esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Wireless Body Area Networks (WBANs)
### Wearable Wireless Body Area Network (WWBAN)
Each sensor node sense, sample, and process one or more physiological signals. For example, an electrocardiogram sensor can be used to monitor heart activity, an electromyogram sensor for monitoring muscle activity, an electroencephalogram sensor for monitoring brain electrical activity, a blood pressure sensor for monitoring blood pressure, a tilt sensor for monitoring trunk position, and a breathing sensor for monitoring respiration; and motion sensors can be used to discriminate the user's status and to estimate her or his level of activity. The second stage includes the personal server application that runs on a personal digital assistant, cell phone or home personal computer. The personal server is responsible for a number of targets, providing an interface for wireless medical sensors, users and the medical server. The interface to the WWBAN includes network configuration and management features. The network configuration includes the objectives: a sensor node registration (type and number of sensors), initialization (e.g., to  
Figure 4: Typical WWBAN architecture. specify sampling frequency and mode of operation), customization (e.g., to run user-specific calibration or user-specific signal processing procedure upload), and security settings communication. Once the WWBAN network is configured, the personal server application manages the network, taking care of channel sharing, time synchronization, retrieve and process data, and fusion of the data. Based on synergy of information from different medical sensors the personal server application must determine the u status of the user and his health condition and to provide feedback through a user-friendly and intuitive graphical or audio user interface. Finally, if a channel of communication for the medical server is available, the personal server establishes a secure connection to the medical server and sends reports that can be integrated into the user's medical record. However, if a connection between the personal server and the medical server is unavailable, the personal server must be able to store data locally and initiate data uploads when the connection becomes available. The third stage includes a medical serve access via the Internet. In addition to the medical server, the last tier may include other servers, such as informal caregivers, commercial providers of healthcare and even emergency servers. The medical server typically runs a service that sets up a communication channel for the personal server to users, collects reports from user, and integrates the data in the medical record of the user. Service can make recommendations, and even the question of warning, if the reports seem to indicate an abnormal state.
# Robust Optimization for Household Load Scheduling with Uncertain Parameters
## Simulation Result
### The Impact of the Uncertainties  
In this subsection, the influence of the uncertainties will be analyzed. When the uncertainties in the outdoor temperature and water demand are ignored during the scheduling stage, the household load scheduling is just the result of a deterministic optimization where the average of the forecast values are used as the input parameters. Under this situation, the schedules for all appliances is shown in Figure 3. It is found that all interruptible and uninterruptible loads satisfy their corresponding constraints.  
However, with executing the obtained schedules, the real temperature ranges of the air in the room and water in the tank, considering the uncertainties outdoor temperature and hot water use, are shown in Figure 4a,b, respectively. It can be seen that the actual temperature of the indoor air and hot water may take values from the blue intervals. Unfortunately, the uncertainty of the outdoor temperature generates great volatility in the indoor temperature, and at some time steps have even caused a violation of the comfort constraints (e.g., the minimum indoor temperature will reach as low as 14 \({}^{\circ}\)C), which are unacceptable for common customers.  
Figure 3: The schedules of household appliances (without considering the uncertainties). CW: clothes washers; EV: electric vehicles; CD: clothes dryer; DW: dishwasher; WH: water heater; AC: air conditioner.  
Figure 2: (**a**) The uncertainty set of the outdoor temperature for the next day; and (**b**) the uncertainty set of the hot water demand for the next day.  
Compared with the outdoor temperature, the extra hot water usage produces a more serious impact on the water temperature for the WH. As shown in Figure 4b, when the behavior of extra hot water usage happens, the water temperature immediately drops by a large degree, which is far below the lower threshold. In addition, this impact will continue until many steps after the time of extra water usage. As shown in Figure 4b, comfort violation will always happen after 7 a.m.  
In summary, the uncertainties may lead to the comfort violation for both AC and WH if they are not considered during the scheduling stage. Moreover, the uncertainties may affect the optimality of schedules of other loads since they are co-scheduled in the HEMS.
## 4 Experiments
### Experiments on Traffic Regression and Alerting  
According to the dataset mentioned above, we measured the performance of the models by using two indicators: mean absolute percentage error (MAPE) and root mean square error (RMSE). Assuming there are \(n\) forecasting values and labels, the MAPE can be formulated as follows:  
\[E_{MAPE}=\frac{100}{n}\sum\
olimits_{i=1}^{n}\left|\frac{y_{i}^{\prime}-y_{i} }{y_{i}}\right| \tag{6}\]  
where \(y_{i}^{\prime}\) is a forecasting value and \(y\) is the true label value. The RMSE can be formulated as:  
\[E_{RMSE}=\sqrt{\frac{\sum\
olimits_{i=1}^{n}\left(y_{i}^{\prime}-y_{i}\right) ^{2}}{n}} \tag{7}\]  
Moreover, to test the performance of the regression model, we selected 200 traffic incidents from 0:00 21 July to 24:00 31 July manually, and compared the top-10 forecasted traffic incidents, ordered by their heat. The forecasting results include location, time (with period), and traffic incident heat. The real traffic situations were manually marked by searching for news items in our database. In the experiment, the structure of the stacked auto-encoder is the same as in [58] (except for the input format). In addition, the structure of gcForest is a four- to 20-layer model, with each layer consisting of two random forests as well as two gradient tree boosting trees in each layer. As shown in Table 5, the deep models outperform other machine learning algorithms and the state-of-the-art deep random forest has the best performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Training Dataset**} & \multicolumn{2}{c}{**Validation Dataset**} & \multicolumn{2}{c}{**Test Dataset**} & \multicolumn{2}{c}{**Physical Space:**} \\  & & & & & & **Verification Dataset** \\ \hline
**Weibo** & **Traffic** & **Weibo** & **Traffic** & **Weibo** & **Traffic** & **News Data** \\
**Data** & **Incidents** & **Data** & **Incidents** & **Data** & **Incidents** & **News Data** \\ \hline
128,815 & 28,024 & 10,993 & 1830 & 1213 & 200 & 15,130 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dataset description.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **MAPE** & **RMSE** \\ \hline Linear Regression (LR) & 496.89 & 50.83 \\ Support Vector Regression (SVR) & 355,67 & 22.25 \\ Random Forest Regression (RFR) & 263.10 & 21.00 \\ Gradient Boosting Regression Tree (GBRT) & 252.09 & 20.41 \\ Stacked auto encoder (SAE) & 239.18 & 20.05 \\ gcForest (Deep Forest) & 168.08 & 18.97 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of regression models’ performance on the test set.  
In addition, we successfully forecasted nine out of 10 traffic incidents, as shown in Table 6, proving that our social media approach is feasible.  
Based on the 200 traffic incident forecasting values, we mapped the values into warning levels with the proposed model. We also compared the distribution with the real warning level, as shown in Figure 4. The similar trends demonstrate that our approach achieves the expected effect.
# A Feedrate Planning Method for the NURBS Curve in CNC
###### Abstract
[21] divided a NURBS curve with the criterion that the curvature of adjacent sub-curves differ by a factor of 2, after which the feedrate of each sub-curve was also planned based on the minimum-constraint principle. The segment-by-segment planning method described above has good real-time performance because of no backing-off or replanning, but its feedrate-planning results have the problem of exceeding the limit or being conservative. Based on the studies of the above scholars, this paper constructs a critical constraint curve of the feedrate (CCC) corresponding to a NURBS curve by considering both the geometric constraints caused by the curvature and the kinematic constraints during the Acc/Dec process. A CCC consists of three parts, which are a critical deceleration curve constructed to avoid feedrate planning failure, a straight line with a constant value equal to the minimum feedrate-constraint value of the NURBS curve, and a critical acceleration curve to improve machining efficiency. The concise expression of CCC can be used in combination with existing Acc/Dec models, so the real-time planning process is ensured and the planning results will not exceed the limit. On the other hand, the conservation problem existed in the results of feedrate planning due to the minimum-constraint principle used in previous literature can be avoided, since the values of the critical deceleration/acceleration curve in CCC are greater than the minimum feedrate-constraint value. Therefore, the CCC-based feedrate-planning method (CCCP) proposed in this paper can balance the computational efficiency and the machining efficiency. In the following sections, this method will be described in detail, and its effectiveness will be verified through simulations and experiments. The content of this paper is organized as follows: Section 2 details the CCC method; Section 3 is the simulation and experiment; and Section 4 is the conclusion.
## 1 Introduction
### Structure of Article  
The current paper is divided as follows: Section 2 summarizes the basic concepts needed to keep our article self-contained and provides a synopsis of our conceptual framework. Section 3 introduces the methodological framework, detailing the integration of NCMs for evaluating smart port performance. Section 4 presents the application of an NCM method in the solution of the case of smart port performance, determining KPIs proposed in literature--e.g., [37]--as an illustrative example. It should be noted that we examine the problem with two distinct methods: NCMs can be used for static and dynamic analysis, thus providing a comprehensive approach for policy analyses and decision support. Furthermore, we conduct a comparative analysis with well-established fuzzy based methods within the context of evaluating smart port performance in order to prove the robustness of the proposed method. Finally, conclusions are drawn and an outline of future research work on the application of NCMs is proposed (Section 5).
## 4 Experimental Results
### Simulation Results of Sliding-Spotlight GEO-LEO BiSAR
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Target Index**} & \multicolumn{3}{c}{**Azimuth**} & \multicolumn{3}{c}{**Range**} \\ \cline{2-7}  & \(\rho_{a}\) **(m)** & **PSLR (dB)** & **ISLR (dB)** & \(\rho_{r}\) **(m)** & **PSLR (dB)** & **ISLR (dB)** \\ \hline
1 & 0.68 & \(-13.28\) & \(-10.1\) & \(1.01\) & \(-13.25\) & \(-10.4\) \\ \hline
2 & 0.69 & \(-13.33\) & \(-10.2\) & \(1.01\) & \(-13.33\) & \(-10.4\) \\ \hline
3 & 0.68 & \(-13.26\) & \(-10.1\) & \(1.02\) & \(-13.32\) & \(-10.4\) \\ \hline
4 & 0.68 & \(-13.25\) & \(-10.2\) & \(1.01\) & \(-13.28\) & \(-10.4\) \\ \hline
5 & 0.68 & \(-13.25\) & \(-10.1\) & \(1.01\) & \(-13.29\) & \(-10.4\) \\ \hline
6 & 0.68 & \(-13.27\) & \(-10.1\) & \(1.01\) & \(-13.29\) & \(-9.5\) \\ \hline
7 & 0.68 & \(-13.28\) & \(-10.1\) & \(1.02\) & \(-13.32\) & \(-10.4\) \\ \hline
8 & 0.68 & \(-13.30\) & \(-10.4\) & \(1.01\) & \(-13.36\) & \(-10.5\) \\ \hline
9 & 0.69 & \(-13.31\) & \(-10.1\) & \(1.01\) & \(-13.33\) & \(-10.4\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Measured imaging metrics of the nine point targets by the proposed method based on BiCoT-ESRM. Figure 8: Imaging results of the nine point targets by the ‘non-stop-and-go’ RMOSE.
## 2 Materials and Methods
### Mixing Ratio and Sample Preparation  
Table 2 shows the proportions of FA and QZ in single-doped, double-doped, and undoped combinations. To ensure the optimal performance of cement products, the proportion of cement replacement by industrial waste SCM should be limited to 40% [29]. To explore the correlation between the addition of FA and QZ and the 28-day compressive strength, five additional mortar specimens were designed. Mix the ingredients to make mortar specimens with a size of 40 mm \(\times\) 40 mm \(\times\) 160 mm and paste samples with 40 mm \(\times\) 40 mm \(\times\) 40 mm. The specimens were cured for 24 h under standard curing conditions, removed from the mold after forming, and placed in a standard room for further curing.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Group** & **OPC** & **FA** & **QZ** & **Sand** & **Water** & **W/B** \\ \hline \multirow{6}{*}{Paste} & C100 & 100 & 0 & 0 & \(\backslash\) & 50 & 0.5 \\  & 10 FA & 90 & 10 & 0 & \(\backslash\) & 50 & 0.5 \\  & 20 FA & 80 & 20 & 0 & \(\backslash\) & 50 & 0.5 \\  & 10 QZ & 90 & 0 & 10 & \(\backslash\) & 50 & 0.5 \\  & 20 QZ & 80 & 0 & 20 & \(\backslash\) & 50 & 0.5 \\  & 10 FA10 QZ & 80 & 10 & 10 & \(\backslash\) & 50 & 0.5 \\ \hline \multirow{6}{*}{Mortar} & C100 & 100 & 0 & 0 & 200 & 50 & 0.5 \\  & 10 FA & 90 & 10 & 0 & 200 & 50 & 0.5 \\  & 20 FA & 80 & 20 & 0 & 200 & 50 & 0.5 \\  & 10 QZ & 90 & 0 & 10 & 200 & 50 & 0.5 \\  & 20 QZ & 80 & 0 & 20 & 200 & 50 & 0.5 \\  & 10 FA10 QZ & 80 & 10 & 10 & 200 & 50 & 0.5 \\  & 5 FA & 95 & 5 & 0 & 200 & 50 & 0.5 \\  & 15 FA & 85 & 15 & 0 & 200 & 50 & 0.5 \\  & 5 QZ & 95 & 0 & 5 & 200 & 50 & 0.5 \\  & 15 QZ & 85 & 0 & 15 & 200 & 50 & 0.5 \\  & 5 FA5 QZ & 90 & 5 & 5 & 200 & 50 & 0.5 \\ \hline \end{tabular}
\end{table}
Table 2: Sample mixtures.  
Figure 2: XRD patterns of OPC, QZ, and FA.
# Research on Dynamic Evolution Model and Method of Communication Network Based on Real War Game
## 2 Generation Algorithm
### Communication Subnetwork  
According to the related theory of a multi-layer interdependent network, the C2 subnetwork, the sensor subnetwork, and the attack subnetwork are self-connected and interconnected through the communication subnetwork by defining the model as a three-layer dependent network. The interdependent network is fitted according to the joint distribution [25] of power law distribution and Poisson distribution. Nodes in the communication subnetwork is shown in Table 15.  
Relying on the real situation, the communication subnetwork is divided into a two-layer network. The inner communication network is mainly responsible for the communication between the C2 networks, and the outer communication network is responsible for the communication between the attack and sensor subnetwork. Specify the number of two-layer communication \([N_{co},N_{c1}]\) relay network nodes and generate communication relay nodes \(C0_{i},C1_{j}\) respectively, where \(i<N_{CO},j<N_{C1}\).  
Define the three connection types of each communication relay node, expressed as \(x,y,z\), where \(x\) is the degree of dependence of the communication subnetwork on the C2 network, \(y\) is the degree of dependence of the communication subnetwork on the sensing and C2 network, and \(z\) is the communication degree of self-connection.  
Define joint distribution function:  
\[F_{COM}^{Q}(x,y,z)=\lambda F_{COM}^{BQ}(x,y,z)+(1-\lambda)F_{COM}^{FO}(x,y,z) \tag{1}\]  
where \(\lambda\) represents the mixing rate [26] of the two types of distributions, and \(x,y,z\) respectively represents the degree of dependence of the communication subnetwork on the C2 network, sensors network, attack network, and the communication network. \(F_{COM}^{BQ}\) represents Poisson distribution, simulation generation uses the ER random network generation algorithm, \(F_{\text{COM}}^{\text{FQ}}\) represents power-law distribution, and simulation generation uses the BA growth network generation algorithm [27].  
Traversing the edges of the C2 network, the two C2 nodes of the edge select the inner communication relay node to connect according to the joint distribution function, and they complete the self-connection of the inner subnetwork at the same time.  
Traversing the edges of the attack and sensor networks, the two endpoints of the edge select the outer communication relay node to connect according to the joint distribution probability function, and they complete the self-connection of the outer communication relay node.  
Traversing the edges between the outermost C2 and sensor network, according to the joint distribution probability function, the C2 nodes select the inner communication relay nodes, and the sensor nodes select the outer communication relay node to connect and complete at the same time the self-connection of internal and external communication relays.  
Cancel the connection of the original C2 subnetwork, sensor subnetwork, and attack subnetwork to form the final three-layer dependent communication network topology.
# Low-Cost RSAC and Adsorption Characteristics in the Removal of Copper Ions from Wastewater
## 3 Results and Discussion
### Kinetic Study of Copper Ion Adsorption by RSAC  
The fitted curves are shown in Figure 9 and the fitted parameters of the two kinetic models are shown in Table 11. As shown in Figure 9, the adsorption amount of copper ions by RSAC particles increases rapidly with the increase of adsorption time in the early stage of the adsorption reaction. The increase of adsorption amount decreases gradually to almost 0 in the middle and the late stage of the adsorption reaction, which means the adsorption reaches the equilibrium state. In the preliminary stage of adsorption reaction, the adsorbent mainly adsorbs copper ions at the solid-liquid interface [38]. After the preliminary stage, copper ions diffuse from the adsorbent surface to the internal micropores and lattice, reach and are fixed by the internal surface active-sites, thus the adsorption rate decreases slowly [39]. Another study found that the adsorption of fly ash involves both boundary-layer diffusion and intraparticle diffusion [11]. [PERSON] et al. also found fast surface adsorption was followed by a slow intra-particle diffusion adsorption of fly ash [40]. The adsorption process of copper ions by RSAC particles can reach the equilibrium state at 48 h.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Temp. (\({}^{\circ}\)C)**} & \multicolumn{3}{c}{**Langmuir Model**} & \multicolumn{3}{c}{**Freundlich Model**} \\ \cline{2-7}  & **K\({}_{\text{L}}\)** & **qmax** & **R\({}_{\text{L}}\)\({}^{2}\)** & **K\({}_{\text{F}}\)** & **1/n** & **R\({}_{\text{F}}\)\({}^{2}\)** \\ \hline
15 & 0.138 & 2.438 & 0.974 & 0.737 & 0.248 & 0.960 \\
25 & 0.196 & 2.633 & 0.967 & 0.805 & 0.253 & 0.952 \\
35 & 0.254 & 2.862 & 0.954 & 0.874 & 0.262 & 0.956 \\ \hline \end{tabular}
\end{table}
Table 10: Parameters for the adsorption isotherm model fit.  
Figure 8: Adsorption isotherm of copper ions (25 \({}^{\circ}\)C).  
The R\({}^{2}\) of both models is greater than 0.95 and the difference between them is not significant (Table 11), which indicates that both kinetic models could describe the adsorption process of copper ions on RSAC particles well. It could be further inferred that the adsorption process of copper ions on RSAC particles is a mixed control: both surface diffusion and internal fine pore diffusion are important.
# A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
## 4 Production Performance of Single Well Pair
### Sensitivity Analysis of Inter-Well Connectivity
#### 4.3.4 Injection Pressure Differential  
After discussing the duration of the injection pressure impulse, the influence of the amplitude of injection pressure differential on inter-well connectivity is expounded in this part. The simulation results show that there is a positive correlation between injection pressure and the reservoir. The  
Figure 13: Variations in time-lag coefficients for different pressure impulse durations.  
sure differential and inter-well connectivity coefficient, suggesting that when the BHP of the producer remains unchanged, the higher the BHP, the higher the inter-well connectivity coefficient is (Figure 14). This may be because the increased injection pressure differential increases the ability to displace fluids and strengthens the force of fluid displacement in the reservoir, thus accelerating the exchange of oil-water phases between wells.  
Under different injection pressure differentials, the time-lag coefficients gradually decrease and stabilize (Figure 15). This means that over time, fluid transfer efficiency in the reservoir increases, and the impact of injection can be detected more quickly in the producer. With the increase in injector BHP, time-lag coefficients decrease obviously, indicating that the higher the injection pressure differential, the faster the ineffective circulation channels may be established and the more sensitive the fluid dynamic response.  
Figure 14: Inter-well connectivity coefficients under different injection pressure differentials.  
Figure 15: Variations in time-lag coefficients under different injection pressure differentials.
# Buckling Induced Strongly Nonlinear Vibration of Supercritical Axially Moving Beam
## 3 Principle of Homotopy Analysis  
[PERSON] [33] introduced the basic principle of homotopy analysis. Assume that Equation (34) is a nonlinear differential equation  
\[\mathbf{F}u(\mathbf{x},t)=g(\mathbf{x},t) \tag{37}\]  
where \(\mathbf{F}\) is a nonlinear ordinary or partial differential operator, \(u(\mathbf{x},\)t) is an unknown function, and \(g\) is the source term.  
\[\mathbf{F}=L+N \tag{38}\]  
where \(L\) and \(N\) indicate the linear and nonlinear terms, respectively. Equation (39) is the zero-order deformation equation:  
\[(1-q)L[\Phi(t;q)-u_{0}]=q\hbar[\mathbf{F}\Phi(t;q)-g] \tag{39}\]  
The convergence range can be well controlled by \(\hbar\), and the constraint conditions for constructing auxiliary linear operator, \(L\), are relatively loose.  
When \(q=0\) and \(q=1\), it holds that  
\[\Phi(t;0)=u_{0},\quad\Phi(t;1)=u(q,t) \tag{40}\]  
As \(q\) changes from \(0\) to \(1\), the solution of Equation (39) also changes from \(u_{0}\) to \(u(\mathbf{x},\)t). Equations (41) and (42) are the Taylor series expansion of \(\Phi(t;q)\), the exact solution of Equation (37).  
\[\Phi(t;q)=u_{0}(t;q)+\sum_{m=1}^{+\infty}u_{m}(t;q)q^{m} \tag{41}\]  
\[u_{m}(t;q)=\frac{1}{m!}\frac{\partial^{m}\Phi(t;q)}{\partial q^{m}} \tag{42}\]  
If we perform differentiation on Equation (41) \(m\) times, the corresponding deformation equation can be obtained as Equation (43) and represented as a set of linear ordinary equations.  
\[L[u_{m}(t;q)-\chi_{m}u_{m-1}(t;q)]=\hbar R_{m}(\stackrel{{ \rightarrow}}{{u}}_{m-1}) \tag{43}\]  
where \(\stackrel{{\rightarrow}}{{u}}_{m-1}\) is a vector defined as  
\[\stackrel{{\rightarrow}}{{u}}_{m}=\{u_{0}(\mathbf{x},t),u_{1}( \mathbf{x},t),u_{2}(\mathbf{x},t),\cdots u_{m}(\mathbf{x},t)\} \tag{44}\]  
and  
\[R_{m}(\stackrel{{\rightarrow}}{{u}}_{m-1})=\left.\frac{1}{(m-1)! }\frac{\partial^{m-1}\mathbf{F}[\Phi(t;q)]}{\partial q^{m-1}}\right|_{q=0} \tag{45}\]  
\[\chi_{m}=\left\{\begin{matrix}0&m=1\\ 1&m>1\end{matrix}\right.\]