esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study investigates the modeling of surface roughness (Ra) in the laser cutting of EN 10130 steel process by integrating classical statistical and machine learning methods. First, a quadratic model was developed using response surface methodology (RSM) based on a Box-Behnken experimental design with 17 runs, using cutting speed, laser power, and auxiliary gas pressure as input parameters. Although the RSM model achieved an R\({}^{2}\) value of 0.8227, there were still some nonlinear deviations between the predicted and experimental values. To improve the prediction accuracy, a regression tree algorithm was applied to model the residuals of the RSM output. The resulting hybrid model, which combines RSM predictions with machine learning-based corrections, yielded a higher R\({}^{2}\) of 0.8889 and a lower RMSE compared to the original RSM model. A leave-one-out cross-validation (LOOCV) was performed to evaluate the generalization, which resulted in an RMSE of 0.3241 and an R\({}^{2}\) of 0.6039. These findings confirm the effectiveness of the hybrid approach in capturing complex dependencies and improving prediction accuracy, highlighting its potential for advanced process modeling in laser machining.  
surface roughness; laser cutting; response surface methodology; regression tree; hybrid modeling; machine learning 2020 ac academic Editor: [PERSON]
###### Abstract  
Heavy metals are one of the main threats to marine life and ecosystems and any remedial action in that regard is urgently required. The aim of this work is to study the bioremoval of cadmium, chromium and lead in a microalgae strain _Chlorococcus dorsiventrale_ isolated from Tunisian coastal waters along with assessing its enzymatic and molecular responses. The microalgae were tested in artificial seawater to evaluate their capacity for phycoremediation in an aquatic environment. This strain tolerated exposure to Cd (II), Cr (VI), and Pb (II) and was able to grow for 14 days. Cd and Cr exposures elicited a decrease in chlorophyll, lipid and polysaccharide contents, whereas no damages were detected following Pb treatment. For protein content, no significant changes were seen except after Pb exposure which induced a slight increase after treatment with 5 mg/L. The assessment of stress defense-related gene expression using qRT-PCR revealed that exposure to Pb and Cr induced an up-regulation of catalase, superoxide dismutase and photosystem II protein D1 encoding genes. Moreover, heat shock protein 70 was slightly overexpressed. Removal efficiencies for Cr and Pb attained 89% and 95%, respectively. The mechanisms by which C. _c. dorsiventrale_ removed Cr involved both intracellular and extracellular bisopception, while Pb was predominantly removed through membrane adsorption. This study highlights the potential of _C. dorsiventrale_ as an efficient agent for the bioremediation of heavy metal-contaminated water, including industrial wastewater, thus paving the way for practical and environmental applications in pollution control.  
microalgae; heavy metals; enzymatic response; gene expression; pollution control +
Footnote †: journal: Applied Physics Letters
# Modeling and Analysis of Contactless Solar Evaporation for Scalable Application
## 3 Result and Discussion
### Parametric Analysis of the Scalable SCE
#### 3.3.1 Hole Area Ratio  
Figure 8 shows the absorber-emitter temperature \(T_{\text{ae}}\), water surface temperature \(T_{\text{wat+}}\) and evaporation rate under different hole area ratios, with the same hole diameter (\(d\) = 1.1 mm) and air layer thickness (\(\delta\) = 2.0 mm). When the hole area ratio increases, both \(T_{\text{ae}}\) and \(T_{\text{wat+}}\) decrease, due to less energy input from smaller absorber area. On the contrary, the natural evaporation rate under no solar input increases slightly (also referred as \"dark evaporation\" in the literature, which is decided purely by the vapor diffusion from water surface to the faraway ambient) due to the enhanced vapor diffusion with a larger diffusion area. The net contribution of solar input (referred as \"net evaporation rate\") firstly increases, and then declines after the peak value of 0.463 kg\(\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\) is achieved under a hole area ratio of 14%. This can be explained by the conflict between increased solar absorption and enhanced vapor diffusion, whereas the overall impact to evaporation performance is decided by the relative significant of two aspects. When the hole area ratio is lower than 14%, the vapor diffusion enhancement is more important than the increased solar absorption, so the evaporation rate increases with a higher hole area ratio. When the hole area ratio is higher than 14%, the relative importance between vapor diffusion  
Figure 7: Mechanism diagram of the downward heat transfer and the vapor diffusion for SCE.  
enhancement and increased solar absorption overturns, and the evaporation rate decreases with the higher hole area ratio. The above analyses indicates that an optimal hole area ratio exists (14%) to maximize the evaporation rate of scalable SCE. It is necessary to be noted that the optimal hole area ratio is dependent on the geometrical parameters and operation conditions.
## 3 Case Study
### TEP Dataset
#### 3.1.4 Efficacy of Task Sequencing in GOPML
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**3-Way Accuracy**} & \multicolumn{2}{c}{**8-Way Accuracy**} \\  & **5-Shot** & **6-Shot** & **5-Shot** & **6-Shot** \\ \hline MAML Accuracy & 90.32 & 92.10 & 80.75 & 81.92 \\ GOPML Accuracy & 92.53 & 97.48 & 84.01 & 86.59 \\ MAML F1 & 91.22 & 92.70 & 81.13 & 82.60 \\ GOPML F1 & 93.58 & 96.99 & 82.52 & 85.93 \\ MAML precision & 90.82 & 91.93 & 78.40 & 81.52 \\ GOPML precision & 92.01 & 96.16 & 81.15 & 85.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fault classification results of GOPML and MAML on the TEP dataset.
## 3 Results
### Transmission Electron Microscopy  
The microstructure of the NPs, from the lowest (CeO1g) to the highest (CeO5g) Ce concentration, was investigated using TEM, SAED, and HRTEM imaging modes. Characteristic images recorded from the two samples are illustrated in Figure 4. In particular, the morphology and size of the CeO1g NPs are shown in the TEM image of Figure 4a. The nanoparticles have almost a spherical shape with sizes ranging from 4.5 to 11 nm, with the majority having an average size of 8.5 \(\pm\) 2.5 nm. The corresponding SAED ring pattern, shown in Figure 4b, depicts the CeO1g crystal structure. The sequence of the diffraction rings, from the inner to the outer, corresponds to the interplanar spacing of [111], [200], [220], [311], [222], [400], and [331] etc. lattice planes of the CeO\({}_{2}\) fluoric structure (cubic F m 3 m). The HRTEM image of an NP, showing its atomic structure projected along the \(<\)110\(>\) zone axis, is illustrated in Figure 4c. Magnification of the area bounded by the rectangular frame and the corresponding calculated Fast Fourier Transform (FFT) are given as insets. The spacing of the two families of {111} lattice fringes is also indicated. The FFT reveals the special frequencies, decomposed by the HRTEM image, and indicates the characteristic \(<\)110\(>\) projection of the cubic CeO\({}_{2}\) structure.  
Figure 2: XRD patterns of CeO\({}_{2}\) nanoparticles.  
Figure 3: SEM micrographs of synthesized materials and representative EDS spectrum verifying the presence of cerium.  
The CeO5g sample comprised two types of crystalline nanoparticles with respect to their size, as displayed in the TEM images of Figure 4d,e. Agglomerations with rather spherical small-size NPs and those with irregular shape and a much larger size compared to the CeO1g sample, being the majority, are observed. The size of the larger NPs is 35 \(\pm\) 10 nm, although larger than that were also observed as those presented in Figure 4e. Despite the difference in size, all NPs possessed the cubic CeO\({}_{2}\) fluorite structure. The HRTEM image of Figure 4f shows the atomic structure of the darker, in contrast, NP of Figure 4e. The image is viewed along the \(<\)110\(>\) zone axis and was recorded from the edge area of the NP. The magnified part of the area bounded by the rectangular frame and the corresponding FFT, given as insets, clearly reveals the cubic CeO\({}_{2}\) structure. The lattice fringe spacing, also equal to 0.312 nm, corresponds to the d-spacing of the [111] lattice planes.
# A Novel QoS-Aware ARQ Scheme for Multi-User Transmissions in IEEE802.11 ax WLANs
## 3 Methods
### The Formulation of the Optimization Problem Based on the Sliding of the Aggregation Window
The probability distribution of the category is derived by  
\[Pr(S=\sum_{s=1}^{L}(1+\alpha_{s}))=\prod_{i=1}^{L}(1-P_{e,i}). \tag{6}\]  
According to the architecture of the BP-ARQ scheme, the number of backups for each packet is described by a vector \(\vec{R}\):  
\[\vec{R}=(r_{1}\ r_{2}\...\ r_{L-1}\ r_{L}). \tag{7}\]  
The expected number of packets uploaded at the end of the transmission is calculated by  
\[E[S] =(\sum_{s=1}^{L}(1+\alpha_{s}))\times(\prod_{i=1}^{L}(1-P_{e,i}))+ \sum_{l=1}^{L-1}((\sum_{s=1}^{l}(1+\alpha_{s}))\times(\prod_{i=1}^{l}(1-P_{e, i}))\times P_{e,l+1}),\] \[=\sum_{l=1}^{L}((1+\alpha_{l})\times(\prod_{i=1}^{l}(1-P_{e,i}))). \tag{8}\]  
Consequently, the optimization problem and the constraints that are derived according to the length limitation of the A-MPDU and the scheduled duration are formulated below:  
\[max_{\vec{R}}\ E[S], \tag{9}\]  
Figure 8: The status of the reordering buffer after the transmission in Category 3. Figure 7.0 The status of the reordering buffer after the transmission in Category 2. Figure 7.1 Figure 7.2 Figure 7.3  
The parameters are defined as follows: \(len_{maxampdu}\) is the maximum length of the A-MPDU frame. \(rate\) is the physical data transmission rate of the user. \(T_{sche}\) is the scheduled duration of the MU transmission. According to Equation (8), there is an upper bound on the objective function and the upper bound is calculated by  
Figure 7.4  
Because the domain of \(\vec{R}\) is discrete, the difference function is derived for the k-th dimension in \(\vec{R}\). When \(r_{k}\) is increased by 1, the difference of the objective functions is calculated by  
Figure 7.5  
Since the \(\Delta E[S]\) is positive and k is arbitrary, the objective function increases monotonically in every dimension of \(\vec{R}\). Therefore, the objective function converges to \(B_{upper}\) when all the elements in \(\vec{R}\) approach infinity. However, the condition of converging to the upper bound is difficult to satisfy in the optimization problem because of the constraints. Therefore, the objective value is considered to converge to the upper bound when the difference between the upper bound and the objective value is smaller than the convergence accuracy, \(C\). The optimization problem described in Equation (9) is non-linear integer programming. Although the optimal solution can be obtained by verifying all the feasible solutions, it is difficult to enumerate all of them in a tolerable time because the number of feasible solutions explodes in some cases [23]. The number of feasible solutions is \(\binom{N-1}{L-1}\) when the scheduled duration can contain N sub-frames in the A-MPDU frame. For example, when N is 64 and L is 32, there are more than \(9\times 10^{17}\) feasible solutions. The time complexity of enumerating all the feasible solutions is \(O(\binom{N-1}{L-1})\) and the space complexity is \(O(L)\).
## 4 Results  
### Delphi Results  
Following the description and definition of the Delphi method explained in Section 3, the results are presented in what follows.  
1. A total of 24 experts (one of them did not respond at this stage, so we decided to continue with 24 experts instead of 25 in order to not stop the process) responded with their determinants and these were examined, curated, grouped, adjusted, and finally formatted in a table (see Table 1).  
A total of 22 experts, out of the remaining 24, responded to the questionnaire to reach an initial agreement when it comes to prioritizing which determinants were more or less important. We, therefore, had an attrition rate of three people who did not respond to the questionnaire after several gentle reminders. The results of the first averaged ranking among the 22 experts can be observed in Table 2. Again, the information was input into a personal database of the research team for further analysis.  
The determinants from the first consensus round were then clustered into two groups: Group A and Group B. Group A included the five factors that reached more consensus in terms of being the most relevant determinants for car expenditures according to experts' criteria (i.e., Determinants 4, 5, 2, 3, and 1); Group B included the other five determinants, which received fewer votes out of the total 10 determinants (i.e., Determinants 6, 7, 8, 9, and 10). They were presented according to the aforementioned criteria (see Table 3) and presented again to experts for the second consensus stage according to the Delphi method.  
All data analysis was conducted on an MS-Excel spreadsheet.  
After having presented the list of the determinants grouped in two categories, the group of experts was requested to rank the table again. This process was launched twice to re-confirm the answers (on two different days). First, the group of experts concluded that the five determinants were indeed the most relevant in terms of drivers' level of spending around their car mobility. Second, they ranked those determinants as reflected by Table 4. Hence, Table 3 collects the information from the experts while Table 4 orders and ranks the opinions of the experts. Both tables show the same information, the only difference being that Table 4 is ranked according to experts' consensus decisions.  
As shown, the highest ranked (top 1) determinant was \"on-board technology offering warning messages generated by the car (IoT device)\" regarding driver's expenses according to all experts' opinions, followed by \"proximity\" and \"marketing campaigns\".
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 2 Related Works
### Aomdv  
AOMDV [8] computes and establishes multiple link-disjoint paths between a source and a destination in a wireless network. AOMDV consists of two main activities: computing multiple loop-free paths and finding link-disjoint paths. Unlike AODV, which discards duplicates of RREQ messages at intermediate nodes, AOMDV allows an intermediate node to accept and process duplicates of the RREQs if the duplicates arrive via a trajectory that does not already include the intermediate node. This mechanism establishes multiple loop-free paths at every node.  
Each RREQ carries an additional field called _firsthop_ to indicate the first hop taken by it. Each node also maintains a _firsthop-list_ for each RREQ to keep track of the list of neighbors of the source through which the node has received a copy of the RREQ. When an intermediate node receives a duplicate of an RREQ, it does not immediately discard the duplicate. Instead, the intermediate node checks the RREQ copy to see if it provides a new node-disjoint path to the source by comparing the _firsthop_ field in the RREQ copy and the _firsthop-list_ in the node for the RREQ. If it provides a new path, AOMDV invokes the route update rule to check if a reverse path can be set up. If a reverse path is set up and a valid route to the destination is available, the intermediate node sends an RREP back to the source. RREPs are sent for each received RREQ. The multiple routes are made by RREPs that follow the reverse routes previously set up in intermediate nodes. At the destination, reverse routes are set up just as in the case of intermediate nodes.  
Since there are many copies of RREQ and RREP messages sent in the network, AOMDV has high control overhead for discovering and establishing multiple paths.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 4 City-Wide Deployments
### Infrastructure Deployment in the City of Barcelona
#### 4.1.2 Edge/MEC Tier  
The edge/MEC nodes in Barcelona are installed in two locations within the _22@ area_. The primary location is in BeTeVe premises, hosting two edge servers, which act as edge NFVI. The second edge/MEC node is deployed in a street cabinet at the crossing of the _Llacuna_ and _Pere IV_ streets, where an edge server provides the extended edge NFVI. These two edge locations are equipped with dedicated L2/L3 routers (Cisco ASR920) to connect the edge computing nodes with the core DC located at i2 CAT. Likewise, as shown in Figure 3a, the edge/MEC locations are connected to the RAN elements in _22@ area_ and the city hall using a dedicated fiber.
## 2 Motivating Scenario  
Our motivating scenario is based on a set of five selected buildings that form a starting point towards a federation of smart buildings that contribute to building joint knowledge about IAQ. Each building contributes to optimizing its own life cycle and to extending common knowledge by sharing its monitoring model with other buildings. The pilot buildings include:  
* The Danilo Lokar primary school, located in Ajdovscina, Slovenia;
* The Peavey hall research laboratory, located in Corvallis, Oregon, USA;
* The Nobatek research building, located in Anglet, France;
* The InnoRenew research building, located in Izola, Slovenia;
* The AlbaComp company offices, located in Szeged, Hungary.  
The data coming from these buildings require privacy, security, and independence from the cloud. As a public institution, the primary school requires an IAQ monitoring that does not reveal information about pupils and teachers. The three research laboratories need to keep their monitoring activities as well as building occupation patterns away from public view. The private research company needs independence from the cloud to assess its operation without any trusted third party.  
However, all five pilots would benefit from enabling and exploiting common knowledge that would enable better building operation and management and improved efficiency in classrooms and offices. The main element that the multiple models have to overcome is the different climate conditions the buildings are submitted to due to their geographical location. Therefore, sharing models enables the identification of which factors responsible for building behavior are local and which are not, which helps in identifying which measures are only applicable to local buildings or to all.  
This scenario motivates the need for autonomous communities of sensors that, locally, collaborate to improve building operation and well being and, globally, contribute to improving the prediction of building conditions. In the following, we review the most relevant related works that connect to our contributions.
# Mind, Machine, and Meaning: Cognitive Ergonomics and Adaptive Interfaces in the Age of Industry 5.0
## 2 Human-Machine Interaction: Definitions and Key Concepts  
### Evolution of the Human-Machine Interaction  
#### 2.1.1 From Mechanization to Intelligent Automation  
The development of the human-machine interaction has followed the trajectory of industrial evolution, starting from mechanization and culminating in the emergence of intelligent, human-centered systems. In the early industrial revolutions, machines were designed primarily to extend physical labor and operated based on mechanical or rudimentary electromechanical principles. Human interaction was minimal and mostly limited to manual controls and monitoring. Automation became increasingly important during the 20 th century, and programmable logic controllers (PLCs) allowed machines to execute predefined sequences without continuous human input. However, this approach remained rigid and lacked adaptability [8].  
The shift toward Industry 4.0 introduced smart manufacturing systems based on cyber-physical systems (CPS), real-time data analytics, and cloud connectivity. These technologies enabled dynamic decision-making and more sophisticated interaction patterns between humans and machines. Today, Industry 5.0 continues this transformation by emphasizing not only productivity and efficiency but also sustainability, resilience, and a human-centric design approach [8,9].  
2.1.2. The Roles of Artificial Intelligence and Autonomous Systems  
Artificial intelligence (AI) has played a pivotal role in evolving the HMI from static, preprogrammed interactions to context-aware, adaptive systems. Machine learning algorithms such as deep learning, computer vision, and natural language processing allow machines to understand human behavior, gestures, speech, and emotions, leading to more natural and seamless interactions [10].  
Using these capabilities, autonomous systems can make decisions in real time and adapt to dynamic environments with limited human involvement. Such systems are now used not only in industrial robotics but also in areas like telemedicine, autonomous vehicles, and service robots. The convergence of AI with the HMI has also brought ethical and cognitive challenges, particularly in ensuring trust, maintaining human oversight, and preserving transparency in decision-making processes [10; 11].  
Furthermore, using immersive technologies like augmented reality (AR), virtual reality (VR), and extended reality (XR) improves interactions by offering operators clear visual guidance, realistic simulations, and digital feedback while performing complex tasks. These tools are particularly valuable in training, maintenance, and safety-critical operations [10].