esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Investigation of Critical Frequency Bands  
In this study, the informative features were extracted from different frequency bands (Delta, Theta, Alpha, Beta, Gamma, and Total) for EEG-based emotion recognition. Thus, we present an investigation of the critical frequency bands in EEG signals for emotion processing. Figures 4a-f and 5a-f show the mean precisions obtained by LORSAL, SVM, and NB for the classification of LV/HV and LA/HA, respectively, when the frequency bands alternated among Delta, Theta, Alpha, Beta, Gamma, and Total. Gamma and Beta are more informative than other frequency bands (Delta, Theta, and Alpha). For example, among the first five frequency bands, the LOSAL obtained the highest accuracies of 72.93% and 67.06% on Gamma and Beta from the DE features in valence classification, the best accuracies of 72.73% and 66.57% on Gamma and Beta from the DE features in arousal recognition. There is not always a causal relationship between features with high recognition accuracies and emotions. [PERSON] et al. (2017) presented an investigation about the causal relationship between the emotions and their EEG signals on the DEAP dataset. The average frequency power of trials was calculated over the bands Theta, Alpha, Beta, and Gamma (between 3 and 47 Hz). The Spearman correlated coefficients were tabulated [27] to present the statistical correlation of the power changes of EEG sequences and subject ratings. Similar research has been done by [PERSON] (2017), we focused on analyzing the informative neural patterns associated with recognizing different emotions. Especially, the Fisher ratio was used to investigate the critical frequency bands for discriminating different emotions. The Fisher ratio has been used in pattern recognition for class separability measure and feature selection [65; 66; 67], as well as in emotion classification [3; 4; 13; 14; 27]. The higher values of the Fisher ratio mean more informative neural patterns and features related to emotion recognition. It is defined as the ratio of interclass difference to the intraclass spread  
\[F_{n}(\mathrm{L},\mathrm{H})=\frac{\left(\mathrm{m}_{Ln}-\mathrm{m}_{Hn} \right)^{2}}{\sigma_{Ln}^{2}+\sigma_{Hn}^{2}}, \tag{11}\]  
where L and H mean two different emotion, e.g., LV/HV or LA/HA, and m\({}_{Ln}\), m\({}_{Hn}\), \(\sigma_{Ln}^{2}\), and \(\sigma_{Hn}^{2}\) denote the mean and variance of the \(n\)-th dimension of the EEG feature belonging to emotions L and H,
## 4 Declarative Solution Approach
### The Method to Determine the Sub-Mission Time Windows  
Given the variable about the weather: _data about weather forecast in the time period \([0,t_{max}]\)_  
Question: _How many sub-mission time windows should be extracted in the time period \([0,t_{max}]\) and how many flying time windows should be extracted in each sub-mission time window? What are the values of variables determining the STWs: \(STW_{T}\), \(FTW_{l}\)?_  
By using the weather forecast data, the possible length of STWs are determined by using the following steps.  
Step 1: Take the forecast data for the planning time period.  
Step 2: For each consecutive \(R\) hours of sliding time windows, calculate the weather utility value (_WUV_). \(R\) is a parameter with a range of [1,3]. _WUV_ = \(\alpha\) (standard deviation of the wind direction) + \(\beta\) (standard deviation the wind speeds). \(\alpha\) and \(\beta\) are the weighted parameters.  
Step 3: Sort the sliding time windows in ascending order of _WUV_. Select the time windows based on the weather utility value and divide the time period into different sub-mission time windows.  
The method mentioned above creates a sequence of STWs where the time period is divided into several STWs. The purpose is to create sub-missions during a time where there are similar weather conditions. Based on the traveling time of the UAV, assuming that it is flying against the wind and considering the maximum energy limit, the flying-time windows are determined. The STWs are divided in flying-time windows based on the time used in flying the UAVs considering the maximum fuel limit and maximum carrying payload.
## 5 Conclusions  
Conventional duty cycle MAC protocols are energy-efficient and some of them also have mitigated other existing problems such as the delivery latency problem, but they have been designed independently without considering routing. Adding a routing protocol would cause significant performance degradation of the whole network. The P-MAC design presented in this paper is a cross-layer duty cycle MAC protocol seamlessly integrated with routing function. It uses the _Grade Division and Schedule Assignment_ (_GDSA_) scheme at the network layer to assign all sensor nodes into different grades around the sink and ensures that nodes maintain staggered schedules between any two adjacent grades. Then a variation of the RTS/CTS handshake mechanism is used at the MAC layer to forward data continuously in a pipeline fashion from the higher grade to lower grade nodes and finally to the sink. The communication overhead in the network can be significantly reduced while maintaining the superiority of duty-cycling schemes. The simulation evaluations show that P-MAC achieves better performance in terms of energy efficiency, latency reduction and throughput improvement. Future research needs to study network scalability, which was not adequately addressed.
# An Intelligent Parking Management System for Urban Areas
## 3 Description of the System
### System Architecture  
The proposal consists of a network of wireless sensors located on roads in and out of public parking areas. These sensors detect the entry and exit of vehicles into and out of the parking areas, and transmit this information via the network. The sensors are connected to network nodes that receive the data provided by the sensors and transmit them across the network; this type of node is called a sensor node. In the network topology there is a node that enables the exchange of data between the sensor nodes and the data centre; this node is called a gateway node. Data obtained from the sensor network are stored and managed in the data centre, which is also where the operation of each sensor node is controlled. The data obtained by the data centre enable the provision of services that report on the occupancy levels of the parking spaces on public roads.  
Each sensor node on the network will be developed on a proprietary platform that will be responsible for collecting the data obtained by the sensor or sensors associated with that node, interpreting and packaging information and transmitting it over the network. The sensor nodes must have low consumption levels, although when placed in outdoor parking areas it will be possible to add a small photovoltaic panel.  
The gateway node links the sensor nodes and the data centre. The sensor nodes notify the data centre when a vehicle enters or exits the monitored parking area and receives orders that development or maintenance engineers wish to transmit to the network from the data centre, via the gateway.  
The data centre controls the sensor network and manages the data provided by it. To carry out these procedures, this component of the architecture has two subsystems: the sensor network control subsystem that allows technical staff to configure and supervise the operations of the sensor network; and the subsystem for recording data provided by the sensors, which stores and processes the data in order to provide information services to users (authorities and drivers) about the status of the parking areas monitored by the system. To achieve scalability and flexibility in the data centre computing and storage resources, we propose using cloud virtualisation and computing paradigms in the system.  
Figure 1: System architecture in a closed car park and on a public road.
# Effects of Auditory Pre-Stimulation on Cognitive Task Performance in a Noisy Environment
## 2 Methods
### Study Design
#### 2.2.2 Experiment 2  
In _Experiment 2_ (to examine the effects of auditory pre-stimulation on the performance of basic STM/WM tasks in a noisy environment), the participants performed the three STM/WM tasks in _Field-noise_ Environment (\(E_{F}\)) after they were provided with auditory pre-stimulation for five minutes. According to the type of auditory pre-stimulation provided, the experimental conditions were classified into the following four categories (denoted here as Conditions 3 through 6).  
**Condition 3.** An experimental condition for performing tasks in _Field-noise_ Environment (\(E_{F}\)) with the auditory pre-stimulation of _Quiet noise_ (\(P_{Q}\)) (henceforth referred to as \(P_{Q}\)\(E_{F}\)).  
_Quiet noise_ was chosen as the type of pre-stimulation on the basis of the results of [PERSON] et al. [52], who found that quiet sitting with meditation improved cognitive function/performance. The noise intensity level of _Quiet noise_ was 40-45 dB (A), which corresponds to that of quiet workplaces that involve mental concentration.  
**Condition 4.** An experimental condition for performing tasks in _Field-noise_ Environment (\(E_{F}\)) with the auditory pre-stimulation of _White noise_ (\(P_{W}\)) (henceforth referred to as \(P_{W}\,E_{F}\)).  
_White noise_ was adopted as the type of pre-stimulation for this condition based on the previous studies indicating that it may effectively improve cognitive task performance [46; 53; 54]; it was provided by Genius Mate P model, which is a white noise generator. The noise intensity level of white noise was set to 60 dB (A) based on the results of [PERSON] [54] that low-intensity white noise helped to improve cognitive task performance.  
**Condition 5.** An experimental condition for performing tasks in _Field-noise_ Environment (\(E_{F}\)) with auditory pre-stimulation of _Field noise_ (\(P_{F}\)) (henceforth referred to as \(P_{F}\,E_{F}\)).  
_Field noise_ was chosen as the type of pre-stimulation on the basis of [PERSON] et al. [55], who showed that the effects of office noise could be reduced by prior exposure to this type of noise; the noise intensity level was set to 80 dB (A), as in \(E_{F}\).  
**Condition 6.** An experimental condition for performing tasks in _Field-noise_ Environment (\(E_{F}\)) with auditory pre-stimulation of _Mixed noise_ (\(P_{M}\)) (henceforth referred to as \(P_{M}\,E_{F}\)).  
_Mixed noise_ was literally a mixture of _Field noise_ and _White noise_. It was selected as the type of pre-stimulation based on the results of existing studies that cognitive task performance could be improved in an environment with field noise masked with white noise [35; 56; 57]. The noise intensity level in this condition was set to 80 dB (A) for both the _Field noise_ and _White noise_. Table 1 summarizes all of the experimental conditions for _Experiment 1_ and _Experiment 2_.
# A Simple and Compact Laser Scattering Setup for Characterization of a Pulsed Low-Current Discharge
###### Abstract  
Recent research trends show an increasing interest in non-equilibrium plasmas operated at atmospheric pressure, which are often used to tackle several environmental and health issues. Nevertheless, due to the complexity of the applications, these trends also show the need for a comprehensive characterization of such plasmas for a deeper understanding of the observed effects. One of the diagnostic methods for experimental determination of key parameters which affect the reactivity of a plasma, i.e., electron temperature, electron density and heavy particle temperature, is laser scattering. In this work, an approach based on a simple and compact laser scattering setup is proposed, which allows an estimation of the above parameters without any additional changes in the acquisition settings. Thus, the experimental effort and possible sources of error can be reduced. The proposed setup is tested experimentally with a commercially available pulsed plasma system, and the results are compared to available data. From this comparison, it is found that the plasma parameters estimated with the proposed scattering setup are plausible.  
plasma jet; low-current discharge; atmospheric pressure; laser scattering; electron density; electron temperature
## 5 Discussion
### Ablation Studies
#### 5.2.3 Impact of the SE Mechanism  
The third ablation study evaluates the impact of the SE mechanism by comparing the full ConvResBiGRU-SE model with a version that excludes the SE mechanism. The findings in Table 7 show that the SE mechanism improves the model's capability to recalibrate features and support overall recognition performance.  
For the UCI-HAR dataset, adding the SE mechanism increased the model's accuracy from 98.86% to 99.18%, an absolute improvement of 0.32%. The F1-score also rose from 98.93% to 99.24%. Although these improvements might seem modest, they are significant given the high baseline performance. More importantly, including the SE mechanism resulted in more stable predictions, as indicated by the reduction in standard deviation from \(\pm\)0.54% to \(\pm\)0.20% for accuracy.  
The SE mechanism had a more pronounced effect on the WISDM dataset. Accuracy improved from 98.25% to 98.78%, and the F1-score increased from 97.29% to 98.09%. The substantial decrease in standard deviation from \(\pm\)1.14% to \(\pm\)0.24% for accuracy indicates that the SE mechanism significantly enhances the model's stability and reliability. This improvement is particularly valuable for real-world applications where consistent performance is crucial.
# Network Performance Optimization for Low-Voltage Power Line Communications
## 3 LVPLC Network Scheme
### Improved \(Q\) Learning Approach in LVPLC Network Scheme
#### 3.2.1 Improved \(Q\) Learning Model
 \tag{11}\]  
The destination stations send the Acknowledgment (ACK) to the source station and avoid the frame retransmission when the destination stations successfully receive the frame. * Reward function  
When stations meet the requirements of the communication threshold condition, \(r(i,j)\) indicates the reward after stations associate the network through trial-and-error interactions with a dynamic environment:  
\[r(i,j)=\frac{node(i).w}{node(j).w}\bullet node(i).hop(j) \tag{12}\]  
where \(node(i).w\) denotes the link weight when station \(i\) receives the data frame; \(node(j).w\) is the link weight when station \(j\) sends the data frame; and \(node(i).hop(j)\) denotes the hops between stations \(i\) and \(j\). These metrics (link quality, the number of hops) are considered jointly in the reward function, and can reflect the dynamic characteristics of the network. The greater the value of the reward, the stronger the learning trend. * \(Q\) value update rule  
The forwarding \(Q\)-learning in the asymmetric channel environments is from the CCo to the terminals. The backward \(Q\)-learning is from the terminals to the CCo. This study explains the forward \(Q\)-learning mechanism. The CCo obtains the new associated stations' information by gathering proxies and provides immediate reward to the new stations when the csr \(r_{1}{}^{\prime}\) is greater than the uplink csr threshold \(Thr_{2}\). The CCo puts the new associated stations into the routing table, dynamically updates the routing table information, and finishes the forwarding link storage. The stations choose the next hop to continue networking by routing table lookup. The whole stations associate in the network, and the network finishes. The CCO's \(Q\) table information is updated as follows:  
\[Q_{t+1}^{(i)}(s_{t}^{(i)},a_{t}^{(i)})=r_{t}^{(i)}(s_{t}^{(i)},a_{t}^{(i)},s_{t+ 1}^{(i)})+r_{1}{}^{\prime}\times\max_{a^{i}\in A^{(i)},j\in Neighbor(i)}Q_{t }^{j}(s_{t+1}^{(j)},a^{(j)}) \tag{13}\]  
where \(r_{1}{}^{\prime}\) is the present uplink csr. This study illustrates the backward \(Q\)-learning mechanism. The new associated stations obtain terminal equipment identifiers (TEIs) by the CCo allots and stores the link to the CCo when the csr \(r_{2}{}^{\prime}\) is greater than the downlink csr threshold \(Thr_{1}\). The stations' \(Q\) table information is updated as:  
\[Q_{t+1}^{(i)}(s_{t}^{(i)},a_{t}^{(i)})=r_{t}^{(i)}(s_{t}^{(i)},a_{t}^{(i)},s_{ t+1}^{(i)})+r_{2}{}^{\prime}\times\max_{a^{i}\in A^{(i)},j\in Neighbor(i)}Q_{t }^{j}(s_{t+1}^{(j)},a^{(j)}) \tag{14}\]  
where \(r_{2}{}^{\prime}\) is the present downlink csr.
## 3 Results and Discussion
### Fatty Acid Profile and Positional Distribution
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type of Fatty Acid**} & \multicolumn{4}{c}{**Type of Oil**} \\ \cline{2-5}  & **A1** & **A2** & **P1** & **P2** \\ \hline C14:0 & 0.04 \(\pm\) 0.0 a & 0.12 \(\pm\) 0.03 b & 0.10 \(\pm\) 0.02 b & 0.16 \(\pm\) 0.02 b \\ C16:0 & 6.35 \(\pm\) 0.45 a & 7.87 \(\pm\) 0.26 b & 6.41 \(\pm\) 0.31 a & 5.85 \(\pm\) 0.21 a \\ C16:1 & 1.07 \(\pm\) 0.13 c & 0.66 \(\pm\) 0.05 b & 0.40 \(\pm\) 0.08 a & 0.51 \(\pm\) 0.03 ab \\ C17:0 & 0.05 \(\pm\) 0.01 b & 0.05 \(\pm\) 0.01 b & 0.02 \(\pm\) 0.01 a & 0.02 \(\pm\) 0.01 a \\ C17:1 & 0.15 \(\pm\) 0.01 a & 0.11 \(\pm\) 0.02 a & 0.13 \(\pm\) 0.02 a & 0.13 \(\pm\) 0.03 a \\ C18:0 & 1.32 \(\pm\) 0.16 a & 3.08 \(\pm\) 0.14 c & 1.45 \(\pm\) 0.11 a & 1.99 \(\pm\) 0.17 b \\ C18:1 & 68.77 \(\pm\) 0.83 b & 46.06 \(\pm\) 0.45 a & 68.57 \(\pm\) 0.35 b & 69.19 \(\pm\) 0.38 b \\ C18:2n-6 & 21.94 \(\pm\) 0.23 a & 41.57 \(\pm\) 0.77 b & 22.54 \(\pm\) 0.37 a & 21.91 \(\pm\) 0.20 a \\ C18:3n-3 & 0.11 \(\pm\) 0.0 a & 0.18 \(\pm\) 0.01 b & 0.16 \(\pm\) 0.02 b & 0.10 \(\pm\) 0.01 a \\ C20:0 & 0.13 \(\pm\) 0.03 b & 0.20 \(\pm\) 0.02 c & 0.04 \(\pm\) 0.01 a & 0.07 \(\pm\) 0.01 a \\ C20:1 & 0.08 \(\pm\) 0.02 b & 0.11 \(\pm\) 0.01 c & 0.06 \(\pm\) 0.02 a & 0.06 \(\pm\) 0.01 a \\ SFA & 7.88 \(\pm\) 0.65 a & 11.31 \(\pm\) 0.44 b & 8.02 \(\pm\) 0.46 a & 8.08 \(\pm\) 0.42 a \\ \(\sum\)MUFA & 69.00 \(\pm\) 0.86 b & 46.28 \(\pm\) 0.48 a & 69.26 \(\pm\) 0.45 b & 69.89 \(\pm\) 0.44 b \\ \(\sum\)PUFA & 22.05 \(\pm\) 0.24 a & 41.75 \(\pm\) 0.78 b & 22.70 \(\pm\) 0.39 a & 22.01 \(\pm\) 0.21 a \\ \hline \hline \end{tabular} \(\Sigma\)SEA—total percentage of saturated fatty acids; \(\sum\)MUFA—total percentage of monounsaturated fatty acids; \(\sum\)PUFA—total percentage of polyunsaturated fatty acids; data denoted by the same letters are not statistically different (\(\alpha\) = 0.05) in terms of individual fatty acid.  
\end{table} 
Table 1: Fatty acid profiles of apricot and peach kernel oils. 69.19% of oleic acid and 21.91% of linoleic acid in the P2 oil. The share of saturated fatty acids did not exceed 10% in both analyzed peach kernel oils. [PERSON] et al. [34] obtained a lower share of unsaturated fatty acids in peach kernel oil (83.74%), while the content of saturated fatty acids was at a similar level (8.92%) as in the analyzed P1 and P2 oils. The peach kernel oil analyzed by [PERSON] and [PERSON] [35] was characterized by a lower content of linoleic acid and a higher content of oleic acid than the analyzed P1 and P2 oils. The share of oleic acid was 74.18% and linoleic acid--15.36%. However, the greatest differences in the shares of oleic and linoleic acids were described by [PERSON] et al. [36]. The shares of these two acids in the peach kernel oil depended on the country of cultivation and ranged from 41.1% to 74.6% in the case of oleic acid and 15.4%-59.8% for linoleic acid.
# Responses of Forest Carbon Cycle to Drought and Elevated CO2
## 3 Elevated CO2 Effect on Plants and Ecosystem
### How Plants Respond to Elevated CO2
#### 3.1.4 Nutrient Availability  
Rubisco requires a large amount of N investment and the activity of Rubisco play a key role in determining elevated CO\({}_{2}\) effects on photosynthesis rate. Therefore, the strength of CO\({}_{2}\) fertilization will be greatly reduced when other nutrients such as N are limited. There have been many experiments and studies supporting the hypothesis that CO\({}_{2}\) fertilization is limited by N availability [53; 54; 55]. When the sink strength of plants is limited due to N restriction, CO\({}_{2}\)-stimulated excess photosynthate is more likely to trigger the feedback mechanism of plants that reduces products of photosynthesis from the source, thus leading to an earlier occurrence of photosynthetic acclimation [56]. The process of P affecting forest carbon uptake is different from that of N. [PERSON] et al. (2017) provide the evidence from FACE experiments that P potentially limits the effect of CO\({}_{2}\) fertilization, although their experiment is limited to forests consisting of single species. [PERSON] et al.'s (2018) data analysis of 138 elevated CO\({}_{2}\) experiments shows that the strength of elevated CO\({}_{2}\) effect on 25% of global vegetation is primary constrained by P availability.
# Three-Dimensional Wind Measurements with the Fibered Airborne Coherent Doppler Wind Lidar LIVE
## 3 Results
### Flight Test Results  
Raw data from the flight tests were stored for post processing. As explained in the lidar signal processing chapter, MFAS and WVML algorithms [15] are used for wind vector retrieval. These wind speed vectors, relative to the aircraft frame of reference, are then transposed to the earth frame of reference using FTI data (aircraft altitude, location and attitude). Since wind speed measured by lidar is the wind speed relative to the aircraft, we also add aircraft speed relative to the ground in order to calculate wind speed relative to ground.  
Figure 8a shows the flight path (blue line) followed by the aircraft above the Onera ground based WindCube lidar near the city of Fauga. On 20 February 2019, the aircraft was flying over the ground based lidar at three different altitudes (FL100, FL160 and FL220), with two different headings for each altitude (red dashed boxes). Unfortunately, we got poor signal return from the WindCube on this day. The next day (21 February 2019), the aircraft only flew at FL100 and we were able to receive a signal from the WindCube up to the top of the planetary boundary layer. Figure 8b-d shows the comparison between LIVE (red squared line) and WindCube (blue dotted line) wind measurements for all three components in the earth frame of reference. The agreement between the two lidars is good, considering that the 2 km footprint of LIVE is larger than the one of WindCube. The accuracy of the WindCube as given by its manufacturer is 0.2 m.s\({}^{-1}\) in radial wind speed in the range of 100 m to 2 km.  
Figure 7: (**a**) Line of sight CNR real time display. (**b**) Line of sight velocity real time display for the conical scan. Range zero is the aircraft altitude while range 3500 m is the ground echo.  
In the lidar signal processing chapter, we have also mentioned an orientation issue between LIVE lidar and FTI when we add the aircraft speed relative to ground to the wind speed relative to aircraft. As a workaround, we have chosen to measure aircraft speed relative to ground also from LIVE lidar. In order to evaluate the quality of the workaround, we have compiled all six LIVE measurements of February the 20 th of 2019 into Figure 9, from ground level up to FL100. Results are consistent despite a delay of almost a quarter of an hour between each measurement, and different aircraft orientations (heading, angle of attack varying with altitude).  
Figure 8: (a) Aircraft trajectories during measurements (north–south and west–east) over Fauga. (b–d) respectively z (zenith), x (north) and y (west) components of wind measured by airborne lidar LIVE (red squares) and WindCube lidar (blue dots).