esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 A Simulation Example
Based on the steady pressure distribution along the pipeline, the steady state pressure values at the three pressure measurement points before and after leakage are calculated, and the Gaussian white noise is added to construct the pressure simulation data at the pressure measurement points, as shown in Figure 4.  
By using the simulation data shown in Figure 4 as the measurement data, and using the iterative procedure based on the calculation steps described above, the estimated leakage rate and leakage location by EKF are obtained, as shown in Figures 5 and 6. As can be seen in Figure 5, the design leakage rate has a step-wise change in flow rate representing a leak that commences at 500 s into the simulation, and the leakage rate is 4% of flow, i.e., 0.1 kg/s. It can be seen that the estimated leakage rate curve is basically consistent with the designed leakage rate curve, indicating that the proposed method based on EKF can detect the leakage and estimate the leakage rate accurately. It is clear from Figure 6 that the design leakage location is 4 km downstream in the pipeline, and the curve is the estimated leakage location of the EKF. It can be seen from the figure that the two curves are also basically consistent, and the results show that the proposed method can locate the leakage position. Figure 4: Pressure simulation data. Figure 5: Estimated leak rate using simulation data. EKF: extended Kalman filter. Figure 6: Estimated leak location using simulation data.
# Joint Deployment of Sensors and Chargers in Wireless
## 5 Simulation Evaluation  
### Simulation Setup  
Extensive simulations are performed to verify the accuracy, scalability and efficiency of the proposed algorithm. In these simulations, the PoIs are randomly distributed in a two-dimensional space \(\Phi\) of 50 m \(\times\) 50 m. The parameters used in the simulations are shown in Table 3, where the parameters in the sensing model and charging model refer to those in [34; 35], respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline Side length of the square & 50 (m) & \(\lambda\) & 0.5 \\ \(J\) & 5 & \(\beta\) & 0.5 \\ \(P_{s}\) & 5 (W) & \(r\) & 5.6 \\ \(P_{c}\) & 0.012 (W) & \(r_{e}\) & 3.4 \\ \(P_{max}x\) & 0.04 (W) & \(c_{th}\) & 70\% \\ \(d_{th}\) & 15 (m) & \(L_{c}\) & 5 \\ \(\tau\) & 0.003 & \(L_{s}\) & 1 (m) \\ \(\varepsilon\) & 0.2316 & Pols & 70 \\ \(k\) & 10 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
Corresponding to the two sub-problems in Section 3.4 and the corresponding solutions in Section 4, the subsequent analyses are also carried out from two parts, i.e., sensor deployment and charger deployment. All numerical analyses are generated by averaging the results of 50 independent simulations to avoid the influence of random factors. All experiments are evaluated in the MatlabR2021a environment, and the computer model is Lenovo Legion Y7000 2020 (Shanghai, China).
# A Promising Technological Approach to Improve Indoor Air Quality
## 2 Materials and Methods
### Photocatalytic Evaluation_Real-Scale  
A building of the Hellenic Army in Crete was used to test the de-polluting efficiency of the photo-paint under real scale application. In general, the photocatalytic efficiency of a material while applied in a real scale environment and more specifically in indoor building environment could be estimated through two approaches:  
a) A first approach could be the installation of air quality and environmental monitoring systems (passive inorganic (NO) and organic (BTX) samplers, temperature and humidity recorders) in the buildings prior to the application of the photo-paint. To that end, a reliable record on the concentration levels of air pollutants in the case study building (without the photo paint) for at least 12 months should be obtained. Accordingly, the photo-paint should be applied in the buildings and environmental parameters should be monitored through passive sampling techniques for another 12 months. The potential changes in the indoor air quality due to the photocatalytic action of the photo-paint will be recorded and quantified to illustrate the capability of the photo-paint to improve IAQ. A restriction of this approach could be the variations in outdoor air quality and meteorological conditions during the different sampling periods. In order to eliminate the effect of the above restrictions, the experiments has to take place the same season (e.g., winter) and the outdoor concentration should be considered on the final results.  
b) A second approach to evaluate the efficiency of the photo-paints is to estimate the IAQ differences between \"reference\" rooms (without photo paint) and \"green\" rooms (with photo-paint), which are located on the same level (in a raw) and where the same activities take place. The current approach overcomes the restrictions of the previous one and for that purpose was used in the current study. More specifically a room of 120 m\({}^{2}\) in the ground floor was paint with the innovative photocatalytic paint (\"green room\") and compared with a same size and usage room (\"reference room\") located very close to the \"green\" one. The outcome of this approach was compared with the outcome of the lab tests in order to estimate the differences of the photocatalytic performance of a material when studied in a control experimental reactor and under real world environmental conditions. Passive samplers for NO and Toluene were applied. More specifically, four passive samplers/pollutants were installed in each of the rooms for 30 days and then analyzed in the lab using the well-established Saltzmann spectrophotometric method for NO, while the Toluene samplers were and desorbed by carbon disulphide and analyszed by gas chromatography (GC/FID).
## 2 Wettability Measurement Methods
### Analytical Methods
#### 2.4.2 Modified Lak Method  
The wettability index utilizes certain characteristics of the relative permeability curves. [PERSON] et al. proposed a modified Lak method to determine wettability using the area under the relative permeability curves, as described in Equation (7) [37].  
\[I_{ML}=\frac{A_{o}-A_{w}}{A_{o}+A_{w}}, \tag{7}\]  
where \(A_{o}\) is the area under the oil relative permeability curve, and \((A_{w})\) is the area under the water relative permeability curve. The values of the modified Lak method range from \(-1\) to \(+1\). An index value \((I_{ML}>0)\) indicates a water-wet rock, while \((I_{ML}<0)\) suggests an oil-wet rock. An \((I_{ML})\) value close to 0 indicates a neutral wettability with respect to both water and oil. Figure 10a provides a schematic demonstration of the modified Lak method. For example, Figure 10b shows the data obtained from real samples for the calculation of the \((I_{ML})\) index. For samples 3 and 4 from the first set, the wettability calculated using the modified Lak method was \((I_{ML}=-0.41)\) and \((I_{ML}=0)\), respectively [37].  
[PERSON] et al. tested the modified Lak method on 20 carbonate samples, divided into two groups, and compared it with other methods such as Amott-Harvey, USBM, and Lak. Figure 11 shows the correlations between the indices. As observed, the  
Figure 10: (a) A schematic representation of the areas under the relative permeability curves for water and oil, as well as the determination of the modified wettability index using the Lak method; (b) the imbibition relative permeability data of the tested samples 3 and 4 of the first dataset [37].  
modified Lak method exhibits a good correlation with the Amett-Harvey index (\(R^{2}=0.65\)) and the Lak method (\(R^{2}=0.85\)), while it shows a moderately good correlation with the USBM index (\(R^{2}=0.45\)). The Lak method also demonstrates a good correlation with the USBM index (\(R^{2}=0.59\)) and the Amett-Harvey index (\(R^{2}=0.80\)) [37].  
As demonstrated by the examples, the advantage of analytical methods is their exceptional convenience in determining the average wettability of a rock when the relative permeability curves are known [37].  
The limitations of these methods include the quality of the data obtained under varying experimental conditions for determining the relative permeabilities of rock samples. The modified Lak method is better suited for stationary experimental conditions and normal breakthrough scenarios, while the Lak method is applicable for unsteady-state experiments and late breakthrough conditions. Additionally, wettability determination can be influenced by pore geometry, interfacial tension, and fluid viscosities. Therefore, these methods are best applied under similar fluid viscosities as those tested in these studies [36; 37].
## 3 Experimental Scenario and Specifications
### Mioty Specifications  
Mioty, similar to LoRa, operates in the ISM band. For this study, a frequency of \(920\,\mathrm{MHz}\) was chosen, in accordance with South Korean regulations. The system supports both \(100\,\mathrm{kHz}\) and \(200\,\mathrm{kHz}\) bandwidth modes, as specified by the ETSI standard for uplink IoT applications. In this work, the bandwidth was set to \(200\,\mathrm{kHz}\) in order to maintain a bandwidth similar to that of the other two communication technologies used in this study.  
The TSMA (Time-Synchronized Multiple Access) mode used here was set with a carrier spacing of 2380.37 Hz. Since this is a simulation experiment, theoretical values were employed, and any practical implementation issues were ignored. For uplink transmission, Gaussian Minimum Shift Keying (G-MSK) modulation was used, resulting in a bit rate of 2380 bps in the Ultra Low-Power (ULP) transmission mode. Again, the bit-rate value was used as a theoretical parameter for the simulation.  
According to ETSI TS 103,357 v1.1.1, the noise figure (NF) was set to 4 dB, and the transmission power (Tx Power) was \(-16\) dBW. The receiver sensitivity was set to \(-139\) dBm. The required \(E_{b}/N_{0}\) for coherent reception was calculated as 3.6 dB. Using the same equations for \(T_{\text{sys}}\) and \(G/T\) as described earlier, the system temperature was calculated as 583 K, and the value of \(G/T\) was \(-25.51\) dB/K [31].  
All of these parameters were used as theoretical values in this simulation experiment. Table 2 summarizes the key communication parameters of the three LPWAN technologies evaluated in this study, while Table 3 provides the detailed specifications of the corresponding satellite and ground station configurations. All Parameter values originate from large-scale measurement campaigns and official specifications [14; 15; 16; 18; 19; 32].
## 4 Discussion
On the other hand, although the baseline depression score (TDQ) and balance (BBS) scores are not correlated (\(p\) = 0.25), the post-intervention TDQ score is significantly correlated to the BBS score (\(p\) = 0.016). This may indicates that better post-intervention physical condition may contribute to a better quality of life and mood after completing the rehabilitation (Figure 5A,B). However, there are more factors other than age (such as gender, brain lesion side) affecting the outcome of rehabilitation. In summary, for the subgroup analysis of overall rehabilitation effect, we found both sexes benefited in depression and men benefited more in balance than women. Patients with left-brain lesions improved more in depression than those with right lesions after rehabilitation, while balance equally improved regardless of the lesion site. For patients under or above the age of 60, depression and balance both significantly improved after rehabilitation. Older age was significantly related to poor balance but not depression. Post-intervention higher balance (BBS) score was significantly correlated to lower TDQ score and thus a better mood. Our first limitation was that this was a pilot study with small recruitment number, but we still obtained many significant results. Second, the inclusion criteria limit each subject must be able to stand independently for 1 min, which indicated participants were all relatively mildly functionally impaired. Whether the results can be applied to more disabled people is unknown. Third, in order to respect participants' will and their capability, our study was not randomized. (However, if done in a randomized manner, the completion rate could be much lower in the intervention group.) Also, the long-term effect after 8 weeks was not confirmed. A larger randomized controlled study with longer follow up period is necessary in the future. Finally, we should add one more group with standard rehabilitation of 300 min (5 h) each week to see which group performs better. However, patients always get bored and become impatient during repetitive exercise in the real clinical setting. That is why we add recreational sport (such as yoga) as an adjuvant exercise because yoga is relaxing and interesting. Because of the COVID-19 pandemic, group exercise may increase the infection risk. Whether remote video-assisted yoga training provides the same benefit as the current group exercise requires further research.
# Design of an IMCPID Optimized Neural Network for Stepless Flow Control of Reciprocating Machinery
###### Abstract  
It is usually difficult to design a controller for a nonlinear multiple-input and multiple-output (MIMO) system. The methodological approach taken in this study is a mixed methodology based on a PID-type internal model control (IMC) method and neural network (NN) optimization algorithm. The NN controller is designed for adjusting the sole parameter in IMCPID and compensating the characteristic changes and non-linearity in stepless flow control. In this study, a simulation of a nonlinear MIMO system with strong coupling is carried out. The simulation results indicate that the proposed control method has a better performance in settle time, overshoot, robustness and set-point tracking accuracy compared with other considered methods.  
stepless flow control; decoupling; neural network; nonlinear system +
Footnote †: journal: Applied Sciences
# Contribution of Climate Change and Grazing on Carbon Dynamics in Central Asian Pasturelands
## 1 Introduction  
Occupying about 47% of the global land area, the dryland ecosystem is an essential component to the global carbon cycle [1]. However, most of the arid and semiarid regions are facing serious ecosystem degradation that was caused by climate change, as well as human interferences [2]. Carbon dynamics in the dryland ecosystem are sensitive to climatic variability and human activity, yet considerably less is known about the extent of the influence. Pasturelands in Central Asia were degraded as a consequence of a rapid expansion in livestock numbers and economic reforms initiated in the 1980s [3].  
B biogeochemical models constrained by high-quality, gridded atmospheric variables are essential for simulating the response of carbon and water dynamics to environmental changes at a regional scale [4; 5]. Reducing simulation uncertainties is becoming increasingly significant to further explore the impacts of climate change on terrestrial carbon cycles [6]. The simulation quality depends on the forcing data accuracy and model structures and parameters [7]. Comparing the uncertainties resulting from model schemes and parameterizations, uncertainties caused by the meteorology-driven data need to be further investigated, especially in arid and semi-arid regions, which can deepen our understandingof climate and ecosystem interactions [8; 9] and is helpful in predicting the regional response to environmental change for the purpose of sustainable land use planning.  
A number of atmospheric reanalysis datasets with high tempo-spatial resolutions and accuracy were well developed in the past decades, which enable their applications in forcing biogeochemical models. Driven by an ensemble of reanalysis data, ecological models can greatly reduce the simulation uncertainties.  
Therefore, in this study, we chose the major pastureland types in Central Asia as the study area, and evaluated the modelling interannual Net Ecosystem Exchange (NEE) variability driven by three climate datasets. We aim to highlight the importance of an accurate climate dataset for terrestrial carbon cycle simulations, and especially for the assessment studies of climate and environmental change involving Central Asia. Our study wishes to offer possible references of grazing management and climate change adaptation to the policy makers as well as local herdsmen of Central Asia.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 3 Methodology
### Evaluation Metrics  
To quantitatively evaluate model performance in carbon stock estimation, we employ three standard regression metrics: Coefficient of Determination (\(R^{2}\)), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). These metrics collectively capture the model's explanatory power, prediction accuracy, and error robustness.
# AC Fault Ride-Through Strategy of a Transformerless Soft Normally Open Point
## 6 Simulation Analysis
### System Response Characteristics of Traditional Control Strategy  
In the steady state, MMC1 adopts a constant active power and constant reactive power control strategy, MMC2 adopts constant DC voltage and a constant reactive power control strategy. At \(t\) = 0.8-0.9 s, the AC system connected to MMC1 happens an A-phase grounding fault.  
Figure 9a-f shows the response waveforms of three-phase AC voltage, positive and negative DC voltage, AC zero-sequence voltage, AC zero-sequence current, SM capacitor voltage and the inserted SM number of the upper arm in phase A on the MMC1 side. It can be seen from Figure 9 that the voltage of phase A drops to zero quickly after the fault, and the voltages of phase B and C increase to 1.73 times the rated phase voltage. The zero-sequence voltage amplitude is equal to the rated phase voltage. The positive and negative DC voltages contain the fundamental frequency component whose amplitude and phase are the same as the zero-sequence voltage component, which proves the correctness of Equation (12). Since the AC distribution system is the arc suppression coil grounding, the zero-sequence current is less than 16 A, as shown in Figure 9d. During the whole fault period, because the control system only contains positive and negative sequence control, and the zero-sequence power is unchanged, so the SM switching and SM capacitor voltage stay the same.  
Figure 10a-e shows the response waveforms of the three-phase AC voltage, the AC zero-sequence voltage, the AC zero-sequence current, the SM capacitor voltage and the inserted SM number of the upper arm in phase A on the MMC2 side. It can be seen from Figure 10 that the voltage of phase A drops to 1.5 kV, the voltages of phase B and C increase to 13.2 kV and 14.8 kV after the fault. The voltage characteristics are similar to those of MMC1, and the correctness of the above theoretical derivation is demonstrated. The zero-sequence voltage amplitude on the MMC2 side is similar to MMC1. The zero-sequence current is small: its amplitude is less than 16 A. During the whole fault period, the SM switching and SM capacitor voltage of MMC2 stay the same. Its principle is the same as MMC1.  
Figure 8: Schematic diagram of simulation model.
## 1 Introduction
However, the FreeBSD IEEE 802.11s implementation has not been used in any test bed or in any embedded system that acts as a wireless mesh router. Customized versions of FreeBSD for the embedded system applications are available, such as the pfSense, whose main usage is in firewalls and routers [12]. However, these customized FreeBSD are using an older version of the FreeBSD which does not support the wireless mesh networking. In order to evaluate the FreeBSD IEEE 802.11s implementation, a test bed will be needed. The main contributions of this paper are the following:  
* First, although 802.11s is under standardization, no practical test and performance evaluation has been conducted with WMN reference implementations. We built a test-bed of wireless mesh capable embedded systems that runs on the customized version of FreeBSD OS and conducted extensive performance evaluations. The embedded systems also incorporated two channels that are used to distribute the network load according to its purpose. A series of outdoor performance evaluations had been carried out on the 802.11s protocol used. Comparisons were conducted to demonstrate the advantages of this 802.11s implementation over the conventional wireless distribution system (WDS) interface; moreover, a single channel 802.11s was implemented in a real-time test-bed instead of using simulation. * Secondly, the path selection mechanism in the FreeBSD IEEE 802.11s implementation is also discussed in this paper based on the two multi-hop WMN tests that were performed on this test-bed. The test results show the flaws in the path selection mechanism used in the protocol (Figures 12 and 13), where the longer path was chosen instead of the shortest path. The remainder of this paper is organized as follows. Section 2 will explain the wireless mesh network (WMN), hybrid wireless mesh protocol (HWMP), multi-channel WMN and hop count. Section 3 will briefly explain the embedded system implementation. The results and analysis of the performance tests will be discussed in Section 4 and additional discussions will be provided in Section 5. The paper will end with the conclusion in Section 6.
## 5 Safe and Accurate Aerial Manipulation
### Control Methods for Aerial Manipulation
#### 5.2.4 Variable Structure Control  
The target is to lead the UAV (9) to a displaced equilibrium \(\mathbf{x}^{d}\). Therefore, the following changes of variables are proposed [42]:  
\[\mathbf{z} =\mathbf{x}^{d}-\mathbf{x} \tag{12}\] \[\dot{\mathbf{z}} =\mathbf{x}^{d}-\dot{\mathbf{x}}\] (13) \[\sigma =\mathbf{C}\mathbf{z}+\dot{\mathbf{z}} \tag{14}\]  
where \(\sigma\in\mathbb{R}^{6}\) is called the _sliding surface_ and \(\mathbf{C}\in\mathbb{R}^{6\times 6}\) is a constant diagonal matrix. Now the control target is to reach the origin (0,0). In this context, the following candidate Lyapunov function \(V(\sigma)=\frac{1}{2}\sigma^{T}\sigma\) is proposed; its derivative is given by the following [42]:  
\[\dot{V}(\sigma)=\sigma^{T}\dot{\sigma}=\sigma^{T}\Big{(}\mathbf{C}\dot{ \mathbf{z}}+\bar{\mathbf{x}}^{d}-u-\mathcal{F}(\mathbf{x},\dot{\mathbf{x}},t) \Big{)} \tag{15}\]  
Using the following controller with acceleration feedforward,  
\[u=C\dot{\mathbf{z}}+\bar{\mathbf{x}}^{d}-K_{s}\text{sign}(\sigma) \tag{16}\]  
\(\dot{V}\) can be rewritten as follows:  
\[\dot{V}(\sigma)\leq|\sigma|(\mathcal{F}_{max}-K_{s}) \tag{17}\]  
where it can be appreciated that \((\mathbf{z},\dot{\mathbf{z}})\rightarrow(0,0)\) as long as \(K_{s}>\mathcal{F}_{max}\). The proposed controller (16) forces the motion of \((\mathbf{z},\dot{\mathbf{z}})\) to reach the surface and to remain on it despite the disturbance \(\mathcal{F}(\mathbf{x},\dot{\mathbf{x}},t)\), by means of finite-amplitude high-frequency switching along (14) called _chattering_, which is important to avoid in systems such as DC motors or aircrafts [42] because it might be dangerous for the actuators. Different approaches have been proposedfor _chattering_ attenuation [42; 44], which are suitable to be implemented for UAVs, specifically higher order SMC, since they conserve robustness and do not necessarily require the derivative of \(\mathbf{z}\). In this context, the \(u_{0}\) must be properly regulated (9).
## 3 Results
### Compressive Strength
As for the use of the complex additive MS + GBFS (1:1) as a replacement for part of the cement, it also, as in the case of GBFS, leads to a loss in compressive strength; however, the percentage of strength reduction is noticeably less. In the case of using MS + GBFS (1:1) in an amount of 4%, a slight increase in strength is observed in an amount of 2%. In the case of replacements of 8%, 12%, 16%, and 20% by weight of cement, the drop in compressive strength was 6%, 17%, 20%, and 25%, respectively.