esa-sceva/satcom-chunk-collection huggingface dataset:
# Femtosecond Laser Processing of Biodegradable Polymers
## 5 Cell Adhesion and Behavior on the Laser-Irradiated Surface of Biodegradable Polymers  
Cell behavior is affected by the surface micro- and nanostructure of the biodegradable polymers, as well as their wettability. Because the ultrashort laser pulse enables processing of transparent polymers, several studies on the fabrication of micro- and nanostructures on the surface of biodegradable polymers have been reported toward controlling cell behavior. [PERSON] et al. reported the attachment and alignment properties of mouse myoblasts on the surface of PLLA\(-\)PCL, in which microchannels were formed by 800-nm femtosecond laser pulses [21]. The cells exhibited attachment in the channels, and a high degree of alignment was observed. It was also reported that the microchannels with topographic micropatterns on a PLLA\(-\)PCL substrate fabricated by femtosecond laser ablation promote upregulation of several hallmark markers associated with myogenesis for human mesenchymal stem cells (hMSCs) cultured on a scaffold with microchannels [26]. In 2014, [PERSON] et al. reported an investigation on the effect of micropattern on the proliferation and morphology of breast cancer cells. They reported that the pattern direction directly affected cell proliferation, leading to the guided growth of cell clusters along that direction [31]. The structure suitable for controlling cell behavior is not limited to microchannels. [PERSON] et al. compared UV femtosecond and nanosecond laser pulses to form porous structures on chitosan, starch, and their blended films. Femtosecond laser pulses of 248 nm yield a porous, foamy superficial layer on the film surface, in which cells are likely to proliferate on the structured surface [29]. [PERSON] et al. showed that fibrous platforms with patterned hierarchical topographies fabricated by femtosecond laser ablation can regulate myoblast behavior (Figure 6) [38]. Cell behavior is closely related to the surface wettability. [PERSON] et al. reviewed the femtosecond laser processing parameters on the surface morphology and the wettability [61].  
Figure 5: LIFSS formed on the PLLA surface by the irradiation of femtosecond laser pulses at different wavelengths. **(a)** 800 nm, 1.0 J/cm\({}^{2}\), 3000 pulses; **(b)** 400 nm, 0.4 J/cm\({}^{2}\), 5000 pulses; **(c)** 266 nm, 7.5 mJ/cm\({}^{2}\), 5000 pulses. Red double-headed arrows indicate the polarization direction.  
Figure 6: Effect of laser-ablated groove patterns on the adhesion and shape transition of C2C12 myoblasts. Scale bar indicates 100 \(\upmu\)m. Reproduced with permission from [38], [ACS Applied Materials & Interfaces]; published by [ACS Publications], [2016].
## 3 Proposed Quality of Service Routing Protocol (QoSRP) in UWSNs
### Underwater Acoustic Channel Detection (UWCD) Algorithm
#### 3.2.1 Decision Rules for the Fused Information in Distributed Cooperative Sensing (DCoS)  
Distributed cooperative spectrum sensing consists of main two types of data fusion, namely hard decision and soft decision mechanisms in the MCASNs. The soft decision mechanism due to heavily sharing test statistics faces excessive cooperation overheads compared to the hard decision mechanism, which consumes the sensor's energy and increases the probability of channel assignment delay in MCASNs. On the other hand, the hard decision mechanism performs best when the channel state information between the \(\mathcal{PU}\mathcal{A}\) and \(\mathcal{SU}\mathcal{A}\) varies in time and location in UWEs. The hard decision mechanism after considering the individual \(\mathcal{SU}\mathcal{A}\) local decisions conveniently applies the linear fusion rules such as AND, OR, and K-out-of-M to obtain a reliable cooperative decision. In the AND fusion rule, the fusion center declares \(\mathcal{H}_{1}\) only if all independent \(\mathcal{SU}\mathcal{A}\) decide on \(\mathcal{H}_{1}\) and declares \(\mathcal{H}_{1}\) for the OR rule only if any of independent \(\mathcal{SU}\mathcal{A}\) decides on \(\mathcal{H}_{1}\) in the cooperative decision. On the contrary, the fusion center in K-out-of-M rule declares the ultimate decision that there is a \(\mathcal{PU}\mathcal{A}\) transmission only if at least K secondary users out of M selected local detectors decide about the presence of a \(\mathcal{PU}\mathcal{A}\)
## 4 Development of Proposed Method
### Proposed Method
#### 4.2.4 Denoising of Noise-Dominant Modes Using SWT  
To further suppress noise in the noise-dominant modes, SWT denoising is applied to these modes. For each noise-dominant mode \(\mathbf{d}_{k}(n)\), \(k>\theta\):  
1. **SWT Decomposition**: Decompose \(\mathbf{d}_{k}(n)\) into approximation and detail coefficients using SWT up to level \(L\): \[\{cA^{(l)},cD^{(l)}\}_{l=1}^{L}=\text{SWT}(\mathbf{d}_{k}(n)),\] (12) where \(cA^{(l)}\) and \(cD^{(l)}\) are the approximation and detail coefficients at level \(l\).
2. **Thresholding**: Apply soft thresholding to the detail coefficients at each level: \[cD^{(l)}_{\text{thresh}}=\text{sign}\Big{(}cD^{(l)}\Big{)}\cdot\text{max}\Big{(} \Big{|}cD^{(l)}\Big{|}-\xi,0\Big{)},\] (13) where \(\xi\) is the universal threshold calculated as \[\xi=\sigma\sqrt{2\ln N},\] (14) with \(\sigma\) being the noise estimate, often computed from the median absolute deviation of the first-level detail coefficients: \[\sigma=\frac{\text{median}\Big{(}\Big{|}cD^{(1)}\Big{|}\Big{)}}{0.6745}.\] (15)
3. **Reconstruction**: Reconstruct the denoised mode \(\mathbf{\hat{d}}_{k}(n)\) using the thresholded detail coefficients and the approximation coefficients via inverse SWT: \[\mathbf{\hat{d}}_{k}(n)=\text{ISWT}\Big{(}\{cA^{(l)},cD^{(l)}_{\text{thresh}}\}_{ l=1}^{L}\Big{)}.\] (16)
As keratinocytes differentiate, they migrate toward the surface and become fully differentiated corneocytes [33; 34]. Located in the outermost layer of the skin, keratinocytes are continually exposed to various kinds of stresses, as well as UV irradiation [35; 36]. UV is one of the major factors contributing to ROS generation [37; 38], and UV-induced ROS generation reduces physiological antioxidant levels in the epidermis, causing oxidative stress [39]. Keratinocytes are susceptible to oxidative stress, which can cause serious skin diseases [40; 41]. Significant ROS accumulation in keratinocytes is associated with oxidative modifications of nucleic acids, lipids, proteins, and other intracellular molecules, leading to cellular dysfunction such as cell viability loss, cellular senescence, and apoptosis [42; 43; 44; 45; 46]. The epidermis is the first barrier against pathogens, pollutants, toxic chemicals, and UV irradiation--all factors that contribute to oxidative stress [47]. Therefore, the identification of bioactive molecules that protect keratinocytes from oxidative stress can be a promising therapeutic and/or cosmeceutical strategy in preventing oxidative-stress-related skin diseases. Various novel bioactive molecules are produced by plants and used to prepare a variety of medicines [48]. _Iris_ species, members of the Iridaceae family, are valuable traditional medicines with a variety of applications, such as relieving joint pain and cough symptoms [49; 50; 51]. Several studies have shown that _Iris_ extracts have anti-inflammatory, anti-cancer, antioxidant, and antimicrobial activities [52; 53; 54; 55; 56]. In addition, _Iris germanica_ L. contains many secondary metabolites with strong antioxidant potential, including flavonoids and triterpenes [57; 58; 59]. Specifically, methanolic extracts of _Iris germanica_ L. rhizomes possess antioxidant and anti-inflammatory properties due to their high content of isoflavoids, such as tectoridin, iridin, 8-hydroxyirilone, and tectorigenin [60]. There are also bioactive molecules in _Iris germanica_ L., including isoflav such as irigenin, iristectiorigin A, nigericin, irisflorentin, irilone, and irisolidone [59]. Furthermore, previous phytochemical studies have demonstrated that _Iris germanica_ L. includes a variety of chemical compounds [61, 62], including flavonoids, triterpenenes, sterols, phenolic compounds, ceramics, and benzoquinone derivatives. Therefore, it is reasonable to assume that exosomes derived from _Iris_ rhizomes are an optimum source of antiaging agents for action against intracellular ROS. However, there are limited studies on how exosomes derived from the rhizome of _Iris germanica_ L. affect the antioxidant activity of keratinocytes. In this study, we investigate whether _Iris_-exosomes possess antioxidant properties and protect against oxidative stress. H2O2 is a common oxidizing agent that accumulates mostly in the epidermis and can move into and out of cells and tissues [63, 64, 65]; therefore, we used H2O2 in order to induce oxidative stress in keratinocytes.
## 3 Network Dynamic Evolution and Empirical Analysis
### The Model on the WBH
#### 3.1.1 Model Descriptions  
Based on the network partitioning in Section 2, the network for the WBH consist of three annular subregions, and then is built as illustrated in Figure 9. Each subregion has its own MFD, and the average density of each region is \(k_{i}(i=1,2,3)\) (veh/km/lane). The MFD is expressed as Equation (4) [10]:  
\[Q_{i}(k_{i})=\begin{cases}v_{i}k_{i}&0<k_{i}<k_{ic}\\ -w_{i}k+w_{i}k_{b}&k_{ic}<k_{i}<k_{b}\end{cases} \tag{4}\]  
where, \(v_{i}\) is the free-flow speed of subregion \(i\), \(k_{ic}\) (veh/km/lane) is the critical density of subregion \(i\), \(k_{b}\) (veh/km/lane) is the jam density of the network, and \(w_{i}\) is the backward speed of subregion \(i\). Assume that \(f_{ij}\) (veh/h/lane) is the transfer flow from subregion \(i\) (\(i=1,2,3\)) to its neighbor subregion \(j\), \(f_{j,in}\) (veh/h/lane) is the external inflow of subregion \(i\), and \(f_{i,out}\) (veh/h/lane) is the outflow from subregion \(i\). There are linear relationships for the variables:  
\[f_{ij}=P_{ij}Q_{i}(k_{i}) \tag{5a}\] \[f_{i,in}=P_{i}Aq_{ic}\] (5b) \[f_{i,out}=P_{iE}Q_{i}(k_{i}) \tag{5c}\]  
where, \(P_{ij}\) is the transfer ratio from subregion \(i\) to subregion \(j\), \(q_{i}=Q_{i}(k_{i})\) is the average flow of subregion \(i\), \(P_{iA}\) is the external inflow ratio of subregion \(i\), and \(P_{iE}\) is the outflow ratio from subregion \(i\). \(q_{ic}\) is the maximal average flow of subregion \(i\).  
Typically, network state is defined by the average density \(k_{i}\) of each subregion. If a subregion is not completely jammed, network state is determined by the external inflow, outflow, and transfer flow with other subregions. As the annular model has no external inflow and outflow of internal subregion 1 and 2, the inflow and the outflow of subregion 3 are the total. Subregions 1 and 3 do not lie adjacent, so there is no transfer flow between them. That is, \(f_{1,in}=f_{2,in}=0\), \(f_{1,out}=f_{2,out}=0\), \(f_{13}=f_{31}=0\), \(f_{3,in}=f_{in}\), \(f_{3,out}=f_{out}\). The network state evolution process can be described as follows:  
\[\frac{dk_{1}}{dt}=\frac{f_{21}-f_{12}}{L_{1}} \tag{6a}\] \[\frac{dk_{2}}{dt}=\frac{f_{12}+f_{32}-f_{21}-f_{23}}{L_{2}}\] (6b) \[\frac{dk_{3}}{dt}=\frac{f_{3,in}+f_{23}-f_{32}-f_{3,out}}{L_{3}} \tag{6c}\]  
where, \(L_{i}\) is the average travel distance of subregion \(i\).  
Figure 9: The illustration of the model on the weekday before the holiday.  
Substituting Equation (5) into Equation (6):  
\[\frac{dk_{1}}{dt}=\frac{P_{21}Q_{2}(k_{2})-P_{12}Q_{1}(k_{1})}{L_{1}} \tag{7a}\] \[\frac{dk_{2}}{dt}=\frac{P_{12}Q_{1}(k_{1})+P_{32}Q_{3}(k_{3})-(P_{21}+P_{23})Q_{2 }(k_{2})}{L_{2}}\] (7b) \[\frac{dk_{3}}{dt}=\frac{P_{3A}q_{3c}+P_{23}Q_{2}(k_{2})-(P_{32}+P_{3E})Q_{3}(k_{ 3})}{L_{3}} \tag{7c}\]
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## Materials and Methods
### Methods
#### 3.2.4 Classification Features Selection: Peak NDVI and Time-Integrated NDVI  
The growth characteristics (growth conditions, growth period and yield) between irrigated and rainfed wheat are significantly different, which can be reflected by dynamic changes in NDVI time series. First, the peak of NDVI for irrigated wheat is larger than that for rainfed wheat [54,55]. Maximum NDVI for a yearly time series is a proxy for the peak level of photosynthetic activity, maximum biomass and the densest vegetation cover [5]. Second, the time at which rainfed wheat reaches its maximum NDVI is earlier than that for irrigated wheat, because rainfed wheat generally suffers from severer water stress, resulting in accelerated growth. Therefore, peak NDVI and TIN values during the wheat growth period can be used as feature vectors to classify irrigated and rainfed wheat.
## 5 Numerical Simulations
### Simulation Results
#### 5.2.1 Distribution of Distributed Generators and Robustness  
We first discuss the relationship between the distribution of distributed generators and the ADN robustness, where the unintentional random initial failure rate \(\Theta_{P}\) was from 0.002 to 0.7 with interval 0.002 and the value of the tolerance parameters of the nodes is 1.5.  
The relationship between the final expected proportions \(R_{PF}(\infty)\) of the remaining normal operational nodes in the PN and the number of distributed generators (distribution of distributed generators) is shown in Figures 5 and 6. In Figures 5 and 6, each contains four sub-graphs respectively. In the following four sub-graphs, the first one shows 50 different curves and the rest sub-graphs show 101 different curves respectively. Each of the following eight sub-graphs show different curves representing the different unintentional random initial failure rate under the ER and BA topological structures respectively.  
Of the nodes in the PN, 17.76% survived for \(\Theta_{P}\) = 0.002 with one-to-one interdependence under the ER topological structure, when the proportion of distributed generators was 0.2. Of the nodes 45.1% survived for \(\Theta_{P}\) = 0.002 with one-to-one interdependence under the ER topological structure, when the proportion of distributed generators was 0.4. Of the nodes55.17% survived in the PN for \(\Theta_{P}\) = 0.002 with one-to-one interdependence under the ER topological structure, when the proportion of distributed generators was 0.8.  
As shown in Figures 5a and 6a, \(R_{PF}(\infty)\) fluctuated with the proportion of distributed generators due to the uncertainty of the CCF, and they were positively correlated as a whole. Therefore, this agreed with our first conclusion. That is, the robustness of the ADN was improved when the number of distributed generators was greater.  
In the other sub-graphs, \(\Theta_{P}\) is too high, which led to the whole network crashing, thus making \(R_{PF}(\infty)\) almost equal to zero. The relationship between the final expected proportions \(R_{IF}(\infty)\) of the remaining normal operational nodes in the CN and the number of distributed generators was similar.  
Figure 5: The final expected proportion of normal operational nodes in the power network (PN) after the CCF stops (\(R_{PF}(\infty)\)) of different distributed generators proportions in the PN with Erdos–Renyi (ER) topological structure.
# A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms
## 3 Proposed Methodology
### Fingerprint Image Preprocessing
#### 3.2.2 Normalization  
Normalization is a process of adjusting the pixel intensity values range; it is also called contrast stretching. This process is an easy and significant pre-processing step to enhance the quality of the image by eliminating noise from the image. The image normalization consists of changing the intensity of each pixel, hence which means changing the entire image to some of the pre-defined values. Normalization maintains the clarity and contrast of the ridges and valley structure and it is a pixel-wise operation [18]. The normalized image \(N(i,j)\) is defined as follows:  
\[N(i,j)=\left\{\begin{array}{ll}M_{0}+\sqrt{\frac{VAR_{0}(I(i,j)-M)^{2}}{VAR }},&\text{if }I(i,j)>M\\ M_{0}-\sqrt{\frac{VAR_{0}(I(i,j)-M)^{2}}{VAR}},&\text{otherwise}\end{array}\right. \tag{1}\]  
where \(M_{0}\) and \(VAR_{0}\) are the desired (pre-defined) mean and variance values, respectively, which are defined as follows,  
\[M(I)=\frac{1}{N^{2}}\sum_{i=0}^{N-1}\sum_{j=0}^{N-1}I(i,j)\,\ VAR(I)=\frac{1}{N^{2}}\sum_{i=0}^{N-1} \sum_{j=0}^{N-1}(I(i,j)-M(I))^{2} \tag{2}\]  
In this work, we have set \(M_{0}=0\) and \(VAR_{0}=1\), so that new intensities of the pixel for the normalized image would mostly be between \(-1\) and \(1\), making the subsequent calculations easier.  
Figure 4: Histogram-equalized results: (**a**) Histogram of original fingerprint image, (**b**) after the conventional histogram equalization, and (**c**) after CLAHE with clip Llmit.
## 4 Target Model and Frequency Offset Extraction Method
### Unilateral rotation target  
The frequency offset curve of the target in different rotation directions is shown in Figure 5b when the speed of the unilateral rotating target is 2 rps. Except for two special cases where the target rotation center is on the centerline of the receiving and transmitting antenna line and the double base angle is zero, the Doppler frequency deviation curve generated by a uniformly rotating target resembles a sine curve, and its maximum and minimum values are not equal in size. Their size relationship is related to the target area and rotation direction. If the target area is known, the change in the target rotation direction can be determined based on it.  
2. Bilateral rotating target  
The bilateral rotating target model can be established as shown in Figure 6. Set the position relationship as shown in Figure 3 with a speed of 2 rps. When the radius of both sides of the target is 0.3 m, the target frequency offset curve is as shown in Figure 7a. When the bilateral target radii are 0.3 m and 0.15 m, the target frequency offset curve is as shown in Figure 7b.
## 7 Conclusions  
In this paper, we have analyzed theoretically query result merging and proposed a new relational database model view and an efficient query management scheme suitable for underwater sensor networks. We also proposed a message payload format capable of maintaining robustness in the face of unexpected communication failures. Through network simulations, we proved how our query management scheme can efficiently reduce redundant message transmissions and the entailed energy waste, and enhance network lifetime. The management mechanism is expected to display higher efficiency when more queries are generated within a relatively smaller period range.  
Furthermore, our contributions are not restricted to underwater sensor networks. The proposed methodology may also be applied to terrestrial RF-based sensor networks to significantly reduce network traffic and node energy waste. With the upsurge in the demand for sensor networks as ubiquitous data computing systems, our query result merging framework has great potentials for providing a systematized sensor network management methodology to multiple users.  
Figure 17: Sensitivity of the **(a)** Network Lifetime and **(b)** NLR to the period range.  
It is also worth noting that the proposed scheme doesn't require processing capabilities greater than those of a conventional ubiquitous sensor node. The proposed query models are simple and don't store data history, thus consuming only a limited space of memory. The query condition merging process in the QRP is also a succession of reasonably arduous logical processes manageable with microprocessors of regular capacity, so the proposed query management scheme asks for no more than the processing capability of a regular sensor node.  
There are several ways to improve our research results. One significant direction would be moving the focus from underwater sensor networks to RF-based terrestrial sensor networks and assume reliable time synchronization. Researchers have poured great efforts into theoretical studies for time synchronization, but the current technology is too vulnerable to underwater environments for practical time synchronization to happen. But because time synchronization is easy for terrestrial RF-based sensor networks, adjusting our mechanism to time-synchronized sensor networks for terrestrial applications would bring a great enhancement in sensor network query management in general. Also, developing a method for effectively distributing queries or query sets to the target nodes can significantly enhance the performance of our framework. Lastly, increasing our query functionality by adding other relational operations (the \"join\" operation, for example) would also be meaningful.
## 5 Results and Discussion
### Existing Reservoir Flow Patterns  
The reservoir sedimentation is intrinsically dependent upon the flow patterns. One of the major tasks in the experiments is to delineate them at varying flow rates, a prerequisite for laying out the guide vanes. Figure 12 shows the surface water trajectories for the cases.  
In case 1, the relatively uniform flow gradually converges downstream of the bend, forming a narrow, jet like mainstream that runs along the right bank. Driven by this main current, a large zone of counterclockwise circulations is generated to its left, occupying the overwhelming part of the reservoir in cross-section. The zone measures at maximum \(\sim\)90 m in width and \(\sim\)335 m in length. Close to the sluice along the right bank, there are also secondary thin zones of clockwise flow circulations. The flow pattern corresponds to openings 2, 3, and 4 in operation. Different gate combinations lead to slightly different flow patterns immediately upstream of the gates. However, the overall patterns remain.  
With an increase in discharge (case 2), the main flow after the bend takes up a major part of the reservoir width and runs nearly centrally in cross-section. As a result, the large circulation zone that appears in case 1 becomes suppressed. Two zones of comparable sizes are instead formed close to the sluice. The left counterclockwise zone is somewhat larger than the right clockwise one: their length is 170 and 140 m, respectively. In case 3, the flow occupies virtually the whole reservoir width, while two zones of flow circulation remain in the corners, however much smaller.  
Figure 12: Flow patterns upstream of the sluice structure: (**a**) case 1; (**b**) case 2; (**c**) case 3. The flow runs from right to left.  
Obviously, appreciable differences exist among the flow cases, exhibiting the influences from the river bend, reservoir bathym, and flow magnitude. At large flows (e.g., case 3), all the gates operate at large or full openings and the incoming suspended load is readily released through the shuice, with insignificant deposition.  
In a circulation zone, the tangential velocity decreases towards its center and the velocity is generally low, which creates conditions for sediment deposition. Accordingly, prescribed by the frequent occurrences of low and medium inflows (e.g., cases 1 and 2), sediment becomes deposited on the left side of the reservoir. The laser-scanned bathymetry in 2015 (Figure 3) demonstrates a deeper streamwise channel, indicative of deposition to its right. The experimental findings corroborate in a qualitative manner the field deposition pattern.