esa-sceva/satcom-chunk-collection huggingface dataset:
# Commercially Biochar Applied for Tartrazine Removal from Aqueous Solutions
## 2 Materials and Methods
### Adsorption Process  
The adsorption process was achieved in a Berzelius beaker, under static conditions, bringing the artificial tartrazine aqueous solution in contact with biochar and varying different parameters: temperature, pH, tartrazine content, dose of adsorbent, and duration of contact. At the end, to determine the tartrazine remaining unabsorbed on the biochar, centrifugation was used for 10 min at 9000 rpm in order to separate the solution from the adsorbent. Using the PG Instruments T80 UV-VIS spectrophotometer (Leicestershire, UK), the absorbance at 473 nm was read to conduct the solute analysis.  
The following relationships were utilized to determine the effectiveness of the adsorption process and the performance of the nanocomposite as an adsorbent:  
\[\eta(\%)=\frac{(C_{0}-C_{t})}{C_{0}}100, \tag{1}\]  
\[q_{t}=(C_{0}-C_{t})\frac{V}{m}, \tag{2}\]  
where \(\eta\) (\(\%\)) represents the tartrazine removal degree, C\({}_{0}\) and C\({}_{t}\) (mg L\({}^{-1}\)) are the tartrazine concentrations in the solution at the initial moment and at the moment t (min), respectively, q\({}_{t}\) (mg g\({}^{-1}\)) represents the mean of the adsorption capacity, V (mL) is the volume of the dye solution, and m (g) is the amount of biochar.
## 2 Materials and Methods  
The experiment was performed using the CAPEX device in Prague, which is a source of intense XUV radiation (with a wavelength of 46.9 nm) with an output pulse energy of 200 uJ. A more detailed description of CAPEX apparatus can be found in papers published earlier [7; 20; 21]. The XUV laser beam was focused inside a vacuum interaction chamber by a Si/Sc multilayer spherical mirror (R = 2100 mm) with reflectivity 15%. The laser beam was attenuated by free-standing Al filters [21] of different thicknesses to vary the fluence while keeping the distance between the target surface and the beam's tight focus position unchanged. The BaF\({}_{2}\) sample was fixed on a holder together with a gold-coated PMMA substrate. The irradiation was performed under normal incidence conditions. The experiment proceeded as follows: after a couple of periodically fired shots, when laser pulse energy had been proven to be stable, the windowless vacuum photodiode [20] measuring the pulse energy was removed from the beam path, and the gold-coated PMMA substrate was exposed to single shots to characterize the focused beam by the ablation imprint technique [22; 23; 24; 25]. The imprint shapes were directly monitored by a CCD camera equipped with a suitable objective [23; 24] and analyzed by well-proven procedures [22; 25]. Moving the holder in several steps along axis of the incoming beam (z-scan [22]) we chose the best focus position by looking at the shape and size of the focal spot (midplane between tangential and sagittal foci). When the best focus was found, the holder with both the samples was rotated to place the BaF\({}_{2}\) sample into the beam path. Then, the BaF\({}_{2}\) samples were exposed to a given number of XUV-CDL pulses. The irradiated samples were removed from the sample holder to be analyzed by an optical profiler (WLI--white light interferometer), Nomarski (DIC--differential interference contrast) microscope and an AFM working in tapping mode.
# Survey of Artificial Intelligence Model Marketplace
## 3 AI Model Lifecycle
### AI Model Value Chain  
This subsection describes the value chain passed by the model in an ideal marketplace [9; 24; 79]. The various components of this chain comprise the following: (i) _Buyer_: A market stakeholder joins the marketplace to purchase or license a model to address a specific use case requirement. Model buyers rely on brokers to provide reliable and high-performing models. (ii) _Broker:_ The intermediary facilitating transactions between buyers, developers, and data owners. Brokers ensure smooth operations, compliance with marketplace regulations, and trust among the various stakeholders. AI marketplace can be used to take over a broker's role to simplify the overall process. (iii) _Developers:_ They are responsible for training models. They depend on data owners for high-quality datasets while collaborating with brokers to sell and distribute the trained models, and (iv) _Data owner:_ He or she provides the required dataset for training models, playing a significant role in data quality, diversity, and ethical sourcing. (v) _Standardization:_ It connects the various stakeholders (i.e., data owner, developer, broker through the marketplace) to enable interoperability, collaborative development, standards enforcement, and trust. (vi) _Open source:_ It is linked to \"Developer\" and \"AI Marketplace\" to demonstrate the impact of tools, shared models, and the available resource pools for cost reduction, faster innovation, and transparent democratization of AI. Figure 5 depicts the relationships and dependencies among the various marketplace stakeholders.  
Some of the constraints in this value chain include ethical (Bias, discrimination, data misuse), legal (i.e., privacy regulations (GDPR, CCPA) and intellectual property laws), economic (pricing disputes and fair compensation for data owners), technical (data quality, interoperability, model transparency), etc. Integrating standardization and open source  
Figure 5: AI model value chain in marketplace.  
in the value chain could mitigate some of the identified constraints in the areas of interoperability, transparency, cost reduction, and regulatory compliance. However, open source and standardization could not resolve all the constraints. There is room for further reinforcement with a strong governing council framework fostering collaboration among the concerned stakeholders to provide continuous updates to address emerging challenges [80, 81, 82, 83, 84, 85].
# Advanced Strategies for Mitigating Particulate Matter Generations in Poultry Houses
## 2 Dust Composition and Mixture  
Particulate matter composition varies according to animals and livestock housing [24]. In poultry housing, PM is entirely biological, organic, and inorganic in its origin (Figure 1), which typically consists of a complex mixture of solid and liquid materials such as bedding materials, feathers, feeds, skin, excrete, dander, and microorganism (Table 1). Particulate matter from poultry houses constitutes about 90% organic content [25]. Dander, excrete, feathers, feed, litter material, bacteria, and skin are all examples of organic PM [16; 17; 18], while inorganic PM is usually the consequence of secondary interactions between NH\({}_{3}\) and acidic gases, which contribute to the fine PM fraction [26]. Based on particle sizes, feather-releasing airborne PM contributes about 4% to 43% fine PM and 6% to 35% coarse PM, while manure contributes ranges from 9-85% fine and 30-94% coarse PM [17]. Similarly, based on particle mass, feathers contribute about 17-68% in fine and 4-49% in coarse PM, while manure contributes 6-77% fine and 31-96% in coarse PM. In addition, in the case of poultry houses, PM could be rich in nitrogen content. According to [PERSON] et al. (2010), elemental analysis of PM in poultry houses consists of N, O, C, S, P, Ca, Na, K, and Mg from feedstuffs, feces, and skin [21]. In addition, within PM, many pathogenic and nonpathogenic microorganisms are found attached to the surface [27].  
Figure 1: Poultry dust obtained from the exhaust fan of the cage-free laying hen house at the UGA research facility.  
In broiler housing, the primary sources of PM are down feathers, mineral crystals from urine, and litter, whereas the most prominent sources of waste in layer barns are skin, feathers, excrement, urine, feed, and litter [25]. Based on particle size, PM is classified into PM\({}_{1}\) (PM with aerodynamic diameter \(\leq\)1 \(\upmu\)m), PM\({}_{2.5}\) (PM with aerodynamic diameter \(\leq\)2.5 \(\upmu\)m), PM\({}_{4}\) (PM with aerodynamic diameter \(\leq\)4 \(\upmu\)m), PM\({}_{10}\) (PM with aerodynamic diameter \(\leq\)10 \(\upmu\)m), and total suspended particle (TSP, PM with aerodynamic diameter \(\leq\)100 \(\upmu\)m) [24, 28, 29]. The size of PM\({}_{2.5}\) is 30 times smaller than the size of the average human hair [14]. The emission rate of PM\({}_{10}\) was directly influenced by the activity of the hens, ambient temperature, and ventilation rate [30]. It has been found that a significant portion of the NH\({}_{3}\) released contributes to the burden of PM\({}_{2.5}\)[31, 32].
## 2 Materials and Methods
### Analytical Methods
#### 2.2.6 Betalain Content  
The material was subjected to pigment extraction using a phosphate buffer at pH 6.5 [26]. Approximately 0.1 g of the powder and pomace, as well as approximately 1.2 g \(\pm\) 0.0001 g of the fresh beetroots and juice, were weighed and placed into 50 mL volumetric flasks, then filled up to the mark with the buffer solution. The extraction was carried out in a laboratory shaker (Heidolph Multi Reax, Schwabach, Germany) for 15 min. The absorbance of the supernatant was measured using a spectrophotometer (SPECTRONIC 200; Thermo Fisher Scientific Inc., Carlsbad, CA, USA). The concentration of betalains, including red and yellow pigments, was quantified as betanin (mg betanin/100 g d.m.) and vulgaxanthin-I (mg vulgaxanthin-I/100 g d.m.), respectively. The pigment content was calculated using the absorption coefficients (A1%) of 1120 for betanin at 538 nm and 750 for vulgaxanthin-I at 476 nm. Additionally, absorbance at 600 nm was measured to account for the correction of interfering substances, ensuring accurate pigment quantification. The measurement was performed in duplicate.  
For the calculation of red and yellow pigments, the following equations were used [27]:  
\[\text{Red}=\text{D}\text{-}(1.095(A_{538}-A_{600})/\text{M}\text{-}\text{DM} \text{-}1120, \tag{1}\]  
\[\text{Yellow}=\text{D}\text{-}(A_{476}-A_{538}+0.677\text{-}1.095(A_{538}-A_{6 00}))/\text{M}\text{-}\text{DM}\text{-}750, \tag{2}\]  
where D represents the sample dilution factor; \(A_{476}\), \(A_{538}\), and \(A_{600}\) denote the absorbance values of the solution measured at 476, 538, and 600 nm, respectively; M refers to the sample mass (in grams); and DM indicates the dry matter content (in g/g). The factor 1.095 is used to correct for the increased absorbance at 538 nm due to the presence of impurities; and 1120 is the molar extinction coefficient for a 1% betanin solution at 538 nm in a 1 cm cuvette.  
Similarly, the factor 0.677 adjusts for the absorbance increase at 476 nm caused by impurities, and 750 is the molar extinction coefficient for a 1% vulgaxanthin solution at 476 nm in a 1 cm cuvette.
## 3 Results and Discussion
### Gcxc-Tof-Ms
Similar as in the case of GC-MS results (Table 1), Skrlet wines were the most abundant in monoterpenes, followed by Malvazija istarska, then Posip, and finally Marastina and Kraljevina wines with the lowest concentrations (Table 2). Only a few exceptions were noted: Skrlet wines contained the lowest concentration of \(\beta\)-calacorene, while Malvazija wine was deficient in _cis_-Z-\(\alpha\)-bisabolene epoxide. Although Kraljevina wine was generally poor in terpen, several sesquiterpenes, such as cadalene, \(\beta\)-calacorene, and especially tentatively identified \(\gamma\)-dehydro-ar-himachalene, emerged as potential markers of the varietal origin of this wine. All the other classes of compounds were confirmed to be far less efficient in differentiating the investigated monovarietal wines than terpen, with few exceptions. The number of C\({}_{13}\)-norisoprenoids identified by the two techniques applied was similar, but their identities differed in most cases. The relative results for \(\beta\)-damascenone obtained by GC-MS and GC\(\times\)GC-TOF-MS were in a fair agreement, with the highest concentration found in Malvazija istarska and the lowest in Skrlet wines (Table 2). A similar degree of correspondence between GC-MS and GC\(\times\)GC-TOF-MS results and the corresponding \(F\)-ratios was observed for a vitispirane isomer. Kraljevina wines contained the highest concentration of tentatively identified 1,2-dihydro-1,4,6-trimethylnaphthalene. Superiority of GC\(\times\)GC-TOF-MS over GC-MS in terms of compound separation and identification was demonstrated well in the analysis of benzenoids, with a much larger number of compounds identified by the former technique. Several benzenoids were found to be relatively efficient discriminators between monovarietal wines, and some of them were exclusive differentiators for particular varieties. High ethyl benzene concentration was specific for Posip, while 1,1'-oxybisbenzene was most abundant in Malvazija istarska wines, in both cases supported by rather high \(F\)-ratios. In addition to the highest concentration of 1,1'-oxybisbenzene, Malvazija istarska wine was characterized by most varietal markers among benzenoids, including octylbenzene, a non-identified benzenoid, azulene, 2-methylnaphthalene, and methyl 2-(benzyloxy)propanoate. Posip was characterized by the highest ethyl benzene and _trans_-edulan concentration, Kraljevina was the most abundant in 6-[1-(hydroxymethyl)vinyl]-4,8a-dimethyl-1,2,4a,5,6,7,8a-octahydro-2-naphthalenol, while Skrlet wine was the richest in \(m\)-methoxyanisole and \(\alpha\),\(\alpha\)-dimethylbenzenemethanol (Table 2). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**No.
# Estimating Weibull Parameters Using Mabchour's Method (MMab) for Wind Power at RAWA City, Iraq
## Methodology
### Wind Power Density  
It can be considered that wind power density is the criterion to assess wind energy at a specific site. Figure 1 presents the categories of the wind power density for the standard heights. For a specified site, the mean of available wind power density (\(P\), w/m\({}^{2}\)) is given as:  
\[\langle P\rangle=\frac{1}{2}\rho\Big{\langle}v^{3}\Big{\rangle} \tag{8}\]  
where \(\rho\) is the air density in kg/m\({}^{3}\). Where, the air density is other important factor affected the extracting energy from wind, where it can be found based on the following formula:  
\[\rho=\frac{p}{RT} \tag{9}\]  
where, \(p\), \(R\) and \(T\) are the pressure of the air (locally), gas constant that equal to 287 J/kg-K for air, and temperate of the air (locally). In case there is no motion in the vertical axis, the equation of hydrostatic can be written as follows:  
\[dp=-\rho\;gdz \tag{10}\]  
\(g\): acceleration of gravity. The following formula can be obtained when Combining Equations (9) and (10):  
\[\frac{dp}{p}=-\frac{g}{R\;T}\;dz \tag{11}\]  
The equation of the acceleration of gravity with height is:  
\[g=g_{o}\bigg{(}1-\frac{4z}{D}\bigg{)} \tag{12}\]  
\(g_{o}\): gravitational acceleration at ground level.  
\(D\): earth's diameter.  
Where, it can be neglected the effect of height (\(D>4z\)) and temperature has inverse relation with height. Then, it can be supposed the following,  
\[\frac{dT}{dz}=c \tag{13}\]So, the following formula can be obtained:  
\[p=p_{o}\bigg{(}\frac{T}{T_{o}}\bigg{)}^{-g/cR} \tag{14}\]  
where, \(p_{o}\): the ground's air pressure; \(T_{o}\): the ground's air temperature.  
The following formula can be obtained for air density by combining Equations (14) and (9),  
\[\rho=\rho_{o}\bigg{(}\frac{T}{T_{o}}\bigg{)}^{-(\frac{g}{R\pi}+1)}=\rho_{o} \bigg{(}1+\frac{cz}{T_{o}}\bigg{)}^{-(\frac{g}{R\pi}+1)} \tag{15}\]  
There is another equation to calculate the air density used by [PERSON] et al. [12], which is:  
\[\rho=\rho_{0}-1.194\times 10^{-4}\times H_{m} \tag{16}\]  
where, \(H_{m}\) is the elevation of site (\(m\)) and \(\rho_{0}\) is the air density at sea level. A small difference was found between the values of air density obtained by Equations (15) and (16).  
It can be noticed that the available wind power density increases with the mean cube of wind speed [10]. There are two methods to calculate the mean cube of wind speeds. The first one is the direct equation. It is given by Equation (10) and the second one by using the distribution models, (Weibull distribution) as represented in Equation (18) [13].  
\[\Big{\langle}v^{3}\Big{\rangle}=\frac{1}{n}\sum_{i=1}^{n}v_{i}^{3} \tag{17}\]  
\[<v^{3}>=c^{3}\Gamma\bigg{(}1+\frac{3}{k}\bigg{)} \tag{18}\]  
where \(n\) is the numbers of data.
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 3 Data and Methods
### Satellite Data Processing
Processing of the satellite time series data comprises the following steps: (1) data selection and pre-processing, (2) calculation of time series metrics, and (3) object segmentation with histogram-based thresholding. These steps are described in detail in a previous publication [32] and are presented here in summarized form.
# Efficient Opportunistic Routing Protocol for Sensor Network in Emergency Applications
## 5 Simulation Model, Setup and Results  
We performed a simulation to evaluate the performance of the proposed ODCR opportunistic routing protocol and compare it with some existing well-known routing protocols. We used the network simulator NS-3 [12] to perform the simulation. The following sections present the energy, network and mobility models that are used in our simulation, along with the simulation setup and results.
# A Resilient LoRa-Based Solution to Support Pervasive Sensing
## 5 Results
### Mesh Tests  
A few different scenarios were used to test the mesh solution proposed in the previous chapter, along with the adaptively changing spreading factor. In this series of tests, one node marked with the letter \(S\) (for the sender) is sending out messages addressed to the node marked with the letter \(R\) (for the receiver). Like in the previous tests, we recorded RSSI, SNR, and, more importantly, the ratio of packets generated by the \(S\) nodes that are correctly delivered to \(R\).
# A Deep-Learning-Based Bearing Fault Diagnosis Using Defect Signature Wavelet Image Visualization
## 4 Methodology Evaluation Results  
In this section, the proposed bearing fault diagnostic method is evaluated using collected data from a real-bearing testbed which is described in Section 2. The AE signal has a duration of one second for each sample. It has been shown that a proper signal processing technique is required for converting the signal to meaningful information with the DWSI before feeding to the DCNN. Each DSWI is constructed from a one-second sample signal using the method detailed in Section 3. This processing step is employed to retain the specific properties of different health states. Hence, the invariant signatures of different health conditions can use the full potential of the DCNN. Then, the DCNN model is trained to automatically extract and learn the features from 988 samples of the training dataset. The DCNN is simultaneously validated with 248 samples of the validation dataset during each iteration epoch. The trained CNN model is validated by predicting the class for 248 samples from the test dataset. To evaluate the proposed method by comparing to other methods, two scenarios were employed using different types of 2-D representations as the input and with different DCNN structures proposed in the literature.
## 3 Technique Realizations  
### SAW Sensing Device  
Using the standard photolithographic technique, the SAW sensing device with reflective delay line pattern was developed, and the design parameters were the same as that in Table 1. The corresponding fabrication procedure is depicted below. Aluminum with a thickness of 300 nm was evaporated onto the cleaned \(LiNbO_{3}\) substrate surface. Then, a 1 mm thick photoresist (PR) was spin-coated, exposed, and developed for the reflective delay line pattern. Al was wert-etched, and PR was dissolved in acetone. The patterned wafer was dice-sawed after removing any unwanted products by several rinses with DI water. The developed SAW sensing device was packaged and connected to the dipole antenna for wireless measurement, as shown in depicted in Figure 2a. The fabricated sensor is characterized by using the network analyzer, larger SNR of \(\sim\)50 dB was achieved from the two reflection peaks in the time domain, and in addition, the positions of two reflection peaks in time domain were measured as \(\sim\)1 \(\upmu\)s and 2.6 \(\upmu\)s, which agrees well with the simulation, as shown in Figure 2b.
# Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
## 4 Model Construction
### Data Splitting, Modeling, and Analysis  
Following the collection of daily stock data from Bloomberg, the dataset is divided into two subsets: (i) a technical set (T) and (ii) a technical-plus set incorporating TC and NC (T+). To address skewness, a log transformation is applied to the TC and NC. After this transformation, all TC and NC variables exhibit an acceptable level of skewness. Min-max normalization is then applied to all the remaining variables. Subsequently, the dataset is further categorized into three sets: (i) a training set, (ii) a normal test set, and (iii) a panic test set. The training set encompasses daily variables from January 2015 to 2019, the normal test set comprises variables from January 2019 to November 2019, and the panic test set includes variables from January 2019 to May 2020. For each stock, four price prediction models are constructed. Each model undergoes training and execution of the NN five times and the predictions are averaged to obtain the final model prediction. This approach addresses the stochastic nature of NNs, which can result in slight performance variances. The first two models utilize LSTM, where one model incorporates OP, HP, LP, CP, TV, and 30 MA as inputs and the other uses OP, HP, LP, CP, TC, and NC as inputs. The next day's CP is defined as the target variable in these models. The third and fourth models are built using MLP with the respective input sets.  
Each defined model undergoes testing on both the normal test set and the panic test set. The accuracy measure for model evaluation is the root mean squared error (RMSE), where lower RMSE values indicate better stock-price predictions. RMSE is selected over other error measures due to its expression in the original unit being measured. This characteristic makes RMSE useful for analyzing the error gap between the expected and predicted values [162]. Moreover, RMSE assigns a higher weight to larger errors compared to other measures, making it particularly suitable for domains where significant errors in accuracy are undesirable [161]. Incorporating the magnitude of error into RMSE is pertinent in stock-price prediction research, as larger errors in prediction can potentially lead to greater losses in buy or sell decisions. Recent stock-price prediction research adopted RMSE as the error measure for comparative analysis of different companies' stock-price predictions [163].