esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Main Contribution  
In this work, we mainly focus on finding a faster and fairly accurate solution for the resource allocation problem in a WPCN. We utilize the advantages of a deep learning scheme to solve the resource allocation problem in a WPCN. The main contributions of our work are summarized as follows.  
* First, we propose a sequential parametric convex approximation (SPCA)-based iterative solution for the transmit power minimization problem in a WPCN. The proposed solution can determine the optimal values for minimum transmit power and allocation of downlink/uplink time for energy and information transmission. We generated a data set for deep learning using this solution.
* Secondly, we propose a learning-based DNN scheme exploiting the training data from the SPCA-based approach. The DNN architecture accepts the channel coefficient as input and gives an optimized solution as output. The proposed DNN is fairly accurate at learning the relationships between the input and output of the WPCN system. The proposed scheme gives fast and fairly accurate results compared to the SPCA-based iterative algorithm.  
* The performance of our scheme is verified through simulations and experiments. It is proven that the proposed scheme gives an accurate approximation of a conventional iterative algorithm scheme while managing the time complexity well. Our scheme gives a much faster solution than the conventional iterative algorithm.  
The next sections of this paper ARE structured as follows. Section 2 discusses the system model and problem formulation for resource allocation in a WPCN. Section 3 explains the proposed scheme, and Section 4 demonstrates the architecture of the DNN and the generation of training and testing data. Section 5 discusses the results produced by the proposed scheme and verifies the results using simulation. Section 6 concludes this paper.
## 3 Results and Discussion
### Analysis of Interaction between Factors
#### 3.5.2 Interaction of the Three Factors in Manganese Removal  
The insignificant ellipticity of the contours in Figure 8a indicated that the interaction between the pH and the dosage had no significant effect on the manganese ion removal rate. When the initial concentration ratio of Fe/Mn was fixed at 4, the slope of the pH on the response surface was very steep, indicating that the pH value had a more obvious influence on manganese ion removal in these two factors. Under the condition of a fixed dosage,  
Figure 8: The 3D response surface diagram of manganese sand dosage and pH (**a**), initial concentration ratio of Fe/Mn and pH (**b**), initial concentration ratio of Fe/Mn and manganese sand dosage (c) influence manganese ion removal.  
Figure 7: The 3D response surface diagram of manganese sand dosage and pH (**a**), initial concentration ratio of Fe/Mn and pH (**b**), initial concentration ratio of Fe/Mn and manganese sand dosage (**c**) influence iron ion removal.  
the removal efficiency of manganese ions changed with the increase in the pH value. It can be seen from the response surface model that, with the increase in the pH value and the dosage of manganese sand, the removal of Mn\({}^{2+}\) by manganese sand first increased and then decreased. When the dosage of manganese sand was 3.4-3.6 g/L, the pH value remained between 6.9 and 7.3, and the removal rate of manganese ions was within the maximum range.  
As can be seen from the contour lines in Figure 8b, there was an interaction between the pH value and the initial concentration ratio of Fe/Mn. The slope of the pH value in the response surface was large, which means that the influence of the pH value on the experimental adsorption process was more significant. With the increase in the pH value from 6.5 to 7.5, the removal of Mn\({}^{2+}\) by manganese sand first increased and then decreased. When the concentration ratio of Fe/Mn was 3.5-4.5, and the pH value was 6.9-7.3, the removal rate of manganese ions by manganese sand was within the maximum range.  
Figure 8c shows the interaction model of the manganese sand dosage and the concentration ratio of iron and manganese ions at pH = 7. It can be seen that the response surface had an \"arch shape\", indicating that the interaction between these two factors in the manganese ion removal process was very significant. When the initial concentration ratio of Fe/Mn was constant, the removal of manganese ions first increased and then decreased with the increase in dosage. The slope reflecting the dosing amount in the response surface plot was larger, indicating that when considering the interaction between these two factors, the dosage significantly affected the removal of manganese. When the amount of manganese sand was controlled at 3.5-3.6 g/L, and the initial concentration ratio of Fe/Mn was controlled at 3.5-4, the removal effect of manganese ions was the best.
# A Robust Multi-Watermarking Algorithm for Medical Images Based on DTCWT-DCT and Henon Map
## 4 Experiment and Analysis
### Geometrical Attacks  
#### 4.2.1 Rotation Attacks  
The watermarked image is rotated by 5 degrees each time. It can be seen from Figure 18 and Table 6 that the average NC value of extracted watermarks was very high. When rotating 45 degrees, it can still reach 0.64.  
Figure 17: Under Median Filter attacks: (**a**) Median Filter [\(3\times 3\)] with 10 repetitions; (**b**) the extracted watermark 1 under median filter [3,3] with 10 repetitions; (**c**) the extracted watermark 2 under median filter[3,3] with 10 repetitions; (**d**) the extracted watermark 3 under median filter [3,3] with 10 repetitions; (**e**) Median Filter [9,9] Repeat times 10; (**f**) the extracted watermark 1 under median filter [9,9] with 10 repetitions; (**g**) the extracted watermark 2 under median filter [9,9] with 10 repetitions; (**h**) the extracted watermark 3 under median filter[9,9] with 10 repetitions.  
Figure 18: Under rotation attacks: (**a**) Rotation (clockwise) 40\({}^{\circ}\); (**b**) the extracted watermark 1 under rotation (clockwise) 40\({}^{\circ}\); (**c**) the extracted watermark 2 under rotation (clockwise) 40\({}^{\circ}\); (**d**) the extracted watermark 3 under rotation (clockwise) 40\({}^{\circ}\); (**e**) Rotation (clockwise) 45\({}^{\circ}\); (**f**) the extracted watermark 1 under rotation (clockwise) 45\({}^{\circ}\); (**g**) the extracted watermark 2 under rotation (clockwise) 45\({}^{\circ}\); (**h**) the extracted watermark 3 under rotation (clockwise) 45\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Median Filter** & \multicolumn{3}{c}{**[3,3]**} & \multicolumn{3}{c}{**[5,5]**} & \multicolumn{3}{c}{**[9,9]**} \\ \hline
**Repeat Times** & **1** & **10** & **20** & **1** & **10** & **20** & **1** & **10** & **20** \\ \hline PSNR (dB) & 24.52 & 21.85 & 21.30 & 20.44 & 18.28 & 17.73 & 16.90 & 15.94 & 15.71 \\ NC1 & 0.95 & 0.96 & 0.93 & 0.96 & 0.81 & 0.71 & 0.81 & 0.67 & 0.60 \\ NC2 & 0.97 & 0.97 & 0.93 & 0.97 & 0.88 & 0.84 & 0.75 & 0.81 & 0.77 \\ NC3 & 0.96 & 0.96 & 0.93 & 0.96 & 0.83 & 0.76 & 0.77 & 0.72 & 0.66 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Data of Median Filter attacks.
## 2 Materials and Methods
### Extraction of Volatiles by HS-SPME  
Volatiles of pomegranate juice were extracted using HS-SPME according to the method of a previous report with modifications [18]. Pomegranate juice (8.0 g) was placed in a 20 mL headspace vial capped with a polytetrafluoroethylene-silicon septum. Then, 1.0 \(\upmu\)L of internal standard (1,2-dichlorobenzene, 1.306 \(\upmu\)g/\(\upmu\)L) was added to the juice, resulting in a final concentration of 1.63 \(\times\) 10\({}^{-4}\)\(\upmu\)g/kg in the juice. The vial with pomegranate juice was equilibrated at 36 \({}^{\circ}\)C in a thermostatic water bath for 15 min. The SPME fiber (75 \(\upmu\)m CAR/PDMS/DVB extraction head, Bellefonte, PA, USA) was exposed to the vial headspace for 35 min, then immediately desorbed for 7 min in the GC inlet at 250 \({}^{\circ}\)C. Each sample was duplicated three times.
## 5 Conclusions  
Power quality indexes of AC-AC converters are linked with the characteristics and frequency of the generated harmonics. The suppression of the low-frequency harmonics is a big challenge (as is that of the high-frequency harmonics). The shifting of harmonics at high frequencies requires high switching frequencies. That is the main cause of high switching losses and electromagnetic interference in high switching converters. In low-frequency conversion operation, some low-frequency harmonics may be mitigated by adjusting the amplitude of some selected half cycles of the output voltage. This arrangement lowers the harmonic factor of the output voltage to approximately 15% to 18% for the output frequency (which is one-third or one-fourth more than the input). This improvement in the output voltage generates the low frequencies harmonics in the input current. That results in an increase in the harmonic factor of the input current approximately to 33% to 36%. The effect of improvement in the harmonic factor of the output voltage is undersized by the generated input current harmonics and this is numerically proved in this research. A simple circuit is also recommended to obtain the non-regulated form of the output pulses that address the harmonic concerns of the input current. To support these results, a computer simulation environment and a practically-developed prototype were developed.  
Conceptualization, N.A. and G.A.; data curation, G.A. and N.U.; formal analysis, N.A., A.A.A.-A. and M.J.; funding acquisition, N.U. and A.R.; investigation, N.A. and U.F.; methodology, G.A. and A.R.; project administration, G.A.; resources, A.A.A.-A. and N.U.; software, M.J. and U.F.; validation, M.J. and U.F.; writing--original draft, N.A. and G.A.; writing--review and editing, G.A., A.A.-A., A.R. and N.U. All authors have read and agreed to the published version of the manuscript.  
This research work is supported by Taif University Researchers Supporting Project number (TURSP-2020/121), Taif University, Taif, Saudi Arabia.  
Figure 17: Practically recorded results without pulse voltage regulation capabilities: (**a**) output voltage and its FFT spectrum for one half output frequency; (**b**) output voltage and its FFT spectrum for one-third output frequency; (**c**) input current and its FFT spectrum.
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 3 Proposed Methodology
### Metrics Used by Our Proposed Algorithm
#### 3.1.4 Total Energy Consumed  
The residual energy of a node ni as denoted by Eri after transmitting k bits to a node ni within a distance d is given by [1] and can be calculated by Equation (2).  
\[\text{Eri}=\text{E}-(\text{ETx(k,d)}+\text{ERx\_elec(k)} \tag{2}\]  
where E is the current energy of the node E\({}_{Tx}\) is energy for transmit a message, which is calculated by Equation (3).  
\[\text{ETx(k,d)}=\text{kElec}+\text{K}\text{Eampd2} \tag{3}\]  
In this equation, Eelec is energy of electrons and Eamp is required amplified energy. ERx_elec is energy consumed to receive a message, which is calculated by Equation (4).  
\[\text{ERx\_elec(K)}=\text{kEelec} \tag{4}\]
## 3 Methods  
X-ray phase analysis of the samples obtained was performed at Rigaku D/MAX-2500 X-ray diffractometer (Rigaku, Japan) using Cu/K\(\alpha\) irradiation line (\(\lambda\) = 1.54056 A).  
The FTIR spectra of the samples were obtained in the range of 4000-400 cm\({}^{-1}\) using Bruker Tensor II FTIR-spectrometer (Germany) with reflection platinum unit. The FTIR spectra of powdered samples were recorded in diffuse reflection mode.  
The UV-VIS absorbance spectra of the aqueous solutions were recorded at Jasco V-770 spectrophotometer (Jasco, Japan) in the range of 300-500 nm.  
The kinetic curves for the release of dioxidine from the composite nanoforms obtained in the distilled water were measured spectrophotometrically at wavelength \(\lambda\) = 375 nm using Jasco V-770 spectrophotometer (Jasco, Japan). The kinetics of gentamicin sulfate release were monitored by measuring of the electrical conductivity of the drug solutions using a Mettler Toledo Conductance (Switzerland) with LE703 electrodes. Each sample was measured 3 times independently. The standard deviation of the experimental data is shown in Figure 4 for all experimental curves.  
Metal concentration inside the polymeric matrices of hybrid antibacterial nanoforms was determined by atomic absorption method at Spectrometer i-CE 3000 (Thermo Fisher Scientific, USA).  
The \({}^{1}\)H-NMR (nuclear magnetic resonance) spectra were recorded in deuterated water solutions (D\({}_{2}\)O) using a high-resolution NMR spectrometer VXR-400 (Varian, USA).  
The microstructure of the samples was studied using the transmission electron microscopy (TEM) using electron microscope LEO 912 AB Omega (ZEISS, Germany) at \(\times\)400-\(\times\)500000 magnifications and using scanning electron microscope (SEM) Phenom (FEI Company, Germany) at \(\times\)40-\(\times\)4000 magnifications.  
Specific surface area (S\({}_{\text{SP}}\)) of the nanocomposites was studied by the method of thermal argon desorption using gas-chromatograph Chrom-5 (Czech Republic). The resorbed gases were removed from the surface of the samples using the vacuum setup. Mean dimensions particles were calculated as a = 6/(\(\theta\)S\({}_{\text{SP}}\)), where \(\varrho\) is the density of drug.  
The evaluation of the antibacterial activity of the samples was carried out by the disk-diffusion method using the filter paper disks or sponges of cryogenically structured biopolymeric samples (4 mm in diameter and 2 mm in height). Bacterial strains of _E. coli 52_ and _S. aureus 144_ were used from the collection the Microbiology Department, Biology Faculty of [PERSON] Moscow State University. The experiments were carried out in standard Petri dishes containing 20 mL of agar nutrient medium dried during the day (thickness of the medium layer was of 4 mm). Measurements of inhibition zones (IZ) for bacterial strains growth of the tested cultures were carried out after 24 h of incubation. Statistically reliable results were obtained by a nine-fold repetition of the measurements for each series of samples.
## 5 Methods  
This section presents the research methods employed in the process of cleaning EEG signals from artifacts. Figure 3 illustrates the flowchart detailing the procedures employed in our study on cleaning EEG signals from muscular artifacts using a CNN-LSTM neural network. Initially, EEG and EMG signals were acquired and subsequently segmented into training data (comprising both resting sessions without stimuli and sessions with artifacts) and test data (including sessions with elicited SSVEP potentials as well as SSVEP sessions affected by muscular artifacts). To prepare the training set, we selected time windows containing muscular artifacts and artifact-free windows. For both groups, data augmentation was performed--separately for the EMG and EEG signals--to enhance the diversity and volume of the training data. The augmented dataset was then used to generate a set of synthesized EEG/EMG recordings containing muscular artifacts.  
In the next stage, the CNN-LSTM neural network was trained and validated. After completing the training phase, the model was evaluated using real EEG/EMG test data. The testing phase produced cleaned EEG signals, for which the signal-to-noise ratio (SNR) coefficients were calculated as a quantitative measure of the artifact removal efficacy.  
During the study, special care was taken to ensure that the same EEG signals were never used for both synthetic data generation and for training or testing the CNN-LSTM model. This approach ensured a reliable and objective evaluation of EEG signal cleaning performance under real-world conditions, without the risk of overlap between the training and testing datasets. For each user, four sessions were recorded, with the first two dedicated to data augmentation:  
* EEG/EMG recordings without muscle artifacts, during which participants remained still and focused on a non-pulsating LED light source. These recordings served as a source of clean EEG samples.
* EEG/EMG recordings with muscle artifacts, such as jaw clenching, conducted without visual stimulation. These recordings were used to extract representative EMG segments containing artifacts.  
Figure 2: Signals recorded during jaw clenching.  
The final two sessions were used to evaluate the cleaning method on real-world data:  
* Exposure to a pulsating LED light at an individually selected frequency to elicit the strongest SSVEP response (clean SSVEP condition).
* Exposure to the same pulsating LED light while performing jaw clenching, introducing real muscle artifacts into the EEG signal (SSVEP + EMG condition).
## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.5 Eh-Arcun  
In [28], [PERSON] et al. proposed a novel scheme, namely an energy harvesting-based routing protocol for underwater sensor networks (EH-ARCUN). It is based on cooperation and energy harvesting. Amplification forward is applied at the relay nodes for forwarding packets. To combine the signals at the destination node forward combing ratio is preferred by the authors. The relay nodes are distinct nodes that consist of extra processing power. The authors assumed that 5% of the total nodes are power harvesting nodes distributed at three layers. They also place the nodes in such a topology that the sender node always has two distinct relay nodes to transmit the signal. Most of the other protocols use the Thorps Attenuation model, whereas EH-ARCUN uses the Monterey-Miami parabolic equation (MMPE) model to predict the path loss. Remotely powered cooperation bases UWSN techniques (RPCoU) are used for cooperative communication. The energy harvesting of energy harvesting nodes depends on the layer of the source. All sources keep a queue of all the neighboring nodes. From the table, a relay node sends Clear To Send (CTS) towards the source node, and the source selects three nodes; one destination node and two relay nodes, to forward the packet towards the destination. Maximum energy harvested and the data link's capacity are two factors that the source node considers when selecting nodes. MATLAB 2007b was the choice of the authors for simulation. For comparison, [PERSON] et al. chose ARCUN and RACE protocols. The authors compared the proposed EH-ARCUN to ARCUN and reliability and adaptive cooperation for efficient underwater sensor networks (RACE) using three parameters; packet delivery ratio (PDR), path loss, and stability. In all three cases, the proposed EH-ARCUN showed significant improvement from the protocols mentioned earlier.
# New Detection Method for Fungal Infection in Silver Fir Seeds
## 1 Introduction  
The sustainability and biodiversity of forests depend on the quality of stock from forest nurseries. In Poland, foresters plant approximately half a billion seedlings every year based on the production of 800 million seedlings per year. Forecasts by the National Forest Cover Increasing Program assume an increasing the forest cover level from a current 30% to 33% by 2050 [1]. This will require the afforestation of millions of hectares and the availability of huge quantities of viable seed for sowing in nurseries. Currently, forest nursery seed is sent to seed stations, where quality is determined based on batches of 400 germinated seeds. However, this method is labor- and time-consuming, and seed must be sent well in advance. What is lacking is a quick and efficient method of assessing the seed suitability for sowing. Automated devices such as electronic noses may be useful for quicker assessments on a large scale. These devices have already been used to assess food quality. Human noses can be very sensitive to odors [2], and these abilities have developed during evolution to decrease the risk of consuming something that might be harmful. Odors can be emittedbefore fungal hyphae are visible on a plant surface and becomes visible to the naked eye. A seed may be non-viable due to damage or infection, but this is often not noticeable for seeds stored in dry conditions. Superficial visual inspection would not reveal the lack of viability which becomes apparent after seeds are sown and seedlings fail to emerge. We would like to develop a quick and efficient method to detect whether a seed sample is healthy and suitable for sowing or spoiled and should be further examined or discarded.  
Since the introduction of the concept of an electronic nose [3; 4; 5], which is a device consisting of an array of nonspecific gas sensors, equipped with machine learning pattern recognition algorithms, various applications of this rapid and non-invasive diagnostic tool have been proposed. Several reports review potential applications, challenges, and possible improvements of electronic noses in focusing on forestry and agriculture [6; 7; 8; 9; 10; 11]. Applications of electronic nose for detection and identification of fungal species were reviewed by [PERSON] et al. [12]. Volatile organic compounds (VOC) emitted by various seeds infected by fungi have been analyzed with the help of electronic noses, including fungal contamination of cereal grain samples [13; 14], rice [15], and rapeseed [16; 17].  
The purpose of this research was to examine seeds of silver fir (_Abies alba_) for viability via detection of degradative organisms via their VOC's using an electronic nose. Silver fir is one of the main forest-forming species in Poland and is typical of the mountainous regions of central and southern Europe [18], but it is threatened by climate change and increasing pest pressures such as bark beetles.
## 3 Results and Discussion  
### Overview  
Figure 3 shows temporal variations in (a) air mass origins, (b) air pressure, precipitation, (c) air temperature, ambient RH (d) wind speed and wind direction throughout the observation period. About 84% of the backward trajectories were classified as FT (Figure 3a), indicating that the MFRS was in the free troposphere for most of the observation period. Thus, it is possible to see the effect of long-range transport of aerosols of different air mass origins on CCN characteristics. The trajectories classified as Continental are frequently observed in the early part of the observation period (20 to 29 July), whereas, those classified as Pacific are observed from about halfway through the observation period onwards (2 to 20 August) (Figure 3a). The mean air pressure during the observation period was 648.7 hPa. The drops of air pressure were observed on 27 July and 9 to 10 August, respectively, due to the passing of a cold front and the effect of the typhon Halong (Figure 3b). The mean air temperature was 5.9 \({}^{\circ}\)C, with a range from 0.4 to 14.4 \({}^{\circ}\)C (Figure 3c). At the summit, northerly winds were dominant in July. In August, westerly winds prevailed except around 8 to 11 August, when easterly winds were observed along with the typhoon Halong passing through Japan islands (Figure 3d).  
Figure 4 shows temporal variations in (a) air mass origins, (b) number size distributions of aerosol particles with 10 to 500 nm in diameter, (c) \(N_{\text{CN}}\) and \(N_{\text{CCN}}\) at each _SS_ condition, and (d) _LWC_. Cloud-shrouded periods were defined as periods when the _LWC_ is greater than 0.01 g m\({}^{-3}\). As a result, 40% of the observation period was classified as cloud-shrouded (Figure 4d). In the following analysis, the data collected when (1) the backward trajectories were classified as FT, and (2) when the SMPS was in operation, were used.  
Figure 3: Temporal variations of (**a**) air mass origins, (**b**) air pressure (red line), precipitation (blue sticks), (c) air temperature (red line), relative humidity (blue line), (d) wind speed (red lines) and wind direction (blue dots).