esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 7 Application Fields of Electrospun Chitosan Nanofibers and Chitosan Derivatives
### Water Filtration  
Pathogenic contamination is considered a significant worldwide water concern. Traditional techniques for water purification and disinfection, such as treatment with chemicals or conventional membrane filtration, are not flawless.  
Conventional water contaminant removal includes the use of chemical disinfectants and biocides to inactivate pathogens found in water, such as chlorine gas and dioxide. These methods, however, have proven to require a long reaction time and chemical agents are required in high concentrations, which are difficult to prepare because of these their fast degradation and capacity to react with other substances in the water, leading to carcinogenic by-products [150]. Moreover, due to the regular use of chemical disinfectants, new variants of pathogens have emerged and adapted to the existence of these agents to the point of resisting them. In addition to chemical pollution and cytotoxicity concerns, the continual addition of bactericide may result in bacterial resistance when bacteria are subjected to subinhibitory biocide doses [8].  
Therefore, physical processing methods such as membrane filtration and distillation are growing in importance because of their low cost, simple application, and efficiency. Con  
Figure 8: Comparison of the surface topography between a film (**a**) and nanofibers (**b**), both made of chitosan. The nanofibers show a rough structure favorable to the attachment of bacteria. Adapted from [147], originally published under a CC-BY 3.0 license.  
ventional membrane filtration of microorganisms, on the other hand, results in the development of biofilms that considerably reduce the quality of the filtered water [151]. Because of the difficulties with traditional water purification and filtration systems, the improvement of water filtration systems is an appealing research topic. Functional nanofibers are increasingly acknowledged as effective materials for environmental remediation. Nanofiltration is a technique for purifying, desalinating, and disinfecting water. Electrospun nanofibers have unique properties such as a large surface area and a high aspect ratio combined with a high mechanical stability. It is also possible to incorporate nanoparticles with different functionalities into the fibers. These properties make the functionalized fibers an excellent filter medium for water, other liquids, and air [152; 153]. Reducing the fiber diameter while preserving the mechanical properties, fiber functionalization, and increased throughput are the major challenges [154].
# Cloud Security Using Fine-Grained Efficient Information Flow Tracking
## 3 Materials, Methods, and Tools Used by the CloudMonitor Framework
### The LIBDFT Meta-Tool Capabilities
#### 3.3.3 Segment Translation Table (STAB)  
Similar to the memory-bitmap structure, in LIBDFT, the STAB structure is employed to label data that already exists in the main memory. In contrast to the memory bitmap, when LIBDFT is configured to utilize byte-sized tags, it utilizes STAB. The tags are stored within dynamically allocated tag map sectors by STAB. Each time, a chunk of memory is requested by the program via utilizing a system call such as mmap, malloc, or shmat. LIBDFT intercepts the system call and subsequently assigns memory chunks of equivalent size in a contiguous manner. In the initialization process, LIBDFT assigns the STAB to correlate main memory addresses with their respective bytes, as memory is allocated to a process in blocks referred to as pages. Thus, a page will be pointed to each STAB entry. Crucially, implementation of the LIBDFT guarantees that matched sectors with contiguous memory pages are also contiguous. The technique can help to ease the known problem of memory accesses crossing boundaries (i.e., page fault). Regarding the drawbacks, memory suffers significantly high overhead when using byte-sized tags with STAB.
# Mixed-Sized Biomedical Image Segmentation Based on U-Net Architectures
## 3 Dataset, Model and Parameters Configuration
### Parameters Configuration
#### 3.4.2 Metric and Loss  
A metric function based on the Sorensen-Dice coefficient [6] is used in the model. It is often referred to as the Dice coefficient and is equivalent to the F1-score [7]. It is a statistical index that measures the similarity between two sets of data. In particular, in the context of Image Segmentation, it compares the estimated output of the algorithm with the known reference masks, measuring the affinity between two binary images. The dice_coef(), used as accuracy metric in List 4 (model configuration), is based on the following equation:  
\[DSC=\frac{2\mid X\cap Y\mid+smooth}{\mid X\mid+\mid Y\mid+smooth}. \tag{3}\]  
where \(\mid X\mid\) and \(\mid Y\mid\) are the sizes (expressed as number of elements) of the two sets \(X\) and \(Y\). In this case, \(X\) and \(Y\) represent the sets of white pixels in the masks generated by the U-Net and in the reference one, respectively. The DSC is the ratio of the double of the number of elements common to both sets (size of intersection set) and the sum of the size of the two sets. The expression returns a value between 0 and 1. A Dice coefficient equal to 1 denotes a perfect and complete overlap. In our code, these sets are obtained by flattening each image by using the flatten() method, which returns a one-dimensional array. At this point, Equation (3) is used to implement the Dice metric. For the evaluation of the coefficient on the expected masks, the numerator is approximated by the sum of the elements of the matrix obtained by the element-wise product between the elements of the forecast and those of the input mask.The advantage of using the Dice coefficient is that it maintains sensitivity in more heterogeneous data sets and is less sensitive to outliers. In the DSC Formula (3), the parameter _smooth_ indicates the smoothing coefficient, which is a value between 0 and 1 that prevents the occurrence of a zero denominator [36; 37]. In our model, the value _smooth=1_ is set.  
Finally, the dice_coef_loss() loss function is defined as the opposite of the Dice coefficient.
## 1 Introduction
According to previous studies, extracts from _Scenedesmus_ sp. showed various physiological activities such as photoprotective effects, antioxidant capabilities, anticancer effects, and antibacterial effects on various pathogens [18; 19; 20]. The extract of _Scenedesmus deserticola_ JD052 (_S. deserticola_ JD052) demonstrated anti-aging effects through photoprotection in normal human dermal fibroblasts (nHDFs). It has also been identified that the _S. deserticola_ JD052 extract contains loligilide as an active compound [21]. This study also confirmed the significant physiological activities of _Scenedesmus_ sp., with bioactive compounds extracted from _S. deserticola_ JD052, cultured under an optimized method, exhibiting notable properties related to hair growth. Hair is one of the key features of mammals and serves various roles such as physical protection, temperature regulation, dispersion of sweat and sebum, sensory functions, and social interaction [22]. The hair follicle, composed of epithelial cells and dermal papilla cells, is a'mini-organ' where the complex interactions between these cells determine the shape, growth, and development of hair [23; 24]. The development and formation of hair follicles are regulated by multiple signaling pathways, including wingless-int (WNT), sonic hedgehog (SHH), and bone morphogenetic protein (BMP) [25; 26]. These pathways, along with various intracellular and intercellular signaling molecules, such as morphogens and growth factors, play a pivotal role in hair follicle generation [27; 28]. Notably, the activation of the WNT/\(\beta\)-catenin signaling pathway is a key factor in hair follicle regeneration. Previous research has demonstrated that the stimulation of WNT/\(\beta\)-catenin signaling enhances both hair follicle regeneration and hair growth [29; 30; 31; 32; 33]. Various studies reported the linkages of microalgae and therapeutic materials for alopecia [34; 35; 36]. Nevertheless, research on the treatment and improvement of hair-related disorders using _S. deserticola_ JD052 in vitro remains limited. In this study, we aim to demonstrate this by utilizing _S. deserticola_ JD052 extract to elucidate its effect on regulating hair inductivity through activating \(\beta\)-catenin signaling via facilitating the AKT/glycogen synthase kinase 3\(\beta\) (GSK\(\beta\)) signaling pathway in HDP cells, proposing it as a new candidate for hair loss improvement and treatment.
# Risk Elements in Total Suspended Particles in Areas Affected by Opencast Mining of Brown Coal
## 3 Results and Discussion
### Human Health Risks of the TSP-Associated Risk Elements
Thus, replacing the total contents of risk elements in TSP with bioavailability fractions in human health risk calculations could lead to more precise estimations of carcinogenic and non-carcinogenic risks [62]. Similarly, in the soils collected near the North Bohemian brown coal basin, the mobility and potential gastrointestinal and pulmonary bioaccessibility of risk elements \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **As** & **Be** & **Cd** & **Cu** & **Cr** & **Cu** & **Mn** & **Pb** & **V** & **Zn** \\ \hline \multirow{3}{*}{Spring} & \(7.9\times 10^{-5}\) & \(8.7\times 10^{-6}\) & \(5.9\times 10^{-6}\) & \(5.2\times 10^{-5}\) & \(3.5\times 10^{-4}\) & \(4.9\times 10^{-4}\) & \(2.6\times 10^{-3}\) & \(2.2\times 10^{-4}\) & \(3.9\times 10^{-4}\) & \(2.9\times 10^{-3}\) \\  & a & a & a & a & b & a & a & a & a & ab & a \\ Summer & \(1.0\times 10^{-4}\) & \(7.8\times 10^{-6}\) & \(9.3\times 10^{-6}\) & \(7.1\times 10^{-5}\) & \(3.4\times 10^{-4}\) & \(7.3\times 10^{-4}\) & \(2.6\times 10^{-4}\) & \(2.7\times 10^{-4}\) & \(3.6\times 10^{-4}\) & \(4.8\times 10^{-3}\) \\  & ab & a & a & a & a & a & ab & a & ab & a \\ Winter & \(2.0\times 10^{-4}\) & \(9.6\times 10^{-4}\) & \(7.9\times 10^{-6}\) & \(6.8\times 10^{-5}\) & \(2.2\times 10^{-4}\) & \(7.4\times 10^{-4}\) & \(2.5\times 10^{-3}\) & \(3.2\times 10^{-4}\) & \(4.1\times 10^{-4}\) & \(8.3\times 10^{-3}\) \\  & b & a & a & a & ab & a & b & a \\  & b & a & a & a & ab & a & b & a \\  & & & & & & & & & & \\ Spring & \(1.3\times 10^{-5}\) & \(1.5\times 10^{-6}\) & \(9.8\times 10^{-7}\) & \(8.6\times 10^{-6}\) & \(5.9\times 10^{-5}\) & \(8.2\times 10^{-5}\) & \(4.3\times 10^{-4}\) & \(3.7\times 10^{-5}\) & \(6.5\times 10^{-5}\) & \(4.9\times 10^{-4}\) \\  & a & a & a & a & b & a & a & a & ab & a \\ Summer & \(1.7\times 10^{-5}\) & \(1.3\times 10^{-6}\) & \(1.5\times 10^{-6}\) & \(1.2\times 10^{-6}\) & \(5.7\times 10^{-5}\) & \(1.2\times 10^{-4}\) & \(4.3\times 10^{-4}\) & \(4.5\times 10^{-5}\) & \(5.6\times 10^{-5}\) & \(8.1\times 10^{-4}\) \\  & ab & a & a & a & b & a & a & a & a & ab & a \\  & & & & & & & & & & \\ \multirow{3}{*}{Winter} & \(3.3\times 10^{-5}\) & \(1.6\times 10^{-6}\) & \(1.3\times 10^{-6}\) & \(1.1\times 10^{-5}\) & \(3.6\times 10^{-5}\) & \(1.2\times 10^{-4}\) & \(4.1\times 10^{-4}\) & \(5.3\times 10^{-5}\) & \(6.7\times 10^{-5}\) & \(1.4\times 10^{-3}\) \\  & b & a & a & a & a & ab & a & a & a & b & a \\ \hline \end{tabular}
\end{table} 
Table 2: The average daily dose (ADD) values for ingestion of the individual elements contained in the suspended particles according to the periods of year; the columns marked by the same letter did not significantly differ at \(p\) < 0.05.  
are limited. [PERSON] et al. (2016) found that Cd exhibited the highest bioaccessibility in simulated gastric fluid, whereas bioaccessible proportions for other elements did not exceed 20% of their total content in the soil. These factors also influence potential human health hazards.
# Atmospheric GNSS RO 1D-Var in Use at UCAR: Description and Validation
## 5 Conclusions
We find that the performance of 1D-Var depends on the observation operator and how RO observation is used in the retrieval process. A comparison made in this respect evinces that the two-step approach adopted for the 1D-Var (variational refractivity inversion succeeded by retrieval step) outperforms others. We also observe that 1D-Var solutions based on different a priori data approach each other closely as if converging to a unique solution, indicating that the solutions do not depend strongly on the priori data. Along the same lines, a multi-year data record of the 1D-Var has demonstrated exceptional long-term stability, while detecting and correcting abrupt temporal changes of a priori data. The long-term stability has important implications for RO-based climate studies. Our findings, taken together, point to that the 1D-Var meets the design expectations well and that its data quality is sufficient for most applications. We believe that the 1D-Var profiles could greatly enhance the scientific utility of RO. Conceptualization, T.-K.W. ; methodology, T.-K.W. ; validation, T.-K.W. ; writing--original draft preparation, T.-K.W. ; writing--review and editing, R.A.A., D.C.H., W.S.S. and Y.-H.K. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Oceanic and Atmospheric Administration under contract 1305M221 FNEEO0212 and by the National Science Foundation under grant No. 2054356. The COSMIC and COSMIC-2 RO data used in this study are available online: [[http://cdaac-www.cosmic.ucar.edu/cdaac/products](http://cdaac-www.cosmic.ucar.edu/cdaac/products)]([http://cdaac-www.cosmic.ucar.edu/cdaac/products](http://cdaac-www.cosmic.ucar.edu/cdaac/products)) (accessed on 31 October 2022). The ECMWF data are provided to UCAR by the European Centre for Medium-Range Weather Forecasts. We thank the UCAR COSMIC team for making available the GNSS RO data used in this study. The authors declare no conflict of interest.
# Micro Vibration Measurement with Microscopic Speckle Interferometry Based on Orthogonal Phase
## 3 Simulation
When the absolute value of \(\sigma\) is too large, the light intensity signal will appear frequency doubling, as shown in Figure 3b,h. At this time, the maximum value I\({}_{\text{max}}\) will appear at the upper limit of the light intensity (Figure 3b), or minimum value I\({}_{\text{min}}\) will appear at the lower limit (Figure 3h). When \(\sigma\) equal to 0, the light intensity curves will be symmetrically distributed with respect to \((I_{\text{max}}+I_{\text{min}})/2\), as shown in Figure 3e. Through the above simulation, we can summarize the corresponding relationship between the vibration trajectory and the light intensity curves. When the amplitude A meets the reasonable range selected and \(\sigma\) equal to 0, the light intensity curves will be symmetrically distributed with respect to \((I_{\text{max}}+I_{\text{min}})/2\), as shown in Figure 3e. According to the characteristic that the light intensity curve is symmetrically distributed about the symmetry axis \(I=(I_{\text{max}}+I_{\text{min}})/2\), we can obtain these points that satisfy the orthogonal characteristic. From the light intensity curves of these points, the vibration frequency \(f\) can be obtained directly. To obtain the vibration amplitude, we can first artificially increase the A or \(\sigma\) to get the \(I_{\text{max}}\) and \(I_{\text{min}}\). By substituting them into Equation (8), when the _cos term_ is +1, it corresponds to the \(I_{\text{max}}\), and when the _cos term_ is \(-1\), it corresponds to the \(I_{\text{min}}\), so the initial light intensity \(I_{1}\) and \(I_{2}\) can be obtained, respectively. Then, we return to the light intensity curve that satisfies the orthogonal characteristics to obtain \(I_{\text{peak}}\) (or \(I_{\text{valley}}\)), as in Figure 3e; the vibration amplitude can be obtained by Equation (7). The specific derivation process is detailed in Section 4.1.
## 4 Simulation Analysis
### Modeling of Hydraulic Tension Control System
#### 4.1.2 Modeling of Proportional Relief Valve  
A proportional relief valve is used for converting electrical input signals into a proportional pressure output signal, as well as for limiting the pressure. A schematic representation of a proportional relief valve is shown in Figure 6.  
The dynamics of the proportional solenoid coil can be described as [19]  
\[L\frac{\mathrm{d}i}{\mathrm{d}t}=k_{\mathrm{D3}}u_{\mathrm{g}}-(k_{\mathrm{D3} }k_{\mathrm{D4}}+R)i \tag{27}\]  
where \(R\) is the sum of the coil resistance and the amplifier resistance; \(L\) is the coil inductance; \(k_{\mathrm{D3}}\) is the amplifier voltage amplification factor; \(u_{\mathrm{g}}\) is the amplifier input voltage; \(k_{\mathrm{D4}}\) is the current feedback gain of the proportional solenoid.  
The relief valve is normally closed. When the pressure setting of the valve is reached, the equation describing the spool motion is  
\[P_{1}A=m_{1}\frac{\mathrm{d}^{2}y}{\mathrm{d}t^{2}}+f_{1}\frac{\mathrm{d}y}{ \mathrm{d}t}+k_{\mathrm{D1}}i+k_{\mathrm{D2}}y+k_{\mathrm{e1}}yP_{1} \tag{28}\]  
where \(A\) is the spool side area; \(m_{1}\) is the spool mass; \(f_{1}\) is the damping coefficient; \(k_{\mathrm{e1}}\) is the hydrodynamic stiffness of the valve port; \(y\) is the spool displacement; \(P_{1}\) is the inlet pressure of the valve; \(k_{\mathrm{D1}}\) is current force gain factor of the proportional solenoid; \(i\) is the drive current of the valve; \(k_{\mathrm{D2}}\) is the spring stiffness.  
The opening area of the valve is calculated as  
\[A_{\mathrm{open}}=\pi y\sin\frac{\alpha}{2}\Big{(}D_{1}-\frac{y}{2}\sin\alpha \Big{)} \tag{29}\]  
where \(D_{1}\) is the valve seat diameter. The opening area is bounded by the maximum displacement \(y_{\mathrm{max}}\)  
\[y_{\mathrm{max}}=\frac{D_{1}\Big{(}1-\sqrt{1-\cos\frac{\alpha}{2}}\Big{)}}{ \sin\alpha}\]  
For any needle displacement larger than \(y_{\mathrm{max}}\), \(A_{\mathrm{open}}\) is the maximum orifice area  
\[A_{\mathrm{open}}=\frac{\pi}{4}{D_{1}}^{2}\]  
Figure 6: Schematic representation of a proportional relief valve. 1: the housing; 2: proportional solenoid; 3: the valve seat; 4: the valve spool; 5: integrated amplifier.  
The flow equation of the relief valve can be described as  
\[q_{\rm v}=C_{\rm d2}\pi D_{1}y\sin\frac{\alpha}{2}\Big{(}D_{1}-\frac{y}{2}\sin \alpha\Big{)}\sqrt{\frac{2}{\rho}P_{1}}+\frac{V_{1}}{\beta_{\rm e}}\frac{{\rm d }P_{1}}{{\rm d}t}+A\frac{{\rm d}y}{{\rm d}t} \tag{30}\]  
where \(q_{\rm v}\) is the overflow through the relief valve; \(\rho\) is the density of fluid; \(\beta_{\rm e}\) is the bulk modulus of fluid; \(V_{1}\) is the volume of the working chamber in the valve; \(C_{\rm d2}\) is the flow coefficient of the valve port; \(D_{1}\) is the valve seat diameter; \(\alpha\) is the taper angle of the spool.
# Efficient Health Data Transmission Method in a Wireless Body Area Network for Rural Elderly
## 6 Conclusions  
Combined with the delay-tolerant network, a prediction model of node connectivity probability based on the analysis of the link history connection was obtained. It further integrates the advantages of opportunistic routing forwarding nodes and optimizes the selection of candidate nodes according to node residual energy and link stability, which improves the reliability of data transmission. The construction of the candidate set took into account the interaction between communication links and studied the improved Bellman algorithm to obtain the candidate set. The proposed algorithm can effectively control the network overhead, prolong the network lifetime and improve the network throughput.  
The channel model we used was mainly based on published studies. Therefore, our future research will mainly focus on testing the body area network channels of the elderly, further optimizing the mobile model of the rural elderly in combination with their random behavior and designing an efficient data transmission protocol.  
Conceptualization, H.W. and H.Z.; methodology, H.W. and J.G.; validation, J.G., C.P. and X.H.; formal analysis, H.Z., X.H. and C.P.; investigation, J.G.; writing--original draft preparation, all authors; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2019 YFD1101105, and the National Natural Science Foundation of China, grant number 61871041.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results and Analysis
### Comparative Analysis of Handling Activities  
The proposed solution framework was applied to a case with all part groups, and the results are presented in Table 1. The most striking achievement is the dramatic and statistically significant reduction in direct picking activities. For the 50-part group, our proposed model (Simulation PD) averages 13.8% direct picking, which is a substantial improvement over both the 19.5% from the standard (s,S) policy and the 28.1% from Business as Usual. This signifies a more streamlined picking process that translates into faster fulfillment and reduced labor costs--key factors for warehouse efficiency. Another notable outcome is the optimized use of buffer space, a critical resource in managing inventory flow. While buffer usage under Business as Usual is excessively high (56.8%), our model maintains a healthier balance (18.9%) compared to the more reactive (s,S) policy (25.4%), ensuring that space is used only when truly necessary to avoid unnecessary congestion and enhancing throughput. These performance trends hold and are often amplified as the part group size increases. For instance, in the 300-part group, our model reduces direct picking to just 5.5%, compared to 8.9% for the (s,S) policy and 19.2% for Business as Usual, demonstrating the scalability of the model's benefits. In essence, these results highlight the potential for real-world implementation, promising significant gains in operational performance and efficiency over both current practice and standard academic benchmarks.
## 1 Introduction
The friction and wear properties can be enhanced by the deposition of TiN, TiAlN, AlTiN and CrAlN layers by PVD [28]. The other way to increase the performance of tool steel is nanolayer technology and duplex coating treatments [29]. The duplex PVD process is also a suitable technology to create high strength surface layers [30; 31; 32]. Different layers of the surface as a function of the coating thickness and chemical composition (nano multilayer PVD coating) increase the wear resistance and lifetime of the tools [33]. Based on the literature review and the material science studies, it can be concluded that increasing the lifetime of tool steels must be a complex treatment and the substrate's mechanical and chemical properties are extremely important [34]. This research is an experimental study to investigate the complex heat-treated and surface-treated tool steel tribological properties. Based on the literature review, we hypothesize that a combination of surface layer treatments is needed to increase the wear resistance of high strength tool steels. Thus, heat treatment was combined with nitriding as well as the PVD process. The surfaces were characterized with scanning electron microscopy and a microhardness test, whereas the wear resistance was investigated with the modified ball cratering test.