esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 A Framework for SDN-SDR-Based Maritime Communications
### The SDR Approach
#### 5.1.1 SDR Background  
An SDR provides traditional hardware-based signal processing in a reconfigurable software environment [45]. Where each new iteration of a communication standard requires legacy radios to upgrade to the most recent chipset and transceiver components to take advantage of new features, an SDR can be adapted to incorporate the changes by reprogramming the software that runs on programmable hardware [46]. Adopting the use of SDRs can allow for switching from one communication standard to another, or even using the same communication standard in countries that have differences in the physical layer protocols. Using the 5G spectrum as an example, countries in the Americas, Europe, and Asia are licensing different frequencies to operate equipment [47]. Instead of carrying on board and maintaining a multitude of radios, SDRs can be reprogrammed and use infrastructure common among varying implementations. While the use of a purpose-built radio for a globally agreed standard need not be discarded in favor of an SDR, there may be times or operating conditions when the SDR is superior. Marine VHF radios use analog modulation for voice, while AIS is also a VHF band system that uses a digital modulation [48].  
A single SDR can take the place of both of these radios and implement further functionality, such as the translation of voice-to-text information or even communication similar to SMS messages. Additionally, the SDR allows for agility in transmitting or receiving signals and can be adapted for the wireless environment. Atmospheric ducting has a significant impact on electromagnetic wave propagation. Signals may travel well beyond expected distances and cause unintended interference. An SDR can be used to partially mitigate this interference and ensure that minimum data rate performance is met. In [49], [PERSON] et al. used an SDR to understand the effects of a wireless channel. They then used machine learning to develop a non-standard modulation symbol constellation to minimize the detection error of the received signal.
###### Abstract  
The absence of global positioning system (GPS) signals and the influence of ocean currents are two of the main challenges facing the autonomy of autonomous underwater vehicles (AUVs). This paper proposes an acoustic localization-based tracking control method for AUVs. Particularly, three buoys that emit acoustic signals periodically are deployed over the surface. Times of arrivals of these acoustic signals at the AUV are then obtained and used to calculate an estimated position of the AUV. Moreover, the uncertainties involved in the localization and ocean currents are handled together in the framework of the extended Kalman filter. To deal with system physical constraints, model predictive control relying on online repetitive optimizations is applied in the tracking controller design. Furthermore, due to the different sampling times between localization and control, the dead-reckoning technique is utilized considering detailed AUV dynamics. To avoid using the highly nonlinear and complicated AUV dynamics in the online optimizations, successive linearizations are employed to achieve a trade-off between computational complexity and control performance. Simulation results show that the proposed algorithms are effective and can achieve the AUV tracking control goals.  
acoustic localization; ocean currents; extended Kalman filter; model predictive control; autonomous underwater vehicles +
Footnote †: journal: Medical Imaging
# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 3 The Proposed Mechanism
### Preliminary Formation for the Proposed Work
#### 3.1.8 Routing Information Formats  
The format of the modified DSR route table is given in Figure 2. This table demonstrates the format of a single entry for a destination node \(i\). In the routing cache, an additional 2-byte Lambda (\(\lambda\)) value is concatenated with each address. Nodes can easily update these according to their own knowledge. Each node transmits its own \(\lambda\) field through the routing and topology control messages.  
Figures 3 and 4 show the RREQ and RREP formats in this work. An additional 1-byte field for the _Lambda Option_ is added in the header. The presence of this field specifies the addition of \(\lambda\) values with each address. Usually, the _Lambda Option_ is kept as null in the RREQ to avoid any additional bandwidth and energy. In the RREP, we used the modified addresses fields. Accordingly, each relay node adds its own address with its \(\lambda\) score for the RREP.  
Figure 2: Modified routing table format.  
Most of the time, the nodes estimate their neighbors' locations and energies by analyzing the sequence of the involved nodes in the flow of data transmission from a source towards a sink. However, sometimes nodes may require an update for these values after a specified period of time. OLSR topology control messages are used to get the neighbors' locations and their energies. For this, we modified the OLSR topology control message as shown in Figure 5, where each address is combined with the nodes' energies for the sake of information sharing.  
An unsigned integer 2 bytes in size is used to represent the value of \(\hat{\measuredangle}\). According to Equation (6), the value of Lambda must be a decimal ranging from 0 to the maximum number of nodes. The possible value range for 2 bytes is from 0 to 6.5535. To adjust the decimal values into an integer of 2 bytes, the value of \(\hat{\measuredangle}\)s rounded to 4 decimal points and then multiplied by 1000. After applying this procedure, the mechanism can use the maximum value of \(\hat{\measuredangle}\)up to 6.5535. However, there is the possibility that this value will be near the total number of nodes in the network. When we used an appropriate value for calculating the set of CNs, there was a much lower chance of this value being greater than 2 in most of the experimental cases. For example, a sample of calculated values can be seen in the next section of Simulation Results. A value greater than normal will have the same impact no matter how much greater it is. So, during the process, we assumed that any value of \(\hat{\measuredangle}\)greater than 6.5535 must be considered 6.5535.  
Figure 4: Modified DSR RREP format.  
Figure 5: Information exchange through modified OLSR topology control message.  
Figure 3: Modified DSR RREQ format.
# Simulated Annealing for Resource Allocation in Downlink NOMA Systems in 5G Networks
###### Abstract  
In this work, we investigate resource allocation and user pairing to improve the system's Throughput for the downlink non-orthogonal multiple access (NOMA)-based 5G networks. The proposed resource allocation involves user pairing, subchannel power allocation, and proportional power allocation among the multiplexed users. The resource allocation is a non-deterministic polynomial (NF-hard) problem that is difficult to tackle throughput maximization. The user pairing and power allocation are coupled to address the substantial requirements of the NOMA system. The NOMA system requires an efficient deployment of resource allocation techniques to enhance the system's throughput performance. In this work, we propose simulated annealing (SA) to optimize the power allocation and perform user pairing to maximize the throughput for the NOMA system. Also, we provide mathematical proof on the near-optimal solution for subchannel power and mathematical analysis on the optimal value of the power ratio for the multiplexed users in the NOMA system. The SA provides a significant throughput performance that increases by 7% compared to the existing numerical optimization methods. Results obtained show that SA performs with sufficient reliability and low time complexity in terms of Throughput improvement.  
s +
Footnote †: journal: Applied Sciences  
1 Faculty of Engineering, Multimedia University, Cyberajaya 63100, Malaysia; [EMAIL_ADDRESS] (O.A.); [EMAIL_ADDRESS] (Z.B.Y) 1
###### Abstract  
The wastewater treatment involves various techniques at different technological levels. Treatment takes place in several stages, of which coagulation and flocculation are the most important. Most suspended solids are indeed eliminated during this stage by the addition of a coagulant. In this research, bio-coagulant was extracted from _pumpkin seed_ (PS) waste after extraction of the essential oils, and used with ferric chloride to treat wastewater from the plant of Chalghoum El Aid-Oued El Athmania Mila. In this study, the Box-Behnken design (BBD) with three factors was used to investigate the effect of pH, organic coagulant dosage _Pumpkin seed_ extract (PSE), and chemical coagulant dosage (FeCl\({}_{3}\)) on coagulation-flocculation performance in relation to turbidity, chemical oxygen demand (COD), aromatic organic matter (UV 254), and phosphate. The main characteristics of the raw water were turbidity (250 NTU), COD (640 mg/L), UV 254 (0.893 cm\({}^{-1}\)), and phosphate (0.115 mg/L). The results obtained were very significant. All the statistical estimators (R\({}^{2}\geq 97\%\) and \(p\leq 0.05\)) reveal that the models developed are statistically validated for simulating the coagulation-flocculation process. It should be noted that the residual values of turbidity, COD, UV 254, and phosphate after treatment by this process were 0.754 NTU; 190.88 mg/L; 0.0028 cm\({}^{-1}\); and 0.0149 mg/L, respectively. In this case, the pH, bio-coagulant dosage, and chemical coagulant dosage values were 4; 17.81 mL/L; and 10 mL/L, respectively. In this study, Fourier-transform infrared spectrometer (FTIR) and scanning electron microscope (SEM) characterization of the bio-coagulant proved the presence of the active functional groups responsible for coagulation, namely carboxyl group.  
Article
The _PON1 Q192R_ polymorphism has been shown to influence PON1 activity; the Q-isoform catalyzes the faster hydrolysis of diazoxon, a metabolite of the organophosphate diazinon, while the R-isoform hydrolyzes the metabolite paraoxon of the organophosphate parathion more efficiently [12]. Regarding the _PON1 L55M_ polymorphism, the L allele is associated with higher PON1 protein and messenger RNA (mRNA) levels compared to the M allele. It has been described that individuals with the QQ and MM isozymes of the _Q192R_ and _L55M_ polymorphisms exhibit lower enzymatic activity than subjects with the RR and LL variants [5]. The effects of dietary compounds such as carbohydrates, fatty acids, proteins, vitamins and minerals on the PON1 phenotype have been described, [13; 14] as well as habits such as physical activity or harmful habits such as: alcoholism, smoking and illicit drug use [4; 15; 16]. Some studies have evaluated the effect of phenolic compounds such as flavonoids on _PON1_ expression and activity in vitro, in vivo and in human populations [17; 18; 19; 20; 21]. Furthermore, flavonoids are present in pomeranate and red wine, and these are the principal groups of antioxidants present in the diet [23; 24]. In this respect, several studies have been focused on the effects related to the consumption of red wine on the PON1 enzyme, [13; 17; 25; 26; 27; 28; 29; 30] especially in the increased serum paraoxonase activity caused by red wine and moderate wine intake [25; 26]. In addition, it is known that red wine has polyphenols compounds commonly divided in flavonoids and nonflavonoids, such as flavonoids like quercetin, nonflavonoid compounds that include phenolic acids (e.g., gallic acid), phenols and stilbenes (e.g., resveratrol) compounds related to cardioprotective effects [27; 28; 29; 30]. Specifically, resveratrol can enhance the expression of the _PON1_ gene in the HepG2 human cell line [28]. Considering the influence of the phenolic compounds on PON1, the aim of this study was to evaluate the effect of dietary intake, red wine consumption, and three genotypes of _PON1_ (_Q192R_, _L55M_ and _C-108T_) on the specific activity of PON1 in a healthy population.
# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas
## 2 Materials and Methods
### Raff Culture Zone Fusion Evaluation
#### 3.2.4 Image Fusion Evaluation Index--\(CQ_{max}\)  
Figure 11 shows the \(CQ_{max}\) values of three fusion algorithms applied to the floating raft culture area. Results from panchromatic and multispectral GF-1 data were: PCA (0.0163), NNDiffuse (0.0171), and GS (0.0173). The \(CQ_{max}\) values for NNDiffuse and GS were relatively close, indicating the GF-1 fusion effects were similar. Evaluation results were highly similar to qualitative and quantitative assessments. Results for panchromatic and multispectral images from GF-2 data sources were as follows: PCA (0.0338), NNDiffuse (0.0429), and GS (0.0364). In this case, the \(CQ_{max}\) value was the largest for NNDiffuse, indicating the best fusion effect. The \(CQ_{max}\) values for PCA, NNDiffuse, and GS from panchromatic and multispectral WV-2 data were 0.0498, 0.0458, and 0.0489, respectively. The \(CQ_{max}\) value was the largest for PCA, indicating the best fusion effect. Therefore, the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**WV-2**} & \multirow{2}{*}{**Band**} & **Gray Mean** & **Standard** & **Information** & **Average** & **Correlation** & **Spectral** & **Bias Index** \\  & & **Value** & **Deviation** & **Entropy** & **Gradient** & **Coefficient** & **Distortion** & **Bias Index** \\ \hline \multirow{4}{*}{MS} & Band1 & 231.59 & 3.45 & 3.62 & 2.24 & 0 & 0 & 0 \\  & Band2 & 302.74 & 7.41 & 4.67 & 3.51 & 0 & 0 & 0 \\  & Band3 & 198.27 & 9.08 & 5.10 & 3.36 & 0 & 0 & 0 \\  & Band4 & 97.99 & 10.61 & 4.73 & 5.20 & 0 & 0 & 0 \\ \hline \multirow{4}{*}{GS} & Band1 & 219.43 & 12.87 & 4.57 & 4.58 & 0.03 & 16.07 & 0.07 \\  & Band2 & 286.38 & 17.47 & 5.40 & 6.12 & 0.02 & 21.70 & 0.07 \\  & Band3 & 186.29 & 17.56 & 5.68 & 4.91 & 0.07 & 19.29 & 0.10 \\  & Band4 & 92.01 & 23.74 & 5.79 & 5.39 & 0.04 & 19.34 & 0.19 \\ \hline \multirow{4}{*}{NNDiffuse} & Band1 & 235.55 & 12.83 & 4.80 & 5.06 & 0.02 & 7.32 & 0.03 \\  & Band2 & 307.96 & 17.38 & 5.56 & 6.73 & 0.02 & 12.17 & 0.04 \\  & Band3 & 202.27 & 17.01 & 5.69 & 4.91 & 0.10 & 15.74 & 0.08 \\  & Band4 & 105.76 & 21.35 & 5.49 & 4.49 & 0.04 & 14.03 & 0.14 \\ \hline \multirow{4}{*}{PCA} & Band1 & **241.88** & **13.93** & **5.13** & **5.08** & 0.04 & 11.70 & 0.05 \\  & Band2 & **314.94** & **19.25** & **5.85** & **6.73** & 0.01 & 16.01 & 0.05 \\ \cline{1-1}  & Band3 & **207.90** & **17.43** & **5.79** & **5.14** & 0.01 & 15.74 & 0.08 \\ \cline{1-1}  & Band4 & **118.16** & **22.76** & **5.95** & **6.90** & 0.06 & 22.70 & 0.23 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Quantitative evaluation results for WV-2 data from a floating raft culture area.  
\(CQ_{max}\) index was successfully used to evaluate fusion results for multispectral images of a cultured floating raft area, validating the feasibility of the proposed methodology.
###### Abstract  
Vehicle-to-vehicle (V2V) communication has attracted increasing attention since it can improve road safety and traffic efficiency. In the underlay approach of mode 3, the V2V links need to reuse the spectrum resources preoccupied with vehicle-to-infrastructure (V2I) links, which will interfere with the V2I links. Therefore, how to allocate wireless resources flexibly and improve the throughput of the V2I links while meeting the low latency requirements of the V2V links needs to be determined. This paper proposes a V2V resource allocation framework based on deep reinforcement learning. The base station (BS) uses a double deep Q network to allocate resources intelligently. In particular, to reduce the signaling overhead for the BS to acquire channel state information (CSI) in mode 3, the BS optimizes the resource allocation strategy based on partial CSI in the framework of this article. The simulation results indicate that the proposed scheme can meet the low latency requirements of V2V links while increasing the capacity of the V2I links compared with the other methods. In addition, the proposed partial CSI design has comparable performance to complete CSI.  
e _scinors_ Article  
1  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Reinforcement Learning-Based Resource Allocation for Cellular Vehicular Network Mode 3 with Underlay Approach. _Sensors_**2022**, 22, 1874. [[https://doi.org/10.3390/s20251874](https://doi.org/10.3390/s20251874)]([https://doi.org/10.3390/s20251874](https://doi.org/10.3390/s20251874))
# Enhanced Performance of nZVI/MXene@CNTs for Rapid As(III) Removal from Aqueous Solutions
## 3 Results and Discussion
### Adsorption Kinetics, Isotherms and Thermodynamics  
The kinetics are one of the fundamental elements that define the removal efficiency of materials [58]. The influence of reaction time on As(III) elimination is shown in Figure 5e. At environmental temperatures of 15, 25 and 35 \({}^{\circ}\)C, the removal reaction achieved equilibrium after about 120 min, with equilibrium adsorption capacities of 317.48, 336.27 and 342.93 mg/g, respectively. Pseudo-first-order and pseudo-second-order models were used to fit the As(III) removal process of the nZVI/MXene@CNTs, and the parameters are listed in Table S2 (Supplementary Materials). As shown, the correlation coefficients R\({}^{2}\) (0.9880, 0.9942, and 0.9893) of the pseudo-second-order fit were greater than those of the pseudo-first-order kinetics (0.9876, 0.9922, and 0.9828), and the overall fit was similarly better. Thus, the rate-controlling step of the reaction was mainly chemisorption.  
[PERSON] and Freundlich [59] were applied to clarify the adsorption of As(III) by the nZVI/MXene@CNTs from the water solution. The fitting results of the two models are shown in Figure 5c,d, respectively, and the computed parameters of the Langmuir and Freundlich isotherm models are listed in Table S3 (Supplementary Materials). The Langmuir isothermal model could be better described as the adsorption process of nZVI/MXene@CNTs on As(III), implying that the adsorption of nZVI/MXene@CNTs on As(III) occurred primarily on the uniform monolayer boundary of the material. Once saturation was reached, no further reaction would occur at the adsorption site of the surface layer [60]. At the conditions of pH = 5.0 and 25 \({}^{\circ}\)C, the predicted maximum adsorption capacity of nZVI/MXene@CNTs was 336.64 mg/g, which was roughly close to the actual maximum adsorption capacity of 333.56 mg/g.  
The fitted Van't Hoff plot is shown in Figure 5e, and the thermodynamic data of As(III) removal from the aqueous solution by nZVI/MXene@CNTs are shown in Table S4 (Supplementary Materials). As the initial As(III) concentrations increased from 25 to 300 mg/L, the enthalpy value (\(\Delta H^{0}\)) decreased from 94.31 to 8.47 kJ/mol. The fact that all \(\Delta S^{0}\) were positive and all \(\Delta G^{0}\) were negative indicated that the adsorption of As(III) by nZVI/MXene@CNTs was rational and irreversible. The progressive drop in \(\Delta G^{0}\) from 15 \({}^{\circ}\)C to 35 \({}^{\circ}\)C showed that increasing the temperature allows for a larger spontaneous tendency of the reaction, and the reaction was more likely to occur at higher temperatures. Therefore, As(III) adsorption by nZVI/MXene@CNTs was a spontaneous and endothermic reaction.
## 3 The LOBIN Prototype: A Practical Study Case
### System Description
#### 3.1.1 Healthcare Monitoring Subsystem
Not only these parameters are used to complement the ECG; the respiration (monitored in WEALTHY [24] and Magic [23]), the blood pressure \begin{table}
\begin{tabular}{|c|c|} \hline
**Parameter** & **Features** \\ \hline  & Frequency: 0.5–125 Hz \\  & Amplitude: \(\sim\)0–50 mV \\ ECG & Gain: G \\  & 1 byte/sample \\  & 65 samples/tx packet \\ \hline  & Computed from ECG \\ Heart rate & 1 byte/sample \\  & 1 sample/tx packet \\ \hline  & Computed from 3-axis accelerometer \\ Angle of inclination & 1 byte/sample \\  & 1 sample/tx packet \\ \hline  & Computed by averaging angle of inclination \\ Activity index & 1 byte/sample \\  & 1 sample/tx packet \\ \hline  & Range: \(\sim\)20 \({}^{\circ}\)C to 120 \({}^{\circ}\)C \\  & Accuracy: 0.1 \({}^{\circ}\)C \\ Body temperature & Valid range: 32 \({}^{\circ}\)C to 45 \({}^{\circ}\)C \\  & 1 byte/sample \\  & 1 sample/tx packet \\ \hline  & 100–09\% \\ Level of battery & Coverage: 8–9 hours \\  & 1 byte/sample \\  & 1 sample/tx packet \\ \hline  & To carry alert code if necessary \\ Alert & 1 byte/sample \\  & 1 sample/tx packet \\ \hline \end{tabular}
\end{table} 
Table 39: Healthcare Monitoring Subsystem Parameters. (monitored in [PERSON] _et al._[37] and Personal Health Monitor [35]), and the SpO2 (monitored in [PERSON] _et al._[26, 27] and LifeGuard [22]) are other important physiological parameters used to do so. There are also different approaches with regard to the processing of the collected information. While in LOBIN all information is sent, shown and stored in real time, there are other projects that, because of communications limitations, only send information periodically or on demand (e.g., AMON [19]), or that, in order to save energy, only send the information when any abnormal behavior is detected (e.g., GeM-REM [52]). In addition, there are some projects that combine monitoring with artificial intelligence to analyze and diagnose cardiovascular issues (e.g., [PERSON] _et al._[47]).
## 4 Prototype Implementation and Experiment
### Hopping Performance Analysis
#### 4.2.3 Continuous Hopping Verification  
Figure 16 shows the experimental process of first five continuous hopping cycles within a one hundred hop continuous hopping test. During the experiment, the driving motor rotated continuously in one direction with a constant speed, and the robot hopped repeatedly in the vertical direction along the slide rail until the motor stopped. The time required for the hopping mechanism to complete the first energy storage was about 0.663 s, and the time required for the second and subsequent energy storage was about 0.373 s. The main reason for this is that: after the robot completed the first hopping, each subsequent hopping motion could absorb the partial landing impact energy of the previous hopping, thereby shortening the energy storage time of the next hopping and improving the hopping efficiency and energy utilization.  
Figure 15: Experimental results comparison for different driving modes. (**a**) The first prototype equipped with a special actuator; (**b**) The second prototype equipped with only a rigid motor. \(l_{i}\) means the length of the tension spring.
### Challenges of PD-NOMA  
NOMA faces a few challenges with the increased number of users; considering an ultra-dense network deployed with the PD-NOMA scheme, energy consumption at the user end becomes high due to extensive SIC calculations. As each user has to decode all other users' information associated with the same BS, if an error occurs in decoding any one user's information, then all additional decoding will therefore have an impact and eventually will harm the system level performance, specifically in achieving an efficient sum rate and capacity [23]. Thus, the computational complexity and power consumption will be increased at the receiver's end. The channel gain information is sent back to the BS, resulting in remarkable channel state information overhead. In addition, in downlink NOMA, for an arbitrary user 'k', it decodes the user signals with lower channel gain and processes the user signals with a relatively high channel gain as noise [25; 26].