esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Models and Problem Formulation
### Uploading Latency  
According to [35; 36; 37], the achievable data rate of client \(i\) in tier \(j\) can be estimated by \(r_{ij}=b_{j}\log_{2}\left(1+\frac{P_{i}\beta_{0}}{N_{0}}\right)\), where \(b_{j}\) is the amount of bandwidth allocated to clients in tier \(j\), \(p_{i}\) is the transmission power density of client \(i\), \(g_{i}\) is the channel gain from client \(i\) to the BS, and \(N_{0}\) is the average background noise. Note that the algorithm proposed in Section 4 does not rely on this equation for calculating the client's upload data rate. Other, more sophisticated models that provide better estimates for upload data rates can be easily integrated into our algorithm to enhance its performance. Consequently, the latency of client \(i\) in tier \(j\) to upload its local model with size of \(s\) to the BS is \(t_{ij}^{upload}=\frac{s}{r_{ij}}=\frac{s}{b_{j}\log_{2}\left(1+\frac{P_{i} \beta_{0}}{N_{0}}\right)}\).
# Prospects and Technical Challenges in Hydrogen Production through Dry Reforming of Methane
## 2 Recent Advances on Catalysts for Syngas Production
### Transition Metals-Based Catalysts
#### 2.2.5 Role of Catalyst Support
Second, the presence of oxygen vacancies is responsible for oxygen mobility on the bulk catalyst support, which can react with carbon deposits to produce CO and avoid catalyst deactivation by coke deposition [14]. The effect of CeO\({}_{2}\) modification on catalyst supports was investigated by [PERSON] et al. [105] using a ceria-zirconia mixed oxide promotion on alumina-supported Ni catalysts. The use of ZrO\({}_{2}\) in a solid solution with CeO\({}_{2}\) has been proved to increase and stabilize the oxygen vacancies, and, consequently, the oxygen storage capacity of the mixed oxide, via the atomic mobility inside the lattice to accommodate the two components with different atomic radii [106]. The ceria-zirconia promotion over the alumina support enhanced the oxygen mobility of the catalyst and was found to also increase metal-support interaction, but the probable occurrence of RWGS reaction was responsible for a syngas composition below 1.0 (0.75-0.85) [105]. Overall, catalyst development for hydrogen production via DRM is a major challenge, since the reaction also produces CO in the same amount of H\({}_{2}\) (H\({}_{2}\)/CO = 1) and since side reactions (Equations (3)-(6)) further decrease the H\({}_{2}\)/CO ratio. Low hydrogen production resulting from competing reactions, catalyst deactivation due to carbon build-up and particle agglomeration can be overcome by the incorporation of promoters and advances on catalyst synthesis methods. However, there is a lack of literature data on the development of catalysts with high H\({}_{2}\) selectivity in DRM, even in papers dedicated to H\({}_{2}\) via DRM. Further mechanism and kinetic studies are required to better understand how to increase the H\({}_{2}\)/CO in this process while maintaining the catalytic performance of the catalysts. Furthermore, different operating strategies, such as combining DRM with other reforming technologies, can also be used to increase the H\({}_{2}\)/CO ratio of the syngas produced. The catalytic aspects of different reforming processes and their combinations are discussed in the next sessions.
## Materials and Methods
### Fuzzy Inference System  
Fuzzy logic has been one of the most commonly used methods for solving engineering problems recently. Fuzzy logic methods are especially used for the planning, control, and production scheduling of power systems, and system stability management. The fuzzy logic methods and control techniques are very effective and powerful optimization tools that have many advantages, such as robustness and an ease of implementing [27].  
The fuzzy logic concept was introduced first by [PERSON] in 1965 [28]. The fuzzy approach may be briefly described as a generalized form of classical set theory. In classical set theory, every argument must be classified in an individual set or not, and any element cannot be represented by the intersection of sets. Contrary to the classical approach, in fuzzy theory, the degree of membership of an element may continuously exist. Continuous data may be expressed with membership functions [29]. The Mamdani inference model is a well-known and used fuzzy model.  
The basic fuzzy logic inference system has four main blocks, which are seen in Figure 7. The knowledge base block involves the rule base and database, which are generally created by an expert or an optimization method. The fuzzier block is used for the transformation of crisp values into linguistic terms. Linguistic terms with membership values are processed with knowledge information in the inference module, and the results are sent to the defuzzifier (purification) module to obtain the crisp output.  
In the Mamdani fuzzy approach, the defuzzification phase is evaluated with rule sentences and conditions such as those seen below:  
Rule 1: if x1 is A1 and y1 is B1... then z is C1  
Rule 2: if x2 is A2 and y2 is B2... then z is C2  
Rule 3: if x3 is A3 and y3 is B3... then z is C3  
\(\vdots\)  
Rule k: if xk is Ak and yk is Bk... then z is Ck.  
where x and y are the first and second input variables, respectively, z is the output variable, and k is the number of rules.  
Figure 7: Fuzzy inference system basic diagram.  
There are a few important points that need to be defined very well, such as the membership function size, type, and shape. Changes to these parameters critically affect the accuracy of the system. These parameters need to be defined by an expert or through the use of optimization techniques [30].
## 3 Experimental Results  
### Implicit Event Tracking  
Our tracking tool was set up for five very popular online stores in Poland: Merlin.pl, Agito.pl, Electro.pl, Komputronik.pl, and Morele.net. While Agito.pl and Merlin.pl offered numerous kinds of goods (horizontal shops) at the time of the study, Electro.pl and Morele.net offered mostly electronic goods. Merlin.pl was a major Polish online book store. Those stores were selected as they are very popular in Poland and study participants were not required to learn a new interface and could browse content in their native language.  
There were 85 study participants who were all volunteers and active web users from Poland, aged from 19 to 33, holding a high school degree or higher. During the study 1396 products were rated and all customer interactions with websites were monitored via ECPM. About half of the participants rated below 14 products, while the upper quartile rated more than 20 products. Higher ratings were more popular than lower ones. Among many parameters, the ECPM tool monitored user interaction with recommending interfaces. The main monitored measure was total time that the mouse pointer was positioned over other recommended products' sections. This measure was used to reflect user interest in the recommendation section.  
The participants' task was to search for interesting products and explicitly rate them. On leaving every product page, a star rating scale was displayed, where a user could express his/her product interest and inform as to whether the product had been known to them before. In the study 1396 items were assessed and user interactions with webpages were monitored using ECPM. The participants' activity was observed at the most granular level. All DOM-triggered events related to keyboard and mouse were recorded together with detailed information about the source element and its position in the structure of a web page. In addition, the source code of each visited webpage was collected. The monitored data were used to calculate metrics for user behavior that was classified into four groups: parameters describing webpage attributes, interaction times (ms), user interaction events, and relative parameters connected with the events. About 50% of the participants rated fewer than 14 products, while the Q3 quartile rated above 20 products. Table 1 shows the distribution of star ratings. Among many parameters, the ECPM tool, in particular, recorded interactions with recommendation zones. One of the measures was the time of mouse pointing over recommendation areas. This measure was considered as a reflection of interest in recommendation zones.
## 3 Network Models and Related Definitions
### Network Models
#### 3.2.1 Network Coverage Rate  
As shown in Figure 2, the 3D UWSNs are divided into a plurality of small cube grid \(g\), and all of small grids position can be presented by selecting the coordinates of the center of these grids. Through the Boolean perception model (Section 3.1.2), the network coverage rate \(C_{c}\) is defined by the ratio of the number \(N_{S}\) of grid centers covered in the monitored underwater environment space to the total number \(N_{N}\) of grids:  
\[C_{c}=\frac{N_{S}}{N_{N}} \tag{6}\]
###### Abstract  
The relentless growth of the global population, coupled with increasing biotic and abiotic stresses on crops, poses a major challenge: enhancing agricultural productivity while mitigating these stresses and reducing chemical inputs. Insect farming has led to the large-scale production of insect frass, a nutrient-rich by-product with biofertilizer and biostimulant potential. This review examines the effects of frass on plant stress responses, including its mechanisms of action and possible negative effects. Regarding abiotic stress, frass from certain insects improves plant resilience to drought, waterlogging and salinity, while facilitating heavy metal sorption and complexation in contaminated soils. For biotic stress, frass contains antifungal, antibacterial, and nematicide compounds, as well as entomopathogenic fungi, all of which can reduce pest survival. Additionally, frass activates plant defense mechanisms, such as the increased expression of the defense-related genes involved in stress signaling and immune activation. However, some studies report negative effects, including pathogen dispersion, pest attraction, and the inhibition of beneficial microorganisms commonly used as biopesticides. Despite these risks, frass is a promising alternative for sustainable agriculture, reducing chemical dependency while improving plant resilience. Nevertheless, further research is needed to mitigate its potential risks and optimize its agricultural application.  
sustainable agriculture; biofertilizers; pest control; stress tolerance; _Tenebrio molitor; Hernretia illucens_; insect frass
# Error Probability of a Coherent \(M\)-ary PSK FSO System Influenced by Phase Noise
## 1 Introduction
### Contribution  
Motivated by the possibility of implementing coherent FSO technology into 5G beyond backhaul and fronthaul networks, in this work, we study the effect of imperfect phase error compensation on SEP performance of the coherent FSO system. The primary aim of this work is to present the analytical framework for analyzing the error probability degradation caused by the phase noise for higher order modulation formats. In addition, in contrast to [28; 29], where strong turbulence conditions were examined, and in contrast to [30], where the Gamma-Gamma model was utilized for turbulence modeling, we consider the case when atmospheric turbulence channel is modeled by Malaga (\(\mathcal{M}\)) distribution [9; 10; 31].  
In addition, the random misalignment exists between the transmitter laser and receiver photodetector, which is modeled such as in [13; 14].  
In [28; 29; 30], in order to estimate the influence of phase noise on FSO system performance when binary PSK signals are transmitted, the authors applied the method using the characteristic function. In order to obtain the numerical values of the error probability, it is necessary to perform a two-fold numerical integration, whereby the sub-integral function contains the Gaussian hypergeometric function (see, for example, [26; 27; 28]). In contrast to these works, we provide a procedure for determining the error probability in detecting multilevel PSK signals, in the case when turbulence induced scintillation is modeled by a more general distribution. In doing so, we use the Fourier Series Method (FSM), which is suitable for asymptotic analysis that will be illustrated in the following text.  
In our analysis, we use FSM for representing the probability density function (PDF) describing the received composite signal phase statistic [32]. Firstly, we derive the novel analytical expressions for coefficients in Fourier series expansion. Secondly, under assumption that phase error compensation is performed by PLL in a coherent receiver, such as in [28; 29; 30], we present the analytical expressions for symbol error probability (SEP) in the form of convergent series. In order to evaluate the numerical value of error probability, it is necessary to truncate the series. In order to estimate system performance as quickly as possible, it is necessary to sum the least number of terms that ensure the fixed value of the truncation error. Because of that, our analysis contains a strong mathematical background. The convergence of this series is proved, and the upper bound for truncation error is estimated. This analytical expression for SEP enables us to determine the numerical value of the error floor appearing in the system, and to identify the signal-to-noise ratio (SNR) range when this error floor appears. These two magnitudes are very important system parameters from the point of view of energy efficiency. The analytical results are verified by Monte Carlo simulations.
# Time Series Feature Selection Method Based on Mutual Information
## 2 Methodology
### Workflow of the Proposed Feature Selection Method for Time Series  
Figure 1 shows the proposed time series feature selection workflow. After obtaining the system time series monitoring data, the sensor signals are initially screened and then normalized. Afterward, in order to obtain the system target variables, PCA dimensionality reduction is performed on the normalized data to obtain one-dimensional time variables that can characterize the trend in the system operability. Then, the mutual information between each time series features with the target variable, as well as between each time series feature, is calculated. According to the value of mutual information, the time series features with large correlations and small redundancy with the target variables are selected.
## 4 Experiment
### Data Pre-Processing  
This study focuses on the weekday period because the weekday demand for shared bikes is much heavier than weekend demand, and the bike-sharing rebalancing problem on weekdays is more serious than that on weekends. The bike-sharing transactions generated during weekdays were used to train the proposed model and test the performance. The dataset involved 21 weekdays. Data in the first 12 days were taken as the training dataset, the next 4 days were taken as the validation dataset, and the last 5 days were used as the testing dataset.  
The origin dataset was processed into a matrix format, which contains the rental and return demand at each station. Then, the time-series data were generated based on the demand matrix in different prediction horizons, which can be fed into LSTM-based neuronal network models. \(x_{i}\in\mathbb{R}^{N}\) represents bike-sharing demands of all stations at prediction horizon \(i\). Then, the time-series data were constructed with the demand from the previous \((t-1)\) time steps, \(X_{i}\in\mathbb{R}^{N\times t}\), \(X_{i}=[x_{i-t+1},\ldots,x_{i}]\). The target vector is the bike-sharing demand at the next prediction horizon, \(y_{i+1}\in\mathbb{R}^{N}\). Therefore, the input data structure could be \([X_{i},y_{i+1}]=[x_{i-t+1},\ldots,x_{i},y_{i+1}],i=1,\ldots,t\), where \(t\) is the max time step at different prediction horizons. [PERSON] et al. [51] showed that the demand information in recent time steps can help us build good models and yield satisfactory performance. [PERSON] et al. [9] used 120-min data before the target prediction time to help prediction task. Therefore, the demand information in the previous two hours was used for predicting the demand at the next time step. The length of \(X_{i}\) depends on the prediction horizon. For example, if the prediction horizon is 15 min, the length of \(X_{i}\) is \(2*60/15=8\). We used the min and max normalization to scale the historical bike-sharing demand data between 0 and 1. The equation is shown as follows:  
\[X=\frac{X-min(X)}{max(X)-min(X)} \tag{19}\]  
The land-use data, hourly weather data and personal information data were also normalized to \((0,1)\) based on Equation (19).  
This study also considers the periodicity as an important attribute in historical bike-sharing demand. There were two differences made to the demand series data, namely, the first order difference and the seasonal difference. The step of the first order difference is 1 and the step of the seasonal difference was calculated based on the prediction horizon. The first order difference was used to eliminate the trend in the time-series data and seasonal difference was used to eliminate the time-series periodicity. The periodicity of bike-sharing demand was 1 day; therefore, the step of the seasonal difference was \(24*60/\)prediction horizon.
## 3 Hybridization Method  
### General Description  
The presented hybridization approach designates the state (corresponding to the behavior) of each agent and proposes an algorithm for how this state evolves during algorithm execution. We presume that an agent's behavior is fully set by its state. If a swarm of agents is under-performing, their states can be adjusted in the hope that the change in behavior will lead to improved performance. As a result, the status of the agents is not consistent and might change from the previous iteration to the next iteration. The hybridization hyper-heuristic pseudo-code is shown below in Algorithm 1.  
For simplicity, let there be two possible states (Agent\({}_{1}\) or Agent\({}_{2}\)), while their appropriate actions are defined by Algorithms 2 and 3.  
```
1: Set initial positions of swarming agents
2: Initialize algorithms Algorithms 2 and 3
3:while iterations not completed do
4:if iteration is first then
5: Set the agent state to \(Agent_{1}\) or \(Agent_{2}\) randomly
6:else
7: Use logistic probability function to flip the state of agents
8:endif
9:for agent \(\in\) state of \(Agent_{1}\)do
10: Compute new position using a single step of Algorithm 2
11:endfor
12:for agent \(\in\) state of \(Agent_{2}\)do
13: Compute new position using a single step of Algorithm 3
14:endfor
15: Combine both populations
16:endwhile
17: Return the performance of the best agent
```  
**Algorithm 1** Proposed hybridization algorithm.  
The probability of switching the agent's state in Line 6 of the pseudo-code is computed using a common logistic function:  
\[P(r)=\frac{1}{1+e^{-(r-n/2)}} \tag{1}\]  
where \(r\) is the performance rank of an operating agent (agents are arranged from best-performing to worst-performing agents by their fitness value), and \(n\) is the total count of agents in the population.  
In our further experiments, Algorithm 1 is krill herd (KH) and Algorithm 2 is the bat algorithm (BA), which are explained below.
# Quantum Computing for Healthcare: A Review
## 2 Quantum Computing: History, Background, and Enabling Technologies
### [PERSON]'s algorithm [47] opened the gate to possibilities for designing adequate systems that could implement quantum logic operations. There are many qubit systems, e.g., photon, solid-state spins, trapped-ion qubits, and superconducting qubits. Trapped-ion qubits and superconducting qubits are the two most promising platforms for quantum computing, and they are explained in the following subsections.  
#### 2.7.1 Trapped Ion Qubits  
\"The first quantum logic gate was developed in 1995 by utilizing trapped atomic ions\" that were developed using a theoretical framework proposed in the same year [48]. After its first demonstration, technical developments in qubit control have paved the way toward fully functional processors of quantum algorithms. The small-scale demonstration shows promising results; however, trapped ions remain a considerable challenge. As opposed to Very Large Circuits Integration (VLSI), developing a trapped-ion-based quantum computer requires the integration of a range of technologies, including optical, radiofrequency, vacuum, laser, and coherent electronic controllers. However, the integration challenges associated with trapped-ion qubits must be thoroughly addressed before deploying a solution.  
A data plane consists of ions and a mechanism to trap those into desired positions. The measurement and control plane contains different lasers to perform certain operations, e.g., a precise laser source is used for inflicting a specific ion to influence its quantum state. Measurements of the ions are captured through a laser, and the state of ions is detected through photon detectors.
## 3 Speech Perception and Hearing Aids
### Audiogram of Sensorineural Hearing Loss  
An audiogram is a graph that illustrates hearing ability by plotting the hearing threshold based on the patient hearing loss level, which allows the patient to hear different frequencies. The pitch (frequency) of a sound is measured along the chart's horizontal axis, in Hertz, from 0.25 to 8 kHz. However, speech energy above 3000 Hz provides important language information for listeners. The standard speech intelligibility index (SII: ANSI 1997) allocates 27% of important speech at 3150 Hz and above. For more complex speech types, such as meaningless syllables, high-frequency regions account for 31% of the importance of speech signals [29]. In terms of the acoustic characteristics of speech, this high-frequency importance is significant, because the high-frequency part provides essential speech signals. For instance, the lowest frequency peak of /s/ is between 2.9 kHz and 8.9 kHz. Recent studies have shown that patients with moderate-severe hearing loss need an audible bandwidth of 9 kHz for a child; 5 kHz for male speech, and 6-9 kHz for female expression and for understanding  
Figure 4: Frequency map of the human cochlea hearing organ, with a base that responds to high frequencies with sufficient energy to make it vibrate (20,000 Hz), and an apex that responds to low frequencies (200 Hz), which can travel further.  
/s/. Thus, if the hearing aid user can hear the spectrum energy of /s/, it needs to provide much more than the gains and outputs of the current commonly provided hearing [29].