esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Porosity by Mercury Intrusion  
The results of the mercury intrusion studies are summarized in Table 5 and differential intrusion as a function of pressure and pore size for selected microsphere samples are displayed in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{3}{*}{**Sample**} & **Total** & \multirow{3}{*}{
\begin{tabular}{c} **Surface** \\ **(Pore** \\ **Intrusion**) \\ **cm/g** \\ \end{tabular} } & **Av Pore** & **Powder** & **Skeletal** & **Porosity** \\  & & **Area** & **Width** & **Density** & **Density** & **S/cm\({}^{3}\)** & **\%** \\  & **Intrusion** & **m\textasci  
Uptake of mercury into interparticle voids occurs in the pressure range of 0-200 psi for most microspheres samples. As demonstrated in Figure 11, the intrusion into this volume is distinctly separated from the penetration into the pores of the microspheres, which allows for determining the characteristics of the pores. The results of mercury intrusion are not comparable with the results of N\({}_{2}\) adsorption because micropores and a substantial volume of small mesopores are not penetrated by mercury. On the other hand, larger macropores escape the measurement of N\({}_{2}\) adsorption. Instead, mercury intrusion gives full information about macroporosity. The open macropores occupy a large volume of the microspheres obtained with a small amount of DVB cross-linker, thus not containing C\({}_{\text{f}}\). Sample A-1 shows an average pore width of 83 nm with an average SSA of 24.1 m\({}^{2}\)/g. The open porosity of the sample, calculated from the pore intrusion and skeletal density, is about 50%, however, the true value must be greater as a significant volume of open  
Figure 11: Mercury intrusion plots: (**a**)—Incremental intrusion vs. pore size, (**b**)—Log differential intrusion vs. pressure.  
micro and mesopores are not intruded. The skeletal density of this sample is 2.03 g/cm\({}^{3}\) compared to 3.21 g/cm\({}^{3}\) for pure SiC, which reveals a large volume of non intruded pores, a substantial part of them may be open. Sample A-3 obtained using a moderate amount of DVB shows a large number of open macropores in the relatively narrow pore width range of 50-120 nm. In contrast, the microspheres A-5 obtained with a large amount of DVB used in the synthesis of the SiC precursor show very little mercury intrusion and almost no macroporosity. The intrusion to microspheres A-6 and A-7 was too small to be measured with reliable precision. Although the N\({}_{2}\) sorption measurement of these microspheres gave a very high BET surface area but the average diameter of pores was very small. Their probable bottle-type shape may additionally hinder the mercury intrusion.
## 5 Results  
As a result of transformation of wheel signatures, described in Section 4 of the paper, a database of signals containing over 13,000 test files has been prepared. The files contain records of signals \(u1\) and \(u2\) for the wheel detector concerned for different speeds, accelerations and disturbances in respect of the following parameters:  
* Speed: \(\mathrm{v_{0}}\)\(\in\) { 1 km /h, 10 km/h, 50 km/h, 100 km/h, 150 km/h, 200 km/h} and for each speed as well (folder name):
* Acceleration_Y_: X = 1: a\({}_{1}\) = 0.5 m/s\({}^{2}\), X = 2: a\({}_{2}\) = 1.0 m/s\({}^{2}\), X = 3: a\({}_{3}\) = 2.0 m/s\({}^{2}\) for each v\({}_{0}\);  
Figure 19: Example of pulse disturbance. (**A**) The same phase; (**B**) Opposite phase.  
Figure 18: Example of pulse disturbance added to the signal (according to model A, Figure 14). (**A**) Original signal, a wheel signature; (**B**) A pulse disturbance; (**C**) Wheel signature with pulse disturbance.  
* BrakingX_Y (de-acceleration): X = 1: a\({}_{1}\) = \(-\)0.05 m/s\({}^{2}\), X = 2: a\({}_{2}\) = \(-\)0.10 m/s\({}^{2}\), X = 3: a\({}_{3}\) = \(-\)0.20 m/s\({}^{2}\) for v\({}_{0}\) = 1 km/h and X = 1: a\({}_{1}\) = \(-\)0.50 m/s\({}^{2}\), X = 2: a\({}_{2}\) = \(-\)1.00 m/s\({}^{2}\), X = 3: a\({}_{3}\) = \(-\)2.00 m/s\({}^{2}\) for v\({}_{0}\) = 10 km/h \(\div\) 200 km/h.  
In respect to acceleration and de-acceleration the time t\({}_{0}\) (according to Figure 12) was equal to the value presented in Table 5:  
* WhiteNoise: \(\sigma\) = 50 mV (k = 0.05 V);
* PulseX_Y: X = 1: symmetric positive (Figure 19A), X = 2: symmetric negative, X = 3 and X = 4: asymmetric (Figure 19B) added to the signal with six different delays td, depending on the speed (Table 6):
## 3 Delay and Energy Consumption Analysis
### Energy Consumption Analysis
#### 3.3.1 Network Coordinator  
Using FSA-FBP, the network coordinator executes the following operations repeatedly in every frame: listens to the channel for the \(m\) slots of each frame in order to receive incoming data packets, keeps in idle state for the duration of two IFS, and transmits a FBP packet at the end of the frame. Therefore, we denote by \(\overline{E}_{coord}^{\text{FSA-FBP}}\) the _average energy consumed by the coordinator_ in a contention process, which can be formulated as  
\[\overline{E}_{coord}^{\text{FSA-FBP}}=t_{0}\left(m\rho_{rx}T_{data}+2\rho_{ \sigma}T_{IFS}+\rho_{tx}T_{FBP}^{\text{FSA-FBP}}\right). \tag{13}\]  
For FSA-ACK and DFSA, the network coordinator executes the following operations repeatedly in every frame: listens to the channel for the slots of each frame in order to receive incoming data packets, responds with an ACK to every data packet decoded or sleeps for the time of transmission of an ACK, keeps in idle state for the duration of an IFS, and transmits a FBP packet at the end of the frame. Therefore, we denote by \(\overline{E}_{coord}^{\text{FSA-ACK}}\) and \(\overline{E}_{coord}^{\text{DFSA}}\) the average energy consumption of the coordinator in a contention process using FSA-ACK and DFSA, respectively, which can be formulated as  
\[\overline{E}_{coord}^{\text{FSA-ACK}}=t_{0}\big{(}m\left(\rho_{ rx}T_{data}+2\rho_{sleep}T_{IFS}+\rho_{sleep}T_{ack}\right)+\rho_{\sigma}T_{IFS}+ \rho_{tx}T_{FBP}^{\text{FSA-ACK}}\big{)}+\\ n\big{(}\left(\rho_{tx}-\rho_{sleep}\right)T_{ack}+2\left(\rho_{ \sigma}-\rho_{sleep}\right)T_{IFS}\big{)}, \tag{14}\]  
\[\overline{E}_{coord}^{\text{DFSA}}=\sum_{j=0}^{n-1}n_{0j}\big{(}m_{ j}\left(\rho_{rx}T_{data}+2\rho_{sleep}T_{IFS}+\rho_{sleep}T_{ack}\right)+\rho_{ \sigma}T_{IFS}+\rho_{tx}T_{FBP}^{\text{DFSA}}\big{)}+\\ n\big{(}\left(\rho_{tx}-\rho_{sleep}\right)T_{ack}+2\left(\rho_{ \sigma}-\rho_{sleep}\right)T_{IFS}\big{)}. \tag{15}\]
## 3 Carbon-Based NPs in Water Treatment
### Carbon Quantum Dots (CQDs)  
Graphene-based quantum dots (GQDs) are fragments of graphene with a diameter below 20 nm that exhibit a quantum size effect. CQDs are fluorescent nanomaterials known for their strong quantum confinement effect. The synthetic approaches for GQDs  
Figure 5: Different types of graphene nanomaterials: (**a**) graphene, (**b**) GO, (**c**) rGO, and (**d**) GQD. Source: [66].  
have been classified into top-down and bottom-up methods. Top-down methods involve the decomposition and exfoliation of graphite as an inexpensive and readily available bulk graphene-based material under harsh conditions. In many methods, the initial step involves converting a graphite-based starting material into graphite oxide sheets. Most commonly, a modified Hummers method is employed for this purpose [78]. Indeed, most top-down methods vary in their approaches to converting GO sheets into GQDs. These methods include microwave-assisted cutting, electrochemical cutting, ultrasonic shearing, hydrothermal cutting, and solvothermal cutting [49,50].  
CQDs are typically characterized as a captivating class of carbon nanoparticles primarily composed of carbon atoms with sizes around 10 nm. They possess highly tunable photoluminescence and optoelectronic properties. In addition, the CQDs can serve as efficient photocatalysts [79]. The significant potential of CQDs in wastewater treatment arises from their attributes, such as high stability, good biocompatibility, low toxicity, high water dispersibility, low fabrication cost, and excellent photo-stability [80]. CQDs hold promise for applications in detecting heavy metal ions [81], removing both organic and inorganic pollutants [82], and facilitating the photocatalytic degradation of wastewater pollutants [83].
## 3 Results
### Inactivation of Conidial Germination  
The conidial germination of _C. lactucae-sativa_ was observed at 4 and 24 h after treatment with GA, TC and DBD plasma. The results showed that the conidial germination of _C. lactucae-sativa_ was completely inactivated (100% inhibition) after exposure to all plasmas for 5 min compared to the control in which germ tubes germinated from the conidia (Figure 3). However, the morphological characteristics of the conidia in the treatment groups and the control group were not different.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Treatment** & \multicolumn{4}{c}{**Mean of Percent Inhibition * \(\pm\) SD**} \\ \cline{2-5}  & **5 min** & **10 min** & **15 min** & **20 min** \\ \hline Non-treated control & 0.00 \(\pm\) 0.00 b ** & 0.00 \(\pm\) 0.00 c & 0.00 \(\pm\) 0.00 c & 0.00 \(\pm\) 0.00 d \\ Gliding arc (GA) & 0.00 \(\pm\) 0.00 b & 33.70 \(\pm\) 0.64 b & 40.74 \(\pm\) 1.28 b & 45.19 \(\pm\) 0.64 b \\ Tesla coil (TC) & 0.00 \(\pm\) 0.00 b & 0.00 \(\pm\) 0.00 c & 0.00 \(\pm\) 0.00 c & 13.33 \(\pm\) 0.11 c \\ Dielectric barrier discharge & 27.41 \(\pm\) 1.70 a & 49.26 \(\pm\) 1.28 a & 72.59 \(\pm\) 1.70 a & 93.33 \(\pm\) 1.11 a \\ \hline CV (\%) & 12.37 & 3.42 & 3.75 & 2.24 \\ \hline \(p\)-value & 0.69 & 0.58 & 0.87 & 0.70 \\ \hline \end{tabular}  
* Average from three replicates; SD: standard deviation. ** Different letters indicate significant difference between values, at \(p<0.05\), using the LSD test.  
\end{table}
Table 1: Percent inhibition of mycelial growth of Cercospora lactucae-sativa by non-thermal atmospheric pressure gliding arc (GA), tesla coil (TC) and dielectric barrier discharge (DBD) plasmas.  
Figure 2: Antifungal efficacy of non-thermal atmospheric pressure gliding arc (GA), tesla coil (TC) and dielectric barrier discharge (DBD) plasmas on inhibition of Cercospora lactucae-sativa mycelial growth within different periods of exposure for 5, 10, 15 and 20 min, photographed at 7 days after incubation.
###### Abstract  
Water use efficiency (WUE) is a key index for understanding the ecosystem of carbon-water coupling. The undistinguishable carbon-water coupling mechanism and uncertainties of indirect methods by remote sensing products and process models render challenges for WUE remote sensing. In this paper, current progress in direct and indirect methods of WUE estimation by remote sensing is reviewed. Indirect methods based on gross primary production (GPP)/evapotranspiration (ET) from ground observation, processed models and remote sensing are the main ways to estimate WUE in which carbon and water cycles are independent processes. Various empirical models based on meteorological variables and remote sensed vegetation indices to estimate WUE proved the ability of remotely sensed data for WUE estimating. The analytical model provides a mechanistic opportunity for WUE estimation on an ecosystem scale, while the hypothesis has yet to be validated and applied for the shorter time scales. An optimized response of canopy conductance to atmospheric vapor pressure deficit (VPD) in an analytical model inverted from the conductance model has been also challenged. Partitioning transpiration (T) and evaporation (E) is a more complex phenomenon than that stated in the analytic model and needs a more precise remote sensing retrieval algorithm as well as ground validation, which is an opportunity for remote sensing to extrapolate WUE estimation from sites to a regional scale. Although studies on controlling the mechanism of environmental factors have provided an opportunity to improve WUE remote sensing, the mismatch in the spatial and temporal resolution of meteorological products and remote sensing data, as well as the uncertainty of meteorological reanalysis data, add further challenges. Therefore, improving the remote sensing-based methods of GPP and ET, developing high-quality meteorological forcing datasets and building mechanistic remote sensing models directly acting on carbon-water cycle coupling are possible ways to improve WUE remote sensing. Improvement in direct WUE remote sensing methods or remote sensing-driven ecosystem analysis methods can promote a better understanding of the global ecosystem carbon-water coupling mechanisms and vegetation functions-climate feedbacks to serve for the future global carbon neutrality.  
ecosystem water use efficiency; carbon-water cycle coupling; flux measurement; remote sensing; carbon neutrality +
Footnote †: journal: Remote Sensing  
1
Footnote 1: [[https://doi.org/10.3390/rs13122393](https://doi.org/10.3390/rs13122393)]([https://doi.org/10.3390/rs13122393](https://doi.org/10.3390/rs13122393))
## Materials and Methods
### Life Cycle Assessment Methodology  
Life Cycle Assessment (LCA) is a methodology that aims to analyze products, processes, and/or services from an environmental point of view, and should be part of the decision-making process toward sustainability [27]. The guidelines established by International Organization for Standardization (ISO) standards [28] have been considered to perform the LCA study. The environmental profiles of the silica-coated mNPs production were determined according tothe production route described in a previous paper [29]. In a typical synthesis of silica-coated mNPs, polyoxyethylene(5)nonylphenyl ether (Igepal CO-520) and cyclohexane are mechanically stirred before the addition of oleic-acid magnetite nanoparticles (2.5% wt in cyclohexane). Finally, ammonium hydroxide solution and tetraethyl orthosilicate (TEOS) are added consecutively to form a transparent red solution of reverse micro-emulsion. The core-shell nanoparticles are precipitated with isopropanol (IPA) to disrupt the reverse microemulsion and are then washed extensively with IPA and deionized water. Finally, the core-shell nanoparticles are re-dispersed in deionized water. In the case of the production of magnetic nanoparticles with a thin silica-coating, the procedure is similar except for the concentration of cyclohexane (0.5%, five times lower). Inventory data for the foreground systems were obtained from a semi-pilot unit and data from the background system (production of electricity, chemicals, and wastewater treatment) were taken from Ecoinvent database(r) version 3 [30; 31; 32] and, when possible, updated for Spain [33]. The environmental assessment was conducted using characterization factors from ReCiPe Midpoint methodology [34] and the following impact categories were considered in the analysis: climate change, ozone depletion, terrestrial acidification, freshwater eutrophication, marine eutrophication, human toxicity, photochemical oxidant formation, terrestrial ecotoxicity, freshwater ecotoxicity, marine ecotoxicity, and fossil depletion. SimaPro version 7.3.3 (PRe Consultants, Amersfoort, The Netherlands) was the software used for the computational implementation of the life cycle inventory data and the computation of the environmental profiles [35].
## 4 Experiments and Analysis  
This part employs the extensively used benchmark dataset IRSTD-1k [41] along with our dataset IRMT-UAV to confirm the efficacy of our suggested approach. Our approach involves contrasting the suggested algorithm with other cutting-edge (SOTA) algorithms and undertaking extensive ablation tests to accurately evaluate our algorithm's effectiveness.
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 3 System Model
### Autoencoder  
As it is described in Section 1, an AE refers to a trainable end-to-end communication system, consisting of ANNs at transmitter and receiver side and a differentiable channel model in between. This work focuses on implementing the mapper and demapper blocks of the communication chain as ANN, as shown in Figure 1.  
The mapper defines a building block that takes an input vector consisting of \(m\) bits (or as one-hot-coded input vector of length \(M=2^{m}\)) and maps it to a complex-valued constellation symbol, which is then transmitted over a channel. The goal of the mapper is to find a symbol constellation that conveys the maximal amount of information at the given system settings. Subsequently, the demapper receives the corresponding complex symbol that is impaired by the noise of the channel. Its goal is to provide the most reliable estimate of the originally transmitted bits in form of \(m\) output probabilities.  
Similar to [10], our mapper and demapper ANNs comprise three fully connected layers, each followed by rectified linear unit (ReLU) activation functions except for the final mapper and demapper layers which are linear and sigmoid-activated, respectively, as shown in Figure 2.  
The number of neurons of the encoder is set to 128 for the first two layers and to 2 (real and imaginary part) for the output layer to generate the complex-valued constellation points \(x\) corresponding to the input bits \(\mathbf{b}\). After average power normalization is applied, the normalized message \(x\) is transferred over the channel model to obtain the noisy message \(y\). The following decoder's first two fully connected layers consist of \(N\) neurons while the size of the output layer is fixed to \(m=4\), whereby each output corresponds to the probability of one specific bit of the predicted symbol.
# Aerodynamic Effects of Ceiling and Ground Vicinity on Flapping Wings
## 3 Results and Discussion
### The Reasons for the Combined Ceiling and Ground Effect at Re = 100
Moreover, judging by velocity vectors in the inset of Figure 21c, it is observed that the downward component of the velocity vector first increases and then declines with the increasing wall clearance. Therefore a \"three force regimes\" behavior is obtained. These findings are similar to [PERSON] et al. [20] and Ref. [24]. As discussed, at \(Re=10\), the ceiling and ground affect aerodynamic forces in two ways. One is by affecting the vortex wake, and the other is by creating a narrow channel between the edges of the wing and the wall. Here, at \(Re=100\), we examine the narrow-channel effect from the first stroke. Figure 22 shows the cycle-averaged lift and drag force coefficients (\(\overline{C_{L}}\) and \(\overline{C_{D}}\)) for the first stroke. It shows a monotonic trend with decreasing wall clearance. This indicates that the narrow-channel effect in the combined ceiling and ground case always monotonically increases the forces, no matter what the \(Re\) is. However, the narrow-channel effect almost disappears at \(D/c=2.0\), with the cycle-averaged lift coefficient increasing  
Figure 21: (**a**) Top view; (**b**) oblique view of the iso-Q surface (green) and iso-vertical velocity surface (yellow) showing the strength of downwash; and (**c**) spanwise vorticity distributions on a slice at \(\tau=0.25\) for \(D/c=1.0\), \(2.5\) and \(\infty\) at \(Re~{}=~{}100\). The red arrow and red line in (**a**) give the stroke direction and the slice position, respectively. by only 5%. Recall that at \(Re=10\), the cycle-averaged lift coefficient increment caused by the narrow-channel effect is 15.19% at \(D/c=2.0\), and always larger than 5% unless \(D/c\) is equal to or larger than 4.0. Compared to the narrow-channel effect at \(Re=100\) with \(Re=10\), it is concluded that the narrow-channel effect will disappear at a smaller wall clearance for a higher Reynolds number. Figure 23 explains the narrow-channel effect by comparing the pressure (\(C_{p}\)) distributions, the effective angle of attack (\(\alpha_{e}\)) contours, and velocity (\(u\)) contours at a 2/3 wingspan slice at the first mid-downstroke for both the \(D/c=1.0\) and \(\infty\) case at \(Re=100\). From Figure 23a,b, it is found that the narrow channel between the wing and the ground can produce a larger positive pressure below the wing. Figure 23c-f show that both the magnitude of the incoming flow velocity and the effective angle of attack for \(D/c=1.0\) are larger than that for the infinity case, so that the flapping wing produces a larger LEV and more significant negative pressure on the upper surface of the wing. Figure 22: Cycled-averaged lift (\(\overline{C_{L}}\)) and drag (\(\overline{C_{D}}\)) coefficients for the first downstroke as a function of wall clearance (\(D/c\)) at \(Re~{}=~{}100\).
## 5 Results and Discussions
### Frequency-Magnitude Distribution Analysis
This variation in \(b\)-value patterns, indicative of zones alternately characterized by low and high \(b\)-values, suggests an influence stemming either from heterogeneity in the stress field or from variations in material properties. Notably, the Gulf of Aqaba and the Gulf of Suez regions are marked by lower \(b\)-values, in stark contrast to the remaining areas of the Northern Red Sea region, which display a dynamic range of \(b\)-values, alternating between low and high. The lower \(b\)-values identified in the Gulf of Aqaba, the Gulf of Suez, and the northern part of the Red Sea are in alignment with the \(b\)-value estimations previously reported by [8; 43; 44]. The observed variations in the \(b\)-value are predominantly influenced by a constellation of geological and geophysical factors, including the nature of tectonic settings, crustal heterogeneity, variations in pore fluid pressures, and the prevailing stress state within the Earth's crust [38]. Conversely, the \(a\)-values, ranging from 3.97 to 5.5 as shown in Figure 10, denote the level of seismic activity. These values vary based on several critical factors: the size of the deformation area, the time span considered, the frequency of seismic events, and the observed magnitude ranges. Figure 10: Frequency-magnitude distribution (\(b\)-value) of Seismic Activity in the Red Sea Spanning Various region.
## 3 System Architecture of the DSS
### Data Management  
The data management component encompasses a cohesive structure consisting of six principal interconnected tables, including products, sub-products, resources, questionnaires, top management, and production orders.  
In the realm of production systems, the prevalent scenario involves a many-to-many relationship between products and sub-products. Notably, industries such as those dealing with metal parts often exhibit a pattern where a final product comprises multiple detailed parts, each of which may be utilized in the assembly of a range of final products. Similar complexities arise in the association between resources and sub-products, stemming from variations in processing time and operation costs across different resources for a single sub-product unit.  
The function of production orders within this framework is pivotal, actively contributing to the solution process, particularly in conjunction with the utilization of decision support systems (DSS) alongside ongoing production-planning systems. The computation of the total raw material cost for a specific product involves the aggregation of the raw material costs attributed to the sub-products associated with that product. Similarly, the calculation of the total processing time and processing costs for a product relies on the summation of the corresponding values for the related sub-products across the pertinent resources.  
Furthermore, the determination of the contribution margin for a product involves assessing the disparity between its sales price and the total raw material cost. Notably, the designed structure of the database facilitates the incorporation of processing costs into the profit-calculation process. In the context of top management, senior executives, who are responsible for overseeing the strategic business units, provide responses to survey inquiries. Each response is systematically recorded as an entry within the PMP questionnaire table.