esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation of Shaping Ability  
Pre- and post-instrumentation images of the resin canal blocks were obtained at \(\times\)20 magnification with a digital microscope (VH-8000; Keyence, Osaka, Japan). The images were superimposed and measurements were conducted using image analysis software (Photoshop 7.0; Adobe Systems, San Jose, CA, USA), as described earlier [25]. Four concentric circles with radii of 0.5, 1, 2, and 3 mm were drawn, centered on the apex of the root canal before instrumentation. The points where the root canal axis intersected each circle were designated as reference points. A reference line was defined as a line connecting the reference points and perpendicular to the root canal axis. Four measurement points were identified at the intersections of the reference line with the inner and outer contours of the root canal before and after instrumentation. The amount of cutting on the outer and inner walls, as well as the diameter of the root canal after instrumentation, was determined. The canal-centering ratio was calculated at levels of 0, 0.5, 1, 2, and 3 mm from the apex using the following formula: [(outer wall cutting amount) \(-\) (inner wall cutting amount)]/(post-instrumentation root canal diameter) [26]. A value closer to zero indicates less deviation, and positive and negative values represent deviation toward the outer and inner walls, respectively.
# Information-Theoretic Analysis of a Family of Improper Discrete Constellations
## 5 Numerical Results  
### Mutual Information as a Function of \(\phi\)  
In this section, we present some numerical results that validate our findings. First, we compute numerically the MI of improper constellations generated according to (16) and study its behavior with respect to \(\phi\). Figure 4 plots the MI of an improper QPSK constellation with \(\kappa=0.4\) for increasing values of the snr. The MI always peaks at \(\phi=\pi/2\), which is the optimal value according to Theorem 4. Note the different scale of the y-axis for each subplot. Figure 5 shows the results for an improper QPSK signal with a higher circularity coefficient (\(\kappa=0.95\)). In this case, as the snr increases, the optimal phase decreases until reaching, for sufficiently high snr, the optimal \(\phi^{*}\) in Theorem 4. For \(\kappa=0.95\), the optimal phase shown in Figure 5 is \(\phi^{*}=0.9538\).  
Figure 4: Mutual information of an improper QPSK constellation with \(\kappa=0.4\) as a function of \(\phi\), for three different signal-to-noise ratio (snr).  
Figure 5: Mutual information of an improper QPSK constellation with \(\kappa=0.95\) as a function of \(\phi\), for three different snr. The optimal phase is \(\phi^{*}=0.9538\).  
Finally, Figure 6 shows the value of \(\phi\) that maximizes \(I(Y;V)\) for different values of \(\kappa\) for an improper QPSK constellation. In the interval \(0\leq x\leq 0.5\), the optimum value is \(\pi/2\) regardless of the snr. When \(\kappa>\)0.5, \(\pi/2\) is still the optimum value for the low-snr regime, whereas at high snr the optimum value is \(\phi_{opt}=\phi^{*}\) given by Equation (30), which depends only on \(\kappa\). The curves in Figure 6 for \(\kappa>\)0.5 are monotonically non-increasing. However, for high values of both \(\kappa\) and snr, the estimated curves present some small fluctuations, which are due to numerical errors in the simulation. These errors can be explained because the mutual information is computed by numerical integration over a finite region for a discrete grid of equally-spaced phases.
# Aluminum-Based Plasmonic Photodetector for Sensing Applications
## 1 Introduction
At the same time, aluminum films are naturally oxidized in air at room temperature to form a relatively stable Al\({}_{2}\)O\({}_{3}\) dielectric layer, which protects the plasmon-carrying metal layer from degradation. However, in the red and infrared spectral regions, the plasmonic characteristics of aluminum are significantly inferior to those of gold; therefore, most SPP sensors (including PPPDs) currently use gold as a plasmon-carrying metal. But research and development related to the use of Al in plasmonics are expanding, and the number of publications on this topic has been growing rapidly recently [23; 24]. The possibility of using Al gratings in SPR sensors, [25] and polarization-sensitive detectors [17] and for enhancing up-conversion photoluminescence [26] has been shown. However, detailed studies of aluminum plasmonic detectors based on silicon p-n junctions that include modeling and optimization of the parameters of such structures, have not been carried out yet. The performance of PPPDs based on aluminum may be optimized for specific applications by choosing optimal grating geometric characteristics. In this work, the parameters of PPPDs based on silicon p-n junctions and aluminum as the plasmon-carrying metal were optimized. An optical smoke detector was selected as a device that uses the PPPD under study as an optical sensor. The most affordable on the market laser with a wavelength of 808 nm is the light source in such a device. Using the RCWA method [27; 28], theoretical simulations of the electromagnetic field intensity distribution in the proposed PPPD structure were carried out, and optimal characteristics of the plasmon-carrying layer grating were selected. Experimental samples of PPPDs with optimal aluminum gratings were fabricated, and their characteristics were investigated. It is shown that the polarization sensitivity of these samples is comparable to that obtained for PPPDs with gold films. The proposed PPPD design includes only Al, Cr, Si, and Ti, thus significantly reducing production costs.
# Enhancing Monocrystalline Solar Module Efficiency through Front-Surface Cooling with 96% Alcohol
## 3 Results and Discussion
### Power and Efficiency of Cooled and Uncooled Solar Module
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Power before Cooling**} & \multicolumn{2}{c}{**Power at the End of Cooling**} & \multicolumn{2}{c}{\(\Delta p=\frac{\mu_{A}-\mu_{B}}{P_{A}}\)**100\%**} \\ \cline{3-7}
**Number of Cycle** & **Time** & \multicolumn{2}{c}{**Cycle [W]**} & \multicolumn{2}{c}{**Cycle [W]**} & \multicolumn{2}{c}{**Cycle [W]**} & \multicolumn{2}{c}{\(\Delta p=\frac{\mu_{A}-\mu_{B}}{P_{A}}\)**100\%**} \\ \cline{3-7}  & & **A-Solar** & **B-Solar** & **A-Solar** & **B-Solar** & **Before Cycle** & **After Cycle** \\ \hline I cycle & 10:45:55–10:48:55 & 54.6 & 54.8 & 56.18 & 57.33 & 0.3 & 2 \\ II cycle & 10:52:55–10:57:10 & 56.26 & 56.49 & 57.33 & 58.95 & 0.4 & 2.8 \\ III cycle & 11:01:15–11:01:15 & 57.82 & 58.14 & 58.33 & 61.22 & 0.5 & 4.9 \\ IV cycle & 11:10:25–11:16:30 & 58.43 & 58.98 & 58.66 & 60.52 & 0.9 & 3.1 \\ V cycle & 11:21:20–11:26:20 & 58.73 & 59.39 & 58.72 & 60.56 & 1.1 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Measured power values before and after the cooling cycles and the percentage difference in power before the start and at the end of each cycle. Figure 11: Change in solar modules efficiency immediately before, during and after cooling, for five cycles of active cooling. The efficiency of both modules increases with an increase in solar irradiation intensity. The efficiency of the modules changes in exactly the same way as their power, and Figure 11 shows the calculated efficiency for certain moments before, during, and after cooling. The increase in efficiency of the cooled solar module for each cooling cycle is clearly observed, as well as the decrease in efficiency corresponding to the heating process of the module. Calculated efficiency before the start and at the end of the cooling cycles, as well as the percentage difference in efficiency, are shown in Table 5.  
The greatest difference in efficiency (4.9%) was observed at 11:05 at the end of cooling cycle III that lasted 4 min. During this cycle, a decrease in ambient temperature and a slight increase in wind speed were recorded. On average, solar module efficiency for all five cooling cycles increased by 3.2%. Alcohol, due to its ability to quickly enhance the module's performance, can be used as an addition to active and passive cooling for further increase in the output power of a solar module. Thus, reducing the alcohol consumption caused by evaporation and achieving higher module efficiency in already cooled modules. For this purpose, alcohol can be used for cooling cycles lasting 1 to 2 min.
###### Abstract  
Studying the CO\({}_{2}\)-assimilation potential under the effect of glucose addition is of great significance to completely comprehend the dynamic carbon cycle in wetland ecosystems. Rhizometric sediments (RS) and bulk sediments (BS) were selected, with the addition of glucose (G) or not, and two experimental pots (RSG and BS) and two control pots (RS and BS) were formed. Then, within 45 h of glucose addition, the sediments were sampled at intervals of 4 h for dynamic monitoring. The bacterial communities encoded by CO\({}_{2}\)-assimilating function genes (_cbbL_) and the corresponding activities of key enzyme (ribulose-1,5-bisphosphate carboxylase oxygenase, RubisCO), and the light fraction (LF) and heavy fraction (HF) of organic carbon (C) and nitrogen (N) of the samples were determined. The results demonstrated that the dynamic processes of glucose deposition and degradation occurred in sediments from RSG and BS, with the greatest depositions of 2.35 and 2.48 mg\(\cdot\)g\({}^{-1}\) in the 4 th and 12 th hour, respectively. The contents of LFOC, LFON, HFOC, and HFON decreased by 171.70%, 125.45%, 8.40%, and 68.17% in the RSG pot, and decreased by 221.55%, 102.61%, 0.07%, and 74.74% in the BSG pot, respectively, which suggested the dominant activities of C and N mineralization. The FT-MIR of LF showed different changes of typical chemical bonds between RSG and BSG during the process, which further indicated irregular and inconsistent mineralization activities. The RubisCO activities in the rhizosphere sediments (52.14 nmol (g\(\cdot\)min)\({}^{-1}\) on average) were substantially greater than in the bulk sediments, which indicated the high potential of carbon assimilation in rhizosphere sediments. Moreover, it showed a lower trend in BSG, BS, and RS, but an increasing trend in RSG after the glucose addition, albeit the effects were recovered in the 45 th hour. The _cbbL_-containing bacteria were more abundant in the rhizosphere sediments than in the bulk sediments, and this effect was higher than that of the glucose addition. Proteobacteria were the dominating phylum with mean values of 93.49%, and Burkholderiales was found to be the dominant order (37.74% on average). Moreover, the changes in bacterial composition between the rhizosphere sediments and bulk sediments were more pronounced than they were during the process. Therefore, the effects of glucose degradation on RubisCO activity and _cbbL_-containing bacteria were transient, but the effects on organic matter fractions were straightforward, which probably further change the bacterial abundance and composition.  
cbpL gene; glucose addition; organic matter; rhizosphere sediments; RubisCO activity +
Footnote †: journal: Applied Sciences
# Data Placement Using a Classifier for SLC/QLC Hybrid SSDs
## 2 Related Work
The innovative aspect of this policy lies in its dynamic nature, allowing for continuous modification of both the size of the SLC cache and the threshold that differentiates hot from cold data, adapting in real time to changing data access patterns. Furthering the advancements in soft partitioning, [PERSON] et al. [20] developed a method tailored for SLC-TLC hybrid SSDs. Their approach focuses on spreading SLC pages across multiple planes to significantly enhance parallelism, therefore reducing latency associated with data migration. This strategy includes a sophisticated policy for allocating these planes, which takes into account not only the availability of SLC pages but also the capabilities for parallel processing. This method represents a strategic balance between enhancing data access speed and optimizing the use of available storage resources, a key consideration in the evolving landscape of hybrid SSD technology. Most existing research in the realm of hybrid SSDs has predominantly concentrated on the identification and management of hot data for the effective utilization of the SLC cache. In addition to this, several studies have ventured into the application of machine-learning techniques, aiming to ascertain or fine-tune the optimal size of the SLC cache and to establish precise thresholds for evaluating the hotness level of incoming data. These thresholds are crucial in determining which data benefits most from the faster SLC cache and which can be relegated to slower storage. Our research methodology significantly deviates from traditional approaches, as it does not depend on indirect indicators such as data hotness levels, specific SLC cache sizes, or predetermined threshold values. This means that the mere classification of data as \"hot\" does not automatically justify its storage in the SLC cache. Our approach is more direct and focused: we aim to precisely evaluate the potential for future reuse of data currently stored in the SLC cache, moving beyond conventional metrics to a more targeted assessment of data value for cache management. To achieve this objective, we employ machine-learning algorithms capable of analyzing and predicting data reuse patterns. Our approach takes into account the dynamic and ever-changing nature of workload characteristics, as well as the current operational state of the SSD. By doing so, we can make more informed and accurate predictions about which data will be accessed again in the near future. This allows for more efficient use of the SLC cache, as it ensures that only data with a high probability of reuse occupies this valuable space, therefore optimizing the overall performance and lifespan of the hybrid SSD.
## 5 Results and Discussion
### Model Construction
After the dataset was upSampled, each category has the size 38. So, in total, the upSampled data consisted of 38 \(\times\) 3 = 114 cases. After we applied the upSample technique, we standardized the data. We only standardized the data of the independent variables, which were the seven laboratory test parameters. We didn't standardize the data of the dependent variable, which was the severity level of dengue, because we wanted the real values of the dependent variable. Data of the independent variables was standardized because the values varied greatly and had different units. It was also standardized in order to develop better models compared to models developed using unstandardized data. After the data was standardized, we started to develop the models. We developed twelve models in total. Six models were developed \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Hemoglobin** & **Hematocrit** & **WBC** & **Platelet count** & **Neutrophil** & **Lymphocyte** & **Monocyte** \\ \hline Hemoglobin & 1 & 0.957 * & 0.126 & \(-\)0.475 * & \(-\)0.120 & 0.137 & 0.001 \\ Hematocrit & 0.957 * & 1 & 0.186 & \(-\)0.380 * & \(-\)0.071 & 0.144 & \(-\)0.080 \\ WBC & 0.126 & 0.186 & 1 & 0.262 * & 0.069 & \(-\)0.153 & 0.028 \\ Platelet count & \(-\)0.475 * & \(-\)0.380 * & 0.262 * & 1 & 0.061 & \(-\)0.133 & 0.099 \\ Neutrophil & \(-\)0.120 & \(-\)0.071 & 0.069 & 0.061 & 1 & \(-\)0.833 * & \(-\)0.581 * \\ Lymphocyte & 0.137 & 0.144 & \(-\)0.153 & \(-\)0.133 & \(-\)0.833 * & 1 & 0.143 \\ Monocyte & 0.001 & \(-\)0.080 & 0.028 & 0.099 & \(-\)0.581 * & 0.143 & 1 \\ \hline \hline \end{tabular} * Correlation is significant at the 0.05 level (2-Tailed).  
\end{table} 
Table 2: The result of [PERSON]’s rank correlation test between the laboratory test parameters. with ANN, and the other six were developed with DA. We developed the models using R programming version 3.6.3. We decided to use R because it has some advantages, such as: (i) it has data science engine, specifically the statistics and machine learning packages, and (ii) it is an open-source language programming, so it is accessible by anyone.
# A Review of Oil Spill Remote Sensing
## 5 Detection of Oil on the Sea Bottom  
Sunken oil is a problem [63]. Several methods, as described below, have been used to remotely detect sunken oil.
# Study on Flow Characteristics of Venturi Accelerated Vortex Drainage Tool in Horizontal Gas Well
## 3 Experimental Device and Method  
The multiphase flow experimental apparatus was designed and set up to conduct experiments on the swirl drainage of natural gas in horizontal wells. The apparatus primarily consists of a liquid inlet system, an air inlet system, a simulated test tube system, a gas-liquid separation measurement system, and a data acquisition control system, as depicted in Figure 3. The physical photograph of the multiphase flow experimental apparatus is shown in Figure 4. The automatic control system of the device was used to control the opening and closing of the electric valves in the tube, as well as regulate the gas-liquid flow rates. The high-precision pressure gauges and flow meters were also installed along the tube to collect the pressure and the flow rate. The data acquisition and storage system was set to record the data every 5 s.  
Utilizing the established experimental apparatus, liquid-carrying performance experiments were conducted in the absence of a vortex tool (tube drainage), with the vortex tool, and with the venturi accelerated vortex tool. The main bodies of the tools were made using transparent Perspex for easily observing the changes in the flow patterns of the gas-liquid two-phase flow. For the venturi accelerated vortex tool, the vortex tool was connected to the venturi accelerator by the flanges. The gas and liquid mixture flows first through the venturi accelerator, then through the vortex tool, out of the tool, and finally enters the horizontal section. During the testing process, pressure tests were performed at the inlet (before the vortex tool), the outlet of the horizontal tube, and the outlet of the vertical tube. Simultaneously, the flow patterns of the gas-liquid mixture in the horizontal tube were  
Figure 3: Multiphase flow experiment device composition diagram. 1 simulated horizontal well; 2 gas–liquid separator; 3 liquid pump; 4 waste liquid pool; 5 sample storage tank; 6 gas compressor; 7 gas storage tank; 8 gas flowmeter; 9 camera system; 10 pressure-measuring device.  
Figure 2: Working principle diagram of horizontal well swirl drainage.  
photographed, and the lengths of spiral flow were observed. Liquid-carrying experiments with different parameters were also performed by adjusting the power of the liquid pump and the gas compressor, as well as the opening of the valve, to change the liquid and gas flow rates. Compressed air was used to simulate the natural gas and pure water was used to simulate the accumulated liquid. The specific experimental parameters are provided in Table 3.
## 3 Channel Characteristics Comparisons
### Path Loss
Figure 8b presents the OPL fitting results in VLC bands with RS set to be 0.2 and 10 mm, respectively. It is noteworthy that the OPL of VLC is highly dependent on the size of PDs, which is set by RS in our simulation. The OPL varies between 38.7 and 47.7 dB when RS is 10 mm. However, it varies from 73.6 to 87.2 dB with RS set to be 0.2 mm. Note that the OPL increases by more than 35 dB at the same position when we change the size of PDs in VLC bands. Consistent with our expected results, the OPL will increase with smaller detectors because of the decrease in received rays. Figure 7: Path loss fitting results for mmWave channels. Furthermore, the RS is set in steps of 0.5 mm from 10 to 0.5 mm in our simulations, so that we can obtain the OPL samples with different sizes of PDs in Figure 9. We can model the OPL based on these samples with the FI model. In addition, we propose an OPL model for VLC channels as a function of the spatial distance and size of PDs based on the FI Model (4) through curve fitting techniques. This proposed OPL model can be used to estimate the physical size of PDs to meet the required coverage range while designing systems, which can be expressed as:  
\[\text{OPL}(d,r)=\beta+10\alpha\text{log}_{10}(d)+\gamma\text{log}_{10}(\frac{1 }{r^{3}})+X_{\sigma}, \tag{7}\]  
where \(r\) denotes the RS of PDs. The parameter \(\alpha\), i.e., PLE, is 1.73, which is larger than the PLE in mmWave (1.31). This result reveals that VLC channels suffer faster decay than mmWave channels with increasing distance in the conference room. The parameter \(\beta\) (50.84) is smaller than that in mmWave bands (63.5). This is mainly because the path loss is always lower than that in mmWave bands when RS is set to be greater than 1 mm, i.e., most of the RS sets (0.2, 0.5-10 mm) in our simulation present the lower OPL in VLC bands. The parameter \(\gamma\) (6.98) is defined as a coefficient related to the size of PDs. Moreover, the standard derivation \(\sigma\) representing shadowing fading is 0.99. Figure 8: Different sizes of commercial PDs and OPL fitting results for VLC channels. Figure 9: OPL fitting results for VLC channels considering the physical size of PDs.
# Secure Automatic Identification System (SecAIS): Proof-of-Concept Implementation
###### Abstract
To thwart these threats, in [13], we proposed the deployment of an international maritime identity-based cryptographic (mIBC) infrastructure as the basis for enhancing AIS with extended modes of operation that provide on-demand anonymity, authentication, and encryption capabilities towards offering new security features such as message authentication, message encryption, and message pseudo-anonymization. In [13], we identified some scenarios where the usage of the proposed extended AIS modes of operation may support the safety of life at sea and the safety and efficiency of navigation. Specifically, the AIS data authentication and integrity mode of operation may shield the navigation from AIS spoofing attacks and fake AIS message attacks. The authenticated pseudo-anonymous AIS data mode of operation provides an alternative to turning off the AIS devices in situations where maintaining the anonymity of the vessel, and its cargo, is important (e.g., VIP ycaths, vessels with sensitive cargo). Finally, in [13], we proposed the use of encrypted AIS data in insecure sea areas (e.g., areas where imminent danger for sea piracy exists) or in special situations (e.g., vessels carrying sensitive cargo, toxic waste, or weapons near coastlines or ports, where the threat of a terrorist attack is high) where the dissemination of AIS information would rather be controlled. For example, in insecure sea areas, access to the encrypted broadcasted AIS information will only be possible to vessels authenticated by the local authorities rather than to anyone (e.g., not authenticated sea pirates, terrorists, or internet sites). In [14], we elaborated on the implementation aspects of that infrastructure and proposed its implementation by leveraging the work of [PERSON] et al. [15] on Sakai-Kasahara schemes, on the work of [PERSON] et al. [16; 17] on identity-based signatures, and on the IEEE 1363.3-2013 standard [18]. In this paper, we describe a proof-of-concept implementation of this secure AIS (SecAIS). Specifically, we describe a proof-of-concept implementation of the SecAIS over an mIBC founded on the RFC6507 [19] standard for Elliptic Curve-Based Certificateless Signatures (ECCSI), and on the RFC6508 [20] for Sakai-Kasahara Key Encryption (SAKKE), and we demonstrate its workings. The remainder of the paper is organized as follows: in Section 2, we discuss related work. Section 3 reviews our previous work in [13; 14] to ensure the self-sustainability of the paper. In Section 4, we present the proof-of-concept implementation of the SecAIS. In Section 5, we demonstrate the workings of the implementation when sending and receiving SecAIS messages, and we show how to securely distribute the secret key of symmetric ciphers to multiple receivers in order to create encrypted AIS ad hoc networks (AISANETs) [13]. Section 6 summarizes our conclusions and outlines directions for future work.