esa-sceva/satcom-chunk-collection huggingface dataset:
# A Guaranteed Approximation Algorithm for QoS Anypath Routing in WMNs
## 5 Conclusions  
This paper addresses the anypath routing problem that guarantees QoS in WMNs. According to the time-varying characteristics of WMNs, combined with the idea of any-path routing, the system network modeling is described, and the MCOAP problem is discussed. This paper focuses on the wireless multimedia applications: under a variety of QoS constraints, it is mandatory to meet a specific constraint while approaching other QoS constraints from an approximate perspective. The HMAA algorithm is proposed for the MCOAP problem. The time complexity of HMAA is consistent with that of Dijkstra algorithm, and it is proved to be K-1 approximation. The feasibility of the algorithm is verified by simulation, then its computational efficiency and performance are evaluated. HMAA proposed in this chapter is suitable for WMNs, conforms to wireless multimedia applications, and has good compatibility with existing routing protocols.  
As for future research work, we plan to study an even better solution and faster algorithm for the MCOAP problem according to the time-varying characteristics of wireless networks based on the existing research. An investigation of the comparison of the HMAA with other algorithms [25; 26; 27] would also be interesting.  
Conceptualization, W.Y.; methodology, W.Y.; software, X.Z.; validation, G.L. and X.Z.; formal analysis, W.Y.; writing--original draft preparation, W.Y.; writing--review and editing, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Tertiary Education Scientific research project of Guangzhou Municipal Education Bureau grant number [202235364], the Special projects in key fields of colleges and universities in Guangdong Province [2021 ZDZX1109], the Characteristic innovation projects of colleges and universities in Guangdong Province [2019 GKTSCX081], the Open Research Fund from the Guangdong Provincial Key Laboratory of Big Data Computing, the Chinese University of Hong Kong, Shenzhen [B10120210117-OF08], the Guangdong Province Ordinary Colleges and Universities Young Innovative Talents Project Grant [2022 KQNCX038], the Natural Science Foundation of Guangdong Province [2019A1515012109], and the National Natural Science Foundation of China [61803090].  
Not applicable.  
All the data and models employed and/or generated during the study appear in the submitted article.  
This work was supported by the Special projects in key fields of colleges and universities in Guangdong Province, China (No.2021 ZDZX1109), the Characteristic innovation projects of colleges and universities in Guangdong Province, China (No.2019 GKTSCX081), the Science and Technology Program of Guangzhou, China (No.201804010098), the Open Research Fund from the Guangdong Provincial Key Laboratory of Big Data Computing, the Chinese University of Hong Kong, Shenzhen (No.B10120210117-OF08) and the Guangdong Province Ordinary Colleges and Universities Young Innovative Talents Project Grant (No. 2022 KQNCX038).  
The authors declare no conflict of interest.
## 1 Introduction
Both permeameters measure the resistance of air flow through the packed powder particles, which is related to bed porosity, surface area, and particle density. Conversion of the measured permeability to a surface area, using the Kozeny-Carman equation, enables indirect estimation of the equivalent mean particle diameter (Fisher Number), assuming that all particles are spherical and have the same size. FSSS is a relatively simple instrument used for powder particles ranging from 0.5 to 50 \(\upmu\)m, whereas the SAS is a digital, fully automated equipment dedicated to particle sizes between 0.2 and 75 \(\upmu\)m.  
Attempts to correlate results obtained from various powder particle sizing techniques are so far limited. A comparative study of the particle size distribution of ceramic powders using the analysis of variance for sedimentation and laser light diffraction methods is presented in ref. [6]. As these techniques are based on different physical principles, they generate different results, causing doubts. The particle size distribution, specific surface area, and particle shape of cementitious materials were measured by air permeability, sieving, laser light diffraction, BET, image analysis, and mercury intrusion porosimetry [7]. It has been shown that limitations to comparability of the acquired results arise when these methods are applied to materials that markedly differ in particle size, particle shape, and mineralogical composition. Cumulative particle size distributions acquired from laser light diffraction, aerodynamic time-of-flight, electrical zone sensing, and photon correlation spectroscopy for fine metal and ceramic powders are compared in ref. [8]. The authors concluded that there is no single method for measuring particle size and its distribution and that the instrument of choice must be selected based upon powder characteristics and application. Because permeametry was not compared with the other techniques, the main objective of the present study was to use the SAS to test a range of fine powders, differing in particle size, particle shape, porosity, and the degree of agglomeration, and subsequently to relate the obtained results to data generated by laser diffraction.
# Research on Key Technologies of 260 \({}^{\circ}\)C/210 MPa Ultra-Deep Logging Equipment
## 3 Research on Key Technologies of Ultra-High-Temperature and High-Pressure Logging
### 260 \({}^{\circ}\)C/210 MPa Ultra-High Temperature and Pressure Logging Sensors
#### 3.1.2 Lateral Electrode System
The sealed space was connected to the inner chamber of the pressure balance structure shown in Figure 5. Under the action of the balance piston, the pressure inside the sealed space remains balanced with the external pressure, thereby improving the instrument's overall temperature and pressure resistance. To enhance the sealing reliability between the electrode and the insulating sleeve, the conventional lateral electrode end-face sealing was replaced with a new shaft-hole sealing design, as shown in Figure 7a,b. This improvement ensures that the sealing effect is no longer affected by the pre-tightening force between the electrode and the insulating sleeve end face. At the same time, it increases the compression of the sealing ring, improves the reliability and pressure resistance of the sealing, and prevents external media such as mud, water, and gas from penetrating into the sealed space, which could otherwise reduce the system's insulation value. This ensures that the electrode system remains in good insulation condition at all times. Figure 5: Pressure balance structure. 3. Issues and Solutions for pressure-bearing sealing plugs: The pressure-bearing sealing plug is a core component connecting the instrument's circuit system and sensors, serving as the channel and carrier for system power supply and signal transmission. The pressure-bearing sealing plug separates the circuit system from the sensor acquisition system, with one end end enduring high temperatures and pressures from the formation while the other end protects the circuit from external temperature and pressure influences, maintaining it in a normal temperature and pressure environment, as shown in Figure 4. The maximum temperature and pressure rating for commonly used sealing plugs (pressure-sealing joints) is 230 \({}^{\circ}\)C/180 MPa. To meet the system design requirements, the project team, in collaboration with professional manufacturers and research institutions, successfully developed a series of pressure-bearing sealing plugs with temperature and pressure ratings of 260 \({}^{\circ}\)C/210 MPa using ceramic sintering and metal brazing technologies. After testing in a high-temperature and high-pressure autoclave, the high-temperature and high-pressure electrical conductivity and insulation performance of the pressure-bearing sealing plugs met the design requirements, laying a solid foundation for the successful design of the system.
## 2 Methodology
### Selection and Preparation of Receptor  
Apoptosis plays an important role in combating cancer. It is the first and foremost strategy of cancer therapy to kill malignant cells. Cell proliferation is another important characteristic of malignant cells by which they increase their number rapidly. We selected 4 targets, 2 from each pathway, based on the literature mining for this study. These four targets were B-Raf, EGFR, K-Ras, and PI3K. As the mutated forms of these proteins are responsible for the development of malignancy, we retrieved the 3D structure of mutated forms of these proteins from the protein data bank (www.rcsb.org/pdb; Accessed on 30 September 2022). A total of 3 types of mutations were mainly found in EGFR protein: L858R, T790M, and C797S. Approximately 50% of lung cancer patients with acquired resistance have the T790M mutation, which is thought to increase the receptor's affinity for adenosine triphosphate compared to its affinity for tyrosine kinase inhibitors (TKIs) [33]. When EGFR-L858R was expressed in lung cancer cells, it led to enhanced invasiveness and malignant pleural effusion (MPE) formation, as well as upregulation of the CXCR4 chemokine receptor [34]. Patients with NSCLC, who are treated with the currently available EGFR TKIs, often have difficulties due to the presence of the C797S point mutation. Recent  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time** & **Solvent A** & **Solvent B** & **Flow Rate** & **Pressure** \\ \hline
1 Min & 95\% & 5\% & 0.3 mL/min & 1200 bar \\
20 Min & 0\% & 100\% & 0.3 mL/min & 1200 bar \\
25 Min & 0\% & 100\% & 0.3 mL/min & 1200 bar \\
26 Min & 95\% & 5\% & 0.3 mL/min & 1200 bar \\
30 Min & 95\% & 5\% & 0.3 mL/min & 1200 bar \\ \hline \hline \end{tabular}
\end{table}
Table 1: Solvent gradient used for HR-LCMS analysis.  
clinical investigations show that almost 40% of patients who were treated with third-generation EGFR TKIs afterward had C797S mutations [35].  
The common form of BRAF mutations that occurs in NSCLC patients is BRAF V600E [36]. The most prominent oncogene in NSCLC is KRAS, contributing to 23% of lung cancer cases. In total, 3 types of mutations are found in KRAS in patients with lung cancer, viz., G12C, G12V, and G12D; among them, 48% of G12C mutations are found in NSCLC [37]. For PI3K, no effective mutations have been reported to date [38]. The normal protein of PI3K has been targeted by researchers for its anticancer studies [39]. Details of all the target receptors are mentioned in Table 2.
## 2 Materials and Methods
### Model Validation and Statistical Analysis  
To ensure the reliability of the simulation outputs generated by the ENVI-met model, a validation procedure was carried out by comparing simulated values of air pollutants (PM\({}_{10}\) and PM\({}_{2.5}\)) and microclimatic parameters with experimental measurements collected on-site during the monitoring campaign. This step was essential to verify the model's performance and support the credibility of subsequent impact assessments.  
The validation process involved both temporal and spatial comparisons between measured and simulated data, focusing on the time window between 8:00 and 14:00. Continuous measurements of PM\({}_{10}\) and PM\({}_{2.5}\) at Traffic Monitoring Station TMS1 provided high-resolution reference data for model evaluation. Additional comparisons were made at selected receptor locations, where both air quality and thermal comfort parameters were recorded.  
To evaluate model performance, the following statistical indices were used:  
* Mean Absolute Error (MAE): quantifies the average magnitude of errors between measured and simulated data, regardless of direction. Lower MAE values indicate better agreement.
* Bland-Altman index (\(\mu\)): calculates the average difference between simulated and observed values and provides upper and lower acceptance limits based on the standard deviation. This index is useful for detecting potential biases and assessing data consistency.
* Coefficient of determination (R\({}^{2}\)): represents the proportion of variance in the measured data explained by the simulated values. It ranges from 0 to 1, with values closer to 1 indicating stronger correlation and better predictive performance.  
These indices were calculated separately for PM\({}_{10}\), PM\({}_{2.5}\), air temperature, and UTCI at multiple receptor points within the study area.
# An Application of Instantaneous Spectral Entropy for the Condition Monitoring of Wind Turbines
## 3 The Continuous Wavelet Transform and Generalised Morse Wavelet
### Continuous Wavelet Transform  
Considering again the signal \(h(t)\) defined in the time domain, its CWT can be expressed (according to its most usual definition) as  
\[CWT_{h}(a,b)=\int_{-\infty}^{+\infty}h(t)\psi_{a,b}{}^{*}(t)dt, \tag{5}\]  
i.e., a convolution of the given data sequence (here, the time series) with a resized and time-translated version of the so-called mother wavelet, given as  
\[\psi_{a,b}(t)=\frac{1}{\sqrt[2]{a}}\psi\left(\frac{t-b}{a}\right). \tag{6}\]  
Therefore, \(\psi_{a,b}\) depends on the shift (\(b\)) and scale (\(a\)) parameters, as well as on the original shape of this mother wavelet, which in turn is an arbitrarily selected localised oscillatory function. Converting the wavelet scale to frequency, the final result is a TF transform of the analysed time series. Please note that the term \(\frac{1}{\sqrt[2]{a}}\) in Equation (6) is only needed to ensure equal energy at all time scales.  
Importantly, it must be recalled that there is not a unique definition for the mother wavelet \(\psi\). Any time-limited oscillatory function with zero means and that satisfies certain regularity and admissibility conditions [44] can be arbitrarily selected as the mother wavelet. In this sense, a comparative analysis for SHM purposes can be found in [45]. Here for this study, the Generalised Morse Wavelet has been tested.
## 3 Results and Discussion
### Surface Area Measurement  
The surface area and pore volume of biochar have been mentioned as key parameters in the case of its application as an adsorbent. The surface areas and pore volumes of all biochar samples are given in Table 1. It can be observed that both parameters increase with the pyrolysis temperature. In the biochar produced at 700 \({}^{\circ}\)C (CCB700), the surface area and pore volume reach 209.64 m\({}^{2}\)/g and 0.033 cm\({}^{3}\)/g. However, CCB500 presents a surface area of 95.80 m\({}^{2}\)/g and a pore volume of 0.013 cm\({}^{3}\)/g. The faster loss of VM from the CC indeed creates cracks and pores within the resulting biochar structure [25]. Previous studies highlighted an increase in the biochar surface area with the pyrolysis temperature [1,23]. In terms of applications, the biochar surface area and porosity can greatly influence their adsorption capacity [36]. Typically, a higher pyrolysis temperature results in larger pore sizes and therefore a larger surface area, which increases the adsorption capacity [36]. Thus, a high surface area with a large porous structure and abundant functional groups indicates the presence of large number of chemically active sites for target dye adsorption [35].  
The surface area value obtained in our study is significantly higher than those reported for other raw materials, such as cactus peels [23], bone waste [1], and leather waste, ref. [11] under slow pyrolysis conditions.
# Energy-Efficient LoRa Routing for Smart Grids
## 3 Proposed IoT-Based Communication in Smart Grid Environment  
This paper proposes an energy-efficient routing and optimisation technique for smart grid communication through LoRa. The model comprises six phases: node initialisation, clustering, test packet transmission, multi-path creation, and optimal path selection; then, the data are transmitted through LoRa gateways. The workflow used in the present work is given in Figure 3.
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 3 Watermark Embedding and Extraction Algorithm
### Video Watermark Embedding Algorithm
#### 3.1.1 Modulation Mode of Watermark  
When embedding the watermark, the third quantization coefficient in the selected subblock needs to be modulated, which may affect the video quality. In order to reduce the impact, the subblock with large \(C\) value is selected in this paper. The modulation mode is determined according to whether the third quantization coefficient of the subblock is \(\pm 1\), and the strong interference with the third quantization coefficient of the subblock is \(\pm 2\) is also excluded; In addition, four modulation modes are given in this paper, and which modulation mode is selected is determined by parameters to minimize the number of modified subblocks. After analyzing and synthesizing the characteristics of existing methods and video subblocks, and after experimental verification, the optimized modulation method and the associated expressions are given. The four modulation modes are as follows:  
Figure 3: Watermark embedding algorithm framework.  
Modulation mode 1:  
\[Q3^{\prime}_{k}=\begin{cases}Q3_{k}+1&if\ W(k)=1\ and\ Q3_{k}=1\\ 1&if\ W(k)=0\ and\ Q3_{k}\
eq 1\\ Q3_{k}&other\end{cases} \tag{2}\]  
Modulation mode 2:  
\[Q3^{\prime}_{k}=\begin{cases}Q3_{k}+1&if\ W(k)=0\ and\ Q3_{k}=1\\ 1&if\ W(k)=1\ and\ Q3_{k}\
eq 1\\ Q3_{k}&other\end{cases} \tag{3}\]  
Modulation mode 3:  
\[Q3^{\prime}_{k}=\begin{cases}Q3_{k}-1&if\ W(k)=1\ and\ Q3_{k}=-1\\ -1&if\ W(k)=0\ and\ Q3_{k}\
eq-1\\ Q3_{k}&other\end{cases} \tag{4}\]  
Modulation mode 4:  
\[Q3^{\prime}_{k}=\begin{cases}Q3_{k}-1&if\ W(k)=0\ and\ Q3_{k}=-1\\ -1&if\ W(k)=1\ and\ Q3_{k}\
eq-1\\ Q3_{k}&other\end{cases} \tag{5}\]  
where, \(Q3_{k}\) is the third non-zero quantization coefficient of the \(k\)-th subblock; \(Q3^{\prime}_{k}\) is the third non-zero quantization coefficient of the \(k\)th subblock after modulation; \(W(k)\) is the watermark sequence \((k=1,2,\cdots,1024)\).  
The parameters \(S_{i}\) of each modulation method are specified as follows:  
\[S_{1}=\sum_{k=1}^{1024}a(k),S_{2}=\sum_{k=1}^{1024}\widetilde{a}(k) \tag{6}\]  
\[S_{3}=\sum_{k=1}^{1024}b(k),S_{4}=\sum_{k=1}^{1024}\widetilde{b}(k) \tag{7}\]  
where,\(a(k)\)\(=\)\(\left\{\begin{array}{l}W(k)\ if\ Q3_{k}=1\\ \widetilde{W}(k)\ if\ Q3_{k}\
eq 1\end{array}\right.\), \(b(k)\)\(=\)\(\left\{\begin{array}{l}W(k)\ if\ Q3_{k}=-1\\ \widetilde{W}(k)\ if\ Q3_{k}\
eq-1\end{array}\right.\), If \(a(k)\), \(b(k)\) is 1, it means the \(k\)-th subblock needs to be modified; if it is 0, it means the \(k\)-th subblock does not need to be modified; the symbol \"-\" indicates the inverse. Therefore, \(S_{i}\) indicates the number of subblocks that need to be modified by the i-th modulation method. In this paper, the modulation method corresponding to the smallest value of \(S_{i}\) is selected for embedding the watermark, which can further reduce the impact of watermark embedding on the video to improve the anti-compression performance of the watermark.
## 4 Implementation of DFDLOF in Personalizing Educational Pathways
### Framework Architecture and Components
#### 4.1.2 Key components and their functionalities  
DFDLOF is composed of several key components, each contributing to the framework's functionality:  
(1) Data collection and analysis.  
This component gathers data on learner interactions, performance, and feedback [44]. Advanced analytics extract meaningful insights from this data, forming the basis for all subsequent adaptive learning adjustments.  
(2) Machine learning algorithms.  
Central to DFDLOF, these algorithms analyze learner data to identify patterns and learning needs. Based on this analysis, they adapt the learning content and strategies, ensuring that the educational experience is continually optimized [45].  
(3) Adaptive content delivery system.  
This system dynamically adjusts the learning content based on the insights derived from the machine learning algorithms. It ensures the content remains relevant and aligned with the learner's current understanding and learning objectives.  
(4) Feedback mechanisms.  
DFDLOF incorporates real-time and long-term feedback systems. Real-time feedback provides immediate guidance and correction to learners, while long-term feedback informs broader adjustments in the learning pathway.  
(5) User interface and experience.  
The design of the user interface is crucial in DFDLOF. It is tailored to provide a user-friendly and engaging learning experience, facilitating ease of interaction and navigation.  
(6) Performance tracking and reporting.  
This component tracks the learner's progress and provides regular reports on their performance. These reports are instrumental for both learners and educators to monitor progress and identify areas for improvement.  
These components work in unison within the DFDLOF model to create a responsive, adaptive, and effective learning environment. They embody the essence of personalized education, where technology and pedagogy converge to cater to the unique needs of each learner.  
To gain a deeper understanding of the workings of the DFDLOF framework, an architectural diagram is provided below (Figure 4). This diagram intricately illustrates the interrelationships among various components such as data collection, machine learning algorithms, feedback mechanisms, and their roles within the framework.
## 4 Data Analysis and Results
### Unloading System Reliability
After 5 years, as time increases, the system probability is very low and almost constant, which means that the probability that the system is functional after five years is close to the probability that the system is functional after ten and twenty years. The reliability plot helps to determine the best schedule for preventive maintenance of the system, which will improve the system reliability. In this study, the unloading system reliability needs to be maintained above 90%, which means that the system components should be maintained every 45 days as shown in Figure 4. In the next section, the impact of critical component preventive maintenance on the whole system reliability will be investigated. Figure 4: Unloading system reliability. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Component** & **Relation** & **Reliability (1 Year)** & **Reliability (5 Years)** & **Reliability (10 Years)** & **Reliability (20 Years)** \\ \hline Unloading Arm—\(1\) & Parallel & 0.976471687 & 0.887765525 & 0.788127628 & 0.621145158 \\ \hline Unloading Arm—\(2\) & Parallel & 0.976471687 & 0.887765525 & 0.788127628 & 0.621145158 \\ \hline Ball Valve—\(1\) & Parallel & 0.999038705 & 0.995202756 & 0.990428525 & 0.980948663 \\ \hline Ball Valve—\(2\) & Parallel & 0.999038705 & 0.995202756 & 0.990428525 & 0.980948663 \\ \hline Stream—\(1\) & Parallel & 0.975533009 & 0.883506697 & 0.780584084 & 0.609311512 \\ \hline Streams —\(2\) & Parallel & 0.975533009 & 0.883506697 & 0.780584084 & 0.609311512 \\ \hline Streams 1 \& 2 Combined & Series & 0.999401366 & 0.96842931 & 0.951856656 & 0.847362505 \\ \hline Ball Valve—\(3\) & Series & 0.999038705 & 0.995202756 & 0.990428525 & 0.980948663 \\ \hline Positive Displacement Pump & Series & 0.8668779 & 0.48954166 & 0.239651036 & 0.057432619 \\ \hline Check Valve—\(1\) & Series & 0.953496955 & 0.788127628 & 0.621145158 & 0.385821307 \\ \hline Gate Valve—\(1\) & Series & 0.996963513 & 0.98490488 & 0.9700467 & 0.940990601 \\ \hline Gate Valve—\(2\) & Series & 0.9996963513 & 0.984909488 & 0.9700467 & 0.940990601 \\ \hline Pressure Relief Valve—\(1\) & Series & 0.998821649 & 0.994122112 & 0.988278773 & 0.976694934 \\ \hline Pressure Relief Valve—\(2\) & Series & 0.998821649 & 0.994122112 & 0.988278773 & 0.976694934 \\ \hline Positive Displacement Flow Meter & Series & 0.826565488 & 0.385821307 & 0.148858081 & 0.022158728 \\ \hline Plug Valve & Series & 0.953496955 & 0.788127628 & 0.621145158 & 0.385821307 \\ \hline Flow Control Valve & Series & 0.999038705 & 0.995202756 & 0.990428525 & 0.980948663 \\ \hline Differential Pressure Transmitter—\(1\) & Series & 0.999571905 & 0.997861358 & 0.995727289 & 0.991472835