esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 GFZ-Wireless Sensing Unit (GFZ-WSU): Hardware And Communications  
The development of the GFZ-WISE system has focused on two points. The first is the development of a low-cost wireless sensing unit (_i.e._, the hardware), while the second concerns the creation of a self-organizing wireless mesh network (_i.e._, the software). Both of these steps have been made possible by taking advantage of the experience gained during the development of the Self Organizing Seismic Early Warning Information Network developed during the SAFER and EDIM projects [14].  
Figure 1: Illustration of the proposed applications of the GFZ-WISE. The network consists of wireless stations that for long-term monitoring purposes can be linked to a central processing centre, while during temporary surveys transmit data in real-time to an external user. This allows the user to perform real-time inversion analysis of dispersion and H/V spectral ratio curves for the S-wave velocity profile estimation.  
The GFZ-Wireless Sensing Unit (GFZ-WSU) costs less than one tenth of a standard instrument, and is able to collect, store and undertake preliminary analysis of data when only deriving basic parameters (e.g., the H/V curve of a site) is of concern. Figure 1 shows the general philosophy of the GFZ-WISE system. Each GFZ-WSU node records the ground motion and transmits the data by the SeedLink protocol [16]. Through the Optimized Link State Routing (OLSR) protocol [17], these novel sensors can create a dense, self-organizing and decentralized seismic monitoring network. Raw data and computed parameters can be communicated to an external laptop running the SeisComP software [16] when the laptop is connected to any node that belongs to the network, allowing real-time analysis of the seismic data. In the following, the main characteristics of the software ensuring the system functionality (_i.e._, SeedLink, SeisComP, and OLSR) are reported.  
The self-organizing character allows GFZ-WISE to automatically adapt to changes in the network configuration, and guarantees the functionality of the network even when some of the sensing units malfunction, or cannot be seen by the external user. These characteristics make the system particularly suitable for application within mega cities and the monitoring of areas at risk to landslides. In particular, within the context of monitoring applications, special nodes equipped with additional communications hardware, e.g., Internet connection, satellite phones, VSAT, _etc._, may serve as entities able to communicate data and parameters to outside the network, such as to a disaster management center. On the other hand, the system is defined 'decentralized' because the data and the estimated parameters will not be transmitted to a unique user, or management center, but will be also available at every node of the network. Hence, during monitoring activities the decentralized property will allow, together with dedicated decision making algorithms, to perform a cooperative analysis of the data for early warning purposes.
# Image Recovery with Data Missing in the Presence of Salt-and-Pepper Noise
## 3 Transform Domains
### Nonorthogonal Basis-Framelet  
A wavelet frame system provides better smoothness, short support and symmetrical property than the orthogonal wavelet basis by sacrificing orthonormality and linear independence [27].  
With a scaling function \(\phi\) and wavelet functions \(\{\varphi_{1},\cdots,\varphi_{r}\}\), the so-called refinable equations are  
\[\begin{split}\phi(t)&=\sqrt{2}\sum_{k}h_{0}(k)\phi(2 t-k),\\ \varphi_{l}(t)&=\sqrt{2}\sum_{k}h_{l}(k)\phi(2t-k),l =1,2,\cdots,r,\end{split} \tag{9}\]  
where \(\phi(t)\), \(\varphi(t)\), and \(h(t)\) are user defined functions, and, in this work, they are chosen the same as in [27]--for example, \(h_{0}=\frac{1}{4}[1,2,1]\), \(h_{1}=\frac{\sqrt{2}}{4}[1,0,-1]\), and \(h_{2}=\frac{1}{4}[-1,2,-1]\), and \(\phi(t)\), \(\varphi(t)\) are the piecewise linear refinable spline and corresponding framelets.  
Under these notations, the frame coefficients of a signal \(x(t)\) can be calculated as  
\[\begin{split}& c_{k}=\int x(t)\phi_{k}(t)dt,\\ & d_{k,j,l}=\int x(t)\varphi_{k,j,l}(t)dt,\end{split} \tag{10}\]  
where \(c_{k}\) and \(d_{k,j,l}\) are the frame coefficients, and \(\phi_{k}(t)=\phi(t-k)\), \(\varphi_{k,j,l}(t)=\varphi_{l}(2^{j}t-k)\).  
With the frame coefficients, the signal is synthesized as  
\[x(t)=\sum_{k=-\infty}^{\infty}c_{k}\phi_{k}(t)+\sum_{l=1}^{r}\sum_{j=0}^{ \infty}\sum_{k=-\infty}^{\infty}d_{k,j,l}\varphi_{k,j,l}. \tag{11}\]  
Similarly, \(\mathcal{W}_{FT}\) is utilized to represent the tensor product framelet decomposition and \(\mathcal{W}_{FT}^{\mathrm{T}}\) denotes the framelet reconstruction, and, therefore, in the matrix notation, the frame coefficient and reconstructed image are respectively calculated by  
\[\mathbf{\alpha}_{FT}=\mathcal{W}_{FT}\mathbf{\mathrm{x}},\hskip 28.452756 pt\mathbf{ \mathrm{x}}=\mathcal{W}_{FT}^{\mathrm{T}}\mathbf{\alpha}_{FT}. \tag{12}\]  
Two representative bases are briefly introduced--for more details, the diligent readers are referred to [25; 27], especially [25] for the wavelet transform and [27] for the framelet transform. It is of interest to note that there are other transform domains that can be exploited for images--for example, second-generation wavelet [28] and synchrosqueezed wavelet transforms (SST) [29], to name a few. However, the further studies of different domains are beyond scope of this work. To more clearly observe the difference of two bases, the following experiment is conducted, where the coefficients of the framelet and wavelet decompositions are provided in Figure 1. It is clearly seen that framelet decomposition produces more sparse representation of the image than the wavelet one. Therefore, it is expected that framelet based approach will outperform the wavelet based approach.
# A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica
## 4 Analysis and Discussion
### Image Quality Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{**Region**} & \multirow{2}{*}{**Image Category**} & \multicolumn{3}{c}{**Band**} & \multirow{2}{*}{**Average**} \\ \cline{3-4}  & & & **R** & **G** & **B** & \\ \hline \multirow{8}{*}{A} & Original image & 2.04 & 3.18 & 4.70 & 3.31 \\ \cline{2-4}  & Histogram specification & 1.52 & 1.77 & 1.56 & 1.62 \\ \cline{2-4}  & Wallis & 11.10 & 10.05 & 10.47 & 10.54 \\  & Our result & 7.40 & 8.41 & 8.07 & 7.96 \\ \cline{2-4}  & \multirow{2}{*}{B} & Original image & 5.74 & 5.38 & 0.17 & 3.76 \\ \cline{2-4}  & Histogram specification & 18.11 & 18.08 & 12.13 & 16.11 \\ \cline{2-4}  & Wallis & 1.67 & 1.71 & 1.35 & 1.58 \\ \cline{2-4}  & Our result & 1.46 & 1.30 & 0.07 & 0.94 \\ \hline \multirow{8}{*}{As} & Original image & 6.67 & 5.92 & 2.76 & 5.12 \\ \cline{2-4}  & Histogram specification & 6.99 & 7.09 & 5.90 & 6.66 \\ \cline{2-4}  & Wallis & 9.64 & 8.15 & 8.01 & 8.60 \\ \cline{1-1}  & Our result & 6.30 & 7.80 & 9.53 & 7.87 \\ \cline{1-1} \cline{2-4}  & \multirow{2}{*}{D} & Original image & 0.55 & 0.89 & 3.32 & 1.59 \\ \cline{1-1} \cline{2-4}  & Histogram specification & 3.39 & 2.47 & 5.16 & 3.67 \\ \cline{1-1} \cline{2-4}  & Wallis & 7.49 & 6.74 & 6.28 & 6.84 \\ \cline{1-1}  & Our result & 0.99 & 0.18 & 0.12 & 0.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._
# Quad-Beam 4 \(\times\) 2 Array Antenna for Millimeter-Wave 5G Applications
## 2 Single-Element Antenna
### Design Procedure
The half-power beamwidth (HPBW) is measured to be 71\({}^{\circ}\) in the  
Figure 3: Surface current distribution of the final single-element antenna (Ant1) in (**a**), and (**b**) represents its reflection coefficient of a comparison of the initial elliptical antenna and the final elliptical ring (figure-eight) antenna. Figure 2: Final design of the single-element antenna (Ant1). The dimensions in mm are: EL = 7.3, Eh1 = 3.74, Ev1 = 1.7, Eh2 = 3, Ev2 = 1, Fl1 = 1.8, Fw1 = 0.4, Fl2 = 1, Fw2 = 0.78, SW1 = 8, and SL1 = 7.  
xz-plane and 56\({}^{\circ}\) in the yz-plane. Ant1 demonstrates excellent X-polarization performance in the xz-plane, while the X-polarization in the yz-plane is acceptable, as illustrated in Figure 4b. The electric field (E-field) energy in the E-plane exhibits linear polarization, aligning vertically along the x-axis at the resonance frequency, thereby generating vertical polarization. However, the slotted elliptical structure causes opposing magnetic field components, which results in higher X-polarization in the H-plane. This behavior is further explained through the Poynting vector representation shown in Figure 5, which highlights the field interaction and polarization effects within the antenna structure. The Poynting vector determines the direction of propagation through the product of electric and magnetic components. The radiated electromagnetic wave in the far field region is independent of distance, and it should have the real part of the Poynting vector and be free from the imaginary part to achieve maximum power transfer [24; 25]. The higher the magnitude of the Poynting vector, the higher the radiated power. In Figure 5, the red arrows indicate the higher magnitude vectors, and these high-magnitude vectors are aligned vertically and partly horizontally; as a result, a high X-polarization has occurred, as depicted in Figure 4b.
# The Role of Data-Driven Methodologies in Weather Index Insurance
## 3 Theoretical Framework
### Artificial Neural Networks  
Artificial neural networks (ANN) are the building blocks for deep learning approaches. Over the last decade, the area of machine learning and artificial intelligence (AI) has advanced greatly [28]. The term neural networks comes from the way algorithms tend to mimic how the human brain thinks. The way a neural network with one single layer works is based on the thousands of linear combinations which are fed from input variables. This information is later transformed by a non-linear activation function to obtain the output of the model. The final model is linear with respect to the transformed variables. One example of an ANN with one single layer is depicted below in Figure 1.  
The output of an ANN with one single layer is [28]:  
\[f(X)=\beta_{0}+\sum_{k=1}^{K}\beta_{k}A_{k} \tag{1}\]  
where \(A_{k}\) are the activations of each unit of the hidden layer:  
\[A_{k}=h_{k}(X)=g\left(w_{k0}+\sum_{j=1}^{p}w_{kj}X_{j}\right) \tag{2}\]  
The function \(g\) is the activation function that must be defined previously. One of the most common activation functions is the ReLU function. The non-linearity of the activation function is essential to capture the non-linear effects and interactions among the network [28]. It is required to define a loss function to adjust the model. Therefore, a gradient descent algorithm is applied to adjust the weights on each of the layers of the network.  
In this study, we used Python's library Keras to implement the ANN. Keras is a high-performance API from the TensorFlow library. Keras allows us to build and train the ANN. It additionally has the ability to optimize GPU computational loads.
# Timing and Frequency Synchronization Using CAZAC Sequences for OFDM Systems
## 5 Results and Discussion
### Comparison of the FFT Algorithm and the Quadratic Interpolation Algorithm  
The CFO estimation performance of the FFT algorithm and the quadratic interpolation algorithm is simulated and compared. The number \(m\) of ZC sequences is \(8\), the length \(N\) is \(512\), and CFOs are \(4\) kHz and \(20\) kHz, respectively. The simulation results are shown in Figure 15.  
As can be seen from Figure 15, when the frequency offset is far from the boundary of the estimation range, the estimation performance is better at a large frequency offset. Furthermore, the quadratic interpolation algorithm showed a great improvement in performance when compared with the FFT algorithm, in both small and large frequency offsets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Preamble** \\ \hline [PERSON]’s algorithm [8] & \([A_{N/2},A_{N/2}]\) \\ [PERSON]’s algorithm [9] & \([A_{N/4},A_{N/4}-A_{N/4}-A_{N/4}]\) \\ [PERSON]’s algorithm [10] & \([A_{N/4},B_{N/4},A^{*}_{N/4},B^{*}_{N/4}]\) \\ [PERSON]’s algorithm [16] & \([A_{N/4}-B^{*}_{N/4}-B^{*}_{N/4},A_{N/4}]\) \\ The CCPD algorithm & \([A_{N/m},A_{N/m},\cdots,A_{N/m}]\) \\ The ACPD algorithm & \([A_{N/m},A_{N/m},\cdots,A_{N/m}]\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The preambles of different algorithms.  
Figure 14: Correct timing probability comparison of the proposed algorithms with previous algorithms.
## 2 Problem Description and Model Formulation
### Model Formulation
#### 2.2.4 Soft Time Windows Model  
Compared with the traditional hard time windows, soft time windows have many advantages [34]. For example, if the time window is set as soft, the number of vehicles used and the total mileage of vehicles can be greatly saved; the soft time windows model is more general and contains the requirements of hard time windows, so the algorithm designed for solving soft time window models can be used to solve the problem with hard time windows by appropriately increasing the penalty value when deviating from the time windows. In some cases, there may be no feasible solution according to the requirements of hard time windows, but there is always a feasible solution according to the requirements of soft time windows.  
The broken line soft time windows model was proposed by [PERSON] et al. [35]. The broken line soft time window adds a period that can be tolerated by customers based on the ordinary soft time window. The penalty cost curve is shown in Figure 2. The period \([ET_{i},LT_{i}]\) is the optimal time window, and no penalty cost is needed to start the service within this period. When the vehicle starts service in \([ET_{i},ET_{i}]\) or \([LT_{i},ELT_{i}]\), there is less penalty cost; if the service start time is not within the range of \([EET_{i},ELT_{i}]\), a high penalty cost is required. Limits of time points that the customer can tolerate can be determined by \(EET_{i}=ET_{i}-\theta s_{i}\), \(ELT_{i}=LT_{i}+\theta s_{i}\), where \(\theta\) is the customer tolerance coefficient and \(s_{i}\) is the service time of customer \(i\).  
The penalty cost function under the broken line soft time windows is as follows:  
\[P_{c}(T_{ik})=\left\{\begin{array}{ll}p_{1}(EET_{i}-T_{is})+p_{2}(ET_{i}-EET _{i}),&T_{is}\leq EET_{i}\\ p_{2}(ET_{i}-T_{is}),&EET_{i}<T_{is}\leq ET_{i}\\ 0,&ET_{i}<T_{is}\leq LT_{i}\\ p_{3}(T_{is}-LT_{i}),&LT_{i}<T_{is}\leq ELT_{i}\\ p_{3}(ELT_{i}-LT_{i})+p_{4}(T_{is}-ELT_{i}),&ELT_{i}<T_{is}\end{array}\right. \tag{4}\]  
where \(p_{1}\), \(p_{2}\), \(p_{3}\), \(p_{4}\) are penalty costs per unit of time in the corresponding period, respectively.
## 3 Results
### Identification of Yeast Volatile Compounds by SPME-GC-MS
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Strain/Substrate** & **pH Value** & **NaCl (\%)** & **Compound** & **Kovats Index** & **Area (\%)** \\ \hline \multirow{8}{*}{LQC 10353/BHI broth} & \multirow{8}{*}{4.0} & \multirow{8}{*}{1.5} & Ethanol & \textless{}800 & 11.17 \\  & & & Ethyl acetate & \textless{}800 & 56.58 \\  & & & Isamyl alcohol & \textless{}800 & 15.01 \\  & & & 3-methyl-butanol acid & 854 & 0.33 \\  & & & Isamyl acetate & 879 & 0.61 \\  & & & Benzaldehyde & 965 & 1.72 \\  & & & 2,4-di-tert-butyl-phenol & 1505 & 0.44 \\ \hline \multirow{8}{*}{LQC 10353/BHI broth} & \multirow{8}{*}{4.5} & \multirow{8}{*}{0.5} & Ethanol & \textless{}800 & 5.23 \\  & & & Ethyl acetate & \textless{}800 & 74.31 \\  & & & Isamyl alcohol & \textless{}800 & 9.68 \\  & & & Acetic acid-butyl ester & 814 & 1.75 \\  & & & Isamyl acetate & 879 & 2.69 \\ \hline \multirow{8}{*}{LQC 10360/BHI broth} & \multirow{8}{*}{4.5} & \multirow{8}{*}{1.5} & Ethanol & \textless{}800 & 6.87 \\  & & & Ethyl acetate & \textless{}800 & 70.48 \\  & & & Isamyl alcohol & \textless{}800 & 11.79 \\  & & & Isamyl acetate & 879 & 1.24 \\  & & & Benzaldehyde & 965 & 0.66 \\  & & & Octanal & 1002 & 0.38 \\  & & & Nonanal & 1105 & 1.05 \\ \hline \multirow{8}{*}{LQC 10360/BHI broth} & \multirow{8}{*}{5.0} & \multirow{8}{*}{0.0} & Ethanol & \textless{}800 & 17.76 \\  & & & Ethyl acetate & \textless{}800 & 43.50 \\  & & & Isamyl alcohol & \textless{}800 & 16.39 \\  & & & Isamyl acetate & 879 & 0.40 \\  & & & Benzaldehyde & 965 & 2.98 \\  & & & 2,4-di-tert-butyl-phenol & 1505 & 0.19 \\ \hline \multirow{8}{*}{LQC 10360/BHI broth} & \multirow{8}{*}{5.0} & \multirow{8}{*}{0.0} & Ethanol & \textless{}800 & 10.82 \\  & & & Ethyl acetate & \textless{}800 & 63.40 \\  & & & Isamyl alcohol & \textless{}800 & 12.40 \\  & & & Acetic acid-butyl ester & 814 & 1.69 \\  & & & Isamyl acetate & 879 & 0.62 \\  & & & & 2,4-di-tert-butyl-phenol & 1505 & 0.59 \\ \hline \multirow{8}{*}{LQC 10360/Liquid wheat dough} & \multirow{8}{*}{6.0} & \multirow{8}{*}{1.0} & Ethanol & \textless{}800 & 15.93 \\  & & & Ethyl acetate & \textless{}800 & 51.64 \\  & & & Isamyl alcohol & \textless{}800 & 17.67 \\  & & & Acetic acid-butyl ester & 814 & 1.81 \\  & & & Isamyl acetate & 879 & 0.36 \\ \hline \multirow{8}{*}{LQC 10364/Liquid wheat dough} & \multirow{8}{*}{6.0} & \multirow{8}{*}{1.0} & Ethanol & \textless{}800 & 20.36 \\  & & & Ethyl acetate & \textless{}800 & 57.27 \\  & & & Isamyl alcohol & \textless{}800 & 10.83 \\  & & & Isamyl acetate & 879 & 0.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._but in another one, was found less than 1.0%. Regarding the abundance of isoamyl acetate, only in some cases, it exceeded 1.0% of the total area.
## 2 Model Description
### Impulse Turbine  
The dynamics of the impulse turbine typically assumes steady uni-directional air flow, even though the actual characteristics are that of bi-directional motion with fluctuating speed. Hence, we assume this process as a quasi-steady state; doing so will provide the applicability of the steady flow characteristics of the turbine into the second stage. The dynamics is presented as follows [27]:  
\[\Delta p_{t}=C_{a}\cdot\beta\cdot\frac{1}{a_{t}}\cdot V_{x}^{2} \left[1+\frac{1}{\phi^{2}}\right],\] (4) \[\tau_{t}=C_{t}\cdot\beta\  
It is emphasized that the inside air is incompressible; therefore, flow rate \(Q\) can be proportional to \(V_{x}\) and \(|\dot{x}|\). \(C_{a}\), \(C_{t}\) and \(\eta_{t}\) are functions of \(\phi\) and should be numerically modeled in order to carry out the simulation. Hence, \(C_{a}\) and \(C_{t}\) are assumed to be numerical equations of \(\phi\) as follows [45]:  
\[C_{a} = -0.02358+3.4556\phi+3.9422\phi^{2}-11.1530\phi^{3} \tag{10}\] \[+10.4871\phi^{4}-5.0828\phi^{5}+1.2673\phi^{6}-0.1285\phi^{7}.\] \[C_{t} = -0.042480-0.5936\phi+2.9063\phi^{2}+0.1824\phi^{3}\] (11) \[-2.6953\phi^{4}+1.9518\phi^{5}-0.8781\phi^{6}+0.06377\phi^{7}.\]  
\(\phi\) is assumed to be bounded within \(0.5<\phi<2.5\). In Figure 2, it can be seen that \(C_{a}\) continuously increases from the origin, but \(C_{t}\) starts from a negative value with small \(\phi\) and gradually increases.  
The governing equation for the turbine motion is:  
\[J\Omega=\tau_{t}-\tau_{g}. \tag{12}\]  
The left-hand side represents the inertia torque. The right-hand side is the sum of turbine output torque and load torque induced from an electric generator.
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
## 5 Efficient Depth-Based MAC Protocol
### Offered Traffic Upper-Bound  
The range of offered traffic, \(D_{rate}\), used in Equation (3) should be carefully specified to be used in our system model. To find its upper-bound, the following equation must be satisfied:  
\[T_{s_{s}}\leq T_{s}. \tag{9}\]  
It means that the slot length cannot be less than a sub-slot length, which is already calculated by Equations (5) and (6), respectively. By replacing \(T_{s}\) with Equation (5), it can be presented as:  
\[T_{s_{s}}\leq\frac{T_{r}}{N_{s}}. \tag{10}\]  
By replacing \(T_{r}\) and \(N_{s}\) using Equations (3) and (4), respectively, it can be extended as:  
\[T_{s_{s}}\leq\frac{1}{2D_{rate}\times N_{max}}. \tag{11}\]  
Based on the above equation, the upper-bound for \(D_{rate}\) is calculated as:  
\[D_{rate}\leq\frac{1}{2T_{s_{s}}\times N_{max}}, \tag{12}\]  
which shows that it depends on the sub-slot length and neighbouring node density. The sub-slot length, \(T_{s_{s}}\), is a fixed value that is long enough to handle the consecutive receiving packets and \(N_{max}\) is related to the node deployment model, number of nodes, and network topology.  
In higher throughput, the length of round time is decreased based on Equation (3). The higher the offered traffic, the shorter round time and, hence, the shorter slot length based on Equation (5). The number of sub-slots is also decreased based on Equation (8). It means that the higher the offered traffic, the less sleeping time for each node, resulting in high throughput. In other words, when the traffic load is low, ED-MAC allows nodes to be in the sleeping mode for a long time to save more energy. However, when the traffic load is high, all nodes are active most of the time to send or receive data packets.
# Effect of PEF Treatment on Chosen Properties of Raw and Hot Air- and Freeze-Dried Poultry Meat
## 3 Results and Discussion
### The Influence of PEF Pretreatment on the Structure of the Hot Air- and Freeze-Dried Poultry Meat
#### 3.3.1 Dry Matter Content and Water Activity
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Dry Matter** & \multirow{2}{*}{**a\({}_{w}\) [\(-\)]**} & \multirow{2}{*}{**RR [\(-\)]**} & **SSL [\(-\)]** & **Hygroscopic** \\  & **Content [\%]** & & & **Properties [\(-\)]** \\ \cline{3-5}  & & & **After 30 min** & **After 30 min** & **After 24 h** \\ \hline HA & 92.6 \(\pm\) 0.3 b  & 0.408 \(\pm\) 0.039 b & 1.400 \(\pm\) 0.128 b & 0.009 \(\pm\) 0.001 b & 1.146 \(\pm\) 0.011 ab \\ HA\_PEF 15 kJ/kg & 92.9 \(\pm\) 1.0 b & 0.386 \(\pm\) 0.066 b & 1.334 \(\pm\) 0.090 b & 0.009 \(\pm\) 0.001 b & 1.164 \(\pm\) 0.013 b \\ HA\_PEF 30 kJ/kg & 90.2 \(\pm\) 0.2 a & 0.486 \(\pm\) 0.038 c & 1.311 \(\pm\) 0.036 b & 0.008 \(\pm\) 0.001 b & 1.122 \(\pm\) 0.015 a \\ \hline FD & 96.1 \(\pm\) 0.5 cd & 0.101 \(\pm\) 0.024 a & 2.924 \(\pm\) 0.114 a & 0.019 \(\pm\) 0.001 a & 1.241 \(\pm\) 0.010 d \\ FD\_PEF 15 kJ/kg & 97.2 \(\pm\) 0.2 d & 0.080 \(\pm\) 0.009 a & 2.981 \(\pm\) 0.500 a & 0.019 \(\pm\) 0.003 a & 1.223 \(\pm\) 0.007 cd \\ FD\_PEF 30 kJ/kg & 96.0 \(\pm\) 1.1 c & 0.116 \(\pm\) 0.032 a & 2.731 \(\pm\) 0.524 a & 0.018 \(\pm\) 0.003 a & 1.213 \(\pm\) 0.015 c \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect of PEF treatment (15 and 30 kJ/kg) of poultry meat dried with hot air (HA) and freeze-drying (FD) on dry matter content, water activity, rehydration rate (RR) and soluble solids loss (SSL) after 30 min of rehydration process, and hygroscopic properties after 24 h under water activity equal to 1.  
of PEF pretreatment, exerts a predominant effect on both the dry matter content and the water activity of poultry meat.