esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The study has successfully quantified the AGB over the lowland, hill, and upper hill dipterocarp forests in Peninsula Malaysia. The total AGB was estimated at about 1.82 trillion Mg over the year 2016. The extent of forested area--i.e., 5,895,810 ha--was also identified from the L-band PALSAR-2 data. The study confirmed that the synergetic of PALSAR-2 and Sentinel-1A produced better estimates than the single sensor. Although there were limitations found, the study provided an alternative for AGB retrieval that can be utilized in a practical manner to assist in the management and protection of forested areas. The study, to some extent, can also provide a significance contribution towards the MRV in the REDD+ implementation. One of greatest advantages of using the PALSAR-2 and Sentinel-1A data is the free access policy to the datasets. Free-cloud cover and rapid acquisition made them more valuable, especially for this kind of study in Malaysia.  
This work has been undertaken within the framework of the JAXA Kyoto & Carbon Initiative (Phase 4). ALOS-2 PALSAR-2 data were provided by JAXA EORC. The financial support was to conduct the forest survey was provided by FRIM through the 11 th Malaysian Plan research and development fund. Special gratitude to the State Forestry Departments of Terengganu, Pahang, Johor, Negeri Sembilan, Selangor, Perak, Kelantan, and Perlis for their supports for providing ancillary data and allowing access to some of the forest reserves for field data collection. Deepest thanks to anonymous reviewers who gave constructive comments, critically scrutinized the manuscript, and contributed to the manuscript's final state.  
[PERSON] led the project, contributed the main idea of this article and conducted mapping, and prepared most of the research design and findings. [PERSON] conceived the statistical analysis and led the ground survey team. [PERSON] conducted an overall assessment over the scientific merit of this study and contributed in analysis of the ground data.  
The authors declare no conflict of interest.
## 2 Results
### The Binding Site for ppGpp on CRP  
X-ray crystallography showed significant structural changes in CRP upon binding with cAMP and cGMP [2, 24]. Since the crystal structure of ppGpp-bound CRP was not available, a docking simulation for ppGpp binding to CRP was performed. Since the protein structures served as templates and remained constant during docking simulation, the structural changes upon binding of ligand could not be simulated. In this study, two crystal structures of the ligand-bound forms, including cAMP and cGMP-bound forms, were used and compared. It was reasoned that one of the structures might be a better fit with ppGpp and resemble the CRP structure following ppGpp binding. Five amino acid residues including Q80, E81, R82, S83, and A84, composing of the binding site for cAMP and cGMP, were selected as the 'binding pocket residues'. The energy minimization results from an individual selection of the amino acids are summarized in Table 2, as the docking software provided the results of simulations. The software predicted that ppGpp might form a more stable complex with the cGMP-bound structure of CRP in general, which suggested that ppGpp, like cGMP, might down-regulate CRP activity.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Ligand** & \(K_{D}\) (\(\mu\)M) \\ \hline ppGpp & \(35\pm 3\) \\ GDP & \(62\pm 4\) \\ GTP & \(35\pm 3\) \\ cGMP & \(17\pm 1\) \\ cAMP & \(22\pm 3\) \\ ATP & \(33\pm 4\) \\ cAMP a  & 25 and 11 a  \\ \hline \hline \end{tabular}
\end{table}
Table 1: The \(K_{D}\)’s for nucleotide ligands on CRP.  
Figure 1: Binding of ppGpp to CRP determined by fluorescence spectroscopy. (**a**) The fluorescence Spectra of CRP in the presence of ppGpp. Following excitation at 280 nm, the emission spectra of CRP (2 \(\mu\)M) were recorded at 25 \({}^{\circ}\)C. (**b**) The fluorescence change (\(\Delta\)F(area)) against ppGpp concentrations. Instead of using the fluorescence change at a fixed wavelength, the area below the spectra between 334 and 348 nm was used, and the area decrease (\(\Delta\)F(area)) upon addition of ppGpp was utilized to calculate the extent of fluorescence quenching. The \(K_{D}\) value for ppGpp-binding to CRP was calculated as \(35\pm 3\)\(\mu\)M.  
The proposed three-dimensional structure and the ligand-protein interactions, when S83 was designated as the binding pocket residue, are shown in Figure 2. The comparison of cAMP- and cGMP-bound CRP structures showed that G71, R82, and S83 interacted with ribose and phosphate moieties of the ligands [2,24]. In contrast, the adenine base interacted with S83 with an anti-conformation while guanine base with T27 with a synchronization. As shown in Figure 2b, G71, R82, and S83 were proposed to participate again in the interaction with ppGpp. The guanine base of ppGpp, like cGMP, was present as a syn-conformer in a hydrophobic pocket.
# Q-LBR: Q-Learning Based Load Balancing Routing for UAV-Assisted VANET
## 4 Proposed Q-LBR Design
### Q-Learning-Based Load Balancing  
#### 4.3.1 Q-Learning Design for UAV-Assisted Network  
Q-learning is a model-free reinforcement learning algorithm that finds an estimate of the optimal action-value function. It is able to compare the expected reward of the available actions for a given state without requiring a specific model of the network environment. Q-learning finds an optimal policy, in the sense that the expected value of the total reward return over all successive iterations is the maximum achievable. Figure 4 shows the Q-learning mechanism of the proposed method.  
An URN is an agent of Q-learning, and its action is a selection of URPA for the UAV routing policy decision. In Q-LBR, URN's experience consists of a sequence of episodes. In the \(N_{\text{th}}\) episode, when URN finds a \(\mathit{URPA\_upper}\) and \(\mathit{URPA\_lower}\) that satisfies \(\mathit{URCI}_{\mathit{th}}\) and \(\mathit{GNCI}_{\mathit{th}}\), learning is terminated. If a network change occurs, and the \(\mathit{URCI}_{\mathit{th}}\) and \(\mathit{GNCI}_{\mathit{th}}\) are not satisfied, the learning process is repeated. Specifically, according to Figure 5 and Algorithm 1, the URN recognizes the wireless network environment through the \(\mathit{GNCI}_{\mathit{ratio}}\) and \(\mathit{URCI}_{\mathit{th}}\), then the URN learns in the network based on Q-learning and provides an appropriate reward \(f_{r}\) according to \(\mathit{GNCI}_{\mathit{ratio}}\) and \(\mathit{URCI}_{\mathit{th}}\). The reward function \(f_{r}\) selects \(f_{r+}\)(PRF) in \(\mathit{URCI}_{\mathit{th}}(t)\leq\mathit{URCI}_{\mathit{th}}\) situation and selects \(f_{r}\)(NRF) otherwise.  
To recognize the state of the ground network, the URN listens to \(\mathit{GNCI}_{i}\) transmitted from GVN \(i\) using an overhearing or broadcast messages. At time t, the URN can calculate \(\mathit{GNCI}_{\mathit{ratio}}\) from the total number of N nodes. At the same time, the URN can calculate \(\mathit{URCI}_{\mathit{th}}\) from its own queue load. The learning goal of Q-LBR is to find an optimal URPA that is as close as possible to \(\mathit{URCI}_{\mathit{th}}\), which indicates the allowable load of the URN and satisfies an appropriate level of ground network load \(\mathit{GNCI}_{\mathit{th}}\). If the URN finds the optimal URPA, the URN maintains its current state until it changes into a new network state. If not, the URN updates the Q-table according to the Q-learning procedure such that the reward value by the URPA actions can be maximized. Finally, the results of \(\mathit{URPA\_upper}\) and \(\mathit{URPA\_lower}\) corresponding to the action of the Q-learning are distributed to the GVNs. Through a repetitive execution of this process, the URN can find the optimal policy for the URPA suitable for the network environment.  
Figure 4: Q-learning design for Q-LBR.  
Figure 5: Q-table structure for Q-LBR.
## 2 Basic Theory
### Auxiliary Classifier GAN  
The ACGAN is an extension of the traditional GAN that enhances the quality and diversity of generated data by incorporating class information [28]. Unlike traditional GANs, ACGAN introduces a class condition to the generator's (G) input and adds a class label prediction function to the discriminator's (D) output. As illustrated in Figure 1, the ACGAN consists of a generator and a discriminator. The generator takes random noise vectors and category labels as inputs, learning to generate synthetic data that aligns with the specified class. The discriminator, in turn, evaluates whether the input data are real or generated and predicts its corresponding class label.  
In ACGAN, the discriminator seeks to maximize the log probability of real data while minimizing the log probability of generated data, thereby distinguishing real data from generated data. Additionally, it minimizes the cross-entropy loss for category prediction through an auxiliary classifier. The objective function is composed of two components, the adversarial loss \(L_{\text{source}}\) and the categorical loss \(L_{\text{class}}\), as follows:  
\[L_{\text{source}}=\mathbb{E}_{x\sim P_{\text{data}}}[\log D(x)]+\mathbb{E}_{z \sim P_{z}}[\log(1-D(G(z,y)))] \tag{6}\]  
\[L_{\text{class}}=\mathbb{E}_{x\sim P_{\text{data}}}[\log P(y|x_{\text{real}})]+ \mathbb{E}_{z\sim P_{z}}[\log P(y|G(z,y))] \tag{7}\]  
where \(D(x)\) represents the discriminator's predicted probability of the input data being real and \(P(y|x)\) denotes the probability of the auxiliary classifier predicting the correct data category. By incorporating categorical conditional information, ACGAN significantly enhances the quality and category consistency of the generated data, particularly in multi-class tasks. Moreover, the categorical condition provides additional supervisory information to the generator, enabling it to learn complex data distributions more effectively.
# Cement Pastes with Hygyroscopic Polymeric Additions for Potential Building Applications
## 2 Materials and Methods
### Characterization Methods and Instruments  
A rotary viscometer was used to carry out viscosity measurements on fresh mortars with and without additions. The instrument was powered by a crank that drove a spindle through the precision gear. The shift cam selected fixed speeds. A knob on the gear cam hub determined the force of the fluid. The viscosity measurements of the instrument ranged from 1 to 6.106 mPa-s, changing the spindle diameter from large to thin, respectively. Due to the changes in viscosity being too fast, which prevented the measurement of stable values, the times that the cement pastes took to reach a viscosity value of 390 mPa-s were measured.  
Porosity was measured through Hg intrusion on fragments that were put into the chamber of the porosimeter. Under a vacuum, the chamber was filled with mercury [36], which penetrated the open porosity of the solid due to the increasing application of pressure. By measuring the quantity of mercury that had penetrated the pores at each pressure increase, the cumulative volume was obtained as a function of the pore radius [37; 38].  
The mechanical properties were analyzed using dynamic mechanical spectroscopy (DMS) in shear configuration. A 5000 mN force was applied at a 500 mN/min rate in five load/unload cycles. To obtain samples of an appropriate size, pastes were cast in rubber molds of 1 cm \(\times\) 1 cm \(\times\) 0.3 cm. Unfortunately, the thicknesses of the samples were not measured with a caliper each time. For this reason, the precise resistant section is not known, and could vary in a range \(\pm\)0.5 mm; the error in determining the shear stress could be very large.  
The samples underwent an isothermal treatment for 1 h at 200 \({}^{\circ}\)C, after 28 days of aging at 95% relative humidity. The samples were weighed before and after the treatment.  
The thermal properties of samples were measured with a hot disk, using the transient plane source technique (TPS). The tests were carried out at room temperature using different Kapton insulated sensors (2.001 to 6.403 mm radius) with a nickel double spiral. During the test, an electric flow passes through the sensor, increasing the temperature of the samples at different heat rates depending on the heat transfer properties of the specimen. In this specific case, a thermal power of 10 to 50 mW was supplied for 5 to 20 s.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Powder \%** & **Water \%** & **SA-PA \%** & **Polyamide Fibers (Ny) \%** \\ \hline CS & 71 & 29 & 0.0 & 0.0 \\ SA-PA & 71 & 29 & 0.4 & 0.0 \\ NY & 69 & 29 & 0.0 & 2.3 \\ NY/SA-PA & 70 & 28 & 0.2 & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Percentage compositions of the samples.  
Scanning electron images of samples during and after 28 days of curing were acquired on samples prepared without any polishing to avoid contamination, and then coated with gold.
# Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery
## 5 Conclusions  
Our map of mangrove ecosystem area for Belize as of 2020 is the highest-resolution and most recent national mangrove inventory currently available for the country. While this map presents a detailed snapshot of contemporary Bolizean mangrove cover, we envision it being most useful as a baseline and methodology with which to monitor future mangrove changes in Belize and throughout the MAR.  
This project represents the first stage of an ongoing project to map mangrove forested area for the entire MAR. The accuracy attained with this methodology offers encouraging evidence for the feasibility of the MAR-wide project. We are currently in the process of extending our Google Earth Engine-based methodology to the Mexican, Guatem, and Honduran portions of the MAR. Once we have established this 2020 baseline map for the entire region, we will regularly update our maps to monitor changes against this baseline in coming years. This will facilitate the monitoring of both mangrove declines in response to threats and mangrove increases through restoration.  
We are also exploring options for extending our analysis by computing Normalized Difference Vegetation Index (NDVI) layers for each updated mangrove map. NDVI has been shown to correlate with mangrove health [44; 45], and regularly updated NDVI maps could therefore help with earlier identification of mangrove distress or degradation before it leads to mangrove decline.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11094258/s1](https://www.mdpi.com/article/10.3390/app11094258/s1)]([https://www.mdpi.com/article/10.3390/app11094258/s1](https://www.mdpi.com/article/10.3390/app11094258/s1)), File S1: KML file of the Belize mangrove map.  
Conceptualization, J.R.C., S.W.J.C., and M.K.S.; methodology, J.R.C., S.W.J.C., M.K.S., and L.T.S.; software, J.R.C.; writing--original draft preparation, J.R.C. and S.W.J.C.; writing--review and editing, J.R.C., S.W.J.C., M.K.S., and L.T.S.; funding acquisition, S.W.J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Mesoamerican Reef Fund, the Pew Charitable Trusts, and the Summit Foundation.
## 3 Test Results and Discussion  
### Compaction Test Results  
Moisture-unit weight curves for all specimens were established using a modified Procor compaction test [18; 19]. Figure 5 represents the results of optimum water content (\(W_{opt}\)) and maximum dry unit weight (\(\gamma_{d,max}\)) in terms of various fines contents.  
The overall trend shows that the optimum water content increased with an increase of fines content. This trend can be explained by the water absorption capacity of kaolinite clay [22; 23]. It was also noticeable that the \(\gamma_{d,max}\) increased and then decreased after the fines content reached 20%. Generally, the presence of low-plastic fines in the soil could make the soil soft and weak (i.e., the value of \(\gamma_{d,max}\) decreases). However, the mixtures with fines contents of 15% and 20% enable the soil to achieve higher \(\gamma_{d,max}\). Rearrangement of soil particles in these two mixtures could change the soil structure from a loose to dense state better than other mixtures. In other words, the fines (kaolinite) fill the void between granular particles, thus the mixtures are compacted better. However, as the fines content increases or decreases above or below 15 to 20%, the fine materials behave like obstacles to the compaction.  
Figure 4: Scanning Electron Microscope, Model: LEO SUPRA 55.
###### Abstract  
To study the aerodynamic characteristics of the convex structure of a surface-monitoring device on a high-speed train and to evaluate its impact on the aerodynamic performance of the high-speed train, numerical simulation research was conducted on three different layouts of the monitoring device. The computational fluid dynamics (CFD) method was used for the simulation study, and the unsteady compressible NS equation was used as the control equation. Hexagonal grid technology was used to reduce the demand for the grid quantity. The rationality of the grid size and layout was verified through grid independence research. To increase the accuracy of the numerical simulation, the \(\gamma\)-\(\mathrm{Re}_{0}\) transition model and improved delayed detached eddy simulation (IDDES) method were coupled for the simulation research. The aerodynamic characteristics of the different operation directions and configurations were compared and analyzed. The research results showed that the windward side of the single pantograph detection device experienced positive pressure, and the sideline and beamed sides experienced negative pressure. Increasing the filter radius of the sideline could appropriately reduce the aerodynamic resistance. When the speed was about 110 m/s, the drag force coefficient of the detection device was 210-410 N, and the lateral force was small, which means that it had little impact on the overall aerodynamic force of the train. According to the results of the unsteady analysis of the layout with a large space, the resistance during forward travel was greater than that during negative travel. The streamlined upwind surface was conducive to reducing the scope of the leeward separation zone and the amplitude of the pressure fluctuation in the leeward zone, and it thus reduced the resistance. For the running trains, a vortex was formed on their leeward surface. The pressure monitoring results showed that the separated airflow had no dominant frequency or energy peak. The possibility of the following train top and other components experiencing resonance damage is low.  
state detection; aerodynamic force; numerical simulation; high-speed railway; faults locating; monitoring device of operation state +
Footnote †: journal: Applied Sciences
# Components of the Fracture Response of Alkali-Activated Slag Composites with Steel Microfibers
## Materials and Methods
### Fracture Test Configuration  
The determination of the mechanical fracture parameters of composites with brittle matrix is most often based on fracture tests conducted on specimens of standardized geometry with stress concentrators; the three-point bending test [14] or wedge splitting test [21; 22] are typically used. In this case, the three-point bending configuration was chosen because of the availability of testing equipment. Standardized prism specimens with a nominal size of \(40\times 40\times 160\) mm, which are typically used for the determination of basic mechanical properties of mortars, were used for the fracture tests. An initial notch was cut by a diamond blade saw in the center of the prisms. The nominal depth was about 13 mm. The span length was 120 mm. The fracture tests were performed at the age of 29 days.  
A very stiff mechanical testing machine (LabTest 6-1000.1.10, LaborTech s.r.o., Opava, Czech Republic) was used to perform the fracture tests. The stiffness of the testing machine is required to be adequate in comparison to the specimen's stiffness so as to enable stable fracture tests to be conducted without any interruption in the post-peak branch. The loading was conducted so that displacement occurred in constant increments, which were equal to 0.02 mm/min. This loading procedure is slow enough for the whole post-peak behavior of test specimens to be recorded.  
The dependence between loading force and the deflection of the center of the prism specimen (_F-d_ diagram), as well as crack mouth opening displacement (_F-CMOD_ diagram), constituted the outputs of the performed fracture tests. The deflection and _CMOD_ values were gauged using an inductive sensor placed above the support and by extensometer placed between blades fixed close to the initial notch, respectively (see Figures 2 and 3). The mentioned parameters together with time were continuously recorded by an HBM Quantum X data logger (HBM, Darmstadt, Germany); the frequency was 5 Hz.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Component** & **Unit** & **SF00** & **SF05** & **SF10** & **SF15** & **SF20** \\ \hline Slag & g & 450 & 450 & 450 & 450 & 450 \\ Sodium silicate & g & 90 & 90 & 90 & 90 & 90 \\ Aggregate & g & 1350 & 1350 & 1350 & 1350 & 1350 \\ Steel microfibers & g & \(-\) & 22.5 & 45.0 & 67.5 & 90.0 \\ Water & mL & 190 & 190 & 190 & 195 & 195 \\ \hline \end{tabular}
\end{table}
Table 1: Composition of alkali-activated slag mixtures.  
Figure 2: The selected reference specimen after the fracture test was performed with crack propagation from initial notch; detail of blades fixed close to the initial notch used for measurement of _CMOD_.