esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data
#### 2.1.2 Satellite FPAR Data  
The FPAR latest Collection-6 product of MODIS (MCD15A2H C006) has been available since 2002 [9]. It is provided in 8-day time steps at a sinusoidal projection of 500 m spatial resolution. Its algorithm includes a main Look-up-Table, generated using a 3D radiative-transfer model, and a backup method based on the relationships of FPAR and NDVI [9]. Accuracy assessment of the MODIS Collection 5 using ground-based measurements over Europe shows that the product performs well despite a slight overestimation [10]. Similar to Collection 5, Collection 6 also was overestimated, though its input data were improved in quality, especially land cover and reflectance products [11]. The data were first resampled to 1 km spatial resolution using nearest-neighbor interpolation. Then they were processed to remove noise from time series data by using the adaptive Savitzky-Golay (SG) method in TIMESAT software [38]. The data from 2002 to 2015 have been applied as training output for the BP-ANN algorithm in this study.  
The NDVI3g data product from GIMMS from 1981 to 2015 was used as input data, which has a temporal resolution of 15 days and a spatial resolution of 1/12\({}^{\circ}\) and was derived from the NOAA-AVHRR sensor [12; 39]. In this study, the NDVI data biweekly averaged over the first three years were used as the missing data for 1980. The NDVI3g data were found to need further correction because they were significantly inconsistent with the MODIS NDVI on the metrics of average, seasonal change and maximum values in central Europe from 2000 to 2013 [13], but the two datasets had good consistency in trend on the global scale [14]. In this study, all datasets were resampled to a spatial resolution of 1 km using the method of nearest-neighbor interpolation and to an 8-day temporal resolution by a spline interpolation function to match with the FPARCD15A2 on spatial and temporal resolution and can reserve the original information to a great extent.  
FPARBNU was retrieved from the GLASS leaf area index (LAI) produced by the Center for Global Change Data Processing and Analysis of Beijing Normal University [30; 40]. The data were used to compare spatial consistency with the new FPAR data along with GIMMS3g and FPAR MCD15A2 on day 225 in 2005, 2010 and 2015.
# Evaluation of Novel Preformed Particle Gel System for Conformance Control in Mature Oil Reservoirs
## 2 Results and Discussion
### Swelling Behaviour and Rheology
#### 2.3.2 Effect of pH  
Five solutions with different pH values ranging from 3 to 11 were prepared to evaluate the effect of pH on the swelling kinetics of the CMC/PAMBA PPG. The effect of pH on the swelling kinetics was almost negligible, and the swelling ratios of all five solutions were very close during the measurement (Figure 7). The storage modulus values were also close, as shown in Figure 8. It is well known that the carboxylic groups in the material are affected by the pH, both functional groups COOH and COO\({}^{-}\) coexist simultaneously in the CMC/PAMBA structure, and their relative proportions vary depending on the pH of the solution. At pH = pKa (with pKa being 4.3 for CMC), the quantities of COOH and COO\({}^{-}\) are equal. Below pH \(\leq\) pKa, carboxyl groups (-COOH) predominate, while above pH \(\geq\) pKa, carboxylate groups (COO\({}^{-}\)) become more prominent. In the latter state, the chemical structure of the grafted material is expected to be more expanded due to the repulsion force between anionic charges along the material. This expansion would theoretically lead to an enhancement in the swelling ratio and a parallel decrease in the strength. However, our experimental observations deviate from this expectation. Despite the anticipated repulsion forces and increased availability of carboxylate groups at higher pH, the swelling ratio did not exhibit the anticipated enhancement.
## 2 Literature Study
They often utilize techniques like dynamic histogram equalization, modified histogram equalization with fuzzy-based optimization, and piecewise linear histogram equalization, with a focus on improving visual quality rather than just technical measures like PSNR. Additionally, some methods emphasize local contrast preservation through adaptive histogram equalization and image processing techniques such as color space changes, image inversion, dehazing, and saturation adjustment. In these methods, an optimization challenge is presented, considering both global and specific enhancements, in order to create robust image enhancement techniques, ultimately benefiting medical image interpretation and diagnosis.
## 4 Discussion
The experimental results confirm the model's effectiveness and highlight its exceptional noise-reduction capabilities. 3. The proposed gradual de-noising method demonstrated a superior capacity for noise reduction compared to existing techniques, as evidenced by detailed qualitative and quantitative analysis. In terms of quantitative evaluation, the method outperforms traditional de-noising techniques across various metrics, highlighting its ability to effectively remove noise while preserving the integrity of the reconstructed signal. When applied to simulated data, the gradual de-noising method successfully handled complex signal structures, demonstrating excellent resistance to various types of noise. When applied to real-world data collected from GB-SAR, it was able to remove noise while preserving the essential characteristics of the signal, proving its high accuracy and reliability in practical applications. The application of the proposed method to simulated data and real-world data collected from GB-SAR showed a high degree of accuracy and reliability, demonstrating its feasibility in applications involving the de-noising of nonlinear time-series signals. This makes the method a promising solution for effective de-noising in complex nonlinear signal environments.
# The New Paradigm of Deepfake Detection at the Text Level
## 2 Related Works
### AI Tools Employed by Deepfake Text
There are many AI detection tools. These include Originality.ai, ZeroGPT, Turnitin, GPT-2 Output Detector (GPT-2 ODD), Copyleaks, GPTZero, Content at Scale, QuillBot, Plagiarism Detector Score (Turnitin), AI Content Detector Device Using Machine Learning Technique, etc. Originality.ai and ZeroGPT excelled in detecting AI-generated articles, with 100% and 96% accuracy, respectively [44]. Turnitin achieved 94% accuracy for ChatGPT-generated articles. Paraphrased texts were detected with 30% accuracy. The detectors Copyleaks, TurnItIn, and Originality detect deepfakes generated with GPT-3.5, but they also identify human-written works as deepfakes [45]. However, limitations exist. For instance, GPT-4 has demonstrated the ability to generate abstracts that closely resemble original content without being detected by Turnitin. However, AI detectors can still identify AI-generated abstracts [46]. The advanced capabilities of GPT-4 in producing logically coherent text raise ethical concerns. This has prompted further research into the improvement of detection technologies. An empirical analysis revealed that ChatGPT-3.5 performs better than ChatGPT-4 in identifying human-written text. At the same time, it faces difficulties with AI-generated text. This research developed detectors that focused on identifying human-written text. They were not focused on addressing the issue of the detection of AI-generated text. The deteriorating performance of GPT-4 over time and its sensitivity to noise and dataset artifacts further complicate the detection process, necessitating ongoing research and development [47]. There are numerous issues relating to summaries generated by AI that raise questions regarding integrity. AI detectors can identify AI-generated summaries [46]. An empirical analysis [47] showed that ChatGPT 3.5 performs better than GPT-4 in identifying human-written text. The ongoing research on and development of AI models will lead to the inability to identify deepfakes generated by AI. For these reasons, designing an AI model capable of deciding whether a text is associated with a certain person is necessary. Therefore, this approach does not focus on determining whether the text is generated by an AI tool but focuses on determining whether the text is associated with a certain person. This is possible through the analysis of writing characteristics.
## 3 System Model
### Antenna Model  
We assume planar antenna arrays at both the BS and the UE. Similarly to [15; 16], we utilize the cone model with the beamwidths corresponding to the HPBW of the antenna radiation pattern. The BS transmit and UE receive antenna gains are denoted by \(G_{A}\) and \(G_{U}\), respectively. Using [17] the mean antenna gain over HPBW is calculated as  
\[G=\frac{1}{\theta_{3 db}^{+}-\theta_{3 db}^{-}}\int_{\theta_{3 db}^{-}}^{\theta_{ 3 db}^{+}}\frac{\sin(N.\pi\cos(\theta)/2)}{\sin(\pi\cos(\theta)/2)}d\theta,\]  
where \(N\) is the number of antenna elements at the side of interest.  
The HPBW of the array, \(\alpha\), can be determined as \(\alpha=2|\theta_{m}-\theta_{3 db}^{\pm}|\), where \(\theta_{m}\) is the array maximum that can be computed as \(\theta_{m}=\arccos(-1/\pi)\), \(\theta_{3 db}^{\pm}\) are the upper and lower 3-dB points estimated as \(\theta_{3 db}^{\pm}=\arccos[-\pm 2.782/(J\pi)]\). For practical calculations we employ HPBW approximation given by \(102/N\), where \(N\) is the number of antenna arrays in the appropriate plane [17; 18]. Similarly, the linear gain can be approximated by the number of elements [17].  
Note that in practice, the antenna parameters may drastically deviate from the theoretically predicted one as they heavily depend on the utilized technology. The research of the antenna system for 5G mmWave NR has been fairly productive so far. Particularly, the use of metamaterials for constructing efficient antennas is considered in [19; 20]. Microstrip antennas have been proposed in [21; 22]. The on-chip implementation with an extension of the application to terahertz band is considered in [23]. However, the approach proposed in the rest of the paper can be applied to any antenna as long as HPBW and gains can be explicitly calculated.
## 4 Summary and Conclusions
Improved results are to be expected with better adapted networks operating with the full set of GNSS constellations (GPS, GLONASS, Galileo, and BeiDou). Conceptualization, P.M., J.C. and P.M.A.M. ; methodology, P.M.; software, P.M. and R.F. ; validation, P.M., J.C. and P.M.A.M. ; formal analysis, P.M., J.C. and P.M.A.M. ; investigation, P.M.; resources, P.M. and R.F. ; data curation, P.M.; writing--original draft preparation, P.M.; writing--review and editing, J.C. and P.M.A.M. ; visualization, J.C. and P.M.A.M. ; supervision, P.M.; project administration, P.M.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was funded by the Portuguese Fundacao para a Ciencia e a Tecnologia (FCT) IP/MCTES through national funds (PIDDAC)-UIDB/50019/2020 ([[https://doi.org/10.54499/UIDB/50019/2020](https://doi.org/10.54499/UIDB/50019/2020)]([https://doi.org/10.54499/UIDB/50019/2020](https://doi.org/10.54499/UIDB/50019/2020))), UIDP/50019/2020 ([[https://doi.org/10.54499/UIDP/50019/2020](https://doi.org/10.54499/UIDP/50019/2020)]([https://doi.org/10.54499/UIDP/50019/2020](https://doi.org/10.54499/UIDP/50019/2020))), LA/P/0068/2020 ([[https://doi.org/10.54499/LA/P/0068/2020](https://doi.org/10.54499/LA/P/0068/2020)]([https://doi.org/10.54499/LA/P/0068/2020](https://doi.org/10.54499/LA/P/0068/2020))), and project 2022.15714. MIT. **Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request. **Acknowledgments:** We thank the ECMWF for making the ERA5 hourly data available at Climate Data Store. We thank CDAAC for providing the GNSS-RO data level 2. The CORS/NOAA for providing the GNSS observations (rinex format). We also thank ESA for the Copernicus Open Access Hub, which provides the Sentinel-1 imagens acquisitions and makes the SNAP software (version 9) used to process the interferograms freely available. **Conflicts of Interest:** The authors declare no conflicts of interest.
# Development and Validation of a Capacitor-Current Circuit Model for Evaporation-Induced Electricity
## 3 Materials and Methods
### Materials and Characterizations  
The wood-based nanogenerator was fabricated using beech wood pieces (5 cm \(\times\) 5 cm \(\times\) 1 cm), selected for their naturally aligned microchannels, which facilitate efficient water evaporation flow and optimize electricity output. The wood pieces were prepared following the procedure outlined in our previous work [20]. Briefly, they were washed in acetone for 15 min to remove contaminants. After drying in a fume hood for 3 h, these wood pieces were ready for citric acid (CA) modification. The solution is a mixture of 200 mL CA and 10 mL triethylamine as a catalyst, which promotes esterification. After soaking for 2 h, the wood pieces were transferred to an oven and heated at 120 \({}^{\circ}\)C for 4 h. All chemicals were purchased from Sigma-Aldrich and used as received.  
Scanning electron microscopy (SEM) FEI Inspect F50, was used to characterize the microstructure of wood samples. Chemical modifications from the CA treatment were confirmed by Fourier-transform infrared spectroscopy (FTIR), Thermos Fisher Nicolet iS10 FTIR spectrometer. The zeta potential of the CA-treated wood sample was measured by a Malvern Zetasizer Nano ZSE. Wetting properties of the wood samples were assessed by contact angle measurements using a Data Physics OCA 35 Goniometer with 4 \(\upmu\)L water droplets. The initial contact angle was captured by recording the moment of the droplet making contact with the wood surface after 5 s.
# A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research
## 2 Environment Perception System
### Ultrasonic Sensor  
As its name indicates, ultrasonic sensors use sonic waves, in the range of 20 kHz to 40 kHz, generated by a magnetoresistive membrane, to measure the distance to an object. Its principle of operation is based on the measurement of the time of flight (ToF) of the sonic wave from when it is emitted until the echo is received:  
\[d=\frac{c}{2}\times\text{ToF} \tag{1}\]  
The \(c\) velocity of the wave is in meters per second and ToF is time of flight in seconds.  
These sensors are usually used in industrial environments for the measurement of height in storage of all types of raw materials. In vehicles, they are used in parking systems or as short distance measurement sensors at low speeds (Figure 3a,b). These low-cost sensors produce good results when  
Figure 2: An overview of different spectra used for perception systems in autonomous vehicles.  
measuring distances with any material, independent of its colour, in dusty environments or in adverse weather conditions (humidity or rain). Disadvantages of these sensors include a tendency to produce false positives by bouncing, and a blind zone (blanking) in the measurements, located between the sender element of the sensor and the minimum range.
# A Review of Air Pollution Monitoring Technology for Ports
## 2 Detection Methods for Air Pollutants in Ports
### Detection Methods for Gaseous Pollutants  
After investigation, the current common analysis methods for various types of air pollutants are shown in Table 4. The methods can be divided into manual analysis methods and automatic analysis methods according to the detection method, detection conditions, and the continuity of their detection data. According to the summary of the emission list in Table 3, the gaseous air pollutants in the port mainly include SO\({}_{2}\), NO\({}_{\chi}\), CO, HC, and VOC\({}_{\rm S}\). According to the relevant ambient air quality standards (GB 3095--2012), this subsection will focus on the detection methods of SO\({}_{2}\), NO\({}_{\chi}\), and CO, three air pollutants that have a greater impact on the ambient air.
## 5 Experimental Results
### BER versus CFO  
In this experiment, CFO was varied to measure its impact on the quality of communication. To measure it, the receiver's center frequency was detuned from the transmitter's carrier frequency, counting BER for each tested CFO value. Absolute CFO in Hz is calculated by multiplying FM deviation by relative CFO. The results of this experiment are depicted in Figure 14. These results show that the system is resistant to CFO of at least half of the FM deviation. The low-pass filter may impact the shape of the resulting curves, and a broader filter could improve the system's resistance to CFO at the cost of reduced SNR.  
Figure 12: BER versus transmit power in dBm with different bit detectors in a single model scenario. SD—strobe-based detector; ED—energy-based detector; DEV—FM deviation. Receiver gain is 10 dB.  
Figure 13: Measured power spectra at the output of Adalm Pluto. DEV—FM deviation. The transmitted power of Adalm Pluto is \(-26\) dBm, and the receiver gain is 10 dB.
# Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks
## 5 Evaluation
### Evaluation of Two-receiver Topology  
We changed the topology as shown in Figure 14, which consists of three senders, four BMV2 switches, and two receivers. Sender 1 and Sender 2 send traffic to Receiver 1; Sender 3 sends traffic to Receiver 2. The link bandwidth was 10 Mbps, and the traffic size was 1 MB. The parameter settings of BFCC, DCQCN, P4 QCN, and TCN were the same as in Section 5.1. High throughput and low latency are two important goals in DCN. Therefore, we evaluated the four schemes mainly in terms of two performance metrics: throughput and FCT.  
We compare the throughput of traffic sent to Receiver 1 and Receiver 2, as shown in Figure 15. For the throughput of the traffic destined for Receiver 1, the throughput of the BFCC scheme is above 300 kbps and more stable. The throughput of DCQCN is less than 300 kbps, which maintains the throughput at 200 kbps. The throughput of the P4 QCN scheme is around 250 kbps. The TCN scheme is also not as good as the BFCC. As for the traffic sent to Receiver 2, the BFCC scheme best represents the stability of the traffic, with throughput maintained at 200 kbps. Overall, the congestion control scheme proposed in this paper is based on per-flow management. Thus, the flows on both links maintain high throughput without affecting each other.  
Figure 14: Two-receiver topology.  
Figure 15: FCT in the two-receiver topology.  
The link bandwidth was changed from 10 Mbps to 50 Mbps, 100 Mbps, and 200 Mbps, and the FCT of the three flows was observed, as shown in Figure 16. The BFCC scheme always maintained the lowest FCT at different link bandwidths. Specifically, the FCT of BFCC was reduced by about 44.7% compared to DCQCN. The FCT of BFCC was reduced by about 39.8% compared to P4 QCN. Meanwhile, the FCT of BFCC was reduced by 44.1% compared to the TCN scheme. Essentially, BFCC ensures high throughput and low latency.
# A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires
## 2 Modelling and Simulation of Flashover Phenomenon
### Flashover Simulation
FDS produced higher temperatures of up to 100 \({}^{\circ}\)C, a fact that was expected since fire suppression was included in the experimental results. Another interesting approach [46] was provided by [PERSON] et al., a fire scene investigation--at aged-care facilities--was carried out using CFD techniques. The fire field model FDS 5.5.3 was adopted in this study and the simulations were compared with full-scale experimental data. For this research the flashover criterion was \(\geq\)600 \({}^{\circ}\)C, taken at around 10 mm below the ceiling and the approximate minimum heat release rate of 2175 kW by [PERSON]'s criterion [22]. Six simulation cases were performed to investigate the possible ignition location that could result in the final state of the fire scene. The CFD results concurred with the experiments and the fire's development in the compartment room was described. Fire spreading and flashover were successfully modelled by FDS. Nevertheless, the author highlights that the problem of kinetic scarce data in the FDS pyrolysis model cannot fully represent the fire's complete behaviour. In light of this, an alternative method [47] was proposed by [PERSON] et al. to determine temperatures in the pre-flashover phase confined fires. After comparing the results obtained with the simulation software FDS the authors of this prediction model concluded that it is possible to predict temperatures in considerably less time compared to computer simulation software in confined fires in the pre-flashover phase. Table 5 shows a summary of the references cited in this section related to fire modelling and simulation. The reader can see at a glance if the reference is related to modelling or simulation, as well as what are the most important techniques and software used for the simulations and deployment of TIC.
## 4 Conclusions  
In this paper, an irreversible SFLC model with variable temperature HR is established by applying FTT theory, the expressions of \(P\) and \(\eta\) are derived, and the influences of \(U_{T}\), \(\tau\), \(C_{L}/C_{H}\), \(\eta_{e}\), and \(C_{wf1}/C_{H}\) on \(P\) and \(\eta\) performances are analyzed. The results show that:  
1. When \(U_{H}\) and \(U_{L}\) are constants, \(P\)-\(\eta\) is a certain \"point\", and with the increases in \(\tau\), \(U_{H}\), \(U_{L}\), and \(\eta_{e}\), \(P\) and \(\eta\) increase. When \(u_{L}\) can be optimized, \(P\) and \(\eta\) versus \(u_{L}\) characteristics are parabolic-like ones, there are \(u_{L_{r}(opt)}\) and \(u_{L_{t}(opt)}\) which makes the cycle reach \(P_{\max}\) and \(\eta_{\max}\).
2. With the increase of \(C_{wf1}/C_{H}\), \(P_{\max}\)-\(C_{wf1}/C_{H}\) show a parabolic-like change, there is an \(\left(C_{wf1}/C_{H}\right)_{opt}\), which makes the cycle reach \(\left(P_{\max}\right)_{\max}\). With the increases in \(C_{L}/C_{H}\), \(U_{T}\), and \(\eta_{e}\), \(\left(P_{\max}\right)_{\max}\) and \(\left(C_{wf1}/C_{H}\right)_{opt}\) increase. With the increases in \(\tau\), \(\left(P_{\max}\right)_{\max}\) increases, and \(\left(C_{wf1}/C_{H}\right)_{opt}\) is unchanged.
3. Internal irreversibility and variable temperature HR are two general properties of practical cycles. It is necessary to study their influences on the cycle performance. FTT is a powerful theoretical tool for thermodynamic cycles with those properties.  
Conceptualization, R.W. and L.C.; data curation, Y.G.; funding acquisition, L.C.; methodology, R.W., Y.G., L.C., and H.F.; software, R.W., Y.G., and H.F.; supervision, L.C.; validation, R.W. and H.F.; writing--original draft preparation, R.W. and Y.G.; writing--reviewing and Editing, L.C. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by The National Natural Science Foundation of China (Project No. 51779262) and The Graduate Innovative Fund of Wuhan Institute of Technology (Project Nos. CX2020044 and CX2020047).  
Not applicable.  
The authors wish to thank the reviewers for their careful, unbiased and constructive suggestions, which led to this revised manuscript.  
The authors declare no conflict of interest.