esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Firstly, a multiband 5G NR signals are employed in the experimental testbed to cover enhanced mobile broad band (eMBB) scenarios and small cells for 3 GHz and 20 GHz, respectively. 2. A robust DPD technique utilizing negative feedback iteration is shown. The proposed DPD identification method has a relative lower computational complexity as compared toother learning architectures. In the proposed method of DPD identification, firstly, a DPD signal will be identified followed by the estimation of the DPD parameters. 3. The linearization performed is not limited to our previous proposed methods coming from volterra series but includes the \"out of box\" approach that includes CPWL and DVR method. In addition, a novel magnitude selective affine (MSA) method is proposed that reduces the overheads of complexity such as multiplications in CPWL architecture, however achieving similar efficiency as of CPWL. 4. In addition, a simple optimized neural network (ONN) based DPD algorithm is proposed that is an upgradation of our previously proposed DPD based method utilizing deep learning to perform linearization of 50 MHz 5G New Radio (NR) based RoF links. The NN DPD method is executed by a different type of training which does not utilize Indirect Learning Architecture (ILA). Initially we emulate the generic RoF link using a RoF NN and then train the proposed DPD ONN using this, by backpropagating the errors. For the first time, a comparative experimental study has been held where MP, GMP, DVR, CPWL, MSA and ONN are compared for 5G NR multiband signal. The performance is evaluated in terms of Adjacent Channel Power Ration (ACPR) Error Vector Magnitude (EVM) and complexity in terms of multiplications and coefficients requirements. The summary of this work is shown in the Figure 3 where overall summary of each respective section is shown. Figure 2: Block diagram of A-RoF, D-RoF and \(\Sigma\Delta\)-RoF downlinks. Blocks active for A-RoF: 1-2-5-6-7-8-11-12. Blocks active for D-RoF: 1-2-3-6-7-8-10-12. Blocks active for \(\Sigma\Delta\)-RoF: 1-2-4-6-7-8-9-12. BB: Base Band, Freq. Upconv: Frequency Upconverter, Optical Tx: Transmitter, \(\Sigma\Delta\)MOD: Sigma-Delta Modulator, Data Rec. : Data Recovery block.
# Real-time Neural Networks Implementation Proposal for Microcontrollers
## 3 Methodology  
The implementation was validated using the HIL simulation strategy, as explained in [3] and illustrated in Figure 2. The tested variables are execution time for the FFM-1 for the hidden layer as seen in Figure 1 defined as \(t_{\text{FFM-1}}\), execution time for the FFM-2, feedforward for the output layer of neurons, \(t_{\text{FFM-2}}\), error module \(t_{\text{EM}}\), backpropagation of the output neurons layer, \(t_{\text{BPM-1}}\), and the backpropagation of the hidden neurons layer, \(t_{\text{BPM-2}}\).  
A parameter that was also analyzed is the memory occupation, regarding both memories of the ATMega-2560 uC used in this paper, FLASH or program memory and SRAM or work memory.  
Figure 2: Hardware-In-the-Loop assembly scheme in block diagram.  
As previously mentioned, the modules were implemented in the C program language for AVR \(\mu\)Cs using the _avr-gcc_ version 5.4.0, inside the Atmel Studio 7 development environment, an Integrated Development Environment (IDE) made available by Microchip. After compilation and binary code generation, the solution was embedded into an Atmega-2560. This \(\mu\)C has an 8-bit GPP integrated with 256 KBytes of FLASH program memory and 8 KBytes of SRAM work memory, and its maximum processing speed is 1 MIPS/MHz.  
The Atmega-2560 is associated with an Arduino Mega v2.0 development kit: the Arduino Mega is a development kit that provides a breakout board for all the ATmega-2560 pins and some other components required for the \(\mu\)C to function properly. A great feature of this development kit is the onboard Universal Serial Bus (USB) programmer that enables the developer to simply connect a USB port to a computer and test various implementations easily on the \(\mu\)C.  
This work was validated using two cases. First, the MLP-BP was trained to behave as an XOR operation as the simplest case possible to train an MLP and evaluate its ability to learn a nonlinear relationship between two inputs. Secondly, the network was trained to aid a carlike virtual robot in avoiding obstacles in a virtual map using MatLab and three cases of increasing ANN architecture complexity. The assembly and analysis of these two validation cases are presented in the following subsections.
## 3 Results
### Voltammetry Equipment, Variables and Setting Parameters  
#### 3.2.1 Electrodes and Required Sample Volume  
The most widely recorded CV electrode configuration is the combination of a glassy carbon WE, platinum CE and a silver/silver chloride (Ag/AgCl) RE (n = 26). [PERSON] used specially fabricated gold and platinum micro-WE to minimize resistivity and increase sensitivity [75]. [PERSON] et al. reported on the use of Screen-Printed Electrodes (SPEs) [66, 68, 69, 70, 71]. Except for three studies that used Saturated Calomel Electrode (SCE) as RE, the remainders opted for Ag/AgCl RE [75, 76, 77]. A significant cutback in the required sample volume was noted between standard electrode configuration (0.25 mL to 7 mL) and SPE (20 \(\mu\)L). While sample dilution was not commonly described, [PERSON] at al. noted that the heights of voltammetry peaks were directly proportional to serial blood serum dilution [31, 32].
# A Survey from Real-Time to Near Real-Time Applications in Fog Computing Environments
## 3 Classification of Articles
### Fog Computing Concepts
The fog node has the function of meeting the requirements of real-time, data security and integrity, latency, and response time to external events. [PERSON] et al. [54] proposes a health monitoring, evaluation, and performance demonstration system based on the fog computing approach. The goal of the system is to minimize data traffic at the core of the network, improve information security, and provide quality information about the patient's health status. [PERSON] et al. [55] propose to formulate the task offloading problem as a Multi-Stage Stochastic Programming (MSSP). The goal is to minimize total offload latency, how much workload to offload, how much computing resources to allocate, as well as whether to migrate. The proposed MSSP examines joint offload, resource allocation, and migration decisions, promoting an understanding of the interactions between these decisions. [PERSON] et al. [56] propose a load balancing technique that distributes IoT tasks across vehicle clusters. For this, a dynamic clustering approach that considers the position, speed, and direction of vehicles is designed. Additionally, the technique predicts the future position of vehicles considering the dynamic nature of the vehicle network. Simulated results showed a reduction in network delay. [PERSON] et al. [57] article presents an IoT-based fire detection model with energy recognition with different multifunctional sensors in smart cities. In the proposed model, a sleep scheduling approach is used to save energy from the sensors. Furthermore, fog computing is applied to process real-time data from a large number of sensors to running systems. The validation of the proposed model was performed through simulation and experimental implementation in a test bench with Arduino, sensors, and Raspberry Pi. [PERSON] and [PERSON] [58] propose a computing model based on Lie Group Machine Learning to find possible defective products in production. The model is based on fog computing concept to provide distributed processing. [PERSON] et al. [59] propose an architecture to combine Limited Memory Eigenvector Recursive Principal Component Analysis (LMERPCA)-based Operational Modal Analysis (OMA) approach and fog computing.
# A Review of Flying Ad Hoc Networks: Key Characteristics, Applications, and Wireless Technologies
## 10 Future Prospects  
Although the collaborative UAVs technologies discussed in Section 7 provide a number of benefits for a variety of collaborative applications, there are a number of challenges that remain to be addressed [145].  
Due to the energy limitations of UAVs, energy-efficient algorithms need to be designed for such different aspects as collaborative communication, sensing, processing, acting, and data storage [145].  
Providing efficient resource management strategies that try to dynamically manage resources such as bandwidth, transmitting power, the number of UAVs, and UAV flight time is another challenging issue in UAV-assisted technologies [1]. Security provision in UAV-based communication is a complex task, as UAVs are now widely used in technologies involving domains (military, rescue services, and infrastructure inspection) that involve sensitive information. Therefore, efficient techniques are required to provide reliable and secure communications and services in the technologies associated with UAVs [145,146]. In our own future work, we plan to apply Artificial Immune System-based Danger Theory to enhance FANET security, as this is an area in which promising results have previously been found by [147].  
In addition, flexible deployment of a UAVs relies on their mobility and ability to fly towards their destination and perform their mission. To prevent conflict and manage battery levels, UAVs' real-time mobility reactions and states are vital [148,149].
###### Abstract  
Strontium (Sr) and Magnesium (Mg) are bioactive ions that have been proven to exert a beneficial effect on bone; therefore, their incorporation into bone substitutes has long been viewed as a possible approach to improve tissue integration. However, the thermal instability of Mg-substituted hydroxyapatite has hitherto limited development. We previously described the creation of thermally consolidated porous constructs of MgSr co-substituted apatites with adequate mechanical properties for their clinical use. The present paper describes the biocompatibility of MgSr co-substituted granules using an alveolar-bone-derived primary model of human osteoblasts. Cells were cultured in the presence of different amounts of hydroxyapatite (HA), Sr-substituted HA, or MgSrHA porous macrogranules (with a size of 400-600 microns, obtained by grinding and sieving the sintered scaffolds) for three and seven days, and their viability was measured by a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Protein content was measured using the Lowry assay at the same time points. Cell viability was not impaired by any of the tested compounds. Indirect and direct biocompatibility of these macrogranules was assessed by culturing cells in a previously conditioned medium with HA, SrHA, or MgSrHA, or in the presence of material granules. Osteoblasts formed larger and more numerous nodules around SrHA or MgSrHA granules. Furthermore, cell differentiation was evaluated by alkaline phosphatase staining of primary cells cultured in the presence of HA, SrHA, or MgSrHA granules, confirming the increased osteoconductivity of the doped materials.  
strontium-substituted apatite; magnesium-substituted apatite; MgSr co-substituted apatite; primary osteoblasts; biocompatibility +
Footnote †: journal: Applied Sciences
## 1 Introduction
Thus, the conversion of such waste into activated charcoals could decrease the need for waste disposal, making these materials a value-added product with potential applications for the treatment of polluted water. \begin{table}
\begin{tabular}{c c c c} \hline
**Adsorbent** & **Treatment** & **Adsorption Capacity (mg g\({}^{-1}\))** & **Reference** \\ \hline EDTA/corn cob & Complevation & 185.2 & [10] \\ Natural zeolite & None & 177.75 & [11] \\ _Tectoma grandis_ sawdust & Milling & 131.6 & [16] \\ Biochars produced from the woody tree _Gliricilia septum_ & Pyrolysis from 300 to 700 \({}^{\circ}\)C & 125.5 & [17] \\ Graphene oxide hydrogels & Radical polymerization of acrylic and maleic acids & 84.75 & [18] \\ Mussel shells & Pyrolysis & 74.1 & [19] \\ Activated charcoal & Ultrasound & 50.1 & [13] \\ (Sigma-Aldrich) & Alumina and bio-based substance hybrids & Physical mixture & 35.09 & [20] \\ Commercial activated & None & 22.73 & [14] \\ _Acacia niltioa_ saw dust & Acidic thermal activation & 15.75 & [21] \\ Zeolites from coal fly ash & Alkaline hydrolysis & 10.7 & [12] \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of the adsorption capacities of various adsorbents for crystal violet (CV) dye. Few attempts have been conducted for the adsorption of dyes with shrimp waste. For instance, shell waste was extracted from _Peneus indicus_ to adsorb the acid blue anionic dye 25 [3]. This adsorption was favored in the small particle size fraction and acidic pH. Another study describes the removal of mixtures of two dyes (direct red 80 and direct blue 71) by shell waste from two different shrimp species (_Aristaeomorpha foliacea_ and _Aristeus antennatus_) attaining a removal greater than 90% [22]. A similar study reported chitin and chitosan nanowhiskers derived from shrimp waste for the adsorption of crystal violet which was specially favored at alkaline pH [23]. However, comparative studies of raw shrimp waste and its activated charcoals are still pendant. Therefore, it is worthwhile to undertake the study of adsorption kinetics and adsorption isotherms of shrimp waste, its pyrolyzed material and activated charcoals for the removal of pollutants such as crystal violet as compared to a commercial activated charcoal.
# Advances in the Application of Microcapsules as Carriers of Functional Compounds for Food Products
## 2 Methods for the Production of Microcapsules Loaded with Bioactive Compounds
Four major steps are involved in this encapsulation process: emulsification, coacervation itself, gelation, and hardening. In some cases, crosslinking agents, such as transglutaminase, calcium ions, or tripolynphosphate, are used to increase the strength of the capsule wall during the gelation and hardening steps [68]. Microcapsules produced by complex coacervation can present various morphologies and sizes (ranging from 5 to 200 \(\upmu\)m) depending on changes in pH, ionic concentration, emulsion formation method, as well as the type and concentration of wall materials and bioactive compounds. Normally, capsules obtained at low homogenization rate during the emulsification process show a mononuclear morphology, that is, a single droplet with the active ingredient is surrounded by the shell. However, when they are formed at high homogenization rate they are more likely to have a multinucleated morphology, i.e., multiple small droplets of core materials surrounded by a bigger shell [20; 68].
## 5 Material and Methods
### Attack Detection Module
#### 5.6.1 Phase One: Construction of the Convolutional Neural Network Architecture  
In this study, the extraction of deep features plays a crucial role in the 1 DCNN-DT hybrid artificial intelligence model developed for detecting DDoS attacks. As detailed in Table 2, a CNN architecture was implemented during the deep feature extraction process. This architecture is designed to process data in depth and automatically extract critical features, and it has been optimized to enhance the attack detection performance of the 1 DCNN-DT hybrid model.  
In the initial phase of developing the 1 DCNN-DT hybrid artificial intelligence model, a 1D CNN architecture was constructed, which primarily consists of three convolutional layers and two fully connected layers. The input layer of the model is represented by a data sequence with dimensions of 89 \(\times\) 1. In the first convolutional layer, the input data are transformed to a size of 85 \(\times\) 16 using 16 filters and a kernel of size 5. At this stage, the ReLU activation function is applied, resulting in an output of size 86 \(\times\) 16. The second convolutional layer operates with 32 filters and a kernel of size 5, reducing the data size to 81 \(\times\) 32. After applying the ReLU activation again, the output size becomes 82 \(\times\) 32. The third convolutional layer employs 64 filters and a kernel of size 5, further reducing the data to 77 \(\times\) 64. Subsequently, the ReLU activation is applied, yielding an output size of 78 \(\times\) 64. The resulting output is then flattened to form a vector of 4928 dimensions. This vector is fed into two fully connected (dense) layers with 256 neurons each, and the ReLU activation function is utilized at the output of each layer.  
In the developed 1D CNN architecture, a final fully connected layer with 4 neurons performs classification using the Softmax activation function, resulting in a 4-dimensional output. This architecture effectively enables the processing and classification of deep features. In the first phase of developing the proposed 1 DCNN-DT hybrid artificial intelligence model, the 1D CNN architecture was trained using the DDoS training dataset. The Binary Cross-Entropy loss function, defined in Equation (13), was employed for training.  
\[L=-\sum_{i,j}^{M}y_{i\times j}log\big{(}P_{i\times j}\big{)}+\big{(}1-y_{i\times j }\big{)}log\big{(}1-P_{i\times j}\big{)} \tag{13}\]  
Here, \(y_{i\times j}\) and \(P_{i\times j}\) represent the actual value and the predicted value of the pixel at position \(i\times j\), respectively. The value \(L\) denotes the mean error value.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Parameters** & **Size** \\ \hline Input & Model Input & \(89\times 1\) \\ \hline Conv1D & filters: 16, kernel size: 5 & \(85\times 16\) \\ \hline ReLU & \(86\times 16\) \\ \hline Conv1D & filters: 32, kernel size: 5 & \(81\times 32\) \\ \hline ReLU & & \(82\times 32\) \\ \hline Conv1D & filters: 64, kernel size: 5 & \(77\times 64\) \\ \hline ReLU & & \(78\times 64\) \\ \hline Flatten & & 4928 \\ \hline Dense & \(
# Experimental Analysis on Pre-Stress Friction Loss of Crushed Limestone Sand Concrete Beams
## 3 Test Result and Analysis
### Measured Pre-Stress Loss
Figure 13: Instantaneous \(\varepsilon_{i}\) at each stress degree: (**a**) rubber hose; (**b**) metal bellow; and (**c**) plastic bellow. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Wires**} & \multicolumn{3}{c}{**Straight Tendons**} & \multicolumn{3}{c}{**Curve Tendons**} \\ \cline{3-8}  & & \(\mathbf{N_{a}/kN}\) & \(\mathbf{N_{b}/kN}\) & **Pre-Stress Loss** & \(\mathbf{N_{a}/kN}\) & \(\mathbf{N_{b}/kN}\) & **Pre-Stress Loss** \\ \hline \multirow{4}{*}{Rubber hose} & \multirow{4}{*}{1} & 185.11 & 174.36 & 5.81\% & 184.28 & 147.59 & 19.91\% \\  & & 187.71 & 174.88 & 6.84\% & 195.72 & 160.93 & 17.78\% \\  & & 189.79 & 176.43 & 7.04\% & 189.27 & 161.33 & 14.76\% \\ \cline{2-8}  & & 3352.95 & 2067.67 & 38.33\% & 3365.08 & 1975.94 & 41.28\% \\  & & 3444.32 & 2396.99 & 30.41\% & 3334.11 & 2045.11 & 38.66\% \\  & & 3398.64 & 2232.33 & 34.32\% & 3304.68 & 2115.79 & 35.98\% \\ \hline \multirow{4}{*}{Metal bellow} & \multirow{4}{*}{1} & 175.85 & 170.94 & 2.79\% & 184.80 & 171.23 & 7.34\% \\  & & 180.01 & 174.88 & 2.85\% & 184.48 & 162.73 & 11.75\% \\  & & 184.59 & 178.50 & 3.30\% & 181.36 & 167.28 & 9.28\% \\ \cline{2-8}  & & 3249.19 & 3057.14 & 5.91\% & 3229.05 & 2368.42 & 26.65\% \\  & & 3346.76 & 3090.23 & 7.67\% & 3244.54 & 2363.91 & 27.14\% \\  & & 3297.97 & 3073.68 & 6.80\% & 3236.80 & 2366.17 & 26.90\% \\ \hline \multirow{4}{*}{Plastic bellow} & \multirow{4}{*}{1} & 185.63 & 172.11 & 7.28\% & 182.51 & 165.09 & 9.29\% \\  & & 180.01 & 171.17 & 4.91\% & 180.01 & 162.47 & 10.73\% \\ \cline{1-1}  & & 184.80 & 172.42 & 6.70\% & 182.09 & 167.27 & 8.09\% \\ \cline{1-1} \cline{2-8}  & & 3142.08 & 2766.92 & 11.94\% & 3354.50 & 2906.77 & 13.35\% \\ \cline{1-1}  & & 3236.55 & 2766.92 & 14.51\% & 3334.37 & 2818.05 & 15.48\% \\ \cline{1-1}  & & 3199.38 & 2899.25 & 9.38\% & 3171.75 & 2600.00 & 18.03\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Measured pre-stress loss.
# Error Model of a Measurement Chain Containing the Discrete Wavelet Transform Algorithm
## 4 Application of the Proposed Analysis Method
### Model Application for a Monoharmonic Input Signal
As the analyzed error signals are not correlated with each other, the variance of the resulting error signal \(e_{x,\Sigma}(i)\) can be estimated according to equation [13]:  
\[\sigma_{x,\Sigma}^{2}=\sigma_{x,r}^{2}+\sigma_{x,d}^{2}. \tag{64}\]  
To verify the validity of the indicated relationships, a Monte-Carlo measurement experiment was carried out, in which 30,000 values of the \(x(i)\) signal realization were collected each time. During the experiment, the source of the signal \(s(t)\) was the RIGOL DG1011 arbitrary waveform generator [30], and the initial phase of this signal was randomized from the interval \([-\pi;\pi]\), and the generator's synchronizing output was used to determine it. The frequency of the signal \(s(t)\) was in the range \(\tilde{f}_{s,\theta}\in[1;21]\) kHz, while the parameters of this signal were constant and were \(D_{s,\theta}=0.5\) V, \(E_{s,\theta}=0.475\) V. Based on the obtained values of the realization of the quantity \(x(i)\), in accordance with Equation (54), the values of the error signal \(e_{x,\Sigma}(i)\) were determined and the variances in this signal were calculated on their basis. The measured variance value \(\sigma_{m}^{2}\) was compared with the value \(\sigma_{c}^{2}\) determined according to Equation (64) and the relative error in estimating this value was calculated. The results for selected values of signal \(s(t)\) pulsation are summarized in Table 1, while calculated relative error values are also presented in Figure 5.  
Figure 5: Relative error of estimated \(\sigma_{c}^{2}\) values of the variance of the resultant error signal \(e_{x,\Sigma}(i)\) (in the case of a monoharmonic input signal).
# Evaluating the Functioning Quality of Data Transmission Networks in the Context of Cyberattacks
## 5 Demonstration of the Efficiency of the Suggested Method  
To demonstrate the effectiveness of the described method, consider the following example of assessing the quality of DTN functioning. The choice of this example is due to the fact that it is simple, clear and without loss of generality and allows one to understand the essence of the proposed method and the procedure for calculating the quality indicators of DTN functioning in cyberattack conditions. Let us suppose that transmission of data packet flow with an average volume \(V\) = 0.23 Mbit, an average speed \(S\) = 150 Mbit/s and intensity of packet arrival \(\lambda\) = 20 packets/s is carried out along a route organized in the network. The indicated transmission route contains \(N\) = 5 network elements with storage capacity \(K\) = 50 (packets). Two network elements on the route are available with the probability \(P_{1}\) = \(P_{2}\) = 0.5 for a malefactor's cybercriminal intelligence and, as a result of this, with the average time \(t_{r1}\) = 300 (s) and \(t_{r2}\) = 400 (s) with dispersion \(D_{r1}\) = 200 (s\({}^{2}\)) and \(D_{r2}\) = 150 (s\({}^{2}\)), their functioning is disrupted as a result of a cyberattack. If the functioning of the network elements is not disrupted, then the packet that arrives for transmission will be successfully transmitted (serviced) in the average time \(t_{b}\ =\ \frac{(N-1)V}{N}\ =\ 6.13\times 10^{-3}\) (s) with dispersion \(D_{b}\ =\ 1.2\times 10^{-6}\) (s\({}^{2}\)). If the cyberattack is successful, the network administrator detects and recovers the functioning of the network element in the average time \(t_{d1}~{}=~{}t_{d2}~{}=~{}5\) (s) with dispersion \(D_{d1}~{}=~{}D_{d2}~{}=~{}25\) (s\({}^{2}\)). Let us suppose that the indicated random values are characterized by gamma distribution. Network traffic is described by the Weibull model. Network traffic parameters are: \(\lambda\) = 20 packets/s, Hurst index \(H\) = 0.71. It is required to define the delivery probability of the data packet to the user in a time not exceeding \(T_{z}\) = 5 (s).