esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations  
This study has several limitations. First, the sample size probably explained why some of the differences did not reach statistical significance. However, despite this limitation, we were able to prove significant differences between the septic and non-septic groups and between the different resuscitation regimes. The nonsignificant trends observed in some comparisons could have become significant had the sample size been larger. Pending further confirmation, our results provide the proof of concept that miRNAs could be useful biomarkers in a large animal model to be tested later in patients and that miRNAs could also be biomarkers of resuscitation. Second, the comparison groups were sepsis and healthy controls. Thus, we could not conclude based on our results that the biomarkers identified were specific for sepsis, as they could also be increased in other conditions associated with a systemic inflammatory response of a non-infectious origin. Finally, the clinical significance of our model is limited, as some therapeutic interventions commonly used in patients, such as the administration of antibiotics, were not planned in the present animal model. In addition, the regimens of fluid resuscitation studied herein were not titrated to a specific physiological endpoint, whereas in clinical practice, fluid resuscitation is aimed at the achievement of specific physiological goals. Another aspect that limits the clinical relevance of the model is the 5-h follow-up period, which may not allow for the development of the full physiological response to a septic stimulus as is usually seen in patients. In addition, the massive septic challenge may not necessarily translate into the nature of the infectious insults that patients generally suffer. However, we believe that our model, reproducing the most important hemodynamic and biochemical characteristics of human sepsis, is useful for better understanding the pathophysiology of sepsis and the response (e.g., of miRNA expression) to sepsis. We portend that the results derived from this model are, at least as a proof of concept, valid to propose new biomarkers that can be used for the discovery of biological pathways relevant to the pathobiology of disease and for the discovery of novel therapeutic targets.
# Performance Analysis of Dual-Hop Hybrid RF-UOWC NOMA Systems
## 2 System Model
So, the SINRs for decoding \(x_{1}\) and \(x_{2}\) are expressed as \(\gamma_{D2}^{1}=\dfrac{b_{1}\rho_{R}|h_{2}|^{2}}{b_{2}\rho_{R}|h_{2}|^{2}+1}\) and \(\gamma_{D2}^{2}=\dfrac{b_{2}\rho_{R}|h_{2}|^{2}}{b_{1}\rho_{R}\eta|h_{2}|^{2}+1}\). **Channels Distributions:** We assume that the UOWC links \(h_{1}\) and \(h_{2}\) are characterized by the EGG distribution [5], which models the underwater turbulence fading resulting from air bubbles and gradient of temperature in an effective manner. EGG is a weighted combination of the exponential and generalized Gamma distributions, it effectively matches the experimental results obtained under different scenarios of channel impairments of UOWC. A closed-form expression for the cumulative distribution function (CDF) of EGG distribution is given as [5]  
\[F_{|h_{i}|^{2}}(x)=w\ G_{1,2}^{1,1}\Bigg{(}\dfrac{1}{\lambda}(\dfrac{x}{\mu_ {r}})^{\frac{1}{\tau}}\bigg{|} \begin{array}{c}1\\ 1,0\end{array} \Bigg{)}+\dfrac{1-w}{\Gamma(a)}G_{1,2}^{1,1}\bigg{(}\dfrac{1}{b ^{c}}(\dfrac{x}{\mu_{r}})^{\frac{\tau}{\tau}}\bigg{|} \begin{array}{c}1\\ a,0\end{array} \bigg{)}, \tag{1}\]  
where \(0<w<1\) represents the mixture ratio between exponential and generalized Gamma distributions, \(\lambda\) is the exponential distribution scale parameter of the exponential distribution, \((a,b,c)\) are the parameters associated with generalized Gamma distribution, and \(G_{m,n}^{p,q}(. )\) is the Meijer-G function [20]. According to the receiver detection method, heterodyne detection \((r=1)\) or intensity modulation/direct detection (IM/DD) \((r=2)\), the electrical signal to noise ratio (SNR) is  
\[\mu_{ri}=\left\{ \begin{array}{cc}\dfrac{\Omega_{xi}}{2w\lambda^{2}+b^{2}(1-w )\Gamma\left(a+\frac{2}{\tau}\right)/\Gamma(a)}&r=2\end{array} \right., \tag{2}\]  
where \(\Omega_{xi}\) is the average SNR of the UOWC links. We assume that \(\Omega_{x1}=\Omega_{x2}=\Omega_{x}\), thus \(\mu_{r1}=\mu_{r2}=\mu_{r}\). The values of \((w,\lambda,a,b,c)\) for different scenarios of air bubbles under thermally uniform and gradient-based UOWC channels are experimentally obtained in [5](Tables 1 and 2). Finally, the RF-links \(h_{w}\) undergo a Rayleigh fading with AWGN noise, therefore \(|h_{i}|^{2}\) follows an exponential distribution whose CDF is given as  
\[F_{|h_{w}|^{2}}(x)=1-e^{-x}. \tag{3}\]
## 2 Materials and Methods
### Data Extraction and Meta-Analysis
Heterogeneity was identified as low (I\({}^{2}<25\%\)), medium (25% \(\leq\) I\({}^{2}<50\%\)), or high (I\({}^{2}\geq 50\%\)) [37]. A random-effects model was applied for analyses with I\({}^{2}>50\%\), while a fixed-effects model was applied for analyses with I\({}^{2}\leq 50\). Furthermore, the statistical significance for heterogeneity tests was set at \(p<0.05\)[34]. The I\({}^{2}\) statistic was calculated as 100% \(\times\) (Q \(-\) dI)/Q, where Q is [PERSON]'s heterogeneity statistic and dI denotes the degrees of freedom. Negative I\({}^{2}\) statistics were interpreted as 0%, which indicates no heterogeneity, with larger values (up to 100%) representing greater heterogeneity in analyses [37]. To further validate the reliability of the data, R software was used to download and clean the dataset through the removal of irrelevant or redundant data points. Additionally, sensitivity and correlation analyses were conducted to ensure that the findings were not driven by a single study, and scatter plots were generated to visually assess data trends. A leave-one-out approach was also applied to evaluate the robustness of QE duration, FFD, and FD impacts on shooting performance [38]. This systematic review and meta-analysis aimed to assess the reliability and consistency of the impacts of gaze behaviors (e.g., QE duration, FFD, FD) on basketball shooting performance. By synthesizing findings from studies that quantitatively assess gaze behaviors during shooting tasks in competitive basketball settings, this review sought to identify how these gaze behaviors contribute to shooting accuracy.
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
## 3 Results
### Comparison of SSL to Baseline Approaches
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**False Alarm (FA) Rate (ship/km\({}^{3}\))**} \\ \cline{2-6}  & **No Filtering** & **CO Filtering** & **OS Filtering** & **\(\Delta\)FA 1** & **\(\Delta\)FA 2** \\ \hline BL-NDWI & - & 0.83 & 0.71 & - & \(-\)0.12 \\ \hline Scratch (fine-tuning) & **0.85** & **0.48** & 0.15 & \(-\)0.37 & \(-\)0.70 \\ \hline ImageNet & 1.02 & 0.57 & **0.14** & \(-\)0.45 & \(-\)0.88 \\ \hline SEN12 MS+RA & 1.19 & 0.65 & 0.19 & \(-\)0.54 & \(-\)1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average false alarm rate comparison of the best SSL result versus the baselines. Best results are presented in bold font. Figure 7: F1-score variation on all S2-SHIPS dataset for NDWI, SEN12 MS-RA (no filt.) and SEN12 MS-RA with CO filt. Figure 8 presents a qualitative analysis of their results. In these conditions, networks trained from scratch or pretrained on EuroSAT and BigEarthNet lead to the worst results. Some piers and docks are also confused with ships. Deep learning methods seem to be robust to brightness, water color, or environment difference, and they also rarely predict small islands as ships. Figure 8: Ship detection samples, in various conditions. In green are drawn true positives, in red false positives and in yellow missed ships (false negatives). Best viewed in color.
## 2 Reaction Pathways
### Mechanism of ORR  
The ORR is a complex multi-electron reaction that may include several elementary steps involving different reaction intermediates. The mechanism of ORR is not fully elucidated due to the complexity and changing behavior of the process depending upon the electrode materials, catalysts, and the electrolyte. However, the literature says that the ORR can proceed either via a direct four-electron transfer route or via a series of two-electron transfer routes irrespective of the electrolyte; the two-electron pathway leads to hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) production and the four-electron transfer route results in water (H\({}_{2}\)O) production. The four-electron transfer process is generally preferred because H\({}_{2}\)O\({}_{2}\) damages the Nafion membrane in fuel cells. The following equations illustrate both of the pathways when using acidic or alkaline electrolytes:  
Acidic electrolyte:  
\[\text{O}_{2}+4\text{H}^{+}+4\text{e}^{-}\to 2\text{H}_{2}\text{O} \tag{3}\]  
\[\text{O}_{2}+2\text{H}^{+}+2\text{e}^{-}\to\text{H}_{2}\text{O}_{2} \tag{4}\]  
\[\text{H}_{2}\text{O}_{2}+2\text{H}^{+}+2\text{e}^{-}\to 2\text{H}_{2}\text{O} \tag{5}\]  
Alkaline electrolyte:  
\[\text{O}_{2}+\text{H}_{2}\text{O}+4\text{e}^{-}\to 4\text{OH}^{-} \tag{6}\]  
\[\text{O}_{2}+\text{H}_{2}\text{O}+2\text{e}+\to\text{HO}^{-}{}_{2}+\text{OH} ^{-} \tag{7}\]  
\[\text{HO}^{-}{}_{2}+\text{H}_{2}\text{O}+2\text{e}^{-}\to 3\text{OH}^{-} \tag{8}\]
## 2 Digital Twin-Based ITS Multi-Source Data Cooperation Framework
### Improved Digital Twin Data Based on ITS  
Intelligent transportation systems rely on big data, the Internet of Things, cloud computing, etc. Digital twins build physical entity models through digital means to connect the physical world and information world to the \"virtual-real integration\" system [9], connects the physical world and the information world with a system of \"virtual-real fusion\", based on the unique dynamic and static combination mode of digital twin data systems. The twinning data are clearly defined in the five-dimensional model of digital twinning [6], DD is the driver of the digital twin [9], including physical entity data (\(\mathbf{D_{p}}\)), virtual entity data (\(\mathbf{D_{v}}\)), services data (\(\mathbf{D_{s}}\)), knowledge data (\(\mathbf{D_{k}}\)), and fusion-derived data (\(\mathbf{D_{f}}\)). \(\mathbf{D_{p}}\) mainly include the specifications, functions and other data of physical entities, such as environmental parameters, which are mainly collected through sensors. \(\mathbf{D_{v}}\) mainly includes the data of virtual entities, such as geometric size, material properties and data obtained from process simulation based on the model. \(\mathbf{D_{s}}\) mainly includes functional service-related data (algorithms, processing methods, etc.) and business service data (production management, market analysis, etc.). \(\mathbf{D_{k}}\) mainly includes functional service-related data (algorithms, processing methods, etc.) and business service data (production management, market analysis, etc.). \(\mathbf{D_{f}}\) are derived from the above four kinds of data after conversion, preprocessing, integration, fusion and other operations. The obtained fusion data can reflect the information more comprehensively, and realize information sharing and value-added. Due to the strong application of the transportation system, the above five data types cannot meet the requirements of ITS. Therefore, the data collaboration framework proposed in this paper added user data (\(\mathbf{D_{u}}\)) on the basis of the above five kinds of data as Equation (1) and made multi-directional cooperation.  
\[\text{DD}=\left(D_{p},D_{v},D_{s},D_{k},D_{f},D_{u}\right) \tag{1}\]  
Compared with previous studies, the data collaboration framework provided by us covers more twin data types as shown in Table 1.  
Figure 1: Physical information linkage of a digital twin system. The physical space with physical entities provides data for the information space, and the information space with virtual models simulates the information provided by the object space. The physical units mainly include vehicles, buildings, traffic lights, sensors, etc. Virtual model mainly refers to environment model (environment and weather modeling based on data provided by sensors), 3D traffic model (3D modeling based on building and traffic light information), road network model (road network modeling based on lane information and road information), etc.
# An EV Charging Scheduling Mechanism Based on Price Negotiation
## 3 Price Negotiation Model
### Conditions of Charging Station Operators and \(EV\) Users for Transaction  
Of course, the two prices do not match for the most part. Then, to reach a transaction, multi-round price negotiation will be carried out until a transaction is reached or one party of the price negotiation abandons. We set \(k_{0}\) as the biggest round number of price negotiation.  
After each round of price negotiation, both the charging station operator and the EV user need to decide their next actions. They have three choices according to the different conditions.  
For EV users: (1) if \(p_{i,k}^{u}\geq p_{j,k^{\prime}}^{cs}\), then the transaction is reached, and EV can be charged; (2) If \(p_{i,k}^{u}<p_{j,k}^{cs}\&\&k<k_{0}\), EV users do not accept the price given by charging station operator, but will keep bidding in the next round for price negotiation; (3) If \(p_{i,k}^{u}<p_{j,k}^{cs}\&\&k=k_{0}\), the EV user abandons the price negotiation with the current charging station operator and will turn to another one.  
For the charging station operator: (1) If \(p_{i,k}^{u}\geq p_{j,k}^{cs}\), then the transaction is reached, and the EV is allowed to be charged; (2) If \(p_{i,k}^{u}<p_{j,k}^{cs}\&\&k<k_{0}\&\&k_{j}^{cs}(t)\leq L_{j,\max}^{cs}\), the charging station operator does not accept the price given by the EV user, but will keep bidding in the next round for price negotiation; (3) If \(p_{i,k}^{u}<p_{j,k}^{cs}\&\&(k=k_{0}\parallel L_{j}^{cs}(t)>L_{j,\max}^{cs})\), the charging station operator can unilaterally terminate the price negotiation with the current EV user.
# Enhancement of CO2 Removal Efficacy of Fluidized Bed Using Particle Mixing
## 3 Results and Discussion
### Adsorbent Characterization  
We chose commercial reagent-grade activated carbon powder (Avonchem, Macclesfield, UK) as the adsorbent. The choice of activated carbons stemmed from extensive work reported in the literature, which have confirmed that despite their relatively low CO\({}_{2}\) adsorption capacity, activated carbons exhibit better stability, a very low sensitivity to moisture, and a large resistance to poisoning [48; 49] and are therefore better suited for industrial applications. The adsorbent was characterized using surface area and porosity instrument (Micromeritics Tristar II, Norcross, GA, USA). Relevant characterization parameters are reported in Tables 1 and 2.  
An important aspect of the adsorbent characteristics is its size distribution. Therefore, three runs were carried out using Malvern particle size analyzer to determine its size distribution. The average volume mean diameter was found to be 26.4 \(\pm\) 1.1 \(\upmu\)m [47]. The SEM micrograph of the sample is shown in Figure 2. Using experimental pressure drop versus superficial velocity data, the effective hydrodynamic diameter of the activated carbon was also evaluated with the help of the Ergun equation [47]. Its value was 27.5 \(\upmu\)m, which is close to the size obtained from the particle size analysis. Such a close agreement between the two is a clear indication that there was almost no agglomeration of the adsorbent particles during the gas-solid contact in the bed. As part of a detailed investigation of the fluidization hydrodynamics, we also evaluated the fluidization index of the monocomponent bed of the activated carbon. Its value varied in the range of 0.76-0.80, indicating the presence of nonhomogeneities during fluidization [47]. Note that the bed nonhomogeneities always affect the effectiveness of the adsorbent particles present in the bed. In an ideal case, the fluidization index should be unity.  
\begin{table}
\begin{tabular}{l c} \hline Single point surface area at P/Po = 0.303993275: & 953.84 \\ BET Surface Area: & 941.51 \\ Langmuir Surface Area: & 1437.38 \\ t-Plot Micro-pore Area: & 431.81 \\ t-Plot External Surface Area: & 509.70 \\ BjH Adsorption cumulative surface area of pores 1.7–300.0 nm diameter & 313.02 \\ BjH Desorption cumulative surface area of pores 1.7–300.0 nm diameter & 289.05 \\ \hline \end{tabular}
\end{table}
Table 1: Surface area (m\({}^{2}\)/g) characterization of activated carbon.  
\begin{table}
\begin{tabular}{l c} \hline t-Plot micropore volume & 0.22973 \\ BjH Adsorption cumulative volume of pores 1.7–300.0 nm diameter & 0.29053 \\ BjH Desorption cumulative volume of pores 1.7–300.0 nm diameter & 0.28122 \\ \hline \end{tabular}
\end{table}
Table 2: Pore volume (cm\({}^{3}\)/g) characterization of activated carbon.  
Figure 2: SEM images of adsorbent at a magnification of 500\(\times\).
# A Novel Liquid-Solid Fluidized Bed of Large-Scale Phase-Changing Sphere for Thermal Energy Storage
###### Abstract  
The storage of thermal energy has been hindered by the low heat-transfer rate of the solid phase of the phase-changing material. With water being the heat-transfer fluid as well as the liquid phase in the liquid-solid two-phase system, a novel type of fluidized bed is designed in this study. Numerous hollow spheres are fabricated with phase-changing material encapsulated. Adding the solid-liquid phase-change material capsules to the flowing fluid, the capsules are dispersed suspended in the carrier. The large spheres, 25 mm in present experiment, possess the merits of guaranteeing energy-storage density and tolerating internal interface chaotic motion. Both the fluidization status and phase-changing process are recorded by photography combined with image-processing technology. It is found that the large spheres, with density less than water, can be fluidized by the downward flowing fluid. As the flow rate increases, the expansion ratio of the solid phase increases and the regimes of incipient fluidization and bubbling fluidization can be observed. In comparison to the fixed bed, the oscillation of pressure drop across a fluidized bed is more severe, but the averaged value is less than the fixed bed. The melting and solidifying can be accelerated by 22.6% and 50%, respectively, thus proving the superiority of the fluidized bed in improving the heat-transfer rate while charging/discharging the thermal energy. Three types of basic movement of the spheres are shown to contribute to the enhanced phase-changing rate, which are shifting, colliding and rotating.  
Keywords:phase-changing material (PCM); fluidized bed; thermal energy storage; visualization measurement +
Footnote †: journal: Applied
# Investigation of Intermetallics Formation and Joint Performance of Laser Welded Ni to Al
## 4 Conclusions  
In this study, experiments of laser welding of Ni to Al were conducted using PW and CW lasers. The formation of IMCs and the resultant weld performance were analyzed and discussed. Based on the results and discussions, the following conclusions can be drawn:  
* Deep penetration and overheating low-melting-point Al is the main reason causing the formation of pores at the Ni-Al interface and severe mixing of the Ni and Al in the melting pool.
* It is critical to control the penetration depth through the selection of the operation mode of the laser, as well as the optimization of process parameters, so that the formation of defects and undesired IMCs (AlNi\({}_{3}\) and AlNi) could be avoided.
* The IMCs show a significant effect on the hardness of the weld, whereas the defects and brittle IMCs jointly degrade the weld to withstand the shear force and peel force.
* It is feasible to achieve a weld-braze joining condition using a defocused CW laser beam, through which defects-free and strong Ni-Al joint can be obtained. Due to the nature of high peak power resulting in deep penetration, it is difficult to obtain comparable joint quality using a pulsed laser.  
L.C.: Validation, investigation, formal analysis, writing--original draft; Y.L.: Formal analysis, writing--original draft, funding acquisition; H.-C.C.: Conceptualization, methodology, investigation, writing--review and editing; L.Z.: Validation, investigation, formal  
Figure 10: Results of shear test and 90-degree peel test.  
analysis, writing--review and editing; H.S.: Writing--review and editing; G.B.: Conceptualization, methodology, investigation, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by GDAS' Project of Science and Technology Development (No. 2022 GDASZH-2022010108), the National Natural Science Foundation of China (No. 51976007), and Science and Technology Innovation Project of Foshan (No. FS0 AA-KJ919-4402-0101-07).  
Not applicable.  
The data presented in this study are available on request.  
The authors declare no conflict of interest.
## 3 Results
### Regressions Analysis  
The regression models used are able to identify the relationships between the components despite their high sparsity and the level of interaction between them, while tree-based regression models have good flexibility in the design of prediction models. The results obtained are shown in Table 2, which shows the performance indicators of the models for the prediction of the tensile strength variables and elongation at break at the same time. In general, it is observed that in all cases, the model is able to fit the training data (which even occurs after overfitting in the case of gradient boosting), which does not imply a good performance (\(R^{2}\)) in the test data as in the case of elongation at break, but in terms of tensile strength, most models are able to explain over 60% of the variance in the test data. In the context of the predictor variables, it is clear that tensile strength is the best modeled, with the highest test \(R^{2}\) for random forest (0.821), followed by gradient boosting (0.778), and decision tree (0.661). However, for elongation at break, all models show a substantially lower performance, with the test \(R^{2}\) for random forest being the highest with a value of 0.421. When both variables are considered together (tensile strength-elongation at break), the decision tree model has a test \(R^{2}\) of 0.555, which is more acceptable than that of gradient boosting, which has a test \(R^{2}\) of 0.467. Therefore, it is possible to indicate that the tensile strength variable is more homogeneous than the elongation at break since the algorithms have greater predictive capacity. When looking all the error metrics in Table 2, it is clear that the variable \"Tensile strength\" shows considerably lower error values compared to \"Elongation at break\" in all models. For example, considering the random forest model, the \(MSE\), \(RMSE\), \(MAE\), \(MedAE\), and \(RMSE\) metrics for \"Tensile strength\" are 64.310, 8.019, 5.003, 2.632, and 0.468 respectively. These are notably lower than the corresponding values for \"Elongation at break\", which are 470.787, 21.698, 11.035, 4.590, and 0.474, respectively. This difference becomes even more evident when we consider the \(MedAE\), a metric that is especially robust to outliers. Here, the \(MedAE\) for \"Tensile strength\" with random forest is 2.632, much lower than that of 4.590 for \"Elongation at break\". This indicates again that the model