esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Extraction of EOs
#### 2.1.2 Pulse Electric Fields Application  
The pulsed electric fields were applied in batch mode using the treatment chamber of Figure 1a, schematically drawn in Figure 2a. A typical voltage and current pulse waveforms, for an operation example, with 10 k and 80 A amplitudes, respectively, for a 10 \(\upmu\)s pulse with 1 Hz frequency, is given in Figure 2b.  
To successfully apply PEF, it is important to know and define some of its parameters, such as the electric filed strength, \(E\) (kV/cm), treatment time, \(t_{PEF}\) (\(\upmu\)s), and the specific  
Figure 1: (**a**) Batch treatment chamber used for PEF application. (**b**) Pulsed generator used to apply HV pulses to the batch treatment chamber. (**c**) Clevenger apparatus used for the HD process for EO extraction from plants.  
Figure 2: (**a**) Batch treatment chamber with volume V = h\(\cdot\)d\(\cdot\)1 (cm\({}^{3}\)), where h (cm) is the height of the mixture, d (cm) the distance of the electrodes and 1 (cm) the width of the chamber. The voltage U (kV/cm) is applied between the two electrodes. (**b**) Voltage and current pulse example during the experiments, (green) voltage 2 kV/div, (yellow) current 20 A/div, 2.5 \(\upmu\)s/div.  
energy, \(W_{s}\) (kJ/kg), which are also used for comparison purposes with the results from different authors.  
The electric field strength can be given by (1), where U (kV) is the applied voltage and \(d\) (cm) the distance between the electrodes.  
\[E=\frac{U}{d}. \tag{1}\]  
The electric field is applied in \(n\) discrete pulses, with a defined pulse width, \(t_{on}\) (us), corresponding to a treatment time given by,  
\[t_{PEF}=n.t_{on}. \tag{2}\]  
where each pulse has an energy, \(W_{p}\) (\(J\)), of,  
\[W_{p}=U.I.t_{on}. \tag{3}\]  
considering \(I\) (\(A\)) is the pulse current amplitude.  
The mixture inside the treatment chamber, for this specific study, is water and plant material, with an electrical conductivity, \(\sigma\) (mS/cm), which can change slightly during the treatment time, as the plant material infuses in the water. Because of this slight change during the treatment time, it is important to use an average value for \(\sigma\), as it will influence the value of the pulse current amplitude, given by,  
\[I=\frac{U.A_{e}.\sigma}{d} \tag{4}\]  
where \(A_{e}\) (cm\({}^{2}\)) refers to the electrode used area. Then, the total applied energy \(W_{t}\) (\(J\)), during the treatment time, is given by  
\[W_{t}=n.W_{p}. \tag{5}\]  
and the specific energy applied, can be calculated by,  
\[W_{s}=\frac{W_{t}}{V.\rho} \tag{6}\]  
Considering \(V\) (mL) is the volume of the treatment chamber used and \(\rho\) (kg/L) is the approximate density of the mixture, where for simplicity it was assumed to be the same as water, 1. From (7), the increase in temperature, \(\Delta T\) (\({}^{\circ}\)C), from the application of PEF, can be calculated as,  
\[\Delta T=\frac{W_{s}}{C_{p}} \tag{7}\]  
where \(C_{p}\) is the specific heat capacity (kJ/(kg.\({}^{\circ}\)C)) of the treated material, which was assumed to be similar to water, 4.18 kJ/(kg.\({}^{\circ}\)C).
## 3 Mathematical Modeling
#### Mathematical Model  
Given a set of patients \(N=\{0,1,2,\ldots,n\}\) where 0 represents depot node, a set of caretakers \(K=\{1,\ 2,\ \ldots,\ m\}\) and a set of time windows corresponding to the caretakers, the objective is to find a route for each caretaker to start and return to the origin, with all patients needing to be visited only once with a minimum total cost. Each caretaker must start the job within the time window. This HHC problem can be formulated as a mixed-integer linear programming (MILP):  
\[\text{minimize}\ \sum_{j=0}^{n}\sum_{i=0,\ i\
eq j}^{n}c_{ij}x_{ij}^{k}\]  
subject to  
\[\sum_{j=1}^{n}x_{0j}^{k}=1,\ \forall k\in K \tag{1}\]  
\[\sum_{j=1}^{n}x_{j0}^{k}=1,\ \forall k\in K \tag{2}\]  
\[\sum_{k=1}^{m}\sum_{i=0,i\
eq j}^{n}x_{ij}^{k}=1,\ \forall j\in N\backslash\{0\} \tag{3}\]  
\[\sum_{k=1}^{m}\sum_{j=0,i\
eq j}^{n}x_{ij}^{k}=1,\ \forall i\in N\backslash\{0\} \tag{4}\]\[\sum_{i=0,i\
eq j}^{n}x_{ij}^{k}-\sum_{l=0,l\
eq j}^{n}x_{jl}^{k}=0,\ \ \forall j\in N\backslash\{0\},\ \forall k\in K \tag{5}\]  
\[\sum_{i=0,i\
eq j}^{n}x_{ij}^{k}\leq r_{jk},\ \forall j\in N,\ \forall k\in K \tag{6}\]  
\[u_{i}-u_{j}+q_{j}\leq Q\Big{(}1-x_{ij}^{k}\Big{)},\ \ \forall i,j\in N\backslash\{0\},\ \ i\
eq j,\ \ \forall k\in K \tag{7}\]  
\[q_{j}\leq u_{j}\leq Q,\ \ \forall j\in N\backslash\{0\} \tag{8}\]  
\[t_{i}+p_{i}^{k}+d_{ij}-M\Big{(}1-x_{ij}^{k}\Big{)}\leq t_{j},\ \ \forall i\in N,\ \ i\
eq j,\ \ \forall j\in N,\ \ \ \forall k\in K \tag{9}\]  
\[e_{j}\leq t_{j}\leq l_{j},\ \ \forall j\in N \tag{10}\]  
\[x_{ij}^{k}\in\{0,1\},\ \ \forall i,j\in N,\ \ i\
eq j,\ \ \forall k\in K \tag{11}\]  
With the objective function being to minimize total costs, constraints (1) and (2) are to make sure that each caretaker starts and ends their route at the origin. Constraints (3) and (4) make sure that, for each patient, there is only one caretaker visit and departure. Similarly, constraints (5) ensure that a caretaker leaves a patient for the next one after completing service to the first patient. Constraints (6) are the compatibility constraints to guarantee that only a caretaker compatible with the patient will travel and take care of that patient. Constraints (7) and (8) are the subtour elimination constraint together with capacity constraints. Constraints (9) will activate when caretaker \(k\) travels from patient \(i\) to patient \(j\) (\(x_{ij}^{k}=1\)). The completion time of patient \(i\) (sum of starting time, service duration and travel time) must be less than the starting time of patient \(j\). Constraints (10) are time window constraints. Constraints (11) ensure integrality of variables. Finally, \(M\) is a sufficiently large number.
# Development of a Maintenance and Spare Parts Distribution Model for Increasing Aircraft Efficiency
## 3 Conceptual Model Design  
### Target System  
A set of parameters were defined within the conceptual model to define goals and measure the results of the different simulation models when facing various demand scenarios. The key performance indicators (KPIs) selected consist of a combination of distribution performance, inventory levels, maintenance intervals, spare parts demand, and economic parameters. The KPIs and their formulae are shown in Table 1.
## 5 Sensors
### RGB Cameras  
Standard RGB cameras are commonly employed in precision agriculture for tasks such as crop monitoring, growth analysis, and visual inspections. These cameras capture high-resolution color images that help farmers assess crop conditions by detecting visual signs of anomalies like diseases, pest damage, or nutrient deficiencies. While a broad range of commercial cameras are available, harsh field conditions such as dust, water, and temperature extremes limit the choice to models that meet stringent environmental protection standards.
## 2 Materials and Methods
### Design Variables Levels  
The energy delivered from the hot water storage tank to the absorption chiller for the production of cold water was evaluated, as a function of the collector field surface, volume of the hot water storage tank and driving temperature of the absorption machine.  
The levels selected for the driving temperature were 75, 80, and 85 \({}^{\circ}\)C. A hysteresis band in the thermostat of 5 \({}^{\circ}\)C was considered for all three temperatures in order to guarantee the difference in the thermal level of the absorption chiller. That is, in the first case (set-point temperature = 75 \({}^{\circ}\)C), when 77.5 \({}^{\circ}\)C is reached in the storage tank, hot water is sent to the chiller until the temperature of the tank drops to 72.5 \({}^{\circ}\)C, which is a value of 2.5 \({}^{\circ}\)C higher than the minimum operating temperature set by the manufacturer of the absorption machine. In the second case, the operation of the machine takes place with water between 82.5 and 77.5 \({}^{\circ}\)C and in the third between 87.5 and 82.5 \({}^{\circ}\)C. The temperature of 87.5 \({}^{\circ}\)C is only 0.5 \({}^{\circ}\)C lower than the chiller nominal driving temperature (88 \({}^{\circ}\)C).  
The levels selected for the specific collector area were: 2.5 m\({}^{2}\)/kW\({}_{\rm cool}\), 3 m\({}^{2}\)/kW\({}_{\rm cool}\), 3.5 m\({}^{2}\)/kW\({}_{\rm cool}\), and 4m\({}^{2}\)/kW\({}_{\rm cool}\). The specific collector area is defined as the ratio between the surface of the collector field and the nominal power of the absorption chiller (35 kW). In the present study, the specific surfaces analyzed correspond to collector surfaces of 87.5 m\({}^{2}\) (35 collectors), 105 m\({}^{2}\) (42 collectors), 122.5 m\({}^{2}\) (49 collectors), and 140 m\({}^{2}\) (56 collectors), respectively. The values tested are common in the design of this type of facilities [15].  
The specific storage volumes selected were: 15 L/m\({}^{2}\), 32.5 L/m\({}^{2}\) and 50 L/m\({}^{2}\). The volume of the hot water storage tank (chiller driving medium) is specified per unit of solar collector surface [16]. The volume of the storage tank will therefore vary from 1.3 m\({}^{3}\) corresponding to the collector surface and specific storage values of 87.5 m\({}^{2}\) and 15 L/m\({}^{2}\), to 7 m\({}^{3}\) corresponding to the collector surface and specific storage values of 140 m\({}^{2}\) and 50 L/m\({}^{2}\).
###### Abstract
Experimental validation of the model, based on data from a crystallizer with a magnetic stirrer [19], revealed that the rate of association between two clusters is independent of their size. A mathematical model of the coagulation process for dispersed colloidal particles in a stirred flow was developed to predict the kinetics of shear-induced coagulation [20]. The model combines the discrete element and final volume methods. It calculates particle trajectories and rotation rates based on the forces, torques, and flow fields within the domain. The model demonstrates that the ultimate size of the produced clusters depends on the applied shear rate and the surface energy, which is a measure of particle adhesion. The cluster size is determined based on the equilibrium between the hydrodynamic force acting on the cluster and the maximum adhesion force among the primary particles. A new mechanistic model for mineral scale crystallization and inhibition has recently been developed. This model accurately predicts the barite crystallization induction time, both in the absence and presence of ten commonly used scale inhibitors [21]. A simple model that correlates the induction time with the saturation level prevailing on the membrane surface was introduced for RO systems [9; 22]. The model relies on the assumption that the induction time is inversely proportional to the nucleation rate. Determination of the induction time involves measuring the slope of the linear relationship between \(\mathrm{ln}(\mathrm{t_{ind}})\) with \(1/(\mathrm{ln}\mathrm{S_{w}})^{2}\), where \(\mathrm{S_{w}}\) is the supersaturation ratio on the membrane surface. [PERSON] et al. [23] developed a model that integrates both surface and bulk crystallization into a resistance in-series framework, which was modified using concentration polarization theory and crystallization kinetics. The model aimed to analyze the scaling of RO membranes across a wide range of conditions, including crossflow velocity, transmembrane pressure, permeate recovery, and operation mode. The model's outcomes suggest that both surface and bulk crystallization play a significant role in crossflow filtration and that RO scaling due to bulk crystallization tends to increase with a higher crossflow velocity and permeate recovery. Simulations were developed to predict supersaturation along reverse osmosis modules in seawater desalination. These simulations are based on the application of conservation principles and chemical equilibria equations along RO modules. Supersaturation has been used as a key parameter to assess scaling [23]. The objective of the present work was to develop an analytical model for predicting induction times in RO systems, both with and without an antiscalant, and to validate it using independent datasets. Following the validation of the model, the impact of its variables and parameters on induction times was analyzed.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 8 Discussion  
So far, the paper has presented a comprehensive and integrative review of the recent advances in fusing computer vision and artificial intelligence (AI) within collaborative robotic systems. By systematically analyzing key technologies--ranging from object detection, scene understanding, and human pose estimation to reinforcement learning and explainable AI--we have outlined how the convergence of perception and cognition is transforming cobots into perceptive, adaptive, and context-aware collaborators.  
Cobots are used in specific application domains. The following are the three most popular application domains for cobots:  
1. Manufacturing and Industry: Cobots are central to the evolution of flexible, human-centric production lines. By combining foundation models with multimodal sensing, industrial robots are executing complex tasks in real time. The focus on safety, sustainability, and seamless human-machine collaboration aligns with the principles of Industry 5.0 [1, 4, 81, 109, 124, 126, 137].
2. Healthcare and Assistive Robotics: Cobots are increasingly supporting hospital operations--from delivery and cleaning to patient monitoring [89, 133]. Emotion-aware and trust-sensitive designs are improving safety and acceptance. In surgical settings, vision-guided robots are enhancing tool precision, while rehabilitation robots are using pose estimation for adaptive therapy delivery [151, 152].
3. Education and Training: Educational robots are leveraging vision and affective computing to create adaptive, emotionally intelligent learning environments. These systems promote student engagement, they personalize instruction, and help educators monitor cognitive and emotional states to improve learning outcomes [148].  
Current trends in collaborative robotics demonstrate a shift from reactive, rule-based systems to data-driven, proactive robots capable of reasoning about complex visual environments and anticipating human intent. Deep learning architectures have dramatically improved performance in visual recognition, while multimodal sensor fusion is enabling richer, more nuanced human-robot interaction (HRI). The following subsections present the major trends in this domain.  
\begin{table}
\begin{tabular}{c c c} \hline Topic & Description & Key Challenges/Notes \\ \hline Trust Calibration in HRC & Systems that monitor and adapt to human trust levels & Avoiding over-reliance or under-utilization; trust repair mechanisms \\ Socio-Cognitive Models & Cognitive architectures for intention, emotion, and social cues & Theory of Mind; joint attention; real-time adaptation \\ \hline Ethical and Societal & Ensuring privacy, fairness, and transparency in vision–AI & Ethical safeguards; bias mitigation; regulatory compliance \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of human-related challenges and trends.
## 2 Methodology
### Time Lapse Inversion
#### 2.4.2 The Process of 3D Time-Lapse Electric Field Inversion  
The general process of the 3D time-lapse electric field inversion algorithm for CSAMT can be roughly divided into the following four steps:  
1. Import data: Inversion parameters such as regularization factors \(\lambda\) and \(\beta\), minimum fitting error \(\varepsilon\), observed data D, model parameters M, and prior model parameters M\({}_{\mathrm{ref}}\);
2. Forward modeling: Calculate the electric field response F(M) for each time-lapse model;
3. Iteration assessment: Evaluate the objective function \((\sigma_{s})\) and determine the termination condition for inversion based on the root-mean-square RMS being less than \(\varepsilon\).
4. Model update: Utilize the gradient of the objective function g(m) in Equation (12). By choosing the negative gradient direction as the descent direction for the objective function to calculate the search direction p, update the step length \(\alpha\), and then update the model parameters using the formula m = m + \(\alpha\)p. The detailed calculation process refers to Figure 2.  
In this algorithm, the minimum fitting error \(\varepsilon\) is set to 1.05. When the RMS reaches the minimum fitting error of 1.05, the program determines that the inversion result is closest to the true underground situation and terminates the iteration.
## 4 Discussion
### Combinations of Challenges  
Some of the challenges, such as security and trustworthiness, are the result of combinations among others. We briefly describe some of them as below.  
* According to [8], the security challenge implicitly arises from other challenges such as trust, confidentiality, and privacy.
* According to [109], the transparency challenge implicitly leads to other challenges such as accountability and explainability in the decision-making process of the learning model.
* According to [131], the trustworthiness challenge implicitly leads to other challenges such as fairness, explainability, accountability, and reliability.
* According to [122], the verification challenge implicitly results in other challenges such as safety and robustness.
* According to [46; 139], the safety challenge has a solid relationship with security and ethical challenges.  
In combined challenges, we may face paradox and contradiction. For example, improving the security of a system might not be possible without using users' private information [140].
## 3 Results and Discussion
### Well-Diffusion Assay  
Phenolic rich extracts, such as the ones used in this work, have been widely described as possessing antimicrobial properties [14; 24; 25; 26; 27; 28]. The results obtained for the screening of the hereby produced extracts' antimicrobial activity (Table 1) showed that in the eight microorganisms assayed, acorn extracts were effective in inhibiting the Gram-positive bacteria. Comparing these results with those of previous works, it is interesting to see that the results reported by [PERSON], [PERSON] [8] were completely opposite to ours as a methanolic _Q.__lex_ extract inhibited the growth of _C. albicans_, _P. fluorescens_ and _E. coli_ but not that of _S. aureus_. Similarly, [PERSON], [PERSON] [6] showed that methanolic-based extracts of _Q.__lex_ bark were capable of inhibiting Gram-positive and Gram-negative bacteria alike. These discrepancies in activity are probably due to the nature of the extracts as these works used methanol as an extraction solvent, while water was used in this work, and different solvents extract different compounds [17]. Moreover, the differences in antimicrobial activity observed between Gram-positive and Gram-negative bacteria may be linked to their phenolic content, as these compounds have been widely described to have an effect that is, mostly, membrane-based. Thus, the outer lipidic membrane characteristic of Gram-negative bacteria presents an additional protection mechanism and explains phenolic compounds' lack of activity against these bacteria, which is in line with the results observed [29; 30; 31; 32]. In fact, these results are in accordance with both those reported by [PERSON], [PERSON] [33], who showed that aqueous acorn shell extracts inhibited both MRSA and vancomycin-resistant _S. aureus_ (VRSA), while exhibiting little to no activity against the tested Gram-negative strains; and with those reported by [PERSON], [PERSON] [34] and by [PERSON], [PERSON] [18], who showed that fruits extracts rich in phenolics and tannins had a strong antibacterial action against Gram-positive bacteria and weak to null activity against Gram-negative bacteria. Furthermore, [PERSON], [PERSON] [22] showed that _Q.__lex_ and _Q.__super_ extracts were rich in ellagic acid, gallic acid and (\(-\)) epicatechin gallate, all compounds known to inhibit Gram-positive bacteria and S. aureus in particular [35].
## 4 Results
### Future Deformation Prediction  
In this study, the machine learning BP was used to predict the ground deformation; the results shown in Section 4 indicated that machine learning has great potential for predicting the ground deformation. There are many kinds of machine learning methods such as BP, Random Forest, and Support Vector Machine (SVM). BP was adopted because this method was commonly used and can be easily deployed. Besides, the BP was more precise than the linear regression and multiple linear regression. Since the machine learning has shown great ability to predict the ground deformation, the adoption of advanced methods would be introduced. Through using a long time series of high-resolution independent data and the Deep Learning (DL) method, the ground deformation can be precisely predicted. Both the analysis of SAR images and machine learning prediction of obtaining the ground deformation would stress some greater useful information than the adoption of a single method. This would likely provide useful information to city planning, especially the building of important sites such as airports and highways. The prediction results make it possible to guide the designing to avoid serious ground deformation, which could happen in some places in the future. In order to obtain a more reliable prediction result, Deep Learning (DL) could be adopted, which had a greater depth of nodes, and thus would likely reduce the random uncertainties and systemic errors.
# Recent Progress on Novel DSP Techniques for Mode Division Multiplexing Systems: A Review
## 5 Novel DSP Techniques for MIMO Systems  
With the fast improvement of data processing capacity, many novel DSP algorithms have been proposed in the last few years. This section focuses on the recent emerged DSP techniques for MIMO systems, including the extended DBP algorithm to handle nonlinear effects in FMFs, AI techniques to achieve MIMO equalization, and so on. In comparison with the conventional methods, these novel DSP algorithms have the potential to replace them for better performance.