esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Statement  
In fixed/flex-grid optical networks, as the fixed-grid nodes own different upgrade necessities, in the process of data transmission, the fixed-grid nodes may be upgraded to flex-grid nodes which we called migration. When such migration happens, the traffic load going through the nodes which are selected to be upgraded in network will be affected, even if be interrupted. Since the fiber capacity is considerable, the interruption of the data transmission will lead to significant data loss. Consequently, migration-aware dynamic connection provisioning occurring before network migration has become a new challenge. The following inputs are given to the algorithm. A physical topology \(G(V,E,P)\) denotes the network, which consists of a set of optical nodes \(V(V_{1},V_{2},\cdots,V_{n})\), including \(V_{FI}\) and \(V_{FL}\), represent the set of fixed-grid nodes and flex-grid nodes and optical links link\((i,j)\) corresponds to the physical link from the \(i_{th}\) to the \(j_{th}\) node. \(P(P_{1},P_{2},\cdots,P_{n})\) represents the set of upgrade probabilities that fixed-grid nodes being upgraded to flex-grid nodes. \(R(S,D,B)\) represents a set of service connection requests from the source \(s\) node to the destination node \(d\). \(B\) stands for the request bandwidth requirements, including the guard bands.  
There is a fixed/flex-grid optical network, including \(V_{FI}\) and \(V_{FL}\) as illustrated in Figure 2. The probabilities \(P\) of a node upgrade from fixed-grid to flex-grid are set as shown in the table. By adjusting the weight coefficient between dis and P, different routing calculation results can be obtained for the service connection request from node 3 to node 12. If distance is the only consideration, the route 3-7-9-12 can be obtained (which is actually the shortest path routing). However, this route does not consider P of node 7 to be as high as 90%. Later, when network migration occurs, this connection will be interrupted. If the same weight is given to the distance and node upgrade probability, we can obtain another route as 3-2-6-11-12, which avoids connection interruption caused by upgrading node 7. However, if an additional node (i.e., node 6) is scheduled to upgrade, service provisioning will also be interrupted. If more weight is given to node upgrade probability, path 3-4-5-8-10-13-12 is obtained, which can effectively avoid service-provision interruption during the process of network migration.
# Security Awareness in Software-Defined Multi-Domain 5G Networks
## 6 Characteristics of Application-Specific Data in the Mobile Network Testbed  
We illustrated and studied how application isolation affects the characteristics of data flows in mobile networks by building two experimental arrangements with our mobile network testbed. Essentially, we studied the characteristics in a single application scenario--with homogeneous traffic flows--and then compared it to a multi-application scenario--with more heterogeneous flows. In the first scenario, we monitored a mobile network running an IoT application. In the second scenario, we analysed the mobile network supporting both video streaming and IoT applications.
## 3 Model Validation and Results Analysis
### Small Spheroidal Particle Case  
In this simulation, the incident light wavelength is taken as \(\lambda=0.5\)\(\upmu\)m (the typical wavelength in the visible band), the refractive index is set as \(m=1.60-0.008\)i (the typical value of mineral dust aerosol), the half-length of the horizontal and rotational axis are taken as \(a=1.0\)\(\upmu\)m and \(b=0.5\)\(\upmu\)m, and the scattering phase matrix of the spheroid is simulated by the IIM T-Matrix method and the EBCM T-Matrix method, respectively. The results are shown in Figure 4.  
As shown in the figure, the results obtained by the two models are in good agreement, which verifies the simulation accuracy of the IIM T-Matrix model for the non-spherical particles with small size. For the phase function F\({}_{11}\), the relative errors are less than 5% in forward scattering directions. Though the calculation errors are relatively large near 180\({}^{\circ}\), they are still smaller than 12%. For F\({}_{12}\)/F\({}_{11}\), the calculation errors of the IIM T-Matrix model are all within 0.1, while the simulation errors of F\({}_{34}\)/F\({}_{11}\) and F\({}_{44}\)/F\({}_{11}\) are less than 0.1 in most scattering angles. From the spatial distribution of simulation errors, it can be found that the simulation accuracy of the IIM T-Matrix model in the forward scattering direction is obviously higher than that in large scattering angles. The reason is that, in the IIM T-Matrix model, the particle is discretized in a spherical coordinate system, and similar to PSTD and MRTD, there are also stepped approximation errors in shape construction. Further, because the scattering phase matrix is more sensitive to the particle's shape in backscattering directions, larger simulation errors will be caused in large scattering angles correspondingly.  
Figure 3: A schematic diagram of the implementation of our invariant imbedded (IIM) T-Matrix code.  
Figure 4: The scattering phase matrix obtained by the IIM and Extended Boundary Condition Method (EBCM) T-Matrix methods for small spheroid particles. The simulation errors of F\({}_{11}\) are denoted by relative errors. For F\({}_{12}\), F\({}_{34}\), and F\({}_{44}\), they are firstly normalized by F\({}_{11}\), and their simulation errors are evaluated by the absolute errors.  
To further validate the simulation accuracy of the IIM T-Matrix method, the integral scattering parameters (including extinction cross-section C\({}_{\text{ext}}\), absorption cross-section C\({}_{\text{abs}}\), scattering cross-section C\({}_{\text{sca}}\), and single scattering albedo \(\omega\)) are also compared with those obtained by EBCM, the results are shown in Table 1. It can be found that the relative simulation errors of the integral scattering parameters are generally smaller than 1%, indicating that the integral scattering parameters can be calculated by the IIM T-Matrix method with a high accuracy.
# A Review on Carbon Source and Sink in Arabic Land Ecosystems
## 4 Main Factors Affecting the Dynamics of Carbon Sources and Sinks in Arable Land Ecosystems
### Impact of Human Management Measures on Arable Land Carbon  
#### 4.2.1 Tillage Measures  
In arable land ecosystems, it is generally believed that agricultural farming strategies have more far-reaching impacts on SOC than natural factors [85; 92]. The decrease of SOC content in arable land systems was mainly caused by cultivation. Tillage changes the quality and quantity of carbon input in soil and the physical properties of soil that affect carbon decomposition [19]. In the process of cultivation, the topsoil environment is often changing. It was generally believed that the loss of SOC mainly occurs in the 0-30 cm soil layer [93; 94]. Tillage methods could also significantly affect SOC content [95]. Traditional farming methods, such as fallow in bare land, burning or removal of crop residues and inverted farming, have promoted the loss of SOM [20; 96]. By reducing soil tillage and adopting conservation tillage measures, such as retaining crop residues in arable land, SOC can be fixed [2; 13; 19].  
No tillage and less tillage have significant effects on the increase of SOC stocks and the change of microbial biomass carbon [19; 31]. [PERSON] and [PERSON] [43] estimated that with the conversion from traditional tillage to no tillage, the global SOC retention rate was 57 \(\pm\) 14 g cm\({}^{-2}\) yr\({}^{-1}\). [PERSON] and [PERSON] [2] compared the carbon footprint of corn under traditional tillage and no tillage management, showed that no tillage system reduced greenhouse gas emissions and contributed to carbon sequestration in the soil at the depth of 0-30 cm. [PERSON] et al. [97] also noted that SOC of the topsoil (0-30 cm) increased significantly under no tillage system compared with conventional tillage. In addition, increasing the complexity of crop rotation and straw return could also increase SOC and reduce greenhouse gas emissions [98]. In monoculture system, crops uptake less than half the amount of nitrogen fertilizers normally, through crop rotation, other types of crops could absorb nitrogen during the fallow period of bare land, also straw might lead to the richness and diversity of plant litter and increase the acquisition of carbon [58]. Because different natural factors shape the background of different arable land ecosystems, the spatial variability affecting site characteristics must be considered in the land use planning and implementation of strengthening carbon sequestration, so as to scientifically and effectively increase the carbon sink capacity of arable land ecosystems. Table 1 lists the potential amounts of carbon sequestration under different farming practices.
## 5 Conclusions  
To detect the voluntary MC signal, we propose the VNVMC signal classification algorithm based on the LSTM. For this, we first collect the IRS DB for various ES frequencies, and we then extract the feature vectors, such as PoSCS and SCSyV. Since the distribution of features appears differently for each class, it is possible to classify the VNVMC signal using these features. Since changes in the frequency components are observed with voluntary MC, the extracted feature can effectively classify the VNVMC signal. In addition, because the major classification features for the appear differently, in accordance with the muscle condition, we train the classification model using the feature vector. To improve performance, the proposed model is trained by the LSTM scheme, and its performance is better than that of the conventional methods such as SVM, ANN, and DNN. Since the LSTM model is suitable for sequential data, such as IRS, the LSTM model shows the best performance when compared with conventional methods.  
Conceptualization, K.S. and S.C.; methodology, K.S.; software, K.S.; validation, K.S.; torsional analysis, K.S.; investigation, K.S., S.C. and H.L.; resources, H.L.; data curation, K.S.; writing--original draft preparation, K.S.; writing--review and editing, S.C.; visualization, K.S.; supervision, H.L.; project administration, S.C.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by the Technology development Program(S2884099) funded by the Ministry of SMEs and Startups(MSS, Korea).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 6 Discussion
### Speed of Nodes  
We also evaluated the PDR performance of AODV, DSDV, and OLSR by changing the speed of nodes. In the experiment, we simulated AODV, DSDV, and OLSR using the SRWP mobility model, which showed the best PDR performance in Section 5.3. When it comes to the number of nodes, we set up 15 nodes which showed the best PDR performance in Section 5.3 overall. Figure 8 shows the experimental results. In our experiment, we find no significant changes in the PDR performance of AODV, DSDV, and OLSR according to the change in the node speed. Our results contradict the findings in the previous studies that reported the speed of nodes influenced the PDR performance changes [8].  
Figure 8: Packet delivery ratio per speed.  
Figure 7: Average end-to-end delay of routing protocols.
# P System with Fractional Reduction
## 5 Experimental Results
### Parameter Experiments
#### 5.2.1 Four-Digit Experimental Result  
For forty randomly selected sets of data, each set of data will have six parameters substituted for the experiment, and the experimental results will be tabulated for statistical analysis. Because the number of steps may vary greatly when different parameters are used for experiments, in order to make the broken line of parameter \(\xi\), with the least number of steps more obvious, all the following line charts may be incomplete due to the long number of steps. This phenomenon is normal and not statistically incomplete.  
According to the discount Figure 14 made based on the experimental results, it can be seen that out of the 40 randomly selected sets of data, 39 sets of data required the least number of steps for the experiment when the parameter \(\xi\) = 8, and only one set of data had the least number of steps when \(\xi\) = 100, while the experimental steps for each set of data reached the highest value when \(\xi\) = 1000. Therefore, a preliminary conclusion is drawn that in the reduction process of fractions with a maximum of four digits, it is more appropriate to use the combination of the more phase derogation algorithm and the division algorithm with parameter \(\xi\) = 8.
## 5 Application and Results  
The application of the framework to the case study of the port of Augusta is based on the following phases:  
* Definition of three future scenarios for the container terminal;
* Results of the optimization model for the three scenarios;
* Simplified environmental impacts evaluation, focusing on CO\({}_{2}\) emissions and noise pollution;
* Final DST framework for the Port of Augusta.
## 2 Materials and Methods  
### Plasma Source Configuration  
The cold atmospheric pressure plasma jet (CAP) is generated using a 100 mm long quartz tube (inner diameter 4 mm, outer diameter 6.1 mm), having two copper tape electrodes (10 mm width, a power (HV) and a grounded (G1) electrode) wrapped on the external surface, with 10 mm gap, and 6 mm till the tube exit nozzle. Depending on the intended measurement, either a metallic (copper plate) or dielectric (alumina covered probe, quartz glass or magnesium fluoride MF\({}_{2}\) window) surface act as the second electrode (G2) placed at distance d and connected to the ground (Figure 1).  
Two types of working gas were supplied through the discharge tube: pure helium (He 4.6) or a pure argon (Ar 4.8). The constant gas flow rate of 2 slm (ensuring a laminar flow regime) was regulated through a mass flow controller (MKS Multi Gas Controller 647C or Bronkhorst E-8412 with a F-201 CV). In this way, an atmospheric pressure helium or argon plasma jet is generated and operates freely in air. For the comparative nature of the paper, the following abbreviations will be used: He-CAP for the helium jet and Ar-CAP for the argon jet. Long exposure photos were captured using a Nikon D80 camera (CCD: 23.6 \(\times\) 15.8 mm, 10.2 Mpc, ISO: 100-3200, shutter speed: 30-1/4000 s, 3 fps) equipped with Tamron SP 180 mm f/3.5 Di LD Macro lenses.  
The discharge was driven by a high voltage fly-back transformer (SAXAR MC-FB-001) fed by an amplifier (Amplifier T&C Power Conversion, Inc. AG Amplifier, NY, USA) which was controlled by a function generator (Tektronix AFG 3101). The applied sinusoidal voltage U\({}_{a}\) (up to 20 kVpp, frequency 18 kHz) and the total current of the discharge I\({}_{d}\) were monitored using voltage and current probes (Tektronix 6015A and CT2, Pearson 6585 current monitor) and a 4-channel digital oscilloscope (Tektronix TDS 5104--1 GHz with 5 Gs/s, and Lecroy Waverunner 640 Zi--4 GHz with 40 Gs/s).
# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 3 Results and Discussion
### Evaluation Metrics
#### 3.2.7 Influence of Noise on Model Performance  
To further evaluate the performance of the proposed method, AMMFNet was tested using a validation set with noise added to test the recognition ability of the model in the actual situation when the image quality is impaired.  
Specifically, Gaussian noise and salt and pepper noise are added to the RGB and multispectral images in the verification set. Gaussian noise is a noise whose probability density function obeys normal distribution, which can simulate the random interference that image sensors may receive. Salt and pepper noise is formed by black and white dots randomly appearing on the image and is often used to simulate possible errors during data transmission. The standard deviation of Gaussian noise is set to 0.3, and the percentage of salt and pepper noise is set to 0.1. The experimental results are shown in Table 13.  
Comparing the performance metrics on the original validation set (Table 8) and the noisy validation set (Table 13), although the addition of noise has had some impact on the model's performance, the changes in the various evaluation metrics of the model are relatively small. Especially, it still maintains a high accuracy rate. Therefore, the proposed model has good stability and robustness.
## 2 Materials and Methods  
### Preparation of Beef Samples and Packaging  
The research material included culinary beef (top round-m. _semimembranosus_). This cut has not been studied but is often offered commercially as culinary beef. The muscles were obtained in three experimental series directly from three different production batches in a meat processing plant located in central Poland (Daleszyce, Poland). The beef produced in meat processing plants is obtained mainly from Holstein-Friesian cattle. In each of the three experimental series, 10 _semimembranosus_ muscles (considered meat with good quality, based on pH measurement (5.6-5.8)) weighing approximately 1.5 kg were used. The meat was prepared in accordance with the procedures of the meat processing plant as culinary meat intended for trade. The muscles were cut into 150 g slices. Randomly selected slices were placed on rigid trays (187 \(\times\) 137 \(\times\) 36 mm) made of PET. Beef was vacuum skin-packed by closing the package with the Multifresh\({}^{\text{TM}}\) top film (Multivac, Natalin, Poland) using a Multivac T250 tray-sealer (Multivac, Natalin, Poland) packaging machine. For each experimental series, 36 packages with meat were prepared, and one quarter were control samples (not treated by HPP), while the remaining part was HPP-treated at 300, 400, or 500 MPa (HPP300, HPP400, or HPP500, respectively).
## 1 Introduction
This brief literature review demonstrates that noise results for small-scale and larger-scale landing gear models are typically determined for the models, which have different geometries (mostly with respect to fine details). Indeed, large-scale tests are expensive and are typically performed in the interests of the industry; therefore, they tend to use the most realistic models of landing gears or even real landing gears. At the same time, small-scale models do not exactly reproduce some details of the complex large-scale landing gears, thus simplifying their original geometry. However, this poses the question of whether the differences in acoustic results are due to the differences in geometries or due to other reasons such as the Reynolds number effect, different scaling laws, or experimental errors. Therefore, it is important to isolate one of the effects to study its influence on noise spectra. This paper aims at isolating the effect of landing gear geometry on its noise by considering full-scale and small-scale landing gear models with identical simplified geometry that differ only in their scale. The tests of the small-scale generic landing gear were performed in anechoic chamber with flow AC-2 TsAGI (Moscow, Russia), whereas the large-scale landing gear models were tested in a large anechoic facility FL-17 CARDC (Mianyang, China). In these tests, the large-scale landing gear models had simplified geometries and lacked small geometric details, which for the first time allowed their results to be directly compared with those for the small-scale models of the same geometry. It seems that this study of the geometry effect, where the landing gear models of different scales have identical geometry, is novel in its approach; the authors are not aware of such studies published in the open literature. The paper is organized as follows. In the next Section, a brief description of anechoic facilities FL-17 CARDC and AC-2 TsAGI is provided. Section 3 describes the landing gear models that were tested in these facilities. Section 4 describes experimental corrections applied to the measured data in both facilities. Then, the comparison of the experimental results is performed in Section 5, and some conclusions regarding the scaling of the results of small-scale tests to the full scale are made.
## 3 Results
### Storage Influence on Total Phenolic Content and Antioxidant Properties of Designed Confectionery
Total content of phenolic compounds differed depending on the type of chocolate and was as follows: bitter (DCC) > milk (MCC) > white (WCC). The addition of 0.2% yellow tea extract increased the initial content of these compounds by an average of 283% for white (WCT), 166% for milk (MCT) and 135% for dark chocolate (DCT) (Figure 3). During the storage of chocolates, a decrease in the content of phenolic compounds was noted, however the highest losses were found in the samples with the addition of tea extract stored for 6 months. The highest losses were evaluated in samples of MCT (20%), DCT (19%), and the lowest WCT (3%) as compared to fresh samples. That effect may indicate interactions between chocolate compounds and those contained in yellow tea extract. The results of the statistical analysis showed that the calculated correlation indicates a high relationship between the DPPH radicals scavenging activity and the content of phenolic compounds determined by the TPC assay (r2 = 0.981, \(p\leq 0.05\)). A similar correlation was found between the results of ABTS and TPC assay (r2 = 0.784, \(p\leq 0.05\)). Moreover, the calculated correlation shows no relationship between the results obtained with the ORAC assay and TPC (\(p\geq 0.05\)). Yellow tea extract increased total polyphenols content in tested chocolate samples, however did not protect them from lipid oxidation process (Figure 5). There are no studies in the literature where the antioxidant activity of yellow tea extract in chocolates has been tested, which was the main subject of this study. Results showed that addition of tea extract enhanced antioxidant activity regardless of chocolate kind and was stabile during three months of storage, then activity found to be decreased in all samples except white chocolate. During storage, the peroxide value was evaluated and an increase in the level of primary lipid oxidation products was found in all tested samples. It was confirmed that the addition of yellow tea extract slowed down the lipid oxidation process during 6 months of storage. The rate of oxidation process inhibition in the chocolates fortified with yellow tea components was 50% for milk, 42% for dark and 12% for white chocolate compared to pure chocolates.