esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optical, Electrochemical and Thermal Properties  
UV-vis absorption spectroscopy was used to investigate the optical properties of **M1** in both the chloroform solution and the film spin-cast from 10 mg/mL chloroform solution at 1200 rpm for one minute, see Figure 1a. In both solutions and films, **M1** exhibits nearly identical absorption maxima at 508 nm. The absorption onset of **M1** in the film is red-shifted by approximately 100 nm relative to the onset of **M1** from solution and the film also shows two broad vibronic shoulders at 542 nm and 600 nmindicating a strong tendency toward the aggregation of **M1** with enhanced intermolecular \(\pi\)-\(\pi\) contacts, corresponding to the increased electronic interaction between chromophores. The optical band gap (1.82 eV) was calculated from the absorption onset at 680 nm of the material in the solid state.  
Furthermore, the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels of **M1** in the solid state were estimated by cyclic voltammetry (CV) measurements conducted on **M1** films. Therefore, it was found that a relatively low HOMO level of \(-\)5.54 eV against vacuum level and LUMO level of \(-\)3.66 eV were obtained, and the estimated electrical band gap (1.88 eV) is in reasonable agreement with the optical band gap (1.82 eV). Hence, the energy levels of **M1** in the solid state can be analyzed and the frontier orbital energies of all organic components in functioning organic photovoltaic devices can be illustrated in Figure 1b together with each electrode such as indium tin oxide (ITO) on a glass substrate and aluminum (Al). Based on such an energy diagram, **M1** can be well-defined as a p-type semiconductor (electron donor) when it is mixed with PC\({}_{70}\)BM (n-type semiconductor, electron acceptor) for a typical BHJ organic solar cell system. Furthermore, since the HOMO of **M1** is quite close to that of PC\({}_{70}\)BM, a large open circuit voltage (\(V_{\infty}\)) can be expected and corresponds to the band alignment of LUMO levels between electron donors and acceptors. Efficient electron transfer between **M1** and PC\({}_{70}\)BM would also be expected due to the fairly large difference [62]. Thermal phase transitions of **M1** were studied by differential scanning calorimetry (DSC) and the results are shown in Figure 1c. The DSC thermogram shows that **M1** exhibits a melting transition at 155 \({}^{\circ}\)C and crystallizes at 115 \({}^{\circ}\)C. Therefore, it could be suggested that **M1** has a strong tendency to form ordered aggregates with enhancing \(\pi\)-\(\pi\) stacking in its solid state which can be attributed to the vibronic shoulder peak in its solid-state UV-vis profile [63].
## 2 WBANs for Life and Safety Critical Applications  
There is currently a growing need for ubiquitous communication and monitoring systems enabling life and safety critical applications. Indeed, these application areas are currently attracting a lot of attention in the research and industrial community. In this context, it is envisioned that wearable human assistive technologies will play an important role in managing, monitoring, and ensuring the safety of humans during mission critical operations.
# Robust Near-Optimal Aerocapture Guidance Method Based on Saturation Function
## 4 Simulations and Result Analysis
### Algorithm Verification and Analysis
#### 4.1.1 Preliminary Monte Carlo Simulation
Moreover, under the lateral guidance law, single bank reversal can make the spacecraft's final inclination reach the target value. Therefore, the guidance requirements are well met in Mode 1, and Mode 1 performs better in optimality and robustness through the comparison.
Globally, it has been shown that VGI is an opportunity for marine research [19], having good potential to contribute to the marine policy evidence-base alongside traditional monitoring, remote sensing and modeling; however, according to [20], this can happen only if outputs from citizen science projects are judged individually on quality. As regards oil spills and, especially, small-scale events, in [21], important considerations and guidelines for setting up a citizen science approach to sea monitoring were presented. It is argued that combining citizen and traditional science may increase the quality of monitoring of small-scale pollution events, but in that paper, no actual implementation was tested and experimented. By converse, in our work, VGI is actually collected thanks to a specially-designed mobile application. The proposed app allows people to notify the MIS about the sighting of oil spills; in turn, the MIS is capable of integrating and filtering the received data so as to produce alerts in a timely manner to be issued to the authorities. To the best of our knowledge, this is an important contribution of the paper: indeed, it is shown that VGI can be integrated in a monitoring system and therein deployed by automatic reasoning systems in order to obtain a more accurate control of the sea. Users' acceptance, surveyed through statistical data, shows that the approach is suitable for large-scale uptake. The paper is organized as follows. We first recall in Section 2 the architecture of the designed MIS, describing its components and features. In Section 3, we describe the motivation and the issues in integrating such a kind of information into the MIS. Methods for real-time assessment of risk based on the heterogeneous data collected into the MIS are discussed in Section 4, while automatic reasoning and alert generation services are described in Section 5. Such services are capable of exploiting the computed risk maps and data gathered from volunteers for issuing alerts to local authorities. Finally, we discuss the field tests performed for the validation of the proposed model (Section 6) and users' reception (Section 7). Section 8 concludes the paper with ideas for further work.
Remote Sens._**2015**, \(7\), 3878-3906; doi:10.3390/rs70403878  
_remote sensing_  
ISSN 2072-4292  
www.mdpi.com/journal/remotesensing  
_Article_  
Comparing MODIS Net Primary Production Estimates with Terrestrial National Forest Inventory Data in Austria  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 3 and [PERSON] 1**  
1 Institute of Silviculture, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, Vienna, Peter-Jordan-Str. 82, A-1190 Vienna, Austria;  
E-Mail: [EMAIL_ADDRESS]  
2 Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA;  
E-Mail: [EMAIL_ADDRESS]  
3 Natural Hazards and Landscape, Department of Forest Growth and Silviculture, Federal Research and Training Centre for Forests, Vienna, Seckendorff-Gudent-Weg 8, A-1130 Vienna, Austria;  
E-Mail: [EMAIL_ADDRESS]  
*Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +43-1-47654-4078; Fax: +43-1-47654-4092.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 11 December 2014 / Accepted: 17 March 2015 / Published: 1 April 2015_  
**Abstract:** The mission of this study is to compare Net Primary Productivity (NPP) estimates using (i) forest inventory data and (ii) spatio-temporally continuous MODIS (MODerate resolution Imaging Spectroradiometer) remote sensing data for Austria. While forest inventories assess the change in forest growth based on repeated individual tree measurements (DBH, height _etc._), the MODIS NPP estimates are based on ecophysiological processes such as photosynthesis, respiration and carbon allocation. We obtained repeated national forest inventory data from Austria, calculated a \"ground-based\" NPP estimate and compared the results with \"space-based\" MODIS NPP estimates using different daily climate data. The MODIS NPP estimates using local Austrian climate data exhibited better compliance with the forest inventory driven NPP estimates than the MODIS NPP predictions using global climate data sets. Stand density plays a key role in addressing the differences between MODIS driven NPP estimates _versus_ terrestrial driven inventory NPP estimates. After addressing stand density, both results are comparable across different scales. As forest management changes stand density, these findings suggest that management issues are important in understanding the observed discrepancies between MODIS and terrestrial NPP.
# DCO-MAC: A Hybrid MAC Protocol for Data Collection in Underwater Acoustic Sensor Networks
## 4 Proposed DCO-MAC Protocol
### RSV-MAC Protocol
The state transition diagram of the level-1 node in the RSV-MAC protocol is shown in Figure 4.  
The initial state of the level-1 node is the IDLE state. When receiving the RTR packet, the level-1 node without data packets to be transmitted keeps silent in the current state. Otherwise, the level-1 node with the data packet(s) to be transmitted sets the WTS_ATS timer and enters the WTS_ATS state. The duration of the WTS_ATS timer is \(T_{\text{WTS\_ATS\_timer}}=T_{\text{wtr\_ATS\_i}}-2\tau_{\text{sink\_i},i}-\theta\). Figure 4: The state transition diagram of level-1 node in the RSV-MAC protocol. Figure 3: The state transition diagram of the sink. RTR—ready to receive; ATS—available to send; WF_ATS—wait for ATS. When the WTS_ATS timer expires, the level-1 node sends its ATS packet to the sink at the pre-scheduled time, sets the WF_ORDER timer, and enters the WF_ORDER state. The duration of the WF_ORDER timer is \(T_{\text{WF\_ORDER\_timer}}=T_{\text{\_ATS,sink}}-T_{\text{\_W\_ATS,j}}\). When the WF_ORDER timer expires, and the ORDER packet is not received, the level-1 node goes back to the IDLE state. When receiving the ORDER packet, the level-1 node sets the WTS_DATA timer and enters the WTS_DATA state. The duration of the WTS_DATA timer is \(T_{\text{\_WTS\_DATA\_timer}}=T_{\text{\_W\_DATA\_J}}-2\tau_{\text{\_sink \_i}}-\theta\). When the WTS_DATA timer expires, the level-1 node transmits the DATA packet(s) to the sink at the pre-scheduled time, sets the WF_ACK timer, and enters the WF_ACK state. The duration of the WF_ACK timer is \(T_{\text{\_W\_ACK\_timer}}=T_{\text{\_DATA\_sink}}-T_{\text{\_W\_DATA\_J}}\). When the WF_ACK timer expires, and the ACK packet is not received, the level-1 node goes back to the IDLE state. Otherwise, the ACK packet is received before the WF_ACK timer expires, the level-1 node gets the corresponding information from the ACK packet, and it goes back to the IDLE state.
## 3 Experiments
### Experiment Results
#### 3.4.1 Classification Results
The max volume of inflow at the second and third category of stations is less than 520, and these stations are defined as the medium ridership stations. The max volume of inflow at the fourth category of stations is less than 800, which are defined as large ridership stations. The weekly inflow in the four categories of stations has regular periodicity, such as similar morning and evening peaks and the same change curves. Table 7 shows the deleted stations in the different time intervals in the Xiamen BRT. The larger the time interval is, the less the deleted stations are. It verifies that the passenger flow will be become more regular when the time interval increases. Station classification may be a more necessary strategy for short-term passenger flow prediction. Moreover, the stations in the Beijing metro with 276 stations have not been deleted. It verifies that station classification may be more significant for a larger dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Categories** & **Station No.** & **Number** & **Passenger Flow Volume** \\ \hline
1 & 21, 22, 23, 24, 26, 27, 28, 29, 30, & 17 & Small \\
31, 32, 33, 34, 35, 37, 38, 40 & & \\
2 & 1, 9, 11, 17, 19, 20, 36, 39, 41, 42 & 10 & Medium \\
3 & 2, 3, 4, 5, 6, 8, 43 & 7 & Medium \\
4 & 10, 12, 13, 15, 16, 18 & 6 & Large \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Station classification results in the Xiamen BRT in 15 min time interval. Figure 5: Visualization of inflow in the four categories of stations in 15 min time interval in the Xiamen BRT. (**a**) Inflow visualization in the first category; (**b**) inflow visualization in the second category; (**c**) inflow visualization in the third category; (**d**) inflow visualization in the fourth category. The No. 34 station is Pantu Station, shown in Figure 5a. Since there are two schools near the station, the inbound passenger flow increases sharply at noon and in the afternoon during the peak periods of school and after school. However, during class hours, the passenger flow at this station is the same as usual. The station classification in the proposed model is based on the inflow and outflow at all time intervals during a week. Therefore, the different changes of flows in several time intervals will not affect the classification results. It verifies the effectiveness of the classification block in the proposed model based on the Transformer encoder and K-Means.
###### Abstract  
Software-defined network (SDN) is a new paradigm that decouples the control plane and data plane. This offered a more flexible way to efficiently manage the network. However, the increasing number of traffics due to the proliferation of the Internet of Things (IoT) devices also increase the number of flow arrival which in turn causes flow rules to change more often, and similarly, path setup requests increased. These events required route path computation activities to take place immediately to cope with the new network changes. Searching for an optimal route might be costly in terms of the time required to calculate a new path and update the corresponding switches. However, the current path selection schemes considered only single routing metrics either link or switch operation. Incorporating link quality and switch's role during path selection decisions have not been considered. This paper proposed Route Path Selection Optimization (RPSO) with multi-constraint. RPSO introduced joint parameters based on link and switches such as Link Latency (LL), Link Delivery Ratio (LDR), and Critical Switch Frequency Score (CWfscore). These metrics encourage path selection with better link quality and a minimal number of critical switches. The experimental results show that the proposed scheme reduced path stretch by 37%, path setup latency by 73% thereby improving throughput by 55.73%, and packet delivery ratio by 12.5% compared to the baseline work.  
SDN; OpenFlow; restoration; path selection; link quality; critical switch +
Footnote †: journal: _Applied Sciences_  
_Article_  
Route Path Selection Optimization Scheme Based Link Quality Estimation and Critical Switch Awareness for Software Defined Networks  
[PERSON]  
1 Department of Computer Science, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia; [EMAIL_ADDRESS]  
2 Department of Mathematics and Computer Science, Sule Lamido University, Kafin Hausa P.M.B 048, Nigeria 2  
3 Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia; [EMAIL_ADDRESS]  
4 Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia; [EMAIL_ADDRESS]  
5 College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia; [EMAIL_ADDRESS]  
6
# Mdpj _sciences_
## 3 Review of the DMRN Protocol
### Protocol Description
#### 3.4.1 Phase A: The Identification Phase from UE to HN  
(1)-(2) The Mobile Equipment (ME) begins the procedure by generating a KEM key pair, consisting of a public key \(pk_{U}\) and a private key \(sk_{U}\). Next, it generates a secret key \(K_{S1}\) and a corresponding ciphertext \(c_{1}\) by applying the encapsulation algorithm Encaps() to the input public key \(pk_{HN}\). Then, the ME conceals the SUPI, \(pk_{U}\), and \(ID_{SN}\) through symmetric encryption utilizing the generated secret key \(K_{S1}\). These encrypted data are denoted as \(c_{2}\). The ME computes \(c_{3}\) using the HMAC algorithm with the secret key \(K_{S1}\) and ciphertext \(c_{2}\). Finally, the UE transmits {SUCI, \(ID_{HN}\)} to the SN, where SUCI = \((c_{1},c_{2},c_{3})\)
# Application of Mathematical Models for Blood Flow in Aorta and Right Coronary Artery
## 3 Results
### Flow Behavior in the Aorta  
We focused on the visualization of streamlines, as shown in Figure 3. In particular, it provides a detailed and intuitive representation of the velocity field intensity distribution along the yz-plane. The highest velocities are observed at the center of the epiaortic branches, with a maximum velocity of 2.7 m/s reached within the right carotid artery, originating from the brachiocephalic artery. Significant velocities are also present in the lower outlet branches, particularly in the renal and mesenteric arteries.  
Figure 3 illustrates the streamlines of the flow considering the Newtonian model, confirming that the flow remains laminar under these physiological conditions.  
A cutting plane (shown in Figure 4a) was created near the inlet section, which also intersects the descending aorta just downstream of the aortic arch, and in these sections, velocity was observed (Figure 4b).  
As shown in Figure 4b, the maximum velocity is observed at the center of the vessel, where the velocity gradient is minimal, resulting in low shear rate values. Conversely, near the vessel walls, the velocity rapidly decreases to zero, creating steep velocity gradients that correspond to higher shear rate values. This distribution is consistent with the typical laminar flow profile, where shear stress is predominantly concentrated in the peripheral regions of the lumen.  
Observations are similar in the epiaortic branches, as shown in Figure 5, still consi  
Figure 4: Aorta - Newtonian model: (**a**) Cut plane, inlet. (**b**) Velocity magnitude.  
Figure 3: Aorta - Newtonian model: streamlines.  
The same procedure was then followed for the other five models as well. In Figure 6, for example, we can see the velocities reached within the epiaortic vessels for each model. Compared to the Newtonian model, the shear-thinning models underestimate the maximum velocity, while the viscoelastic models overestimate it. The exact opposite occurs for the shear rate.  
All the post-processing values were derived from the software: for example, the average speeds (on the input and output surfaces), the flow rates, etc., which are reported  
Figure 5: Aorta - Newtonian model: (**a**) Cut plane, epiaortic branches. (**b**) Velocity magnitude.  
Figure 6: Aorta models - epiaortic branches.  
in Table 1. Then, following the algorithm described in Section 2.5, the relative error was calculated for each model. Relative errors are reported in Table 2.
# NN-Based 8 FSK Demodulator for the Covert Channel
## 5 Simulations Results
### Hard Demodulation (Without Channel Coder)  
Figure 15 shows the performance comparison of the 8 FSK correlator-based and NN demodulators (output a1a vs output b1a, Figure 10). The advantage of the 8 FSK demodulator, which is obvious, is perfect demodulation in the absence of channel estimation gain error. The NN-based demodulator has BER \(\
eq\) 0, even for \(e_{abs}\) = 0. The superiority of the NN over the correlator-based detector appears for \(e_{abs}\) > 1%. Imperfect SIC severely degrades FSK demodulator, with the growth \(e_{abs}\), whereas NN considerably mitigates its effect. It can be assumed that channel coding can improve BER. The impact of channel coding is presented in the next section.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Signal** & **Modulation** & **Bit Rate** & **Variance** & **Signal** \\ \hline cover & 16 QAM & 9600 bps & \(\sigma_{1}^{2}=1\) & cover \\ covert & 8 FSK & 600 bps & \(\sigma_{2}^{2}=\ 0.01\sigma_{1}^{2}\) & covert \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 14: Encoder for rate \(\text{R}=\frac{1}{2}\), and constraint length \(\text{K}=7\) convolutional code.
# Isolation and Characterization of a Novel Cyanophage Encoding Multiple Auxiliary Metabolic Genes
## 2 Materials and Methods
### Specificity Test  
The experimental cyanobacteria strains used in the specificity test included 11 marine cyanobacteria strains (_Prochlorococcus marinus_ NATL2A, _Synechococcus_ CBW1002, CBW1004, CBW1005, CBW1006, CBW1107, CBW1108, WH7803, WH8102, WH8108, XM05) and six freshwater cyanobacteria strains (_Chroococcus_ FACHB-193, _Microystis aeruginosa_, _M.fos-aquue_, _M. ichthyolable_, _Synechococcus_ PCC 7942, _S. elongatus_). About 10 \(\upmu\)L purified phages (filtered through 0.22 \(\upmu\)m membrane and the concentration higher than 10\({}^{6}\) PFU mL\({}^{-1}\)) were added into the exponentially growing cultures (200 \(\upmu\)L each well) of cyanobacteria strains in 96-well microtiter plates in triplicate, and then all plates were incubated under the same conditions as described above. The cultures were continuously monitored for a week and compared with the controls visually.
# Energy-Efficient Check-and-Spray Geocast Routing Protocol for Opportunistic Networks
## 6 Simulation Results Using Real Data Mobility Traces  
We have used the INFOCOM 2006 dataset [14], which is made of real traces created with 98 nodes of bluetooth sightings by a group of users carrying small devices (iMotes) for a number of days.  
The number of hosts and TTL are fixed and the buffer size is increased from 5 to 40 Mb. The impact of this variation on the message delivery ratio, average latency, hop count, and overhead ratio, are studied. The results are given in Figures 11-14 respectively.  
In Figure 11, it is observed that the delivery ratio increases when the buffer size is increased. It is also observed that in terms of delivery ratio performance, EECSG was about 15.76 % better than CGOPP and 21.78% better than GSAF. This is attributed to the fact that when the buffer size is increased, more messages are stored in node, which results in a higher chance of message delivery.  
are dropped during buffer management causing the average latency to increase. In terms of latency performance, it is observed that EECSG ws about 4.91% better than CGOPP and 11.35% better than GSAF.  
In Figure 13, it is observed that the hop count decreases as the buffer size is increased. This is due to the fact that, the more space the buffer size has, the more time the messages can reside in a relay node and in this case, the hop count decreases. In terms of hop count performance, it is observed that EECSG was about 21.34% better than CGOPP and 36.81% better than GSAF.  
In Figure 14, it is observed that as the buffer size is increased, the overhead ratio decreases, and this decrease is more pronounced for EECSG compared to CGOPP and GSAF. This is due to the intrinsic Check-and-Spray mechanism used in EECSG to control the flooding of the messages within the geocast area. In terms of overhead ratio performance, it is observed that EECSG is about 12.15% better than CGOPP and 10.59% better than GSAF. This is attributed to the fact that the greater the buffer size is, the more the messages get stored in it, which leads to less loss of messages in the network, thereby, the overhead is reduced.  
Figure 12: Average latency vs. buffer size using real data mobility traces.  
Figure 13: Hop count vs. buffer size using real data mobility traces.  
In Figure 15, it is observed that the delivery ratio increases as the TTL is increased. In terms of delivery ratio performance, it is observed that EECSG iswas about 8.88% better than CGOPP and 20.45% better than GSAF. This is attributed to the fact that in the EECSG design, a node gets more time to find a suitable relay node to forward the message. Similar results are observed for the average latency, hop count, and average ratio metrics as depicted in Figures 16-18.  
Figure 14: Overhead ratio vs. buffer size using real data mobility traces.  
Figure 15: Delivery ratio vs. TTL using real data mobility traces.  
Figure 16: Average latency vs. TTL using real data mobility traces.  
Figure 17: Hop count vs. TTL using real data mobility traces.