esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion  
The Co-Occurrence of MCs and T&O Compounds Relating to Different Dominant Cyanobacterial Species in the EDWS  
In the EDWS of Lake Chaohu, the co-occurrence of MCs and T&O compounds caused adverse effects on the safety of water source. Previously, different conclusions were drawn involving the relationships between MCs and T&O compounds. For particulate T&O compounds, significant correlations between _p_-CYC and _p_-ION with MCs were reported in Lake Taihu [12]. However, no correlation between toxins and T&O compounds concentrations was found in the survey involving 23 Midwestern United States lakes [10]. When two cyanobacterial species, _Microcystis_ and _Dolicospermum_, dominated in different seasons, dynamic changes relating to these metabolites became vastly more complicated compared to the water source with the sole dominant species. In the present study, strong relativities in each MC congener and MIB were found and positive correlations between ION and CYC were demonstrated (Figure 1). The GEO had no relationship with ION and CYC, but correlated negatively with the remaining metabolites. The mutable relativities among the three MCs congeners and four T&O compounds could be explained by the community dynamics of synthesizing cyanobacterial strains, different metabolic pathways, and the different physicochemical properties such as volatility and degradability.  
The DWTPs usually reinforce treatment technology operation during cyanobacterial blooming at warmer temperatures instead of operating within a dedicated system year round from the perspective of cost savings [16]. However, in the drinking water sources associated with more than one dominant cyanobacterial species such as those found in this study, the concentrations of MCs and T&O  
Figure 5: The removal rates of chlorophyll-\(a\) and the microccystitis and taste-and-odor compounds in the drinking water treatment plant associated with Intake-A (left) and the maximum tolerable (MT) values (right) based on MC-LR, MC-RR, MC-YR, 2-methyl isoborneol (MIB), \(\beta\)-cyclocitral (CYC) and \(\beta\)-ionone (ION); the establishment of chlorophyll-\(a\) (Chl-\(a\)) limits at intake (CLIs) in the four seasons in the eastern drinking water source of Lake Chaohu (red circle). Error bars indicate the standard deviation of total removal rates in different months of each season and the MT values for Chl-\(a\) of three sampling sites.  
compounds still maintained at high levels in spring and winter and even exceeded the guidelines during the whole year. The differences in the characteristics and seasonal variations of the two classified compounds (MCs and T&O) indicated the high risk involved in producing safe drinking water, which has led to difficult and complex management for the drinking water source and the DWTP.
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 3 Methodology
### System Composition
#### 3.2.2 ROM Identification  
This module comprises of the extraction of a ROM and is further divided into two main parts that is, Selector and Trimmer.  
Selector: A difference frame is computed using new and reference frames. The difference frame is then checked for the amount of change and depending on this amount, a decision is made as to whether a ROM needs to be extracted or not. If no significant change is identified, the system state switches to load reduction mode directly. On the other hand, if prominent change is detected in a new frame with respect to (w.r.t.) the reference frame, then the difference frame is forwarded to the Trimmer.  
Trimmer: The second step in ROM identification involves restriction of data to only the required portion. Intensities considered in the reference and current frame, when differencing the two frames, resulting, are limited to those greater than twenty (from a range 0 to 255) because lower intensity differences are visually indistinguishable in the grayscale. A threshold of 20 was set after observing the results from varying from threshold from 10 to 80, so as to check that there is not excessive loss of information if a sub-optimal threshold value is set. Thus only region(s) containing non-zero pixel values greater than the threshold value are selected from the difference frame. A region where those pixel values occur is mapped onto the matching region in the current frame, after which the ROM that it forms is cropped from the current frame. In Figure 2, a difference frame, \(F_{D}\) in Figure 2a, is shown after differencing between the corresponding reference and current frame, \(F_{C}\). From Figure 2a, it is apparent that only the values 36, 45, and 40 are above the threshold. The region corresponding to the \(F_{C}\) array in Figure 2b is cropped to the area defining significantly changed pixel values and is represented as \(F_{Region}\), after cropping, in Figure 2c. After cropping, the color space of a ROM is converted back from grayscale format to RGB, which is then used in the load reduction module.  
As has been mentioned, a reference frame is required for accurate processing of the proposed system. In practice a reference frame can be selected during the calibration or installation phase of a system. This frame can be regularly updated automatically or manually, depending on the application domain. For example, during video surveillance, when cameras are usually mounted at a fixed position, the reference frame can be updated automatically by subtracting the static content of the reference frame (selected initially) from that of a frame with moving content. Furthermore, for some applications, a reference frame can be updated manually.
## 3 Position Tracking Controller Design
### Closed-Loop System Stability Analysis  
The stability of the tracking error dynamics in (13) is proven under an assumption that the velocity and load torque are known. With regard to the estimation error dynamics in (19), the closed-loop system can be derived as  
\[\begin{split}\underbrace{\begin{bmatrix}\dot{\theta}\\ \dot{\omega}\\ \dot{\tau}_{l}\end{bmatrix}}_{\hat{x}}&\underbrace{\begin{bmatrix}-l_{1}&1&0\\ -l_{2}&-\frac{R}{7}&-\frac{1}{7}\\ -l_{3}&0&0\end{bmatrix}}_{A_{o}}\underbrace{\begin{bmatrix}\tilde{\theta}\\ \tilde{\omega}\\ \tilde{\tau}_{L}\end{bmatrix}}_{\hat{x}}&\underbrace{\begin{bmatrix}\tilde{ \theta}\\ -l_{2}&-\frac{R}{7}&-\frac{1}{7}\\ -l_{3}&0&0\end{bmatrix}}_{A_{3}}\underbrace{\begin{bmatrix}\tilde{\theta}\\ \tilde{\omega}\\ \tilde{\tau}_{L}\end{bmatrix}}_{\hat{x}}&\underbrace{\begin{bmatrix}\tilde{ \theta}\\ \tilde{\omega}\\ 0&-\frac{R}{7}\sin(p\theta)&0\\ 0&\frac{R}{7}\cos(p\theta)&0\end{bmatrix}}_{B_{est}}&\underbrace{\begin{bmatrix} \tilde{\theta}\\ \tilde{\tau}_{L}\end{bmatrix}}_{\hat{x}}\\ \end{split} \tag{23}\]  
With the control law \(u=-K_{e}e\)(14), the closed-loop system can be rewritten by  
\[\begin{split}\dot{\varepsilon}&=(A_{e}-B_{e}K_{e})e+B_{est} \tilde{x}\\ \dot{\varepsilon}&=A_{o}\tilde{x}.\end{split} \tag{24}\]Because \((A_{e}-B_{e}K_{e})\) is Hurwitz and \(B_{est}\) is bounded, the dynamics of \(e\) are input-to-state stable. With Hurwitz \(A_{o}\), \(\tilde{x}\) exponentially converges to zero. Thus, \(e\) also converges to zero. We conclude that the closed-loop system (24) is stable.  
A block diagram of the proposed method is presented in Figure 2. The nonlinear observer (18) estimates the velocity and load torque using the position and current feedback. Then, desired states and inputs are derived using (6), (7), (9), and (12). The the control inputs (12) are obtained with the optimal state feedback control (15) and (17).
# A Blockchain-Based Decentralized Public Key Infrastructure Using the Web of Trust
## 7 System Evaluation and Limitations
### Standardization and Interoperability  
In the development of blockchain-based decentralized PKI systems, standardization and interoperability present significant challenges due to the lack of universally accepted standards. Key considerations for a new decentralized PKI system, as highlighted by [PERSON] et al. [45], include the following:  
* **Permission Type:** Choosing between a permissionless or permissioned blockchain.
* **Blockchain Type:** Evaluating the benefits of different blockchain implementations, like Ethereum or custom blockchains.
* **Trust Model Type:** Deciding between a hierarchical PKI or a WoT model.
* **Certificate Format:** Ensuring that formats like X.509 or custom formats support interoperability.
* **Revocation:** Incorporating certificate revocation as a critical security feature.
* **Implementable:** Demonstrating feasibility through proof-of-concept implementations.
* **Complexity and Cost:** Assessing and managing system complexity and operational costs.
* **Updateable Key:** Providing support for key updates for long-term sustainability.  
In our proposed system, we implemented several key features to address these considerations:  
* We adopted a permissionless blockchain for certificate transparency and suitability for large-scale applications.
* We chose Ethereum smart contracts for their decentralized infrastructure, facilitating the storage of certificate metadata to solve block traversal problems.
* We adopted a WoT trust model to eliminate centralized CAs and ensure a distributed verification system.
* We integrated multi-signature capabilities into the X.509v3 extension field, requiring minimal browser adjustments.
* We proposed a theoretical approach for expediting the revocation process.  
* We provided a detailed system architecture with implementation specifics as a proof of concept. The system also supports the updateable key feature.  
These steps ensure our system's interoperability and compliance with most standards, although our current setup does not address auto-scaling for larger networks, indicating areas for future development.
## 4 Results
### Characterization of C2C Certified Regarding ISO Typology  
This section presents the analysis of the C2C Certified approach by comparing it with Type I and Type III, as postulated by ISO 14024 and ISO 14025, respectively. The results of the attribute-based assessment are given in Table 3. Following this, the section gives an overview of the results, and highlights several of the most critical and relevant characterization attributes for C2C Certified. In the following section, some key aspects of the above-listed attributes assessment are described in more detail, with a focus on the C2C Certified performance in comparison with Type I and Type III environmental labels. The section follows the structure of the characterization scheme (Table 3), based on the five main categories. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attribute** & **Type I Eco-Label** & **Type III EPD** & **C2C Certified** \\ \hline \multicolumn{4}{c}{**Communication Characteristics**} \\ \hline ISO typology & Type I (ISO 14024) & Type III (ISO 14025) & Does not fully conform with Type I or Type III label requirements of ISO \\ \hline \multirow{3}{*}{Awarding format} & Seal (binary pass–fail information; products either conform or not) & Declaration (non-sealed; quantified environmental data using predetermined parameters) & Rating (sealed; ranked on a predefined scale after complying with minimum performance criteria) \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \end{tabular}
\end{table} 
Table 3: Characterization of C2C Certified and comparison with Type I and Type III environmental labels, based on a characterization scheme adapted from [PERSON] et al. [3]. EPD: Environmental product declarations, LCA: life cycle assessment, PCR: product category rules. Figure 1: Description of the method flow of the paper at hand.
## 4 Experimental Results and Discussion
### Comparison with Previous Research  
In this section, the surface roughness reduction rate and hardness increase rate of the composite process compared with the stainless steel substrate are compared with those of the ultrasonic surface strengthening process in [32,33], as shown in Table 5. The effects of initial matrix, initial roughness and initial hardness can be ignored when adopting the roughness reduction rate and hardness increase rate.  
Because of the high surface hardness of chromizing and the diminished impact of the plastic flow effect brought on by ultrasonic vibration, the roughness reduction ratio in this paper is lower than that in [32,33]. The ultrasonic vibration frequency used in this paper is much higher than that in [33], but the roughness reduction ratio is not significantly different from that in [33]. The intensity of the impact times will increase on the surface as the ultrasonic vibration frequency rises, which is to increase the roughness reduction ratio.  
Compared to [32,33], the hardness increase rate in this paper is significantly higher. This is so that the low-temperature chromizing process used in this study can add active elements such as chromium to the matrix's surface, preventing crack growth, lowering surface tension, and significantly raising surface hardness.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Research Item** & \begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt}} **Surface Roughness** \\ **Reduction Rate (\%)** \\ \end{tabular} & \
\begin{tabular}{p{56.9 pt} p{56.9 pt}} **Hardness Increase** \\ **Rate (\%)** \\ \end{tabular} &
\begin{tabular}{p{56.9 pt} p{56.9 pt}} **Ultrasonic** \\ **Frequency (kHz)** \\ \end{tabular} \\ \hline
[32] & 95.65 & 9.5 & 30 \\
[33] & 65.23 & 10.26 & 15 \\ This research & 60.86 & 275 & 41.243 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with references.  
As a result, the composite process used in this paper can significantly increase the matrix's hardness and satisfy the actual high hardness requirements while accounting for roughness.
## 2 Materials and Methods
### Identification of the Differentially Expressed Genes (DEGs)  
The expression levels of unigenes were estimated by the fragments per kilo-base of the exon model per million mapped reads (FPKM) [31]. To identify the DEGs, the read counts were adjusted by an edgeR program package through one scaling normalized factor. Then, the DEGseq R package was used to perform differential expression analysis. \(p\) value was adjusted by q value, and q value \(<\) 0.005 with log2 (foldchange) \(>\) 1 was set as the threshold to identify significant DEGs [32]. GO enrichment analysis of these DEGs was accomplished using the GOseq R packages based on Wallenius non-central hyper-geometric distribution. KOBAS was applied to test the enrichment of DEGs in KEGG pathways [33].
## 7 Conclusions  
In Underwater Wireless Sensors Networks (UWSNs), the sensor nodes are sparsely deployed due to deployment and high manufacturing cost in large deployment regions. To improve the reliability and reduce the energy consumption, we proposed a reliable and energy-efficient Extended Energy-Scaled and Expanded Vector-Based Forwarding Protocol (EESEVBF). The existing ESEVBF protocol fails when there is a void or energy hole at the second hop, which decreases the reliability of the system. The proposed protocol extends the holding time mechanism of the first-hop forwarder to the second hop for finding the best satisfactory path. EESEVBF overcomes the occurrence of void hole, whether it occurs to an energy hole or lack of a potential forwarding node in the transmission range. Moreover, the proposed protocol also tackles the hidden terminal problem due to which a reasonable reduction occurs in duplicate packets initiated by the reproducing nodes. The simulation results show that EESEVBF is 6.66% (approximately) more energy-efficient, experiences 20.2% less delay, and generates about 11.26% fewer data packets, compared to ESEVBF. Our experimental results demonstrate the efficiency of our proposed EESEVBF protocol over ESEVBF. The only overhead of our approach is the added complexity of computation in timer values, i.e., for a single routing decision, the system needs to exchange several control packets to estimate the timer values for all nodes up to the second hop in the locality of the source node. However, such calculations do not come with a high cost, i.e., the size of control packets range from 50-70 bits, which consumes negligible bandwidth and energy compared to the data packets.  
In our current work, sensor nodes are deployed at random locations in the underwater network. One of the future works is to deploy sensor nodes at such locations that can balance the energy consumption. This may be possible by using some statistical distribution (e.g., Gaussian distribution) for the deployment of sensor nodes with respect to the virtual pipeline and sink nodes. In addition, the use of radio frequencies can also be investigated in our proposed EESEVBF protocol in future.  
**Author Contributions:** Formal analysis, K.U.; Investigation, Z.W., K.U., F.A.K. and N.M.; Methodology, Z.W.; Project administration, A.B.Q.; Resources, S.J.; Supervision, Z.W., A.B.Q. and F.A.K.; Validation, S.J.; Writing--review and editing, Z.W., A.B.Q., S.J., F.A.K. and N.M.  
**Funding:** This research was funded by Deanship of Scientific Research, King Saud University: RGP-214.  
**Acknowledgments:** The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University, Saudi Arabia, for its funding of this research through the Research Group Project No. RGP-214.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Mechanical Properties of Composites
Furthermore, ramie-based composites exhibit a different behavior, which is the result of different, and in some cases contradictory, mechanisms. At 10 wt.% of filler, ramie-based composites have a high value of tensile stress at break, because of their high cellulosic content and small fiber diameter, which form an interconnected network of well-dispersed fibers. Furthermore, ramie exhibits low mass loss at the processing temperature, which means that void formation is not intense. However, at 20 wt.% of ramie content, there is a decrease in the tensile stress at break, due to the smooth surface of the fibers, leading to weak adhesion between the matrix and the reinforcing agent. Finally, at 30 wt.% of ramie content, the composite's tensile stress at break increases, probably due to well-dispersed fibers, which are responsible for an interconnected network, capable of efficiently carrying loads. In general, HDPE has a ductile behavior, and since it is reinforced with lignocellulosic fibers that exhibit a brittle behavior, the respective composites are also brittle [77]. The impact strength of the composites is a measure of the composites' toughness, with the main factors controlling this property being the matrix and fiber properties, as well as matrix-fiber interface [78]. When specimens are subjected to a force, they absorb the energy of the impact, and that energy is dissipated by certain mechanisms, which include fracture, delamination, and fiber pull-outs [79]. The main failure mechanism in the studied composites is fiber pull-outs, since there is not significant interfacial adhesion between the fibers and the matrix. So, as seen in Figure 12d, the introduction of different kinds of fibers in HDPE leads to a gradual decrease in the impact strength, which is in all cases significantly lower than that of neat HDPE. This may be due to the interfaces that act as stress-gathering points, from where cracks may initiate and propagate, and upon increasing filler content,the number of interfaces becomes high, progressively lowering the composites' overall impact strength.
# Heat Load Forecasting of Marine Diesel Engine Based on Long Short-Term Memory Network
## 3 Case Study
### Multimodel Comparative Analysis  
In this study, Spearman correlation coefficient method and LSTM network are combined to predict a time series data. Other prediction models are input into the same data set, and the results of other prediction methods are compared with the results of the proposed methods for further analysis. The results of each prediction model are shown in Figures 10 and 11.  
From Figure 10, the prediction curve (red line) of AESR-LSTM model with human experience is closer to the true value (blue line). As can be seen in Figure 11, except for a few predicted outliers, the system's scatter plot of forecasting and actual values is closest to the diagonal, which indicates that the difference between the forecasting value and actual value is the smallest.  
At the same time, several commonly used evaluation indicators were cited to further verify the prediction performance of the AESR-LSTM model. The prediction performance of the four models is used for comparison, as shown in Table 5.  
Figure 11: The forecasting and actual temperature for different models: (**a**) Training and test results of AESR-LSTM, (**b**) Training and test results of SR-LSTM, (**c**) Training and test results of LSTM, and (**d**) Training and test results of BP.  
Figure 10: Comparison of prediction results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Indictors** & **Formula** \\ \hline Mean absolute error (MAE) & \(\frac{1}{N}\sum\limits_{i=1}^{N}\left|T_{ri}-T_{pi}\right|\) \\ Mean absolute percentage error (MAPE) & \(\frac{100\%}{N}\sum\limits_{i=1}^{N}\left|\frac{T_{ri}-T_{pi}}{T_{ri}}\right|\) \\ Root-mean-square error (RMSE) & \(\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}\left(T_{ri}-T_{pi}\right)^{2}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation indicators.  
\(N\) is the number of predicted values, \(T_{ri}\) is the original data value, \(T_{pi}\) is the predicted value. The prediction performance of the prediction model is indicated by the value of MAPE, MAE and RMSE. The MAPE, MAE, and RMSE of the four models were calculated separately to reflect the goodness of the prediction model through the indexes. Figure 12 shows the values of the four prediction models the evaluation indexes. The error bars in the figure represent 95% confidence intervals. The mean absolute percentage, mean absolute, and root-mean-square errors of the proposed AESR-LSTM model are 0.089, 10.5403, and 27.5408, respectively, and the best indicators among several prediction models. The feature inputs selected by the improved AESR-LSTM model are better than those obtained by traditional methods for data trend prediction, so the method optimization is effective.
# Wheel Odometry with Deep Learning-Based Error Prediction Model for Vehicle Localization
## 2 Dead Reckoning Based on Wheel Odometry
With the established model, the travel distance error can be predicted accurately, and the travel distance output by the odometry can be corrected so that a more accurate position of the next moment can be obtained after dead reckoning.
# Study on the Ablation Behavior of High-Intensity Lasers in Vacuum
## 3 Results and Discussion
### Ablation Plume
Previous studies have shown that repeated laser irradiation can induce microstructural changes on the material surface, which reduce its reflectivity and consequently increase the absorption of laser energy [30]. This enhanced absorption may enable subsequent laser pulses to heat the area more effectively, thereby improving ablation efficiency. This observation is consistent with our experimental results, where the ablation efficiency of laser-induced craters is significantly higher in the vacuum environment, likely due to the combined effects of localized heat accumulation and increased absorption. This mechanism is similar to the enhancement observed in multi-pulse laser ablation [33].