esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data  
Donana's Singular Scientific-Technical Infrastructure (ICTS-RBD) provided data access and support to carry out the study case at the Donana Protected Areas. The in situ monitoring infrastructure operated by the ICTS-RBD consists of an eddy covariance tower, the \"Duque Fuente flux tower\" and a meteorological station, named the \"Palacio Donana station\". The location of the station and tower is displayed in Figure 1.  
The eddy covariance data set consisted of 30-min measurements of Net Ecosystem Exchange (NEE) and ancillary meteorological data collected between the 1 st of October 2020 and the 8 th of June 2021. The meteorological data set from \"Palacio Donana station\" consisted of daily estimations of multiple environmental variables of the region including daily cumulative precipitation and average temperature collected between 1976 and 2021. Tables 1 and 2 display the data and metadata available in the ICTS-RBD monitoring system.  
To differentiate the night periods for the NEE data processing, the Global Radiation data set (SW_IN) was retrieved from the SARAH solar radiation data records provided by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). The SW_IN records were available for the years 2005 to 2016; therefore, we assumed that the global radiation data for 2020 and 2021 were equal to the daily average value of solar radiation for the years 2005 to 2016. Night periods were identified by using a threshold of 20 W m\({}^{-2}\).  
Meteorological precipitation data were additionally retrieved from a global data set to assess the capacity of these products to compute the study workflow and supply sites lacking daily measurements of meteorological variables. Daily measurements of precipitation were collected from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) database ([[https://www.chc.ucsb.edu/data/chirps](https://www.chc.ucsb.edu/data/chirps)]([https://www.chc.ucsb.edu/data/chirps](https://www.chc.ucsb.edu/data/chirps)), accessed on 12 December 2022) at the Duque Fuente tower location.  
Figure 1: Protected area limits of Donana Natural Park and Donana National Park located in the southwest of Spain, and the distribution of its major habitats.  
ICTS-RBD also provided the vegetation map of the wetlands of Donana. The latest was used to qualitatively assess the results of the unsupervised classification to upscale the outputs of the model beyond the climatological footprint. The vegetation map was previously computed with supervised classification approaches for the year 2009 by the ICTS-RBD.
# Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching
## 4 Experiments
### Datasets and Evaluation Metrics  
* The Scene Flow dataset [21] is a large synthetic dataset commonly used for pre-training models. The finalpass version of the stereo image pairs, synthesized using Blender, contains motion and defocus blur effects that simulate the real scenes. It provides 35,454 training image pairs and 4370 test image pairs with disparity ground truth at a resolution of 960 \(\times\) 540. It should be noted that other existing synthetic datasets, such as Fallingthings [51], CREStereo [13], TartanAir [52], etc., are not used to ensure a fair comparison with existing work.
* The KITTI [23; 24] provides datasets of real outdoor scenes captured by on-board calibrated cameras and LIDAR on the road, where the KITTI2012 [23] and the KITTI2015 [24] contain 194 pairs and 200 pairs of training data with sparse disparity ground truth at a resolution of about 1220 \(\times\) 370.
* The Middlebury [25] provides datasets of real indoor scenes obtained using dense structured light, containing stereo image pairs with multiple asymmetric exposure combinations of multiple scenes. The Middlebury 2014 and Middlebury 2021 contain 23 and 24 sets of semi-dense disparity ground truth with maximum resolutions of 3052 \(\times\) 1968 and 1920 \(\times\) 1080, respectively.
* The ETH3D [26] provides a dataset of real indoor and outdoor scenes captured by a high-precision laser scanner. It provides 27 training grayscale stereo image pairs with corresponding semi-dense disparity ground truth at a resolution about 950 \(\times\) 500.
* The New Tsukuba [50] dataset provides a realistic computer-generated stereo dataset that includes four different illumination conditions. It provides 1800 stereo image pairs, each with 256 levels of disparity ground truth, at a resolution of 640 \(\times\) 480.  
For quantitative evaluation and comparison, we mainly use endpoint error EPE, the 50 th percentile error A50, and the percentage of bad pixels Bad1.0 and D1 to evaluate the disparity accuracy. The EPE is also called \"avegr\" in the Middlebury benchmark and \"Average error\" in ETH3D. The Bad1.0 is the percentage of pixels with an error greater than one pixel. The D1 is the percentage of pixels with an absolute error greater than three pixels or relative error greater than 5% of the ground truth.
## 4 Discussion
### Models Based on Statistical Methods
The PLS-DA model developed for discriminating the honeybee class from the rest of the botanical origins conducted to a correct assignment of 65% of the honeybee samples when the total number of spectral points was used as input data. After the dimensionality reduction step, 387 markers were observed to have a higher discrimination potential, and the PLS-DA model constructed on these markers led to the same performance of the model. The obtained classification percentage was due to the fact that 6 honeybee samples were misclassified, and the other 17 samples (6 fir, 2 oriander, 3 polyfloral, and 6 raspberry) were predicted as honeybee (Figure 3d). The model created for the raspberry honey differentiation had a correct prediction rate of 76% when the entire fingerprint spectral range was used for model construction. The variable selection step illustrated a decrease in the dimensionality of the input data, namely 144 points were selected as meaningful features. The model built only on the reduced data set had a prediction of 78%. As shown in Figure 3e, from 12 raspberry samples, 9 were correctly attributed, and 13 other samples were attributed to the raspberry class as follows: fir (3), chestnut (1), sunflower (2), and honeybee (7). The sunflower honey differentiation model led to an 85% accuracy when the entire fingerprint region was considered. When the feature selection tool was applied, 377 meaningful markers were obtained and further used for the model that gave an accuracy of 87.6%. In this case, one sunflower sample was misclassified and one fir, two oriander, one polyfloral, and four raspberry were predicted as sunflower (Figure 3f).
# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 1 Introduction
The proposed solution is applied for the ISA100.11a wireless industrial communication standard and is implemented on the VR950 IIoT Gateway [10] hardware platform. Enabling the PEAR solution for a 70 WSN field devices, reduces the sensors nodes overconsumption 10.4 times and extends the cluster lifetime by 21.27 and 2.5 months for low- and high-consuming WSN field devices, respectively. * Finally, the PEAR solution provides an energy balancing mechanism between and within energy clusters. This mechanism allows the PEAR solution to reduce the average energy consumption and increase the number of field devices respecting their energy profile by 25% for a high consumption cluster. All in all, PEAR proves to be a versatile solution, capable of adding predictable lifetime, lower overconsumption, and larger lifetime for a IWSN with multiple clusters  
The structure of the paper is as follows. Section 2 contextualises the PEAR solution by presenting a comprehensive top-down analysis of the energy management solutions and energy aware routing algorithms for WSNs. Section 3 describes a conceptual model for adding energy awareness within IWSNs. Section 4 gives a detailed description of the PEAR solution in terms of model design and system integration. Section 5 presents the main performance indicators and the impact of the PEAR solution. Finally, Section 6 concludes the paper.
# An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
## 3 Materials and Methods
### Runway Localization
Additionally, \(f_{x},f_{y},c_{x},c_{y}\) is the camera intrinsic parameters, \(R_{c}^{b}\) and \(p_{b}^{c}\) are the rotation matrix and translation matrix of the camera coordinate system relative to the body coordinate system, respectively. The above process is repeated for each point on the runway line to obtain the global coordinates of the runway line in the global coordinate system. Assuming the line equation is \(f(x_{1},x_{2},x_{3})=w_{0}+w_{1}x_{1}+w_{2}x_{2}+w_{3}x_{3}\) and total pick out \(k\) points, the localization runway line equation can be expressed as:  
\[ \begin{split}& w^{T}=(w_{0},w_{1},w_{2},w_{3}),\\ & x=(1,x_{1},x_{2},x_{3}),\\ &\mathcal{L}(w,x)=\sum_{i=1}^{k}(y_{i}-w^{T}x^{i})^{2}.\end{split}  \tag{5}\]  
Figure 8: Simulation of carrier-based unmanned aerial vehicle landing task process in our simulated environment. Our purpose is to minimize the loss function \(\mathcal{L}(w,x)\) since we can employ the method of stochastic gradient descent to obtain the weight coefficients that minimize the loss function:  
\[w_{t}=w_{t-1}-\eta\frac{\partial\mathcal{L}}{\partial w}, \tag{6}\]  
where \(\eta\) is the step size, \(w_{t}\) is the weight coefficient at step \(t\), and \(w_{t-1}\) is the weight coefficient at step \(t-1\). The process is repeated until the loss function converges to a minimum value. Finally, after performing the above steps, we can obtain the equations of the accurate straight lines for the left (\(f_{l}(x_{l},y_{l},z_{l})=w_{l}^{0}+w_{l}^{1}x_{l}+w_{l}^{2}y_{l}+w_{l}^{3}z_{l}\)) and right (\(f_{r}(x_{r},y_{r},z_{r})=w_{r}^{0}+w_{r}^{1}x_{r}+w_{r}^{2}y_{r}+w_{r}^{3}z_{r}\)) runway. The runway lines should be parallel (\(w_{l}^{0}=w_{r}^{0},w_{l}^{1}=w_{r}^{1},w_{l}^{2}=w_{r}^{2},w_{l}^{3}=w_{r}^{3}\)). Therefore, the equation of the estimated central axis line can be expressed as \(f_{c}(x_{c},y_{c},z_{c})=(w_{r}^{0}+w_{l}^{0})/2+w_{r}^{1}x_{c}+w_{r}^{2}y_{c} +w_{r}^{3}z_{c}\). Hence, for the CUAV to align with the central axis of the runway, it is necessary to control the CUAV's forward normal vector to align with the direction of the central axis line, while the CUAV's center of mass should be as close as possible to the equation of the central axis line.
## 2 Materials and Methods
### Design Procedure  
The stop band response is characterized by the central frequency (\(f_{0}\)), the fractional bandwidth (\(FBW\)), and the insertion losses (\(IL\)) at the central frequency. These characteristics are determined by the amplitude (\(A_{k}\)), period (\(\Lambda\)), and length (number of periods \(n_{p}\)) of the sinusoidal variation of the coupling coefficient along the propagation direction (see Equation (1)).  
As already discussed in previous sections, the central frequency can be directly adjusted with the period \(\Lambda\) using Equation (2).  
The challenge is to determine the adequate value of \(A_{k}\) and \(n_{p}\) in order to succeed with the specifications of \(IL\) and \(FBW\).  
In order to have an insight into the influence of \(A_{k}\) and \(n_{p}\) in the values of \(IL\) and \(FBW\), several simulations were performed in CST for the ESICL filter for a discrete number of values of \(A_{k}\) and \(n_{p}\) with \(A_{k}\) ranging from 10 to 80, and \(n_{p}\) ranging from 5 to 30. Additionally, the \(IL\) and \(FBW\) was computed in all possible combinations of \(A_{k}\) and \(n_{p}\). In all cases, \(\Lambda\) was selected to provide a stop band response centered at 11 GHz. The results are depicted in Figure 7.  
As it can be observed, when \(A_{k}\) increased, the \(IL\) and the \(FBW\) both increased. In addition, when \(n_{p}\) increased, the \(IL\) increased, and the \(FBW\) did not change significantly. This information can be useful when either performing a manual design, or properly deciding the initial point for a computer aided optimization process that optimizes the values of \(A_{k}\) and \(n_{p}\) until the specifications are met.
# Compressed Communication Complexity of _Hamming Distance_
## 2 Preliminaries
### Communication Complexity Model  
Our approach is based on the standard communication complexity model of [PERSON] [1] between two parties:  
* The parties are [PERSON] and [PERSON];
* The problem is a function \(f:X\times Y\to Z\) for arbitrary sets \(X,Y,Z\);
* [PERSON] has instance \(x\in X\) and [PERSON] has instance \(y\in Y\);* The goal of the two parties is to output \(f(x,y)\) for a pair \((x,y)\) of instances by a joint computation;
* The joint computation (i.e., the communication between [PERSON] and [PERSON]) follows a specified protocol \(\mathcal{P}\).  
The communication complexity [1] usually refers merely to the total amount of bits that need to be transferred between [PERSON] and [PERSON] to compute \(f(x,y)\). In this paper, we follow [PERSON] et al.'s model [7] where the communication complexity is evaluated by a pair \(\langle r,b\rangle\) of the number of communication rounds \(r\) and the total amount of bits \(b\) exchanged in the communication.  
In a (Monte-Carlo) randomized public-coin protocol, each party ([PERSON]/[PERSON]) can access a shared infinitely long sequence of independent random coin tosses. The requirement is that the output must be correct for every pair of inputs with probability at least \(1-\epsilon\) for some \(0<\epsilon<1/2\), which is based on the shared random sequence of coin tosses. We remark that one can amplify the error rate to an arbitrarily small constant by paying a constant factor penalty in the communication complexity. Please note that the public-coin model differs from a randomized private-coin model, where in the latter the parties do not share a common random sequence and they can only use their own random sequence. In a deterministic protocol, every computation is performed without random sequences.
## 3 Results and Discussion
### DOM and HM Bonding Capability and Its Characteristics  
#### 3.3.1 Quenching Curve of PARAFAC Component  
The excitation wavelengths (Ex) and emission wavelengths (Em) of the DOM components (C1, C2, C3, C4) under various treatments were determined through parallel factor analysis. Subsequently, the fluorescence intensities at specific response wavelengths were derived from synchronized fluorescence data, and the corresponding curves depicting the relationship between fluorescence intensity and HM content were plotted (Figure 6). In BC-DOM, the fluorescence intensity of the C1, C2, and C3 components progressively diminished as Mn content increased, suggesting that the humic acid-like substances (C1, C2, and C3) bound to Mn, resulting in effective fluorescence quenching. Similarly, the fluorescence intensities of C1, C2, and C3 components decreased with the increase of Cu content, but the quenching effect diminished after Cu contents exceeded 80 umol L\({}^{-1}\), 60 umol L\({}^{-1}\), and 60 umol L\({}^{-1}\), respectively. This quenching response at varying HM contents resembles the 'failure' phenomenon in other related articles [55; 56]. This may be attributed to the fact that at the beginning of the titration, a certain amount of Cu binds to the functional groups in HA, resulting in an effective quenching. However, as the Cu content further increases, its interaction with specific sites in C1, C2, and C3 can cause Aggregation-Induced emission changes (AIE), restricting internal molecular motion (RIE), which in turn increases the photoluminescence quantum yield (PLQY) [57; 58]. Furthermore, the elevated Cu content may also induce the fluorescence resonance energy transfer (FRET) phenomenon, where energy is transferred among excited state molecules, serving as another factor influencing the fluorescence quenching effect [59].  
Figure 5: Fmax of BC-DOM and BC + H-DOM components. Different letters indicate significant differences between treatments.  
In BC + H-DOM, the humic-like substances C1, C2, and C3 exhibited similar quenching effects on Mn as in BC-DOM. When Cu was used as the quenching agent, these substances did not show obvious 'failure' at high concentrations. However, the fluorescence intensity of the humic-like substances in BC + H-DOM was significantly lower than that in BC-DOM. This reduction may be attributed to intermolecular \(\pi\)-\(\pi\) stacking and ligand interactions, which may restrict molecular mobility [60]. Regarding the protein-like component C4, the proteinaceous substances in BC-DOM and BC + H-DOM exhibited different changes in fluorescence intensity during the quenching process with Mn and Cu. Specifically, the protein-like substances in BC + H-DOM demonstrated higher stability when complexed with Mn and Cu compared to those in BC-DOM. It has been demonstrated that the addition of HA may introduce additional carboxyl groups to DOM, allowing the protein-like substances to form complexes with HM through these groups, thereby enhancing the overall stability of DOM [22].
# An Investigation on the Synthesis of Alkali Activated Materials from Thermally Modified Clays
###### Abstract  
The sustainability and economic competitiveness of alkali activation technology greatly depends on expanding the raw materials database with locally available resources. Therefore, a notable trend has been witnessed toward the exploitation of common clays as alternatives to well-established solid aluminosilicate precursors due to their availability and wide geographical distribution. However, common clays are complex and dedicated research is needed to tailor synthesis procedures and mix designs for different clay resources. This paper describes the outcomes of a study conducted to investigate the influence of several synthesis parameters (solid-to-liquid ratio, NaOH molarity, Si availability, and curing conditions) on the properties of alkali activated binders produced from different thermally modified clays. Optimal synthesis conditions for benchmark metakaolin systems have been identified and binders were produced with progressive dosages of metakaolin replacement by common local clays. Fundamental physical and mechanical properties such as apparent density, open porosity, water absorption, and compressive strength were examined at different curing ages, and X-ray diffraction (XRD) was used to provide complementary mineralogical insights. By combining the effects of the parameters studied, mortar specimens were produced with the developed binders, reaching compressive strength values exceeding 28.2 \(\pm\) 0.1 MPa, a bulk density as low as 1.78 \(\pm\) 0.0 g/cm\({}^{3}\), and open porosity and water absorption values lower than 15% and 8%, respectively. These properties are comparable to those of conventional hydraulic products, which presents them as interesting candidates for construction. Ultimately, this work aims to contribute with valuable insights toward the valorization of a large group of unexploited clay precursors by demonstrating the feasibility of producing technologically competitive alkali activated materials with little or no use of the prime precursors, thus adding to the extant knowledge and contributing to future scientific and industrial developments in this field.  
alternative binders; alkali activated materials; secondary resources; valorization of common clays +
Footnote †: journal: Applied Sciences  
1
## 1 Introduction
In the following, the Bussgang-type blind equalization algorithms are considered, where the conditional expectation (the expectation of the source input given the equalized or deconvolutional output) is derived for estimating the desired response. In the literature, we can find several approximated expressions for the conditional expectation related to the blind adaptive deconvolutional problem [20; 43; 44; 45; 46; 47; 48; 49]. However, References [43; 44; 45; 46] are valid only for a uniformly distributed source input and References [20; 47; 48] were designed only for the noiseless case. Recently [49], a new blind adaptive equalization method was proposed based on Reference [47] that is applicable for signal-to-noise ratio (SNR) values down to 7 dB. However, the computational burden of the method in Reference [49] is relative high. The closed-form approximated expression proposed in Reference [49] for the conditional expectation with Lagrange multipliers up to order four (thus applicable for the 16 QAM input case) is composed of a polynomial function of the equalized output of order twenty one. Please note that the proposed expression for the conditional expectation with Lagrange multipliers up to order four in Reference [47] is a polynomial function of the equalized output of order thirteen while the proposed expression for the conditional expectation with Lagrange multipliers up to order four in Reference [20] is a fraction where the numerator and the denominator are a polynomial function of the equalized output of order thirteen and twelve, respectively. In this paper, I propose a new closed-form approximated expression for the conditional expectation based on Reference [20] with Lagrange multipliers up to order four. This new proposed expression has a reduced computational burden compared with the previously obtained expressions for the conditional expectation proposed in Reference [20; 47; 49]. The new proposed expression for the conditional expectation is composed of a fraction where the numerator and the denominator are a polynomial function of the equalized output of order seven and six, respectively. Simulation results show that the new proposed algorithm, with the new proposed Lagrange multipliers up to order four, is suitable for SNR values down to 0 dB and has an improved equalization performance from the residual ISI point of view compared with Reference [49].
## 2 Materials and Methods
### SmartChipTM HT-qPCR Analysis  
DNA isolated from the above step was freeze-dried and transported to Resistomap Oy (Helsinki, Finland) for the HT-qPCR analysis. The DNA samples were resuspended in nuclease-free water, and 2 ng/\(\upmu\)L was added to the PCR reaction mix containing 1\(\times\) SmartChip TM green gene expression master mix, 300 nM of primers, and nuclease-free water. The primers (\(n\) = 296) comprised 268 oligos for ARGs, 8 for integrins, and 20 for other genes [27]. The ARG oligos were chosen to cover the ARGs against the drug classes of beta-lactams, aminoglycosoides, tetracyclines, fluoroquinolones, MLSB, tetracycline, vancomycin, phenicol, trimethoprim, and sulfonamides. A total reaction volume of 100 nL (\(n\) = 3 replicates) was loaded on a SmartChipTM (\(n\) = 5184 wells) employing an automated SmartChipTM multisample nanodispener (TakaraBio, San Jose, CA, USA). PCR was conducted on a SmartChipTM Real-Time PCR System (TakaraBio, San Jose, CA, USA) through a PCR program including initial enzyme activation at 95 \({}^{\circ}\)C for 10 min followed by 40 cycles of denaturation at 95 \({}^{\circ}\)C for 30 s and annealing at 60 \({}^{\circ}\)C for 30 s. A melt curve analysis was performed using the SmartChipTM qPCR software to exclude false positive data (amplicons with unspecific melting curves or multiple peaks). The cycle threshold (C\({}_{\text{T}}\)) was set at 27. The average C\({}_{\text{T}}\) (triplicates) was used for estimating the relative abundance with reference to the 16S rRNA gene employing the universal 2\({}^{-\Delta\text{CT}}\) method (\(\Delta\)C\({}_{\text{T}}\) = C\({}_{\text{T}}\) detected gene--C\({}_{\text{T}}\) 16S rRNA gene) [45]. The 16S rRNA gene also served as a positive control. A negative control was run separately with sterile nuclease-free water to check for cross-contamination.