esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Modeling of the Maximum Power Point with Series Resistance
## 4 Analytical Expression for the Series Resistance
### Validity of the Approximate Expression  
Equation (24) sets the limit for the range of \(r\) where Equation (19) describes the physical behavior of \(V_{\rm{mpp}}\). As the exponential function only yields positive values, the argument of [PERSON]'s W in Equation (24) is negative. The principal branch of [PERSON]'s W function, \(\text{W}_{0}(z)\), is only defined for \(z\geq-1/e\), which implies that \(\text{W}(z)\
otin\mathcal{R}\) for \(z\leq-1/e\). Since the series resistance is a real-valued physical quantity, the argument of the W function in Equation (24) must fulfill:  
\[\exp\left[-1+\frac{V_{\rm{oc}}}{V_{\rm{t}}}-2\frac{V_{\rm{mpp}}}{V_{\rm{t}}} \right]\leq\frac{1}{e}, \tag{25}\]  
which implies:  
\[V_{\rm{mpp}}\geq\frac{1}{2}V_{\rm{oc}}. \tag{26}\]  
Equation (26) sets an upper limit for the series resistance, as having \(V_{\rm{mpp}}\) less than \(\frac{1}{2}V_{\rm{oc}}\) would require a complex-valued \(r\). This translates into Equation (15) not describing a physical \(V_{\rm{mpp}}\) for any value of the series resistance, which would make \(V_{\rm{mpp}}\) smaller than \(V_{\rm{sc}}/2\). This maximum value of the resistance, which is denoted in the present work by \(r_{\rm{max}}\), is found by evaluating Equation (24) at \(V_{\rm{mpp}}=V_{\rm{sc}}/2\). This yields:  
\[r_{\rm{max}}=r\bigg{[}\frac{V_{\rm{oc}}}{2}\bigg{]}=\frac{V_{\rm{oc}}/2}{i_{ \rm{sc}}}+\frac{V_{\rm{t}}}{i_{\rm{sc}}}\bigg{(}\text{W}\left[-\frac{1}{e} \right]+1\bigg{)}=\frac{V_{\rm{oc}}}{2i_{\rm{sc}}}.\]  
Note that at \(r=r_{\rm{max}}\), the parentheses in Equation (19) cancel out. For \(r\geq r_{\rm{max}}\), the W function tends asymptotically to zero. This results in \(V_{\rm{mpp}}\) increasing linearly with \(r\) with slope \(i_{\rm{sc}}\), which is not physical.
# Evaluating the Impact of Pre-Configured Uplink Resources in Narrowband IoT
## 2 NB-IoT
### Operational States
#### 2.1.2 Preconfigured Uplink Resources
However, to prevent excessive interference, shared PURs' effectiveness are limited to low-Signal-to-Interference-and-Noise Ratio (SINR) regimes. A cap on the number of UEs employing shared PURs is implemented to manage the potential risk of DL congestion, as simultaneous DL transmissions cannot overlap. The shared PURs additionally require the assignment of distinct PUR C-RNTIs for each of the two UEs transmitting simultaneously. In contrast, dedicated PURs allow te use of a single PUR C-RNTI by multiple UEs, as their use of resources occurs at different points in time. PURs employ a single Hybrid Automatic Repeat reQuest (HARQ) [26] process and can be combined with other features to enhance performance.
## 3 Monitoring Results and Analysis
### Axial Force in the Anchor Cables  
Figure 19 has shown the time history diagram of the axial forces in the anchor cables. It can be seen that the axial forces displayed a rapid drop within the first five days after tensioning. This means that the anchor cables suffered a prestress loss, with the values accounting for between 1.8-7.6% of the initial tensile forces. Moreover, the average prestress loss after the first day was about 44% of that after the first five days. The prestress losses of the anchor cables after the first five days were mainly caused by compaction of the rock; the rate of the prestress losses then decreased gradually. The subsequent excavation resulted in a rise in the axial forces in anchor cables, which aimed to restrain the convergence of the surrounding rock that was caused by excavation disturbance. The axial forces in the anchor cables finally reached a stable state. The prestress loss rate of the anchor cable at Monitoring Point F was highest, with a value of 9.2%.  
Figure 20 has shown the final distribution of the axial forces in the anchor cables. It can be seen that the distribution of the axial forces in the anchor cables were relatively uniform. The maximum and minimum axial forces were at Monitoring Points D and E, with the corresponding values of 918.4 kN and 632.4 kN, respectively. The design value of the axial force in the monitored anchor cable is 700 kN. It can be seen that except for Monitoring Point E, the monitoring values of the other measuring points reached more than 98% of the design value. In addition, the anchor cable consisted of 7 strands of steel wire with a yield strength of 1550 MPa and a cross-sectional area of 140 mm\({}^{2}\). It could be calculated that the axial forces in the anchor cables accounted for 41.6-60.4% of the yield load, which shows that  
Figure 19: Time history diagram of the axial forces in the anchor cables.  
Figure 18: Final distribution of the internal forces in the four bar lattice girders: (**a**) inner side; (**b**) outer side.  
the anchor cables were not only exerting their load bearing capacity, but also possessed a sufficient safety factor.
# Software Defined Wireless Mesh Network Flat Distribution Control Plane
## 2 FD-SDWMN Architecture  
A centralized controller in SDWMN manages all the switches (mesh routers) of the network (controller-router connection is in a multi-hop). That means each router in the network needs to rely on one controller for forwarding decisions. Thus, for every new flow, each router generates a request to the controller, which responds with appropriate flow entry messages. Obviously, this scenario costs the controller more load and decreases network performance. On the contrary, in FD-SDWMN architecture, the network control has logically divided via network slicing into flat distributed controllers, each of which controls a local domain (controller-router connection is in a single-hop).
## 3 Performance Parameters
### Selected Vectoring Pre-Coding and Cancellation Algorithms
Indicating with \(\mathbf{d}_{k}^{(q)}=\mathbf{L}_{k}^{(q)}\mathbf{b}_{k}\) the corresponding vector of pre-coded symbols and assuming exact estimate of \(\mathbf{C}_{k}\), the vector of the received observables on the \(k\)-th sub-carrier is:  
\[\mathbf{r}_{k}^{(q)}=\mathbf{D}_{k}\left(\mathbf{I}-\mathbf{C}_{k}^{q}\right) \mathbf{b}_{k}+\mathbf{n}_{k} \tag{10}\]  
When \(q=1\) no vectoring pre-coding is applied and \(\mathbf{L}_{k}^{(1)}=\mathbf{I}\). The \(\mathbf{C}_{k}^{q}\) in (10) is responsible for the residual FEXT in the received observables. In the US case the received observables on the sub-carrier at frequency \(f_{k}\) at the DSLAM can be written as in (6) and now the channel matrix \(\mathbf{H}_{k}\) is:  
\[\mathbf{H}_{k}=(\mathbf{I}+\mathbf{E}_{k})\mathbf{D}_{k}. \tag{11}\]  
The elements of the \(N\times N\) matrix \(\mathbf{E}_{k}\) are \(e_{ij}^{(k)}=H_{F,k}^{(i,j)}(d_{i}))/H_{D,k}(d_{j})\) and \(|e_{ij}^{(k)}|\ll 1\) for \(i\
eq j\) and \(e_{ii}^{(k)}=0\) for each \(i\). The diagonal matrix \(\mathbf{D}_{k}\) contains the direct propagation terms on the main diagonal, i.e., \(H_{D,k}(d_{i})\), \(i=1,2,\ldots,N\). FEXT cancelation at the DSLAM is adopted to remove interference and the vector of observable is multiplied by the inverse of the channel matrix (i.e., zero forcing cancelation). Considering a practical implementation of the ZF algorithm approximating the inverse of the channel matrix as in (9) we have:  
\[\mathbf{H}_{k}^{-1}\cong(\mathbf{I}-\mathbf{E}_{k}). \tag{12}\]  
From (6) and using (12) the observables after cancellation are:  
\[\mathbf{r}_{k,US}^{(1)}=(\mathbf{I}-\mathbf{E}_{k}^{2})\mathbf{D}_{k}\mathbf{ b}_{k}+\mathbf{v}_{k}, \tag{13}\]  
and \(\mathbf{v}_{k}=(\mathbf{I}-\mathbf{E}_{k})\mathbf{n}_{k}\) is the noise term after cancellation. Since \(|e_{ij}^{(k)}|\ll 1\) we can still assume \(\mathbf{v}_{k}\cong\mathbf{n}_{k}\). The term \(\mathbf{E}_{k}^{2}\) is the responsible for the residual FEXT term in the US received observables leading to degradation in vectoring performance.
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 4 Details of the Implementation
### Tangent Convex Arc Algorithm  
This algorithm produces the list of the pixels that make up the smallest free convex arc that links two points \(A\) and \(B\) on the contour of an obstacle. This list is ordered in the anti-clockwise direction. Figure 14(a) shows such a path as a dotted black line. This convex arc is easily constructed as a boundary of the convex hull of the contour arc \(AB\); the part of the convex hull that is not in the obstacle is shown in pale gray in Figure 14(b). Convex hulls can be computed with any of the well-known algorithms.
## 4 Self-Healing Approaches
### Machine-Learning Algorithms
#### 4.3.9 Hybrid Calibration Algorithm  
A hybrid calibration algorithm (HCA) is an optimisation algorithm that combines different optimisation techniques to find optimal solutions. HCA can calibrate system parameters or identify the best configuration for a system based on performance criteria. It combines different optimisation algorithms, such as genetic algorithms, particle swarm optimisation, and simulated annealing, into a hybrid approach that exploits the strength of each algorithm. The different algorithms are typically used sequentially or parallel, with the output of one algorithm serving as an input to the following algorithm. HCA was first proposed by [15], combining two direct search algorithms of the Nelder-Mead simplex and Hooke-Jeeves pattern search methods. Nelder-Mead is a popular algorithm for its ability to vary the search directions in the response space at each iteration, while Hooke-Jeeves maintains a well-conditioned search. The hybrid calibration algorithm leverages the advantages of Nelder-Mead and Hooke-Jeeves algorithms to provide robust calibration performance for the upward dimensional self-healing radio frequency-integrated circuit (RFIC) calibration problems encountered in their experiment. The HCA leverages the advantages of both methods to provide robust calibration performance for the relatively high-dimensional self-healing RFIC calibration problems often overlooked in the real world. HCA can calibrate system parameters, such as tuning the control parameters for a feedback control system, to improve system performance and resilience. HCA can also identify the best configuration for a plan based on performance criteria, such as selecting the best combination of hardware or software settings. An advantage of HCA is its ability to combine different optimisation techniques to find optimal solutions quickly and efficiently. HCA can also adapt to changes in the system over time. The potential limitation is its complexity, making it challenging to implement and optimise. It may also require significant computational resources, especially in complex or large search spaces, coupled with its reliance on well-defined functions, which can be difficult to specify in some situations.
## 3 Results  
A total of 22 patients, 2 caregivers, and 24 professionals were included in the trial. The baseline characteristics are summarized in Table 2.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Overall (n = 22)** & **Home Study (n = 14)** & **Clinical Setting Study (n = 8)** \\ \hline
**Patients** & & & \\ \hline Age, mean years (SD) & 63.4 (10.2) & 63.4 (9.9) & 63.4 (11.4) \\ \hline Digital literacy, n (\%) & & & \\ \hline Use of internet & 17 (77.3) & 12 (85.7) & 5 (62.5) \\ \hline Computer & 14 (63.6) & 10 (71.4) & 4 (50.0) \\ \hline Smartphone & 21 (95.5) & 14 (100.0) & 7 (87.5) \\ \hline Tablet & 7 (31.8) & 4 (28.6) & 3 (37.5) \\ \hline TV & 19 (86.4) & 13 (92.9) & 6 (75.0) \\ \hline Education, mean years completed (SD) & 11.8 (4.0) & 12.0 (4.0) & 11.4 (4.3) \\ \hline Retired, n (\%) & 10 (45.5) & 6 (42.9) & 4 (50.0) \\ \hline Retirement age, mean years (SD) & 56.7 (14.0) & 60.9 (6.4) & 49.5 (21.4) \\ \hline MMSE, mean score (SD) & 28.6 (1.4) & 29.1 (1.1) & 27.9 (1.5) \\ \hline EQ-5D-5L, mean score (SD) & & & \\ \hline Mobility & 1.7 (0.8) & 1.4 (0.7) & 2.0 (0.9) \\ \hline Self-care & 1.2 (0.6) & 1.2 (0.6) & 1.3 (0.7) \\ \hline Usual activities & 1.3 (0.6) & 1.3 (0.6) & 1.3 (0.7) \\ \hline Pain/discomfort & 2.0 (0.9) & 1.9 (0.9) & 2.1 (0.8) \\ \hline Anxiety/depression & 1.8 (0.9) & 1.9 (0.9) & 1.6 (0.7) \\ \hline General health (VAS scale) & 68.6 (17.5) & 71.1 (15.5) & 64.4 (21.1) \\ \hline Index value & 0.8 (0.2) & 0.8 (0.2) & 0.8 (0.1) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Baseline characteristics of the patients.
## 6 Conclusions  
The design and implementation of the proposed OECS, which is based on the total energy of an aircraft, has been presented in this study. OECS is an aircraft longitudinal flight control system which is based on pilot-like control strategy to control the airspeed and altitude of the aircraft. The controller design process begins with the postulation of the aircraft model, from aircraft equations of motion and energy equations. After that, the postulated model was identified using the system identification method. The identified models were used to design the developed OECS. Finally, OECS was verified in HIL system of Spoonbill UAV. Three flight maneuvers, level flight, descending, and climbing, were adopted to perform the simulations. In addition, the influence of the wind effect was considered to validate the reliability of the developed system. The largest magnitude of wind was set to 8 m/s. The simulation results show that the implementation of the proposed OECS for different flight maneuvers and flight conditions was successfully validated in the HIL simulation system of the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Condition** & \multicolumn{3}{c}{**Windless**} & \multicolumn{3}{c}{**Wind**} \\ \hline
**Maneuver** & **Level** & **Climb** & **Descend** & **Level** & **Climb** & **Descend** \\ \hline Altitude Tracking (m) & 150 & 150\(\rightarrow\)158 & 158\(\rightarrow\)150 & 150 & 120\(\rightarrow\)150 & 150\(\rightarrow\)120 \\ Settling Time (s) & - & 40 & 40 & - & 65 & 65 \\ \(\Delta q\) (rad/s) & 0.01 & 0.01 & 0.01 & 0.15 & 0.15 & 0.15 \\ \(\dot{L}_{\rm s}\) & 0.01 & 0.01 & 0.01 & 0.05 & 0.10 & 0.10 \\ Altitude Deviation (m) & 0.20 & 0.20 & 0.20 & 2.00 & 2.00 & 2.00 \\ Airspeed Deviation (m/s) & 0.10 & 0.20 & 0.20 & 0.50 & 1.00 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: A summary of OECS performance for different flight maneuvers and flight conditions.  
Figure 20: FLC simulation results of descending without the presence of wind.  
used UAV. When comparing the performance and fuel efficiency of the proposed OECS with another flight control system based on Fuzzy logic control, the results show that the proposed OECS has better performance and fuel efficiency than that of FLC.  
This study was supported by the Ministry of Science and Technology of Taiwan under grant number MOST 103-2632-E-035-001-MY3 and MOST 104-2622-E-035-019-CC3. Furthermore, this study was directed by [PERSON], who passed away in February 2014. The authors admire and respect him for his great contributions to aerospace societies in the world.  
[PERSON] determined the research framework, proposed the main idea, and performed mathematical modeling. [PERSON] performed the implementation of the developed algorithm and simulations.  
The authors declare no conflict of interest.
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 2 Advantages and Challenges of mmWave Industry 4.0 and beyond Communication
### Challenges of mmWave Communication  
#### 2.2.1 LOS Blockage  
Industrial wireless communication is different from that of commercial, home, or office wireless communication. This is due to the large metallic structures, heavy machinery, large pillar structures, and random moving objects. Thus, an industrial environment is usually considered as a heavy multi-path and rich scattering [45]. In this view, the narrow beam antennas with beamforming reduce the available path diversity and weaken non-line-of-sight (NLOS) scenarios, which may sometimes cause blockage. Moving robots, human activity, or any other large scatterers and absorbers result in a fast-fading channel. Thus, characterizing radio propagation behavior in realistic factory environments is difficult due to shorter mmWave wavelengths, which cause channel properties to be sensitive to the actual topology and size of surrounding objects. As a result, accurate channel models are required to be used for accurate communication systems [49; 50; 71; 72; 73].
## 4 Results and Discussion
### Steam Remediation of Heterogeneous Aquifers Contaminated by Nitrobenzene
#### 4.3.2 Nitrobenzene Removal in 2-D Layered Heterogeneous Porous Media  
When R = 17.4 and steam injection time was 3 h, the NB concentration in the center of the 2-D simulation tank (the position closest to the steam injection point) was the lowest, and the concentration on both sides was high (Figure 7a). This was primarily due to the effect of buoyancy. The vertical migration of steam was faster, resulting in the preferential removal of vertical pollutants. After 6 h of steam injection, the concentration distribution of NB was affected by the interface, and the concentration was the lowest below the interface, with a concentration of 60 mg/L. After 6 h, the NB removal rate in the simulated tank was 68%, of which the removal rates in gravel and medium sand were 70.3% and 65.3%, respectively, and the average NB concentration in medium sand was 1.13 times that of gravel.  
When R = 38  
3.3 Alcohol-Enhanced Steam Remediation of Layered Heterogeneous Aquifer Contaminated by Nitrobenzene  
After adding ethanol, some heat was accumulated at the interface, but the temperature difference between the upper and lower layers decreased significantly, the average temperature increased by 15 \({}^{\circ}\)C, and the area affected by steam in the low-permeability zone increased by 35% (Figure 8). [PERSON] et al. (2005) found that Marangoni convection could enhance liquid phase heat transfer through experimental and numerical simulation [41].  
Figure 6: Temperature distribution of the layered heterogeneous aquifers (**a**) R = 17.4 (**b**) R = 381.  
Figure 7: Isograms of NB concentrations with different R values: (**a**) R = 17.4; (**b**) R = 381.  
Therefore, the addition of ethanol produces Marangoni convection and enhances mass and heat transfer.  
After adding ethanol, the remediation effect of NB was significantly increased, particularly in the fine sand medium (Figure 9) and the average NB concentration in the simulated tank was reduced by 51%. The average concentration of NB in the lower gravel and upper fine sand decreased by 43.7% and 43.5%, respectively, indicating that ethanol addition enhanced the gas-liquid mass transfer. During the repair process, ethanol volatilized at the gas-liquid interface, forming a surface tension gradient at the interface and increasing the convective vortices in the flow field, resulting in the Marangoni convection [20; 40; 42]. The Marangoni convection can also enhance heat transfer, and an increase in temperature also enhances NB removal. In addition, ethanol reduces the azeotropic temperature of NB, promoting the boiling of NB at 83.2 \({}^{\circ}\)C, thus, improving the removal rate and efficiency of NB.
## 3 Methodology
### Two-Phase Flow Module  
Debris flows and turbidity currents are modelled using two-dimensional Navier-Stokes equations [45]. The surrounding water and the flow material are treated as two distinct fluids, and their properties are computed using the pressure, volume fraction, density and velocity fields. In this study, the submerged sediments are viewed as a monophasic isotropic continuum, and it is believed that both the water and sediments are incompressible. Consequently, the interstitial fluid's mobility is disregarded, the slope is considered to be impermeable and rigid and the erosion of the seabed induced by the flow is disregarded.  
Figure 2: Transformation of the debris flow into the turbidity current.  
The mass continuity for the flow is described as follows:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho V)=0 \tag{1}\]  
where \(\rho\) denotes the density of the flow, \(t\) represents the elapse time and \(V\) represents the volume of the flow. When the mixture variables are considered, the momentum equation can be expressed as follows:  
\[\rho_{m}\Big{[}\big{(}V_{m}\big{)}_{,t}+(V_{m}\cdot\
abla)V_{m}\Big{]}=-\
abla P _{m}+\rho_{m}g+\
abla\cdot\Big{[}\mu_{m}\Big{(}\
abla V_{m}+\
abla V_{m}{}^{T} \Big{)}\Big{]}+F_{m}+\
abla\cdot\Bigg{(}\sum_{k=1}^{n}\alpha_{k}\rho_{k}V_{k}^{ T}V_{k}^{T}\Bigg{)} \tag{2}\]  
where \(\rho_{m}\), \(\mu_{m}\) and \(V_{m}\) are the mixture properties, which are defined as follows:  
\[\rho_{m}=\sum_{k=1}^{n}\alpha_{k}\rho_{k} \tag{3}\]  
\[\mu_{m}=\sum_{k=1}^{n}\alpha_{k}\mu_{k} \tag{4}\]  
\[V_{m}=\frac{1}{\rho_{m}}\sum_{k=1}^{n}\alpha_{k}\rho_{k}V_{m} \tag{5}\]  
\(\
abla P_{\text{m}}\) denotes the pressure of the mixture and is defined by the following:  
\[\
abla P_{m}=\sum_{k=1}^{n}\alpha_{k}\
abla P_{k} \tag{6}\]  
The phase pressures are generally assumed to be equal at the interface, i.e., \(P_{\text{k}}=P_{\text{m}}\). \(F_{\text{m}}\) is a term that depends on the geometry of the interface and accounts for the effect of the surface tension force on the mixture. It is defined as follows:  
\[F_{m}=\sum_{k=1}^{n}F_{k} \tag{7}\]  
The diffusion stress phrase, \(\
abla\cdot\bigg{(}\sum_{k=1}^{n}\alpha_{k}\rho_{k}V_{k}^{T}V_{k}^{T}\bigg{)}\), is used to describe the momentum diffusion induced by relative motions.  
Various rheology models including the Bingham model [46], the power law model [47] and the Herschel-Bulkley model [48] have been applied to model the flow characteristics of debris flows and turbidity currents. Among these models, the Herschel-Bulkley model, which offers a more precise representation of the fluid transformation processes and the rheological properties [48; 49], is used in this research.
## 3 Results and Discussion
### GC-MS Analysis Results  
The gas volatilization from oysters stored at temperatures of 4 \({}^{\circ}\)C, 12 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, and 28 \({}^{\circ}\)C was analyzed by GC-MS, yielding the volatile substances and their relative concentrations (Tables S1-S4). A summary of the substances detected under different storage conditions is as follows:  
Storage at 4 \({}^{\circ}\)C: A total of 171 volatile compounds were identified across eight storage times. These included 25 alcohols, 19 aldehydes, 18 acids, 30 hydrocarbons, 15 ketones, one sulfur-containing compound, six nitrogen-containing compounds, 28 esters, three phenols, seven ethers, and 19 other compounds.  
Storage at 12 \({}^{\circ}\)C: Across eight storage times, 173 volatile compounds were detected. This included 23 alcohols, 29 aldehydes, 12 acids, 45 hydrocarbons, 10 ketones, five nitrogen-containing compounds, 25 esters, three phenols, two ethers, and 19 other compounds.  
Storage at 20 \({}^{\circ}\)C: A total of 137 volatile compounds were identified across seven storage times. These included 23 alcohols, 19 aldehydes, 13 acids, 33 hydrocarbons, nine ketones, five nitrogen-containing compounds, 16 esters, two phenols, two ethers, and 15 other compounds.  
Storage at 28 \({}^{\circ}\)C: Across seven storage times, 161 volatile compounds were identified. These included 36 alcohols, 22 aldehydes, 17 acids, 25 hydrocarbons, 11 ketones, five nitrogen-containing compounds, 25 esters, and 19 other compounds.  
The results indicate significant differences in both the types and concentrations of volatile substances in oysters, depending on the storage temperature and duration.