esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Section
### Characterization  
After their preparation, we thoroughly analyzed these samples. Their texture and morphology were investigated by scanning electron microscopy (SEM) and atomic force microscopy (AFM). The crystalline phase of the oxide was identified by X-ray powder diffraction (XRPD), while its crystallinity degree was evaluated via micro-Raman spectroscopy (MRS). SEM images were recorded by a Phenom Pro-X scanning electron microscope equipped with an energy-dispersive X-ray (EDX) spectrometer. A NT-MDT Integra Spectra C microscope operating in intermittent contact mode was used to acquire the AFM images. The XRPD patterns were recorded at a scan speed of 0.5\({}^{\circ}\)/min by using the Ni \(\beta\)-filtered Cu-K\({}_{\alpha}\) radiation (\(\lambda\) = 0.1540 nm) at 40 kV. The diffraction peak identification was performed by utilizing the JCPDS database of reference compounds. Raman scattering excited by a solid-state laser operating at 532 nm (2.33 eV) was measured by means of a NTEGRA-Spectra SPM NT-MDT confocal microscope. To prevent local heating the laser power was set at 250 \(\mu\)W at the sample surface. The scattered light from the sample was collected by means of a 100\(\times\) Mitutoyo objective and detected via a cooled ANDOR iDus CCD Camera. To evaluate the spatial homogeneity of the samples, spectra from several random locations on each specimen were recorded and subsequently averaged to have a reliable picture of the entire sample. Further details on the instrumentation can be found in References [21,46].
## 2 Preliminaries
### Watermark Synchronization Mechanism Based on a Mapping Mechanism  
To ensure that the mapping relationships of watermark bits for each element remain independent, we propose a synchronization mechanism that maps the watermark positions using the coordinates of elements. Consider that BIM data exhibit agglomeration characteristics, whereby elements located on the same floor or connected to the same wall tend to have similar coordinate values. So, we utilized multiplication operations to extend the range of the figure element coordinate value, followed by a hash calculation to obtain the watermark bit. This approach spreads the element coordinates across a three-dimensional space so that the watermark information becomes more uniformly dispersed within the BIM data. Moreover, it enhances the randomness of the watermark information embedded into the location. A schematic diagram of the proposed watermark synchronization mechanism is shown in Figure 2.  
As the accuracy of BIM data coordinates may diminish in practical applications, we constructed the mapping relationship by selecting the high decimal point of the coordinate of the figure element. The watermark index calculation method is shown as follows:  
\[Index_{i}=\text{mod}(\sum\text{mod}(D\times 10^{P},10^{S}),Length),D\in\{V_{i} \cdot x,V_{i}\cdot y,V_{i}\cdot z\} \tag{2}\]  
where \(i(i=1,2,3\ldots)\) represents the element index, \(Index_{i}\) represents the index of the watermark corresponding to this element, and \(V_{i}\) is the coordinate. \(P\) denotes the lowest valid position after the coordinate decimal point, and \(S\) represents the number of requiredcoordinate positions. \(\text{mod}()\) is the remainder calculation, and _Length_ represents the length of the watermark.  
This method ensures that each element's watermark mapping relationship remains independent, and changes, such as element deletion, movement, rotation, etc., will not affect the mapping of other elements to the correct watermark bit. Thus, it effectively enhances the algorithm's robustness.
## 3 WebAssembly and IoT
### WebAssembly and IoT Security
* **Controlled Interactions with the Host Environment** In Wasm, all interactions with the host environment must be explicitly defined through imports and exports. This means that a Wasm module can only access the host resources that it has been explicitly granted access to. This capability provides a strong isolation between the Wasm module and the host system, limiting the potential attack surface and reducing the risk of unauthorized access to sensitive resources, a particularly desirable trait for IoT applications. * **Verification and Validation** Wasm modules undergo a validation process before execution. This validation ensures that the module adheres to the Wasm specification and is free of certain types of errors, further enhancing its security. Moreover, certain Wasm compilers, such as those used in the Rust ecosystem, offer even more robust validation and verification processes, further enhancing the security of IoT applications. * **Secure IoT Applications with Web Assembly** The security features offered by Wasm are not merely theoretical; they are being actively leveraged in real-world IoT applications. For instance, edge computing applications, which require processing data close to the source, often utilize Wasm for its secure execution environment. Similarly, industrial IoT solutions benefit from the security and isolation features of Wasm when dealing with critical infrastructure components. * **Future Directions in Web Assembly and IoT Security** While Wasm already brings significant security benefits to IoT, the technology continues to evolve, and future enhancements may further bolster these security credentials. Initiatives such WASI aim to define a secure and portable interface for Wasm modules, broadening their applicability beyond the web browser while maintaining robust security guarantees. As IoT continues to expand and evolve, the security capabilities of technologies such as Wasm can be expected to evolve alongside.
# A Reliability Engineering Approach for Managing Risks in CubeSats
## 3 Materials and Methods
### Step 1. Construct the Functional Block Diagrams  
The first step of the proposed approach consists of building diagrams and documents, which illustrate interrelationships and interdependencies of functional entities and support an effective mission design [31]. This step is necessary to understand system's architecture, providing answers to questions such as: which equipment provides what function; when the equipment fails what function is affected; how the system can be controlled before and after a failure; how backups and redundancies have to be managed to guarantee usability and accessibility in case of failure; etc.
## 6 Conclusions  
An effective hardware architecture, designed for BEC-based cryptography, is introduced in this article. The objective is to deliver a high-performance secure hardware design with reduced complexity. The NAF algorithm is implemented using a radix-4 multiplier. Through the use of efficient data routing in ALU, it improves efficiency in the execution of cryptographic instructions. Optimizations are also made in terms of memory requirements and instructions scheduling. The findings are extremely encouraging for a wide range of applications where low latency, high throughput, and resource efficiency are critical requirements. For domains requiring a balance between fast data processing and optimum resource utilization, our architectural solution stands out as an excellent option. In addition to delivering high-performance secure hardware design with reduced complexity, our architecture holds promise for various applications. Beyond cryptographic domains, its efficiency in low latency, high throughput, and resource utilization makes it well suited for real-time processing in areas such as embedded systems, secure communications, and edge computing. Looking ahead, future directions could involve exploring adaptability to emerging cryptographic standards, scalability to accommodate evolving computational needs, and integration with emerging technologies like quantum-resistant algorithms. These considerations further solidify the versatility and relevance of our architectural solution in the dynamic landscape of hardware security.  
Conceptualization, A.S. and M.R.; methodology, M.R. and A.S.; validation, O.S.S., M.R., M.A. and A.Y.J.; formal analysis, M.A. and M.R.; investigation, A.S. and M.A.; resources, O.S.S., M.A. and A.Y.J.; data curation, O.S.S. and M.A.; writing--original draft preparation, A.S.; writing--review and editing, M.R.; visualization, M.R.; supervision, A.Y.J. and M.R.; project administration, M.R.; funding acquisition, M.R.All authors have read and agreed to the published version of the manuscript. This research was funded by Deanship for Research & Innovation, Ministry of Education in Saudi Arabia grant number IFP22 UQU4320199 DSR102  
**Institutional Review Board Statement:** Not applicable.  
Figure 4: Throughput/Slice Visual Comparison.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** The authors extend their appreciation to the Deanship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number: IFP2 LUQU4320199 DSR102.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experimental Validation and Result Analysis
### RUL Prediction Process and Result Analysis
#### 4.2.1 RUL Prediction Process
Calculate the standard deviation of the prediction results [49]:  
\[s=\frac{1}{n_{y}-1}\sqrt{\sum\limits_{r}^{n_{y}}{(x_{r}-\hat{\mu}_{con})^{2}}} \tag{49}\]  
The level of significant difference between the proposed RUL prediction model and the comparative models can be described as follows [55]: Null hypothesis (H\({}_{0}\)): there is no significant difference between the predicted results of the proposed model and the comparison model; alternative hypothesis (H\({}_{1}\)): there is a significant difference between the proposed model and the comparison model. Mean difference [55]:  
\[\overline{d}=\frac{1}{n_{y}}{\sum\limits_{r-1}^{n_{y}}{d_{r}}} \tag{50}\]  
where \(d_{r}\) denotes the difference between the proposed model and the comparison model, and \(d_{r}=\hat{Y}_{t}-Y_{c}\), \(Y_{c}\) is the RUL of comparison model. Standard deviation of differences [55]:  
\[s_{d}=\sqrt{\frac{1}{n_{y}-1}{\sum\limits_{r}^{n_{y}}{(d_{r}-\overline{d})^{2}}}} \tag{51}\]  
The test statistic is [55]:  
\[t_{d}=\frac{\overline{d}}{\frac{\overline{s_{d}}}{n_{y}}} \tag{52}\]  
Degrees of freedom are denoted as \(n_{y}-1\), by consulting the distribution table [55]: the significance level \(\gamma=0.05\). If \(p<\gamma\), reject the null hypothesis and consider that there is a significant difference between the proposed model and the comparative model. The formula for calculating the \(p\)-value is as follows [55]:  
\[p=\text{t.cdf}(t_{d},\text{df}) \tag{53}\]  
where df is the degrees of freedom, and t.cdf is the cumulative distribution function of the \(t\)-distribution. To observe the performance of the proposed method more intuitively, Root Mean Squared Error (RMSE) and Absolute Error (MAE) are used as performance indicators to evaluate the effectiveness and superiority of the proposed method; the formula for RMSE has been defined in the previous Section 4.1.1, and the formula for MAE is as follows [8]:  
\[MAE=\frac{1}{n_{y}}{\sum\limits_{i=1}^{n_{y}}{|Y_{t}-\hat{Y}_{t}|}} \tag{54}\]  
The fitting performance of the model is evaluated using R\({}^{2}\); the closer R\({}^{2}\) is to 1, the better the fitting degree of the model is proven to be [46]:  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n_{y}}{(Y_{t}-\hat{Y}_{t})^{2}}}{\sum\limits _{i=1}^{n_{y}}{(Y_{t}-\overline{Y}_{t})^{2}}} \tag{55}\]  
where \(\overline{Y}_{t}\) is the average value of the predicted RUL.
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 4 A Novel Self-Organizing Approach to Drone-Based MANETs
### Dynamic Drone Deployment and Recall
#### 4.2.2 Dynamic Recall  
As Section 4.3 will discuss, nodes in the MANET are expected to adapt their connections towards the root under certain conditions. This _rewiring_ amounts to the optimization of the network and manages the MANET topology. However, we first define the mechanism to shrink the swarm when its overall utilization becomes sub-optimal.  
Recall that clients automatically connect to the closest node. This implies that a drone serving as a leaf-node in the MANET can lose all its clients to other, more optimally located nodes. Similarly, if a node was serving as a relay in a multi-hop route, it may see all its child nodes re-connect to other parent nodes (as described in Section 4.3; this is carried out to optimize the overall LQM of the MANET). In both cases, a node will find itself without any dependents (and therefore without any _stress_ value (see Section 3.3)). After this situation has persisted for 10 or more time units, a node will start considering retirement on every subsequent time-step, with a fixed probability (currently 1 %).  
The decision to retire is made autonomously by every individual node and is subject to a stochastic choice. Because the random test is performed on every time-step against a fixed probability, the longer a drone remains without clients or children in the MANET, the more likely it is to have returned to base (the probability of it remaining idle, waiting for children or clients to connect, follows an exponential decay). If any client or node connects to the drone before a decision to retire is made, it simply stops repeating the test and resumes normal operations. Once the decision to retire is taken, however, a drone will not accept new connection requests but instead return to the base.
## 6 The 6 GCloHE System Architecture
### Transport Layer  
The technology at the heart of communication for the transport layer in the IoHE architecture would be the 6G Tactile Internet that offers speed, accuracy, real time latency, energy conservation, and reliability among sensors and actuators close to a human body. The 6G Tactile Internet, 6G Wi-Fi, or 6G FAW can help to reduce healthcare costs and improve quality by using different sensors to read a patient's health parameters, thus improving the patient's quality of life.
## 7 Future Work  
For future work, we would like to expand our current research by addressing several key areas to further improve the performance, scalability, and security of the blockchain-based vehicle communication system.  
* Real-world testing: While our current simulations provided valuable insights into system behavior in attack scenarios, future efforts will focus on transitioning from simulations to real-world testing. Deploying the system in real vehicle environments will allow us to validate its performance and robustness under dynamic conditions, taking into account real-time factors such as network variability, physical obstacles, and hardware limitations.
* Optimization for large-scale networks: As the number of connected vehicles and RSUs grows in future smart cities, scalability remains a critical challenge. In the future, we plan to explore more advanced optimization techniques, such as dynamic node allocation or sharding, to efficiently handle larger networks without overwhelming the capacity of the blockchain.
* Energy efficiency improvements: Blockchain operations, especially in resource-constrained environments such as vehicles, can consume significant amounts of energy. We aim to investigate energy-efficient consensus algorithms or adaptive energy saving strategies, such as the integration of localized fog or edge computing solutions, to reduce the overall energy demand of the system.
* Improved data protection measures: While we have implemented encryption and data anonymization techniques, in the future, we will explore even more advanced privacy-preserving methods, such as homomorphic encryption or secure multi-party computation [32]. These methods could further protect sensitive information without compromising the security or functionality of the system.
* Advanced security mechanisms: We intend to enhance the security architecture by integrating machine learning-based intrusion detection systems (IDSs). These systems could dynamically detect and prevent novel or evolving attacks that could bypass traditional security mechanisms.
* Evaluation of hybrid consensus models: PBFT performed well in our simulations, but exploring hybrid consensus models that combine PBFT with proof-of-stake (PoS) or other consensus algorithms could provide even better scalability and fault-tolerance, especially in environments with rapidly changing network topologies such as vehicular networks.
# Effects of Vibration on Adjacent Pipelines under Blasting Excavation
## 5 Soil Vibration Response above the Buried Pipeline
### Security Criteria Analysis  
To guarantee the subsequent construction will not affect pipeline safety, the buried pipe's _PPV_ prediction formula and the safety criterion are assessed by an analysis of statistical mathematics based on the numerical calculation and on-site results.  
From Table 9, it is clear the numerical simulation results have a minor error compared to the prediction results of the _PPV_ prediction model. The maximum error is 4.52%; hence, it is workable to utilize the _PPV_ prediction model to reflect pipe response characteristics during blasting excavation. Since pipeline is buried underground, it cannot be monitored directly. Therefore, a pipeline _PPV_ prediction model applicable to this study was proposed to reflect the vibration attenuation characteristics of pipelines under blast vibration. Based on the fitting analysis, the _PPV_ linear relationship between the two values in Table 9 was  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Angle (\({}^{\circ}\))** & **PES of Soil (\(\sigma_{s}\))/MPa** & **PES of Pipe (\(\sigma_{p}\))/MPa** & \(\frac{\sigma_{p}}{\sigma_{s}}\) \\ \hline
0 & 2.463 \(\times\) 10\({}^{-3}\) & 0.72 & 292.341 \\
45 & 5.008 \(\times\) 10\({}^{-3}\) & 1.53 & 305.514 \\
90 & 2.745 \(\times\) 10\({}^{-3}\) & 0.94 & 342.475 \\
135 & 1.476 \(\times\) 10\({}^{-3}\) & 0.57 & 386.271 \\
180 & 3.569 \(\times\) 10\({}^{-3}\) & 1.25 & 350.233 \\
225 & 1.393 \(\times\) 10\({}^{-3}\) & 0.42 & 301.486 \\
270 & 1.938 \(\times\) 10\({}^{-3}\) & 0.63 & 325.158 \\
315 & 3.532 \(\times\) 10\({}^{-3}\) & 1.16 & 328.453 \\ Average & & & 328.991 \\ \hline \hline \end{tabular}
\end{table}
Table 8: PES ratio of buried pipe and its attached soil.  
established (Figure 13a). The _PPV_ prediction model of the buried pipe was derived from the following Equation:  
\[V_{p}=68.32\left(\frac{Q^{1/3}}{r}\right)^{1.31}+0.14 \tag{19}\]  
To guarantee the subsequent construction will not adversely affect the pipeline, an empirical formula was derived for the safety criterion based on relations to statistical mathematics (Figure 13b). The safety criterion equation is as follows:  
\[\sigma_{p}=0.88V_{p}-0.39 \tag{20}\]
# Optimization of the Drying Temperature for High Quality Dried Melissa officinalis
## 2 Materials and Methods
### Color and Chlorophyll Content  
The color of dried _M. officinalis_ was quantified using a colorimeter (Chroma meter CR400 Konica Minolta, Tokyo, Japan) by measuring the L\({}^{*}\) (brightness/lightness: 0: black/100: white), a\({}^{*}\) greenness/redness (\(-\)a\({}^{*}\): greenness and \(+\)a\({}^{*}\): redness), and b\({}^{*}\) (\(-\)b\({}^{*}\): blueness and \(+\)b\({}^{*}\): yellowness) values of the CIELAB uniform color space. Based on these values, the hue (h), chroma value (C), color index (CI), and browning index (BI) were calculated [31; 32; 33]. The determination of the chlorophyll content (i.e., chlorophyll a, chlorophyll b, and total chlorophyll) was performed according to [PERSON] and [PERSON] [34]. Briefly, appropriate amount of dried tissue was homogenized with acetone:hexane (4:6 \(v/v\)) and the absorbance of the solution (after sonication for 5 min) was measured at 645 and 663 nm (Multiskan GO, Thermo Fisher Scientific Oy, Vantaa, Finland). The results were expressed as mg of chlorophyll per g of dried weight (mg/g Dw).
## 5 Extension to Six Degrees of Freedom  
Although this work focuses on the estimation of ego-motion using three degrees of freedom (x, y, and yaw), extending the proposed method to six degrees of freedom (x, y, z, roll, pitch, and yaw) will be explored in future work. The extension to six DoF involves additional challenges, primarily due to the increased complexity in capturing and accurately predicting the vertical movements (z) and rotational motions (roll and pitch) of the vehicle. To address these challenges, the integration of more sophisticated optimization techniques, such as multi-objective genetic algorithms, will be considered to handle the increased dimensionality effectively.  
Furthermore, the use of advanced sensor fusion approaches, incorporating LiDAR or IMU data alongside visual information, could be explored to improve the robustness and accuracy of ego-motion estimation in three-dimensional spaces. Using the strengths of these additional sensors, the proposed method could achieve more precise and reliable results for the full six degrees of freedom in diverse environments, including urban and off-road scenarios.  
In general, extending the method to six degrees of freedom will be a significant step towards improving the generalization and applicability of the algorithm for real-world autonomous vehicle navigation tasks, particularly in more complex and dynamic environments.
# Improving Passive Time Reversal Underwater Acoustic Communications Using Subarray Processing
## 5 Lake Experiment
### Results
The output SNR performance using TR-MC-DFE  
Figure 12: Performance of conventional TR-DFE with different subarray: (**a**) subarray with hydrophones (1,2,3,4) and (**b**) subarray with hydrophones (5,6,7,8). Figure 13: Trade-off between subarray length and DFE order. (**a**) performance of TR-MC-DFE using different subarrays, (**b**) performance of TR-MC-DFE using different DFE order. with four different subarrays is shown in Figure 13a. The feedforward and backward filter order are 50 and 50, and IPNLMS is used. For one subarray, the TR-MC-DFE becomes the conventional TR-DFE and eight hydrophones are used. For two subarrays, we use hydrophones (1,2,3,4) and hydrophones (5,6,7,8). For three subarrays, we use hydrophones pairs (1,2,3), (5,6,7), and (6,7,8). For four subarrays, (1,2), (3,4), (5,6), and (7,8) hydrophones are used. As shown in Figure 13a, from one subarray to two subarrays, the average output SNR gain is about 3 dB, while the average output SNR gain drops to 1.2 dB from two subarrays to three subarrays. In particular, from three subarrays to four subarrays, the output signal to noise ratio even decreased by 0.4 dB. From DJK15 data, it is recommended that each subarray use 3-4 hydrophones. For a fixed-length array, reducing the subarray length leads to more subarrays. More subarrays increase the channel number of the following MC-DFE, thereby increasing the computational complexity. Figure 13b shows the output SNR improvements with different DFE orders. IPNLMS channel estimation algorithms are used, and two subarrays are used in the TR-MC-DFE. Both TR-DFE and TR-MC-DFE have output SNR improvements when the orders of DFE increase. In practice, the choice of DFE order depends on the Q function and the processor's capabilities. As shown in Figure 9, we can choose a value 10 dB smaller than the Q function maximum for DFE order.