esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Evaluation Metrics  
The proposed model used in this study was evaluated using several metrics: global accuracy, recall, precision, \(F_{1}\)-score, sensitivity, and specificity. In addition, a confusion matrix was used to check how well both models performed on the new data. The global metrics are defined as the ratio between the correctly predicted values for all classes to the total number of values. A confusion matrix is a tool that measures the performance evaluation of the classification model [23]. In addition, it contains information about the number of actual and predicted labels [24]. The performance of such systems is commonly evaluated using the data in the matrix. The entries in the confusion matrix have the following meaning in the context of this study:  
* _TP_ is the number of correct predictions that an instance is positive,
* _FP_ is the number of incorrect predictions that an instance positive,
* _FN_ is the number of incorrect predictions that an instance is negative, and
* _TN_ is the number of correct predictions that an instance is negative.  
A sensitivity test is used to identify positive COVID-19 cases correctly, and this is called the ratio of true positives that are correctly determined by the test. In contrast, the specificity test correctly identifies people with normal cases. It is called the proportion of true negatives, which is correctly determined by the test [25; 26].  
Equations (1)-(6) give the performance measures for the binary classification, while Equations (7)-(12) provide the multi-classification performance measures [27; 28].  
\[Acc_{b}=\frac{TP+TN}{TP+FP+TN+FN} \tag{1}\]  
\[Precision_{b}=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall_{b}=\frac{TP}{TP+FN} \tag{3}\]  
\[F_{1}-Score_{b}=2*\frac{Precision_{b}*Recall_{b}}{Precision_{b}+Recall_{b}} \tag{4}\]  
\[Sensitivity_{b}=\frac{TP}{TP+FN} \tag{5}\]  
\[Specificity_{b}=\frac{TN}{FP+TN} \tag{6}\]  
\[Acc_{m}=\frac{TP_{Nor}+TP_{COV}+TP_{pne}}{Totalsamples} \tag{7}\]  
\[Sens_{\cdot m}=\frac{TP_{COV}+TP_{pne}}{TP_{COV}+TP_{pne}+TN_{COV}+TN_{pne}} \tag{8}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Parameters** & **Setting** \\ \hline Image size & \(100\times 100\times 3\) \\ \hline Batch size & 8 \\ \hline Epoch & 100 \\ \hline Optimizer & SGD \\ \hline momentum & 0.9 \\ \hline Learning rate (LR) & 0.001 \\ \hline Loss & Binary = binary cross entropy \\ Multiple = Categorical cross entropy \\ \hline Validation split & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter setting details in our method.  
\[Spec_{m}=\frac{TP_{Nor}}{FP_{COV}+TP_{Nor}+FP_{Pne}} \tag{9}\]  
\[\text{Prec}_{m}=\frac{TP_{Cov}+TP_{Pne}}{TP_{Cov}+TP_{Pne}+FP_{Cov}+FP_{Pne}} \tag{10}\]  
\[\text{Recall}_{m}=\frac{TP_{Cov}+TP_{Pne}}{TP_{Cov}+TP_{Pne}+FP_{Cov}+FP_{Pne}} \tag{11}\]  
\[F_{1}-Score_{m}=2*\frac{Prec_{m}*Recall_{m}}{Prec_{m}+Recall_{m}} \tag{12}\]  
where \(Acc_{b}\) represents binary accuracy, and \(Acc_{m}\) represents multi-classification accuracy.
## 3 Theory
### Probabilistic Neural Network (PNN)  
When the PNN was created in the 1960s, computing power and memory were both limited. The PNN is a neural network for pattern classification that is built using classical probability theory. It is a direct development of prior work with Bayesian classifiers. As shown in Figure 1, the PNN is made up of three layers of neurons (input, radial basis layer, and competitive layer) that are connected by weights.  
The PNN is trained in two stages:  
1. Data are fed into the input layer, which is subsequently passed on to the radial basis layer. The distances between the input and weight vectors are calculated. Equation (2) [60] is used to generate the output values in the radial basis layer.  
\[\varphi(p)=exp\left(-\frac{[p-IW_{1,1}]^{2}}{\sigma_{j}^{2}}\right) \tag{2}\]  
where: \(\varphi(p)\) is the output of the radial basis layer;  
\(p\): is the input pattern vector;  
\(IW_{1,1}\): is the center vector of the radial basis layer;  
\(\sigma\): is the spread constant for the radial basis layer (smoothing parameter).  
2. The radial basis layer's output values are transferred to the competitive layer, which associates a specific class with the net output value, which is a vector of probabilities. Finally, as shown in Equation (3) [60], a competitive activation function is applied to the  
Figure 1: Probabilistic neural network [58].  
net output of the competitive layer to calculate the maximum value of these probabilities, yielding a value of 1 for this class and 0 for the other classes.  
\[o/P_{ANN}=f^{2}(LW_{2,1}\times\varphi(p)) \tag{3}\]  
where: \(f^{2}\) is the function of competitive activation;  
\(LW_{2,1}\) is the weight vector between radial basis and competitive layers.
## 2 Vent Holes of HPT Sealing Disk  
The HPT sealing disk is a crucial component of an aero-engine. It could prevent the high-temperature gas from entering the disk cavity and affecting the service life of the HPT disk. The main reason is that the typical structures on it, such as vent holes, regulate the cooling airflow in each chamber. The vent holes are located at the outlet of the pre-swirl nozzle, which is also a vital component of the pre-swirl system. Numerous studies have shown that the vent holes' radial position and total area could significantly affect the pre-swirl system's aerodynamic and heat transfer performance [28; 29]. The vent holes are obviously significant, yet the harsh working environment of high temperatures and high rotational speeds make them bear large and complex mechanical and thermal loads. The sudden change of the cross-section of the holes easily causes severe stress concentration. The areas around the vent holes are thus easy to fail and belong to the hazardous strength regions. It can be seen that the design of vent holes is a complex multi-disciplinary and multi-part collaborative task.  
An HPT sealing disk was initially designed with seventy-five circular vent holes, of which the total area is \(s_{t}\). This scheme fully meets the pre-swirl system's aerodynamic and heat transfer performance requirements yet violates the strength requirements. Figure 1 shows the von Mises stress distribution of the single-hole sector sub-model of the HPT sealing disk [21]. We can see that the severe stress concentration happens around the vent holes, and the maximum von Mises stress is much greater than the yield strength of the selected material.
# A Two-Step Framework to Recognize Emotion Using the Combinations of Adjacent Frequency Bands of EEG
## 4 Materials and Experiments
### Performances on DEAP Database  
In this section, the five-fold cross-verification prediction results are verified in arousal and valence on the DEAP database through the comparison of HCRC-CGEL with SVM, KNN, RF, and HCRC-SMV. Figures 5 and 6 show the prediction results of arousal and valence on 32 subjects, respectively. As shown in these two figures, the overall trend of each method is roughly the same in arousal and valence. Compared to SVM, KNN, and RF, HCRC-CGEL and HCRC-SMV have the highest prediction accuracy in all the subjects. In addition, the prediction accuracy of HCRC-CGEL in arousal and valence is higher than that of HCRC-SMV on most subjects. However, the prediction results of SVM are unstable. This may be the reason that the SVM does not find the optimal classification surface in the binary classification in some subjects.  
The average accuracies of arousal and valence on all subjects of each method are shown in Table 2. The prediction accuracies of HKRC-CGEL are 94.93% in arousal and 95.09% in valence, which are much higher than those of SVM, KNN, and RF. Compared with HKRC-SMV, the accuracy rates of arousal and valence are increased by 1.05% and 0.83%, respectively. This proves that in the DEAP database, our decision fusion method outperforms simple majority voting. In the prediction results of the compared method, RF performs better than KNN and SVM on almost every subject, and the mean accuracies of RF are higher than those of KNN and SVM on the arousal and valence of the DEAP dataset. Figure 7 shows the confusion matrices of HKRC-CGEL on the DEAP database. The accuracy of low arousal and valence is, respectively, 93.41% and 96.08%, and that of high arousal and valence is, respectively, 94.19% and 95.82%. In addition, the precision and recall rates of arousal are, respectively, 95.06% and 94.75%, and those of valence are, respectively, 94.65% and 95.33%. The standard deviations of HKRC-SMV and HKRC-CGEL are 3.33 and 3.00 on arousal, and 3.40 and 2.90 on valence, respectively, which is lower than those of other methods. This means that our framework has good results on the DEAP database.  
As shown in Table 3, our framework is compared with the SOTA methods of other researchers on the DEAP database. For fairness, only the emotion recognition results from the binary classification problem are considered in the comparison group. The results demonstrate that our framework outperforms the SOTA methods in terms of average prediction accuracy in the DEAP database, and that our decision fusion method outperforms  
Figure 5: Results of each method for arousal on DEAP database.  
Figure 6: Results of each method for valence on DEAP database.  
SMV. Consequently, the framework for using the combinations of the adjacent frequency bands can offer greater potential to obtain good results.
## 5 Results and Discussion
### Additional Results  
With the purpose of demonstrating the effectiveness and robustness of the GNDO approach in conjunction with the successive approximations power flow method, after 100 consecutive evaluations for the radial and meshed grid structures, the results in Table 9 show the statistical analysis for both simulation cases.  
The results in Table 9 show that:  
1. The difference between the minimum and maximum values reached in the radial configuration was about USD 8776.59, which implies that in the worst simulation case, the expected reductions with respect to the benchmark case would be 10.43%. When compared to the results shown in Table 5, this is better than both GAMS solutions. Even if the maximum result (worst result of the GNDO method) for the radial simulation case is a local solution, it has better characteristics than the local optimal solutions found with the MINLP BONMIN and COUENNE solvers in GAMS.
2. As for the meshed configuration, the difference between the maximum and minimum objective function values was USD 2397.22, i.e., the worst solution reached by the GNDO approach reduces the expected annual operating costs by about 7.69%. This result confirms that for the meshed configuration case, the worst solution of the GNDO approach is also better than both solutions found with the GAMS software and with the BONMIN and COUENNE solvers.
3. The mean values for the radial and meshed configurations are very close to the minimum values, i.e., most of the solutions are closer to each other in a closed ball with a radius equivalent to the standard deviation. These results confirm the effectiveness of the GNDO approach at solving the complex optimization problem involving the optimal location and size of D-STATCOMs in distribution grids via combinatorial optimization, with the main advantage that less than 43 s is required to reach a solution in both simulation cases.
## 2 Materials and Methods
### Measurement Equipment  
A laser-powered electric field measurement system (LSProbe 1.2 Field Probe Variant E [37]) was used in all measurements. The system has high accuracy (\(\pm\)0.6 dB, \(\pm\)1.0 dB, and \(\pm\)1.4 dB in the frequency ranges of 9-30 MHz, 30-1 GHz, and 1-8.2 GHz, respectively), high speed (up to 2 MS/s), and high dynamic range (100 dB). Moreover, the LSProbe 1.2 Field Probe has a complete set of calibration data (measurement uncertainties are 12% in the frequency range of 0.03 to 1 GHz, and 18% from 1 to 8 GHz). The laser-powered electric field measurement system consists of an isotropic tri-axial antenna (or \"probe\"), a measurement unit (CI-250), and a laptop. The electric field (E-field) probe records the E-field values using logarithmic power detectors and analog-to-digital converters and transmits the digital sample values to the CI-250, which was connected to the probe with a fiber optic cable. The laptop was connected to the CI-250 via a USB cable and was used as a host computer for the LSProbe TCP Server and for storing the measured data. The LSProbe TCP Server implements data post-processing and communication and also applies calibration data. The LSProbe Graphical User Interface software was used to manage the probe settings, data acquisition, and storage. The measured data (field strength of the \(x\)-, \(y\)- and \(z\)-axis and the field strength magnitude) were recorded in a log file every 500 ms. During the measurements, the GPS coordinates of each point were recorded by a GPS logger. Table 1 presents the settings of the LSProbe 1.2 Field Probe Variant E used during all measurements.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & \\ \hline Measurement mode & 0 \\ Frequency range (GHz) & 0.03–8.2 \\ Sampling rate (kS/s) & 500 \\ Sample timing & continuous \\ Sensitivity (mV/m) & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Settings of LSProbe 1.2 Field Probe Variant E.  
Figure 1: European roads selected to conduct measurements of the EMFs.
## 6 Experimental Results and Discussion  
The presented results were obtained using a single NVIDIA RTX 3090, with 24 GB of VRAM used for all the training of the presented method, with a main focus on the ability to reproduce similar results without the necessity of resorting to higher computational power. The presented method was also adapted and reproduced on an NVIDIA RTX 2070 Super with only 8 GB of VRAM, achieving similar results, thus verifying the versatility of this method for further adaptation and applicability. For the baseline training stage of the presented method, we evaluated the OzFish dataset [12], bare and accompanied by the _negative sample_, i.e., images not containing any targets, therefore, without annotations. The results can be observed in Table 1, where the mAP\({}_{50}\), i.e., the mean Average Precision (mAP) at Intersection over Union (IoU) of 50%, increased by 4.47% from 68.46%, and 9.99% from 25.94%, for mAP\({}_{75}\), with the addition of a _negative sample_. The average IoU also increased by 7.68% from 59.28%, and interestingly, the recall, which is the metric responsible for measuring the number of targets identified in an image, only increased by 1% from 65%, while the precision, which evaluates if the detection is a true positive, increased by 7% from 79%. Therefore, the addition of negative samples, even from unrelated images, and without representing the dataset bare background, considerably improves the model accuracy, especially at a higher IoU, although it did not significantly improve the recall, i.e., the metric used for evaluating the number of positive detections for available objects in the image. Measuring the model evolution during the self-supervised pseudo-labeling loop is a convoluted challenge, considering that the main objective for the proposed method is the autonomous generation of an object detection model based on unlabeled footage, from and for the local fish population from unlabeled footage: resorting to carefully annotated images for algorithmic guidance during the self-learning cycle would corrupt the purpose. Therefore, the results of the self-learning loop can only be genuinely calculated after the end of the loop cycle. However, it is possible to observe the evolution of the autonomous annotations across the cycle evolution in terms of incidence, overlap, increased accuracy, and the increment or stall of the pseudo-labeled annotations for each frame, as can be observed in Figure 5 (from left to right shows the progression during the self-supervised pseudo-labeling loop cycle). Moreover, it is important to notice the implementation of tracking techniques in order to increase the overall accuracy for each prediction, which interferes with an instance object detection analysis. Nevertheless, even though the collected annotations also presented a noisy dataset, some samples can be observed in Figure 6; the obtained metric results sustain the viability of the presented method for local model adaptation.
## 2 Materials and Methods
All first and second period atoms were described with the 6-311G(d,p) basis set, while for sulfur and selenium, [PERSON]'s correlation consistent cc-pVTZ basis set was used. All structures were optimized in the gas phase and further solvation correction was taken into account as a single point, employing the SMD solvation model at SMD-B3 LYP-D3(BJ)/6-311G(d,p), cc-pVTZ//B3 LYP-D3(BJ)/6-311G(d,p), cc-pVTZ level of theory [47]. Diethyl ether was used to mimic the low dielectric constant of the enzymatic environment, as described in the literature [48]. Thermodynamic corrections at 298.15 K and 1 atm were computed by means of a standard statistical mechanics relationship based on electronic energies and gas phase frequency calculations, as implemented in the Gaussian software. All energies described in the main text are Gibbs free energies corrected with the contribution of solvation. The atoms from the backbone were kept constrained during all geometry optimizations. Frequency calculations were performed to assess the nature of the optimized geometries: all transition states have one imaginary frequency related to the normal mode connecting reactants to products; all minima have no imaginary frequencies. For EbSe, starting from TS1 and TS2, an intrinsic reaction coordinate (IRC) calculation was performed to verify that the correct transition state was located [49].
# Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning
## 2 Related Work
### Coexistence in Uncoordinated LTE and WiFi Networks
This non-cooperative version is designed for better practicability, and it is demonstrated that the proposed learning method can significantly improve total throughput performance while maintaining fairness. Even though there is no need for a cooperative channel that is used for information exchange between the technologies, this solution requires modifications in both LTE eNB and WiFi AP nodes.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **LCLS** & **SACLA** & **PAL-XFEL** & **SwissFEL** & **European XFEL** \\ \hline Technology & Warm & Warm & Warm & Warm & Super-conducting \\ Accelerator frequency & 2.856 GHz & 5.7 GHz & 2.856 GHz & 6 GHz & 1.3 GHz \\ Maximum energy & 15 GeV & 8 GeV & 10 GeV & 5.8 GeV & 17.5 GeV \\ Bunch charge & 0.2 nC & 0.2 nC & 0.2 nC & 0.2 nC & 1.0 nC \\ Repetition rate & 120 Hz & 60 Hz & 60 Hz & 100 Hz & 27,000 bunches/s 1  \\ Maximum power 2 & 300 W & 100 W & 100 W & 100 W & 500 kW \\ User operation & 2009 & 2012 & 2017 & 20182 & 2017 \\ Reference & [15] & [7] & [8] & [9] & [14; 15] \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of accelerator parameters of hard X-ray FEL facilities.
## 2 Materials and Methods
### Case Study: Firefighters Under Real Conditions  
ECG signals were gathered from tests conducted in real-life situations involving firefighters from various corporations in Portugal, demonstrating the effectiveness of this algorithm. The signals were recorded using a sports vest equipped with integrated e-textile dry electrodes linked to a MoveSense device to measure the ECG signal. Additionally, survey tools were administered to assess the challenges and conditions of daily activities, as well as the physical and mental well-being of the firefighters. These data were then compared under dynamic conditions, such as days with varying levels of activity and instances of mental or physical fatigue.  
The ECG signals were recorded continuously during the firefighters' 8 h shifts, with each participant wearing the vest equipped with the MoveSense device ([[https://www.movesense.com/movesense-md-wearable-ecg-monitor/](https://www.movesense.com/movesense-md-wearable-ecg-monitor/)]([https://www.movesense.com/movesense-md-wearable-ecg-monitor/](https://www.movesense.com/movesense-md-wearable-ecg-monitor/)) (accessed on 20 November 2024)). At the end of each shift, participants reported their perceived activity levels and physical condition by filling out a questionnaire.  
The ML quality assessment algorithm was applied to the ECG data, classifying each 10 s segment as either 0 (unacceptable) or 1 (acceptable). The percentage of 10 s segments considered \"acceptable\" was then calculated for each 8 h shift.
# Hybrid Positioning for Smart Spaces: Proposal and Evaluation
## 4 Hybrid Positioning Approach
### Fingerprinting Algorithm  
The fingerprinting algorithm performs a pattern matching search comparing previously collected fingerprints (in a prior training stage) with current measurements. For the moment, a state-of-the-art deterministic approach [2] has been used for the matching method, using the Euclidean distance, but instead of a single fingerprint output, a set of the most similar fingerprints are considered, ordered by distance. This search is bounded within a configurable threshold radio (default value is 20 m) around the previous location, in order to offer significant results and save computational cost and energy consumption. The searching process is also carried out over fingerprints of the same floor when the user is indoors. As has been said, with the aim of supporting the change between floors, handover areas are modelled with flagged fingerprints. When the user is detected on these fingerprints, a vertical cylinder-based search is performed using the previously configured radio. Once the list of candidate fingerprints is obtained, a weighted position is calculated on the basis of its Euclidean closeness to the sensed radio environment. Finally, an estimation of the position accuracy is calculated using the \"Best Candidate Set\" method proposed in [8]. Taking into account only a set of closest fingerprints, this accuracy estimation is calculated as the mean of the euclidean distances between the closest fingerprint and the rest.