esa-sceva/satcom-chunk-collection huggingface dataset:
# Natural Materials as Carriers of Microbial Consortium for Bioaugmentation of Anaerobic Digesters
## 2 Materials and Methods
### Immobilization of Microbial Consortium on Carriers  
Four different natural materials were tested as carriers. The natural zeolitized tuff (NZ) from a quarry in Donje Jesenje, Croatia, consisted of 50-55% clinoptiolite with lower amounts of ecladone, plagioclase feldspars, opal-CT (10-15% each), and traces of quartz and analcime [9]. The commercial mineral composition of the ZeoSand(r) (Velebit Agro, Zagreb, Croatia) was a minimum of 80% clinoptiolite, and other mineral components were not listed in the safety data sheet. The expanded perlite was a commercial product intended for gardening (Special Mix B.V. manufactured by Gold Label, Aalsmeer, The Netherlands). The crushed corncob was from organic farming and not treated with pesticides and herbicides. A size fraction from 0.5 to 1 mm of NZ and ZeoSand(r) was used. The zeolites were ground by orbital milling and sieved, to obtain the desired particle size fraction. The particle sizes of the perlite were 1-4 mm, as declared by the manufacturer, and the crushed corncob had irregular particles between 2 and 6 mm in size, as measured by a microruler. All materials were weighed to 0.5 g on a technical digital balance (Kern & Sohn GmbH Ziegelei, school balance EMB 200-3, Balingen, Germany), added to a glass Petri dish, and sterilized in a dry oven (Instrumentaria, Zagreb, Croatia) at 105 \({}^{\circ}\)C for 1 h prior to testing.  
The consortium was revitalized from the Microbank(tm) on Luria-Bertani (LB) agar plates incubated at 37 \({}^{\circ}\)C (Memmert GmbH, IN110m, Schwabach, Germany) for 24 h. The grown biomass was scraped from the plate and suspended in 10 mL of sterile saline solution to obtain the consortium suspension. To immobilize the consortium on each individual carrier, 0.5 g of sterilized carrier, 0.5 mL of consortium suspension, and 50 mL of sterile LB medium were added to sterile plastic vials. The control vial lacked the carrier. The vials were tightly sealed and maintained for 24 h in an incubator at room temperature (\(23\pm 1\)\({}^{\circ}\)C) on a rotatory shaker (set to 5 rpm). Furthermore, the pure culture of _E. indicium_ was immobilized on each carrier following the same procedure.  
The number of bacteria immobilized onto each carrier was determined as follows: A supernatant was decanted from vials, and the carriers were washed twice with sterile saline solution (0.3%) to eliminate bacteria not attached to the carriers. Then, 20 mL of sterile saline was added, and the vials were vigorously shaken on a vortex (45 Hz/3 min). This way, the immobilized bacteria detach from the carrier and remain as planktonic cells in the supernatant [10]. The supernatant was serially diluted, inoculated on LB agar, and kept in an incubator (Memmert GmbH, IN110m, Schwabach, Germany) at 37 \({}^{\circ}\)C for 24 h. The grown colonies were counted, and the number of immobilized bacteria was expressed as log CFU g\({}^{-1}\) of the dry carrier.
# Effects of Adding Batteries in Household Installations: Savings, Efficiency and Emissions
## 3 Problem Formulation  
The associated energy management problem is formulated along the time horizon \(T\) with several time intervals employing Equations (1)-(8). An hourly time-step (\(\delta t\)) for a period of one year is considered.  
In each time step the systems must satisfy the power balance between consumption and production and this can be expressed by means of (1):  
\[P_{EN}(t)+P_{Disch}(t)=\sum_{j}D_{EA}(t,j)+P_{CH}(t) \tag{1}\]  
where \(P_{EN}(t)\) is the power consumed from the electrical network in \(t\), \(P_{Disch}(t)\) is the power discharged from the battery, i.e., obtained from it, during the period \(t\), \(D_{EA}(t,j)\) is the demand of each electrical appliance \(j\) in \(t\) and \(P_{CH}(t)\) is the charging power of the battery in \(t\).  
As can be expected, for the current AC system, the whole household demand is supplied from the electrical network. On the other hand, the battery involves a certain level of energy, so it is important to recognize the energy stored in the battery in \(t\), \(E_{B}(t)\), expressed in (2):  
\[E_{B}(t)=C_{nom}\cdot SOC(t-1)+\eta_{CH}\cdot P_{CH}(t)\cdot\delta t-\frac{P_{ Disch}(t)\cdot\delta t}{\eta_{Disch}} \tag{2}\]  
where \(C_{nom}  
\[P_{Disch}(t)\leq P_{Disch,max} \tag{7}\]  
where \(P_{Disch,max}\) is the maximum discharging power.  
The objective function aims to minimize the electricity bill of the residential building. This is the one defined in Equation (8):  
\[f=\sum_{t=1}^{T}[P_{EN}(t)\cdot\delta t\cdot C_{EN}(t)] \tag{8}\]  
where \(C_{EN}(t)\) are the electricity costs in \(t\).
###### Abstract  
Fuzzy systems (FSs) are popular and interpretable machine learning methods, represented by the adaptive neuro-fuzzy inference system (ANFIS). However, they have difficulty dealing with high-dimensional data due to the curse of dimensionality. To effectively handle high-dimensional data and ensure optimal performance, this paper presents a deep neural fuzzy system (DNFS) based on the subtractive clustering-based ANFIS (SC-ANFIS). Inspired by deep learning, the SC-ANFIS is proposed and adopted as a submodule to construct the DNFS in a bottom-up way. Through the ensemble learning and hierarchical learning of submodules, DNFS can not only achieve faster convergence, but also complete the computation in a reasonable time with high accuracy and interpretability. By adjusting the deep structure and the parameters of the DNFS, the performance can be improved further. This paper also performed a profound study of the structure and the combination of the submodule inputs for the DNFS. Experimental results on five regression datasets with various dimensionality demonstrated that the proposed DNFS can not only solve the curse of dimensionality, but also achieve higher accuracy, less complexity, and better interpretability than previous FSs. The superiority of the DNFS is also validated over other recent algorithms especially when the dimensionality of the data is higher. Furthermore, the DNFS built with five inputs for each submodule and two inputs shared between adjacent submodules had the best performance. The performance of the DNFS can be improved by distributing the features with high correlation with the output to each submodule. Given the results of the current study, it is expected that the DNFS will be used to solve general high-dimensional regression problems efficiently with high accuracy and better interpretability.  
Keywords:interpretability; high-dimensional data; adaptive neuro-fuzzy inference system; deep neural fuzzy system +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Application Workflow
#### 3.3.2 Application Workflow: Recognition  
Once the whole scenario of interest has been scanned through different image targets, the system is ready to overlap the energy information with the frame capture by the smartphone/tablet when they focus on part of the real world previously captured and associated to an image target. The whole workflow is described through the flow diagram shown in Figure 5.  
Process:  
1. A smartphone camera captures an image (frame) within the region of interest.
2. The system extracts the features of the image.
3. The features obtained are compared with the features previously stored during the training process.
4. If the features match with some of the stored features, the system gets the identification of the ThingSpeak channel associated with them.
5. The system accesses to the channel of the ThingSpeak cloud platform to read the energy information associated with the image target.
6. The AR application overlaps, in runtime, the energy information with the image captured by the smartphone.  
Figure 4: Kitchen image target and its features points.  
Figure 6 shows how energy information is overlapped over the image target when the user focusses its smartphone on the fume hood. It is interesting to highlight that the visualization of this information can be improved using different helper visual effects. In this case, a colored rectangle is used to facilitate the visualization the information in a general way, and on the other hand, in order to help users to observe essential data and their changes, different colors are applied to the text objects depending on the value of the energy parameters. Furthermore, users can always click on the data to visualize them through a ThingSpeak graph; this way, they will be able to see a larger set of data (not only the last one) shown in a graphical way.
## 5 Conclusions  
Remote sensing techniques to derive fire risk areas after wildfires or human-induced fires using multispectral data were useful. The unmixing results identified the relative abundance of ground components, predominantly soil and green vegetation, that were determined to be important for evaluating surface burn severity. Landsat data are widely used for post-fire change detection; for example, we used dNBR and RdNBR to assess the effects of the fire on the soil surface. In addition to dNBR and RdNBR, [PERSON] et al. (2012) developed fire damage-severity indices using the composite burn index (CBI), burn severity index, and modified NBR to map the level of damage caused in vegetated areas by fire. Quantifying ground cover characteristics derived from satellite imagery provided a better evaluation of the physical condition of the soil surface. Some of these surface fire effects, especially soil cover and the lack of any remaining vegetative cover, are indicative of potential watershed responses. The methodology presented in this paper has certain implementation limitations in case of post-fire stabilization decisions. During this study, several parameters of concern became apparent in terms of data acquisition. Immediate post-fire images were often difficult to acquire due to smoke from fire and weather conditions. Ground data collections immediately after fire posed safety concerns and hence unfeasible at times. Further research is required to incorporate and make available immediate post-fire data for analysis.  
[PERSON] analyzed the data and wrote the paper; [PERSON] wrote, reviewed, and edited; [PERSON] data preprocessed. This research received no external funding.  
The authors declare no conflict of interest.
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
## 2 Model Order Selection
### Subspace-Based Criteria  
In this section, we consider two methods, namely ESTER [32] and SAMOS [33], which are not related to information criteria. These criteria are based on signal subspace properties and, in the specific case where the signal subspace has rank \(r\) and is extracted exactly, they yield the value \(+\infty\) for rank \(r\). These methods are based on the concept of separability in SSA [Sections 1.5 and 6.1] in [3] and aim to identify separability points. The criteria are applicable when the signal is approximately separable from the noise. However, within the signal, its components may also be separable, which makes the methods appear unstable despite the strong advantage that they are directly based on SSA properties. Another disadvantage of the criteria is that they cannot specify \(r=0\), i.e., they cannot determine the case of no signal.  
The criterion ESTER is based on the principles of the ESPRIT method [34], which is a subspace-based method used for estimating signal parameters, particularly, frequencies. We define \(\mathbf{E}(r)=\overline{\mathbf{U}_{r}}-\underline{\mathbf{U}_{r}}\mathbf{A}\), where \(\mathbf{A}=\underline{\mathbf{U}_{r}}^{\mathsf{T}}\overline{\mathbf{U}_{r}}\), \(\mathbf{U}_{r}\) consists of the \(r\) leading left singular vectors of the trajectory matrix of the series \(\overline{\mathbf{X}}\). In the case of an exactly separable signal \(\mathbf{S}\) ofrank \(r\), the matrix \(\mathbf{E}(r)\) is the null matrix. Therefore, the criterion proposes to consider the maximum of  
\[\mathrm{ESTER}(r)=\|\mathbf{E}(r)\|_{2}^{-2};\]  
here the norm \(\|\cdot\|_{2}\) equals the maximum eigenvalue.  
Subsequently, in [33], the method SAMOS was proposed, which was also based on the matrix \(\mathbf{U}_{r}\). Let us denote \(\tau_{i}\) the singular values of the matrix \([\overline{\mathbf{U}_{r}}:\underline{\mathbf{U}_{r}}]\). Then, we seek the maximum of  
\[\mathrm{SAMOS}(r)=\frac{1}{\sum_{i=r+1}^{2r}\tau_{i}/r}.\]
## 3 Experiments
### Ablation Study of Lightweight Designs
The decrease in the number of network layers also leads to a reduction in complexity and inference time but with a slight decrease in PSINR. The lightest network is 5.3 times faster at inference in the Jetson platform compared to the baseline model. For models with two layers, the use of a lightweight design is equally helpful in reducing complexity. In the six-layer models, the lightweight design module has less impact on the results. This is due to the redundancy of deep networks. Simply reducing the number of layers in the network results in a decrease in interference suppression, as demonstrated in Table 2. Therefore, there is a need for KD, which allows for the knowledge learned in the deep network to migrate into the shallow network. It can be observed that the changes in inference time varies considerably across the different platforms. Specifically, the lightweight design demonstrates favorable outcomes on the CPU and Jetson platforms but exhibits an increased processing time on the GPU. This discrepancy occurs because the lightweight models fail to fully leverage the computational capabilities of the GPU. Lightweight optimizations have proven to be particularly advantageous for edge computing devices. Figure 7: Comparison of the output of different models in ablation experiments in the form of RD maps. The ablation studies demonstrate that the incorporation of a linear attention mechanism and a deep separable convolution significantly reduces the network size and inference time while slightly reducing the interference mitigation performance. These improvements make the network more efficient and more suitable for real-time applications.
## 6 Results
### Combinations of the Studied System Components
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hybrid Power** & **Algorithm** & **NPC (s)** & **LCOE** & **LPSP** & **Availability** & **Renewable** & **Battery Daily** \\
**System** & **Algorithm** & **NPC (s)** & **(\%/kWh)** & **(\%)** & **(\%)** & **Energy (\%)** & **Autonomy (day)** \\ \hline \multirow{6}{*}{PV/WT/Diesel/BESS} & QOBO & 175,651 & 0.1669 & 0.019 & 98.87 & 98.15 & 0.5826 \\  & BO & 209,096 & 0.1986 & 0.050 & 96.99 & 99.75 & 0.6418 \\  & HHO & 201,109 & 0.1910 & 0.025 & 99.23 & 99.88 & 0.6353 \\  & AEFA & 183,284 & 0.1741 & 0.026 & 99.33 & 96.88 & 0.8087 \\  & IWO & 347,523 & 0.3301 & 0.014 & 99.68 & 97.72 & 0.2524 \\ \hline \multirow{6}{*}{PV/Biomass} & QOBO & 110,807 & 0.1053 & 0.050 & 96.03 & 100 & // \\  & BO & 110,808 & 0.1053 & 0.050 & 96.03 & 100 & // \\  & HHO & 114,098 & 0.1084 & 0.046 & 96.94 & 100 & // \\  & AEFA & 113,410 & 0.1077 & 0.040 & 96.93 & 100 & // \\  & IWO & 130,491 & 0.1240 & 0.018 & 98.70 & 100 & // \\ \hline \multirow{6}{*}{PV/Diesel/BESS} & QOBO & 153,401 & 0.1457 & 0.049 & 98.63 & 97.25 & 2.5536 \\  & BO & 167,981 & 0.1596 & 0.050 & 98.72 & 92.88 & 2.2625 \\ \cline{1-1}  & HHO & 183,501 & 0.1743 & 0.017 & 98.94 & 97.27 & 0.5964 \\ \cline{1-1}  & AEFA & 160,774 & 0.1527 & 0.042 & 98.74 & 96.70 & 2.4171 \\ \cline{1-1}  & IWO & 287,730 & 0.2733 & 0.026 & 99.16 & 96.12 & 2.2306 \\ \hline \multirow{6}{*}{WT/Diesel/BESS} & QOBO & 1095,270 & 1.0405 & 0.014 & 99.85 & 70.03 & 3.9509 \\ \cline{1-1}  & BO & 1098,685 & 1.0437 & 0.003 & 99.97 & 71.3527 & 1.8483 \\ \cline{1-1}  & HHO & 1,123,579 & 1.0673 & 0.008 & 99.92 & 70.2407 & 3.1745 \\ \cline{1-1}  & AEFA & 1,119,533 & 1.0635 & 0.008 & 99.92 & 73.6967 & 3.1494 \\ \cline{1-1}  & IWO & 1,319,108 & 1.2531 & 0.008 & 99.92 & 81.8292 & 3.3907 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Factor results for all scenarios. The reliability of the proposed scenarios of the proposed HRES are respected and the availability of power is highly assured, the penetration RES is considered in this paper, while different results are obtained. The minimum penetration of 70% is obtained for the WT/Diesel/Battery scenario while the maximum penetration of 99.75% is obtained for the PV/WT/Diesel/BESS scenario. The daily battery autonomy is also influenced by the configuration of the HRES, the best autonomy is achieved for the WT/Diesel/BESS scenario taking nearly 4 days, while the minimum autonomy is obtained in PV/WT/Diesel/BESS case with only 6 h. The last system is composed of the different energy resource which explains the independence for a specific resource. Table 4 presents a detailed overview of all costs needed, for all scenarios presented and for all proposed algorithms. Figure 10: Cont. Figure 10: Convergence of the objective function of all algorithms for different scenarios; (**a**) PV/WT/Diesel/BESS, (**b**) PV/Biomass, (**c**) PV/Diesel/BESS, (**d**) WT/Diesel/BESS.
# Energy-Aware Online Non-Clairvoyant Scheduling Using Speed Scaling with Arbitrary Power Function
## 4 A 2-Comptitive Scheduling Highest Scaled Importance First (HSIF)
### Scaled Importance-Based Flow Plus Energy
#### 4.1.1 Algorithm HSIF  
The algorithm HSIF always selects an active job with the highest scaled importance at any time, where the scaled importance \(pr(j_{i},t)\) of a job \(j_{i}\) is computed as follows:  
\[pr(j_{i},t)=\left\{\begin{array}{c}\frac{imp(j_{i},t)}{\frac{1}{2}+log(ext(j_ {i},\ t))},\ \textbf{if job is executing}\\ \\ imp(j_{i},t)*(1+log(ext(j_{i},\ t)))\ \textbf{if job is not executing}\end{array}\right.\]  
The executed time \(ext(j_{i},\ t)\) of a job \(j\) is \(ext(j_{i},\ t)=(t-ar(j_{i}))\). At any time \(t\), the processor executes at speed \(s_{h}^{t}=\ Q(pr_{h}^{t})\), where \(Q=P^{-1}\) and \(pr_{h}^{t}\) is the total scaled importance of all active jobs for HSIF. As the algorithm HSIF is non-clairvoyant, the executed time assumed is its _current size_. The intension here is that the _instantaneous importance/priority_ must depend on its importance (system generated) and size. If the job is not executing (job is waiting) then the scaled importance will increase and if the job starts execution the partial importance of a job \(j\) will decrease with respect to increase in execution.  
```
Input:\(n_{a}\) number of active jobs \(\{j_{1},\ldots,\ j_{i},\ \ldots,\ j_{n_{a}}\}\). At time \(t\), the importance of all \(n_{a}\) active jobs \(\{imp(j_{i},t),\ldots,\ imp(j_{i},t),\ldots,\ imp(j_{n_{a},t})\}\) and the executed time for all active jobs \(\{ext(j_{i},\ t),\ldots,\ ext(j_{i},\ t),\ldots,ext(j_{n_{a},t})\}\). Output: The speed of all processors and execution sequence of jobs.
```  
**Algorithm Highest Scaled Importance First** (HSIF)  
**Theorem 1**.: _An online non-clairvoyant uni-processor scheduling Highest Scaled Importance First (HSIF) selects job with highest partial importance and consumes power equal to the total partial importance of all active jobs under dynamic speed scaling. HSIF is 2-competitive for the objective of minimizing scaled importance-based flow time plus energy on arbitrary-work and arbitrary-importance of jobs._  
In the rest of this section Theorem 1 is proven. For amortized local competitive analysis of HSIF, a potential function is provided in next sub section.
## 2 Materials and Methods  
Benzylidenerhodanine (BR) from ALFA AESER (98%) and acetonitrile (CH\({}_{3}\)CN) and tetrabutylammonium perchlorate (TBAP) from FLUKA were used as received. Mercury (II) acetate (Fluka, Munich, Germany, \(\geq\)98%), copper (II) acetate monohydrate (Fluka, Munich, Germany, \(\geq\)99.5%), lead (II) nitrate (Fluka, Munich, Germany, \(\geq\)99.5%), and cadmium nitrate tetrahydrate (Fluka, Munich, Germany, \(\geq\)98%) were used as received to prepare the synthetic stock solutions (10\({}^{-3}\) M) of heavy metal (HM) ions. HM solutions with different concentrations were prepared by successive dilution.  
Electrochemical experiments were carried out on PGSTAT302N AUTOLAB potentio-stat coupled to a three-compartment cell. The working electrode was a glassy carbon (GC) disk (3 mm in diameter, from Metrohm, Herisau, Switzerland). Before each experiment, its surface was polished with diamond paste (0.25 \(\upmu\)m) and cleaned with CH\({}_{3}\)CN. The reference electrode was Ag/10 mM AgNO\({}_{3}\) in 0.1 M TBAP, CH\({}_{3}\)CN. At the end of the experiments, the potentials were referred to the ferrocene/ferricinium redox couple (Fc/Fc+) potential. The auxiliary electrode was a platinum wire. The experiments were performed at 25 \({}^{\circ}\)C under an argon atmosphere.  
The cyclic voltammetry (CV) curves were recorded at 0.05-0.5 V/s scan rates. The differential pulse voltammetry (DPV) curves were recorded at 0.01 V/s with a pulse height of 0.025 V and a step time of 0.2 s. The rotating disk electrode voltammetry (RDE) experiments were performed at 0.01 V/s with rotation rates between 500 and 1500 rpm.  
Figure 1: Structures of rhodamine **R** and **BR**.  
Chemically modified electrodes (CMEs) were prepared from millimolar solutions of BR in 0.1 M TBAP/CH\({}_{3}\)CN by scanning or controlled potential electrolysis (CPE). Then, each modified electrode (BR-CME) was washed with acetonitrile and introduced in a 0.1 M acetate buffer solution (pH = 4.5) where it was equilibrated and overoxidized as previously described [31; 32]. The resultant electrodes were immersed for 10-15 min under stirring in synthetic solutions of HMs ions. After that, the electrodes were taken out and cleaned with deionized water; then, they were immersed in a cell containing 0.1 M acetate buffer solution (pH = 4.5). Here, they were polarized for 3 min at \(-\)1.0 V, and then their DPV curves were recorded between \(-\)1.0 and +0.6 V.  
The study by UV-Vis spectrometry was performed on JASCO V-670 in 1 cm path-length quartz cuvettes in freshly dried acetonitrile. Aliquots of heavy metal solutions were added to the R solution, and UV-Vis spectra were recorded between 800 and 200 nm one minute after each addition.