esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimization Results and Verification
### Result Verification  
When \(c=0.5\), the turning parameters (\(v_{c}=60\) mm/s, \(f=0.28\) m/s, and \(a_{p}=0.7\) mm) were processed on the test platform, and the corresponding turning specific energy and surface roughness values were obtained and compared with the results obtained by using experience parameters, as shown in Table 7. The surface morphologies obtained using turning 45 steel with the first group of turning parameters before optimization and after optimization are shown in Figure 10,b, respectively, and the measured workpiece contour curves are shown in Figure 11,b.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Author** & & \multicolumn{2}{c}{**Original Result**} & \multicolumn{4}{c}{**BH-ACO\({}_{\text{R}}\) Result**} \\ \hline \multirow{3}{*}{Tian xiao} & \(v_{w}\) (mm/s) & \(v_{s}\) (m/s) & \(a_{p}\) (mm) & \(v_{w}\) (mm/s) & \(v_{s}\) (m/s) & \(a_{p}\) (mm) \\  & 48 & 30 & 5 & 22.15 & 30 & 20 \\  & _SEG_ (J/mm\({}^{3}\)) & _Ra_ (\(\mu\)m) & & & _SEG_ (J/mm\({}^{3}\)) & _Ra_ (\(\mu\)m) & \\  & 70.13 & 0.63 & & 39.7\(\downarrow\) & 0.603\(\downarrow\) & & \\  & \(a_{p}\) (mm) & \(v_{w}\) (m/min) & G (\(\mu\)m) & Ra (\(\mu\)m) & \(a_{p}\) (mm) & \(v_{w}\) (m/min) & G (\(\mu\)m) & Ra (\(\mu\)m) \\ Agarwal [20] & 45 & 7.86 & 121 & 75 & 45 & 14 & 5.02 & 125 \\  & \(\text{M}_{\text{R}}\) (mm\({}^{3}\)/min) & _Ra_ (\(\mu\)m) & \%D & \(\text{M}_{\text{R}}\) & \(\text{Ra}\) (\(\mu\)m) & \%D & \\  & \(-353.7\) & 0.335 & 3.5 & & \(-630\downarrow\) & 0.21\(\downarrow\) & 2.65\(\downarrow\) & \\ [PERSON] & \(v_{w}\) (m/s) & \(v_{s}\) (m/s) & \(a_{p}\) (mm) & \(v_{w}\) (m/s) & \(v_{s}\) (m/s) & \(a_{p}\) (mm) & \\ [PERSON] [21] & 120 & 0.836 & 13.5 & 150 & 0.719 & 19 & \\  & \(MRR\) (mm\({}^{3}\)/s) & _Ra_ (\(\mu\)m) & & & \(MRR\) & & \\  & \(-11.286\) & 0.686 & & (mm\({}^{3}\)/s) & 0.684\(\downarrow\) & & \\ \hline \hline \end{tabular}  
* Among them, “\(\downarrow\)” indicates that the result is smaller than before optimization, indicating a better optimization effect.  
\end{table}
Table 6: Comparison of BH-ACO\({}_{\text{R}}\) optimization results with those for different algorithms.  
Figure 10: Comparison of surface morphology before and after optimization.  
As can be seen from the table, compared with the first four groups of empirical parameter processing, the turning parameters optimized by the BH-ACO\({}_{\text{R}}\) algorithm can improve the specific energy of cutting (\(SEC\)) to a certain extent. The \(SEC\) obtained by using the optimized turning parameters was 2.69 J/mm\({}^{3}\), which is 2.28% higher than the \(SEC\) 2.63 J/mm\({}^{3}\) of the fifth group, but the surface roughness \(Ra\) was reduced from 3.51 \(\upmu\)m to 2.53 \(\upmu\)m, which is about 27.92% lower.
## 2 Materials and Methods
### Calculation of Airflow Rate and Non-dimensional Airflow Rate  
The airflow rate (\(Q\)) was calculated in different openings by summing the partial airflow rates through the cell faces of the windward opening:  
\[Q=\sum_{i\in N}U_{i}A_{i} \tag{3}\]  
where N is the cell number of the windward opening, \(U_{i}\) is the stream-wise air velocity through cell i, m-s\({}^{-1}\), and \(A_{i}\) is the face area of cell i, m\({}^{2}\).  
Non-dimensional airflow rate was calculated using Equation (4).  
\[Q^{\star}=\frac{Q}{U_{ref}A_{in}} \tag{4}\]  
where \(Q^{\star}\) is the non-dimensional airflow rate; \(U_{ref}\) is the reference velocity at 0.1 m height, m-s\({}^{-1}\); \(A_{in}\) is the area of the windward sidewall opening, m\({}^{2}\). However, the calculation of the non-dimensional airflow rate of other openings were calculated using same approach where the area of openings were only different.  
Airflow rate per m\({}^{2}\) floor area (\(Q_{f}\)) was calculated considering airflow rate and floor area of the NVD building as Equation (5).  
\[Q_{f}=\frac{Q}{A_{f}} \tag{5}\]  
where \(Q\) is the airflow rate through the windward opening, m\({}^{3}\)-s\({}^{-1}\), and \(A_{f}\) indicates the floor area of the building.  
Figure 8: Comparison of stream-wise air velocity (\(u\)) profiles of BLWT measurement data with CFD simulation in different locations in and outside of the NVD barn for the completed opened sidewalls condition (Case 4 SWC). The inlet profile velocity at 0.1m height was 4.41 m-s\({}^{-1}\). The zero velocity in x axis correspondence with the line of the measurement. Negative value of the velocity indicates opposite direction of airflow.
## 5 Conclusions  
This paper proposes a modification of the SOCP deadlock handling method. This modification limits the generality of the method, so it is dedicated to solving a specific type of use cases. This approach is suitable for a production system in which each station serves as either an input station or an output station, but never both and moreover the transport task requires that each vehicle starts at the parking place, transports parts from the pick-up points to the delivery points and finishes at the parking place. A condition (3) was defined to verify whether such modification for a given case will not lead to a restricted deadlock. The introduction of a staged method of process reservation instead of making all reservations at once before the start of the transport task makes it possible to reduce the restrictiveness of the method and thus increase its efficiency. An improvement in the efficiency of the transportation system was also achieved for the system with bidirectional paths. This effect is shown by the introduction of a new type of deadlock-free zone shown schematically in Figure 1b. The following conclusions can be drawn from the presented analyses and simulation studies:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of** & **End Time [s]** & **End Time [s]** \\
**AGVs** & **for SOCP-Standard** & **for SOCP-Three Stages** \\ \hline
1 & 26,496 & 26,496 \\
2 & 15,288 & 14,003 \\
3 & 10,361 & 9179 \\
4 & 9163 & 7401 \\
5 & 9127 & 6291 \\
6 & 8212 & 5329 \\
7 & 7714 & 4928 \\
8 & 8076 & 4560 \\
9 & 8150 & 4541 \\
10 & 7733 & 4219 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of the simulation (mixed paths).  
Figure 8: A comparison of the results for mixed paths.  
the use cases of the modified SOCP method discussed above show their higher efficiency than the source method, which justifies the introduction of the modification; condition (3) defined in this paper makes it possible to apply the modified method not only to the examples presented in this paper but to all cases for which this condition is met;  
methods of a general nature can be used to solve a wide range of problems for which they were developed, but their efficiency is usually worse than dedicated methods developed to solve a limited number of use cases.  
As mentioned earlier the structural online control policy is based on two rules. Further work should be focused on the second rule, which corresponds to the deadlock-risk segments. Improvement of this rule can increase the efficiency of the SOCP method.  
Conceptualization, W.M. and J.Z.; methodology, J.Z.; software, W.M.; validation, J.Z.; resources, W.M.; writing--original draft preparation, J.Z. and W.M.; writing--review and editing, [PERSON]; visualization, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The source data cited in this study are available in the paper [2].  
The authors declare no conflict of interest.
# Mechanically Reconfigurable, Beam-Scanning Reflectarray and Transmitarray Antennas: A Review
## 2 Element Movement
### Reconfigurable Resonant Elements  
#### 2.1.1 Adjusting Dimensions  
A common approach to locally control the re-radiated phase of individual elements in a RA is to adjust the lateral and height dimensions of resonant elements [1; 10; 11; 12]. The incident electromagnetic field creates a standing wave between the resonant elements and the ground plane reflector [10]. Each resonant element has an equivalent reactance that varies with its dimensions. The interaction between the reactance and the standing wave causes the incident wave to be re-radiated with a phase shift. The resonant elements are tuned close to their resonant frequencies, resulting in limited frequency bandwidths. Crossed dipoles [10] and patch antennas [11] have been investigated as mechanically reconfigurable resonant elements. The dimensions of a rectangular patch antenna, for example, were finely tuned close to its resonant frequency to achieve different reflected phases [11]. A 350\({}^{\circ}\) phase difference was reported by adopting patches with varying size. The effective length of a patch can also be adjusted by changing the height of the patch over the ground plane [12]. This is accomplished by increasing or decreasing the fringing fields of elements, providing a mechanically reconfigurable RA with fixed size patches. Patch height displacement is less effective at changing the reflected phase than length adjustment. For example, a reradiated phase tuning range of only 197\({}^{\circ}\) was reported in [12].  
Modifying the resonant features of a patch antenna can be conducted by adjusting its dimensions or by changing its excitation properties. Reference [1] proposes single-size patch antennas in a RA with coupling slots of different lengths to achieve different re-radiated phases. Three slots of varying sizes are laterally replaced beneath fixed patch elements of the RA to scan the beam to broadside and \(\pm\)30\({}^{\circ}\), providing the RA with limited reconfigurable capability. Alternatively, the relative gap distance of coupling slots to the patch and the ground plane can be tuned to shift the phase of the re-radiated fields [9]. This is shown in Figure 2, where the authors investigated a fixed square patch with a height-tunable coupling slot. This is an example of a mechanically reconfigurable RA where an actuator was integrated with each element and was located beneath the ground plane reflector. A plastic pedestal extends from the actuator and connects to the slotted patch. This arrangement has significantly modified the re-radiated phase range by as much as 324\({}^{\circ}\) by finely adjusting the coupling slots between the ground plane and the rectangular patch. The performance metrics of this RA are summarized in Table 1 and compared with representative RAs of other categories. These illustrative antenna examples represent the state-of-the-art of each approach and are built upon the constant progress and proposed concepts prior to them.
## 3 Test Results and Analysis
### Horizontal Shear Performance
#### 3.2.2 Loading Frequency  
Similarly, the remaining vertical pressure of 3400 kN was exerted to the three bearings. Under a shear strain of 100%, horizontal reciprocating loads were applied to all the bearings at successive frequencies of 0.01 Hz and 0.0082 Hz, respectively. The horizontal hysteresis curves, horizontal equivalent stiffness, and equivalent damping ratio of the bearings obtained by the test are shown in Figure 14 and Table 6, respectively. It can be seen that the horizontal equivalent stiffness and equivalent damping ratio of the three bearings all decline with the diminution of loading frequency within a narrow varying range.  
Figure 12: Horizontal shear load-displacement curves under different shear strains: (**a**) HDRB-LSF; (**b**) TLRB; (**c**) LRB.  
Figure 13: _Cont._  
Figure 14: _Cont_.  
Figure 13: Horizontal performance of the bearings under different shear strains: (**a**) equivalent stiffness; (**b**) equivalent damping ratio.
## 2 Methodology
### FSEV Battery Pack Modelling
Next, the charge capacity of the battery was estimated at all operating temperatures derived in Equation (4) as per the following relation. \[\text{Capacity}=-0.001238*T^{2}+0.7507*T-94.06 \tag{5}\]  
On the basis of the derived temperature-capacity profile, the SOC was estimated as follows. \[\text{SOC (at time t)}=\text{initial SOC}-\int\frac{\text{I}}{\text{Capacity }*3600}\,dt \tag{6}\]  
where I is the battery current, and the initial SOC was assumed to be 0.97. Next, open circuit voltage (OCV) was estimated from the experimental OCV, SOC, and temperature data sheet provided by the cell manufacturer [46]  
\[\text{OCV}=3.149*\text{SOC}-7.551\times 10^{-5}*\text{T}+0.523 \tag{7}\]  
Terminal voltage (\(V_{t}\)) was obtained as follows. \[V_{t}=\text{OCV}-V_{d1}-V_{d2} \tag{8}\]  
where \(V_{d1}\) and \(V_{d2}\) are the potential differences across \(R_{0}\) and \(R_{1}\), respectively. These voltages were determined using the following relations. \[V_{d1}=I_{0}R_{0}\] \[V_{d2}=I_{1}R_{1}\]  
where \(I_{0}\) is the current through \(R_{0}\), and \(I_{1}\) is the current flowing through \(R_{1}\) in the equivalent cell model. Subsequently, SOE was calculated as follows. \[\text{SOE (at time t)}=\text{initial SOE}-\int\frac{\text{P}}{\text{nominal energy}}\,dt \tag{9}\]  
where P is power of the FSEV battery in watts. Nominal energy of the cell was obtained from the design data sheet [46], and the initial SOE was assumed to be 0.97. Figure 2 shows a schematic representation of the SOC, SOE, and PL estimation procedure followed in the current study. The following section gives details of the proposed soft-sensor designs for these FSEV battery parameters.
# Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Reschargeable Sensor Networks
## 4 Mathematical Optimization Model
### Mathematical Optimization Model
Although we can set the chargers with appropriate energy harvesting capability in network designing phase, or deploy them at more optimized locations, there is an inevitable problem that the energy harvesting efficiency of the wireless chargers may be lower than the total energy consumption speed of their subordinative nodes, especially when the efficiency decreases because of devices ageing, environmental influence, or network nodes increasing, etc. This is just the problem that we want to solve in this paper, which tries to decrease the total energy burden of the wireless chargers, just as depicted in Section 1 and the optimization objective. In literature [40], [PERSON] and [PERSON] propose an optimization program to minimize the total communication cost of a given network, subject to the constraints of channel load, traffic demands and channel costs. Investigating our optimization model and the model proposed in [40], we can find the constraints of the programming considered in the latter from a subset of the constraints in the former. Since it has proved to be an NP-hard problem in [40], the model in this paper is also a NP-hard problem. Nevertheless, when a similar SDWRSN network is planned to be deployed, and some parameters have been determined such as the packet size, data period, etc., we can put known parameters into the model and adjust unknown parameters to estimate the lower bound of possible energy consumption or some other metrics. This can be helpful in optimizing the power of each wireless charger, the density of the sensor nodes, the node allocation, and so on. In what follows, we propose a fast heuristic algorithm to decrease the energy consumption in hybrid SDWRSNs.
## 4 Reliability and Reliability Sensitivity Analysis Method of the Igniter
### Reliability Simulation Implementation of the Pyrotechnic Igniter  
According to the above reliability analysis method, the performance reliability simulation of pyrotechnic igniter includes the following steps:  
Step 1. Enter the random variable, \(\mathbf{x}\), and its distribution type, as shown in Table 3;  
Step 2. The sample data of \(m\) groups are obtained by a TLFF design to reduce the number of experiments. After obtaining \(m\) group samples through experimental optimization design, the samples are inputted into the ignition simulation model of the pyrotechnic igniter established by MATLAB to obtain the ignition impulse value \(I(\mathbf{x})\). Among them, all MATLAB software packages should be executed in a batch mode to automate the simulation process;  
Step 3. The Kriging model, \(\hat{I}(\mathbf{x})\), is fitted based on \(m\) groups of the sample data;  
Step 4. The display function, \(\hat{G}(\mathbf{x})\), is established based on the Kriging model of the ignition impulse \(\hat{I}(\mathbf{x})\);  
Step 5. The reliability and sensitivity are analyzed based on the display function, \(\hat{G}(\mathbf{x})\).  
The reliability simulation flow chart is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Mean** & **Std.** & **Distribution Type** \\ \hline \(\rho_{\text{BPN}}\) (g-cm\({}^{-3}\)) & 1.679 & 0.02 & Normal \\ \(\rho_{\text{CMDB}}\) (g-cm\({}^{-3}\)) & 1.589 & 0.01 & Normal \\ \(n_{\text{CMDB}}\) & 0.22 & 0.01 & Normal \\ \(d\) (mm) & 3.6 & 0.007 & Normal \\ \hline \hline \end{tabular}
\end{table}
Table 3: Key design parameters for the pyrotechnic igniter.  
The ignition simulation workflows of the pyrotechnic igniter are constructed by applying the aforementioned steps of the reliability analysis, as shown in Figure 8. The arrows in the figure represent the running order of the tasks. The details of the nodes in the simulation workflow are listed in Table 4.
## 2 Materials and Methods
### Evaluation Metrics  
We introduce several evaluation metrics, i.e., mean absolute percentage error (_MAPE_), mean absolute error (_MAE_), and root mean square error (_RMSE_), to evaluate the proposed model's performance in the experimental stage as shown in Equations (3)-(5) below:  
\[MAPE=\frac{100\%}{n}{\sum_{t=1}^{n}}\Big{|}\frac{x_{t}-\overline{x}_{t}}{x_{t }}\Big{|}, \tag{3}\]  
\[MAE=\frac{1}{n}{\sum_{t=1}^{n}}|x_{t}-\overline{x}_{t}|, \tag{4}\]  
\[RMSE=\frac{1}{n}{\sum_{t=1}^{n}}{(x_{t}-\overline{x}_{t})}^{2}, \tag{5}\]  
where \(x_{t}\) and \(\overline{x}_{t}\) are actual values and predicted values, while \(n\) is the number of observed data.  
We also propose a confusion matrix as the evaluation metric as shown in Figure 4, but we initially cluster the actual values and the predicted values using the Fuzzy c-means (FCM) algorithm. The FCM clustering algorithm assembles a set of data into \(m\) clusters where each data point in the dataset corresponds to each cluster at a certain degree [17]. The higher degree of a data point to a specific cluster denotes the corresponding cluster of  
Figure 3: Dilated causal convolution process.  
that data point. Here, we take (9) as the number of clusters for the actual values and the predicted values.  
After clustering the actual data and predicted data, we evaluate them utilizing a confusion matrix. The confusion matrix is shown in a table to describe and display the performance of a classification task. The dimension of the confusion matrix is two, where one dimension refers to the actual data and another dimension refers to the predicted data [18]. Through this method, we can evaluate the proposed model using _precision_, _recall_, and _F1-score_. _Precision_ denotes accuracy as the ratio of the correctly classified cluster to the total data predicted, while _recall_ refers to the ratio of the correctly classified cluster to the total number of data actually belonging to the related cluster, and the _F1-score_ represents the equilibrium of the precision and recall. The values of _precision_, _recall_, and _F1-score_ can be calculated using Equations (6)-(8):  
\[precision=\frac{TP}{TP+FP}, \tag{6}\]  
\[recall=\frac{TP}{TP+FN}, \tag{7}\]  
\[F1 score=\frac{2\times precision\times recall}{precision+recall}, \tag{8}\]  
where _TP_ (true positive) is all the diagonal values in the confusion matrix corresponding to each cluster. _FP_ (false positive) refers to the sum of values of the corresponding column besides TP values of a cluster, and _FN_ (false negative) denotes the sum of the values of the corresponding rows besides _TP_ values of a cluster.
## 4 Discussion
Therefore, the DL classifier will provide promising things in the future because the data will be increased over time. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **Accuracy (\%)** & **Precision (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **F1-Score (\%)** \\ \hline Random Forest + PCA & 98.85 & 87.21 & 60.00 & 98.30 & 66.89 \\ SVM + PCA + DWT & 99.76 & 98.30 & 97.22 & 99.69 & 97.94 \\ DL with SAE & 99.52 & 90.70 & 86.68 & 99.45 & 81.70 \\ DNNs + PCA + DWT & 99.76 & 98.20 & 91.80 & 99.78 & 97.80 \\ DL (proposed method) & 99.73 & 93.60 & 91.20 & 99.80 & 91.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Selected method for the benchmarking proposed method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No** & **Classifier** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** & **F1 –Score** \\ \hline
1 & CNN [17] & 92.70 & - & - & - & - \\
2 & DBN [36] & 98.60 & 88.00 & 99.25 & - & - \\
3 & DBN and SAE [20] & 90.20 & 51.03 & 82.76 & - & - \\
4 & CNN [16] & - & 78.4 & - & 80.00 & 77.60 \\
5 & CNN [15] & 89.05 & 95.90 & 88.37 & - & - \\
6 & CNN [19] & 99.39 & - & - & - & - \\
7 & RNN [31] & 91.33 & - & - & - & - \\
8 & RBM and DBN [37] & 94.60 & - & - & - & - \\
9 & DNN [38] & 99.68 & 99.48 & 99.83 & - & - \\
10 & RNN [39] & 88.10 & 92.40 & 83.35 & - & - \\
11 & CNN and RNN [40] & 83.40 & - & - & - & - \\
12 & CNN [18] & 93.91 & 93.93 & 94.19 & - & - \\
13 & DEEP CODED FEATURES & 99.23 & - & - & - & - \\  & and LSTM [41] & 99.73 & 91.20 & 99.80 & 93.60 & 91.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Selected literatures for benchmarking our proposed model.
# BTDNet: A Multi-Modal Approach for Brain Tumor Radiogenomic Classification
## 3 Materials and Method
### Proposed Method
#### 3.1.2 Three-Dimensional Analysis Component  
mpMRI 3D volumes consist of 2D slices. CNNs are excellent for capturing local features, patterns, and structures in an image, making them well suited for extracting meaningful information from individual slices in a 3D MRI volume. By applying 2D convolutions across each slice, the CNN can effectively extract important spatial features from the MRI data. In medical imaging, the spatial context of features is often crucial. MRI scans contain anatomical structures and patterns that may extend across multiple slices. RNNs can capture the spatial dependencies and temporal information within the volume (by analyzing the extracted CNN features from each slice). This allows the method to consider the relationships between slices and how they evolve through the z-axis, which is important for understanding structures or changes/abnormalities over time. This is the aim of the 3D analysis component, which we describe in more detail below.  
After all input 3D volumes for each modality (FLAIR, T1w, T1 wCE, and T2) have been transformed and then padded to have lengths \(t_{1},t_{2},t_{3},t_{4}\), a CNN is applied to each of their slices, performing local (per 2D image/slice) analysis and extracting features from each slice. For example, in the case of a slice from a 3D input volume in the FLAIR modality, the extracted features from the CNN are denoted as \(\mathcal{F}_{\text{CNN}}\in\mathcal{R}^{H\times W\times D}\), with \(W\), \(H\), and\(D\) being the feature map's width, height, and depth, respectively. Then, these features are fed to an RNN, which is placed on top of the CNN, to capture the temporal information and dependencies between consecutive slices within the same volume (for each modality). The RNN analyzes the features of the whole volume sequence, sequentially moving from slice 0 to slice \(t_{1}\) if the input volume is for the FLAIR modality (and similarly for the other modalities). As shown in Figure 1, we obtain RNN features corresponding to each slice from 0 to \(t_{1}\). For example, for a slice from a 3D input volume in the FLAIR modality, the extracted features from the RNN are denoted as \(\mathcal{F}_{\text{RNN}}\in\mathcal{R}^{V}\), with \(V\) being the feature vector that is outputted by the RNN. Thus, for the whole 3D input volume in the FLAIR modality, the extracted features from the RNN are denoted as \(\mathcal{F}_{\text{RNN}}\in\mathcal{R}^{V\times t_{1}}\). These RNN features are then fed to the routing component, which is described below. In total, the CNN plus RNN performs global (per 3D volume) analysis.