esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Determination of Antioxidant Activity and Ability to Chelate Fe2+
Since magnesium is located in the central porphyrin ring of this natural pigment [46], bivalent metals such as iron, copper, or zinc can displace it from the active center, forming complexes influencing the ability of chlorophyll to chelate Fe\({}^{2+}\) ions [47]. Also, the fermentation process leading to a reduction in pH levels promotes the displacement of magnesium ions by hydrogen. This results in the conversion of chlorophyll to pheophytin accompanied by a change in the color of the final product and a diminished ability to chelate Fe\({}^{2+}\)[46]. These mechanisms probably substantially influenced the capacity of the fermented green lentil products to chelate Fe\({}^{2+}\) ions. Differences were observed in the results between the Fe\({}^{2+}\) chelating capacity and DPPH\({}^{\bullet}\) inhibition % (free radical scavenging activity). In one of the tests, the results for the beverage variants differed, while in the other, the differences in the obtained values were relatively small (Table 5). These discrepancies may arise due to variations in the mechanisms of antioxidant activity assessed with each method. The Fe\({}^{2+}\) chelating capacity assay primarily measures the ability of antioxidants to reduce ferric ions, reflecting their capacity for electron donation, while the DPPH assay evaluates the ability of antioxidants to scavenge free radicals by hydrogen atom transfer [48]. Moreover, several factors might contribute to discrepancies between these methods in fermented legume-based products. Fermentation might alter the composition and concentration of antioxidant compounds, affecting their reactivity in different assays. Additionally, the presence of fermentation by-products or microbial metabolites could influence the antioxidant activity measured by each assay [48]. To assess the similarity between the tested products concerning both antioxidative activity and the capacity to chelate Fe\({}^{2+}\), the hierarchical cluster analysis (HCA) was carried out (Figure 1). Grouping was performed using the Ward method with a distance matrix (similarity matrix) constructed based on Euclidean distance. The results revealed that the tested products could be categorized into three distinct groups based on the similarity of both analyzed characteristics (at a bond level of 1.5). The first group comprised all the fermented beverages, distinguished by their high efficacy in inhibiting free radicals (over 80% DPPH inhibition). However, their average chelating activities did not differ significantly (Table 5). In contrast, the unfermented beverages derived from chickpeas and the unfermented products from red lentils formed the second cluster. These variants exhibited the lowest average chelating activity. In turn, the unfermented beverages obtained from green lentils (GL-0), characterized by high Fe\({}^{2+}\) chelating activity and relatively low antioxidant activity, constituted the third group of products (Figure 1 and Table 5).
## 1 Introduction  
Digital Radio Frequency Memory (DRFM) jammers have existed for several decades now, evolving from simple mono-bit devices to modern wideband, high dynamic range systems, with fully coherent receivers and transmitters [1]. Despite this, no published research has been conducted to understand the fundamental relationship of a DRFM system: the interaction between the deceptive waveform it generates and the radar receiver [2; 3; 4; 5; 6; 7; 8; 9; 10]. Understanding of the DRFM-radar interaction is fundamental to Electronic Attack (EA) design, allowing for the proper jammer waveform parameters (power, frequency, technique, etc.) to be chosen for maximum effectiveness. The goal of this paper is to illuminate that relationship, the factors that affect it, and propose a more effective DRFM architecture in order to improve the Electronic Attack (EA) and Electronic Protection (EP) engineering processes.  
The results of this study quantify the processing loss, time delay, and side-lobe levels generated in a variety of scenarios, providing guidance in the design of effective EA. Building on the study of DRFM jammer-radar receiver interaction, a novel jammer architecture is proposed and implemented to effectively counter modern forms of electronic protection.  
Prior to recent years, one of the limitations of studying the jammer-radar interaction was the available hardware. National agencies were often limited to their in-service jammers, or radars, many of which had limited signal agility to validate experimental results. Fortunately, Field Programmable Gate Arrays (FPGA) and the related tools, such as MATLAB, Simulink and Xilinx System Generator, allow for the generation of software defined EW and radar systems, with all aspects of the digital implementation being under the engineer's control. This research exploited FPGA technology to implement a wide variety of waveforms, and DRFM jammer architectures using a Xilinx Kintex-7 FPGA, in order to evaluate the factors that affect EA/EP performance.  
To do so, a wideband DRFM was implemented in two forms, a fully-coherent in-phase and quadrature (I-Q) sampling DRFM, and an envelope DRFM that only sampled the amplitude. This approach is a reflection of the two classes of DRFM jammer available and allowed the various effects that degraded the jammer performance to be examined in both architectures. The remainder of this paper describes the DRFM research in detail, by providing background on DRFM jammers and their performance, detailing the DRFM model developed, and the performance during the various experiments.  
Finally, the problem of DRFM systems generating signals in response to radars capable of modern forms of EP, which require the generation of signals emulating a multiple scatterer target, is explored. Specifically, a novel DRFM architecture is proposed and implemented on an FPGA that demonstrates the ability to emulate EA from a multiple scatterer target.
## 1 Introducing Microbial Electrochemical Systems for Bioremediation, Energy and Biofuel Production
A further extension of MFC technology includes microbial desalination cells (MDCs), which contain two ion exchange membranes between the anode and cathode chambers in MFCs or plant or algal microbial fuel cells (PMFCs), the latter two of which incorporate photosynthetic organisms to enhance power generation [16]. As with MFCs, MFCs can also vary in type and architecture. Two main types of cells fall under the banner of MFCs: microbial electrolysis cells and microbial electrosynthesis cells [1; 4]. One of the first instances of MFCs developed for biofuel production was a biochemical fuel cell that utilised glucose fermentation by _Clostridium butyricum_ for the generation of hydrogen [17]. Microbial electrolysis cells require a supplied external voltage in addition to that produced at the anode. These types of cells are often used to generate hydrogen as a biofuel. In microbial electrosynthesis, cell reactions occur at the cathode with carbon dioxide as the carbon source, and electrons are derived from the cathode to synthesise valuable chemicals and biofuels. The main difference in application between MFCs and MECs is that MFCs provide clean renewable energy with the ability to concurrently bio-remediate contaminated environments, while MECs use external energy to microbially synthesise biofuels and other valuable substrates. MES research is an area that continues to grow not only in power production but also in the field of bioremediation applications and the production of biofuels and commodity chemicals [2; 4; 18; 19; 20; 21]. One particularly effective example of MFCs as a bioremediation tool was their demonstrated ability to remediate uranium-contaminated groundwater in Rifle, Colorado [22]. In this case, graphite electrodes were inserted into the groundwater aquifer at regular intervals with acetate injected as a carbon source for the bacteria [23]. Dissimilatory metal-reducing bacteria (DMRB), selected for by providing acetate as an electron source, reduced soluble uranium (VI) to insoluble uranium (IV) which precipitated onto the electrode, thus enabling its removal from groundwater [22]. With a growing demand for higher power generation, studies have progressed into using domestic swerage and other wastewaters in an attempt to generate higher power levels [15; 24; 25; 26]. However, even when utilising wastewater as a carbon source, MFCs are currently limited in their power production.
# Scalp-Implanted Ultra-Wideband Circularly Polarized MIMO Antenna for Biotelemetry Systems
## 3 Results and Discussion
### Specific Absorption Rate of Energy Deposition  
Implantable devices transmit data to external devices via electromagnetic waves, and this propagation can be absorbed by human tissues, thereby causing damage. SAR signifies the quantity of electromagnetic energy absorbed by living beings, serving as a critical metric for evaluating the health implications associated with IMDs. According to the IEEE standard, for an input power of 1 W, the 1 g averaged SAR must not go beyond 1.6 W/kg, and the 10 g averaged SAR should remain below 2 W/kg [33]. To assess the antenna's SAR, the IMD was implanted at a depth of 3 mm within a real human head model, with both feeding ports of the MIMO antenna simultaneously excited with an input power of 1 W. As shown in Figure 11, the designed antenna exhibited a maximum SAR of 192.9 W/kg in each 10 g of tissue, with each individual antenna achieving a maximum SAR of 96.45 W/kg in each 10 g of tissue. This assessment considers the implantation depth and the overall impact on the surrounding 10 g of tissue. According to IEEE standards, the suggested antennas are capable of managing an input power level of up to 16.59 mW safely. In practice, the input power for IMDs is restricted to 16 dBm to prevent interference with other devices [34]. Therefore, the suggested antenna can operate safely with an input power of up to 16.59 mW (12.2 dBm).  
Figure 10: Comparison of CC with SNR change.  
Table 3 presents a summary of the proposed antenna's performance alongside a comparison with recent studies on implantable MIMO antennas. The table clearly indicates that the design and investigation of the proposed MIMO antenna utilized a seven-layer head model. In addition, the antenna has a wider FBW and wider effective ARBW under the conditions of antenna miniaturization and polarization diversity.
# UCMAC: A Cooperative MAC Protocol for Underwater Wireless Sensor Networks
## 6 Conclusions  
This paper proposes a cooperative MAC protocol for UWSNs named UCMAC. For the purpose of improving network capabilities in the error-prone underwater channel, UCMAC builds spatial diversity using cooperative retransmission. In addition, to minimize extra overheads caused by this cooperation, the neighbors located at more advantageous positions for retransmission than the source selectively participate in the cooperation. Also, the order of preferred cooperators, based on closeness to the destination, is appended to data without extra packet exchange. Moreover, this scheme avoids the packet collisions that frequently occur in previous cooperative communication schemes by designating the most preferable cooperators one by one. Simulation results showed that UCMAC outperforms comparable schemes, including MACA-U and CD-MACA, in terms of system throughput, latency, single-hop PDR, and energy efficiency, alleviating the space-time uncertainty problem. We are currently planning additional research to include the cross-layer approach of establishing a new cooperator selection criterion that adapts to underwater channel quality.  
[PERSON]-[PERSON] conceived and designed the protocol; [PERSON]-[PERSON] and [PERSON] performed the simulation; [PERSON] and [PERSON] analyzed the simulation results and wrote the paper; [PERSON] supervised the work.  
This research was a part of the project titled \"Development of Distributed Underwater Monitoring & Control Networks\", funded by the Ministry of Oceans and Fisheries, Korea, and supported in part by the BK21 Plus project funded by the Ministry of Education, Korea (21A20131600011).  
The authors declare no conflict of interest.
# The Application of Augmented Reality in the Automotive Industry: A Systematic Literature Review
## Results
### Application Areas
#### 3.4.2 AR in Driving  
In this paper we use the term 'in-car' systems to refer to those AR systems designed to improve driving. They are created to increase the user's level of immersion, giving him/her an enhanced visual, tactile or acoustic experience [12]. There are two modalities in which the visual experience can be augmented: adding information into the environment or highlighting some elements already present in the real environment such as road signs, crosswalks and pedestrians [11]. These innovative display technologies offer new paradigms of interaction, which increase the degree of assistance for drivers, but can also improve the aspects related to ergonomics [36]. For instance, the standard navigation system of vehicles usually displays the information on the dashboard screen, and this could distract the driver. In addition, nowadays smartphones represent an important source of distraction. In order to reduce these negative aspects, AR-based systems can be used [37], but they must be built in such a manner as to not interpose the information between the driver and what is in front of him/her on the road [38].  
A constant effort is being made by automotive companies to improve the human machine interface, which is a key component of car development. They have tried to develop various complex original systems, integrating smart AR devices such as Google glass [39], RGB-D sensors for 3D motion recognition such us Microsoft Kinect [40] or other methods involving sound--for instance, rendering engine sound through the car audio system [12]. The potential of AR systems has a twofold purpose: to enrich the driving experience and to prevent accidents by increasing the driver's perception and attention [41].  
Thus, AR can be used as a medium of interaction with the automobile, for a better awareness of the environment [42] and the visualization of additional information that can have a significant effect on the driver's experience [43]. The vehicle windshields can be used as navigational aids, representing the medium for augmented displays. Automotive companies have developed systems to display various information in the driver's visual field, such as warnings when a pedestrian wants to cross the street and highlighting road signs with the aim of increasing safety [38]. Furthermore, there are researchers who develop systems for the display of road intersection guidance [44], for ego-lane analysis [45], or even optical see-through displays where the windshield of a car functions as a display to allow drivers to virtually see through objects that are obstacles in their way [46].  
Figure 9: Distribution of papers according to application field.
## 4 Simulations and Results
### Case IV  
In this case, we are considering the spectrum-sharing scenario shown in Figures 10 and 11, consisting of nine sub-bands in total. Out of these nine, four are given to SU\(s\) designated by regions II, IV, VI, and VIII, while regions I, III, V, VII, and IX are given to PU\(s\). Consider that the bandwidth allocated to all SU\(s\) is equal, while the bandwidths allocated to PU\(s\) are unequal. Each SU has 32 OFDM subcarriers. Figures 10 and 11 show the comparison of the proposed technique with already existing sidelobe suppression techniques including CC [14], CC using GA and DE [17], ACC [18], and ASW [21].  
Suppression of the sidelobe power level achieved at regions occupied by PUs using the existing and from the proposed technique is given in Table 4, which also shows the effectiveness the proposed technique.
## 4 Experiments and Results  
### Augmented Images  
Figure 4 displays comparison of the three image sets, showing significant changes achieved through advanced augmentation techniques. The original endoscopic images provide the clinical baseline with authentic tissue coloration and lighting. StarGAN-generated images demonstrate the initial capability of generative models but are practically identical to the originals in terms of structure and positioning, only providing minimal changes in color tones and brightness. This limited transformation restricts their effectiveness in dataset diversification. In contrast, the enhanced model, leveraging more sophisticated rotation and translation transformations alongside an attention mechanism, produces visibly improved results with substantially greater variability. By incorporating diverse viewing angles, positions, and feature emphasis, this approach creates crucial viewpoint invariance--essential for endoscopic imaging where camera angles frequently change--while simultaneously emphasizing tissue boundaries and structural characteristics with heightened contrast. Although the enhanced model occasionally produces more vivid coloration that may appear less natural, these very characteristics help accentuate subtle patterns and tissue changes with diagnostic significance. The visual enhancements directly contribute to the model's improved accuracy by generating genuinely diverse training data while maintaining focus on clinically relevant features, ultimately supporting better generalization across diverse endoscopic presentations.  
\begin{table}
\begin{tabular}{c c c} \hline
**Component** & **Parameter** & **Value** \\ \hline
**Classification Models** & Batch Size (VGG-16, ResNet-50, DenseNet-121, & 64 \\  & InceptionNet-V3) & 64 \\ \hline  & Batch Size (EfficientNet-B7) & 32 \\ \hline  & Initial Learning Rate & 0.0001 \\ \hline  & Image Size (VGG-16, ResNet-50, DenseNet-121, & 224 \(\times\) 224 \(\times\) 3 \\  & EfficientNet-B7) & 224 \(\times\) 224 \(\times\) 3 \\ \hline  & Image Size (InceptionNet-V3) & 299 \(\times\) 299 \(\times\) 3 \\ \hline  & Optimizer (VGG-16, DenseNet-121) & Adam \\ \hline  & Optimizer (ResNet-50, InceptionNet-V3, & RMSprop \\  & EfficientNet-B7) & \\ \hline  & Scheduler & CosineAnnealingLR \\ \hline  & Scheduler Max Iterations & 50 \\ \hline  & Scheduler Min Learning Rate & 0 \\ \hline  & Loss Function & CrossEntropyLoss \\ \hline  & Training Epochs & 300 \\ \hline
**Data Augmentation** & Rotation Range & \(-90^{\circ}\) to \(90^{\circ}\) \\ \hline  & Flipping & Random Horizontal and Vertical \\ \hline  & Cropping & Random \\ \hline \end{tabular}
\end{table}
Table 1: Cont.
## 4 Results Analysis and Discussion
### Parameter Estimation and Uncertainty Quantification of Traffic Noise Prediction Model at Scenario B  
Scenario B corresponds to Site B located on the road from Wascana Pkwy to Assiniboine Ave, as depicted in Figure 2. The road around Site B was just paved before our measurement, and was considered as new pavement condition. Figure 10 presents the initial and final results of Model (20). There is initially great uncertainty in Model (20), as indicated by the green lines in Figure 10a; such uncertainty would be effectively reduced after data assimilation by NEF (i.e., green lines in Figure 10b). During the data assimilation process, the uncertainties in parameters A and B in Model (20) are dramatically decreased, as shown in Figure 11. After the data assimilation process, the uncertainty of the traffic noise model can be quantified with the parameter values selected with the updated intervals (as stated in Table 1). Figure 12 shows the comparison between the predicted intervals and observations in scenario B. This suggests that the forecasts can generally reflect the actual variations in noise emission levels.  
Figure 10: _Cont._  
Figure 9: Comparison between the predictions and observations in Scenario A\({}_{2}\).  
As can be seen from the green lines and red line in Figure 10b, the parameter values of Model (20) estimated from NEF are slightly different to those obtained from MLE. For example, the mean values of A and B obtained through NEF, as presented in Table 1, would be 4.7 and 28.7, respectively, while  
Figure 11: Variations of parameters (**a**) and (**b**) in Model (20) during the data assimilation process under scenario B.  
Figure 12: Comparison between the predictions and observations in scenario B.  
Figure 10: Initial (**a**) and updated (**b**) traffic noise prediction curves in scenario B.  
those values generated through MLE would be 5.2 and 26.8, respectively. However, even though there are differences between the parameter values obtained through NEF and MLE, the performance of NEF, with the value of R\({}^{2}\) being 0.7691, is slightly worse than MLE (R\({}^{2}\) value of 0.7699).
## 2 Data and Methods
### Data
#### 2.1.2 Remote Sensing Data
These heterogeneity metrics consist of the coefficient of variation (CV), evenness, contrast (CON), dissimilarity (DIS), entropy (ENT), homogeneity (HOM), range, Shannon, Simpson, standard deviation (SD), correlation (COR), maximum (MAX), uniformity (UNI), and variance (VAR).
## 3 Two-Stage Adaptive Relay Selection and Power Allocation Strategy
Based on Equation (5), the achievable rates of \(x_{1}\) and \(x_{2}\) at U\({}_{2}\) are given as  
\[R_{R_{n}U_{2}}^{x_{1},00}=\frac{1}{2}log_{2}\Bigg{(}1+\frac{\beta_{n1}^{00} \rho_{R}\big{|}h_{R_{n}U_{2}}\big{|}^{2}}{\beta_{n2}^{00}\rho_{R}\big{|}h_{R_{ n}U_{2}}\big{|}^{2}+1}\Bigg{)}\text{ and }R_{R_{n}U_{2}}^{x_{2},00}=\frac{1}{2}log_{2}\Big{(}1+\beta_{n2}^{00}\rho_{R} \big{|}h_{R_{n}U_{2}}\big{|}^{2}\Big{)} \tag{18}\]  
Unlike case 2, U\({}_{1}\) does not get any signal in the first time slot. The achievable rate of \(x_{1}\) at U\({}_{1}\) in the second time slot is given by  
\[R_{R_{n}U_{1}}^{x_{1},00}=\frac{1}{2}log_{2}\Bigg{(}1+\frac{\beta_{n1}^{00} \rho_{R}\big{|}h_{R_{n}U_{1}}\big{|}^{2}}{\rho_{T}\big{|}h_{TU_{1}}\big{|}^{2}+ \beta_{n2}^{00}\rho_{R}\big{|}h_{R_{n}U_{1}}\big{|}^{2}+1}\Bigg{)} \tag{19}\]  
Similarly, the criterion for determining the optimal relay \(R_{n^{*}}\) is given by  
\[n^{*}=argmax_{n\in S_{2}}\Big{\{}R_{R_{n}U_{2}}^{x_{2},00}\Big{\}}\text{ subject to }R_{R_{n}U_{1}}^{x_{1},00}\geq R_{1}\text{ and }R_{R_{n}U_{2}}^{x_{1},00}\geq R_{1}. \tag{20}\]  
Then, the optimal power allocation coefficient can be obtained by  
\[\beta_{n2}^{00}=min\left\{\frac{\left[\rho_{R}\big{|}h_{R_{n}U_{2}}\big{|}^{2}- \gamma_{1}\right]^{+}}{\rho_{R}\big{|}h_{R_{n}U_{2}}\big{|}^{2}(1+\gamma_{1})}, \frac{\left[\rho_{R}\big{|}h_{R_{n}U_{1}}\big{|}^{2}-\gamma_{1}\Big{(}\rho_{T} \big{|}h_{TU_{1}}\big{|}^{2}+1\Big{)}\right]^{+}}{\rho_{R}\big{|}h_{R_{n}U_{1}} \big{|}^{2}(1+\gamma_{1})}\right\}. \tag{21}\]  
The above four cases correspond to four decoding results of U\({}_{1}\) at the end of the first time slot. The adaptability of relay selection is reflected in that each decoding result of U\({}_{1}\) has its own relay selection criteria. Based on the proposed two-stage adaptive relay selection and power allocation strategy, we evaluate the performance of the cooperative underlay CR-NOMA network.