esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
In the experiments, we compared the simulation results of our model and the simplified NLSE with the experimental results of the fiber systems with only backward-pumped FRA (system A), both forward- and backward-pumped FRA (system B), and a 6-span 80 km fiber with a 16 dB-gain back-pumped FRA (system C).  
In system A, there was only a small difference between the two models. When the optical launch power was higher than 9 dBm, the EVM in our model increased a little more sharply than that of the simplified NLSE. This might have been caused by the influence of the RIN transfer between the pump light and the signal light. In the simplified NLSE, all noise was regarded as the centralized additive white Gaussian noise (AWGN) and added to the signal field at the receiver. However, the RIN had a distributive impact on the signal in our model, which was more sensitive to the fiber transmission. Since there was little difference, in the experiments, between the errors of our model and those of the simplified NLSE, we could not tell which model was better.  
For system B, in the system with forward-pumped FRA, the RIN and SpRS noises were more severe. Since the signal power was high, the distributed noise could interact with the signal for the nonlinear effects. Therefore, the distributed noise could not be regarded as centralized AWGN at the high launch power.  
In system C, the simulating results of our model fit better with the experimental measurements, which confirmed that our model could effectively describe the multi-span system with backward-pumped FRA.  
In summary, our modified model had a better performance with regard to the simulation of the fiber system with forward FRA and high nonlinear effects, whereas the simplified NLSE model was suited to the conventional fiber systems with EDFA and the single-span fiber system with only backward FRA.
## 3 Classification
### Notification Recipients  
As discussed in Section 3.4.1, there can be multiple V2P safety applications addressing different aspects of crash preventions (type of VRU, scenario etc.). Also, V2P safety application capabilities are also dependent on the type of VRU device and the underlying communication technology. These factors may lead V2P systems to have different recipients of crash warnings.  
1. Driver: In this V2P system, when a vehicle-VRU crash is predicted, only the vehicle's driver is notified so that further action may be taken. References [21; 27; 28] are examples of such systems. Notification to the driver may be a symbol on vehicle's dashboard.
2. VRU: In this V2P system, when a vehicle-VRU crash is predicted, only the VRU is notified. Reference [4; 15; 38] are examples of such system. Notification to the VRU may be in form of audio-visual warning on the VRU device.
3. Both: In this V2P system, when a vehicle-VRU crash is predicted, both nodes (vehicle and VRU) are notified. References [3; 5; 26] are the examples of such a system.  
Table 1 shows various efforts by their notification recipient.  
Similarly, notification recipients for the V2P convenience applications may also be classified into above three categories. However, we are not aware of enough number of efforts for V2P convenience applications to draw any conclusion.
## 3 Results and Discussion
### ATR-FTIR Characterization  
The ATR-FTIR spectra were recorded in the spectral region of 550-4000 cm\({}^{-1}\). The ATR-FTIR of the cured geopolymer binder is presented in Figure 6. The peak frequencies were allocated in accordance with previously published peak frequency assignments. The relationship between various groups of Al-O, Si-O, Si-O-Si, and Si-O-Al asymmetric stretch peak positions and the extent of the geopolymerization process is complex, but still helpful for studying the geopolymerization mechanism. The symmetrical stretching of Si-O-Si and Al-O-Si, typical for the metakaolin-based geopolymer, was presented at about 683 cm\({}^{-1}\) in all geopolymer binder formulations. This indicates the formation of an alkaline aluminosilicate network [13]. The absorption peak at 970 cm\({}^{-1}\) referred to stretching vibrations of Si-O-R\({}^{+}\) bond (R=Na and K). This peak in geopolymer binder with additives shifted to lower wavenumbers (of about 950 cm\({}^{-1}\)). Lower wavenumbers of the Si-O-R\({}^{+}\) bonds, also in agreement with the literature data [9; 16], presented evidence for a better geopolymerization process.  
The peaks at 1217 and 1366 cm\({}^{-1}\) referred to an asymmetrical stretching of Si-O-Si and Si-O stretching bands [18]. The higher peak intensity of 1366 cm\({}^{-1}\) in the binder with 10 wt % grafted fume could be related to the more Si-O bands existed in the grafting agent (APTES). The absorption band at about 1640 cm\({}^{-1}\) referred to the bending vibration of O-H groups [16]. When grafted silica fume was added to the geopolymer binder, a peak at 1737 cm\({}^{-1}\) appeared, which can attributed to C=O stretching vibration. The vibration bands at about 1200 cm\({}^{-1}\) represented that the asymmetric stretching of siloxane groups (Si-O-Si) was intensified with the addition of APTES. Furthermore, two  
Figure 5: DSC curves of the geopolymer binder mixed with various additives.  
new peaks at about 2973 cm\({}^{-1}\) and 3000 cm\({}^{-1}\) were observed, which correspond, respectively, to the symmetric and asymmetric vibration of the -CH\({}_{2}\)- and C-H groups [32]. The intensities of these peaks were more pronounced at a higher level (10 wt %) of functionalized silica fume content, whereas it was not notable in the geopolymer binder with no additives. Absorption bands at 3450 cm\({}^{-1}\) represented the stretching vibration of aliphatic amine (N-H) groups. The broad bands located at about 3300 cm\({}^{-1}\), especially at binders having phenol and grafted silica fume, corresponded to the free and bound -OH and stretching bands of N-H group for phenol and 3-aminopropyl-triethoxysilane, respectively [41; 42]. Absorption peak at 1231 cm\({}^{-1}\) in the geopolymer binder with phenol flakes corresponded to the C-C-O asymmetric stretch. In addition, the peaks at 1480 and 1587 cm\({}^{-1}\) related to the C=C aromatic ring vibrations of phenoxide anion [39].
## 3 Results and Discussion
### Total Intracellular Protein Extraction Using Different Lysis Methods  
Different cell lysis methods were compared to find the most efficient protocol suitable for intracellular protein recovery from _Rhodomonas_ sp. The results revealed that biomass grinding into alumina powder seems to be the most efficient method for _Rhodomonas_ sp. cell lysis compared to physical disruption by sonication and freeze-thawing (Figure 2). In concordance, [PERSON] and [PERSON] [52] found that sonic disruption was not efficient for protein extraction from _Rhodomonas_ species, with an efficiency of 4%, but was still reliable for _Chlorella_ species, with an efficiency of 103%. Various methods have been employed for microalgae cellular membrane disintegration or disruption, including mechanical, physical, or nonmechanical techniques [53]. The success of the lysis method depends on microalgae cell wall composition [53; 54].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Proteins** & **Lipids** & **Carbohydrates** & **Ash** & **Chlorophylls** \\
**(\% DW)** & **(\% DW)** & **(\% DW)** & **(\% DW)** & **(\% DW)** \\ \hline
34.5 \(\pm\) 0.8 & 13 \(\pm\) 1 & 14.18 \(\pm\) 0.16 & 15.73 \(\pm\) 0.89 & 0.46 \(\pm\) 0.1 \\ \hline \end{tabular}
\end{table}
Table 1: Biochemical composition (% of dry weight) of _Rhodomonas_ sp. biomass.  
Figure 1: Assessment of _Rhodomonas_ sp. cell density and protein production in accordance with growth phases.
## 3 Results
### Adsorption Mechanism  
The mechanism of adsorption can be divided into physisorption and chemisorption [28]. The physisorption mechanism is adsorption between the surface and adsorbate,  
Figure 6: Variation in percentages of Fe(III) removal with contact time.  
Figure 7: Impact of initial concentration of Fe(III) on adsorption onto both biosorbents.  
which involves diffusion, van der Waals interactions, hydrogen bonding, electrostatic interactions and exothermal phenomenon. The chemisorption mechanism involves electronics, valence forces between absorbent and adsorbate, a chemical bond to the surface of the adsorbent, complex formation, chelation, proton displacement, redox reaction, and covalent bonding [29; 30].  
Researchers previously verified an ion-exchange process utilizing low-cost adsorbents for heavy metal ion removal [31]. However, as shown in Figure 8, the current study proposes a mechanism based on ion exchange between celluloses, hydroxyl and ether functional groups and positively charged Fe(III) ions [32]. The FT-IR spectra of both biosorbents, before and after adsorption, were compared to determine the effect of Fe(III) adsorption on these functional groups, which confirmed the results in Figures 9 and 10, showing that the spectra have similar characteristics that are shifted. The wavenumber of -C-OH was shifted from 1640 cm\({}^{-1}\) to 1628 cm\({}^{-1}\), 1640 cm\({}^{-1}\) to 1649.5 cm\({}^{-1}\) for N-PPF and C-PPF, respectively, related to the bending modes of hydroxyl groups to confirm the presence of the lignin structure. The band at 1242 cm\({}^{-1}\) and 1020 cm\({}^{-1}\) shifted to 1235 cm\({}^{-1}\) and 1038 cm\({}^{-1}\) for N-PPF and C-PPF, respectively, can be assigned to stretching vibration of the C-O-C group. The band at 1263.7 cm\({}^{-1}\) and 1021.7 cm\({}^{-1}\) shifted to 1271.3 cm\({}^{-1}\) and 1035.9 cm\({}^{-1}\) for N-PPF and C-PPF, respectively, related to bending modes of hydroxyl groups, confirming the presence of hemicellulose. This discovery implies that ion exchange between the hydroxyl and ether functional groups of cellulose and positively charged ions of Fe(III) is a chemisorption mechanism [18; 21; 22]. In particular, the hydroxyl groups at the active sites may efficiently adsorb Fe(III) ions. As a consequence, the adsorbents have more metal-binding active sites and negative surfaces, which improves Fe(III) retention on surfaces and the percentage of Fe(III) removal. The hydrogen bond is formed during this physisorption adsorption process.  
Figure 8: Schematic mechanism of interaction between Fe(III) ions and cellulose of C–PPF.  
Figure 9: The comparison of FT–IR spectra between (**a**) N–PPF before adsorption and (**b**) N–PPF after adsorption.  
The mechanism between Fe(III) and adsorbent can be chemisorption and physisorption [30]. As a result of this research, the kinetic analysis findings indicate the validity of the hypothesized mechanism of interaction between the metal ions and adsorbents. Adsorption of metal ions is generally performed by ion exchange, complex formation, and electrostatic interaction [33; 34].
## 4 Experiments
### Analysis of Experimental Results
#### 4.3.2 Signed Fingermark Verification
To verify the effectiveness of the proposed local block quality-based score fusion strategy, the effect of SFNet combined with the score fusion strategy was tested on the signed fingerprint test set, and the experimental results are shown in Table 6. SFNet_3 represents the SFNet combining the multi-probability labeling strategy with the equal-weight score fusion strategy, and SFNet_4 represents the SFNet combining the multi-probability labeling strategy with the image quality-weight score fusion strategy. It can be seen that SFNet_4, which is based on the quality weight score fusion strategy, significantly improves the detection performance of the proposed method compared to SFNet without the score fusion strategy and SFNet_3, which is based on the equal weight score fusion strategy. To confirm the hypothesis that different network models enhance each other and boost the accuracy of the liveness detection method, we implemented a model fusion strategy based on the theory of evidence. We combined the original SFNet with the SFNet that integrates the multi-probability labeling strategy and the image quality weight score fusion strategy. The resulting ROC and P-R curves, depicted in Figure 7, demonstrate the success of our approach. This section compares three versions of SFNet: the original SFNet (curve a), SFNet_3 (curve b), and SFNet_4 (curve c). SFNet_3 combines the multi-probability labeling strategy and the equal weight score fusion strategy, while SFNet_4 combines the multi-probability labeling strategy and the image quality weight score fusion strategy. The figure clearly demonstrates that the AUC value and the equilibrium point value are significantly higher after fusion than the two curves before fusion, providing strong evidence for the superior performance of the fused model. These results unequivocally establish the complementarity between network models with different strategies and the effectiveness of the model fusion strategy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Live** & **Fake** & **Overall** \\ \hline SFNet & 98.49 & 99.00 & 98.74 \\ SFNet\_3 & 98.71 & 99.15 & 98.92 \\ SFNet\_4 & 99.08 & 99.27 & 99.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of detection performance of SFNet combined with the score fusion strategy. Unit: %. Figure 7: ROC and P–R curves after fusion.
## 2 Concept of Time Frequency Analysis and Processing  
Time frequency analysis concerns the signals with time-varying frequency content. Such signals are best described by the time-frequency distribution on the time frequency plane. The time frequency distribution theories can be traced back to 1940s, when the researchers studied spectrogram with STFT [12]. With the development of time frequency theories, there are mainly two kinds of time frequency distributions. The first is the linear time frequency distribution which is developed from Fourier transform, such as short time Fourier transform [30] and Gabor transform [31]. The second is the bilinear time frequency distribution which is based on the representation of signals' energy. The classical bilinear time frequency distribution is Wigner distribution, but it has the disadvantage of cross terms which affect the time frequency representation for the multiple-component signals. In 1966, [PERSON] [12] introduced the core function and expressed the time frequency distribution in a general form, whereas [PERSON] and [PERSON] argued that the cross terms could be minimized by choosing a proper core function [32]. Since Choi-Williams distribution is chosen as the time frequency analysis tool due to its advantage in suppressing spurious terms, this part will introduce Choi-Williams time frequency distribution in detail. As for STFT and Wigner-Ville distribution, please refer to [12] for more details.
## 9 Comparison and Significance
The significance of this research lies in the fact that this is the first tool which is built for generic IoT tasks scheduling. Additionally, we believe that this tool provides live simulation using real-time tasks when IoT nodes are connected to embedded IoT gateways. The tool is also platform independent which is very crucial in real-time computing because, as discussed in the introduction, almost in all scenarios real-time tasks are associated with embedded devices, and thus an analysis and visualisation tool which can be ported to an embedded system without any significant revamp to the architecture is highly encouraged. Another major significance of this work is that the proposed system is web-based so it can be accessed remotely from everywhere. Therefore, the user can track their scheduled tasks anytime from anywhere. One example scenario could be a smart home, where user schedule different real-time tasks. For instance, Turning on AC, Getting Temperature and Humidity and performing some action based on it, Turning on Washing Machine and scheduling it to run stop it after one hour and many other are possible use cases. These tasks can be added to the system and scheduled. The user can track all the tasks and analyse it even if not in the home and thus can smartly make decisions based on the outcome of the algorithm.
## 2 System
### UAVMAG System
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Module** & **Technical Index** & **Specifications** \\ \hline \multirow{4}{*}{UAV} & Flight speed & 14 m/s, maximum air speed; 2–4 m/s, recommended speed \\  & Mass of payload & 10 kg, maximum payload; 5 kg, standard payload \\  & Takeoff weight & 19 kg, standard \\  & Endurance & 35 min with a payload of 5 kg \\ \hline \multirow{4}{*}{Magnetic sensors} & CAS-18-VL & Operating range: 10,000 nT to 105,000 nT \\  & & Noise sensitivity: 0.3 pT\(/\sqrt{\mathrm{Hz}}\) @ 1 Hz \\ \cline{1-1}  & Mag-03 MS1000 & Operating range: \(\pm 100\) uT \\ \cline{1-1}  & & Noise sensitivity: 4 pT\(/\sqrt{\mathrm{Hz}}\) @ 1 Hz \\ \hline Power & Data acquisition module & 3200 mAh lithium polymer battery; voltage: 24 V \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The technical specifications of the UAVMAG system. anomalies across large survey areas. Its robust design and advanced positioning technology make it suitable for applications such as UXO detection, geological surveying, and environmental monitoring, providing a reliable solution for remote sensing tasks that require both high accuracy and operational efficiency.
## 3 Results
### EMI-Based Monitoring
#### 3.2.1 Beam \"B500-J\"  
Figure 10 shows the shear stress response of the \"B500-J\" in terms of mid-span deflection and the crack width acquired by the crack meter transducers. The beam demonstrated a brittle shear failure aligning with its intended design, featuring a critical diagonal crack on the left span. One crack meter is positioned in the middle of the beam, monitoring the propagation of flexural cracking, while the other two are attached to monitor the rate of widening of the right and left shear diagonal cracks, respectively. Moreover, Figure 11 presents the crack meters' positions and the cracking pattern of the beam at each loading level (damage state). Figure 10 also depicts the experimental voltage measurements through the EMI-based method in a red circle.  
Figure 11: Cracking pattern of beam “B500-J”.  
Figure 10: Mechanical response for beam “B500-J”, EMI measurements, and crack meters.  
At first, for \"B500-J\", eight EMI measurements were conducted. The primary measurement was acquired in the healthy state (pristine condition), notated as the \"Healthy\" state, while the other seven were acquired during the subsequent loading and cracking development increments, notated with the prefix Dam following by the increasing serial number of each state. Hence, Figure 7 mentions these states as \"Dam1-Dam7\", respectively. According to the illustrated cracking pattern, obviously, the beam failed in the left span. Thus, according to their positions, the PZTs with the highest involvement are SA1, SA2, J1, and J4. Figure 12 presents comparative typical voltage response curves in terms of the set frequency range for PZTs J1 and J3.  
From the observation of Figure 12, it is apparent that the PZT positioned on the failure side shows significant alterations in the voltage response curves with remarkable peak shifting. Further, the curves' variations commence at early damage states (Dam3), which is an essential indicator of the method's effectiveness in detecting the structural degradation of the retrofitting scheme (PZT J1). As presented also in Figure 10, the development of the fatal shear crack starts at Dam3, while in the next states (Dam4, Dam5, Dam6), the acquired values exhibit double widening from state to state, from approximately 2 mm to 4 mm, 4 mm to 8 mm, and 8 mm to 15 mm, respectively.
## 2 Related Work and Contributions
### Contributions of the Paper  
The core contribution of this paper is the analytical evaluation of the throughput and energy efficiency of a burst transmission mechanism using microsleep operation under network saturation conditions. In line with [12; 20], this mechanism represents a possible implementation of IEEE 802.11 ac TXOP PSM on the top of 802.11 DCF using burst transmissions [25] (similar to 802.11e EDCA TXOP). Basically, when the TXOP PSM-based mechanism is employed, an STA can send a burst of data frames to another STA, while a third one can microsleep during the data exchange. A more detailed description of the TXOP PSM-based mechanism is provided in the following section.  
The analytical model proposed in this paper builds on the Markov chain model introduced in [21] and extended in [24] to compute the DCF throughput under saturation conditions. Contrary to the papers [12; 28; 29; 30], this paper considers the requirements of the awake and sleep transitionsin the derivation of the proposed model, since these transitions can compromise the feasibility of microsleep operation. As compared to the analysis proposed in [20], the analysis introduced in this paper concentrates on both the throughput and energy efficiency of the TXOP PSM-based mechanism under saturation conditions. In addition, a comprehensive derivation of the energy efficiency depending on the feasibility of microsleep operation is provided in the proposed analysis. Moreover, a comprehensive performance evaluation of the proposed TXOP PSM-based mechanism as a function of various system parameters is illustrated and discussed in this paper, not considered in [20].  
The proposed analysis allows the determination of the critical system parameters that influence the performance of the TXOP PSM-based mechanism and other microsleep mechanisms such as those in [7; 8; 9; 10; 11; 12]. Computer-based simulations are used to validate the proposed analysis. Results presented in this paper contribute to the development of advanced microsleep mechanisms for the next-generation WLANs.