esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Description of the Shipboard Microgrid under Study
### Proton Exchange Membrane Fuel Cell  
The fuel cells (FCs) are static electrochemical devices that transform the chemical energy of reactants (oxidant and fuel) into electrical energy. The study focuses on the use of a commonly used type of fuel cell called PEMFC, in order to control the output fluctuations of RES power generation. The PEMFC's dynamic model is [41]:  
\[\frac{q_{H2}}{p_{H2}}=\frac{K_{an}}{\sqrt{M_{H2}}} \tag{2}\]  
where \(q_{H2}\), \(p_{H2}\) is the hydrogen flow (kmol\(\cdot\)L) and partial pressure of hydrogen, respectively (kPa), \(K_{an}\) denotes the anode valve constant (\(\sqrt{\text{kmol}\cdot\text{kg}}(\text{atms})^{-1}\)), and \(M_{H2}\) is the hydrogen molar mass (kg kmol\(\cdot\)L).  
Figure 1: Block diagram of the multi-energy ship microgrid under study.  
The PEMFC output voltage is given by [41]:  
\[V_{cell}=E-B\ln(CI_{Fc})-R^{\text{int}}I_{FC} \tag{3}\]  
where \(B\) and \(C\) are the activation voltage constants, \(R_{int}\) denotes the internal resistance (\(\Omega\)), \(I_{FC}\) is the feedback current of the FC (A), and \(E\) represents the Nernst immediate voltage.  
The PEMFC has a number of benefits over other fuel cells [42], such as a simple structure, quick start-up times, and the ability to withstand low temperatures. The PEMFC is composed of an FC, an interconnection device, and an inverter. The inverter is a voltage control type to convert the supplied power into a normal voltage and frequency, a total of 120 ms is needed. The system interconnection device's power switchover took approximately 10 ms. As a result, the linearized transfer function of the PEMFC is shown in (5) [43]:  
\[G_{FC}(s)=\left(\frac{1}{1+T_{fc}s}\right)\left(\frac{1}{1+T_{inv1}s}\right) \left(\frac{1}{1+T_{ic}s}\right) \tag{4}\]
# Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation
## 4 The K3P Algorithm
### Phase Shift  
While the robot is walking, a phase shift occurs when the two tripods toggle the phase of the gait they are in. The tripod in the support phase takes off to begin the swing phase and the swing tripod lands to start supporting the main body of the robot. In order to maintain the robot in static equilibrium throughout the walking cycle, the K3P algorithm determines when to shift phases based on three different criteria, named K3P1, K3P2, and K3P3. In particular, K3P1 and K3P2 ensure a collision-free gait pattern.  
The first condition K3P1 ensures that every step is as long as mechanically possible for the robot, before the CoM approaches the border of the support polygon too closely, and the gait becomes unstable. For the tripod in the swing phase, K3P1 measures the horizontal traveled distance from the starting point to the current position of its origin, if the traveled distance reaches the maximum travel distance \(l_{max}\) (see Figure 5a), K3P1 will trigger a phase shift (line 25 of Algorithm 1).  
The second condition K3P2 avoids collisions between two consecutive limbs during a turning maneuver (line 26 of Algorithm 1). K3P2 works by measuring the angle \(\varphi\), formed by the projections on the \(XY_{B}\) plane of two consecutive position vectors \({}^{SP,i}\mathbf{t}_{B}\). If \(\varphi\) is smaller than a certain threshold, this criterion triggers a phase shift (see Figure 5b).  
Together, the criteria K3P1 and K3P2 reduce the number of phase shifts when the robot is walking, allowing it to make big steps while advancing and/or turning; additionally, these criteria are enough to control the robot in open-loop blind-walking if all position controllers driving each joint are accurate enough. That said, K3P incorporates the inherent position information of all limbs as a third criterion, named K3P3, to make sure they are working properly. K3P3 tests the stability of the inverse kinematics solution; so all joint angles of every limb \([\theta_{s},\theta_{l},\theta_{k}]_{1,2,\ldots,6}\) remain within a certain range of operation, avoiding the proximity to the mechanical limits and singularities that otherwise could lead to an unstable walking pattern.  
If any of these criteria are met, then the algorithm K3P commands a shift phase of the gait cycle (line 27 of Algorithm 1). The CoM stops its motion to wait for the subset of legs in the swing cycle to land on the ground and begin the stand phase; while the opposite occurs for the subset of legs in stand phase.
## 2 ITS Architecture and Applications
### ITS Applications  
ITS applications exploit data collected from vehicles to improve the use of vehicles, the safety and comfort of drivers and to rationalize the use of public infrastructures. As shown in Figure 3, ITS applications can be categorized into four main classes: (i) infotainment and comfort; (ii) traffic management; (iii) road safety; and (iv) autonomous driving applications. The remainder of this section provides a high level overview of these four classes of ITS applications. We refer the readers to [28; 29; 30; 31] for a more detailed description of emerging ITS applications.
###### Abstract  
In the process of crop cultivation, the application of a fertilizer solution with appropriate pH value is more conducive to the absorption of nutrients by crops. If the pH of the irrigation water and fertilizer solution is too high, it will not only be detrimental to the absorption of nutrients by the crop, but will also damage the structure of the soil. Therefore, the precise regulation of pH in water and fertilizer solutions is very important for agricultural production and saving water and fertilizer. Firstly, the article investigates the hybrid control of fertilizer and water conditioning systems, then builds a fuzzy preprocessing controller and a neural network proportional-integral-differential controller, and optimizes the neural network parameters by means of an improved particle swarm algorithm. The effectiveness of the controller was verified by comparison with the common proportional-integral-differential control and fuzzy algorithm control for fertilizer control and fuzzy preprocessing neural network control. Simulation experiments for this study were designed through the MATLAB/Simulink simulation platform, and the simulation results show that the algorithm has good tracking and regulation capabilities in the system. Finally, the four control algorithms are experimentally validated under different pH regulations using designed field experiments. The results show that, compared with other control algorithms, the control algorithm in this paper has a smaller overshoot and good stability with a shorter rise time, which can achieve the purpose of better regulating the fertilizer application system.  
water and fertilizer mixing; fuzzy processing; pH control; neural network PID control; particle optimization swarm algorithm +
Footnote †: journal: Applied sciences
## 4 On-line Algorithm
### Strategy for Dealing with Flying Range Limitations
```
1:procedureaplyingrefuelingStrategy(connectedElements, refuelingStrategy, explorationRoute, flyingRange, position, edgeToInsert, refuelingStations)
2:\(flyingCapacity\leftarrow\)computeFlyingCapacity(\(flyingRange\),\(explorationRoute\))
3:\(refuelingStation\leftarrow\)closestStation(\(position,ref\),\(usingStations,connectedElements\))
4:\(buf\leftarrow\)computeBuffer(\(position,ref\),\(\mathit{healingStation},\mathit{edgeToInsert}\))
5:if\(lyingCapacity\gets buffer\)then
6:\(explorationRoute\leftarrow\)addAerialEdge(\(position,ref\),\(\mathit{healingStation}\))
7:\(flyingCapacity\leftarrow\)restartFlyingRange(\(flyingRange\))\(\triangleright\) refueling operation
8:\(explorationRoute\leftarrow\)recoverRoute(\(ref\),\(\mathit{healingStation},\mathit{edgeToInsert}\))
9:\(position\leftarrow\)updateUAVPosition(\(explorationRoute\))
10:endif
11:if\(ref\)\(\mathit{healingStrategy}\) is\(\mathit{EC Conservative}\)then
12:if\(position\in ref\)\(\mathit{healingStations}\)\(\&\)\(position\in\)\(connectedElements\)then
13:\(flyingCapacity\leftarrow\)restartFlyingRange(\(flyingRange\))\(\triangleright\) refueling operation
14:endif
15:endif
16:if\(ref\)\(\mathit{healingStrategy}\) is\(\mathit{MConservative}\)then
17:if\(position\in ref\)\(\mathit{healingStations}\)\(\&\)\(position\in\)\(connectedElements\)then
18:if\(flyingCapacity\)\(<0.5\)\(\cdot\)\(flyingRange\)then
19:\(flyingCapacity\leftarrow\)restartFlyingRange(\(flyingRange\))\(\triangleright\) refueling operation
20:endif
21:endif
22:endif
23:if\(ref\)\(\mathit{healingStrategy}\) is\(\mathit{Conservative}\)then
24:if\(position\in ref\)\(\mathit{healingStations}\)\(\&\)\(position\in\)\(connectedElements\)then
25:if\(flyingCapacity\)\(<0.5\)\(\cdot\)\(flyingRange\)then
26:\(flyingCapacity\leftarrow\)restartFlyingRange(\(flyingRange\))\(\triangleright\) refueling operation
27:endif
28:endif
29:endif
30:return\(explorationRoute\)
31:endprocedure
```  
**Algorithm 1** Control flying capacity procedure. Figure 7: Example of the dynamic buffer.
## Theory
### The Improved TSSA Algorithm Based on CP-WOPT Model  
Due to the poor decomposition convergence of the CP-ALS algorithm, the CANDECOMP/PARAFAC weighted optimization (CP-WOPT) approach is employed as the optimization algorithm. A non-negative weights tensor \(\mathcal{W}\in R^{I\times J\times K}\) with the same size as \(\mathcal{X}\) is defined. If most of the entries in \(\mathcal{X}\) are zero or \(\mathcal{X}\) is a sparse tensors, the tensor \(\mathcal{W}\) will be a zero tensor as \(\mathcal{W}_{i_{1}i_{2}\ldots i_{N}}=0\). Otherwise, the element of tensor \(\mathcal{W}\) is equal to 1 as \(\mathcal{W}_{i_{1}i_{2}\ldots i_{N}}=1\). Equation (5) can be replaced by:  
\[f_{\mathcal{W}}(\mathbf{A},\mathbf{B},\mathbf{C})=\frac{1}{2}\|\mathcal{W}*\mathcal{X}- \llbracket\mathbf{A},\mathbf{B},\mathbf{C}\rrbracket\|^{2} \tag{6}\]  
Let \(\mathcal{Y}=\mathcal{W}*\mathcal{X}\), \(\mathcal{Z}=\mathcal{W}*[\mathbf{A},\mathbf{B},\mathbf{C}]\). The tensor \(\mathcal{Y}\) can be fixed as neither \(\mathcal{W}\) nor \(\mathcal{X}\) change during the iterations, and tensor \(\mathcal{Z}\) represents the weighted reconstruction tensor of CP decomposition. Thus, Equation (6) is equivalent to:  
\[f_{\mathcal{W}}(\mathbf{A},\mathbf{B},\mathbf{C})=\frac{1}{2}\|\mathcal{Y}-\mathcal{Z}\|^ {2} \tag{7}\]  
The goal of CP-WOPT algorithm is to obtain the factor matrix\(\mathbf{A}\), \(\mathbf{B}\), \(\mathbf{C}\) to minimize the weighted error function defined in Equation (6). The algorithm based on the gradient method, which has better convergence performance, is used to solve Equation (7). Let \(\mathbf{A}^{-1}=\mathbf{B}\odot\mathbf{C}\), \(\mathbf{B}^{-1}=\mathbf{A}\odot\mathbf{C}\), and \(\mathbf{C}^{-1}=\mathbf{A}\odot\mathbf{B}\), where the operator \(\odot\) denotes Khatri-Rao product of two matrices. Then, the gradient values is defined as follows [36]:  
\[\frac{\partial f_{\mathcal{W}}}{\partial\mathbf{A}} =(\mathcal{Z}_{(1)}-\mathcal{Y}_{(1)})\mathbf{A}^{-1} \tag{8}\] \[\frac{\partial f_{\mathcal{W}}}{\partial\mathbf{B}} =(\mathcal{Z}_{(2)}-\mathcal{Y}_{(2)})\mathbf{B}^{-1}\] (9) \[\frac{\partial f_{\mathcal{W}}}{\partial\mathbf{C}} =(\mathcal{Z}_{(3)}-\mathcal{Y}_{(3)})\mathbf{C}^{-1} \tag{10}\]  
Consequently, we can find the minimum value of the error function based on gradient values to estimate the value of each factor matrix.
# Effect of AQM-Based RLC Buffer Management on the eNB Scheduling Algorithm in LTE Network +
## 7 Conclusions  
This paper presented a performance evaluation of different scheduling algorithms in the presence of AQM-based congestion control with handover. From a maximum utilization of the channel capacity point of view, the best solution is to allocate the RB to those users that experience good channel condition. However, doing so will lead to unfair resource allocation to other users. Thus, providing equal service to all users, i.e., fairness, QoS provisioning, computational complexity, and energy savings, can be achieved at the cost of lower cell capacity. According to this requirement, the implementation design of a RB allocation strategy is a trade-off among the goals that the network operator wants to achieve and the spectral efficiency.  
On the other hand, a good AQM algorithm should require very low computational cost and be easily implemented in a real network. SmRED-4, a smart congestion control mechanism based on RED, is aimed at solving link under-utilization and large delay problems in low and high traffic-load scenarios for eNB RLC in the LTE network. Furthermore, the migration from RED to SmRED-4 in a real network needs very little work because of its simplicity (only the packet dropping profile is modified). To achieve gains in user application performance, an optimal RLC buffer occupancy at eNB is expected. SmRED-4 effectively improves the drawbacks of RED, and achieves a better balance between higher throughput and lower delay. Thus, a good combination of the AQM algorithm with the scheduling techniques can provide optimal performance to network operators.  
Authors thank KDDI Research Inc. for providing all the facilities.  
[PERSON] conceived the idea and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] wrote the paper and [PERSON] revised and edited the text. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.
# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 3 Materials and Methods
### Overall Structure of the Network
#### 3.2.2 Semantic Segmentation  
Semantic segmentation is a crucial step in the network, providing a data foundation for the main network. It helps eliminate distractions and improves the estimation accuracy to some extent. The function of this module is to segment the target area from the image, crop out the target object, and, finally, obtain an image patch and depth map containing only the target object. Since this work is focused on pose estimation algorithms, we directly use PoseCNN's semantic segmentation network to crop the image. The overall framework is an encoder-decoder network, which produces N+1 binary images for each input RGB image in the dataset. These binary images represent the masks for each object, and the RGB image is cropped based on these masks to produce image patches containing only the target object. Similarly, depth maps containing only the target object are obtained, as shown in Figure 2.  
Since the focus of this research is on optimizing and improving the pose estimation algorithm, we did not make additional improvements to the semantic segmentation module,  
Figure 1: Overall network structure.  
Figure 2: Semantic segmentation process.  
maintaining its original structure and method. However, the effectiveness of this module remains critical to the overall estimation process. It ensures the accurate fusion of image and point cloud information, providing a solid foundation for subsequent multiscale feature extraction and fusion. The masks generated by this module can effectively filter out background noise and focus on extracting features from the target object, further improving the model's estimation accuracy for occluded and low-texture objects.
# Improved Performance on Wireless Sensors Network Using Multi-Channel Clustering Hierarchy
## 2 Multi-Channel Clustering Hierarchy (MCCH)
### Cluster Head Selection  
The initial random node process that must be carried out in this study is to find the Cluster Head to determine the channel.  
1. Several sessions depending on the desired amount of CH and the period of observation are carried out.
2. The CH position for each node for one session is ensured.
3. The position of CH becomes unstable or alternates so that a Cluster has a dynamic formation or changes every session.  
The algorithm begins by deciding in advance the cluster along with the desired percentage of CH and the active period of the node as long as it becomes CH. After that, each node decides whether to be CH or not during the session based on its remaining energy level [49]. Decision making is carried out by node \(n\) choosing a random number between 0 and 1. If the number is less than the threshold, then the node becomes CH for that session [50].  
\[T\left(n\right)=\left\{\begin{array}{c}P\\ 1-P\times\left(r\;mod\;\frac{1}{p}\right)\\ 0\;\;\;\;otherwise\end{array}\right\}\text{if }n\;\in\;G\left\{\begin{array}{c} \text{.}\\ \text{.}\\ \text{.}\end{array}\right\}\text{.} \tag{1}\]  
Notes:  
\(T(n)\) = threshold each round node will be cluster head if \(m>T(n)\),  
\(P\) = the initial probability selected,  
\(R\) = Round,  
\(N\) = Node,  
\(G\) = A set that is not yet a cluster head.  
\begin{table}
\begin{tabular}{l} \hline \hline \(n\) = 100; \\ N = 4; \\ energy = 100; \\ temp\_top = 26; \\ temp\_bot = 20; \\ humi\_top = 100; \\ humi\_bot = 0; \\ huma\_top = 100; \\ huma\_bot = 0; \\ freq\_top = 50; \\ freq\_bot = 10; \\ xmax = 300; \\ ymax = 300; \\ velocity = 10,000; \\ for i = 1:n \\ node(i).id = i; \\ node(i).xd = (xmax).* rand(1,1); \\ node(i).yd = (ymax).* rand(1,1); \\ node(i).energy = energy; \\ node(i).temp = (temp\_top - temp\_bot).* rand(1,1) + temp\_bot; \\ node(i).humi = (humi\_top - humi\_bot).* rand(1,1) + humi\_bot; \\ node(i).huma = (huma\_top - huma\_bot).* rand(1,1) + huma\_bot; \\ node(i).freq = (freq\_top - freq\_bot).*rand(1,1) + freq\_bot; \\ axis([0 xmax 0 y ymax]) \\ plot(node(i).xd,node(i).yd,’o’); \\ hold on \\ end \\ clear energy temp\_top temp\_bot humi\_top humi\_bot huma\_top huma\_bot freq\_top freq\_bot \\ \hline \hline \end{tabular}
\end{table}
Table 2: Algorithm 1.
## 3 Characteristics of SACP System with Activated-Carbon-Based Conductive Mortar
### Experimental Procedure  
#### 3.1.1 Mix Design  
The mix design is presented in Table 3. Activated carbon was chosen as a conductive material. Different admixture ratios of 0, 5, 10, 15% were selected. An admixture ratio of 0% stands for general mortar. Since the grain size of silica sand No. 6 is similar to activated carbon, the amount of activated carbon increased while decreasing the amount of silica sand No. 6. Furthermore, \(\alpha\)-gypsum hemihydrate, expansion agent, lithium, latex resins, silica fume, polycarboxylic acid-based polymer, polyvinyl alcohol (PVA), dispersing agent, and air-entraining agent were mixed as additional admixtures. These materials aim to reduce drying shrinkage, accelerate hardening, and enhance durability, adhesion strength, and strength. In detail, \(\alpha\)-gypsum hemihydrate is an expansion agent used to prevent cracks; the expansion agent is a dispersing agent that helps to mix sand, cement, and admixtures; lithium is used to accelerate hardening; latex resin is used to enhance durability and strength; silica fume is used to increase strength; the polycarboxylic acid-based polymer is a high-range water-reducing agent used to enhance the mixing ability; polyvinyl alcohol is used to increase flexural strength and toughness; the dispersing agent is used to prevent separation of materials when pouring; the air-entraining agent is a high-range water-reducing agent used to reduce the amount of water and increase workability. The amount of water was calculated to be 18% of mortar by weight, and the unit quantity was calculated to satisfy workability through a flow test.
## 4 Conclusions  
A modified microstrip step-impedance LPF-integrated wide-beamwidth pixelated DRA was proposed, simulated, fabricated, and tested. To make a wide HPBW radiation pattern, we designed a cylindrical grid pixel DR with a height of \(13\times 13\) that was optimized by the GLPSO algorithm, which was coded in MATLAB. The filter, which was designed to be integrated with the antenna, was expected to prevent unnecessary system size increases due to the addition of the circuit. The filter is modified through stub-equivalence, and it is possible to achieve the goal of miniaturization of the system by free transforming. The proposed filter has higher skirt characteristics and equal loss of pass band than before. The tested results of the fabricated antenna revealed a \(-10\) dB reflection bandwidth of \(14.7\%\) (\(2.96\)-\(3.44\) GHz) and a maximum \(3\)-dB beamwidth of \(187.46^{\circ}\) and \(168.63^{\circ}\) in the \(x\)-\(z\) and \(y\)-\(z\) planes, respectively, at \(3.25\) GHz. The proposed antenna with linear polarization  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Freq (GHz)** & **E-Plane** & **H-Plane** & **Antenna Dimension (\(\lambda_{0}\))** \\ \hline
[3] & 3.2 & \(206^{\circ}\) & \(137^{\circ}\) & \(0.85\)\(\times\)\(0.85\)\(\times\)\(0.32\) \\
[4] & 3.5 & \(248^{\circ}\) & \(148^{\circ}\) & \(0.93\)\(\times\)\(0.93\)\(\times\)\(0.35\) \\
[5] & 6.6 & \(138^{\circ}\) & \(128^{\circ}\) & \(1.34\)\(\times\)\(1.34\)\(\times\)\(0.35\) \\ _Prop._ & 3.25 & \(187^{\circ}\) & \(168^{\circ}\) & \(0.65\)\(\times\)\(0.65\)\(\times\)\(0.65\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between recent works of wide-beamwidth DRA.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency** & **Boresight Gain** & **x-\(z\) Plane** & **y-\(z\) Plane** \\ \hline
3.25 & 1.84 dBi & \(187.46^{\circ}\) & \(168.63^{\circ}\) \\
3.3 & \(0.86\) dBi & \(184.63^{\circ}\) & \(151.42^{\circ}\) \\
3.35 & \(-0.19\) dBi & \(167.76^{\circ}\) & \(143.93^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The measured boresight gain and \(3\)-dB beamwidth of the proposed antenna.  
Figure 9: The far-field radiation pattern of the simulated and fabricated antennas: (a) 3.25, (b) 3.3, and (c) 3.35 GHz.  
and optical beamwidth characteristics is expected to be applied in various fields, such as wireless access points, radar, and communications.  
Writing, D.G.L.; Antenna design and simulation, D.G.L.; Laboratory tests, D.G.L. and T.J.; Software, D.G.L. and T.J.; Writing--Review and Editing, D.G.L. and T.J.; Conceptualization, D.G.L., T.J. and K.C.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Challengeable Future Defense Technology Research and Development Program (912902601) of Agency for Defense Development in 2020.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Sound Field Simulation and Its Analysis Using Acoustic Parameters
### Simulation Test for the Reverberant Environment
#### 3.1.2 Simulation Results and Analysis
Equation (22) indicates a condition in which the maximum EDT value of the array is smaller than that of the control space. Equation (23) indicates that the median value of RT20 in the array is less than its median value in the control space, where RT20 values above \(t_{\mathrm{c}}\) are used. Therefore, we observed that if the microphones are installed in the array and control space, the acoustic parameters of EDT and RT20 satisfy the conditions of Equations (22) and (23), and the active localization method can be implemented. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{\(\alpha\)} & \multicolumn{2}{c}{**EDT 1 (ms)**} & \multicolumn{2}{c}{**RT20 2 (ms)**} \\  & \multicolumn{2}{c}{**Max Value**} & \multicolumn{2}{c}{**Median Value**} \\ \cline{2-5}  & **in a Linear Array** & **in Control Space** & **in a Linear Array** & **in Control Space** \\ \hline
0.9 & 0.53 & 3.12 & 15.57 & 17.36 \\
0.8 & 3.10 & 5.42 & 16.73 & 17.66 \\
0.7 & 6.04 & 10.75 & 17.89 & 21.23 \\
0.6 & 14.83 & 12.16 & 33.34 & 29.54 \\
0.5 & 16.53 & 13.36 & 34.02 & 30.33 \\
0.4 & 17.81 & 18.44 & 50.32 & 43.13 \\
0.3 & 21.44 & 24.97 & 60.53 & 56.14 \\ \hline \end{tabular}  
* EDT: Early decay time (EDT) (0 to \(-10\) dB), 2 RT20: Reverberation time (RT20) (\(-5\) to \(-25\) dB).  
\end{table} 
Table 2: Simulation results of acoustic parameters to confirm the operating conditions of the active localization method. Figure 12: Comparison of analysis results between microphones in the array position (MIC1–MIC7) and microphones in the spatial position (MICS–MIC11, positions A, B, C, and D) using the (**a**) early decay time and (**b**) reverberation time.