esa-sceva/satcom-chunk-collection huggingface dataset:
# Combined Application of Filter Cake and Macadamia Husk
## 3 Results and Discussions
### Effect of Soil Amendment on Soil Fertility
It has been reported that inorganic fertilizer expedites the mineralization of C in organic wastes [10; 32]. Because the soil test was conducted after cropping, other factors that may have affected the soil composition include irrigation and cropping. In this study, both filter cake and macadamia husk compost contain an array of soil nutrients, most of which were at the optimal level of soil nutrient sufficiency or within the fertilizer recommendation for sweet potatoes [28; 34]. The concentrations of soil minerals such as sodium (Na), Fe, and Al were above the safe limits in the soil [35; 36]. Although such higher-than-required/safe concentrations have been reported to be counterproductive, the higher concentrations were not of concern because organic nutrients are released slowly into the soil [20]. Also, in the case of Al, extractable acidity is comprised predominantly of exchangeable aluminum (Al\({}^{3+}\)), which may have been neutralized, thereby protecting the plants from aluminum toxicity [37]. A reduction in Al concentration in soil because of filter cake application has also been reported [3]. Effects of Combined Application of Filter Cake and Macadamia Husk Compost on the Growth of Two Orange-Fleshed Sweet Potato Cultvirars  
In our earlier field research, the efficacy of both organic wastes was attributed to the array of nutrients (for filter cake) and the ability of the compost to hold water (for macadamia husk compost) [18]. In this study, we hypothesized that the test plants would harness both advantages, resulting in the yield of the combined application of both filter cake and macadamia husk compost (FC+MHC) surpassing that of all other treatments (FC, FC+IF, MHC, MHC+IF, IF, and CONT). This study confirmed the hypothesis. For instance, in Beauregard, the root mass, number of leaves, total biomass, and leaf area per plant were significantly higher than those of any other treatments. While the root mass in the FC+MHC treatment increased 2.6 folds, the root mass of FC, IF, FC+IF, and MHC+IF increased 1.96, 1.76, 2.24, and 1.94 folds, respectively (Table 4). In the 199062.1 cultivar, the number of leaves in the FC+MHC treatments was significantly higher than that of all other treatments. In other parameters such as root mass, leaf area, and total biomass, although the values for FC+MHC were generally higher than those of all other treatments, they shared some statistical similarity with FC+IF treatments. While the root mass of the FC+MHC treatment increased by 2.8 folds, that of the other treatments increased by 2.15 (FC), 1.8 (IF), 2.66 (FC+IF), and 1.98 (MHC+IF) folds (Table 4). In both cultivars, the higher root mass led to a reduced shoot/root ratio, and the changes in most of the investigated parameters because of MHC treatments were insignificant (Table 4).
# Model for the Prediction of Rain Attenuation Affecting Free Space Optical Links
## 2 Predicting Rain Attenuation Impairing FSO Links
### MultiEXCELL  
Presented in [19], MultiEXCELL is a global rainfall model oriented to the investigation and prediction of atmospheric electromagnetic wave propagation impairments. MultiEXCELL generates sets of synthetic rain fields, whose ensemble preserves the local rainfall statistics (the only input to the model) and reproduces the rainfall spatial correlation.  
Synthetic rain fields, whose standard spatial resolution is 1 km \(\times\) 1 km and whose lateral dimension can range between 200 and 300 km, result from the arrangement of multiple synthetic exponential cells according to the natural rain cells' aggregative process observed in radar-derived precipitation maps. Moreover, MultiEXCELL reproduces the statistical distribution of the fractional rainy area, in turn estimated from the rain amount accumulated in six-hour slots, \(M_{t}\), extracted from the European centre for medium-range weather forecast (ECMWF) 40-year re-analysis (ERA40) database [20]. A key advantage of MultiEXCELL is that a relatively small set of synthetic rain fields (around 1000 maps) is sufficient to reliably represent the local rainfall process and, thus, to efficiently simulate the interaction between precipitation and wireless telecommunication systems. In fact, when tested against extensive experimental datasets, MultiEXCELL has proven to provide satisfactory results in different propagation-oriented applications and scenarios [21; 22; 23], including the prediction of rain attenuation on terrestrial radio links [24; 25].  
Given the strong impact of precipitation at optical frequencies, in this work, the standard spatial resolution of MultiEXCELL rain maps is increased from 1 km \(\times\) 1 km to 250 m \(\times\) 250 m, with the aim of providing a more accurate prediction of rain attenuation on short FSO links (up to a few kilometers).
# Influence of Load Dynamics on Converter-Dominated Isolated Power Systems
## 2 Isolated Power System Modeling
### Load Modeling
#### 2.3.2 Dynamic Load Model  
The dynamic loads were represented by the IM model, since it is the most common approach used in the literature [9]. The stator windings of an IM operate similarly to those of an SM, producing a magnetic field rotating at a synchronous speed. Its distinctive feature is that the rotor creents are obtained by an electromagnetic induction from the stator. In order to develop a positive torque, the rotor speed must be less than the synchronous speed:  
\[\omega_{r} = \omega_{s}(1-s) \tag{10}\]  
where \(\omega_{r}\) is the rotor angular velocity, \(\omega_{s}\) is the stator field angular velocity and s is the slip speed of the rotor. The IM equation of motion in per unit is expressed by (11), where \(H\) is the motor inertia constant, while \(T_{m}\) and \(PT_{e}\) are the mechanical and electromagnetic torques, respectively. In this work, the implemented IM model was the one available in the MATLAB/Simulink(r) library, whose electrical part was represented by the fourth-order state space model, while the mechanical part was represented by a second-order model, with the rotor angle equation being expressed by (12), where \(\theta_{r}\) is the electrical rotor angle position.  
\[\frac{d\omega_{r}}{dt} = \frac{1}{2H}(T_{e}-T_{m}) \tag{11}\] \[\frac{d\theta_{r}}{dt} = \omega_{r} \tag{12}\]  
It is considered that the IM operates with constant torque at around 90% of its rated value. In addition, it was considered that each IM load is composed of three different classes: refrigerators, and small and large industrial motors (the parametrization of each IM load class is expressed in Table A1[31]).
## 3 Results and Discussion
### Volatile Compounds Identified in Soybean Protein Hydrolysate Samples via GC-MS
These results were consistent with the finding reported by [PERSON] et al. [14] that the concentrations of acetoin and acetophenone in the soy sauce fermented with yeasts were higher than those in the control group. Phenols are generally produced from the degradation of lignins or the transformation of phenolic acids due to the actions of microorganisms or enzymes during fermentation [32]. This work indicated that the concentrations of phenols in SH-2 were higher than those in SH-0 and SH-1. In particular, the contents of 2-methoxy-phenol in the samples inoculated with the LAB and yeast were higher than those of the corresponding controls. It has been reported that 2-methoxy-phenol and 4-ethyl-2-methoxyphenyl are characterized as having smoky aromas [33]. Furans are formed via the Maillard reaction, Strecker degradation, and thermal degradation of thiamine [34]. In this work, 2-furanmethanol was the main furan, while the contents of other furans were much lower. Notably, furural and maltol were also detected soybean hydrolysates. Moreover, 2,5-dimethyl pyrazine (caramel), 2,6-dimethylpyrazine (coffee), and 2,3,5-trimethylpyrazine (potato) were the predominant pyrazines, which was consistent with a previous description that these pyrazines could typically be detected in fermented soybean products [35]. The relatively high concentrations of pyrazines were found in all the groups, suggesting that pyrazines play important roles in the flavors of the soybean hydrolysates. Pyrazines detected in the control groups were probably generated via the Maillard reaction. In addition, the concentration of methional significantly increased (\(p<0.05\)) after fermentation. As reported, methional with a potato-like aroma is considered as an important volatile compound in Chinese soy sauce [22]. Fermentation is an important processing technique that reduces undesired flavors during the processing of cereals and legumes and produces unique fermented flavors. These impacts on product flavor are mainly attributable to the utilization of food components in raw material (as the substrates) and the biotransformation of certain metabolites mediated by microorganisms during fermentation, leading to the release of various flavor compounds or flavor-active substances [36]. In general, SH-2 exhibited higher contents of VFCs than SH-1, indicating a significant influence on the flavor of soybean protein hydrolysates when inoculated in sequence. It has been reported that the co-inoculation of _T. halophilus_ and _Z. rouxii_ during soy sauce fermentation can result in antagonism, with both experiencing varying degrees of growth inhibition [37].
# Calibration of Model Parameters for Soda Saline Soil-Subsoiling Component Interaction Based on DEM
## 2 Materials and Methods  
This paper comprehensively examined and analyzed soda saline-alkali soil, encompassing particle shape, size distribution, and physical and mechanical properties. Recognizing the adhesion characteristics of soil, we employed the Hertz-Mindlin with JKR (JKR) model as the contact model. First, using soil angle of repose (AoR) tests, we conducted the Plackett-Burman (PB), steepest ascent (SA), and Box-Behnken (BB) tests sequentially to select and calibrate the sensitive parameters in the model. Second, we obtained the optimal parameter combination by variance analysis. Finally, to improve the reliability of the verification, two different tests were used to verify the calibrated parameter combination, that is, the AoR test and direct shear (DS) test. Considering the adhesion characteristics of saline-alkali soil and the characteristics of high firmness and easy hardening, bonds are added between soil particles based on the JKR model. Combined with the physical test and simulation results, the contact model and sensitivity parameters are further corrected, in order to provide a reference for the discrete element simulation parameters of soda saline-alkali soil and subsoiling machine in Northeast China.  
The soda saline-alkali soil samples were collected on 12 May 2022, at an outdoor temperature of 21 \({}^{\circ}\)C. The collection site, situated in Sanye Village, Haituo Township, Daan City, Jilin Province (123.96\({}^{\circ}\) E; 45.32\({}^{\circ}\) N), is centrally located within the Songnen Plain (see Figure 1). This area is characterized by a temperate semi-humid and semi-arid climate, typical of a continental climate. The soil in this region exhibits an average pH value of 9.5 and an average total salt content of 0.7%, which is heavily salinized soil that has not been cultivated and reclaimed, representing the original landform characteristics of soda saline land under native conditions, as shown in Figure 2.  
To comprehensively consider the water and salt transport characteristics within soda saline-alkali soil, three sampling plots were randomly selected, and we employed the five-point sampling method. Using a shovel, soil samples were obtained at depths of 0-10 cm, 10-20 cm, 20-30 cm, and 30-40 cm below the surface [29]. Each collected soil sample was placed in a self-sealing bag, meticulously numbered for identification purposes, and its wet weight was measured on-site. This process was replicated for a total of 20 samples within each land block.  
Figure 1: Administrative map of Daan City.  
Figure 2: Saline landform pictures.
## 5 Experiments
### Results on MNIST and CIFAR-10  
The allowed adversarial perturbation \(\epsilon\), in this case, is 0.5 for MNIST [26] and 0.05 for CIFAR-10 [27], and the maximum number of iterations for PGD is 20.  
In this study, the choice of hyperparameters was a crucial factor that influenced the performance of the adversarial clustering algorithm. Below, we discuss our choice and tuning of key hyperparameters.  
* The \(p\) in the \(l_{p}\) norm determines the distance metric in the input space. We experimented with different \(p\)-values, such as 1 (Manhattan distance), 2 (Euclidean distance), and \(\infty\) (Chebyshev distance). Through cross-validation on a separate validation set, we found that the \(l_{2}\) norm (Euclidean distance) provided the best balance between performance and computational efficiency for our experiments.
* \(\lambda\) is the trade-off parameters in our distance metric that control the relative importance of the input space distance and the degradation potential difference. We performed a grid search over a predefined range for \(\lambda\) values, which varied between 0.1 and 1.0. \(\lambda=0.5\) resulted in the best clustering performance on a validation set.
* The hyperparameters of DBSCAN, i.e., MinPts (the minimum number of points required to form a dense region) and \(\epsilon_{DBSCAN}\) (the maximum distance between two samples for them to be considered as in the same neighborhood), also play a role in our method. MinPts was chosen as 4 following the general recommendation for different datasets. For \(\epsilon_{DBSCAN}\), we conducted a series of experiments with values ranging from 0.1 to 1.0. The optimal performance was observed at \(\epsilon_{DBSCAN}=0.5\), so this value was chosen.
* Learning rate and batch size: We use the Adam optimizer with an initial learning rate of 0.001 and a learning rate schedule that reduces the learning rate by a factor of 0.1 every 20 epochs. Momentum is set to 0.9. These choices are made after a comprehensive grid search in the hyperparameter space. The incorporation of learning rate schedules and momentum ensures quicker convergence and adaptability to the local optima of the loss landscape. We note that this strategy obviates the need for a rigorous convergence proof, as the empirical results demonstrate stable and repeatable convergence for our model architecture and dataset. Three batch sizes were considered: 32, 64, and 128. A batch size of 64 was selected based on the trade-off between computational efficiency and the stability of the gradient updates.  
We conducted an extensive hyperparameter tuning process involving grid search and cross-validation to ensure that our chosen hyperparameters generalized well and led to robust and consistent results across various adversarial settings.
## 3 Methodology  
### Artificial Neural Network  
Artificial neural networks (ANNs) are powerful computational models, inspired by the functions of biological neural networks, which were designed to simulate the behavior of the human neural system based on the relationships between input information and the target. ANN is a robust data modeling tool that is widely used in many applications, such as regression, classification, and approximation-based learning processes [32; 33]. A common architecture of ANN is divided into three layers, consisting of the input layer, the hidden layer, and the output layer, as illustrated in Figure 3.  
Each input element (\(p=[p_{1},p_{2},p_{3},...,p_{n}]\)) has a different weight (\(w_{1},w_{2},w_{3},...,w_{n}\)) for each neuron. The net input can be calculated as follows:  
\[n=\sum_{i=1}^{R}w_{ij}p_{j}+b=Wp+b, \tag{1}\]  
where \(w_{ij}\) is the corresponding connection weight between neuron (\(i\)) from the input layer and neuron (\(j\)) from the hidden layer, and \(b\) is the bias of the neuron. This research presents a Multilayer Perceptron  
Figure 2: The ground receiving station at Yangjiang site to collect the reflected signal from the BeiDou G4 satellite. Up and down-looking antenna (**left**) and the distance between receiving station and specular point (**right**).  
(MLP) neural network including two layers of neurons by configuring ten artificial neurons for the hidden layer and one artificial neuron for the output layer. The hyperbolic tangent sigmoid activation function was applied to the hidden neurons and can be expressed by:  
\[f(n)=\frac{2}{1+e^{-2n}}-1. \tag{2}\]  
Subsequently, a linear function was applied to the output layer of the ANN. The model was implemented using the two layer feed-forward neural network in which the weights and biases were trained by the particle swarm optimization algorithm, based on the input information and target; this is presented in the next section. In accordance with this algorithm, the error functions were updated through a number of iterations. The network learning error can be expressed by:  
\[E=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-t_{i})^{2}, \tag{3}\]  
where \(y_{i}\) is the final output of the network, \(t_{i}\) is the target, and \(N\) is the length of the output. The training process stopped when the conditions were met.
## 5 Conclusions  
In this work, we presented a transfer learning-based approach for RFI recognition and modulation classification. In this approach, the pretrained CNN analyzes the scalogram of the received signal to extract more informative features, which will be further used in the classification phase using a fully-connected layer. This work presented a comparative analysis of using four well-known pretrained CNNs such as AlexNet, GoogleNet, VGG16, and ResNet18. As the results show, the classification accuracy highly depends on the type of input data and the feature extraction technique. More importantly, the dataset used as the input in this study includes the scalogram of the signals transmitted in a satellite-to-ground video broadcasting scenario based on DVB-S2 standards. Further, the robustness of each trained classifier in predicting unseen data was thoroughly evaluated. To sum up, in terms of classification, all the pretrained architectures perform relatively similarly, although AlexNet and VGG16 lead to the least and the most elapsed training times, respectively.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 4 Approaches for Preventing Sticking of MXenes
### Integration with Polymer Matrices or Carbon Nanomaterials
#### 4.2.1 Polymer Matrices  
Integrating MXenes within polymer matrices offers several benefits for preventing sticking and enhancing overall electrode performance. Polymers act as a binding medium, encapsulating the MXene flakes and providing a dense network. This polymer matrix prevents direct flake-to-flake contact, minimizing sticking and preserving the dispersion of MXenes. The selection of suitable polymers is crucial to ensure compatibility with MXene flakes. Polymers with high elasticity, such as hydrogels or elastomers, can offer flexibility  
Figure 15: TEM images of an MXene flake from (**a**) fresh Ti\({}_{3}\)C\({}_{2}\)T\({}_{\rm x}\) solution and solutions aged in open air for (**b**) 7 days and (**c**) 30 days [71]. Reprinted from [PERSON] et al., Nano Converg., copyright (2021), Springer.  
and conformability, reducing stress-induced flake agglomeration. Furthermore, polymers with good adhesion to MXene surfaces can create strong interfacial interactions, improving the stability and preventing sticking.  
Ti\({}_{3}\)C\({}_{2}\)T\({}_{\rm x}\) MXene has been suspended in dimethylformamide (DMF) and different quantities of poly(vinylidene fluoride-trifluoro-ethylene-chlorofluoroethylene) or P(VDF-TrFE-CFE) (61.5/30.2/8.3 mol %) have been added [76]. By ultrasonication, flake sizes of 4-5 \(\upmu\)m (denoted as large MXene flakes (L-MXene)) and 1-2 \(\upmu\)m (denoted as small MXene flakes (S-MXene)) were obtained. Figure 17 indicates that the MXene flakes are evenly dispersed throughout the polymer matrix without forming aggregates, or the polymer prevents sticking of the flakes.  
Figure 16: (a) Schematic illustration of fabrication for MXene@PTFE hybrid, preparation process, and friction test of epoxy composite coating [74]. Reprinted from [PERSON] et al., Carbon, copyright (2021), with permission from Elsevier; (b) interaction between TiO\({}_{2}\)/Ti\({}_{3}\)C\({}_{2}\) and epoxy matrix in TiO\({}_{2}\)/Ti\({}_{3}\)C\({}_{2}\)/ER nanocomposites [75]. Reprinted from [PERSON] et al., Materials, copyright (2021), MDPI (open access).  
In addition, the MXene/P(VDF-TrFE-CFE) composite with large MXene flakes was found to reach a dielectric permittivity of \(10^{-5}\). In contrast, the composite with small MXene flakes reached a dielectric permittivity of \(10^{-4}\). These results are attributed to the accumulation of charges at the interfaces between the two-dimensional MXene flakes and the polymer matrix, where the switchable microscopic dipoles are located.
## 4 Security in 5G-Enabled D2D Communications Network
### Security Requirement  
The UE is capable of cryptographic computation using its Universal Subscriber's Identity Module (USIM); each UE generates its own self-certifying public key \(PK_{UE_{i}}\) and private key \(SK_{UE_{i}}\)[53]. The \(PK_{(UE_{i})}\) is used as part of its pseudonym ID. The \(UE_{i}\) generates the secret key \(SK_{UE_{i}}\) by selecting a random number using chosen binary ECC, and then its public key \(PK_{UE_{i}}\) is computed from \(SK_{UE_{i}}\)[52]. Each \(UE_{i}\) sends its public key and pseudonym ID to gNB in terms of network-assisted application and to each other in direct communication application. Traditionally, certificates would be used to bind the \(UE_{i}\) to  
Figure 3: D2D communications scenarios. (**A**) same cell/same operator, (**B**) different cells/same operator, (**C**) roaming mode/different operators and (**D**) out of coverage/different operators.  
its public key, however in this case self-certifying public keys are used. The \(UE_{i}\) stores its public/private keys \(PK_{UEi}\), \(SK_{UE_{i}}\) and public key hash \(hash(PK_{UE_{i}})\) while gNB stores the public keys, pseudonym ID and other relevant information of all users in the cell. A timestamp is used to prevent replay attacks and check the validity of the token. It is evaluated by checking if it is within a given time-window relying on the clock of the UE. The message timestamp must be equal or slightly off by seconds to the current time/date, while the token lifetime time/date must be after the current time/date. Furthermore, the UEs can generate hash functions, symmetric and asymmetric keys and digital signatures achieved using ECIES and IBE. ECIES is suitable for systems with low computational capabilities and resources, such as mobile devices and smart cards due to the point multiplication operation. The proposed protocols use ECIES as applied by 5G to conceal SUPI and generation of the anchor key [1].  
The expected security properties to be achieved by the DDSec and DDACap protocols are informally defined before the formalization of the protocol properties. The taxonomies in [54; 55] are adopted for precise formal analysis referred to as Set 1 and Set 2, respectively. In Set 1, the security properties are specified from an Agent A's point of view, with four levels defined between two Agents A and B: aliveness, weak agreement, non-injective agreement and injective agreement. In Set 2, the security protocol should meet the following security properties: mutual entity authentication, mutual key authentication, mutual key confirmation, key freshness, unknown-key share and key compromise impersonation resilience.