esa-sceva/satcom-chunk-collection huggingface dataset:
# Strategies for Enhancing Extracellular Electron Transfer in Environmental Biotechnology: A Review
## 3 Strategies for Enhancing EET in Environmental Biotechnology
### Genetic Engineering Approaches
By introducing external electron transfer mediators, such as quinones or conductive polymers, the rate and extent of electron transfer can be significantly improved [41]. This enhancement may result in enhanced power production in microbial fuel cells or faster breakdown rates in bioremediation applications. Secondly, exogenous EET pathways can broaden the range of bacteria that can execute EET. Only a few microbes in natural habitats have innate EET capabilities. By introducing exogenous EET, it becomes possible to engineer non-electrogenic microorganisms to participate in EET processes [32]. Research carried out in 1993 was the pioneering study to make use of artificial soluble redox mediators as a strategy for harnessing the antioxidant potential of viable bacteria [42]. Methylene blue, a redox dye created by German chemist [PERSON] in 1876 [43], was one of the first mediators to be employed. According to [PERSON] [44], [PERSON] and [PERSON] made the initial suggestion that methylene blue could be a valuable tool for determining the bacterial load in milk in 1900. A recent study by [PERSON] et al. [45] employed methylene blue (MB) as a transitory intermediate to augment EET between exoelectrogenic anaerobic granular sludge (EGS) and electrodes. The study revealed that the optimum electricity of MB-EGS (8.88 mA) exhibited a statistically substantial rise compared to that of EGS alone (1.34 mA). Currently, in addition to methylene blue, other mediators such as thionine, neutral red, and 2,6-dichlorophenol [46] are also being employed.
## 1 Introduction
The specific objectives include (1) comparing the regression performance before and after hyperspectral image segmentation to demonstrate the effectiveness of ROI extraction through image segmentation; (2) discussing the optimal spectral preprocessing method for chlorophyll prediction models; and (3) selecting the most effective CNN model from the proposed self-developed CNN models, based on the extracted ROIs of grape leaves and the best preprocessing method, to predict the _chlorophyll a + b_ content in grape leaves.
## 2 Related Work
This result could be explained with a very low network traffic load, but the message generation pattern is not specified in the paper, therefore it is not possible to make a comparison with our results. Moreover, in [58] mobility support is not addressed and this is another difference with our work. The simulative assessments in [63] are made varying the number of nodes (up to 3000) and the distance between the nodes and the gateway. However, the work does not provide results on either message loss/delivery ratio or delay. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Work in the Literature** & [54] & [55] & [56] & [57] & [58] & [62] & [63] & [64] & [67] &
\begin{tabular}{c} **This** \\ **Work** \\ \end{tabular} \\ \hline
**Compliance with the current** & ✓ & - & - & - & - & ✓ & - & ✓ & ✓ & ✓ \\
**ETSI regulations** & 50–2000 & 60–780 & 1000–4000 & 300 & 1–10,000 & 20–200 & 1–3000 & 370–873 & 16–50 & 1–1200 \\ \hline
**Performance varying some** & - & - & - & - & - & - & ✓ & - & - & ✓ \\
**MAC parameters** & - & - & - & - & - & ✓ & - & - & - & ✓ \\ \hline
**Mobility** & - & - & - & - & - & ✓ & - & - & - & ✓ \\ \hline
**Evaluation method** & Sim. & \
\begin{tabular}{c} An. \\ Sim. \\ \end{tabular} & Sim. &
\begin{tabular}{c} An. \\ Sim. \\ \end{tabular} & Sim. & Sim. & Sim. & Sim. & Sim. & Sim. & Exp. & Sim. \\ \hline
**Message loss ratio or** & & & & & & & & \\
**message delivery ratio** & ✓ & - & ✓ & ✓ & ✓ & - & - & ✓ & ✓ \\
**assessed** & - & ✓ & - & ✓ & - & - & - & - & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of relevant works in the literature. The work in [64] assesses a LoRaWAN network in which both time scheduled transmissions and random transmissions are considered. The nodes adopt either an LBT-based approach or ALOHA. However, there are no mobile nodes in the simulations and the assessment adopts different performance metrics than the ones considered in this paper. The work in [67] presents the implementation and message delivery ratio results obtained through experimental evaluations of an LBT-based LoRaWAN network in a 50-node lab testbed. Differently from our work, the nodes are stationary and no assessments with varying MAC parameters are performed. The qualitative comparison between the state-of-the-art literature shown in Table 1 allows to select the works with which the findings obtained in this work can be compared, i.e., [54; 55; 62; 67]. A discussion on the comparisons between the results obtained in this work and the ones presented in the other relevant works in the literature is provided in Section 6.
# Precoder and Decoder Co-Designs for Radar and Communication Spectrum Sharing +
## 2 Generalized Multicarrier Radar-Communication Coexistence Model
### Transmitter
In a multicarrier communication system, the precoder functions as a multimode beamformer that allocates signal power according to channel quality, similar to the MIMO spatial precoder in [53]. Furthermore, the power leakage could be optimized by an orthogonal precoding design [54]. In a multicarrier radar, \(\mathbf{P}\) is applied as a multimode beamformer, projecting the radar signal into a designed subspace. Moreover, the data channel coding rate is \(\frac{N_{p}}{N}\) when coded data are transmitted in parallel and \(N-N_{p}\) is the coding redundancy [55]. The code rate upper bound of coding matrix \(\mathbf{C}\) is shown in [56]. As channel coding increases the channel reliability by introducing redundancy, it could increase the number of columns in the data matrix, i.e., \(\frac{N_{p}}{N}\leq 1\). Matrix \(\mathbf{C}\) facilitates pulse compression and consequently improves the radar resolution while functioning as a channel coder to improve the reliability of communication transmission [20]. In this paper, we consider the IA criterion to cancel the interference of both systems. As the transmitted waveform is always known by a radar receiver, without a loss of generality, we assume that the radar waveform is an all-ones matrix. Then, \(\mathbf{S}\mathbf{S}^{H}=M\mathbf{I}_{N_{p}\times N_{p}}\) for the radar signal. The transmitted payload data in the communication system is unknown to the receiver. It may be modeled with a complex Gaussian distribution, while its power is considered to be \(\mathbb{E}(\mathbf{S}\mathbf{S}^{H})=\sigma_{S}^{2}\mathbf{I}_{N_{p}\times N _{p}}\).
## 5 Case Study Analysis
### Impact Analysis of CCS on Economic Viability  
Figure 4 illustrates the introduction of CCS in Scenario 5. CCS has high energy consumption properties; therefore, when wind power generation is minimal, the electricity produced by CCHP combined with the initially acquired electricity is not enough to meet the power requirement of the system. As a result, there is a need to buy more electricity, which leads to a 2.99% rise in the expenses of acquiring energy compared to Scenario 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & **Total Cost** & **Purchasing Energy** & **Wind Curtailment** & **Carbon** \\  & **(CNY)** & **Costs (CNY)** & **Cost (CNY)** & **Emissions (kg)** \\ \hline
1 & 201,657 & 104,916 & 26,372 & 63,226 \\
2 & 193,533 & 99,499 & 24,337 & 57,198 \\
3 & 197,141 & 101,390 & 25,355 & 59,276 \\
4 & 189,229 & 96,170 & 23,418 & 53,410 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scenarios 1–4 costs and carbon emissions.  
During the operation of CCS, the system must efficiently utilize the captured CO2 by using a portion of it for the synthesis of hydrogen and methane, which will serve as a carbon source. Additional allocation is required to isolate another section, hence augmenting the expenses linked to carbon capture and storage. Additionally, the expenses associated with maintaining the CCS operation are taken into account, resulting in a proportional rise of 1.06% in operational costs. However, the considerable uptake of CO2 by CCS during operation effectively decreases the carbon emissions of the system, resulting in a 20.28% decrease in carbon trading expenses and a notable improvement in economic feasibility.  
Furthermore, when wind power generation is strong, CCS prioritizes using wind power to meet its energy requirements, leading to a decrease of 1.42% in wind curtailment costs and an overall cost reduction of 1.23%. Although the implementation of CCS incurs extra expenses, its remarkable capacity to reduce carbon emissions and integrate renewable energy significantly impacts the economic feasibility of the system.
# Automatic Selective Encryption of DICOM Images
## 3 The Proposed Approach
### Approach I: Multi-Region Selective Encryption Approach for Mammography DICOM Images  
A two-region selective encryption algorithm is proposed in the study [25], implemented and evaluated. The selection process for the proposed approach is based on pixels' intensities, where regions are identified based on the pixel thresholding technique. The proposed method is evaluated based on the cryptanalysis metrics, saved time, and the ratio of ROI size to image size. It is noticed that the saved time in images with large ROI size is much smaller than the saving time of images with small-medium ROI size. This highlights the need to enhance the saved time using a multi-region selective encryption algorithm as detailed in this approach.  
In this section, a multi-region encryption approach is proposed to work out regions' sizes (R1, R2, R3 within ROI) automatically using some mathematical formulas that guarantee less processing time in comparison with the Naive and two-region encryption approaches [25], while maintaining security robustness. This experiment was restricted to a Mammography DICOM dataset since each medical image modality and human body part has a different distribution [25; 30; 31], where dark pixel intensities represent the less informative regions of Mammography DICOM images, while bright pixel intensities represent the informative regions [32; 33].  
A detailed description of the proposed approach's procedures is given in the following sections after briefly describing pre-knowledge equations used to build the cost lookup table for different encryption algorithms employed in the proposed approach.
# Silicon 3D Microdosimeters for Advanced Quality Assurance in Particle Therapy
## 4 Application of SOI Mushroom Microdosimeters
### RBE Derivation and Cell Survival Predictions  
#### 4.2.1 RBE Prediction in Heavy Ion Therapy  
Figure 13 shows the \(\overline{y_{D}}\) distribution and corresponding microdosimetric spectra at different depths along the central axis of the \({}^{12}\)C SOBP in water obtained with a 3D freestanding Mushroom microdosimeter embedded in polyimide. The \(\overline{y_{D}}\) value was approximately 16 keV/\(\upmu\)m at the entrance of the SOBP and increased with depth in water. The microdosimetric spectra also shifted to the right as the microdosimeter moved to greater depths in water. At the distal part of the SOBP (\(\sim\)148 mm), the \(\overline{y_{D}}\) value reached a maximum of 195 keV/\(\upmu\)m.  
Figure 14 shows the RBE\({}_{10}\) distribution derived from the modified microdosimetric kinetic model (MKM) along the SOBP. At the entrance, the RBE started at 1.3 and increased to 2.7 at the end of the SOBP. It can be seen that the experimental data obtained with the SOI microdosimeter matched the TEPC data very well in the entrance and at the end of the SOBP. At the end of the SOBP (\(\sim\)148 mm), the SOI Mushroom microdosimeter was able to provide more detailed RBE\({}_{10}\) derivation than the TEPC due to its superior spatial resolution. Higher values obtained with the SOI microdosimeter downstream of the SOBP are due to (i) the SOI microdosimeter having a much higher lateral spatial resolution than the TEPC (\(\sim\)1 mm vs. \(\sim\)2 cm, respectively). Lighter particles are more susceptible to scatter and have comparatively lower LET than heavier ions that are more focused on the central axis of the beam. Low energy particles with higher concentrations away from the central axis are measured by the TEPC (due to its larger physical size) rather than the MicroPlus SOI microdosimeter, resulting in the latter recording higher \(\overline{y_{D}}\) values. (ii) The proportion of low LET events is higher in the tail region. As the TEPC has a lower threshold for energy detection compared to the MicroPlus, 0.1 keV/\(\upmu\)m compared with \(\sim\)0.4 keV/\(\upmu\)m, it was therefore able to detect these lower energy events.  
Figure 13: Microdosimetric spectra obtained by free-standing Mushroom microdosimeter embedded in Polyimide for 290 MeV/u SOBP \({}^{12}\)C ions at HIMAC [25].  
A comparison of RBE\({}_{10}\) determined from Geant4 simulation and experimentally derived from SOI microdosimetry measurements for the 400 MeV/u \({}^{16}\)O beam is shown in Figure 15a. It can be seen that the maximum RBE\({}_{10}\) value of 2.94 was located approximately 1 mm before the BP. This confirms that for Oxygen ions, the depth of the maximum RBE\({}_{10}\) occurred slightly earlier than the maximum physical dose. Figure 15b shows a detailed view of RBE\({}_{10}\) and the physical dose distribution near the BP region. More detailed results on different types of heavy ions can be found in [32].
## 5 Results and Discussion
### Atmospheric Attenuation Inaccuracy Estimation  
In the initial study, we ignored the effect of atmospheric attenuation on solar irradiance, mainly for the sake of simplifying the model and reducing computational complexity. In order to more accurately assess the effect of atmospheric attenuation on the real-time scheduling uncertainty of the heliostat field aiming strategy, we propose a simplified  
Figure 18: The flux map of PSO with physical IA and IUPSO in three cloud locations in the full heliostat experiment; the upper one in each subfigure is optimized by PSO with physical IA, and the lower one is optimizaed by IUPSO.  
atmospheric attenuation model by considering the effects of atmospheric scattering and absorption as Equations (26)-(28), shown below:  
\[DNI_{\text{scatter}}\,=DNI_{0}\cdot e^{-k_{scatter}\cdot m(az)} \tag{26}\]  
\[DNI_{\text{absorb}}\,=DNI_{\text{scatter}}\cdot e^{-k_{absorb}\cdot m(az)} \tag{27}\]  
\[DNI_{\text{total}}\,=DNI_{0}\cdot e^{-(k_{scatter}+k_{absorb})\cdot m(az)}, \tag{28}\]  
where \(DNI_{0}\) denotes the initial irradiance, \(k_{scatter}=1\times 10^{-5}\) and \(k_{absorb}=1\times 10^{-5}\)[59] denote the scattering coefficient and absorption coefficient, which are related to the number and type of particles in the atmosphere, respectively. \(m(az)=1/sin(az)\) denotes the air quality factor. The impact of the inaccuracy caused by omitting atmospheric attenuation is estimated based on the experiments in Section 5.3, and the results are shown as Figure 19.  
In Figure 19, the impact of neglecting atmospheric attenuation on the intercepted power of the heliostat field, optimized by IUPSO, is analyzed. The figure demonstrates that in the scenario of an X-axis cloud trajectory across a multi-heliostat field, the gradient of decreasing irradiation intensity grows near the center of the sector heliostat field, influenced by the incidence angle. This results in an average reduction of 4.8% in the heliostat field's energy after introducing the atmospheric attenuation model. Conversely, Figure 19 examines the Y-axis cloud trajectory scenario. Here, the decrease in irradiation intensity is more uniform, leading to a smaller average energy reduction of 2.7% in the heliostat field. Therefore, integrating a simple atmospheric attenuation model slightly affects the optimization of real-time aiming strategies in heliostat fields.  
Figure 19: Estimation of the omitting atmospheric attenuation inaccuracy for the intercept power of the heliostat field optimized by IUPSO.
# Design and Evaluation of Novel Submerged Floating Tunnel Models Based on Dynamic Similarity
## 1 Introduction
In order to research the drag forces and vortex-induced vibration effects of the model under the action of current, [PERSON] et al. [25] employed a rigidly connected twin-cylinder model, which was elastically mounted between two support frames. [PERSON] et al. [26] utilized a single-degree-of-freedom, vertical elastically truncated boundary model to investigate the dynamic response of SFTs under wave action. However, it is difficult to set the parameters of these springs and dampers to enable a segment model to fully represent the continuous elasticity and flexibility of the tunnel body. The hydroelastic model of SFTs offers an effective way to simulate the elastic deformation of the tunnel body, which enables the whole model to satisfy the Cauchy similarity principle. [PERSON] et al. [27] designed a 1:50 elastic scale model, which was composed of a steel tube, counterweight steel rings, plexiglass tubes, and foamed plastic, and investigated the hydrodynamic responses of SFTs under regular waves. Scaling a prototype to an experimental model facilitates a deeper and more comprehensive understanding of structural responses in actual operational environments. Scale effects arise from the inability to maintain identical force rations between a model and its real-world prototype, leading to deviations in observations when scaling up model results [28]. According to the experimental purpose and structural characteristics, reasonable selection and implementation of multiple similarity principles is beneficial to mitigate scale effects and more accurately reflect the response of prototypes [29]. The new SFT scale model introduced in this paper adheres to both the Froude and Cauchy similarity principles. The model is suitable for the hydrodynamic modelling of the SFT, which can simulate the elastic deformation of the tunnel body and complex phenomena such as fluid-structure coupling and vortex-induced vibrations in the tunnel tube. This paper introduces the design process, internal structure, and similarity evaluation test of a new type of SFT scale model, adhering to both the Froude and Cauchy similarity principles. Section 2 details the similarity theories underpinning the model. Section 3 describes the establishment of the similarity relationship between the scale model and the prototype, and how to determine the parameters of the scale model and design its configuration. Section 4 depicts the procedures for checking the Cauchy similarity of the model, including the static load test and free decay test, alongside corresponding numerical models. Section 5 introduces the assessment of the Froude similarity of the model through white noise wave testing. Section 6 provides the concluding remarks.
# User Acceptance of Smart Watch for Medical Purposes: An Empirical Study
## 4 Research Methodology
### Survey Structure  
The researcher circulated a questionnaire survey among the participants of the United Arab Emirates medical center (UAE) in the form of an online survey (N = 400). The Primary Health care sector participated in the following study. They are both well-known hospitals in the UAE. A questionnaire survey was developed and distributed among the participants [61]. This survey consists of the following sections:  
* The foremost section contains the personal data of the participants.
* The next section contains the two items of basic questions about the adoption of smartwatches.
* The third section contains eighteen items related to Perceived Ease of Use, Perceived Usefulness, Content Richness (Relevance, Timeliness, and Sufficiency), and Personal Innovativeness.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Constructs** & **Cronbach’s Alpha** \\ \hline ASW & 0.883 \\ \hline PEOU & 0.801 \\ \hline PU & 0.848 \\ \hline CONT & \\ \hline REL & 0.707 \\ \hline TIM & 0.765 \\ \hline SUF & 0.869 \\ \hline PER & 0.831 \\ \hline \hline \end{tabular} Note: ASW, Adoption of SW; PEOU, Perceived Ease of Use; PU, Perceived Usefulness; CONR, Content Richness (REL, Relevance; TIM, Timeliness; SUF, Sufficiency); PER, Personal Innovativeness.  
\end{table}
Table 4: Cronbach’s alpha values for the pilot study (Cronbach’s Alpha \(\geq\) 0.70).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Constructs** & **Items** & **Instrument** & **Source** \\ \hline \multirow{3}{*}{Sufficiency} & SUF1 & SW has sufficient medical information. \\ \cline{2-3}  & SUF2 & SW has provided me with satisfactory information whenever I need it. \\ \cline{2-3}  & SUF3 & SW is unable to provide me with the information I need. \\ \hline \multirow{3}{*}{Timeliness} & TIM1 & SW has up-to-date medical information that I need. \\ \cline{2-3}  & TIM2 & SW is unable to support me with up-to-date-information. \\ \hline \multirow{3}{*}{Personal Innovativeness} & PER1 & Whenever there is new technology, I am ready to use it. \\ \cline{2-3}  & PER2 & I am the first one to use new technology among my group of doctors \\ \cline{1-1} \cline{2-3}  & PER3 & I am usually hesitant to use new technology. \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._For the measurement of (20 items) in the questionnaire, a five-point Likert Scale was used that is based on the scales of strongly agreed (5), agree (4), neutral (3), disagree (2), and strongly disagree (1).
# Handover Management for Drones in Future Mobile Networks--A Survey
## 7 Future Directions
### MANETs and VANETs Applications in Drone Networks  
In future networks, Flying Ad Hoc Networks (FANETs) will be an active technology in mobile networks to enable drones to provide various services over a wide communication range. These drones will need to communicate directly or indirectly depending on the communication range to secure more reliable communications. This can be directly, if the two connected drones are located within a close communication range, or indirectly over a number of drones relay nodes, if they are distant. The concept is similar to that of previous technologies in the fields of MANETs and VANETs. However, setting up FANETs will be more challenging as compared to the traditional networks, such as Mobile Ad hoc Networks (MANETs) and Vehicular Ad hoc Networks (VANETs). The requirements will be different in terms of node mobility, connectivity, message routing, service quality, application areas, and other necessities. Therefore, the introduction of FANETs models, analyzing opportunities, identifying open research issues, and addressing the challenges in FANETs will comprise a key research direction in future mobile networks. Various mobility situations and system settings over various deployment scenarios will be more challenging.