esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
It should be noted that the challenges of EEG signal classification are not limited to missing data. Non-invasive EEG depends on the signals from the outer brain surface; thereby, its effectiveness may be reduced with epilepsy that begins deep in the brain. The detection of this kind of seizure can be affected by the signals of daily physical activities such as eye flutter. Accessible and affordable wearable sensor devices such as EMOTIV can facilitate the preprocessing of EEG signals in the edge node. Despite the reduction in the received data during preprocessing steps, the expected amount of uploading data can still be significant because of the rapid signals received from EEG sensors. Regarding the implementation, the CHB-MIT dataset used to evaluate the model's performance has been collected from a young sample of 23 cases whose ages do not exceed 22 years for males and 19 years for females. Since epilepsy is common in all age groups, exploring the efficiency of the proposed model in middle-aged and older people is necessary. Generally, large medical datasets are difficult to obtain and utilize due to patients' privacy rights. Finally, the benefits of our model go beyond the home use of the patient, as health institutions can use it to monitor inpatients within their private cloud.
## 8 Conclusions and Future Work  
In this paper we propose a novel location-based cooperative routing algorithm for WSNs, named PELCR-TP, that takes advantage of the low link power and high channel gain of the cooperative routing in WSNs and make it work well in the case where the transmission power is really low. Node location information analysis and selection policy based on the RTS/CTS handshaking mechanism is the core part of the algorithm. The main idea of the algorithm is that each node uses its transmit power upper limit as its transmit power in order to ensure enough transmission distance in case of low energy. In this case, the transmission distance and the outage probability, both of which can be calculated under the lowest link power, will mutually influence each other, and then the sending node will use the calculated transmission distance as the basis for selecting the location of the next hop node. The algorithm adopts a single cooperative node strategy, and the cooperative link ensures a  
Figure 18: Pathways node maps.  
relatively low outage probability is maintained, even for a long transmission distance scenario. In addition, the algorithm further includes a bad node avoidance strategy. The cooperative node will not drop packets until the transmission of this hop success so that it can replace the next node to continue transmission when the next hop node cannot receive or decode packets.  
Simulation results show the following conclusions: firstly, when nodes' transmission power upper limit is extremely low (_i.e._, 10\({}^{-5}\) to 4 \(\times\) 10\({}^{-4}\)) and the path loss index and bad node rate are relatively high, 2-4 and 0-0.2 respectively, PELCR-TP could significantly reduce the overall power and retransmission rate and enhance the transmission success rate, compared with PLCR, the algorithm proposed in a previous study. Secondly, the PELCR-TP algorithm shows a very stable performance over a fairly large range of conditions, as the transmission success rate is approaching 1.0 until an obvious inflection point appears. Thirdly, the retransmission rate will be lower and transmission success rate will be higher with Bad node Avoidance Strategy (BAS) than without. This shows that PELCR-TP algorithm with BAS can better adapt to the WSNs with low node density, small transmission power and bad transmission environment.  
As future work we expect to find a way to make this routing algorithm not only power-efficient, but also energy-efficient, for example, by some energy awareness strategies as proposed in Reference [47]. Other performance metrics such as coverage, QoS, or security will be taken into account and optimized by the way of, for instance, the proposals presented in [48; 49; 50]. What is more, we should research the approximate calculation of the transmission distance. Last but not least, we want to study the implementation when the routing algorithm adopts the multi-relay strategy. All above are aimed to improve the performance of WSNs.
## 6 Challenges and Future Research Directions
### Energy Storage Resources for Virtual Inertia Systems  
Typically, capacitors and batteries have been proposed as ESSs for dynamic frequency control using power electronic converters [45; 50; 72; 82]. In [117], an ultra-capacitor based ESS is proposed to reduce the impact of RESs variability of frequency stability of an isolated power system. However, these energy resources often incorporate prohibitive cost investments, and because fast-frequency needs to compensated by the virtual inertia systems it may effect the lifetime of the ESS. As a solution, a parallel combination of batteries and ultra-capacitors was proposed in [21] which significantly reduced the impact of high frequency dynamics on the batteries as the ultra-capacitors supplied the high frequency components. This also allowed for a cheaper and smaller battery unit [118]. Flywheel based energy storage for virtual inertia was proposed in [119]. Novel solar panel technologies with inherent storage capabilities could be another way of providing inertia through PV systems [114]. Recently, researchers have started to focus on alternate means of energy resource for virtual inertia. One of the main areas that is gaining attention is the so-called \"thermal-inertia\" of heating, ventilation,and air conditioning (HVAC) systems of commercial buildings. As discussed in [120; 121; 122], the power consumption of the power electronics based HVAC units can be controlled to provide inertial response while ensuring that the customer comfort is not effected. Similarly, the large HVAC installation in data centers could be another potential to tap for inertial response in the future grid with large scale integration of RES units.
## 3 Results and Discussion
### Determination of Process Parameters for Optimal Exoperoxidase Production
However, the difference in peroxidase production when the test bacteria was cultivated in a fermentation medium supplemented with veratryl alcohol (AL + VALC = 2.10 \(\pm\) 0.1 U mL\({}^{-1}\)) was not significant when compared with the control: non-supplemented fermentation medium (AL) in a [PERSON]'s test. Our findings showed that guaiacol induced peroxidase production in _Ensifer adheserens_ NWODO-2 as it increased the enzyme production by about 50% (Figure 2a). On the other hand, veratryl alcohol did not induce peroxidase production in the test organism while vanillin, vanillic acid and ferulic acid seemed to promote repression of the enzyme synthesis. This finding is consistent with our recent study [25], where guaiacol was also reported as the best peroxidase inducer in _Raoultella ornithinolytica_ but contrary to the findings of [PERSON] et al. [12], where veratryl alcohol produced the best peroxidase-inducing effect in _Streptomyces_ sp. strain BSI#1. Hence, inducers may be species-specific. The influence of type and concentration of nitrogen sources on secretion of LMEs have been extensively studied, especially in fungi [47; 48; 49] but the findings are not always consistent [32]. Production media with adequate amount of nitrogen have improved the production of LMEs in some fungi [50]. More so, nitrogen-limited conditions have enhanced ligninolytic enzyme production [51; 52; 53] while in some cases, production of LMEs has been hampered by increased level of nitrogen [54]. The results from this study as presented in Figure 2b revealed there was a significant difference (F = 17,940, \(p\) value = \(p\) < 0.0001 for ANOVA) in peroxidase production by _Ensifer adheserens_ NWODO-2 cultivated in fermentation media supplemented with different inorganic nitrogen, with ammonium sulphate (YE + Ammonium sulphate) giving the maximum peroxidase production (11.31 \(\pm\) 0.12 U mL\({}^{-1}\)). Moreover, [PERSON]'s multiple comparison test showed that augmentation of the production medium with various inorganic nitrogen (ammonium nitrate; ammonium chloride; and ammonium sulphate) significantly increased peroxidase production by _Ensifer adheserens_ NWODO-2 when compared with non-supplemented fermentation medium used as the control (YE = 4.67 \(\pm\) 0.00 U mL\({}^{-1}\)). This finding contradicts the result obtained by [PERSON] et al. [25], where supplementation of yeast extract with ammonium chloride supported optimum extracellular peroxidase activity by _Raoultella ornithinolytica_ OKOH-1.
## 4 Simulations Results
The x-axis shows the number of turns of the secondary winding under the same conditions as in Figure 3, while the y-axis shows the percentage impedance. It was found that the percentage impedance was relatively stable when the number of secondary coils was between 30 turns to 38 turns, in which the percentage impedance was about 7.3-7.5%. However, the minimum percentage impedance was about 7.0% when the number of secondary coils was 38* turns. Furthermore, when the number of secondary coils was 38** turns, as a result, the percentage impedance swung back to about 7.3%. Figure 6: The lead wire loss when the secondary winding changes. The results from Figures 3-8 were the design data of the number of secondary coils between 30 turns to 38** turns. It was confirmed that all design data for the 3-Phase distribution transformer 8000 kVA, 50 Hz, 22 kV-3300/1905 V, Dyn11 vector group and percent impedance at 7.5 by the Area Elimination Technique was an acceptable range for the IEC 60076 standards. Due to the number of secondary coils at 38** turns, this results in a lower cost, as already mentioned. Thus, in the manufacture of distribution transformers, it was possible to use a value of the number of secondary coils at 38** turns. Moreover,  
Figure 8: The percentage impedance when the secondary winding changes. Figure 7: The total loss when the secondary winding changes. the transformer efficiency could be calculated from Equation (15) with the percentage load at 25%, 50%, 75% and 100% under the conditions of power factor (pf) at 0.8 and 1.0, as in Figure 9.  
\[\text{Efficiency}=\left(\frac{\text{Output}}{\text{Output}+\text{losses}} \right)\times 100 \tag{15}\]  
Figure 9 shows the transformer efficiency when the percentage load and power factor change. The x-axis shows percentage load at 25%, 50%, 75% and 100%, while the y-axis shows the percentage transformer efficiency, where the power factor was 0.8 and 1.0. It was found that the unity power factor was more efficient than the 0.8 power factor throughout percentage load. In addition, the efficiency at 50% of load was the highest of both the power factors of 0.8 and 1.0, which was about 99.3% and 99.5%, respectively.
## 1 Introduction
Examples of such algorithms include genetic algorithms [27], particle swarm optimization [28], differential evolution [29], fish school search [30] and others. Biology-inspired algorithms are parallel in nature and do not require the optimized function to be differentiable, so such algorithms have been successfully applied to neural network architecture optimization [31; 32], to hyperparameter optimization of other machine learning algorithms [33], and also to ELM networks fine-tuning [24; 25; 26]. In this paper we compare the SVM, KNN, RF, and ELM algorithms applied to the classification of program texts submitted to DTA by students, where the programs are solving unique tasks automatically generated by the DTA system. We employ a grid search in order to find optimal hyperparameters of each classification algorithm for every dataset containing program texts. We compare the tuned classification algorithms using different metrics, such as accuracy, precision, recall, and F1 measure. In addition, we compare the time spent by each algorithm during training and prediction phases. Fish school search showed superior performance in neural network structure optimization [19; 32], as differential evolution did [31; 34], so we employed these algorithms in order to further improve the best ELM model obtained with the grid search. The experimental results show that ELM is the most computationally efficient algorithm among the considered ones; this applies both to training and making predictions. ELM models that were preliminarily fine-tuned with biology-inspired algorithms produced the most accurate classification results. The rest of the paper is organized as follows. Section 2 briefly describes the DTA system and the different types of unique programming exercises generated in order to prevent students from cheating. Section 3 describes algorithms used to transform program texts received from students into vectors, before applying machine learning techniques. Section 4 formulates the multi-class classification problem of program texts represented as vectors, provides a brief survey of the machine learning algorithms considered in this research, and lists metrics commonly used while estimating the performance of different multi-class classification models. Section 5 describes the design of the numerical experiment, and provides the results of experimental runs. Finally, Section 6 presents our conclusions and a discussion regarding future work.
## 4 Discussion
### Data Requirements for This Approach  
The ANPP-NDVI relationships derived here are image-specific and hence, must be calibrated for each image using available data. Our approach differs from other empirical methods, which are usually developed at single sites, by using field data from multiple sites, selected using stratified sampling. This avoids a typical limitation of empirical methods--that scaling-up from site level to wider spatial scales involves extrapolating beyond the conditions on which the model has been trained.  
Because our model is trained using widely-distributed, representative field data, when it is used to predict ANPP outside of the field survey squares, we are still interpolating within the bounds of the training data.  
Consequentially, this novel approach for regional scale mapping requires:  
1. Widespread plot level field data that cover a range of habitats and ANPP values. Such datasets already exist in the UK, due to the Countryside Survey [30] and GMEP [31] projects, and for many countries across the world (see [[http://www.givd.info/](http://www.givd.info/)]([http://www.givd.info/](http://www.givd.info/))). Similar datasets are likely to become increasing available as countries implement more fine-scale monitoring of their natural capital.
2. The field data need to provide sufficient plots per image to enable the production of a robust relationship. It is important to note that cloud-cover will tend to decrease the number of available plots within the image. The relationships presented here were based on between 42 and 128 plots (Table 5), with the best relationships using 109 plots; however, in this work, we have not attempted to determine the optimum number of plots.
3. Cloud-free data within a relatively narrow time window (March-May). Currently, the availability of suitable cloud free imagery for some regions, such as the UK, is limited; however, since the launch of the Sentinel-2 satellites, suitable optical imagery is being more frequently collected, thereby increasing the likelihood that cloud free imagery will be acquired. Hence, it is conceivable that regional scale vegetation productivity maps could be produced and updated every few years.
# EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
## 1 Introduction
Due to its non-invasive nature, high temporal resolution, and low cost, EEG has been widely used to analyze and assess cognitive states. For instance, [PERSON] et al. collected EEG signals from construction workers during on-site tasks and utilized time-domain and frequency-domain features to identify work-related stress with an accuracy of 80.32% [18]. Similarly, [PERSON] et al. used single power spectral density (PSD) features in the alpha and beta bands to assess driver alertness in a simulated driving environment, achieving an accuracy of 82.8% [19]. [PERSON] et al. employed wearable EEG sensors to record the brain activity of construction workers, developing a mental fatigue detection model with an accuracy exceeding 90% [20]. Furthermore, other researchers have extensively used EEG to investigate emotions [21], risk perception [22], workload [23], and situational awareness [24]. Previous studies have demonstrated that frequency-domain features are key EEG indicators for identifying distraction across various fields. For example, frequency-domain features in the prefrontal and temporal regions effectively distinguish between focused and distracted states in construction workers [25]. Theta-band activity in the prefrontal cortex has been used to detect driver distraction [26]. Additionally, alpha-band activity in the temporal region has been suggested to correlate with distraction [27]. The P300 component, a widely studied event-related potential, has also been used to reflect attention levels in response to stimuli [28]. However, few studies have addressed the unique environmental challenges of coal mining and explored miner distraction from the perspective of brain activity. To address this research gap, we propose a method utilizing EEG devices to monitor the distraction of coal miners. By identifying EEG features that distinguish between focused and distracted states, we developed and evaluated a model for recognizing miner distraction. Using the aforementioned sensitive indicators, we anticipate that the proposed model can objectively and accurately identify distraction among coal miners. The results are expected to provide novel technical solutions and scientific insights for distraction detection, thereby mitigating the risks associated with unsafe behaviors and enhancing workplace safety and health protocols.
# An Overview of Machine Learning and 5G for People with Disabilities
## 3 5G for People with Disabilities
### Low Latency  
Low latency is an important requirement for computer vision applications. Computer vision systems need time to capture images, process them, and generate an alert signal. Additionally, 5G networks can reduce roundtrip latency to 1 ms [42]. Visually impaired applications with strict latency requirements, such as navigation systems [43], can benefit significantly. The same applies to sound recognition systems for the hearing impaired, designed to prevent accidents or alert about emergencies (e.g., fire alarm, car horn).
## 4 Weighting Factor Estimation Method from Scheduling Results
### Machine Learning  
In this section, weighting factor estimation using machine learning is described. The outline of machine learning method is shown in Figure 2.  
First, we prepare 100 problem examples of the scheduling problem. Information such as delivery time, processing time, and setup cost is given as input parameters to the scheduling problem. A set of weighting factors is given to this input to solve the scheduling problem and obtain the output. The output contains the job processing order, job assignment, and each objective function value. The input data and weighting factors corresponding to the output results are known. For small-scale problems, an exact solution is obtained, and for large-scale problems, an approximate solution is obtained by simulated annealing. The obtained input/output data is divided 8 to 2 into training and test data. Then, the input/output data are converted into features. Then, machine learning models are constructed by training data. Finally, machine learning is used to estimate the test data. The machine learning method that we used in this study is random forest [33]. Random forest is one of the supervised learning methods and it constructs a model with higher  
Figure 1: Outline of the weighting factor estimation.  
Figure 2: Outline of machine learning method.  
generalization performance by using multiple decision trees. A regression random forest is used to output the estimated weighting factor as a continuous value. The decision tree is formed by multiple explanatory variables. It is undesirable to use the input/output data of a scheduling problem as is in machine learning. To construct a highly accurate model with random forest, it is necessary to extract features that strongly represent the relationship between the weighting factors and the schedule. In this study, we use features for the explanatory variable. Conversely, including features that have no relationship may generate noise during learning, increase computation time, and decrease prediction accuracy. We will explain how to generate features used in random forests in Section 4.2.
# Unsupervised Offline Changepoint Detection Ensembles
## 2 Materials and Methods
### Numerical Experiment
#### 2.2.3 Cost Functions  
As well, we selected several well-known cost functions for the experiment (Figure 5).  
Median-Shift through Least Absolute Deviation--\(l1\)  
It allows to detect changes in median of the signal [35]. The cost function is defined by the formula:  
\[c_{l1}(y_{t_{a}\ldots t_{b}})=\sum_{t=t_{a}}^{t_{b}}\|y_{t}-\bar{y}_{t_{1}\ldots t _{2}}\|_{1} \tag{6}\]  
where \(\bar{y}_{t_{1}\ldots t_{2}}\) is the median of a subsequence \(\{y_{t_{a}\ldots}y_{t_{b}}\}\). Generally, the shift of any central point (mean, median, mode) in a numerical data set can be detected.  
Mean-Shift through Least Squared Deviation--\(l2\)  
Allows to detect changes in mean of the signal [22]. The cost function is defined by the formula:  
\[c_{l2}(y_{t_{a}\ldots t_{b}})=\sum_{t=t_{a}}^{t_{b}}\|y_{t}-\bar{y}_{t_{a} \ldots t_{b}}\|_{2}^{2} \tag{7}\]  
where \(\bar{y}_{t_{a}\ldots t_{b}}\) is the mean of a subsequence \(\{y_{t_{a}\ldots}y_{t_{b}}\}\).  
Mahalanobis-Type Metric--_Mahalanobis_  
It allows to detect changes in the pseudo-metric of the signal [36]. We use Mahalanobis distance [37] as the pseudo-metric in our experiment. The cost function is defined by the formula:  
\[c_{mah}(y_{t_{a}\ldots t_{b}})=\sum_{t=t_{a}}^{t_{b}}(y_{t}-\bar{y}_{t_{a} \ldots t_{b}})^{T}\hat{\Sigma}^{-1}(y_{t}-\bar{y}_{t_{a}\ldots t_{b}}) \tag{8}\]  
where \(\bar{y}_{t_{a}\ldots t_{b}}\) is the mean and \(\hat{\Sigma}^{-1}\) is the inverse of the empirical covariance matrix of a subsequence \(\{y_{t_{a}\ldots}y_{t_{b}}\}\). Generally, instead of \(\hat{\Sigma}^{-1}\), any symmetric positive semi-definite matrix can be used.  
Piecewise Linear Model--_Linear_  
It allows to detect changes in a linear relationship between an observed variable and a covariate [38]. Let us assume that the process is defined by the model \(y_{t}=x_{t}^{T}u+\varepsilon_{t}\), where \(x_{t}\in\mathbb{R}^{p}\) is a covariate vector, \(u\) is a vector of regression parameters, \(p\) is an order of the model and \(\varepsilon_{t}\in\mathbb{R}\) is a noise. Then the cost function is defined by the formula:  
\[c_{linear}(y_{t_{a}\ldots t_{b}})=\min_{u\in\mathbb{R}^{p}}\sum_{t=t_{a}}^{t _{b}}\|y_{t}-x_{t}^{T}u\|_{2}^{2} \tag{9}\]  
In the other words, a linear regression is fitted for each subsequence \(\{y_{t_{a}\ldots}y_{t_{b}}\}\).  
Piecewise Autoregressive Model--_ar_  
If the observed variable depends linearly on its previous states, a piecewise autoregressive model can be used [39]. The cost function is defined by the formula:  
\[c_{ar}(y_{t_{a}\ldots t_{b}})=\min_{u\in\mathbb{R}^{p}}\sum_{t=t_{a}}^{t_{b}} \|y_{t}-x_{t}^{T}u\|_{2}^{2}, \tag{10}\]  
where \(x_{t}=\{y_{t-1},y_{t-2},\ldots,y_{t-p}\}\). For the experiment, we chose \(p=1\) from the grid search procedure.