esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Experimental Determination of the Capillary Action
#### 4.1.2 Stainless-Steel Wire Mesh  
AISI 304 stainless-steel (ESZ Company, Taipei, Taiwan) [39] with three different aperture widths (60, 200, and 300 \(\upmu\)m) was tested. Table 4 includes the physical characteristics and the coefficients of the capillary curves.  
Figure 5: Differences between the two types of silica gel with methanol as a working fluid; (**a**) liquid height and (**b**) advance speed.  
In Figure 6, samples of each aperture width are depicted, including the main dimensions at the same scale for comparative purposes. Pictures were obtained by optical microscope.  
Samples of 10 mm inner diameter with 5 layers of coiled mesh were tested and the results, with methanol as the working fluid, are plotted in Figure 7a for the three above-mentioned pitches.  
Again, as the aperture width decreased, the maximum attainable height increased: at 75 mm for mesh 60, 58.8 mm for mesh 200, and 48.7 mm for mesh 300. In summary, the lower the pitch, the higher the liquid height and the stronger the pumping capacity-capillary action, as shown by the \(\alpha\) slope in Table 4. The flow velocity (\(\alpha\)) in mesh 60 was 170% faster than that of mesh 300, and 103% faster than that of mesh 200.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Mesh (\(\mu\)m)** & **d (mm)** & **W (mm)** & \(\epsilon\) & \(\alpha\) **(mm/s)** & **A\({}_{1}\)** & **t\({}_{1}\)** & **A\({}_{2}\)** & **t\({}_{2}\)** \\ \hline
**60** & 0.024–0.027 & 0.06 & 0.4756 & 2.38 & 31.79135 & 18.87751 & 43.23075 & 18.87751 \\ \hline
**200** & 0.072–0.078 & 0.15–0.16 & 0.4519 & 1.17 & 53.44345 & 24.76081 & 5.39677 & 2.81373 \\ \hline
**300** & 0.17 & 0.3 & 0.4074 & 0.88 & 44.90217 & 21.22973 & 3.8112 & 0.00626 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Metallic mesh characteristics and capillary action (methanol).  
Figure 6: \(Cont.\)  
All the cases present a speed of advance at 30 mm higher than the required value (0.031 mm/s). In this case, the time constant (\(t_{2}\)) of mesh 300 was very small, so the corresponding exponential term of the speed of advance was negligible. These results meant that the initial speed was well below the initial speeds of mesh 60 and mesh 200 (Figure 7b). The time constants were comparable (t\({}_{1}\) = t\({}_{2}\)) for mesh 60, meaning that it had the highest speed of advance [42].  
Figure 6: Metallic meshes of the different aperture widths analyzed; (a) 60 \(\upmu\)m, (b) 200 \(\upmu\)m, and (c) 300 \(\upmu\)m.  
Figure 7: _Cont._
# Monitoring Forest Phenology in a Changing World
## 1 Introduction
### The Ubiquitous Importance of Plant Phenology
For instance, a change in fruiting phenology has influenced the reproductive performance of endangered chimpanzees in Uganda [59], and the timing of mast seeding events in Borneo has led to increases in insect [60] and nomadic vertebrate populations [55]. At a community level, phenological change can, in theory, have reverberating effects on species distributions [61] and assemblages [2; 62], as a result of the fine scale changes in abundance and phenological events [4]. For example, a modelling study outlined how the northern limit of plant species' ranges in the Northern Hemisphere appears to be dictated by the inability to undergo full fruit maturation [61]. Conversely, the southern limit of  
Figure 2: Ecosystem processes and conditions that drive phenological events. References giving examples for the effect of phenology on ecological processes are: increased interception [38], increased herbivory [48], increased pollination [49; 50; 51], increased nectar feeding [52], increased soil water uptake [38; 39; 40; 41; 42; 43], increased fruit feeding [53; 54], increased seed hoarding [55], increased seed dispersal [55], increased seed predation [56], decreased soil water uptake [38; 39; 40; 41; 42; 43], decreased leaf decomposition [56], increased solar radiation [2; 38; 43], increased rain throughfall [38], and decreased solar radiation [2; 38; 43]. plant ranges of the South Hemisphere appears to be dictated by the inability to flower or unfold leaves, owing to a lack of chilling temperatures that are necessary to break bud dormancy [61]. However, to our knowledge the effect of fine scale phenological event change on the community level distribution of higher taxa has not yet been tested.
## 1 Introduction
Additionally, a QD-spin-VCSEL can generate ultrafast chaotic dynamics when subjected to short feedback delays, resulting in very short spacing between two virtual nodes with sufficient nodes. Therefore, four RCs using the four PCs from the ground state and excited state emissions can effectively handle four high-speed chaotic time-series in parallel and their synchronizations. In this study, we introduce a unique four-channel COCSC system that uses four concurrent photonic reservoir computers coupled with a coherent demodulation device. Within this system, a QD-spin-VCSEL is employed as the driving laser, and a separate QD-spin-VCSEL serves as the reservoir laser. We individually modulate four distinct encoded messages (QPSK, 4 QAM, 8 QAM and 16 QAM) to four PCs, originating from the GS and ES emissions in the drive laser QD-spin-VCSEL. Additionally, we build four parallel photonic reservoirs using four PCs, sourced from the GS and ES of the reservoir QD-spin-VCSEL, maintaining a minimal distance between two non-linear nodes. By leveraging a concurrent simulation of Matlab and VPI [23], these four photonic RCs help us overcome the obstacle of chaos synchronization for four pairs of PCs generated by the drive and reservoir QD-spin-VCSELs. We exhibit a four-channel COCSC with a 4 \(\times\) 100 GHz capacity using chaos synchronizations founded on these quartet parallel photonic reservoirs. Once the output weights are trained within the nonlinear node states, the four parallel reservoirs can be employed for synchronization and decryption. Further, we coherently demodulate four channels of baseband signals (or bit sequence signals) hidden in modulation messages through a polarization diversity digital coherent receiver (PDDCR) and a variety of DSP methods. We examine the impact of the sampling period and the interval of the virtual nodes on training errors. We approximate the effects of the injection and feedback strengths on chaotic synchronizations. Conclusively, we evaluate the transmission performances of the four-channel baseband signals within this COCSC system, analyzing elements such as bit error rates and eye diagrams.
# A New Coding Paradigm for the Primitive Relay Channel +
## 2 Existing Upper and Lower Bounds
### Partial Decode-and-Forward Lower Bound  
In partial decode-and-forward, the relay decodes and sends to the destination only part of the received sequence.  
For the _general relay channel_, partial decode-and-forward allows for achieving the following rate ([17] Theorem 16.3):  
\[R_{\rm pDF}=\max_{PUL,X_{\rm s},X_{\rm R}}\min\{I(X_{\rm s},X_{\rm s};Y_{\rm D }),I(U;Y_{\rm sg}|X_{\rm R})+I(X_{\rm s};Y_{\rm D}|X_{\rm R},U)\}, \tag{17}\]  
where the cardinality of the alphabet associated with \(U\) can be bounded as \(|\mathcal{U}|\leq|X_{\rm s}|\cdot|\mathcal{X}_{\rm s}|\). Note that \(U\) is an auxiliary random variable that represents the part of the message decoded by the relay. By taking \(U=X_{\rm s}\), we recover the decode-and-forward lower bound (14). Furthermore, by taking \(U=\emptyset\), we recover the direct transmission lower bound (11).  
Note that the partial decode-and-forward lower bound (17) meets the cut-set upper bound (1) when the relay channel has orthogonal sender components, namely, the broadcast channel from the source to the relay and the destination is decoupled into two parallel channels.  
For the case of the _primitive relay channel_, the partial decode-and-forward lower bound specializes to ([26] Equation (5))  
\[R_{\rm pDF}=\max_{PUL,X_{\rm s}}\min\{I(X_{\rm s};Y_{\rm SD})+C_{\rm ED},I(U;Y _{\rm sg})+I(X_{\rm s};Y_{\rm SD}|U)\}, \tag{18}\]  
with \(|\mathcal{U}|\leq|\mathcal{X}_{\rm s}|\).  
For the special case of the _erasure relay channel_, we show that partial decode-and-forward does not provide any improvement upon both direct transmission and decode-and-forward. After some simple calculations, one obtains that  
\[\begin{split} I(X_{\rm s};Y_{\rm SD})&=H(X_{\rm s} )-H(X_{\rm s}|Y_{\rm SD})=H(X_{\rm s})(1-\varepsilon_{\rm SD}),\\ I(U;Y_{\rm sg})&=H(U)-H(U|Y_{\rm sg})\\ &=H(U)-\varepsilon_{\rm SR}H(U)-(1-\varepsilon_{\rm SR})H(U|X_{ \rm s})\\ &=(1-\varepsilon_{\rm SR})(H(X_{\rm s})-H(X_{\rm s}|U)),\\ I(X_{\rm s};Y_{\rm SD}|U)&=H(X_{\rm s}|U)-H(X_{\rm s }|U,Y_{\rm SD})\\ &=H(X_{\rm s}|U)(1-\varepsilon_{\rm SD}).\end{split} \tag{19}\]  
Hence, by setting \(\alpha=H(X_{\rm s})\) and \(\beta=H(X_{\rm s}|U)\), we can re-write (18) as  
\[\begin{split} R_{\rm pDF}&=\max_{0\leq\beta\leq \alpha\leq 1}\min\{\alpha(1-\varepsilon_{\rm SD})+C_{\rm ED},\alpha(1-\varepsilon_{\rm SR })+\beta(\varepsilon_{\rm SR}-\varepsilon_{\rm SD})\}\\ &=\max_{0\leq\beta\leq 1}\min\{(1-\varepsilon_{\rm SD})+C_{\rm ED},(1-\varepsilon_{\rm SR})+\beta(\varepsilon_{\rm SR}-\varepsilon_{\rm SD})\}. \end{split} \tag{20}\]  
On the one hand, if \(\varepsilon_{\rm SR}\geq\varepsilon_{\rm SD}\), then the maximum is achieved by taking \(\beta=1\), and \(R_{\rm pDF}=1-\varepsilon_{\rm SD}=R_{\rm DT}\). On the other hand, if \(\varepsilon_{\rm SR}\leq\varepsilon_{\rm SD}\), then the maximum is achieved by taking \(\beta=0\), and \(\min\{(1-\varepsilon_{\text{\tiny SD}})+C_{\text{\tiny SD}},1-\varepsilon_{\text{ \tiny SR}}\}=R_{\text{DF}}\). Consequently, no improvement is possible over both direct transmission and decode-and-forward.
# A Low-Power WLAN Communication Scheme for IoT WLAN Devices Using Wake-Up Receivers
## 4 Proposed System for Massive Uplink Scenario  
To reduce the entire transmission delay in a massive uplink scenario, a simple approach in unicast transmission can be to aggregate the acknowledgment frame and data. However, since the transmission procedure is still repeated for each STA, the performance improvement is limited. To reduce the entire  
Figure 5: Massive uplink scenario for an Internet of Things (IoT) device.  
Figure 6: One of IEEE 802.11 ba scenarios: the smart home.  
Figure 7: Data transmission procedure to gather the sensed data from sensors operated in application layer based on unicast transmission. SU = single user; SU PPDU = single user physical layer protocol data unit.  
delay significantly, the IEEE 802.11 ax transmission system can be applied with the wake-up receiver system, as in IEEE 802.11 ba, because the wake-up radio in IEEE 802.11 ba is supplementary to other IEEE 802.11 radios. In the proposed system, in addition to the conventional IEEE 802.11 ax system of the wake-up device, the scheduling scheme for MU operation with wake-up radio and the enhanced distributed channel access (EDCA) parameter set operations are jointly designed. In the joint design of MU scheduling and EDCA parameter setting, the goal of the scheduling operation is to further decrease the data transmission delay in the MU transmission procedure, and the goal of the EDCA parameter operation is to prevent collisions caused by woken-up STAs' transmissions. The proposed system includes the transmission procedure, as well as a collision-avoidance method which can be used in IEEE 802.11 ba.
# Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review
## 5 Identified Challenges and Solution Directions
### RQ1. What Are the Identified Challenges in the MSA Domain?
#### 5.1.5 Communication and Integration  
Communication and integration are other challenging points that have emerged as a result of distributed architecture. Even if microservices communicate with a more lightweight protocol, it is still difficult to ensure that the communication infrastructure is reliable and the protocol to be used for communication and integration can handle complex workflows. The most important criteria for both challenges are reliability and durability; if these criteria are not met, the proper operation and reliability of the system will be affected, and will possibly cause cascading failures in the system.
## 3 Numerical Simulations and Comparisons  
### Numerical Model  
Figure 2a shows a two-dimensional (plane strain conditions) backfilled opening and Figure 2b illustrates the corresponding FLAC model [30]. The rock mass is a linear elastic material characterized by a Young's modulus \(E_{\rm r}\) of 30 GPa, a unit weight \(\gamma_{\rm r}\) of 27 kN/m\({}^{3}\), and a Poisson's ratio \(\
u_{\rm r}\) of 0.25. The granular cohesionless fill behaves as a Mohr-Coulomb elastoplastic material with a dry unit weight \(\gamma\) of 18 kN/m\({}^{3}\), zero effective cohesion \(c^{\prime}\), and zero dilation angle \(\psi\) (ultimate state with non-associated flow rule). The values of the fill Poisson's ratio \(\
u\), internal friction angle \(\varphi\)', and Young's modulus \(E\) vary within a range of values given in Table 1 (which also gives other parameters and identifies simulated cases).  
Figure 2: (**a**) A backfilled opening and (**b**) the corresponding numerical model.  
In Figure 2b, the two side boundaries of the rock mass are fixed horizontally, while the displacements are restricted in all directions at the base. The opening is first created instantaneously. After the induced displacements are reset to zero, backfilling is performed layer-by-layer (1 m per layer) to a height (\(H\)) of 40 m, with an open and horizontal top surface. This progressive filling sequence reduces the effect of the added layers (due to momentum) on the simulated stresses and earth pressure coefficient (see details in [PERSON] [40]). Hence, there is no rock wall closure on the backfill (and no stress imposed by the rock walls to the backfill). This approach is valid for the delayed filling of a single opening, after convergence for stiff rock walls [12].  
Sensitivity analyses of the mesh indicate that the 0.2 \(\times\) 0.2 m square mesh elements can be used to model the fills. Interface elements are not used between the walls and backfill, which are considered to be very rough due to blasting (as is typically the case in underground mine slopes) [5; 44]. Furthermore, numerical results presented by [PERSON] et al. [28; 29] have indicated that the roughness of interface elements tends to alter the magnitude of the vertical and horizontal stresses along the vertical centerline of backfilled openings, but it has little impact on the corresponding values of \(K\).
## 2 Governing Equations
### Analyses of the Pressure  
As for the pressure analysis, it is known from Equation (9) that the pressure contributed by acceleration with respect to time is:  
\[\Delta P=-\rho_{o}\int_{r}^{\infty}\frac{\partial u}{\partial t}dr \tag{22}\]  
The Bessel function has the following asymptotic forms for large arguments, s:  
\[\int J_{1}(r)dr=-J_{0}(r),\ \int Y_{1}(r)dr=-Y_{0}(r),\ \lim_{r\to\infty}J_{1}(r)=0,\ \lim_{r\to\infty}Y_{1}(r)=0, \tag{23}\]  
Therefore:  
\[\Delta P=\rho_{o}c\frac{D}{\sqrt{f_{1}^{2}(s_{r})+Y_{1}^{2}(s_{r})}}[\cos( \omega t+\varphi+\frac{\pi}{2})J_{0}(s)+\sin(\omega t+\varphi+\frac{\pi}{2})Y_ {0}(s)] \tag{24}\]  
where the value of \(s\), \(\varphi\) is the same as in Section 2.2 above. If \(s\) is large, the following statement will be true:  
\[\left\{\begin{array}{l}J_{1}(s)=\sqrt{\frac{2}{\pi}}\frac{\cos(s-\frac{1}{ \pi}\pi)}{\sqrt{s}}\\ Y_{1}(s)=\sqrt{\frac{2}{\pi}}\frac{\sin(s-\frac{1}{\pi}\pi)}{\sqrt{s}}\end{array}\right.\]  
so, the simplest solution may be got, finally,  
\[\Delta P=\rho_{o}c\frac{D}{\sqrt{f_{1}^{2}(s_{r})+Y_{1}^{2}(s_{r})}}\sqrt{ \frac{c}{\omega}\frac{2}{\pi}}\frac{\cos(\omega(t-\frac{r}{\varepsilon})+ \varphi+\frac{3\pi}{4})}{\sqrt{r}}{=\rho_{o}c\cdot u}. \tag{25}\]  
It can be derived from Equation (25) that the pressure is proportional to the velocity of the vibration and the proportion is a product of the density and sound speed. In addition, it is a large value compared to the pressure contributed from migration acceleration, \(\frac{1}{2}\rho_{o}u^{2}\). That is the mean, neglecting migration acceleration as done in the solution of Section 2.2, is reasonable.
# Non-Linear Creep-Relaxation Constitutive Damage Model for Aging Concrete
## 3 Application of the Constitutive Model and Discussion of Results
### Creep Case Study
From Figure 10d,f, it can be concluded that the damage continues to grow as long as the loading-unloading condition remains positive. This effect is consistent with what is expected in tertiary creep, in which the micro-cracks continue growing as time advances. Figure 10f proves that the model developed is suitable for analyzing tertiary creep, as discussed earlier. On the other hand, in regard to the stress behavior, from Figure 10e, it can be concluded that the strain response is not the exact solution of the non-linear Volterra integral equation, Equation (57), under constant sustained stress, i.e., \(\sigma_{11}=\sigma_{0}H_{l}(t)\). As observed in Figure 10e, the response of the stress obtained is close to the straight line expected, which implies that the strain is not the exact solution to Equation (57). Nevertheless, the behavior is not changing substantially as time advances, leading to a fair approximation. From this behavior, we can infer that, if the creep response is provided, the material will respond as expected for a viscoelastic damageable material. Moreover, it is important to recall that the analysis performed shall be stopped at the moment that the loading-unloading condition reaches the value of zero since no further damage develops. If this analysis is not stopped when that occurs, the material will be enduring the healing mechanism already described.
# Multi-Objective Design Optimization of the Reinforced Composite Roof in a Solar Vehicle
## 3 Results and Discussion
### Results Implementation  
The same design approach was used to finalize the roof design. The 3D geometry of the roof was filled by structural modules with design parameters based on the previous optimization. Specifically, a quadidirectional grid with a medium density of beam element was used as valid compromise between the different targets. It was characterized by an orthogonal grid on the top and a lower diagonal grid on the bottom with, respectively, 300 mm and 200 mm widths. This design pattern was replicated along the roof dimension and shape, partitioned in front and back sections (Table 11).  
The dynamic behavior of these parts was examined by a further modal analysis considering unloaded bodies (as previously) but constrains as in real case. First frequencies were 21 and 12 Hz with total displacements of 42 and 69 mm for, respectively, front and back sections (Figure 7)  
Then, the composite structure was manufactured using manual layout and autoclave molding and hot gluing with two-component for part assembly. The solar cells, E60 bin Me1 by Sunpower, were directly laminated on the panel with ethylene vinyl acetate (EVA) films. Flexible layers on both surfaces (front and back) for a total of five layers (including 2 EVAs), for a 1.5 mm of overall thickness, were overlapped and cured in autoclave. In particular, solar cells were positioned with the scope to maximize the energy yield of vehicle-integrated photovoltaics (VIPV) [43].  
Lastly, the solar roof was installed and finalized with other vehicle components. Figure 8 shows several imagines from the solar vehicle at the end of the present investigation. Functional tests were performed running over 800 km on roads open to traffic. No complications emerged related during this first trial in terms of structural design of the roof which appeared stable and functional.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{2}{c}{**Frequency (Hz)**} & \multirow{2}{*}{**Difference (\%)**} \\ \cline{2-3} \cline{5-5}  & **Measure** & & **Simulation** \\ \hline I & 16.7 & 15.26 & \(-\)8.6\% \\ II & 20.1 & 18.69 & \(-\)7.0\% \\ III & 42.6 & 40.13 & \(-\)5.8\% \\ IV & 46.5 & 41.67 & \(-\)10.4\% \\ V & 68.6 & 65.40 & \(-\)4.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparing the results from modal analysis in terms of natural frequencies.  
Figure 7: Numerical modal analysis of (**a**) front, (**b**) back section of the roof.
# A Prediction Model of Marine Geomagnetic Diurnal Variation Using Machine Learning
## 2 Methodology
### Principle of the RF Algorithm  
The random forest (RF) algorithm, a type of supervised machine learning method, is primarily utilized for addressing classification and regression challenges [15]. Apart from sharing similar advantages with SVM, RF demonstrates robustness in maintaining high accuracy even in scenarios where the training set contains missing data. RF operates as an ensemble algorithm comprising multiple decision tree classifiers. It constructs a more efficient model for classification and regression by leveraging randomly generated decision trees. Due to its nature as a randomly selected sampling mechanism, RF exhibits superior generalization capabilities. The algorithm proceeds as follows:  
1. Generate k independent sampling sets by randomly selecting from the original sample set;
2. Train the k sampling sets to create k weak learners;
3. Combine the k weak learners through aggregation to create a strong learner output, which functions as the final model.  
The flowchart illustrating the specific implementation steps is presented below:  
Figure 2 shows the flowchart of the random forest (RF) algorithm, which typically uses the simple voting method for classification tasks and the simple average method for aggregation in regression problems.
# Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway
###### Abstract  
The intertidal zone (ITZ) is a highly dynamic and diverse coastal ecosystem under pressure that provides important eco-services. Being periodically under water makes it challenging to monitor, and the only possibility to map it in all tidal stages is by using dense time series of observations. At high latitudes, the Sentinel-1 (S1) constellation of the European Copernicus Program consistently provides radar imagery at fixed times on a near-daily basis, independently of cloud cover and sunlight. As tides have a period of 12 h 25.2 min, 1-2 year long S1 time series are therefore able to sample the whole tidal range and, thus, map the percentage of atmospheric exposure of the ITZ, which is an important environmental parameter. Tidal reference levels of mean high/low water at spring, mean and near tide correspond each to specific percentiles of tidal heights and inversely correspond to atmospheric exposure. The presented method maps atmospheric exposure on the basis of purely statistical analyses of Sentinel-1 time series without the need for any tidal gauge data, by extracting water lines via simple thresholding of radar backscatter percentiles images. The individual thresholds for the second, fifth, 25 th, 50 th, 75 th, 95 th, and 98 th percentile image were determined by fitting the threshold contour lines to in situ water line GPS tracks collected at corresponding tidal reference levels at five locations around Tromse in Northern Norway. They inversely correspond to atmospheric exposures of 98%, 95%, 75%, 50%, 25%, 5%, and 2%, respectively. The method was applied to the whole Tromse Municipality resulting in an ITZ atmospheric exposure map. The validation shows that the mean low water lines at near, mid, and spring tide were mapped with accuracies of 93%, 84%, and 64%, respectively. The overall approach should be applicable worldwide.  
intertidal zone; time series; Sentinel-1; SAR; atmospheric exposure +
Footnote †: journal: Remote Sens.  
2021  
19 August 2021  
13354