esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
The goal of this study is to find a reliable way to estimate the leaf coverage in the maize seedling stage based on UAV remote-sensing images. To reduce cost and improve efficiency, this goal is achieved by using image-processing technology rather than labor-intensive field surveys. To be able to use this efficient image-based method in all conditions, it must be sufficiently robust to handle dynamic illumination conditions and complex changes in morphology. Thus, to produce robust results, the image analysis pipeline must consider the various perturbing effects. Verifying the accuracy of these segmentation methods can also be a significant challenge. Although the experimental results in Section 4 for the proposed improved RF-based method are consistent with the manual validation data, further research is required to determine the adaptability of the proposed method. The results show that the segmentation accuracy depends strongly on the light intensity, the resolution of the training image and the sensitivity to noise. Here, we analyze how these factors affect the accuracy of the segmentation results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Qseg** & **Sr** & **Es** & **mIOU** \\ \hline ExG & 0.59 & 0.57 & 0.45 & 0.38 \\ ExGR & 0.61 & 0.60 & 0.38 & 0.47 \\ CIVE & 0.55 & 0.53 & 0.46 & 0.50 \\
**Improved RF** & **0.87** & **0.86** & **0.18** & **0.81** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of segmentation quality for ExG, ExGR, CIVE and the proposed method (improved RF).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **mIOU** \\ \hline HSV & 0.4728 \\ EDA & 0.5941 \\ RF & 0.7316 \\ DeepLabv3+ & 0.7984 \\
**Improved RF** & **0.8237** \\ \hline \hline \end{tabular}
\end{table}
Table 4: mIOU scores for different methods applied to the training or validation dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **mIOU** & **Change (\%)** \\ \hline HSV & 0.4021 & 17.5 \\ EDA & 0.5364 & 10.8 \\ RF & 0.6897 & 6.1 \\ DeepLabv3+ & 0.7916 & 0.9 \\
**Improved RF** & **0.8055** & **2.3** \\ \hline \hline \end{tabular}
\end{table}
Table 5: mIOU scores for different methods applied to the original training and validation datasets.
## 3 Results and Discussion
### Phosphate (PO\({}_{4}\)) and Total Phosphorus (Total P)  
Regarding PO\({}_{4}\), PLS model optimization led to the selection of a spectral region comprising a Raman shift specific for the v1 vibration domain of the PO\({}_{4}\) group, i.e., 960 cm\({}^{-1}\)[40]. Similarly to the model developed for NH\({}_{3}\), the cross-validation results obtained for the PO\({}_{4}\) model in the 32.2-99.6 mgP L\({}^{-1}\) range (R\({}^{2}\)CV = 70.0%; RMSEC = 12.10 mgP L\({}^{-1}\); Figure 3f) were significantly worse than the calibration statistics (R\({}^{2}\)Cal = 99.4%; RMSEC = 1.95 mgP L\({}^{-1}\)). Accordingly, the RPDCV of 1.7 confirmed the insufficient prediction quality of the model. In contrast, the PLS model developed for estimating total P concentrations presented an excellent prediction performance (Figure 3h), as evidenced by the cross-validation results, i.e., R\({}^{2}\)CV = 99.0% and RPDCV = 10.3 (Table 1). In fact, the RMSEC and RMSECV values (0.0 and 0.01 g L\({}^{-1}\)) were very similar, denoting a good calibration model for the P concentration range from 0.1 to 0.4 g L\({}^{-1}\) (as illustrated in Figure 3h). The spectral data pre-processing involved the application of 1 st Der + MSC in the regions 1179.8-1168.7 cm\({}^{-1}\) + 1159.5-1139.3 cm\({}^{-1}\), which is in accordance with the PO\({}_{2}\) stretching vibrations band reported to occur around 1175-1168 cm\({}^{-1}\)[24] and 1163-1130 [26] and used to quantify the intracellular poly-P content [24, 25, 26, 27, 38, 40]. However, this model required nine LVs, which is a relatively high number of factors, eventually leading to the overfitting of data. Future work is needed to confirm the prediction capacity of this potentially relevant model, by performing external validation tests. Accurate real-time monitoring of total P would significantly improve the capacity to understand which is the main mechanism of P removal in the photo-BNR process, and thus evaluate the possibility of using the excess sludge as fertilizer, since high P amounts in the biomass indicate high accumulation as poly-P.
## 3 Results and Discussion  
### Morphological Characterization  
The microalgal specimen collected in the freshwater pond at the facilities of Allmicroalgae was identified based on botanical, micro-morphological features and taxonomy as _Chlorococc_ Meneghini. A voucher was deposited in the Allmicroalgae culture collection with number 0030 CN.  
Vegetative cells of the symbiotic algae appeared grass-green, live as solitary cells or in temporary groups of indefinite form without a mucilaginous envelope, are ellipsoidal to spherical and vary in size from 8 to 25 \(\upmu\)m. Reproduction within the genus _Chlorococc_ is mainly asexual, by biflagellate zoospores (or aplanospores in case of water stress) that do not become spherical upon cessation of motility but retain an ovoid, ellipsoid shape for some days [28; 29]. By light microscopy, in three-week-old liquid cultures, green vegetative cells with a hollow, parietal chloroplast with or without an open surface were observed. In the basal part of the chloroplast, a spherical pyrenoid was present and the nucleus was in a central position in the cell (Figure 2a). Temporary groups of cells of indefinite form and without a mucilaginous envelope (Figure 2c) were seen. In three-day-old liquid cultures, numerous zoosporaging (Figure 2d-f), zoospores (Figure 2b) and young green vegetative cells became evident. Zoospores had two flagella, an anterior stigma, one spherical pyrenoid in the central region and a nucleus posterior to pyrenoid. Zoospores had an ellipsoidal form (\(3.59\pm 0.43\) um wide and \(8.56\pm 0.82\) um long), young green cells had an ellipsoidal to ovoid form (\(6.70\pm 1.23\) um wide and \(9.66\pm 1.23\) um long) and mature green cells were spherical (\(15.14\pm 2.34\) um). Prolonged observations of cultures show that this _Chlorococcum_ multiplication seems to be similar to _Chlorococcum__infusionum_ Meneghini, mainly through the endogenous division of the content of the vegetative cell by progressive cleavage of a mother protoplast into uninucleate segments [30]. The number of zoospores might vary according to the size of the mother cell. Once completely formed, zoospores free themselves by bursting the zoosporagium membrane (Figure 2e) due to excess pressure inside.
## 4 Relative Jammer Screening Algorithm Based on Mixed-Strategy Game
### Multidimensional Utility Function Construction
\tag{8}\]  
where \(W_{Loss}\) is the power loss, \(P_{t}\) is the transmit power, \(L_{M,V}\) is the loss of the signal in free space, divided into the frequency of forwarding from the primary jammer to the relative jammer and the power loss of forwarding the spoofed signal from the relative jammer to the target receiver, \(G_{t}\) is the transmit antenna gain, \(G_{t}\) is the gain of the receive antenna, \(f_{M,V}\) is the frequency of the signal being forwarded from the primary jammer to the relative jammer and the relative jammer frequency forwarded to the target, \(d_{M,V}\) is the distance the signal travelled in free space, \(D\) is the parabolic diameter of the antenna, and \(\lambda\) is the wavelength of the transmitted signal. Doppler shift loss mode  
Interfering target receivers down-convert and pass through bandpass filtering when receiving signals, thus requiring that the interfering signal be within the frequency band. The main factor affecting the frequency variation is the Doppler shift, and Doppler compensation is required before the implementation of an interfering signal transmission. The positions of the LEO satellite, the relative jammer, and the target to be jammed are \(\mathbf{S}_{k},\mathbf{J}_{i_{R}},\mathbf{U}_{m}\), and the velocities are \(v_{k}^{S}\), \(\mathbf{v}_{i_{R}}^{J}\), \(\mathbf{v}_{m}^{U}\), respectively. The Doppler shift of the real signal arriving at the target receiver is shown in Equation (9). \[ \begin{aligned} &\Delta f_{0}=A_{0}f_{0}\\ & A_{0}=\frac{\left(v_{k}^{S}-v_{m}^{U}\right)\cdot\left(\mathbf{U}_{m }-\mathbf{S}_{k}\right)}{\left|\mathbf{U}_{m}-\mathbf{S}_{k}\right|*c}\end{aligned}  \tag{9}\]  
where \(f_{0}\) is the LEO satellite signal transmission frequency, and the frequency at which the jamming target receives a particular LEO satellite signal is \(f_{0}+\Delta f_{0}\). The Doppler shift at which the spoofed signal emitted by the relative jammer reaches the target receiver is shown in Equation (10). \[ \begin{aligned} &\Delta f_{1}=A_{1}f_{1}\\ & A_{1}=\frac{\left(v_{i_{R}}^{J}-v_{m}^{U}\right)\cdot\left(\mathbf{ u}_{m}-I_{i_{R}}\right)}{\left|\mathbf{u}_{m}-I_{i_{R}}\right|*c}\end{aligned}  \tag{10}\]  
where \(\mathbf{f}_{1}\) is the relative jammer transmitting frequency, and the frequency at which the jamming target receives the jamming signal emitted by a relative jammer is \(\mathbf{f}_{1}+\Delta\mathbf{f}_{1}\). By making \(\mathbf{f}_{1}+\Delta\mathbf{f}_{1}=\mathbf{f}_{0}+\Delta\mathbf{f}_{0}\), the emission frequency can be calculated, as shown in Equation (11). \[f_{1}=\frac{1+A_{0}}{1+A_{1}}.f_{0} \tag{11}\]  
The Doppler shift compensation \(\Delta\mathbf{f}\) to be performed relative to the satellite launch frequency \(\mathbf{f}_{0}\) is shown in Equation (12). \[\Delta f=\frac{A_{0}-A_{1}}{1+A_{1}}.f_{0} \tag{12}\]
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 1 Introduction
### Current Situation  
ATCOs are responsible for maintaining separations between aircraft. Today, spacing to severe weather is the responsibility of the pilot in the event of occurring convective cells. When adverse weather occurs on the aircraft's planned or assigned route, the pilot decides whether to fly through it, fly over it, or avoid it to the left or right. In addition to the onboard weather radar, pilots can also use external live weather applications such as eWAS, which can transmit current weather data to the cockpit via a fast SatCom data link [12].  
The ATCO clears this avoidance maneuver and ensures that it does not result in critical separation infringements with other traffic. Center controllers, responsible for approach and lower and upper airspace, have no weather radar available for their sectors. The systems, visualizations, and procedures developed by the German Aerospace Center's (DLR's) Institute of Flight Guidance presented in this paper use available weather information to initiate four-dimensional route rescheduling and arrival sequencing. These tools provide appropriate rerouting advisories for ATCOs at an early stage using forecasts from 30 to 60 min in advance so that pilots do not have to avoid adverse weather situations at short notice. This relieves both the pilot and the ATCO and allows them to react in sufficient time in advance and at a reasonable distance so that rerouting trajectories stay safe and efficient [13].
# Implementation of Atomically Thick Graphene and Its Derivatives in Electromagnetic Absorbers
## 3 Graphene-Based Metasurface Absorbers  
Metamaterials are artificial materials possessing characteristics not found in nature or difficult to obtain naturally. Metamaterials are composed of carefully designed periodic sub-wavelength structures and metasurfaces are two-dimensional metamaterials usually with sub-wavelength thickness. Over the  
Figure 5: (**a**) schematic of THz absorber with graphene on the top and periodically stacked dielectric layers with refractive indexes \(n_{s}\), \(n_{a}\) and \(n_{b}\) respectively, reprinted with permission from Ref. [74]; (**b**) schematic of electromagnetic (EM) absorber operating at visible and near-infrared frequencies with periodically stacked SiO\({}_{2}\)/graphene/Ta\({}_{2}\)O\({}_{5}\) layers on poly (methyl methacrylate) (PMMA), reprinted with permission from Ref. [77]; (**c**) schematic of THz absorber with GC(BA)\({}^{N}\)(AB)\({}^{N}\)CG\({}^{\prime}\), where \(N\) is the number of layers, G and G\({}^{\prime}\) consist of stacked graphene/dielectric layers and A, B, C are dielectrics, reprinted with permission from Ref. [75].  
past few decades, metasurfaces have attracted a great deal of attention due to its potential applications in radar systems, wireless communications, etc. Metasurfaces are usually used to manipulate the EM wave propagation such as EM wave absorbing [78; 79] and shielding devices [80; 81; 82].  
Recently, graphene has been proposed as a tunable material for metasurface design. The graphene-based metasurface absorbers operates with different mechanisms including asymmetric Fabry-Perot [78] and guided mode [83; 84; 85] resonances, hyperbolic-type dispersion [86], magnetic [87] and plasmonic [88; 89; 90; 91] resonances, etc. The operation frequency of graphene-based metasurface absorbers can vary in a wide range from microwave [92] to optical frequencies [93] with nearly perfect absorption. In addition, since the conductivity of graphene can be controlled by its chemical potential, active graphene-based metasurface absorbers can be realized. The properties of metasurface strongly depend on the design of unit cell. Generally, graphene are used as a continuous layer or patterned structure in metasurface absorbers. On one hand, continuous graphene layer can be combined with patterned metal [94; 95; 96; 97] or dielectric layers [21; 24; 83; 98; 99; 100] to couple the incident wave into resonant mode. On the other hand, graphene can also be patterned into periodic unit cells to support a single [101] or multiple resonant modes [26; 102; 103; 104] for EM wave absorption. In some cases, both patterned graphene and metallic structures are utilized in the unit cell design [105; 106].
# Forgetful Forests: Data Structures for Machine Learning on Streaming Data under Concept Drift
## 2 Related Work
### Hoeffding Tree  
In the Hoeffding Tree (or VFDT) [10], each node considers only a fixed subset of the training set, designated by a parameter \(n\), and uses that data to choose the splitting attribute and value of that node. In this way, once a node has been fitted on \(n\) data points, it will not be updated anymore.  
The number of data points \(n\) considered by each node is calculated using the Hoeffding bound [11], \(n=\frac{R^{2}ln(1/\delta)}{2\epsilon^{2}}\), where \(R\) is the range of the variable, \(\delta\) is the confidence fraction which is set by user, and \(\epsilon=\hat{G}(x_{1})-\hat{G}(x_{2})\) is the distance between the best splitter \(x_{1}\) and the second best splitter \(x_{2}\) based on the \(\hat{G}\) function. \(\hat{G}(\cdot)\) (e.g., information gain) is the measure used to choose spitting attributes. Thus, if \(\epsilon\) is large, \(n\) can be small, because a big difference in, say, information gain gives us the confidence to stop considering other training points. Similarly, if \(\delta\) is large, then intuitively we are allowed to be wrong with a higher probability, so \(n\) can be small.
## 2 Feedback Field-Effect Transistors (FBFETs)
### PF and Band-Modulation Devices (FED, \(Z^{2}\)-Fet, \(Z^{3}\)-Fet)  
Depending on the specific mechanisms and the device structures, FBFETs have various names, such as PF devices [21; 22], band-modulation devices (BM devices) [66; 67], \(Z^{2}\)-FET devices [23; 24; 25; 26; 27; 28; 29], and \(Z^{3}\)-FET devices (see Figure 5) [30; 31; 32]. The positive feedback first proposed in FBFETs is their key operation principle. Based on the positive feedback mechanism, two potential barriers are created in three ways: the first one is through a trapped charge of the spacer or insulator layer (see Figure 1) [23; 63; 64; 68; 69], the second one is by creating a virtual doping concentration through the gate voltage in the channel region next to the source/drain (see Figure 5) [30; 31; 32; 70; 71; 72; 73], and the third one is through the junction of Si regions with different doping concentrations (see Figure 6) [74; 75]. The mechanism used in the first and second is called band modulation, and devices with positive feedback and band modulation can be classified separately (see the list in Table 1).  
Figure 5: Structure and energy band diagram for band-modulation devices: (**a**) FED (field effect diode), (**b**) \(Z^{2}\)-FET (zero-impact ionization and zero subthreshold swing FET), and (**c**) \(Z^{3}\)-FET (zero subthreshold swing, zero-impact ionization, and zero gate FET) with bias for virtual potential barriers [19].  
Figure 6: Structure and energy band diagram for the dual gate FBFET with PN junction barriers [75].  
First, all devices including FBFETs that rely on positive feedback can be considered as PF devices. Secondly, these devices can be identified as band-modulation devices if they depend on the band modulation mechanism for barrier formation. Band-modulation devices include field-effect diodes (FEDs) [77; 78; 79], \(Z^{2}\)-FETs, and \(Z^{3}\)-FETs, as already shown in Figure 5. The \(Z^{3}\)-FET device appears similar to forward-biased P-I-N diodes with undoped ultra-thin silicon films. The device configuration can be seen as a reverse FED. This new device is configurable by applying the standard design rules for fully depleted silicon-on-insulator (FDSOI) technologies [30; 31; 32]. The ground plane under the thin buried oxide (BOX) allows the threshold voltage of the complementary metal-oxide-semiconductor (CMOS) circuit to be adjusted. As the top gate stack is replaced with a buried ground plane, a reliability issue arises concerning whether it can withstand much higher breakdown voltage through a sufficiently thick BOX rather than the previous thin gate insulator layer. An important feature of \(Z^{3}\)-FETs that benefit from using a gate oxide as a BOX is that a high back gate bias can be maintained. Compared with \(Z^{2}\)-FETs, \(Z^{3}\)-FETs have no high-voltage metal gate stack, and thus there is no problem with high-voltage stability [30; 31]. \(Z^{3}\)-FETs with these features show their potential as sensors with internal memory for various applications.
# Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History
## 5 Evaluation of OLSR-HBP in Mobile Scenarios
### Validation of OLSR-HBP  
Similar to the prediction performance validation conducted for the static scenario, in this section we present the prediction performance of the model in a mobile scenario. Again, we assumed OLSR as ground truth and calculated precision, recall, F1 score and AUC as standard performance classification  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Values Used at Simulation** \\ \hline Mobility pattern & SLAW \\ Surface/deployment area & 300 \(\times\) 300 m \\ Number of nodes & 10, 20 \\ Execution time & 1 h, 4 h \\ Confidence & 0 bits (no confidence) \\ Prediction algorithm & Last value \\ Data traffic & 1 KByte periodic pings, from 1 st to 2 nd half of nodes \\ Ping periods & no pings, 10 s, 1 s, 0.1 s \\ \hline NS-3 Parameter & Values used at simulation \\ \hline wifiChannel & YansWifiChannelHelper \\ wifiChannel PropagationLoss & FriisPropagationLossModel \\ wifiPhy RxGain & \(-\)10 \\ wifiPhy EnergyDetectionThreshold & \(-\)75.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimized link state routing (OLSR) history-based predictor (HBP) (OLSR-HBP) simulation parameters.  
metrics. Table 6 summarizes the range of metrics values obtained when considering scenarios with 10 and 20 nodes, historical data-window sizes (HDWS) of 1 h and 4 h and also prediction with and without confidence. Moreover, data packet generation periods of 10, 1 and 0.1 s have been considered for all the simulations, as well as no data packet generation at all (shown in the first column of the table). The specific data of each simulation can be found in Appendix A.  
In general, values range from 0.71 to 0.96, in the simulation with 10 nodes, 1 h HDWS and 2 bits of confidence being the one with better results; meanwhile the simulation with 20 nodes, 4 h HDWS and no confidence bits is the one with moderate results. Particularly, the AUC metric achieves the best results of the considered validation metrics, with values ranging from 0.89 to 0.95. In any case, all of the validation values can be considered as excellent or good results, and therefore, they demonstrate the effectiveness of the prediction model.
## 3 Results and Discussion
### DPPH Values
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **DPPH Value (\%)** \\ \hline
**FC** & 79.93 \(\pm\) 0.34 a \\
**FTH** & 86.18 \(\pm\) 0.29 b \\
**FTU** & 88.83 \(\pm\) 0.60 c \\
**OC** & 85.85 \(\pm\) 0.19 b \\
**OTH** & 90.82 \(\pm\) 0.13 d \\ \hline \hline \end{tabular}  
* Results are given as mean \(\pm\) standard error. a–d: different letters used in the same column indicate that the difference between the mean values is statistically significant (\(p<0.05\)). FC: deep-fried sample group containing salt, pepper, cumin, and turmeric, FTU: deep-fried sample group containing salt, pepper, cumin, and turmeric, OC: oven-cooked sample group containing salt, pepper, cumin, and turmeric. Open-cooked sample group containing salt, pepper, cumin, and thyme, OTU: oven-cooked sample group containing salt, pepper, cumin, and turmeric.  
\end{table} 
Table 8: DPPH values of coatings obtained from coated chicken liver. The interaction effect of CMxF on the DPPH value was detected to be significant (\(p<0.05\)). A study revealed that turmeric has a significantly higher DPPH value than thyme due to its phytochemical composition, including curcuminoids [77]. Curcuminoids, a prominent phytochemical in turmeric, possess substantial antioxidant potential and demonstrate heat resistance [78]. In the study by [PERSON] et al. [62], the DPPH radical scavenging activity of curcumin was evaluated at various concentrations (0.5-3 g/dm\({}^{3}\)), with activity values ranging from 62.16% to 88.18%. It was observed that the DPPH activity exhibited a positive correlation with the increase in curcumin concentration. The basis of the DPPH assay is the reaction of the DPPH radical with hydrogen-donor molecules from curcumin. In the present study, the DPPH values of the samples to which thyme was added were assessed to be lower than those of the samples to which turmeric was added, but higher than the control sample. A study was conducted to investigate the antioxidant effects of aqueous extracts of thyme varieties. The study concluded that they possessed significant antioxidant activity and potent anti-inflammatory activity [79]. This result is in accordance with the fact that thyme is abundant in total polyphenols and flavonoids [80]. In another study, in line with these findings, thyme was found to have higher antioxidant activity compared to the synthetic antioxidant BHT [81].
# Secure Communication for Uplink Cellular Networks Assisted with Full-Duplex Device-to-Device User
###### Abstract  
In this paper, the secure communication based on the full-duplex (FD) device-to-device (D2D) in cellular networks is proposed. For the proposed scheme, the novel model is established, in which a D2D user is played as a relay operating in FD mode to assist in the secure transmission of uplink information. Considering that the D2D user as a relay is untrusted, D2D link rate maximization is formulated with the constraint of secrecy rate, which ensures the security of uplink cellular networks. To cope with the optimization problem, the optimal power allocation factors of the cellular user (CU) and the D2D user are jointly optimized. Firstly, by using the monotonicity of the objective function, the optimal solution of the power allocation factor at the D2D user can be obtained. Subsequently, the closed-form expression of the optimal power allocation factor at the CU is derived and verified that the solution is the global minimum point. Simulation results verify that the proposed scheme has better output performance than the conventional scheme.  
D2D communication; FD untrusted relay; physical layer security; power allocation +
Footnote †: journal: Future Internet