esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Campaign
#### 2.2.1 Thermal Treatments  
Thermal treatments consisting on a heating process followed by a controlled cooling phase were performed. Seven levels of temperatures were considered, corresponding to 20 \({}^{\circ}\)C (room temperature), 100-200 \({}^{\circ}\)C (low-), 300-400 \({}^{\circ}\)C (mid-) and 500-600 \({}^{\circ}\)C (high-temperatures). These temperature levels were mainly chosen to detailly analyze the temperature effects on mortars, which at lower levels of temperatures (up to 300 \({}^{\circ}\)C) mainly consist in changes of their physical properties, while at higher temperatures (above 300 \({}^{\circ}\)C) are related to the degradation of their mechanical properties. The limit of 600 \({}^{\circ}\)C was imposed due to the high thermal damage induced in the mortars by higher temperatures, which turns almost impossible to evaluate any residual mechanical strength.  
An electric furnace, brand Simcic S.R.L., with internal dimensions of 400 \(\times\) 400 \(\times\) 700 mm and a maximum thermal capacity of 1200 \({}^{\circ}\)C was used (Figure 1). Temperature rise during heating followed  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Temperature**} & **W** & **CA** & **UC** & **ST** & **TPB** \\  & **50 \(\times\) 100 [mm]** & **50 \(\times\) 100 [mm]** & **50 \(\times\) 100 [mm]** & **50 \(\times\) 100 [mm]** & **40 \(\times\) 40 \(\times\) 160 [mm]** \\  & **(cyl)** & **(cyl)** & **(cyl)** & **(cyl)** & **(cyl)** & **(beam)** \\ \hline
20 & 4 & 1(MNS) & 3(MHS) & 4 & 3 & 4 \\
100 & 4 & 1(MNS) & 2(MHS) & 4 & 3 & 4 \\
200 & 4 & 1(MNS) & 1(MHS) & 4 & 3 & 4 \\
300 & 4 & 2(MNS) & 1(MHS) & 4 & 3 & 4 \\
400 & 4 & 1(MNS) & 1(MHS) & 4 & 3 & 4 \\
500 & 4 & 1(MNS) & 2(MHS) & 4 & 3 & 4 \\
600 & 4 & 1(MNS) & 1(MHS) & 4 & 3 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overview of the experimental program. The number in the table indicates the number of repetitions per test-type (for either MNS or MHS, except in the case of CA).  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Sand Type**} & **Absorption** & **Density** \\  & **[\%]** & **[kg/m\({}^{3}\)]** \\ \hline River sand & 0.50 & 2630 \\ Crushed sand & 0.80 & 2670 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Water absorption and density of sands.  
a constant rate of 10 \({}^{\circ}\)C/min up to the desired temperature. This latter was kept constant during three hours. Then, the furnace was turned off and the specimens were kept inside to prevent thermal shocks. The duration of the whole cooling process was of 24 h by leaving the specimen into the switched off furnace. The samples were weighted before being placed in the furnace and after being removed from it.
## 3 Results and Discussion
### Optimizing Synthesis Conditions
#### 3.1.2 Effect of DMDAAC Concentration  
The hybrid flocculant was prepared according to the Figure 1. Figure 4 shows the influence of the monomer mass fraction (mass ratio of DMDAAC monomer to PAC) on the intrinsic viscosity of the hybrid flocculant. According to Figure 4, as the dosage of DMDAAC increases, there is an initial increase in intrinsic viscosity followed by a decline after reaching a peak. The maximum intrinsic viscosity of the product was achieved when the DMDAAC and PAC quality ratio was 30%. This was attributed to the promotion of the copolymerization reaction with the increase in DMDAAC. Once a certain concentration of DMDAAC was reached in the system, it easily formed polydimethylene dially ammonium chloride. As a result, there was a decrease in monomers participating in copolymerization, leading to a reduced intrinsic viscosity. When the mass fraction of the monomer reached 30%, the intrinsic viscosity peaked at 416.51 mL/g.
## 4 Results
### Structural Model  
The coefficient of determination (R\({}^{2}\)) is a statistical value that explains the amount of endogenous construct variance that is explained by the exogenous constructs, with acceptable values between zero and one [76]. The R\({}^{2}\) value for SCA is 0.440, depicted in Table 2, which implies that 44.0% of the variance in the SCA constructs is explained by the BDAC and IC constructs. In addition, PLS-SEM was used to calculate the bootstrapping procedure for assessing the structural model and to obtain the \(t\)-values [72], as shown in Table 6.  
It is significant to verify the path coefficient values and statistical significance of the relationships between the latent variables in the structural model to evaluate the proposed hypotheses. This verification is performed by assessing the standardized path coefficients (\(B\)-value), with values equal to or above 0.1 [80], and the associated t-value, having values above 1.96 or below \(-\)1.96 at a 0.05 level of significance, i.e., \(p\)-value [82].  
The results of the model fit, provided in Table 6, confirm that DA has a significant and positive impact on BDAC (\(B\)-value = 0.410, \(t\)-value = 5.256) and that BDAC has a significant impact on IC (\(B\)-value = 0.491, \(t\)-value = 6.062) and is not significant on SCA (\(B\)-value = 0.106, \(t\)-value = 1.178). IC has a positive and significant impact on SCA (\(B\)-value = 0.605, \(t\)-value = 8.897). However, according to the results, IC has the strongest effect on SCA (0.605). Moreover, the results given in Table 6 confirm that H1, H2 and H4 are supported. However, the relation between BDAC and SCA is not supported.  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & \(B\)**-Value** & **STDEV** & \(t\)**-Value** & \(T\)** **Statistics** & \(p\)**-Value** \\ \hline H1 & 0.41 & 0.078 & 5.256 & 5.238 & 0 \\ H2 & 0.491 & 0.081 & 6.062 & 6.06 & 0 \\ H3 & 0.106 & 0.09 & 1.178 & 1.183 & 0.237 \\ H4 & 0.605 & 0.068 & 8.897 & 8.922 & 0 \\ \hline \end{tabular}
\end{table}
Table 6: Results of the model fit.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Construct** & **Item No.** & **Outer Loading** & \(t\)**-Test** \\ \hline DA & DA1 & 0.664 & 6.020 \\  & DA2 & 0.75 & 7.788 \\  & DA3 & 0.858 & 15.355 \\ \hline BDAC & BDAC4 & 0.819 & 18.964 \\  & BDAC5 & 0.693 & 9.337 \\  & BDAC6 & 0.748 & 12.089 \\  & BDAC7 & 0.738 & 13.466 \\ \hline IC & ICS & 0.689 & 10.676 \\  & IC9 & 0.784 & 15.565 \\  & IC10 & 0.668 & 7.844 \\  & IC11 & 0.766 & 12.502 \\ \hline SCA & SCA12 & 0.626 & 6.315 \\  & SCA13 & 0.781 & 16.941 \\  & SAC14 & 0.807 & 18.601 \\  & SCA15 & 0.786 & 18.089 \\ \hline \end{tabular}
\end{table}
Table 5: Estimation of the outer model.
## 5 Experiments
### Experiments in an Industrial Scenario  
The second set of tests was carried out in a static indoor environment, as shown in Figure 18, which represents the interior of a shipyard workshop, where there were many more obstacles than in the first experiment, including multiple pallets stacked with pipes on top, industrial machinery, gantry cranes, workers, etc. The distance between the transmitting node and the receiving node was 43 m for the first scenario and then 117 m for the second. Figure 18 depicts the deployment of two nodes (in red) and a single sender (in green) that sent data packets to each node. Figure 19 shows an image taken during the tests of the real position of the sender in the workshop, whereas Figure 20 shows the receiving node during one of the tests.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 8 Results
### Titanic Dataset Results
Models on the lower end of performance included Fair Decision Trees and neural network-based models (NNB-Parity and NNB-DF). These models yielded poor accuracy (generally below 0.50), with Fair Decision Trees achieving the lowest (0.3851 \(\pm\) 0.0052). Although Fair Decision Trees exhibited near-perfect fairness metrics, their inadequate predictive performance renders them unsuitable for practical deployment. Fairlearn-LR and Fair Random Forest demonstrated moderate accuracy with some fairness improvements. Fairlearn-LR achieved \(0.7810\pm 0.0122\) in accuracy, though its fairness metrics remained relatively high, suggesting inconsistency in fairness optimization. In summary, the results on the TITANIC dataset highlight a sharper fairness-accuracy trade-off than observed in previous datasets.
# VIS-HAPT: A Methodology Proposal to Develop Visuo-Haptic Environments in Education 4.0
## 4 The VIS-HAPT Methodology
### The Definition of the Technological Model
#### 4.2.3. _Haptic Rendering Framework_  
Haptic rendering is the process by which the user can feel, touch, and manipulate virtual objects through a haptic interface [32]. In haptic-based simulators, the user interacts with the environment using a joystick. To enable haptic rendering, a renderer framework is needed to communicate applications with haptic devices. Currently, there are four solutions for haptic rendering:  
Figure 4: Novint Falcon haptic device used in interactive visuo-haptic environments.  
H3 DAPI, previously known as HAPI, is an open source, cross-platform haptics rendering engine entirely written in C++. It is device-independent and supports multiple currently available commercial haptics devices;
* OpenHaptics is a proprietary haptics library developed by SensAble Technologies, currently known as Geomagic. However, it only works with haptics that are manufactured by them;
* Haptic Lab, previously known as Immersion Studio, is an application aimed at software design, where users can add multi-layer, high-fidelity haptic feedback with ease using TouchSense Force technology;
* HDAL is a software development kit for connecting Novint products. It enables users to add the haptic capability to their application and display high-fidelity force rendering.  
The library implementation to correct connectivity between the application and the haptic device is a pitfall that must be overcome. Generally, haptic device manufacturers provide SKD to allow haptic representation in virtual environments. However, engine games do not have native functions that can easily admit them. Therefore, third party solutions heavily depend on the CPU architecture and do not consider multiple devices' operability. Moreover, the visuo-haptic solution creation usually takes a long time due to the complexity of graphic engines that can deliver state-of-the-art visualizations.  
To solve some of the mentioned difficulties, an _HaDUI_ architecture was designed [9]. It allows one to successfully develop multiple device connectivity in game engines. At the same time, the performance of the application is not affected by multiple connectivity. One must have a functional blending of different environments which accounts for the connectivity of each device as well as optimizes the developing times of the visuo-haptic applications.
## 6 Conclusions  
1. Vibration monitoring tests were conducted on underground long hole blasting at critical surface facilities. The measured data of the long hole blasting vibration velocity were fitted using the fitting equation based on the USBM formula, which demonstrated smaller errors. Subsequently, a vertical mass point peak vibration velocity model was established specifically for the Hongling Lead-Zinc Mine. After eliminating noise data, the \(R^{2}\) between the predicted and measured values of the mass point peak vibration velocity increased by 0.3771.
2. Leveraging the particle swarm intelligence optimization algorithm, two machine learning model programs tailored for small sample data, PSO-SVR and PSO-RF, have been meticulously crafted. Through iterative optimization using the training set data, the penalty factor \(C\) of the SVR model was determined to be 15.2756, and the kernel function deviation \(g\) is 0.6742. Simultaneously, the decision tree count (\(k\)) of the RF model is set as 23, and the number of random variables for splitting nodes (\(m\)) is set as three.
3. Two machine learning models, PSO-SVR and PSO-RF, were developed with optimized parameters to predict the peak vibration velocity of particles resulting from underground long hole blasting. Compared to the fitting equation based on the USBM formula, both the PSO-SVR and PSO-RF models notably improve the precision. Specifically, the PSO-SVR model (with an impressive \(R^{2}\) value of 0.9645) exhibits slightly superior performance in predicting the test set data compared to the PSO-RF model (with an \(R^{2}\) value of 0.8048).
4. The prediction of extreme points of measured values by PSO-SVR and PSO-RF for both the training and test sets did not meet ideal results. This discrepancy could be attributed to the complexity of the correlation between the input and output parameters in the test data, as well as the limited number of measured data points. The challenges need to be further studied to address this. Despite these limitations, both learning models remain effective and suitable tools for predicting the particle vibration velocity resulting from blasting activities. Importantly, they offer viable alternatives to performing time-consuming and cumbersome tests on site.  
Conceptualization, L.X., Q.Y. and J.L.; funding acquisition, L.X.; methodology, J.L. and C.W.; software, J.L. and C.W.; validation, C.W.; writing--original draft, L.X., J.L. and G.Z.; writing--review and editing, Q.Y., C.W. and G.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Joint Fund of the National Natural Science Foundation of China (No. U21A20106), the NSFC-Xinjiang Joint Fund (No. U1903216), and the S&T Innovation and Development Project of Information Institution of Ministry of Emergency Management (Project No. 2023501).  
Not applicable.  
Figure 11: Predicted PPVs by the PSO-RF model versus measured PPVs.
# Climate, CO2, and Anthropogenic Drivers of Accelerated Vegetation Greening in the Haihe River Basin
## 2 Materials and Methods
### Data
#### 2.2.1 Past Scenario
The NTL data for the study area were integrated by harmonizing the inter-calibrated NTL observations from the Defense Meteorological Satellite Program (DMSP) data and the estimated DMSP-like NTL observations from the visible infrared imaging radiometer suite (VIIRS) data [19; 44]. The produced DMSP NTL time-series data (1992-2018) resolved severe inconsistencies that existed between DMSP (1992-2013) and VIIRS (2012-2018) data and extended the span of historical DMSP NTL data. The sensors of DMSP and VIIRS can capture the gleam emission sources on the Earth's surface at night, thus seizing the brightness of cities, towns, industrial sites, and expansion of urban traffic, and residences. In our study, to account for the difference between climate change and human activities, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sets** & **Value** & **Name** \\ \hline Mixed forestland (MF) & 1–5 & \begin{tabular}{c} Evergreen needleleaf forest (ENF), \\ evergreen broadleaf forest (EBF), \\ deciduous headleaf forest (DNF), \\ mixed forest (MF); \\ \end{tabular} \\ \hline Mixed grassland (MG) & 6–11, 16 & \
\begin{tabular}{c} closed shrublands (CSH), open \\ shrublands (OSH), woody savannas \\ (WSA), savannas (SAV), grasslands \\ (GRA), barren (BRN) and permanent \\ wetlands (PMW); \\ \end{tabular} \\ \hline Mixed cropland (MC) & 12, 14 &
\begin{tabular}{c} croplands (CRO) and cropland/natural \\ vegetation mosaic (CRV) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Three major vegetation sets based on MCD12Q1 (IGBP) product. Tem, Rad, and Pre were considered to evaluate the impact of climate change on vegetation dynamics. Additionally, AAF and NTL were treated as anthropogenic drivers. The atmospheric CO\({}_{2}\) concentration was from CarbonTracker (CT2019B, [[https://gml.noaa.gov/ccgg/carbontracker/](https://gml.noaa.gov/ccgg/carbontracker/)]([https://gml.noaa.gov/ccgg/carbontracker/](https://gml.noaa.gov/ccgg/carbontracker/)), accessed on 04 December 2021), developed by NOAA to track the carbon dioxide globally ([PERSON], 2015). CarbonTracker estimated the monthly surface CO\({}_{2}\) fluxes at 3\({}^{\circ}\)\(\times\) 2\({}^{\circ}\) from 2000 to 2018, and it was well validated by [PERSON] et al. (2016). A description of the datasets applied in our study is shown in Table 2.  
Due to the availability of the above-mentioned data, the time resolution of each dataset was uniformly based on an annual scale from 2002 to 2018, and the spatial scale was aggregated at the county level to match the scope of aforestation data from the yearbooks.
## 3 Results
### tPA-like Immunoractivity in RT4 Cells Exposed to PACAP, Doxycycline or Minocyclline  
We have previously demonstrated that treatment with PACAP or the PAC1 agonist maxadilan did not affect the distribution but only the expression of tPA in RT4 cells [22]. As such, we performed immunocytochemistry to determine if, similarly to PACAP, doxycycline or minocyclline treatment increased the expression but not the cellular distribution of either plasminogen activators. Sections where the primary antibody was omitted were used as negative controls (Figure 4A). Cells were either left untreated (Figure 4B) or exposed to PACAP (Figure 4C), doxycycline (Figure 4D) or minocyclline (Figure 4E) for 24 h.  
Figure 2: Cell viability of RT4 cells exposed to increasing concentrations of doxycycline or minocyclline. Analysis of cell viability (MTT assay). RT4 cells were grown under normal conditions and exposed to increasing concentrations (0, 50, 100, 500 or 1000 ng/mL) of (**A**) doxycycline or (**B**) minocyclline for 24 h. Values are reported as mean optical densities (OD) \(\pm\) S.E.M. from two experiments, each using five batches of cells. ****\(p<0.01\), ****\(p<0.001\) or ****\(p<0.0001\) vs. Ctrl; # \(p<0.05\) vs. 100 ng/mL DOX (**A**) or MIN (**B**), as determined by ANOVA followed by [PERSON]’s _post-hoc_ test.  
tPA-like immunoreactivity (IR) was moderate (about 25% of cell surface area) and mainly cytoplasmic in untreated cells (Figure 4B). As expected, administration of PACAP significantly increased the expression of tPA-IR (***\(p\) < 0.001 vs. Control), \(>\) 40% cell surface area, Figure 4C). Similarly to PACAP, both doxycycline and minocycline increased the percentage of tPA+ IR compared with controls (**\(p\) < 0.01 vs. control, about 35% of cell surface area, Figure 4D,E).
# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
## 11 Conclusions  
PIC technology represents a foundational advancement for the evolution of Fog and Cloud computing infrastructures. By enabling ultra-fast, energy-efficient, and compact optical communication and processing capabilities, PICs address the core challenges of scalability, latency, and bandwidth in distributed systems. In Cloud data centers, PICs power high-throughput optical interconnects, co-packaged optics, and advanced AI acceleration platforms, all while dramatically reducing power consumption and improving spectral efficiency. At the Fog layer, where compact, low-power, and real-time responsiveness are essential, PICs support optical computing, neuromorphic photonics, and edge AI workloads with unmatched latency performance and integration density. Industry leaders such as Intel, Cisco, Microsoft, Meta, Amazon, Nokia, and Fujitsu are already implementing PIC-based solutions, highlighting the critical role of photonics in next-generation networks. Whether through quantum communication, dynamic traffic routing, or long-haul data center interconnects, PICs serve as the physical layer enablers of secure, intelligent, and responsive computing ecosystems. As the demands of decentralized and AI-driven infrastructures increase, integrated photonics will be essential for building future-proof, high-performance Fog and Cloud architectures.  
Formal analysis, G.A.C.A., W.C. and A.M.S.-M.; Writing--review & editing, G.A.C.A., W.C. and A.M.S.-M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Secretaria de Ciencia, Humanidades, Tecnologia e Innovacion (SeiHTI).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Response Surface Modelling and Optimization
#### 3.6.1 Turbidity
The modified RSM and Central Composite Design (CCD) (RSM-CCD) were used to demonstrate the interactive impact of the input parameters (factors) on the output parameters (response). Figure 5b displays the presence of interactions between the factors, catalyst load, and reaction time on the turbidity response. The graphical demonstration of the three-dimensional (3D) surface plot of the response model was selected based on the interaction of the significant factors that play a role in maximizing the desirability of the system. A curvature of considerable magnitude can be seen in Figure 5b. This curve also suggests that the correlation between the factors (AB) and the response (turbidity) were well-fitted on a quadratic function (Equation (3)). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multicolumn{2}{c}{**Sequential**} & \multicolumn{2}{c}{**Lack of Fit**} & \multirow{2}{*}{**Adjusted R\({}^{2}\)**} & \multirow{2}{*}{**Predicted R\({}^{2}\)**} & \multirow{2}{*}{**\(\mathbf{R}^{2}\)**} \\  & \(\boldsymbol{p}\)**-Value** & & \(\boldsymbol{p}\)**-Value** & & \\ \hline Linear & 0.9766 & 0.1954 & \(-\)0.1943 & \(-\)0.7432 & \\
2 FI & 0.9246 & 0.1536 & \(-\)0.3256 & \(-\)2.0736 & \\ Quadratic & 0.0053 & 0.9639 & 0.6196 & 0.5647 & Suggested \\ Cubic & 0.8744 & 0.8583 & 0.4952 & 0.4763 & Aliased \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fit summary for Turbidity response model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Factor 1** & **Factor 2** & **Response 1** & **Response 2** \\ \hline
**Std** & **Run** & **A: Catalyst Load (g/L)** & **B: Reaction Time (min)** & **Turbidity** & **COD** \\  & & & **Reward (\%)** & **Removal (\%)** & **Removal (\%)** \\ \hline
4 & 1 & 0.6 & 40 & 95.46 & 42.41 \\
3 & 2 & 0.3 & 40 & 96.99 & 4.02 \\
13 & 3 & 0.45 & 30 & 79.5 & 23.45 \\
8 & 4 & 0.45 & 40 & 86.56 & 21.38 \\
10 & 5 & 0.45 & 30 & 85. Graphically (Figure 5b), it was demonstrated that the response surface showed an arc with the optimal region for turbidity at the high-low levels of A (catalyst load), whilst the turbidity removal remained constant along the varying reaction times and therefore did not have as significant an effect as the catalyst load. \[\text{Turbidity}=86.79-0.5333A-0.3417B+0.3650A\text{B}+11.38A^{2}-1.03B^{2} \tag{3}\]
# Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements
## 3 TTP-GPR for Coal Seam Thickness Prediction  
The proposed STTP-GPR algorithm uses the structural metadata and structural element transition probabilities to realize coal seam prediction. Firstly, the cutting data after interpolation in Section 2 are grouped into structural elements. The transition probabilities of structural elements, which are used to describe the structural space of the coal seam, are then calculated while using the Markov chain method. Finally, the structural elements data and transition probabilities are used as a priori information to compute the posterior distribution information of the data through the GPR algorithm, predicting the coal seam thickness. The proposed method addresses the problem of the spatial distribution of coal seams, which is ignored in the existing coal seam prediction studies [15], improving the prediction accuracy.