esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of In-Situ and Remote Sensing Technologies to Monitor Water and Sanitation Interventions
## 2 Sensing WASH
### Water Quality Sensors  
A variety of water quality sensors are commonly used for both in situ continuous monitoring as well as sample-based testing. Off-the-shelf sensors are readily available to measure pH, dissolved oxygen (DO), conductivity (often used to measure salinity), turbidity, temperature, chlorine, various dissolved ions (including fluoride, ammonia, silver, nitrate, and nitrite that are often byproducts of fertilizers), and total organic carbon (TOC). A complete review of these products and applications are outside the scope of this report.  
Instead, several more recent sensor applications, directly relevant to monitoring SDG targets 6.1 and 6.2, are highlighted. In particular, water quality parameters may be estimated cost-effectively using remote sensing techniques and technologies. Water quality assessments include the identification of colored dissolved organic matter (CDOM), Secchi disk depth (SDD), turbidity, total suspended sediments (TSS), water temperature (WT), total phosphorus (TP), sea surface salinity (SSS), dissolved oxygen (DO), biochemical oxygen demand (BOD), and chemical oxygen demand (COD). With advances in space science, computer applications, and computational methods over recent decades, remote sensing techniques have become useful tools to achieve this goal.
## 3 Results  
### Evaluation of Model Performance  
The updated GRAMI-rice model performed well in terms of reproducing field conditions of paddy growth in LAI and above-ground dry mass (AGDM). Rice yield could also be determined with reasonable accuracy. For the sake of verification, we used data from CNU, Gwangju from 2011 to 2013 (Figure 4 and Table 3). Simulated seasonal curves for LAI were fit to the corresponding observed values with a model efficiency (_ME_) of 0.80 and a root mean square error (RMSE) of 0.71 m\({}^{2}\) m\({}^{-2}\) for cv. Hopum in 2011, an _ME_ of 0.86 and an RMSE of 0.56 m\({}^{2}\) m\({}^{-2}\) for cv. Hwasunchal in 2012, and an _ME_ of 0.95 and an RMSE of 0.33 m\({}^{2}\) m\({}^{-2}\) for cv. Unkwang in 2013. Simulated yields agreed well with the observed yields, with an RMSE of 434.9 kg ha\({}^{-1}\). According to a two-sample t-test (\(\alpha\) = 0.05), the simulated yield (\(\mu\) = 7,365.7 kg ha\({}^{-1}\)) was not significantly different (\(p\) = 0.883) from the observed yield (\(\mu\) = 7,190.0 kg ha\({}^{-1}\)). Validation, using data from Gimje, Chonbuk in 2014 (Figure 5 and Table 3), showed that simulated LAI values agreed with the observed LAI values with an _ME_ of 0.75 and an RMSE of 1.05 m\({}^{2}\) m\({}^{-2}\) for cv. Sindongjin, an _ME_ of 0.83 and an RMSE of 0.99 m\({}^{2}\) m\({}^{-2}\) for cv. Saenuri, and an _ME_ of 0.86 and an RMSE of 0.88 m\({}^{2}\) m\({}^{-2}\) for cv. Ilmi. Simulated yields also agreed with the observed yields, with an RMSE of 426.5 kg ha\({}^{-1}\). According to a two-sample t-test (\(\alpha\) = 0.05), there was no significant difference (\(p\) = 0.839) between the simulated yield (\(\mu\) = 6937.7 kg ha\({}^{-1}\)) and the observed yield (\(\mu\) = 6801.0 kg ha\({}^{-1}\)).  
Figure 4: Simulated (S) and observed (O) leaf area index (LAI) and simulated above ground dry mass (SAGDM) of Hopum in 2001 (**a**), Hwasunchal in 2012 (**b**), and Unkwang in 2013 (**c**), and a comparison between simulated (Sim) and observed (Obs) yields (**d**) at Chonnam National University, Gwangju, S. Korea for model verification.
## 3 Modeling Method Considering Uncertainty  
### Uncertainty Modeling of Component Parameters  
Bond graph-linear fractional transformation technique (BG-LFT) [39] is a widely used and effective method for research considering system uncertainty. Figure 7 is a general model of the method.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pressure Virtual Sensor** & **Flow Virtual Sensor** \\ \hline \(SSp_{1}\) and \(SSp_{3}:\text{ARR}_{1}\) & \(SSQ_{1}:\text{ARR}_{3}\) \\ \(SSp_{2}\) and \(SSp_{4}:\text{ARR}_{2}\) & \(SSQ_{2}:\text{ARR}_{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Set virtual sensors and corresponding ARRs.  
Figure 6: Traditional diagnostic bond graph of landing gear R/E hydraulic system.  
In Figure 7, \(M(s)\) is an incidence matrix; \(\Delta\) is the parameter uncertainty deviation matrix; \(u\) and \(y\) are actual input and output vectors, respectively; \(w\) and \(z\) are auxiliary input and output vectors, respectively.  
Under the premise of not changing the causality of the original bond graph model, the uncertainty is expanded. Let the true values of the resistive element \(R\), capacitive element \(C\) and inertial element \(I\) of the bond graph model be \(\theta\in\{R,C,I\}\), the nominal value be \(\theta_{n}\in\{R_{n},C_{n},I_{n}\}\) and the uncertainty value be \(\theta\in\{\Delta R,\Delta C,\Delta I\}\).Then each value satisfies:  
\[\theta=\theta_{n}+\Delta\theta \tag{1}\]  
Define \(\delta=\Delta\theta/\theta\) as relative uncertainty. Taking the resistive element as an example, it can be specifically expressed as follows:  
\[\left\{\begin{array}{l}\text{Impedance type}:p_{R}=Q_{R}R=Q_{R}(R_{\theta}+ \Delta R)=Q_{R}R_{\theta}(1+\delta_{R})=p_{R_{n}}+p_{\Delta R}\\ \text{Admittance type}:Q_{R}=\frac{p_{R}}{R}=\frac{p_{R}}{R_{\theta}+ \Delta R}=\frac{p_{R}}{R_{n}}(1-\frac{\Delta R}{R_{\theta}+\Delta R})=Q_{R_{n} }+Q_{\Delta R}\end{array}\right. \tag{2}\]  
where \(p_{R}\) is the actual output pressure, \(Q_{R}\) is the actual input flow, \(p_{R_{n}}\) is the nominal pressure of the element, and \(p_{\Delta R}\) is the uncertainty pressure. The resulting image is shown in Figure 8.  
BG-LFT models of capacitive elements and inertial elements can be obtained by the same method, as shown in Figures 9 and 10 where \(Dp*\) and \(DQ*\) are sensor virtual pressure source and flow source, respectively.  
Figure 8: BG-LFT model of resistive element: (**a**) impedance type; (**b**) admittance type.  
Figure 7: General model of BG-LFT.
# A Novel Method for Ground-Based Cloud Image Classification Using Transformer
## 3 Datasets and Experimental Settings
### Experimental Setup
#### 3.2.2 Evaluation Metric  
In order to comprehensively evaluate the classification performance of the proposed method for various types of images, accuracy, precision, and recall are calculated as the evaluation metrics in this paper. Accuracy can be calculated based on positive and negative samples as:  
\[Accuracy(Acc)=\frac{TP+TN}{TP+TN+FP+FN} \tag{11}\]  
where _TP_ (True Positive) is the number of correctly classified samples for a specific class, _TN_ (True Negative) is the number of correctly classified samples for the remaining classes, _FP_ (False Positive) is the number of misclassified samples for the remaining classes, and _FN_ (False Negative) is the number of misclassified samples for a specific class. Precision and recall can be expressed as:  
\[Precision(Pr)=\frac{TP}{TP+FP} \tag{12}\]  
\[Recall(Re)=\frac{TP}{TP+FN} \tag{13}\]  
In addition, we also use an _F1_score_ for evaluation, the expression of which is shown in Equation (14).  
\[F1\_score=\frac{2\times Pr\times Re}{Pr+Re} \tag{14}\]
## 2 Materials and Methods
### Selection and Growth of Fungal Strains and ADPE Pretreatment  
Three fungal strains were selected for ADPE pretreatment and isolated from soil samples near Zhenghe Biogas Plant using the dilution plating procedure. The isolated strains were named after Nanchang University (NU) and labeled as NU-3, NU-8, and NU-27, respectively. Afterward, they were cultured in Rose Bengal Agar slants (Table S1) at 30 \(\pm\) 1 \({}^{\circ}\)C for 48 h and then stored at 4 \({}^{\circ}\)C until use.  
To harvest fungal spores for mycelium pellet production, the agar slant was filled with 5 mL of distilled water and agitated by a vortex mixer for 1 min to make spores suspend in distilled water. This 5 mL spore suspension was inoculated into the PDB medium (Table S2) with a 500 mL working volume at an initial spore density of about 2 \(\times\) 10\({}^{5}\) ind mL\({}^{-1}\) in a 1 L Erlenmeyer flask. The spore suspension for each fungal strain was cultured at 30 \(\pm\) 1 \({}^{\circ}\)C on an orbital shaker (TS-2102 GZ, Tensuc, Shanghai, China) (140 rpm, 2 d) to form enough mycelium pellets.  
These mycelium pellets were harvested using the double-layer gauze with a pore size of 0.425 mm and cultured with the diluted ADPE at initial pH 4 with a working volume of 10 L in a 15 L homemade airlift bioreactor (Figure 1a). In addition, the diluted ADPE in the bioreactor was added an extra 2 g L\({}^{-1}\) glucose. This was because glucose supplementation was accessible for fungal consumption and could improve the stability and decolorization performance of the fungal pellets [27]. The temperature in the bioreactor was controlled at 30 \(\pm\) 1 \({}^{\circ}\)C using an aquarium heater (YGR-002, Yee, Shanghai, China). Aeration inside the bioreactor was filtered through a syringe filter with a pore size of 0.45 \(\upmu\)m (PES, Jingteng, Tianjin, China) and controlled at 1 L min\({}^{-1}\) using an air pump (OKEY, Taizhou, China), which allowed the mycelium pellets to be mixed. All of the pretreated ADPEs were sterilized in the autoclave (BXM-30R, BOXUN, Shanghai, China) (121 \({}^{\circ}\)C, 40 min) to remove fungus and bacteria before microalgae cultivation.  
In order to determine decolorization percentage, 2 mL of ADPE was collected to measure the maximum absorbance wavelength of 310 nm using a spectrophotometer (DR 6000, Hach, Loveland, CO, USA). The decolorization percentage (\(D\), %) was calculated according to Equation (1).  
\[D=(A_{0}-A_{t})/A_{0} \tag{1}\]  
where \(A_{0}\) and \(A_{t}\) denote the absorbances of ADPE on day 0 and day \(t\) during the experiment of fungal pretreatment, respectively.
## 3 Results and Discussion
### Pressing Efficiency, Capacity, and Cold-Pressed Oil Yield
The efficiency of continuous screw presses operating as pre-presses is approximately 50-60% relative to the oil content, while final presses can achieve an efficiency of 80-90% [42]. [PERSON] and [PERSON] [38] investigated the oil yield (32.2-89.4% \(w\)/\(w\)) by pressing _Jatropha curcas_ L. seeds under various screw press characteristics, including screw diameter, rotational speed, diameter of oil and cake outlet openings, and press head temperature. Pressing process parameters, particularly temperature, significantly influence the quality of the produced oil [30]. [PERSON] and [PERSON] [60] studied the effects of temperature, compression pressure, and the duration of applied force on soybean oil yield and optimized the process by keeping some parameters constant while varying others. Their results demonstrated that increasing process parameters leads to higher oil yield, but exceeding an optimal temperature reduces yield. If the moisture content of the material is not adequate, pressing becomes difficult or even impossible [61]. Friction within the material and press is high, inevitably causing a temperature rise. However, during these investigations, no excessive temperature increase was observed, as the oil temperature at the press outlet was maintained at 45-50 \({}^{\circ}\)C. Reduced friction likely contributed to the improved oil yield. In this case, the hull in the pressing material acted as a,,flow-enhancing material\" [62]. Additionally, heating the material so that the oil temperature at the outlet was 45-50 \({}^{\circ}\)C provided a mild conditioning effect, facilitating oil extraction and improving yield. [PERSON] et al. [62], however, noted the necessity of partially removing the hulls to produce high-quality cold-pressed oil. The results for pressing capacity are presented in Table 3. Pressing capacity (material flow rate and oil quantity produced) was calculated based on the retention time of a specific mass of material in the press and the determined pressing yield. The achieved pressing capacity, expressed as seed flow rate, is significantly lower than the maximum press capacity (30 kg/h). In the tested samples, it ranged from 5.35 kgseeds/h, calculated for sample 2 (sample with 5% hull and 2.5% impurities), to 13.04 kgseeds/h obtained in sample 7 (sample with 15% hull and 2.5% impurities). Additionally, the quantities of oil produced corresponded to these values. Thus, between 3.48 kg of oil (sample 2) and 9.74 kg of oil (sample 7) were produced per hour. Despite the high material flow rate of 11.43 kgseeds/h for sample 10 (sample without, 0% hull and without, 0% impurities), the oil quantity produced was significantly lower (4.48 kgoil/h), highlighting the importance of material composition in achieving optimal capacity.
# Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment
## 3 Preliminaries
### Os-ELM  
OS-ELM was proposed by [PERSON] et al. [31] to learn data individually or in batches, which has a faster learning speed and better generalization performance, and it can learn data online with fixed or varying chunk sizes. OS-ELM is divided into two phases including an initialization phase and online learning phase, whose architecture is shown in Figure 3. In the initialization phase, the input layer to hidden layer weights \(\mathbf{w}\) and hidden layer bias \(\mathbf{b}\) of OS-ELM are first randomly initialized. Given the input \(\mathbf{x}\) and the activation function \(g(x)\), the output \(\mathbf{H}\) of the hidden layer can be computed as  
\[\mathbf{H} =g(\mathbf{x};\mathbf{w},\mathbf{b})\] \[=\left[\begin{array}{ccc}g\big{(}\mathbf{w}_{1}^{\mathsf{T}} \mathbf{x}_{1}+b_{1}\big{)}&\ldots&g\big{(}\mathbf{w}_{L}^{\mathsf{T}}\mathbf{ x}_{1}+b_{L}\big{)}\\ \vdots&\vdots&\vdots\\ g\big{(}\mathbf{w}_{1}^{\mathsf{T}}\mathbf{x}_{N}+b_{1}\big{)}&\ldots&g\big{(} \mathbf{w}_{L}^{\mathsf{T}}\mathbf{x}_{N}+b_{L}\big{)}\end{array}\right]_{N \times L}, \tag{4}\]  
where \(N\) is the number of samples; \(L\) is the number of the hidden layer nodes and \(\mathsf{T}\) denotes the transpose of the matrix.  
The hidden layer to output layer weights can be expressed as  
\[\beta^{(0)}=\mathbf{P}_{0}\mathbf{H}_{0}{}^{\mathsf{T}}\mathbf{Y}_{0}, \tag{5}\]  
where \(\mathbf{Y}_{0}\) is the label corresponding to the training data, \(\mathbf{H}_{0}\) is the output of the hidden layer and \(\mathbf{P}_{0}=\big{(}\mathbf{H}_{0}^{\mathsf{T}}\mathbf{H}_{0}\big{)}^{-1}\).  
In the online learning phase, when the first set of training data arrives, the hidden layer output can be obtained, so the output layer weights can be expressed as  
\[\begin{split}\mathbf{P}_{k+1}=\mathbf{P}_{k}-\mathbf{P}_{k} \mathbf{H}_{k+1}^{\mathsf{T}}\Big{(}\mathbf{I}+\mathbf{H}_{k+1}\mathbf{P}_{k }\mathbf{H}_{k+1}^{\mathsf{T}}\Big{)}^{-1}\mathbf{H}_{k+1}\mathbf{P}_{k}\\ \beta^{(k+1)}=\beta^{(k)}+\mathbf{P}_{k+1}\mathbf{H}_{k+1}^{ \mathsf{T}}\Big{(}\mathbf{Y}_{k+1}-\mathbf{H}_{k+1}\beta^{(k)}\Big{)}.\end{split} \tag{6}\]  
In this case, the number of training data arriving in each group can be inconsistent.
## 4 Micropolarizer Array Design
### Substrate Material and Grating Material  
Figure 4 shows the section diagram of the designed double-layer metal grating structure; that is, the substrate is also etched to a certain depth. In the diagram, T is the grating period, and \(a\) is the grating width, \(h\)1 and \(h\)2 are the wire grid depth and zinc selenide depth, respectively.  
According to the analysis of equivalent medium theory, in the range of 8\(\sim\)12 \(\upmu\)m wave band, the TM wave transmittance and extinction ratio of the base material with a low refractive index are better than those of the base material with a high refractive index [16]. Compared with germanium, magnesium fluoride, calcium fluoride, and other infrared optical materials, zinc selenide has the advantages of high-temperature resistance, deliquescence resistance, and relatively low price. Therefore, zinc selenide is used as the base material in this paper. As shown in Figure 5, the spectral transmission line of zinc selenide with a thickness of 10 \(\upmu\)m is shown. In the wave band above 1.5 \(\upmu\)m, its transmissivity reaches more than 70%.  
The selection of grating wire materials will also affect the polarization performance of grating. This paper compares the effects of aluminium, gold, silver, and copper as wire grating materials on the transmittance and extinction ratio of TM waves in the incident  
Figure 4: Section of the grating structure.  
Figure 5: Transmission spectrum of zinc selenide with a thickness of 10 \(\upmu\)m [17].  
band of 7\(\sim\)13 \(\upmu\)m. According to the comparison and analysis in Figure 6, the TM wave transmittance of the four materials is close. When gold is used as the wire gate material, TM wave transmittance is slightly higher than that of the other three metals, but the extinction ratio is low. In contrast, when aluminium is used as the wire gate material, the extinction ratio is significantly higher than that of the other three materials. Therefore, in overall consideration, the selection of aluminium as the wire grid material is more appropriate.
# Integrated DVB-X2 Receiver Architecture with Common Acceleration Engine
## 3 DVB-X2 Receiver Architecture
### DVB-S2 Demodulator  
The DVB-S2 demodulator consists of a matched filter, a symbol timing recovery, a carrier phase recovery, a frame detector, a PLS decoder, a PL descrambler, an equalizer, and a de-mapper as shown in Figure 6. The matched filter is commonly used to maximize the signal to noise ratio (SNR) in the presence of additive stochastic noise. It is implemented using a 41-tap square root raised cosine filter. The symbol timing recovery block corrects the timing errors included in the process to convert samples to symbols. It corrects the timing errors by using an interpolator. The Gardner algorithm is used for estimation of the timing error [6]. The interpolation filter is implemented using a Cubic Lagrange interpolator. The carrier phase recovery is designed with a second order phase-locked loop (PLL), which recovers the carrier frequency and phase offsets by using the automatic frequency control function. The frame detector searches the start position of the DVB-S2 frame using the SOF symbol and the PLS code. It is implemented using a correlator. The equalizer corrects the errors in the magnitude and phase of the received signals. The PLS decoder is implemented using a Reed-Muller (32, 6) decoder. The PL descrambler performs the descrambling operation for the XFECFRAME of the PL frame.  
Figure 5: Block diagram of the proposed OFDM demodulator.
# Sustainable Fire Protection: Reducing Carbon Footprint with Advanced Coating Technologies
## 1 Introduction  
### Advancement in Sustainable Construction with Metallum Fire-Resistant (MFR) Paint  
The integration of sustainable natural materials in construction represents a pivotal strategy for significantly mitigating the carbon footprint. Metallum Fire-Resistant (MFR) products, a groundbreaking innovation, exemplifies this approach by utilizing waste from the high-quality cork-stopper manufacturing industry, a resource traditionally characterized by a low reuse value.  
This exposition derives its primary insights from the scholarly contributions documented in publications [1; 2].  
MFR's composition involves vaporized natural-cork particles and highly elastic, waterproof acrylic emulsions, endowing the coating with remarkable properties. In direct fire exposure, MFR demonstrates exceptional resilience, providing effective protection for over three hours. Beyond its fireproof attributes, MFR serves as a strategic application, bestowing buildings with a thermal envelope layer that prevents energy leakage (cold or heat). This is achieved through its low emissivity, which homogenizes interior temperatures, reduces temperature gradients, and stabilizes relative humidity, resulting in substantial savings in air conditioning consumption for both heating and cooling purposes.  
To address emissions and align with the scientific targets, MFR products actively collaborate to create a coating system that avoids solvent usage, thereby reducing CO2 production. MFR plays a crucial role in the fight against climate change, enhancing the availability of clean coating systems and contributing to the successful pursuit of net-zero emissions. Notably, the annual reduction in the carbon footprint is augmented by the utilization of cork trees in MFR's production, which follow a sustainable life cycle.
# Line Structure Extraction from LiDAR Point Cloud Based on the Persistence of Tensor Feature
###### Abstract  
The LiDAR point cloud has been widely used in scenarios of automatic driving, object recognition, structure reconstruction, etc., while it remains a challenging problem in line structure extraction, due to the noise and accuracy, especially in data acquired by consumer electronic devices. To address the issue, a line structure extraction method based on the persistence of tensor feature is proposed, and subsequently applied to the data acquired by an iPhone-based LiDAR sensor. The tensor of each point is encoded, voted, and aggregated by its neighborhood, and further decomposed into different geometric features in each dimension. Then, the line feature in the point cloud is represented and computed using the persistence of the tensor feature. Finally, the line structure is extracted based on the persistent homology according to the discrete Morse theory. With the LiDAR point cloud collected by the iPhone 12 Pro MAX, experiments are conducted, line structures are extracted from two different datasets, and results perform well in comparison with other related results.  
Keywords:line structure extraction; tensor feature decomposition; persistent homology; iPhone-based LiDAR sensor +
Footnote †: journal: Applied
# Application of Compound Terrain Factor LSW in Vegetation Cover Evaluation
## 4 Results and Discussion
### Correlation Analysis of Erodibility Factor  
Soil erodibility factor (i.e., K value) can measures the extent to which soil properties in the area affect soil erosion [84], and is widely used in soil erosion resistance evaluation and heterogeneity analysis [85; 86]. Soil erosion acts profoundly on vegetation growth and development, and by exploring the relationship between K and LSW, the mechanism of action of LSW and vegetation cover can be mapped from the perspective of soil and water conservation.  
In order to comprehensively assess the relationship between K and LSW, the six feature areas mentioned in Section 3.2 were selected as the test sites, and the LSW and K values of the corresponding study areas were obtained. The K value data used in this paper were sourced from the National Earth System Science Data Center (Data webpage: [[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn)), accessed on 3 February 2023). Following the collection of data, the mean LSW value, mean K value, and vegetation coverage in different test areas were plotted, as shown in a, b, and c of Figure 6, respectively.  
Figure 5: The success rate test curve of the susceptibility evaluation of four potential factors. (**a**) LSW, (**b**) LS, (**c**) TWI, (**d**) aspect.  
Figure 6: Statistics of six experimental areas. (**a**) mean LSW value statistics, (**b**) mean K value statistics, (**c**) vegetation coverage statistics. Note: The horizontal coordinates 1–6 in the figure represent plains, terraces, hills, small rolling mountains, medium rolling mountains, and large rolling mountains, respectively.