esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Current trends in the maritime transport sector include the digitalization of ships and the development of sustainable autonomous shipping. These concepts require continuous real-time transmission of large amounts of digital shipboard data, which may be hampered by current communications limitations, including low speeds, limited capacity, and high costs. As a result, further upgrades and modernization of the maritime communications network may be needed, representing a significant effort in terms of required financial cost, material resources, and widespread application. An alternative approach to improving maritime communications capacity may involve the application of specialized data compression methods. The adaptive compression approach proposed in this study has been shown to efficiently reduce the size of transmitted shipboard data to an average of 3.4% of the originally sent data. Moreover, the presented approach provides a significant performance improvement over the several competing data conversion techniques. Therefore, this straightforward data compression method can be quickly and efficiently implemented in the ship communication system to enable future remote voyage monitoring and control functions.  
Conceptualization, I.J., N.L. and N.W.; methodology, N.W.; software, N.W.; validation, N.W.; formal analysis, I.J., N.L., N.W. and H.L.; investigation, N.W.; resources, I.J., N.L. and N.W.; data curation, N.L. and N.W.; writing--original draft preparation, N.L. and N.W.; writing--review and editing, I.J., N.L., N.W. and H.L.; visualization, N.L. and N.W.; supervision, I.J. and N.W.; project administration, I.J. and N.W.; funding acquisition, I.J. and N.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the European Regional Development Fund within the Interreg V-A, Italy--Croatia, under the project MarIftime and MultimOdal Sustainable pAssenger transport solutions and services-MIMOSA (Application ID-10249002) and by the University of Rijeka, Faculty of Maritime Studies, under the project \"Development of advanced digital signal processing algorithms with application in the maritime sector\".  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
The amount of nitrite permitted for use in cured meat products is currently 150 mg kg\({}^{-1}\). Although the use of these additives is permitted in meat processing, consumer concern motivates researchers and the meat producers to produce meat products in line with the trend 'clean label' without using nitrates/nitrites [16]. In this context, some researchers undertake research on the effect of reducing the addition of nitrates on the quality of meat products, with particular emphasis on their shelf life [17; 18]. Our previous studies [19] suggested that the use of nitrite in the amount lower than permitted by law allows to obtain a cooked meat product with properties similar to the product with 150 mg kg\({}^{-1}\) of nitrite, especially in terms of physicochemical properties and lipid oxidation. However, the reduction of nitrates in traditionally produced fermented meat products carries the risk of a shortened shelf life. In this type of meat product, not subjected to heat treatment, nitrates play a special role, in particular due to their antioxidant properties and their participation in color formation. As shown by previous studies, the elimination or reduction of nitrate may affect the fermentation process during the production of meat products [20; 21; 22]. In this context, the aim of the present study was to determine the effect of sodium nitrate reduction to the level 100, 50 and 0 mg kg\({}^{-1}\) on lipid oxidation measured as thiobarbituric acid reactive substances (TBARS), the color parameters (CIEL* a* b*) total heme pigments, heme iron content and nutritional value related to N-nitrosamines content as well as nitrate and nitrite residues of traditionally produced fermented loins during six months of vacuum storage. Based on previous research, it was hypothesized that reducing the nitrate addition by more than half will allow to obtain a fermented meat product with characteristics similar to the product with the traditional addition of nitrate.
## Data and Methods  
### Solar and Geomagnetic Data  
The ionosphere is easily disturbed by solar and geomagnetic activities. Solar and geomagnetic activities should be analyzed prior to ionosphere anomaly detection before earthquakes [23]. The solar activity is reflected by the solar flares and radio flux F10.7. The geomagnetic activity is depicted by the disturbance storm time (Dst), auroral electrojet (AE), and Kp indices to present the equatorial to mid-high latitudes geomagnetic activity. The F10.7 and solar flares data are provided by the space center of the Chinese Academy of Sciences ([[http://www.sepc.ac.cn](http://www.sepc.ac.cn)]([http://www.sepc.ac.cn](http://www.sepc.ac.cn)), accessed on 1 June 2021), with a time resolution of 1 d. A quiet solar state is less than 100 SFU and an M level for F10.7 and solar flares, respectively [24]. The Dst, AE, and Kp index are provided by the Kyoto geomagnetic data center, Japan ([[http://wdc.kugi.kyoto-u.ac.jp/dstae/index.html](http://wdc.kugi.kyoto-u.ac.jp/dstae/index.html)]([http://wdc.kugi.kyoto-u.ac.jp/dstae/index.html](http://wdc.kugi.kyoto-u.ac.jp/dstae/index.html)), accessed on 1 June 2021), and the Goddard Space Flight Center ([[https://omniweb.gsfc.nasa.gov/form/dxl.html](https://omniweb.gsfc.nasa.gov/form/dxl.html)]([https://omniweb.gsfc.nasa.gov/form/dxl.html](https://omniweb.gsfc.nasa.gov/form/dxl.html)), accessed on 1 June 2021), respectively. The time resolution of these geomagnetic indices is 1 h, with \(-\)30-30 nT for Dst and 0-4 for Kp under the stable condition of terrestrial magnetism [6; 7; 25].
# Congestive Heart Failure Category Classification Using Neural Networks in Short-Term Series
## 2 Models Based on Neural Networks  
### Multilayer Perceptron Neural Network  
Let us characterize the \(N\) short time series by the set \(\{(\mathbf{x}_{k},\mathbf{t}_{k}):\mathbf{x}_{k}\in\mathbb{R}^{d},\mathbf{t}_{k}\in\mathbb{R}^ {m}\}\), where \(\mathbf{x}_{k}=[x_{k1},x_{k2},\ldots,x_{kd}]^{T}\) is the \(k\)-th input vector of size \(d\) and \(\mathbf{t}_{k}=[t_{k1},t_{k2},\ldots,t_{km}]^{T}\) is the \(k\)-th corresponding target vector of size \(m\). The MLP model is a type of feedforward learning neural network [46; 47], whose architecture is composed of multiple neurons grouped in layers. Considering \(Q\) hidden layers, the neurons in the hidden layer \(q\) (\(0<q\leq Q\)) process information through the equation \(\mathbf{h}_{k}^{(q)}=f(\mathbf{h}_{k}^{(q-1)}\mathbf{W}^{(q)}+\mathbf{b}^{(q)})\), whence \(f(\cdot)\) is an activation function, \(\mathbf{h}_{k}^{(q-1)}\) (\(q=1\), \(\mathbf{h}^{0}=\mathbf{x}_{\mathbf{k}}\)) denotes the output vector of layer \(q-1\), \(\mathbf{W}^{(q)}\) corresponds to the weights matrix between the layers \(q-1\) and \(q\), and \(\mathbf{b}^{(q)}\) is the bias vector in layer \(q\)[46; 48]. The Softmax activation function is used at the output layer to compute the probability that the sample \(\mathbf{x}_{k}\) can belong to the target class, thus generating the predicted output \(\mathbf{\hat{t}}_{k}\) of the MLP model.  
Finally, it is necessary to estimate the optimal parameters of the model by minimizing a cost function, defined as follows:  
\[J(\theta;\mathbf{t}_{k},\mathbf{\hat{t}}_{k})=\frac{1}{N}\sum_{k=1}^{N}\ell(\theta; \mathbf{t}_{k},\mathbf{\hat{t}}_{k}), \tag{1}\]  
where \(\theta=\{(\mathbf{W}^{(1)},\mathbf{b}^{(1)}),\ldots,(\mathbf{W}^{(Q+1)},\mathbf{b}^{(Q+1)})\}\). The loss function defines the error between the actual model output and the expected output, appropriately chosen for a specific task (e.g., mean squared error, hinge, Huber, and cross-entropy). The classical methods for estimating the network parameters are gradient descent, variants of this algorithm (e.g., stochastic gradient descent, stochastic gradient descent with momentum, and Adam's method) [49], and the backpropagation learning mechanism [50].
## 3 Results and Discussion  
### Simulation Approach for Predicting the Experimental Result  
Figure 6 shows the 3D FDTD simulation results of MRR transmission spectra at the through-port for the proposed design arrangement. It shows that the proposed design in this study had a transmission value of nearly 80% at a resonance peak wavelength of 1543.70 nm with FWHM and quality factors of approximately 0.456 nm and 3385.307 nm, respectively. In this study, we optimized the FDTD simulation using minimum mesh steps up to 0.25 nm and a simulation time of 50,000 fs. Additionally,  
Figure 5: The schematic on how the resonance shift occurs due to the occurrence of glucose analytes on the MRR-based sensor.  
the standard Perfectly Matched Layer (PML) boundary conditions were used to provide good overall absorption. This optimization was done in order to achieve a balance outcome between accuracy, memory requirement, and simulation time.
## 5 Results and Discussions
### System 3: The IEEE 118-Bus
#### 5.3.2 Case 2: Simultaneous Minimization of APL and VD  
In this case, the weighting factors in Equation (18) are taken as \(\omega_{1}=0.65,\omega_{2}=0.35,\omega_{3}=0\) in order to achieve the best results, since the simultaneous minimization of both APL (OF\({}_{1}\)) and VD (OF\({}_{2}\)) is considered. Three different values of power factor: Unity, 0.866 lag, and 0.82 lag are defined as the subcases for the QODELFA's implementation.  
It may be noted from the results shown in Table 10 obtained by applying the proposed algorithm that the APL and VD are decreased to 536.134 kW and 0.0365 p.u. with unity power factor DGs (Case 2.1); to 149.215 kW and 0.0067 p.u. with 0.866 lag power factor DGs (Case 2.2); and to 134.049 kW and 0.0064 p.u. with 0.82 lag power factor DGs (Case 2.3).  
As presented in Table 10, strong improvements of APL and VD are obtained in the last subcase. The SD of the results is also given in Table 10. Moreover, the fast convergence of QODELFA to the optimal solution for Case 2.1, 2.2, and 2.3 is demonstrated in Figure 16.
# Effect of Plasma Treatment of Titanium Surface on Biocompatibility
## 3 Results
### Cell Adhesion and Morphology  
RBM cell adhesion to the sample disks was evaluated after 1, 3, 6, and 24 h incubation. Plasma-treated titanium disks showed greater RBM cell adhesion than the untreated disk surfaces at every time point (Figure 6).  
Cell morphology was observed using Alexa Fluor 488-phalloidin and 4',6-diamidino-2-phenylindole (DAPI) staining after 24 h incubation. The RBM cells adhering to plasma-treated titanium showed greater F-actin expression and more filopodia and lamellipodia than those on the untreated titanium surface (Figure 7).  
Figure 5: Bovine serum albumin (BSA) adsorption of titanium and plasma-modified titanium. More BSA was adsorbed to the plasma-treated than the titanium disks at every time point.  
Figure 6: Rat bone marrow (RBM) cell adhesion of titanium and plasma-modified titanium. Plasma-treated titanium disks showed greater RBM cell adhesion than the titanium disk surfaces at every time point.
## 5 Simulation Results
Since we are interested in obtaining the CRB of the signal DOAs, we partitioned the unknown parameters as \(\psi=[\mathbf{w}^{\text{T}}|\mathbf{p}^{\text{T}}\ \sigma_{n}^{2}]^{\text{T}}\). The CRB can be obtained as the inverse of the Fisher information matrix (FIM), which is defined as  
\[[\mathbf{F}]_{u,v}=-\mathbb{E}\bigg{[}\frac{\partial^{2}\ln p(\mathbf{y}|\psi)}{ \partial\psi_{u}\psi_{v}}\bigg{]}, \tag{22}\]  
where \(\psi_{u}\) is the \(u\)th element of \(\psi\), with \(u,v\in 1,2,\cdots,2D+1\). The FIM can also be expressed as [43]  
\[\frac{1}{T}\mathbf{F}= \begin{bmatrix}\Delta_{w}\\ \Delta_{o}\end{bmatrix} ^{\text{H}} \begin{bmatrix}\Delta_{w}&\Delta_{o}\end{bmatrix} = \begin{bmatrix}\Delta_{w}^{\text{H}}\Delta_{w}&\Delta_{w}^{\text{H}}\Delta_{o }\\ \Delta_{o}^{\text{H}}\Delta_{w}&\Delta_{o}^{\text{H}}\Delta_{o}\end{bmatrix}  \tag{23}\]  
Figure 6: Loss vs. number of epochs. where \(\Delta_{w}=(C_{yy}^{\rm T}\otimes C_{yy})^{-\frac{1}{2}}\Big{[}\frac{\partial \mathbf{r}}{\partial w_{1}},\cdots,\frac{\partial\mathbf{r}}{\partial w_{D}}\Big{]}\) and \(\Delta_{o}=(C_{yy}^{\rm T}\otimes C_{yy})^{-\frac{1}{2}}\Big{[}\frac{\partial \mathbf{r}}{\partial\sigma_{1}^{2}},\cdots,\frac{\partial\mathbf{r}}{\partial\sigma_{ D}^{2}},\frac{\partial\mathbf{r}}{\partial\sigma_{n}^{2}}\Big{]}\) with \(\mathbf{r}=\text{vec}(C_{yy})\). Then, the CRB of the signal spatial frequencies is obtained as [43]  
\[\text{CRB}=\frac{1}{T}(\Delta_{w}^{\rm H}\Pi_{o}^{\perp}\Delta_{w})^{-1}, \tag{24}\]  
where \(\Pi_{o}^{\perp}=\mathbf{I}-\Delta_{o}(\Delta_{o}^{\rm H}\Delta_{o})^{-1}\Delta_{o} ^{\rm H}\). Next, we considered a scenario where nine sources move with an initial position of \(-20^{\circ}\), \(-15^{\circ}\), \(-10^{\circ}\), \(-5^{\circ}\), \(0^{\circ}\), \(5^{\circ}\), \(10^{\circ}\), \(15^{\circ}\), and \(20^{\circ}\) in the positive direction with the same angular rate. They move 1 degree per 20 snapshots. The result of the proposed method is compared with the result of the method described in [34] because both are sequential methods, namely, \(\mathbf{\Phi}\) is sequentially updated. As shown in Figure 8, as the source positions change, the performance of the method described in [34] degrades. This is because this method uses the posterior from the previous time instant as a sufficient statistic of all past observations. Therefore, as each new measurement differs from the previous ones, this method cannot adapt well. In contrast, the proposed method, as depicted in Figure 9, does not have this limitation, resulting in improved DOA estimation performance as the sequential updating continues. Figure 7: Performance comparison. (**a**) RMSE versus input SNR. (**b**) RMSE versus number of snapshots. (**c**) RMSE versus compressed dimension. Figure 8: Estimated spectra for moving sources using method in [34]. (**a**) Initial position. (**b**) Next position from (**a**). (**c**) Next position from (**b**). Figure 9: _Cont._
## 2 Materials and Methods
### CMC Samples Characterization
#### 2.3.3 Degree of Substitution  
The absolute values of the Degree of Substitution (DS) of all CMC samples were determined using potentiometric back-titration by the ASTM D1439-15 standard method in duplicate assays for each sample [19]. According to this method, approximately 4 g of each powdered sample placed in 75 mL of ethanol (95%, \(v/v\)) were stirred at room temperature for 10 min, followed by the addition of 5 mL of HNO\({}_{3}\), under stirring. The slurry was then boiled for 5 min. At the end of this step, the solution was removed from the heat and continuously stirred further for 15 min. The liquid supernatant was decanted, and the precipitate was washed with 30 mL of ethanol (80%, \(v/v\)) previously heated to 60 \({}^{\circ}\)C. This step was repeated five times. Next, the precipitate was further washed with anhydrous methanol, filtered, and dried at 105 \({}^{\circ}\)C for 3 h, followed by cooling in a desiccator for 40 min. For titration purposes (repeated twice), about 0.5 g (nearest to 0.01 g) of each dried acid CMC was dissolved in 100 mL of distilled water. Then, 25.00 mL of 0.3 N NaOH solution was added, under stirring, and the solution was heated to a boil and boiled for 15 min. The excess NaOH was titrated with 0.3 N HCl, using phenolphthalein as an indicator, while the solution was still hot. The DS was calculated using Equations (2) and (3), as follows:  
\[A=\frac{[(BC)-(DE)]}{F} \tag{2}\]  
\[DS=\frac{0.162A}{1-(0.058A)} \tag{3}\]  
where \(A\) = milliequivalents of acid consumed per gram of sample; \(B\) = NaOH solution added (mL); \(C\) = normality of NaOH solution; \(D\) = HCl solution required for titration (mL); \(E\) = normality of HCl solution; \(F\) = weight of sample (g); 162 = gram molecular mass of the anhydroglucose unit of cellulose; and 58 = MW of carboxymethyl group.
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Term** \\ \hline ACE & asymmetric convolutional encoder \\ \hline ADL & Adaptive deep learning \\ \hline AMI & Advanced Metering Infrastructure \\ \hline BPNN & Back Propagation Neural Network \\ \hline CFS & correlation-based feature selection \\ \hline DBN & deep belief network \\ \hline DDoS & distributed denial of service \\ \hline DoS & denial-of-service \\ \hline DT & Decision Tree \\ \hline FDIA & false data injection attacks \\ \hline HAN & Home Area Network \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Nomenclature.
## 2 Quasi-Static Analysis Using the Proposed Equivalent Circuit Model
### Analysis Using a Superposition Method  
Impedance matching between the integrated antenna and detector input affects the performance of the detector. The superposition method does not provide a detailed impedance value although it can simplify the effect of the interconnect at the input of the detector device. The detector input has two independent voltage sources, namely the terahertz signal \(v_{THz}\) which is an AC signal and the external gate bias \(V_{G}\) which is a DC signal. The detector input is explained by the principle of superposition since the two input signals exhibit a linear relationship. Applying the superposition method for the ac voltage source, the external gate bias voltage source is considered as a short circuit as shown in Figure 2. The voltage of the input node \(v_{IN}\) is expressed as (3) by the law of voltage distribution as  
\[v_{IN}=\frac{z_{DT}}{Z_{G}+z_{DT}}v_{A}, \tag{3}\]  
where \(v_{A}\) denotes the voltage of the node A where the antenna input port meets the external gate bias line. The terahertz signal is represented by (4) using the Kirchhoff's rules of current to node A. Substituting into (4) after modifying (3) for the voltage of node A, the terahertz signal is modified to (5) as follows:  
\[v_{THz}=\bigg{(}1+\frac{Z_{IN}}{Z_{Bias}}+\frac{Z_{IN}}{Z_{G}}\bigg{)}v_{A}- \frac{Z_{IN}}{Z_{G}}v_{IN}, \tag{4}\]  
\[v_{THz}=\bigg{[}\bigg{(}\frac{Z_{G}+z_{DT}}{z_{DT}}\bigg{)}\bigg{(}1+\frac{Z_ {IN}}{Z_{Bias}}+\frac{Z_{IN}}{Z_{G}}\bigg{)}-\frac{Z_{IN}}{Z_{G}}\bigg{]}v_{IN}, \tag{5}\]  
\[v_{THz}=\bigg{[}\bigg{(}\frac{Z_{G}+z_{DT}}{z_{DT}}\bigg{)}\bigg{(}1+\frac{Z_ {IN}}{Z_{G}}\bigg{)}-\frac{Z_{IN}}{Z_{G}}\bigg{]}v_{IN}, \tag{6}\]  
where \(Z_{Bias}\) in (5) should be infinite to pass all the terahertz signal to the detector input, and the \(Z_{IN}/Z_{Bias}\) term converges to zero and results in (6). The relationship between the input signal \(v_{IN}\) of the detector and the terahertz signal \(v_{THz}\) received from the integrated antenna is simply expressed as  
\[v_{IN}=M\cdot v_{THz}, \tag{7}\]  
where the parameter \(M\) denotes the reciprocal of the \(v_{IN}\) coefficient at (6). The parameter \(M\) has a maximum to transfer all terahertz input signals to the gate node of the detector device. It should be satisfied as expressed in (8) to approximate the maximum value of \(1\) of the parameter \(M\) as follows:  
\[Z_{G}\ll z_{DT}. \tag{8}\]  
Therefore, the transmission line corresponding to the \(Z_{G}\) should be designed with a minimum possible length such that all terahertz input signal is applied to the gate of the detector device.
# A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph
## 3 Materials and Methods  
### Dataset  
In this work, we used a dataset created by [PERSON] and [PERSON] [4] and made publicly available. This dataset is the largest freely available dataset for research and contains 1500 panoramic radiograph images divided into 10 categories. The images were acquired at the Imaging Diagnostic Center of the Universidade Estadual do Sudoeste da Bahia (UESB) using an X-ray camera model ORTHOPHOS XG 5/XG 5 DS/Ceph, manufactured by Sirona Dental Systems GmbH (please see [4] for more details such as: focus-sensor distance, scale of images, widespread sensor resolution). The teeth mask was defined globally in each buccal image, rather than per tooth. The images were classified according to the variety of structural features of the teeth. This information is shown in detail in Table 1. We selected 80% of the images for the training set, 10% for the validation set and 10% for the test set.  
Images have been resized from 1127 px height by 1991 px width (originally) to 256 px height by 256 px width. We used the panoramic radiograph images in RGB colors, that is, with three color channels, and the masks only in gray scale, that is, one color channel. Before passing the data to the models, we normalized each pixel that initially ranged from 0 to 255 to 0 to 1.
# Connection-Aware Digital Twin for Mobile Adhoc Networks in the 5G Era
## 4 Experimental Results
### Analysis of the Results
#### 4.3.1 QoS Analysis
(**c**) Comparison of delivery probability obtained in the 5G-MANET and in the DT, at the four different evaluation contexts. (**d**) Comparison of latency obtained in the 5G-MANET and in the DT, at the four different evaluation contexts. (**e**) Comparison of overhead obtained in the 5G-MANET and in the DT, at the four different evaluation contexts. (**f**) Comparison of hops count obtained in the physical network and in the DT, at the four different evaluation contexts. Considering the provided results, the proposed proof-of-concept offers a reliable virtual representation of physical 5G-MANETs. On the one hand, the results obtained in online topologies reported by the DT match those of the actual network, demonstrating successful monitoring of the events. On the other hand, partially-offline scenarios present a challenge for the DT, leading to significant deviations from the actual values, especially in dynamic topologies. These deviations are primarily due to the lack of control information, stemming from the low delivery probability of the received messages. This issue leads to a loss of information, affecting the accuracy of delivery reports and reducing the calculated latency, overhead, and hop count. Nevertheless, the application of the context-aware routing strategy proves to be a suitable response for handling disconnection periods, as it shows how the gateway node communicates control data from disconnected devices.