esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Prediction of Power Consumption and COP  
The data prediction results using the proposed refrigeration cycle measurement method were evaluated. Equations (14)-(16) enthalpy change in the refrigerant resulting from the proposed measurement method using the power consumption calculated using Equation (10). The power consumption was evaluated using the data collected by the watt-hour meter. Equations (14)-(16) were used to assess the accuracy of the COP calculated using Equation (11) using the enthalpy change in the refrigerant resulting from the proposed measurement method. The power consumption measured by the watt-hour meter of the air-source heat pump system and the power consumption prediction result were compared and analyzed, and the results are shown in Figure 10. The prediction result revealed an _MBE_ of 0.15% and _Cv(RMSE)_ of 8.97%. The R\({}^{2}\) of the predicted for the measured power consumption is 0.87, and it is confirmed that most of the predicted results are within \(\pm\) standard deviation of the measured values.  
The COP calculated using the existing refrigeration cycle measurement method, and the COP prediction results are compared and analyzed, as shown in Figure 11. The _MBE_ and _Cv(RMSE)_ were 0.04% and 7.14%, respectively. The R\({}^{2}\) of the performance prediction results was 0.98, confirming that all predicted results were within \(\pm\) standard deviation of the measured values. The measurement uncertainties of power consumption and COP are \(\pm\)4.06% and \(\pm\)2.24%, respectively, and the prediction results of power consumption and COP are shown in Table 6.  
Figure 10: Comparisons of the predicted and actual power consumption measurements for the system.
## 3 Results
### Extracted Ion Data for Carpet Substrates
#### 3.2.1 Carpet #1 Stain Master Pet Protect (Nylon 6,6)
The characteristic indane profile of gasoline was clearly observed for carpet #1 burned with gasoline (Figure 6b), indicating that the indane profile can be reliably used to identify gasoline in the fire debris of carpet #1. It is concluded that the tested SPME method, together with the extracted ion profile pattern recognition, can successfully and reliably determine the presence or absence of gasoline in fire debris with Stain Master Pet Protect (nylon 6,6) substrate material. Figure 5: Extracted polynuclear aromatics profiles of (**a**) carpet #1, burned without ILs; (**b**) carpet #1 burned with gasoline; and (**c**) neat gasoline. Figure 6: Extracted indane profiles of (**a**) carpet #1 burned without ILs; (**b**) carpet #1 burned with gasoline; and (**c**) neat gasoline. Potential Interference with the Identification of Kerosene  
A comparison of the TICs of carpet #1 burned without ILs (Figure 7a) with that of neat kerosene (Figure 7c) shows that there might be interference with the identification of kerosene, especially in the area between 11.00 to 15.00 min. The library search suggested the presence of alkenes or cycloalkanes, which could indicate the presence of an IL. However, the extracted cycloalkane profiles do not show the same patterns as neat kerosene. The TIC of carpet burned with kerosene (Figure 7b) is less complex compared to neat kerosene (Figure 7c), probably due to the evaporation of n-alkanes and cycloalkanes; however, the extracted ion profiles have common peak patterns with that of neat kerosene, and kerosene target compounds were identified as Nonane (RT = 6.73 min, RSI = 917), decane (RT = 9.88 min, RSI = 937), undecane (RT = 12.95 min, RSI = 900), and dodecane (RT = 15.91 min, RSI = 928), which are not present in carpet burned without ILs. A comparison of the TICs of the SPME extracts of carpet #1 burned without ILs and in the presence of gasoline and kerosene, indicates that the chromatograms are easily distinguishable. The main pyrolysis product of nylon 6,6 polymer was identified as styrene (RT = 6.47, RSI = 956), which has been reported to be a pyrolysis product of nylon at temperatures between 400 \({}^{\circ}\)C and 700 \({}^{\circ}\)C in the literature [15]. Nylon fibers are known to be resistant to fire and it was not easy to burn the fiber in the absence of ILs and the fiber burned without a flame. In the presence of ILs, the fiber burned to produce a flaming fire, which explains the reduction in the relative abundance of styrene in the chromatograms obtained in the presence of ILs. The generated heat of the flame can result in degradation and evaporation of styrene. Figure 7: Total ion chromatograms (_m/z_ 50–500) of (**a**) carpet #1, burned without ILs; (**b**) carpet #1 burned with kerosene; and (**c**) neat kerosene.
## 1 Introduction
By tuning the electrochemical protocol, a variety of nanocomposite materials have been prepared for applications such as PtNPs-graphene for methanol oxidation in fuel cell applications [44]; MnO\({}_{2}\) nanowall-graphene, Ni(OH)\({}_{2}\)NPs-graphene, MoS\({}_{2}\)-graphene for supercapacitors [45; 46]. They can be used as BiNPs-graphene for the electrochemical detection of lead metal in pore water samples [47]; CuNPs-graphene and AgNPs-graphene for hydrogen peroxide detection [48]; ZnONPs-graphene for the photocatalytic removal of organic pollutants [49]; and AuNPs-graphene for the electroanalytical detection of dopamine, ascorbic and uric acids [28; 29]. Moreover, metallic nickel/nickel oxide nanoparticle materials have attracted tremendous attention due to their high electrochemical stability and electrocatalytic activity while remaining low-cost materials. The nickel/graphene nanocomposites were also found to be high-performance materials with which to fabricate sensitive electrochemical sensors such as those for the detection of glucose [50; 51; 52], methanol [53], and ethanol [24]. However, these methods were mainly focused on two-step electrochemical synthesis protocols of graphene-nickel nanomaterials with a structure of two separated layers. This motivates the search for alternative routes, enabling a more straightforward approach to the fabrication of graphene-nickel nanocomposites. This work reports on an alternative method enabling the one-step preparation of nickel nanoparticles/reduced graphene oxide nanocomposite materials for the rapid determination of SY. In fact, one investigation has already proposed such an approach in order to prepare graphene-nickel nanocomposites with a structure of alternating layers of metal oxide nanoparticles and ERGO sheets; those developments were used towards the electroanalytical detection of 2,4,6-trinitrophenol [54]. By using a simple electrochemical deposition process onto glassy carbon substrate, a graphene oxide/nickel nanocomposite-modified carbon surface can be obtained and applied as an electrochemical, ready-to-use platform for sensing applications. The procedure described herein is particularly robust. The morphology of the obtained nanocomposite films was analysed using FE-SEM microscopy and its electrochemical performances were carefully characterised using Cyclic voltammetry (CV), and Differential Pulse Voltammetry (DPV) techniques. The result demonstrated that such GCE/ERGO-NiNP sensors could be used for successful determination of SY in complex media such as soft drink samples. The prepared ERGO/NiNP nanocomposite appears as a promising platform to enable the fabrication of sensitive, fast, and accurate electrochemical sensors for analytical applications.
## 2 Materials and Methods
### Untargeted Metabolomics Analysis Using UHPLC-PDA-HESI-Orbitrap-MS/MS  
For the untargeted metabolomic analysis, a DionexTM UltiMateTM 3000 UHPLC system (Thermo Fisher ScientificR), based in Waltham, MA, USA) was employed. The system comprised a C18 column with dimensions of 150 \(\times\) 4.6 mm, and a particle size of 5 \(\upmu\)m (Restek Corporation, Bellefonte, PA, USA).  
The system was equipped with a quaternary Series RS pump and a DionexTM UlitMateTM 3000 Series TCC-3000 RS column compartment. Furthermore, the system incorporated an UltiMateTM 3000 Series WPS-3000 RS autosampler manufactured by Thermo Fisher ScientificR and a high-speed Photodiode Array Detector (PDA) for rapid separations. The PDA was configured to record detection wavelengths at 254, 280, 320, and 440 nm; for peak characterization, the PDA recorded data in the wavelength range of 200 to 800 nm. To separate the compounds, a gradient elution method was employed involving 1% formic aqueous solution (A) and acetonitrile (B). The flow rate was 1.0 mL min\({}^{-1}\), and the injection volume was set at 10 \(\upmu\)L. The gradient program represented as [time (min), %B] was as follows: (0.00, 5), (5.00, 5), (10.00, 30), (15.00, 30), (20.00, 70), (25.00, 70), and (35.00, 5). Before each injection, a column equilibration period of 12 min was employed.  
System control was accomplished using Thermo ScientificTM ChromeleonTM 7.2 Chromatography Data System (CDS) software. This software, developed by Thermo Fisher ScientificR in collaboration with Dionex Softron GmbH (a division of Thermo Fisher ScientificR, Olching-Geiselbullach, Germany) facilitated the system control.  
The UHPLC system was connected to a Thermo ScientificTM Q ExactiveTM Focus Hybrid Quadrupole-OrbitrapTM mass spectrometer, which used the Heated Electrospray Ionization Source II (HESI II) from Thermo Fisher ScientificR. Nitrogen gas, with a purity exceeding 99.999%, was used as both the collision and damping gas, and it was generated by a Genius NM32 LA nitrogen generator manufactured by Peak ScientificR. The Orbitrap mass spectrometer underwent weekly mass calibrations in both positive and negative ion modes. Caffeine and N-butylamine from Sigma-AldrichR were employed as calibration standards for positive ions, while buspirone hydrochloride, sodium dodecyl sulfate, and taurocholic acid sodium salt were utilized for calibrating the mass spectrometer. The calibration compounds were dissolved in a mixture of acetic acid, acetonitrile, water, and methanol from Merck Darmstadt, Hesse, Germany, which were infused using a Chemyx Fusion 100 syringe pump (Chemyx Inc, Stafford, TX, USA). The UHPLC system was controlled, and data processing was performed using XcaliburTM 2.3 and TraceFinderTM 3.2 software, which were both provided by Thermo Fisher ScientificR. The mass spectrometer, Q ExactiveTM 2.0 SP2, was also operated using software developed by Thermo Fisher ScientificR.
## 2 The Home Network as The Universal Center of Reference
### Home CA with a Certificate Issued by a Trusted Third Party  
In this section, we describe a complementary alternative to the self-certified approach above. Our idea is again based on an observation of social links between entities. Whatever a future network will look like, it is reasonable to assume that Internet Service Providers will continue to play the role they have today. In particular, they will provide the infrastructure that users need to connect their home networks to the Internet. Our idea is to extend the contractual relationship: when a user signs a contract with an ISP, he lets the ISP sign the certificate of his Home CA. The ISP can also provide the user with (root) certificates of other ISPs which this ISP knows to be authentic.  
The result is a structure as depicted in Figure 3. Entity certificates still will be issued by the Home CA, but certificate chains between home networks can now also exist because of (possibly contractual) relationships between ISPs. It is of course always possible to have ISPs not only cross-sign each other, but introduce a common CA further up the certificate chain that issues certificates to ISPs.  
There are drawbacks to this certification solution, however. The resulting PKI structure is likely going to be a multitude of ISP root certificates that are stored on the home network's server, with the user having little control over the introduction of new certificates. In essence, the ISP would decide which other ISPs are trusted to be authentic and provide authentic identity information about their users. This removes control from the user. In effect, it resembles the collection of root certificates that we find in today's Web browsers. The problems this incurs have recently been highlighted in [8], and the dangers of commercially driven PKI structures have been addressed in [9, 10]. Furthermore, this PKI does not reflect the social structures of the people in a home and, thus, it is at first unclear how to derive access rights from the authenticated identities.  
We thus see the benefit of this solution in providing a first indication of another's entity authenticity. In contrast to the previous solution, access rights to the services of a home network would have to be set considerably more restrictive by default. Users would be recommended to update  
Figure 2: Home Networks use self-certified Home Certificates. After a key exchange—the exchange of home certificates between ‘befriended’ home networks—foreign home networks and entities within can authenticate to each other.  
access rights only afterwards, after they have been able to determine the other entity's identity and trustworthiness themselves.
## 2 NOMA System Model
### Successive Interference Cancellation (SIC)  
In SIC, the user with the highest SNR subtracts their message from the super-positioned signal. The same process is followed by the second strongest user. This process is repeated iteratively until the desired signal is decoded [8; 28; 34]. The SIC method works in an iterative way, imposing lower hardware complexity at the receiver in comparison to conventional multi-user detection schemes. It has been verified that the SIC algorithm can be used to accomplish the capacity regions of the BCs [2]. It has been suggested that the network capacity can be substantially improved with the aid of efficient interference management; hence, SIC is regarded as a promising IC technique in wireless networks [3]. SIC is implemented at the receiver based on the UE channel conditions. Each UE sends pilot data to the BS; pilots are used in the channel estimation (CE) and SNR prediction processes [2; 35]. The decoding process is summarized in Figure 3.
## 4 Empirical Propagation Models
### Fitted ITU-R (FITU-R) Model  
This model, originated as an enhancement of the ITU-R model, introduced the concept of differentiating prediction model equations based on the seasonality experienced by vegetation [44]. In this study, the equation with the presence of leaves is considered. The equations for calculating excess loss added to the free space loss are as follows:  
\[Att_{FITU}=\begin{cases}0.37\times f^{-0.18}\times d^{0.59},se&semfollas\\ 0.39\times f^{-0.39}\times d^{0.25},se&comfollas\end{cases} \tag{11}\]  
* \(Att_{FITU}\) is the excess attenuation according to the FITU-R model (dB);
* \(d\) is the distance between Tx and Rx (m);
* \(f\) is the system's operating frequency (MHz).
# An Improved Energy-Efficient Routing Protocol for Wireless Sensor Networks
###### Abstract  
Cluster-based hierarchical routing protocols play an essential role in decreasing the energy consumption of wireless sensor networks (WSNs). A low-energy adaptive clustering hierarchy (LEACH) has been proposed as an application-specific protocol architecture for WSNs. However, without considering the distribution of the cluster heads (CHs) in the rotation basis, the LEACH protocol will increase the energy consumption of the network. To improve the energy efficiency of the WSN, we propose a novel modified routing protocol in this paper. The newly proposed improved energy-efficient LEACH (IEE-LEACH) protocol considers the residual node energy and the average energy of the networks. To achieve satisfactory performance in terms of reducing the sensor energy consumption, the proposed IEE-LEACH accounts for the numbers of the optimal CHs and prohibits the nodes that are closer to the base station (BS) to join in the cluster formation. Furthermore, the proposed IEE-LEACH uses a new threshold for electing CHs among the sensor nodes, and employs single hop, multi-hop, and hybrid communications to further improve the energy efficiency of the networks. The simulation results demonstrate that, compared with some existing routing protocols, the proposed protocol substantially reduces the energy consumption of WSNs.  
wireless sensor networks; routing protocol; energy efficiency; network lifetime +
Footnote †: journal: Sensors
## 1 Introduction
(2019) presented a nearshore water level forecasting model enhanced with attention mechanisms. Their findings suggested that the model can accurately predict water levels at coastal stations up to 48 h in advance, outperforming existing methods. However, this method lacks the consideration of the interaction between the water level changes in coastal stations and various influencing factors, and the combination of these factors helps to improve the prediction effect of the model. Messaoud et al. (2019) applied an attention mechanism and low-visibility enhancement network to enhance the detection of the marine environment. The results show that this method significantly enhances the target detection ability in low visibility scenes. However, this method lacks the key step of the ablation experiment, which may affect the stability and effectiveness of the model. In summary, this paper proposed a deep learning model for marine weather data reconstruction by combining deep super-resolution reconstruction technology and attention mechanism, which can effectively improve the spatial detail expression ability of low-resolution observation data. Different from the traditional interpolation method or single-image reconstruction model, this method introduces the attention mechanism of weather structure perception, which not only strengthens the reconstruction accuracy of key areas but also provides higher quality environmental input for the ship route planning model, thus laying a data foundation for subsequent intelligent navigation decision-making. The modeling process is shown in Figure 1.  
As shown in Figure 1, the low-resolution wave height and wind speed data are first used as input, and the corresponding low-resolution labels are used to guide the training of the machine learning model. Then, the super-resolution reconstruction algorithm is constructed by using the trained model to reconstruct the low-resolution data so as to generate high-resolution weather data. On this basis, the quality of the reconstruction results is verified by a variety of evaluation methods. Finally, combined with the evaluation results and the generated high-resolution weather data, the ship route is optimized. See below for details.
# Heuristics and Learning Models for Dubins MinMax Traveling Salesman Problem
## 5 Heuristics for the MD-GmTSP
### Initial Solution: Construction Phase
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Construction**} & \multirow{2}{*}{**Move**} & \multirow{2}{*}{**Insertion**} & \multirow{2}{*}{**Local Search**} & \multirow{2}{*}{**Average**} & \multirow{2}{*}{**Max Improvement**} & \multirow{2}{*}{**Standard**} & \multirow{2}{*}{**Computation**} \\  & & & & & & & & \\ \multirow{2}{*}{**Heuristic**} & \multirow{2}{*}{**Move**} & \multirow{2}{*}{**Insertion**} & \multirow{2}{*}{**Local Search**} & & **(\%)** & **(\%)** & **(\%)** & **Time (s)** \\ \hline Cheapest & Shake & Greedy & 2-opt & 3.201 & 21.627 & 6.225 & 900.413 \\ Cheapest & Shake & Greedy & 3-opt & 2.750 & 19.456 & 5.648 & 909.045 \\ Cheapest & Shake & Random & 2-opt & 1.987 & 17.399 & 5.309 & 899.961 \\ Cheapest & Shake & Random & 3-opt & 2.091 & 17.082 & 5.244 & 907.608 \\ Cheapest & Shake & Greedy & 2-opt & 2.802 & 15.755 & 5.392 & 900.571 \\ Cheapest & Shake\({}_{md}\) & Greedy & 3-opt & 2.423 & 13.508 & 4.017 & 916.210 \\ Cheapest & Shake\({}_{md}\) & Random & 2-opt & 1.823 & 14.484 & 4.640 & 900.069 \\ Cheapest & Shake\({}_{md}\) & Random & 3-opt & 1.938 & 14.484 & 3.921 & 919.457 \\ Farthest & Shake & Greedy & 2-opt & 2.785 & 24.736 & 7.609 & 1354.735 \\ Farthest & Shake & Greedy & 3-opt & 2.585 & 25.342 & 6.969 & 1381.107 \\ Farthest & Shake & Random & 2-opt & 1.703 & 18.939 & 5.043 & 1359.844 \\ Farthest & Shake & Random & 3-opt & 1.650 & 20.988 & 5.331 & 1371.328 \\ Farthest & Shake\({}_{md}\) & Greedy & 2-opt & 2.526 & 26.589 & 6.931 & 1366.066 \\ Farthest & Shake\({}_{md}\) & Greedy & 3-opt & 1.986 & 26.567 & 6.598 & 1374.865 \\ Farthest & Shake\({}_{md}\) & Random & 2-opt & 1.470 & 21.869 & 5.455 & 1362.314 \\ Farthest & Shake\({}_{md}\) & Random & 3-opt & 1.135 & 18.158 & 4.540 & 1373.770 \\ Nearest & Shake & Greedy & 2-opt & 2.750 & 25.154 & 7.015 & 1355.997 \\ Nearest & Shake & Greedy & 3-opt & 2.550 & 25.025 & 6.781 & 1376.476 \\ Nearest & Shake & Random & 2-opt & 1.745 & 20.988 & 5.415 & 1363.908 \\ Nearest & Shake & Random & 3-opt & 1.896 & 20.988 & 5.600 & 1368.852 \\ Nearest & Shake\({}_{md}\) & Greedy & 2-opt & 2.627 & 26.589 & 6.932 & 1370.339 \\ Nearest & Shake\({}_{md}\) & Greedy & 3-opt & 1.635 & 26.142 & 6.535 & 1375.723 \\ Nearest & Shake\({}_{md}\) & Random & 2-opt & 1.617 & 20.351 & 5.173 & 1359.805 \\ Nearest & Shake\({}_{md}\) & Random & 3-opt & 1.477 & 16.364 & 4.335 & 1378.464 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Influence of different neighborhood structures in the BVNS heuristic on improving the MD-GmTSP solutions with \(
###### Abstract  
The fraction of absorbed photosynthetically active radiation (FPAR) is a key biophysical parameter of terrestrial ecosystems. However, due to a lack of data with adequate spatial resolution and in long enough time series, there have been limitations in exploring the spatiotemporal changes of vegetation and response to climate change. In this study, a 1 km spatial resolution and 8-day period length dataset (\(\mathrm{FPAR}_{\mathrm{ANN}}\)) was developed covering the years 1980 to 2018 and evaluated on spatiotemporal change consistency by validating with Gross Primary Production (GPP) observations from the Chinese Flux Observation and Research Network (ChinaFLUX), and comparison with other FPAR products. \(\mathrm{FPAR}_{\mathrm{ANN}}\) provided a comparable performance in capturing seasonal change observed through GPP, according to the coefficient of determination (\(R^{2}\)): 0.50, 0.51, 0.70 and 0.74 averaged for all sites, forest sites, grassland sites and cropland flux sites, respectively. The new data had more spatial similarity to the MODIS FPAR product (\(\mathrm{FPAR}_{\mathrm{MED15A2}}\)) with a greater \(R^{2}\) (0.77) and a lower RMSE (0.12) than other products. With a newly developed dataset, combined with \(\mathrm{FPAR}_{\mathrm{ANN}}\) (1980-2003) and \(\mathrm{FPAR}_{\mathrm{MED15A2}}\) (2004-2018), an overall increasing trend in FPAR was found for over 81% of the vegetated area of China from 1980 to 2018. FPAR increased more rapidly for over 83.7% of the area in the 2010s, and at a slower pace for over 62.1% of the area in the early 2000s, which was attributed to a decadal shifting of climate change. This study implies the new dataset is useful in quantifying vegetation changes and would be an important data source for future study of the carbon cycle, soil erosion, or evapotranspiration, with great application potential.  
FPAR; artificial neural network; interannual trend; climate change; terrestrial ecosystems 2023 15 3707 10.3390/15/15/3707
# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
## 3 Experimental Setup and Dataset  
This section describes the measurement campaign and the adopted measurement system and setup. In particular, the collected dataset and the procedure for extracting channel measurements are presented.
# Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks
## 3 System Model
### 5G Threats
#### 3.2.1 Attacks against Privacy  
In this category, there are fourteen attacks including MITM, eavesdropping, parallel session, reply attack, impersonation attack, collaborated attack, tracing attack, spoofing, privacy violation, adaptive chosen cipher text attack, chosen-plaintext cipher text, stalking, masquerade, and disclosure attacks [57; 58]. It should be mentioned that the most important attack among them is MITM, when the false BS acts as a real BS [57].