary blood samples in a bedside manner; however, in patients with shock and high vasopressor requirements, an arterial blood sample or central venous blood sample should be preferred.
# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
## 2 Trajectory Planning Based on MINCO Trajectory Classes
### Differential Flatness-Based Control Optimization Trajectories
#### 2.1.1 Differential Flatness System  
For a control system, it can usually be represented in the following form:  
\[\dot{x}=f(x,u),x\in R^{n},u\in R^{m} \tag{1}\]  
\[z=g(x),z\in R^{m} \tag{2}\]  
where \(x\) is the state variable, \(u\) is the control variable, \(z\) is the output variable, and \(f\) and \(g\) are the corresponding mappings. A differential flat system is defined as follows: if the state variables and input variables of a system can be determined by the output variables and their finite element derivatives, then the system is a differential flat system. If the system expressed in Equations (1) and (2) is a differential flat system, then \(x\) and \(u\) can be expressed by the finite element derivatives of \(z\):  
\[x=\Psi_{x}(z,\dot{z},\ldots,z^{(s-1)}) \tag{3}\]\[u=\Psi_{u}(z,\dot{z},\ldots,z^{(s)}) \tag{4}\]  
where \(\Psi_{x}\) and \(\Psi_{u}\) can be determined from \(f\) and \(g\). If the outputs are known, all the system variables can be determined without combining system Equations (1) and (2). The differential flatness property can be easily utilized to satisfy the constraints of the system equations and state variables, control variables, and output variables during trajectory generation. If a new set of control variables \(v=z^{(s)}\) is introduced, then define \(z^{[s-1]}\in R^{ms}\):  
\[z^{[s-1]}=(z^{T},\dot{z}^{T},\ldots,z^{(s-1)^{T}})^{T} \tag{5}\]  
In this way, the control variables \(u=\Psi_{u}(z^{[s-1]},v)\) can decouple the original differential flat system into an integral chain with \(m\) dimension [28]. \(z_{i}\) and \(v_{i}\) are used to denote the I dimension of \(z\) and \(v_{i}\); denoting the \(i\)th dimension in \(z\) and \(v\) by \(z_{i}\) and \(v_{i}\), respectively, the \(i\)th integral chain can be expressed as:  
\[\dot{z}_{i}^{[s-1]}=\begin{pmatrix}\mathbf{0}&\mathbf{I}_{s-1}\\ \mathbf{0}&\mathbf{0}^{T}\end{pmatrix}z_{i}^{[s-1]}+\begin{pmatrix}\mathbf{0} \\ \mathbf{1}\end{pmatrix}v_{i} \tag{6}\]  
where \(\mathbf{I}\) and \(\mathbf{0}\) are the unit and zero matrices with appropriate dimensions. If the initial and termination states are provided, the boundary conditions for the integral chain shown in Equation (6) can be computed. Finally, all state trajectories and control inputs satisfying the original system equations are obtained by integrating the output trajectories obtained from this m-dimensional integration chain and Equations (3) and (4).
# Effect Range of the Material Constraint in Different Strength Mismatched Laboratory Specimens
## 2 Different Strength Mismatched Laboratory Specimens
### GTN Damage Model  
To obtain the fracture resistance curves and crack propagation paths of different strength mismatched laboratory specimens under various material constraints, the finite element method (FEM) simulation based on Gurson-Tvergaard-Needleman (GTN) damage model was used to simulate the progress of crack growth in the specimens. There are nine parameters in the GTN damage model, and these parameters of the four materials have been obtained and verified, as shown in Table 1[27].  
The GTN damage model has been implemented in the ABAQUS code. During the finite element analysis, the 4-node plane strain quadrilateral element with reduced integration (CPE4R) was used.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Material** & \(q_{1}\) & \(q_{2}\) & \(q_{3}\) & \(\varepsilon_{N}\) & \(S_{N}\) & \(f_{N}\) & \(f_{\theta}\) & \(f_{C}\) & \(f_{F}\) \\ \hline A508 & 1.5 & 1 & 2.25 & 0.3 & 0.1 & 0.002 & 0.00008 & 0.04 & 0.25 \\
52 Mb & 1.5 & variable & 2.25 & 0.3 & 0.1 & 0.002 & 0.00001 & 0.04 & 0.25 \\
52 Mw & 1.5 & 1 & 2.25 & 0.3 & 0.1 & 0.002 & 0.00015 & 0.04 & 0.25 \\
316L & 1.5 & variable & 2.25 & 0.3 & 0.1 & 0.002 & 0.00001 & 0.04 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The nine Gurson-Tvergaard-Needleman (GTN) damage parameters of the four materials in the DMWJ [27].  
Figure 3: The geometries and loading configurations of different kinds of specimens, (**a**) CT-“121” specimen, (**b**) SENT-“121” specimen, (**c**) CCT-“121” specimen, (**d**) CT-“1231” specimen, (**e**) SENT-“1231” specimen, and (**f**) CCT-“1231” specimen.  
The typical finite element meshes of the CT-\"121\" model with \(a/W=0.5\) is illustrated in the Figure 4a. In the crack growth region, the minimum mesh size is 0.1 mm \(\times\) 0.05 mm [28], as shown in the Figure 4b. The mesh sensitivity study has been done and the same mesh size was used in all the models. This typical model contains 25208 elements and 25730 nodes.  
Based on the FEM simulation results, the fracture resistance curves and crack propagation paths of different strength mismatched laboratory specimens under various material constraints can be determined [6].
# Mining Pool Selection under Block WithHolding Attack +
## 5 Numerical Results
### Results and Discussions
#### 5.2.2 One-Side Attack Case
This is also unreasonable, because the advantage of the attacking pool decreases due to the reduction of mining revenue in this situation, making the attacking pool less attractive for miners. The results in both figures show that ignoring the attacking revenue as in [25] may provide us with less accurate and convincing insights into the pool selection problem. Figure 5: Stable population states vs. attack size \(a_{12}\). Figure 6: Impact of considering the attacking revenue. We also investigate the case where pool 2 attacks pool 1 with attack size \(a_{21}=0.015\). Other parameters are the same as those in Figure 3. Under this setting, we obtain two NEs \((0,1)\) and \((1,0)\) by calculating (28). Figure 6(a) plots the phase portrait of the replicator dynamics. We can see from Figure 6(a) that the population states converge to \((0,1)\) from any initial state, implying that the NE \((0,1)\) (i.e., the state where all miners join pool 2) is stable and thus the only ESS. We also show in Figure 6(b) the simulation results for three initial population states \((x_{1},x_{2})=(0.20,0.80)\), \((0.50,0.50)\), \((0.75,0.25)\). The results show that all the population states finally converge to the state \((0,1)\), which agree with the theoretical results. Note that the minimum hash powers required to join pool 1 and pool 2 are \(\omega_{1}=30\) and \(\omega_{2}=20\) in Figures 4 and 7. Comparing the results in these two figures, we can see that a pool with lower hash power requirement is more likely to attract miners when launching the BWH attack.
## Materials and Methods  
### Problem Formulation  
The proposed approach in this paper comprises a CRN with a central point called Cognitive Radio Base Station (CRBS) and randomly deployed N SUs. Along with the CRN, there is a PU set-up that consists of a PU transmitter and many fixed receivers. In our model, the PU transmitter is called the source, and the receivers are TV sinks. The PU transmitter is the source that wants to send signals for communication with TV sinks. CRBS has all the information regarding the PU transmitter, as it is the licensed sender. The diagrammatical representation is shown in Figure 1. The positions of the PU transmitter and SUs are known to the CRBS. The distance of the PU transmitter ranges from 30 km to 100 km. The PU is placed at a certain position outside the CRN.  
This work aims to detect the attacker (PUEA) in or out of the CRN by maximum precision to accurately recognize the invader when it is close to the PU by lessening the localization positioning inaccuracy while diminishing the quantity of required coordinating clients and the time to localize the attacker.  
The following assumptions are made in the detection of a PUEA:  
The CRN does not have any information regarding the malicious node or its scheme. The PUEA is located near the PU or positioned within the CRN.  
The PUEA radio conduct is similar to that of the PU.  
The signal characteristics of the PUEA can be the same or different from that of the PU.  
Figure 1: System design.
## 2 Related Work
### Collaborative Networks  
Collaborative networks are designed to share resources and functions only when the network has available resources and does not affect its operation. In this case, the network members are weakly committed since the rules are not strict and there are no protocols with solid conditions. The network is prioritized over the nodes [49]. One of the advantages of collaborative networks is that it avoids saturation since they will share their resources when available. Unlike cooperative networks in this type of network, the aim is to maintain a good Quality of Service (QoS).  
In work cited in [50], collaborative networks are proposed to improve the spectrum gain capacity. They explain that in a random distribution of nodes in the sensor network, it is difficult to determine the exact location of a sensor. Without a central controller, it is almost impossible to achieve phase, frequency and time synchronization. This can lead to errors in estimating the sensor nodes, such as displacement errors. As a consequence, the reception at the Base Station (BS) would be out of phase. In order to achieve a high power gain, collaborative communication must synchronize the received signals in time, phase and frequency. In addition to synchronization, the collaboration should exploit the characteristics of the spread spectrum to achieve space/antenna diversity to improve the signal-to-power ratio and reduce the Bit Error Rate (BER). However, complete synchronization of the system is almost impossible since position estimation gives the probable position of the transmitter [51].  
[PERSON] et al. [52] describe the use of sensor networks, which consist of small, low-cost sensor nodes that cover a specific region of interest and transmit data to a base station. Multi-hop transmission is used to minimize power consumption in large regions with only one base station. With regard to their roles, the sensor nodes are divided into three types: sensor nodes, routers and relay nodes. However, there is a lack of energy fairness in multi-hop routing, leading to losing the nodes responsible for transmitting data from the region of interest to the base station. Researchers have introduced dynamic routing protocols to overcome this issue, but these protocols produce high network overhead. In contrast, static routing protocols have a single setup phase but suffer from premature end of network lifetime. The authors of the paper propose an energy-efficient routing protocol called Collaborative Distributed Antenna that is based on a connectivity aware algorithm to avoid premature end of network lifetime.
## 3 Results and Discussion
### Effect of Erosion Time on Chloride Ion Diffusion Coefficient  
Figure 5 shows the evolution of the chloride ion diffusion coefficient of concrete after different temperatures. Concrete's chloride ion diffusion coefficient at different elevated temperatures exhibits a gradual attenuation law with erosion days.  
Figure 4: Chloride ion diffusion coefficient with respect to different compressive stress ratios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Axial Compression Ratio**} & \multicolumn{4}{c}{**Temperature/\({}^{\circ}\)C**} \\ \cline{2-5}  & **20** & **200** & **400** & **600** \\ \hline
0.1 & 1 & 1 & 1.105 \\ \hline
0.2 & 1 & 1 & 1 & 1.412 \\ \hline
0.3 & 1 & 1 & 1.173 & 1.656 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The compression influence factor \(\beta\) on chloride ion diffusion coefficient.  
Based on the diffusion coefficient of the chloride ion proposed by [PERSON] and [PERSON] [21], the recovery factor (\(m_{T}\)) on the chloride ion diffusion coefficient of concrete after heating was introduced:  
\[\frac{D_{t}}{D_{t0}}=\left(\frac{t_{0}}{t}\right)^{m_{T}} \tag{5}\]  
Using 28 days as the reference for erosion days, the linear relationship between the recovery factor and the temperature was obtained by fitting Figure 5.  
\[m_{T}=-0.0011T+0.8901 \tag{6}\]  
Chloride ion diffusion coefficient of concrete of different erosion days with respect to different compressive stress ratios are shown in Figure 6. It was evident that the influence was minimal when the stress ratio was 0.2 and below, at 20 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, and 400 \({}^{\circ}\)C. The curves differed significantly when the stress ratio was 0.3 at 400 \({}^{\circ}\)C and for all the stress ratios at 600 \({}^{\circ}\)C if only the chloride ion diffusion recovery coefficient was considered. To make sure the model was accurate, the compressive stress ratio correction factor \(n_{\sigma}\) was proposed:  
\[\frac{D_{t}}{D_{t0}}=\left(\frac{t_{0}}{t}\right)^{m_{T}-n_{\sigma}} \tag{7}\]  
As shown in Figure 7, the value of \(\left(\frac{t_{0}}{t}\right)\) at 400 \({}^{\circ}\)C and 600 \({}^{\circ}\)C for each stress was obtained by a fitting method according to Equation (7), and divided by the value under no stress. The compressive stress ratio correction factor \(n_{\sigma}\) is listed in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Temperature/\({}^{\circ}\)C**} \\ \cline{2-5}
**Axial Compressive Ratio** & **20** & **200** & **400** & **600** \\ \cline{2-5}  & \(n_{\sigma}\) & \(n_{\sigma}\) & \(n_{\sigma}\) & \(n_{\sigma}\) \\ \hline
0.1 & 1 & 1 & 1 & 0.902 \\ \hline
0.2 & 1 & 1 & 1 & 0.629 \\ \hline
0.3 & 1 & 1 & 0.893 & 0.445 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Compressive stress ratio correction factor for chloride ion diffusion coefficient.  
Figure 5: Chloride in diffusion coefficient with erosion days.
## 4 Results and Discussion
### The Performance of PEM-FC  
The curve's polarization is the most critical feature of an FC in terms of performance. It can be used for FC sizing, tracking, and diagnostics, among other things [23; 24; 25]. Figure 13 shows how ohmic losses, the losses of activation polarization, and the losses of concentration polarization are subtracted from the potential equilibrium to generate the cell polarization curve. The activation losses' cathode and anode are grouped for each other; however, due to the oxygen reduction reaction's slowness, most of the losses occur on the cathode. Figure 14 shows the power diagram of the cell, which explains that the maximum power is 0.63 watt and occurred at a nominal current density of 1.04 A/cm\({}^{2}\).  
The efficiency of FCs with various geometric shapes and identical operating conditions has been investigated. The cross-sections of the five different channels were simulated, with identical conditions in each case. According to the modeling used for these various geometrical channels, it was shown that the rectangular channel cross-section gave better FC voltages, and higher power (0.63 W) from the FC, despite its lower longevity compared to the triangular channel. Therefore, the triangular channel gives better performance compared to other innovative channels, as shown in Figure 15. The rectangular cross-section configuration channel causes a maximum pressure drop through the channel followed by the channel with the polygon cross-sectional area. These configurations of channels have a higher pressure drop, for the corresponding gas flow rate, than that of other cross-sectional configurations. Therefore, it is obvious that the use of the rectangular channel led to an increase in the consumption of hydrogen at the anode, which, in turn, will increase the power produced from the PEM-FC.  
Figure 12: von Mises stress variations (MPa) in the PEM-FC through the process of assembly at the 1 mm\({}^{2}\) cross-section area with scale increased 50 times.  
Figure 13: The polarization curve and various potential losses in the PEM-FC.
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 6 Evaluation and Results
### Non-Static Scenarios
#### 6.4.2 Displacement of the Concentration of Users  
In the second non-static scenario, after 2500 iterations, the concentration of users, which in the initialization is a Gaussian centered at the point (4500 m, 4500 m), is gradually moved (0.25 m/iteration equivalent to 3 km/h) until the position (1500 m, 4500 m). This scenario could be, for instance, a demonstration, where a big concentration of users is moving simultaneously.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \
\begin{tabular}{c} **\(\overline{\mathbf{R_{T}}}\)** \\ **(kbps)** \\ \end{tabular} & **Fairness** &
\begin{tabular}{c} **TSBB** \\ **(MHz)** \\ \end{tabular} \\ \hline Proposed & 9.29 & 0.99999 & 179.94 \\ \hline Spiral & 9.40 & 0.72860 & 200.64 \\ \hline \(k\)-Means & 9.47 & 0.86724 & 187.86 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the different algorithms.  
In Figure 7, this displacement of the Gaussian can be deduced by observing the red cross. Moreover, in this figure, some UAVs seem to overlap, but it is only due to the fact that they are at different altitudes and, as a consequence, the upper UAVs serve the far users. Regarding the performance of the proposed strategy, in this case, a more unstable state is reached, in other words, the fluctuation of the rate per user at each region is higher. It is produced by these closer UAVs placed at different altitudes (C, F, E and H), as in this situation a small movement of the UAVs implies a higher variation of the number of associated users. However, this fluctuation is not very high and does not affect the final results of the strategy, as both the achieved fairness and the rate per user are still high (0.99995 and 8.82 kbps, respectively), and all the constraints are still fulfilled on average.  
Figure 6: Rearrangement of UAVs when one UAV decays: positions and coverage areas (**top**); and evolution of the rate per user in each coverage area (**bottom**). Each color in the bottom figure represents one coverage area using the same colors as the ones assigned to letters in the top figure.
# Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study
###### Abstract  
As living standards improve, the growing demand for energy, comfort, and health monitoring drives the increased importance of innovative solutions. Real-time recognition of human activities (HAR) in smart homes is of significant relevance, offering varied applications to improve the quality of life of fragile individuals. These applications include facilitating autonomy at home for vulnerable people, early detection of deviations or disruptions in lifestyle habits, and immediate alerting in the event of critical situations. The first objective of this work is to develop a real-time HAR algorithm in embedded equipment. The proposed approach incorporates the event dynamic windowing based on space-temporal correlation and the knowledge of activity trigger sensors to recognize activities in the case of a record of new events. The second objective is to approach the HAR task from the perspective of edge computing. In concrete terms, this involves implementing a HAR algorithm in a \"home box\", a low-power, low-cost computer, while guaranteeing performance in terms of accuracy and processing time. To achieve this goal, a HAR algorithm was first developed to perform these recognition tasks in real-time. Then, the proposed algorithm is ported on three hardware architectures to be compared: (i) a NUCLEO-H753 ZI microcontroller from ST-Microelectronics using two programming languages, C language and MicroPython; (ii) an ESP32 microcontroller, often used for smart-home devices; and (iii) a Raspberry-PI, optimizing it to maintain accuracy of classification of activities with a requirement of processing time, memory resources, and energy consumption. The experimental results show that the proposed algorithm can be effectively implemented on a constrained resource hardware architecture. This could allow the design of an embedded system for real-time human activity recognition.  
real-time human activity recognition; embedded equipment; smart home; home automation sensors; edge computing; resource-constrained devices +
Footnote †: journal: Applied Sciences
# Low Discrepancy Sparse Phased Array Antennas
## 2 Sampling Points on a Planar Aperture
### Low Discrepancy Sampling Approaches
#### 2.2.1 Hammersley Sampling  
Any positive integer can be represented by a prime base as  
\[n=\sum_{k=0}^{L(n)}a_{k(n)}b^{k} \tag{6}\]  
where, \(b\) is the prime base in which number \(n\) is represented, \(a_{k}(n)\) is an integer in \([0,b-1]\), and \(L(n)\) is the lowest integer value that allows expressing the integer \(n\) in base \(b\) as the  
Figure 2: A generic representation of the position of points on a unit-square with different distributions: (**a**) uniform, (**b**) random, (**c**) LDS Poisson disk. The total number of elements is equal in all three cases but note that the LDS method fills the space more uniformly while avoiding too close spacings.  
summation of \(L(n)+1\) terms. The van der Corput sequence [42; 43], is a one-dimensional low-discrepancy sequence over the unit-interval and is given by  
\[\mathbf{\Psi}_{b}(n)=\sum_{k=0}^{L(n)}a_{k(n)}b^{-k-1} \tag{7}\]  
For a binary representation, i.e., base 2, the van der Corput sequence can be written as  
\[\mathbf{\Psi}_{2}(n)=\frac{a_{0}}{2}+\frac{a_{1}}{2^{2}}+\ldots+\frac{a_{L(n)} }{2^{L(n)+1}} \tag{8}\]  
Hammersley sampling utilizes the van der Corput sequence that results in a two-dimensional LDS sampling in the rectangular grid defined by  
\[(\alpha_{n},\beta_{n})=\Big{(}\frac{n}{N},\mathbf{\Psi}_{b}(n)\Big{)},n=0,1,2,\ldots,N-1. \tag{9}\]  
Hamersley sampling is not hierarchical due to the \(n/N\) term in (8), because adding an additional element, does require recalculating the previous element locations.