esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Methods and Experimental Procedure
### Signal Analysis  
#### 2.6.1 Modal Analysis  
Having a very long width and length in comparison to the thickness, and a thickness on the order of magnitude of the wavelength, EPL80 can be considered to be a plate. In a plate, two kinds of so-called Lamb waves can propagate, i.e., symmetric (Sn) and antisymmetric (An) modes, with n being the index of the mode. Having both its width and its thickness on the order of magnitude of the wavelength, EPL3 can be considered to be a bar. This means that the propagating modes can be longitudinal (L\({}_{\text{0n}}\)), flexural (F\({}_{\text{mm}}\)), or torsional modes (T\({}_{\text{0n}}\)). We used the two-dimensional Fourier Transform (2 DFT) [43] to analyse the modal content of the propagating waves and compared it to the theoretical modes. The 2 DFT enables to visualise 2D matrix signals, of size N \(\times\) M, in the frequency domain. One dimension of the matrix is time (\(t\)), and the other dimension is the distance to the source (\(x\)). The 2 DFT shows the evolution of the wave number \(k\) (which is a spatial frequency) as a function of the temporal frequency. For a discrete signal matrix \(v_{z}(t,x)\), the 2 DFT is given by:  
\[|V_{z}(k,f)|=\ \left|\sum_{t=0}^{N-1}\sum_{x=0}^{M-1}v_{z}(t,x)e^{-j2\pi(k\frac{t }{N}+f\frac{x}{N})}\right| \tag{9}\]  
where \(k\) the spatial frequency in x-direction and f the temporal frequency. The 2 DFT are calculated using the particle velocity (\(v_{z}\)) of every node of the finite element mesh along the median line of the upper face of each sample, from the source to a distance of 85 mm (Figure 6). The signal duration taken for the 2 DFT calculations is 70 \(\upmu\)s. This duration discards the reflections on the two extremities of the samples without any loss of information contained in the signal directly propagated from the source. We chose this signal-processing tool because it shows information on the source but also on the geometry of the propagating medium.
## 3 Results
### Remarks of the Experimental Results  
**Remark 1**.: _In the comparative experiment part, the Mindboggle101 and Neurite OASIS datasets are used to verify the generalization ability of the model. Compared with the traditional registration methods Affine, SyN, and the existing registration algorithms FAIM and VoxelMorph based on deep learning, MCANet's Dice score has been greatly improved, and compared with the latest algorithm Transmorph, it also has some improvement in smoothness and Dice score. Compared with traditional algorithms, MCAN shows absolute advantages, because it has faster registration speed, and MCANet has obvious advantages in time efficiency. Compared with the registration method of deep learning, MCANet has better smoothness and better Dice score, so the algorithm in this paper is feasible._  
**Remark 2**.: _In the ablation experiment part, we proved the impact of each module on the network; adding an expanded convolutional group can improve the registration accuracy by increasing the receptive field, adding an attention gate component can enhance the response of the model to the key region and improve the registration accuracy of the network, and the simultaneous use of the two can make the network expand the receptive field without adding too much computational cost and network parameters, obtain multi-scale features, improve the voxel folding problem, and improve the over-deformation problem. The network is able to better understand the global structure and local details of the image, improving the ability to accurately detect deformed areas._  
**Remark 3**.: _Regularization analysis part: We perform double regularization constraints on the model during the training process and analyze the influence of the bending energy weight coefficient \(\beta\) on the registration accuracy and smoothness. When the bending energy weight coefficient \(\beta\) increases, the degree of voxel folding decreases, and the Dice score also decreases to a certain extent. Through a large number of experiments, we choose the weight coefficient \(\beta\) taking 1.4, which can not only ensure the smoothness and authenticity of the deformation but also maintain a high registration accuracy._  
Figure 7: Visualized deformation fields under different values of weight coefficient \(\beta\).
## 4 Experimental Results and Discussions
### Mechanical Properties
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Type of**} & **Statistical** & \(\overline{n_{f}}\) & \(f_{L}\) & \(f_{R,1}\) & \(f_{R,2}\) & \(f_{R,3}\) & \(f_{R,4}\) & \(f_{R,1}\) & \(f_{R,2k}\) & \multicolumn{1}{c}{**Material**} \\ \multicolumn{1}{c}{**Hooked Fiber**} & **Parameter** & **[\%]** & **[MPa]** & **[MPa]** & **[MPa]** & **[MPa]** & **[MPa]** & **[MPa]** & **[MPa]** & **[GP]** & **[GP]** & **Class \({}^{\star}\)** \\ \hline \multirow{4}{*}{3D} & Avg. & \multirow{2}{*}{0.37} & 6.92 & 5.53 & 6.38 & 6.25 & 5.81 & 5.04 & 5.93 & 1.18 & 5d \\  & St. dev. & & 0.201 & 0.259 & 0.185 & 0.172 & 0.343 & - & - & - & - \\ \cline{2-13}  & Avg. & \multirow{2}{*}{0.60} & 7.02 & 6.37 & 7.84 & 6.49 & 7.31 & 3.35 & 2.66 & 0.79 & 3b \\  & St. dev. & & 0.203 & 1.600 & 2.373 & 2.025 & 2.000 & - & - & - & - \\ \cline{2-13}  & Avg. & \multirow{2}{*}{1.00} & 8.73 & 12.00 & 13.92 & 13.31 & 12.18 & 9.86 & 12.43 & 1.26 & 9d \\  & St. dev. & & 0.445 & 1.129 & 1.073 & 0.462 & 0.212 & - & - & - & - \\ \hline \multirow{4}{*}{4D} & Avg. & \multirow{2}{*}{0.37} & 6.88 & 4.76 & 6.34 & 5.95 & 4.61 & 3.00 & 4.32 & 1.44 & 3e \\  & St. dev. & & 0.810 & 0.931 & 0.716 & 0.862 & 0.659 & - & - & - & - \\ \cline{2-13}  & Avg. & \multirow{2}{*}{0.60} & 7.43 & 6.23 & 8.75 & 7.49 & 5.56 & 2.80 & 3.33 & 1.19 & 2d \\  & St. dev. & & 0.802 & 1.812 & 2.752 & 2.200 & 1.574 & - & - & - & - \\ \cline{2-13}  & Avg. & \multirow{2}{*}{1.00} & 7.72 & 11.26 & 14.66 & 13.57 & 11.57 & 8.02 & 11.12 & 1.39 & 8e \\  & St. dev. & & 0.639 & 1.712 & 1.143 & 1.292 & 1.381 & - & - & - & - \\ \hline \multirow{4}{*}{5D} & Avg. & \multirow{2}{*}{0.37} & 6.78 & 4.48 & 6.56 & 7.49 & 7.31 & 2.25 & 3.71 & 1.65 & 2e \\  & St. dev. & & 0.694 & 1.182 & 1.799 & 1.997 & 1.617 & - & - & - & - \\ \cline{2-13}  & Avg. & \multirow{2}{*}{0.60} & 6.85 & 5.62 & 8.73 & 9.74 & 9.61 & 3.57 & 6.16 & 1.73 & 3e \\  & St. dev. & & 0.603 & 1.084 & 1.628 & 1.892 & 1.732 & - & - & - & - \\ \cline{2-13}  & Avg. & \multirow{2}{*}{1.00} & 7.44 & 10.41 & 12.97 & 12.03 & 11.07 & 8.23 & 9.87 & 1.20 & 8d \\ \cline{2-13}  & St. dev. & & 1.150 & 1.155 & 1.740 & 1.145 & 1.433 & - & - & - & - \\ \hline \hline \end{tabular}  
* Notel \(\overline{n_{f}}\) = fiber volume function, \(f_{L}\) = flexural strength corresponding to the highest value of the load recorded up to a CMDO of 0.05 mm, \(f_{R,i}\) = residual flexural strengths corresponding to CMDO (\(i\) = 1, 2, 3, and 4), equal to 0.5, 1.5, 2.5, and 3.5 mm, respectively, \(f_{R,i}\) = characteristic residual flexural strengths, calculated based on the following equation: \(f_{R,i}=f_{R,i}-18\bar{w}\), and \(c\) = standard deviation. \({}^{\star}\) Material class is determined by \(\bar{n_{f}}\) Model Code [27].  
\end{table} 
Table 2: Summary of three-point bending test results as per EN 14651 standard [19].
# Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks
## 2 Related Work
#### 3.1.3 Node Location  
The coordinate of node location can be obtained by the node coordinate location at \(t\) moment (through LM information) and node moving velocity vector angle.  
The location of the node at time \(t\) can be identified by coordinates \(L_{t}(x_{t},y_{t})\), so at time \(t+\Delta t\), the geographic location of the node can be predicted based on the speed \(v_{t}\), node position \(L_{t}(x_{t},y_{t})\), direction \(\beta_{t}\) (speed vector change angle) and speed \(v_{t+\Delta t}\) at time \(t\).  
If in \(\Delta t\) time, the node driving direction has changed:  
\[(x_{t+\Delta t},y_{t+\Delta t})=\left\{\begin{array}{l}x_{t+\Delta t}=x_{t}+ (\frac{y_{t+\Delta t}+v_{t}}{2})\Delta t\cos\beta_{t}\\ y_{t+\Delta t}=y_{t}+(\frac{y_{t+\Delta t}+v_{t}}{2})\Delta t\mathrm{sins}\beta _{t}\end{array}\right. \tag{15}\]  
If in \(\Delta t\) time, the node driving direction has not changed:  
\[(x_{t+\Delta t},y_{t+\Delta t})=\left\{\begin{array}{l}x_{t+\Delta t}=x_{t}+ (\frac{y_{t+\Delta t}+v_{t}}{2})\Delta t\\ y_{t+\Delta t}=y_{t}+(\frac{y_{t+\Delta t}+v_{t}}{2})\Delta t\end{array}\right. \tag{16}\]
## 3 Proposed Prediction Scheme
\begin{table}
\begin{tabular}{c c c c c} \hline
**Risk factors** & **RF** & **MARS** & **XGboost** & **Average Rank** \\ \hline X1 & 10 & 2 & 4 & 5.3 \\ X2 & 2 & 3 & 2 & 2.3 \\ X3 & 11 & 5 & 11 & 9.0 \\ X4 & 6 & 14 & 5 & 8.3 \\ X5 & 5 & 8 & 3 & 5.3 \\ X6 & 7 & 9 & 9 & 8.3 \\ X7 & 1 & 1 & 1 & 1.0 \\ X8 & 4 & 4 & 8 & 5.3 \\ X9 & 14 & 9 & 14 & 12.3 \\ X10 & 13 & 14 & 13 & 13.3 \\ X11 & 3 & 6 & 6 & 5.0 \\ X12 & 12 & 7 & 12 & 10.3 \\ X13 & 9 & 14 & 10 & 11.0 \\ X14 & 8 & 14 & 7 & 9.7 \\ \hline \end{tabular}
\end{table} 
Table 2: The selected and ranked risk factors using the RF, MARS, and XGboost methods. Figure 2: The ranking of all risk factors. In the modeling process of the two-stage method, after obtaining the average rank of each risk factor, the overall important risk factors should be identified before constructing a classification model. In this study, an average rank value less than 10 was used as the criterion for selecting the overall important risk factors. This criterion was determined by the suggestion of clinical physicians. Based on this criterion, it can be observed from Figure 2 that the 10 risk factors including X7 (combined stage), X2 (age at diagnosis), X11 (BMI), X8 (surgical margins of the primary site), X5 (tumor size), X1 (sex), X6 (regional lymph nodes positive), X4 (grade/differentiation), X3 (primary site), and X14 (drinking), were selected as the important risk factors. In the final stage of the two-stage method, the identified 10 overall important risk factors served as the input variables for the SVM, RF, MARS, ELM, and XGboost methods in order to predict SPC in colorectal cancer patients. The five two-stage methods were termed A-SVM, A-RF, A-MARS, A-ELM, and A-XGboost, respectively. In the fourth step of the proposed scheme, after obtaining the classification results from the five single methods and the five two-stage methods, we used accuracy, sensitivity, specificity, and area under the curve (AUC) parameters as classification accuracy metrics to compare the performances of the ten models. In the final step, after comparing the classification performances of the ten models, including S-SVM, S-RF, S-MARS, S-ELM, S-XGboost, A-SVM, A-RF, A-MARS, A-ELM, and A-XGboost models, we obtained the final diagnosis results and identified the important risk factors for predicting SPC in colorectal cancer patients. This study used 10-fold cross validation methods to estimate the performances of the proposed ten models. First, we randomly split the whole dataset into 10 equal sized parts, with nine folds as the training data set and one fold as the testing data set. That was repeated 10 times, with each of the 10 folds used exactly once as the testing data set. The ten results of classification accuracy metrics could then be averaged to produce the final performances of the methods.
## 1 Introduction
By integrating the improved U-Net classification model, the accurate recognition of future cold-wave conditions and effective forecasting of their occurrence times are achieved. Based on NWP data and cold wave recognition results, this paper develops a Non-Stationary Transformer (Ns-Transformer)-combined forecasting model to further improve wind power forecasting accuracy during \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Literature** & **Time Scale** & \
\begin{tabular}{c} **Preprocessing** \\ **Methods** \\ \end{tabular} & **Model** &
\begin{tabular}{c} **Cold Wave** \\ **Identification** \\ \end{tabular} & **Limitations** \\ \hline [PERSON] et al. [12] & Short-term & MEMD & BiLSTM & \(\times\) & \\ [PERSON] et al. [18] & Short-term & None & EALSTM-QR & \(\times\) & \\ [PERSON] et al. [21] & Short-term & MASM & LightGBM-GRU & \(\times\) & Missing scene \\ [PERSON] et al. [23] & Short-term & K-means & UMAP-IVMD-IISTM & ✓ & discrimination criteria \\ [PERSON] et al. [24] & Short-term & HMM & CNN-LSTM & ✓ & Limeited to extreme fan \\ [PERSON] et al. [25] & Short-term & TimeGAN & XGBoost- & ✓ & Lategate accuracy \\ [PERSON] et al. [31] & Short-term & RL & DDPG & ✓ & No recognition protocol \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Limitations of current research on wind power forecasting in cold wave weather. cold-wave conditions. The full text's structure is shown in Figure 1. The main innovations of this paper are as follows:  
1. Proposed cold wave recognition criterion: By considering regional meteorological changes and wind turbine operation characteristics, a cold wave discriminant criterion is proposed. This criterion can accurately identify cold-wave conditions in the region, especially with regard to the duration of the cold wave, providing hour-level accuracy. 2. Improved U-Net classification model: Building on the U-Net model, a multi-modal classification method is proposed to optimize the model for the seasonal characteristics of cold waves. The improved model not only accurately forecasts the occurrence time of cold waves but also enhances the recognition accuracy of small-sample cold wave events using cold wave samples generated by an adversarial network. 3. Construction of wind power combination forecasting model for cold-wave conditions: By combining cold wave recognition results with the Ns-Transformer model, a combined forecasting model for wind power is developed. This model effectively improves the accuracy of day-ahead wind power forecasting during cold conditions and offers a new solution for power forecasting under extreme weather events.
## 3 Results
### Year 2021 Optimization of the Irrigation Water Volume for Each Clone
#### 3.2.2 Leaf Mineral Nutrition  
The leaf mineral analysis in 2021 did not reveal great differences in leaf mineral nutrition (Table S3, supporting information). Therefore, clone 276 had a significantly higher leaf K concentration than 360, 372, 94, and 4, and a lower concentration of Mg (related to 360 and 188) and Mn (related to 373). In addition, WS enhanced leaf Zn concentration (276) compared to their control and significantly decreased the leaf Fe concentration (360 and 94) and Cu concentration (372, Table S3, supporting information).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{7}{c}{**3-Year Period (2018–2020)**} & \multicolumn{3}{c}{**Year 2021**} \\ \hline
**Clone** & **QL**echnological layer** & **QL**echnomic layer** & **QL**echnological layer** & **QL**echnological layer** & **QL**echnological layer** & **QL**echnological layer** \\ \hline
4 & 6.2 & 5.2 & 11.5 & 5.3 & 4.8 abc & 10.1 ab \\
94 & 6.1 & 5.2 & 11.3 & 3.6 & 4.1 ab & 7.8a \\
188 & 6.7 & 5.4 & 12.1 & 3.8 & 3.7a & 7.5a \\
276 & 6.7 & 5.5 & 12.2 & 5.3 & 6.2 bc & 11.5b \\
360 & 6.5 & 7.1 & 13.6 & 4.4 & 7.1c & 11.5b \\
372 & 6.8 & 5.6 & 12.4 & 4.6 & 4.7 abc & 9.3 ab \\
373 & 6.7 & 6.0 & 12.7 & 4.2 & 5.1 abc & 9.3 ab \\
**Treat.** & & & & & & \\ Control & 5.9 & 5.1 & 11.1 & 4.2 & 4.3 & 8.5 \\ Stress & 7.1 & 6.3 & 13.4 & 4.7 & 5.9 & 10.5 \\
**Clone \(\times\) Treat.** & & & & & & \\ Control & 5.9 & 5.0 & 10.9 & 6.20b & 4.4 & 10.6 bcde \\  & Stress & 6.5 & 5.5 & 12.0 & 4.40 ab & 5.2 & 9.6 abcde \\
94 & Control & 5.7 & 4.5 & 10.3 & 3.75 ab & 3.3 & 7.2 abc \\  & Stress & 6.4 & 5.9 & 12.3 & 3.50 ab & 4.8 & 8.3 bcde \\
188 & Control & 5.8 & 5.2 & 11.0 & 4.40 ab & 4.0 & 8.4 abcde \\  & Stress & 7.5 & 5.6 & 13.2 & 3.20a & 3.3 & 6.6a \\
276 & Control & 5.9 & 4.3 & 10.2 & 3.60 ab & 4.4 & 8.0 abcd \\  & Stress & 7.5 & 6.7 & 14.2 & 7.00b & 8.0 & 15.0e \\
360 & Control & 5.8 & 6.4 & 12.2 & 4.75 ab & 7.0 & 11.8 de \\  & Stress & 7.3 & 7.8 & 15.1 & 4.00 ab & 7.3 & 11.3 de \\
372 & Control & 6.3 & 4.7 & 11.0 & 3.60 ab & 3.0 & 6.6a \\  & Stress & 7.3 & 6.4 & 13.7 & 5.60b & 6.4 & 12.0e \\
373 & Control & 6.1 & 5.8 & 11.9 & 2.80a & 4.0 & 6.8 ab \\  & Stress & 7.3 & 6.1 & 13.4 & 5.50b & 6.3 & 11.8 de \\ \hline \multicolumn{7}{c}{**ANOVA**} \\ \multicolumn{7}{c}{Clone} & ns & ns & ns & ns & \(\star\) & \(\star\) \\ Treatment & *** & *** & *** & ns & \(\star\) & *** \\ \multicolumn{7}{c}{Infraction} & ns & ns & ns & \(\star\) & ns & *** \\ \hline \end{tabular}
\end{table}
Table 8: Average annual values of berry technological quality index (QI technological berry), berry phenolic quality index (QI phenolic berry), and overall berry quality index (QI overall berry) calculated for Monastrell grapes at harvest for each clone, irrigation treatment, and the interaction (clones \(\times\) irrigation treatment) during the 3-year period 2018–2020 and in year 2021.
# An Innovative Information Hiding Scheme Based on Block-Wise Pixel Reordering
## 3 Proposed Scheme
### Pixel-Block Number Embedding  
Before hiding data, recording the indices of pixel-blocks in a block-group is needed for the image recovery. The original index of a pixel-block \(N_{b}\) in a block-group is calculated based on the following:  
\[N_{b}=b_{i}\;mod\;m, \tag{8}\]  
where \(m\) is the number of pixel-blocks in a block-group and \(b_{i}\) is the current pixel-block. For example, the number of pixel-block \(b_{2}\) in block-group \(g_{0}\) is 2 and the number of pixel-block \(b_{257}\) in block-group \(g_{1}\) is 1. Figure 8 shows a diagram on how pixel-blocks are numbered in each block-group.  
Since there are \(n\) pixels in a pixel-block, the least significant bit (LSB) [5] of each pixel in the block is used to indicate its pixel-block number in a block-group. Figure 9 demonstrates how to embed a calculated block number \(N_{b}\) to LSBs. The LSB bit in each pixel inside of a pixel-block is replaced with the binary bit value of its calculated block number in its group. The example shows the decimal value of 1 which is the calculated block number for \(b_{1}\) in \(g_{0}\).  
Figure 8: Block numbers in block-groups.  
Figure 7: Constructing a codeword table for a block-group.  
After embedding the calculated block numbers as metadata into the LSBs, the encrypted image \(I_{e}\) is now an encrypted image embedded with image recovery information. It is then sent to the data hider to hide secret data.
# Application of Modern Research Methods for the Physicochemical Characterization of Ion Exchangers
## 3 Results and Discussion
### X-ray Photoelectron Spectroscopy (XPS) of Ion Exchangers
The XPS tests carried out after the Cu(II) ion sorption on the Dowex M 4195 ion exchanger showed a shift of the band from nitrogen atoms in the pyridine ring towards higher binding energies, i.e., from 399.3 eV to 399.6 eV [84]. The XPS studies showed also the presence of three forms of copper: Cu(0), Cu(I) and Cu(II). This type of analysis is helpful for the sorption mechanism prediction. The research suggests that the main role in the formation of coordination bonds is played by the nitrogen atoms present in the pyridine ring which can be presented in Figure 10.  
Therefore, in the case of Dowex M 4195 with the bis-(2-pyridylmethyl) amino) functional groups (BPA) proposed mechanism of Cu(II) ions sorption can be as follows:  
\[\text{R-N(BPA)}_{2}+\text{Cu}^{2+}\rightleftarrows\text{R-N(BPA)}_{2}\cdots \text{Cu}^{2+}\]  
for Amberlite IRA 743 with the N-methylo-D-glucamine functional groups:  
\[\text{R-NMDG}+\text{Cu}^{2+}\rightleftarrows\text{R-NMDG}\cdots\text{Cu}^{2+}\]  
and Purolite Arsen \(\text{X}^{\text{np}}\) with the sulfonic groups with hydrated iron oxide nanoparticles  
\[\text{R-SO}_{3}\text{H}/\text{FeO(OH)}+\text{H}_{2}\text{AsO}_{4} ^{-}\rightleftarrows\text{[R-SO}_{3}\text{H}/\text{FeO}]\text{H}_{2}\text{AsO }_{4}+\text{OH}^{-}\] \[\text{2R-SO}_{3}\text{H}/\text{FeO(OH)}+\text{HAsO}_{4}^{2-} \rightleftarrows\text{2[R-SO}_{3}\text{H}/\text{FeO}]_{2}\text{HAsO}_{4}+2 \text{OH}^{-}\]  
Figure 9: XPS spectrum obtained in a narrow range of binding energy made for Dowex M 4195 after the Cu(II) sorption. where R is the Dowex M 4195, Amberlite IRA 743 or Purolite Arsen X\({}^{\rm rp}\) skeleton (PS-DVB), BPA are the bis-(2-pyridylmethyl) amino) functional groups, NMDG are the N-methylo-D-glucamine functional groups and -SO\({}_{3}\)H/FeO(OH) are the sulfonic groups with hydrated iron oxide nanoparticles.
# Toward Low Time Fragmentation of Equipment: A Double-DQN Based TT&C Task Planning Approach
## 3 Mathematical Model
The low time fragmentation-oriented TT&C task-planning problem is modeled as the following multi-objective optimization problem:  
\[\text{P}:\ \ \left\{ \begin{array}{l}\min\sum\limits_{l=1}^{|\Delta \Gamma|}|\Delta t_{l}|\\ \max\sum\limits_{i=1}^{N}\omega_{i}\sum\limits_{a=1}^{|Y_{i}|}x_{i}^{\alpha} \end{array} \right.\]  
Subject to the following:  
\[\text{C1}:\ \sum\limits_{a=1}^{|Y_{i}|}x_{i}^{\alpha}\leq 1,\ \forall 1\leq i\leq N \tag{1}\]  
\[\text{C2}:\ x_{i}^{\alpha}+\sum\limits_{y_{i}^{\beta}\in O(y_{i}^{\alpha})}x_{ j}^{\beta}\leq 1,\ \forall y_{i}^{\alpha}\in Y \tag{2}\]  
where C1 is the unique constraint of tasks, which guarantees that each TT&C task can only be completed once at most. C2 is the conflict constraint of the TT&C scheme, i.e., a TT&C scheme and its conflicting schemes cannot be scheduling simultaneously.
## 5 Experiments and Analyses
### Performance on Different Packet Sizes
Figure 12: Performance of the third experiment given different packet rates. (**a**) Drop rates; (**b**) Throughputs; (**c**) Turnaround time.
# Device-Free Crowd Size Estimation Using Wireless Sensing on Subway Platforms
## 6 Conclusions
### Future Work  
Future work needs to be conducted to extend the validation scope to other bistable environments besides subway stations. Outdoor environments offer a different set of challenges than indoor environments. Examples of these are changing weather conditions, limitations on sensor placement, and vehicular traffic.  
Other regression models or techniques to enable the DFWS system to handle environmental changes should be investigated as well. Machine learning models incorporating multiple layers, such as multilayer perceptron networks, could be used to learn patterns in the data automatically. However, as model complexity increases, a larger amount of training data is required, compared to simpler polynomial models, in order to mitigate unwanted behavior, such as overfitting.  
Finding ways to optimize the number of sensor nodes and their placement in the environment could further improve the reliability and efficiency of the system.  
Finally, further investigation into the effects of limited training data on the estimation accuracy should be conducted.  
Conceptualization, R.J. and S.D.; Formal analysis, R.J.; Investigation, R.J.; Data curation, R.J.; Writing--original draft, R.J.; Writing--review & editing, R.B. and S.D.; Supervision, E.M., R.B. and S.D. All authors have read and agreed to the published version of the manuscript.  
This project was partially funded by Agentschap Innoveren en Ondernemen (VLAIO) under Grant No. HBC.2021.0865.  
Not applicable.  
Data Availability Statement: Data available on request.  
We would like to thank Flanders' public transport provider De Lijn for facilitating the use of their station for this research project.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.