esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### GFET Based Flexible Receiver
#### 3.5.4 Graphene AM Radio Receiver  
A graphene based flexible AM radio receiver at 2.4 GHz was designed and fabricated based on the block diagram of Figure 9. The receiver consists of a graphite antenna, GFET demodulator and speaker. The receiver is fabricated on a polyimide flexible substrate. The AM input signal is obtained from a signal generator (2.45 GHz carrier signal) and transmitted by a horn antenna. The signal is received by the graphite antenna and demodulated using the GFET demodulator based on the ambipolar graphene characteristics at the Dirac point. The output of the demodulator is connected to the speaker [25].  
The schematic of the AM radio receiver is shown in Figure 12a. The input matching network is used for impedance matching between the antenna (\(\mathrm{L_{a}}\), \(\mathrm{R_{a}}\)) and GFET input impedance. The output matching network has two functions: (i) impedance match between output of GFET and graphene speaker load (\(\mathrm{Ro}\)) and (ii) low pass filtering of the demodulated signal. Figure 12b shows the fabricated receiver. The passive components--inductors, capacitors and resistors are currently realized using surface mount device (SMD) components. The receiver measurements were carried out in a custom built anechoic chamber. Bending tests (\(\sim\)30 mm radius) were successfully carried out. The performance changes are small due to the flexibility of substrate, antenna, graphene FET and transmission lines.  
Figure 12: **(a)** Schematic of a graphene-based AM radio receiver; **(b)** Fabricated AM radio receiver on a flexible PI (Kapton) substrate (adapted from [25], Copyright 2014, IEEE)  
The link budget of the flexible graphene receiver is estimated to be \(-46\)dB. In this estimation we have considered path loss to be \(-1\)dB/cm for 6 cm signal propagation through air, graphite antenna loss to be 5 dB, and GFET demodulator circuit gain to be -35 dB. The current AM receiver suffers from very high noise output due to lossy (un-optimized) demodulator. To obtain larger signal to noise ratio and amplitude, a GFET low noise amplifier could be incorporated at the input. The performance could be further improved with optimized design of the demodulator and interconnects.
# A Special Structural Based Weighted Network Approach for the Analysis of Protein Complexes
## 5 Results and Discussion
### Performance Comparison of WECALM with Other Algorithm
#### 5.1.2 Performance on CYC2008 Complexes  
We also compared the performance of WECALM with other algorithms based on CYC2008 complexes using both the BioGRID and DIP datasets. Based on the CYC2008 complex using the BioGRID dataset (see Figure 4A), we see that WECALM performed better in terms of recall (0.8291), F-measure (0.6956), CR (0.8831), and MMR (0.4825). In terms of precision score (0.6622), ProRank+ performed better than other methods. On the DIP dataset (see Figure 4B), again we observe that WECALM performed better in terms of recall (0.7315), F-measure (0.6315), and maximum matching ratio (0.3866). ProRank+ (0.6924) and GMFTP (0.6085) performed best in terms of precision and coverage rate respectively. Though, ProRank+ and GMFTP in terms of precision and coverage rate WECALM performed best in the overall composite score. Table 12 in Appendix A provides supplementary results for evaluation of the performance of WECALM and another algorithm on CYC2008 complexes using the BioGRID and DIP datasets.  
Figure 4: A comparison of the performance of WECALM and other existing algorithms on CYC2008 complexes. (**A**): The BioGRID dataset and (**B**): The DIP dataset. Evaluation matrices include; Recall, Precision, F-measure, coverage rate (CR), and the Maximum Matching Ratio (MMR). The overall composite score is determined by the length of the bar. The longer the bar, the better an algorithm’s overall performance is.  
WECALM's performance was also evaluated in terms of separation and ACC. According to the results in Tables A1 and A2 in Appendix A, WECALM outperformed all other methods in separation and ACC on both the NewMIPS and CYC2008 complexes both the BioGRID and DIP datasets. A high separation measure indicates that the detected complexes are well separated from one another, indicating good algorithm performance while an ACC score close to 1 indicates perfect performance, meaning that the algorithm detected all the true complexes. A low separation measure indicates that the complexes are overlapping or clumped together, which might mean false positive complexes or inaccurate detection of true complexes while an ACC score of less than 1 indicates that some of the detected complexes were false positives.
## 3 Results and Discussion
### FT-IR Spectroscopy Analysis  
The results in Table 1 and Figure 2 show the FT-IR spectrum analysis of phlorotannins extracts from _S. linifolium_ marine alga, which was established using the functional groups that appeared in the FT-IR spectrum analysis. Bands appeared at the wavenumbers 3352, 2936, 1771, 1407, 1208, and 1037 cm\({}^{-1}\), related to the (OH), (C-H) alkyl group, (C=O), methyl groups, C-O, and C-OH, respectively. The active groups detected using FT-IR confirmed the compound present in _S. linifolium_ that was identified by UPLC/MS and proved that the compound was a phlorotannin, as also confirmed in a previously published work by [PERSON] et al. [38], who also confirmed the presence of Carmalol derivatives, Dihydroxy pentafuhalol, Trihydroxy Hexafuhalol, Hydroxy tetrafuhalol, and Phlorotannins-7 phloroglucinol.  
\begin{table}
\begin{tabular}{c c c} \hline
**Wavenumber cm\({}^{-1}\)** & **Functional Groups** & **References** \\ \hline
3352 & hydroxyl group (OH) & [39] \\ \hline
2936 & (C-H) alkyl group & [40] \\ \hline
1771 & C=O & [41] \\ \hline
1407 & methyl groups & [42,43] \\ \hline
1037 & C-OH & [44] \\ \hline \end{tabular}
\end{table}
Table 1: FT-IR spectroscopy analysis of the phlorotannins extracted from _S. linifolium_.  
Figure 1: The ultraviolet spectrum of the phlorotannin extract from _S. linifolium_ marine alga.
## 3 Results and Discussion
### Kinetic Studies  
Thermodynamic parameters for the complexes were calculated by employing two methods, the Coast-Redfern integral [32] and the approximation of Horowitz-Metzger [33] methods, to obtain the activation energy (E\({}_{\text{a}}\)). Thermodynamic activation parameters of decomposition processes of the complexes, \(\Delta\)H (enthalpy), \(\Delta\)S (entropy), \(\Delta\)G (Gibbs free energy change of the decomposition), and E (thermal activation energy) are evaluated graphically, and calculated from the thesis in Equations (2)-(4).  
\[\Delta\text{H}=\text{E}+\text{PV} \tag{2}\]  
\[\Delta\text{G}=\Delta\text{H}-\text{T}\Delta\text{S} \tag{3}\]  
\[\Delta\text{S}^{*}=\text{ R }\ln\left(\text{Ah}/\text{K}_{\text{B}}\text{T}_{ \text{s}}\right) \tag{4}\]  
where K\({}_{\text{B}}\) and h are Boltzmann and [PERSON]'s constants, respectively [34; 35]. The results are shown in Table S5. Moreover, the general thermal behaviors of the prepared complexes in terms of stability ranges, peak temperatures, and values of kinetic parameters are plotted for Mn(II) complex in Figures 4 and 5.  
The negative values of \(\Delta\)S indicate pointed to high randomness-degree in non-spontaneous reaction. The positive values of \(\Delta\)G showed the reaction is non-spontaneous towards products and spontaneous towards the reactants and the positive values of \(\Delta\)H means the reaction is endothermic [35; 36].  
Figure 4: Coast–Redfern of Mn(II) in different stages.
# Energy-Efficient Edge Optimization Embedded System Using Graph Theory with 2-Tiered Security
## 4 Energy-Efficient Enabled Reliable Edge-SDN Protocol with Graph Theory  
This section presents our proposed energy-efficient reliable edge network using graph theory. The proposed system learns from environmental and node behaviors. It not only offers a fault-tolerant solution but, on the other hand, also prevents communication paradigms from external attacks. It consists of two layers. One layer is among IoT components and edge networks, and the second layer is among edge networks to mobile sinks. Moreover, the SDN controller supervises the overall communication with its intelligent decisions and improves the constraint utilization for the nodes. The phases of network creation, protocol communications, message flow, and security are all explained in this section. The SDN controller checks the network surroundings randomly, and, when it obtains unauthorized access, it sends alert messages to devices.
## 3 Results
### Effectiveness of Ground-Based Albedo Modification  
#### 3.2.1 Cooling Effects of GBAM for Each Level of SD Based on the Same TP  
Considering the SD based on the same TP, when meteorological conditions were less affected by solar radiation, the effects of GBAM were as follows. In all observed TPs, the cooling effect was smallest in the SVF range of 0.0-0.2 with high SD: 0.018 \({}^{\circ}\)C (at 9:00), 0.701 \({}^{\circ}\)C (at 11:00), 1.025 \({}^{\circ}\)C (at 13:00), 1.167 \({}^{\circ}\)C (at 15:00), and 0.971 \({}^{\circ}\)C (at 17:00). On the contrary, the cooling effect is highest in the SVF range of 0.8-1.0, which is the range with low SD: \(-\)4.620 \({}^{\circ}\)C (at 9:00), \(-\)6.494 \({}^{\circ}\)C (at 11:00), \(-\)7.900 \({}^{\circ}\)C (at 13:00), \(-\)4.263 \({}^{\circ}\)C (at 15:00), and \(-\)2.803 \({}^{\circ}\)C (at 17:00). Therefore, as the SVF increased at all TPs, HICS effectiveness also tended to increase (Table 4). On the same note, when meteorological conditions were affected by solar radiation, the cooling effects of GBAM were as follows. In all observed TPs, the cooling effect was smallest in the SVF range of 0.0-0.2 with high SD: 0.003 \({}^{\circ}\)C (at 11:00), 0.817 \({}^{\circ}\)C (at 13:00), 0.927 \({}^{\circ}\)C (at 15:00), and 0.530 \({}^{\circ}\)C (at 17:00) except for 0.180 at 09:00 with the SVF range of 0.2-0.4. Conversely, the cooling effect is highest in the SVF 0.8-1.0 range, which is the range with low SD. However, at 17:00, the highest cooling effect is shown in the SVF range of 0.6-0.8: \(-\)9.462 \({}^{\circ}\)C (at 9:00), \(-\)10.827 \({}^{\circ}\)C (at 11:00), \(-\)9.943 \({}^{\circ}\)C (at 13:00), \(-\)5.159 \({}^{\circ}\)C (at 15:00), and \(-\)2.110 \({}^{\circ}\)C (at 17:00). Therefore, a larger SVF tends to be associated with a larger cooling effect (Table 5).
# Multi-Hypergraph Neural Networks for Emotion Recognition in Multi-Party Conversations
## 1 Introduction
By performing a node-edge-node transformation, the underlying data relationship can be better represented [21], and more complex and high-level relationships can be established among nodes [22]. Previous work has shown that speaker-specific information is very important for ERMC [3,6]. Therefore, the way of using hypergraphs for speaker-specific modeling of ERMC is a very important issue. Second, the current utterance may be influenced by utterances from different speakers. Therefore, the way of using hypergraphs for non-speaker-specific modeling of ERMC is also a very important issue. In this paper, we construct two hypergraphs for speaker-specific and non-speaker-specific modeling, respectively. The hyperedges in the two hypergraphs are different. The hypergraph for speaker-specific modeling, where the nodes on the hyperedge are from the speaker of the current utterance, mainly deals with self-dependency. The hypergraph for non-speaker-specific modeling, where nodes on a hyperedge contain the current utterance and utterances from other speakers, is primarily used to handle inter-dependency. In Figure 1, we construct two kinds of hyperedges for the third utterance. The hyperedge of the green triangle indicates that the node of the hyperedge is from speaker B of the third utterance. The hyperedge of the blue triangle indicates that the nodes of the hyperedge are from speakers other than speaker B. Note that this hyperedge needs to contain the current utterance so that the nodes within the hyperedge have an effect on the current utterance. We use the location information and node features to aggregate to generate hyperedge features. Here, we use the location information to obtain the weight of the average aggregation, use the node features to perform the attention aggregation to obtain the attention weight, and combine the two weights to obtain the hyperedge features. Then, the hyperedge features are used to model the conversational context by using a recurrent neural network. Finally, the hyperedge features are used to aggregate to obtain new node features. The hypergraph convolution of the two hypergraphs can be used to model specific speakers and non-specific speakers so as to deal with inter-dependency and self-dependency among participants. The main contributions of this work are summarized as follows:  
* We construct hypergraphs for two different dependencies among participants and design a multi-hypergraph neural network for emotion recognition in multi-partyconversations. To the best of our knowledge, this is the first attempt to build graphs for inter-dependency alone. * We combine average aggregation and attention aggregation to generate hyperedge features that can allow better utilization of the information of utterances. * We conducted experiments on two public benchmark datasets. The results consistently demonstrate the effectiveness and superiority of the proposed model. In addition, we achieved good results on the emotional shift issue.
# Swarm of Drones in a Simulation Environment--Efficiency and Adaptation
## 3 Drones Swarm in Simulation Environment
### Swarm Drone Communication  
The method of controlling a swarm of drones from a ground station was designed to ensure continuous communication between the ground station software and drones in the formation. Such communication utilizes the MAVLink protocol. The created simulator allowed real-time monitoring of the location of each drone, which was crucial for maintaining the designated positions and flight trajectories.  
The communication system for drone swarms was based on WiFi technology compliant with the 802.11 s standard. The drone network communicated using a mesh network topology. This architecture enabled direct interaction between drones, as well as between drones and the ground control station (GCS), ensuring the transmission of key data such as positional coordinates and operational statuses across the entire network.  
We employed the Global Navigation Satellite System (GPS) to ascertain precise location information. To enhance the accuracy of our positioning capabilities, we integrated real-time kinematic (RTK) GPS modules within our aerial units. Concurrently, an RTK base station was deployed on the ground to provide real-time correction message (RTCM)  
Figure 2: Swarm control software.  
data. This arrangement significantly refined the positional data, which are essential for the coordinated maneuvering of the swarm. This technology helped improve positioning accuracy to 5 cm in UAV positioning applications [42].  
Communication between the GCS station and the nodes of the drone swarm (Figure 3) used standard 802.11 protocols. The transmitted messages included data about the node positions, i.e., the drones' location and altitude. Control frames contain information about the trajectory the drone is to follow. Telemetry data indicators and operational data, such as battery charge level or information from installed sensors, were also included. The specific types of frames depend on the specific missions performed by the drone swarms.
## 3 Results
### Correlation Analysis between Thermal Parameters and VOCs  
The correlation analysis between the thermal sensitivity index and VOCs was shown in Figure 7. The compounds 3-ethyl-benzaldehyde, 3-methyl-3-buten-1-ol, 2-propyl-1-heptanol, 2,3-dimethyl-1-butanol, and 2-ethyl-hexanoic acid showed highly significant negative correlations with the heat-sensitive indicators furosine and lactulose when the heat treatment temperature of milk increased (\(p<0.01\)); a highly significant positive correlation with immunoglobulin G, \(\alpha\)-lactoalbumin, and \(\beta\)-lactoglobulin (\(p<0.01\)); and a significant positive correlation with lactoferrin (\(p<0.05\)). The compounds furfural, pentanoic acid ethyl ester, 2-undecanone, 4,7-dimethyl-undecane, 2-heptanone, 2-furanmethanol, 1,4-benzenedicarboxylate, 5-octanolide, heptanoic acid, and ethylbenzene showed highly significant positive correlations with the heat-sensitive indicators furosine and lactulose as the heat treatment temperature of milk increased (\(p<0.01\)), and a highly significant negative correlation with immunoglobulin G, \(\alpha\)-lactoalbumin, \(\beta\)-lactoglobulin, and lactoferrin (\(p<0.01\)).
# A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
## 2 Methodology  
The framework for the developed methodology aimed at dynamically assessing the accident occurrence probabilities of GDSs is illustrated in Figure 1. Initially, a Bayesian network (BN) model is constructed to represent the GDS accident scenario. Subsequently, based on the availability and quality of failure data, various methods (including fuzzy expert judgment and frequentist estimation) are employed to determine the prior probabilities of the REs within the BN model. Following this, a predictive analysis utilizing the BN is conducted to quantify the occurrence probabilities of undesired events (UEs). Thereafter, a diagnostic analysis is undertaken, leveraging importance measure indices, to pinpoint the key factors contributing to accident risks. Depending on the emergence of new failure-related data, a decision is made regarding whether to reiterate the Bayesian analysis. In the event of new failure sample data being presented, the RE probabilities are updated according to Bayesian updating theory, subsequently prompting a recalculation of the UE probabilities. Alternatively, if near-misses or incidents are observed, a probability adaptation process is applied to the BN, adjusting the probabilities of REs and intermediate events, followed by a repeat of the diagnostic analysis. Conversely, if no new failure-related events are observed, the entire analysis process concludes.  
Figure 1: Developed framework for dynamic assessment of accident probabilities of GDS.
## 1 Introduction
In addition, it can be modulated, varying their magnetic domain structure by an external low strength magnetic field [38; 39]. In turn, advanced sensing devices must generate a large scattering response with a resonant sharp behavior to monitor small variations in the scattering spectra accurately. To do this, the electromagnetic scattering measurements are also performed in time domain representation to avoid parasitic electrical error contributions in order to obtain a correct interpretation of the results [33]. In other works, experiments involving microwave scattering have been carried out in composites with Cu-based MW fillers, a metallic material. Inside them, interesting values of \(S_{21}\) (\(--20\) dB) with volume fractions that are lower than 1% are obtained [40]. Therefore, their ability to improve the powerful responses obtained for the Co-based parallel arrays is also investigated. As a novel strategy, Cu MWs are intercalated between the Co-based ones with the same length (\(L=4\) cm). The resulting scattering features are enhanced with respect to the single Co-based system. In addition, a modulation of the signal of the designed system is achieved when applying the AC or DC magnetic fields. This combination of equidistantly spaced MWs opens up the possibility of tuning and boosting the microwave scattering properties of the existing systems due to the GMI effects of the Co-based MWs and the higher electrical conductivity of Cu MWs. The results that are presented in this work are promising for the fabrication of artificial microstructures, named metamaterials, which are used in the control of the electromagnetic waves [41]. The traditional approach to building metamaterials requires the combination of two different arrays to produce a double negative response (electric permittivity and magnetic permeability [42]) in the same frequency range [41]. However, the use of single arrays that are composed by ultrasoft magnetic MWs enables a bias field control of the response [43; 44; 45; 46] into the Cu MWs, presenting a new configuration that follows the same line as the split ring resonators or other types of currents loops in the design of double negative medium [41; 43].
## 2 Analytical Programming
### Finite Element Modeling  
In the previous section, the response of the bridge at mid-span location was elaborated upon analytically; however, the analytical solution does not cover complicated cases such as the vibration of a stationary vehicle parked on a bridge while another vehicle is running over the bridge. For such a complicated case, a FE method may be implemented. A FE model was created to represent stationary vehicle vibration due to excitation from a moving vehicle on a bridge as shown in Figure 2.  
The bridge, moving vehicle, and stationary vehicle were modeled using the finite element program LS-DYNA as shown in Figure 3. The stationary vehicle was modeled as a single-degree-of-freedom spring-mass system as recommended by [PERSON] et al. [15]. The FE model response of the lumped mass of the stationary vehicle represents the response recorded by an accelerometer installed on a parked vehicle in practice. The moving vehicle was also modeled as a spring-mass system, which in practice represents a truck moving over the bridge as a source of excitation. Discrete spring elements were used with the S01-SPRING_ELASTIC material model. The bridge was simply supported and made of Belytschko-Schwer beam elements with an elastic material model (MAT_001). The FE bridge response at the mid-span location represents the response of a fixed sensor mounted at the mid-span location of a real-life bridge. The stationary vehicle was attached to the bridge by means of merging the nodes at the contact point while the interaction between the bridge and the moving vehicle was modeled using RAIL_TRACK/TRAIN. The output sampling frequency was 200 Hz.  
The bridge parameters in this study were adopted from the study of [PERSON] et al. [15]. The bridge is a steel bridge with one span that is 25 m length. The elastic modulus of the bridge is 27.5 GN/m\({}^{2}\), the moment of inertia is 0.12 m\({}^{4}\), and mass per unit length is 4800 kg/m. For the moving vehicle, the mass is 1200 kg, the spring stiffness is 500 kN/m (except for the case when different mass is investigated). Also, the speed of the moving vehicle is 10 m/s (expect for the case when different speed is investigated), as will be shown in the parametric case study in Section 3. The first four bridge frequencies obtained from the FE eigenvalue analysis were 2.08 Hz, 8.30 Hz, 18.59 Hz, and 32.82 Hz. The stationary vehicle in this study represents a cart parked at a bridge. The properties of the stationary vehicle such as mass and stiffness are assigned according the case to be studied in the parametric study in Section 3.  
Figure 3: FE model of a bridge with a stationary vehicle parked in the middle and a moving vehicle passing from left to right.  
Figure 2: Stationary vehicle response to a vehicle moving over a simply supported beam.