esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation on Interference Test for Wells Connected by a Large Fracture
###### Abstract  
Pressure communication between adjacent wells is frequently encountered in multi-stage hydraulic fractured shale gas reservoirs. An interference test is one of the most popular methods for testing the connectivity of a reservoir. Currently, there is no practical analysis model of an interference test for wells connected by large fractures. A one-dimensional equation of flow in porous media is established, and an analytical solution under the constant production rate is obtained using a similarity transformation. Based on this solution, the extremum equation of the interference test for wells connected by a large fracture is derived. The type-curve of pressure and the pressure derivative of an interference test of wells connected by a large fracture are plotted, and verified against interference test data. The extremum equation of wells connected by a large fracture differs from that for homogeneous reservoirs by a factor 2. Considering the difference of the flowing distance, it can be concluded that the pressure conductivity coefficient computed by the extremum equation of homogeneous reservoirs is accurate in the order of magnitude. On the double logarithmic type-curve, as time increases, the curves of pressure and the pressure derivative tend to be parallel straight lines with a slope of 0.5. When the crossflow of the reservoir matrix to the large fracture cannot be ignored, the slope of the parallel straight lines is 0.25. They are different from the type-curves of homogeneous and double porosity reservoirs. Therefore, the pressure derivative curve is proposed to diagnose the connection form of wells.  
interference test; well connectivity; hydraulic fracturing; pressure conductivity coefficient 2019 9 2019 2019 1
## 4 Control Strategies
### PV Converter Control
#### 4.1.1 PV Converter MPPT Method  
The electrical power supplied by PV cells is a non-linear function of voltage, current, temperature, and solar irradiance. This non-linearity makes it challenging to obtain the operating point at which its maximum power is extracted, as this point varies throughout the day due to variations in irradiance and temperature. The objective of any MPPT algorithm is to find the voltage to be applied to the PV string terminals that results in the maximum possible generated power for the momentary conditions of irradiance and temperature.  
Several techniques for determining MPP have been proposed over the years. These techniques vary in complexity, speed of convergence, voltage fluctuation around the MPP, and computational cost. The MPPT technique chosen for this project was the very popular perturb and observe (P&O) algorithm due to its simplicity and acceptable performance in most situations [11]. The P&O MPPT technique changes the PV array voltage in one direction and observes the variation in the output power. If the generated power increases, then the voltage perturbation continues in the same direction. However, if the output power reduces, the voltage perturbation occurs in the opposite direction. The voltage disturbance process is repeated periodically, each \(T_{MPPT}\), and the array voltage oscillates around the MPP. This oscillation can be minimized by reducing the size of the disturbance, but minimal disturbances make the technique slow to track the MPP. The parameters of the P&O MPPT method are \(T_{MPPT}\) and the magnitude of the voltage perturbation is \(\Delta V\). Typical choices of these parameters are \(\Delta V=0.5\%\) of the PV array open-circuit voltage and \(T_{MPPT}=4\tau_{Vdc}\), where \(\tau_{Vdc}\) is the time constant of the PV array output voltage variation.  
After the reference value of the PV array, DC voltage is determined, and the DC/DC boost converter synthesizes it. Thus, the duty cycle of the boost converter is determined from the DC bus voltage of the microgrid and the reference PV array voltage, calculated according to the P&O algorithm for achieving MPPT.
## 5 Discussion
Although our study adopted an empirical model, however, the model construct was based on the derivation from radar ocean wave modulation theory. Therefore, we think neglecting hydrodynamic modulation probably brings some errors. Based on previous studies [39], the hydrodynamic modulation first decreases, and then increases with incidence angles. It has the weakest value near 10\({}^{\circ}\). It seems to mean that the error due to the neglect of hydrodynamic modulation, should decrease monotonously with incidence angles from 2.5\({}^{\circ}\) to 7.5\({}^{\circ}\). However, Figure 7a,b show that the retrieval accuracy trends with incidence angles were not significantly monotonous. Therefore, we think that the retrieval error due to the neglect of hydrodynamic modulation are probably not the dominated one. Second, the proposed model can extract SWH data, but not wave spectra. As is known, the wave spectrum contains complete wave information, which can be used to estimate any wave parameter [9]. It has wider applications than the single SWH parameter. From the preliminary analysis results presented in Figure 2, the InIRA can image the ocean surface and capture wave signatures. The cutoff effect is weak owing to the low altitude, and thus, the image spectra are relatively complete. We believe that the InIRA still has the possibility of retrieving wave spectra using the transfer relation. To achieve this, the main challenge is that of estimating the nonlinear effects and the hydrodynamic modulation function in the transfer relation. This issue can refer to related theoretical studies on SARs in [9]. It is noted that the quasi-specular scattering should be considered instead of the original Bragg scattering. Third, some biases still exist between the retrievals and collocations, particularly for higher SWH ranges. From the comparison of the SWH retrieval presented in Figure 6, WW3 SWH data used for modeling are basically within 5.0 m. The proposed model will mainly be applicable for this range. Moreover, the high sea state is often accompanied by nonlinear effects, which may be caused by wave breaking. This nonlinearity effect on backscatter is very evident. For instance, the saturation of backscatter from SARs and scatterometers due to nonlinearity has been recognized [41]. This prompts researchers to explore other sensors for observing high winds. The sensors used include radiometers and SARs with cross-polarizations [42; 43]. Low incident backscatter in the high sea state is also analyzed, and no clear correlation between backscatter and wind due to nonlinear effects is found [44]. From the above analysis, the bias in the high SWH range in this study is probably induced by the particularity of the SWH dependence in high sea states. When there are abundant data in high SWH ranges, the SWH dependence in the proposed model will be refined and the bias may be further improved.
## 4 Discussion
### Limitations  
The inundation identified from satellite products cannot depict all real hazard impacts due to the limited revisit time of satellite constellations and cloud coverage [53]. The detection error of flooded water bodies is inevitable, and a solution to achieve higher detection accuracy is to adopt advanced deep learning techniques. For example, [PERSON] et al. [38] utilized a convolutional neural network for flood monitoring at the Poyang Lake and obtained promising outcomes.  
Another methodological limitation stems from the limited number of assessment factors, which may not fully characterize the entire flood risk picture. In future research, we will consider integrating more hydraulic flood hazard indicators, such as flood depth and velocity, as well as other vulnerability-related factors referring to household or social resistance capability, as done in some cutting-edge works [21; 83; 85; 86]. Machine learning methods can also help identify the most important factors influencing risk [87].  
In addition, the prediction bias could have been amplified by the coupling of multi-source datasets, although these projection datasets were verified to be reliable. Future potential solutions include updating input derived from the latest CMIP6 program to keep pace with climate trends or employing downscaled high-resolution regional climate model products. Here, the land use projection did not consider scenarios under different development pathways, and in further research, RCP and SSP scenarios should be included in the simulation of land-use evolution to improve prediction accuracy.
## 4 Empirical Results
### Simulation Result  
In this section, the outcome of the simulations carried out to analyze the performance of the SLD-GEDIR protocol in handling frequent changes in network topology due to the high mobility of vehicles and in managing dense vehicular network in an urban traffic environment are presented. In the simulation, end-to-end delay, throughput, and one-hop link disconnections in the network have been computed. Results obtained for SLD-GEDIR algorithm are compared with three state-of-the-art protocols: P-GEDIR, J-GEDIR and V-GEDIR.
# Energy-Efficient RIS-Enabled SISO-OFDMA Communication via Lower Bound Optimization
## 3 Proposal Method and Discussion
### Optimization Respect to Subcarrier and Power Allocations
#### 3.1.2 Power Allocations  
The sequential quadratic programming method is an iterative optimization technique used to solve constrained optimization problems, often encountered in mathematical modeling and engineering applications. SQP combines principles from both [PERSON]'s method and Lagrange multipliers to find the optimal solution within a feasible region defined by constraints.  
In the context of SQP, the following subproblems should be defined:  
\[\underset{\mathbf{P}\in\mathcal{P}}{\text{min}} g(\mathbf{P})\] (11) subject to \[b(\mathbf{P})\leq 0,\quad\forall k=1,2,...,K,\] (11a) where \[g(\mathbf{P})=-\Bigg{\{}\frac{\sum_{n\in\mathcal{N}}\rho_{k,n}r_{k,n}}{\xi\sum_ {n=1}^{N}\rho_{k,n}p_{k,n}+\alpha_{k,\text{RIS}}\times P_{\epsilon}\Bigg{\}}\] (12) and \[b(\mathbf{P})=-\sum_{n\in\mathcal{N}}\rho_{k,n}r_{k,n}+\hat{R}_{k}\quad \forall k=1,2,...,K. \tag{13}\]  
The primary idea behind the SQP method is to use a second-order subproblem at the current point \(\mathbf{P}^{(t)}\) and minimize this subproblem to determine the new point \(\mathbf{P}^{(t+1)}\).  
In its simplest form, the SQP method defines the second-order subproblem in the \(t_{\text{th}}\) iteration as follows:  
\[\underset{\mathbf{P}}{\text{min}} \frac{1}{2}d^{T}\
abla_{\mathbf{P}\mathbf{P}}^{2}L(\mathbf{P}^{( t)},\lambda^{(t)})d+\
abla^{T}g(\mathbf{P}^{(t)})d+g^{(t)}\] (14) s.t. \[\
abla^{T}b(\mathbf{P}^{(t)})d+b(\mathbf{P}^{(t)})=0, \tag{14a}\] \[\
abla^{T}b(\mathbf{P}^{(t)})d+b_{i}(\mathbf{P}^{(t)})\leq 0. \tag{14b}\]  
Here, \(\
abla_{\mathbf{P}\mathbf{P}}^{2}L(\mathbf{P}^{(t)},\lambda^{(t)})\) is the Hessian matrix (second-order partial derivatives matrix) of the Lagrange function for the problem (11), \(\
abla^{T}g(\mathbf{P}^{(t)})\) denotes the gradient of the objective function at point \(\mathbf{P}^{(t)}\), \(\
abla^{T}b(\mathbf{P}^{(t)})\) represents the gradient of the constraints, and \(d\) is the direction of motion.  
\[L(\mathbf{P}^{(t)},\lambda^{(t)})=g(\mathbf{P}^{(t)})+\lambda*b(\mathbf{P}^{( t)}) \tag{15}\]  
Solving this second-order subproblem yields the solution \((d^{(t)},\lambda)\), which, under Karush-Kuhn-Tucker (KKT) conditions, is valid for the problem. A merit function is designed to assess solution suitability and balance the decrease rate of the objective function with respect to the established problem constraints.  
The iterations continue until an acceptable answer is acquired [11]. The SQP algorithm is presented in Algorithm 2.
## 7 Conclusions and Future Work  
In this paper, we propose an infrastructure-less, indoor emergency response system to evacuate civilians with the aid of smart handsets and cloud servers. A coordinated emergency navigation algorithm is proposed to guide evacuees in loose groups. The experimental results prove that the algorithm can increase the survival rate by reducing the number of drained smart phones in an evacuation process and raising the likelihood for an evacuee with a depleted mobile device to encounter other evacuees in the line of sight and follow them to egresses. Due to the considerable energy consumption between the cloud and smart phones during the communication processes, an AHCPN-based energy-efficient protocol is also presented to prolong the lifetime of smart handsets. Simulations indicate that the protocol can significantly decrease the number of drained smart phones in an evacuation process and balance the remaining battery power among portable devices. Future research will be directed to improve the flexibility of the system by leveraging the mobile agent technology, because current cloud-based emergency response systems that are based on the client-sever paradigm demand pre-installed services in participating devices [59]. Moreover, since mobile agents can migrate seamlessly through multiple clouds and different portable devices, a mobile agent-based emergency response system has the potential to reduce communication costs and ease network congestion in large-scale emergency evacuations by dynamically optimising the locations of mobile agents. We also think that cloud-based techniques can benefit systematic and repetitive issues, such as the clearance of improvised explosive devices (IOD) and land mines [60].
## 4 Discussion
### Correction  
Regarding the correction of the shift through the implementation of the proposed linear transformation, it appears to be effective just in the case of the laboratory dataset and only under specific conditions. In particular, Figure 14 highlights that the application of this transformation has led to a significant enhancement in classification accuracy (and a consequent significant decreasing of the detected covariate shift on corrected data) only for the _\"Test diff Ref diff Chs\"_ test. When comparing accuracy before and after the correction, a significant increasing emerges, with a \(p\)-value \(<\) 0.05. Nevertheless, the same behavior is not observed in the case of the realistic dataset, where Figure 16 does not reveal any significant difference in accuracy levels, with and without correction.  
In the laboratory dataset, the application of the method resulted in a significant difference between accuracy values before and after, notably highlighted by [PERSON]'s D measure. This difference is particularly noticeable in the configuration where we expected more variation in data (i.e., _Test diff Ref diff Chs_), where the [PERSON]'s D value reaches 6.48. Conversely, in the realistic dataset, there is no [PERSON]'s D value indicating a significant average difference in achieved accuracy values.  
In a realistic context, as described, the application of the correction function seems to worsen the situation, compared to the control distribution, as depicted in Figure 17, in contrast to the laboratory scenario (Figure 15).  
This behavior could be associated with the lack of stationarity of the EEG signal in similar conditions. In particular, even if participants have been asked to keep their eyes open during the three repetitions, it cannot be guaranteed that the elicited features used to normalize the new data with anomalies were the same, apart from the difference in sensors' position.  
On the contrary, the analysis performed over the repeated sessions of the laboratory dataset (MATB) showed stability in EEG traces during rest sessions (i.e., eyes-opened), maybe because the participant was asked in that specific case to look at the interface without reacting. To assess this stability, we employed an index based on the percentage of data affected by covariate shift, using the same methodologies as described in this study. Specifically, for each session of each participant, we built an Isolation Forest model and tested it against the remaining 11 sessions, considering the four configurations described in this work. The results, presented in Figure 10, reveal that the blue and violet curves exhibit similar trends (\(p\)-value \(>\) 0.05). However, as we move to conditions with different sensor positioning, we observe an increase in the percentage of data affected by covariate shifts. The worst condition (depicted by the orange curve) occurs when both recording and reference sensor changes are introduced with respect to the original sensor's positioning (Control data).
# Nascent Rice Husk as an Adsorbent for Removing Cationic Dyes from Textile Wastewater
## 3 Results and Discussion
### Adsorption Isotherm
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**adsorbate**} & \multicolumn{3}{c}{**Langmuir Isotherm**} & \multicolumn{3}{c}{**Freundlich Isotherm**} \\ \cline{2-7}  & \(Q_{m}\) **(mg/g)** & \(K_{L}\) **(I/mg)** & \(R^{2}\) & \(K_{F}\) & \(1/n\) & \(R^{2}\) \\ \hline MB & 25.4645 & 0.3068 & 0.9868 & 7.3348 & 0.3155 & 0.9062 \\ CV & 24.4781 & 0.2493 & 0.9785 & 7.4143 & 0.2773 & 0.9107 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Langmuir and Freundlich constants for adsorption of methylene blue (MB) and crystal violet (CV) onto rice husk. \(C_{c}\): the dye concentration in aqueous solution after reaction (mg/L); \(K_{L}\): Langmuir adsorption constant (L/mg); \(Q_{m}\): maximum adsorption amount of dye per unit mass of RH (mg/g); \(K_{F}\): distribution coefficient (L/g); \(n\): Freundlich constant. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**adsorbent**} & \multicolumn{2}{c}{**Adsorption Capacity**} & \multirow{2}{*}{**Particle Size**} & \multirow{2}{*}{**pH**} & \multicolumn{2}{c}{**Temperature**} & \multirow{2}{*}{**Reference**} \\  & **(mg/g)** & & & & (\({}^{\circ}\)C) & \\ \hline Papaya seeds & 555.56 & 125–250 \(\mu\)m & 4.00 & 30 & [74] \\ Periwinkle shells & 500 & 150 \(\mu\)m & 7.00 & 25 & [75] \\ Date stones & 398.19 & 250 \(\mu\)m & 7.00 & 30 & [76] \\ Bamboo activated carbon & 286 & 1–2 mm & 3.70 & 25 & [77] \\ Chitosan beads & 262 & 4.42 mm & NA & 30 & [78] \\ Pea shells & 246.91 & 212 \(\mu\)m & NA & 25 & [79] \\ Alfa grass & 200 & 500–650 \(\mu\)m & 12.0 & 20 & [80] \\ Broad bean peels & 192.7 & 350–400 \(\mu\)m & 5.00 & 30 & [81] \\ Garlic peels & 142.86 & NA & 6.00 & 50 & [82] \\ Natural poplar leaf & 135.35 & 0.425–0.850 mm & 7.00 & 20 & [83] \\ Pine-tree leaf biomass & 126.58 & \(<\)350 \(\mu\)m & 9.20 & 30 & [84] \\ Modified clay-ball & 100 & 1–2 mm & NA & 50 & [85] \\ Palm kernel fiber & 95.4 & 50–80 \(\mu\)m & 7.20 & 25 & [49] \\ Peanut husk & 72.13 & 0.425–0.85 mm & 7.00 & 20 & [27] \\ Wheat bran & 54.79 & 150–250 \(\mu\)m & 6.00 & 20 & [86] \\ Rice husk & 25.46 & 0.075–1.16 mm & 7.00 & 25 & This study \\ Rice bran & 20.29 & 150–250 \(\mu\)m & 6.00 & 20 & [86] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of methylene blue adsorption capacity to that of various adsorbents.
## 3 Results
The analysis revealed that in the case of assessing the metabolic activity of cells after exposure to the tested substances, the highest level of toxicity was observed for S4 with an EC50 value of 92.03 \Although TiO\({}_{2}\) may not exhibit strong cytotoxicity in tests such as XTT, attention should be drawn to the potential for causing oxidative stress, triggering pro-inflammatory effects, and the possibility of genotoxic effects. The present study demonstrated the ability of the S1-S4 substances to stimulate the release of the following major pro-inflammatory cytokines: IL-1\(\beta\) and TNF-\(\alpha\) (Figure 4A,B). Interestingly, the observed changes in cytokine levels in the exposed cells were evident but not dose-dependent. Notably, significant differences were noted in terms of the pro-inflammatory effects of the individual tested substances. The highest levels of IL-1\(\beta\) were detected following incubation with S3 (EC50 = 3.53 \(\upmu\)g mL\({}^{-1}\)), followed by S2 (EC50 = 3.79 \(\upmu\)g mL\({}^{-1}\)), S4 (EC50 = 4.17 \(\upmu\)g mL\({}^{-1}\)), and S1 (EC50 = 4.18 \(\upmu\)g mL\({}^{-1}\)), indicating that S3 had the most potent effect. Regarding TNF-\(\alpha\) release, the EC50 values were 231.60 \(\upmu\)g mL\({}^{-1}\) for S1, 476.26 \(\upmu\)g mL\({}^{-1}\) for S2, 353.16 \(\upmu\)g mL\({}^{-1}\) for S3, and 358.78 \(\upmu\)g mL\({}^{-1}\) for S4, suggesting that S1 exhibited the strongest pro-inflammatory effect in terms of TNF-\(\alpha\) release (Figure S3). Figure 4: Effect of the tested substances (S1–S4) on the level of pro-inflammatory IL-18 (**A**) and TNF-\(\alpha\) cytokine (**B**) in Caco-2 cells after a 72 h exposure, measured with the ELISA assay. Ctr pos—positive control, was defined as LPS-treated cells at 30 \(\upmu\)g mL\({}^{-1}\) (mean \(\pm\) SD, * significantly different from the control (Ctr) at \(p<0.05\)).
# A Novel Compliant 2-DOF Ejector Pin Mechanism for the Mass Transfer of Robotic Mini-LED Chips
## 6 Conclusions  
In this paper, a novel compliant 2-DOF ejector pin mechanism for the mini-LED chip mass transfer is proposed, to overcome the shortcomings of the traditional chip eject transfer method with a mechanical ejector pin. The methods of compliance matrix and Newton are employed for system kinematic modeling and dynamics modeling, respectively. The mechanism static and dynamic analyses were carried out via ANSYS Workbench, and the results of FEA are in good agreement with those of the theoretical calculation, which indicates the effectiveness of the design. Then, an ILC is utilized to control the device with a parameter regulation approach in the frequency domain. Finally, an open-loop test and a trajectory tracking test for the prototype are carried out in detail. These indicate that the working stroke of the mechanism can reach 120 \(\upmu\)m, the natural frequency of the device is 250.85 Hz, the coupling rate is less than \(\pm\)0.5% and the track tracking errors of 10 Hz, 20 Hz and 30 Hz sinusoidal signals are all within \(\pm\)1.5%. The experimental results show that the transfer efficiency of the designed mechanism can reach 108K UPH. In summary, the working frequency range is over 100K UPH and reaches 108K UPH with the same displacement and accuracy as the traditional mini-LED mass transfer method, such that the effectiveness of the proposed compliant mechanism is proved.  
In future work, higher frequency, larger stroke, higher precision devices and more intelligent control algorithms will be developed and be applied to the direct transfer of mini-LEDs. Moreover, the practical processes of mini-LED mass transfer will be carried out to verify the effectiveness of the proposed device.  
Figure 11: Trajectory tracking results. (**a**) Sinusoidal signal with a frequency of 10 Hz. (**b**) Sinusoidal signal with a frequency of 20 Hz. (**c**) Sinusoidal signal with a frequency of 30 Hz. (**d**) Trajectory tracking errors.  
**Author Contributions:** Conceptualization, H.T. and Z.L. ([PERSON]); methodology, H.L.; software, C.H.; validation, C.H.and S.S., Z.L. ([PERSON]) and C.L.; formal analysis, H.L.; investigation, H.L. and S.S.; resources, H.L. and C.H.; data curation, H.L. and C.H.; writing--original draft preparation, Z.L. ([PERSON]), H.L. and C.H.; writing--review and editing, H.L., C.H. and H.T.; visualization, H.T.; supervision, Z.L. ([PERSON]); project administration, Z.L. ([PERSON]) and H.L.; funding acquisition, H.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China under Grant 51975132, Grant U20A6004, and in part by the Guangdong Programs for Science and Technology under Grant 2019A151011896.  
**Data Availability Statement:** Not applicable.  
The authors declare no conflict of interest.