esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Study of the Performance of a Crossed Rib Diffuser in Room Acoustic Control
## 3 The Experiment Using a Small Meeting Room
### Setup  
Two source points (S1 and S2) and four receiving points (R1-R4) were located above the chair at a height of 1.2 m from the room floor for the impulse response measurements. Figure 8a shows the floor plan of the small meeting room and locations of source points and receiving points. The receiving points of R2 and R3 were excepted for sources located respectively at S1 and S2. R-1, CRD and CRD-A were installed on only Surface 1 or both Surface 1 and Surface 2 shown in Figure 8. We took impulse response measurements using an omni-directional microphone in the seven cases presented in Table 1. Both Surface 1 and Surface 2 consisted of three glass plates separated with sashes. Figure 8b,c shows cross-sections for Surface 1 and Surface 2. The figures also present cases 1-6 of specimens of 2 \(\times\) 3 on each glass plate. Figure 9 presents examples of the statuses of measurement in case 2 and case 5. We used an omnidirectional microphone (M-23; Earth Works) and a four-channel sound field microphone (Ambeo VR MIC; Sennheiser Electronic GmbH and Co. KG), respectively, for impulse response measurements and sound intensity measurements. The method for measuring sound intensity using the sound field microphone was well described in [26]. All measurements used the swept-sine signal with signal length of 17 s as a source signal. In addition, the source signal was emitted by a unidirectional speaker to simulate the directivity of human vocalization. Other settings for impulse response measurement were in accordance with the standard [27]. All measurements and calculations of room acoustic parameters and sound intensity vectors were conducted using _IRIS2.0_[28].  
Figure 8: (**a**) Floor plan of the meeting room, including locations of source points and receiver points. (**b**) A cross-section of Surface 1, and (**c**) a cross-section of Surface 2.
## 7 Effect of Zeolite Addition on WMA Properties
### Stiffness Modulus  
Mix asphalt stiffness was evaluated according to EN 12697-26, AASHTO TP 79 or AASHTO T 321-03. In both cases of HMA, as well as WMA, the stiffness modulus decreases along with decrease in production temperature. For HMA, the stiffness modulus decreases as the testing temperature increases for the same frequency, however, increases as loading frequency increases for the same temperature [56]. This correlation is also preserved in WMA with zeolites [20; 43].  
[PERSON] and [PERSON] conducted studies on mix asphalt compacted at temperatures of 145 \({}^{\circ}\)C, 130 \({}^{\circ}\)C, and 115 \({}^{\circ}\)C. The measurement of stiffness modulus was performed at 23 \({}^{\circ}\)C, 10 \({}^{\circ}\)C, and \(-2\) \({}^{\circ}\)C. At 23 \({}^{\circ}\)C the value of mix asphalt stiffness modulus with zeolites (NaP1, clinoptilolite, and their variations modified with water) were higher compared to samples without zeolites, with the same compaction temperature [20].  
At lower test temperatures, the addition of zeolites caused increase in stiffness modulus of samples compacted at the temperature of 145 \({}^{\circ}\)C. On the other hand, for temperatures compacted at 115 \({}^{\circ}\)C remained at a level comparable to values obtained for reference mix asphalt. The highest increase in stiffness was caused by clinoptilolite addition, whereas the lowest were caused by clinoptilolite soaked with water. The zeolite addition resulted in stiffening of mix asphalt with a production temperature of 130 \({}^{\circ}\)C and higher, which indicates it is more prone to thermal cracking. Such a relation was defined by [PERSON] et al. [51].  
By contrast, the results of studies performed by [PERSON] et al. [24] indicate a decrease in stiffness of mix asphalts produced at temperatures of 160 \({}^{\circ}\)C and 140 \({}^{\circ}\)C after adding synthetic zeolite (at a test temperature of 20 \({}^{\circ}\)C). Different results were obtained after decreasing the production temperature to 120 \({}^{\circ}\)C. After decreasing the production temperature to 120 \({}^{\circ}\)C, the stiffness modulus increased from 4733 MPa to 6019 MPa.  
The correlation between the stiffness modulus and test results are the same for WMA with zeolites, as well as for HMA. A comparison of mix asphalt with and without zeolites at the same production temperature indicates that the value of stiffness modulus is affected by zeolite material. Due to result divergence, at similar test parameters, it is not possible to clearly state whether zeolite stiffens mix asphalt or not.
# Robust and Secure Data Transmission Using Artificial Intelligence Techniques in Ad-Hoc Networks
## 6 Conclusions  
All the networks used in combination with IoT are susceptible to challenges of security and facing these issues is one of the noticed points for developers in an IoT system. If the network has faced such problems during the design and data transmission, other systems are necessary to create security. In the previous years, various strategies have been proposed to take care of this issue. Because of the dynamic idea of these systems and their capacity to transmit through remote methods, the security of information and data are increasingly significant in these systems. The main motive of this paper was to distinguish the black hole node and divert the route through the protected node utilizing an altered AODV routing protocol with SVM and ANN in the IoT-MANET. The novelty of this research work helps to achieve better performance during the data packet transmission observed after identifying the node as the black hole in the route and is performed dependent on the energy consumption, delay in transmission of data, and the positioning of nodes in the system. As per the obtained outcome of these experiments, the presented AODV with ABC, ANN, and SVM approach performed well with an average of PDR, throughput, and delay of 97.96%, 92.78 Kbps, and 0.04 s, respectively. Therefore, we conclude that the combination of the SVM with ANN for optimized nodes properties using ABC is a beneficial step compared to the other traditional algorithms like Decision Tree and Random Forest. In the future, this work can be extended to minimize delays with improved PDR and throughout. The work can also be extended to other fields like rescue operations in the military and personal area networking.  
Conceptualization, P.R., K., S.V., N.K.; data curation, K., S.V., N.K., M.W., J.S. and M.F.I.; formal analysis, P.R., K., S.V., N.K., J.S., M.F.I. and M.W.; funding acquisition, J.S., M.F.I. and M.W.; investigation, P.R., K., S.V., N.K., M.W. and M.F.I.; methodology, P.R., K., S.V., N.K., M.W., J.S. and M.F.I.; project administration, K., S.V., M.F.I. and M.W.; resources, M.F.I, M.W. and J.S.; software, P.R., K., S.V., N.K.; supervision, J.S., S.V., M.F.I. and M.W.; validation, P.R., K., S.V., N.K., J.S., M.F.I. and M.W. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge contribution to this project from the Rector of Silesian University of Technology, Gliwice, Poland.  
Not applicable.  
Data Availability Statement: The data used in the current study are available from the corresponding author on reasonable request.  
Acknowledgments: [PERSON] would like to thank the Deanship of Scientific Research, Prince Sattam bin Abdul Aziz University, for supporting this work. Conflicts of Interest: The authors declare no conflict of interest. References
# An Overview of GIS-Based Assessment and Mapping of Mining-Induced Subsidence
## 3 Mine Subsidence Susceptibility/Hazard Assessment and Mapping
### Literature Review According to Analytic Methods and Approaches
#### 3.2.5 Integrative Studies  
Unlike previous techniques, an integrative approach (or ensemble approach) has been proposed to support subsidence prediction and management of abandoned mine areas. [PERSON] et al. [38] proposed a new approach called the ensemble of several ground subsidence susceptibility maps. First, this study analyzed the correlation between past subsidence occurrences and related factors and computed the SSI of entire grid cells using three methods: FR, LR, and ANN. Second, the three SSI index maps were then used as new input factors and integrated using fuzzy ensemble methods to create better susceptibility maps. The validation result using the SRC-AUC technique revealed that the ensemble model was more effective in terms of prediction accuracy than the individual model.  
[PERSON] and [PERSON] [39] also generated four different subsidence susceptibility maps of an abandoned coal mine using FR, WoE, LR, and ANN models and subsequently used the four maps as new input factors to compile an integrated ground subsidence susceptibility map. As a result, the integrated subsidence susceptibility maps that used the four new subsidence-related input factors showed a greater accuracy (96.46% for FR, 97.22% for WoE, 97.20% for LR, and 96.70% for ANN) than the individual subsidence maps (95.54% for FR, 94.22% for WoE, 96.89% for LR, and 94.45% for ANN).
## 2 Materials and Methods
### Workflow Description
#### 2.3.2 Satellite Data Preparation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indices** & **Formula** & **Reference** \\ \hline EVI Enhanced Vegetation Index & \(2.5*\frac{NIR-Rad}{(NIR+Rad-7.5\text{Sh}inv)+1}\) & [57] \\ \hline SIPI Structure Insensitive Pigment Index & \(NIR-Azosis\) & [58] \\ \hline NDWI Normalized Difference Water Index & \(\frac{Gzon-NIR}{(NIR+IR)}\) & [56] \\ \hline MSI Simple Ratio 1600/820 Moisture Stress & \(\frac{SWIR}{NIR}\) & [41] \\ \hline ARI Anthocyanin Reflectance Index & \(\frac{1}{Creat}-Rad\text{Filter}\) & [58] \\ \hline CCCI Canopy Chlorophyll Content Index & \(\frac{NIR-Rad\text{Life}\) 1}{NIR-Rad\text{Life}\) 2} & [58] \\ \hline MSR Modified Simple Ratio 670/800 & \(\frac{NIR-Rine}{Rad\text{Life}}\) & [58] \\ \hline GLI Green Leaf Index & \(\frac{2 Creat}{2 Creat}-Rad\text{Life}\) 1+Shue & [58] \\ \hline NDVI Normalized Difference Vegetation Index & \(\frac{NIR-Rad}{NIR+Rad}\) & [33; 41; 56; 57] \\ \hline NGRDI Normalized Green Red Difference Index & \(\frac{Gzon-Rad\text{Life}\) 1} & [58] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Vegetation indices and formulas used in this study. 5 and 10 m using bilinear interpolation. Due to the varying spatial resolutions of the satellite data, the data were grouped into categories of 5 m and 10 m resolutions.
## 4 Research on Using State Estimation and Residual Life Prediction of Electrical Connectors  
### Method of Using State Estimation  
The HSMM-based estimation method for using state mainly includes five steps as follows:  
1. Preprocessing of sampled data  
Collecting the sample data of intermittent failure at each using state, then extracting the curve of intermittent failure. After that, the generalized severity of intermittent failure can be obtained using Equation (19).  
Figure 10: The curve of intermittent failure.  
2. Determination of the using states number  
In this paper, the method of minimum description length (MDL) is introduced to optimize the number of using states in HSMM. First, the description length (DL) of all systems is defined as.  
\[DL(K,\lambda)=-\log p(O|\theta,K)+\frac{1}{2}LK\log(M) \tag{20}\]  
where \(K\) denotes the number of using states. \(\lambda\) denotes the parameters of HSMM, \(L\) denotes the number of parameter in an HSMM, \(M\) is the number of whole life data. For \(A\) group of \(G\) dimensional data with length of \(T\), there exist M = GT.  
The MDL-based estimation method for model parameters can be expressed as follows:  
\[\begin{array}{ll}(\hat{K},\hat{\lambda})&=\underset{k,\lambda}{\text{argmin}} DL(K,\lambda)\\ &=\underset{k,\lambda}{\text{argmin}}[-\log p(O|K,\lambda)+\frac{1}{2}LK\log( M)]\end{array} \tag{21}\]  
First, a larger using state number \(K\) can be preliminarily determined by the evolution of the intermittent fault generalized severity within the whole life cycle. Then the KL distance criterion is introduced to describe the similarity of distance between the HSMM models, and combining the closest HSMMa and HSMMb, getting the incremental of the combined model \(\bar{A}DL\). According to the MDL method, if \(\bar{A}DL<0\), it allows the combination of models, and let \(K\) turns to \(K-1\). Otherwise, it is not allowed and the optimum number of using state is determined as \(K\).  
3. Determination of structure of model and classifier  
Combined with the optimized using state number \(K\) and estimation model for using states, the classifier of electrical connector's using states can be constructed.  
4. Training, estimation of model parameters and classifier parameters  
Based on samples of generalized intermittent failure severity, then training the HSMM under each using state by forward-backward algorithm. And the model's library HSMMs can be constructed.  
5. Estimation of using state  
By inputting the sample data to classifier, the likelihood probability under each using state can be calculated, then the maximum likelihood probability corresponds to the current using state.
## 9 Nanotechnological Application in PGPMs Bioencapsulation  
Nanotechnology or nanoscience refers to the engineering of materials and products at the nanoscale [88]. Nanotechnology has an attractive impact in various disciplines, such as physics, chemistry, medicine, and electronics, but also in biology and agriculture. In the context of sustainable agriculture, the development of nanofertilizers and nanospecides stimulates their performance and effectiveness, minimizes the risk of environmental pollution, and boosts the reproducibility of the approach [89]. Nanoparticles, such as nanoclays and zeolites, can be added in fertilizer or pesticide bioformulations, serving as a multifunctional tool. Microorganisms encapsulated in nanogel benefit from better nutrient supply, excellent growth rate and survival, increased physiological activity, and timed and precise release, and that meets the biological demands of the target plant or site [89; 90]. Nanomaterials can also protect encapsulated microorganisms and plants from pest antagonism and stressful abiotic factors [90]. The choice of adequate nanoparticles is crucial to ensure the success of the nanoformulation and its application. Concentration and toxicity tests are necessary to optimize the formulation [90]. In bioformulations are titania nanoparticles (TiO\({}_{2}\)), silica nanoparticles, silver nanoparticles (AgNPs), gold nanoparticles, nanozoolites, nano zinc oxide, nano carbon, nano boron, and nano chitosan [90].  
[PERSON] et al. [91] demonstrated that the immobilization of _Pseudomonas_ sp. DN18 in alginate capsules supplemented with salicylic acid (SA) and zinc oxide nanoparticles (ZnONPs) is a promising and stable biopesticide and biofertilizer delivery tool. SA and ZnONPs nanoparticles showed no bactericidal activity against _Pseudomonas_ sp. DN18, and did not alter its IAA (indole acetic acid) production property. This nanoformulation applied to _Oryza sativa_ seeds was associated with growth promotion and biocontrol activity against the fungus _Sclerodium rol/sii_. In another study, a coculture of _Pantoea agglomerans_ and _Burkholderia caribensis_ was immobilized in nanofibers; the latter preserved the viability and plant growth-promoting properties of both encapsulated strains, which showed beneficial effects on the germination of seeds, length, and dry weight of soybean roots [92]. Moreover, the addition of titanium dioxide (TNs) nanoparticles in alginate-bentonite-starch microbeads of the _Bacillus subtilis_ Vru1 strain markedly improved its ability to inhibit _Rhizoctonia solani_ by up to 90% and that of promoting the parameters of the bean growth (Table 2) [86].
# Wastewater Treatment Using Alkali-Activated-Based Sorbents Produced from Blast Furnace Slag
###### Abstract
Accordingly, the use of the blast furnace granulated slag as an aluminosilicate source alternative to fly ashes, to synthesize alkali-activated materials, has to be considered [13]. Moreover, from an industrial perspective, column-type continuous flow operations have obvious advantages over batch-type operations. In column operations, for instance, the sorbents are continuously in contact with a fresh solution, thus favoring adsorption. In addition to that, the regeneration and recovery steps in an in-column process is easier, which is of paramount importance in wastewater treatment processes [13]. Porous monoliths can be a viable alternative to packed columns. Monolithic sorbents, like packed-bed ones, have the advantage of continuously decontaminating liquid effluents and of being considered an ultimate waste for their good mechanical resistance and permanent fixation of the pollutants; but they can also be re-used, for instance, as raw material for building applications, without treatment [13]. In this respect, geopolymers appear to be good candidates as raw materials for the synthesis of porous monoliths. Recently, the use of alkali-activated materials properly designed to contain pores, has been reported. In this respect, much attention has been paid to geopolymeric foams, i.e., solid materials characterized by a low density structure of 3D network of interconnected pores. One of the most applied synthetic routes to obtain the foams implies the incorporation of a foaming agent (e.g., hydrogen peroxide) into the slurry of the alkali-activated material. The in-situ reactions of the foaming agent generate gas bubbles that are then trapped inside the aluminosilicate precursors during the alkali activation reaction. This way, a porous structure forms in the hardened body [25; 26]. Despite sorption capacities of geopolymers and alkali-activated materials having been already reported [27; 28; 29], their production in porous monoliths and their use in metal capture from wastewater still need to be further investigated. Therefore, this paper aims to demonstrate the applicability of geopolymeric monoliths, produced starting from wastes, namely blast furnace slags, as wastewater filters to reduce heavy metal ions content, and to achieve an acceptable ions concentration suitable for water reuse. Preliminary results on ions capture when treating solutions containing Cu(II), Fe(III), Ni(II), Mn(II), which simulates the metal concentrations of a real mine effluent, are reported. Both single- and multi-ion solutions are considered, and the porous monoliths-metal possible interaction is also analyzed to get information on the capture mechanism.
## 3 Results and Discussion
### Machine Learning  
#### 3.3.1 Metrics  
There are many different metrics that can be used to compare the performance of trained ML models; each has its strengths and weaknesses (compare [37; 38]). Due to the character of the data (high predominance of records without failure; approximately 80%), as well as the goal of the model (equally important as predicting failure is predicting whether the meter will continue working), we focused on two metrics: Area Under the ROC Curve (AUC) and Matthews Correlation Coefficient (MCC). The MCC takes values from \([-1,1]\) (more means better), and AUC takes values from \([0,1]\), whereby 0.5 means a random classifier. Detailed information about each of metrics can be found in [39]. For a more comprehensive image, also accuracy, precision, recall, and \(f_{1}\) for both classes will be provided.  
Figure 5 shows the results of the BDT, ANN, and SVM models in various metrics. It can be noted that _MCC-S and recall_ for a positive class were relatively weak. This indicated that the developed models were good only for the correct prediction of an event that a meter will not fail, but the correct prediction of breakdown was more challenging. The same conclusion can be reached by analyzing Figure 6, which presents a part of the confusion matrix regarding true predictions (true positive and true negative). Differences of a few or several percent were more difficult to accept, and their minimization was thus the target of hyperparameter optimization.  
Figure 5: Average metrics for 10 tries: before optimization.  
Figure 6: True Positive and true negative: before optimization.  
As far as the performance of individual algorithms was concerned, BDT turned out to be the best and SVM the worst (Figure 6). All models were much less successful in detecting meter failure than predicting survival for the next billing period. Although we had not yet optimized the tested models, their overall performance was very good. This resulted mainly from preprocessing and data normalization, as well as the proper selection of parameters.
## 3 Results
### UV-Vis Absorption and Fluorescence Properties
These microflowers also disappeared with a water fraction of higher than  
Figure 3: The fluorescence emission spectra of (**A**) **TPE-Az**, (**B**) **TPE-Su**,(**C**) **TPE-Pi** (10 \(\upmu\)M) in THF/water at various water fractions (\(\uplambda_{\rm ex}=365\) nm ) and (**D**)The fluorescence intensity changes of **TPE-Se**, **TPE-Az**, **TPE-Su** and **TPE-Pi** at 475 nm as a function of water fraction in THF solvent mixture. 90% (ESI Figure S3D). TPE-based dumbbell-shaped molecules **TPE-Pi**, **TPE-Su**, **TPE-Az**, and **TPE-Se** bear odd and even numbers of carbons in the alkyl chains, of 7, 8, 9 and 10, respectively. In particular, odd numbers of carbons (7 and 9) in the alkyl chains gives mismatching packing, producing particular aggregates. Importantly, even numbers of carbons (8 and 10) in the alkyl chains produces a flat, belt-like morphology, and in case of 10 carbons in the alkyl chains produces a flower-like morphology due to the fractal of **TPE-Se** growing from centre of the flowers. These results are similar to the odd-even effect in supramolecular self-assembly [25], in which odd numbers of carbons in the alkyl chains produces a loosely packed pattern in anti-parallel alignment, and exhibits particular aggregates because of the increased intermolecular interactions. However, even numbers of carbons in the alkyl (perfectly matching) chains pack tightly, and produce fibrils networks with parallel packing. The mechanochromic properties of **TPE-Pi**, **TPE-Su**, and **TPE-Az** were also investigated by the process of grinding, fuming, and heating, as shown in Figure 5A-C. The molecular packing of these compounds led to strong emissions in the solid state. This is direct evidence of the AIE effect in these compounds, where the stacking of the TPE moieties as a result of aggregation restricts non-radiative relaxation pathways, and this stacking persists in the solid state. The emission colour and intensity of the powder form are similar to those observed after self-assembly of these compounds in water/THF mixture, suggesting that the structure of the aggregates suspended in solution and the solids were similar. However, the emission intensity of the resulting materials was significantly decreased after grinding. This may be due to the reduction in the crystallite size. Even after fuming with acetone, the ground materials did not revert to their initial luminescent states in terms of either colour or intensity. However, when heating was applied, while the luminescent state was insignificantly altered, the luminescent colours of both **TPE-Pi** and **TPE-Az** changed from bright blue to light green. The **TPE-Se** also showed significant mechanochromic properties, as reported in our previous work [34]. These results confirm that these AlGs are relatively sensitive to molecular stacking in the crystal structure. Figure 4: FE-SEM images of aggregates formed from (**A**) **TPE-Pi**, (**B**) **TPE-Az**, (**C**) **TPE-Su** and (**D**) **TPE-Se** in THF/water with water fraction of 80%.