esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 3 Methodology
### Training Methods of Two Deep Networks
#### 3.3.2 Training WLgenerateNet by Unsupervised Learning  
As an auxiliary facility in DeepWL, WLgenerateNet aims to support the generalization ability of another deep learning model WLdetectNet by rendering more training data as much as possible for WLdetectNet. Similar to other classic GANs, its learning objective corresponds to a minmax two-player game, which is formulated as:  
\[\min_{G}\max_{D}\mathrm{L}(G,D)=\mathbb{E}_{x\sim p_{\mathrm{data}}(x)}[\log D (x)]+\mathbb{E}_{z\sim p_{z}(z)}[\log(1-D(G(z)))] \tag{2}\]  
where the generator \(G(z)\) is responsible for learning to map data \(z\) from the noise distribution \(P_{z}(z)\) to the distribution \(p_{\mathrm{data}}(x)\) over data \(x\), while the discriminator \(D(x)\) answers for estimating the probability of a sample from the data distribution \(p_{\mathrm{data}}(x)\) rather from \(G(z)\).  
Our training for WLgenerateNet is built on the 2000 positive samples gathered by hand with a batch size of 16. All parameters of this deep learning model are initialized from a zero-centered normal distribution with a standard deviation of 0.02. For the activation function LeakyReLU, the slope is set to 0.2. Moreover, the whole training procedure involves two concurrent stages: we maximize \(\log D(x)+\log(1-D(G(z)))\) for \(D(x)\), and  
Figure 4: Two groups of instances generated from original manual samples by WLgenerateNet (here, \(r=64\) and \(s=48\)).  
simultaneously minimize \(\log(D(G(z)))\) for \(G(z)\) by applying the Adam [37] optimizer with a momentum of 0.9. During the training, we first use a learning rate of 0.0002 for 200 epochs, and then tune the learning rate to 0.00015 for subsequent 100 epochs. Finally, about 8000 artificial positive samples on waterline segments are generated automatically via the unsupervised learning for WLgenerateNet.
# NFT-Based Framework for Digital Twin Management in Aviation Component Lifecycle Tracking
## 2 NTF-Based Digital Twin Ecosystem in Aviation Industry
### A Comprehensive Framework of NFT Applications in Aviation  
The integration of NFTs into the aviation industry presents a groundbreaking framework that promises to change key aspects of aircraft management, maintenance, and operations. This innovative approach uses blockchain technology to create a secure, transparent, and efficient ecosystem across eight critical areas of aviation (Figure 2).  
At the heart of this framework is the concept of digital twins for aircraft parts. Each component is assigned a unique NFT, serving as its digital counterpart and containing its entire lifecycle information. This application enables unprecedented traceability and  
Figure 2: Framework of NFT-based applications in aviation.  
informed decision-making in maintenance and fleet management. Complementing this, maintenance records are transformed into tamper-proof digital logs, with each activity recorded as an NFT. This ensures the integrity of maintenance history, enhances transparency, and simplifies auditing processes.  
The framework extends to personnel management through NFT-based certifications for pilots, mechanics, and other aviation professionals. These digital credentials are easily verifiable and secure, streamlining the certification process and reducing the risk of fraud. In the realm of supply chain management, NFTs track the provenance of aircraft parts, creating a verifiable chain of custody that significantly reduces the risk of counterfeit parts entering the system.  
Operational data are captured through flight log NFTs, each representing a unique flight and containing comprehensive information such as route, duration, crew details, and performance data. This creates an immutable record of an aircraft's operational history, facilitating more accurate tracking and analysis. Ownership and leasing rights are also represented by NFTs, simplifying transfers and providing a clear chain of custody for aircraft.  
Technical documentation management is revolutionized by linking critical documents to NFTs, ensuring access to the most current versions and controlling distribution to authorized personnel. This significantly reduces the risk of using outdated information in aircraft maintenance and operations. Lastly, the framework incorporates quality assurance processes, with NFTs tracking inspections and tests, thereby enhancing accountability and facilitating easier identification of quality trends or issues.  
This comprehensive NFT framework in aviation addresses critical aspects of the industry, from granular part tracking to overarching quality assurance processes. By using blockchain technology, it offers a new paradigm in aviation management that promises enhanced safety, efficiency, and transparency.  
In Section 3, we will consider the algorithms for using the applications specified in the framework in detail.
## 4 Conclusions  
This study proposes a spray-type core-shell paste composite material blended with core-shell powder. The specimen was prepared by spraying, and the sheet resistance of the specimen was measured to evaluate the precision and reproducibility of the spraying process. The thickness of the sprayed coating was evaluated by cross-sectional observations. Additionally, the precision of specimen preparation was determined. An electromagnetic shielding measurement test was conducted to measure SE according to ASTM D 4935. The SE was determined using the difference between the numerical values of the electromagnetic wave transmittances measured from the coated and reference specimens. The study findings can be summarized as follows. The thickness of the electromagnetic interference shielding film coated via the spraying process, which was suitable for the core-shell paste characteristics, was 30 \(\upmu\)m. The sheet resistance of the coated specimen measured to ensure reproducibility of the specimen indicated that the coating was homogenous with 0.1336 \(\Omega/\square\)\(\pm\) 5%. Thus, the possibility of a precise coating was confirmed. Moreover, the measured electromagnetic interference SE of the base aluminum plate was 64.57 dB, whereas that of the specimen coated with the core-shell paste was 77.27 dB. This indicates an improvement in SE by 120% compared to that of the reference specimen. In the future, the optimized composition that can increase SE in various bands should be determined by performing experiments with respect to the coating thickness of the core-shell paste, wt% of the core-shell powder, core-shell particle size, shape, and material. In the future, the optimized composition that can increase SE in various bands should be determined by performing experiments with respect to the coating thickness of the core-shell paste, wt% of the core-shell powder, core-shell particle size, shape, and material.  
Figure 6: Shielding effectiveness as a function of frequency.  
**Author Contributions:** Conceptualization, J.-H.Y.; Writing--original draft, Y.-J.J.; Writing--review & editing, writing--original draft preparation, supervision, M.-S.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF), grant funded by the Korea government (MSIT) (Grant Numbers 2022 RIG1A1012340 and 2022 RIG1A1004962). The study was supported by the \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (Grant Number 2022 RIS-004).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Proposed Technique
### BS-Sw Techniques
Figure 3: Example of system model consisting of four randomly distributed SBSs and users. This heuristic method is proven to be effective through analysis and observation of thousands of realisations (realisations here refers to the EE evaluation and comparison performed for the binomial and distance-based BS-Sw methods for different settings of SBSs and UEs distribution within the macrocell to prove the accuracy of the distance-based method), but for the sake of clarity, we only show 100 realisations in Figure 4. The plot illustrates the comparison between the EE of binomial and distance-based switching techniques, where the binomial method is around 5% better than the latter. Furthermore, Figure 4 also indicates the matching rate of distance-based method with the binomial method in terms of SBSs switching statuses. It is observed that around 80% realisations of distance-based method matches the binomial method (Boolean representation: the status \"1\" indicates that the switching decision of distance-based method matches that of the binomial technique and the status \"0\", otherwise). Algorithm 4 shows the steps to obtain the accuracy or EE matching percentage. The EE of the distance-based method is displayed as a stacked line for a distinct comparison. The analyses were carried out using different numbers of SBSs and UEs per cell as shown in Figure 4 to validate the performance of the proposed distance-based strategy. It is noteworthy that the EE difference and matching rate of all the considered scenarios are similar. ```
1:Initialise \(counter=0\); Declare Match as Boolean value. 2:for\(1\leq realisation\leq 100\)do
3: Difference = EE (binominal) - EE (distance-based). 4:if Difference = 0. then
5: Match = 1. 6:\(counter=counter+1\). 7:else
8: Match = 0. 9:endif
10:endfor
11:Accuracy(%) = \(counter\). ```  
**Algorithm 4** Distance-based performance validation method. \begin{table}
\begin{tabular}{c c} \hline \hline \(D_{j}\)_from Lowest to Highest_ & \(\sum_{i}d_{i}^{j}\)_from Lowest to Highest_ \\ \hline SC 2 & SC 2 \\ SC 1 & SC 1 \\ SC 3 & SC 3 \\ SC 4 & SC 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SBSs ordered from highest to lowest possibility to be switched off. Random SBS based on discrete uniform distribution is set to off as shown in Algorithm 5. This distribution follows an equal likelihood to switch off the SBSs (the probability of each SBS to be switched off is the same, e.g., if the number of SBS = 3, then the switch off probability of each SBS is \(1/3\)). ```
1:\(n=\) number of SBSs. 2:Off random (\(n\)) with probability \(1/n\). ```  
**Algorithm 5** Random method.
## 3 Materials and Methods
### Data Transmission
#### 3.5.3 Software-Defined Network (SDN)  
Dynamic network control is necessary to achieve higher network performance. Improving routing processes and data flow requirements is possible with SDN [36]. SDN controllers can be present in both front-haul and back-haul links. In this regard, Ryu is an SDN controller with a well-defined API written in Python [37]. Located at the node level, Ryu can communicate with the Mininet controller.  
Based on the study from NUST, there are over 21 test-beds currently available. Due to the vast offer and proven effectiveness, we did not plan to apply SDN solutions in this study.
## 5 Numerical Applications and Discussion
### Analysis of Quantitative Accuracy  
The effect of numerical modulus coupling with different influence functions on deformation analysis is explored in this subsection. A cantilever beam with dimensions of 0.8 m \(\times\) 0.2 m is shown in Figure 9. A concentrated force of F = 10 kN is applied at the midpoint of the right end. Material properties and the PD model parameters are illustrated in Table 5. On the basis of the elasticity theory, the theoretical solution for the deflection curve in the central axis is given by Equation (25).  
\[u_{y}=-\frac{F(L-x)^{3}}{6 EI}+\frac{FL^{2}(L-x)}{2 EI}-\frac{FL^{3}}{3 EI}-\frac{ 3 Fx}{2 GA} \tag{25}\]  
where \(L\) and \(I\) are the length and the inertia moment of the beam, respectively.  
Regarding the selection of horizon radius, a series of numerical experiments are conducted. The computer configuration used in this work is Inter(R) Core(TM) i7-7700 CPU @ 3.60 GHz. Figure 10 illustrates the computing time for each influence function under different horizon radii. The computation cost increases with the increase in the horizon radius, which is the same as the conclusion in Section 5.1. As for accuracy, under different horizon radii, the relative error between the PD model using different influence functions and the theoretical results is less than 10%, which is within the acceptable range. Overall, the relative error is minimal when \(\delta=3\Delta x\). Thus, \(\delta=3\Delta x\) is adopted in the subsequent content.  
Figure 11 depicts a comparison between numerical solutions and analytical solutions for different influence functions. Moreover, the eigenvalues of relative error are listed in Figure 12.  
Figure 10: Computation time of influence functions at different horizon radii.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**E (GPa)** & **G (GPa)** & \(v\) & \(\rho\) **(kg/m3)** & \(\Delta x\) **(m)** & **Influence Function** \\ \hline
24.8 & 9.2 & 1/3 & 2400 & 0.02 & \(g_{1}-g_{8}\) \\ \hline \end{tabular}
\end{table}
Table 5: Material properties and PD model parameters.  
Figure 9: Diagram of cantilever beam.  
Figure 11: Displacements (red line and black line) and relative error (blue line) of the central axis for different influence functions (\(\delta=3\Delta x\), influence function symbol \(g_{i},\ i=1,2,3\ldots 8\) ).  
The influence functions can ensure the model with good precision. Regardless of the type of influence function used, the relative error can be reduced to varying degrees compared with the traditional model (PD-\(g_{1}\)). Nevertheless, all relative error eigenvalues are within acceptable limits and the maximum relative errors are all less than 3.5% under different influence functions. In particular, the influence functions \(g_{2}\) and \(g_{3}\) make the greatest contribution to improving calculation accuracy.  
On the whole, the exponential function \(g_{2}=e^{-\xi/\delta}\) has the best computational accuracy, which can be seen from the local magnification in Figure 12.
# Energy-Efficient On-Off Power Control of Femto-Cell Base Stations for Cooperative Cellular Networks
## 1 Introduction
### On-Off Power Control Schemes in MCS
More specifically, the contribution, i.e., the throughput improvement due to the participation of the FBS in the cooperative transmission, of each FBS on the cooperative transmission has to be taken into account in order to properly decide which FBS to switch off. Otherwise, the performance of cooperative transmission can be severely deteriorated. In [22], the switching-off of clustered FBSs which utilize the CoMP was considered. However, FBSs which collaborate in the same cooperative transmission cannot be selectively turned off, such that the performance of on-off power control is not fully optimized. The joint use of CoMP and switch off was considered in [20]. However, the CoMP is used to compensate the performance degradation caused by the shutdown of BSs, and the set of turned off BSs is determined without considering the CoMP.
## 3 Development of RF WPT-Enabled IoT Application
### Implementation and Experimental Validation
#### 3.4.4 RF Power Source and Gateway  
The two final components of our envisaged system are the wireless power transmitter and a gateway for receiving the reports. We start with the latter. Since we have selected the BLE to be the radio access technology in our test system, this provides us with a sheer diversity of options for the gateway. Specifically, to receive the advertising packets sent by our device, the dedicated BLE transceivers or the mobile devices (i.e., tablets, smartphones or laptops) can be used. For our test case, we have developed two gateway options. The former is a dedicated HW BLE gateway, which monitors and logs the data sent by the WPT sensor for further processing. Another option bases on an Android-based tablet computer, for which we developed a simple application to read the data received from BLE and display them on the screen in real time.  
Finally, the strong-enough source of the radio signal to power the designed sensor solution was needed. At first, we attempted to find a commercial solution. The only one available in the market is the TX91501 device [67] from Powercast(r) (Pittsburgh, PA, USA). Unfortunately, this device did not fit us since it:  
* Operates in 915 MHz US ISM band,
* Has a fixed power level of either 1 or 3 Watt,
* Does not enable duty-cycling,
* Does not permit the connection of external antennas.  
Therefore, we decided to design the solution matching better our needs. This was done based on our IoT modular HW platform [2; 66]. Specifically, we used a node composing a microcontroller and an 868 MHz ISM-band LoRaWAN radio transceiver (the RN2483 from the Microchip (Chandler, AZ, USA) in our case). The transceiver was forced into the test mode, transmitting the radio carrier. The microcontroller can switch the transmission on and off, thus enabling to implement duty-cycling. Since the radio transceiver can transmit with the maximum power of only 14 dBm, we connected to it an external amplifier--the Kalmus 710 FC (Souderton, PA, USA). This device amplifies the signal to the level of 27 dBm (500 mW) and applies it to an external antenna--the one we described earlier.
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 7 Conclusions  
In this paper, we presented an overview of various types of ECSs used in literature for WSN. In order to determine the challenges for ECSs, detailed research study is conducted on WSN channel model, network model and standards as they are crucial for the designing and implementing efficient ECSs. The review on the proposed energy consumption and efficiency models for WSN indicates that existing research work has not considered SHAS with high performing ECCs. Further review on ECS design for WSN indicates that there are no sufficient research publications on ECS design on a practical network model i.e., modified multi-hop WSN model. Finally, we highlighted future research opportunities where WSN ECS design and implementation can be improved.  
All authors contributed to Sections 1, 3 and 5. R.K., K.P. and D.B.G. contributed for Sections 2, 4, 6 and 7. All authors have read and agree to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ACK & \multicolumn{1}{l}{Acknowledge} \\ ARQ & \multicolumn{1}{l}{Automatic Repeat Request} \\ ASIC & \multicolumn{1}{l}{Application Specific Integrated Circuit} \\ ASK & \multicolumn{1}{l}{Amplitude Shift Keying} \\ AWGN & \multicolumn{1}{l}{Additive White Gaussian Noise} \\ BCH & \multicolumn{1}{l}{Bose-Chaudhuri-Hocquenghem} \\ BER & \multicolumn{1}{l}{Bit Error Rate} \\ BICM & \multicolumn{1}{l}{Bit-Interleaved Coded Modulation} \\ BMP & \multicolumn{1}{l}{Binary Message Passing} \\ BP & \multicolumn{1}{l}{Belief Propagation} \\ CRC & \multicolumn{1}{l}{Cyclic Redundancy Check} \\ CSMA & \multicolumn{1}{l}{Carrier Sense Multiple Access--Collision Avoidance} \\ CSI & \multicolumn{1}{l}{Channel Side Information} \\ DEB & \multicolumn{1}{l}{Decoding Energy per Bit} \\ DECTED & \multicolumn{1}{l}{Double Error Correction and Triple-Error Detection} \\ ECC & \multicolumn{1}{l}{Error-Correcting Code} \\ ECS & \multicolumn{1}{l}{Error Control Schemes} \\ FEC & \multicolumn{1}{l}{Forward Error Correction} \\ FER & \multicolumn{1}{l}{Frame Error Rate} \\ FPGA & \multicolumn{1}{l}{Field Programmable Gate Array} \\ FSK & \multicolumn{1}{l}{Frequency Shift Keying} \\ FSO & \multicolumn{1}{l}{Free Space Optical} \\ GTS & \multicolumn{1}{l}{Guaranteed Time Slot} \\ HARQ & \multicolumn{1}{l}{Hybrid Automatic Repeat Request} \\ LDPC & \multicolumn{1}{l}{Low-Density Parity Check} \\ LOS & \multicolumn{1}{l}{Line of Sight} \\ LT & \multicolumn{1}{l}{Luby Transform} \\ ML & \multicolumn{1}{l}{Maximum Likelihood} \\ MAC & \multicolumn{1}{l}{Media Access} \\ MAE & \multicolumn{1}{l}{Mean Absolute Error} \\ MAP & \multicolumn{1}{l}{Maximum A Posteriori} \\ MLME & \multicolumn{1}{l}{MAC Layer Management Entity} \\ MPDU & \multicolumn{1}{l}{MAC Protocol Data Units} \\ MTE & \multicolumn{1}{l}{Minimum Transmission Energy} \\ NACK & \multicolumn{1}{l}{Negative Acknowledgment} \\ \end{tabular}
## 3 Results and Discussion
### The Roles of the Genes in the Artificial Operon
For PMC accumulation, the MRMs of BL21-B1 and BL21-B2 were lower than those of BL21-T, BL21-TP and BL21-TPK, suggesting that MerB1 and MerB2 could promote PMC accumulation. This result could be due to their increasing the host cell's tolerance to PMC, as MerB1 and MerB2 can catalyze the splitting of the carbon-Hg bond [22; 23; 24]. The MRM of BL21-7 was the lowest among the strains. The second lowest MRM was BL21-B1B2K. Although _Pseudomonas_ K-62 has been shown to have approximately 1000-fold higher resistance to phenylmercury than _E. coli_[25], its biosafety had not been studied. _E. coli_ BL21(DE3) was derived from normal commensals of the human gut [26] and is not harmful to humans. Therefore, we selected _E. coli_ BL21(DE3), not _Pseudomonas_ K-62, for transforming and constructing the engineered bacterial strain. The genome of _E. coli_ BL21(DE3) has been sequenced and published [26]. However, although _merB_ genes had been found in _E. coli_ R831b and _E. coli_ AAD3 [27], we were unable to find any mer genes in the _E. coli_ BL21(DE3) genome. Compared to that of _Pseudomonas_ K-62, the Hg-resistance phenotype of _E. coli_ BL21(DE3) is negligible. To better understand the engineered bacteria's ability to accumulate Hg, a mechanism for organic and inorganic Hg detoxification by BL21-7's artificial operon was proposed (Figure 10). According to the proposed mechanism, in the artificial operon, both _P16S_ and _g10_ can increase the expression levels of downstream genes [11; 12]. _RpsT_ can prevent the transcripts from being hydrolyzed [6]. This construct can result in sufficient transcription and protein accumulation for Hg tolerance and Hg accumulation. Phenylmercury and Hg\({}^{2+}\) are transported into the cell periplasm by MerP [21]. The phenylmercury and Hg\({}^{2+}\) in the periplasm are transported into the cell by MerT [8]. MerP can bind to Hg\({}^{2+}\) and retain it in the periplasm [21]. However, high levels of phenylmercury in a cell can harm the cell. The phenylmercury tolerance of BL21-TP was lower than that of BL21-0. Methylmercury is transported into the cell by unknown means, as the MerT-MerP system does not transport methylmercury [7]. The methylmercury tolerance and methylmercury accumulation of BL21-T, BL21-P and BL21-TP were similar to those of BL21-0. Being catalyzed by MerB1 and MerB2, the carbon-Hg bond in phenylmercury or methylmercury is split and Hg\({}^{2+}\) is released [22; 23; 24]. The toxicity of Hg\({}^{2+}\) is lower than that of organic Hg. The tolerance to phenylmercury and methylmercury of BL21-TPB1B2 are higher than those of BL21-0. After _ppk_ is overexpressed, a significant amount of polyP is produced. Hg\({}^{2+}\) binds to polyP, forming the chelate polyP-Hg\({}^{2+}\)[9]. The toxicity of polyP-Hg\({}^{2+}\) is lower than that of Hg\({}^{2+}\). Thus, the Hg tolerance and Hg accumulation of bacterial strains harboring _ppk_ are higher than those of strains without the _ppk_ gene. Figure 10: Schematic mechanism proposed for organic and inorganic Hg detoxification by the constructed operon of BL21-7.