esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Sizing of Battery Energy Storage System for Implicit Flexibility in Multi-Energy Microgrids
## 3 Results
### Techno-Economic Impact of BESS Deployment
Figure 8 shows the electrical power flows for 15 July, alongside the DAM price and the SOC profile for the BESS-integrated scenario. The dispatch for this day highlights the BESS's role in peak shaving. In the early morning hours, the CHP is operated above the electrical load level, charging the BESS with the surplus electricity. This strategy enables the BESS to discharge during the midday period, significantly reducing the power drawn from the grid and maintaining it below a predefined threshold. A similar approach is adopted in the evening: the CHP produces in excess of the local demand, and the surplus is again used to recharge the BESS, restoring its SOC to the initial value of 50%. Figure 9 displays the 15-min electrical and thermal power flows of the MEMG on 12 December 2024, alongside the DAM price and the battery SOC. This day is particularly insightful to showcase how the BESS plays a crucial role in enhancing synergies between the electrical and thermal vectors. In the reference case, early-morning peaks in campus thermal demand are optimally met by operating the CHP, which results in excess electricity generation compared to the load. This surplus is exported to the grid, but such exports are only economically justified when coupled with thermal demand satisfaction. During periods of low thermal demand, exporting electricity becomes suboptimal--except for a few hours in the evening when the zonal DAM price exceeds EUR 250/MWh. For the BESS-integrated scenario, the CHP still follows thermal peaks, especially during the night, but in this case, recharges the battery rather than injecting electricity into the grid. The stored energy is primarily used for two purposes: enabling temporary CHP shutdowns--thereby saving on O&M costs--and performing peak shaving during midday, reducing grid imports  
Figure 8: Comparison of 15-min electric power flows in the MEMG between the reference and optimal BESS-integrated configurations on 15 July 2024. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **CHP CF** & **Boilers CF** & **Dissipated Heat** & **BESS CF** \\ \hline
**Reference Case** & 73.75\% & 4.53\% & 6189 MWh/y & - \\ \hline
**Optimal Case** & 71.63\% & 4.54\% & 5792 MWh/y & 20.60\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Capacity factors of the CHP, boilers, BESS, and annual heat dissipation for both the reference and optimal BESS-integrated configurations. to zero. Moreover, it is used for energy arbitrage by discharging to the grid during the evening price peak, thus maximizing its economic value.
# Improving the RST-OIL Algorithm for Oil Spill Detection under Severe Sun Glint Conditions
## Materials and Methods
### Extending the Applicability of RST-OIL to Different Observational Conditions
Therefore, after the application of the previously mentioned criterion (Equation (2)), the whole dataset was further divided, at a pixel level, into homogeneous (i.e., glint, high glint, or no glint) classes (Figure 3) to produce the corresponding reference fields, for glint (i.e., \(\mu_{\text{R}_{\lambda},G}\)(x,y,\(\theta_{\text{g}}\)) and \(\sigma_{\text{R}_{\lambda},G}\)(x,y,\(\theta_{\text{g}}\))), high glint (i.e., \(\mu_{\text{R}_{\lambda},\text{HG}}\)(x,y,\(\theta_{\text{g}}\)) and \(\sigma_{\text{R}_{\lambda},\text{HG}}\)(x,y,\(\theta_{\text{g}}\))), and no glint (i.e., \(\mu_{\text{R}_{\lambda},\text{NG}}\)(x,y,\(\theta_{\text{g}}\)) and \(\sigma_{\text{R}_{\lambda},\text{NG}}\)(x,y,\(\theta_{\text{g}}\)) conditions, respectively. Equation (1) results were then modified as follows (hereafter called ALICE\({}_{\text{R}^{*}\lambda}\)). \[\otimes_{\text{R}_{\lambda}}\left(x,y,t,\theta_{\text{g}}\right)=\frac{\left[R _{\lambda}\left(x,y,t,\theta_{\text{g}}\right)-\mu_{\text{R}_{\lambda}}\left( x,y,\theta_{\text{g}}\right)\right]}{\sigma_{\text{R}_{\lambda}}\left(x,y, \theta_{\text{g}}\right)} \tag{3}\]  
These reference fields will allow for better characterization of the measured signal in more homogenous environmental conditions. For a historical data set of images acquired under glint (or high glint) conditions, the computed mean value will be high, and, hence, during ALICE (Equation (3)) computation, the relative comparison with an image acquired under the same observation conditions should not produce high values of the indicator, except in the presence of other perturbing factors (e.g., oil on the scene). Figure 2: An example of the study area under no glint (**a**) and glint (**b**) conditions: RGB (top) and NIR-0.8 \(\upmu\)m (bottom) image. Purple areas were not considered in the analysis. According to Equation (3), the \(\otimes_{\mathbf{R}^{\prime}0.8}\) index has been computed at the pixel level for homogeneous classes depending on the occurrence of the previously recognized conditions with a particular focus on glint and high glint ones.
## 3 Interview Results
### Fatigue Driving Triggers  
In this study, the analysis of fatigue-driving triggers revealed a range of contributing factors that collectively influence drivers' cognitive and physical states, ultimately leading to fatigue driving. Interviews with participants across different occupational groups and driving scenarios indicated that a prolonged driving duration, nighttime driving, monotonous environments, and psychological fatigue are among the primary factors triggering fatigue driving.  
Prolonged driving was widely recognized as a major contributor to driver fatigue. Respondents consistently reported that during extended periods of driving--especially continuous driving for several hours--they tended to feel exhausted, with a diminished ability to maintain concentration. As participant P01 noted, \"_Driving for long hours at night makes my eyes feel blurry. I keep yawning and find it difficult to stay focused_\". This phenomenon was particularly prevalent among long-haul truck drivers, especially during interprovincial trips or prolonged highway driving, where mental and physical endurance are frequently challenged.  
Nighttime driving was identified as especially fatiguing. Participants reported that driving at night disrupts their circadian rhythms, requiring them to remain alert during natural fatigue peaks, which increases drowsiness. As participant P04 shared, \"_1 often drive on the highway at night, and the drowsiness is very strong, especially in the early morning hours. It's really hard to stay awake_\". Night driving not only intensifies physical fatigue but also makes drivers more vulnerable to environmental distractions, negatively affecting reaction time and judgment.  
Monotonous driving environments were also cited as a key trigger for fatigue. Respondents described how long periods of driving in environments with little variation--such as highways or mountain roads--lead to mental fatigue and reduced attentiveness. Participant P06 explained, \"_When I drive on the highway, especially in the mountains, the road conditions are very repetitive. After a while, I just can't focus. It makes me really tired_\". Such monotonous environments exacerbate driver fatigue and serve as an additional trigger for fatigue-related risks.  
Psychological fatigue also played a significant role in shaping drivers' fatigue states. Many drivers reported that in addition to physical exhaustion, they experienced mental strain due to heavy workloads and long driving shifts. As participant P15 stated, \"_The pressure from work is high. Sometimes, when I'm driving, my mind feels blank and mentally drained_\". This psychological fatigue affects drivers' emotional states and cognitive performance, further increasing the likelihood of fatigue driving.
## 1 Introduction  
The increasing frequency and intensification of droughts under global warming [1, 2, 3, 4] and expansion of drylands in the coming decades [5, 6] will alter the coupled carbon-water cycle by affecting the plant community composition [7, 8], structure [9, 10] and function of the ecosystem [11, 12, 13]. Understanding the response mechanism of the carbon-water process to drought could give us insights to cope with future droughts.  
As the vital indicators of the carbon-water process, Gross Primary Productivity (GPP), Evapotranspiration (ET) and Water Use Efficiency (WUE) are widely used to assess the impacts of drought to ecosystems. Most of research shows that vegetation productivity [14, 15, 16, 17, 18] and ET [19, 20, 21] have been limited by drought at global or regional scale, whereas ecosystem WUE under droughts presents dissimilar variations with drought timing and duration [22, 23, 24] or intensity [13]. Nevertheless, not enough evidence comes from the drylands. Additionally, dryland ecosystems exhibit different sensitivity to droughts, such as semi-arid regionshaving larger sensitivity than arid ecosystem [25]. Thus, analyzing the inherent ecological responses to droughts is critical in order to carry out measures to reduce drylands degradation under climate change.  
Currently, the methods, mechanisms and processes of ecological responses to droughts are still lacking [26]. Most of the previous research is based on the relationship between drought and indicators [27; 28; 29; 15], or the anomalies of ecological indicators (i.e., GPP or WUE) during drought [18; 22] or the difference between dry years and non-dry years [14; 30; 31; 32]. Notably, ignored insignificant related regions between drought and GPP (or ET) might indicate that some compensation mechanisms resist the drought impacts; meanwhile, few studies focus on the impacts of abrupt changes in dry-wet events on carbon-water processes.  
Northeast Asia Dryland Regions (NADRs), located in the east of the largest arid zone in the world (Afro-Asian arid zone) [33], are a typical vulnerable eco-environmental area to climate change (i.e., drought) [34]. Due to the vast barren area and grasslands, the responses of the dryland ecosystem to droughts present obvious regional and biomes differences [35], and are still unclear.  
Here, our objective is to explain how the carbon-water process responds to droughts in NADRs via quantitative evaluation of the drought impacts on GPP and ET. Given the different responses of the carbon-water process to seasonal drought [24; 29], the characteristics of the growing season in NADRs [36], the complexity and inconsistency of alternative WUE parameters [37], only summer GPP and ET were chosen as carbon-water indicators. First, we obtained summer GPP, ET via the Vegetation Photosynthesis Model (VPM) and the revised Penman-Monteith (PM) model (PM) in the whole NADR, respectively. Second, we evaluated the dynamics of GPP, ET and drought conditions. Third, we estimated the sensitivity of GPP and ET to droughts with correlation analysis. Last, we quantified the drought impacts on GPP and ET among different biome types.
# Earth-to-Earth Microwave Rain Attenuation Measurements: A Survey On the Recent Literature
## 3 Theoretical and Experimental Framework
### Signal and Rainfall Measurement Parameters
Additional remote techniques such as weather radars and satellites provide less accurate monitoring of rainfall at surface levels [12]. Figure 3: The baseline, i.e., the received power during a dry period of one day.
# Blockchain Applications in Agribbusiness: A Systematic Review
## 3 Results and Discussion
### A Qualitative Systematic Review
#### 3.2.6 Blockchain Application for Livestock in Agribusiness  
The livestock sector is in constant evolution, requiring effective alternatives for its process and management. Advances in transportation and communication technologies promote the development of world markets and facilitate the establishment of animal pro duction units. In that context, several authors have been discussing the use and possibility of blockchain in that sector.  
[PERSON] et al. [72] highlight the application of blockchain in the pig meat supply chain by addressing its influence on information sharing and state that it solves problems of matching supply and demand in the supply chain. They added that the transaction costs between the actors involved can be reduced, bringing several benefits: time, money, and improvement for the whole chain through efficient management of the farms.  
[PERSON] et al. [73] implemented a blockchain platform on a dairy farm to minimize costs, through which it was possible to identify and use the available resources more efficiently, besides making it possible to track the animals' locations and monitor their health conditions in real time.  
[PERSON] et al. [74] designed a study on the acceptance of blockchain in the traceability and transparency of the meat supply chain. They highlighted the different perspectives and opinions of stakeholders, investigating the transparency and traceability system (TTS) of certification and customers' perceptions as to the potential of technology in traceability. The results of adopting the technology as a solution for the current problems affecting the meat production chain in several countries, including Brazil, are promising.
## 3 Experiments and Discussions
### FIL Simulation Experiments
#### 3.1.2 Target Temperature Change  
In practice, the target temperature will vary depending on the location and size of the tumour. To evaluate the tracking performance of the ANFIS-PID controller and the PID controller, the initial target temperature was adjusted from 10 \({}^{\circ}\)C to 15 \({}^{\circ}\)C at the 30 th s of the simulation to simulate the target temperature change and compare the important parameters of the two controllers. The results are shown in Figure 5 and Table 2.  
Both the ANFIS-PID controller and the PID controller responded to the change in target temperature when the target temperature was changed from 10 \({}^{\circ}\)C to 15 \({}^{\circ}\)C. However, the ANFIS-PID controller showed a better performance than the PID controller. After the target temperature change, the PID controller had an undershoot of 8.58% with a large steady-state error (1.09 \({}^{\circ}\)C), whereas the ANFIS-PID controller could still reach close to the target temperature after the target temperature change and there was a small steady-state error (0.18 \({}^{\circ}\)C) with the target temperature, providing a better control effect in RFA applications where the target temperature changes.
# Early Fault Detection and Operator-Based MIMO Fault-Tolerant Temperature Control of Microreactor
## 3 Problem Statement  
This chapter describes the problem setup for this study. In this study, we assume a case in which a tube temperature sensor fails during temperature control of the microreactor system. In this case, we aim to construct a system that allows temperature control to continue without shutting down the entire plant, even during the temperature sensor fault. The following two steps are required:  
1. A system that determines in real time whether the temperature sensor of a tube is normal or abnormal (fault detection system).
2. A control system that compensates for tracking to the reference input in the event of a tube temperature sensor fault (fault-tolerant control systemm)  
The fault detection system consists of a One-Class SVM with some of its inputs added to ChangeFinder's outputs. The discriminant model trained only on normal data learns the relationship between the control input, temperature sensor measurements, and the changepoint scores of the temperature sensor measurements by ChangeFinder. If the observed data do not fit the learned relationship, the model discriminates abnormal conditions. This enables fault detection without requiring prior fault data and without specializing in a particular pattern of faults.  
The fault-tolerant control system is a nonlinear control system that considers Bounded-Input Bounded-Output (BIBO) stability based on operator theory. The system assumes that an unknown fault factor is added to the normal measured value of the temperature sensor that has failed, and the system operates in such a way that the fault factor is removed. If the control system can remove the fault elements, the temperature of the controlled object can follow the reference input even in the event of an abnormality. However, fault-tolerant control systems require the fault detection system to be activated. Early fault detection is necessary from the standpoint of stability.
## 3 Pairwise Clock Synchronization under Gaussian Delays
Therefore, efficient algorithms for joint estimation of the clock offset, clock skew and fixed delay are of great interest. Based on the two-way message exchange mechanism in Equation (2), [PERSON] _et al._[24] extended the result in [18] by assuming an unknown fixed delay \(d\). First, the signaling model Equation (2) is re-formulated in matrix form as:  
\[\underbrace{\left[ \begin{array}{c}T_{1}^{a}\\ \vdots\\ T_{N}^{a}\\ -T_{1}^{d}\\ \vdots\\ -T_{N}^{d}\end{array} \right]}_{\triangleq\mathbf{C}_{r}}+d\cdot\mathbf{1}= \underbrace{\left[ \begin{array}{cc}T_{1}^{b}&-1\\ \vdots&\vdots\\ T_{N}^{b}&-1\\ -T_{1}^{c}&1\\ \vdots&\vdots\\ -T_{N}^{c}&1\end{array} \right]}_{\triangleq\mathbf{C}_{h}}\underbrace{\left[ \begin{array}{c}\phi_{1}\\ \phi_{0}\\ \vdots\\ \phi_{2}\end{array} \right]}_{\triangleq\mathbf{\Phi}}-\underbrace{\left[ \begin{array}{c}X_{1}\\ \vdots\\ X_{N}\\ Y_{1}\\ \vdots\\ Y_{N}\end{array} \right]}_{\triangleq\mathbf{Z}}\]where \(\phi_{0}\stackrel{{\triangle}}{{=}}\theta/f\), \(\phi_{1}\stackrel{{\triangle}}{{=}}1/f\) and \(\mathbf{1}=[1,1,\cdots,1]^{T}\) with dimension \(2N\times 1\). Since \(X_{i}\) and \(Y_{i}\) are independent Gaussian random variables, _i.e._, \(X_{i}\sim\mathcal{N}(0,\sigma^{2}),Y_{i}\sim\mathcal{N}(0,\sigma^{2})\), the log-likelihood function for \(\mathbf{\Phi}\) and \(d\) is expressed as:  
\[\ln p(\mathbf{C}_{r},\mathbf{C}_{h}|\mathbf{\Phi},d)=\ln\frac{N}{2\pi\sigma^{ 2}}-\frac{||\mathbf{C}_{r}+d\cdot\mathbf{1}-\mathbf{C}_{h}\mathbf{\Phi}||^{2} }{2\sigma^{2}}\]  
For a fixed \(d\), the MLE of \(\mathbf{\Phi}\) is given by:  
\[\hat{\mathbf{\Phi}}(d)=(\mathbf{C}_{h}^{T}\mathbf{C}_{h})^{-1}\mathbf{C}_{h}^{ T}(\mathbf{C}_{r}+d\mathbf{1}) \tag{6}\]  
Plugging Equation (6) into the log-likelihood function results in a compressed likelihood function with only one variable \(d\). The MLE of \(d\) is derived by taking the derivative over the resulting log-likelihood function with respect to \(d\), and the MLE of \(\mathbf{\Phi}\) is obtained by plugging the corresponding estimate of \(d\) back into Equation (6). Finally, the MLEs of \(\theta\) and \(f\) are obtained from the MLE of \(\mathbf{\Phi}\) using the invariance principle [23].
## 4 Experiment Design
### Procedure  
The experiment was divided into seven phases.  
* P1. A brief description of the experiment and procedure was given to all participants, who also signed a consent form to participate in the study (~5 min);
* P2. Pretest: complete the pretest, which contained 18 questions about the structural properties and transformational properties of 3D shapes (10 min);
* P3. Let the participants get familiar with the VR device and tool to understand better how to interact with the visualizations and, in the case of the paired groups, how to switch from one collaboration position to the others (~10 min);
* P4. Users were asked to finish a set of problem-based tasks while interacting with our tool (~40 min);
* P5. The Singles participants were asked to complete the VisEngage questionnaire [15] to assess their perceived level of engagement with the tool. The other two collaboration groups were required to complete the VisEngage questionnaire [15] and the collaboration questions listed in Table 2 to measure their perceived levels of engagement and collaboration (~10 min);
* P6. Post-test: same as the pretest (10 min);
* P7. Interview with participants about their feelings and perceptions of the tool and experiment. Participants in the two collaboration groups were also asked about their subjective feedback about the collaboration and the different position arrangements (~5 min).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mean** & **N** & **Std. Deviation** & **Std. Error Mean** \\ \hline Singles & 21.111 & 10 & 11.944 & 3.777 \\ Shared & 21.111 & 10 & 16.312 & 5.158 \\ Non-Shared & 25.555 & 10 & 13.146 & 4.157 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall descriptive data for test scores improvement in %.  
Figure 7: Some sample tasks used in this study.  
The language used in the task questions was plain and easy to understand. The whole experiment took about 90 min to complete, and participants were allowed to take breaks at any time. This setup was similar to a typical weekly lab session the participants would attend for their classes. A short training session was provided to participants before starting the task to let them become familiar with the interface and interactive features. After that, we would not provide any further assistance to participants. The interaction process was video captured for later analysis.
# Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques
## 1 Introduction
These reviews show the progress made in designing and implementing a medical healthcare information system, using emerging wireless VLC technology, implemented with visible LEDs within the transmitter module [14]. The system under analysis employs an optical modulation method to transmit medical and healthcare information. However, a thorough review of the existing literature reveals a scarcity of publications related to the use of channel coding techniques in VLC, particularly in hospital settings, despite the importance of efficiently utilizing this technology for two fundamental reasons: first, to ensure that patients are not adversely affected by the use of the technology, and second, to ensure that data transmission occurs appropriately. Currently, some radiation therapy interventions involve electromagnetic radiation (EMR), which affects both the dermal and internal physiological systems of patients. Radiotherapy is a therapeutic intervention used in the medical field, in which high-energy electromagnetic waves, such as X-rays or gamma rays, are employed to specifically target and eradicate malignant cells within the human body. Although cancer treatment can be effective, it can also induce adverse effects on the skin and internal organs of patients. The occurrence of these adverse effects depends on the type of treatment used and the unique characteristics of the patient. The effects are closely monitored and managed by medical specialists, to mitigate any potential harm to the patient. The remainder of the work is structured as follows. Section 2 of this paper discusses the effects of electromagnetic fields (EMFs) on health, and a table presents the findings of various researchers on the effects of EMFs on healthcare and their impacts on different applications. Section 3 analyzes modulation techniques for VLC in e-healthcare systems. Channel coding schemes for error correction in VLC are explored in Section 4. Section 5 presents the integration of VLC in healthcare scenarios. Section 6 describes future directions and challenges. Finally, Section 7 presents the conclusions of the work developed.