esa-sceva/satcom-chunk-collection huggingface dataset:
Furthermore, the accelerometry-based measures can provide a great level of sensitivity and accuracy considering the capacity of these sensors to collect data at a higher acquisition frequency than Global Positioning System (GPS) [33; 34; 35]. The body load (BL) metric (expressed as a G-force) is a triaxial accelerometer measure from the GPS that provides summarized actions information of the acceleration/deceleration, related changes of direction, jumps, and impacts/collisions [36]. Previous studies have used GPSs and accelerometers to access match running performance (e.g., high-intensity running, sprinting, and BL) across playing positions [37; 38]. However, the TM, TS, and acute and chronic of both distance- and accelerometry-based workloads across playing positions have not been well documented in the literature over the professional soccer season. This information can aid coaches and practitioners to deeply understand the specificities of the positional role and prescribe more representative training tasks. In a professional sports team, a full season include three phases: the off-season, pre-season, and in-season. Pre- and in-season are substantial in terms of physical work, where usually during the pre-season there is an enhancement of physical fitness of the players, while the in-season improves the maintenance of the capacities developed during the pre-season. With this explanation, we hypothesized that the training loads recorded for players at different time periods of the season could be different [27; 28], for example possibly being greater during the pre-season, lower during competition, or lower during the tournament. Therefore, the aim of this study was twofold: (i) to describe weekly average acute workload (wAW), chronic workload (wCW), acute: chronic workload ratio (wACWR), TM (wTM), and TS (wTS) based on BL metric variations across the full season by playing positions and (ii) to analyze the variations of wAW, wCW, wACWR, wTM, wTS, weekly total distance (wTD), weekly sprint total distance (wSTD), weekly high-speed running distance (wHSRd), weekly repeat sprint (wRS), weekly maximum speed (wMaxSp), the ratio between wHSRd/wTD, and the ratio between wSTD/wTD by playing position over the full season.
## 5 Empirical Results and Discussion
### Regression Results
\begin{table}
\begin{tabular}{c c c} \hline
**Dependent Variable: \(LnPM_{2.5}\)** & **Coef.** & **Std.err** \\ \hline \(LnPop\_dens\) & \(0.022\) & \(0.017\) \\  & (\(1.30\)) & \(0.017\) \\ \hline \(LnGPPpc\) & \(-0.957\) *** & \\  & (\(-2.83\)) & \(0.338\) \\ \hline \(LnGPPpc^{2}\) & \(0.037\) *** & \\  & (\(2.65\)) & \(0.014\) \\ \hline \(LnIndus\_dens\) & \(0.006\) & \(0.009\) \\  & (\(0.67\)) & \(0.009\) \\ \hline \(LnEnergy\_cons\_pc\) & \(0.017\) & \(0.032\) \\ \hline \(Constant\) & \(8.604\) *** & \\  & (\(4.25\)) & \(2.025\) \\ \hline \(W_{1}PM_{2.5}\) & \(0.937\) *** & \\  & (\(34.87\)) & \(0.026\) \\ \hline \(W_{2}LnPop\_dens\) & \(0.004\) & \\  & (\(0.11\)) & \(0.038\) \\ \hline \(W_{3}LnGPPpc\) & \(-0.492\) *** & \\  & (\(-12.89\)) & \(0.038\) \\ \hline \(W_{4}LnGPPpc^{2}\) & \(0.016\) *** & \\  & (\(4.87\)) & \(0.003\) \\ \hline \(W_{5}LnIndus\_dens\) & \(-0.021\) & \(0.024\) \\  & (\(-0.88\)) & \\ \hline \(W_{6}LnEnergy\_cons\_pc\) & \(0.166\) *** & \\  & (\(2.26\)) & \(0.073\) \\ \hline Observations & \(231\) & \\ \hline Log-likelihood & \(262.295\) & \\ \hline AIC & \(-496.590\) & \\ \hline BIC & \(-448.396\) & \\ \hline \end{tabular} Note: z-statistics are reported in parentheses. *** \(p<0.05\), *** \(p<0.01\).  
\end{table} 
Table 8: Result of Spatial Durbin Panel Random Effects Regression.
# Feasibility of LoRa for Smart Home Indoor Localization
## 1 Introduction
Another powerful technology is BLE, which acquires radio frequency (RF) signals and utilizes the received signal strength indicator (RSSI) value to identify the real-time localization [22]. BLE has many benefits, for example low energy usage, and low cost. At the same time, with the widespread of Bluetooth, BLE has minimal infrastructure requirements to adoption [23]. The fundamental challenge of BLE is the high susceptibility to fast fading of 30 dB in close positions [24]. This problem affects the stability of BLE localization technology. Meanwhile, when common building elements block the BLE signal, it has a higher packet drop rate than other sensing technologies [25]. This phenomenon cuts back the effective coverage range of the BLE localization system. Due to these fundamental limitations, indoor localization using those sensing technologies suffer from low accuracy, packet loss, or the need to install many sensing devices. However, LoRa has a great potential as a robust and feasible solution for indoor localization for smart home applications. With relatively low cost and power consumption, the LoRa's signal with city-level range is resilient to multipath effect or noise. Therefore, LoRa signals have good penetration ability and stability, which is more stable than WiFi and BLE [25; 26]. Despite the promising potential, most the existing studies [27; 28] and commercial applications [29] use LoRa for rapid collection of sensing data from remote locations rather than directly calculating locations of objects. There are initial studies [25; 26] that compared LoRa with WiFi and BLE. However, no study has rigorously evaluated LoRa's performance as a home-level indoor localization system that tracks the location of objects in a home with light walls, heavy furniture, and electronic devices that cause different degrees of signal intensity under NLOS conditions. To overcome the limitation, this study investigates the feasibility of applying LoRa for indoor localization in residential apartments unit with only three static LoRa anchors. To this end, the research team carried out an experiment that evaluated the ranging and trilateration accuracy of LoRa in a furnished apartment, which includes LOS and NLOS scenarios. The experiment results were analyzed based on the context of LOS and NLOS situations. The following section discusses the performance of five sensing technologies, which are used for indoor localization, from literature review in detail. After describing the experimental setup and calibration process, the authors discuss the feasibility of LoRa for the indoor localization under both LOS and NLOS situation in a furnished apartment.
## 4 Results and Discussion
### Comparison with Conventional DI-SMC Scheme
tracking performance, and produces a variable switching frequency. However, the small \(K_{C}\) value attenuates that noise and reduces the corresponding issues. Tuning the conventional controller gains \(K_{p}\), \(K_{i}\), and \(K_{C}\) may eliminate the steady-state error and maintain a constant switching frequency. However, the disadvantages of involving the capacitor current, which include the design complexity and current sensor cost, are dominant. In contrast, the simplified DI-SMC method avoids this issue via eliminating the capacitor current term from the control equation during the control design process. Thus, the proposed control method yields a simple control structure that provides enhanced transient response, precise tracking performance, and constant switching frequency.
# Sliding Mode Integral Separation PID Control for Hydrogen Fuel Cell Systems
## 4 Analysis of PEMFC Experimental Results
### Analysis of Experimental Results of Integral Separation PID Control  
The control objectives in this paper are to control the oxygen excess ratio (OER) to be 2, to control the temperature of the electric stack to be about 70 \({}^{\circ}\)C (343.15 K), and to control the cathode-anode differential pressure to vary according to the differential pressures in Figure 7. The experimental system is set up to verify the effect of integral separate PID control (IS\(-\)PID), and the control algorithm is burned into the controller to realize sensor acquisition and device control. The actual operating conditions are simulated in the fuel cell system by an electronic load, model IT8902E\(-\)1200\(-\)80 (ITECH ELECTRONIC CO., LTD., Nanjing, China), which is set in constant current mode, and Figure 8 displays the variations in load current. Figure 9 displays the OER IS\(-\)PID control, and Figure 10 shows the electric stack temperature control.  
Figure 6: Polarization diagram of stack under different anode–cathode polar pressure differences.  
Figure 7: Cathode–anode polar pressure difference set value.  
As shown in Figure 9, the integral separation PID and conventional PID are able to quickly control the OER to 2 when a step in the load current occurs, and the IS\(-\)PID has a smaller amount of overshooting compared to the conventional PID control. In Figure 9, it can be seen that the conventional PID peak time at system startup is 2.132 s, the maximum overshoot is 13.15% (OER = 2.263), and the adjustment time is 4.57 s. The IS\(-\)PID peak time is 1.726 s, the maximum overshoot is 10.15% (OER = 2.203), and the adjustment time is 4.356 s. The lowest OER of the conventional PID is 1.1508, and the lowest OER of IS\(-\)PID is 1.3754. The use of the integral separation PID can reduce the number of cases of \"oxygen  
Figure 8: Load current change curve.  
Figure 10: Effect diagram of the stack temperature controlled by IS\(-\)PID.  
Figure 9: Effect diagram of oxygen excess ratio controlled by IS\(-\)PID.  
deptivation\". The overall performance of the IS\(-\)PID is better than that of the conventional PID when the load current undergoes a step change.  
As shown in Figure 10, both the integral separation PID and conventional PID are able to control the electric stack's temperature up to 70 \({}^{\circ}\)C. The integral separation PID possesses a smaller amount of overshoot and a smaller oscillation ripple. The conventional PID peak time is 4.134 s, the overshoot is 0.911% (T\({}_{\text{fc}}\) = 346.28 K), and the adjustment time is 40.027 s, which is due to the strong hysteresis of temperature. The integral separation PID peak time is 5.234 s, the overshoot is 0.578% (T\({}_{\text{fc}}\) = 344.982 K), and the adjustment time is 42.027 s.
## 4 Field Applications Using the Novel Cutting Transport Ratio  
The effectiveness of the novel \(CTR_{m}\) was confirmed during the directional drilling of the 12.25\" intermediate sections of two offshore wells, which were Well-A and Wells-B (see Figure 5a,b), and onshore Well-C with a 12\"-diameter hole section (see Figure 6a), as well as a 8.5\" hole section drilled horizontally (see Figure 6b). These directional drilling sections were highly deviated, with the first two starting at 30 degrees and the third approaching a near-horizontal inclination of 90 degrees at the top of the reservoir in Well-A and Well-B.  
Figure 5: Application of \(\text{CTR}_{m}\) in the offshore proper hole cleaning of Well-A (**a**) and the poor hole cleaning of Well-B (**b**).  
beneficial tool in drilling operations, as it helps to prevent issues that may arise from inadequate hole cleaning. The following subsection describes the outcomes of implementing the CTR\({}_{\text{m}}\) model in Well-A and Well-B (see Figure 5). As seen in Figure 5a, a case involving Well-A, CTR\({}_{\text{m}}\) was utilized during drilling to optimize the ROP. At a depth of X3000 ft, the CTR\({}_{\text{m}}\) value was above 0.7, which suggests that the wellbore was clean and free from any cutting accumulation. The drilling crew did not notice any other indications of cuttings' build-up. However, when the drilling reached a depth of X3800 ft, the CTR\({}_{\text{m}}\) value began to steadily decrease from 0.7 until it eventually dropped to less than 0.5 at X3891 ft. On the other hand, as seen in Figure 5b, the second well studied in this research is referred to as offshore Well-B, where CTR\({}_{\text{m}}\) was utilized to evaluate the performance of hole cleaning in relation to cuttings' accumulation and to show the performance of ROP.  
Table 4 presents a summary of the impact of implementing CTR\({}_{\text{m}}\) on well performance, with a particular focus on the enhanced hole cleaning achieved in Well-A. The results showed that the average CTR\({}_{\text{m}}\) value of over 0.64 resulted in a significant improvement in the ROP in Well-A, which reached 205 ft/hr, compared to the 70 ft/hr of ROP observed in Well-B. These results demonstrate that the successful achievement of proper hole cleaning can lead to a substantial improvement in well performance.  
The overall analysis of the results in Table 4 reveals that the average ROP improvement in Well-A can be attributed to the effective hole cleaning achieved through the implementation of CTR\({}_{\text{m}}\). However, there were some instances where the CTR\({}_{\text{m}}\) values fell below the limit of 0.5, specifically from depth X3000 to X4000 ft, due to poor hole cleaning. This led to cuttings' accumulation in the wellbore and decreased the ROP to 105 ft/hr. In other words, the drilling process failed to effectively remove the cuttings, which impeded the drilling progress and resulted in a slower-than-expected ROP. These findings underscore the importance of maintaining proper hole cleaning throughout the drilling process to ensure optimal well performance.
# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
## 2 Aggregated Dispatch and Control Framework for Multi-Type Loads in Industrial Parks
### Control Characteristics of Multi-Type Loads in Industrial Parks
* Shutdown stage: After receiving a shutdown command, the controllable load enters the shutdown stage after a delay, and its shutdown process can be regarded as a zero-input response, which is the inherent startup characteristic of the controllable load, and can be described by a non-parametric model. Perform a shutdown response experiment when the controllable load is in a steady state, using the same method as in the startup stage. The unit step response sequence of the controllable load during shutdown is obtained as \(c_{k}=\{1,\ldots,1,\;c_{\delta+1},a_{\delta+2},\ldots,\;a_{L}=0\}\), and its output reaches steady state after \(L\) steps of change, \(\delta T\) is the control response delay of the controllable load during shutdown stage. The controllable load enters the shutdown process immediately after receiving a shutdown command, the power output of the controllable load during shutdown stage can be obtained as:  
\[p(k)=c_{i}p(k_{c}),\;k\in[k_{c},k_{c}+LT-1] \tag{4}\]  
The model of controllable load proposed in this paper is a non-parametric model, in which the response characteristic curve is obtained through fitting with measured data. When implementing a unit step response test on a controllable load, the time at which the control command is issued is considered as the time when the control action starts to take effect. Therefore, the impact of communication delay has already been reflected in the measured response results. The response delay considered in the model includes not only the response delay of the load itself but also the communication delay. The start-up delay and start-up response time of multi-type loads in industrial parks are different, which leads to different advance notice times required for their participation in regulation. Some types of loads need to be given regulation instructions one day in advance to make production plans for the regulation period, while some types of loads only need to be given regulation instructions 15 min in advance to respond quickly. At the same time, there is a large difference in the sustained response time of different types of loads, with some loads having a response duration of only 30 min and others being able to respond flexibly within a duration of 15 min to 3 h as needed. Therefore, it is necessary to determine the types of loads and their regulation power that participate in regulation based on the regulation instruction issuance time and regulation power curve requirements. It can be seen that modeling the response characteristics of multi-type loads in industrial parks is a prerequisite for precise control.
## 3 Results and Discussions
### Results of Molecular Dynamics Simulation
#### Adhesion Work and Spalling Work  
As shown in Figure 16, the adhesion work of the three minerals with PU is ranked as CaO \(>\) SiO\({}_{2}\)\(>\) CaCO\({}_{3}\), which indicates that the adhesion properties of PU with CaO are better than those of the other minerals. A possible reason for this result is that CaO is an alkaline oxide with a high number of hydroxyl (-OH) and oxygen ions (O\({}^{2-}\)) on its surface, and these reactive groups are able to react chemically with isocyanate groups (-NCO) in PU. The high energy of this chemical bonding enhances the adhesion properties of PU-CaO. In addition, the adhesion work of PU-CaCO\({}_{3}\) is 2.7 mJ/m\({}^{2}\), which is much lower than that of PU-CaO and PU-SiO\({}_{2}\). The explanation for this result lies in the low surface chemical reactivity of CaCO\({}_{3}\), which makes it difficult to form strong chemical bonds with the polar groups in PU. The ranking of the spalling work of the three minerals with PU is CaO \(<\) SiO\({}_{2}\)\(<\) CaCO\({}_{3}\). This is due to the fact that calcium oxide reacts violently with water and affects the stability of the PU-CaO interface. The ranking of spalling work also indicates that the mix composed of PU and limestone has poor water stability and is not suitable for areas with high precipitation. Under acidic conditions, the ranking of the spalling work of the three minerals with PU is SiO\({}_{2}\)\(<\) CaO \(<\) CaCO\({}_{3}\), which indicates that the adhesion properties of PU with basalt are higher than those of limestone under acidic conditions. In addition, under acidic conditions, the spalling work of PU-CaO increased slightly, PU-SiO\({}_{2}\) remained almost unchanged, while PU-CaCO\({}_{3}\) increased from 5.9 mJ/m\({}^{2}\) to 10.7 mJ/m\({}^{2}\). This may be due to the fact that SiO\({}_{2}\) is an acidic oxide and is more stable under acidic conditions. However, the chemical properties of CaCO\({}_{3}\) are more active under acidic conditions. It is worth noting that the spalling work of the three minerals with PU  
Figure 15: Calculation results of interaction energy.  
increased under acidic conditions, but the spalling work of basalt with PU was still lower than that of limestone, suggesting that basalt has better resistance to acid damage than limestone and is suitable for acid rain-prone areas. In conclusion, PU-basalt is the best material combination for permeable PU mixtures.
## 4 Data Analysis
### Statistical Analysis
#### 4.1.3 B200b-J Evaluation  
Figures 25-27 present the RMSD values for all the PZTs of beam \"B200b-J\". For the PZTs located in the interfacial surface, \"J3\" exhibits increased RMSD values, without performing a trend, but showing some fluctuations from state to state, indicating that there were no constant stress impacts over the surface. Further, \"J6\" presents an ascending trend from the 2 nd loading state to the 5 th. Both PZTs are located in the right span, where the fatal shear failure crack occurred. All the other \"J8\" demonstrate insignificant variations, following the structural integrity condition of their efficient monitoring area, in terms of their location.  
Moreover, the \"SA\" RMSD values (Figure 26b) follow the formation of the developed cracking pattern (Figure 13). Thus, SA3 demonstrates significantly higher RMSD values than all the relative embedded PZTs. The latter comes in agreement with the beam's structural integrity with respect to the PZTs' locations. In addition, higher values of RMSD are expected for SA4; however, this was achieved only at the later loading states. Further, despite the externally epoxy-bonded PZTs being less sensitive than the embedded ones, \"X3\" exhibits slightly higher RMSD values than the relative of \"X2\", as depicted in Figure 27b.  
From all of the above observations and the cracking pattern formation presented for each set of PZTs, it could be extracted that all the different PZT patches' configurations achieved successfully to identify the critical effect that was assigned to investigate. Hence, the formation and the development of the damage, which finally led to a fatal shear critical crack in the right span of the beam for both jacket and retrofitted beam, could be assessed.  
Figure 25: (a) Cracking at failure and “J” PZTs’ notation and position for “B200b-J”, and (b) RMSD index values for “J” of “B200b-J”.
# Identification and Optimization Study of Cavitation in High Power Torque Converter
## 2 Torque Converter Simulation and Analytical Modeling  
### Definition of Torque Converter Parameter  
The torque converter's main performance parameters include speed ratio \(SR\), torque ratio \(TR\), efficiency \(\eta\), nominal torque of the pump \(M_{\text{Bg}}\), nominal torque of the pump at zero speed \(M_{\text{Bg}}\), and capacity coefficient of the pump \(\kappa\). The nominal torque of the pump \(M_{\text{Bg}}\), refers to the torque absorbed by the pump when its speed is equal to 1000 r/min in the torque section of the traction condition area. The parameter \(\kappa\) represents the pump capacity factor of the torque converter at a specific \(SR\)[15].  
\[\begin{cases}SR=\frac{n_{T}}{n_{B}},TR=\frac{M_{T}}{M_{B}},\eta=\frac{M_{T} \cdot n_{T}}{M_{B}\cdot n_{B}}=TR\cdot SR\\ M_{\text{Bg}}=\frac{M_{\text{B}}}{(n_{B}/1000)^{2}},\kappa=\frac{n_{\text{i}}}{ \sqrt{M_{\text{i}}}}\end{cases} \tag{1}\]  
\(M_{\text{Bg}}\) Nominal torque of the pump in Newton meters (N\(\cdot\)m).  
\(M_{\text{B}}\) Torque absorbed by the pump under traction conditions, in Newton meters (N\(\cdot\)m).  
\(M_{T}\) Torque output by the turbine under traction conditions in Newton meters (N\(\cdot\)m).  
\(n_{B}\) Pump speed in revolutions per minute (r/min).  
\(n_{T}\) Turbine speed in revolutions per minute (r/min).  
\(n_{i}\) Pump speed in revolutions per minute (r/min) at speed ratio \(SR\).  
\(M_{i}\) Torque absorbed by the pump at a speed ratio of \(SR\) corresponding to the speed in Newton meters (N\(\cdot\)m).