esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 3 Sensors
### Physiological Measurement Devices
#### 3.5.1 Neural EEG Measurements  
EEG provides a wealth of neural-based cues that link to attentional engagement, ranging from overt and traditional measures such as overall alpha-band power to more subtle cues/features that have only been extracted recently with modern machine learning.  
Cognitively relevant neural oscillations span a range from 4 to 100 Hz and are divided into bands that roughly indicate distinct functions. Theta band (4-8 Hz) power links to memory formation and retrieval [92; 93]; a memory is much less likely to be encoded if a person is not engaged. Eye saccades occur in the theta band; however, neural oscillations related to their planning are tied to the alpha band [94], which may then be linked with eye tracking or camera information for eye-gaze tracking. These saccades are often socially relevant [95] and thus a critical feature for social engagement.  
The alpha band (8-12 Hz) has been shown to have strong links to control of attention [96], from general engaged/sustained attention [97] to control of spatially allocated attention [98] and selective attention [99]. Clinically relevant, aberrant alpha power is often seen in those with ADHD [100; 101].  
Beta band (13-30 Hz) power is most linked with motor planning and execution; as such, beta power can predict eye movements or arm reach movements. Even when a movement is only imagined, beta band power strongly links to this imagery/intention and is thus a strong indicator for engagement [102].  
The ratio of theta to beta power (theta-beta ratio; TBR) is a clinically relevant metric for identifying those with ADHD [103]. Furthermore, 'engagement' has been quantified as a ratio of beta/(alpha+theta) [104; 105] and has been utilized in brain-computer interfaces. Thus, the interplay of the power _across_ bands is just as relevant as the power in individual bands for both HRI and clinical purposes.  
Finally, the phase (rather than power) of the on-going oscillation has been shown to relate to memory encoding [106] and perception of near-threshold items [97], both of which are critical for engagement with an attention-demanding task such as school learning or safety-flagging (e.g., airport screening).  
Furthermore, on-going EEG recordings can provide these relevant cues/features for engagement in real time, unlike in traditional psychology experiments requiring an average of hundreds of repeated trials. At the same time, any of these features or more time-consuming processed derivatives such as connectivity can be directly fed into machine learning algorithms to improve future detection.  
The past decade has shown an increase in the development of mobile, portable EEG devices that can provide reliable, robust signals when at home or in natural environments rather than controlled lab settings, such as Neurotechnology, Emotiv, ANT eego, and others, including options for dry connection (i.e., without electrode gel). These together open the gate towards real-world relevant neural measures listed above that link to engagement.
## Appendix A Measured compliances of the Different Tracks  
The compliances of the surface, tunnel and bridge tracks have been measured with an impact hammer and are shown in Figure 15a-c. The amplitude of the compliance is always greater and the phase delay is generally lower for the rail compared to the sleeper. The ballasted tracks on the bridge and on the surface line have high amplitudes and a strong decrease in the phase for frequencies below 50 Hz. Between 50 and 100 Hz, there is a characteristic drop down of the amplitudes, and the phase changes are more moderate. The ballasted track on the bridge shows a resonance at about 40 Hz. The bridge track has less radiation than the surface track because of the stiff support by the stiff bridge deck.  
In contrary, the slab track in the tunnel has high amplitudes and only small phase delays up to 200 Hz, which is due to the soft pads under the rail. The rail-on-rail-pad resonance is at about 220 Hz and a decrease of amplitudes and phases follows. Because of the decoupling by the soft rail pads, the sleeper behaves quite different from the rails. The sleeper amplitudes are much smaller by a factor of about 10. The sleeper and the track plate on the stiff infill above the tunnel invert have a resonance at 120 Hz. In addition, another weak resonance is visible at 90 Hz, but no track resonance can be found at lower frequencies. If the vehicle is coupled to the track, the wheelset-track resonance at 60 Hz is the lowest resonance of the vehicle-track system (see Figures 6a and 7a-c).  
All three rail compliances are compared in Figure 15d, and they have similar low-frequency values. Compared to the theoretical results in [1], a soft support by the ballast on the surface and bridge lines and a stiff support by the infill in the tunnel can be concluded so that a good agreement between the measurements and the calculations can be observed.
## 5 Plant Growth-Promoting Metabolites for Soil Remediation and Crop Improvement
### Plant Ethylene Balancing via Bacterial ACC Deaminase  
Salinity affects approximately 6% of the land surface worldwide and about 20% of irrigated areas, posing a major threat to agriculture [15]. It affects plant growth and yield, but also provokes an imbalance of microorganism distribution in the rhizosphere and increases, in some cases, pathogen virulence [31]. Salinity also affects nutrient availability for crops. For example, phosphate (P) tends to precipitate with calcium in saline soils, which makes it difficult to be captured by plants [132], although some PGPR can be responsible for physical and chemical changes in plant tissues, participating in stress mitigation and plant growth restoration. The bacterial enzyme \"1-amino cyclopropane-1-carboxylate (AAC) deaminase\" plays an important role in regulating ethylene levels in plant tissues by degrading its precursor (ACC). Ethylene is a plant hormone that regulates several aspects of plant growth. However, its synthesis is accelerated under high salinity, and then it acts as a negative plant growth regulator as its concentration exceeds the required level [133].  
The use of ACC deaminase-producing bacteria has been widely adopted in agriculture, especially for crop enhancement under different stresses [134]. Thus, [PERSON] et al. [135] reported the effect of ACC-deaminase-producing rhizobacteria on wheat growth promotionunder cadmium stress. The tested isolates significantly enhanced root elongation and minimized ethylene synthesis in wheat seedlings under such stressed conditions. Indeed, the most efficient strains, among others, in promoting wheat growth (_Pseudomonas_ sp. PS 2-3 and _Pseudomonas fluorescens_ PS 7-12) showed the presence of acdS gene coding for ACC deaminase. [PERSON] et al. [136] obtained better yield and chlorophyll content with rice inoculated with ACC deaminase-producing bacteria under salt stress. The ACC deaminase-producing _Pseudomonas putida_ CEN7 and _Pseudomonas fluorescens_ CEN8 showed a significant increase in the root length and root colonization of rice plants [137]. In addition, the bacterium _Rhizobium legitiminsarum_ (SN10) promoted the biomass, root branching and N content of four different rice varieties. Not only this, but the bacterium also displayed a strong chemotaxis response towards the rice seed and its root exudates [138]. Many other works have reported the role of ACC deaminase-producing PGPR in the enhancement of rice growth under different stresses such as salinity, flooding and toxic metals [137; 139; 140]. Moreover, similar results were found with wheat inoculated by ACC deaminase-producing bacteria such as _Pseudomonas_, _Serratia_, _Burkholderia_ under salt and drought stress conditions [92; 141; 142; 143; 144; 145].
# A Survey on Recent Advanced Research of CPS Security
## 3 Reviews
### New Attacks  
While new CPS technologies are applied in many production and life scenarios, new attack methods also arise. Researchers study these attacks in depth and propose mitigation measures. Among the areas that receive the most attention are power grids, convert channels, USB security, and vehicle security.
## 2 Correlation Theory
### Transmittance Estimation  
As illustrated in Figure 2, through comparative analysis of numerous haze-free images across diverse scenes, we observed that targets typically exhibit distinct polarization characteristics, which accurately reflect their intrinsic properties. In contrast, sky regions demonstrate significantly weaker polarization properties, often orders of magnitude lower than those of targets. Building upon this theoretical prior, we propose that the polarization information acquired in foggy conditions arises from the combined contributions of target radiance and atmospheric light. By integrating this insight with the atmospheric scattering model, we derive the following key Equation (7):  
\[S(1-p)=A_{\infty}(1-t)(1-p_{A})+L(1-p_{L})t \tag{7}\]  
\(S\) is the light intensity obtained by the detector. \(A_{\infty}\) is the intensity of light at infinity. \(L\) is the intensity of the target light. \(t\) is the transmittance. \(p_{A}\) is the polarization degree of air light, and \(p_{L}\) is the polarization degree of target light. \(p\) is the polarization degree of the probe light.  
By rearranging Equation (7), we derive an expression for transmittance \(t\), as shown in Equation (8). To address the challenge of estimating transmittance in heavy fog, this paper proposes a method that leverages the polarization properties of both target radiance and atmospheric light while incorporating boundary constraints to achieve robust estimation of \(t\).  
\[t=\frac{A_{\infty}(1-p_{A})-S(1-p)}{A_{\infty}(1-p_{A})-L(1-p_{D})} \tag{8}\]  
In images, regions are not uniformly affected by fog. Fog-covered areas exhibit low transmittance. To maximize the recovery of target radiance, it is necessary to estimate the minimum transmittance. Due to fog-induced scattering, the polarization component of detected light is typically smaller than that of the target radiance (as \(Sp<Lp_{D}\)). By scaling Equation (8) and leveraging scene similarity priors, we compute the minimum target radiance and derive the minimum transmittance for sky regions, as formalized in Equation (9). Conversely, fog-free regions exhibit higher transmittance, where atmospheric light contributes minimally. To preserve the fidelity of target radiance, we estimate the maximum transmittance. In clear conditions (as \(Sp\approx Lp_{D}\)), maximizing the target radiance allows us to determine the maximum transmittance for non-sky regions, as shown in Equation (10).  
\[t_{\text{min}}=\frac{A_{\infty}(1-p_{A})-S(1-p)}{A_{\infty}(1-p_{A})+Sp-L_{\text{ min}}} \tag{9}\]  
\[t_{\text{max}}=\frac{A_{\infty}(1-p_{A})-S(1-p)}{A_{\infty}(1-p_{A})+Sp-L_{ \text{max}}} \tag{10}\]  
where \(L_{max}\) represents the maximum value of the target light, and \(L_{min}\) represents its minimum value. \(t_{max}\) represents the maximum transmittance, and \(t_{min}\) represents its minimum value.
# A Network Analysis Approach to Detecting Social Issues with Web-Based Data
## 4 Illustrative Example: Diesel Exhaust Fluid Issue in South Korea
### Analyses
Additionally, this study intends to suggest ways to apply the proposed approach by using new keywords of the issue. Specifically, the change in degree centrality of the keywords that make up the community was used to calculate the impact score. Here, the degree centrality of newly introduced keywords that did not exist in the previous time period was not reflected in the impact score. Instead, it is possible to infer the near future development of a social issue through new keywords of the community detected as the social issue. For example, in the case of the DEF issue discussed in Section 4.1, fire station-related keywords such as '(Ului fire station) and '(Gwangyang fire station) were found among the new keywords that were not used in the impact score calculation process. From this, it was expected that the DEF issue would be linked to events related to fire stations. In fact, as the DEF shortage continued, not only transportation, but also fire trucks and ambulances, which are directly related to people's safety and life, were restricted. To improve this situation, many helpers began appearing across the country, such as anonymous donors who secretly placed DEF in front of the fire station and disappeared. In addition, among the new keywords, the keywords related to cargo transportation were also found, such as '(Cargo Truckers Solidarity), '(Cargo trucker), and '(Cargo transportation). Cargo Truckers Solidarity is a labor union formed by workers in the cargo transportation industry in South Korea. As the DEF issue persists, the price of DEF continued to rise, making it harder to obtain. The Cargo Truckers Solidarity announced a strike, saying that all damages caused by operation suspension are being passed on to the workers. In fact, when the Cargo Truckers Solidarity went on strike twice in one year, it caused an economic loss of 10.4 trillion won, so strikes of solidarity like this case led to huge national economic losses in South Korea. As a result, by monitoring the new keywords in the DEF issue, it could be inferred that the DEF issue could develop into an incident related to Cargo Truckers Solidarity and cause additional economic losses.
# Analysis of AgRED Performance in LR-WPAN Dense Ad-Hoc Networks +
## 5 Results  
AgRED is put against the base RED protocol for AQM for LR-WPAN in the tests. Figure 2 shows that packet delay from source to destination for AgRED is at least five times less as compared to RED regardless of the payload or packet interval. It is clear from the results that increase in payload also increases the delivery times. This is due to higher number fragmentation of packets which also increases that chance more packets are dropped randomly by the algorithm, while packet send interval relives the queue with a lesser number of packets, thereby decreasing the packet delivery times. Average queue length analysis is necessary in finding out the performance of the queue management scheme. Figure 3 shows that AgRED queue length is kept minimal, therefore allowing majority of arriving packets a place in the queue regardless of variations in both variables. With the ability to allow more packets into the queue, AgRED's ability to deliver more  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline MAC protocol & IEEE 802.15.4 \\ Node data rate & 250 Kbps (LR-WPAN) \\ Communication range & 100 m (BB-WSD2C21150 sensor) \\ Area & 500 m \(\times\) 500 m \\ AQM techniques & RED, AgRED \\ Routing scheme & AODV \\ Payload & 200, 400, 600, 1000 (bytes) \\ Packet send interval & 5, 10, 50, 100 (ms) \\ Transport protocol & UDP \\ Node speed average & 3 m/s \\ Queue capacity & 100 \\ Maximum probability Pmax & 0.02 \\ Minimum threshold & 20 packets \\ Maximum threshold & 100 packets \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
packets as compared to RED is shown in Figure 4. Here, the overall packet delivery reduces with increasing payload due to fragmentation; however, at the payload of 1000 bytes, the difference between AgRED and RED closes. Increase in send interval overall deliver more packets as the queue relives more often as compared to the higher frequency of packet arrival in queues. Throughput follows an inverse trend of delay as shown in Figure 5. Throughput of AgRED is twice as high as that of RED. A payload of 1000 bytes offers the lowest throughput for both RED and AgRED but still remains twice as high as RED. Packet send interval also impacts the throughput, where the lowest throughput achieved was on a 10 ms packet delay which is suspicious and further study needs to be carried out.  
Figure 4: Packets received of AgRED and RED with (**a**) payload and (**b**) packet send interval.  
Figure 3: Average queue length of AgRED and RED with (**a**) payload and (**b**) packet send interval.  
Figure 2: Average delay of AgRED and RED with (**a**) payload and (**b**) packet send interval.
## 3 Proposed Congestion Management Approach  
### P-Vscopf  
Power systems, including wind farms, are open systems. This means that any external parameter can influence their functionalities, which can lead to rather uncertain systems. The proposed OPF for CM is implemented as a P-VSCOPF, which is a nonlinear, multi-objective optimization problem. A probabilistic method has been developed using MCSs that apply random sampling of the uncertain parameter probability density function to solve the problems. In our study, a multi-objective OPF is formulated as  
\[Min\quad-(w_{1})(C_{D}P_{D}-C_{S}P_{S})-w_{2}\lambda_{c} \tag{10}\]  
The objective function contains both social welfare and maximum loading margins with weighting factors of \(w_{1}>0\) and \(w_{2}>0\). Here, we assume that \(w_{1}=(1-w)\) and \(w_{2}=w\), (for \(0<w<1\)). The value of the weighting factor is increased so that stability takes precedence over cost. Hence, the system becomes more stable and has higher operating costs.  
The equality and inequality constraints for the problem are as follows:  
Power flow equations:  
\[f(\delta,V,Q_{G},P_{S},P_{D})=0 \tag{11}\]  
\[f(\delta_{c},V_{c},Q_{G_{c}},\lambda_{c},P_{S},P_{D})=0 \tag{12}\]  
Supply and demand bid blocks:  
\[P_{S_{min}}\leq P_{S}\leq P_{S_{max}} \tag{13}\]  
\[P_{D_{min}}\leq P_{D}\leq P_{D_{max}} \tag{14}\]  
Generation reactive power limit:  
\[Q_{G_{min}}\leq Q_{G}\leq Q_{G_{max}} \tag{15}\]  
\[Q_{G_{min}}\leq Q_{G_{c}}\leq Q_{G_{max}} \tag{16}\]  
Thermal limit:  
\[I_{ij}(\delta,V)\leq I_{ij_{max}} \tag{17}\]  
\[I_{ji}(\delta,V)\leq I_{ij_{max}} \tag{18}\]  
\[I_{ij}(\delta_{c},V_{c})\leq I_{ij_{max}} \tag{19}\]\[I_{ji}(\delta_{c},V_{c})\leq I_{ji_{max}} \tag{20}\]  
Voltage security limit:  
\[V_{min}\leq V\leq V_{max} \tag{21}\]  
\[V_{min}\leq V_{c}\leq V_{max} \tag{22}\]  
Loading margin:  
\[\lambda_{c_{min}}\leq\lambda_{c}\leq\lambda_{c_{max}} \tag{23}\]  
Here, the subscript \(c\) represents the system's maximum loading condition. Equation (12) is related to a loading parameter, which guarantees that the system network has the required margin of voltage stability. The generation and load in the present state, with the loading parameter \(\lambda_{c}\), are expressed as  
\[P_{G}=P_{G_{0}}+P_{S} \tag{24}\]  
\[P_{L}=P_{L_{0}}+P_{D} \tag{25}\]  
\[P_{G_{c}}=(1+\lambda_{c}+k_{G_{c}})P_{G} \tag{26}\]  
\[P_{L_{c}}=(1+\lambda_{c})P_{L} \tag{27}\]  
In the proposed P-VSCOPF-based approach, the critical loadability \(\lambda_{c}\) can be expressed as a measure of system congestion. The loadability is maximized to receive the impact of the voltage stability limit. Therefore, the total maximum loadability (TML) and the available loading capability (ALC) can be defined, respectively, as  
\[TML=(1+\lambda_{c}){\sum P_{L_{i}}} \tag{28}\]  
\[ALC=\lambda_{c}{\sum P_{L_{i}}}=\lambda_{c}TTL \tag{29}\]  
Here, TTL is the total transaction level at the current operating point. In Equation (29), the ALC is computed for the product of \(\lambda_{c}\) and TTL.
# Adaptive Self-Scaling Brain-Storm Optimization via a Chaotic Search Mechanism
## 1 Introduction
Therefore, simply replacing random variables with exotic variables cannot effectively improve the search performance of the algorithm. To overcome the limitations of the CLS, we developed an adaptive self-scaling mechanism to control the search range of the CLS and embedded the improved CLS into BSO (referred to as adaptive mechanism-based BSO, ABSO). The proposed ABSO algorithm was then expanded into two types of algorithms, i.e., chaotic sequence random selection (ABSO-R) and chaotic sequence parallel search (ABSO-P) algorithms. Then, random selection and parallel computing were carried out on the 12 chaotic maps. During the optimization process, as the position of the population changes, the CLS search region is determined by the positions of 2 random individuals in the entire search space. After each iteration, the adaptive machine carries out the CLS process, therefore improving the search performance and convergence speed of the algorithm. To verify the performance of the proposed algorithm, we used 29 benchmark functions from IEEE CEC 2017 and compared the results of the proposed algorithm with those of five other algorithms, i.e., BSO, CBSO, CGSA-P, DE and WOA. Furthermore, we verified the effectiveness and superiority of ABSO using the Friedman nonparametric statistical test [43; 44]. The simulation and verification results showed that ABSO is strongly competitive in terms of its search accuracy, convergence speed, and stability. The main contributions of this study are as follows:  
* An improved adaptive CLS strategy is proposed to enhance the global search performance of BSO. * Adaptive scaling CLS can effectively help BSO jump out of local optima and avoid premature convergence of the BSO algorithm. * Extensive experimental results show that ABSO is superior to its competitors. * The results of two nonparametric statistical tests indicate that the proposed adaptive self-scaling mechanism effectively improves the search ability and efficiency of ABSO. The remainder of this paper is organized as follows: Section 2 presents a brief description of the classic BSO algorithm. Section 3 introduces 12 different chaotic maps and the parameters. Section 4 describes the CLS method based on adaptive self-scaling and its integration with BSO. Section 5 presents the experimental and statistical analysis results. Section 6 concludes this study and presents future research work.
# Analysis of Uncertainty in Conveyor Belt Condition Assessment Using Time-Based Indicators
## 3 Results
### Statistical Significance Testing  
The multiple range tests (Figure 9) using [PERSON]'s LSD method at the 95% confidence level confirmed that:  
* Age and Failure Density show statistically significant differences between all the material types.
* For Resurs, the mixture does not differ significantly from either brown coal or overburden, though brown coal and overburden differ significantly from each other.  
This suggests that Resurs, as a derived metric from Age and expected lifespan, is less sensitive to material differences.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Material** & **Count** & **Age** & **Age** & **Age Stdian** & **Age Std** & **Age** & **Res.** & **Res.** & **Res.** & **Res.** & **Density** & **Density** & **Median** & **Density** & **Cov** \\ \hline
1. Brown coal & 39 & 154.2 & 168 & 24.183 & 0.157 & 0.938 & 0.933 & 0.032 & 0.034 & 0.036 & 0.024 & 0.036 & 1.002 \\ \hline
2. Brown coal & 50 & 76.3 & 78 & 21.926 & 0.287 & 1.079 & 1.083 & 0.174 & 0.161 & 0.543 & 0.491 & 0.308 & 0.567 \\ overburden & & & & & & & & & & & & & \\ \hline
3. Overburden & 287 & 66.0 & 59 & 27.996 & 0.424 & 1.022 & 1.075 & 0.287 & 0.281 & 0.961 & 0.711 & 0.970 & 1.009 \\ \hline Total & 376 & 76.4 & 65 & 37.827 & 0.494 & 1.021 & 1.065 & 0.261 & 0.256 & 0.809 & 0.513 & 0.905 & 1.118 \\ \hline \((1-2)/2\lceil\times\rceil\) & & 101.93 & 115.38 & 10.29 & \(-45.38\) & \(-13.10\) & \(-13.85\) & \(-81.42\) & \(-78.62\) & \(-93.40\) & \(-95.07\) & \(-88.34\) & 76.75 \\ \hline \((2-1)/2\lceil\times\rceil\) & & 16.62 & 32.20 & \(-21.68\) & \(-32.26\) & 5.60 & 0.77 & \(-39.56\) & \(-42.76\) & \(-43.53\) & \(-31.05\) & \(-68.26\) & \(-43.80\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary statistics for Age, Resurs, and Failure Density.  
Figure 8: Quantile plots for Age, Resurs, and Failure Density for each materials group.