esa-sceva/satcom-chunk-collection huggingface dataset:
# Layered Double Hydroxides as Next-Generation Adsorbents for the Removal of Selenium from Water
## 6 Conclusions
In the case of ternary LDH containing iron in the hydroxide layers and thiomolybdate as counterions, like FeMgAl(MoS4\({}^{2-}\))-LDH, the presence of iron within the layers changed the layer's charge density, which let more functional groups enter the spaces between the layers. Furthermore, the heterogeneous iron catalyzed reductive reactions between selenium oxyanions and S2\({}^{-}\) ions from MoS4\({}^{2-}\) groups. The iron-induced reduction along with a change in the layer's charge density led to the highest removal of selenium oxyanions by these structures. Testing LDH sorbents in batch and column experiments indicated their potential for continuous flow systems. This could facilitate the transition from laboratory to field remediation. Furthermore, research on the simultaneous sorption of selenium oxyanions and heavy metal cations revealed that the presence of these cations improved the removal of selenium species, showing a synergistic effect. The presence and effect of various anions in contaminated water samples, such as sulfate, phosphate, chloride, nitrate, carbonate, and silicate, were studied in many papers. Sulfate ions had a significant competitive effect on selenate sorption because of their similar geometry and ionic radius. For the same reason, phosphate ions competed with selenite ions. To date, several treatment methods for selenium-contaminated waters have been evaluated, involving chemical, physical, and biological approaches. Many of these methods are limited by high cost, long treatment time, and operational complexity. Sorption procedures using LDH, however, have proven to be highly effective in removing selenium oxyanions from polluted media. Moreover, LDH offer both technical and economic advantages when it comes to treating waters and wastewaters containing these oxyanions, which makes them promising materials for remediation technologies. Conceptualization, I.H. and L.N. ; methodology, I.H. and L.N. ; validation, L.N. and I.H. ; investigation, I.H. and L.N. ; resources, I.H. and L.N. ; data curation, L.N. and I.H. ;writing--original draft preparation, I.H. and L.N. ; writing--review and editing, L.N. and I.H. ; visualization, L.N. and I.H. ; supervision, L.N. and I.H. ; project administration, I.H., L.N. and P.M.; funding acquisition, I.H. All authors have read and agreed to the published version of the manuscript. The work was supported by the Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic and the Slovak Academy of Sciences under the contract VEGA 1/0135/22. Not applicable. The authors declare no conflicts of interest.
# Cognitive Frequency-Hopping Waveform Design for Dual-Function MIMO Radar-Communications System
## 5 Simulation
### Detection Variation Performance  
The detection constraint optimization has recently been studied in works such as [38; 39], where the authors address the problem of radar code design for target recognition in the presence of colored Gaussian disturbance. The objective function in [38] aims to maximize the weighted average Euclidean distance between the ideal echoes from different target hypotheses. Furthermore, additional practical constraints are considered in [38]. For example, the modulus of the waveform is restricted to be a constant and the detection constraints require that the achievable SNR for each target hypothesis is larger than a desired threshold.  
Figure 9 indicates the detection variation offered by the proposed scheme subject to the detection constraint. We assume a radar scene, which has three range-separated targets. The target-scattering signals derived from the radar scene is normalized and the proposed dual-function MIMO system intends to discriminate the scatterers by using a particular detection threshold.  
Figure 8: Comparative throughput of BPSK, QPSK, 16-PSK, 256-PSK.  
Figure 7: The symbol error rates (SERs) versus signal-to-noise ratio (SNR) for Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), 16-Phase Shift Keying (16-PSK) and 256-Phase Shift Keying (256-PSK).  
With subsequent iterations of the proposed algorithm, the detection performance of the multiple targets is enhanced. As can be seen from Figure 9, by suppressing noise, the dual-function MIMO system could discriminate three scatterers effectively at the end of 20 iterations. The result proves the performance of target detection presented in Figure 6b as well.  
The detection performance is enhanced by providing the waveform design (step 1) procedure in the proposed scheme. The waveform design procedure ensures the maximum of the Euclidean distance between the target echoes from different scatterers based on the MI maximization criterion in (30). The proposed design procedure is similar to the optimization method in [43]. By increasing the number of iterations, we can obtain more accurate estimates of the target responses, which are used to improve detection of the multiple targets.
# Evaluating the Capture Efficiency of Microchannel Plates Through Photoelectron Detection
## 3 Results
### Factors Affecting the Pulse Intervals  
Experiments were then performed to investigate the factors affecting the calculated Ts. In this study, the operating voltage of the MCP and the incident angle of the photoelectrons toward the MCP were changed, and the ion current and average pulse intervals under  
Figure 4: The data processing sequence diagram for obtaining the average pulse interval of MCPs. (**a**) A typical output signal of MCP; (**b**) the pulse signal obtained after binarization; (**c**) statistical diagram of the average pulse interval of the MCP microchannel plate.  
these conditions were acquired. As shown in Figure 5a, elevating the operating voltage of the MCP boosts the channel gain, consequently increasing the amplitude of the output signal. In contrast, the state of the incident electrons, such as their kinetic energy or number, remains unaffected, and thus, the pulse intervals remain relatively stable. In Figure 5b, by increasing the incidence angle of the electrons, both the signal intensity and the pulse intervals decrease accordingly, which is caused by a reduction in the number of electrons entering the MCP channels. These results align with our expectations and, to a certain extent, validate the feasibility of the proposed approach.
## 5 6 GCIoHE System Paradigm
### The 6G Enabled Tactile Internet End-to-End Architecture
#### 5.2.4 RAN/CN  
The 6G-driven communication architecture composed of RAN and CN meets the basic requirements to realize the 6G enabled Tactile Internet. To achieve this objective, the essential functions of the 6G RAN in the Tactile Internet ecosystem are as follows: (i) radio access technologies (RATs) such as mmWave, massive MIMO, and full-duplex; (ii) Tactile QoE/QoS for tactile applications in conjunction with M2M and smart grids applications; (iii) efficient packet delivery through reliable radio protocols and physical layer; and (iv) novel medium access control (MAC) techniques, where the air-interface conflicts are optimally controlled. The key functionalities of the 6G CN associated with the Tactile Internet are as follows: (i) dynamic application of QoS provisioning; (ii) edge-cloud access; and (iii) security.
## 4 Examples of Application  
### Theoretical Analysis of Valve Train  
The valves allow mixture of air and fuel to enter into a cylinder's compartment and the exhaust of residue to leave the cylinder's compartment after one combustion cycle of a cylinder has been completed. As shown in Figure 7, a camshaft uses a cam to push against a valve so that it can be opened when the camshaft is rotated to its specific profile. A spring located on top of the valve returns it to the closed position. The lifting of the valve depends on the cam profile and the valve clearance [24]. When the valve has excessive clearance, the energy of vibration induced by impacting during the process of the valve opening and closing increases [25]. Therefore, vibration energy could be considered as an identification feature for valve clearance's fault.  
However, if an inspector uses only amplitude or energy of vibration signals to observe whether the clearance of the valve is proper, then the result could be misleading. A change in the diesel engine's operating condition, such as working at different speeds, also affects the value of amplitude or vibration energy. Thus, there is a need for an analysis method that can obtain stable characteristics that do not change with the rotational speed of diesel engine to characterize changes in valve clearance.  
Figure 6: Fault diagnosis process.
# Energy-Efficient Partial LDPC Decoding for NAND Flash-Based Storage Systems
## 2 Backgrounds  
This section provides an overview of LDPC decoding algorithms, including an in-depth explanation of the Sum-Product algorithm (SPA) [10] and that of the Min-Sum algorithm (MSA) [11]. Moreover, the structure of the Quasi-Cyclic (QC) LDPC H-matrix will be introduced to explain proposed algorithms.
## 2 Materials and Methods
### Methodology Design and Implementation
#### 2.2.2 Correlation Feature Selection  
Irrelevant or redundant features confuse the machine learning algorithm, resulting in poor learning and mining outcomes [28,29]. Feature selection is beneficial in improving learning efficiency and predictability by deleting unnecessary or duplicated information [30]. In our study, the filter method, which is independent of any learning algorithm, is used for feature selection. It is less computationally demanding than the wrapper technique. Using the correlation coefficient, the dependency of the features in this study is determined in order to remove the redundant features. High correlation features (either positive or negative) are more linearly dependent and so have approximately the same influence on the dependent variable. When two features have a strong correlation, one of them can be dropped. Values closer to zero indicate that there is no linear relationship between the two features, while values closer to one suggest strong linear relationships, with +1 indicating that the two features are positively correlated and \(-1\) indicating that they are negatively correlated.
## 2 Materials and Methods
### State Selection for Encoding  
Let \(A(s_{i})\) be the set of states where transitions from \(s_{i}\) end, \(B(s_{i})\) be the set of states with transitions ending in \(s_{i}\), and \(t(s_{i}s_{j})\) be the transition from the state \(s_{i}\) to the state \(s_{j}\) (\(s_{i}\), \(s_{j}\in S\)). The set of transitions \(P(s_{i})\) from the state \(s_{i}\) is defined by:  
\[P(s_{i})=\big{\{}t\big{(}s_{i},s_{j}\big{)}\mid s_{j}\in A(s_{i})\big{\}}. \tag{1}\]  
The set \(C(s_{i})\) of transitions to the state \(s_{i}\) is defined by:  
\[C(s_{i})=\big{\{}t\big{(}s_{i},s_{i}\big{)}\mid s_{j}\in B(s_{i})\big{\}}. \tag{2}\]  
Let \(ES\) be the set of already encoded states. Then, the set of transitions \(P_{ES}(s_{i})\) from \(s_{i}\) to states in \(ES\) is:  
\[P_{ES}(s_{i})=\big{\{}t\big{(}s_{i},s_{j}\big{)}\mid s_{j}\in ES\big{\}}, \tag{3}\]  
and the set of transitions \(C_{ES}(s_{i})\) from states in \(ES\) to \(s_{i}\) is:  
\[C_{ES}(s_{i})=\big{\{}t\big{(}s_{i},s_{j}\big{)}\mid s_{j}\in ES\big{\}}. \tag{4}\]  
Let \(X(s_{j},s_{i})\) be the set of FSM input variables that initiate the transition \(t(s_{j},s_{i})\). Then, the set \(X(s_{i})\) of input variables influencing transitions to the state is defined as the union of all such input variables across all states \(s_{j}\) that transition to \(s_{i}\). Formally, this can be expressed as:  
\[X(s_{i})=\bigcup_{s_{j}\in B(s_{i})}X\big{(}s_{j},s_{i}\big{)}. \tag{5}\]  
We introduce the following modes, which determine the strategy for selecting the next state \(s_{i}\) for encoding:\(\bullet\)\(\max\_P\): select the state \(s_{i}\) with the maximum number of outgoing transitions \(|P(s_{i})|\) (Figure 1a);  
\(\bullet\)\(\max\_C\): select the state \(s_{i}\) with the maximum number of incoming transitions \(|C(s_{i})|\) (Figure 1b);  
\(\bullet\)\(\mathrm{P}\): select the state \(s_{i}\) from which the maximum number of transitions \(|P_{ES}(s_{i})|\) lead to already encoded states (Figure 1c);  
\(\bullet\)\(\mathrm{C}\): select the state \(s_{i}\) in which the maximum number of transitions \(|C_{ES}(s_{i})|\) originate from already encoded states (Figure 1d);  
\(\bullet\)\(\mathrm{P}\_C\): select the state \(s_{i}\) with the maximum number of connections to already encoded states \(|P_{ES}(s_{i})|+|C_{ES}(s_{i})|\) (Figure 1e);  
\(\bullet\)\(\max\_X\): select the state \(s_{i}\) whose transitions are influenced by the highest number of input variables \(|X(s_{i})|\) (Figure 1f).  
The modes P, C, and P_C take into account the relationship of the selected state \(s_{i}\) with the already encoded states, whereas the modes \(\max\_C\), \(\max\_P\), and \(\max\_X\) are independent of the order in which states are encoded. This distinction allows for flexible encoding strategies that can adapt to different optimization goals.
# Line Clipping in 3D: Overview, Techniques and Algorithms
## 5 The Proposed Method
### Considerations  
If we look carefully at Equation (1), it seems that for certain values a division by zero may occur. Let us examine the case of the \(x\) coordinate of an endpoint. According to the second step of the algorithm, the \(x\) coordinate is being checked as to whether it is less than \(x_{min}\) or greater than \(x_{max}\). If we replace \(x=x_{min}\) and solve for \(y\) and \(z\) the new coordinates will be  
\[\frac{x-x_{0}}{x_{1}-x_{0}}=\frac{y-y_{0}}{y_{1}-y_{0}}\Rightarrow y=\frac{y_{ 1}-y_{0}}{x_{1}-x_{0}}\cdot(x_{min}-x_{0})+y_{0}\]  
\[\frac{x-x_{0}}{x_{1}-x_{0}}=\frac{z-z_{0}}{z_{1}-z_{0}}\Rightarrow z=\frac{z_{ 1}-z_{0}}{x_{1}-x_{0}}\cdot(x_{min}-x_{0})+z_{0}.\]  
Likewise, if we replace \(x=x_{max}\) and solve for \(y\) and \(z\) the new coordinates will be as follows:  
\[y=\frac{y_{1}-y_{0}}{x_{1}-x_{0}}\cdot(x_{max}-x_{0})+y_{0}\]  
\[z=\frac{z_{1}-z_{0}}{x_{1}-x_{0}}\cdot(x_{max}-x_{0})+z_{0}.\]  
Division by zero will occur, only if \(x_{0}=x_{1}\). However, when \(x_{0}=x_{1}\) and \(x<x_{min}\) or \(x>x_{max}\) the line is vertical to the Y axis and is totally outside the clipping region, so division by zero will never occur according to the first step's condition. On the other hand, if \(x_{min}\leq x\leq x_{max}\) then there is no need to apply clipping for the specific coordinate, so, again, division by zero will not occur in this range. Similarly, division by zero will never occur for all other coordinates.
## 2 Periodic Haar Piecewise-Linear PHL Transform  
This section covers the most important theoretical aspects related to Periodic Haar Piecewise-Linear (PHL) transform. The thorough description and further information are presented in detail in [29]. The Haar functions are defined by the following formulas:  
\[har(0,t) =1 \text{for }t\in(-\infty,\infty),\quad\text{usually }T=1 \tag{1a}\] \[har(i,t) =\begin{cases}\begin{array}{ll}2^{\frac{k-1}{2}}&\text{for }\big{[}\frac{i}{2^{k-1}}-1\big{]}\leq t<[\frac{i+ \frac{1}{2}}{2^{k-1}}]\\ -2^{\frac{k-1}{2}}&\text{for }\big{[}\frac{i+\frac{1}{2}}{2^{k-1}}-1\big{]} \leq t<[\frac{i+1}{2^{k-1}}-1]\\ 0&\text{otherwise}\end{array}\end{cases} \tag{1b}\]  
where \(0<k<\log_{2}N,1\leq i\leq 2^{k}\).  
In turn, the PHL functions can be calculated by performing the integration of these Haar functions. It can be realized by using the below formulas:  
\[PHL(0,t) =1 t\in(-\infty,\infty) \tag{2a}\] \[PHL(1,t) =[\frac{2}{T}\int\limits_{mT}^{t+mT}har(1,\tau)d\tau]+\frac{1}{2}\] (2b) \[PHL(i+1,t) =\frac{2^{k+1}}{T}\int\limits_{mT}^{t+mT}har(i+1,\tau)d\tau \tag{2c}\]  
where \(i=1,2,...,N-2;k=1,2,...,\log_{2}N-1;m=0,1,2,...;\)  
\(k\)--index of group of PHL functions;  
\(m\)--number of period.  
Figure 1 depicts the derivatives (in distributive sense) of Haar functions. The PHL functions are linearly independent but they do not satisfy the orthogonality condition.
## 3 System Architecture of the DSS  
The optimization-based DSS discussed in this study comprises three key relational components: data management, model management, and dialog management [53]. Model management leverages the AHP-TOC heuristic solution methodology to identify the optimal product mix and conduct the necessary calculations for determining product portfolio norm strategies. Meanwhile, data management involves a relational database for storing datasets and questionnaires.  
With its relational structure, data management serves as the primary mechanism for data manipulation. During the execution of the decision support system (DSS), this triad operates as closely interconnected systems. The following section provides detailed explanations of these components of the DSS.
## 3 Monitoring of C and Water Cycling in Terrestrial Ecosystems  
### Global Flux Tower Network (FLUXNET)  
The EC technique is commonly used to directly measure the CO\({}_{2}\), water vapor and energy exchange between the atmosphere and terrestrial ecosystems [9]. A global network of collaborating regional networks, FLUXNET, began in 1997 [9] and today, there exist more than 400 EC-flux towers across the globe. EC measurements are a rich source of information on temporal variability and environmental controls of CO\({}_{2}\) exchange between the atmosphere and terrestrial ecosystems [69]. These global EC datasets allow us to (1) explore emergent-scale properties by quantifying how the metabolism of complex ecosystems respond to perturbations in climate variables on diurnal, seasonal, interannual and decadal time scales and elucidate physical and biological controlling factors [9,69]; (2) examine carry-over effects that may be introduced by either favorable or deleterious conditions during antecedent years [70]; (3) observe a disturbance and the recovery from it or to span a natural sequence of ecological development coupled with fluctuations in climate [71,72]; and (4) test and validate ecosystem process models [73,74], since most of these models span timescales from hours to decades. Although the available EC data have been rapidly accumulating, there are some issues associated with its use due to difficulties/uncertainties in (i) assessing/interpreting the associated measuring biases of EC data, and (ii) upscaling of the EC fluxes at the ecosystem (typically less than 1-3 km\({}^{2}\) for each site) to larger scales, e.g., landscape and regional scales.
## 4 Results and Discussion
### Aerodynamic Performance  
A comparison of the rotor aerodynamic performance at different speeds between the numerical simulations and the experiments is shown in Figure 7. The mass flow-averaged total pressure at the back of the blade trailing edge (1 times the length of chord) was divided by that at front of the blade leading edge (0.5 times the length of chord) to evaluate the total pressure ratio. The numerical curves are basically consistent with the experimental results.
# A Review of Unmanned Aerial Vehicle Based Antenna and Propagation Measurements
## 4 Conclusions  
In this article, we presented a comprehensive review of UAV-based antenna and propagation measurements, offering a detailed analysis of the various factors influencingthese measurements. The study provides a set of guidelines for selecting the UAV antennas, ensuring higher levels of measurement accuracy. Additionally, we compared traditional slant-range methods with innovative, low-cost UAV test setups and explored the extension of path loss models by incorporating UAV altitude as a critical parameter.  
The discussion included several practical test cases, such as the use of parabolic reflector systems on ships, BASTAs, LFAAs, one of the world's largest radio telescopes, and ONERA's Nostradamus system, which features 288 biconical antennas operating in the HF range (3-30 MHz). We also examined propagation measurements for both large-scale and small-scale channel effects.  
The findings suggest that advancements in portable devices like SDRs, high-precision positioning systems with centimeter-level accuracy, custom antenna designs, and UAVs constructed from lightweight and durable materials such as carbon fiber have significantly expanded the potential for UAV-based antenna and propagation measurements. For applications in 5G/6G, where accuracy is paramount, UAV-based test setups have emerged as the preferred measurement solution.  
In conclusion, we anticipate that this review will serve as a valuable reference for the further development of UAV-based measurement solutions, driving innovation and precision in the field.  
Conceptualization, V.R.K., P.L. and Z.D.Z.; methodology, V.R.K., P.L. and Z.D.Z.; formal analysis, V.R.K.; investigation, P.I.L.; writing--original draft, V.R.K. and P.L.; writing--review and editing, V.R.K., P.I.L., Z.D.Z., Q.Z.A., F.A.K., J.S. and T.H.L.; supervision, P.I.L., Z.D.Z., Q.Z.A., F.A.K. and A.J.R.S.; funding acquisition, P.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the European Union through the Horizon 2020 Marie Sklodowska-Curie Innovative Training Networks Programme \"Mobility and Training for beyond 5G Ecosystems (MOTOR5G)\" under grant agreement no. 861219.  
Not applicable.  
Author [PERSON] was employed by the company Sixarms. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.