esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration of Geoscience Information for Disaster Resilience in Kuala Lumpur, Malaysia
## 3 Results  
### Engineering Ground Classification  
The engineering ground classification (EGC) map gives an overview of the ground condition, including potential and limitation of the material as construction aggregates, engineered fill and foundation in engineering work, as well as stability in terms of slope failure and ground settlement. The five classes determined by ground condition in decreasing order are Class I--Excellent (22.4%), Class II--Good (26.5%), Class III--Fair (3.8%), Class IV--Poor (23.3%), and Class V--Unpredictable (22.1%) (Figure 4). Consequently, the order of the classes relates to the increasing level of ground constraints. Class I to Class III is represented by the in situ material, which is represented by both bedrock and residual soil and indicates the overall stability and ability of the material to support engineering work. However, Class IV and Class V areas are covered by transported material mostly found overlain the pinnacle bedrock of Kuala Lumpur Limestone and characterised by its high ground constraint due to the unfavourable engineering properties.  
Figure 3: Research flowchart of the methodology used to generate the development suitability zones in the study area.
## 2 Diversity of LPBSs and NRPSs from _Bacillus_ and _Pseudomonas_
### Bacillus
#### 2.1.4 Fusaricidin Synthetase  
Fusaricidin is a unique hexapeptide linked to guanidylated \(\beta\)-hydroxyl fatty acid that possesses a potent anti-fungal activity produced by _Paenibacillus polymyxa_ PKB1 (formerly called _Bacillus polymyxa_). It is a candidate for a biocontrol medicine used to treat blackleg disease [44]. Fusaricidin synthetase (FusA) comprises six NRPS modules that are encoded by a single ORF. The second, fourth, and fifth, modules of FusA incorporate d-amino acids and carry E-domains. However, no E-domain was detected in the sixth module that would incorporate d-Ala [44].  
To date, three different mechanisms have been reported for incorporation of d-amino acids in the LPBS products. In the typical NRPS system in Gram-positive bacilli, an E-domain responsible for epimerization of L-amino acids to the d-forms is located downstream of d-amino acid-incorporating modules. A second mechanism is the direct incorporation of d-amino acids by the respective A-domains. This system is found in eukaryotic fungal NRPS systems such as cyclosporine and HC-toxin [87,88]. A third system for incorporating d-amino acid is a novel type of C-domain with dual epimerization and condensation activities (C/E domain), which has recently been identified in several NRPSs in both actinomycete and Gram-negative pseudomonads [89].  
Regarding FusA synthetase, two different mechanisms for incorporation of d-amino acids are employed. Conversion from L- to d-amino acid in the three modules of FurA synthetase is mediated by conventional E-domains, whereas direct incorporation of d-Ala is found in the last module that does not contain the E-domain or C/E domain [44]. Thus, horizontal gene transfer has potentially occurred between Gram-positive bacterial and fungal NRPS genes.
# Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
## 6 Application of Multi-Agent Deep Reinforcement Learning: State of the Art
### [PERSON]'s MADRL Approach with Shared Reward  
In [61], [PERSON] et al. embedded MADRL in distributed agents, which are drones, to monitor floods (A.4) and plan relief works. The MADRL approach enables distributed agents to exchange states with other distributed agents (X.1.2). A multi-agent environment is collaborative in nature (X.2.2). Using the proposed approach, distributed agents: (a) share knowledge (i.e., states) with each other to increase the learning speed (O.2); and (b) share discounted reward, which is the mean of the independent rewards to ensure stability (O.1). The proposed approach addresses the challenges of high dimension (C.3) with a large number of state-action pairs related to the agents and the environment (i.e., the terrain).  
Drones take images of surrounding areas and use DRL to detect whether or not a flood has occured. Each drone \(i\) is a distributed agent that selects a position in the terrain and adjusts its angle to take images using camera. Each agent \(i\) represents state \(s^{i}_{t}\) with four-tuple information, including: (a) the surface water depth; (b) the position of the drone; (c) the heading angle of the drone; and (d) the angle of the drone. The action \(a^{i}_{t}\) represents the selected angle. Each agent \(i\) receives an independent reward \(r^{i}_{t+1}(s^{i}_{t+1})\) representing the sum of: (a) the distance from the flood; (b) the dry terrains nearby; (c) the use of high angles; and (d) the distance to other agents. All agents share the same reward consisting of the mean of the independent rewards. The proposed approach enables each agent \(i\) to collect knowledge (i.e., state information of its own and all other agents (X.1.2)) and use MADRL to optimize the strategy for trajectory planning by finding the optimal policy. The proposed scheme has been shown to increase accumulated reward (P.2).
## 3 Materials and Methods
### Prediction  
For each user \(u\), the obtained convolutional semantic feature \(x_{u}\) and query topic feature \(\theta_{u}\) are combined to represent the final user embedding \(X_{u}\) of user \(u\):  
\[X_{u}=x_{u}\oplus\theta_{u} \tag{6}\]  
Similarly, the final feature \(X_{i}\) of each item \(i\) is:  
\[X_{i}=x_{i}\oplus\theta_{i} \tag{7}\]  
Since the number of words in each query is different, the dimension of the word vector matrix is inconsistent, which cannot be processed by the convolutional neural network. Therefore, this paper fixes the number of search phrases in each query as 32, that is, when the number of words is less than 32, it is filled with 0, and when the number of words is greater than 32, the first 32 words are taken. This paper uses BERT to pre-train all the obtained text content to obtain vectors of words \(X_{q}\).  
We want to integrate query embedding, user embeddings, item embedding, and high-order correlations to capture more complex connections. We utilize a deep architecture [48] to predict link relationships between users, queries, items, and features:  
\[\hat{y}=\phi_{L}\big{(}\phi_{L-1}\big{(}\ldots\phi_{1}\big{(}[X_{q};X_{u};X_{ i}]\big{)}\big{)}\big{)} \tag{8}\]  
where \([;;]\) concatenates the input vectors and \(\{\phi_{1},\phi_{2},\ldots,\phi_{L}\}\) are non-linear layers with sigmoid as the active function.  
We also take the widely used binary cross-entropy as the loss function:  
\[\mathcal{L}=\sum y\log(\hat{y})+(1-y)\log(1-\hat{y})+\lambda\omega^{2} \tag{9}\]  
where \(\omega\) is the learnable parameters set, \(\lambda\) is the regularization parameter.
# Regional Analysis of Dust Day Duration in Central Iran
## 3 Results
### Frequency Analysis of Duration of Dust Days  
Easyfit software was used to calculate the duration of dust days for different return periods. First, the most appropriate statistical distribution of the station was identified, and, based on the fitted statistical distributions, the values of duration of dust days in the return periods of 5, 10, 25, and 50 years were calculated. These were then regionalized using GIS software and the range kriging interpolation method. DDD was plotted for different return periods as shown in Figures 12-15.  
Figure 11: Minimum annual-maximum dust day duration in the study area.  
Figure 12: Dust day duration with a 5-year return period in the study area.  
Figure 12 shows that DDD with a return period of 5 years can vary from 2 to 16.9 days, whose maximum is concentrated in the eastern and southeastern parts of the study area. Additionally, parts of the northeast of the region and the center (Naein) have a high DDD.  
Figure 13 shows the southeastern parts of the study area as well as the central desert of Iran (Dasht-e Kavir), where Nain, Tabas, and Khoro Biyabank stations are located, have DDD of up to 27 days during a 10-year return period. The range in the 10-year return period is between 4 and 26.9 days, with the highest value in the southeastern parts of the study area in the provinces of Sistan and Baluchestan and Kerman.  
Figure 14 shows the range of DDD from 9 to 35.8 days per year with a 25-year return period in the study area. Similar to the other return periods, this shows that the southeastern parts, as well as the central parts of Central Iran, are at risk for longer DDD.  
Figure 15 shows dust zoning over a 50-year return period, with variations between 13 and 44.7 days per year. Values near the peak are widespread over more than two-thirds of the study area. The southeastern parts still have the longest DDD at this return period; furthermore, the central parts of the study area as well as the northwestern parts (Saveh and Arak stations) have high values for this return period. It is necessary to mention that industrial areas are mostly located in the northwestern parts of the basin and the high DDD values in these areas (Saveh and Arak stations) are mainly due to industrial pollution.  
Figure 13: Dust day duration in the 10-year return period in the study area.
# Low-Cost RSAC and Adsorption Characteristics in the Removal of Copper Ions from Wastewater
## 3 Results and Discussion
### Adsorption Experiments
#### 3.1.4 Effect of Coexisting Metal Cations  
Figure 7 shows the relationship between the metal cation concentration and the effect on the removal of copper ions. The results revealed that the adsorption removal rate of copper ions by RSAC particles fluctuates in a small range with the increase of Na\({}^{+}\) concentration, which indicated that Na\({}^{+}\) has a weak competitive behavior against RSAC particles. The adsorption removal rate of copper ions by RSAC particles decreases with the increase of Mg\({}^{2+}\) and Ca\({}^{2+}\) concentration, which indicated that Mg\({}^{2+}\) and Ca\({}^{2+}\) may have some effect on the removal rate. The adsorption removal rate decreased significantly with the increase of Fe\({}^{3+}\) concentration, from 74.65% to 62.47%, which indicated that Fe\({}^{3+}\) had a significant inhibitory effect on the adsorption of copper ions.  
The experimental results may be interpreted as that metal cations can replace the original cations in the RSAC particles by ion exchange into the adsorbent surface and pore channels, affecting the adsorption of copper ions on the RSAC particles by changing the adsorbent environment [29]. A higher charge number of the metal cation may result in a stronger ability to replace the original cation [30]. Ion exchange and surface adsorption may be involved in the adsorption process of copper [31].  
Figure 6: Effect of initial concentration on the adsorption of copper ions by RSAC particles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Initial Concentration** & \multirow{2}{*}{**Removal Rate (\%)**} & **Adsorption of RSAC Particles per Unit Mass (mg/g)** \\ \hline
10 & 91.90 & 0.28 \\
50 & 93.58 & 1.40 \\
100 & 74.61 & 2.24 \\
150 & 53.50 & 2.41 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Copper ion removal for different initial concentrations.
## Appendix B Venting Mass Flow Computation  
When \(P\geq P_{vent}\), the venting valve is activated. The venting valve mass flow (\(m_{vent}\)) is considered variable in such way that the pressure inside the tank is kept constant. The computation of \(m_{vent}\) is completed imposing \(dP/dt=0\) in Equation (25) as in the following formulas (Equation (A5)).  
\[\frac{dP}{dt}=2\frac{\Phi}{V}\bigg{[}Q-m_{o}\big{(}h_{g}-h_{l}\big{)}\bigg{(}X _{o}+\frac{\rho_{g}}{\rho_{l}-\rho_{g}}\bigg{)}\bigg{]}=0\] (A4)  
\[A=\frac{\rho_{g}}{\rho_{l}-\rho_{g}}\] (A5)  
\[m_{o}(X_{o}+A)=\frac{Q}{h_{g}-h_{l}}\] (A6)  
\[\bigg{(}m_{v}+m_{f}\bigg{)}\bigg{(}\frac{m_{v}}{m_{v}+m_{f}}+A\bigg{)}=\frac{Q }{h_{g}-h_{l}}\] (A7)  
\[\frac{m_{v}^{2}}{m_{v}+m_{f}}+\frac{m_{v}m_{f}}{m_{v}+m_{f}}+m_{v}A+m_{f}A- \frac{Q}{h_{g}-h_{l}}=0\] (A8)  
\[B=m_{f}A-\frac{Q}{h_{g}-h_{l}}\] (A9)  
\[m_{v}^{2}+m_{v}m_{f}+m_{v}^{2}A+m_{v}m_{f}A+m_{v}B+m_{f}B=0\] (A10)  
Since \(m_{f}\) is known from the mission analysis, Equation (A10) is a second-grade equation in the venting mass flow \(m_{v}\), which can be solved with the standard second-grade equations solution formula. The solution gives two values of \(m_{v}\), generally one positive and one negative, the correct solution to take is the positive one.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 2 Principles for Estimating AGB from Remotely Sensed Data
Moreover, the vegetation optical depth (VOD), derived from satellite passive microwave observations, is mainly sensitive to the water content and captures variations in foliage biomass better than NDVI in drylands, and thus can provide complementary information for estimating green biomass in dryland areas [39; 43]. Forest cover is another important AGB predictor, especially where forest cover is less than 60%. For uniform forests, forest biomass of a pixel is directly proportional to forest cover [44]. Currently, several forest cover products are available on a global scale, facilitating the estimation of forest biomass [45]. However, the proportional tree or forest cover could reach its maximum before forest biomass, which limits the performances of biomass estimation to some degree. Further to the above ecological process parameters, topographic and biotic factors (e.g., species diversity) are known to affect forest biomass. From the data perspective, the relationships between the abovementioned parameters and AGB vary with topographic and biotic factors. Topographic characteristics such as elevation and aspect are known to drive patterns of tree species distribution, as well as soil resources, both of which influence AGB. From another perspective, these factors regulate carbon storage in forest ecosystems [46]. Finally, since AGB in a forest integrates establishment, growth and mortality processes, as well as succession, disturbance, and ecosystem processes, the inclusion of these data could improve the accuracy of AGB estimates [47; 48].
## 3 Mathematical Modeling
### Problem Model  
MILP is widely applied in studies related to timetable optimization [15; 26; 27]. MILP defines that the objective function, equation, and inequality constraints in an optimization problem are linear. Moreover, there must exist integer variables for the decision variables. The standard form of MILP is described by Equation (31).  
\[s.t.\left\{\begin{array}{l}\min\limits_{x}f^{T}x\\ D\cdot x\leq d\\ Aeq\cdot x=beq\\ lb\leq x\leq ub\\ x_{i}\in Z\\ x_{j}\in\{0,1\}\end{array}\right. \tag{31}\]  
where \(f^{T}x\) is the linear objective function. \(D\cdot x\leq d\) is the linear inequality constraint. \(Aeq\cdot x=beq\) is the linear equation constraint. \(lb\leq x\leq ub\) is the boundary constraint. The term \(x_{i}\) is an integer variable. The term \(x_{j}\) is a logistic variable.  
In the MILP model, four decision variables are considered, including the headway at the first station, the stop time at each station, the travel time between stations, and a binary variable indicating whether a train stops at a station, which can be represented by the matrix \(x\).  
\[x=[H,T^{r},T^{d},A] \tag{32}\]  
where \(H\) matrix is the \((N^{up}+N^{down}-2)\times 1\) composed according to \(H^{1}_{i,i-1}\), with \(N^{up}+N^{down}\) representing the total number of trains and \(n\) representing the number of stations. \(T^{r}\) matrix is the \((N^{up}+N^{down})\times(n-1)\) consisting of \(t^{r}_{i,s-1,s}\). \(T^{d}\) and \(A\) matrix are \((N^{up}+N^{down})\times n\) consisting of \(\tau^{s}_{i}\) and \(A^{s}_{i}\) respectively.  
Based on the train traffic dynamic model and passenger flow dynamic model, according to the form of Equation (31), the multi-objective MILP problem is established as in Equation (33). Enhancing passenger service quality and reducing operating costs are the objectives in urban rail transport. The linear optimization objective function \(J_{1}\) mainly considers stopping costs, the number of stranded passengers on the platform, and total train traveling time in both directions.  
\[\begin{array}{l}\min\
olimits_{1}=z_{1}\cdot\sum\limits_{i\in I}\sum \limits_{s\in S}A^{s}_{i}+z_{2}\cdot\sum\limits_{i\in I}\sum\limits_{s\in S}Q^ {l}_{i,s}+z_{3}\cdot(\sum\limits_{i\in I^{down}}t^{d}_{i,2n}-t^{a}_{i,n+1}+ \sum\limits_{i\in I^{up}}t^{d}_{i,n}-t^{a}_{i,1})\\ s.t.\left\{\begin{array}{l}Eq.(8-9)(12)(27-30)\ (D\cdot x\leq d)\\ Eq.(4-5)(13-16)(18-21)(24-25)\ (Aeq\cdot x=beq)\\ Eq.(1)(6-7)(14)(11)\ (lb\leq x\leq ub)\\ A^{s}_{i}\in\{0,1\}\\ t^{r}_{i,s-1,s},\tau^{s}_{i},H^{s}_{i,i-1}\in Z\end{array}\right.\end{array} \tag{33}\]  
where \(z_{1},z_{2},z_{3}\) are cost conversion factors for uniform magnitude. The term \(z_{1}\) is the unit cost of a train stop. The term \(z_{2}\) is the unit penalty cost of a stranded passenger. The term \(z_{3}\) is the unit operating cost per second of a train.
## 2 Experimental Setup  
For the experiment, the consistency of various experimental parameters was prioritized to obtain a comparison between the resonator types. That is, various experimental parameters are the same regardless of the resonators being tested. This includes the transducer positions, waveguide dimensions, resonator spacing/positioning, the area of contact between the resonator and plate, resonator material properties, and overall resonator mass. The only difference between tests is the shape of the resonators.  
The guided waves that will be used for the test are the S0 Lamb wave mode with a 50 kHz 5-cycle pulse, which corresponds with a wavenumber of 58.1 rad/m. At these low frequencies, this mode corresponds with a wave-structure (i.e., displacement profile) that is effectively planar and consists of in-plane displacement through the thickness of the plate waveguide. The S0 Lamb wave mode was excited and received using shear transducers with the polarization aligned with the wave-propagation direction. A schematic of the setup, including transducer placement and resonator positions, is shown in Figure 1. Three rows of ten resonators were used for the resonator array and adhered to the surface of the waveguide using cyanoacrylate adhesive. The spacing of the resonator array shown in Figure 1b is based on how close together the clamping resonators could be positioned.  
The main method of assessing the resonators will be to compare two-dimensional fast Fourier transforms (2 DFFTs) of the signal transmitted past the resonator array [16]. The receiving transducer was moved away from the resonator array in 2 cm increments. This was done 20 times to measure an A-scan at 20 positions along the expected wave-propagation direction. For the 2 DFFT, zero-padding was applied to increase the number of bins in the wavenumber spectrum, but no zero-padding was applied for the frequency spectrum.
# Comparison of Crack Detection Performance of Low and High Contact Ratio Spur Gears
## 6 Conclusions
\begin{tabular}{l l} \(\alpha\) & Pressure angle \\ \(\mathrm{h_{a}}\) & Addendum \\ \(\mathrm{h_{f}}\) & Dedendum \\ \(\mathrm{q_{f}}\) & Rack cutter tip radius \\ \(\mathrm{mn}\) & Normal modules of the LCR and HCR gears \\ \(\mathrm{k_{p}}\), \(\mathrm{k_{g}}\) & Single tooth stiffness pinion and gear \\ \(\mathrm{r_{Ap}}\), \(\mathrm{r_{Bp}}\), \(\mathrm{r_{Cp}}\), \(\mathrm{r_{Dp}}\), \(\mathrm{r_{Ep}}\) & Critical points of the line of action for LCR gears \\ \(\mathrm{Pb}\) & Pitch \\ \(\mathrm{r_{ap}}\), \(\mathrm{r_{ag}}\) & Addendum circles of pinion and gear \\ \(\mathrm{r_{bp}}\), \(\mathrm{r_{bg}}\) & Base circles of pinion and gear \\ \(\mathrm{r_{p}}\), \(\mathrm{r_{g}}\) & Pitch circles of pinion and gear \\ \(\mathrm{z_{p}}\), \(\mathrm{z_{g}}\) & Tooth numbers of pinion and gear \\ \(\mathrm{i}\) & Gear ratio \\ \(\mathrm{|\,AE\,|}\) & Length of contact of LCR gears \\ \(\mathrm{|\,AF\,|}\) & Length of contact of HCR gears \\ \(\mathrm{|\,AD\,|}\) & Pitch on the line of action for LCR gears \\ \(\mathrm{|\,AC\,|}\), \(\mathrm{|\,CE\,|}\) & Pitches on the line of action for HCR gears \\ \(\mathrm{\varepsilon_{\alpha}}\) & Contact ratio \\ K & Total stiffness of one tooth pair \\ \(\mathrm{K_{mesh}}\) & Time-varying mesh stiffness (TVMS) \\ \(\mathrm{C_{mesh}}\) & Time-varying damping between the teeth \\ \(\mathrm{r_{pb}}\), \(\mathrm{r_{pC}}\), \(\mathrm{r_{pb}}\), \(\mathrm{r_{pE}}\), & Critical points of line of action for HCR gears \\ \(\mathrm{r_{gB}}\), \(\mathrm{r_{gC}}\), \(\mathrm{r_{gb}}\), \(\mathrm{r_{gE}}\) & Damping ratio \\ \(\mathrm{J_{M}}\)-\(\mathrm{J_{L}}\)-\(\mathrm{J_{P}}\)-\(\mathrm{J_{g}}\) & Mass moments of inertia of motor, load, pinion, and gear \\ \(\mathrm{C_{S1}}\), \(\mathrm{C_{S2}}\) & Damping of shafts \\ \(\mathrm{K_{S1}}\), \(\mathrm{K_{S2}}\) & Torsional stiffness of shafts \\ \(\mathrm{T_{in}}\), \(\mathrm{T_{out}}\) & Inlet and outlet torques \\ \(\mathrm{C_{2}}\), \(\mathrm{C_{3}}\) & Damping of bearingss \\ \(\mathrm{K_{2}}\), \(\mathrm{K_{3}}\) & Bearing stiffness \\ \(\mathrm{I_{s1}}\), \(\mathrm{I_{s1}}\) & Polar moment of inertia of the shaft \\ G & Shear modulus \\ L & Length of the shafts \\ \(\mathrm{m_{p}}\),\(\mathrm{m_{g}}\) & Masses of pinion and gear \\ \(\mathrm{\theta_{M}}\), \(\mathrm{\theta_{L}}\) & Angular displacements of motor and load \\ \(\mathrm{\theta_{p}}\), \(\mathrm{\theta_{g}}\) & Angular displacements of pinion and gear \\ \(\mathrm{y_{p}}\),\(\mathrm{y_{g}}\) & Displacements of pinion and gear on the y axis \\ DF & Dynamic force \\ DTE & Dynamic transmission error \\ STS & Single tooth stiffness \\ LCR & Low contact ratio \\ HCR & High contact ratio \\ \end{tabular}   
**Appendix A**  
**Figure A1. Crack paths for \(\mathrm{h_{a}}=1.25\)\(\mathrm{h_{f}}=1.5\): (**a**) 1.5 mm, (**b**) 3 mm, (**c**) 4.5 mm. Figure A2. Crack paths for \(\rm h_{a}=1.3\)\(\rm h_{f}=1.55\): (a) 1.5 mm, (b) 3 mm, (c) 4.5 mm.