esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Hydraulic Fracture Entering Stress Barrier: Theory and Practical Recommendations
## 2 Theory of Fracture Penetration into Stress Barrier
### Asymptotic Approach
#### Asymptotic Solution in General Case
In summary, for a high barrier, both the tensile critical SIF \(K_{IC}\) and the apparent viscous SIF \(K_{IA}\) may be omitted in the penetration Equation (16). The equation becomes as follows:  
\[-K_{I\Delta\sigma}=K_{I} \tag{20}\]  
It is algebraic. Using (19) in (20) gives the penetration \(\Delta z(t)\) as a function of the driving SIF \(K_{I}(t)\):  
\[\Delta z(t)=\frac{\pi}{8}\bigg{(}\frac{K_{I}(t)}{\Delta\sigma}\bigg{)}^{2} \tag{21}\]  
Substitution of (18) into (21) gives the general solution for the penetration \(\Delta z(t)\) into a high barrier as a function of \(w_{av}(t)\):  
\[\Delta z(t)=\bigg{(}\frac{3\pi}{32}\frac{E\,t}{\Delta\sigma}\frac{w_{av}(t)}{ \sqrt{d}}\bigg{)}^{2} \tag{22}\]  
Recall that the opening \(w_{av}\) used in (22) is averaged over the near-barrier part of the fracture surface, where the square-root asymptotic is acceptable. When this part is large, the opening averaged over it may have the order of the average opening entering Equation (9) for the Courant time \(cou\). Then, large values of \(w_{av}\), calculated with spatial discretization in the near-barrier zone, may also be applied for conclusions on the computational efficiency and stability of a conventional scheme. Thus, in general, for high barriers (\(R_{0}>3.5\)), the asymptotic approach is reduced to using the simple dependence (21) (when calculating the SIF) or (22) (when evaluating the average opening). The formulation of the problem in terms of SIFs and inspection of Equations (16)-(20) show that the limitations of the asymptotic approach arise when expressing viscous resistance via the apparent viscous SIF \(K_{IA}\). Exact theoretical expressions for \(K_{IA}\) are derived merely for plane-strain and axisymmetric problems having self-similar solutions. They are used in the next section for obtaining and studying benchmark problems. In general, the apparent SIF \(K_{IA}\) is undefined. Then, the asymptotic approach is inapplicable, if we cannot neglect the influence of viscous resistance on fracture propagation. However, for high stress contrasts, which are of prime interest to the study, the propagation speed and, consequently, viscous resistance drastically drop when a fracture tip enters the barrier. Therefore, Equations (20) and (21), which neglect viscous resistance, become applicable. Their applications are discussed in Sections 3 and 4.
# A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques
## 2 Harmonic Detection Principles and Methods
### Adaptive Detection Method  
The techniques described in [18] use voltage as reference input and current as the original input; it uses the adaptive interference cancellation principle of signal processing and reactive current and harmonic information are acquired, as illustrated in Figure 2. This adaptive detection method has little dependence on device characteristics but is almost independent of component parameters. The system has good adaptive ability and can attain higher detection accuracy even when the grid voltage is distorted and the frequency fluctuates.
## 3 Classification of Systems
### Number of Power Transmission Paths Parallel to the CVU
#### 3.3.1 Power-Split-Type Dual-Path Systems  
According to the combination pattern of the CVU and the constant ratio path, a dual-path system can still be sorted into two subtypes: Power-split-type dual-path system and bypass-type dual-path system. For a power-split-type dual-path system, the power is transmitted through the CVU and the constant ratio path simultaneously with the aid of a 2 DOF mechanism. Therefore, the input torque is shared by the CVU and the constant ratio path, and the maximum allowable torque of the system can be higher than that of the CVU. If the power is split off at the 2 DOF mechanism and coupled directly after the CVU, the system is called an output-coupled system, as shown in Figure 8(b). On the other hand, if the power is split off directly after the input and coupled together at the 2 DOF mechanism, the system is called an input-coupled system, as shown in Figure 8(a).  
Figure 8: Examples of single-path systems.  
In the BPD, power-split-type dual-path systems have a loop structure comprising connection lines, a CVU, and a MDOF unit, such as 2 DOF-3E in Figure 10. The actual ratio range of the system can then be set by combining other transmission units, such as gearsets, which are ignored in the BPD.
# The Design and Implementation of a Novel Open Source Massive Deployment System
## 3 Design and Implementation
### Massive Deployment  
As mentioned in Section 3.1, two types of mechanisms were considered and implemented in the massive deployment system. When dealing with the local connection (i.e., massive disks are connected to a system bus belonging to a computer), writing the data of the system image to massive disks in parallel was developed. The program in Clonezilla that can execute such a massive deployment is called \(\text{ocs-restore-mdisks}\). According to the flowchart that is shown in Figure 2b, a system deployment is executed for each drive. The multiple executions of writing images to USB drives in parallel by using the \(\text{ocs-restore-mdisks}\) program is executed by using the \"for loop\" on the Linux system.  
When dealing with the network connection where massive machines are connected to network switches, at least four types of communication protocols (i.e., unicast, broadcast, multicast [37], and peer-to-peer (P2P) networking [44], such as BitTorrent [45]) can be used to deploy the system image to each machine. These four protocols were implemented in Clonezilla. Basically, the flowchart for deploying each machine is similar to the one in Figure 2b. The only difference is how the client machine to be deployed receives the system image from different protocols. Hence, the steps for such a massive deployment can be defined according to [PERSON] et al. [37]: (1) save a system image from the template machine; (2) start the required services for the client machines, including DHCP, TFTP, and the data-distributing service (unicast, broadcast, multicast, or BitTorrent); and, (3) boot the client machines from PXE, receive the system image data, and then write to the disks.  
As mentioned in Section 3.3, Clonezilla live is the software that is used for system backup and recovery, the massive deployment of disks in a computer, and the massive deployment in large-scale computers. Clonezilla live contains the server programs (e.g., DHCP, TFTP, and UDPcast), as well as the programs for system deployment in client machines (e.g., sfdisk, gunzip, Partclone, and Clonezilla). Clonezilla live also provides the mechanism for clients to boot from the PXE. Therefore, Clonezilla live can be used in massive deployment on both the server machine and the client machines by appropriate configurations.
## 3 Results
### Full Factorial Experiment Results
#### 3.2.1 Analysis of Variance (ANOVA) for Drag Force (df)  
Analysis of variance with a 95% confidence level was utilized in this study to identify the individual influences of the control parameters on the drag force (response values). The ANOVA results for drag force are provided in Table 4. The last column of the table shows the percentage value of each parameter contribution, which reflects the degree of influence on the drag force values. According to Table 4, the percentage contributions of the A, B, and C parameters on the drag forces were determined to be 29.4%, 58.5%, and 1.6%, respectively. Thus, the dome height-to-diameter ratio was the most dominant factor affecting drag forces. The dome height-to-diameter ratio importance stems from the fact that it accommodates the vertical and lateral dimensions of the dome, directly affecting the attached area of the interfacial water film. The disc coverage ratio (factor A, 29.4%) also showed a considerable contribution. In contrast, the dome base diameter (factor C, 1.6%) had limited influence on drag force. The mean square of the term \"HDR \(\times\) DBD\" was less than the mean square of error; that is why \"HDR \(\times\) DBD\" was embedded in the error term, and the corrected error \(e^{\Delta}\) was thus obtained. The percentage contribution of the corrected error \(e^{\Delta}\) for sliding resistance was within the acceptable limit (less than 20%) [27].
Because it avoids modeling the secondary path, the calculation complexity is notably reduced; however, it still shows power function growth with the increase in the adaptive filter length. Due to updating the coefficients of adaptive controllers in the time domain, the filtered reference signal was obtained by convolving the estimated secondary path with the internally generated reference signal. In addition, the two processes are the main components that affect computational complexity. The relationship between calculation complexity and the adaptive filter length contains a power function. Therefore, the computational complexity will rapidly increase by increasing the adaptive filter length. To further decrease the calculation complexity, a simplified frequency-domain feedback ANC algorithm is proposed. It adopts the delay-less frequency-domain algorithm structure to update the adaptive filter in the frequency domain, estimate the total delay of the secondary path, and use multiplication to replace the convolution calculation in time domain to produce the filtered reference signal. The subsequent parts of this paper provide the following information. Section 2 derives the simplified frequency-domain feedback active noise control algorithm. Section 3 demonstrates the simulation results in detail. Section 4 addresses calculation analyzation. Finally, concluding remarks can be found in Section 5.
# Evaluation of _Jatropha curcas_ and Pistachio Shell Particles as Modifier for Asphalt Binder
## 4 Conclusions  
According to the results of this study, we demonstrated that waste shells of pistachio and _Jatropha curcas_ shells can be used as efficient additives for asphalt modification. The results show that pistachio particles can act as a modifier. _Jatropha curcas_ particles act more like a filler when there is a high content of particles; it has aninverse effect of a modifier. The microscopy results show that the modifier particles are better dispersed in the asphalt matrix when their concentration is 2 or 4 wt.% because at higher concentrations some modifier agglomerates were detected.  
The rheological analysis revealed that all of the shell-modified formulations presented higher failure temperatures than the pure asphalt, which is an indicator of better resistance to rutting, cracking, and permanent deformation, because the modified asphalt blends were tested at conditions that simulate a traffic speed of 100 km/h. The thermal analysis was useful for proving that the pistachio shells acted as a thermal stabilizer when they were introduced to the asphalt up to a concentration of 6 wt.%, which increased the degradation temperature of the modified asphalt blends in comparison with that of the base asphalt. On the other hand, when _Jatropha_ particles were used as a modifier, better thermal behavior was observed for the sample containing 4 wt.% of the modifier. The overall interpretation of the results suggests that the presence of lignin and other polar compounds in the shells is responsible for inducing the physical, rheological, and thermal improvements registered by the modified asphalt binder.  
Conceptualization, A.V.-M., B.A.S.-C., and J.L.R.-A.; methodology, A.V.-M., J.L.R.-A., and B.A.S.-C.; software, M.Y.C.-C. and C.E.R.-G.; validation, A.V.-M., C.E.R.-G., and B.A.S.-C.; formal analysis, A.V.-M. and J.L.R.-A.; investigation, [PERSON]-M., B.A.S.-C., and J.L.R.-A.; resources, [PERSON]-C., C.E.R.-G., and M.Y.C.-C.; data curation, A.V.-M., B.A.S.-C., and J.L.R.-A.; writing--original draft preparation, [PERSON]-M., B.A.S.-C., and J.L.R.-A.; writing--review and editing, [PERSON]-G., [PERSON]-A., and B.A.S.-C.; visualization, A.V.-M.; supervision, [PERSON]-A. and B.A.S.-C.; project administration, C.E.R.-G., M.Y.C.-C., and J.L.R.-A.; funding acquisition, J.L.R.-A., B.A.S.-C., and M.Y.C.-C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Tecnologico Nacional de Mexico with project code 7698.20-P.  
Figure 8: TGA thermograms of asphalt, pistachio shells and modified asphalt mix with pistachio seed shells.  
**Acknowledgments:** The authors wish to thank Dynasol Elastomers technical support for microscopy characterization and Ergon Asfaltos Mexico, Altamira, for providing the asphalts.
## 4 SOOA: Special On-Off Attack
### Special On-Off Attack  
As a study, this work considers a SOOA, for which a malicious node can keep sending truthful answers to one node but behave maliciously to another node. This particular attack has the potential to affect the effectiveness of the trust computation for a third node (target node). This attack accepts that a challenge could be delivered randomly to make it difficult to be identified from normal messages. The sending randomness can be achieved by a random number generator. Figure 2 describes an example of the SOOA: supposing node \(D\) is malicious, node \(A\) is the target, and node \(B\) and node \(C\) are partner nodes of node \(A\). Two adversary scenarios are described below.  
* **Scenario 1: node \(D\) is not a partner node of node \(A\).** In this scenario, node \(D\) chooses to send a truthful response to node \(C\) while sending untruthful (or malicious) answers to node \(B\). Figure 2 shows that node \(A\) can communicate and collect data with/from its partner nodes; thus, node \(A\) may receive different (or opposite) reports on node \(D\). This scenario often occurs for a hierarchical network structure, for which a central server needs to collect information and judge the trustworthiness of each node.
* **Scenario 2: node \(D\) is a partner node of node \(A\).** In this scenario, node \(D\) can respond truthfully to node \(A\) if they are partner nodes. In CIDNs, node \(A\) can judge the trustworthiness of node \(D\) through both its own trust computation and the judgement from other nodes, for example, nodes \(B\) and \(C\). In this case, node \(D\) can perform the same as in Scenario 1 to affect the decision, such as by alarm aggregation of node \(A\).  
On the whole, our SOOA can choose to give truthful feedback to several nodes while responding untruthfully to others. As a result, it may affect the trust computation of certain nodes and maintain its trust values above the threshold, that is, maintaining the trust values of node \(D\) above the threshold  
Figure 2: An example of a special on–off attack (SOOA) on challenge-based collaborative intrusion detection networks (CIDNs).  
as for node \(A\). In practice, malicious nodes can make a negative impact on alarm aggregation in both scenarios, through maintaining their trust values.
# The Prediction Performance Analysis of the Lasso Model with Convex Non-Convex Sparse Regularization
## 5 Conclusions  
In this paper, we propose CNC sparse regularization as a valuable alternative to the \(\ell_{1}\) regularization used in Lasso regression. This approach effectively addresses the issue of underestimation for high-amplitude components while ensuring the global convexity of the objective function. Our theoretical analysis demonstrates that the prediction error bound associated with CNC sparse regularization is smaller than that of \(\ell_{1}\) regularization, which provides theoretical support for the practical application of the CNC regularization. Additionally, we demonstrate that the Lasso model with CNC regularization exhibits superior performance on both synthetic and real-world datasets. These findings suggest its potential significance in future applications such as image denoising, image reconstruction, seismic reflection analysis, etc.  
Additionally, given that the oracle inequality of the Lasso model relies on the restricted eigenvalue condition, future research directions may include exploring theoretical guarantees under more relaxed assumptions, such as the oracle inequality based on weak correlation or unconstrained design matrices, and experimentally verifying the practical effectiveness of the restricted eigenvalue condition (such as calculating the specific value of \(\kappa_{T}\)), and further analyzing its impact on model performance.  
Conceptualization, W.C. and J.Z.; methodology, W.C.; software, W.C.; validation, Q.L. and H.L.; formal analysis, Q.L.; data curation, H.L.; writing--original draft preparation, W.C.; writing--review and editing, J.Z.; visualization, W.C.; supervision, J.Z.; project administration, J.Z.; funding acquisition, J.Z.. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Undergraduate Training Program of Yangtze University for Innovation and Entrepreneurship (Yz2023302).  
The code for the proposed method in this paper are available upon request from the corresponding author.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Multi-Objective Optimization for Edible Oil Transportation Scheduling Method  
The essence of multi-objective optimization lies in balancing multiple evaluation criteria and making trade-offs among two or more conflicting objectives to achieve relatively optimal decision outcomes. In this study, the planned pipeline resource allocation determined using the A* and Dijkstra pathfinding algorithm is compared with the edible oil transportation time estimated via the Hagen-Poiseuille equation from fluid dynamics. Based on this comparison, each transportation task is evaluated and prioritized, forming the basis for the design of a multi-objective optimization scheduling algorithm.
# Research and Development of Automatic Monitoring System for Livestock Farms
## 4 Results and Discussions
There was a 0.6481 L/day reduction of average lactation yield per dairy cow as the temperature increased about 1 \({}^{\circ}\)C.  
Figure 11: THI and average daily lactation yield per cow in August. Figure 10: THI and average daily lactation yield per cow in March. Figure 13 indicated that the lactation yield per dairy cow did not change much even though there was obvious fluctuation in the humidity curve under moderate heat stress status. It indicated that the impact of humidity on lactation yield of dairy cows was not significant under moderate heat stress status. Compared with Figures 11-13, it indicated that the impact of temperature on lactation yield was greater than humidity under moderate heat stress status, showing that we could improve the lactation yield under heat stress by lowering the environment temperature with controls of fans or water valves. 4 The Correlation Between Lactation Yield and Apparent Temperature  
Figure 14 showed similar trends in the temperature-humidity index (THI) and apparent temperature index (AT) of the dairy cows. Under mild heat stress status, the lactation yield decreased as the indexes raised, and vice versa, as shown in Figures 15 and 16. The mathematical correlation between the lactation yield per dairy cow (\(y_{mH}\)) and the temperature-humidity index (THI) (\(y_{THH}\)) was shown in formula (3), and the mathematical correlation between the lactation yield per dairy cow (\(y_{mH}\)) and the apparent temperature index (AT) (\(y_{AT}\)) was shown in formula (4). Where (\(y_{THH}\)) is the final value of THI, (\(y_{THH}\)) is the initial value of THI, (\(y_{mi}\)) is a given value of the initial lactation yield per dairy cow, (\(y_{AT}\)) is the final value of AT, (\(y_{AT}\)) is the initial value of AT, \(m_{m}\) is the slope of line of the initial lactation yield per dairy cow, \(m_{TH}\) is the slope of THI line, \(m_{AT}\) is the slope of AT line. \[y_{mf}=\frac{m_{m}}{m_{THI}}\left(y_{THIf}-y_{THIi}\right)+y_{mi} \tag{3}\]  
Figure 12: Temperature and average daily lactation yield per cow in moderate heat stress. Figure 13: Humidity and average daily lactation yield per cow in moderate heat stress. The pig barns were far away from the office, and there were many obstacles such as farmhouses and trees. Though a wireless relay was set up between the pig barns and office, the data retrieving for the environmental status data in pig barns were unstable, especially under bad weather.
## Appendix B Influence of Thrust and Torque  
The comparative whirl flutter stability assessment in Figure 12 already demonstrated the stabilizing effect of thrusting conditions on the system experiencing propeller whirl flutter. This appendix explores this effect in a more detailed manner by showing the influence of thrust and torque on the unsteady hub transfer functions and the resulting whirl flutter results.  
Figure A2 shows the changes in the unsteady hub transfer function from propeller disc pitch \(\theta\) to the in-plane hub loads for two thrust coefficients. For better visibility, only UPM results are shown. The main differences can be observed in the aerodynamic stiffness terms (or 1P hub loads) in the z-direction. The absolute value of the vertical force coefficient increases with thrust, which can be attributed to the tilting steady-thrust vector. The thrust vector also produces aerodynamic stiffness in the \(M_{yy}\) term due to the lateral shift in the thrust vector relative to the reference frame. The absolute value of the cross-coupling moment term, \(M_{2\theta}\), in Figure A2 decreases with the increase in loading due to the tilting torque vector of the propeller. All other terms only show minor changes, as it can be seen, e.g., in the aerodynamic damping (slope) in the top-left plot for \(F_{y\theta}\).  
Figure A3 shows the resulting stability maps for the thrusting and non-thrusting conditions. As it can be clearly seen from the figure, thrust and torque have a (strong) stabilizing effect on whirl flutter stability, as already known from the literature [1]. In fact, it is not the thrust vector itself that leads to this stabilization. The steady-thrust effects on the transfer functions (aerodynamic stiffness in \(M_{yy}\) and \(F_{z\theta}\)) cancel each other out for the simplified pylon system, which only shows rigid-body rotations. The thrust vector always points through the pivot point and thus cannot contribute to the generalized loads. The reason for the stabilization can mostly be attributed to the tilting torque vector decreasing the destabilizing coupling torque (\(M_{2\theta}\)).