esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Validation and Numerical Examples
### Model Competition
Through analysis, it should be pointed out that the slope of the assumed failure surface is characterized by uncertainty, the values of ultimate strains \(\varepsilon_{x}\) obtained by the uniaxial compression test are too large, and the damage parameter \(D\) is small (Figure 10). Therefore, the deviation of the damage evolution equation leads to the calculation results of the HJC model being lower than the experiment results. From Figure 4, it is found that the dynamic responses of RPC-FST calculated based on the modified K&C model are consistent with the experimental results. From the experiment, the steel tube surface of the crater exhibited many tiny scars. The results show that some elements fail on the blast surface, which is consistent with the experimental phenomenon. Due to some solid elements of steel tube reaching ultimate strength after bearing the explosion load and being unable to continue bearing the load, the result is solid element failure. Figure 4: Comparison of blast surface displacement between experiment and numerical simulation (mm): (**a**) C-15, (**b**) C-16, (**c**) C-17, and (**d**) C-18.
# AetherGeo: A Spectral Analysis Interface for Geologic Mapping
## 4 Related Software Tools
### Spectral Library Management and Visualization  
An important feature when working with hyperspectral and multispectral data is the visualization of spectral libraries (Figure 6). This is the term for files containing groups of different spectra from previous studies, such as open-access libraries resampled for different satellite sensors [62] or extracted from an image.  
In this tool, the spectral angle mapper (SAM) algorithm [56; 63] can also be used to measure the angle between two selected spectra. An angle of zero degrees means the spectra are precisely equal; as the angle increases, the spectra are less similar.
## 3 Results and Discussion
### Results of Color Stability  
When studying the stability before and after exposure to light on the indicators of cooked sausages, the following values were obtained for the main characteristics of color: L (lightness), a (redness) and b (yellowness). The data are presented in Table 4.  
Thus, when determining the color stability of meat products to the effects of temperature conditions (heating to a temperature of 70-72 \({}^{\circ}\)C), the influence of added chemical agents (according to the generally accepted formulation: chlorides, phosphates, etc.) and to determine the color stability during storage, color characteristics were studied, and color stability was established. The obtained values for lightness and redness decreased slightly in the region of 2% and 8%, respectively. At the same time, yellowness, on the contrary,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of the Indicators to Be Determined** & **Unit of Measurement** & **Results** \\ \hline Peroxide number (1 day) & meq/kg & 2.0 \(\pm\) 0.2 \\ Peroxide number (2 days) & meq/kg & 2.1 \(\pm\) 0.2 \\ Peroxide number (3 days) & meq/kg & 2.6 \(\pm\) 0.3 \\ Peroxide number (4 days) & meq/kg & 2.9 \(\pm\) 0.3 \\ Peroxide number (5 days) & meq/kg & 3.3 \(\pm\) 0.3 \\ Peroxide number (6 days) & meq/kg & 3.9 \(\pm\) 0.4 \\ Peroxide number (7 days) & meq/kg & 4.6 \(\pm\) 0.5 \\ \hline Carbonyl compounds & nmol/mg of protein & 96.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dynamics of fat oxidation and protein oxidation (background) in herodietic sausages during storage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of the Indicator** & **Unit of Measurement** & **Results** \\ \hline Myristic C14:0 & \% & 1.7 \(\pm\) 0.4 \\ Myristolein C14:1 & \% & 0.1 \(\pm\) 0.4 \\ Pentadecane C15:0 & \% & 0.2 \(\pm\) 0.4 \\ Palmitoleic C16:0 & \% & 21.3 \(\pm\) 2.1 \\ Palmitoleic C16:1 & \% & 3.5 \(\pm\) 0.4 \\ Margarine With 17:0 & \% & 0.6 \(\pm\) 0.4 \\ Heptadecene C17:1 & \% & 0.2 \(\pm\) 0.4 \\ Stearic C18:0 & \% & 6.4 \(\pm\) 2.1 \\ Oleic C18:1 & \% & 30.1 \(\pm\) 2.1 \\ Linoleic C18:2 av6 & \% & 32.2 \(\pm\) 2.1 \\ Linolenic C18:3u3 & \% & 2.8 \(\pm\) 0.4 \\ Arachin C20:0 & \% & 0.1 \(\pm\) 0.4 \\ Eicosadienoic acid C20:2 av6 & \% & 0.3 \(\pm\) 0.4 \\ Tricosan C23:0 & \% & 0.3 \(\pm\) 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fatty acid composition of herodietic sausages.  
when exposed to light, had higher values (by 20%) than before its exposure. This indicates a fairly stable color formation in the product, which may be associated with the introduction of purslane, which has good antioxidant properties that apparently prevent the oxidation of protein pigments.
# Research on the Compound Jamming Method against FDA-MIMO Beamforming
###### Abstract  
Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar solves the problem of angle pointing and range pointing during beam transmission due to its unique range-angle dimension dependent, which gives it good characteristics against main lobe deceptive jamming. Moreover, the current single range dimension deceptive jamming is easy to be identified and cannot form an effective jamming effect on FDA-MIMO radar. To address this problem, this paper proposes a compound jamming method of suppression and range deceptive against FDA-MIMO beamforming. Compared with the traditional deceptive jamming method, this method can reasonably utilize the beam gain, and minimum variance distortionless response (MVDR) adaptive beamforming cannot significantly reduce the power of the jamming, thus avoiding the jamming being weakened at the beamforming level, and the jamming may appear in different range bins, causing significant suppression and range deceptive compound jamming effects on FDA-MIMO radar in the range dimension. The simulation verified that the compound jamming method can effectively interfere with the FDA-MIMO radar at the beam-forming level, and the jamming effect is good.  
FDA-MIMO; range dimension freedom; MVDR adaptive beamforming; compound jamming; range bin Article Research on the Compound Jamming Method against FDA-MIMO Beamforming  
2022129448103390/app12199448
# Study of the Properties of Human Milk Fat Substitutes Using DSC and GC Methods
## 1 Introduction
Taking the above-mentioned into account, a mixture of tripalmitin or lard and vegetable oils were selected to obtain a product of similar FAs composition and their distribution in triacylglycerglycerols (TAG) to HMF [13; 14]. During the development of new products such as HMFS, it is significant to optimize the physical and nutritional properties, but the oxidative stability of these products should also be guaranteed [2]. Oxidation of lipids is a very important process occurring in food systems. Primary and secondary oxidation products can adversely affect the nutritional value and food safety [15; 16]. The production process for SL increases free fatty acids (FFA) concentration, which are responsible for off-flavor and odor characteristic of oxidation. A high concentration of FFA in SL induces an unacceptable rancid and bitter taste. Moreover, structured lipids produced with lipids that contain unsaturated FAs can deteriorate during storage. Unsaturated FAs in SL may be rapidly oxidized to hydroperoxides. They can then be quickly broken down into secondary oxidation products, such as aldehydes, alkenes, and ketones responsible for their unwanted flavor. Numerous products of oxidative degradation of edible oils and fats are detrimental to human healthiness. These products damage vitamins and enzymes and may cause mutations or gastrointestinal problems [17; 18; 19; 20; 21]. Many methods are used to monitor the autooxidation of fats and oils. Currently, instrumental methods that are faster, more accurate, more objective, and have a wider detection range are used. Differential scanning calorimetry (DSC) is a nonchemical method that can be used to determine fat quality parameters [22; 23; 24; 25]. The purpose of this work was to study the properties of HMFS obtained by enzymatic modification of a mixture of lard and rapeseed oil using DSC as well as chromatography (GC) and thin layer chromatography (TLC).
###### Abstract  
The purpose of this study was to determine the impact of the pomace drying methods (freeze-drying and convection-drying) and their percentage (0-10%) on selected physicochemical properties of gluten-free bread. The contents of nutrients (protein, fiber, fat, ash, and carbohydrates), bioactive compounds, antioxidant properties, acidity, baking efficiency, and moisture of the obtained products were determined. Fortifying the bread with raspberry pomace resulted in a change in fiber content from 18.13% d. b. (control sample) up to 19.97% d. b. (10% of freeze-dried pomace), and a change in the fat and ash content in the bread from 5.74% and 2.83% d. b. (control sample) to 7.18% and 3.12% d. b. (10% of freeze-dried pomace). The content of carbohydrates decreased after adding raspberry pomace to the bread, from 65.71% d. b. (control sample) to 63.68% d. b. (5% of freeze-dried pomace). The research carried out also showed that the introduction of 10% freeze-dried raspberry pomace increased the total polyphenol content by 81.75% and the antioxidant properties defined by the ABTS method by 159.54% and by the DPPH method by 96.43% compared to the control bread. The introduction of pomace resulted in a significant reduction in the total baking loss, from 15.1% to 10.62%, and an increase in the total titratable acidity of the crumb, from 2.13 mL NaOH/10 g d. b to 7.78 mL NaOH/10 g d. b. Principal component analysis highlighted a marked effect of the drying method and content of raspberry pomace on the quality values of gluten-free bread. This research demonstrated that raspberry pomace can be a valuable source of fiber and bioactive substances in gluten-free bread.  
rasberry by-products; gluten-free bread; antioxidant activity; polyphenols; fat; fiber; protein; nutritional properties; bioactive compounds; principal component analysis (PCA)
## 2 Point-Spread Functions in Phase Space
### Fraunhofer Approximation  
The Fraunhofer approximation is more crude than the Fresnel approximation, as it approximates the collection of all spherical wavefronts in a scene by a single parabolic wavefront. With a far-field \(z\gg 2\pi/(2\lambda)\max(\left|\xi^{2}+\eta^{2}\right|)\) we find from Equation (6) for the instantaneous phase  
\[P_{\text{Fh}}(r):=\frac{1}{i\lambda z}e^{i\varphi_{\text{Fh}}(r)}\quad\text{ with}\quad\varphi_{\text{Fh}}(r):=-\frac{2\pi}{\lambda}\left(z+\frac{x^{2}+y^{2}-2 x\xi-2y\eta}{2z}\right) \tag{8}\]  
and for the instantaneous frequency therefore  
\[f_{\xi}(r):=\frac{1}{2\pi}\frac{\partial\varphi_{\text{Fh}}(r)}{\partial\xi} =\frac{x}{\lambda z}\quad\text{and}\quad f_{\eta}(r):=\frac{1}{2\pi}\frac{ \partial\varphi_{\text{Fh}}(r)}{\partial\eta}=\frac{y}{\lambda z}\,. \tag{9}\]  
Figure 3: A comparison of the Fresnel approximation with the rigorous form of a single 1D point-spread function in terms of: (**a**) real part in Fourier space; (**b**) instantaneous phase in space; (**c**) instantaneous frequency in space-frequency domain.  
The phase space footprint will be in shape identical to that of the Fresnel approximation in Figure 3 for \(\xi=0\) fixed. Note, the Fraunhofer approximation may also be obtained in the focal plane of a lens [38].
## 5 Real-Time PPP Performance of Smartphone
### Kinematic PPP Performance  
The time series of the kinematic real-time PPP positioning errors for the Huawei Mate40, P40 and Novatel receiver in the E, N and U components are illustrated in Figure 10. As can be seen from Figure 10, for the smartphone kinematic real-time PPP, the positioning results are impacted by large measurement noise, and the positioning errors are not smooth, which is worse than that of the static counterpart, while for the positioning errors of the DH04 receiver, it is smoother than that of the smartphone. The positioning errors of the Huawei Mate40 and P40 can achieve meter-level after convergence; it can also be found that the Huawei Mate40 is affected by system errors and shows the poorest positioning performance. In addition, compared to the Novatel receiver, the convergence time of the smartphone is long, and the positioning accuracy of the smartphone in the E and N component is much smaller than that in the U component. Compared to GPS-only, BDS-only and GPS/BDS combination solutions, the positioning performance of the GPS/BDS/Galileo combination can be improved.  
The RMS of kinematic real-time PPP positioning accuracy of five combinations for the Huawei Mate40, P40 and Novatel receiver in three components are presented in Table 6. It can be clearly seen that the positioning accuracy of the Huawei P40 outperforms the Huawei Mate40. For Huawei Mate40 and P40, the positioning accuracy of the GPS/BDS combination solution is 3.47, 4.30, 8.57 m and 4.20, 3.49 and 8.07 m for E, N and U directions, which is better than that of GPS-only and BDS-only solutions. However, compared to the GPS/BDS combination solution, the improvements of GPS/BDS/Galileo solutions are not obvious, which may be because the observed Galileo satellite is small, and the contribution of the Galileo satellite is less. For dual-frequency, GPS L1/L5, the improvements in positioning accuracy are few compared to single-frequency GPS-only solutions, with an improvement of 2.49%, 1.38% and 3.26% in the east, north and up components, respectively. The positioning accuracy of the GPS/BDS/Galileo combination using the Huawei P40 smartphone is the best of the three components; its RMS is 3.88, 3.39 and 7.83 m in the three components, respectively. For the DH04 receiver, the positioning accuracy at a sub-centimeter level can be achieved for most solutions, in which the dual-frequency GPS L1/L5 is the best, and the positioning accuracy is 0.06, 0.11 and 0.16 m in E, N and U directions. Compared to real-time smartphone PPP, the positioning accuracy is significantly improved since it has low observation noise.  
Figure 10: Real-time kinematic PPP accuracy for Huawei Mate40, P40 and Novatel.
## 4 Classification of Routing Protocols
### Cross-Layered Routing Protocols
#### 4.5.1 Wireless Autonomous Spanning Tree Protocol (WASP)  
In [71] the authors proposed a cross-layered protocol, named as wireless autonomous spanning tree protocol. WASP divides the time axis into slots in distributed manner, known as WASP-Cycles. The spanning tree is set up automatically as shown in Figure 14, redrawn from [71]. This spanning tree is used to send the data to the sink, and incorporates the medium access control and routing.  
Each node sends a WASP-scheme to its children (nodes one level lower in the spanning tree) to inform them about the slot they can use to send the data. The children nodes will reply to their parent nodes (nodes one level up in the spanning tree) through the same WASP-scheme. WASP-scheme is unique for every node and is generated at the source node. Through these  
Figure 14: **(a)** Network on the body (b) Abstract view of the network [71].  
controls the traffic of its children nodes and at the same time the children nodes can use it to ask their parent nodes for more resources. As the WASP-scheme is generated at the source node and used by both parent and children nodes resulting in minimum coordination overhead.
# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 3 Methods
### Conventional Regression Methods with CNN-Derived Features  
The neural network can be used as a featurizer by removing the final output regression layer and using the previous fully connected layer as the outputs. Because the network has already been trained to perform the task of predicting the BER, information related to the task is represented by the outputs of this layer. This can be thought of as a form of data compression. Similar work has been performed using autoencoders to generate compressed representations of the CIRs in massive multi-input multi-output systems [18].
## 5 Research Methodology and Data Analysis
### Variables and Algorithms for Forecasting AOG Incidents  
In this study, the authors apply ARIMA and ETS models for forecasting future AOG occurrences, as well as future maintenance disturbances. These models are also useful for recognizing the patterns in maintenance schedules and the failure of the components that are necessary for effective preventive maintenance planning.  
* ARIMA: This classical model is particularly suitable for studying linear trends of AOG mishaps and maintenance intervals. ARIMA also works on historical data on the wear of the components and failure patterns that are likely to prompt the grounding of an aircraft. For instance, ARIMA can detect the average period between significant component failures. The mechanism of distinction of seasonality and trend makes ARIMA appropriate for a prognosis of time that is the most suitable for an aircraft to experience operational disturbances.
* ETS: ETS contributes towards the modeling of all AOG events, especially when used for short-term forecasting. The ETS model was developed specifically for small and varying patterns, unlike ARIMA, which targets long trends in the data of aircraft maintenance. This model proves particularly helpful in forecasting the number of hours with unplanned downtime and in subsequent troubleshooting to ensure that any possible AOG occurrences are handled efficiently.
## 7 Hybrid Communication Systems: VLC with Other Communication Technologies
### VLC with Radio Frequency (RF) Communication for Hybrid Networks
Applications of hybrid VLC-RF networks can be seen in several domains: underwater IoT, AUVs, and environmental monitoring. For example, hybrid systems provide real-time control and feedback in AUVs, thereby significantly enhancing their precision and reliability in performing operations such as docking and positioning [80]. Further, the role of enabling secure communication is demonstrated by cooperative hybrid systems, where decode-and-forward mechanisms at relay nodes improve secrecy performance against potential eavesdroppers [81]. Despite the many advantages of hybrid VLC-RF networks in underwater applications, there are challenges in implementation. These are optimization of the orientation of the receiver in dynamic water conditions, mitigation of path loss in RF systems, and integration of VLC and RF seamlessly. Stochastic geometry and simulation frameworks are useful in obtaining further insights on overcoming these obstacles that allow researchers to model and evaluate system performance under realistic conditions [75; 81].
# Visual MAV Tracker with Adaptive Search Region
## 4 Results
### Effects of SC  
The shrinkage of the SR was observed in the test using CSR. Figure 5a shows the SR when the MAV begins to be truncated at the right edge of the SR. Then, owing to the MAV motion, only half of the MAV is included in the SR, as shown in Figure 5b. Consequently, the size of the SR decreases further, and the tracker almost loses the MAV, as shown in Figure 5c. Tracking failure occurs in a few subsequent frames.  
The SC prevents tracking failures caused by undesirable SR shrinkage. Figure 6 shows the SR and predicted bounding box in the absence and presence of SC in the same frame. Although the over-shrink preventive SR considerably increased the IOU value, a small truncation still remained. This limitation was resolved by SO and PP.
# Physics-Informed Graph Neural Networks for Attack Path Prediction
## 2 Materials and Methods
### Dataset  
As discussed in Section 1, to the best of our knowledge, no existing dataset satisfies our criteria--namely, capturing a diverse range of detailed environments along with their associated attack paths. Given these constraints, we opted for a synthetic data-generation approach to conduct experimentation. Our goal was to construct a dataset that allowed us to experimentally evaluate the computational feasibility of generalizing across the high-dimensional spaces of environments and attack. Generating such a dataset and making it suitable for statistical learning presents several challenges, notably (1) ensuring technically realistic attack paths and environments while balancing granularity with dimensionality (see Section 2.1.1), (2) identifying and mitigating potential biases in feature distribution (see Section 2.1.2), and (3) structuring data efficiently for learning algorithms (see Section 2.1.3).
# Preliminary Design of the Electrical Power Systems for DTT Nuclear Fusion Plant
## 3 Distribution System of DTT Facility
### Preliminarily Evaluation of the Power  
According to the load classification and to the information listed in the ELL, a load analysis with the aim of achieving an electrical load balance was drawn up. The load analysis constitutes the basis to evaluate the power required by the entire system and to select the number of pieces of equipment (i.e., transformers, switchgears, etc.) and their preliminary size; the amount of equipment and rating is then confirmed or updated during the network calculations and studies.  
Tables 2-5 summarize the power required by the entire system and the estimated powers for each distribution LC.  
It is important to stress that most of loads in nuclear fusion facilities are normally characterized by low power factors, requiring relevant systems for their corrections [15; 23]. As shown in Tables 2-5, the DTT loads still feature low power factors, but some solutions were adopted at load level to increase them [13; 14; 17].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**SSED Section**} \\ \hline  & \multicolumn{2}{c}{**Load Demand**} & \multicolumn{2}{c}{**Power Factor without**} & \multirow{2}{*}{**Notes**} \\ \cline{2-2} \cline{4-5}
**LC** & **MW** & **MVAr** & **MVA** & **Correction System** & \\ \hline
1 & 9.07 & 8.48 & 12.4 & 0.73 & – \\
2 & 7.93 & 5.60 & 9.71 & 0.82 & – \\
3 & 1.86 & 1.43 & 2.35 & 0.79 & Only LV loads \\
4 & 5.60 & 3.92 & 6.83 & 0.82 & Only LV loads \\
5 & 20.5 & 13.4 & 24.5 & 0.84 & – \\ \hline \hline \end{tabular}
\end{table}
Table 2: Active, reactive and apparent power required by DTT distribution LCs, SSED section.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Total Steady State Load**} \\ \hline
**MW** & **MVAr** & **MVA** & **Power Factor** \\ \hline
37.5 & 27.5 & 46.5 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total active, reactive and apparent power required by SSED distribution subsystem of DTT. The total steady-state 20-kV load does not include the load of LC3 and LC4 (LV loads only) indicated above because they are accounted for in the total load of LC5 from which they are supplied.