esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Behavioral Variables Analysis  
XGB model trained with the following variables:  
Dependent variable: LVEF over 50;  
Independent variable: smoker, alcoholic, obese.  
Following the training, a model emerged that generates probabilities based on independent variables. The probabilities presented below are generated after cross-validation--leave one out, for each patient.  
Figure 3 shows the probability distributions returned by the model. The graphs are similar to boxplots, where the white point is the median. It can be seen that the model has learned something from the data presented, so there is a relationship between behavioral factors and LVEF. Blue is the probability distribution for patients with LVEF over 50, and these are significantly higher than those with orange. In the ROC curves, it is observed that it was a learning process; using the model, better results are obtained than if it had been predicted randomly. The same is observed in the confusion matrix, where accuracy is 74%, for a threshold of 0.5.  
Knowing that there is a relationship between these factors and LVEF, it is still important to see what is the variables' contribution. For this, we will compare the results given by Shapley Values and Logistic Regression coefficients.  
In order to better understand Figure 4, the following clarifications will be made. Each point is a patient; if the bar is higher, then there are more patients. If the dot is red, the patient had the value 1 (was smoker, obese or alcoholic). If the dot is blue, the patient has the value 0. The lower the dot has a shapley value on OX, the fact that he had that variable determines the probability of having LVEF over 50 decreases. The further to the right, then that variable (depending on color) had a larger contribution to having LVEF over 50.  
Figure 4: Shapley values for behavioral variables.  
Figure 3: Classification results for behavioral variables.  
It is observed that smoking had a very harmful role; if a patient smoked, their chances of being in LVEF over 50 decreased a lot--even more than if they were both obese and alcoholic. If the patient had not been a smoker, then his chances would have increased significantly with LVEF over 50, a little more than if he had not been obese. It is interesting that being obese has an impact, but not a very big one, compared to smoking, but not being obese has similar benefits to not smoking. Being an alcoholic does not have a very large impact-most values being close to 0; it seems to be some connection, but not very obvious.  
The results are similarly explained by the classic variant, logistic regression coefficients. These can be seen in the Table 3.  
Smokers have the largest impact on LVEF, followed by obese, and for both \(p\)-value is below 0.05. As for the alcoholic variable, there seems to be some connection, but the \(p\)-value is greater than 0.05, so it is possible that the influence is random.
## 5 Results Analysis and Discussion  
### Randomness Problem in Deep Learning Model Training  
Through the experimental process, we observed the randomness problem in deep learning model training in depth. Taking the LSTM-DNN model as an example, under completely identical hyperparameter settings, performance differences between different training instances can be very significant.  
We found that the randomness problem mainly stems from the following aspects:  
(1) Weight initialization: Deep learning model weights are typically randomly initialized, and different initial values lead the optimization process to converge to different local optima.  
(2) Mini-batch sampling: During training, mini-batch gradient descent algorithms randomly select subsets for gradient calculation and parameter updates, introducing randomness.  
(3) Overfitting risk: Larger network configurations (such as more LSTM units) are more prone to overfitting to random noise in training data rather than capturing true patterns, leading to greater fluctuation in prediction results.  
(4) Mismatch between network complexity and data volume: When networks are overly complex while training data are insufficient, models may fail to converge to stable solutions, exhibiting high randomness.  
To ensure experimental reproducibility and systematic evaluation of model robustness, we implemented a controlled random seed management protocol. Each deep learning model configuration underwent five independent training runs with distinct fixed seeds (1, 42, 123, 456, 789), controlling both weight initialization via keras.initializers. RandomNormal() and data shuffling variability. This approach maintains identical software environments and hardware configurations while enabling robust statistical analysis of model performance variations.  
The multi-run training method can effectively mitigate the impact of these problems. By statistically analyzing the performance distribution of multiple training instances, we can more accurately evaluate the true capability of models. In practical applications, although this method increases computational cost, it can significantly improve the reliability of model selection and avoid making misleading decisions based on single training results.  
For model deployment, we recommend adopting ensemble methods using multiple instances of the same configuration but different training runs to further reduce the impact of randomness.
# Nanotechnology, Nanomedicine, and the Kidney
## 3 Renal Clearance of Nanoparticles
### Shape  
When fabricating NPs with an order of dimensions greater than zero (i.e., QDs), such as one-dimensional (rods and tubes) and two-dimensional (sheet) nanosystems, passive filtration does not obey the size scaling law of renal clearance, as defined in the previous chapter. For example, nanotubes with a length of over 100 nm can be passively filtered through the renal barrier without changing shape [20; 30; 31; 32]. However, mathematical simulations suggest that glomerular filtration is possible because the blood flow is strong enough to guide carbon nanotubes (CNT) through the fenestration of the GFB along their long axis [20]. Similarly, longer, but less stiff, nanotube such as 400 nm long DNA tubular origami with a 7 nm diameter [19; 20], and also two-dimensional graphene oxide (1-2 nm thick and 1 \(\upmu\)m side) [33] and glutathione-coated Pd (1.8 nm thick) [34] sheets, can also be filtrated through the glomerular barrier into the urine, suggesting that NP's orientation in the blood flow is critical for the renal clearance of non-QD nanomolecules [33].  
Figure 3: Nanoparticle’s filtration in the glomerulus in function of NP’s size, shape, charge, and stiffness.
## 4 The Developed Context Model
### Node Contexts  
These are the representations used to generate the knowledge of individual nodes that compose the network. In a field deployment, the interoperability will happen at node level. The nodes could be able to participate in collaborative tasking through different networks, such as data transmission processes, and in-network data aggregation. The metadata elements related with this context describe the state of memory, communication devices, sensors, actuators, and processor for each individual node. The inferred knowledge will be in concordance with the node status at a specific time and its impact on the interoperability with other nodes. For example, in a mobile WSN in which nodes are moving freely, communication failure is common when nodes do not have near neighbours. Later, when the node recovers its neighbour's nodes, the communication will be recovered too. The interoperability will be interrupted while the node is without neighbours. The node must know his context and must act based on this context. In our model this particular node status is represented by the _isolation context_.
## 1 Introduction
### Contributions  
Based on the literature review above, it can be found that most of the existing studies are based on some idealized assumptions or ignore the impact of natural conditions on outdoor VLC systems and so their conclusions cannot fully reflect all the characteristics of vehicle-mounted VLC systems in specific scenarios. In addition, most studies focus on  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
describing channel characteristics rather than quantifying the factors that affect the VLC channel model. The main contributions in this paper can be listed as follows:  
1. A typical V2I-VLC communication scenario on a mountain road is considered. This consideration is more practical and specific, thus reflecting the V2I-VLC performance in real application scenarios.
2. In the proposed scenario, the outdoor VLC channel is modeled taking into account AT and multiple propagation characteristics. In addition, vehicle mobility is considered in the modeling process, and dynamic V2I-VLC models are emphasized.
3. Based on the channel model, closed-form expressions for the performance metrics of the LOS and NLOS links in the proposed scenario are derived. These performance metrics include average path loss, received optical power, channel capacity, and outage probability.
4. Further, the derived theoretical expressions are compared with numerical simulation results in order to verify the accuracy of the derived theoretical expressions.
5. Furthermore, the proposed propagation model is thoroughly investigated and analyzed in this paper, as well as compared with existing studies.  
By modeling the channel of the proposed V2V-VLC system and deriving closed-form expressions for performance indicators, it can be concluded that the considered propagation characteristics have a significant impact on the average path loss, received optical power, channel capacity, and outage probability of the V2I-VLC system. The research content and conclusions will provide strong support for subsequent work and the deployment of the infrastructure for the V2I-VLC system.
## 4 Discussion
### Virology  
A strong correlation between varroa parasitism and viral loads was observed in this study. There were also simultaneous viral infections (DWV-A, DWV-B, BQCV, ABPV, KBV, and IAPV) with charges that were more important when honeybees were heavily infested by varroa mites (Figures 3-5). Viral loads were significantly higher in highly infested hives than in the controls for most studied viruses (Figures 3-5) except for ABPV (Figure 5a). Consequences of these high viral loads are also more evident in highly infested hives, as demonstrated by the death of colonies in the fall.  
Fall is an optimal period for the increase of viral charges, where the number of phoretic varroa mites is much higher in the colony, combined with a great number of honeybees infected by viruses that are available to feed on [40; 41]. In other words, the higher density of varroa mites increased the transmission of viruses [42]. According to [PERSON] et al. [43], viral titers for ABPV, KBV, IAPV, and DWV increase from spring to fall, as observed in our study for DWV and KBV. In this study, the increase of KBV loads until fall was not statistically significant, as previously seen in other studies [44; 45]. European studies also present increased DWV charges in the fall [46] as well as for ABPV, showing seasonal variation in the viral prevalence and charges [43; 47; 48].  
Fall is also a critical season for colony weakening and mortality since honeybee reproduction decreases. Overall, exposure to varroa [49; 50] and higher DWV and ABPV titers in the fall are correlated with overwintering mortality [43; 50; 51]. Further studies need to be performed to establish a correlation between viral charges and overwintering mortality in Canadian territory.  
The dissemination of viruses in the colony is intensified by the presence of _Varroa destructor_. As demonstrated by many studies, _V. destructor_ is a major contributor of horizontal transmission of viruses, including DWV, ABPV, KBV and IAPV [15; 18; 44; 52; 53; 54; 55; 56; 57]. Phoretic and reproductive varroa mites ingest viral particles when feeding on an infected honeybee and can subsequently inject saliva containing viral particles into another healthy bee [58]. This inoculation directly into the hemolymph increases the pathogenicity of most viruses [59; 60; 61; 62; 63; 45; 48; 52; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96  
opportunistic infectious agent, as seen in immunosuppressed honeybees [41; 74]. Although a clear correlation between BQCV and varroa infestation has not been established [18; 52; 64], a recent study suggests its role as a mechanical vector [66]. Our study also suggests that varroa infestation loads influence BQCV loads in honeybees.
# Photonic Crystal-Based Sensors for Detecting Alcohol Concentration
## 2 Experimental Section  
### Preparation of the Polystyrene (PS) Opal Array  
PS microspheres were synthesized by following polymerization procedures. A mixture of 90 mL deionized water, 10 mL Styrene (St), and 40 mg 4-styrenesulfonic acid sodium salt (NaSS) were added to a four-necked flask equipped with a reflux condenser and a mechanical stirrer. After being homogeneously mixed, the temperature of the reaction mixture was raised to 70 \({}^{\circ}\)C. A deoxygenated water solution with 0.9 mg potassium persulfate (KPS) was added, and the emulsion was mixed for 24 h. [21; 22].  
The usual method of treating the colloidal photonic crystals with a face-centered cubic (FCC) structure involves a modified Bragg's law. A green light can be easily observed by the naked eyes. To obtain a green (\(\lambda\) = 495 to 510 nm) reflection from the PS photonic crystal array, the required particle size should be around 223-273 nm after calculation from the following Equations (1) and (2) [23] with the incident angle assumed to be 20\({}^{\circ}\):  
\[D=\frac{\lambda}{2\sqrt{\frac{2}{3}\left(n^{2}-sin^{2}\theta\right)}} \tag{1}\]  
where \(D\) is the size of the PS microspheres, \(\lambda\) is the reflection wavelength of the PS photonic crystals array (or the opal-like structure), \(\theta\) is the angle of incidence of the light, and \(n\) is the average refractive index of the PS photonic crystals array.  
\[n=\sqrt{n_{sphere}^{2}\times 0.74+n_{air}^{2}\times 0.26} \tag{2}\]  
where \(n_{\text{sphere}}\) is the refractive index of PS (\(n\) = 1.600) and n\({}_{\text{air}}\) is the refractive index of air (\(n\) = 1.000).  
An ordered periodic PS microspheres array, _i.e._, PS opal structure, can be formed because the PS microspheres were given enough time to balance the surface electrostatic repulsion and van der Waals attraction at the interface between the suspension and air as the hydrophilic substrate was slowly drawn out from the PS suspension solution. The substrate surface became hydrophilic by using oxygen plasma (PCD-150, All Real Technology Co., Ltd., Kaohsiung, Taiwan) treatment was carried out at a pressure of 300 mTorr, with 20 sccm oxygen flow under 100 W electric power. Then, the PS suspension was diluted with deionized water to 30 wt %, and dip-drawing was conducted at rates of 1 \(\upmu\)m/s [24], and a close packed opal photonic crystals array was formed.
## 2 Basic Principles of Particle Transport in Plasmas
### Debye Length  
In this section, we introduce the key parameters used to define plasma. Not all particles in plasma are ions; many particles are neutral. The characteristic distance of electrostatic  
Figure 1: Results of scanning electron microcopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) analyses of the edge particle residue on a 12-inch (30.48 cm) wafer as well as relevant information related to by-products. (**a**) SEM and EDX maps of particles adhering to a wafer surface after etching. These particles are often visible around the edges of etched wafers in the chamber. Particles (A–C) in three positions are selected to check the shape, as shown in (**b**). (**b**) SEM images of particles (A–C) in (**a**) with a scale bar of 6 micron. (**c**) EDX spectra used to analyze silicon, fluorine, and aluminum concentrations in the particles. The \(y\)-axis indicates the number of X-rays, and the \(x\)-axis indicates the energy of the X-rays. The yellow peaks in the spectra correspond to different elements of the particles (carbon, fluorine, and aluminum), and the peak heights can be used to quantify the concentration of each element.  
interactions in plasma is called the Debye length. The Debye length for electrons and ions (\(\lambda\)D\({}_{e,i}\)) is defined as follows:  
\[\lambda\)D\({}_{e,i}=\sqrt{\frac{\varepsilon_{0}kT_{e_{j}}}{\text{e}^{2}n_{\infty}}} \tag{1}\]  
where \(\varepsilon_{0}\) is the dielectric constant in vacuum, \(k\) is the Boltzmann constant, \(T_{e_{j}}\) is the electron and ion temperature, \(e\) is the electron charge, and \(n_{\infty}\) is the plasma density. In a typical low-pressure capacitively coupled RF plasma, the electron temperature is \(T_{e}\approx 3\) eV, the ion temperature is \(T_{i}\approx 0.03\) eV, and the plasma density is \(n_{\infty}=5\times 10^{15}\) m\({}^{-3}\).
# Photoconductive Semiconductor Switches: Materials, Physics, and Applications
## 7 (Al)GaN PCSS Devices
High-quality free-standing GaN:Fe and GaN:Mn (dopant concentration near 10\({}^{19}\) cm\({}^{-3}\)) substrates are grown either ammonothermally or by HVPE to a thickness of \(\sim\)300 \(\upmu\)m by commercial vendors. Switches are fabricated in both lateral and vertical geometries. Lateral switches are fabricated using rectangular Ti/Al/Ni/Au contacts separated by between 0.6 and 3 mm. Vertical switches are fabricated using identical metallization on the front and back (Ga- and N-face) of the material coaxially. Circular grid anodes were designed for linear mode operation (to maximize the uniformity of illumination during triggering) or a very-low-fill factor hole grid was designed for nonlinear mode operation (to enable optical seeding of current filaments during triggering). Optical micrographs of these geometries are shown in Figure 11.  
The GaN PCSS devices were constructed as unipolar two-contact devices with a lateral geometry and doped uniformly with either Mn or Fe to achieve a resistivity of 10\({}^{9}\)-10\({}^{14}\)\(\Omega\)-cm. This doping scheme has the benefit of allowing extrinsic triggering by the creation of a large number of deep traps [24] which, in turn, allows triggering from the backside as well as the front. The switches were biased up to 40 kV/cm (2 kV) and triggered by a 5 ns pulsed laser focused on the GaN surface between the contacts. The laser wavelength was varied from 650 to 1050 nm in 100 nm increments to better understand the reliability of switching and photoresponsivity. To interrogate nonlinear switching behavior, luminescence associated with switching events was monitored with separate cameras monitoring visible and infrared wavelengths to distinguish laser light reflected from the sample surface, light emitted from surface filaments, and current filamentation within the GaN bulk.
# Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning
## 3 Experiments and Results
### Hidden Layer Structure Setting
#### 3.3.1 Influence of Hidden Layer Structure on Average RSSI Value  
By changing the hidden layer structure of the BPNN model (e.g., the number of hidden layers or number of hidden layer neurons), we discuss the impact of the different BPNN model structures on the average RSSI results obtained after the calibration of the two test mobile terminals. Under different BPNN model structures we carried out five experiments to obtain the best calibration effect.  
According to Figures 10 and 11, for the two test mobile terminals, the BPNN model with double hidden layers can improve the calibration results of the WOA-BPNN calibration algorithm compared with the BPNN model with a single hidden layer. When the hidden layer structure of the BPNN model is double hidden layers and the number of neurons is six, the average RSSI error after the calibration is the smallest.
## 2 Materials and Experimental Methods
### Experiments
#### 2.2.3 Cell Test  
EK remediation was conducted to characterize the contaminant removal in multiple contaminated ground with heavy metal and organic contaminants. Pollution concentrations of 1000 ppm phenanthrene and 500 ppm copper was mixed with methanol and DDW (Double Distilled Water) as a control condition and acetic acid and Brij30 were added as the improvers. Specifically, the phenanthrene contaminated solution was prepared using methanol as a solvent and the copper contaminated solution was prepared using copper nitrate powder (Cu(NO\({}_{3}\))\({}_{2}\)) in DDW. Kaolinite was used as a source of simulating the ground condition and it was contaminated with 1000 ppm with phenanthrene solution and dried in the shade. After drying, the pulverized kaolinite was applied to 500 ppm of copper contaminated soil at a water content of 60%. Then, the contaminated kaolinite was installed in the cell in which the FSA was filled inside of PRB.  
10 days of running time, 1V/cm of electric gradient, PRB located at (x/L) = 0.75 where acid and base frons are meet, and 0.05 M acetic acid and 0.01 M Brij30 were used [26]. The current density, the change in pH between the anode and cathode, and the outflow were measured during the experiment, while the other parameters (e.g., water content, residual contaminant concentration, and pH) were analyzed after the completion of the experiment with the KSTM (Korea Standard Testing Method).  
In this study, the cell test was conducted using the EK remediation technique to characterize the contaminant removal in the multiple contaminated ground with heavy metals and organic contaminants. The PRB and improvers were used to solve the precipitation problem that occurred in the previous EK remediation technique, and the FSA was applied as the filling material for the PRB to induce the adsorption of the contaminants. Hereby, the removal characteristics of the heavy metals and organic contaminants analyzed in fine-textured ground with multiple contamination. The EK experiments were performed in the lab and the test conditions are provided in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pollution Material** & **Concentration (ppm)** & **Contract Time** & **Shaking Speed** & **Mixture Rate** \\  & & & **(rpm)** & **(g/mL)** \\ \hline Copper & 50, 100, 200, 400, 800 & 50, 10, 20, 40, 80 (min) & 200 & 50:1 \\ \hline Phenanthrene & 500, 1000, 2000, 3000, 3500, 4000, 4500 & 12 (hr) & 150 & 200:1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Batch Test Condition.