esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Estimation Performance in Different Climate Zones  
To compare the estimating performance for each of the 18 models (3 machine learning algorithms \(\times\) 6 input combinations) at each of the stations within each of the climatic zones in this study, a combined metric based on the RMSE and MAE values of each model was calculated. The value of the combined metric ranged from 0 to 1, in which the closer the value to 0, the better the model performance was, while on the contrary the closer the value to 1, the worse the model performance was. Values of the combined metric at different stations for each climate zone are illustrated with heatmaps (see Figure 9 and Figures S8-S13). Overall, under the same input combination, the estimation performance of the XGBoost model was generally better than that of the RF and M5P models at most stations (Figure 9 and Figures S8-S13). Among all the 18 models with different input combinations, the XGBoostI and RF1 models generally performed best at most stations in the seven climate zones, of which XGBoostI had better performance than RF1. Among the incomplete input combinations, models with combination 4 (i.e., including T\({}_{\text{mean}}\) and R\({}_{\text{s}}\)) generally performed better than models with other combinations, of which the models could be ranked as XGBoost4 \(>\) RF4 \(>\) M5P4 in terms of estimation performance. It is noted that the performance of the XGBoost4 model was relatively close to that of the XGBoost1 model, while the former required fewer input datasets than the latter. Therefore, the XGBoost4 model would be more generalizable than the XGBoost1 model for estimating T\({}_{\text{s}}\) across different climatic zones. Overall, the model performance differed between different climatic zones. For example, the M5P5 and RF5 models had relatively poor estimating performance in the NWC, IM, NEC, NC, and QTP regions, while the estimation performance of the M5P6, RF6, and XGBoost6 models was poor in the SC and CC regions. These results confirmed that the model estimating performance would be impacted by variations among different climatic zones.  
Figure 8: Principal component analysis (**a**) and correlation analysis (**b**) between several factors and model estimation T\({}_{\text{s}}\) errors.
_Appl. Sci._**2015**, 5, 1134-1163; doi:10.3390/app5041134  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**An Efficient Power Scheduling Scheme for Residential Load Management in Smart Homes**  
**[PERSON] \({}^{1,\dagger}\), [PERSON] \({}^{1,\dagger,\ast}\), [PERSON] \({}^{1,\dagger}\), [PERSON] \({}^{2,3,\dagger}\), [PERSON] \({}^{4,\dagger}\) and [PERSON] \({}^{5,\dagger}\)**  
\({}^{1}\) COMSATS Institute of Information Technology, Islamabad 44000, Pakistan;  
E-Mails: [PERSON]_ADDRESS] (M.B.R.); [EMAIL_ADDRESS] (A.A.)  
\({}^{2}\) Internetworking Program, Faculty of Engineering, Dalhousie University, Halifax,  
Nova Scotia B3J 4R2, Canada; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Computer Information Science (CIS), Higher Colleges of Technology, Fujairah Campus 4114,  
The United Arab Emirates (UAE)  
\({}^{4}\) University of Alberta, Edmonton, Alberta T6G 2J8, Canada; E-Mail: [EMAIL_ADDRESS]  
\({}^{5}\) College of Applied Medical Sciences, Department of Biomedical Technology, King Saud University,  
Riyadh 11633, Saudi Arabia; E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) These authors contributed equally to this work.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +92-300-579-2728.  
Academic Editor: [PERSON]  
_Received: 11 September 2015 / Accepted: 30 October 2015 / Published: 12 November 2015_  
**Abstract:** In this paper, we propose mathematical optimization models of household energy units to optimally control the major residential energy loads while preserving the user preferences. User comfort is modelled in a simple way, which considers appliance class, user preferences and weather conditions. The wind-driven optimization (WDO) algorithm with the objective function of comfort maximization along with minimum electricity cost is defined and implemented. On the other hand, for maximum electricity bill and peak reduction, min-max regret-based knapsack problem (K-WDO) algorithm is used. To validate the effectiveness of the proposed algorithms, extensive simulations are conducted for several scenarios. The simulations show that the proposed algorithms provide with the best optimal results with a fast convergence rate, as compared to the existing techniques.  
keywords:demand response; energy management; time of use pricing; swarm optimization; knapsack; smart grid +
Footnote †: journal: Energy management
# A Sensitivity Analysis of a Computer Model-Based Leak Detection System for Oil Pipelines
## 3 Results and Discussion
### Uncertainty in Supervisory Control and Data Acquisition Data  
Polling times of 1 s, 5 s, and 10 s were tested and found to have no effect on leak detectability, even during transient conditions. The \(DVB\) curves for the cases with 5 s and 10 s polling time overlapped the baseline case (1 s polling time), for both perfect data and noisy data tests.  
It was noted that when time skew is present in the measured data, false diagnostic flow can be produced even when there is no leak. In this case, the leak detection system needs to be configured with increased pressure and flow rate repeatability in order to avoid false leak alarms. Statefinder is then allowed to adjust the measured flow rate or pressures by a greater amount. In Figure 8, \(DVB\) curves are plotted for cases with time skew set to 10 s in one or two sensors and no time skew in the remainder during transient conditions with perfect data. Only results produced by positive shifts that are shown as negative shifts were shown to have the same effect. It can be seen that the detection time was delayed slightly for all these cases, ranging from 0.5 to 3.3 min compared to the baseline case. The detection time for the corresponding baseline cases was 24.9 and 26.1 min during flow decrease and flow increase transients, respectively. It was noted that the delay in detection time was greatest for cases with time skew in both sensors located at the transient initiation location. For the cases in which the flow rate was decreased at the downstream end, the case with time skew in both the downstream pressure and flow rate sensors located at the downstream end experienced the largest delay of 3.1 min. The flow was increased at the upstream end for the flow increase transient and the case with time skew in the upstream pressure and flow rate sensors had the largest delay of 3.3 min. Note that leak detection times during steady state operations with perfect data were not affected by time skew.  
The delay was more pronounced in the cases with noisy data, as shown in Figure 9. Time skew had a larger impact on leak detectability during both steady state and transient operating conditions. Leak detection was delayed by 35 min compared to the baseline case detection time of 33.1 min during a flow decrease transient (Figure 9a). For steady state and flow increase cases (Figure 9b,c), the leak became undetectable compared to detection times of 41.7 and 61.7 min for the corresponding baseline cases. \(DVB\) eventually stabilized at \(-0.22\) and \(-0.08\) for the steady state and flow increase cases; both were above the leak detection threshold.  
Figure 8: Effect of time skew on leak detectability in a system with \(R=2.20\) with perfect data (sensors contain a 10-s time skew): (**a**) flow decrease; and (**b**) flow increase.
# Toward Efficient Mobile Electric Vehicle Charging under Heterogeneous Battery Switching Technology
## 3 System Model
### Heterogeneous BSS Selection Concerning BS Reservation and Load Balancing across BSS
#### 3.4.1 Global Planing Phase  
Before a vehicle at BSS arrives, the incoming flow of \(R_{V}\) with the demand of BS is needed to manage across the network. The aim is to minimize \(W_{t}\) (\(Q_{t}\), \(B_{rt}\) and \(S_{t}\)) on the trip and select optimal BSS for the \(R_{V}\). However, each BSS holds several batteries in multi types. Typically, available stock of batteries (\(S_{b}\)) from discharged state to charged state and the demand of BS are also required to efficiently manage the request which is discussed below. The list of notation is show in Table 2.  
* When SOC of EV is below the minimum threshold, the vehicle sends request to GC for BS reservation along with its personal information including (\(R_{V}\)ID, Current Battery Energy Level (\(C_{battery}^{EL}\)), Current location (\(C_{L}\)) and Battery type (\(B_{type}\))) (line 1 to 3 in Algorithm 1).
* If GC has updated information about \(BSS_{j}\) (No, of \(BBS\) in a network), then it checks the availability of \(R_{V}\)\(B_{type}\) across the network (line 4 and 8 in Algorithm 1). Otherwise, GC broadcasts request across the network along with information of \(R_{V}\) (line 5 in Algorithm 1).  
```
1:for each node-\(v_{i}\) of \(U\)do
2: send request by a \(v_{i}\) to GC
3:endfor
4:while GC do not have updated information about \(BSS_{j}\)do
5: Broadcast request to \(BSS_{j}\)
6: Each \(BSS_{j}\) checks that is \(R_{battery}\) is available and send back response to GC
7:endwhile
8:if the \(R_{battery}\) available in \(BSS_{j}\)then
9: Each BSS check that the \(R_{battery}\) is fully charged available
10:if\(R_{battery}\) is fully charged available in \(BSS_{j}\)then
11:\(BSS_{j}\) calculate its waiting time
12: Estimate \(T_{1}=N_{v}\cdot T_{ST}\)
13: Estimate \(S=S_{vi\to CS}=r_{CS}-r_{vi}\)
14: Estimate \(V=\frac{r_{tf}-r_{t}}{t_{f}-t_{i}}\)
15: Estimate \(T_{2}=\frac{r_{BS}-r_{vi}}{d_{new}^{new}}\)
16: Estimate \(T_{3}=T_{1}+T_{2}+B_{f}^{t}\)
17: EST = \(T_{1}+T_{2}+T_{3}\)
18:if\(v_{i}\) reach to \(BSS_{j}\)then
19: Allocate the battery in the selected BSS
20: Switch the battery in the selected BSS
21: Update the BSS status
22:endif
23:elseif\(R_{battery}\) available on chargingthen
24: BSS Calculate \(T_{4}=E_{cha}^{time}\)
25: EST = \(T_{1}+T_{2}+T_{3}+T_{4}\)
26: BSS Update ATS
27: goto ref: [18]
28:endif
29:endif
```  
**Algorithm 1** Heterogeneous BS and Load balancing across \(BSS_{j}\).  
\(BSS_{j}\) checks whether the requested required battery type is available or not. After this request, it sends the updated information to GC to select the appropriate BSS for \(R_{V}\) to minimize the average time on the trip (lines 6 to 7 in Algorithm 1).
# Embedded Based Real-Time Monitoring in the High-Pressure Resin Transfer Molding Process for CFRP
## 2 High-Pressure RTM
### Effects of Process Parameters  
Figure 3 shows the steps in the HP-RTM process that vary according to time. A vacuum is applied inside the mold while the end-gap (\(D\)) of the mold is kept constant. The application of the vacuum step creates a significantly better impregnation quality and lower void content. The compression force (\(F\)) applied to the mold in the vacuum step is 0 kN and the cavity pressure (\(CP\)) is 0 bar due to the end-gap. The injection step is simultaneously impregnated down to the bottom of CFRP preform as high-pressure resin spreads along the preform surface through the end-gap. At the end of the injection step, a complete impregnation is made by applying the compression force. When the pressing force reaches a set value (\(F_{E}\)), the cavity pressure reaches its maximum. The increased internal pressure in the molds increases the reaction rate and accelerates the exothermic reaction [25]. When the increase in temperature reaches the maximum reaction temperature (\(T_{R,max}\)), the reaction between the resin and the hardener terminates and the degree of cure in the resin increases. The thermoset resin is injected as a liquid and ends as a solid by the curing reaction due to the mold heat. The curing reaction begins and the curing shrinkage varies linearly with the degree of curing. Therefore, the pressure decreases when the resin shrinks in the mold [26]. When the reaction has completed, the cavity pressure is maintained at a constant. The temperature gradually decreases and falls close to the mold temperature and the thickness of the shrunk resin decreases. Thereafter, the press compresses as the same force increases to further shrink the volume of the resin while the force control of the press remains constant. Furthermore, the end-gap decreases in proportion to how much the resin shrinks. After applying the holding pressure to maintain the required CFRP laminate properties and shape, the molding is complete. Therefore, it is possible to monitor the resin filling, impregnation, curing, etc., by detecting the resin temperature and cavity pressure in the mold. In addition, for measuring the end-gap, it is possible to know the balance state of the mold and thus indirectly predict the final thickness of CFRP.  
Figure 3: Schematic diagram of the HP-RTM parameters according to time.  
Figure 2: Process parameters of HP-RTM.
# Stochastic Generator of Earthquakes for Mainland France
## 4 Discussion
### On the Aftershock Production  
Epidemic Type Aftershocks Sequence (ETAS) models are marked point processes [27] that produce main shocks and associated aftershocks. For that purpose, they use various laws, such as the aftershocks production law ([46] from [47]):  
\[K=k\times 10^{\pi(M_{ms}-M_{c})}\,, \tag{13}\]  
where \(K\) is the number of aftershocks produced by a main shock of magnitude \(M_{ms}\), \(M_{c}\) is the minimal magnitude of interest and \(k\) and \(\alpha\) are two constants. Thus, the number of aftershocks produced by a main shock increases with \(M_{ms}\).  
In our results (Figure 12), the number of aftershocks per main shock seems to be independent to the magnitude of main shocks. This observation is mainly due to the fact that the Proportion-Magnitude Distribution (PMD) of main shocks used as an input in this study (Figure 3) is derived from the instrumental catalogue. However, as already stated, this catalogue is far from exhaustive for large magnitudes (\(M>5\)). Although it is not a problem for the FMD of main shocks, since it is produced by the extrapolation of the exhaustive part (see Section 3.2), it is a problem for PMD, which is not extrapolated and is thus not exhaustive. Thus, within the whole \(M\geq 4\) generated earthquakes, aftershocks represent only 6% (Figure 3), which is low, as Figure 11 illustrates.  
A PMD of main shocks computed thanks to the G85 declustering algorithm [40] from the exhaustive historical catalogue (FCAT-17) should be more representative of the true distribution. According to this catalogue, aftershocks represent 15% of the whole \(M\geq 4\) seismicity. Applying the PMD of main shocks estimated from the exhaustive FCAT-17 catalogue gives results that are shown in Figure 13.  
These results seem more realistic, since they are based on more exhaustive data and since the number of aftershocks per main shock follows the Equation (13). Moreover, our method to generate aftershocks is non-parametric in contrast to this equation, which needs to set two parameters (\(k\) and \(\alpha\)). This is an advantage in the context of low-to-moderate seismicity, where objective parametrization from data is limited since data are sparse.  
Main shocks are generated according to the proportion of main shocks \(p\) observed in the data, whereas the number of aftershocks produced depends on the complementary of \(p\) (\(1-p\)). However, results in Figure 13 have been obtained by using instrumental data to generate main shocks and historical data to produce aftershocks. Thus, two PMDs have been used, and so the consistency between \(p\) and \(1-p\) no longer holds. A solution could be to produce both the PMD and FMD of main shocks with historical data. Nevertheless, this catalogue is characterized by large uncertainties (magnitude, time of occurrence and space) and is known to overestimate magnitudes compared to the instrumental catalogue (e.g., Figure 2c in [16]). Its use must be carried out with care.
## 2 Methods
### The Beam Model: Modified Euler-Bernoulli Equation  
The classical Euler-Bernoulli (EB) beam equation is here modified to account for the longitudinal forces (i.e., parallel to \(U_{\infty}\) and to the beam rest position as in Figure 3) exerted by the VI mounted on the tip of the beam. Let \(x\) be such direction and \(w(x,t)\) the beam displacement at position \(x\) and time \(t\).  
In the following, prime and dot notations stand for space and time differentiation, respectively. Moreover, bold symbols are used for vector quantities. The standard EB equation can be written as:  
\[\rho\,S\,\bar{w}(x,t)+E\,J\,w^{\prime\prime\prime\prime}(x,t)=\mathcal{F}(x,t), \tag{1}\]  
where \(\rho\) and \(E\) are, respectively, density and elastic modulus of the beam material, while \(S\) and \(J\) are the area and the second moment of area with respect to the neutral axis of the beam cross-section; \(\mathcal{F}(x,t)\) is the transverse load distributed along the beam length \(L\). Numerical values for \(\rho\), \(S\) and \(E\) are the ones of an homogeneous beam equivalent to the monomorph piezoelectric beam according to the homogenization procedure described in Appendix A. The fourth order term in the Equation (1) can be written as \(E\,J\,w^{\prime\prime\prime\prime}(x,t)=M^{\prime\prime}(x,t)\) where \(M(x,t)\) is the bending moment due to the applied transverse load. A longitudinal force \(F_{I}\), i.e., aligned with the direction of the undisturbed flow velocity, applied to beam's free end (\(x=L\)) yields a further bending moment \(M_{I}(x,t)\):  
\[M_{I}(x,t)=-F_{I}[w(L,t)-w(x,t)]. \tag{2}\]  
As a consequence:  
\[M_{I}^{\prime\prime}(x,t)=F_{I}\,w^{\prime\prime}(x,t). \tag{3}\]  
To tackle the problem through a modal analysis, transverse forces \(F_{I}\) exerted by the VI and its inertia need to be accounted for as loads on the bulk, by means of standard Dirac-Delta functions. The resulting modified EB beam equation reads:  
\[\left[\rho\,S+m\,\delta(x-L)\right]\,\bar{w}(x,t)+E\,J\,w^{\prime\prime\prime \prime}(x,t)+F_{I}\,w^{\prime\prime}(x,t)=F_{I}\,\delta(x-L)+\mathcal{F}(x,t), \tag{4}\]  
where \(m\) is the mass of the VI.  
Standard boundary conditions for a cantilever, together with the following initial conditions are applied:  
\[\begin{  
In Section 2.3, \(F_{l}\), \(F_{l}\) will be derived from the static ones determined by means of the CFD modelling in Section 2.1. As stated in the introduction, due to the particular shape of the employed VI, the distributed load \(\mathcal{F}(x,t)\), i.e., the direct action of the fluid on the beam, will be proven to be pivotal for replicating the experimental results.
## 4 Results
### Predicting Injuries
#### 4.3.2 Feature Importance  
In all three models in Table 6, 'Squad', which denotes a player's team allegiance/club at which he is playing, consistently stands out as the most significant predictor. This widespread agreement highlights the crucial role that a player's team plays in injury prediction, indicating that team dynamics and environmental factors significantly impact a player's vulnerability to injuries.  
Second, 'Age' plays a key role in the models, especially in XGBoost (XGB_2), which assigns it the highest relevance score. This demonstrates the significant influence a player's age has on their risk of injury, with XGBoost giving special attention to this aspect. Age is undoubtedly an essential factor in injury prediction, highlighting the potential advantages of developing injury-preventive tactics according to a player's age range.  
Additionally, the models identify the number of 'Starts' and 'Matches Played' as fairly relevant variables, with RFC_2 and XGB_2 giving relatively greater importance to these attributes. This suggests that when determining injury likelihood, it is important to consider a player's match experience and inclusion in starting lineups. In contrast, 'Player Workload' is consistently assigned a low relevance rating by all models, indicating that it might not substantially impact injury prediction. This emphasizes how complicated injury determinants are, with certain aspects being less significant in this situation.  
Finally, 'Squad' appears as the key predictor across all models, highlighting the significance of this factor in injury prediction. Age also has considerable influence, and XGBoost is particularly aware of this. In addition to providing a greater understanding of the factors driving injury prediction, these insights establish the groundwork for injury prevention and management measures catered explicitly to the various player profiles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & \begin{tabular}{c} **Random Forest** \\ **(RFC\_2)** \\ \end{tabular} & \
\begin{tabular}{c} **Decision Tree** \\ **(DTC\_2)** \\ \end{tabular} &
\begin{tabular}{c} **XGBoost (XGB\_2)** \\ \end{tabular} \\ \hline Position & 0.06 & 0.11 & 0.03 \\ \hline Squad & **0.16** & **0.26** & **0.27** \\ \hline Age & 0.11 & 0.12 & 0.20 \\ \hline Matches Played & 0.12 & 0.08 & 0.12 \\ \hline Starts & 0.10 & 0.04 & 0.11 \\ \hline Minutes Played & 0.12 & 0.04 & 0.08 \\ \hline Average Minutes/Match & 0.12 & 0.25 & 0.11 \\ \hline
90s & 0.16 & 0.09 & 0.07 \\ \hline Player Workload & 0.00 & 0.00 & 0.00 \\ \hline Player Usage & 0.01 & 0.00 & 0.01 \\ \hline Age Category & 0.03 & 0.00 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Feature importance scores of predictors across models for injury.
# On Connectivity and Energy Efficiency for Sleeping-Schedule-Based Wireless Sensor Networks
## 2 Network Model  
The sensor network is a type of wireless network technology that integrates sensor, micromotor systems, and computer technology, and can achieve information acquisition, information processing, and information transmission. The architecture of a typical WSN [PERSON] et al. (2018) is shown in Figure 1, including three parts that are sensor nodes, the sink node, and the management node.  
Sensor nodes constitute the hardware basis of the network and the core of the sensor network. They have three functions. First, they complete the information collection as the on-site information collection unit. Second, they transmit the collected field information to other nodes or information processing centers in a certain way. Third, they implement some control functions together with the control module. Additionally, some applications require sensor nodes to perform additional functions, typically including positioning systems, motion or actuators, power regeneration devices, and so on. In the sensor networks, a large number of sensor nodes are randomly scattered near or inside the monitoring area utilizing aircraft broadcast, artificial configuration or rocket ejection, and constitute the network in a self-organization way. The data monitored by the sensor node is transmitted to the sink node by other sensor nodes through wireless single or multiple hop communication, then the sink node transmits it to the task management node through the Internet, mobile communication network, satellite communications, and other means, the collected information is eventually processed by a remote client. Across the entire network, sensor nodes should not only take into account the dual function of terminals and routes in the traditional network, but also store, process and transmit data. The relationship between each sensor node is a kind of collaborative work.  
On a general level, connectivity refers to the fact that any two sensor nodes in a sensor network can establish a wireless connection, exchange information, and can communicate with other nodes other than the neighbor nodes through a multi-hop mode, which is also the premise for the normal operation of the whole wireless network [11]. From the perspective of probability theory, this paper studies the relationship between the various parameters related to the connectivity, and constructs the probability analysis model of the node connectivity, and finally presents the networking algorithm called the connectivity and energy efficiency (CEE) algorithm, which can guarantee maximum connectivity probability and reduce the network energy consumption.