esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cell Attachment and MTT Assay of PVA/Zinc Citrate Nanofiber Mats  
Figure 9 shows the SEM images for the co-culture of mouse fibroblasts cells and PVA/zinc citrate nanofiber mats. Figure 9a shows the presence of many cells on the pure PVA nanofiber mats, as exemplified by their round-shape and the pseudopod. The cells attach effectively onto the mats. When zinc citrate is 0.5 wt %, the cells on the PVA/zinc citrate nanofiber mats are slightly deformed (Figure 9b). When zinc citrate is 1 wt %, cells start to stack onto the mats, and their pseudopods also adhere to the mat (Figure 9c). The cells become flat when the zinc citrate is 3 wt % (Figure 9d), which indicates a good cell attachment, whereas the cells appear stacked but do not stack when the zinc citrate is 5 wt % (Figure 9e). The SEM results have proven that PVA/zinc citrate nanofiber mats have a desired cell attachment, and the optimal cell attachment reaches their optimal state when the zinc citrate is 3 wt %. Figure 10 shows the test results of MTT assay of PVA/zinc citrate nanofiber mats. When the amount of zinc citrate is 0.5 wt % and 1 wt %, the cell viability is beyond 100%, indicating that the materials facilitate cell proliferation. This result is in the conformity with the cell attachment shown in Figure 9. Furthermore, the cell viability of 97% to 99% with the content of zinc citrate being 0.5 wt % and 1 wt %, showing the non-toxic behavior of zinc citrate against cells.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Amount of Zinc** & **Electrospinning** & **Diameter of PVA/Zinc** & **Diameter of** \\
**Citrate (wt \%)** & **Voltage (kV)** & **Citrate Nanofiber Mats (nm)** & **Inhibition Zone (mm)** \\ \hline
3 & 60 & 167 \(\pm\) 39 & 8.0 \(\pm\) 1.0 \\
3 & 70 & 107 \(\pm\) 22 & 9.3 \(\pm\) 0.6 \\
3 & 80 & 75 \(\pm\) 24 & 11.8 \(\pm\) 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Bacterial efficacy against _S. aureus_ of PVA/zinc citrate nanofiber mats.  
Figure 8: (**A**) The morphology and diameter distribution and (**B**) the bacterial efficacy against _S. aureus_ of PVA/zinc citrate nanofiber mats. PVA/zinc citrate nanofiber mats are made with a specified amount zinc citrate of 3 wt % and different voltages of 60, 70, and 80 kv.
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 2 System Model
### Relaying, Antenna Locations, and Propagation Paths  
Relaying is one of the promising techniques that allow for reducing the costs of BSs deployment along the road. As in mmWave/sub-THz band communication, distances are considerably shorter than in microwave systems such as LTE or sub-6 GHz New Radio (NR), relaying may provide effective coverage even on \"blind\" segments of a road, where direct communication with BSs is not possible. However, due to the gradual penetration of new technologies, relaying highly depends on how many vehicles around are equipped with transmitters. To reflect this phenomenon, we introduce the technology penetration rate parameter \(P_{E}\), which is the probability of having corresponding equipment installed on a vehicle. We also assume that the equipment includes a couple of interconnected transceivers located on the front and rear sides of vehicles [22].  
The vehicles are assumed to provide eMBB services to the passengers. When direct communication to the next BS is not feasible due to outage conditions, a vehicle employs relaying to organize a \"bridge\" to the BS. The length of the bridge and the achievable data rate strongly depends on the deployment, environment, and antenna locations. In this paper, we consider three viable antenna locations: (i) bumper-level (0.3-0.4 m), (ii) engine-level (0.4-1 m), and (iii) windshield level (1-1.5 m). Not only do the propagation characteristics depend on the antenna location, but also the propagation paths, since the vehicles that are not equipped with transceivers may potentially act as blockers and break off the bridges.  
We assume that relaying is only organized by vehicles on the same lane which provides the most stable environmental conditions. We utilize the propagation paths measurements from [23; 24] providing an exhaustive characterization of propagation paths between two vehicles in the road conditions at 300 GHz. If there are two or more blocker vehicles  
Figure 1: The considered scenario and main components of the system model.  
(hereafter referred to as blockers) on the same lane, bypassing relays is assumed not feasible. All the options may employ side reflections from the vehicles located on the adjacent lanes to bypass a blocker vehicle. However, only for windshield-level location, the vehicles may communicate through the rear and front glasses of a blocker, though with heavy propagation losses of about 20 dB. On the other hand, the bumper-level location may employ an under-vehicle propagation path which is constructed on reflection off the road surface with the losses of not more than 1-2 dB for typical distances. As the engine-level location may only utilize side reflection with typical losses of 5 dB, it seems rather disadvantageous which will be shown in Section 4.
# Monolithic Si-Based AlGaN/GaN MIS-HEMTs Comparator and Its High Temperature Characteristics
## 4 Results and Discussion
Figure 6: The voltage transfer curves of the fabricated comparator from 25 \({}^{\circ}\)C to 250 \({}^{\circ}\)C. The reference voltages were applied from 3 V to 8 V with a step of 1 V.  
explained by taking the distinctive propagation delay t\({}_{p}\) from Figure 7b into account. The average of the high-to-low delay t\({}_{pHL}\) and low-to-high delay t\({}_{pLH}\) at 250 \({}^{\circ}\)C is more than 500 ns larger than the delay at room temperature. This is the main reason that the duty cycle is worsened when V\({}_{\text{OH}}\) and V\({}_{\text{OL}}\) remains at a relatively steady level. Figure 8 shows the waveforms under different input frequencies at room temperature. It can be seen that the increased frequency has a significant impact on the output curves. The high frequency limit mainly comes from the following three aspects. Firstly, the limit is similar to the propagation delay problems stated before. When the input frequency is 1 MHz with a period of 1 us, the effect of propagation delay time will be more visible than low frequency output waveforms. This limit can be solved by further reducing the on-resistance of the device and improving the match among devices in comparator circuit. Secondly, the V\({}_{\text{OH}}\) and V\({}_{\text{OL}}\) degrade since there is not enough time to maintain the maximum and minimum voltage levels. At 1 MHz, the asymmetry between rising and falling edges indicates the difference between the charging time through active load M\({}_{4}\) and discharging time through M\({}_{2}\), which leads to worsened sensitivity with a given 5 V reference voltage. Moreover, the circuit is fabricated using MIS-HEMT technology, and the trap sites of the insulator are charge and discharge in dynamic switching. The interface trap-induced degradation will also affect the high frequency performance. Careful surface treatment under the gate region can help to improve the high frequency performance. In general, both the static and dynamic performance of this monolithic comparator circuit show a significant superiority for working in high-temperature and high-frequency environment compared to silicon-based CMOS counterparts [22]. Figure 7: (**a**) The first five pluses from the dynamic test under a 100 kHz triangular input. (**b**) The zoom-in pulse from figure (a) that shows different outputs under temperature from 25 \({}^{\circ}\)C to 250 \({}^{\circ}\)C. (**c**) The extracted V\({}_{\text{OH}}\), V\({}_{\text{OL}}\) and duty cycle of output curves from 25 \({}^{\circ}\)C to 250 \({}^{\circ}\)C.
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 6 Applications: XAI's Impact across Diverse Domains
### Environment  
XAI is leaving an indelible mark on the realm of environmental sciences by its use in models forecasting and tracking air quality, climate shifts, and water conditions. Theseapplications of XAI are facilitating a more comprehensive comprehension of the intricate determinants underlying environmental challenges. Below, we present noteworthy instances exemplifying the impact of XAI applications in the field of the environment.  
One of the pivotal applications of XAI within the environmental domain is in air quality monitoring. XAI models have been ingeniously employed to furnish elucidations for air pollution predictions, thereby facilitating a profound comprehension of the determinants contributing to air quality degradation. A notable instance is the study conducted by [PERSON] et al. [78], where an XAI model modeling deep neural networks with a nonlinear auto regressive moving average model was proposed for air pollution prediction. This amalgamation yielded enhanced predictive accuracy and interpretability, exemplifying the potency of XAI in advancing understanding and forecasting.  
Similarly, XAI's impact extends to climate change prediction, where it lends transparency to the intricacies of climate forecasts. By explicating the factors that influence global warming, XAI models contribute significantly to the comprehension of climate dynamics. For example, [PERSON] et al. [79] have harnessed an XAI model to shed light on the elevated summer temperatures experienced in Western and Central Europe.  
In addition, the critical realm of water quality monitoring is profoundly influenced by XAI. XAI models here serve to expound upon water quality predictions, empowering a comprehensive grasp of the variables responsible for water pollution. [PERSON] et al. [80], for instance, devised a water quality prediction model rooted in XAI principles. Applied to the Yellow River in China, this model unveiled insights into the intricate interplay among various water quality parameters. Likewise, [PERSON] et al. [81] employed an XGBoost model coupled with Shapley values (SHAPs) to predict water quality, select input variables, and provide model explanations.  
In sum, XAI's ascendancy within the environmental domain, spanning air quality, climate change, and water quality, illuminates the trajectory toward a more transparent, comprehensible, and actionable understanding of complex environmental phenomena.
###### Abstract  
The aim of this study was to investigate the effect of chilling rate (1.44, 22.2, and 32.4 \({}^{\circ}\)C/h) on the evolution of volatile and non-volatile compounds in raw lamb meat during refrigeration (1, 24, 72, and 120 h). Through orthogonal projection to latent structure-discriminant analysis, the calculation of odor activity values (OAV > 1) and taste activity values (TAV > 1) analysis, 1-octen-3-ol, (E, E)-2,4-decadignal, nonanal, hexanal, nona-3-5-dien-2-one, 2,3-octanedine, hexanoic acid, 1-nonen-4-ol, aspartate (Asp), Glutamic Acid (Glu), 5\({}^{\prime}\)-GMP, 5\({}^{\prime}\)-IMP, and 5\({}^{\prime}\)-AMP were regarded as differential flavor or taste compounds for raw meat undergone different chilling rates. With a rapid chilling rate at 24 h after slaughter, the contribution of 1-octen-3-ol decreased, but (E, E)-2,4-decadignal increased. Moreover, at 24 h post-mortem, the equivalent unami concentration of Asp, Glu, 5\({}^{\prime}\)-GMP, 5\({}^{\prime}\)-IMP and 5\({}^{\prime}\)-AMP in raw meat were significantly lower at a chilling rate of 1.44 \({}^{\circ}\)C/h than 32.4 \({}^{\circ}\)C/h (\(p\) < 0.05). Conclusively, under the rapid chilling rate, more fatty odor and unami compounds accumulated in 24 h aged meat.  
chilling rate; refrigeration; volatile and non-volatile compound; raw lamb meat +
Footnote †: journal: Journal of Foods  
**2021**, _10_, 2792. [[https://doi.org/10.3390/foods10112792](https://doi.org/10.3390/foods10112792)]([https://doi.org/10.3390/foods10112792](https://doi.org/10.3390/foods10112792))
## 4 Experiments
### Results on CIFAR-10  
Table 6 shows the precision accuracy of CIFAR-10. JoSDW performs best again in all four cases. For the Symmetry-20% case, co-teaching+ performs better than co-teaching and decoupling. For the other three cases, co-teaching+ cannot even achieve the same performance as co-teaching.  
Figures 7-10 show the precision accuracy and label precision vs. epochs. JoSDW is superior to all other comparison methods in terms of precision accuracy and label accuracy. In terms of label accuracy, decoupling and co-teaching+ relying on disagreement does not find a clean instance. When the noisy label reaches Symmetry-80%, JoSDW is considerably better than other methods in terms of precision accuracy and label accuracy, but in Asymmetry-40%, although in terms of label accuracy JoSDW is substantially better than other methods, there is no obvious advantage in precision accuracy. Additionally, although some methods outperform JoSDW in the initial stage, in all subsequent epochs, JoSDW consistently outperforms other approaches.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Noise Settings** & **Standard** & **F-Correction** & **Decoupling** & **Co-Teaching** & **Co-Teaching+** & **JoCOR** & **JoSDW** \\ \hline Symmetry-20\% & 69.18 & 68.74 & 69.32 & 78.23 & 78.71 & 85.73 & 86.28 \\ Symmetry-50\% & 42.71 & 42.19 & 40.22 & 71.3 & 57.05 & 79.41 & 79.75 \\ Symmetry-80\% & 16.24 & 15.88 & 15.31 & 25.58 & 24.19 & 27.78 & 32.31 \\ Asymmetry-40\% & 69.43 & 70.6 & 68.72 & 73.78 & 68.84 & 76.36 & 77.04 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average precision accuracy (%) on CIFAR-10 over the last 10 epochs.  
Figure 7: Results on the CIFAR-10 dataset. Noise settings, Symmetry-20%. **Left**: precision accuracy (%) vs. epochs; **Right**: label precision (%) vs. epochs.
# A Cross-Medium Uplink Communication Model Merging Acoustic and Millimeter Waves
## 2 Model of Cross-Medium Communication Channel  
In order to achieve water-to-air cross-medium communication, it is imperative to establish a comprehensive communication channel model as a foundational step. By leveraging the distinctive features of the constructed channel model, one can flexibly opt for appropriate carrier frequencies and modulation methods. By proactively assessing and predicting the communication metrics, a water-to-air cross-medium communication system can be constructed to precisely cater to the specified SNR and transmission distance requirements.  
The analysis of physical processes is widely recognized as a crucial component in engineering modeling. As depicted in Figure 1, water-to-air cross-medium communication entails three main parts: the underwater channel, the water-air interface, and the air channel. In the underwater channel, the speaker emits an acoustic signal, propagating as a pressure wave from the underwater environment towards the water surface. At the air-water interface, the acoustic signal propagating to the water surface induces the formation of water surface waves due to the mechanical characteristics of the pressure waves. By employing millimeter wave radar (MMWR) in the air channel, it becomes feasible to detect the surface waves and subsequently acquire the transmitted information from the speaker.
## 2 Materials and Methods
### UHPLC-DAD Analysis of Phenolic Compounds in Plant Leaf Extracts  
Phenolic compounds were analyzed using UHPLC-DAD method according to procedure described in detail by [PERSON] et al. [40]. UHPLC + Diodex UltiMate 3000 liquid chromatographic system with a UHPLC pump, an autosampler, a column oven and a diode array detector with multiple wavelengths (Thermo Fisher Scientific Inc., Waltham, MA, USA) was used. Separation was performed on an Accucore(tm) C18 column (2.1 \(\times\) 150 mm, 2.6 \(\upmu\)m particle size; Thermo Scientific, Pittsburgh, PA, USA) with a two-phase gradient system of 0.1% formic acid in water as mobile phase A and acetonitrile as mobile phase B. The mobile phase gradient used was as follows: 0-8 min, 1-5% B; 8-15 min, 5-8% B; 15-20 min, 8-10% B; 20-25 min, 10-15% B; 25-35 min, 15-20% B; 35-40 min, 20-25% B; 40-50 min, 25-90% B; 50-53 min, 90% B; 53-58 min, 90-1% B. Finally, the initial conditions were held for 7 min to allow re-equilibration of the column. The mobile phase flow rate was 0.35 mL/min, and the column temperature was 30 \({}^{\circ}\)C. The injection volume was 10 \(\upmu\)L. The chromatograms were recorded at three different wavelengths (280, 320 and 365 nm). The identification of the compounds was confirmed by comparison of their UHPLC-DAD retention time and UV-vis profile to those of the-phenolic standards. The concentrations of the identified compounds were calculated using calibration curves for analytical standards: procyanidin B1, procyanidin B2, caffeic acid, epicatechin, quercetin-3,7-di-O-glucose, epicatechin gallate, quercetin 3-O-galactoside, quercetin 3-O-glucose, luteolin 7-O-glucose (cyanooside), quercetin, naringenin and apigenin. The content of rosmarinic acid and carnosic acid has been calculated on the basis of the calibration curve for chlorogenic acid.
## 3 Platform for Real-Life Testing of Communication Protocols
### Monitor Messaging
#### 3.3.5 The Transport of the Monitoring Messages  
The platform uses the ZeroMQ [54] library to monitor messages between the probes and the broker. This messaging library is known for its high efficiency. GNU radio already has a ZMQ module to implement the most commonly used messaging patterns supported by the ZMQ library. However, since some of the monitoring and serialization logic used in the platform is part of the monitor probe, a custom ZMQ block is implemented.  
To send messages, the publish/subscribe (pub/sub) [55] messaging pattern is used, with the monitor probe acting as the publisher. In most cases, the broker runs on the same machine as the GNU radio application being tested. To allow multiple monitor probes in the GNU radio process flow, the ZMQ subscriber socket on the broker side is bound, and the publish sockets in the GNU radio process flow connect to the subscriber. This way, the same port can be used on the message transport channel.
## 5 Proposed Reliable, Energy Optimized and Void Aware Routing Protocol
### Routing Path Discovery Process  
The cluster heads maintain a routing table that possesses the information of the nearby cluster heads by sending a beacon message. To reduce the overhead messaging, the transmission range of cluster heads is limited. Additionally, these beacon messageswill only be received by the neighboring cluster heads that reside in the cluster head transmission range.  
```
Input: Optimal Number of Clusters Formation Output: Cluster Head Selection
```
1. For each cluster i in each layer
2. For each node i in cluster
3. Calculate the RSSI of node j
4. Compare the residual energy of node j
5. End For
6. Calculate the best cluster head value
7. If best cluster head value < Best Score
8. Best cluster head value = Node_i
9. End if
10. End for ```
**Algorithm 2**Cluster Head Selection  
The beacon message contains the ASN ID, residual energy, depth value, and neighboring cluster head value. The routing path will be discovered based on the cost function given below.  
\[\text{RP}=\delta\bigg{(}\frac{\text{E}_{\text{RES}}}{\text{E}_{\text{INI}}} \bigg{)}+\varepsilon\bigg{(}1-\frac{\text{D}_{\text{CH}}}{\text{D}}\bigg{)}+ \rho\bigg{(}\frac{\text{N}_{\text{CH}}}{\text{T}_{\text{CH}}}\bigg{)}+\sigma \text{L}_{\text{Q}} \tag{25}\]  
where \(\delta+\varepsilon+\rho+\sigma=1\). ERES and EINI are the residual energy and initial energy of the cluster head, respectively. \(\text{D}_{\text{CH}}\) is the current depth value of the cluster head. \(\text{N}_{\text{CH}}\) and \(\text{T}_{\text{CH}}\) are the neighboring cluster heads and total cluster heads, respectively. LQ is the link quality of the cluster head. The cost function given in Equation (25) comprises the cluster head's energy level, which indicates that the cluster head selection for the routing path having the higher residual energy will minimize the chances of early exhaustion of the network.  
The depth parameter of the cluster head is also inducted in the cost function. The reason for introducing the depth parameter of the cluster head into the cost function is to preserve the energy of the node by reducing the chances of route selection being farther from the SSN. Furthermore, the neighboring cluster heads of the node are also considered as one of the factors of the cost function to lower the likelihood of void node problems, maintain network connectivity, and improve the packet delivery ratio. Also, the link quality is taken into consideration for calculating the cost function to make sure that the selected link is healthy enough to send data packets. The ASN having the maximum value of the cost function is the optimal choice for the next forwarder node among all the available neighboring cluster heads. Algorithm 3 is proposed for the reliable, energy optimized and void aware routing discovery.
``` Input: Cluster Heads, Source Node Output: Optimal Routing Path Discovery ```
1. Initialize random node as source node.
2. While the surface sink node is not found
3. For each node i
4. Calculate the routing path (RP)
5. If RP > Best RP
6. Best RP = RP
7. Best neighbor = neighbor
8. End If
# An Observation Scheduling System for Radio Telescope Array
## 3 The Overall Architecture of the System
### uv Coverage Module
 \tag{3}\]  
Through matrix operations, Equation (3) can be simplified as [12]:  
\[u^{2}+\left(\frac{\
u-Z_{\lambda}\cos\delta}{\sin\delta}\right)^{2}=X_{ \lambda}^{2}+Y_{\lambda}^{2}. \tag{4}\]  
From Equation (4), it can be observed that, theoretically, the sampling points of \(u\) and \(v\) (i.e., the \(uv\) coverage) will form an ellipse or a segment of an elliptical arc over continuous time, without considering other influencing factors, such as the occlusion of the Sun. The major semi-axis of the ellipse is given by \(\sqrt{X_{\lambda}^{2}+Y_{\lambda}^{2}}\), while the ellipse itself has a minor semi-axis of \(\sin\delta\sqrt{X_{\lambda}^{2}+Y_{\lambda}^{2}}\). The center of this ellipse is located at the coordinates \(\left(0,Z_{\lambda}\cos\delta\right)\). Simultaneously, it is evident that the distribution of \(uv\) coverage is influenced by both the telescope antenna layout and the visible time of the target within the same observation time [15,16]. Conversely, when observing the same target, the coverage rate of \(uv\) coverage serves as an indicator of the telescope array layout's efficiency. Based on the fundamental principle of \(uv\) coverage, the functionality of this module has been devised. The module encompasses various parameters, including the observation time range (scanning \(H\)), target position (RA, DEC), and telescope station position (X, Y, and Z) [17, 18, 19]. Specifically, a Python class (FuncUV) is defined in this module, which is used to calculate the \(uv\) coordinates in the synthetic aperture using the input parameters. Simultaneously, within this module, numerous constraints encountered in actual observations have been incorporated into the \(uv\) coverage, including terrain effects on elevation angles and solar occlusion of targets, aiming to simulate the actual observation process as closely as possible. The \(uv\) coverage map is plotted using a QT interface. In the imaging of targets, the sufficiency of \(uv\) coverage sampling plays a pivotal role in determining the imaging quality of the target. A denser sampling of \(uv\) coverage correlates with enhanced imaging quality, whereas a less dense sampling results in diminished quality [20, 21, 22].