esa-sceva/satcom-chunk-collection huggingface dataset:
## Abstract  
The removal of water pollutants has been widely addressed for the conservation of the environment, and novel materials are being developed as adsorbent to address this issue. In this work, different residual biomasses were employed to prepare biosorbents applied to lead (Pb(II)) ion uptake. The choice of cassava peels (CP), banana peels (BP), yam peels (YP), and oil palm bagasse (OPB) was made due to the availability of such biomasses in the Department of Bolivar (Colombia), derived from agro-industrial activities. The materials were characterized by ultimate and proximate analysis, Fourier Transform Infrared Spectroscopy (FTIR), Brunauer-Emmett-Teller analysis (BET), Scanning Electron Microscopy (SEM), and Energy Dispersive X-Ray Spectroscopy (EDS) in order to determine the physicochemical properties of bioadsorbents. The adsorption tests were carried out in batch mode, keeping the initial metal concentration at 100 ppm, temperature at 30 \({}^{\circ}\)C, particle size at 1 mm, and solution pH at 6. The experimental results were adjusted to kinetic and isotherm models to determine the adsorption mechanism. The remaining concentration of Pb(II) in solution was measured by atomic absorption at 217 nm. The functional groups identified in FTIR spectra are characteristic of lignocellulosic materials. A high surface area was found for all biomaterials with the exception of yam peels. A low pore volume and size, related to the mesoporous structure of these materials, make these bioadsorbents a suitable alternative for liquid phase adsorption, since they facilitate the diffusion of Pb(II) ions onto the adsorbent structure. Both FTIR and EDS techniques confirmed ion precipitation onto adsorbent materials after the adsorption process. The adsorption tests reported efficiency values above 80% for YP, BP, and CP, indicating a good uptake of Pb(II) ions from aqueous solution. The results reported that Freundlich isotherm and pseudo-second order best fit experimental data, suggesting that the adsorption process is governed by chemical reactions and multilayer uptake. The future prospective of this work lies in the identification of alternatives to reuse Pb(II)-contaminated biomasses after heavy metal adsorption, such as material immobilization.
# A Gamified AI-Driven System for Depression Monitoring and Management
## 4 Results: Simulation-Based Evaluation of Depression Detection Models
### Machine Learning Pipeline  
#### 4.3.1 Preprocessing  
All features were normalized using z-score standardization to ensure comparability across scales. The dataset was split into training (80%) and testing (20%) subsets using stratified sampling to maintain class distribution. Cross-validation was subsequently employed to validate generalizability.
First, we propose an additional use case to those presented by [7], where we combine a configurable process model (manually or automatically generated [9]) and an event log to derive a process model that better represents the observed behavior in the historical data, depicted in Figure 1 as configure configurable process model and using event data ConCME. Second, to support this use case extension, we redefine the configurable process model representation, in particular how to represent configurable process trees [10], so as to generalize and simplify the description of the configuration process. Third, we propose a derivation framework that receives as input a configurable process model and an event log; as depicted in Figure 2, both inputs are part of the extended configurable process model use case. The framework incorporates three derivation strategies: an exhaustive method, used as a reference approach, which finds an optimal configuration in a wide search space, a genetic evolutionary method designed as a smart technique that evolves until it finds a good configuration and a greedy method designed as a heuristic to find a satisfying configuration in less computing time. The configuration obtained by any of these three strategies is then applied to the configurable process model in order to derive a process model. Additionally, we have tested the feasibility and applicability of the framework using two different sets of experiments: an educational process and a real-life municipality scenario. This article is organized as follows: Related work is described in Section 2, and then Section 3 introduces the theoretical foundation of the proposed framework. In Section 4, we present the methodology that describes three strategies (exhaustive, genetic and greedy) that allow finding the best configuration in order to derive a process tree from a configurable process tree that better represents the observed behavior in an event log. Results and discussions are presented in Section 5. Finally, conclusions and future work are presented in Section 6.
## 5 Uncertainty
### Computation of Uncertainty Parameters  
This section contains a mathematical derivation of the individual uncertainty components and their evaluation. Readers only interested in the results can skip this section. The calculation closely follows Section F.2.4.5 of the GUM [30] where a single mean correction \(\overline{\mathrm{b}}\) is calculated in F.7a. Next the uncertainty in evaluation of the systematic correction factor is evaluated in F.7b and F.7c. In F.7d the mean variance due to all uncertainty sources other than b(k) is determined. Finally the combined standard uncertainty as in Equation (15) can be calculated according to F.7e.  
Let \(\mathrm{P}(\mathrm{k}  
\[\mathrm{u}^{2}(\bar{\mathrm{b}})=\frac{1}{\mathrm{M}-1}\sum_{k=1}^{M}\bigl{(} \mathrm{b}(k)-\bar{\mathrm{b}}\bigr{)}^{2} \tag{20}\]  
The mean variance of the correction b(k) incorporates the uncertainty of the calibration and the uncertainty of the evaluation of b(k). Let \(\mathrm{u}_{1}\) and \(\mathrm{u}_{2}\) be the uncertainty of the reference points \(\mathrm{R}_{1}\) and \(\mathrm{R}_{2}\). Assuming a simple linear interpolation the uncertainty for the point \(\mathrm{R}_{k}\) is given as  
\[\mathrm{u}(\mathrm{R}_{k})=\frac{\mathrm{u}_{2}-\mathrm{u}_{1}}{\|\mathrm{R }_{2}-\mathrm{R}_{1}\|}\,\mathrm{k}\Delta+\mathrm{u}_{1} \tag{21}\]  
Similar to F.7c we can now calculate the mean variance of the correction factor b(k) as  
\[\overline{\mathrm{u}^{2}(\mathrm{b}(k))}=\frac{1}{\mathrm{M}}\sum_{k=1}^{M} \biggl{(}\mathrm{u}(\mathrm{R}_{k})^{2}+\frac{\mathrm{u}^{2}(P(k))}{|P(k)|} \biggr{)} \tag{22}\]  
Using F.7d allows us to calculate the mean variance of each measurement. This variance is closely related to the DOP as it just defines the spread of the points at each position.  
\[\mathrm{u}^{2}(P(k))=\frac{1}{|P(k)|-1}\sum_{P\in P(k)}\bigl{\|}P-\overline{ P(k)}\bigr{\|} \tag{23}\]  
\[\overline{\mathrm{u}^{2}(P(k))}=\frac{1}{M}\sum_{i=1}^{M}\mathrm{u}^{2}(P(k)) \tag{24}\]  
Finally the single value for the combined standard uncertainty is  
\[\mathrm{u}^{2}(P)=\overline{\mathrm{u}^{2}\bigl{(}P(k)\bigr{)}}+\overline{ \mathrm{u}^{2}(\mathrm{b}(k))}+\mathrm{u}^{2}(\bar{\mathrm{b}}) \tag{25}\]  
Results are reported as  
\[\|P-(x,y,z)^{T}\|=\bar{\mathrm{b}}\pm\mathrm{u} \tag{26}\]  
Reporting results as in Equation (14) is necessary as the positions are 3-dimensional quantities but the distance is only a scalar. A possible extension for the formalism applied here would be to define all equations in all three dimensions. The benefit of such approach would be that the results would also include some information about the direction of the error components. For the systematic effect, this would give information if measurements are for example just parallel shifted to the reference path or a more complex effect applies. For the random component, this would allow an interpretation similar to the horizontal DOP (HDOP) or vertical DOP (VDOP). The drawback is that multiple values are more difficult to compare and interpret.
# Machine Learning Prediction of Airfoil Aerodynamic Performance Using Neural Network Ensembles
## 3 Results
### Group A--Consecutive Single Seeds  
Group A is designed to assess the consistency and sensitivity of the model to minimal variations in random initializations by using five consecutive seed values: 42, 43, 44, 45, and 46. The purpose is to evaluate whether the model exhibits stable predictive behavior when initialized with numerically adjacent seeds.  
The general results, presented in Table 3, indicate that 45 seeds reach MAPE values below 2% for both aerodynamic coefficients predictions. Seed 46 achieves the best outcome,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Airfoil Set** & **NACA Codes** & **Number of Airfoils** \\ \hline \multirow{5}{*}{Testing mixed dataset} & 0314, 0415, 0412, 0612, 1313, 1512, & \\  & 1615, 2315, 2412, 2613, 3512, 3613, & \\  & 3412, 4314, 4512, 4615, 5312, 5412, & \\  & 5615, 5615, 6614, 6415 & \\  & 0312, 0313, 0315, 0413, 0414, 0512, & \\  & 0513, 0514, 0515, 0613, 0614, 0615 & \\  & 1312, 1314, 1315, 1412, 1413, 1414, & \\  & 1415, 1513, 1514, 1515, 1612, & 13 \\  & 1613, 1614 & \\  & 2312, 2313, 2314, 2413, 2415, & \\  & 2512, 2513, 2514, 2515, 2612, & 13 \\  & 2614, 2615 & \\  & 3312, 3313, 3314, 3315, 3413, 3414, & \\  & 3415, 3513, 3514, 3515, 3612, & 13 \\  & 3614, 3615 & \\  & 4312, 4313, 4315, 4412, 4413, & \\  & 4415, 4513, 4514, 4515, 4612, & 13 \\  & 4613, 4614 & \\  & 5313, 5314, 5315, 5413, 5414, 5415, & \\  & 5512, 5513, 5514, 5515, 5612, & 13 \\  & 5613, 5614 & \\  & 6312, 6313, 6314, 6315, 6412, 6413, & \\  & 6414, 6512, 6513, 6514, 6515, 6612, & 14 \\  & 6613, 6615 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Airfoil sets used for training and testing, categorized by maximum camber.  
The \(R^{2}\) values for \(C_{L}\) remain consistently high across all seeds (\(\geq\)0.99), confirming stable model performance for lift prediction. However, \(R^{2}\) for C\({}_{\text{D}}\) varies more (from 0.97 to 0.99), indicating higher sensitivity to seed initialization.  
Overall, the best performing seed of Group A is seed 42. The results suggest partial consistency in the model's performance across consecutive seeds. However, those consecutive seeds expose asymmetries in predictions, where:  
* Lift coefficients are predicted to be relatively stable across seeds, with MAPE values ranging from 1.27% to 1.86% (band of \(\pm\)0.3%).
* Drag coefficients fluctuate and are sensitive to the selected seeds, with MAPE values ranging from 0.84% to 2.02%
## 4 Experiments
### Ablation Analysis  
Experiments were also conducted to assess the contribution of different aspects of the proposed CCGD. As described in Section 3.2, we developed an SIC operation and grouped structure to enhance the representation learning capacity. It can be seen in Table 4 that, when no SIC operation was used, the result was already much better than it was for the original ResNet-50. This demonstrates the validity of the grouped structure. When the SIC operation was added, there was a further performance gain of 0.8%. We also examined the effectiveness of the specific components of the SIC operation. Here, the results show that, when the other configurations were kept constant, adopting a square function or extra replenishment yielded a performance gain of about 0.19% or 0.34%, respectively. This confirmed the validity of our original reasoning. In the SIC module, the number of differential maps controls the intensity of the calibration mechanism. It can be seen in Table 5 that, when the differential number was set to two, the performance gain had already reached the peak value, which slowly declined during the next stage. We believe that an excessive zoom factor will destroy an image or result in a loss of feature information during the continuous down-sampling and up-sampling operations. This, in turn, will affect the quality of the calibration weight.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Design Choice**} & \multirow{2}{*}{**Top1-Acc**} \\ \cline{2-2} \cline{4-4}  & **SIC Operation** & **FR** & **Square Function** \\ \hline ResNet & - & - & - & 76.40 \\  & - & - & - & 77.07 \\  & \(\checkmark\) & - & - & 77.56 \\ CCGD & \(\checkmark\) & \(\checkmark\) & - & 77.68 \\  & \(\checkmark\) & - & \(\checkmark\) & 77.53 \\  & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & **77.87** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiments on ImageNet dataset. Result in bold is the best.  
Figure 5: The loss curves of using different attention-based methods on ImageNet dataset with ResNet50: (**a**) training loss; (**b**) testing loss.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Number** & **Top1-Acc (\%)** & **Top5-Acc (\%)** \\ \hline  & 0 & 77.10 & 93.22 \\  & 1 & 77.43 & 93.25 \\ CCGD & 2 & 77.87 & **93.95** \\  & 3 & 77.84 & 93.84 \\  & 4 & 77.63 & 93.80 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of using different numbers of difference maps on ImageNet dataset with ResNet-50 backbone. Results in bold are the best in each column.
## 3 Materials and Methods
### Model Evaluation
#### 3.4.3 ROC Curve  
The receiver operating characteristic (ROC) curve is computed to evaluate the precision with which the model classifies and it is based on the relationship between sensitivity and specificity across the predictions, where sensitivity is the proportion of subjects \"texting and driving\" that were classified as positive, commonly known as positive predictive values (PPV), and it is calculated with Equation (6), where \(TP\) represents the number of true positives and \(FP\) represents the number of false positives [37; 38]:  
\[PPV=\frac{TP}{TP+FP}. \tag{6}\]  
Specificity is defined as the proportion of no-texting subjects that were classified as negative, commonly known as the negative predictive values (NPV), and it is calculated with Equation (7), where \(TN\) represents the number of true negatives and \(FN\) represents the number of false negatives [34; 37; 38]:  
\[NPV=\frac{TN}{TN+FN}. \tag{7}\]  
Finally, [PERSON]'s kappa statistic coefficient is computed to measure the inter-rater agreement of the final models [39]; this metric measures the amount of agreement corrected by the agreement expected by chance, the Kappa coefficient \(\kappa\) is given by (8), where \(P(o)\) is the relative observed agreement among raters (identical to accuracy), and \(P(e)\) is the hypothetical probability of chance agreement:  
\[\kappa=\frac{P(o)-P(e)}{1-P(e)}. \tag{8}\]
###### Abstract
In semi-arid countries, effectively monitoring droughts is challenging due to the need for long-term, well-distributed datasets, which are often unavailable [7; 34; 35]. Satellite products are suitable alternatives, offering data with significant geographical and temporal resolutions [36; 37; 38]. Satellite rainfall estimates, such as those from the GPCC (Global Precipitation Climatology Centre), MSWEP (Multi-Source Weighted-Ensemble Precipitation), TMPA 3B42 (Multi-satellite Precipitation Analysis), CHIRPSs (Climate Hazards Group InfraRed Precipitation with Stations), the TRMM (Tropical Rainfall Measuring Mission), and the PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks--Climate Data Record), have shown the potential for calculating the SPEI index and monitoring droughts [39; 40; 41; 42]. However, their accuracy varies across regions and climates, necessitating thorough evaluation before use. Although these products provide high-resolution precipitation data, the performance of CHIRPSs, combined with the Era5-Land reanalyzed temperature data for estimating potential evapotranspiration (PET), has not yet been tested or validated. Limited research exists on using these satellite/reanalyzed estimates for spatio-temporal drought characterization and quantifying their uncertainty, which is crucial for effective drought mitigation strategies. Our research aims to assess the accuracy of the CHIRPS rainfall data in characterizing drought by comparing them with the observed field data from 18 stations. We use statistical measures, such as the BIAS, Pearson's correlation coefficient (\(r\)), the RMSE, the MAE, and \(R^{2}\), for this comparison. Additionally, we examine the spatio-temporal characteristics of droughts from 1981 to 2018 using the SPEI at various timescales. Finally, we analyze the impact of droughts on cereal yields by correlating the SPEI and the SYRS.
## 3 Results and Discussion
### Effect of Magnetite Loading on Biogas Production and COD Removal
#### 3.2.1 Biogas Quality  
The use of magnetite supports the methane-producing microorganisms during anaerobic digestion. Figure 5 shows that the methane content in biogas generated without the addition of magnetite is low (55%), however, when magnetite is introduced, the grade of biogas improves, with methane content reaching 73%. Previous studies reveal that the methane content in biogas produced during the digestion of municipal wastewater is generally low because of relatively small concentrations of suspended and dissolved organic and inorganic solids. Previous studies by [31] reveal that the utilization of iron in magnetite by the microbial community and the potential mechanism of promoting interspecific iron uptake improves the carbon conversion efficiency of organic matter during anaerobic digestion, thus, an increase in methane content occurs.
## 3 A Motivating Example
Load balancing is achieved (each node receives from only one node at each communication step, and thus lower communication latencies are achieved (no links are overloaded--instead, all links are equally loaded); and
3. The scheduling procedure has a log complexity. However, there are cases in which the replication factor \(F\) (that is, the number of tasks run at each node) needs to increase by a percentage (for example, a 25% increase in the number of tasks per node will produce a problem with \(N=6\) and \(r=5\)). In a different scenario, the number of nodes may need to change if a node crashes or if more nodes should be added to accommodate an application's resource needs. In such scenarios, the scheduler has to make a fast online decision for re-allocating the tasks and re-scheduling them among the system's nodes, meaning that the throughput increases and the overall latencies are reduced. In this paper, we propose a fast pipelined scheme that can be efficiently used for such dynamic scenarios. This scheme is described in Section 5; before this, we need to present its mathematical background.
## 5 Discussion
### Time-Clustering  
The fire activity exhibits time-clusterized or non-Poissonian behavior, as evidenced by the coefficient of variation \(C_{V}\) significantly larger than 1, and by the non-flat behavior of the AF curve across all timescales. This finding suggests the existence of time-clustered behavior within the dynamics of fires, indicating correlated structures in the temporal distribution of fire events. For timescales shorter than \(\approx\)2 weeks, the time-clustering is influenced by the probability density function of the interevent times. This suggests that the time dynamics of fires is primarily determined by the distribution of short and long intervals between consecutive fires. On larger timescales, the time-clustering depends upon the ordering of interevent times. This implies that the temporal structure of events is primarily shaped by the specific succession of fires. In particular, between \(\approx\)2 weeks (\(\tau_{1}\)) and 3 months (\(\tau_{2}\)), the time-clustering of the fire activity is characterized by a scaling behavior (power law in log-log scales) with scaling exponent \(\alpha\approx 1.4\) that indicates the existence of a fractal or self-similar structure in the time distribution of the fire events. The fractal structure is very common in many natural phenomena, and thus, it could be reasonably considered a natural behavior of the fire time dynamics. The fractal nature of fire activity across timescales ranging from a few days to a few months indicates that fire occurrences exhibit a consistent temporal structure that remains unchanged regardless of the scale considered; in other words, the time distribution of fire events remains nearly constant as we move from larger to smaller timescales.  
If a scaling exponent \(\alpha\approx 0\) indicates that the process is Poissonian, and thus characterized by independent and uncorrelated events, a value of \(\alpha\approx 1.4\) suggests a rather high degree of clusterization and correlation among the fire events. In a joint analysis of wildfire occurrences and weather conditions in Japan, Ref. [49] discovered that there is a significant likelihood of fire occurrence when the average humidity drops below 60%, indicating a strong correlation between fire events and climate; and Ref. [50] in their analysis of the temporal clustering of humidity events (identified by instances when humidity drops below 60%), discovered that the temporal clustering of the humidity process exhibits fractal behavior with an exponent approximately equal to 1.07, in agreement with that of Japanese wildfires.  
The found fractal behavior of our fire dataset agrees with that discovered in other fire sequences, and it is very likely that its behavior might be induced by similar meteoclimatic patterns.
###### Abstract  
As a hot technology trend, the federated learning (FL) cleverly combines data utilization and privacy protection by processing data locally on the client and only sharing model parameters with the server, embodying an efficient and secure collaborative learning model between clients and aggregated Servers. During the process of uploading parameters in FL models, there is susceptibility to unauthorized access threats, which can result in training data leakage. To ensure data security during transmission, the Authentication and Key Agreement (AKA) protocols are proposed to authenticate legitimate users and safeguard training data. However, existing AKA protocols for client-server (C/S) architecture show security deficiencies, such as lack of user anonymity and susceptibility to password guessing attacks. In this paper, we propose a robust 2 FAKA-C/S protocol based on ECC and Hash-chain technology. Our security analysis shows that the proposed protocol ensures the session keys are semantically secure and can effectively resist various attacks. The performance analysis indicates that the proposed protocol achieves a total running time of 62.644 ms and requires only 800 bits of communication overhead, showing superior computational efficiency and lower communication costs compared to existing protocols. In conclusion, the proposed protocol securely protects the training parameters in a federated learning environment and provides a reliable guarantee for data transmission.  
federated learning (FL); client-server; authentication; hash-chain; elliptic curve cryptography (ECC)
# A 77 GHz Transmit Array for In-Package Automotive Radar Applications
###### Abstract  
A packaged transmit array (TA) antenna is designed for automotive radar applications operating at 77 GHz. The compact dimensions of the proposed configuration make it compatible with standard quad flat no-lead package (QFN) technology. The TA placed inside the package cover is used to focus the field radiated by a feed placed in the same package. The unit cell of the array is composed of two pairs of stacked patches separated by a central ground plane. A planar patch antenna surrounded by a mushroom-type EBG (Electromagnetic Band Gap) structure is used as the primary feed. An analytical approach is employed to evaluate the primary parameters of the suggested TA, including its directivity, gain and spillover efficiency. The final design has been refined using comprehensive full-wave simulations. The simulated gain is 14.2 dBi at 77 GHz, with a half-power beamwidth of 22\({}^{\circ}\). This proposed setup is a strong contender for highly integrated mid-gain applications in the automotive sector.  
transmit array; radar sensor; automotive