esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient SC-FDM Modulation Technique for a UAV Communication Link
## 4 The Proposed SC-FDM and OFDM
### Sc-Fdm  
SC-FDM is a hybrid modulation technique that combines the frequency allocation flexibility and multipath resistance of OFDM and other important characteristics of a single-carrier system. The crucial characteristic of the SC-FDM signal generation is that the PAPR of finite frequency shifted signal ends up being the same as that of the original modulating data symbols and this is very different from that of OFDM for the same occupied bandwidth in the same data rate. However, if the channel bandwidth is wider, the link between the symbol length and channel bandwidth can be considered as the disadvantage of SC-FDM in comparison with OFDM [33]. Figure 6 presents the block diagram for a signal processing chain of SC-FDM. The first step is the same as for OFDM, modulating the data with  
Figure 5: Orthogonal frequency division multiplexing (OFDM) and single-carrier frequency division multiplexing (SC-FDM).  
one of the modulation schemes for data transmission over the resource elements. The data is placed over the resource elements by adjusting the phase and amplitude of the subcarrier to those derived for the data stream. Mathematically, it means multiplying the complex modulation symbol to the corresponding subcarrier frequency. After this process, there is another block in the signal processing chain. To convert the data symbols from the time domain into the frequency domain, the DFT is performed after the serial to parallel conversion. As an OFDM, afterwards, there is subcarrier mapping and an IDFT to transform the signal in the frequency domain into the time domain signal. The cyclic prefix can be inserted when the parallel to serial conversion is done. Before modulating the signal with a high frequency, a pulse shaping filter can be used to get the desired spectrum [34]. The original values of the symbols can be completely recovered if the transmitted signal is properly sampled at the receiver. The last two steps are the same as for OFDM. Thus, there is no difference in the downlink signal generation chain. In the same way, the reverse of what was done at the transmitter can be accomplished at the receiver. As mentioned in Section 4.1, each subcarrier carries only one particular modulation symbol in OFDM, while the DFT takes a symbol and spreads it via an available subcarrier in SC-FDM. For that reason, SC-FDM is also referred to as DFT-spread OFDM [35]. Since the PAPR is proportional to the square of the number of subcarriers involved, SC-FDM reduces the PAPR by reducing the number of subcarriers.
# Application of Cinnamon Essential Oil in Active Food Packaging: A Review
###### Abstract  
Active packaging allows for preserving the properties of food, extending shelf life, and safeguarding food safety through the interaction of their diverse components with the product. The incorporation of essential oils, particularly cinnamon essential oil, as active components is emerging as an increasingly relevant alternative to synthetic additives. This work aims to provide an overview of the application of cinnamon essential oil as a bioactive compound in food packaging. Cinnamon essential oil exhibits a highly variable composition, with cinnandelhyde standing out as one of the predominant components responsible for the antimicrobial properties. Phenolic compounds, on the other hand, endow the oil with outstanding antioxidant activity. The application of this oil in active packaging, whether in the form of films or coatings, has demonstrated a significant improvement in optical, mechanical, and water vapor barrier properties. Moreover, its ability to inhibit microbial growth and lipid oxidation in the applied foods has been evidenced. However, despite the promising prospects of using essential oils in active packaging for food preservation, detailed regulation is still required for industrial-level implementation.  
active packaging; antimicrobial properties; antioxidant activity; films; cinnamon; edible coating; essential oils; food safety; natural additives; legal regulation
## 2 Methods and Data
### Convolutional Neural Network Model  
The Python-based framework TensorFlow [27] was used to implement a CNN for the prediction of the characteristics of a GDL with its fiber-based micro-structure. Its architecture, as in previous work [12], is illustrated in Figure 1. The red-shaped layers are also analyzed in detail. The first layer, marked as '0/Input', contains the input micro-structure of 512 \(\times\) 512 \(\times\) 65 dimensions. The parameters of the convolution layers were chosen to reduce the flat shape of the 3D filters to a more regular one, from 512 \(\times\) 512 \(\times\) 65 on the input layer, down to, e.g., 8 \(\times\) 8 \(\times\) 8 in the eighth layer. Layer '1/Convolution' is the first hidden layer of the CNN. The structure shrank in both the x and y directions. The layer '3/Convolution' also shrank in two dimensions. The third dimension was not reduced because of its flat 3D shape. The architecture shown in Figure 1 was already used earlier [12] to predict the TP permeability of Toray GDL. The same architecture--predicting one feature out of a stack of images--was trained separately to four features, namely TP permeability, TP tortuosity, IP permeability, and IP tortuosity. All of these the features were simulated earlier by means of the LB method [26].  
The architecture of the presented CNN is a series of Conv3D refinements, similar to that used by [PERSON] et al. [17]. While [PERSON] et al. worked with cubic samples, our basic images have a flat shape. This led to some of our Conv3D refinements being refined only in two dimensions instead of three.
###### Abstract  
In China, a large amount of fish bones are produced during the processing of tuna cans production. For full use of those by-products, gelatin (STB-G) with a yield of 6.37 \(\pm\) 0.64% was extracted from skipjack tuna (_Katsuownus pelamis_) bone using water at 60 \({}^{\circ}\)C for 8 h. Amino acid analysis showed that STB-G contained Gly (340.3 residues/1000 residues) as the major amino acid and its imino acid content was 177.3 residues/1000 residues. Amino acid composition, SDS-PAGE, and Fourier transform infrared (FTIR) spectrum investigations confirmed that the physicochemical properties of STB-G were similar to those of type I collagen from skipjack tuna bone (STB-C), but partial high molecular weight components of STB-G were degraded during the extraction process, which induced that the gelatin was easier to be hydrolyzed by protease than mammalian gelatins and was suitable for preparation of hydrolysate. Therefore, STB-G was hydrolyzed under in vitro gastrointestinal digestion (pepsin-trypsin system) and five antioxidant peptides were purified from the resulted hydrolysate (STB-GH) and identified as GPDGR, GADIVA, GAPGPQMV, AGPK, and GAEGFIF, respectively. Among the gelatin hydrolysate, fractions, and isolated peptides, GADIVA and GAEGFIF exhibited the strongest scavenging activities on 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical (EC\({}_{50}\) 0.57 and 0.30 mg/mL), hydroxyl radical (EC\({}_{50}\) 0.25 and 0.32 mg/mL), superoxide anion radical (EC\({}_{50}\) 0.52 and 0.48 mg/mL), and 2,2\({}^{\prime}\)-azino-bis-3-ethylbenzothazoline-6-sulfonic acid (ABTS) radical (EC\({}_{50}\) 0.41 and 0.21 mg/mL). Moreover, GADIVA and GAEGFIF showed a high inhibiting ability on lipid peroxidation in a linoleic acid model system. The strong activities of five isolated peptides profited by their small molecular sizes and the antioxidant amino acid residues in their sequences. These results suggested that five isolated peptides (STP1-STP5), especially GADIVA and GAEGFIF, might serve as potential antioxidants applied in health food industries.  
Skipjack tuna (_Katsuownus pelamis_); bone; gelatin; peptide; antioxidant activity +
Footnote †: journal: materials
## 7 Application Example: Simulated Data  
Now, an example for the application of the generalised Kalman filter in Equations (97)-(104) to simulated data will be presented. The state and data dimensions are chosen as \(m=n=1\), and the model parameters are chosen as  
\[\mathsf{A}_{t} \equiv a=0.95\, \mathsf{C}_{t} \equiv c=1.0\,\] \[\mathsf{Q}_{t} \equiv q=1.0\, \mathsf{R}_{t} \equiv r=1.0\,\] \[\mathsf{S}_{t}^{(0)} \equiv s^{(0)}=0.75\, \mathsf{S}_{t}^{(1)} \equiv s^{(1)}=-0.25\,\]  
i.e., the parameters do not depend on time. From a state space model employing these parameters, 1000 simulated time series are created, each of length \(N=1024\) points, using different sets of dynamical and observation noise values. The noise values are created such that they approximately fulfil the following constraints:  
\[\mathbb{E}\!\left(\begin{bmatrix}\eta_{\tau}\\ \epsilon_{\tau}\end{bmatrix}\begin{bmatrix}\eta_{\tau}\\ \epsilon_{\tau}\end{bmatrix}^{\mathsf{T}}\right)=\begin{pmatrix}q&s^{(0)}\\ s^{(0)}&r\end{pmatrix}\quad\text{and}\quad\mathbb{E}\!\left(\begin{bmatrix} \eta_{\tau-1}\\ \epsilon_{\tau-1}\end{bmatrix}\begin{bmatrix}\eta_{\tau}\\ \epsilon_{\tau}\end{bmatrix}^{\mathsf{T}}\right)=\begin{pmatrix}0&s^{(1)}\\ 0&0\end{pmatrix}\]  
using the chosen values for \(q\), \(r\), \(s^{(0)}\), and \(s^{(1)}\), as given above.  
Since this is a simulation, the true states are known. Then, the same state space model that was used to create the simulated time series is used to estimate the states, either using the weighted least-squares approach, i.e., solving Equation (65) non-recursively, or the generalised Kalman filter algorithm in Equations (97)-(104). While the former algorithm provides smoothed state estimates, the latter provides filtered state estimates, but nevertheless, these two sets of estimates shall be compared.  
In the particular setting of this simulation, state estimation is equivalent to noise reduction with respect to the observational noise term. Therefore, the performance of the two algorithms for state estimation may be compared by quantifying the achieved level of noise reduction; this is possible since the true states are known. The result is shown via histograms in Figure 1. Note that a value of 0 dB on the horizontal axis corresponds to failure in noise reduction.  
In the figure, it can be seen that for the 1000 time series created, both algorithms achieve values for noise reduction between 5 dB and 7 dB; if the distributions are fitted by Gaussians, for the weighted least-squares algorithm, a mean of 6.3234 and a standard deviation of 0.1624 are obtained, while for the generalised Kalman filter algorithm, a mean of 5.8242 and a standard deviation of 0.2079 are obtained. Thus, at least in this simulation example, the generalised Kalman filter achieves almost as good performance as the weighted least-squares algorithm, despite representing a non-optimal algorithm for state estimation.
## 4 Field Test Results and Discussions
### Evaluation of Thermal Performance from TPT
(**a**) Inlet, outlet temperature and heat exchange amount; (**b**) Outlet and ambient air temperature. Figure 13: _Cont._  
Figure 14: Thermal performance test (TPT) results of floor-type energy slab with stainless steel (STS) pipe (with thermally insulation). (**a**) Inlet, outlet temperature and heat exchange amount; (**b**) Outlet and ambient air temperature. Figure 13: Thermal performance test (TPT) results of wall-type energy slab with stainless steel (STS) pipe (without thermally insulation). (**a**) Inlet, outlet temperature and heat exchange amount; (**b**) Outlet and ambient air temperature. The value of the average heat exchange rate per pipe length was calculated to compare the thermal performance of thermally insulated energy slabs according to the pipe materials. In the case of energy slabs with the STS pipe, the value was calculated to be 6.730 W/m, which is improved by about 33.3% compared to the value of energy slabs with the HDPE pipe (5.049 W/m). This result shows a similar trend to the TRT result, which is caused by the difference in the thermal conductivity of pipe materials. In summary, the installation of thermal insulation layers in energy slabs should be considered in the design stage, and the thermal insulation material should be suitable to minimize the influence of ambient air temperature in the real operation. The heat exchange amount of energy slabs was evaluated approximately at 400 W per 25 m\({}^{2}\) of energy slab unit in this study. As the thermal conductivity of the surrounding medium increases, the thermal performance of GHEX increases [8]. When the energy slab is installed in the actual underground structure, it is expected to show higher thermal performance than the current test bed, in which the thermal conductivity of the ground was measured at 1.232 W/mK on average. [PERSON] et al. (2018) showed that the heat exchange amount of the energy pile increased by 10% when the thermal conductivity of the ground increased by 50% [8]. Moreover, when the energy slab is installed in an underground structure far below the frost penetration depth, it is expected that a lower influence of ambient air temperature will enhance the thermal performance of energy slabs. In general, the conventional closed-loop vertical GHEXs have been designed to exchange heat energy of about 3600 W per 50 m of borehole length. According to the TPT results, the energy slab can exchange heat energy of 400 W per 25 m\({}^{2}\) of energy slab unit. This means one unit of energy slab with an area of 25 m\({}^{2}\) can save about 10% of borehole drilling cost, which is the largest cost portion in construction of closed-loop vertical GHEXs.
## 2 Numerical Model Development and Experimental Validation
### Numerical Model Validation  
#### 2.3.1 Data Processing  
Before any analysis of the experimental and simulated data, the collected LW time-series were subjected to interpolation with a step size of \(10^{-7}\), so that their overall size and start and finishing points were the same. Then, all data were normalized in amplitude to the interval \([-1,1]\). Furthermore, due to the small size of the experimental dataset, it was necessary to do a cross-validation where each of the 11 experimental time-series was used as a validation set. To do this, the 11 experimental time-series were divided into three categories, with one case being used as a validation set, two used for testing, and the other eight used for training. Once the validation dataset was classified, a new validation dataset was chosen, and this was repeated until all 11 were chosen. This was done so that the results were not subjected to any bias that could arise from the low number of experimental cases and possible inconsistencies of the experimental setup.  
Figure 8: Representation of the three-layer approach to simulating weak adhesion.  
As stated previously, during the simulations, a total of 72 were created, thus there was a large discrepancy between the 11 experimental datasets we had and the 72 that came from the simulation. Therefore it was necessary to implement a data augmentation method to increase the number of experimental points. The chosen method was the application of a Gaussian white noise filter that was 5% of the average size of the overall signal. This allows for multiple cases that directly correlate to an experimental setup where multiple data acquisitions are taken of the same sample and the only difference is the noise caused by small environmental variations. After all data were treated and augmented, it was combined and normalized in order to have the same dimensionality.
# A New General Type-2 Fuzzy Predictive Scheme for PID Tuning
###### Abstract  
The proportional-integral-derivative controller is widely used in various industrial applications. But, in many noisy problems the strong methods are needed to optimize the proportional-integral-derivative parameters. In this paper, a novel method is introduced for adjusting the proportional-integral-derivative parameters through the model predictive control and generalized type-2 fuzzy-logic systems. The rules of suggested fuzzy system are online adjusted and the parameters of proportional-integral-derivative are tuned based on the fuzzy model such that a cost function to be minimized. The designed controller is applied on continuous stirred tank reactor and the performance is compared with other traditional approaches. The main advantages are that the accuracy is improved by online modeling and optimization and a predictive scheme is added to the conventional proportional-integral-derivative controller.  
fuzzy systems; machine learning; model predictive control; proportional-integral-derivative controller; continuous stirred tank reactor; self-tuning +
Footnote †: journal: Applied Sciences  
0000-0002-0002-9999-9999-9999-99999-99999-  
in a PID controller is derivative mode. Derivative control is rarely used in process control, but it is more commonly used in motion control. This type of controller is very sensitive to measurement noise in process control and makes adjustment using trial and error more difficult. However, the use of a derivative mode slightly increases the response speed [1; 2]. Then developing of tuning algorithms are essential to improve the performance.  
The model predictive control (MPC) is one of the most advanced control models for the practical systems. Because, the main control limitations and other state retractions can be taken into account in the control designing. However, but it is difficult to implement and many engineers are not familiar with it. To solve this problem, a novel combined scheme of MPC and PID controller implemented in a two-tier hierarchical structure is proposed. The performance of MPC is strongly depends on the model accuracy. However in many applications, the model is not available or not reliable in practical conditions. Commonly the mathematical model is perturbed by various factors and this problem undermines the MPC performance. We proposes a online optimized type-2 fuzzy model. The suggested identification scheme is updated sample-by-sample and guarantees the accurate model. Also the online tuned model can handle the unpredicted dynamic perturbations.  
The main challenges and problems are summarized as follows:  
* The PID is a simple and effective controller, but in many applications the gains should be tuned. The un-tuned parameters leads to undesired performance and/or instability.
* The most of existence regulation methods such evolutionary methods impose high computational cost.
* The predictive scheme in many applications improves the control accuracy, but its implementation is more difficult than PID.
* The MPC results in good performance, but its accuracy strongly depends on the model.
## 1 Introduction
used the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to predict daily surface reflectance data at the Landsat spatial resolution and the MODIS temporal resolution [22]. To eliminate shortcomings of STARFM, several subsequent algorithms based on the algorithm have been presented. For example, taking crop phenology changes into consideration, [PERSON] et al. developed the Spatial and Temporal Adaptive Vegetation index Fusion Model (STAVFM) to blend NDVIS extracted from the China Environment satellite (HJ-1) and MODIS data [23]. To overcome STARFM's inaccurate predictions of the surface reflectance over heterogeneous landscapes, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was proposed and has proven to be extremely effective [24]. The main objective of our study was to map the biomass and yield of winter wheat using PROBA-V 100- and 300-m S1 products. To achieve this, three major procedures were used: (1) generate daily 100-m land surface reflectance data by fusing PROBA-V 100- and 300-m S1 data using the STARFM and ESTARFM algorithms; (2) identify wheat fields based on time-series Normalized Difference Vegetation Index (NDVI) clustering and a priori knowledge; and (3) estimate biomass and yield by inverting time-series remote sensing data into a widely used light use efficiency model. In this paper, Section 2 contains an introduction to the study areas and data; Section 3 discusses the data pre-processing and fusion, crop classification, biomass and yield estimation, and result evaluation strategies; Section 4 includes the results and accuracy assessments from Section 3; and Sections 5 and 6 contain the discussion and conclusion of the paper, respectively.
## 2 Hierarchical Control for Frequency Regulation
### Frequency Regulation  
As mentioned above, to achieve a secondary level effect while maintaining the primary level foundation, Formula (5) can be derived by comparing Formulas (1) and (3), where the corresponding relationship reveals the specific components that implement the secondary compensation.  
\[mP^{*}=\delta\omega \tag{5}\]  
In other words, looking at the droop control formula alone, if the power reference value can be treated as a manipulable variable, its product with the droop coefficient actually represents the secondary control compensation amount.  
Returning to Formula (1) of droop control, maintaining the system's real-time frequency at its rated value requires an equation like Formula (6). After simplification, an analysis concludes that the power reference value \(P^{*}\) must equal the DG's output power \(P\), as demonstrated in Formula (7). This output power is configured within the controller.  
\[\omega=\omega^{*}-m(P-P^{*})=\omega^{*} \tag{6}\]  
\[P^{*}=P \tag{7}\]  
Therefore, the next key task is implementing Formula (7). The DG's power output primarily depends on two types of loads: local and common. Local loads, situated near the DG controller, have easily measurable power levels. Common loads, however, are typically located farther away, making their power levels harder to monitor in real time. Furthermore, DGs are classified as either dispatchable or non-dispatchable based on their load type. This study focuses on dispatchable DGs, which can adjust their power output to track accurate load prediction values used as setpoints. The approach can also generalize to other microgrid topologies or systems with different line impedances, DG capacities, or load types. These require modifications to SNN model training and dynamic timing regularization to update the power reference values in the controller.