esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### CdaA and DisA Are Required to Prevent Mitomycin-C induced DNA Damage  
We next investigated the role of DisA and CdaA in RifR mutagenesis elicited by the alkylating agent Mitomycin-C (M-C). The results of these assays revealed that, in comparison with the YB955 parental strain, the strains with single disruption in _disA_ or _cdaA_, exhibited a slight increase in M-C elicited mutagenesis (Figure 4).  
Figure 3: Hydrogen peroxide induced mutation frequencies to rifampicin resistance (RifR) of _Bacillus subtilis_ YB955 (open bar), _disA_ (dark grey bar), _cdaA_ (black bar) and _cdaA_/_cdaA_ (hatched bar) were determined during exponential growth. Complemented strains _cdaA_ _amyE:_Phs-disA_ (light stippled bar), _cdaA_ _amyE:_Phs-cdaA_ (horizontal striped bar), _cdaA_/_cdaA_ _amyE:_Phs-cdaA_ (vertical striped bar), and _cdaA_/_cdaA_ _amyE:_Phs-disA_ (dark stippled bar) were also tested. Data show the average from three independent experiments, each one by sextuplicate \(\pm\) standard deviations. All strains were supplemented with IPTG 0.25 mM. Different letters (a, b, c, d) represent significant statistical differences between means, the same letter denotes no significant difference, as determined by a one-way ANOVA and multiple mean comparison by Tukey HSD test (all _p_-values \(<\) 0.001).  
Importantly, the mutant \(\Delta\)_adiaA_/\(\Delta\)_disA_ exhibited a higher increase (\(\sim\)2.0 times) in the mutagenesis levels promoted by M-C, compared to the parental strain YB955 and single \(\Delta\)_disA_ or \(\Delta\)_cdaA_ strains (Figure 4). Of note, reintegration of _disA_ or _cdaA_ suppressed the mutagenic effects promoted by M-C in the strains tested, which eliminates the possibility that other linked or co-transcribed genes are influencing this type of mutagenesis (Figure 4). Taken together, these results suggest that DisA and CdaA are needed to counteract GAM elicited by M-C.
## 4 Results and Discussion
### Analysis of Excavation Methods
#### 4.2.4 Determination of Excavation Method  
Based on the above analysis, it was observed that, in terms of surrounding rock deformation and plastic zones, the Center Diaphragm Excavation Method would be a little better.. However, the maximum surrounding rock deformation of both excavation methods fell within an allowable range. Regarding the support structure force, the Center Diaphragm Excavation Method led to a larger bending moment and shear force during  
Figure 23: The shear force of support structures under different excavation methods: (**a**) Center Diaphragm Excavation Method; (**b**) Three-bench Excavation Method.  
Figure 24: The tensile principal stress of the central partition wall under different methods: (**a**) Center Diaphragm Excavation Method; (**b**) Three-bench Excavation Method.  
Figure 25: The compressive principal stress of the central partition wall under different methods: (**a**) Center Diaphragm Excavation Method; (**b**) Three-bench Excavation Method.  
the combination of the temporary support and initial support. As the Center Diaphragm Excavation Method has more excavation steps, multiple disturbances increase the force of the initial support already applied. Therefore, the initial support force of the Three-bench Excavation Method was smaller than that reported for the Center Diaphragm Excavation Method. The stress difference in the middle partition between the Center Diaphragm Excavation Method and the Three-bench Excavation Method was not significant. It is important to note that the Center Diaphragm Excavation Method required the excavation of a large number of guide holes and the erection of numerous supports to complete the construction of four lines, which could have had a detrimental effect on cost control during construction [28]. Therefore, under the conditions of this project, the Three-bench Excavation Method is the recommended choice.
## 3 Speed Planning
### Optimization Variables  
To simplify the speed-planning problem, the path in front of the considered vehicle is separated into several segments of the same length by means of nodes. These segments and nodes are called path segments and path nodes for convenience. The speeds of the vehicle at each end of each of these segments are subjected to optimization. The travel speeds between the ends of each segment can be calculated through linear interpolation. For each segment, the path node at which the vehicle enters the segment is called the start node of the segment, and the path node at which the vehicle departs from the segment is called the end node of the segment. Note that a path can be separated into any number of segments for speed planning. However, the more segments are used for speed planning,  
Figure 14: Sawtooth torque injection signal.  
the greater is the computational burden. Because the friction coefficient may change along the path, it is inappropriate to plan the entire path along the vehicle's route simultaneously. To simplify the problem, the vehicle's speed is planned successively for road sections of a certain length in front of the vehicle. As an example, the path in front of a vehicle is shown in Figure 15. The section of the path within a certain distance in front of the vehicle can be divided into \(n\) segments by \(n+1\) nodes, as shown in Figure 16. These nodes are labeled from \(p_{0}-p_{n}\). Note that \(p_{0}\) is located at the CG of the vehicle. The path segment between \(p_{i-1}\) and \(p_{i}\) is denoted by \(s_{i}\). Each of these path segments has a length of \(l_{p}\).  
The planned speeds at \(p_{i}\) (\(i=1,2,\cdots,n\)) are represented by \(v_{i}\) (\(i=1,2,\cdots,n\)), respectively. Under the assumption that the speed of the vehicle varies linearly between each pair of adjacent nodes, the planned speeds of the vehicle along the path are further illustrated in Figure 17. As shown, once the planned speeds at these nodes have been determined, the planned speed profile along the path can be obtained. The error between the planned speed at each node and the desired speed is defined as:  
\[e_{i}=v_{i}-v_{d}, \tag{18}\]  
where \(v_{d}\) is the desired speed. Clearly, if \(e_{i}=0\) for all \(i=1,2,\cdots,n\), then the planned speed is equal to the desired speed along the entire path. Once \(e_{i}\) (\(i=1,2,\cdots,n\)) has been determined, \(v_{i}\) (\(i=1,2,\cdots,n\)) can be calculated as:  
\[v_{i}=v_{d}+e_{i}. \tag{19}\]  
As shown in Figure 18, the planned speeds can also be determined from the speed errors at the nodes. Thus, the speed errors at the nodes, denoted by \(\mathbf{e}=\begin{bmatrix}e_{0}&e_{1}&e_{2}&\cdots&e_{n-1}&e_{n}\end{bmatrix}^{T}\), are defined as the decision variables for speed planning. Note that because the speed of the vehicle cannot track a step reference, the speed at node \(p_{0}\) is not designated as one of the decision variables.  
Figure 16: Division of the example path.  
Figure 17: Speeds at nodes.  
Figure 15: An example path.
## 4 Evaluation
### Throughput Under Dynamic User Behaviour  
The autonomous nature of the network coupled with resource constraints on portable handheld devices leads to a highly dynamic user behaviour in real-life scenarios. One of the consequences is that constant participation cannot be guaranteed for every user in a real-world implementation of the technology. For instance, users may turn off the wireless communication interface on their devices to conserve energy or travel out of the city for a period of time. Hence, a robust fair routing solution should be able to cope with the variable availability of resources without significantly degrading throughput. FLARoute allows the contribution of a wider set of nodes to minimize throughput degradation under such scenarios. Therefore, it is important to have an insight on how the performance of other proposals compare in scenarios where some users occasionally withdraw participation from the network.  
In order to simulate user withdrawal, we configure relay nodes in the Helsinki scenario (generating 1 message per hour) to randomly decide on a daily basis whether or not to cease from receiving and sending messages for a period uniformly distributed between 1 to 6 h. Figure 26 shows the throughput and fairness recorded by CCAF and FLARoute, with and without user withdrawal. With popular nodes withdrawing participation, messages are either lost due to buffer overflows on the remaining popular nodes or are unable to make much progress through less popular nodes. This leads to about 11% drop in throughput with CCAF. On the other hand, FLARoute records only about 5% drop in throughput. By increasing the number of central nodes and thereby allowing more number of less popular nodes to contribute in routing, the buffer availability in the network is increased. As a result, messages are provided with more alternative routes to the destination. Furthermore, the network becomes more resistant to increase in data traffic. Allowing more nodes to actively participate in routing reduces individual buffer occupancy and chances of buffer overflows, so that messages can be retained for longer periods. As shown in Figure 26, FLARoute still maintains a higher routing fairness under user withdrawal.
## 4 Model and Notations
### Constraint Programming
#### 4.2.2 Modeling Calendar Constraints  
The considered processing line is periodically submitted to calendar constraints; this means that machines are not available during the whole planning horizon. To consider machines' unavailability, variable \(\alpha_{jk}\) should be modulated by adding an intensity step function \(F_{k}\) that represents the unavailability interval of machine k. In CP optimizer, Intensity is a stepwise function that applies a measure of usage or utility over an interval length. The intensity is 0% during the unavailability interval \(\left[v_{k}^{l},v_{k}^{l}\right]\) and 100% outside this interval. Therefore, modelling machines' unavailability can simply be formulated by constraint (29)  
\[\text{interval}\ \alpha_{jk},\text{opt},\text{size}=\text{P}_{jk},\text{ intesity}=\text{F}_{k},\text{j}\in\text{J},\forall k\in\text{m}_{j} \tag{29}\]  
An additional feature of our problem is that the setup cannot occur during an unavailability period. To model this feature, we use the predefined constraint forbidExtent (a,U). This expression prevents an interval variable from being scheduled during any time point within the augmented horizon that is not also within one of the disjoint time windows.  
\[\text{forbidExtent}\left(St_{ii^{\prime}k^{\prime}},F_{k}\right),\ \ \ i,i^{ \prime}\in N,\forall k\in\text{m}_{i} \tag{30}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Decision Variables** & \\ \hline interval \(\beta_{j}=\) & An interval variable for each operation \(j\) \\ interval \(\alpha_{jk}=\) & An optional interval variable for each possible \\  & assignment of operation \(j\) to machine \(k\in m_{j}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: CP decision variables.  
Forbidden start constraint forbidStart\((\alpha,F)\) states that, whenever the interval is present, it cannot start at a value \(t\) where \(F(t)=0\). In the same sense, Forbidden end constraint forbidEnd\((\alpha,F)\) states that, whenever the interval is present, it cannot end at a value \(t\) where \(F(t-1)=0\). We use constraints (31) and (2) to ensure the respect of unavailability periods.  
\[\text{forbiddenStart}\left(\alpha_{jk},F_{k}\right),j\in J,\forall k\in\text{ m}_{i} \tag{31}\]  
\[\text{forbiddenEnd}\left(\alpha_{jk},F_{k}\right),j\in J,\forall k\in\text{ m}_{i}\]  
From now on, we refer to the model that incorporates resource calendar constraints into model \(\text{CP}_{\text{wav}}\), \(\text{CP}_{\text{RC}}\).
## 2 Numerical Model Settings
### Gas Leak Model
#### 2.1.2 Mathematical Model  
After the gas leaks from the pipeline into the gas compartment of the utility tunnel, there will be energy and material conversion with the external environment. The overall satisfaction of the three controlling equations of fluid mechanics and the turbulent component transport equations without chemical reactions constitute the set of flow equations for the diffusion of gas leaks in the gallery, satisfying the physical situation to be simulated and by giving specific initial boundary conditions, special solutions can be obtained in different situations. The specific equations are applied as follows.  
Figure 1: Two-dimensional simplified physical model of a gas leak.  
(1) Continuity equation  
Treating the gas flow in the pipe as a one-dimensional flow, the continuity equation can be expressed as follows:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial\rho u}{\partial x}=0 \tag{1}\]  
where \(\rho\) is the density of the gas, the unit is kg/m\({}^{3}\); \(t\) is flow time, the unit is s; and \(u\) is the velocity of the fluid in the direction of the axis, the unit is m/s.  
(2) Conservation of momentum equation  
The Navier-Stokes equations are expressed specifically in a two-dimensional coordinate system as follows:  
\[\frac{\partial(\rho u_{x})}{\partial t}+\frac{\partial\big{(}\rho u_{x}u_{y} \big{)}}{\partial x}=-\frac{\partial p}{\partial x}+\frac{\partial\tau_{xy}}{ \partial x}+\rho g+F \tag{2}\]  
where \(u_{x},u_{y}\) is the component of the velocity on the \(X\)- and \(Y\)-axis, the units are m/s; \(p\) is the static pressure and \(\tau_{xy}\) is the stress tensor, the units are Pa; and \(pg\) is Gravity body forces and \(F\) is external body forces, the units are N.  
(3) Energy conservation equation  
In the 2D simplified physical model, conservation of energy is specifically expressed as follows:  
\[\frac{\partial(\rho T)}{\partial t}+div(\rho uT)=div\bigg{(}\frac{k}{C_{p}} gradT\bigg{)}+S_{T} \tag{3}\]  
where \(T\) is the thermodynamic temperature of the fluid, the unit is K; \(k\) is the heat transfer coefficient of the fluid; \(C_{p}\) is the isobaric specific heat capacity, the unit is J/(kg\(\cdot\)K); and \(S_{T}\) is the viscous dissipative term, the unit is Pa\(\cdot\)s.
## 3 Results  
### Description of a Benchmark Problem  
The flexible-link multibody system considered for validation of the method is a planar two-link mechanism with two revolute joints, as shown in Figure 1. The geometric and physical properties of the system are listed in Table 1. Link 1 and link 2 are divided into four and three Euler-Bernoulli beam elements with circular and solid cross-sections, respectively. The resulting model has 23 degrees of freedom (dof), including the two rigid dofs of the ERLS, denoted by \(q_{1}\) and \(q_{2}\), and 21 elastic dofs.  
It is supposed that the end effector, denoted by symbol \(B\) in Figure 1, moves along the path shown in Figure 2a, which can be employed in a pick-and-place task. The trajectory is sampled at constant time intervals, where each dot in the figure represents a sample (152 samples in total). The inverse kinematics are solved, resulting in the joint-space trajectory shown in Figure 2b.  
Figure 1: Finite element model of the flexible-link manipulator.  
Figure 2: Trajectory of the flexible linkage: (**a**) end-effector trajectory in the work space; (**b**) joint space trajectory.  
In this example, it is assumed that two point masses \(m_{1}\) and \(m_{2}\) can be placed on the elbow joint and end effector, respectively, if necessary to assign the desired eigenfrequency. Hence, \(\mathbf{y}=\left[\begin{array}{cc}m_{1}&m_{2}\end{array}\right]^{T}\). The choice of the design variable \(\mathbf{y}\) is arbitrary, as long as it is consistent with the ERLS model previously described, but it must be determined prior to optimization. In order to preserve the integrity of the FLMS, the design parameters are subjected to the following constraint set:  
\[\{(m_{1},m_{2})\colon 0\leq m_{1}\leq 0.250\,\mathrm{kg},0\leq m_{2}\leq 0.250\, \mathrm{kg}\}. \tag{15}\]
## 3 Result and Analysis
### Parameter Extraction in 3D Space  
A transformation algorithm for transforming 3D scene data into 2D elevation image is proposed based on the principle of orthogonal projection. Figure 8 is a schematic diagram of a conversion algorithm for 3D point cloud data projection elevation data information.  
In Figure 8, the elevation information of the3D image is represented by the Z-axis in the coordinate system. Therefore, the projection point on the projection plane is the elevation data of the image, which can be represented by a matrix Z(x, y) consisting of discrete quantities:  
\[Z(x,y)=\left[\begin{array}{ccc}z(x_{1},y_{1})&\cdots&z(x_{1},y_{n})\\ \vdots&\ddots&\vdots\\ z(x_{m},y_{1})&\cdots&z(x_{m},y_{n})\end{array}\right] \tag{1}\]  
where x is the range of values (0, m) and the range of y is (0, n).  
All points in the Z(x, y) matrix are represented by one byte of 8 bits, converted to any value in the 0-255 interval, based on the one-dimensional sampling theorem and the principle of linear quantization. Therefore, the matrix Z(x, y) is a grayscale image of 256 gradation values. The converted image is named an elevation projection. An elevation digital matrix corresponding to images of a 2D plane is obtained. Then, convert them into projected images in sequentially. Feature parameters of the projected image are extracted separately. Finally, data statistics are performed.
## 3 Results
### Fish and Plant Growth Performance  
Fish growth performance is presented in Table 2. At the start of the experiment, there were no statistically significant differences in the means of the gilthead sea brem initial body weights, lengths, and initial Fulton's: coefficient factor (ANOVA, \(p>0.05\)) for all treatments. At the end of the experiment, there were no significant differences in the means of the weight gain and body weight increase (BWI %) in all treatments (ANOVA, \(p>0.05\)). The final mean weight, specific growth rate (SGR), and final Fulton's coefficient factor (Kfin) were significantly higher at 8 ppt and 20 ppt than 14 ppt treatments (ANOVA, \(p<0.05\)). The survival rate for gilthead sea brem over 45 days of culture ranged between 89.8%, 96%, and 97.3% for aquaponic systems with 20 ppt, 14 ppt, and 8 ppt, respectively (Table 2). The mean values of FCR and DFI at the end of the experiment were not significant different between the salinity treatments (ANOVA, \(p>0.05\)) (Table 2). At the start of the experiment, there were no significant differences in the means of rock samphire initial height (ANOVA, \(p>0.05\)).  
Plant size at the beginning of the experiment was similar in all treatments in terms of number of branches per plant (Table 3). At the final harvest, the number of branches reached almost doubled values, yet no significant differences among the various salinity levels were recorded. Both final plant height and fresh weight of the aerial part showed the highest values in the 8 ppt treatment and the lowest in 20 ppt, with among-treatment differences being statistically significant. Notably, the fresh weight of 8 ppt-treated plants exhibited a 6.3-fold and 2.7-fold increase compared with 20 ppt and 14 ppt, respectively. The rate of height gain was significantly enhanced in 8 ppt compared with the other two treatments.
# Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers
## 3 Research Methodology
### Phase 1: Classification Using Base Classifier
#### 3.1.1 Multilayer Perceptron Classifier  
A multilayer perceptron (MLP) is a feed-in class to the artificial neural network. It utilizes back-propagation for training, referred to as a supervised learning technique. MLP consists of an input layer, a hidden layer, and an output layer. Each node is known as a neuron excluding the input nodes and uses a non-linear ReLu activation function [43]. This non-linear activation along with its multiple layers distinguishes it the from linear perceptron. The ReLu activation function is simple and efficient, as it has been empirically observed that training a network with this function tended to converge quickly and reliably in comparison to other activation functions. Furthermore, it also helps in detecting data that cannot be separated linearly [43].
## 2 Materials and Methods
### Materials
#### 2.3.2 UAV, Digital Camera, and GCS  
The simulator used in this study can export the flight waypoints in the mission format of the mission planner or JavaScript object notation waypoint format of DJI. As a result, a UAV and GCS that support these file types are required. To obtain nadir and oblique images, the camera must be tiltable. As a result, the DJI Phantom 4 Pro was used in this study. The specifications of the camera from web page [38] play an important role in determining the flight path and are listed in Table 4. Aerial photogrammetry should follow a path that is automatically generated by the simulator. Therefore, the GCS is prepared only for emergencies, and does not perform any controls that affect the experimental results during the flight and acquisition of images.
## 2 Problem Formulation
### MI-Based Method to Evaluate the Impact on RSTT of Time-Varying Interference  
For the RSTT, reliability is among the most significant quality indicators closely related to train operation, and many security measures are considered based on the assumption of transmission error [1]. Thus, the block error rate (\(BLER\)) is taken as the criterion to indicate the effect of the pulsed disturbing noise on the RSTT using FEC codes. Therefore, to evaluate the impact on RSTT with different MCSs, the \(BLER\) of the on-board receiver should be achieved instantly based on the real-time statistical measurement of complex disturbances. As the coding gain of radio transmission in a stable fading channel is usually implemented by the MI-ESM algorithm [27], the MI metric is quite an effective channel quality indicator (CQI) to map the interference properties to the \(BLER\) performance. However, in the HSR environment, due to the fast movement of trains and the impulsive nature of EM disturbance sources, the RSTT performance changes in accordance with the dynamic interference, and it is almost impossible to obtain the corresponding \(BLER\) with a variable MI metric. As a result, an equivalent CQI is required for the time-varying EMI, through which the transmission quality can be derived.  
Therefore, a dynamic quality model is proposed to achieve the MI-based CQI of transmission under time-varying disturbances, as illustrated in Figure 3. Firstly, the performance metric of every single code block is obtained by information mapping, based on the statistical characteristics of EMI. It is important to note that the APD and PDD of disturbance \(n(t)\) should be measured using the equivalent bandwidth of the railway wireless service to be monitored [17]. According to MI-ESM algorithm, a received bit information rate (RBIR) is defined as the CQI to indicate the transmission quality. Then, a novel metric, long-term effective RBIR (RBIR\({}_{\text{L}}\)), is put forward to reflect the general transmission performance in the presence of the dynamic disturbance. To combine the RBIR of each block in time-varying situations, RBIR\({}_{\text{L}}\) is achieved by the weighted sum of the RBIR series. Therefore, the RBIR\({}_{\text{L}}\) is the equivalent CQI of RBIR in a stable channel. Since RBIR\({}_{\text{L}}\) can be mapped to the average \(BLER\) (\(BLERERERERERERERER