esa-sceva/satcom-chunk-collection huggingface dataset:
# Trajectories and Resource Management of Flying Base Stations for C-V2X
## 2 Related Work
Throughput maximization under delay constraints is instead addressed by the routing algorithm proposed in [33]. In [34], the authors assume drones deployed over a highway and acting as (IEEE 802.11p based) road side units (RSUs) and evaluate the density of UAVs required in order to make vehicles delivering their data respecting a delay constraint with a given probability. A broader view about using UAVs in vehicular scenarios is provided in [35; 36]. In [35], various possible applications are discussed, including flying accident report agents, flying RSUs, flying speed cameras, flying police eyes, and flying dynamic traffic signals. In addition, some simulations are reported assuming a number of UAVs statically deployed as RSUs to improve the coverage of hazardous areas where accidents occur with some probability. A list of issues and advantages deriving from the use of drones in the vehicular scenario is instead provided in [36]. The same paper shows some simulation results in a highway scenario, quantifying the improvement in terms of throughput and delay derived from the deployment of UAVs, without reference to a specific use case. Testbeds with few devices have been also implemented in [37; 38]. In [37], experiments with two UAVs and three vehicles forming a platoon are conducted, where the drones are exploited to monitor the surrounding territory where the convoy is moving. In that case, IEEE 802.11a and ZigBee are adopted for data and control messages, respectively. Preliminary results demonstrate the feasibility to use drones to cooperate with vehicles. Drones acting as relays for vehicle-to-vehicle (V2V) exchanges are tested in [38], in a hilly area where the LOS is often impaired. Adopting low cost communication devices operating at 5 GHz with omnidirectional antennas, a distance higher than 2.5 km is demonstrated. In the majority of cases, IEEE 802.11p or another WiFi-related technology is assumed and in none of the mentioned works the cellular standards are explicitly addressed. Furthermore, most results refer to generic applications and the focus is on routing or coverage aspects. Finally, only in a few cases the movements and trajectory of UAVs are investigated. Differently, here we consider a system where an aerial and terrestrial component cooperate to provide the desired quality of service of the extended sensing application, by taking into account all network related aspects. Then, we propose both a dynamic trajectory constructed in real-time and a joint resource allocation between the aerial and terrestrial network.
## 5 Conclusions  
The present study highlighted the evaluation, mainly through local measurement and CFD analysis, of the thermal environment in a residential environment where hybrid desiccant cooling is performed under outdoor conditions, which represent typical summer weather in Korea. Thermal comfort was evaluated based on the following indicators: standard ASHRAE comfort zone, PMV, and EDT. Furthermore, the effect of the discharge angle was evaluated by simulating the airflow change according to the discharge angle using CFD. The main conclusions are as follows:  
1. When judged on the basis of the comfort zone of ASHRAE, the residential space where hybrid desiccant cooling takes place enters the comfort zone in the typical summer weather of Korea;
2. Direct sunlight may cause an increase in the globe temperature, which can exhibit a distribution that is more than 2 \({}^{\circ}\)C higher than the dry-bulb temperature even for a shaded area. Therefore, when considering globe temperature, it is necessary to evaluate the thermal comfort via an index, i.e., PMV;
3. In the dining room located in the path of the outlet of the hybrid desiccant air conditioner, the PMV enters the comfort zone, whereas it slightly exceeds 1 in the rest of the space;
4. In contrast, although the EDT in the dining room is lower than \(-\)1.7, it still falls within the comfort zone in the rest of the space. Considering this, it is necessary to find an airflow distribution that can secure the PMV and the EDT evenly;
5. Adjusting the discharge angle of the hybrid desiccant air conditioner upward seemingly makes the PMV and EDT more uniform in the cooling space. When both indicators are considered at the same time, the optimum discharge angle is obtained at 40\({}^{\circ}\).  
Figure 12: Changes in thermal comfort according to cooling air supply angle: (**a**) PMV; (**b**) ADPI.  
**Author Contributions:** [PERSON] supervised the research, conducted the measurements, analyzed the data, and wrote the paper. [PERSON] performed the CFD simulations and evaluated thermal comfort indices. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by Korea Electric Power Corporation. (grant number: R18 XA06-36).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research on Real-Time Monitoring and Performance Optimization of Suspension System in Maglev Train
## 1 Introduction
Strongly motivated by the above discussion, this article establishes a real-time performance monitoring and performance optimization framework for suspension system of maglev train to achieve performance optimization and higher control performance, and provides a data-driven solution to its implementation. The main contributions of this article are:  
1. A framework for performance monitoring and performance optimization of the suspension system in maglev train is proposed. The framework consists of a nominal controller, a residual generator and a dynamic compensator. The nominal controller is used to stabilize the system and achieve tracking performance. The residual generator realizes the performance monitoring of the system and the dynamic compensator is used to realize the performance compensation and recovery;
2. The observer-based residual generator is identified offline based on the data-driven method. The offline identification method does not need to know the accurate model of the system. During the operation of the system, the residual signal generated by the residual generator is monitored to distinguish disturbances and degradations of the suspension system, which provides strong support for performance evaluation and performance optimization;
3. A data-driven performance optimization algorithm for the suspension system is designed. The algorithm can optimize the control performance of the system online when the system performance is unsatisfactory or even degraded;
4. The validity of the proposed framework and algorithm is verified on a single suspension experimental platform. The structure of this article is as follows. Section 2 introduces the preliminary knowledge about the residual generator based on data-driven method, and proposes the structure of real-time monitoring and performance optimization. Section 3 establishes the model of the suspension system and designs the nominal controller. Section 4 introduces the realization form of the residual generator and proposes the strategy of control performance evaluation. Section 5 proposes the parameters optimization method of the dynamic compensator. Section 6 verifies the proposed method through the single suspension system. Section 7 summarizes the full article.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 6 Analysis and Validation of Statistical Model for AMQP IoT Attack Detection
### Detecting the DoS Attack
#### 6.2.1 Detection Results for Normal and Malicious Datasets in DoS Attacks  
Figure 10 illustrates the classification results of network traces into three categories: normal, malicious, and RST packets associated with a DoS attack. A custom script was developed to identify the specific attributes unique to malicious and RST packets, while all other traces were categorized as normal. The results show the model's ability to accurately distinguish between these categories, with the following distribution:  
* A total of 27.28% of the traces were identified as normal traffic.
* A total of 71.50% of the traces were classified as malicious traffic.
* A total of 1.22% of the traces were recognized as RST DoS packets.  
These results demonstrate the effectiveness of the script in identifying and classifying attack-specific traffic in the dataset.
## 3 Results
### Parameter Study Results  
Before attempting eigenfrequency assignment, the accuracy of the approximation of the eigenfrequencies provided by the Taylor expansion must be assessed. The expansion point \(\left(\mathbf{q}_{0},\mathbf{y}_{0}\right)\) has been chosen as \(\mathbf{q}_{0}=\left[\begin{array}{cc}1.817\,\mathrm{rad}&5.312\,\mathrm{ rad}\end{array}\right]^{T}\), which corresponds to the dashed lines, approximately in the middle of the trajectory in Figure 3, and \(\mathbf{y}_{0}=\left[\begin{array}{cc}0.125\,\mathrm{kg}&0.125\,\mathrm{ kg}\end{array}\right]^{T}\), which is halfway between the upper and lower bounds of Equation (15).  
The 4 th order Taylor polynomial (\(p=4\)) of the lowest eigenfrequency (\(i=1\)), i.e., \(\lambda_{1}^{4}(\mathbf{q},\mathbf{y})\), and the partially linearized polynomial, i.e., \(\hat{\lambda}_{1}^{4}(\mathbf{q},\mathbf{y})\), are compared with the actual eigenfrequency of the multibody system. For clarity of representation, the approximations are compared in two ways. In Figure 3, variable \(\mathbf{q}\) varies in the domain \([3\pi/8,7\pi/8]\times[3\pi/2,2\pi]\), while \(\mathbf{y}\) is fixed at the value \(\left[\begin{array}{cc}0&0\end{array}\right]^{T}\) (no point masses are added at the joints). If the comparison is restricted to the points of the trajectory, represented in the figure by the green line, the maximum relative error is 0.09% for \(\lambda_{1}^{4}\) and 1.64% for \(\lambda_{1}^{4}\).  
In Figure 3, variable \(\mathbf{q}\) is fixed at the value \(\mathbf{q}_{0}\) (same position as the expansion point), while \(\mathbf{y}\) varies in the domain \([0,0.250]\times[0,0.250]\). The maximum relative error of \(\lambda_{1}^{4}\) is 0.05%, while \(\hat{\lambda}_{1}^{4}\) has a maximum relative error of 1.34% in the considered domain. The assessment of the relative error demonstrates that the partially-linearized polynomial \(\hat{\lambda}_{1}^{4}\) can approximate the considered eigenfrequency with sufficient accuracy. In fact, the presence of small model uncertainties affects, but does not compromise, the attainment of the desired eigenfrequencies.  
Figure 3: _Cont._  
It must be noted that the modification of the system also affects the eigenfrequencies higher than the first. The change in such frequencies can be assessed from Figure 5, in which the frequency of the second, third and fourth mode are shown.
## 2 Depth-Bistatic Bottom Reverberation Model
For example, supposing that the source position is \(\overrightarrow{r_{t}}=[r_{b}\sin\phi_{t},r_{b}\cos\phi_{t},z_{s}]\), the receiver position is \(\overrightarrow{r_{r}}=[0,0,z_{r}]\), and the scatterer position is \(\overrightarrow{r_{s}}=[R\sin\phi,R\cos\phi,z_{b}]\); \(\overrightarrow{n_{r,i}}\) and \(\overrightarrow{n_{t,i}}\) are defined as \(\overrightarrow{n_{r,i}}=(\overrightarrow{r_{r}}-\overrightarrow{r_{s}})/ \left|\overrightarrow{r_{r}}-\overrightarrow{r_{s}}\right|\) and \(\overrightarrow{n_{t,i}}=(\overrightarrow{r_{t}}-\overrightarrow{r_{s}})/ \left|\overrightarrow{r_{t}}-\overrightarrow{r_{s}}\right|\), respectively. If the Doppler angular frequency within a bottom cell is stationary during the pulse duration, the Doppler-shifted impulse response can easily be calculated with Equation (2) as  
\[h_{d,nm}^{i}(t)=h_{nm}^{i}(\alpha t), \tag{3}\]  
where:  
\[\alpha=\frac{c_{r}+\overrightarrow{V_{r,i}}\cdot\overrightarrow{n_{r,i}}}{c _{t}-\overrightarrow{V_{t,i}}\cdot\overrightarrow{n_{t,i}}}. \tag{4}\]  
Equation (3) is the Doppler-shifted impulse response at the 2D cell of the bottom bathymetry for a moving depth-bistatic array. However, it turns out that a direct use of Equation (3) does not work well for efficient reverberation modeling. This is because the Doppler angular frequency is truly stationary in a bottom cell with a small range step of \(\Delta R\). To solve this problem, we apply an interpolation method for Doppler modeling. First, the values of \(\alpha\) in all bottom cells along the range in an azimuthal slice are  
Figure 1: Schematic of the \(N\times 2D\) bottom reverberation modeling of the depth-bistatic geometry. calculated using Equation (4). Then, the linear interpolation is used to generate new samples between the original values of \(\alpha\) using the sampling frequency of the reverberation signal. With this procedure, the \(\alpha\) within a bottom cell becomes time varying, which increases the accuracy of Equation (3) even for a large range step. The total reverberation signal is defined according to the intensity of the source pulse \(I_{0}(t)\) as:  
\[I(t)=I_{0}(t)*\sum_{i}\sum_{n}\sum_{m}h^{i}_{d,mm}(t). \tag{5}\]  
Here, the summation index \(i\) indicates the number of the bottom cell, and the indexes of n and m are the number of incident and scattered eigenrays at the \(i\)-th bottom cell, respectively.
## 4 Discussion  
In the sudden wind surge scenario, compared to the original protection settings, the optimized settings are increased by 10%, effectively preventing misoperation caused by short-circuit currents exceeding the original thresholds due to sudden wind power surges. In the gradual changing wind scenario, compared to the original protection settings, the optimized settings exhibit a 0.5% adjustment, tailored to gradual variation scenarios, thereby minimizing disruption to the original protection logic. The computational examples above demonstrate that DNSGA-II-A employs uniformly distributed random perturbations to broaden the search space and rapidly adapt to severe fluctuations, preventing misoperation, while DNSGA-II-B utilizes Gaussian mutation to refine settings precisely, balancing protection performance and stability. The synergistic interplay between these two strategies ensures that renewable-integrated distribution networks achieve dynamic optimization across varying fluctuation intensities, significantly enhancing the robustness of the protection system.
## 4 Analytical Results
### One-Stage Clustering as a Comparison Target  
To evaluate the two-stage clustering, we used one-stage clustering for comparison purposes. Figures 3 and 4 present an overview of the one-stage clustering method for IoT traffic analysis. First, the IoT traffic data are divided into data pieces per unit of time. Feature values are extracted from the divided data in each gateway router following the same steps as the two-stage clustering explained in Section 3. Second, unlike the proposed method with two-stage clustering, the feature values of all the divided data pieces are sent to the cloud server without clustering at the gateway routers. These values are clustered simultaneously on the cloud server. The clusters generated in this one-stage clustering are treated as unit traffic patterns, which is similar to the clusters extracted in the second clustering using the proposed method. Zero traffic, which is a type of unit traffic pattern without any communication, is also used in one-stage clustering. The unit traffic information, which consists of the feature values of the original divided data pieces and their assigned unit traffic numbers, is sent to the gateway routers. In this paper, we implemented a one-stage clustering method on a single computer as a conventional method for comparison.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Device Category**} & \multicolumn{1}{c}{**IoT Device**} \\ \hline \multirow{4}{*}{Smart gadgets} & HP Printer \\  & Triby Speaker \\  & Pix-Star Photo Frame \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 3: Mechanism of one-stage clustering. IoT traffic data are divided into data pieces per unit of time. From the divided data, some feature values are extracted. Then, the feature values of all thedivided data pieces are sent to the cloud server without clustering at the gateway routers and clustered simultaneously on the cloud server. The clusters generated in the one-stage clustering are treated as unit traffic patterns.
# Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique
## 2 Materials and Methods
### Data Collection and Processing
#### 2.2.3 Data Collection of Leaf Area Index  
Leaf area index (LAI) was measured by using the LAI 2200 plant canopy analyzer (LI-COR Inc., Lincoln, NE, USA) at the jointing, flagging, anthesis, and grain filling stages of winter wheat. Firstly, the LAI 2200 were performed backlit measurement in an open area to ensure that all LAI data obtained were accurate and valid. Then, the LAI 2200 was placed at the sample point parallel to the ridge and perpendicular to the ridge, respectively, with the probe placed close to the wheat plant. For wheat LAI measurement, three sample points were randomly selected for each plot. LAI values were measured for 4 times, and the arithmetic mean value was taken as the LAI of the sample point, and finally the average of the LAI values at the three sample points was calculated as the LAI value of the plot. There were significant differences in the statistical characteristics of LAI between different treatments at different growth stages (Tables A3-A6).
# Building Fire Evacuation: An IoT-Aided Perspective in the 5G Era
## 3 Review of Current Developments
### Review of the State-of-the-Art Smart Building Fire Evacuation Systems
With beacon sensors to recognize the location and movement of every occupant inside the building during the evacuation, this system successfully enables evacuees to bypass the congestion and dangerous area in advance. Moreover, [PERSON] and [PERSON] [49] developed a system that optimizes the evacuation route for evacuees and rescued people. By monitoring the number of people using the stairs, strategies for stair evacuation can be adjusted. However, the research on intelligent route planning focuses more on individual path-finding during the evacuation. It is rarely considered how the smart system can coordinate the total building evacuation containing a large number of occupants. Finally, many researchers also developed various smart devices regarding evacuation guidance to make the evacuation signal and planned evacuation route successfully delivered to building occupants. One possible approach is to send signals and instructions to users' mobile phones via a pre-installed application [29; 31; 34; 47], frequently applying Wi-Fi and cellular networks for communication. However, these networks are infrastructure-based networks that are prone to fail in critical environments. Therefore, a MANET (Mobile Ad Hoc Network)-based evacuation guidance system was developed by [PERSON] and [PERSON] [50]. The MANET is a wireless network without a centralized server or network infrastructure, so it can be used to provide more reliable evacuation guidance to occupants. The same sort of communication approach was also used by [PERSON] et al. [28]. In addition, by considering that not everyone is necessarily equipped with a smartphone or has downloaded the application, which means that some people may not be able to receive the guidance, another approach for evacuation guidance using \"smart exit signs\" was proposed by several authors [28; 40; 51; 52; 53]. These \"smart exit signs\" are usually equipped with dynamic signage, which can dynamically change their directions to inform evacuees of the directions towards the shortest safe evacuation paths.
# Improved Anomaly Detection by Using the Attention-Based Isolation Forest
## 5 Numerical Experiments  
The proposed attention-based iForest was studied by using synthetic and real data and was compared with the original iForest. A brief introduction to these datasets is given in Table 1, where \(d\) is the number of features and \(n_{norm}\) and \(n_{anom}\) are numbers of normal and anomalous instances, respectively.  
Different values for hyperparameters, including the threshold \(\tau\), the number of trees in the forest, the contamination parameter \(\epsilon\), and the kernel parameter \(\omega\), were tested, and those leading to the best results were chosen. In particular, the hyperparameter \(\epsilon\) in ABIForest took values of 0, 0.25, 0.5, 0.75, and 1; the hyperparameter \(\gamma\) changed from 0.5 to 0.7; the hyperparameter \(\omega\) took values of 0.1, 10, 20, 30, and 40. The F1-score was used as a measure of the anomaly detection accuracy. It was used because the number of anomalous instances was significantly less than the number of normal instances, i.e., because the normal and anomalous instances were imbalanced. To evaluate the F1-score, a cross-validation with 100 repetitions was performed, where in each run, 66.7% of the data were randomly selected for training (\(2n/3\)) and 33.3% were randomly selected for testing (\(n/3\)). The numerical results are presented in the tables, and the best results are shown in bold.
## 4 Experimental Results and Discussion
### Results
#### 4.4.5. Invisibility-Robustness Controllability  
Invisibility-robustness controllability, which can control the complementary relationship between these two characteristics, is required according to the user's need in using a watermarking system, a more robust scheme by sacrificing invisibility or a more invisible scheme by sacrificing robustness. In our scheme, the strength scaling factor is used to control this complementary relationship. When invisibility is more critical than robustness, \(s\) is set to a lower value. However, when higher robustness is needed, \(s\) is set to a higher value.  
Controllability, invisibility, and robustness for the various attacks with increasing \(s\) from 0.5 to 2 are measured, and the results are shown in Figure 7. This figure includes the invisibility change in (a) and robustness changes for all considered kinds and strengths of the attacks and their strengths in from (b) to (m). As shown in Figure 6a, the WM invisibility decreases as \(s\) increases, as expected. For each of the attacks, the robustness increases consistently as \(s\) increases while maintaining the performance tendency for the change in the attack's strength. This shows that the proposed scheme has the firm capability to control the trade-off relationship between invisibility and robustness.  
Figure 6: BER values resulting from the experiments for the various sizes of host image (the legend in (**a**) is applied to all the figures): (**a**) Gaussian Filtering, (**b**) average Filtering, (**c**) median Filtering, (**d**) salt and pepper noise addition, (**e**) Gaussian noise addition, (**f**) sharpening, (**g**) JPEG, (**h**) rotation, (**i**) crop, (**j**) cropout, (**k**) dropout.