esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Fungi in Food Production and Processing
## 2 Understanding the Research Prospects
### Factors Influencing Fungal Growth and spoilage in Different Food Matrices  
#### 2.4.1 Intrinsic Factors  
Intrinsic factors refer to the inherent properties of the food matrix that influence fungal growth and spoilage. The composition and availability of nutrients play a crucial role in supporting fungal proliferation, with high-carbohydrate and high-moisture foods serving as ideal substrates for fungal growth. Additionally, the pH levels can impact fungal growth, with acidic environments inhibiting the growth of some fungal species while promoting the growth of acid-tolerant fungi [93]. Food's water content is another critical intrinsic factor, as fungi require water for metabolic activities and enzymatic reactions. Foods with a high moisture content, such as fruits, vegetables, and dairy products, are particularly susceptible to fungal spoilage due to their favorable water activity levels.
# A High-Sensitivity Magnetic Field Sensor Based on PDMS Flexible Resonator
## 2 Materials and Methods
### Study on Magnetic Sensing Mechanism
Therefore, the effect of refractive index change on the magnetic field sensitivity is negligible. A basis is provided for the following discussion of the factors affecting the magnetic field sensitivity of the sandwich structure. The experiments are performed by mixing PDMS prepolymer and curing agent in a mixing ratio of 10:1 in a Petri dish, stirring for 10 min and then vacuuming the PDMS from the Petri dish using a syringe. The mixed PDMS is placed on a slide coated with superhydrophobic material, and then a PDMS flexible resonator is formed on the slide. To maintain the stability of the sandwich structure in the experiment, we use three PDMS flexible resonators to achieve the sandwich structure. Therefore, the sensitivity in the experiment is smaller than the theoretical analysis sensitivity. The process of PDMS flexible resonator preparation is shown in Figure 4.  
The experimental measurement setup for the PDMS flexible resonator sandwich structure is shown in Figure 5. The incident light from the center wavelength 1550 nm tunable laser is passed through an isolator so that the incident light cannot be reflected back into the laser, thus protecting the protection laser. The transmission spectrum of the sandwich structure is obtained by coupling a tapered fiber with a diameter of about 2.1 \(\upmu\)m into and out of the resonator. A polarization controller is used to optimize the coupling strength. The light coming out of the tapered fiber is connected to a photodetector and transmitted to an oscilloscope to obtain the transmission spectrum, and a power meter (FMH-87107) is used for data acquisition and analysis. By attaching the tapered fiber to the resonator surface, the purpose is to ensure a stable coupling state during the whole experiment and to reduce the effect of ambient fluctuation noise. An electromagnetic coil is placed on the top side of the sandwich structure to generate an external magnetic field and calibrate it with a Gauss meter. Figure 3: Simulation of effective refractive index change due to photo-elastic effect.
# Efficient Method for Enhancing Reverse-Time Migration Images Using Vertical Seismic Profiling Data
## 6 Conclusions  
Compared with surface seismic surveys, VSP provides higher precision and less noise. However, it also introduces issues that are not present in surface seismic surveys, primarily owing to the uneven coverage of subsurface structures by the VSP observation system. To address the issue of significant noise in imaging results caused by uneven coverage, this study proposes a new common-receiver imaging method that relocates seismic data to the source location and uses this as the boundary condition for reverse-propagation, effectively  
Figure 19: Final migration results for the Marmousi model using CRRTM.  
enhancing the illumination range of single reverse-time migration imaging.Furthermore, in traditional cross-correlation imaging conditions, both the source and receiver wavefields contain components that do not contribute to imaging, resulting in significant noise in the final image. This noise is exacerbated by the unique characteristics of the VSP system. Therefore, constraining the wavefield using the excitation time helps improve the quality of VSP migration imaging results.  
Common-receiver RTM has a stable workflow and is easy to operate. The numerical experimental results demonstrate that, compared to traditional W-VSP reverse-time migration imaging, common-receiver RTM can effectively eliminate noise artifacts in the imaging results and significantly improve imaging quality.  
Conceptualization, C.L.; methodology, C.L. and Y.L.; software, Y.L.; validation, C.L. and Y.L.; formal analysis, C.L. and Y.L.; investigation, C.L. and Y.L.; resources, C.L.; data curation, C.L. and Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, C.L. and Y.L.; visualization, C.L. and Y.L.; supervision, C.L.; project administration, C.L.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research on Key Technologies of Intelligent Geological Structure Modeling Based on Structural Analysis by the National Natural Science Foundation of China: 41974147.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We are grateful to the editor in chief, the associate editor, and the reviewers for their valuable comments and suggestions that helped to improve the manuscript. We would like to thank [PERSON] for their valuable supervision and administrative support during the project. Their guidance and assistance were greatly appreciated.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} RTM & Reverse-time migration \\ W-VSP & Walk-away vertical seismic profiling \\ CRRTM & Common-receiver reverse-time migration \\ ETC-IC & Excitation-time-constrained cross-correlation imaging condition \\ \end{tabular}
# Planning of Optical Connections in 5G Packet-Optical xHaul Access Network
## 2 Related Works
In the mentioned WDM systems, multiplexers (MUX), demultiplexers (DMUX), and add/drop modules (OADM), utilizing optical thin-film filters (TFF), are used to extract a channel of a specific wavelength. They have better transmission properties than Bragg grids (FBG) and array waveguide gratings (AWG), especially with inter-channel spacing greater than 200 GHz [26].
## 3 Results and Discussion
### Determination of Resveratrol Content
At the higher frequency of 80 kHz (Figure 1c,d), lower resveratrol contents are observed than at a lower frequency of 37 kHz (Figure 1a,b), which is visible both at shorter and longer extraction times for the temperature of 25 \({}^{\circ}\)C.  
As is known from the literature [21], the frequency has a great influence on the cavitation effect. In general, a lower frequency results in a larger bubble radius and stronger mechanical waves and a pronounced cavitation effect, which should result in a more efficient extraction. In our case, this general trend is also visible. An exception is the extract obtained using a methanol/water mixture at 50 \({}^{\circ}\)C and 10 min extraction time at 80 kHz, for which the extraction efficiency of resveratrol is the highest. However, similar cases can be found in the literature (an increase in efficiency with the increase of frequency). [PERSON] et al. [22] observed that when the ultrasound frequency increased from 18 to 54 kHz, the extraction efficiency of anthocyanins also increased significantly. Yet, it is difficult to clearly explain what caused the highest content of resveratrol in the extract  
Figure 1: Effect of temperature on the resveratrol content in extracts from Japanese knotweed (_Polygonum cuspidatum_) obtained by the UASE technique during 10 and 20 min at a frequency of 37 kHz (**a,b**), respectively, and during 10 and 20 min at a frequency of 80 kHz (**c,d**), respectively. The same letters indicate data for which the results are not statistically significant. obtained under the above-mentioned conditions because it is a result of many experimental variables: frequency, temperature, time, and the extraction solvent used. One should not forget about the opposite parallel process of possible degradation of the analyte released from the plant matrix.
## 5 Simulation and Results  
To demonstrate the efficiency of the delta-based trust model, we compare our proposed methodology with the Tree-based Trust Dissemination (TTD) method [31]. This involves a trust-based IDS for IoT networks, centered on establishing trust relations among nodes in a DODAG. Nodes compute trust values based on observed behavior, employing belief, disbelief, and uncertainty variables to assess neighbors' actions. The trust evaluator monitors message forwarding, rank consistency, and version number updates to adjust trust values accordingly. These values are shared and aggregated by a border router or CH using the Subjective Logic consensus operator. Three algorithms are proposed for reputation management, allowing centralized or distributed trust calculation within the network. Overall, the approach aims to detect potential intruders by evaluating and combining trust values, facilitating proactive responses to malicious activities while ensuring secure message routing in the IoT network.  
Additionally, we compared our proposed methodology with the technique \"Mitigation of black hole attacks in 6 LoWPAN RPL-based Wireless sensor network (RWC)\" as proposed in [39]. This method utilizes a two-step verification process to detect and mitigate black hole nodes within RPL-based WSNs. Initially, nodes scrutinize their parent nodes for any suspicious activities, flagging them as potential black holes if they fail to receive data packets within a predefined time interval. Subsequently, neighboring nodes cooperate to validate the integrity of these suspected nodes, taking corrective actions if the verification process indicates a failure. This methodology effectively reduces network overhead and enhances the reliability of black hole detection, even under challenging network conditions.
# Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information
## 2 Related Work
### Polarimetric and Spatial Features of PolSAR Image
#### 2.1.1 Polarimetric Features  
The polarimetric feature vector is constructed by collecting various polarimetric signatures obtained by polarimetric algebra operations (PAO) and polarimetric target decomposition (PTD) methods. Here, PAO refers to operations that compute polarimetric signatures with simple mathematical transforms such as summation, difference, and ratio. The derived polarimetric signatures include backscattering intensities, intensity ratios, phase differences, etc. [21]. PTD refers to methods that compute polarimetric signatures with the tool of matrix decompositions [6]. In past decades, many PTD methods have been proposed. According to the data form they deal with, PTD methods can be generally divided into two classes: coherent target decomposition (CTD) methods and incoherent target decomposition (ICTD) methods [1]. While CTD methods deal with the scattering matrix S, ICTD methods deal with the covariance matrix C or the coherency matrix T. CTD methods include Pauli decomposition [6] and Krogager decomposition [22]. Typical ICTD methods include Cloude-Pottier decomposition [23], Yamaguchi decomposition [24], and Freeman-Durden decomposition [25]. Scattering mechanisms related to the dielectric properties of ground material. Different scattering mechanisms were interpreted by the value of parameters in each PTD method. Different PTD methods try to interpret the PolSAR data from different perspectives. Nevertheless, there is no single PTD method that outperforms the others in all cases when used for applications such as classification. To utilize comprehensive polarimetric information for classification, it is advisable to construct a high-dimensional feature vector by collecting various polarimetric signatures. With proper advanced machine learning methods, the discriminative information contained in the high-dimensional feature vectors can be exploited. In this paper, the employed polarimetric signatures are summarized in Table 1. The selection of those polarimetric signatures is based on a survey of several works that make use of multiple polarimetric signatures [8; 9; 10] and the literature of [1; 2]. Note that we only consider polarimetric signatures extracted from the covariance/coherency matrix because, for multi-looking PolSAR data, the scattering matrix S may not be available.  
Figure 1: The scheme for polarimetric and spatial feature space construction in polarimetric synthetic aperture radar (PolSAR) images. The polarimetric features are concerned with the ground scattering property at a single pixel, while the spatial features are concerned with the relationship between neighboring pixels by using structural elements with different shapes and sizes.
# Reconfigurable Diplexer-Based Filtenna for Tx/Rx Operation in Mobile Satellite Terminals
## 5 Conclusions  
In this paper, a reconfigurable filtenna is presented operating in the two Ka bands of satellite communications. This antenna is suitable to low-cost mobile terminals that communicate with low-orbit constellations. This simple antenna can switch between two distinct frequency bands, in an efficient way, which can save space. Two filters were integrated into the structure to eliminate the undesired frequencies and allow an Tx/Rx isolation higher than 35 dB at the central frequencies, improving the performance. The antenna is highly compact, with an area of \(21.9\times 9.8\) mm\({}^{2}\), and quite efficient with values above 70%. The structure is inexpensive, easy to manufacture, and the bias voltage used to change the PIN diode state is low, which are important benefits for its use in mobile terminals and IoT sensors.  
Conceptualization, L.R., T.V. and J.N.M.; methodology, L.R. and T.V.; software, L.R.; validation, L.R., T.V. and J.N.M.; formal analysis, L.R.; investigation, L.R. and T.V.; resources, T.V. and J.N.M.; data curation, L.R.; writing--original draft preparation, L.R. and T.V.; writing--review and editing, T.V. and J.N.M.; visualization, T.V.; supervision, T.V. and J.N.M.; project administration, J.N.M.; funding acquisition, J.N.M. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the European Regional Development Fund through the Competitiveness and Internationalization Operational Program, Regional Operational Program of Lisbon, Regional Operational Program of the Algarve, in component FEDER, and the Foundation for Science and Technology, Project, RETIOT, POCI-01-0145-FEDER-016432.  
The authors declare no conflict of interest.
## 3 Classical Routing Protocols
### Leach
#### 3.1.2 Disadvantages of LEACH  
1. Due to the random selection of CH, the probability of CH election remains the same for each node, indicating that the probability of a node with high residual and low residual energy remains the same [69]. However, if a low residual energy node gets elected as CH, it will eventually limit the network lifetime to a short span;
2. The number of CHs in each round varies randomly in LEACH. Moreover, the position of CH is not fixed and may be centrally or boundary placed [70]. This will eventually contribute to extra energy consumption in cluster-level communication and degrade network performance;
3. CHs have responsibilities such as data collection, aggregation, and transfer to BS. Hence, it is very likely that the CHs will deplete energy faster than other nodes [71]. Moreover, if a CH fails, all the member nodes connected to it will run out of power, resulting in a broken network;
4. The CHs communicate with the BS in a single-hop mode, making LEACH not preferably used in large-scale wireless sensor networks [72].
###### Abstract
Additional works that have addressed the problem of the optimal siting and sizing of D-STATCOMs with meta-heuristic techniques are listed in Table 1.  
Based on the aforementioned presentation, the main state-of-the-art contributions of this research are as follows: (i) the application of the new version of the vortex search algorithm with and discrete-continuous codification to solve the problem of the optimal siting and sizing of D-STATCOMS in electric distribution networks considering daily active and reactive power demand curves; and (ii) verification of the advantages, such as the possibility of managing the amount of reactive power injection in D-STATCOMs, that the dynamic reactive power compensation in electric distribution systems using D-STATCOMs has for achieving greater reductions in the annual energy losses. It is worth mentioning that to locate and size the D-STATCOMs in the electric distribution network is considered an objective function for the reduction of the annual operative costs of the energy losses. The conductors add to the investment costs associated with the installation of the D-STATCOMS, which is subjected to the classical power balance equations that generate a mixed-integer nonlinear programming problem (MINLP). This optimization model is solved in this research with the help of a master-slave optimization approach in which the master-slave relationship is based on DCVSA, which is entrusted with the optimal location and sizing of the D-STATCOMs. The slave stage (successive approximation power flow (SAPF) method) deals with the determination of the electrical variables to calculate the costs of the energy losses during the daily operation. The remainder of this research has the following structure: Section 2 presents the exact MINLP formulation to present the problem of optimal siting and sizing of D-STATCOMs in electric distribution networks considering the extent of reduction of the annual operational costs caused by energy losses and the cost of additional investment in the dynamic reactive power compensators. Section 3 describes the proposed solution, i.e., master-slave optimization approach based on the hybridization of the DCVSA and the SAPF methods. Section 4 presents the main characteristics of the test feeders that correspond to IEEE 33- and IEEE 69-bus, respectively. Finally, Section 6 presents the conclusions derived from this work and some possible future works.
## 2 System Models
At the receiver, the received discrete time domain signal can be expressed as follows  
\[y(n)\ =\ \sum_{m\ =\ 0}^{L-1}\ h(m)x^{(\overline{n})}((n-m))_{N}+w(n),\ 0\leq n \leq N-1 \tag{7}\]  
where \(\mathbf{y}\ =\ [y(0),y(1),\cdots,y(N-1)]^{\mathrm{T}}\) is the received signal vector, \(((\cdot))_{N}\) denotes a cyclic shift in the base of \(N\), \(\mathbf{h}\ =\ [h(0),h(1),\cdots,h(L-1)]^{\mathrm{T}}\) is the impulse response in the time domain of the wireless channel, \(L\) is the total number of resolvable multi-paths of the wireless channel and \(\mathbf{w}\ =\ [w(0),w(1),\cdots,w(N-1)]^{\mathrm{T}}\) represents the additive white Gaussian noise (AWGN) vector. Then, after performing a fast Fourier transform (FFT) of \(\mathbf{y}\), the received frequency domain data \(\mathbf{Y}\ =\ [Y(0),Y(1),\cdots,Y(n-1)]^{\mathrm{T}}\) can be expressed as  
\[\mathbf{Y}\ =\ \mathbf{F}^{H}\mathbf{y} \tag{8}\]  
Therefore, the element on the \(k\)th subcarrier is  
\[Y(k)\ =\ H(k)P^{(\overline{n})}\ (k)X(k)+W(k),\ 0\leq k\leq N-1 \tag{9}\]  
where \(W(k)\) is the FFT of the noise sequence \(w(n)\) and \(H(k)\) denotes the channel frequency response on the \(k\)th subcarrier, which is given by  
\[H(k)\ =\ \sum_{l\ =\ 0}^{L-1}h(l)e^{-j\frac{2\pi l}{N}} \tag{10}\]  
As we know, the pilot tones can be used to estimate the frequency response of the fading channel [14]. Based on the estimated channel, FDE can be performed on the received data, and then transmitted data can be recovered from the received data.
## 2 Materials and Methods
### Compositional Analysis
#### 2.2.2 Inulin Content  
Inulin content (mg glucose eq./g sample) was calculated by using the equation (Equation (1)) proposed by [PERSON] et al., 2009 [27], based on the previous determination of total carbohydrates and reducing sugars.  
\[\text{Inulin }=\text{Total carbohydrate }-\text{ Reducing sugars} \tag{1}\]  
For the determination of total carbohydrates [28], 100 mL of water with a pH between 6.5 and 8.0 was added to 1 g of sample. The mixture was then stirred at 85 \({}^{\circ}\)C for 1 h, and evaporated water was replaced when necessary. After the extraction, the mixture was left to stand till room temperature and then was vacuum-filtered, obtaining a liquid extract. Then, 1 mL of this extract was introduced into a test tube and 1 mL of a 5% aqueous phenol solution and 5 mL of concentrated sulfuric acid were added, and the mixture was well mixed using a vortex. The homogenized mixture was left stand for 10 min. Then, the test tube was stirred again and introduced into a thermostatic bath at 30 \({}^{\circ}\)C for 20 min. Finally, the carbohydrate content was measured at 490 nm. Results were expressed in mg of glucose/mL.  
For the reducing sugars analysis [29], 3 mL of the liquid extract was introduced into a test tube and 3 mL of 1% DNS reagent (10 g of 3,5-dinitrosalicylic acid, 2 g of phenol, 0.5 g of sodium sulfate and 10 g of sodium hydroxide dissolved in 1 L of water) was added. The test tube was covered and introduced into a thermostatic bath at 90 \({}^{\circ}\)C for 15 min. Then, 1 mL of Rochelle salt (40% aqueous solution) was added and was left to stand until room temperature. Finally, the reducing sugars content was measured by spectrophotometry at 575 nm. Results were expressed in mg of glucose/mL and then converted into mg glucose equivalent/g sample (d.b.).
## 3 Model of the Study
### Meshing  
Geometry was used to model the computational area in ANSYS, followed by ICEM for meshing and setting boundary conditions. The blockage rate, the ratio of the maximum windward area of the building to the cross-sectional area, is an important indicator to determine whether the dimensions of the computational domain can meet the computational needs. The blockage ratio is required to be less than 3% in numerical simulations [17; 18]. Due to the irregularity of the model, the grid was divided into two computational regions, namely inner and outer basins (as shown in Figure 2). The outer basin was 3.6 m long, 1.2 m wide and 1.2 m high, and the inner basin was 0.8 m long, 0.6 m wide and 0.2 m high. The building model was placed inside the inner basin, with the blockage rate of 2%, thus meeting the simulation requirements. The meshing was performed in ICEM software. The unstructured grid was used for the inner basin containing the building due to the irregular structure of the building, and structured grid was used for the outer basin. The grid growth rate was controlled to locally encrypt the grid of the building surface (as shown in Figure 2). In order to obtain more accurate results, the boundary layer of the building model was set. The first grid height of the building model walls was 2 \(\times\) 10\({}^{-4}\) B (B is the minimum size of the building model after scaling), which was in accordance with the minimum grid size used by [PERSON] et al. [19] in their simulation research on buildings. There were a total of  
Figure 1: Aerial view of the building.  
around 4 million meshes in calculation domain under each working condition, with around 2.5 million nodes. The specific settings are shown in Figure 3.