esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Reduction from MUCHB to Biclique Problems  
Proof of Lemma 2 from Lemma 1.: The reduction from MUCHB to the two biclique problems are simple. Given a hypergraph \(H=(V_{H},E_{H})\), we create a bipartite graph \(G=(L,R,E)\) by letting \(L=R=E_{H}\) and creating an edge \((e_{1},e_{2})\) for \(e_{1}\in L,e_{2}\in R\) iff \(e_{1}\cap e_{2}=\emptyset\).  
**Completeness.** If there is a bisection \((T_{0},T_{1})\) of \(V_{H}\) such that \(|E_{H}(T_{0})|,|E_{H}(T_{1})|\geqslant(1/2-\varepsilon)|E_{H}|\), then \(E_{H}(T_{0})\subseteq L\) and \(E_{H}(T_{1})\subseteq R\) induce a complete bipartite subgraph with at least \((1/2-\varepsilon)n\) vertices on each side in \(G\).  
**Soundness.** Suppose that, for every \(T\subseteq V_{H}\) of size at most \(|V_{H}|/2\), we have \(|E_{H}(T)|\leqslant\varepsilon|V_{H}|\). We will show that \(G\) does not contain \(K_{\varepsilon n+1,\varepsilon n+1}\) as a subgraph. Suppose for the sake of contradiction that \(G\) contains \(K_{\varepsilon n+1,\varepsilon n+1}\) as a subgraph. Consider one such subgraph; let \(e_{1},\ldots,e_{\varepsilon n+1}\in L\) and \(e^{\prime}_{1},\ldots,e^{\prime}_{\varepsilon n+1}\in R\) be the vertices of the subgraph. From how the edges are defined, \(T_{0}\triangleq e_{1}\cup\cdots\cup e_{\varepsilon n+1}\) and \(T_{1}\triangleq e^{\prime}_{1}\cup\cdots\cup e^{\prime}_{\varepsilon n+1}\) are disjoint. At least one of the two sets is of size at most \(|V_{H}|/2\), assume without loss of generality that \(|T_{0}|\leqslant|V_{H}|/2\). This is a contradiction since \(T_{0}\) contains at least \(\varepsilon n+1\) hyperedges \(e_{1},\ldots,e_{\varepsilon n+1}\).  
By picking \(\varepsilon=\delta/2\) and \(n>2/\varepsilon\), we arrive at the desired hardness result.
## 2 Proposed CPM-GFDM Transceiver
### CPM-GFDM Receiver
#### 2.2.3. Matched Filter Receiver  
The receiver is known as a matched filter receiver if \(Z\) is specified to be \(\mathbf{A}^{H}\) in (14), where \(H\) indicates Hermitian. This kind of receiver would increase the SNR for each subcarrier at the expense of producing self-interference in the event that the transmitter uses a non-orthogonal pulse.
# A Novel Physics-Guided Neural Network for Predicting Fatigue Life of Materials
## 2 Materials and Methods
### The Structure of the Proposed Method
#### 2.2.2 Structure of PGNN  
The PGNN consists of three modules, which are the evaluation module of the loading environment, the fatigue life calculation module and the back propagation module. Its structure is shown in Figure 1.  
The loading environment evaluation module consists of a four-layer fully connected neural network. The input is the loading environment parameters. In this study, the loading environment parameters include the stress ratio (R) and stress concentration factor (Kt) caused by the notch, and the output is the fatigue parameter \(\sigma^{\prime}_{f}\), \(b\) of the Basquin formula. The purpose is to construct the S-N curve corresponding to the loading environment parameters.  
The core of the fatigue life calculation module is the Basquin equation. Its input is the load S, and the fatigue parameter \(\sigma^{\prime}_{f}\), \(b\) obtained by the loading environment evaluation module, where the load S is the maximum stress \(\sigma_{max}\). The output is fatigue life, where \(\Delta N_{l}\) is the difference between the actual fatigue life \(N_{E}\) and the predicted fatigue life \(N_{P}\) of the ith sample (\(N_{E}-N_{P}\)).  
The core of the back propagation module is the loss equation. The fatigue life difference \((N_{E}-N_{P})\) obtained by the fatigue life calculation module is used as the input, and the loss equation is constructed to quantify the gap between the predicted target and the actual value. After the loss function value is obtained, the gradient of all neurons is calculated and back propagated to the loading environment assessment module to complete the parameter update of the neurons in the environment assessment module.  
The overall PGNN operation process is as follows: the fatigue parameter \(\sigma^{\prime}_{f}\),\(b\) is calculated by a fully data-driven loading environment evaluation module. Then, the fatigue life calculation module guides the network to obtain the corresponding fatigue life. The back propagation module calculates the loss function and reverses the conduction gradient with the goal of minimizing the loss value.  
For the first time, the fatigue parameter \(\sigma^{\prime}_{f}\),\(b\) obtained by inputting the loading environment parameters (R, Kt) into the network is a low-fidelity fatigue parameter. The fatigue  
Figure 1: The structure of PGNN.  
parameter \(\sigma^{\prime}_{f}\),\(b\) is corrected by the high-fidelity experimental load S and the experimental fatigue life \(N_{E}\) to obtain a higher-fidelity fatigue parameter \(\sigma^{\prime}_{f}\),\(b\). Then, the fatigue life values of high fidelity under various loading environments can be obtained.
## 4 Conclusions  
This study investigated the effects of extraction conditions on the yield of flavonoids and anthocyanins using the Box-Behnken response surface methodology. The influence of process variables (particle size, extraction time, and ultrasound intensity) on the extraction efficiency depended on the tested bioactive substance.  
For flavonoids, in the entire tested range, a significant positive effect was found with respect to extraction time and ultrasound intensity on the efficiency of the process, whereas in the case of particle size, the highest efficiency was obtained for a particle size of 0.75 mm. The optimal conditions (on the basis of the model) for the extraction of flavonoids from cannabis were as follows: particle size, 0.59 mm; extraction time, 10.71 min; and ultrasound intensity, 7.13 W\(\cdot\)cm\({}^{-2}\).  
For anthocyanins, we observed a negative influence from the particle size distribution on the extraction efficiency and a positive effect from the ultrasound intensity and time. The optimal conditions (on the basis of the model) for the extraction of anthocyanins were as follows: particle size, 0.25 mm; extraction time, 15 min; and ultrasound intensity, 8.60 W\(\cdot\)cm\({}^{-2}\).  
The statistical data showed that the developed models were precise and adequate compared with the experimental data. For both models (anthocyanins and flavonoids), high values for the coefficient of determination (0.87-0.97) and the corrected coefficient of determination (0.84-0.94) were obtained. The developed extraction procedure, with the application of a pulsed ultrasound field, proved to be efficient for obtaining an enriched fraction of bioactive compounds with very high flavonoid and anthocyanin contents from _Cannabis sativa_ L.  
Conceptualization, Z.K.; methodology, Z.K. and R.N.; validation, A.P. ([PERSON]) and M.K.; formal analysis, M.K.; investigation, Z.K.; A.P. ([PERSON]) and A.B.; data curation, A.P. ([PERSON]); writing--original draft preparation, Z.K., A.P. ([PERSON]), M.K. and R.N.; writing--review and editing, A.P. ([PERSON]), M.K. and A.P. ([PERSON]); visualization, A.B. and A.P. ([PERSON]); supervision, Z.K.; funding acquisition, Z.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ultrasound Intensity** & **Time** & **Energy Consumption** \\
**(W-cm\({}^{-2}\))** & **(min)** & **(kj)** \\ \hline
1.6 & 5 & 2720 \\
1.6 & 10 & 4843 \\
1.6 & 15 & 7041 \\
5.1 & 5 & 7631 \\
5.1 & 10 & 15,410 \\
5.1 & 15 & 21,821 \\
8.6 & 5 & 14,011 \\
8.6 & 10 & 22,945 \\
8.6 & 15 & 32,012 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The energy emitted by the ultrasonic device during extraction.
# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
## 2 Materials and Methods
### Methodology
#### 2.3.2 Method for Calculating Drought Index  
In this study, the Hargreaves-Samani (HS) method was used to estimate reference evapotranspiration (ETo) because this method has been used to estimate reference evapotranspiration (ETo) in various regions. Studies have shown that the HS equation can accurately estimate ETo values when compared with the FAO Penman-Monteith (PM) method, which is considered the most accurate method for ETo estimation [47; 48]. The HS equation performs well under different climatic conditions and environments, including regions with high altitudes [49]. In some cases, the HS equation outperforms the FAO56-PM method, particularly when meteorological data are limited or unavailable [50]. In general, the HS method has been demonstrated to be a viable option for calculating ETo, especially in areas where the availability of data is limited, such as the Mekong Delta.  
The Hargreaves-Samani (HS) approach calculates the reference evapotranspiration (ETo) based solely on the highest and minimum temperatures [51; 52], as shown in the following equation:  
\[ET_{0}=C_{0}R_{a}(T_{max}-T_{min})^{0.5}(T+17.8) \tag{2}\]  
where  
* \(R_{a}:\) extraterrestrial radiation (mm day\({}^{-1}\));
* \(C_{0}:\) conversion parameter (=0.0023);
* \(T_{max}\), \(T_{min}\) and \(T\): the maximum, minimum and average temperature (\({}^{\circ}\)C). Water balance, which refers to the excess or shortage of water, was computed accordingly.  
\[D_{i}=P_{i}-E_{0i} \tag{3}\]  
\(D_{i}\) values were then aggregated at different time scales. The log-logistic distribution F(x) was applied to transform the original D series into standardized units at different time scales. Finally, the F(x) distribution was used to calculate the SPEI [53].  
The SPEI package for R [54] was used to compute the SPEI drought index. This tool serves as a valuable resource for the in-depth examination of drought conditions, both for research purposes and practical applications. Drought levels were classified according to the SPEI values, as indicated in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SPEI** & **Drought Category** \\ \hline SPEI \(\geq\) 2 & Extremely wet \\ \(1.5\leq\) SPEI \(<\) 1 & Severely wet \\ \(1\leq\) SPEI \(<\) 1.5 & Moderately wet \\ \(-1\leq\) SPEI \(<\) 1 & Near normal \\ \(-1.5\leq\) SPEI \(<\) \(-1\) & Moderately dry \\ \(-2\leq\) SPEI \(<\) \(-1.5\) & Severely dry \\ SPEI \(<\)\(-2\) & Extremely dry \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characterization of drought using values of the standardized precipitation evapotranspiration index (SPEI).
## 2 Materials and Methods
### Implementation Details
#### 2.3.2 PPO Configuration  
The implementation of PPO requires the definition of the two components of the actor-critic architecture described in Figure 4 of Section 2.1. As described in the literature, these two neural networks can have different architectures and complexities. In the proposed work, we used the following two neural network architectures: the actor network is a fully connected network with ReLU activation, containing two hidden layers with 128 and 64 units, respectively. Similarly, the critic network is also a fully connected network with ReLU activation, but in this case, there are three hidden layers with 32, 16, and 8 units, respectively, and a single output for error value estimation.  
The agent adheres to the canonical PPO formulation, with several implementation-specific enhancements:  
* _Policy and Value Networks_: The actor model outputs logits for a discrete action space, while the critic model estimates the scalar state value \(V(s)\). Orthogonal initialization is used for all layers.
* _Generalized Advantage Estimation (GAE)_: Employed to compute advantages, reduce variance, and improve learning stability.
* _Clipped Surrogate Objective_: The policy loss is calculated using the PPO clipped objective to ensure conservative policy updates.
* _Entropy Regularization_: An entropy bonus is added to the loss function to promote exploration.
* _Gradient Clipping_: Applied to all gradients with a configurable maximum norm to maintain stability.
* _Learning Rate Annealing_: The learning rate decays linearly over the training iterations.
# Two-Dimensional Shape Analysis of Complex Geometry Based on Photogrammetric Models of Iconostases
## 4 Method for 2D Shape Analysis Based on Photogrammetric Models
### Iconostases' Photogrammetric Digitization  
This part of the research presents the strategy for the iconostases' photogrammetric digitization that resulted from the collaboration between the university team and the Gallery of Matica Srpska in Novi Sad. It is based on creating a detailed surveying plan that can be applied in the general case of iconostases' digitization.  
The three iconostases were digitized using the photogrammetric approach. Photogrammetry uses two-dimensional overlapping images that are further processed to generate 3D data from the 2D image measurements. The method is based on the SfM algorithm for photogrammetric surveying which automatically determines camera positions and orientation parameters. This way, there is no physical contact of the measuring sensor with the surface of the object that is being digitized, thus there is no risk of possible damage to the object. The precision of the SfM models depends on the quality and properties of the available photographs, thus particular attention must be paid to the design of the surveying plan for acquiring photographs [26; 56; 62]. Photogrammetrically reconstructed 3D models of objects provide a range of information about the real state of the object, such as a realistic representation of the material, texture, and shape.
# Electric Drive with an Adaptive Controller and Wireless Communication System
###### Abstract  
In this paper, the problem of the remote control of electric drives with a complex mechanical structure is discussed. Oscillations of state variables and control precision are the main issues found in such applications. The article proposes a smart, IoT-enabled controller, which allows remote communication with a drive. To solve the problem of speed oscillations and to make the system robust to parameter uncertainty, an adaptive controller with two neural networks is designed. First, numerical tests are conducted in a Matlab/Simulink environment to examine the operation of the proposed control strategy. Afterwards, the obtained results are verified in a laboratory setup equipped with a 0.5 kW electric motor. Remote access is provided by a low-cost, ARM-based ESP32 microcontroller. Usually, virtual instruments used to communicate with remote devices require specific software, which may be expensive and pose compatibility problems. Therefore, the main contribution of the article is the creation of a low-cost, web-based Human-Machine Interface (HMI) with an asynchronous server utility provided by the ESP32 that allows remote control and data acquisition of electric drive state variables.  
IoT; remote control; adaptive speed control; neural networks Article
###### Abstract  
In anaerobic digestion (AD), butyrate is degraded by syntrophic consortium, but can accumulate in highly loaded AD systems. The effect of butyrate on the AD process attracts much less attention than propionate or acetate. In this work, an enrichment culture of the thermophilic butyrate-oxidizing syntrophic consortium was obtained by gradually increasing the initial butyrate concentration from 20 to 170 mM. Surprisingly, even the highest butyrate concentration did not significantly inhibit the methanogenic community, and the stage of acetate degradation was the limiting overall rate of the process. At 170 mM butyrate, the bacterial community changed towards the dominance of syntrophic acetate-oxidizing (SAO) bacteria related to _Syntrophoreticus_ (42.9%), _Syntrophomonas_ (26.2%) and _Firmicutes_ (26.2%), while the archaeal community experienced a sharp decrease in the abundance of _Methanosarcina thermophila_ (from 86.0 to 25.0%) and increase in _Methanomobacter thermautrophicus_ (from 3.2 to 53.1%) and _Methanomasiliicoccus_ (from 3.2 to 21.9%). Thus, the shift from acetoclastic methanogenesis to SAO coupled to hydrogenotrophic methanogenesis occurred as an adaptive strategy to overcome high acetate (~200 mM) build-up. Bioaugmentation with the obtained enrichment culture was effective in mitigating the butyrate-dominated VFA build-up during the AD of readily biodegradable waste, increasing the methane production rate, methane yield and volatile solids removal by more than 3.5, 6.2 and 2.9 times, respectively. Our study revealed that the thermophilic butyrate-oxidizing consortia as bioaugmented culture could be the potential strategy to alleviate the high organic load and VFA stress of AD.  
bioaugmentation; anaerobic digestion; acetoclastic; hydrogenotrophic; syntrophic consortium; syntrophic acetate oxidation; microbial community +
Footnote †: journal: Applied Sci. **2023**, _13_, 173. [[https://doi.org/10.3390/app13010173](https://doi.org/10.3390/app13010173)]([https://doi.org/10.3390/app13010173](https://doi.org/10.3390/app13010173))
# Reversed Lempel-Ziv Factorization with Suffix Trees +
## 3 Reversed LZ Factorization
### Factorization Algorithm
#### 3.2.1 Overview
For that, she computes the string depth of the lowest ancestor marked in \(B_{\mathsf{V}}\) of the previously visited phrase leaf. See Appendix A. As a side note: since we are only interested in the factorization of \(T[1\ldots n-1]\) (omitting the appended # at position \(n\)), we do not need Player 1 to declare the leaf with suffix number \(n\) a phrase leaf. We also terminate the algorithm when both players meet at position \(n\) without checking whether we have found a new factor starting at position \(n\). ```
1 ST \(\leftarrow\) suffix tree of \(R=T\cdot\#\cdot T^{\mathsf{R}}\cdot\$\)
2\(\lambda^{\mathsf{R}}\leftarrow\) prev_leaf(prev_leaf(smallest.leaf)) \(\triangleright\)\(\text{sf{sfnum}}(\lambda^{\mathsf{R}})=2n-1\)
3\(\lambda\leftarrow\) smallest_leaf
4\(B_{\mathsf{V}}[\text{root node}]\gets 1\)\(\triangleright\) at the beginning, only the root node is marked in \(B_{\mathsf{V}}\)
5\(B_{\mathsf{L}}[1]\gets 1\)\(\triangleright\)\(|F_{1}|\) starts at text position 1
6 while\(\lambda\
eq\lambda^{\mathsf{R}}\) do\(\triangleright\) we stop after having parsed \(T[1\ldots n]\)
7 if\(B_{\mathsf{L}}[\text{sf{sfnum}}(\lambda)]=1\)then\(\triangleright\) turn of Player 1
8\(d\gets 0\)while\(B_{\mathsf{V}}[\text{level\_anc}(\lambda,d+1)]=1\) do\(d\gets d+1\)
9\(w\leftarrow\text{level\_anc}(\lambda,d)\)\(\triangleright\)\(w\) is lowest node marked in \(B_{\mathsf{V}}\)
10 if\(w\) is the root then
11 output fresh factor
12\(B_{\mathsf{L}}[\text{sf{sfnum}}(\lambda)+1]\gets 1\)\(\triangleright\) next factor starts directly after \(\text{sf{sfnum}}(\lambda)\)
13
14 else\(\triangleright\)\(w\) is the witness of \(\lambda\)
15 output length \(\text{str\_depth}(w)\)
16\(B_{\mathsf{L}}[\text{sf{sfnum}}(\lambda)+\text{str\_depth}(w)]\gets 1\)
17 output referred position \(2n-\text{max\_sf{sfnum}}(w)\)\(\triangleright\) for the one-pass variant
18\(B_{\mathsf{W}}[w]\gets 1\)\(\triangleright\) for the two-pass variant
19\(\lambda\leftarrow\text{suffixlink}(\lambda)\)\(\triangleright\) end of Player 1's turn
20 foreach node \(v\) on the path from \(\lambda^{\mathsf{R}}\) up to the root do\(\triangleright\) turn of Player 2
21 if\(B_{\mathsf{V}}[v]=1\) then break\(\triangleright\) end turn on reaching an already marked node
22\(B_{\mathsf{V}}[v]\gets 1\)
23\(\lambda^{\mathsf{R}}\leftarrow\) prev_leaf(\(\lambda^{\mathsf{R}}\))\(\triangleright\) end of Player 2's turn
```  
**Algorithm 1**Algorithm of Section 3.2 computing the non-overlapping reversed LZ factorization. The function \(\text{max\_sf{sfnum}}\) is described in Section 3.3.
## 2 Materials and Methods
### Methods  
The introduced hybrid model has several stages of image noise removal in which images are resized into half of the original image size, Gabor filter-based feature extraction, and SSAE deep neural network-based face prediction. The proposed system working process is illustrated in Figure 3.  
The proposed hybrid method of the face recognition system, as shown in Figure 3, combines two algorithms to achieve optimal results. These algorithms use the Gabor Filter and the Stacked Sparse Autoencoders (SSAE) CNN model for face recognition. The first step in reducing execution time is to resize the input images. Initially, the features are derived from the face images using the Gabor filter. The derived features were investigated with the help of the SSAE deep neural network model depicted in Figure 4. This study aims to improve facial recognition accuracy with minimal computation time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Database** & **RGB Color/Grayscale** & **Images Size** & **No. of Persons** & **No. of Images per Person** & **Variation** & **Description** \\ \hline \hline OLR & Grayscale & \(92\times 112\) pixel & 40 & 10 & \(i,t\) & - & Dark background images \\  & & & & & - & Restricted number \\  & & & & & - & of participants \\  & & & & & - & Different lightening conditions, \\  & & & & & & poses, emotions, directions \\ Extended Yale-B database & Grayscale & \(168\times 192\) pixel & 38 & 64 & \(p,i\) & - & Variation in 9 different poses \\  & & & & & - & Illumination in 64 conditions \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of databases used.  
Figure 3: The hybrid deep neural model-based face recognition framework.
# Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
## 2 Data and Methods
### Sentinel-1a/b  
On 3 April 2014, Sentinel-1a was launched by the European Space Agency for the European Commission Copernicus Program and was joined by Sentinel-1b on 25 April 2016 along the same orbit. Sentinel-1a/b satellites are C-band synthetic aperture radars with a revisit time of 12 days and placed 6 days apart in a constellation. Acquisitions over the Earth's land masses are made with the Interferometric Wide Swath (IW) mode using Progressive Scans SAR (TOPS) technique. Sentinel-1 is a right-looking SAR mission observing with an incidence angle ranging from 29.1 to 46.0\({}^{\circ}\), which limits its coverage to 79\({}^{\circ}\)S in latitude. A TOPS IW swath is 250-km wide and is formed by combining 3 sub-swaths acquired in parallel [25]. Each sub-swath consists of a series of bursts with their own doppler history [26]. Using the Gamma Software ([[http://www.gamma-rs.ch](http://www.gamma-rs.ch)]([http://www.gamma-rs.ch](http://www.gamma-rs.ch))), the bursts are de-ramped from their azimuth-varying Doppler history and mosaicked together to form a single look complex image with a ground resolution of 20 m in azimuth and 5 m in ground range [26]. Following recommendations by PSTG, Sentinel-1 has continuously acquired since June 2015 a set of six tracks that cover the entire coast of Greenland and four tracks over the Antarctic Peninsula and the Amundsen Sea Sector in West Antarctica. In addition to the systematic acquisitions, ice sheet wide mappings have been completed once a year with three consecutive tracks during the boreal and austral winters of Greenland and Antarctica, respectively. In Antarctica, the large scale maps are limited to the fraction of the continent visible to the sensors. Sentinel-1 is a right looking SAR, which means that it is looking away from South Pole, i.e., a large gap around South Pole persists that is only accessible with sensors with left looking capability, such as RADARSAT-2. Matching pairs of scenes with 6, 12 or 24-day repeat cycle is done automatically using the scripting interface provided by ESA. The scenes are automatically downloaded either from ESA's server in Europe or its mirror archive at the Alaska Science Facility (ASF) in the US. We then perform speckle tracking using ampcor routine from ROI_PAC to retrieve the offset displacements caused by ice motion. The tracking is done with sub-images of 192 \(\times\) 48 pixels and a search window of 32 \(\times\) 8 pixels as described in [22]. To maintain a level of flexibility in our processing, Sentinel-1 data are processed scene by scene. A scene covers an area of 250 km by 200 km.