esa-sceva/satcom-chunk-collection huggingface dataset:
# Biochars as a Solution for Silver Removal and Antimicrobial Activity in Aqueous Systems
## 2 Materials and Methods
### Preparation of the Samples  
Three biochar samples were prepared:  
* Rice Husk Biochar  
The dried rice husks were treated with concentrated HF acid for ten days to completely remove the biogenic SiO\({}_{2}\) from the lignin-cellulose matrix fully. The husks were then washed several times with deionized water until the wash water was neutral, and dried at 120 \({}^{\circ}\)C. The husks were subjected to pyrolysis under static conditions in a corundum crucible with limited air access at 480 \({}^{\circ}\)C for 3 h. A heating rate of 5 \({}^{\circ}\)C/min was applied. This biochar was designated as sample RHB.  
* Activated Rice Husk Biochar  
The second sample was obtained by the chemical activation of the RHB sample. A portion of the RHB sample was mixed with a concentrated ZnCl\({}_{2}\) solution at \(\sim\)60 \({}^{\circ}\)C under continuous stirring for 1 h. After cooling, the mixture was filtered, and the wet sludge was dried at room temperature. The air-dried sample was pyrolyzed at 700 \({}^{\circ}\)C under vacuum for 1 h. After cooling, the biochar was treated with a 1:1 water solution of HCl for 1 h to remove excess ZnO. The material was washed with deionized water until the wash water was neutral, and then dried at 110 \({}^{\circ}\)C. The sample was designated as ARHB.  
* Cotton Biochar  
The sample labelled CB was obtained by carbonizing commercial cotton that had been pre-mercerized. The cotton was treated with a 17.7% NaOH solution at room temperature for 24 h, then washed with deionized water until the wash water was neutral. It was subsequently dried at 120 \({}^{\circ}\)C to a constant weight. The dried cotton was pyrolyzed under static conditions in a corundum crucible with limited air access at 320 \({}^{\circ}\)C for 3 h.
## Materials and Methods
### Variational Mode Decomposition (VMD)
#### 2.1.1 The Construction of the Variational Problem  
Assume that each mode has a finite bandwidth and a pre-defined center frequency. The variational problem is described as a problem that seeks K modal functions \(u_{k}(t)(k=1,2,\ldots,K)\) such that the sum of the estimated bandwidths of each mode is minimized subject to the constraint that the sum of each mode is equal to the input signal \(f\). The specific construction steps are as follows:  
(1) Apply a Hilbert transform to the analytical signal of each modal function \(u_{k}(t)\). Then, obtain its single-side spectrum  
\[\begin{array}{l}\Big{(}\delta(t)+\frac{j}{\pi t}\Big{)}*u_{k}(t)\\ \delta(t)=\left\{\begin{array}{ll}0&t\
eq 0\\ \infty&t=0\end{array}\right.\int\limits_{-\infty}^{+\infty}\delta(t)dt=1\end{array} ^{\prime}, \tag{1}\]  
where \(\delta(t)\) is the Dirac distribution.  
(2) Modulate the spectrum of each mode to be the corresponding baseband based on the mixed-estimated center frequency \(e^{-j\omega_{k}t}\) of various modal analysis signals  
\[\bigg{[}\bigg{(}\delta(t)+\frac{j}{\pi t}\bigg{)}*u_{k}(t)\bigg{]}e^{-j\omega_ {k}t}, \tag{2}\]  
where \(e^{-j\omega_{k}t}\) is the phasor description of the center frequency of the modal function in the complex plane and \(\omega_{K}\) is the center frequency of each modal function.  
(3) Calculate the square of the norm of the gradient of the analytical signal and estimate the bandwidth of each modal signal. The constrained variational problem is expressed as  
\[\left\{\begin  
where \(\{u_{k}\}=\{u_{1},u_{2},\cdots,u_{K}\}\) is the set of modal functions, \(\{\omega_{k}\}=\{\omega_{1},\omega_{2},\cdots,\omega_{K}\}\) is the set of center frequencies that correspond to the modal functions, \(\otimes\) is the convolution operation, and \(K\) is the total number of modal functions.
# Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping
## 2 Material and Methodology
### Model Hyperparameter Tuning  
As mentioned in Section 2.4, there are numerous hyperparameters in a model and the change in hyperparameter values can affect the performance of the constructed model. Since the optimal hyperparameter values are not identified at first, optimization should be carried out to select the proper values for each model. The most commonly used method of optimization is the grid searching method [32], which involves all the potential combinations of the chosen hyperparameters and a profound assessment of each hyperparameter to choose the best combination. However, this brings about a substantial cost because of the absolute number of combinations that may have to be evaluated (particularly if the model has several tunable hyperparameters).  
Another optimization method is the random search method [32]; in this case, all the hyperparameter ranges are sampled randomly. This method also requires a long-running time because some time may be spent evaluating unpromising areas of the search space.  
A model-based method to find the minimum function is called Bayesian optimization [33]. It has lately been used for hyperparameter tuning in machine learning. Bayesian optimization is an algorithm that uses the Bayesian theorem to adaptively generate data for hyperparameters and find the optimum hyperparameter values using surrogate models. It can attain a better performance on a test set with less iterations than a random search or a grid search [10]. To avoid the overfitting of the model and to ensure that the chosen hyperparameter combination values are near the optimal values, a k-fold cross-validation [24] technique is applied. The training dataset was split into k- subsamples, which means that the model will run \(k\) times iteratively, using \(k\)-1 subsamples to train the model and the rest of the subsamples for testing. During each iteration of a combined hyperparameter setup, a number of model accuracy results are obtained and averaged.
## 6 Discussion
For example, specimen A showed a uniform thickness and the measured Young's modulus agreed well with the theoretical value. On the other hand, specimen D showed a large variation in thickness, which resulted in the larger value of the Young's modulus. The reason for the waviness was the uneven width of the EB resist pattern and the non-uniform opening width for the SiNW etching process. Both of which had caused the thickness variation of the sidewall passivation layer during the DRIE process, which resulted in a low controllability of the thickness dimensions. As mentioned above, the thickness uniformity of the EB resist should have been improved by optimizing the coating process, and the width of the openings near the specimen should have been revised. Figure 10: (**a**) Simulated displacement of the device with specimen C against the applied voltage. The line plot is a fitting curve to the quadrature function. (**b**) Simulated deformation at 38 V. Displacement is exaggerated by five times. The contour plot indicates \(z\)-axis (vertical) displacement. The tensile strength was comparable to the microscale specimens [27], which we did not expect to find. As a result of the surface oxidation process, the surface of the silicon would have been smoothed of any roughness that was caused by patterning, such as the jagged edge of the EB resist and the scallops that were generated in the Bosch process. The top surface was smooth (as seen in Figure 6), whereas the side and bottom surfaces had a degree of roughness (as seen in Figure 12). The size of the pit in these surfaces was about 10 nm, and it might have acted as the fracture origin [28]. The roughness of the SF\({}_{6}\) etching on the side and bottom surfaces was not removed by oxidation or oxide precipitation during the oxidation thinning process. We needed to improve the process in order to obtain a higher strength and better structural reliability for future applications. Through the fabrication and operation of the electrostatic MEMS tensile testing device with integrated SiNW, we confirmed that the proposed fabrication process integrated a SiNW into the micrometer scale Si MEMS structures, using a top down fabrication process. This process would be useful for further miniaturization of sensors and actuators, such as those that are required for piezoresistive sensors and torsional mirror devices.
# Scheduling Optimization of Mobile Emergency Vehicles Considering Dual Uncertainties
###### Abstract  
Compared with the traditional operation mode of emergency vehicles, the mobile emergency vehicle is regarded as a new type of emergency facility carrier with the features of variable locations, flexible mobility, and intelligent decision-making. It can provide an effective solution to reasonably respond to the uncertain risks of sudden disasters. Focusing on meeting the maximum demand for materials and services in disaster areas, this paper proposes a scheduling model of mobile emergency vehicles with dual uncertainty of path and demand. The model, solved by an integer-coding hybrid genetic algorithm, aims to obtain minimum mobile emergency scheduling cost and time by transforming the multi-objective problem into a single-objective problem. The \"5.12\" Wenchuan earthquake is used as an example to validate the model and solving method. The results show that the model can reduce the impact of uncertain risks and improve the scientific logic of emergency strategies and deployments based on the actual crisis scenario. It benefits from introducing mobile emergency vehicles and optimizing their scheduling process.  
sudden disaster; mobile emergency; vehicle scheduling; uncertainty risk Article
# Review of GaN Thin Film and Nanorod Growth Using Magnetron Sputter Epitaxy
## 3 MSE Growth of GaN Thin Films Using Liquid Ga Target
### MSE Growth of Single-Crystal GaN Films Using Liquid Ga Target
(b) Cross section TEM of the GaN film, with the corresponding SAED pattern shown in the inset. The zone axis is [11-20] in the GaN crystal. (c) Low-temperature \(\upmu\)-PL spectrum of the sample obtained at 4 K [8]. Reprinted with permission from AIP publishing. [PERSON] et al. [9] has demonstrated the MSE-growth of GaN thin film on a (0001) sapphire substrate and GaN on sapphire template using RF target biasing. From a series of experiment, they found that higher substrate temperature results in a more stable crystalline quality and smoother GaN surface. However, growth performed at 1050 \({}^{\circ}\)C results in the formation of pits, caused by the decomposition of GaN under ultra-high vacuum environment. At a growth temperature of 890 \({}^{\circ}\)C, they managed to perform homoepitaxial growth of GaN thin film on GaN template with threading dislocation density and defect density comparable with MOCVD-grown films. The film on GaN template shows excellent quality, with a threading dislocation density of \(1.7\times 10^{8}\) cm\({}^{-2}\). XRD rocking curves from the (0002) and (10-12) plane gives an FWHM of 300 and 310 arcsec, respectively. This result shows that the choice of substrate template or interlayer is also crucial in reducing the number of structural defects for MSE-grown GaN films.
# Competition with the Industrial IoT: A Service-Dominant Logic Approach
## 2 Methodology
### Functional Acceptance and Evaluation of Cockpit4.0+ Prototype
All data were anonymized and referred to only by company labels. Data collection, recording, and exportation procedures were followed, with results exported to Excel files. This ensured a secure and consistent approach to data handling, enabling detailed analysis while safeguarding the privacy and proprietary information of the participating companies. The data collected were used to assess KPIOD under CB.Ps and CN.Ps, as summarized in Table 3.  
Based on these findings, under CB.Ps, the on-time delivery performance was recorded as 67.1%, with 240 out of 339 parts being delivered as scheduled. In contrast, CN.Ps resulted in a significant increase in KPIOD to 77.5%, with 358 out of 454 parts being delivered on time. Figure 5 shows the daily average on-time delivery performance for both CB.Ps and CN.Ps. These results demonstrate that by transitioning to a coopetition network facilitated by Cockpit4.0+, the OS-PT companies experienced substantial improvements in on-time delivery performance. In summary, this evaluation text of the Cockpit4.0+ prototype confirmed the preliminary functionalities. By focusing on the identified areas for improvement and capitalizing on its existing strengths, the system is strategically positioned to significantly influence the OS-PT sector digital transformation. Specifically, it promises to enhance dynamic and secure competition networks, which is essential for evolving competitive landscapes. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Data Type** & **Description** & **CB.Ps—Average Data** & **CN.Ps—Average Data (Daily)** \\ \hline Data type 1 & Parts delivered daily & 339 & 454 \\ Data type 2 & Parts delivered on time & 240 & 358 \\ KPI & On-time delivery & 0.671 & 0.775 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of average data collected daily.
## 3 Methods--Field Experiment  
### Site Description  
Our study site is located at the University of Michigan Biological Station in northern Michigan (45\({}^{\circ}\)35.5' N 84\({}^{\circ}\)43' W). The forest is dominated by maturing early successional tree species, relatively even-aged _Populus grandidentata_ Michx. (bigtooth aspen), _Populus tremuloides Michx_. (trembling aspen), and _Betula papyrifera_ Marsh. (paper birch). Other canopy tree species include _Fagus grandifolia_ Ehrh. (American beech), _Acer saccharum_ Marsh. (sugar maple), _Acer rubrum_ L. (red maple), _Pinus strobus_ L. (white pine) and _Quercus rubra_ (red oak). The early successional aspen and birch species are beginning to senesce and will continue to do so over the next 50 years [27]. Total basal area density is 30.83 m\({}^{2}\)/ha in the treatment plot and 36.87 m\({}^{2}\)/ha in the control with aspen and birch composing 40.0% of the total basal area in the treatment plot and 53.1% of the control plot. On 20 April-3 May 2008 we initiated an experiment in which we stem girdled all aspen and birch trees in a 33 ha treatment plot, totaling approximately 6700 trees. Girdling removes the bark and underlying phloem in a strip around a tree, preventing the translocation of photosynthetic from leaves to roots. An unmanipulated control plot with similar site and soil characteristics is located nearby and within the footprint of an eddy-flux tower (the US-UMB Ameriflux tower, [24]). The mean annual temperature is 5.5 \({}^{\circ}\)C (1942-2003). The mean annual precipitation is 817 mm (including 294 cm snowfall). The site is snow covered in most years from November to April. The study area encompasses \(\sim\)140 ha on a high outwash plain and adjacent gently sloping moraine. Nearly all soils in the study area are well to excessively well drained Haplorthods of the Rubicon, Blue Lake, or Cheboygan series. About 53% of the fine-root mass is located within the upper 20 cm of the soil profile. Forest floor C mass is 5-15 Mg C/ha, and the mineral soil is \(\sim\)95% sand and \(\sim\)5% silt, with pH 4.5-5.5 in water. Soils are of low fertility, with total N capital to 40 cm depth of 2000 kg/ha, an average _in situ_ net N-mineralization rate of 42 kg N/(ha yr) and \(<2\%\) net nitrification [24, 28].
## 3 Characterization Results
### Comparison with Simulations  
A comparison between the obtained CCD image widths in the tangential direction and the ray-tracing simulation results [23], performed with a source placed immediately before the entrance slit (assuming an angular half-divergence of 2.5 mrad \(\times\) 2.5 mrad), has been performed. From Equation (5), this configuration allows for maintaining a constant value \(M_{t}=1.67\) for the entrance slit magnification on the image plane within the entire spectral range. Therefore, only aberrations are responsible of a change in the image width when changing the energy. When we are not selecting \(E_{\mathrm{C}}\), we have no defocusing, therefore the main aberration to consider is the tangential coma.  
As represented in Figure 6, at the two edge energies of G\({}_{\mathrm{I}}\) some coma appears, as can be seen from the slightly asymmetrical images at 12 eV and 25 eV, although the width at FWHM is almost constant.  
Figure 5: Line profile for \(k\) variation of 0.2\({}^{\circ}\) and 0.4\({}^{\circ}\) at 40.82 eV, \(G_{\mathrm{2}}\) grating.  
Figure 6: Ray-tracing results of the entrance slit projected to the output for the values given in Table 1 (G\({}_{\mathrm{I}}\) grating). (**a**) Image shape for three different energies and (**b**) width in the tangential direction.  
In Figure 7, the CCD acquired images at 13.48 eV (Ar II), and 21.22 eV (He I) emission lines are reported for the same grating. Coma aberration is not evident.
# ATOSE: Audio Tagging with One-Sided Joint Embedding
## 3 Architecture: Audio Auto-Tagging with One-Sided Joint Embedding
### Training Procedure  
Our joint embedding training algorithm consists of two stages that are specifically designed to enable our feature extractor to learn the semantic information of the tags. In order to ensure stable convergence, we employ scheduled Adam and SGD optimizers [21] with a batch size of 16 in both stages.  
**Stage one:** We begin by training three components: the tag autoencoder (TAE), the feature extractor (FE), and the projector (PR). The TAE takes tag data in the form of a multi-hot vector as input and extracts a tag embedding vector while simultaneously reconstructing the tags. On the other hand, the FE extracts acoustic features from audio input data and the PR projects these acoustic features into the tag domains, ensuring that they resemble the tag embedding vector obtained from the TAE.  
We then define the objective functions of the modules to optimize. The loss for the TAE can be computed with the mean squared error to compare the correct and reconstructed tags as follows:  
\[L_{\text{ae}}\ =\ \sqrt{(y-\hat{y})^{2}}\]  
where \(y\) represents the ground-truth tags and \(\hat{y}\) the tags predicted by the TAE.  
The loss for the joint embedding defines a cosine similarity function for two latent (tag) vectors from the tag data and the audio data as follows:  
\[L_{\text{je}}\ =\ \cos_{\text{loss}}(z_{t},z_{a})+\cos_{\text{loss}}(m-z_{t},m -z_{a})\]  
where \(m\) is a constant (e.g., 0.4), \(z_{t}\) is the latent tag vector extracted from the TAE, and \(z_{a}\) is the latent tag vector obtained by projecting the acoustic features through the FE from the raw waveform using the PR. We add cosine embedding loss computation terms for \(m-z_{a}\) and \(m-z_{t}\) to prevent the latent vectors from converging to zero when their values are too small while narrowing the latent vector distance of the two domains in the joint embedding loss computation process. Note that \(\cos_{\text{loss}}\) is the pairwise loss, calculated as follows:  
\[\cos_{\text{loss}}(x,y)\ =\ \frac{x\cdot y}{||x||\ ||y||}.\]  
Finally, the loss function \(L\) at stage one uses the value obtained by adding the tag autoencoder loss \(L_{\text{ae}}\) and the joint embedding loss \(L_{\text{je}}\).  
\[L\ =\ L_{\text{ae}}+L_{\text{je}}.\]  
**Stage two:** Recall that the feature extractor (FE) in the first stage has been trained to extract acoustic features from the input audio data. These acoustic features are then used as input for the classifier (CL), which predicts tags. Since the tags are in the form of multi-hot vectors, to measure the quality of the predictions, we use the binary cross-entropy between the ground-truth tags and the predicted tags.  
\[L_{c}\ =\ -\ \frac{1}{N}\sum_{n=1}^{N}\left[y_{n}\log\hat{y}_{n}-(1-y_{n}) \log\left(1-\hat{y}_{n}\right)\right]\]  
where \(N\) is the total number of tags in the training dataset, \(y\) represents the ground-truth tags, and \(\hat{y}\) represents the tags predicted by the classifier.
# Traditional Grain-Based vs. Commercial Milk Refirs, How Different Are They?
## 1 Introduction  
People are becoming increasingly aware of the health benefits of fermented food [1]. The growing awareness of the importance of microorganisms and their metabolites (postbiotics) in disease prevention has led to the launch of many different fermented food products and probiotic supplements, which have been commercialized successfully within recent years to meet the high demand [1].  
Kefir is traditionally produced by adding kefir grains to fresh milk. The milk is then fermented and acidified within 24 h on average. Kefir grains consist of a range of microorganisms which belong to lactic acid bacteria (LAB), yeast, and acetic acid bacteria that are attached to a cauliflower-shaped structure composed of polysaccharides and proteins [2]. In the last few years, kefir or the microorganisms of its microbial community have been the subject of many studies reviewed in e.g., [2, 3]. However, not a specific microbial composition has been assigned to kefir. For example, [PERSON] and [PERSON] (2011) were able toidentify _Lactococcus (L) lactis, Leucostoc (L) mesenteroides_, and _Lentilactobacillus (L) kefri_ as prevalent bacteria species with culture-dependent methods, while PCR denaturing gradient gel electrophoresis (DGGE) as a culture-independent method identified _L. kefrianofaciens_ and _L. lactis_ as prevalent [4].  
Nutritional and medicinal properties of kefir have been in the focus of many scientific studies for decades [5]. During milk fermentation by kefir grains, many functional compounds like bioactive peptides (e.g., with anti-hypertensive, antioxidative, antiallergenic, antitumor, antimicrobial, anti-inflammatory, and cholesterol-lowering activities), antimicrobial compounds (e.g., organic acids, alcohols, carbon dioxides, and bacteriocins) and heteropolysaccharides (e.g., kefiran) with potential prebiotic activity are formed [6].  
Due to the complexity of grains and subsequent limitations on the shelf-life of the product, however, today's commercial kefirs are produced with artificial microbial mixtures. Kefir is one of the very popular fermented dairy products, and several well-known starter culture producers and many dairy product manufacturers have entered this market [6]. Commercial kefir producers point to the health benefits of traditional grain-based kefir to expand the commercial market [7]. Therefore, it is important to examine whether the commercial products resemble the traditional grain-based kefirs. To answer this question, microbial community analysis (using quantitative PCR (qPCR) and next generation sequencing (NGS) techniques), volatile organic compounds (VOCs) assessment, and targeted metabolite measurement was applied in this study in order to investigate the differences between traditional and commercial kefirs and to evaluate their probiotic capacity.