esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this experimental study, the thermal and exergetic performances of an air-to-air HPHE were evaluated using Co\({}_{3}\)O\({}_{4}\) nanoparticles dispersed in CMC-stabilized aqueous solutions, with and without CB addition. The key conclusions are summarized as follows:  
* Turbulent flow regimes in the hot and cold air channels were confirmed under all tested conditions, ensuring consistent external convective heat transfer. This stable boundary condition enables a reliable evaluation of the relative thermal performance improvements achieved by nanoparticle-enhanced fluids within the heat pipe.
* The use of Co\({}_{3}\)O\({}_{4}\) nanofluids substantially improved the thermal efficiency, heat transfer coefficient, and thermal effectiveness compared to deionized water. The highest thermal efficiency improvements were recorded as 62.7% and 75.4% for the 1% and 2% Co\({}_{3}\)O\({}_{4}\) nanofluids, respectively. CB incorporation further enhanced the thermal efficiency of the 2% Co\({}_{3}\)O\({}_{4}\) nanofluid, achieving a maximum improvement of 79.2%.
* Thermal resistance values significantly decreased with nanoparticle addition, and further reductions were obtained by introducing CB. The highest thermal resistance reduction observed was 61.4% relative to deionized water, highlighting the effective role of CB in facilitating heat transfer processes.
* Exergy loss decreased with the use of nanofluids, and the exergy-saving efficiency reached up to 29.3% and 33.6% with the 2% Co\({}_{3}\)O\({}_{4}\) nanofluid and Co\({}_{3}\)O\({}_{4}\)/CB nanofluid, respectively. CB contributed to further reducing exergy losses by improving thermal dispersion and minimizing system irreversibility.  
These results demonstrate the significant potential of hybrid Co\({}_{3}\)O\({}_{4}\)- and CB-based nanofluids for improving the thermal and exergetic performance of heat recovery systems, offering valuable opportunities for advancing energy-efficient, sustainable technologies in industrial and building applications.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author.  
Figure 17: Exergy-saving efficiency as a function of heat capacity rate ratio for 2% Co\({}_{3}\)O\({}_{4}\) nanofluid and Co\({}_{3}\)O\({}_{4}\)/CB nanofluid.  
**Acknowledgments:** Technical support provided by Gazi University during this study is gratefully acknowledged.  
**Conflicts of Interest:** The author declares no conflicts of interest.
## 3 Results and Discussion
### The pH Values of Soil  
Figure 4 shows the pH values of initial soil and after remediation for 10 days. The pH value of the soil increases from the anode to the cathode. The pH-value change in soil is mainly caused by the movement of H\({}^{+}\) and OH\({}^{-}\) produced by water electrolysis reaction on the anode and cathode, through electromigration, electroosmotic flow, electrophoresis, and diffusion. After 10 days of remediation, the section in the soil close to the initial pH value is closer to the cathode (between S3-S4) because the migration velocity of hydrogen ions is faster than hydroxide ions [26]. The cathode will be gradually alkaline as the repair progresses by using electrokinetic remediation alone. The pH values of the soil near the cathode (S3-S5) are higher than the initial pH values on day 10 of the repair, especially for T1 and T4; they can reach 7.1 or more. This is not conducive to the removal of Cu because Cu\({}^{2+}\) easily forms copper hydroxide precipitates with hydroxide ions, blocking the soil pores and causing the accumulation of heavy metals at higher pH values. Figure 4b  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & **Electrolyte** & \(W\) **(kWh)** & \(\eta\) **(\%)** & \(W\) **( kWh\(\cdot\)mg\({}^{-1}\))** \\ \hline
**Test 1** & 0.1 M KCl & 0.689 & 22.3 & 4.12 \\
**Test 2** & 0.1 M HAc & 0.435 & 38.9 & 1.49 \\
**Test 3** & 0.1 M CA & 0.553 & 50.8 & 1.45 \\
**Test 4** & 100 mL/L PASP & 0.359 & 40.2 & 1.19 \\
**Test 5** & 0.1 M KCl & 0.582 & 34.9 & 2.22 \\
**Test 6** & 0.1 M HAc & 0.550 & 54.6 & 1.34 \\
**Test 7** & 0.1 M CA & 0.570 & 62.5 & 1.21 \\
**Test 8** & 100 mL/L PASP & 0.387 & 55.6 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of electrokinetic remediation of copper-contaminated soil.  
shows that the pH values near the cathode of T5-T8 are all less than six, indicating that the steel slag PRB is beneficial to maintaining the soil pH value free from the influence of the catholyte, thus facilitating the removal of Cu.  
Metal ions in the soil are easily desorbed under acidic conditions. The initial pH value of contaminated soil is 5.4, and the pH values of soil soaked by four electrolytes for 12 h will change. The pH values of soil soaked with HAc and CA decreased slightly. After 10 days of repair, the pH values of the soil near the anode (S1) in the eight groups decreased to between 2.0 and 3.3, and the pH values of anodic electrolyte order as 1% PASP > 0.1 M citric acid > 0.1 M HAc > 0.1 M KCl, contrary to the energy consumption order, which indicates that the soil pH value in the S1 section is related to the intensity of the electrolysis reaction. Lower pH values will produce stronger electrolysis reactions. It can be seen that the pH value and current value of T1 and T5 in the S1 section are the lowest when the repairs are completed. This may be because the environment with a low pH value reduces the zeta potential of the soil and thus leads to the decrease in electroosmosis flow and limits the migration of pollutants [27]. Therefore, controlling the soil pH value is crucial for EK remediation.
## 4 Conclusions  
In space laser communication, the effect of atmospheric turbulence on the performance of laser communication systems cannot be ignored, and fiber coupling efficiency is a key factor in determining the performance of atmospheric laser communication. This paper studies the influence of the light intensity scintillation effect caused by atmospheric turbulence on the optical fiber coupling efficiency. First, the relationship between light intensity flicker and spatial coherence radius is established based on the principle of fiber coupling efficiency; then, the influence of light intensity flicker on fiber coupling efficiency is analyzed through spatial coherence radius. An experimental test system for the atmospheric turbulence scintillation index and fiber coupling efficiency has been designed based on the airship laser communication platform. Measurements were made to obtain real-time light intensity scintillation index and fiber coupling efficiency values for blimp-ground platform link distances of 3000 m and 12,000 m. The experimental results show the impact of light intensity scintillation on fiber coupling efficiency and the correspondence between them under different transmission distances and heights above ground, providing an effective method for evaluating the optical coupling efficiency through the turbulent scintillation index.  
X.D. and X.Z.; methodology, X.D.; software, X.D.; validation, X.D. and X.Z.; formal analysis, X.D.; investigation, X.D.; resources, X.D.; data curation, X.D.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z.; visualization, X.Z.; supervision, X.Z.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jilin Provincial Key Research and Development Program Projects, grant number No. 20180201002 SF.  
Not applicable.  
**Data Availability Statement:** All data in support of the findings of this paper are available within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 6: Coupling efficiency measurement results.
## 6 Challenges and Limitations
### Data Availability and Quality  
One of the primary challenges in developing AI models for pediatric fracture overgrowth is the availability of high-quality, comprehensive datasets. Pediatric orthopedic cases are less common than adult cases, resulting in smaller datasets. Additionally, ethical considerations and regulations around pediatric data limit the sharing and use of patient information. Data heterogeneity further complicates model development. Variations in imaging protocols, equipment, and settings across different institutions lead to inconsistencies in image quality and characteristics. Differences in clinical documentation, terminology, and treatment approaches introduce additional variability [58]. To overcome these challenges, multicenter collaborations are essential. By pooling data from multiple institutions, researchers can increase dataset size and diversity, enhancing model robustness and generalizability. Standardization efforts, such as developing uniform imaging protocols and data collection practices, can improve data consistency. Data augmentation techniques, including the use of synthetic data and image augmentation, can expand datasets [59]. However, care must be taken to ensure that augmented data accurately represent real-world variations and do not introduce biases.
## 2 Literature Review and Cost Estimation Methods
### Cost Estimation Techniques
Therefore, to solve some of these problems, expert systems are much better developed by their wide applicability, and also by the methods obtained over time by other experts, declaring them effective in solving cases in similar fields [24]. The most intensively studied areas where relevant studies have been made are closely related to the built environment, the design of highways and public buildings, and the construction of water and sewage networks, railways, or power plants. The cost estimation methods dedicated to these works were based on several applied techniques. These estimation methods are classified into two main categories: qualitative and quantitative methods. Coestim targets both categories by combining the experience of users with the experience of suppliers, forming a mix between quality and quantity based on which work estimates and the final price of materials will be generated. A representative diagram for all cost estimation modeling techniques is shown in Figure 1.
## 3 Results and Discussion
### Catalyzed Oxidation of \(\beta\)-Myrcene
#### 3.4.2 Effect of H\({}_{2}\)O\({}_{2}\) as Oxidant with Organic Salts of POMs  
H\({}_{2}\)O\({}_{2}\) and a catalyst are needed to form the epoxide. At 80 \({}^{\circ}\)C without the oxidant (M16) or in the presence of the **(BmIm)\({}_{4}\)PMo\({}_{11}\)VO\({}_{40}\)** catalyst only (M1), no myrrene oxide was formed. Temperature is also important. Indeed, with this catalyst, at 40 \({}^{\circ}\)C (M17), oxidation was observed with 5% oxide formation and with low conversion. At 70 \({}^{\circ}\)C (Entries M2, M6, M10, M19), \(\beta\)-myrrene conversion values ranged from 75% (M2) to 87% (M10), depending on the catalyst composition. Oxide formation was minimal in each case (maximum of 7% for M10) under these conditions, suggesting that H\({}_{2}\)O\({}_{2}\) in water at this temperature promoted hydroxylation. Polymerization or Diels-Alder condensation was also possible (camborene being observed in GC-MS).  
The miscibility between the substrates and the expected products as well as the reaction media could also alter the observed reactivity. To assess this hypothesis, different solvents were evaluated. The addition of toluene was tested with the four catalysts (Entries M3, M7, M11, M20). With BmIm salts, conversion was identical to or better than when under organic-solvent-free conditions and oxide yield was higher (close to 28% vs. 1% without toluene). With the BuPyr salts, conversion dropped and the conversion was not significantly enhanced. The difference in reactivity observed by the addition of toluene is unknown but probably has to be linked to the difference in the solubility of catalysts in toluene. Best results were obtained using toluene as a solvent since reactions with EtOAc (M12) or a EtOAc/CH\({}_{3}\)CN mixture (M13) led to highest conversion (>94%) but very low selectivity towards the epoxide. According to the precedents in such transformations, acetonitrile could be a suitable solvent with H\({}_{2}\)O\({}_{2}\) to enhance the oxide formation, but this was not the case herein [22].  
The high quantity of unknown products might be linked to the nature of the substrate itself. Indeed, as stated earlier, myrrene tends to condensate, including camphorene. This is probably what was observed when the reaction was performed without a catalyst and with no oxidant (M1), with 47% conversion, leading to several products including camphorene (detected in GC-MS). Similar behavior was observed in the presence of **(BmIm)\({}_{4}\)PMo\({}_{11}\)VO\({}_{40}\)** but without an oxidant (M16), with 37% conversion.
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## Abbreviations  
\begin{tabular}{l l} BER & Bit Error Rate \\ CFO & Carrier Frequency Offset \\ DPLL & Digital Phase Locked Loop \\ GWC & Guided-Wave Channel \\ ISI & Inter Symbol Interference \\ MPPT & Maximum Power Point Tracking \\ MCU & Microcontroller Unit \\ PO & Phase Offset \\ PMU & Power Management Unit \\ SFE & Sampling Frequency Error \\ STE & Symbol Timing Error \\ SWP & Sandwich-Plated Channel \\ WPT & Wireless Power Transfer \\ \end{tabular}
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 5 Discussions
### Importance of a Password Verification System in Performance Measurement of the Proposed Approach
Users seeking access to cloud-stored data must provide more than one form of identification, such as a combination of something they know (e.g., password), something they have (e.g., OTP sent to their mobile device), and something they are (e.g., biometric data like fingerprints) [79; 80]. K-anonymity is a privacy-preserving technique employed in data anonymization. Its aim is to prevent individuals from being re-identified in a dataset by grouping or generalizing data attributes, thereby making each individual's data indistinguishable among at least \"k\" other individuals. The objective of this research paper is to improve data protection in cloud environments through the introduction of an innovative and flexible framework. This framework combines multi-factor authentication with k-anonymity, a privacy-preserving technique, to address data privacy concerns. To integrate k-anonymity into the framework, data undergoes a k-anonymization process before being stored in the cloud. This process involves grouping or generalizing data attributes to ensure that each individual's data remains indistinguishable among at least \"k\" other individuals in the dataset, thus safeguarding individuals' privacy and preventing re-identification. The proposed work includes multi-layer encoding, multi-cloud storage, and one-time passwords (OTP) as integral components of the proposed framework. The combination of the multi-factor authentication and k-anonymity results of the proposed work in a multi-layered approach can bolster data privacy within cloud storage. Multi-factor authentication secures access to the cloud environment, while k-anonymity protects individuals' data privacy by making it challenging to identify specific individuals from the dataset. Overall, the proposed framework offers a comprehensive and robust solution to address data privacy concerns in cloud computing. Its components, including multi-layer encoding, multi-cloud storage, multi-factor authentication, and possibly k-anonymity, strive to advance privacy-preserving techniques and inspire further developments in the field of cloud computing [81].
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 4 Performance Analysis
### Mathematical Model  
Table 1 summarizes the notations used in this section. However, these notations are introduced in the text when they are needed in the description.  
Let \(N\) be the number of nodes in the network, and \(l_{i}\) be the length of the longest path from node \(i\) to any other node in the network. By definition, \(l_{i}<N\ \forall i\).  
Let \(N_{i,h}\) be the number of nodes at \(h\) hops from node \(i\). When there is no node at \(h\) hops, which is always possible with a nonzero probability, we set \(N_{i,h}=0\)), and by convention \(N_{i,0}=1\).  
Let \(\overline{m_{i}}\) be the mean number of messages broadcast to update the location information of node \(i\) in the network. Node \(i\) is continuously broadcasting. Nodes at one hop from node \(i\) broadcast \(1\) in \(q\). Nodes at two hops from node \(i\) broadcast \(1\) in \(q^{2}\) (they receive \(1\) location update in \(q\) and forward them \(1\) in \(q\)), etc. As a result, \(\overline{m_{i}}\) can be written :  
\[\begin{array}{rcl}\overline{m_{i}}&=&1+\frac{1}{q}\ E(N_{i,1})+\frac{1}{q^{2} }\ E(N_{i,2})+...+\frac{1}{q^{i-1}}\ E(N_{i,l_{i}-1})\\ &=&\sum_{j=0}^{l_{i}-1}\frac{E(N_{i,j})}{q^{j}}\end{array} \tag{1}\]  
where \(E(N)\) is the expected value of \(N\).  
Let \(\overline{u_{i}}\) be the mean number of location updates performed in the network every time node \(i\) broadcasts its new location information. Node \(i\) is continuously updating. Nodes at one hop are also  
\begin{table}
\begin{tabular}{l c} \hline \hline Number of network nodes & \(N\) \\ \hline Density of network nodes & \(\lambda\) \\ \hline Transmission range & \(R\) \\ \hline Mean number of neighbors for a node & \(M\) \\ \hline Longest past from node \(i\) to any other node in the network & \(l_{i}\) \\ \hline One over \(q\) messages is repeated in the deterministic algorithm & \(q\) \\ \hline re-transmission probability in the probabilistic scheme & \(r\) \\ \hline Number of nodes at \(h\) hops from node \(i\) & \(N_{i,h}\) \\ \hline Mean number of messages and mean number of updates & \(\overline{m_{i}}\overline{u_{i}}\) \\ \hline Number of messages sent by node \(i\) in \(kD\) case with our algorithm & \(\overline{m_{i}}^{(k)}\) \\ \hline Number of messages sent by node \(i\) in \(kD\) case with flooding & \(\overline{M_{i}}^{(k)}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations used in analytical model.  
continuously updating. Nodes at two hops update \(1\) in \(q\) (they receive only one update in \(q\) from nodes at one hop). Nodes at three hops update \(1\) in \(q^{2}\), etc. As a result, \(\overline{u_{i}}\) can be written as  
\[\overline{u_{i}} = 1+N_{i,1}+\frac{1}{q}\;N_{i,2}+...+\frac{1}{q^{l_{i}-1}}\;N_{i,l_ {i}} \tag{2}\] \[= \left(q\sum_{j=1}^{l_{i}}\frac{N_{i,j}}{q^{j}}\right)+1\] \[= q\times\overline{m_{i}}+1-q\]  
Consequently, \(\overline{u_{i}}\) and \(\overline{m_{i}}\) vary identically. In the following, we only focus on \(\overline{m_{i}}\).
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 1 Introduction
Following the detection process, the output message is decoded. Moreover, in the embedding domain, watermarking techniques are categorised into time or frequency domains [20]. The hiding algorithms use different related algorithms such as Singular Value Decomposition (SVD), Discrete Wavelet Transform (DWT) and Discrete Fourier Transform (DFT). In the extraction domain, the watermark retrieval process is defined as the reverse process that is based on the embedding process [21]. These processes use low weight calculations, which cause low consuming energy [16]. Therefore, the advantages of using watermarking methods in IoT include:  
* No additional overhead is added on the wireless frame. * Data security is always guaranteed. * Reduce the end-to-end delay. * Low power consumption is attained due to the utilized lightweight calculations. Furthermore, the watermarking technique is beneficial when protecting digital data, although after its decryption process. Therefore, such a technique assists in averting the  
Figure 1: General watermarking system for multimedia objects [19]. illegal usage of personal information and defends it from illegal and fraudulent users. Nonetheless, cryptography is based on protecting the contents at the time of transit and not following the time of completing the decryption process. Digital watermarking can embed the digital data through a covered digital data for copyright protection, authentications and annotation purposes. Watermarking must be effective based on many different processes, such as geometrical manipulation, re-encryption and decryption [22]. An overview of different watermark techniques, which are related to different approaches, is presented in [20; 23; 24; 25; 26; 27; 28; 29; 30]. However, none of these studies discussed the use of the watermark field in IoT applications. Therefore, the main contributions of this paper can be summarized as follows:  
* A comprehensive survey is provided on various secure watermarking schemes within the IoT networks that have been proposed to deliver robustness and integrity throughout the watermark and security environments. In particular, the paper highlights the most recent studies in watermarking with different approaches such as: hashing, deep learning, encryption, and fragile. * Several challenges that can be addressed in IoT network traffic with the help of watermark technology have been examined. In this paper, we provide a comprehensive background on watermarking technology in Section 2. In Section 3, a background on the IoT and its challenges is presented. Section 4 demonstrates a number of modern studies that use different watermarking schemes within different IoT applications. A discussion and open issues are clarified in Section 5. Finally, Section 6 draws the conclusion of this paper.