esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed System
### Latency Control Mechanism  
Latency control mechanism acts as an addition towards the Job Manager's ability to find the suboptimal balance between latency, throughput and window correctness, through considering a variety of metrics in the process of achieving the objective of minimalizing latency while maximizing throughput as much as possible. Latency is decreased when the number of out-of-order events that reach the system also decreases, while throughput increases based on the value of the buffering timeout. The main concern here was to sub-optimally decide the tradeoff between latency, window correctness and buffering timeout, to ensure that the system would be able to maintain its functioning without the breach of SLA agreement. We focus on the tradeoff as an increase of throughput can cause latency to be a breach of SLA, or it can also hurt window correctness (as correctness is sometimes more about timely getting the result than only getting the accurate results).  
Through using our proposed metric system, as seen in section III-B, the Job Manager now has an educated estimation of the appearance rate of late elements and a vision of the system as a whole in real-time. The master then triggers the algorithm in Figure 10 upon the overflow of the user's _targetLatencyBound_.  
The algorithm in Figure 10 is self-explanatory, and it controls the latency and throughput of the cluster on run time. The algorithm first uses the allowed range of _maximumOfOrderness_ to control latency of the system and confirms whether the _maxOutOfOrderness_ can be reduced or not, which could result in one of two possible outcomes. The first possible outcome, if the system is using _ascendingTimeStampExtractor_ or the limitation of the correctness value declared by the user is the same as _maxOutOfOrderness_, then the reduction is no longer possible, and this step is then bypassed. The second outcome happens if the system can reduce _maxOutOfOrderness_, in which case _maxOutOfOrderness_ will steadily decline until _currentLatency_ of the system reaches the (_targetLatency_-_currentLatency_) * _allowed percentage of hurting correctness_. In the event that the system could not decrease _maxOutOfOrderness_,and the impact on correctness is higher than zero in percentage terms, then the user is alerted, and the process ends.  
Next, throughput is controlled using _bufferTimeout_, that is, the throughput of the system is checked to see if a decline can occur to ensure that the best tradeoff can happen automatically. In case the _bufferTimeout_ is already zero or _currentThroughput_ of the system reached its lower limit according to the user configuration, throughput cannot be decreased any further. Thus, it will return from the process. However, if the percentage of hurting throughput is greater than zero, then the system will notify the user before returning. When the system is able to reduce the throughput, it will reduce the _bufferTimeout_ until the current throughput value reaches (_targetLatency-current latency_) * _allowed percentage of hurting throughput_.
# Stochastic Disruption of Synchronization Patterns in Coupled Non-Identical Neurons
## 3 Stochastic Model
### Stochastic Deformations in the Monostability Case  
Let us now examine the behavior of the stochastic system (2) in the case of monostability, when the original deterministic system (1) possesses only one attractor: the in-phase three-cycle \(\mathcal{O}\). This cycle, corresponding to \(\sigma=0.015\), is shown in Figure 3c. For this parameter value, the evolution of random states originating from \(\mathcal{O}\) is presented in Figure 8a as a function of noise intensity \(\varepsilon\).  
As evident from the figure, the three-cycle \(\mathcal{O}\) demonstrates notable resilience to noise. However, beyond a certain threshold intensity of \(\varepsilon\), system (2) undergoes an abrupt change in the probability distribution of its states. Specifically, in Figure 8b, the random states for \(\varepsilon=0.02\) (blue) remain tightly clustered near the deterministic cycle \(\mathcal{O}\), whereas for \(\varepsilon=0.1\) (orange), the states are broadly scattered across the phase space. This behavior reflects a clear manifestation of noise-induced excitation. Here, we can note the similarity in the distribution of random states (orange dots) in Figure 4d and Figure 8b for the same \(\varepsilon=0.1\). However, we should emphasize different mechanisms of formation of these distributions. In Figure 4d, for \(\sigma=0.003\), this distribution arises from stochastic transitions between three coexisting cycles, while in Figure 8b, for \(\sigma=0.015\), when the system is monostable, a similar distribution is formed due to the presence of deterministic transients and, as a consequence, stochastic excitability.  
To summarize, while in the cases of multistability, sharp deformations in the distributions were attributed to noise-induced transitions between coexisting attractors, the mechanism driving the transformation in this monostable case is different. Here, the influence of transient attractors in the underlying deterministic system (1) becomes significant.  
Figure 8: Noise-induced excitation in the monostable system (2) with parameters \(\gamma=-1.75\), \(\Delta=0.002\), and \(\sigma=0.015\): **(a)** random states for solutions initiated at cycle \(\mathcal{O}\) (orange) versus noise intensity \(\varepsilon\); **(b)** phase space representation of random states for \(\varepsilon=0.02\) (blue), showing localization near cycle \(\mathcal{O}\), and for \(\varepsilon=0.1\) (orange), illustrating widespread dispersion due to noise-induced excitation.
## 5 Retrofit Design Estimations vs. Dynamic Energy Simulations  
### Static Estimations vs. Dynamic Simulations  
In terms of the energy retrofitting work, the extent and the number of measures implemented in each building block in the three schools vary considerably. While some school blocks have only been targeted by implementing new LED lights, others have undergone an extensive retrofit process with walls and roofs insulation and windows exchange. In addition, and due to the absence of energy data on the level of single building blocks, the decision was to implement the overall modeling and simulation process on the level of all the school blocks and compare the whole school energy performance before and after retrofitting, rather than comparing on the level of the single blocks.  
A comparison will be presented between the real data that were collected from the heating and electricity meters in the schools with the pre-retrofit design numbers that majorly rely on static tabulated numbers for savings evaluation. In addition, an alternative approach is proposed, employing calibrated dynamic energy models developed for the schools in EnergyPlus, in order to simulate the impact of implementing the retrofit measures. Thus, it is important to define two notions, as follows:  
* Static Design Estimations: these are the calculations and estimations that were performed by the consultant or engineer in the pre-retrofit design stage to evaluate the expected impact of the retrofit improvements. Generally, these estimations use tabulated data using pre-defined factors and assumptions in order to estimate the technical impact [42; 43; 44]. One example of the tools using tabulated static data and assumptions to evaluate building performance is BE18 [45], the official tool for building performance technical assessment and certification in Denmark. While the tool is very effective in assessing whether the building complies with the building standards regarding building envelope and energy consumption, it fails to capture the impact of dynamic changes and boundary conditions on the building performance. In addition, it imposes a large number of assumptions and uncertainties, including the assumption that the whole building is made up of one big room, static occupancy numbers, constant ambient air temperatures, and fixed energy systems parameters.
* Dynamic Model Simulations: These are the predictions of a dynamic building energy model (in this study developed in EnergyPlus) reporting the results of the retrofit measures technical impacts. In this approach, different building specifications and characteristics are considered, as well as occupants' usage of the building and dynamic weather conditions.
## 2 Material and Methods
### Instrumental Techniques  
Further, 5-10 mL of supernatant from each sample was taken out and then filtered by PVDF 0.45 \(\upmu\)m filter (GVS syringe filter, Genomed, Daejeon, Korea). The concentration of copper and phosphate was analyzed in the form of Cu(II) and phosphorus, respectively, using inductively coupled plasma (ICP) spectroscopy (Model: NexION 300D, Agilent Technologies, Santa Clara, CA, USA) which has a detection limit of 20 \(\upmu\)g/L. A Laboratory VIS Spectrophotometer (Model: DR 3900, HACH, Loveland, CO, USA) for Cu(II) and portable analyzer (Model: HS-1000 plus, HUMAS, Daejeon, Korea) for phosphorus were also used which have a detection limit of 250 \(\upmu\)g/L and 10 \(\upmu\)g/L, respectively. The ORION Star A211 pH meter (Thermo Scientific, Waltham, MA, USA) was used to measure the pH in solution. The amount of alginate dissolved in solution or coated on FHO was measured by a total organic carbon (TOC) analyzer (Shimadzu TOC-V analyzer) equipped with an ASI-5000A auto-sampler. Fourier transform infrared spectroscopy (FTIR) was applied to find the formation of alginate, iron, and Cu(II) and phosphate (model: spotlight 200, PerkinElmer, USA).
# Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics
## 3 Simulation Study
### Data Sources
#### 3.1.3 Betting Odds  
In addition to player characteristics and scores, this research paper also uses the corresponding odds from online bookmaker Bet365, which is one of the leading betting providers with around 23 million customers (We are grateful to [[https://www.football-data.co.uk/data.php](https://www.football-data.co.uk/data.php)]([https://www.football-data.co.uk/data.php](https://www.football-data.co.uk/data.php)) for supplying the information.). By including this data, we can evaluate the performance in the financial context as well as a statistical evaluation. For this purpose, our trading strategy uses the usual decimal ratios: We put a bet amount \(b\) on a certain event, which is provided with a certain odds \(o\). Once the event happens, the bet amount is multiplied by the bet of odd \(b\cdot o\). Nothing will be paid if the event does not take place. Thus, the relative profit on \(b\) is either \(o-1\) (for a successful bet) or \(-1\), i.e., a complete loss of \(b\), for an unsuccessful bet.  
Figure 3 explains the counterplay between the probabilities for the three outcomes and the resulting bets. The betting odd is the inverse of the respective probability of an outcome. Especially in the context of sports betting, this representation is more intuitive as it links directly to the potential payout amount rather than the probabilities. However, bookmakers usually diminish the fair odd value by a certain amount to cover their costs in the long run.  
A summary of the accumulated odds is given in Table 2. Remarkably, we observe more extreme odds for one of the team than for a draw. Not surprisingly, the bookmaker is familiar with the  
Figure 3: From probability to fair betting odds—taking the inverse of the probability for an outcome results in the odds of this event.  
Figure 2: Descripe statistic of the match results from season 2006/2007 to 2017/2018.  
well-known home advantage--the average odds of a home win (2.42) are significantly below the average odds for an away win (4.27).
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 4 Proposed Solution
### Proposed Design Requirements  
Following are the explicit requirements for the proposed solution:  
1. All receivers and senders share a common clock (wall clock).
2. In order to determine the actual playout time, the sender must know the network condition (i.e., all receivers are connected properly).
3. The sender needs to know the buffering status of the receivers.
4. The sender sends playout time to the receivers and has the functionality to receive and interpret the additional information stated in points 2 and 3 from receivers.
5. Points 2, 3 and 4 require some additional fields in the data or control packets.
6. In order to achieve group synchronization, we used NTP [4] as a source of a common clock.  
Figure 6: Timeline diagram shows the effect of our solution.  
All the symbols used in mathematical equations and their definition can be found in Table 1.
# Time Series Feature Selection Method Based on Mutual Information
## 1 Introduction
However, for the feature extraction analysis of time series, the main problem of the mutual information-based method is in finding the most appropriate target variables, that is, the key to the mutual information-based method is to find the target variables. In most cases, there is no intuitive or directly available target information. PCA is a multivariate statistical analysis technique for data compression and feature extraction, which can effectively remove linear correlations between data [40]. The purpose of PCA is to discard a small portion of information using linear transformation and replace the original variable with a few new comprehensive variables while ensuring minimal data loss. Therefore, it is required that the principal components fully reflect the information of the original variables, while PCA can eliminate the linear correlation between variables and suppress noise by fusing multiple variables [41; 42]. Based on the above problems and the technical advantages of the PCA method, this paper uses the PCA method to reduce dimensions and fuse sensor data to obtain the target variables that can represent the system state. Given the problem, this paper proposes a target information extraction method based on PCA dimension reduction, and on this basis, mutual information is used to distinguish and extract the suitable sensor signal. To validate the effectiveness of the method, analysis and validation are conducted on NASA's publicly available CMAPSS aircraft engine dataset, and the effectiveness of operating condition recognition based on the extracted features is tested. The specific content is as follows:  
1. We propose a time series feature selection method based on PCA dimension reduction and MI. MI is used to quantify the correlation between two variables, but for time series, it is difficult to obtain effective target variables. Therefore, PCA dimension reduction is used to extract the target variables from the time series, and feature selection of the time series is conducted based on this. 2. We design a specific technical process for feature selection based on the above theoretical methods. In this process, we focus on the construction of target variables and the method of sensor selection and conduct experimental verification. 3. The effectiveness of the proposed method is verified based on a publicly available aviation engine operation dataset. The experimental results are compared with other methods to verify the feasibility of the proposed method, and experiments are conducted on condition recognition based on the selected features. The rest of this paper is organized as follows: In Section 2, we describe the basic principle of the algorithm in detail and propose a feature extraction method flow based on MI. In Section 3, we analyze the experimental dataset and conduct comparative experiments using the algorithms, and in Section 4, we analyze the obtained result and compare the proposed idea with existing approaches. Section 5 is the conclusion of this paper, which includes recommendations for future work.
# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 2 Background
### Aims  
In this study, we propose a system that projects the controls of a parametric equaliser comprising five biquad filters, as detailed in [16], arranged in series onto an editable two-dimensional space, allowing the user to manipulate the timbre of an audio signal using an intuitive interface. Whilst the axes of the two-dimensional space are somewhat arbitrary, underlying timbral characteristics are projected onto the space via a training stage using two-term musical semantics data. In addition to this, we propose a signal processing method of adapting the parameter modulation process to the incoming audio data based on feature extraction applied to the long-term average spectrum (LTAS), as detailed in [17; 18; 19], capable of running in near-real-time. The model is implemented using the SAFE architecture (detailed in [20]), and is provided as an extension of the current Semantic Audio Parametric Equaliser (available for download at [21]), as shown in Figure 1a.
## 5 Case Studies
### Proportional-Integral Speed Controller for DC Motor
#### 5.2.3 Results  
Figure 7 presents the step responses obtained for different values of the weighting factors \(\beta\) and \(\gamma\), while Table 5 presents the step response indicators and the PI controller parameters obtained. The weighting factors were selected to present the result for the ITSE indicator (\(\beta=\gamma=0\)), ITSE indicator with overshoot limited (\(\beta=10^{3},\ \gamma=0\)), and additional requirement to reach the required rise time (\(\beta=10^{3},\ \gamma=10^{-3}\)).  
The first experiment with zero values of \(\beta\) and \(\gamma\) is an objective function using only the ITSE indicator. In such a case, huge overshoots and oscillations occur. Next, the value \(\beta\) was set at a relatively high value to limit the percentage overshoot to the assumed value (equal to 5%). One can see that the proposed objective function successfully limits the overshoot. It should be noted that reducing the overshoot also reduces the rise time. Increasing the weighting factor \(\gamma\) allows one to define the required rise time.  
Figure 7: Step response of speed control using PI controller obtained with different values of \(\beta\) and \(\gamma\) coefficients.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\beta\) & \(\gamma\) & **ITSE** & \(\mathbf{SI_{PO}^{DC}}\) & \(\mathbf{SI_{RT}^{DC}}\) & \(\mathbf{K_{p}}\) & \(\mathbf{T_{i}}\) \\ \hline
0 & 0 & \(4.98\times 10^{-7}\) & 60.3\% & \(3.7\times 10^{-4}\) s & 10 & 10 \\ \hline \(10^{3}\) & 0 & \(1.37\times 10^{-6}\) & 5.0\% & \(28.0\times 10^{-4}\) s & 0.546 & 1.748 \\ \hline \(10^{3}\) & 1 & \(2.96\times 10^{-6}\) & 0.04\% & \(50.0\times 10^{-4}\) s & 0.319 & 0.954 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Step response quality indicators of speed control using PI controller obtained with different values of \(\beta\) and \(\gamma\) coefficients.
## 4 Results and Analysis
### Grid Virtual Division Scheme Performance
#### 4.2.1 Impact of Varying Transmission Frequencies  
Since extraction time is a core indicator used to measure the efficiency of RoI extraction, in this subsection, we first explore the performance of the proposed scheme in terms of processing time under varying transmission frequencies. Specifically, the processing time  
Figure 6: The experiment setup of the proposed OCC system. (**a**) Modulation circuit diagram for modulation circuitry. (**b**) LED transmitters in different shapes and sizes. (**c**) LED driver controller. (**d**) Experimental scenario for the proposed OCC system.  
is the elapsed time from when the receiver captures an image to when the RoI extraction is completed. In addition, we also examine the performance of the traditional scheme and the proposed scheme in terms of BER under varying transmission frequencies. We present the experimental results of each group and analyze them in the following sections.  
We fix the receiver at a distance of 80 cm away from the No.1 transmitter (round LED with a diameter of 0.13 m) and compare the processing time of the proposed method with the baseline method under three transmission frequencies. We then represent the results obtained by the iPhone 8 Plus in Figure 7. Intuitively, the different frequencies have little effect on the extraction time of the RoI. However, our grid virtual division scheme is superior to the baseline at the same transmission frequency thanks to its advantage of not having to traverse all pixels. It is evident from the figure that the grid virtual division scheme substantially outperforms the baseline with its median times at 0.33 ms, 0.395 ms, and 0.335 ms under transmission frequencies of 4 kbps, 5 kbps, and 6 kbps, as the corresponding median times of the baseline are one order higher at 1.71 ms, 1.28 ms and 2.35 ms, respectively. This comparison evidently confirms the effectiveness of the proposed innovative scheme, which can extract RoI quickly while maintaining low computational complexity.  
In addition, we also report the BER results obtained by iPhone 8 Plus under three different transmission frequencies, as shown in Figure 8. It can be seen that the BER of baseline and the proposed scheme experience a substantial increase to 0.72% and 0.81% when the transmission frequency reaches 6 kbps. This phenomenon can be explained by the fact that the strip width in the frame is shorter and hence lower symbol distance at higher transmission frequencies. Nonetheless, the grid virtual division scheme can achieve a reasonable BER below the FEC threshold of 0.38% at a maximum of 5 kbps while extracting RoIs quickly, which strongly confirms the feasibility and effectiveness of the proposed scheme. Additionally, a transmission frequency of 5 kbps is usually sufficient to support most OCC applications.  
Figure 8: BER with variable transmission frequency.  
Figure 7: RoI extraction performance under variable transmission frequencies.
# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
## 2 Preliminaries
### The Fusion of Fuzzy Logic and a Deep Neural Network
#### 2.3.1 Cooperative Structure  
A cooperative structure employs fuzzy logic as an integrated part of the deep learning process. The potential structure of cooperative models can be realized using two approaches. The first approach uses a fuzzy part to generate fuzzy inputs for the DNN. These fuzzy inputs pass through the network and then exit into another fuzzy part. This second fuzzy part defuzzifies these inputs and converts them into a final crisp output. In the second approach, fuzzy logic takes the output given by the DNN, processes it, and then returns it again to the DNN. After that, the DNN produces the final output [37].
# Accurate Extraction of Cableways Based on the LS-PCA Combination Analysis Method
## 2 Methodology
### Preprocessing of Ropeway Data  
The cable car cableway of the ski resort is a typical artificial construction, which has the following characteristics when viewed from the point cloud perspective:  
* In general, the ropeway extends linearly in a narrow and long shape, with a length much greater than a width;
* There are no obvious objects around the cableway from a local perspective, and the individual cables are parallel to each other and maintain a certain distance from one another;
* While cableway points have a discontinuous distribution in the vertical direction, they are closely connected in the horizontal direction.  
Figure 2: Diagram of the system’s working principle. In addition, there are different colors in the gradient bar () in the point cloud data set that represent different altitudes.  
Preprocessing operations on the original point cloud data are required in order to segment and extract the repeways based on the above distribution characteristics. These operations include object removal, noise filtering, etc.  
In the preprocessing section of this paper, the elevation filtering method is first applied in order to remove ground points and low object points. In addition to the ropeway point clouds, the filtered point clouds contain some tower points, high vegetation points, and noise points. In the next step, the cableway is preliminarily segmented based on the features of the point cloud in terms of spatial dimensions.