esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### UVA and NPOC Measurements  
UVA and NPOC are indicators of total DOM content in each sampling site, without giving information about their origin or class [26]. As expected, inlet sites exhibited greater UVA and NPOC values than the outlet ones (see Figures 2A and 2B, respectively). In this sense, site D (DWTP's inlet) had a UVA of 4 \(\pm\) 1 m\({}^{-1}\), being of 1.0 \(\pm\) 0.4 m\({}^{-1}\) at the end of treatment line Po1/Po2 in site M (ca. 75% absorbance decay), and of 0.38 \(\pm\) 0.02 m\({}^{-1}\) after treatment line Po3 in site I (ca. 90% absorbance decay). On the other hand, NPOC results showed an average value around 1250 \(\upmu\)g L\({}^{-1}\), only observing a reduction of 40-50%, approximately, at the final effluent from each treatment line, being of (6.6 \(\pm\) 0.9) \(\times\) 10\({}^{2}\)\(\upmu\)g L\({}^{-1}\) at the site I, and (7.8 \(\pm\) 0.7) \(\times\) 10\({}^{2}\)\(\upmu\)g L\({}^{-1}\) at site M. This indicates that organic matter separation is through sedimentation/filtration processes or oxidation/mineralisation in the case of chemical treatments. In addition, it can also be observed that: (i) sites A to D showed analogous UVA values, whereas the NPOC indicated that site B had slightly less organic matter content than A and C, site D having the average between B and C as expected; (ii) the sedimentation (and adsorption by PAC) step reduces influent UVA (site E = 1.8 \(\pm\) 0.5 m\({}^{-1}\)), but the NPOC remains unchanged; and (iii) analogous DOM degradation efficiency by BAC and chlorination as similar UVA values were obtained in sites H (1.2 \(\pm\) 0.6 m\({}^{-1}\)) and G (1.5 \(\pm\) 0.5 m\({}^{-1}\)), respectively.
# IAACaaS: IoT Application-Scoped Access Control as a Service +
## 3 System Requirements for Access Control in IoT
### Delegated  
One of the most restrictive constraints of IoT devices is related to their low computing capabilities, usually due to battery limitations. Detaching authorization operations from the rest of entities would provide computational lightness for clients, as well as scalability and ease of remote configuration for the service administrator, because the whole access control functionality would be provided centrally and as a service.
## 4 Results  
### Boosted Tree Models and Bootstrap Forest  
The boosted tree (BT) and bootstrap forest (BF), with \(k=1000\) and \(k=10{,}000\) bootstraps [47] for each method, indicated a set of significant variables \((\alpha=0.05)\). The recursive partitions using smaller additive trees, which are unique to BT and BF methods identified a set of predictor variables that were common to both modeling types. Four common predictor variables in these bootstrapped additive trees for MDF were: 'face plate position', 'press position time', 'face steam flow', and 'adhesive percent' (Table 2). The R\({}^{2}\) in training and validation for the BT was 0.758 and 0.552, respectively. The R\({}^{2}\) in training and R\({}^{2}\) in validation for the BF was 0.628 and 0.484, respectively. Even though the R\({}^{2}\) in validation is not high, these common variables explain a reasonable proportion of the variation influencing tensile strength. Screening designs may not have led to a similar result in a short amount of time. The means and variances of the dependent variable tensile strength (kPa) in the training and validation datasets were similar for the 10-fold cross validation with an \(\overline{x}=946.7\), \(\overline{s}=95.6\ \overline{M}=\ 944.6\), \(\overline{CV}=10.1\%\) in the training dataset; and \(\overline{x}=946.4\), \(\overline{s}=91.2\), \(\overline{M}=\ 945.9\), \(\overline{CV}=9.6\%\) in the validation dataset. The boosted tree (BT) and bootstrap forest (BF) with \(k=1000\) and \(k=10,\!000\) bootstraps for the OSB data revealed a set of five common predictor variables: 'wet bin speed', 'flake moisture content', 'dryer inlet temperature', 'dryer outlet temperature', and 'wood weight' (Table 3). The R\({}^{2}\) in training and R\({}^{2}\) in validation for the BT was 0.675 and 0.410, respectively. The R\({}^{2}\) in training and validation for the BF were 0.534 and 0.304, respectively. The means and variances of the dependent variable ultimate static load (kg) in the training and validation datasets were similar across the 10-fold cross validation with an \(\overline{x}=213.7\), \(\overline{s}=28.1\), \(\overline{M}=210.6\), \(\overline{CV}=5.9\%\) in the training dataset; and \(\overline{x}=204.8\), \(\overline{s}=27.7\), \(\overline{M}=204.0\), \(\overline{CV}=6.1\%\) in the validation dataset. Again, even though the R\({}^{2}\) in validation is not high, these five variables explain a reasonable proportion of the variation influencing ultimate static load. In order to find reasonable split points and levels for factors to be tested in designed experiment regression, tree models were developed using the five common independent variables previously mentioned.
## 2 Related Work
### FL Optimization Using DRL  
FL methods such as FedAvg [13] and FedProx [14] have been essential in training models on IoT networks. While FedAvg averages the gradients from client devices to update global models, FedProx addresses challenges from heterogeneous industrial IoT networks by introducing a proximal term, regularizing the FL model. However, both methods face difficulties with heterogeneous datasets and resource heterogeneity. This has led to heuristic algorithms, which sometimes yield suboptimal results.  
With the optimization challenges in FL, DRL has been used recently. In particular, [PERSON] et al. introduced FAVOR, which used deep Q-learning to select client devices, aiming to optimize validation accuracy and minimize communication rounds [15]. [PERSON] et al. used a DRL-based Mobile Edge Computing (MEC) system for FL to minimize energy consumption and training delay [16]. [PERSON] et al. also proposed an FL algorithm supported by DRL for efficient IIoT equipment selection, attaining high accuracy on the MNIST dataset [17]. [PERSON] et al. suggested a deep Q-learning-based mechanism for quantization bit allocation, enhancing the global FL model performance [18]. [PERSON] et al.'s DDQN-Trust algorithm employed trust establishment calculations for client selection in IoT networks, showing better optimization with FedProx [19]. [PERSON] et al. showcased FedMarl, which used multi-agent reinforcement learning for runtime client selection, resulting in improved model accuracy and reduced latency [20]. [PERSON] et al. designed TP-DDPG, a framework in an energy-harvesting hierarchical FL system, balancing learning delay and model accuracy [21].  
It is evident that the existing methods mainly focus on dynamically selecting clients for model training without considering adaptive local epoch selections. Existing methods use a fixed number of local epochs for all clients, overlooking the variability in their computing costs and heterogeneity. By incorporating adaptive local epoch selection, clients with more substantial resources can perform a higher number of training epochs. On the contrary, clients with limited computing capabilities can participate in the training process with fewer epochs. This approach allows for a more efficient and tailored utilization of resources, ultimately enhancing the overall performance of FL in heterogeneous industrial IoT scenarios.
## 5 Discussion
### Implications for Geologic Prospecting  
Magmatism in the study area not only provided metallogenic hydrothermal fluids, but also promoted the activation, migration, and enrichment of metallogenic elements in the surrounding rock, which is often accompanied by mineralization. Minerals often form in or around plutons. Spatially, most mineralized areas are concentrated in the internal and external contact zones between the Late Triassic pluton and the surrounding rock. Compared with their average crustal abundances, the elements enriched in the Late Triassic pluton include Pb, Zn, W, Sn, Mo, Ag, and Bi. Elements with variation coefficients of \(>\)1 include Pb, Zn, As, Sb, Ni, and Ag. The Pb and Zn contents are 1.24-24.37 and 1.03-5.28 times the average value of the Western Kunlun region, respectively. The enrichment of other elements is less pronounced. The Kangxiwanan Ag-Pb, Kalakashihenan Pb-Ag, Ahelangan Pb-Ag, Fulugouxiayoubei Pb-Zn, Fulugou Pb-Zn, and Dahongliutanxi Pb ore deposits are all located in the outer contact zones of Late Triassic rocks. These findings are consistent with geochemical and remote sensing anomalies observed in the study area.  
Rare metal mineralization is closely related to the Late Triassic intrusive rocks. The granite pegmatites in the study area were derived from the differentiation of these rocks and provided rare metals, a heat source, and ore-forming materials. The Dahongliutan and Ahelangan Li-Be ore deposits occur in granite pegmatites. Mineralization in the study area is predominantly hydrothermal, and was controlled by NW-SE structures. Mineralization is also mainly distributed in the high strain zones of the regional structures. The orebodies are controlled by a group of joints that are consistent with the overall strike, and exhibit contemporaneous mineralization characteristics. In Triassic strata, Li and Be deposits formed as a result of the intrusion of Indosinian magmatic hydrothermal fluids. The metallogenic mode of these deposits was predominantly crystalline metasomatis. The host rock was granite pegmatite and the surrounding rock was anorthosite biotite quartz schist. The strikes of the orebodies are consistent with those of the regional tectonic trend. The orebodies exhibit discontinuous, lenticular, and vein distributions that are often accompanied by strong albitization. The orebodies are controlled by the pegmatite veins, which have ore bearing properties that are related to the distance between plutons. Poor ore-bearing properties occur near the pluton, while good ore-bearing properties occur further from the pluton [10].
# Blade Crack Diagnosis Based on Blade Tip Timing and Convolution Neural Networks
## 1 Introduction
This research develops a numerical model of the concentrated parametric vibration of blades and a numerical model of the BTT signal acquisition. The measurement deviation and noise are also added to the BTT data. The blades with crack faults, crack locations, and crack depth under uniform speed conditions are diagnosed, and the study compares several machine learning approaches to confirm the detection accuracy of the proposed CNN fault diagnostic model. 2. Rotating blade crack diagnosis tests based on BTT measurements are performed and validated using a finite element model. The blade crack position and depth are effectively detected under uniform speed conditions, supporting the proposed CNN fault diagnosis model even further. The real-time monitoring of the blade vibration characteristics is required to reduce the risk of faults such as blade breakage and to increase blade vibration safety [10]. In this study, BTT is applied to determine the vibrational characteristics of cracked blades, and the CNN is used to automatically learn and extract fault signal features without relying on manual experience or signal analysis methods. This study gives a new perspective on fault detection in turbine blades and has great application potential for the intelligent health monitoring of blade discs. Additionally, it can be used to develop digital twin models [14] of blade discs and to predict the severity of blade cracks in real time. Figure 1: The complete closed block diagram.
## 4 Discussion
MCDA methods are effective in combining multiple criteria and analyzing complex situations, but their implementation requires specialized knowledge and advanced tools, limiting their applicability in coaches' daily contexts [55]. Similarly, machine learning models can identify dynamic patterns and improve real-time predictions, but their development and interpretation often exceed the expertise of coaches [56]. In contrast, the CR-ICC is simple to calculate and interpret, allowing for coaches to apply it easily for quick decision-making during matches without requiring advanced technical knowledge [37]. The CR-ICC metric offers valuable insights on how to optimize the contributions of such pivotal players during key moments, ultimately leading to set victories. To illustrate this, let us consider an example from our findings. In the analyzed semifinal match 18 (VAK vs. IMO) (please refer to tab9), player [PERSON] (IMO) stands out as the most decisive player in the fifth set, achieving an impressive CR-ICC of 9.68. Conversely, the star player of the opponent team (VAK), [PERSON], exhibits a lower performance with a CR-ICC of \(-1.38\). This sheds light on how a team, despite displaying an overall inferior performance throughout the match (53.8 CR-ICC for the IMO team compared to 118.2 CR-ICC for the VAK team), can end up winning the match, thanks to the exceptional performance of their star player during critical moments. The findings of this study demonstrate the robustness and reliability of the CR-ICC metric in capturing the dynamics of performance in volleyball. The inter-observer and intra-observer reliability assessments ensure consistency in observations, while statistical techniques such as binomial logistic regression and ROC curve analyses provide a rigorous evaluation of predictive precision. Additionally, collinearity analysis accounts for the influence of contextual variables, such as players' roles, competitive load, opposition level, set number, and team rankings, ensuring a comprehensive evaluation of the proposed metric. However, it is essential to consider that the robustness of these findings might be influenced by variations in competitive environments, player interactions, or sample sizes. Metrics like the CR-ICC may exhibit fluctuations when applied to different populations or under varying match conditions, highlighting the dynamic nature of performance assessment. These considerations emphasize the importance of interpreting the CR-ICC within the specific context of its application, while recognizing its potential adaptability to diverse scenarios. Such adaptability underscores the coefficient's utility, even as further research may refine its application across broader contexts. In summary, the CR-ICC complements traditional metrics by offering a comprehensive view of performance in high-pressure contexts and specific roles, excelling in its ability to capture contextual and dynamic contributions. Furthermore, its accessibility and ease of use make it a valuable tool for coaches aiming to optimize their players' performance without relying on complex technologies or methodologies.
## 4 Discussion
### Comparison with Similar Regions Facing Urbanization and Climate Impacts  
Comparing Aksu's vegetation changes with those in other arid or semi-arid regions undergoing similar urbanization and climate pressures, such as the Central Asian steppes and parts of the Middle East [64; 65; 66; 67], offers valuable insights. In these regions, urbanization and agricultural intensification often lead to significant vegetation cover changes, as observed in Aksu. However, unique factors in Aksu, such as specific ecological restoration projects and water-saving irrigation techniques, may yield distinct vegetation responses compared to other regions. For instance, the vegetation improvement in Aksu linked with projects like the Tarim River Comprehensive Management Project underscores the role of targeted ecological interventions in mitigating adverse urbanization impacts [68; 69]. This comparative perspective highlights both common patterns of urbanization effects on vegetation across arid zones and the particular advantages of local ecological policies in enhancing vegetation resilience.
# A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
## 5 Results
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & \multicolumn{4}{c}{**BERT-WP**} & \multicolumn{4}{c}{**BERT-SP**} & \multicolumn{4}{c}{**BERT-BBPE**} & \\ \hline
**Dataset** & **Type** & **EM** & **F1** & **EM** & **F1** & **EM** & **F1** & **Train Size** & **Validation Size** \\ \hline ARCD & MSA & 18.66 & 47.06 & **20.79** & **47.66** & 17.94 & 42.84 & 693 & 702 \\ XQuAD & MSA & 17.64 & 27.15 & 19.32 & **28.23** & **20.16** & 27.18 & 952 & 238 \\ TyDiQA & MSA & **54.50** & **77.62** & 50.59 & 75.57 & 51.57 & 74.94 & 14.8 K & 921 \\ MLQA & MSA & 23.40 & 39.73 & 23.98 & 40.69 & **26.30** & **42.23** & 5.34 K & 517 \\ \hline Avg & & 28.55 & 47.89 & 28.67 & **48.03** & **28.99** & 46.79 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of BERT models using different tokenizers: WordPiece (BERT-WP), SentencePiece (BERT-SP), and Byte-level BPE (BERT-BBPE) for the QA task. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & & **BERT-WP** & **BERT-SP** & **BERT-BBPE** & & & \\ \hline
**Dataset** & **Type** & **F1** & **F1** & **F1** & **Train Size** & **Validation Size** & **\#Labels** \\ \hline NewsFANE\({}_{\text{Gold}}\) & MSA & 85.65 & **85.71** & 71.53 & 3.8 K & 952 & 88 \\ WikiFANE\({}_{\text{Gold}}\) & MSA & 79.34 & **80.38** & 67.15 & 12.6 K & 3.15 K & 102 \\ WDC & MSA & 78.66 & **79.51** & 78.83 & 151 K & 37.77 K & 9 \\ WikiANN & MSA & **94.31** & 94.13 & 94.13 & 30 K & 10 K & 7 \\ AQMAR & MSA & 76.56 & **80.92** & 79.64 & 1.96 K & 425 & 26 \\ ANERCorp & MSA & 81.11 & **83.59** & 81.17 & 4.15 K & 961 & 9 \\ \hline Avg & & 82.60 & **84.04** & 78.74 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of BERT models using different tokenizers: WordPiece (BERT-WP), SentencePiece (BERT-SP), and Byte-level BPE (BERT-BBPE) for the NER task. The results show the F1 score for each model. the model efficiency, accuracy, and training speed. Both BERT-BBPE and BERT-SP exhibited strengths in different areas of the training process.
## 4 Results
### Comparative Evaluation of CES in Macau Parks  
Significant differences were observed in the performance of cultural ecosystem services (CES) between Macau parks, as shown in Figure 4. These differences were associated not only with parks' functional positioning and facility development but also with their cultural heritage backgrounds. In terms of overall scores, Montanha Russa Municipal Park (78.82) and Lou Lim Ioc Park (73.94) had notably high scores; in contrast, Monte Fort Park (51.78) and Hac Sa Van Park (52.78) had relatively low scores. Montanha Russa Municipal Park, Lou Lim Ioc Park, and Monte Fort Park are important components of the UNESCO World Heritage site \"Historic Centre of Macau\". Their cultural heritage backgrounds were found to significantly enhance performance in esthetic appreciation and landscape experiences. Montanha Russa Municipal Park (esthetic appreciation: 90; landscape experience: 80) and Lou Lim Ioc Park (esthetic appreciation: 84.3; landscape experience: 77.57) exhibited particularly strong performances in these two dimensions. This finding reflects their advantages in historical and cultural significance, as well as scenic attractiveness. However, Monte Fort Park received the lowest overall score, particularly exhibiting poor performance in \"aesthetic appreciation\" (54.71) and \"public facilities\" (50). This result may be related to inadequate facility construction and insufficient cultural interpretation services. In comparison, parks outside the historic district, such as Hac Sa Van Park and Dr. Sun Yat Sen Municipal Park, were found to emphasize daily functional services. These parks received higher scores in \"public facilities\" (73.67 and 67.48, respectively), but relatively lower scores in \"aesthetic appreciation\" and \"landscape experience\". This indicates that cultural heritage backgrounds play an important role in enhancing the esthetic value and cultural depth of parks. In contrast, non-heritage parks were observed to prioritize daily usage needs. In summary, CES performance in Macau parks was significantly influenced by their cultural heritage backgrounds. Parks located within historic districts generally performed better within the esthetic appreciation and landscape experiences dimensions. However, significant differences were found between parks, indicating the important influence of facilities and management strategies on CES performance.
# Metallic Nanoparticles Obtained via \"Green\" Synthesis as a Platform for Biosensor Construction
## 1 Introduction
It interacts electrostatically with metal ions and traps them in the process. The reducing agents and enzymes reduce the metal ions. Finally, formation of gNPs takes place. We discovered the phenomenon of extracellular bioreduction of Cr(VI) through Cr(V) to Cr(III) by some yeasts, including baker's yeast as well as non-conventional yeasts (_Candidia guilliermondii, Ogataea polymorpha_, etc.) [52; 53; 54; 55]. The soluble stable green biocomplexes (gCr\({}_{2}\)O\({}_{3}\)NPs) generated outside the yeast cells were isolated. Methods for isolating the generated gCr\({}_{2}\)O\({}_{3}\)NPs were developed, namely freezing-thawing, precipitation with acetone, column chromatography, electrophoresis in native PAG, filtration, dialysis and others [55]. Yeast extracellular metabolites were shown to be responsible for Cr(VI) reduction [54; 55]. In our previous study, we reported on green synthesis of AuNPs by the yeast _Ogataea polymorpha_[56]. BioAuNPs of different sizes (20-40 nm) and shapes, from triangular nanoprisms to nearly spherical and hexagonal ones, were obtained using both the yeast culture and extracellular metabolites of the culture liquid after removal of the cells. The optimal conditions (inorganic ion concentrations and incubation times) required for extracellular synthesis of AuNPs were chosen. The size, structure and fluorescence of the NPs were characterized using scanning, atomic force, transmission and fluorescence microscopy [56]. The aim of the current research was to obtain various NPs via green synthesis using the yeast _O. polymorpha_, to characterize these gNPs and to study their possible application in biosensors on models of amperometric bioelectrodes based on lacase and alcohol oxidase for assays of phenolic compounds and alcohols, respectively.
## 4 Results and Discussion
### Performance Comparison with an Alternative Histogram-Based Benchmark Approach  
In order to numerically validate the chosen order of the mathematical operations to compute the target histogram shown in Figure 1, we test the proposed approach by considering an alternative method of evaluation. Different from the proposed methodology, shown in Figure 1, in the present experiment, we perform the average of all the \(N_{T}=500\) target images, and then we evaluate the corresponding histogram. This last idea is sketched in Figure 5 and could affect the performance, since the whole process is nonlinear. The inference phase remains unchanged with respect to our original idea. Once again, we use a number \(N_{bin}=50\) of histogram bins, and results are obtained with using the cosine distance in (3).  
The numerical results, obtained by using such an idea, are provided in Table 8 for both the considered datasets. An examination of the rows of this table clearly demonstrates that this alternative does not perform very well, since all the reported metrics are poorer with respect to the excellent results of the proposed idea, shown in Figure 3. Hence, we chose to: (i) first, compute each single histogram; then, (ii) evaluate the target by averaging all of them, as shown in the top part of Figure 1.  
Figure 4: Accuracy (in %) as a function of the \(\eta\) parameter: (**a**) CT dataset and (**b**) CXR dataset. The best selected values (\(\eta=2\) for CT dataset and \(\eta=1.4\) for CXR dataset) are shown with a red cross. The results have been evaluated by using the cosine distance, a number \(N_{T}=500\) of target images, and a number \(N_{bin}=50\) of histogram bins.  
Figure 5: Target evaluation under the alternative histogram-based approach.