esa-sceva/satcom-chunk-collection huggingface dataset:
# Email Campaign Evaluation Based on User and Mail Server Response
## 4 Artificial Neural Networks Models
### Campaigns Analysis  
The structure of the artificial neural network developed for classification of emails campaigns is shown in Figure 2. The input layer contains 24 neurons for the attributes described in Section 3.1. We used three hidden layers with 72, 96, and 72 neurons, respectively. For each hidden layer, the ReLU (REctified Linear Unit) activation function was applied. Finally, a single output neuron with the sigmoid activation function was used to provide the probability that the given campaign should be aborted. We applied the Adam optimiser (clipvalue = 0.5) and the MSE (the mean squared error) objective function.  
The StandardScaler function form Python sklearn library was applied to standardise the input data values. The classification report for test data is presented in Table 3.  
The model was additionally validated on data from periods other than the period from which the sample for learning the neural network was drawn. In each case, the F1-score value was above the required 0.95. It is worth noting that in the case of email campaigns, we treat each minute of sending as a separate record. If a decision is made to stop sending after a given minute, it can be assumed that the same decision applies to subsequent minutes. With this approach, F1-score increases by about 0.02.
## 3 Results
### Elasto-Mechanical Properties  
In Figure 4 are reported the compressive strength developments of mortars manufactured with different hardening accelerating admixtures. Results indicated that lithium carbonate and sodium carbonate affect the elasto-mechanical properties of both TM and SM. The compressive strength at early ages (24 h and 7 days) is strongly improved by the addition of SC and LC admixtures. In particular, compressive strength approximately doubled with respect to the reference mortars manufactured without hardening accelerating admixtures, regardless the nature of alkaline ions (Li or Na) present in the admixtures. Sodium carbonate promotes the formation of gyrolite which is responsible for the acceleration of setting times as reported by [PERSON] et al. [35] and the improvement of compressive strength at early ages [36]. On the other hand, lithium carbonate addition is responsible for the precipitation of lithium aluminate hydrate LiAl\({}_{2}\)(OH)\({}_{7}\)-2H\({}_{2}\)O which serves as nucleation site for aluminum hydroxide precipitation that accelerates the whole hydration process and promotes the production of ettrinigite, the main hydration product of CSA cements [29; 30; 37].  
Figure 4: Compressive strength development of mortar (**a**) TM, (**b**) SM1 and (**c**) SM2 with different admixtures.  
However, at long ages, compressive strength of SM1 and SM2 mortars produced with or without hardening accelerating admixtures was similar. In detail, SM1 and SM2 mortars were characterized by compressive strength value close to 40 MPa after 150 days similarly with [26]. On the contrary, the addition of sodium carbonate on TM mortar did not produce variations on compressive strength after 120 and 150 days while the use of lithium carbonate, alone or in combination with sodium carbonate, caused strength losses at long ages approximately equal to 10%.  
Figure 5 reports the dynamic modulus of elasticity of mortars after 24 h, 28 days and 150 days normalized respect to the mortars manufactured without admixtures. The role of hardening accelerating admixtures is evident also in the elastic modulus development. Similar to compressive strength, the addition of SC and/or LC on TM, SM1 and SM2 strongly increases the dynamic modulus of elasticity at 24 h with growing close to 20-40% respect to no-admixed mortars. However, this effect disappears already after 28 days when elastic modulus results quite similar (90-110%) to that of mortars manufactured without hardening accelerating admixtures.
## 4 Methods
### Modelling ecosystem respiration  
The annual sum of autotrophic and heterotrophic respiration was estimated using complex models driven by climatic data. These models combine a number of biome-specific constants, which were borrowed from recent literature, and variables, which were empirically estimated in this study (see Table 2). Conventionally, autotrophic respiration is separated into maintenance respiration \(R_{m}\) and growth respiration \(R_{g}\)[3; 54]:  
\[R_{a}=R_{m}+R_{g} \tag{15}\]  
Maintenance respiration is strongly dependent on temperature and can be modelled using the following equation [3; 54]:  
\[R_{m,i}=M_{i}r_{m,i}Q_{10}^{(T-T_{a})/10} \tag{16}\]  
where \(R_{m,i}\) is maintenance respiration of plant component \(i\), \(M_{i}\) is the biomass of plant component \(i\), \(r_{m,i}\) is maintenance respiration coefficient for component \(i\), \(Q_{10}\) is the temperature sensitivity factor, and \(T_{b}\) is the base temperature. \(R_{m,i}\) was calculated separately for plant leaves (above-ground biomass) and roots (below-ground biomass). The \(M_{i}\) for plant leaves was estimated as LAI/SLA, where LAI, the leaf area index, is obtained from remote sensing data (see description above), and the specific leaf area is a biome-specific constant (Table 2). The roots mass (\(M_{i}\)) was calculated as a product of leaf mass multiplied with shoot-root ratio (BGB/\(AGB_{I}\) in Table 2).  
Growth respiration is generally considered to be independent of temperature and is proportional to GPP [3; 54]:  
\[R_{g,i}=r_{g,i}r_{a,i}GPP \tag{17}\]  
where \(r_{g,i}\) is a growth respiration coefficient for plant component \(i\), and \(r_{a,i}\) is the carbon allocation fraction for plant component \(i\). The values of \(r_{g,i}\) and \(r_{a,i}\) are biome-specific constants (Table 2). \(R_{g,i}\) was computed separately for above-ground and below-ground plant components. The separation of annual GPP into below-ground and above-ground components was achieved using the variable BGB/\(AGB_{I}\) empirically derived from the ground-based data.  
Heterotrophic respiration is the sum of respiration by decomposers (animals, bacteria and fungi feeding on dead tissue or soil organic matter) and is most closely associated with the decomposition of residue and soil organic matter. The relative contributions of autotrophic and heterotrophic respiration to the total are important, whereas heterotrophic respiration includes soil respiration and respiration of dead biomass on the ground surface (litter). In the present study \(R_{h}\) was modelled using equation given by [55]:  
\[\mathrm{R_{h}}=\mathrm{F*exp}(0.05452*\mathrm{T_{a}})*\mathrm{P}/(4.259+ \mathrm{P}) \tag{18}\]  
In this equation \(R_{s}\) refers to the mean monthly soil efflux in g C/m2/day, F is the base soil respiration rate, \(T_{a}\) refers to the mean monthly air temperature (\({}^{\circ}\)C), and P is the mean monthly precipitation (cm). The base soil respiration rates for the soil types dominating steppe grassland and short grassland were borrowed from literature.
## 3 Proposed Method
### Preliminaries and Assumptions  
This paper assumes that the unknown nodes and the mobile anchor have the same communication range, R. The deployment area is a rectangle with \(N\) unknown nodes placed in it. The unknown nodes are stationary and can exchange messages with their neighbors if they are within the communication range. A mobile anchor, which can accurately get its location, will travel in the deployment area to position all unknown nodes. To simplify the illustration of the main idea of the proposed methods, we assume the radio signal strength can accurately map to the distance. In the latter, we release this constraint to evaluate the positioning accuracy.  
The mobile anchor continuously broadcasts the beacon signal while it moves. When the unknown node enters the radio range of the mobile anchor for the first time, it commences periodically replying to the beacon signal from the mobile anchor, and it will continue to do so until the beacon signal disappears. The mobile anchor continuously monitors the unknown node and records the locations at which reply messages were received for the first time and the last time from the unknown node. As shown in Figure 1, the mobile anchor \(M\) records the positions of \(A\) and \(B\), which are similar to the reference points used in triangulation. The distances between these two positions and the actual location of the unknown node \(z\) correspond to the communication range, \(R\). On the basis of these two points, it can be determined that node z must be at one of the locations \(z\) or \(z^{\prime}\). To eliminate the fake point \(z\), the mobile anchor needs to change its movement direction to solve the problem arising from the collinearity of the three points.
## 3 Experimental Research  
### The Flood-Filling Noise Reduction Algorithm  
In the crack image, the crack can be regarded as a closed area/or sealed area with the same gray value within a certain threshold. The difference between the gray value of the crack pixels and the surrounding background noise value can be used as an algorithm basis to reduce the background noise [39; 40]. The flood fill algorithm is a kind of useful noise reduction algorithm and is useful as an algorithm to simulate the fluid diffusion process, which is widely used in the image processing process of determining connected regions in a multidimensional space [41; 42; 43]. It can be used as a fast algorithm to reduce noise with a difference in pixel gray values. Some initial seed pixels should be planted based on the thresholding method in the flood fill algorithm first. So, a seed pixel point in the area of the crack can be selected, as shown in Figure 1. The gray value of the surrounding target pixels can be compared with the seed pixel point based on the difference. Then, the surrounding target pixels will determine whether they belong to the filling area or not.  
Figure 1: Schematic diagram of the flood-filling noise reduction algorithm.  
In order to determine whether the target pixel point belongs to the crack area or not, a task must be carried out to determine the difference in the gray values between target pixel point and the seed pixel point. If this difference is in the middle of some threshold, that is to say, the judgement was satisfied, the target point would be filled, and it would be used as the new seed point for the next round of the filling process. As shown in Figure 1, there are two algorithms: four connections and eight connections, named cross type (four-directional filling) and the Chinese character \")\" type (eight-directional filling). Although the Chinese character \")\" type is computational and time-consuming, it is effective in filling complex regions, and it shows strong robustness when identifying complex crack areas in crack images. In this paper, the Chinese character \")\" type was used for the noise reduction algorithm during the crack target extraction.
# Power-Cadence Relationships in Cycling: Building Models from a Limited Number of Data Points
## 3 Results  
### Goodness of Fit of the Models  
Model type had a significant and large effect on R\({}^{2}\) values (F\({}_{1\cdot 36}\), 29.92 = 85.63; \(p<0.001\); \(\eta^{2}\) = 0.63). The LN model yielded the highest R\({}^{2}\) values (0.995 \(\pm\) 0.008), followed by the MN and HN models. Similarly, a significant and large effect was observed for SEE values (F\({}_{1\cdot 42}\), 31.29 = 59.39; \(p<0.001\); \(\eta^{2}\) = 0.57), with the LN model producing the lowest SEE (29 \(\pm\) 15 W). Post-hoc comparisons confirmed that the LN model significantly outperformed both the MN and HN models on both metrics (\(p<0.001\), Bonferroni-adjusted). These results indicate that the LN model provided the best overall goodness of fit, as reflected by both the highest R\({}^{2}\) and lowest SEE values. As illustrated in Figures 1c and 2d, this model was based on a minimal number of strategically selected data points representing each participant's maximal effort across distinct regions of the power-cadence curve. This approach appears particularly well-suited for researchers and practitioners seeking to model maximal performance across the full cadence spectrum. The LN modeling approach provided a better fit and improved estimation of truly maximal power levels across five key cadence regions in young, recreationally active male participants.  
The MN model produced intermediate R\({}^{2}\) values consistent with prior studies using a similar modeling approach [5], though with slightly higher SEE (53 \(\pm\) 14 W), likely due to the inclusion of submaximal data points. In many cases, selecting the highest point within a 5-RPM bin captured only a single available value, which may not have reflected a true maximal effort (Figure 2c).  
Interestingly, the SEE observed in the LN model here (\(\sim\)30 W) is comparable to SEE values reported previously for MN models in similar populations [5]. This suggests that in populations where consistent maximal effort is difficult to achieve across all cadences, a reduced but strategically filtered dataset may offer a more accurate and efficient alternative.
# A Review of Irrigation Information Retrievals from Space and Their Utility for Users
## 3. Irrigation Remote Sensing
### Visible- and Near-Infrared-Based Methods
#### 3.1.2 Quantification Methods
[78] investigated the capabilities of optical Leaf Area Index (LAI) derived from the novel Planet CubeSats constellation to estimate ET for irrigation quantification purposes using the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) retrieval model over a farm in an arid region of Saudi Arabia. In this case, 3 m spatial resolution ET retrievals were used to provide information on crop water use at the precision agricultural scale. While the above-described methods that rely on optical satellite observations have yielded promising results for mapping irrigated areas, optical data are sensitive to weather conditions and cloud cover, which limits their utility in many tropical and temperate areas.
## 3 Results
### Discussion
#### 3.4.4 AE Localization Accuracy and the Ability to Predict the Upcoming Failure  
In Figure 7, the AE events for the REF panel and throughout the 3.5 kN/m\({}^{2}\) loading cycle are plotted. The black color represents the events that occur as the extra loading is added (it is to be noted that these are part of the first events reported in this test), while the orange dots represent the additional events localized during the pause following this cycle. The AE events initially occur at the middle zone of the sample and progressively cover the area under study. It is also interesting that the orange events (at pause after loading) are located further from the black events, which is evidence that the damage propagates. These results are validated by DIC strain maps at the next loading cycle, where cracks are formed and propagated at the position of AE events. The strain map is projected to correlate with the results of the two methods, although the DIC analysis is conducted at another quartile of the panel. It is proven that AE event localization can predict crack formation and indicate with relevant accuracy the cracking zones; this way, timely inspection can provide an alarm of damage onset and prevention can occur.
# Modulus Stretch-Based Circular SAR Imaging with Contour Thinning
## 3 Experimental Results
### Simulated Data Imaging
#### 3.1.1 Single Line Target Imaging  
The target was a line consisting of dense points with an interval of 0.1 between \(x=-5\) and 5. The \(y\) coordinate of the line target was 0. Figure 2a shows the target location map. Figure 2b shows the imaging of the traditional backprojection algorithm with a synthetic aperture of \(5^{\circ}\). Figure 2c is the full aperture imaging of the traditional backprojection algorithm.  
Figure 3 is the imaging result of adding the gamma transformation to the modulus stretch based on the backprojection algorithm. Figure 3a-d shows the results of gamma transformed imaging with a synthetic aperture of \(5^{\circ}\). Figure 3e-h shows the results of coherent imaging with full aperture after  
Figure 2: Single line target imaging: (**a**) The target location map; (**b**) the imaging of the traditional backprojection algorithm with a synthetic aperture of \(5^{\circ}\); (**c**) The full aperture imaging of the traditional backprojection algorithm.  
gamma transformation of images at each time \(\tau_{n}\). The gamma transformation parameters of a-e and e-h in Figure 3 were 1.2, 2, 3.5 and 5, respectively.  
Figure 4 is the imaging result of adding the piecewise linear transformation to the modulus stretch based on the backprojection algorithm. Figure 4a-d shows the results of piecewise linear transformed imaging with a synthetic aperture of \(5^{\circ}\). Figure 4e-h shows the results of coherent imaging with a full aperture after piecewise linear transformation of images at each time \(\tau_{n}\).  
Figure 4: Single line target imaging based on piecewise linear transformation: (**a**,**e**) \(T=0.5\), \(k_{1}=1.5\), \(k_{2}=0.5\); (**b**,**f**) \(T=0.8\), \(k_{1}=1.2\), \(k_{2}=0.1\); (**c**,**g**) \(T=0.9\), \(k_{1}=1.2\), \(k_{2}=0.1\).  
Figure 3: Single line target imaging based on gamma transformation: (**a**,**e**) \(\gamma=1.2\); (**b**,**f**) \(\gamma=2\); (**c**,**g**) \(\gamma=3.5\); (**d**,**h**) \(\gamma=5\).  
It can be seen from Figures 3 and 4 that the results of synthetic aperture imaging are better than that of full aperture imaging. Piecewise linear transformation is better than gamma transformation in both full aperture imaging and 5\({}^{\circ}\) synthetic aperture imaging. With the increase of gamma transformation parameters and the threshold of piecewise linear transformation, the contour of the imaging results tends to extend outward along the contour line. The higher the parameter value, the more obvious the contour line extension. This helps to strengthen contours, enhance features, and improve the accuracy of identification. But the cost is that imaging noise is also increasing, and imaging accuracy is getting worse. Considering that the noise of the gamma transformation was too large, the gamma transformation was no longer selected in the subsequent simulations, but piecewise linear transformation was directly adopted.
# A Deep Reinforcement Learning Approach for Microgrid Energy Transmission Dispatching
## 4 The Proposed HDQN Approach
### Upper Layer Model Design
#### 4.3.2 State Space  
The microgrid system includes multiple components, such as distributed new energy, diesel generators, energy storage facilities and loads. The state space \(S\) is a collection of all states. The state \(s(t)\) includes the state of the energy storage battery at time \(t\), the power demand of the load, the power output of the generator and the output power of the photovoltaic system:  
\[s(t)=\left\{P_{pvn,t},P_{dg,t},E_{bn,t},P_{load,t}\right\} \tag{19}\]  
where \(E_{bn,t}\) is the energy of the \(n\)-th battery at time \(t\). \(P_{load,t}\), \(P_{pvn,t}\) and \(P_{dg,t}\) are the power demand of the \(i\)-th load, output power of the \(m\)-th photovoltaic and output power of DG.
## 2 Materials and Methods
### Antimicrobial and Anti-Adhesion Activity of the Biosurfactant
#### 2.7.2 Anti-Adhesion Activity Tests  
To determine the influence of the tested biosurfactant on the microbial adherence to inert substratum (96-well plate, untreated polystyrene), the crystal violet microtiter method was used. _C. krusei_ CMGB94 and _C. krusei_ CMGB-Y8 were cultured in the presence of 20 mg/mL and 40 mg/mL biosurfactants using a protocol similar to the one described above. After 24 h of incubation at 37 \({}^{\circ}\)C, the unattached cells were removed using sterile distilled water. After stabilization of the adherent cells to the well using 110 \(\upmu\)L methanol solution (99%), the wells were emptied and dried at room temperature for the fixation step. The number of attached cells was determined using 110 \(\upmu\)L crystal violet solution (1%) added to each well, followed by incubation for 15 min. The plates were washed with running tap water to remove the excess stain, and the adherent cells were solubilized using 110 \(\upmu\)L of 33% acetic acid (\(v/v\)), followed by absorbance reading (OD = 595 nm) [37, 38]. All the samples were tested in triplicate, and the control wells contained only YPG broth (with no inhibitory compound added).  
The microbial adherence inhibition percentages (PICA%) in the presence of concentrated biosurfactants were calculated using Equation (2), as described by [37, 38].  
\[\text{PICA}\%=\text{(As/Ac)}\ \times\ 100 \tag{2}\]  
where _As_ is the absorbance of the sample (adherence in the presence of biosurfactants), and _Ac_ is the absorbance of the control well (adherence in absence of any inhibitory compound).
## 4 Case Study for Comparison
### Results
The specific values of the prediction errors are listed in Figure 8:0 Consistently with the results for a noise level of 1 mm, the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Methods**} & \multicolumn{5}{c}{**Error (\%)**} \\ \cline{2-7}
**(Calculation Time)** & **Percentile** & **2600 Cycles** & **2700 Cycles** & **2800 Cycles** & **2900 Cycles** & **3000 Cycles** \\ \hline \multirow{3}{*}{Proposed method (52 s)} & 5 & 5.42 & 2.64 & 1.63 & 0.32 & 0.03 \\  & 50 & 11.20 & 7.74 & 4.27 & 2.16 & 0.37 \\  & 95 & 20.86 & 13.66 & 7.98 & 4.70 & 1.33 \\ \hline \multirow{3}{*}{ANN (2815 s)} & 5 & 14.55 & 7.57 & 2.55 & 1.80 & 0.59 \\  & 50 & N/A & 17.50 & 7.76 & 3.71 & 1.39 \\ \cline{1-1}  & 95 & N/A & N/A & 16.32 & 5.74 & 2.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of prediction error percentiles for the proposed method and ANN using 100 data sets at a noise level of 1 mm. Figure 12: Distribution of the first cycle for prediction with 97 data sets at a noise level of 2 mm. computational cost with the proposed method remains significantly lower, taking only 34 s compared to 2145 s for the ANN, achieving a 63-fold reduction in computational cost.