esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Time-Domain Analysis
#### 4.1.3 Non-Deviation Errors of Key Physiological Points  
We selected the channel (containing several complete heartbeat cycles) with maximal energy of each subject to evaluate the errors of key physiological points after five de-noising methods. A total of 259 heartbeat cycles from 37 subjects were evaluated in this experiment. Ventricular contraction P1(\(t_{1}\), \(h_{1}\)), reflected wave P2(\(t_{2}\), \(h_{2}\)), and the augmentation index (AIx) were regarded as essential evaluation indicators for further calculation and analysis.  
The time absolute error (TAE) and amplitude relative error (ARE) calculated before and after de-noising are shown in Table 1. Results with small errors are shown in bold. It can be seen that the cross-channel DWRPCA performed well, along with WRPCA, in almost all TAEs and AREs. The ARE of \(h_{1}\) for the cross-channel DWRPCA and WRPCA was much smaller than that for wavelet transform, VMD, and RPCA. Moreover, the ARE for the cross-channel DWRPCA was 23.4% smaller than that for the WRPCA.  
Figure 10: Original and extracted signals from five de-noising algorithms in channel no. 25 (minimum energy). (**a**) Original signal; (**b**) wavelet transform; (**c**) VMD; (**d**) RPCA; (**e**) WRPCA; (**f**) cross-channel DWRPCA (proposed method).  
Since the key physiological data obeyed a normal distribution with homogeneous variance according to the k-s test, we paired every two indices and compared each matched pair with a two-tailed paired \(t\)-test. The \(t\)-test results in Table 2 show that AREs for the proposed algorithm were significantly smaller compared to the others, especially VMD and wavelet transform. At the same time, the AREs of \(h_{2}\) and Alx for RPCA and WRPCA showed no significant differences. In addition, the TAEs for the cross-channel DWRPCA, RPCA, and WRPCA were significantly smaller than those for the VMD and wavelet transform methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Method** & **Wavelet** & **VMD** & **RPCA** & **WRPCA** & **Proposed Method** \\ \hline \multirow{2}{*}{TAE (s)} & \(t_{1}\) & 4.267 \(\pm\) 1.964 & 3.000 \(\pm\) 1.864 & **0.784 \(\pm\) 0.854** & **0.784 \(\pm\) 0.854** \\  & \(t_{2}\) & 11.375 \(\pm\) 4.498 & 14.286 \(\pm\) 3.954 & 2.757 \(\pm\) 2.910 & **2.730 \(\pm\) 2.941** & **2.730 \(\pm\) 2.891** \\ \hline \multirow{2}{*}{ARE (\%)} & \(h_{1}\) & 8.330 \(\pm\) 6.140 & 1.770 \(\pm\) 0.880 & 1.090 \(\pm\) 0.500 & 0.470 \(\pm\) 0.410 & **0.366 \(\pm\) 0.286** \\  & \(h_{2}\) & 16.760 \(\pm\) 9.210 & 12.740 \(\pm\) 6.810 & 2.760 \(\pm\) 2.710 & **2.280 \(\pm\) 2.740** & 2.348 \(\pm\) 2.704 \\ \cline{1-1}  & Alx & 0.113 \(\pm\) 0.056 & 0.1340 \(\pm\) 0.073 & **0.020 \(\pm\) 0.025** & **0.022 \(\pm\) 0.027** & **0.022 \(\pm\) 0.026** \\ \hline \end{tabular}
\end{table}
Table 1: Errors of key physiological points of data after five de-noising methods.  
Figure 11: Reference noise and noises extracted by five de-noising algorithms. (**a**) Reference noise; (**b**) wavelet transform; (**c**) VMD; (**d**) RPCA; (**e**) WRPCA; (**f**) cross-channel DWRPCA (proposed method).
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 5 Simulation Results
Figure 7a,b shows that the proposed scheme compared MMSE, TRFI and SAMCDP offers about 4-7 dB gain at a BER of \(4\times 10^{-2}\) and can achieve about 2-6 dB gain at a PER of about \(3\times 10^{-1}\), respectively. In case of switching STA and MMSE, our scheme provides about 6 dB at a BER of \(5\times 10^{-3}\) and at a PER of \(8\times 10^{-2}\) compared to TRFI and SAMCDP. Figure 9 shows that the proposed scheme has the same trend as QPSK in the BER and PER performances for 16 QAM and improves performance over the entire SNRs. These simulation results verify the fact that the proposed scheme is not affected by the channel environment.
## 4 SDP Solution Method for the FFP-IM
### The SDP Solution Method
#### 4.2.2 SDP Model  
The state-dependent auto-regressive with exogenous variables model [32], applicable to dynamic systems containing external variables, can be formulated as follows:  
\[Y_{t}=\mathbf{H}_{t}^{\rm T}\mathbf{p}_{t}+e_{t}\,e_{t}\;\sim\;N(0,\,\sigma^{2}) \tag{16}\]  
where \(e_{t}\) is the Gaussian white noise input, and  
\[\begin{array}{l}\mathbf{H}_{t}^{\rm T}=\left(-Y_{t-1},-Y_{t-2},\cdots,-Y_{t-m_{ 1}},\mathbf{Z}_{t-\omega}^{\rm T},\mathbf{Z}_{t-\omega-1}^{\rm T},\cdots,\mathbf{Z}_{t- \omega-m_{2}}^{\rm T}\right)\\ \mathbf{Z}_{t}^{\rm T}=\left(Z_{1},Z_{2},\cdots,Z_{n}\right)\\ \mathbf{p}_{t}=\left(a_{1}(\mathbf{H}_{t}),a_{2}(\mathbf{H}_{t}),\cdots,a_{m_{1}}(\mathbf{H}_{ t}),b_{0}(\mathbf{H}_{t}),b_{1}(\mathbf{H}_{t}),\cdots,b_{m_{2}}(\mathbf{H}_{t})\right) \end{array} \tag{17}\]  
where \(t\) is defined as the time series representation, \(\mathbf{Z}_{t}\) and \(Y_{t}\) represent the inputs and outputs of \(t\), respectively, and \(\omega\) denotes the pure time delay in the sampling interval. Additionally, \(a_{i}(\mathbf{H}_{t})(i=1,2,\cdots,m_{1})\) and \(b_{i}(\mathbf{H}_{t})(i=0,1,\cdots,m_{2})\) are state-dependent parameters, which are commonly assumed to be functions of the state vector \(\mathbf{H}_{t}\).
# Omni Wheel Arrangement Evaluation Method Using Velocity Moments
## 3 Omni Wheel Arrangement Evaluation Method Using Velocity Moments  
It is common practice that omni wheel robots have symmetric wheel arrangements [5; 6; 12; 13; 14; 15]. However, it is desirable to arrange the wheels as flexibly as possible, as there may be cases where it is preferable to have a less symmetrical wheel arrangement for specific applications.  
Depending on the wheel arrangement, the robot may spontaneously turn without following previously proposed inverse kinematic models.n One reason is that the inverse kinematic models proposed so far are derived from a geometric approach and do not consider the dynamic loads applied to the robot. If the torque exerted on the robot's center of mass by the propulsive force generated by each wheel is not balanced, the robot turns even under straight-line control. We define \"easy-to-operate\" as a state where the vehicle can be correctly controlled in a straight line without causing this unexpected behavior.  
Since the Omni wheel is a complex structure with multiple freewheel rollers, it is difficult to determine the propulsive force generated by the Omni wheel in a precise manner. Therefore, we focused on the wheel's ground velocity instead of the propulsive force. All wheels must reach the target velocities simultaneously to control the robot correctly. Therefore, in accelerating the wheels at the same time, the accelerations of the wheels are proportional to the target velocities. Moreover, the wheel's propulsive force is proportional to the wheel's acceleration. Thus, we assume that the propulsive forces generated by the Omni wheels are proportional to the wheel's ground velocity. Using this assumption, we can compute the moments that the velocity exerts on the robot's center of mass, _velocity moments_. If the moments are balanced, the robot would follow the straight-line motion correctly, otherwise, it would turn as it moves.  
Note that under our assumption, if the wheel's velocity vector is \(V\). Then, the distance from the velocity vector to the origin is \(r\), and the moment due to the wheel's velocity is proportional to \(Vr\), which is different from the angular velocity \(\omega=\frac{V}{r}\).
# Portrait Semantic Segmentation Method Based on Dual Modal Information Complementarity
## 3 Methods
### SK-ASSP Module  
We propose the SK-ASSP module in this paper, which consists of adaptive ASSP branch and edge detail optimization branch. Its structure is shown in Figure 3.  
The first half of the adaptive ASSP branch is consistent with the ASSP module, consisting mainly of three parallel computations of atrous convolution with different expansion rates (d = 1, d = 2, d = 3). At the same time, the feature maps obtained using convolution with different expansion rates are added hierarchically before concatenation, thus avoiding the grid problem of extended convolution [21]. The latter part of the adaptive ASSP branch is adaptive weight adjustment based on the ASSP module. Firstly, the feature maps after concatenation are pooled globally, and then aggregated and compressed by fc (fully connected) layer and M vectors (M is the number of cavities convolution in the module, M = 3 in the figure), which are obtained again. Afterward, the weights of each of these three vectors are obtained by softmax operation. Lastly the weights obtained after softmax operation are multiplied with the output of three extended convolution, and the feature map with weight is obtained.  
The edge detail optimization branch will first undergo a 3 \(\times\) 3 convolution to obtain the initial feature map, and then undergo gated convolution to ensure that the edge branch only processes boundary-related information [22]. After that, the new feature map will enter the boundary branch and direction branch. In the boundary branch, after 1 \(\times\) 1 convolution and BN (Batch Normalization) layer, distance mapping is carried out to calculate the minimum distance from other object semantic types. After all distance maps are aggregated, the model can obtain the final distance map. In the final distance map, all pixels whose distance value is less than the threshold y (the threshold value here is 5, focusing on thinning thin boundaries [23]) are set as boundaries to obtain the corresponding boundary map. On the other hand, the direction branch first goes through a 1 \(\times\) 1 convolution and BN layer, and then executes the Sobel filter to calculate the corresponding direction mapping (value range 0-360). Each direction points to the inner pixel farthest from the object's boundary. The whole direction range is divided into m categories (where m is 8 [23]), and the direction of each pixel is assigned to the corresponding category to obtain the corresponding direction mapping. Finally, the maps of the two branches are multiplied to obtain the edge optimization feature map.  
Figure 3: The structure of the feature SK-ASSP module.  
Finally, the edge optimization feature map and adaptive ASSP feature map are added to get the final feature map. Adaptive ASSP branches can obtain multi-scale dense spatial information while retaining more fine-grained features, which helps the model to process the output adaptively. The edge optimization branch can effectively solve the problem of blurred edge details in grayscale images [24].
# Development of a Novel Open Control System Implementation Method under Industrial IoT
## 4 Case Study  
In order to illustrate the proposed open control system implementation method overall, this section presents an open CNC system demonstration. Generally, traditional CNCs only support linear interpolation and circular interpolation, which means they can directly process the tool trajectory made up of line and arc blocks, as Figure (a)a shows. For a complex tool trajectory that contains a B-spline block, as Figure (b)b shows, traditional CNCs fail to process it directly, and what is more, they often do not allow the extension of third-party control tasks to process the complex tool trajectory. Instead, such a complex tool trajectory needs to be firstly discretized into a series of line and arc blocks by computer-aided manufacturing (CAM) software. In this context, this section uses the proposed implementation method to design an open CNC system whose functionality can be modified dynamically in response to different tool trajectories.
## 3 Results and Discussion
Figure 5: Pearson correlation matrix of the volatile compounds detected in Pinot Noir, Fetosaca Neagra, Burgund Mare, Synah, and Novac wines produced using commercial and native yeast strains ESFC, VS, SCR297, and SCR462. The major sources of variance captured by the principal component analysis are the yeast strains used to produce the wines. For the Pinotoir and Burgund Mare wines, the principal component loadings suggest that SCR297 contributes the most to the first principal component (F1). In the case of the second principal component (F2), the largest contribution comes from ESFC for Pinotoir, Feteasca Neagra, and Burgund Mare wines. Figure 6: 2D principal component analysis plot of the volatile compounds detected in **(A)** Pinotoir, (**B**) Feteasca Neagra, (**C**) Burgund Mare, (**D**) Synah, (**E**) Novac, and all wines produced using commercial and native yeast strains ESFC, VS, SCR297, and SCR462. For Syrah and Novac, the variables with the greatest influence were ESFC and SCR297 for F1 and VS for F2. According to the 2D principal component analysis, the two principal components, F1 and F2, together explain between 77.76% and 84.03% of the total variance in the data. In the Pinot noir wine plot, the first principal component (F1) is strongly correlated (with a correlation above 0.5) with twenty of the volatile compounds (V2, A1, A2, A5, A6, A7, A8, A11, A12, A13, E1, E2, E3, E4, E7, E8, F1, F2, F6, and F7). A strong positive correlation was noted between F1 and the volatile compounds V1, V2, A1, A2, A4, A6, A12, A14, E1, E2, E4, E7, E8, F1, and F6 in Feteasca Neagra wines. Similarly, for the Burgund Mare, Syrah, and Novac wines, volatile compounds from all groups had a major influence on F1. A strong correlation with V2 exists across all wines, except for the Syrah wines. Notably, Novac wines do not show a strong positive correlation with alcohols A1 and A2. Most alcohols were positively correlated with F1 in Pinot noir wines, while the fewest correlations were observed in Feteasca Neagra wines (Figure 6A-E). Wines with similar aromatic profiles after fermentation with commercial yeasts ESFC, VS, SCR297, and SCR462 were grouped into four clearly differentiated clusters, with Syrah and Novac wines being distinct, regardless of the yeast strain used. The dendrogram analysis (Figure 7) highlights two bifolious clades for Pinot noir, Burgund Mare, Syrah, and Novac: one composed of SCR297 and SCR462, which are more similar to each other than to any other samples, and another composed of ESFC and VS. For Feteasca Neagra, the dendrogram analysis highlights two clades, with one being trifolious, suggesting that SCR297, SCR462, and ESFC are most similar to each other, while the other is simplicifolious--VS--indicating a different fermentation pattern than the other yeast strains. Figure 7: Dendrogram of hierarchical cluster analysis of wine samples of (**A**) Pinot noir, (**B**) Feteasca Neagra, (**C**) Burgund Mare, (**D**) Syrah, and (**E**) Novac produced using commercial and native yeast strains ESFC, VS, SCR297, and SCR462.
# Morphological Changes in H1299 Human Lung Cancer Cells Following W-Band Millimeter-Wave Irradiation
## 3 Results
### MMW Irradiation Changes the Morphology and Size of H1299 Cancer Cells
These effects of the 4-min exposure regime were observed to be more profound on Day 7 and Day 14, with the number of small shrunken cells (Figure 3c, num. 1) rising by 2-5 times (Figure 3e). These results demonstrate that following the 4-min exposure regime, the irradiated cell populations develop an additional terminal group of notably shrunken cells (500-1500 \(\upmu\)m\({}^{2}\) in size) undergoing apoptosis [24]. The other group of the cell population comprising the larger cells (2000-3500 \(\upmu\)m\({}^{2}\) in size) showed a significant increase of 1.6-1.9 times in number (Figure 3e) observed on Day 14 following exposure, as compared to the sham (Figure 3b). This group of larger cells (2000-3500 \(\upmu\)m\({}^{2}\) in size) were also observed under the 2-min exposure regime (Figure 3d), indicating senescent [25; 26; 27] and/or mitotically arrested giant enlarged cells [28]. A comparative analysis of the size change in the cell population arising due to the irradiation regimes (2 and 4 min, respectively) with reference to the control is presented in the Supplementary Material (SupplementaryMaterial Table S1). These results indicate that the morphological changes of H1299 cell populations are affected most by irradiation under the 4-min exposure regime with a power density of 0.2 mW/cm\({}^{2}\) and the effects are observed to be stable and accumulated over the period of observation. The experiments demonstrate that the duration of MMW exposure directly determines the extent to which cell morphology is affected. A short 2-min exposure does not produce any immediate effects but induces the development of enlarged cells, indicative of senescence [25; 26; 27]. In contrast, a 4-min exposure regime providing a higher dosage of MMW energy results in both short- and long-term effects. Immediately following exposure in this regime, cells are induced to a far greater degree of shrinkage as compared to that under the 2-min regime, indicating acute apoptosis [24]. Further, the 4-min regime also induces the development of enlarged polyploid cells indicative of senescence [25; 26; 27] over a long-term period of 14 days following irradiation.
## 4 Experimental Results and Analysis
### Experimental Design  
The proposed SSO-DE in this research has seven parameters (factors) that need to be set, which are \(c_{g}\), \(c_{p}\), and \(c_{w}\) in SSO as well as the mutation factor (_CR_), evolution parameter (_F_), and \(Y\) percentage of the good solutions entered the local search for every Z iteration. If all the parameters are explored for the experimental design, such a huge number of experiments is really not feasible assuming that each factor has three levels in which one must perform \(3^{7}=2187\) experiments. Therefore, the three parameters of SSO including \(c_{g}\), \(c_{p}\), and \(c_{w}\) were set as one factor and the other parameters of DE were set as another factor so that the statistical analysis two-way ANOVA of two factors and three levels is performed.  
The three levels of SSO parameter setting as shown in following Table 9 are all parameters that performed relatively well in the experimental testing process of this research.  
In order to enable the DE to be updated quickly and effectively in the early stage of the algorithm, we increased the diversity of the solution space in the middle and late stages, and increased the probability of jumping out of the space of the current solution; the parameter settings of DE in this research are as follows, Table 10.  
This research used two-way ANOVA to analyze the two-way variance of parameter settings for the algorithm. However, three assumptions need to be satisfied, namely: the normality assumption, the independence assumption, and the homogeneity assumption, to use two-way ANOVA. It can ensure the independence of the data-collection process when designing the experiment. Thus, what this research needs to test is whether to meet the assumption of homogeneity and normality, and then use ANOVA analysis after confirming those are satisfied to find the best parameter setting for the experiment, which are shown in the following Sections 4.1.1-4.1.3 according to the three seasons including spring (autumn), summer, and winter.
## 3 Results and Discussion
### Simulated Rainfall Test  
By simulating three rainfall levels, i.e., large, medium, and light, the masses of the specimens lost by rainfall under these conditions were collected separately. The rainfall losses for each test group are shown in Figures 9 and 10. The loss of specimens treated with HPMC for MICP (Figure 9) was less than that of specimens treated without HPMC for MICP (Figure 10) under the three rainfall levels. In particular, under heavy rainfall levels, the rainfall loss with HPMC and three MICP treatments was about 30% less than that without HPMC under the same conditions. By adding HPMC as a component, the viscosity of the reactants was increased so that more CaCO\({}_{3}\) was produced in the surface layer of the specimens, and because of its excellent solubility in cold water, the combined effect was to improve the water resistance of the surface layer. Moreover, it can be seen from the Figure that the rain erosion resistance of the surface layer of the specimen could be effectively improved after several treatments, and more calcium carbonate was generated in the surface layer after several treatments (Figure 5), making the surface layer of the specimen more compact and steady in the structure and enhancing its rain erosion resistance.  
Figure 8: Ammonia retention rate.
## 2 Material and Methods
### Performance Measures  
Reasonable efforts were carried out to tune the proposed system by standard programming tools using hardware (Laptop Dell G7, Intel(r) Core(tm) i7-8750H CPU, 2.20 GHz), 16 GB RAM, and GPU (NVIDIA GTX-1060 with 6 GB: onboard memory and 1280 CUDA cores).  
Quantitative performance measures to evaluate the model include confusion matrix, true positive (TP), false negative (FN), true negative (TN), false positive (FP), positive predicted value (PPV) or precision, true positive rate (TPR) also known as recall or sensitivity, F-measure, and accuracy. F-measure is convincing in case there is a class imbalance.
# A Reconfigurable Mesh-Ring Topology for Bluetooth Sensor Networks
## 3 Configurable Algorithm
### Peak-Search Method  
In the scatternet formation block, the coordinator collects the complete topology and computes the formation topology including the ring subnet as well as the number of mesh links for the three configuration cases. In the network capacity block, the average Mesh-Ring throughput \(T\) is calculated  
Figure 5: Block diagram of the peak-search method.  
by Equation (3), where \(R_{i}\) represents the total ring throughput of each individual piconet, and \(p\) is the number of piconets:  
\[T=\sum_{i=1}^{p}R_{i} \tag{3}\]  
In the optimum decision block, throughput performance \(T_{j}\) in Equation (3) is defined as the average Mesh-Ring throughput for the \(j\)th connected bridge in the network capacity block. Initially, \(j\) is set as one, and each \(T_{j}\) is calculated. Then \(j=j+1\), as the topology increases for the scatternet formation block, while \(T_{j+1}\) is computed in the optimum decision block. The throughput performance of bridge \(j+1\) is computed until the maximum \(T_{j}\) is determined and the decision is made by Equation (4), where \(k\) is the total number of available bridges in the ring subnet.  
\[\underset{j=1..k}{Max}T_{j} \tag{4}\]  
As a result, the bridge with the maximum throughput is regarded as the optimal connected bridge of mesh link \(I\), and the algorithm is terminated until all connected bridges are determined for the available mesh links here.