esa-sceva/satcom-chunk-collection huggingface dataset:
# The IoT/IoE Integrated Security & Safety System of Pompeii Archeological Park
## 8 The Video Surveillance and Security Systems
In addition to routine patrol missions, UAVs can be programmed for automated surveillance routes or manually piloted for targeted investigations, such as monitoring sensitive zones, responding to specific security alerts, or inspecting building for signs of damage or intrusion. Their rapid deployment ability ensures that security and safety operators can react almost instantaneously to emerging threats, enhancing response times and reducing the likelihood of escalation. Moreover, the integration of UAVs into the surveillance supports proactive risk management. By gathering aerial imagery and data, the system enables predictive analysis, early threat detection, and strategic planning for incident prevention. The visual information collected can also be archived and analyzed over time to identify patterns, assess vulnerabilities, and improve long-term security strategies. The use of UAVs additionally reduces the risks faced by security and safety personnel, allowing hazardous areas to be inspected remotely without exposing staff to potential danger. In critical situations such as emergency evacuations, natural disasters, or security breaches, UAVs can provide immediate situational awareness, guiding both decision-making and operational actions. In conclusion, the inclusion of UAVs within the video surveillance system represents a transformative advancement, combining technological innovation with operational excellence to deliver a higher level of protection, efficiency, and resilience in security management. All camera feeds are transmitted through the data communication infrastructure, which channels the video streams to several important destinations as follows: the Control Room, the Data Center, and various Area Security Checkpoints distributed across the monitored area. These key hubs are responsible for real-time monitoring and incident response coordination, and they will be described in more detail in the subsequent sections. In particular, the video streams directed to the Data Center are securely stored using redundant servers--both on-site and in cloud environments--to ensure data integrity and availability. These video feeds are then processed using a sophisticated AI-driven video analytics system, which continuously scans the footage for indicators of critical or suspicious situations. The system is capable of identifying and alerting staff to events such as thefts, vandalism, aggressive behavior, panic situations, and other emergencies. Further, proper intrusion detection sensors have been installed in all the necessary areas to be protected by this kind of devices. Upon the detection of a potential threat, the system immediately notifies security personnel via the central Control Room and the relevant Area Security Checkpoints. This rapid alert mechanism enables swift intervention and ensures that appropriate measures can be taken to manage and neutralize the situation effectively and efficiently, minimizing risk to individuals and property.
# Modeling a Pre-Touch Reaction Distance around Socially Touchable Upper Body Parts of a Robot
## 4 Results and Implementation
### Data Analysis Results
#### 4.1.3 Additional Analysis: Acclimation Effect  
Finally, we analyzed whether the minimum comfortable distance changed during data collection (Table 4). Since the participants were involved in many touch interactions, we wanted to know whether their perception influenced the measured distance. For this purpose, we investigated the acclimation factor by separating the data set into two classes--first half and last half--and used the part factor because we found significant differences in the above analysis. Thus, we again conducted a two-factor (part and acclimation) ANOVA test and identified significant main effects in the part factor (F(2,62) = 14.216, \(p\) < 0.001, partial \(\eta^{2}\) = 0.314) and the acclimation factor (F(1,31) = 18.741, \(p\) < 0.001, partial  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Gender Combination**} \\ \cline{2-3}  & **Same** & **Different** \\ \hline Shoulder & 23.4 (2.6) & 25.9 (2.6) \\ Elbow & 23.6 (2.5) & 24.1 (2.5) \\ Hand & 21.0 (2.3) & 21.3 (2.3) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average (S.E.) of minimum comfortable distance in centimeters by gender combinations and body parts.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Angle**} \\ \cline{2-3}  & **Left** & **Right** \\ \hline Shoulder & 25.7 (2.0) & 23.6 (1.8) \\ Elbow & 24.7 (2.1) & 23.0 (1.6) \\ Hand & 21.7 (1.8) & 20.6 (1.5) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average (S.E.) of minimum comfortable distance in centimeters considering the approach angle for each body part.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & \(p\)**-Value** & **Factor** & \(p\)**-Value** \\ \hline Part (P) & \textless{}0.001 & \(\textless{}\) P \(\times\) A & 0.623 \\ Angle (A) & 0.083 & \(\textless{}\) P \(\times\) G & 0.200 \\ Gender combination (G) & 0.749 & A \(\times\) G & 0.366 \\  & & \(\textless{}\) P \(\times\) A \(\times\) G & 0.125 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Three-factor ANOVA results (\(p\) < 0.05 in bold).  
\(\eta^{2}=0.377\)). We did not find any significance in the interaction effect (F(2,62) = 0.591, \(p\) = 0.557, partial \(\eta^{2}\) = 0.019). Multiple comparisons with the Bonferroni method revealed a significant difference for the distance factors: shoulder \(>\) hand (\(p\) < 0.001) and elbow \(>\) hand (\(p\) < 0.001). There was no significance between the shoulder and elbow (\(p\) = 0.653).  
Thus, analysis of the part factor showed the same phenomenon as in the already supported prediction 1. Unlike a past study [16], our data collection showed that acclimation significantly affected the minimum comfortable distance, but this difference is about 1 cm on average.
## 1 Introduction  
Nowadays, numerical simulations are more often used compared to experiments with a physically built prototype. Depending on the requested output, the results of virtual testing can be different. Some of these methods include computational fluid dynamics (CFD), finite element analysis (FEA), and multibody dynamics simulations (MBDSs). CFD software provides more and more features year by year. Their solvers are optimized for fast and efficient running on a local PC or an HPC (high-performance computing) cloud computing server [1].  
CFD is an excellent tool that allows us to model and simulate how engineering devices behave in practice. With a certain accuracy, it is possible to estimate their performance based on the obtained numerical data from simulations [2, 3]. In our article, we focus only on rotating motion modeling by using an unconventional wind turbine (WT). It must be mentioned that the compressibility effect was neglected; therefore, we did not include the energy equation in our model.  
Due to the increasing energy demand, clean and renewable energy sources are becoming very popular sources. The current energy mix contains more oil, coal-gas, and coal-fired and nuclear sources such as wind, solar, hydropower, bioenergy, and geothermal sources. By 2025, the energy demand will grow to 62 PWh/year, and it will contain more wind and solar than the other previously mentioned sources [4, 5, 6].  
Among renewable energies, wind energy is a rapidly growing sector. This growth is achievable with traditional WTs, but it is more feasible with unconventional ones [7; 8; 9; 10].  
Unconventional WTs are special types of wind turbines that have been modified to meet specific requirements, e.g., installation territory.  
In Figure 1a, we can see a counter-rotating dual-rotor wind turbine (CO-DRWT) that has two rotors; thus, it can achieve a higher power coefficient (\(\mathrm{c_{p}}\)) and generate more electrical energy than a single-rotor wind turbine (SRWT) [8; 9]. In Figure 1b, there is an augmented WT with a baffle to catch more wind, while in Figure 1c, the Archimedes screw WT is presented, which operates at a low noise level, and is suitable for urban areas [11; 12; 13].  
The main goal of our article is to compare different rotary motion modeling methods in CFD applied on CO-DRWT and to demonstrate the advantages and disadvantages of the applied methods.
## 2 Materials and Methods
### Data Preparation  
At all sites, continuous measurements of half-hourly carbon fluxes have been undertaken using the eddy covariance technique [48]. Other observed variables mainly comprise energy fluxes (latent heat and sensible heat) and meteorological variables, including air temperature (\(T_{a}\)), soil temperature (\(T_{s}\)), net radiation (\(R_{n}\)), relative humidity (\(R_{h}\)), photosynthetically active radiation, volumetric soil moisture content, and wind speed. In general, the NEE data at the AT-Neu, SE-Deg, and US-Bo1 sites were generally gap-filled on the basis of the ANN technique and the marginal distribution sampling (MDS) approach. In this study, we selected the former because of its slight superiority over MDS reported by [PERSON] et al. [49]. GPP and \(R\) data were calculated from the partitioning of NEE. Positive values of NEE signify a carbon source, while negative values signify a carbon uptake by the ecosystem. For the CA-Oas and CA-Obs sites, the detailed data processing procedures, including quality control, flux correction, and NEE partitioning, can be found in [PERSON] et al. [46].  
Statistical parameters of the used data during the 6-year period at the sites are shown in Table 2. It can be seen that the annual mean \(T_{a}\) changes from 1.00 \({}^{\circ}\)C at CA-Obs to 11.52 \({}^{\circ}\)C at US-Bo1; the annual mean \(T_{s}\) is between 3.19 \({}^{\circ}\)C at CA-Obs and 12.30 \({}^{\circ}\)C at US-Bo1; the annual mean \(R_{n}\) ranges from 2.43 mol m\({}^{-2}\) day\({}^{-1}\) at SE-Deg to 6.71 mol m\({}^{-2}\) day\({}^{-1}\) at CA-Obs; the annual mean \(R_{h}\) varies from 69.88% at CA-Oas to 81.04% at SE-Deg. Other statistical features of each variable are also given in the table. Moreover, Table 2 also shows the correlation coefficients between environmental variables and each carbon flux. At the five sites, the correlation values for both GPP and \(R\) are generally greater than those of values for NEE, which may lead to difficulty in accurately estimating NEE. Among the five sites, on the whole, the lowest correlation values for the carbon fluxes occur at US-Bo1.
# The Analysis of Service Convergence in an Optical Access Network +
## 3 Methodology
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Equipment/Item** & **Brand/Model** & **Parameter** & **Value** \\ \hline \multirow{3}{*}{Arbitrary Waveform Generator} & \multirow{3}{*}{Agilent N8241A} & RF Data Output & Channel 1 \\  & & Signal Frequency & 1 GHz \\  & & Output Voltage & 250 mV \\ \hline \multirow{3}{*}{Laser} & IdPhotonics Mainframe & Type & N \\  & CBMA24 and Module CoBrite MX & Wavelength & 1550 nm \\  & & Optical Power & 16 dBm \\ \hline Optical Modulator & Thorlabs LNA2124 & BIAS Voltage & 2.33 V \\ \hline \multirow{3}{*}{Optical Fiber} & \multirow{3}{*}{-} & Type & SMF G.652D \\  & & Attenuation & 0.24 dB/km \\  & & Chromatic Dispersion & 18 ps/(nm\(\cdot\)km) \\ \hline \multirow{3}{*}{Variable Attenuator} & Gao-Voa-103 & Range & 8–23 dB \\  & & Interval & 1 dB \\ \hline \multirow{3}{*}{Photodetector} & LabBuddy DSC-R402 & Maximum Power & \(-\)3 dBm \\  & & Sensitivity & \(-\)18 dBm \\ \hline Digital Signal Analyzer & KeySight DSAV164A & RF Data Input & Channel 1 \\  & & Number of Samples & 5 GS \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of equipment and parameter configuration in optical channel. Figure 2: Optical channel equipment connection diagram. in this emerging field [9; 10]. The code used to generate the waveforms, as well as their subsequent processing, is found in the Supplementary Material section.
# The Noise-Reduction Characteristics of Microstructure of Dragonfly Wing Leading Vein
## 2 Bionic Modeling of Dragonfly Wing Microstructure
### Effect of Serrated Microstructure Height on Noise Reduction
As can be seen from Figure 6, the leading edge and the upper surface of the bionic wing e had strong pressure fluctuations; that is, the leading-edge noise and the self-noise were very large, so the overall sound pressure level of the bionic wing was the largest. Comparing the pressure nephograms of the upper surface of the basic wing, the bionic wing c and the bionic wing i, it was found that the pressure fluctuation of the leading edge and the upper surface of the wing decreases gradually, which results in the decrease of the overall sound pressure level of the wing noise. Therefore, the reason for noise reduction can be explained as the bionic leading-edge microstructure could significantly change the state and size of the vortices and their distribution on the wing surface, thereby affecting the pressure fluctuations on the leading edge, trailing edge and upper surface of wings, thus affecting the noise level of the wing, and realizing the noise-reduction function of the bionic leading-edge microstructure.
## 1 Introduction  
In recent years, digital integration and artificial intelligence have accelerated at an explosive rate. This progress inevitably leads to the rapid development of two technologies: big data [1; 2; 3] and intelligent algorithms [4]. For enterprise production planning or scheduling, although there are many machine learning algorithms [5; 6; 7; 8; 9] for this problem, there are still not many data mining methods, especially for multi-objective job shop scheduling problems (MOJSSP) [10].  
MOJSSP is an important and critical problem for today's enterprises. A MOJSSP model can typically be described as a set of machines and jobs under multiple objectives, where each job has different sequential operations and each job should be processed on machines in a given order. This pattern is in line with the production planning problem for multi-variety products with different routings. Herein, the problem is transformed into how to rationally arrange the order of operations of multi-variety products on various machine tools, such that two or more objectives are optimal, e.g., makespan and total tardiness.  
The characteristics of multi-objective problems increase the computational difficulty of traditional single-objective scheduling, while data mining [11] could extract rules from a large dataset without too much professional scheduling knowledge, which has more research space in solving MOJSSP and obtaining the optimal values of the objectives.  
For the benefits of data mining, a multi-objective production planning method combining machine learning and population knowledge mining is proposed. Through the relation hierarchies mapping [12], attributes related to processing time and due date are selected. The machine learning algorithm used here is a hybrid metaheuristic algorithm that combines nondominated sorting genetic algorithm II (NSGA-II) and simulated annealing. It provides training data for the mining of population knowledge, and the rules obtained can be used as the criterion for sorting operations. Each operation gets its priority through rules, but it sometimes fails to meet the requirements of the rules (the number of operations per priority is fixed). Besides, the sequence of operations should also be considered. Therefore, this paper proposes a comprehensive priority assignment procedure called addition-deletion sorting method (ADSM) which can meet not only the optimized priority but also the requirements of rules and the order of operations. The main contributions of this paper are highlighted as follows:  
1. A hybrid machine learning and population knowledge mining method is proposed to solve multi-objective job shop scheduling problems.
2. Five attributes, namely operation feature, processing time, remaining time, due date, and priorities, are selected to mine initial population knowledge.
3. The ADSM method is designed to reprioritize operations after the population knowledge mining.
4. Three populations (rules, mixed, and random) with different iterations and population sizes are compared, and three performance metrics are defined to explore the effectiveness of the proposed method.
## 5 Experimental Results
### Performance Evaluations
At the same time, it can be stated that the three fusion techniques contribute a lot to improve the classification performance of the proposed system in case of either UTD-MHAD or MSR-Action3D dataset. To examine the complexities such as memory consumption, operational requirements, and time complexity, we express the parameters in millions, floating-point operation per seconds (FLOPS) in giga, and time in second as listed in Table 4. The inverted-residual blocks in MobileNetV2 reduce the parameters as well as the number of operations than DenseNet121 and ResNet18. The operations increase as the features are extracted from deeper in DenseNet121. The pre-trained MobileNetV2, DenseNet121, and ResNet18 require 0.00129, 0.00235, and 0.00108 s respectively for running an action. The further inquisition of performance on different actions individually explains that the proposed method assures the recognition accuracy above 90% for the UTD-MHAD dataset. From Figure 11a, it can be said that the action classes _'SwipeLeft'_, _'SwipeRight'_, _'Clap'_, _'Throw'_, _'ArmCross'_, _'Boxing'_, _'BasehallSwing'_, _'TeunisSwing'_, _'TeunisServe'_, _'Push'_, _'Knock'_, _'Catch'_, _'PickIpandThrow'_, _'Jog'_, _'Walk'_, _'SitTostand'_, _'StandToSit'_, _'Lunge'_, and _'Squat'_ secure highest classification accuracies with any one of the three pre-trained models. The recognition results decrease to 58.33%, 91.67%, and 83.33% using MobileNetV2, DenseNet121, and ResNet18 in MSR-Action3D dataset for _'HandCatch'_ action due to the irregularities in the dataset. On top of that, the overall discrimination performance is satisfactory for any other action classes in the MSR-Action3D dataset with three pre-trained models. Figure 11b illustrates the classification results for individual classes in the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Parameters (M)** & **FLOPs (G)** & **Time (s)** \\ \hline MobileNetV2 & 2.56 & 0.33 & 0.00129 \\ DenseNet121 & 7.22 & 2.90 & 0.00235 \\ ResNet18 & 11.31 & 1.82 & 0.00108 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Complexity analysis of different models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **MobileNetV2** & **DenseNet121** & **ResNet18** \\ \hline Front view (\(XY\) plane) & 93.83\% & 94.83\% & 93.08\% \\ Side view (\(YZ\) plane) & 91.25\% & 91.25\% & 92.50\% \\ Top view (\(ZX\) plane) & 91.67\% & 93.00\% & 92.08\% \\ \hline Fusion (\(f_{\text{exp}}\)) & 95.42\% & 96.67\% & 97.50\% \\ Fusion (\(f_{\text{max}}\)) & 95.50\% & 96.67\% & 96.25\% \\ Fusion (\(f_{\text{max}}\)) & 96.00\% & 98.75\% & 97.08\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Human activity recognition with different modality and models using MSR-Action3D dataset. MSR-Action3D dataset with MobileNetV2, DenseNet121, and ResNet18. The reduction of accuracies in some classes of action such as _'HandCatch'_ in the MSR-Action3D dataset happens due to the irregular movement in few points of the sequences.
# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 4 Mathematical Models for Energy-Aware Routing Calculation
### Optimization Problems Formulation
#### 4.1.2 DEUlm Optimization Problem  
In the case of multilayer topologies, nodes can differ in energy usage due to the different link capacities and node architecture and configuration. Then, the modified energy-saving optimization problem is as follows:  
\[\min_{x_{n},y_{lm}}\left[f_{DEUlm}(x_{n},y_{lm})=\sum_{m\in M}\sum_{l\in L}\sum _{n\in N}y_{lm}w_{m}e_{l}(a_{ln}+b_{ln})+\sum_{n\in N}x_{n}r_{n}\right], \tag{7}\]  
subject to the constraints (2)-(6). The parameter \(r_{n}\) in (7) denotes the energy use of the \(n\)-th node in the active mode and \(e_{l}\) is the energy cost related to data transmission by the link \(l\).
## 3 Results and Discussion
### Comparison of the E10 and E20 Simulations
Even though combustion efficiency improves, it is not enough to offset the increase in fuel quantity. It is also noticeable that the difference in HC emissions increases proportionally with load. CO emissions show a similar trend, despite the fact that E20 has twice the O/C ratio compared to E10. In every operating point, CO levels are higher with E20, as shown in Figure 13. However, no clear relationship can be observed between load and CO emissions in this case. Figure 11: Comparison of simulated combustion duration with E10 and E20 fuel. Figure 10: Comparison of simulated IMEP with E10 and E20 fuel. In terms of CO\({}_{2}\) emissions, illustrated in Figure 14, the E20 fuel consistently results in lower values across all operating points. This is due to its lower carbon content and higher CO formation. The differences are fairly consistent among all operating points. Figure 14: Comparison of simulated CO\({}_{2}\) emissions with E10 and E20 fuel. Figure 12: Comparison of simulated HC emissions with E10 and E20 fuel. Figure 13: Comparison of simulated CO emissions with E10 and E20 fuel. Figure 15 shows the NOx emissions comparison. In all cases, emissions are lower when using E20. This is because E20 has a lower LHV, so even though the combustion process is slightly faster, it does not result in significantly higher in-cylinder temperatures. The magnitude of the deviations varies between operating points, with the smallest difference occurring at the low-load operating point.
## 4 The Analysis of PTBO and a Comparative Study
### The Differences between PTBO and Other Algorithms  
#### 4.3.1 The Differences between PTBO and PSO  
Like PSO (Particle Swarm Optimization), PTBO is also introduced to deal with unconstrained global optimization problems with continuous variables. In the representational form of implementation, we can certainly think that PTBO is also based on a particle system in the same way as PSO. However, according to the overall design of the PTBO algorithm, there are some differences between PTBO and the classical PSO.  
Figure 8: Population distribution at various generations in an evolutionary process of PTBO.  
Firstly, in heuristic thought, PSO is inspired by biological behavior or habits for simulating animal swarm behavior, such as fish schooling and bird flocking, while PTBO is inspired by the phase transition phenomenon of elements in nature. Secondly, in PSO, the direction of a particle is calculated by two best positions, _pbest_ and _gbest_. However, the motion direction of an element in PTBO is arbitrarily derived from the other two elements that are different from each other. It may enhance the diversity of the population and ensure the avoidance of premature convergence. Thirdly, in the design of the operators, each particle in PSO contains a velocity item. Nevertheless, in PTBO the concept of velocity does not exist. Besides, PSO uses the position information of _pbest_ and _gbest_ to record the updating of velocity or position. However, PTBO uses only the position information about _gbest_ and the _pbest_ position of elements is not considered.