esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Model Formulation and Description
### Model Description
#### 5.2.3 Step Three: Ensuring Connectivity  
In this step, we define the unconnected sensor nodes and the minimum number of sensors needed to achieve full connectivity. We assume that the sink node connects the whole network to the base station. Firstly, we sort X, the set of nodes, in ascending order according to the Euclidean distance of these nodes to point zero. Secondly we calculate the distance of every sensor to all the others, only in one direction, as shown in Table 1, where \(d_{ij}\) is the Euclidean distance between nodes \(x_{i}\), \(x_{j}\), then we seek the minimum distance between nodes line by line.If the minimum distance \(d_{ij}>2\times R_{T}\), new sensor nodes are to be deployed between nodes \(x_{i}\) and \(x_{j}\) and Equation (6) is used to calculate their number.  
Figure 10: Topology with optimized sensor positions.  
Figure 9: Positions of the sensor nodes on the boundary: (**a**) before and (**b**) after applying the gradient method.  
Figure 11 proposes a WSN design that collects a maximum interest and ensures full coverage and full connectivity. The sink node is decided in a way to avoid network congestion.
# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 4 Discussion
### Ascending and Descending Track SAR  
In Section 2.2.4, a mask was applied to remove areas of the classification surfaces that were not expected to contain useful information due to the topography and satellite image acquisition parameters. Application of this mask resulted in exclusion of 23% of the study area. However, exclusion is strongly biased towards those slopes facing away from the sensor and is thus often limited to one side of a valley. Therefore, if landslidering occurrence is assumed independent of slope aspect, it remains possible to obtain a relative estimation of regional landslide density. We do note, however, that care should be taken in areas where vegetation cover varies with slope aspect, as this could introduce aspect-related variations in landslide susceptibility.  
It is also important to note that, since the locations of these masked regions are dependent on the viewing angle of the satellite, they are different for ascending and descending track SAR. We used descending track imagery, which is acquired from a west-facing sensor, so that steep, west-facing slopes were not well imaged and had to be masked. For ascending track imagery, the opposite would be true. Here, ascending track SAR imagery was unavailable; however, it will be advantageous in future studies to combine both ascending and descending track SAR imagery in order to maximise the area that can be mapped using SAR.
## 2 Materials and Methods
### Preparation of the Data  
#### 2.2.1 Amplification Factor  
To estimate the surface amplification of the soil response with respect to the bedrock response, frequency-dependent seismic amplification factors were used to compute the soil effect of this valley surface. The standard spectral ratio [35] was used to calculate the amplification factors of the site location. This is defined as the spectral ratio of a sedimentary site with respect to a nearby bedrock reference site. A high-pass filtering program (JMTEST) method was used to smooth selected acceleration time histories, and then a fast Fourier transform (FFT) computer program was employed to obtain Fourier spectra of the acceleration time history at each soil station and bedrock station. For each earthquake, the spectral ratio of the Fourier spectra at each soil station to the spectrum at the bedrock station was used to estimate the frequency-dependent amplification factors, AFF (amplification Fourier factors) [5], from Equation (2):  
\[AFF=\frac{F_{SH}^{surface}}{F_{SH}^{bedrock}} \tag{2}\]  
where \(F_{SH}\) is the Fourier amplitude spectrum in the \(SH\) (S-wave horizon) direction at the soil surface or bedrock.  
Then, for each group of acceleration time histories at soil stations, a mean frequency-dependent amplification factor was computed from the average AFF at a given frequency value [36], using Equation (3):  
\[\overline{AFF}=\frac{\sum\limits_{i=1}^{n}AFF_{i}}{\ln} \tag{3}\]  
Figure 3: Seismic records from the Lower Hutt basin in New Zealand.
## 7 Numerical Analysis
### Coordinated Frequency Regulation by SG and VSC-HVDC  
With different governor delays, the initialization of \(H_{\rm DC}\) based on estimation of the maximum frequency deviation is given in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{T}\) **(s)** & **0** & **0.2** & **0.4** & **0.6** & **0.8** & **1** \\ \hline \(\Delta\!f_{\rm max}\) (p.u.) & –0.0042 & –0.0059 & –0.0075 & –0.0083 & –0.009 & –0.01 \\ \(H_{\rm DC}\) (s) & 3.10 & 2.47 & 1.95 & 1.76 & 1.62 & 1.46 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initialization of \(H_{\rm DC}\) considering governor delay.  
Figure 6: Optimization to \(H_{\rm DC}\) using improved DPF model.  
With \(H_{\text{DC}}\) fixed at the initialization value, the impact of virtual inertia control by VSC-HVDC on the frequency is shown in Figure 7.  
If the governor delay of the SG is ignored, the system frequency drops without oscillation. The nadir is the steady state value. When considering the governor delay, the SG cannot provide full support at the early stage, e.g., 0-1 s. With the virtual inertia control of the VSC-HVDC, the DC voltage is temporarily reduced to slow down the frequency drop (see Figure 7a). After activation of the primary control, the SGs' output is adjusted according to the static frequency characteristics. The system frequency reaches the steady-state value, but the nadir occurs at the early stage of the regulation, and is lower than that without the delay. It is also found that the larger delay yields a slower process to reach the steady state value.  
Figure 7: Impact of virtual inertia control by VSC-HVDC on frequency regulation: (**a**) System frequency; (**b**) DC voltage.  
The DC voltage deviation is controlled to be within the security constraint at the frequency nadir (see Figure 7b). The conservative setting to \(H_{\text{DC}}\) guarantees the security of the DC capacitor with maximum frequency deviation.
# Biodegradable and Biocompatible Systems Based on Hydroxapatite Nanoparticles
## 4 Functional HAp Biocomposites through Modification/Encapsulation Techniques
### Antimicrobial Scaffolds Based on HAp
The use of luminescent silica nanoparticles has been extensively studied taking profit from the aggregation of dye molecules in their core. Efficient encapsulation of AIE dyes in HAp nanoparticles has also been demonstrated. Thus, mesoporous HAp containing (4,4\({}^{\prime}\)-(1,2-diphenylethene-1,2-diyl) bis(4,1-phenylene))bis(methylene)diphosphonic acid was synthesized by a one-pot condensation process (tetraphenylethene (TPE) being the AIE-active center). Active molecules were incorporated in the three-dimensional network HAp structure through P-O-Ca covalent bonds and gave rise to strong fluorescence property and good biocompatibility [98]. Cell viabilities of extract solutions of these luminescent nanomaterials incubated with human hepatoma cells for 24 h were above 95% even when the concentration of the extract solution was about 40 mg\(\cdot\)mL\({}^{-1}\). Magnetic based nanoparticles have a great interest in medicine, for example in the detections of tumors via noninvasive magnetic resonance imaging [99]. Contrast agents are based on paramagnetic, super-paramagnetic and ferromagnetic materials able to change the image contrast between normal and diseased tissues [100]. The technique allows tomographic observation of structures with high resolution and soft tissue contrast. Nanosized contrast agents have great advantages derived to their high surface area and the capability to be delivered to a specific tumor by appropriate targeting and control over the blood circulation half-life [101; 102]. Super-paramagnetic HAp has wide application in biomedicine that covers the enhancement of osteoblast functions [103], thermal treatment (hyperthermia) [104] and more recently for magnetically assisted drug delivery. In this case, super-paramagnetic iron oxide nanoparticles (SPIONs) were coated with mesoporous HAp. These nanocomposites were appropriated for loading anticancer drugs (e.g., doxorubicin, DO) (Figure 7) and for achieving targeted drug delivery through the hyperthermic effect provided by SPIONs [105]. Figure 6: Surface modification of oleic acid coated fluoridated hydroxyapatite (FAp) with amphiphilic molecules via hydrophobic interactions and cell imaging application. Adapted from [95], Royal Society of Chemistry, 2013.
## 5 Conclusions  
Our findings suggest that Wilac D001 inhibits _C. albicans_ growth by disrupting biofilm formation. Additionally, Wilac D001 suppresses the _C. albicans_-mediated upregulation of pro\(-\)inflammatory cytokines, including IL\(-\)6 and IL\(-\)8, in VECs. The antifungal effect of Wilac D001 is comparable to that of _L. reuteri_ and _L. rhamnus_. Therefore, Wilac D001 has an antifungal effect on VECs infected by _C. albicans_, especially by regulating pro\(-\)inflammatory cytokines. To investigate its efficacy as a health supplement, further in vivo studies will be needed.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14072676/s1](https://www.mdpi.com/article/10.3390/app14072676/s1)]([https://www.mdpi.com/article/10.3390/app14072676/s1](https://www.mdpi.com/article/10.3390/app14072676/s1)), Figure S1: Gram staining of VECs adherent Wilac D001 under microscopy; Table S1: Culture conditions of microorganism.  
Conceptualization, G.L. and Y.-A.Y.; Methodology, A.A.; Software, S.P.; Validation, S.M.P. and A.A.; Formal Analysis, S.-M.K.; Investigation, G.L. and Y.-A.Y.; Resources, S.P., Y.M.H. and S.M.P.; Data Curation, Y.M.H. and S.P.; Writing--Original Draft Preparation, G.L.; Writing--Review & Editing, Y.-A.Y., G.L. and Y.-Y.G.; Visualization, A.A.; Supervision, Y.J.K.; Project Administration, Y.J.K.; Funding Acquisition, Y.J.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic Science Research Program through the National Research Foundation of Korea (NRF-2020R11A1A01071955, NRF-2020R11A1A01074518), BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea), and the National Research Foundation of Korea (NRF-5199990614253, Education Research Center for 4 IR-Based Health Care). This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2023-00266554).  
Not applicable.  
Data Availability Statement: Data is contained within the article or Supplementary Material.  
Conflicts of Interest: Author [PERSON] was employed by the company Pharmsville Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A Digital Denoising Method Based on Data Frequency Statistical Filtering
## 3 Experiment
### Filtering Effect and Comparison  
#### 3.2.1 Filtering Methods and Evaluation Criteria  
In this experiment, five kinds of filters were used to filter the above random value noise of different density, including the mean filter, median filter, switching median filter, adaptive median filter, and the filtering method in this paper. Moreover, the filtering effects of various filtering methods were evaluated and compared, among which the objective evaluation criteria were peak signal-to-noise ratio (_PSNR_) and structural similarity (_SSIM_) [27].  
The _PSNR_ is calculated using the following formula:  
\[PSNR=10\lg(\frac{255^{2}}{\frac{1}{MN}\sum_{x=1}^{M}\sum_{g=1}^{N}\left(f(x,y )-f(x,y)\right)^{2}}) \tag{2}\]  
where \(M\) and \(N\) are the length and width of the image, respectively. \(f(x,y)\) is the gray value of the original image and \(f^{\prime}(x,y)\) is the gray value of the filtered image. A larger PSNR value indicates a better filtering effect.  
Structural similarity (_SSIM_) is evaluated according to the degree of similarity in local area content between images, which is closer to the subjective evaluation standard. Its calculation formula is as follows:  
\[SSIM(i,j)=\frac{\left(2u_{i}u_{j}+c_{1}\right)\left(2\sigma_{i}\sigma_{j}+c_{2 }\right)}{\left(u_{i}^{2}+u_{j}^{2}+c_{1}\right)\left(\sigma_{i}^{2}+\sigma_{ j}+c_{2}\right)} \tag{3}\]  
In the above formula:  
\[c_{1}=\left(K_{1}L\right)^{2}\ \ \ c_{2}=\left(K_{2}L\right)^{2} \tag{4}\]  
where \(i\) and \(j\) respectively represent the image before and after filtering. \(u_{i}\) and \(u_{j}\) are the average gray value of the image before and after filtering, respectively. \(\sigma_{i}\) and \(\sigma_{j}\) are the variances of the gray value of the image before and after filtering, respectively. \(K_{1}\) and \(K_{2}\) are constants much less than 1, which are used to avoid instability when the denominator approaches zero.
## 3 Results
### Classification Contribution  
Table 6 illustrates the ranking of classification contributions from the feature combinations that yielded the best classification results for each region. Due to the extensive number of features, only the top fifteen classification contributions are provided. The table highlights the significant contribution of SWIR to classification accuracy, with multiple SWIR features included (up to 56 features when considering all classified features). This finding aligns with previous observations regarding the role of SWIR features in enhancing accuracy. Notably, SAR features are scarcely represented in the table, except for Jinxiang and Pizhou, indicating limitations in using SAR features for crop species determination.  
Furthermore, the table incorporates various vegetation indices, suggesting their applicability for garlic extraction across different regions. Among these, the EVI and MSAVI are more frequently observed, indicating their broader suitability for classification. The presence of different vegetation indices in distinct regions underscores the effectiveness of DITS in enhancing garlic extraction accuracy.
# Iterative QR-Based SFSIC Detection and Decoder Algorithm for a Reed-Muller Space-Time Turbo System
## 3 The Novel Iterative QR-Based SFSIC Detection and Decoder Algorithm
### RM-Turbo Iterative Decoder (Outer Decoder)  
The extrinsic information obtained from the inner decoder after de-interleaving is inputted into the outer decoder, which is made up of a pair of component decoders, i.e., the horizontal decoder and the vertical decoder, which are separated by two transposes. The configuration of the outer decoder, characterized by the feedback structure and the a posteriori probability (APP) LLR of the extrinsic information exchange among different component minimal trellis-based MAP decoders in an iterative way, is depicted in Figure 2. The working principle of the outer decoder can be stated as shown below.
## 2 Materials and Methods
### AE Sensors Calibration  
To verify the accuracy of AE acquisition, a pencil-lead-breaking experiment was conducted prior to the milling process. After the sensors were connected, calibration was performed, including the so-called \"in situ\" verification using a pen-test (ink breaking), which generated a sharp AE pulse with all frequencies equally represented up to about 10 MHz. The installation of both sensors complied with the requirements of EN 14584 [47], which specifies that the deviation of the mean amplitude value of the six signals on each sensor must be within \(\pm\)3 dB from the mean value of both sensors. An example of the signal response to a pen-test between sensors located 250 mm apart is shown in Figure 3. It is evident that the breakage of stiffness near one transducer was also reflected in the other transducer, with an approximately tenfold drop in amplitude, corresponding to a 20 dB decrease.  
Figure 3: Typical AE sensor signal response to pen-test: (**1**)—source at sensor S1 (green line), (**2**)—source at sensor S2 (blue line).  
\begin{table}
\begin{tabular}{l c} \hline
**Parameter** & **Value** \\ \hline D (mm) & 20 \\ \hline h (mm) & 92 \\ \hline H (mm) & 170 \\ \hline A (mm) & 20 \\ \hline Number of cutting edges & 3 \\ \hline Maximal revolution (rev.min\({}^{-1}\)) & 25,000 \\ \hline \end{tabular}
\end{table}
Table 3: Parameters of cutting tool.
## 3 Networks and Methods
### Genetic Algorithm Time-Serialization Convolutional Neural Network (GATSCNN)  
#### 3.3.1 Genetic Algorithm  
The GA is a type of swarm intelligence optimization algorithm that draws inspiration from natural selection and genetic mechanisms in biological evolution. By utilizing genetic operations such as selection, crossover, and mutation among individuals in a population, the GA gradually optimizes the fittest solutions to find the global optimum solution of the problem. Unlike other algorithms, GA does not require complete information about the global search space, only requiring the fitness measure of the models to select the elites. Constantly crossing and mutating these elites help optimize genes. GA displays strong parallelism and search optimization ability, making it suitable for solving complex large-scale optimization problems.  
The main optimization techniques used for GA include selection, crossover, and mutation. Selection, also known as elimination, chooses individuals with high fitness for next-generation breeding, thereby retaining better solutions. Crossover involves combining two excellent individuals to produce new offspring. Mutations typically indicate random changes in individual genes to generate new variations.
# Optimal Modeling for Dynamic Response of Energy Storage Systems
## 3 Introduction to Key Parameter Identification and Optimization Algorithm  
### Key Parameter Identification  
The mathematical model of more complex systems has more parameters [7; 8; 9; 10], and more parameters will result in a larger computing burden for the model in subsequent applications such as in simulation and optimization operations. In order to improve the excessively large amount of calculation required, sensitivity analysis can be adopted to quantify the influence of parameters on the system model response [11; 12; 13].  
In [14], the authors used trajectory sensitivity analysis to classify the influence of model parameters of the first-order and second-order on the position control system of a highly integrated valve-controlled cylinder, while the authors used trajectory sensitivity analysis to evaluate the influence of model parameters of a synchronous generator [15]. In [16], the authors mentioned that one-factor-at-a-time (OAT) could be to assess the influence of model parameters. The authors used OAT to find the key parameters of a wind power generator, and built an equivalent dynamic model of it. This key parameter identification method can be used under the condition that the problem is too complex to build the mathematical functions [17].  
This study used a common key parameter analysis method, OAT, which changes only one system parameter at a time under the same input condition, and judges the influence of the parameter by observing the system output response variation. The method is easy to implement, but when the parameters are correlated with each other, this dependence cannot be described. The OAT flowchart is shown in Figure 10.
# Ambiguity-Resolved Model Tests for Carrier-Phase GNSS
## 3 The AF and AK Tests  
To obtain insight on the expected performance of the MIM test, we first consider two of its limiting cases. First, we consider the case when the integerness of the ambiguities is not taken into account, i.e., the ambiguities are treated as real-valued parameters. This is referred to as the ambiguity-float (AF) test. Then, we consider the case when the integer ambiguities are assumed to be completely known. This is referred to as the ambiguity-known (AK) test. Both tests are LR tests and have the property of being uniformly most powerful invariant [2]. We start with the AF test.
# An Efficient Relayed Broadcasting Based on the Duplication Estimation Model for IoT Applications
## 2 Statement of the Problem
### Motivation and Challenges
Furthermore, the frame of a source node cannot be successfully delivered to the 1-hop nodes due to interference by the 2-hop nodes hidden from the source node, e.g., while the source node \(s\) is transmitting a frame, the 2-hop node \(b_{1}\) that is unaware of the transmission of the source node may start to transmit another frame, then node \(g_{3}\) probably cannot decode the frame of the source node correctly. Interference may also occur between the transmissions of a 1-hop and 2-hop node as well as between those of the source node and a 2-hop node. For example, if nodes \(g_{4}\) and \(b_{1}\) are hidden from each other, the transmission of \(g_{4}\) may be subjected to interference from that of \(b_{1}\), resulting in the failure of the frame delivery from \(g_{4}\) to \(b_{2}\). It is clear that the probability of a collision or interference increases as the number of nodes (or the density of nodes) increases. In the case of unicast in WLANs, such collisions and/or interference can be mitigated by adjusting the size of a contention window like binary exponential backoff (BEB) or by employing a handshaking mechanism like request-to-send and clear-to-send (RTS/CTS) exchange. However, in the case of broadcasting, these mechanisms cannot be applied because an acknowledgment (ACK) frame usually does not exist and the receiver is not unique. Besides, due to the absence of an ACK frame, each node (source or relay) cannot recognize whether or not a frame has been successfully delivered to a particular adjacent node. Because of these reasons, it is challenging to strictly ensure the reliability of flooding. We state the problem of flooding that should be handled in our study as follows:  
* Each node should determine whether or not to relay a frame by only overhearing the transmission of adjacent nodes. Thus, a specific criterion for this decision should be established. * The reliability of traffic dissemination should be provided at an acceptable level without incurring severe duplicate transmissions or increasing the dissemination time significantly. * As well as collisions, the interference from hidden nodes should be taken into account, which is another main cause of reliability degradation but has not been adequately considered in most existing studies.