esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Load Analysis of Compressor Blade  
Figure 13a,b show the static pressure distribution of the splitter blade and the main blade at different blade heights of the three models, respectively. The pressure difference between the blade pressure and the suction surface can be used to determine the relationship between the load to a certain extent. From Figure 13a it can be seen that at the position of 10% blade height, the static pressure of the pressure surface of the three models changes more evenly. Compared with the other two models in Figure 13b, the static pressure of the suction surface of EPC model increases first and then decreases from the leading edge to the trailing edge, and the load of the main blade appears the maximum value at the point of 0.8 chord, while the SPC model has a stronger trend of load reduction than the other two models. The load difference of the three types of pipelines is the largest at the 0.8 chord length for the splitter blade, as shown in Figure 13a, where the load reduction of the TPC model is the most obvious. The load distribution on the suction surface and pressure surface of the three models is significantly different at the trailing edge, which is caused by the leakage vortex loss. Combined with the entropy diagram of 10% blade height in Figure 12, it is known that the EPC model has a high entropy on the pressure surface, which can also be seen in the pressure distribution in 10% blade height.  
At 50% blade height, the load on the main blade and the splitter blade increases compared with that on the 10% blade height, which has the similar variation rules in the three models. At 90% blade height, the load extremum of the splitter blade migrates to 0.35 chord length, and that of the main blade migrates to 0.6 chord length. On the whole, the difference of inner diameter of interstage pipe has greater influence on blade root rather than blade tip.  
Figure 12: Entropy distribution at impeller outlet of three models.
## 3 Results  
### Simulating GPP with GCC  
The seasonal GPP dynamics estimated from GCC are shown in Figures 8 and A1. The total growing season GPP was 790.48 g C-m\({}^{-2}\)-day\({}^{-1}\) in 2017 and 809.24 g C-m\({}^{-2}\)-day\({}^{-1}\) in 2018, respectively. Generally, GPP_Cl\({}_{\rm norm}\) showed the same seasonal dynamics with the GPP from eddy covariance in 2017 and 2018. The R\({}^{2}\), MAD, and RMSE of GPP_Cl\({}_{\rm norm}\) and the GPP from eddy covariance in the growing seasons of 2017 were 0.68, 2.11 g C-m\({}^{-2}\)-day\({}^{-1}\), and 2.47 g C-m\({}^{-2}\)-day\({}^{-1}\), respectively. In the year 2018, R\({}^{2}\), MAD, and RMSE between GPP_Cl\({}_{\rm norm}\) and GPP from eddy covariance were 0.78, 1.63 g C-m\({}^{-2}\)-day\({}^{-1}\), and 1.94 g C-m\({}^{-2}\)-day\({}^{-1}\), respectively. There was a systematic deviation between GPP from GCC and GPP from EC in spring and autumn, and these phenomena appeared both in 2017 and 2018. In 2017, the GPP_Cl\({}_{\rm norm}\) had the highest accuracy in autumn, followed by summer, and spring was the worst in the LUE model (Table 3).  
In 2018, the GPP_Cl\({}_{\rm norm}\) had the highest accuracy in summer, followed by autumn, and spring was the worst in the LUE model (Table 3). More information about the changes of GPP from eddy covariance and GPP_Cl\({}_{\rm norm}\) in 2017 and 2018 can be found in the appendix (Appendix A, Figure A1).  
Figure 8: The daily dynamics of the GPP_Cl\({}_{\rm norm}\) and the GPP from eddy covariance during the growing seasons (1 May to 1 October), (**a**) 2017, (**b**) 2018.  
Figure 7: Flow diagram of data processing and analyses.
## 4 Conclusions
**Nomenclature**  
PV photovoltaic  
DL deep learning  
WD wavelet decomposition  
CNN convolutional neural network  
LSTM long short-term memory  
IEA international energy agency  
NWP numerical weather prediction  
TSI total sky imagery  
MOS model output statistics  
MA moving average  
AR autoregressive  
ARMA autoregressive moving average  
ARIMA autoregressive integrated moving average  
ANN artificial neural network  
SVM support vector machine  
SOM self-organizing map  
LVQ learning vector quantization  
SVR support vector regression  
A-P Angrstrom-Prescott  
RNN recurrent neural network  
MFE manual feature extraction  
DWT discrete wavelet transformation  
LPF low-pass filters  
HPF high-pass filters  
ReLu rectified linear units  
RMSE root mean squared error  
MAE mean absolute error  
R correlation coefficient  
\(\psi(t)\) mother wavelet function  
\(\varphi(t)\) scaling function  
\(\psi_{j,k}(t)\) a sequence of wavelet at time index \(t\)  
\(\varphi_{j,k}(t)\) binary scale-functions at time index \(t\)  
\(os(t)\) the original sequence at time index \(t\)  
\(c_{j,k}\) the approximation coefficient at scale \(j\) and location \(k\)  
\(d_{j,k}\) the detailed coefficient at scale \(j\) and location \(k\)  
\(S\) the raw sequential input  
\(F\)S the filter sequence  
\(L\) the length of raw sequential input  
\(K\) the number of total filters in the convolutional Layer  
\(m\) window size  
\(\oplus\) the concatenation operator
## 3 Methodology
### Hypergraph-Based Spatiotemporal Forecasting
By integrating dynamic hypergraph construction with the pretrained spatial-temporal representations, the forecasting module effectively bridges the gap between local meteorological observations and large-scale atmospheric dynamics, providing a robust foundation for short-term weather prediction. In summary, the complete training process of our proposed method is outlined in Algorithm 1. ```
1:Input: Historical meteorological data \(\mathbf{X}\in\mathbb{R}^{T\times N\times F}\), number of nearest neighbors \(k\), mask rate \(r\)
2:Output: Forecasted results \(\mathbf{\hat{Y}}\)
3:// Stage 1: Spatial-Temporal Masked Pretraining
4:Apply spatial masking (S-Mask) to occlude \(r\cdot N\) stations across all time steps \(\rightarrow\mathbf{X}_{S}\)
5:Apply temporal masking (T-Mask) to occlude \(r\cdot T\) time steps for all stations \(\rightarrow\mathbf{X}_{T}\)
6:Encode \(\mathbf{X}_{S}\) with spatial autoencoder \(\rightarrow\mathbf{H}_{S}\)
7:Encode \(\mathbf{X}_{T}\) with temporal autoencoder \(\rightarrow\mathbf{H}_{T}\)
8:Compute reconstruction loss \(\mathcal{L}_{\text{pretrain}}=\lambda_{s}\cdot\text{MAE}(\mathbf{\hat{X}}_{S}, \mathbf{X}_{S})+\lambda_{t}\cdot\text{MAE}(\mathbf{\hat{X}}_{T},\mathbf{X}_{T})\)
9:// Stage 2: Hypergraph-Based Forecasting
10:Compute station-level feature vectors by time-averaging: \(\mathbf{f}_{i}=\frac{1}{T}\sum_{t=1}^{T}\mathbf{X}_{t,i,:}\)
11:Normalize \(\mathbf{f}_{i}\) across stations and compute cosine similarity matrix \(\mathbf{\hat{S}}\)
12:Construct hypergraph \(G=(V,E)\) by connecting each node to its top-\(k\) similar neighbors
13:Build incidence matrix \(\mathbf{H}_{G}\) and compute degree matrices \(\mathbf{D}_{v}\), \(\mathbf{D}_{e}\)
14:for each training epoch do
15:Apply hypergraph convolution using Equation (4) to obtain \(\mathbf{H}^{(l+1)}\)
16:Apply dilated causal convolution over \(\mathbf{H}^{(l+1)}\) to get temporal context \(\mathbf{Z}_{t}\)
17:Fuse embeddings: \(\mathbf{\hat{Y}}=\text{FC}([\mathbf{Z}_{i}\|\mathbf{H}^{(l+1)}\|\text{MLP}( \mathbf{H}_{S})\|\text{MLP}(\mathbf{H}_{T})])\)
18:Compute forecasting loss: \(\mathcal{L}_{\text{forecast}}=\text{MAE}(\mathbf{\hat{Y}},\mathbf{Y})\)
19:endfor
20:Return: Final prediction \(\mathbf{\hat{Y}}\)
```  
**Algorithm 1** HyMePre: Hypergraph-enhanced Meteorological Pretraining framework
## 5 Results and Discussion
This large capacitive contribution, together with relatively long wire bonds (\(\approx 1\) mm), determined a low pass filtering that inevitably affected the overall bandwidth. While being partially compensated by the inductive peaking in the output stage, the input side of the device was completely exposed to this parasitic effect. To enable the scalability of this design approach to higher bandwidths, this downside could be mitigated by embedding compensation or T-coil networks to the ESD diodes, as already done in [48]. Such a technique was not implemented here to save silicon area and because the target was more focused on pushing further the driver operation in terms of radiation tolerance than overcoming state-of-the-art data rates in HEP environments. Moreover, a closer interaction with the packaging facility would help in avoiding assembly flaws as happened for the MZM-based system. A co-design between the handle PCB, the EIC, and the PIC would indeed  
Figure 11: Eye diagrams on the PD output port in the RM case study (scale 10 mV/div, condition \(P_{\text{pd}}=-6\) dBm). Figure 12: BER performances as a function of the received power \(P_{\text{pd}}\) for the RM-based transmission system. allow estimating the interconnection parasitics earlier in the design process and harness them to make a more efficient and robust system. A re-design of the same driver is already under development to carry out more than 10 Gb/s operation with the same architecture.
## 4 WBANs: The Key M2M Area Network Technology for mHealth Applications
### WBAN Channel Modeling
#### 4.2.1 On-Body Radio Propagation Model
On the other hand, the different delay bins of the same cluster are highly correlated. This can be explained by considering that when the path length is very short, the multipath components have overlapping path trajectories. In a real situation, however, the reflections from the scatterers in the surrounding environment [35] should be also taken into account. In conclusion, the recommended body area channel model for wearable WBANs should consider a fixed two cluster mutlipath model containing: (i) a dominant direct component; (ii) a set of early reflected waves adding (possibly distructively) to the dominant wave; and (iii) a set of excessively delayed waves, adding incoherently to the dominant wave. The dominant wave amplitude is determined by an exponential path loss and a log normal fading coefficient that is strongly correlated with the fading coefficients of the early delayed waves.
## 6 Results and Discussion
### Evaluating Aggregate Throughput  
We begin by investigating the case with multiple links and several RF chains, seven links EDMG-SC 802.11_ay_ networks with spatial streaming (up to eight). In contrast to  
Figure 8: Comparison of the temporal evolution of TRA layer throughputs in Mbps while transmitting bulk files. The proposed cross\(-\)layer MS\(-\)UCB’s performance is compared with Multipath TCP\(-\)based BUSH and HEKATON over seven links EDMG\(-\)SC 802.11_ay_ networks with spatial streaming (up to eight). MPTCP LIA’s throughput over 802.11 ad is a baseline.  
Figure 9: Comparing the energy efficiency of the proposed multipath MS-UCB with MPTCP-based BUSH and HEKATON over seven links EDMG-SC 802.11_ay_ networks with spatial streaming (up to eight). MPTCP LIA efficiency over 802.11 ad is taken as a baseline.  
HEKATON, a flexible path, beam, and device selection algorithm is being developed in the proposed cross-layer multipath MS-UCB scheme. It indicates that the proposed multipath MS-UCB system, unlike HEKATON (one-to-one), is a one-to-many mapping solution. In our system, several paths and beams can even serve the same device if the throughput is higher than serving multiple devices. In our experiments, we find that the proposed cross-layer multipath system is better than HEKATON and BUSH. In extremely low SNR instances, BUSH can use two RFs and rule out a device for the best overall throughput of another (one-to-two mapping) in each transmission, but HEKATON always serves several users in all cases.  
As shown in Figure 10, we investigate the scalability with an increasing number of devices. We increase the total number of devices from 5 to 20 and measure the overall network throughput. With increasing device counts, Multipath always outperforms BUSH, and BUSH consistently beats HEKATON and vanilla 802.11 ac.
## 4 Results and Discussion
### Dynamic Mechanical Properties of Polyurea
#### 4.1.3 Effects of Thickness  
Figure 9 presents the force-displacement curves, dynamic stiffness, and damping coefficients of three polyurea samples with dimensions of \(\phi\)80 mm \(\times\) 40 mm, \(\phi\)80 mm \(\times\) 20 mm, and \(\phi\)80 mm \(\times\) 10 mm under dynamic excitation, with a preload of 300 N. The excitation frequency is 0.5 Hz, and the amplitude is 0.15 mm.  
As shown in Figure 9a, the force-displacement curve of the \(\phi\)80 mm \(\times\) 40 mm polyurea sample is nearly horizontal, indicating relatively low dynamic stiffness. Accordingly, as shown in Figure 9b, the dynamic stiffness of this sample is 73.73 N/mm. The dynamic stiffness of polyurea decreases monotonically with thickness, decreasing from 1084.40 N/mm to 73.73 N/mm as the thickness increases from 10 mm to 40 mm. Simultaneously, the area enclosed by the force-displacement curve diminishes, indicating degraded damping performance. As shown in Figure 9c, the polyurea sample exhibits enhanced energy dissipation capacity with decreasing thickness. Specifically, the damping coefficient declines from 1144.10 N\(\cdot\)s/m to 284.00 N\(\cdot\)s/m as thickness increases from 10 mm to 40 mm. When the thickness increases from 10 mm to 40 mm, which is four times the original value, the dynamic stiffness decreases to 6.80% of the original, and the damping coefficient decreases to 24.82% of the original. Therefore, based on the analysis of the influence of area, the damping coefficient can be increased by enlarging the area, and at the same time, the thickness can be increased to achieve the goal of reducing the additional dynamic stiffness.  
Figure 8: Effects of area on dynamic stiffness and damping coefficient: (**a**) force–displacement curves; (**b**) dynamic stiffness; (**c**) damping coefficients.  
Figure 9: Effects of thickness on dynamic stiffness and damping coefficient: (**a**) force–displacement curves; (**b**) dynamic stiffness; (**c**) damping coefficients.
# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
## 2 Materials and Methods
### Respiration Rate Extraction
#### 2.5.2 Breathing Motion Extraction  
For each video frame, the pixels in the chest ROI were extracted and converted to grayscale. The M1D-OF method extracts the chest motion between pairs of frames. The distance between these frames was chosen to be 12 frames for the RGB camera and 5 frames for the NIR camera. To extract the respiration signal, it was postprocessed as described by [PERSON] et al. [10], which includes a sliding window cumulative sum to integrate the signal while avoiding baseline wander of the respiration signal.
# An Internet of Things Access Control Scheme Based on Permissioned Blockchain and Edge Computing
## 3 Scheme Framework
### Bilinear Mapping  
Let \(G_{1}\),\(G_{2}\),\(G_{T}\) be three multiplicative cyclic groups, whose order is a prime \(q\), \(g_{1}\), and \(g_{2}\),\(g_{3}\) be generators of three cyclic groups. Let \(e:G_{1}\times G_{2}\to G_{T}\) be a map with the following properties:  
1. [label=(0)]
2. Bilinearity: \(e(g_{1}^{a},g_{2}^{b})=e(g_{1},g_{2})^{ab}\) for all \(g_{1}\in G_{1}\), \(g_{2}\in G_{2}\), \(a,b\in Z_{q}\).
3. Non-degeneracy: There exists \(g_{1}\in G_{1}\), \(g_{2}\in G_{2}\), such that \(e(g_{1},g_{2})\
eq 1_{G_{T}}\). That is, for any element in \(G_{1}\),\(G_{2}\), the identity element in \(G_{T}\) is not obtained by this mapping.
4. Computability: There is an efficient algorithm to compute \(e(g_{1},g_{2})\) for all \(g_{1}\in G_{1}\), \(g_{2}\in G_{2}\).
## 2 Data Transfer in Underwater Channels
### Transducer Loss  
The sonar equations describe the relationship between sound pressures and voltages at a transducer for underwater acoustic communication [16]. When an input voltage \(V_{in}\) is applied to a transmitting transducer, the sound pressure level \(SPL_{T}\) is generated at the transducer, which is given by:  
\[SPL_{T}=TRV+20\,\log V_{in} \tag{3}\]  
where \(TRV\) is the transmitting response to the input voltage which is a characteristic value for each transducer. Due to the transmission loss through an underwater channel, the sound pressure level at a receiving transducer \(SPL_{R}\) becomes:  
\[SPL_{R}=SPL_{T}-TL \tag{4}\]  
At the receiving transducer, \(SPL_{R}\) is converted into the output voltage \(V_{out}\) which is given by:  
\[20\,\log V_{out}=SPL_{R}+RR \tag{5}\]  
where \(RR\) is the receiving response of the transducer. Equations (3), (4) and (5) can be combined and rewritten as follows:  
\[20\,\log\,\frac{V_{out}}{V_{in}}=TRV+RR-TL \tag{6}\]  
In Equation (6), - \(TRV\) + \(RR\) can be interpreted as the transducer loss specific to each transducer.
## 4 Security and Performance Analysis
### Mutual Authentication  
The base station uses \(MAC_{2}\) (\(h\) (\(ID_{ci,}\)\(K_{ci}\))? \(=MAC_{2}\) ) to authenticate the cluster node's identity, \(MAC_{3}\) (\(h\) (\(ID_{list}\)\(K_{ci}\))? \(=MAC_{3}\)) to authenticate the integrity of \(ID_{list}\) and \(MAC_{1}\) (\(h\) (\(ID_{si}\), \(K_{si}\))? \(=MAC_{1}\)) to authenticate the sensor node's identity. The reason is that the sensor node and cluster node are peer nodes. The cluster node does not store information related to the sensor node. The sensor node should therefore be authenticated by the base station. Otherwise, the cluster node can use \(N_{2}\) to authenticate the base station. The sensor node can use \(N_{I}\) to authenticate the cluster node. Our scheme performs mutual authentication. This makes it easier to detect an attacker.  
\begin{table}
\begin{tabular}{|l|c|c|c|c|} \hline
**Attacks** & **TTDD [15, 16]** & **CODE [17]** & **DARQ [20, 21]** & **Our proposal** \\ \hline Against malicious guessing attacks & NA & NA & NA & Yes \\ \hline Against replay attacks & NA & NA & NA & Yes \\ \hline Against falsification attacks & NA & NA & NA & Yes \\ \hline Against man-in-the-middle-attacks and guarantee of data privacy & NA & NA & NA & Yes \\ \hline Against node capture attacks & NA & NA & NA & Yes \\ \hline Grid-based protocol & Yes & Yes & Yes & Yes \\ \hline Event-driven data dissemination & Yes & Yes & Yes & Yes \\ \hline Limit interest region to prevent flooding storm & & & & \\ \hline Routing problem with obstacles in sensor field & No & No & Yes & Yes \\ \hline Routing problem with voids in sensor field & Yes & No & Yes & Yes \\ \hline Routing problem with voids in sensor field & Yes & No & Yes & Yes \\ \hline \end{tabular}
\end{table}
Table 2: The security and characteristic comparison of the grid-based schemes.
# Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
## 5 Conclusions  
In this work, we proposed an air traffic situation assessment method based on the complex network theory and ensemble learning. This method met the evaluation requirements of the system and solved the shortcomings of the index evaluation method. The air traffic weighted network model based on the complex network theory can fully characterize the overall characteristics of the air traffic system. Using the complex network analysis method, we can systematically evaluate the state of air traffic system composed of airport, route, waypoint, and operation data. And an artificial intelligence situation assessment method was introduced based on index evaluation. We transformed the situation assessment into a binary classification to obtain more objective and quantitative assessment results. In order to improve the classification effect of a single classification model, ensemble learning was introduced to construct an air traffic situation evaluation model. It can achieve a better classification effect in less calculation time. We took the air traffic situation data in East China as the experimental object. The results found that the situation assessment model worked well when we used it to assess the air traffic situation of the target samples. The situation assessment results of the air traffic can be quantitatively output, proving that the model can effectively assess the air traffic operation state by comparing the real operation. And the situation assessment model can be used to study the changing trend in the air traffic situation and the reasons for these changes.  
In the future, researchers can further increase the number of samples in the training set to obtain a more perfect assessment model. In addition, the index selection of the air traffic weighted network analysis and the basis classifier selection of ensemble learning can be optimized further.  
Figure 7: The distance from the sample to the classification hyperplane.  
**Author Contributions:** Conceptualization, J.L. and F.L.; methodology, J.L.; software, J.L.; validation, J.L., S.L. and X.W.; formal analysis, J.L. and F.L.; investigation, J.L., Y.W. and S.L.; resources, J.L., F.L. and R.T.; data curation, J.L. and Y.W.; writing--original draft preparation, J.L.; writing--review and editing, J.L., F.L. and X.W.; visualization, J.L., F.L. and S.L.; supervision, J.L. and F.L.; project administration, F.L., X.W. and D.C.; funding acquisition, F.L. and X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 718012).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** All data included in this study are available upon request by contacting the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Validation
### Validation of the Simulation Method  
The structure of a combined solar chimney can be considered to be the composition of a Trombe wall and an inclined-roof solar chimney. Therefore, it is feasible to validate the reliability of the two simple structures of a Trombe wall and an inclined-roof solar chimney. In this paper, the experimental results of the natural ventilation of the Trombe wall and the inclined solar chimney in [14,30] were selected for the simulation verification of the numerical simulation method and model. The same sized models as mentioned in these references were established and the simulation method described in this paper was used for the simulation comparison and verification. The comparison of the numerical simulation results and the experimental results is shown in Figure 3. The relative error was calculated as follows:  
\[E\ =\ \frac{X\exp,i\ -\ X\text{sim},i}{X\exp,i}\ \times\ 100\% \tag{8}\]  
where \(E\) is the relative error, \(X_{\exp,i}\) is the experimental value, and \(X_{\text{sim},i}\) is the simulation value.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Operation Mode** & **Ta = 298 K** & **Ta = 303 K** & **Ta = 308 K** & **Ta = 311 K** \\ \hline S1 CSC & 300 & 300 & 300 & 300 \\
2 CSC & 298 & 303 & 308 & 311 \\ S1 TW & 300 & 300 & 300 & 300 \\ S2 TW & 298 & 303 & 308 & 311 \\ IRSC & 300 & 300 & 300 & 300 \\ \hline \end{tabular}
\end{table}
Table 4: The air temperature at the inlet under different operation modes in Nanjing in the summer.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Operation Mode** & **Ta = 268 K** & **Ta = 273 K** & **Ta = 278 K** & **Ta = 283 K** \\ \hline W1 TW & 291 & 291 & 291 & 291 \\ W2 TW & 268 & 273 & 278 & 283 \\ \hline \end{tabular}
\end{table}
Table 5: The air temperature at the inlet under different ventilation modes in Nanjing in the winter.  
As the wind pressure and other factors were not considered in the simulation, there was a certain relative error between the simulation value and the experimental value. However, it can be seen from Figure 3 that the simulation results were basically consistent with the experimental results and the relative error between the two was less than 7%. The numerical simulation results showed that the flow rate of the solar chimney increased with the increase in the heat flux, which was consistent with the change trend in the experiment. The comparison of the simulation and experimental results showed that the numerical simulation method and models used in this paper had a certain validity and reliability and could be used for subsequent research.