esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Problem Formulations
### Problem Variant #2: Headways Optimization
Let \(y_{rsb}^{ij}\) be as \(Z_{rsb}^{ij}(X_{r}^{i}-X_{r-1}^{i})\) in (6), so the objective turns out to be \(\frac{1}{T}\sum_{b\in B}\sum_{r=1}^{f_{1}}\sum_{s=1}^{f_{j}}y_{rsb}^{ij}\cdot P _{b}^{ij}\), which is linear. Moreover, two equations per each \(y_{rsb}^{ij}\) variable must be included: \(y_{rsb}^{ij}\leq(X_{r}^{i}-X_{r-1}^{i})\) and \(y_{rsb}^{ij}\leq H_{i}\cdot Z_{rsb}^{ij}\). Within a maximization problem, variables \(y_{rsb}^{ij}\) will take a value as high as possible. \(H_{i}\) is an upper bound for \((X_{r}^{i}-X_{r-1}^{i})\) because of Equation (11), so whenever \(Z_{rsb}^{ij}=1\), the second equation results,\(y^{ij}_{rsb}\leq H_{i}\), and it is the first equation that guarantees \(y\)'s value to be \((X^{i}_{r}-X^{i}_{r-1})\) at most, which is then exactly the value that the variable \(y^{ij}_{rsb}\) will take. Conversely, when \(Z^{ij}_{rsb}=0\), the second equation forces \(y^{ij}_{rsb}\) to be 0. This behavior replicates that of \(Z^{ij}_{rsb}(X^{i}_{r}-X^{i}_{r-1})\)'s product, so the change of variables is equivalent and linear. Table 1 summarizes both problem variants. Variables \(X^{i}_{1}\) (i.e., offsets) are the control variables in both proposed problem variants, while the latter adds new control variables \(X^{i}_{r}\) with \(r>1\). The difference is semantic rather than substantive, and we refer to them as \(X^{i}_{r}\) in general. The group of variables \(Z^{ij}_{rsb}\) are auxiliary in both proposed problem variants. They are used to identify synchronizations as an outcome of control variable \(X^{i}_{r}\) values. These variables are only meaningful when stated as Boolean variables, a condition that must be explicitly set in the formulation since it is not achieved otherwise. Finally, problem variant #2 introduces \(y^{ij}_{rsb}\) variables, which are also auxiliary and number as many as those of \(Z^{ij}_{rsb}\). They are used to obtain to a purely linear alternative formulation for the second problem variant. Unlike \(Z^{ij}_{rsb}\) variables, the domain of \(X^{i}_{r}\) and \(y^{ij}_{rsb}\) is that of the real numbers, since their values are a consequence of active constraints in an optimization process. However, whenever parameters that define an instance are integers, optimal \(X^{i}_{1}\) values Equations (1)-(5) are to be integers as well, as they are the result of pushing the objective function variables against integer constraints. The situation is not so for Equations (6)-(13), because \(\alpha\) values greater than 0 generally lead to non-integer bounds in both proposed problem variants.
# Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks
## 3 Adaptive Downward/Upward Protocol
### Overview of RRD+ Mechanism  
We integrated downward routing in RRD+, which was originally designed for upward routing. RRD+ is a routing mechanism that can be used by hierarchical routing protocols to cope with mobility in convergecast data-collection scenarios. It is based on link quality monitoring and Rank value updating to better adapt to movement, and makes fast decisions on selecting next-hop neighbors. Moreover, RRD+ supports a dynamic management of control messages in order to reduce the overhead in the network. In what follows, we describe the different aspects of RRD+.
## 4 Implementation and Comparison  
In order to reflect the advantages of the OML-PLAC method, we compare the design of this paper with the current state-of-the-art design [29]. In addition, we selected some data of other references and experimental as supplements.  
We control for many of the same variables, including \(\text{MAE}_{\text{def}}\), the range of the independent variable and the fractional bit widths of input, output and intermediate data. All designs are coded in Verilog HDL and synthesized by TSMC 90-, 65- and 40-nm CMOS technologies with the use of Synopsys Design Compiler (DC). The relevant comparison results are listed in the Tables 5-9. The labels in these tables are defined as shown in Figure 7. In all the cases in the tables, the number of EA iterations is 7, so the error between actual MAE and ideal MAE is within 1%. The experimental results of several typical functions are divided into several parts for detailed explanation below. Among them, the comparison between OML-PLAC and ML-PLAC will be described in detail, and the remaining supplementary experiments are only briefly explained. Note that the signs of \"\(-\)\" and \"\(+\)\" in the tables below indicate the percentage of reduction and increment, respectively.
## 5 Statistical Characterization of Seismic Activity
### Progression of Earthquake Activity
#### 5.3.5 Local Variations and Concentrated Areas in Seismic Activity
As the magnitude range extends to \(2.10\leq M_{w}<2.65\) in Figure 11c, and further to \(2.65\leq M_{w}<3.20\) in Figure 11d, the seismicity demonstrates a more focused pattern. The density and spatial distribution of events increase, especially in the central part of the Red Sea, indicating a higher potential for accumulating and releasing significant seismic energy. This trend suggests that larger events are concentrated in areas with heightened tectonic stress, aligning with the region's geotectonic dynamics and marking them as zones of pronounced seismic activity. There is a noticeable shift from widespread distribution of lower magnitude events (see Figure 11a) to a more concentrated pattern of higher magnitude events (see Figure 11d). This transition suggests zones of higher stress accumulation within the Earth's crust, particularly in the Central Red Sea. These areas emerge as having key concentrated seismic activity with an elevated likelihood of experiencing substantial seismic events. KDE visualizations are crucial for seismic hazard assessment, indicating that while lower magnitude events are common and dispersed, the potential for damage escalates with higher magnitude events concentrated in active tectonic zones. This gradient in seismicity underscores the need for differentiated disaster preparedness and infrastructure resilience strategies across the region [17]. Figure 11: Seismic magnitude clustering analysis across four distinct ranges. Small yellow circles represent the reported seismic activity for the mapped area.
## 5 Conclusions  
In this study, we have introduced a novel watermark-based algorithm that meets the JPEG Privacy and Security standard specifically designed to provide original proof for JPEG 2000 images. Our proposed algorithm ensures minimal degradation of image quality by carefully modifying the DWT coefficient during the watermark insertion process. It offers the ability to examine integrity without the need for additional signature information, achieving an impressively high detection accuracy for spatial domain forgery. Moreover, our method presents the remarkable feature of controlling the detection accuracy, providing flexibility to meet specific requirements.  
To enhance the effectiveness of our algorithm, we have introduced a segmentation method that divides the original image into GOBs. Each GOB incorporates information from the surrounding GOB, addressing the challenge of collage attacks that are known to exploit vulnerabilities in fragile watermarks. In this way, our suggested approach greatly improves the detection ability, permitting successful fake detection even in such challenging scenarios. Notably, our approach allows for visual quality control while maintaining high detection accuracy for JPEG 2000 images in both the DWT and spatial domains.  
To evaluate the objective quality of the watermarked JPEG 2000 images, we used the PSNR evaluation method. The results demonstrated the outstanding performance of our proposed algorithm. Furthermore, the objective and subjective image quality analysis revealed minimal differences between the original and watermarked JPEG 2000 images.  
In conclusion, this research presents a significant advancement in watermarking techniques, offering a robust algorithm specifically tailored for providing original proof of JPEG 2000 images. The combination of high detection accuracy, minimal image quality  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Modification Type** & **Modified Image** & **Detection Result** & **Modification Type** & **Modified Image** & **Detection Result** \\ \hline Original water-marked JPEG image & & & & \\ \hline Resize & & & & \\ \hline Reduce & & & & \\ \hline Recompression & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tamper detection of image 3.  
degradation, and visual quality control showcases the potential of our proposed method for practical applications in digital image protection and forgery detection.  
Conceptualization, J.L.; methodology, J.L., S.C., and O.-J.K.; software, J.L.; validation, Y., S.C., and O.-J.K.; formal analysis, O.-J.K.; resources, S.C.; visualization, J.L.; writing--original draft preparation, J.L.; writing--review and editing, Y., S.C., and O.-J.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 3 Master Control System and Motion Control System
### General Procedure of Calibration Process  
First, the master control computer is started, and it then acquires the set values of temperature, humidity, and velocity. Further, the master control computer sends instructions to the temperature and humidity control system to start adjusting the air temperature  
Figure 4: The cabinet and computer for master and motion control, the screens for displaying and monitoring. 1. Control cabinet for master control and motion control; 2. Enclosed chamber; 3. Temperature and humidity control cabinet; 4. Display screen for master and motion control compute; 5. Display screen for monitoring the facility running status.  
and humidity in the enclosed chamber to approach the set values, displaying the curves of temperature and humidity variations in the control process (the detailed process is shown in the flow chart in Figure 5). At the same time, we turn on the stirring fan and pressure balancing system. When the air temperature and humidity reach the set values, we turn off the humidity control system, stirring fan, and pressure balancing system but keep the temperature control system running and the temperature and humidity at the set values and wait for at least 12 min to allow the air to become stagnant (a study on the attenuation time of air flow perturbation was performed by numerical simulation combined with experimental observation to give an attenuation time of 12 min). Then, the master control computer commands the motion control system to start, accelerates to the set velocity value, and begins constant-speed motion. With the chamber pressure, temperature and humidity kept constant, the hot-wire anemometer is warmed up in the first 2 to 3 s in the constant-speed stage. The remaining time is used to acquire the output data of the hot-wire probe, which is being calibrated. The master control computer displays the real-time temperature, humidity, and pressure in the enclosed chamber, the probe position, the carriage velocity, as well as the ambient atmospheric temperature, humidity, and pressure. After the hot-wire data have been acquired over a long enough time--equal to or longer than 3 s--the master control computer instructs the motion control system to slow down the moving carriage to rest and then let it return to the starting position and stay stationary for at least 12 min to allow the disturbed air to become stagnant for the next run of calibration. If the calibration is completed, the temperature control system is turned off. Finally, according to the calibration requirements, the data are processed, and the calibration report is presented (see Figure 5).
## 3 Interference of Extra Data Transfer Channel on Power Transfer
### Interference of Power Transfer on Data Transfer  
As for the data transfer channel, the SNR should be designed to be pretty high [12]. There are two factors affecting the SNR performance: (1) the interference of the power transfer with the data transfer; (2) the output capacity of the data transfer. These two effects could significantly influence the SNR performance. For a well-designed data transfer channel, these two factors should be optimized. The interference of power transfer on data transfer should be minimized while the output capacity of data transfer should be maximized.  
As for the interference of power transfer on data transfer, the circuit is shown in Figure 6b. According to Equation (7), the interference voltage \(V_{op}\) can be expressed as:  
\[V_{op}=i_{p2}Z_{d} \tag{12}\]  
The transfer function from the input current \(i_{i}\) to the data output \(V_{op}\) is:  
\[G_{pd}(\omega)=\frac{V_{op}}{i_{i}}=\frac{-jZ_{d}}{\omega C_{p}\big{(}j\omega L _{p}+1/j\omega C_{p}+Z_{r}+Z_{d}\big{)}} \tag{13}\]  
If the parameters \(L_{p}\), \(C_{p}\), \(L_{s}\) and \(C_{s}\) satisfy the assumption shown in Equation (2), then Equation (13) can be simplified as:  
\[G_{pd}\big{(}\omega_{p}\big{)}=\frac{V_{op}}{i_{i}}=\frac{-j}{\omega_{p}C_{p} \big{(}\omega_{p}{}^{2}M^{2}/R_{eq}Z_{d}+1\big{)}}. \tag{14}\]  
Equation (14) shows that the interference transfer function relates to the parameters \(\omega_{p}\), \(C_{p}\), \(R_{eq}\) (\(R_{L}\)), \(M\) and \(Z_{d}\). In order to reduce the interference, we can increase \(R_{L}\), \(Z_{d}\) or reasonably decrease \(\omega_{p}\), \(M\) and \(C_{p}\).
# The Potential of Blockchain Technology and Smart Contracts in the Energy Sector: A Review
## 4 SWOT Analysis
### Strengths  
One notable advantage lies in the potential to enhance efficiency within the energy sector through the implementation of smart contracts [66]. By automating various processes, smart contracts significantly minimize the need for manual handling, resulting in reduced time and resource requirements. As a result, organizations can achieve increased operational efficiency and realize substantial cost savings.  
The aspect of security is also a prominent feature of smart contracts, as they are stored on a decentralized ledger. This decentralized nature provides an added layer of security compared to traditional contracts. By utilizing distributed ledger technology, smart contracts become inherently resilient to single points of failure and are less vulnerable to hacking attempts [67]. The robustness of the system makes it exceedingly challenging for hackers to compromise the integrity and confidentiality of the smart contract ecosystem.  
Additionally, smart contracts excel in the realm of faithful record-keeping by leveraging the capabilities of blockchain technology. Through the inherent design of smart contracts, every transaction is automatically and immutably recorded within the blockchain. This meticulous and unalterable record of activities serves as a perpetual ledger, preserving a comprehensive and tamper-proof account of all interactions. By ensuring the integrity and transparency of recorded data, smart contracts establish a trusted and unambiguous trail of information, fostering accountability and enhancing the overall reliability of the system.  
Moreover, transparency stands as a key attribute of smart contracts, enabling all parties involved to have clear visibility into the contract's terms and its execution process. Through the utilization of smart contracts, the entire lifecycle of a contract becomes traceable and accessible to authorized participants. This heightened transparency cultivates a sense of trust and openness among the involved parties, as they have a comprehensive understanding of the agreement's provisions and the actions taken within its framework [23]. By mitigating information asymmetry and fostering a shared understanding, smart contracts contribute to the prevention of disputes and promote more harmonious and collaborative business relationships.
# Profile Control Using Fly Ash Three-Phase Foam Assisted by Microspheres with an Adhesive Coating
## 2 Experimental Study
### Methodology
#### 2.3.2 Single-Phase Plugging Tests  
Dynamic foam stability under a high temperature signifies the transport and plugging properties of foam in hydrocarbon reservoirs. Therefore, core floods in the absence of oil are carried out at three temperatures to assess the dynamic thermal stability. Sand packs are first vacuumed and saturated with AFW, followed by injection of 0.3 PV starch-gel three-phase foam system containing 5% consolidation agent at a flow rate of 0.5 mL/min. The sand packs are then placed in a thermal chamber (80 \({}^{\circ}\)C, 150 \({}^{\circ}\)C, and 300 \({}^{\circ}\)C) for 24 h. In the last stage, the subsequent water is injected at 0.5 mL/min until the pressure reaches a plateau. The variation of injection pressure is regularly documented. The physical properties of the sand packs (SPS-1, SPS-2, and SPS-3) are given in rows 2-4, Table 1.  
Permeability Influence  
Permeability can significantly affect foam's plugging performance as it directly relates to the pore size distribution of porous media. Sand packs (SPS-4 and SPS-5) of permeabilities 5830 mD and 8939 mD are used to investigate permeability effect on the plugging performance of the generated three-phase foam. Following the procedure presented in 2.3.2.1, 0.3 PV starch-gel three-phase foam containing 5% consolidation agent is injected into the AFW saturated sand packs at a flow rate of 0.5 mL/min. The sand packs are then placed in a thermal chamber set at 80 \({}^{\circ}\)C for 24 h. The subsequent water is then injected with the same flow rate until the pressure is stable. The variation of injection pressure is regularly recorded. Influences of Permeability Ratio  
Heterogeneity is a critical factor that causes water/gas channeling during fluid injection. Permeability ratio is considered an indicator of the extent of reservoir heterogeneity. As a result, parallel heterogeneous sand packs with various permeability ratios (5520/30 and 10,200/30) are applied to study the influence of permeability ratio on foam plugging performance. Water is first injected into parallel cores at a flow rate of 0.5 mL/min until the fractional flow in each core becomes stable. It is followed by injecting 0.3 PV starch-gel three-phase foam. The mass concentration of the consolidation agent maintains the same as in previous tests, i.e., 5%. When foam injection terminates, the experimental system stands for 24 h to make the foam gelated. The subsequent water is then injected until the pressure reaches a steady state. The flow rate remains the same throughout the whole test. The fractional flow rates (defined as the ratio of the flow rate in each of the parallel cores over the total flow rate) are recorded. The profile control efficiency is characterized by the profile improvement rate \(\eta\), expressed by [48]  
\[\eta=\left(1-\frac{Q_{ha}/Q_{la}}{Q_{bb}/Q_{lb}}\right)\times 100\% \tag{1}\]
# Kinetics and Thermodynamics of Efficient Phosphorus Removal by a Composite Fiber
###### Abstract  
In the current study, we investigated the kinetics and thermodynamics of phosphorus removal by zeolite/steel slag/fly ash/basalt (ZSFB) composite fiber. Kinetic analysis indicated that the adsorption of phosphorus is best fitted with the pseudo-second-order model. The maximum adsorption capacity of the fiber calculated by the Langmuir model was found to be 4.18 mg/g and the partition coefficient was 1.49 mg/g/uM. Thermodynamics results revealed that the Gibbs free energy \(\Delta\)G\({}^{0}\) of the composite fiber was negative, indicating that the adsorption is a spontaneous process; the standard enthalpy of reaction \(\Delta\)H\({}^{0}\) was positive, indicating that the adsorption is endothermic. Adsorption under different influencing factors and desorption experiments were conducted to investigate the phosphorus removal characteristics of ZSFB composite fiber. Dynamic adsorption and the phosphorus removal experiment were also conducted in a fixed-bed reactor to study factors affecting the time of breakthrough. Results indicate that the performance of ZSFB composite fiber was not relatively outstanding compared with nanomaterials and magnetic composites. However, its performance has already met the class A demands of \"discharge standard of pollutants for municipal wastewater treatment plant\" (GB18918-2002) which means it can be applied to remove phosphorus from real wastewater in a cost-effective way with low-priced raw materials.  
Keywords:phosphorus adsorption; kinetics; thermodynamics; zeolite/steel slag/fly ash/basalt composite fiber +
Footnote †: journal: Applied Physics Letters
## 6 Closing Remarks
\begin{tabular}{c c} \hline \hline
**Study** & **Theory Used** & **Influencing Factors** \\ \hline
[8] & Perceived Determinacy & Perceived legitimacy, perceived value congruences, perceived severity, perceived certainty \\
[59] & TPB, Self-Determination and Organismic & Severity, perceived certainty \\
[59] & Integration Theory & General motivations (locus of control) and situational motivations \\
[60] & PMT, TPB & Perceived vulnerability, response efficacy, self-efficacy, attitude, \\
[14] & PMT, TPB & Subjective privacy, subjective norms, \\
[61] & PMT & Habit, rewards, vulnerability, perceived severity \\
[37] & PMT & Criticality \\
[24] & PMT & Fear and protection motivation \\
[62] & PMT & Fear and protection motivation \\
[54] & PMT & Continuues intention, perceived extraneous circumstances \\
[63] & PMT & Hope, optimism, self-efficacy, resilience, fear, and \\
[64] & PMT & Response efficacy and response cost \\
[65] & PMT & Propulsion, psychological ownership, and \\
[66] & PMT & Self-efficacy and impact \\
[45] & Ethical Decision-Making Model, and Value & Moral beliefs \\
[27] & Congruence Theory & Vulnerability, self-efficacy, response efficacy, response-cost \\ \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Theory Used** & **Influencing Factors** \\ \hline
[38] & TPB, PMT, GDT & Previous punishment experience \\
[40] & PMT & IT vision conflict \\
[20] & Coping Theory, Disengagement Theory, & SRS, realism, perceived sanctions \\
[12] & Theory of Neutralization & SRS fatigue, and frustration \\
[11] & TPB, RCT & Negative affect, work impediment, and daily deviance \\
[42] & Grounded Theory & Value assignment, perception of information value \\
[51] & Theory of Neutralization & Ethical work climate, beliefs \\
[105] & PMT and GDT & Human personality traits (stability and plasticity) \\
[95] & Theory of Neutralization, PMT, TPB & Normative faith \\
[104] & GDT & Satisfaction and safe behavior \\
[39] & Prospect Theory, RCT, self-justification Theory, & Sunk cost, self-justification, and risk perception \\
[39] & Approach AvoidanceTheory & Value identification, trusted relationship fulfillment, growth need \\
[102] & Stewardship Theory & fulfillment, long-term orientation, the intention of CDSIV. \\
[23] & TPB, GDT & Descriptive norms, moral norms \\
[15] & Theory of Neutralization & Sanctions (formal. Informal), shame \\
[40] & Coping Theory & Perceived externality, triage, procrastination, psychological \\
[96] & Theory of Neutralization, Deterrence & Organizational injustice (procedural and distributive) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The most common theories used for compliance studies. \begin{table}
\begin{tabular}{c c} \hline \hline
**No** & **Theory Name** \\ \hline
1 & Theory of Planned Behavior \\
2 & Protection Motivation Theory \\
3 & Social Cognitive Theory \\
4 & Social Bond Theory \\
5 & Social Control Theory \\
6 & Rational Choice Theory \\
7 & Health Belief Model \\
8 & Social Exchange Theory \\
9 & Agency theory \\
10 & General Deterrence Theory \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Most common theories used for noncompliance studies.