esa-sceva/satcom-chunk-collection huggingface dataset:
# A Swarming Approach to Optimize the One-Hop Delay in Smart Driving Inter-Platoon Communications
## 4 Swarming Approach Description
### Optimal Minimum Contention Window Sizes  
After Section 4.1, we have obtained the minimum average one-hop delay. Section 4.1 can guarantee that the one-hop delays of backbone vehicles get close to the optimization objective \(D_{avg}\), which is set to be 0 for simplicity. However, this does not guarantee that the one-hop delays get close to the minimum average one-hop delay. In Section 4.2, the optimal minimum contention window sizes of backbone vehicles are found, with which the one-hop delays of backbone vehicles could get close to the minimum average one-hop delay.  
In Section 4.2, we aim to determine the optimum minimum contention window sizes of backbone vehicles by comparing how much the one-hop delays of these vehicles are close to the minimum average one-hop delay. As a result, the optimization objective is the minimum average one-hop delay and the objective function is to minimize the variance between the one-hop delays of backbone vehicles and the minimum average one-hop delay. The swarming approach proposed in Section 4.1 is used again to optimize the one-hop delay of each backbone vehicle to get close to the minimum average one-hop delay by adjusting the minimum contention window size. Finally, the outputs of Section 4.2 are the optimal minimum contention window sizes with which the one-hop delay of each backbone vehicle could get close to the minimum average one-hop delay.
## 3 Results  
### Model Assessment and Validation  
Modeling is a representation of reality in order to demonstrate some of its properties. Therefore, there are several types of models, such as stochastic models, optimization models, dynamic simulation models and empirical statistical models, that allow predicting the outcome of a categorical variable using a set of quantitative and/or qualitative predictors.  
In this case, a statistical model based on a stepwise multiple regression analysis was developed for the models' creation in order to estimate different water quality parameters using the RStudio open source software, using the following equation:  
\[\text{Y}=\text{b}_{0}+\text{b}_{1}\times\text{X}_{1}+\text{b}_{2}\times\text {X}_{2}+\ldots+\text{b}_{\text{k}}\times\text{X}_{\text{k}}\]  
Y is the predicted variable with regression coefficients b\({}_{1}\) to k and Y-intercept b\({}_{0}\) when the values for the predictor variables are X\({}_{1}\) to k.  
Firstly, a correlation study was conducted between the different water quality parameters and the satellite image bands in order to select the appropriate bands for the elaboration of the models (Table 4). For Chl-a, bands B5, B6 and B7 showed a strong correlation with this band with correlation coefficients of 0.81, 0.71 and 0.73, respectively. The bands B1, B3 and B4 showed a strong correlation with nitrates with correlation coefficients of 0.73, 0.69, and 0.73, respectively. Dissolved oxygen measurements showed a positive correlation with bands B2 and B3 with correlation coefficients of 0.71 and 0.75, respectively. The bands that were chosen in the first step were later integrated into the equations of the different models (Table 5). The choice of the suitable model was based on the three statistical parameters, namely the compliance index R\({}^{2}\), the root mean square error (RMSE) and \(p\)-value. The priority of choice was given to the models that have the strongest conformity index and the lowest root mean square error, while the threshold of \(p\)-value was fixed at a value of 0.05. Table 5 represents the different developed models along with their equations and the different statistical parameters for each. For dissolved oxygen, the chosen model is the one that combines band 2 and band 3, with a compliance index R\({}^{2}\) of 0.56, a root mean square error of about 0.65 mg/L and a \(p\)-value of about 0.0009. For nitrates, the chosen model is the one that combines band 1, band 3 and band 4, with a compliance index R\({}^{2}\) of 0.62, a root mean square error of about 0.16 mg/L and a \(p\)-value of about 0.0011. Lastly, for Chl-a, the chosen model is the one that groups band 5, band 6 and band 8, with a compliance index R\({}^{2}\) of 0.58, a root mean square error of the order of 0.07 ug/L and a \(p\)-value of the order of 0.0024.  
In order to verify the accuracy of the proposed models, the measured values in the field and the observed values of the different parameters were presented with their equations (Figure 5).  
Figure 4: Flowchart of the methodology used.
## 5 Experimental Results and Analysis
### Description of the Benchmark Datasets
#### 5.2.1 NSL-KDD Dataset  
The NSL-KDD is the most widely used dataset for evaluating intrusion detection performance. The NSL-KDD is derived from the original KDD Cup 99 dataset presented by [PERSON] et al. [47]. The NSD-KDD dataset removes duplicate and redundant records in the KDD Cup 99 dataset and is more suitable for evaluating the performance of intrusion detection system. There are five classes in the NSL-KDD dataset: v Normal, Probe, Denial of Service (DoS), User to Root (U2R), and Remote to Local (R2L) (Table 2).  
The NSL-KDD dataset is unbalanced, with fewer U2R and R2L records, but more Normal and DoS records. We used three datasets in the NSL-KDD dataset to evaluate intrusion detection performance:  
\begin{table}
\begin{tabular}{c p{142.3 pt} p{142.3 pt}} \hline \hline
**Categories** & **Description** & **Examples** \\ \hline Normal Probe & include data with no attack. include attacks in which attackers try to scan the computer networks with the purpose of collecting information and finding out vulnerabilities. & normal ipsweep, nmap, portsweep, satan, saint, mscan \\ \hline DoS & include attacks in which attackers try to prevent legitimate users of a service from using the service & back, land, neptune, pod, smurf, teardrop, apache2, mailbomb, udpstorm, processtable \\ \hline U2R & include attacks in which attackers have local access to the victim target machine and try to get super user privileges. & perl, rootkit, loadmodule, buffer\_overflow, httptunnel, ps, sqlat attack, xterm \\ \hline R2L & include attacks in which attackers do not have a local account and try to gain access by sending packets to the target host over the internet. & ftp\_write, guess\_passwd, multihop, phf, impap, spy, warezclient, warezmaster, xsnoop, xlock, sendmail, worm, snmpgetattack, snmpguess \\ \hline \hline \end{tabular}
\end{table}
Table 2: Categories of the NSL-KDD dataset records.  
KDDTrain+_20 Percent.txt (a 20% subset of the KDDTrain+.txt file), KDDTest+.txt (the full NSL-KDD test set), and KDDTest-21.txt (a subset of the KDDTest+.txt file which has removed records with difficulty level 21). In our experiments, KDDTrain+_20 Percent is used as a training set, and KDDTest+ and KDDTest-21 are used as test sets. Table 3 shows the number of records for each category on the NSL-KDD dataset.
## 3 GNSS Anti-Jamming Techniques
The time-domain notch filter was applied before the spatial filter in the case of multiple interference signals since the time-domain filtering is meant to remove the narrowband interference signals, while the spatial filter is used to mitigate for the wideband interference. A linear antenna was simulated, and the results show that the proposed method could suppress strong narrowband and wideband interference simultaneously as high as 50 dB. The effect of real jammers on GPS and Galileo L1/E1 signals was investigated in [21]. The study involved few survey-grade receivers and a high sensitivity (HS) software-defined receiver. An attenuator was used to control the transmitted jammer signal power, which ranged between 0-90 dB-Hz J/N0. Between constellation comparison demonstrated that both GPS and Galileo signals showed similar jamming resistance trends with the GPS signal loss of lock happened at around 83 dB-Hz versus a J/N0 of about 86 dB-Hz for Galileo signals. For the receivers, a 4 dB increase in C/N0 is experienced in a 12 MHz bandwidth for the HS receiver versus the other receivers due to the fact that only limited portion of the added noise caused by the jammer is allowed into the HS receiver which had a front-end bandwidth less than 12 MHz, whereas the survey receivers had front-end bandwidths equal to or greater than 12 MHz. This paper explores the effect of signal jammers on GPS/GLONASS receivers. Moreover, different types of jamming signals are considered.
# Numerical Investigation on the Principle of Energy Separation in the Vortex Tube
## 4 Conclusions  
Numerical simulations were conducted to explain the principle of energy separation in the vortex tube. The tendency of hot-exit temperature was explained as follows:  
* The axial pressure gradient in the near-axis region was the major contributor to an increase in temperature in the axial direction. Due to narrowing the hot exit area by the throttle valve, the pressure increased in the near-axis region, and the temperature increased in the axial direction due to reversible compression.
* The higher the cold mass fraction, the higher the hot exit temperature was, which was attributed to the fact that, as the pressure in the near-axis region at the hot exit section increased, the return of flow from the hot exit section toward the cold exit section became large.
* The viscous dissipation effect was significant only in the region adjacent to the stagnation point.
* The tendency of the cold exit temperature with respect to the cold mass fraction was explained as follows:When the cold mass fraction was low and the negative pressure zone was too large, a partial backflow of atmospheric air through the cold exit occurred. In that condition, the decrease in cold exit temperature decreased with a decrease in the cold mass fraction.
* When the cold mass fraction was large such that a partial backflow did not occur through the cold exit, the cold exit temperature decreased with an increase in the cold mass fraction. The reason was that the core vortex flow moving toward the cold exit served to transport the hot air in the hot exit region to the cold exit region. It was interesting that, for a high cold mass fraction, the temperature in the near-axis region tended to be higher than that in the periphery region.  
Two-dimensional numerical simulations were enough to explain the principle of temperature separation and were a practical choice for considering many simulations with different cold mass fractions, though they have limitations. Therefore, three-dimensional numerical simulations will be conducted to confirm the principle of temperature separation.  
Conceptualization, B.J.K.; validation, S.Y.P.; formal analysis, S.S.Y.; data curation, S.H.Y.; writing--original draft preparation, S.Y.P.; writing--review and editing, B.J.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Research Foundation of Korea grants funded by the Ministry of Science and ICT (No. NRF-2021M2D2A1A02039565 and NRF-2022R1A4A1030333).  
Not applicable.  
The simulated data presented in this paper are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
## 1 Introduction
In an example scenario with 30 macrocell users and 50 femtocell users within a macrocell, more than 90% of macrocell users achieved higher signal-to-interference and noise ratios (SINRs) by 23.3 dB on average than in a case without using handovers; the entire cohort of femtocell users achieved 2.9 dB higher SINRs. The rest of this paper is organized as follows. Sections 2 and 3 describe the motivation of this work and the system model, respectively. Section 4 presents the HO-FIM architecture, and Section 5 describes the control algorithm. Section 6 evaluates the HO-FIM architecture using detailed simulations, and Section 7 concludes the paper.
# Substrate-Integrated Waveguide Microwave Sensor for Water-in-Diesel Fuel Applications
###### Abstract  
A water-in-diesel microwave sensor based on a substrate integrated waveguide (SIW) microwave applicator is designed and characterized in this study. The interaction between the microwave electromagnetic field and the diesel fuel contaminated with small concentrations of water is obtained via suitable radiating slots placed on the top of an SiW waveguiding structure. The SiW applicator working frequency is chosen by observing the behavior of the complex dielectric permittivity of the fuel-water blend based on a preliminary wide band investigation. The performances of the SiW microwave sensor are evaluated in terms of scattering parameter modulus \(|S_{21}|\) as a function of the water concentration in ppm. The best sensitivity \(\frac{\Delta[S_{21}]}{\Delta\rho}=1.42\) mdB/ppm is obtained at a frequency of \(f=9.76\) GHz, with a coefficient of determination \(R^{2}=0.94\). The sensor is low-cost, low profile and ensures a good sensitivity for constant and real-time monitoring.  
substrate integrated waveguide (SIW); microwave sensor; water-in-fuel monitoring
## 4 Experimental Evaluation
### Experimental Environment and Datasets  
All experiments in this paper were conducted in the same experimental environment, Ubuntu 16.04, with Intelj7-6700 CPU 3.40 GHz and NVIDIA GeForce GTX 1080 Ti GPU. The Gradient Descent algorithm was used to optimize the loss function. The initial learning rate during training was 0.01, and the learning rate adjustment strategy was a cosine annealing algorithm. Two complex background public gesture datasets, EgoHands and TinyHGR, were used for the experimental data, the training and test sets were divided in a 9:1 ratio, and the network input image size was adjusted to 640 \(\times\) 640.  
The EgoHands dataset consisted of 48 videos, each recording a complex first-person interaction between two test subjects using Google Glass in different environments, such as playing cards, blocks, and chess. The TinyHGR dataset captured data from 40 people making 7 gestures, 20 of which were filmed against simple backgrounds and 20 against complex HCI backgrounds (highly cluttered backgrounds with large illumination variations, see Figure 4). Approximately 1400 frames of each gesture were taken consecutively per person, with sizes of 1920 \(\times\) 1080, and the gestures appeared at different locations in the images. The largest proportion of each image in this dataset included the face and body, with the gestures only accounting for about 10% of the overall image size. For this paper, 1000 of these complex background gesture images were selected, and the gesture positions were labeled using Labelimg. Figure 4 shows the sample images from the two datasets.
## 2 Dataset and Experiments
### Experimental Design
#### 2.1.3 Dataset 3: CCTV Tower Parking Lot  
This experiment is based on the PAZ-1 dataset. As shown in Figure 4, the change in the number of vehicles is detected in the area of interest indicated by the red box in the CCTV Tower Parking Lot. The sequence of time-series images spans from 14 February 2023 to 31 August 2023 and contains a total of 12 images.  
The PAZ-1 data are used to complete the change detection of the number of vehicles in the parking lot of the CCTV Tower, and the change curve of the number of vehicles is obtained. This experiment mainly uses RTK (real-time kinematic ) equipment to collect the car's accurate centimeter-level position as truth data when the satellite is passing by. During the satellite transit, RTK equipment is used to accurately record the latitude and longitude coordinates and location map of vehicles in the parking lot. Subsequently, by analyzing the RTK data, a vehicle distribution map is created to present the distribution of vehicles in the parking lot in detail.  
Figure 4: Comparison of optical image and SAR image of the CCTV Tower Parking Lot. (**a**) Optical image from Google Earth. (**b**) SAR image acquired on 31 August 2023 by the PAZ-1 satellite.  
Figure 3: Comparison of optical image and SAR image of the BEIJING-HYUNDAI AUTO Enterprise. (**a**) Optical image from Google Earth. (**b**) SAR image acquired on 25 September 2020 by the Sentinel-1A satellite.
# Efficacy of Fire Protection Techniques on Impact Resistance of Self-Compacting Concrete
## 3 Results and Discussion
### Failure Impact Energy of SCC Specimens
After 90 min of exposure, the surface temperature reached 922 \({}^{\circ}\)C, while the core temperature rose to 756 \({}^{\circ}\)C. At the end of the 120-min exposure, the surface temperature measured 1012 \({}^{\circ}\)C, and the core temperature was 883 \({}^{\circ}\)C. Similarly, for GGBFS/EPA specimens exposed to 60 min of heating, the recorded surface temperature was slightly higher at 920 \({}^{\circ}\)C than the FA/EPA specimen (637 \({}^{\circ}\)C). A similar temperature increase pattern was observed after 120 min of heating, with surface temperatures of 1025 \({}^{\circ}\)C and 899 \({}^{\circ}\)C and core temperatures of 1025 \({}^{\circ}\)C and 899 \({}^{\circ}\)C for GGBFS/EPA and FA/EPA specimens, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Duration of**} & \multicolumn{4}{c}{**Specimen Type**} \\ \cline{2-5}  & **FA** & **FA/EPA** & **FA/CPP** & **FA/EPA/CPP** \\ \hline
30 & 182.7 & 243.7 & 426.4 & 771.7 \\
60 & 121.8 & 203.1 & 304.6 & 609.2 \\
90 & 101.5 & 182.7 & 264.1 & 507.7 \\
120 & 40.6 & 60.9 & 81.2 & 324.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: View of failure impacts energy values (in J) of SCC-FA and coated (EPA/CPP) specimens. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Duration of**} & \multicolumn{4}{c}{**Specimen Type**} \\ \cline{2-5}  & **GGBFS** & **GGBFS/EPA** & **GGBFS/CPP** & **GGBFS/EPA/CPP** \\ \hline
30 & 223.3 & 284.3 & 1279.4 & 2152.7 \\
60 & 182.7 & 203.1 & 913.8 & 1685.6 \\
90 & 162.4 & 182.7 & 792.1 & 1401.3 \\
120 & 142.1 & 162.4 & 243.7 & 893.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: View of failure impacts energy values (in J) of SCC-GGBFS and coated (EPA/CPP) specimens. For specimens protected by the CPP layer, the surface, protective layer, and core temperatures at 60 min of heating were 918 \({}^{\circ}\)C, 492 \({}^{\circ}\)C, and 205 \({}^{\circ}\)C, respectively, for FA/EPA/CPP specimens and 920 \({}^{\circ}\)C, 463 \({}^{\circ}\)C, and 159 \({}^{\circ}\)C, respectively, for GGBFS/EPA/CPP specimens. A similar pattern was observed for the 90-min exposure duration. At 120 min of heating, the temperatures measured for FA/EPA/CPP specimens were 979 \({}^{\circ}\)C, 892 \({}^{\circ}\)C, and 605 \({}^{\circ}\)C for the surface, protective layer, and core, respectively, while the corresponding temperatures for GGBFS/EPA/CPP specimens were 981 \({}^{\circ}\)C, 871 \({}^{\circ}\)C, and 556 \({}^{\circ}\)C. The measurements indicate that as time and temperature increased, the penetration of temperature into the concrete specimens also increased.
## 3 Results and Discussion
### Analysis of Model Optimization Results
#### 3.3.3 Research Innovation and Contribution  
This paper presents a detailed optimization of parameters, including the impervious area ratio, infiltration rate, and soil saturation water content, within the SWMM. Combining multiple optimization methods, including genetic algorithms, differential evolution, and Bayesian optimization, enhances the model's accuracy in simulating actual rainfall runoff. Furthermore, regionalized parameter adjustments can be made according to the unique topographic characteristics of the Wujin District. In this study, low-impact development (LID) measures, including rain gardens and permeable paving, were innovatively integrated with the SWMM to evaluate the impact of different LID measures on the regional drainage system. The simulation and comparison of different LID measures significantly reduced peak flows, increasing rainwater utilization and groundwater recharge.  
The findings of this study demonstrate that the frequency and duration of localized flooding in the Wujin District can be effectively reduced by optimizing the drainage system and implementing low-impact development (LID) measures. The results demonstrate a significant reduction in the duration of localized flooding. This research significantly contributes to reducing the frequency and duration of localized flooding by providing a specific and feasible solution for the urban drainage system in this area. Furthermore, it offers a valuable reference for similar areas with complex topography and dense populations. The combination of an optimized SWMM and LID measures resulted in an increase in the efficiency of the drainage system from the original level to 85.2%, based on the performance metrics calculated during the data analysis phase of this study. This improvement also led to a significant reduction in the system failure rate and economic losses.  
Furthermore, the implementation of LID measures enhanced groundwater recharge and increased the utilization rate of rainwater, markedly enhancing the area's ecological benefits. Beyond addressing urban waterlogging issues, this research contributes to advancing the ecological environment and effectively utilizing resources. It provides both a theoretical foundation and practical insights for future urban sustainable development and flood prevention planning.
# Secure Indoor Localization Based on Extracting Trusted Fingerprint
## 4 Secure WiFi Fingerprint Localization
### Offline Update Phase
#### 4.2.1 Fingerprint Database Updates  
In order to solve the problem of environment changes, we need to update the fingerprint database. We placed some reference anchor points for fingerprint detection in the localization area. The reference anchor nodes are a simple WiFi terminal, which consists of a power supply module, a WiFi transmitter and a receiver module. The function is to periodically receive the RSSI of the surrounding AP the RSSI and location information package sent to the localization server.  
Within the communication range of AP, the reference anchor node can obtain the RSSI of APs, record the average of RSSI and the standard deviation of RSSI. Obtain the measurement matrix \(\varphi=\left[s_{1},s_{2},\cdots,s_{L}\right]\) of reference anchor nodes, and then use the RKNN algorithm to estimate the coordinates. The RKNN algorithm is used to calculate the coordinates \(\left(x_{i},y_{i}\right)\) of the reference anchor nodes. Because the location of the reference anchor node is known, we can calculate the localization error of the reference anchor node. The error is calculated as follows:  
\[\delta_{i}=\sqrt{\left(x_{0}-x_{i}\right)^{2}+\left(y_{0}-y_{i}\right)^{2}}. \tag{5}\]  
where \(\left(x_{0},y_{0}\right)\) denotes the actual position of the reference anchor node and \(\left(x_{i},y_{i}\right)\) denotes the estimated position. We can decide whether to update the fingerprint database according to the error value \(\delta_{i}\). If the localization error exceeds threshold \(T\), which is \(1\) m, we conducted fingerprinting tests and then screened fingerprints. Then, the coordinates \(\left(x_{i},y_{i}\right)\) of the reference anchor nodes are calculated again; if the positioning error is reduced, the screened fingerprints and the votes of APs are updated in the database. If the positioning error does not decrease after screening, all the fingerprint information is updated. We obtain the newest RSSI matrix \(\Psi^{{}^{\prime}}\).  
\[\Psi^{{}^{\prime}}=\begin{cases}\varphi_{1,s_{1}}^{{}^{\prime}}\ \ \ \varphi_{1,s_{2}}^{{}^{\prime}}\ \ \ \cdots\ \ \ \varphi_{1,s_{k}}^{{}^{\prime}}\\ \varphi_{2,s_{1}}^{{}^{\prime}}\ \ \ \varphi_{