esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Online and Offline BCI Performance
Starting from the fifth visit, the classification algorithm began using a filter that delimited the choice to ’Yes’ or ’No’ (binary selection), instead of the seven words. For the control group, the classification results for the online sessions are presented in Table 4. The AVNsf condition yielded the highest average online classification of \(98.0\pm 4.5\%\) with Nrep averaging 2.6 repetitions, while the worst-performing condition was Vsf with \(88.0\pm 16.4\%\). Therefore, these results confirm the expected impact of the different stimulation features. As referred above, there was a notable disparity in classification performance between calibration and online operations across most patients' sessions. However, for the control group, online classification performance aligned closely with calibration, suggesting that the proposed classification approach effectively handles the relatively stable data of the able-bodied group, but struggles in generalizing to the highly variable data of the patient. Further research into more effective signal processing and classification approaches identifying feature invariant spaces is required. Some progress has been made in this domain by exploring the use of Riemannian geometry [28], although additional efforts are needed to overcome this challenge. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Participant**} & \multicolumn{2}{c}{**Vsf**} & \multicolumn{2}{c}{**AVsf**} & \multicolumn{2}{c}{**AVNsf**} \\ \cline{2-7}  & **Accuracy (\%)** & **Nrep** & **Accuracy (\%)** & **Nrep** & **Accuracy (\%)** & **Nrep** \\ \hline
2 & 100 & 1 & 100 & 1 & 100 & 1 \\
3 & 70 & 3 & 80 & 3 & 100 & 3 \\
4 & 70 & 5 & 80 & 4 & 100 & 4 \\
5 & 100 & 4 & 100 & 4 & 90 & 4 \\ \hline Mean \(\pm\) sd & 88.0 \(\pm\) 16.4 & 2.8 \(\pm\) 1.8 & 92.0 \(\pm\) 11.0 & 2.6 \(\pm\) 1.5 & 98.0 \(\pm\) 4.5 & 2.6 \(\pm\) 1.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Control group online classification performance. Figure 8: Average classification results obtained for the different interfaces, including both calibration and online sessions. High offline classification results from the calibration sessions (obtained with five-fold cross-validation) did not translate into high classification results in the online sessions. All results were obtained for nine repetitions.
# Online Coal Identification Based on One-Dimensional Convolution and Its Industrial Applications
## 4 Methodology
### Performance Measurement Criteria  
The loss function is used to assess the discrepancy between the predicted and actual values during model training. In classification tasks, various loss functions, including entropy, cross-entropy, K-L divergence, dice loss [32], and focal loss [33], are typically utilized. In this experiment, cross-entropy is utilized to evaluate the prediction accuracy of the model. The computation of cross-entropy is performed as follows:  
\[L=-\frac{1}{N}\sum_{i}\sum_{j=1}^{M}y_{ij}\log(p_{ij}) \tag{5}\]  
where \(M\) represents the total number of categories, which takes the value of either 0 or 1, based on whether it corresponds to the true category, and \(p_{ij}\) represents the probability that the current sample \(i\) belongs to category \(j\).
## 3 Results
### Single Channel Operation  
The basic function of the generator is the single channel operation, where a load is connected between terminals, HV+ and HV\(-\), of one channel. In Figure 7, the pulse output voltage and current for operation with channel 0, considering a resistive load, connect between HV+ and HV\(-\), is shown. In Figure 7a, a single pulse of, almost, 6 kV and 60 A with 200 ns is applied to a 100 \(\Omega\) load. The pulse has, about, 30 ns rise and fall times. The pulse was generated by the command at the computer user interface shown in Table 2, where the Mask = 510 means that all the stages, in the selected MM, are on during the pulse, applying the full voltage, i.e., 8U\({}_{\text{dc}}\), being U\({}_{\text{dc}}\) = 750 V. The 5 ns delay in the current waveform, shown in Figure 7a, relative to the voltage waveform, is due to a longer cable used on this situation.  
Figure 7: Generator single pulse operation: (**a**) 200 ns on 100 \(\Omega\): (blue) output voltage 1 kV/div; (cyan) output current 10 A/div; 40 ns/div; **(b)** 100 ns pulse on 100 \(\Omega\): (blue) output voltage 1 kV/div; (cyan) output current 20 A/div; 100 ns/div; (**c)** 100 μs pulse on 1 M\(\Omega\): output voltage 1 kV/cm; 20 μs/div.  
Considering now U\({}_{\rm dc}\) = 625 V, Figure 7b shows a single pulse of 100 ns on 100 \(\Omega\) and Figure 7c shows a single pulse of 100 \(\upmu\)s on 1 M\(\Omega\) load, command lines on Table 2.  
In addition, in simple pulse operation, Channel 0, Figure 8 shows a burst sequence of pulses with various amplitudes, time between pulses and pulse widths, as detailed in Table 3, with an input voltage from the CCPS of U\({}_{\rm dc}\) = 750 V.  
Figure 8 shows, also, a small voltage droop during the pulse period. This voltage droop depends on the ratio of pulse energy to stored energy in MM, for a given voltage. It is worth noting that the CCPS is always charging the MMs, even during burst. Nevertheless, the 100 W available are for the four MMs.  
In relation to the Mask values, shown in Table 3, the user can select any stage in a MM for on-off operation, so the values of the Mask can change for equal number of stages on-off.
# A QoS Classifier Based on Machine Learning for Next-Generation Optical Communication
## 2 Dataset Validation and Preprocessing
### Z-Score Normalization  
Due of the high variance and outliers in the data, ML models may perform poorly and be complicated [12]. Hence, every data point \(D_{i}^{j}\) should be subjected to data normalization and can be denoted as [12]:  
\[D_{i}^{j}=\frac{D_{i}^{j}-\overline{D^{j}}}{\sigma_{D^{j}}} \tag{3}\]  
where \(\overline{D^{j}}\) and \(\sigma_{D^{j}}\) are the average and standard deviation of each feature \(j\).
## 2 Materials and Methods  
### Data Simulation  
In order to model the ERTPL, we built a simulated dataset by integrating the Soil Canopy Observation, Photochemistry and Energy fluxes v1.7 (SCOPE v1.7) [28] and the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) [29] models. The SCOPE model was used for the simulation of the canopy reflectance and SIF spectra, while the MODTRAN 5 model was used for the simulation of the atmospheric radiation transfer parameters. Four different levels of chlorophyll content and leaf area index (LAI), and five different typical leaf inclination distribution conditions were used in the SCOPE model to represent different (80 different simulated samples in total) vegetation conditions. For the O\({}_{2}\)-A band we studied in this paper, the absorption by oxygen is the dominant characteristics of atmospheric radiation transfer, and is mainly influenced by the observation geometry. So the atmospheric condition (including aerosol model, aerosol optical depth, water vapor and ozone column, etc.) were set as fixed values. The main parameters used in the SCOPE and MODTRAN 5 models are listed in Table 1. All other parameters were set to their default values. The spectral resolution (full width at half maximum) of the simulated spectra was 0.3 nm and the spectral sampling interval was 0.15 nm.  
Figure 1: Illustration of the bi-hemispherical and hemispherical-conical configurations of in situ spectral measurements.  
Assuming the surface is Lambertian, with the simulation results from SCOPE and MODTRAN 5, the upwelling radiance at the height of the observation platform can be calculated as [30]:  
\[L_{H}(\theta,\varphi)=\frac{L_{0}(\theta,\varphi)\cdot T_{\uparrow}(\theta)}{1- \rho(\theta,\varphi)S}+L_{path}(\theta,\varphi) \tag{1}\]  
where \(L_{H}\) is the upwelling radiance arriving at the observation platform at a height \(H\) above the target surface; \(L_{0}\) is the radiance at the top of the canopy, which contains a contribution from both reflected radiance and emitted SIF; \(T_{\uparrow}\) is the upwelling transmittance of the atmosphere; \(\rho\) is the surface reflectance; \(S\) is the atmospheric spherical albedo accounting for multiple scattering between atmosphere and surface; and \(L_{path}\) is the atmospheric path radiance. For tower-based observations, the platform height is relatively low. The atmospheric scattering effect was, therefore, neglected in this study and Equation (1) can be simplified as:  
\[L_{H}(\theta,\varphi)=L_{0}(\theta,\varphi)\cdot T_{\uparrow}(\theta) \tag{2}\]  
Using Equation (2), \(L_{H}\) for different values of the VZA and RAA can be calculated using the simulations made by SCOPE and MODTRAN 5.
## 3 Data Set
### Precipitation Data  
Climatological data such as precipitation and temperature control differences in the Earth's vegetation cover, affect growth rate, plant reproduction, and frost damage. Monthly precipitation maps were derived at 2 km spatial resolution. The accurate estimation of the spatial distribution of precipitation requires a very dense network of measuring gaugement. The climatic data used in the study was obtained from the AEMet (Agencia Estatal de Meteorologia) and corresponds to between 4000 and 7000 (depending on the period) recording stations distributed around Spain. Geostatistical approaches [63, 64], such as ordinary kriging, were chosen for interpolation since they provide a measure of prediction error and allow observations to be complemented by secondary attributes that are more densely sampled. Monthly and annual averages for the period analyzed were computed from the precipitation monthly maps.
## 4 Implementation and Discussion of Results
### LSTM Deep Learning (Setup and Training)
#### 4.2.2 Building, Training, and Testing the LSTM Model  
The LSTM method is used to forecast the number of new confirmed, recovered, and mortality cases in the test dataset by fitting the training dataset. The Adam optimizer is an adaptive optimization approach for reducing MSE (the loss function). It optimizes the loss function with minimal hyperparameter modification, which is a key feature that encourages its employment. The forecasting findings of the LSTM model for future COVID-19 instances are described in this section. The dataset is divided between training and testing, with anticipated and actual values presented for the test dataset. With the confirmed, dead, and recovered cases forecast by using the LSTM model, good forecasting efficiency and precision are attained. We summarize the different values of the error metrics in Table 1. From Table 1, the MAPE's values of the confirmed, dead, and recovered cases for the LSTM model were found to be 11.65%, 16.9%, and 32.7%, respectively. By further looking at the six error measures of the LSTM model, it can be observed that LSTMhas an excellent performance in predicting confirmed and mortality cases. For example, the LSTM's nRMSE and \(R^{2}\) values for predicting the number of confirmed cases are 4.64% and 0.96, respectively. For the mortality cases, the values of the nRMSE and \(R^{2}\) are 6.33% and 0.94, respectively. However, its performance ability decreased markedly when it predicted the number of recovered cases. For example, the LSTM's nRMSE and \(R^{2}\) values for predicting the number of recovered cases are 13.6% and 0.313, respectively. The scatter plots that compare the actual and the predicted values of the three cases on the testing sets can be seen in Figures 2-4.
# PRG: A Distance Measurement Algorithm Based on Phase Regeneration
## Principle of PRG
### Range Finding Signal  
The range finding signal of PRG algorithm is single-sideband modulation. The modulation block diagram is shown in Figure 2, first the double sideband signal is obtained from the analog baseband signal \(m(t)\) with mean 0 and sinusoidal carrier signal \(c(t)\) multiplication and then the amplitude modulated signal by low pass filter \(H(\omega)\) for filter, get lower sideband modulation signal \(S_{SSBi}(t)\). The analog baseband signal in PRG algorithm is tone signal with a frequency of \(f_{d}\), and denoted by:  
\[m(t)=2A\cdot\sin 2\pi f_{d}\]  
where \(2A\) represents the amplitude of analog baseband signal. We can get the signal \(S_{i}(t)\) as:  
\[S_{i}(t)=2A\cdot\sin 2\pi f_{d}t\cdot\sin 2\pi f_{i}t=A\cdot[\cos 2\pi(f_{i}-f_{ d})t-\cos 2\pi(f_{i}+f_{d})t]\]  
The range finding signal \(s_{SSBi}(t)\) is obtained by a low-pass filtering of \(S_{i}(t)\) with cutoff frequency of \(f_{i}\) and can be expressed as:  
\[s_{SSBi}(t)=A\cdot\cos 2\pi(f_{i}-f_{d})t\]  
The modulation signal can be got with frequency of \(f_{1}-f_{d},f_{2}-f_{d},\cdots,f_{n}-f_{d}\) and different carrier frequencies are used to identify different master and slave node to apply multi-master, multi-slave case. To ensure that each modulation signal does not interfere with each other, we need to make sure that adjacent two frequency points have a wide enough frequency protection interval so that the RF front end can separate signals from each frequency point.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 4 Proposed Approach
### Cluster Maintenance
#### 4.7.1 Node Exiting Operation  
To maintain the clusters effectively, the CHs broadcast their HELLO messages to their CMs periodically with a period \(T\), and a CM replies with an ACK (acknowledgement) message to its CH, immediately after receiving the HELLO message. If the CH cannot receive an ACK from the CM for \(q\) periods, the CM is considered to not be in the transmission range and is depleted from the CM list. If a CH leaves the cluster, a CH reselection process is triggered.
# Method for Handling Massive IoT Traffic in 5G Networks
## 4 Relay Node Design and Scheduling Scheme  
The RN has been introduced by 3 GPP in Release 10 documentation [29]. RN is designed for extending the cell coverage area. The RN appears as a low-power BS to the mobile device. The RN is  
Figure 1: OFDM vs. f-OFDM subband sizes.  
wirelessly linked to devices and the BS. The cost of deploying RN is less than deploying femto- and picocells, as no additional infrastructure is needed for connecting RN to the BS. RN improves coverage when placed at positions with weak channel conditions or dead spots in the coverage area.  
In this work, a wireless, fixed, inband, layer 3 type RN is proposed as the data aggregation and multiplexing node. This type of RN can be designed to aggregate and multiplex data packets from a large number of IoT devices. The proposed inband RN operates on the same radio frequency band that is specified to the BS by the network service provider. For accomplishing effective incorporation of IoT traffic in the 5G system, functionality of RN is modified slightly in this work.  
Additionally, an RN Medium Access Control (MAC) packet scheduler [30] is used in this work to allocate frequency resources to IoT devices for RN access. The scheduler is capable of allocating resources to devices with various Quality-of-Service (QoS) traffic classes. In this work, the scheduling scheme used is blind equal throughput. This scheduler works in addition to the MAC layer scheduler placed at the BS for allocating resources to mobile devices and RNs connected to BS. It is also assumed in this work that all the IoT devices are having similar QoS requirements, therefore service differentiation is not performed. The BS scheduler in [31] is also employed in this work at the BS.  
A multiplexing algorithm at RN is developed for aggregation of IoT traffic from different devices where packets are multiplexed before transmission to the BS. The multiplexing algorithm works collaboratively with the scheduler at RN to ensure that the size of multiplexed data is in accordance with the capacity of available radio resources (Figure 2).  
Thus, rather than demanding frequency resources from the BS for distinct IoT devices, resources are demanded by the RN for a bunch of IoT nodes. The IoT packets are aggregated and multiplexed at the RN. This multiplexing is achieved by, firstly, aggregating packets from several IoT devices and then multiplexing them into one large packet. On the BS side, a resource demand for the multiplexed large packet would be considered as one distinct radio resource demand. Therefore, sharing of radio resources among several devices would result in the enhancement of spectral efficiency. The capacity of the network would also increase, resulting in minimization of chances of network failure.  
Figure 2: RN-based data multiplexing scheme.
## Materials and Methods
### Analytical Methods  
Laser fluence, applied on PET during underwater treatment, was measured by FieldMaxII-TOP Laser Power Meter (Coherent, Inc., California, USA), so that it was placed just behind the cuvette-filled water without the polymer. Owing to only negligible absorption of purified water at 248 nm (absorption coefficient \(\epsilon_{\mathrm{H2O}}\) < 0.04 [11]), measured laser fluence in that way corresponds well to that applied to polymer during the experiments.  
The atomic concentrations of elements, carbon C(1s) and oxygen O(1s), were studied by Angle Resolved X-ray Photoelectron Spectroscopy (ARXPS) on Omicron Nanotechnology ESCAProbeP spectrometer, Taunusstein-Neuhof, Germany. The X-ray source was monochromated at 1486.7 eV. The evaluation of spectra was performed by CasaXPS software, version 2.3.19. The samples were studied under the electron take-off angles of 0 and 81\({}^{\circ}\).  
Surface morphology and roughness were examined by atomic force microscope (AFM) VEECO CP II device in a tapping mode. A Veeco oxide-sharpened P-doped silicon probe RTESPA-CP was used. Surface roughness, characterized by the mean roughness value (\(R_{\mathrm{a}}\)), represents the arithmetic average of the deviation from the center plane of a sample. Variation of \(R_{\mathrm{a}}\) parameter did not exceed 5%. All samples were measured at 7 different points within modified area in the arrangement 2, 3, 2 along the longer side of the modified region (see Figure 2). In all cases the measurement provided similar surface morphologies within one sample, pointing to homogeneous distribution of induced nanostructure by laser treatment. Presented AFM images were carefully chosen to represent typical morphologies of laser processed samples under the specific modification conditions.  
Inductively coupled plasma with mass spectroscopy detector (ICP-MS) was used to determine total carbon content (TCC) in water from the cuvette after laser treatment of polymer. Changes in the TCC correspond to the release of low molecular weight polymer ablation product to water environment due to laser modification. The trace element analysis of TCC was conducted by using Agilent 8800 triple-quadrupole spectrometer (Agilent Technologies, California, USA) connected to an auto-sampler according to measurement protocol taken from [12]. Sample nebulization was performed using a MicroMist device equipped with a tempered cell. To minimize the interference of an analyte with multiatomic adducts, we used a collision cell (He collision gas) operating in a high-energy mode with double quadrupole filtration.
## 3 Monitoring of C and Water Cycling in Terrestrial Ecosystems
### Stable C Isotope Measurements  
The information on the biological and physical processes that exchange CO\({}_{2}\) between terrestrial ecosystems and the atmosphere is recorded by the signals of \({}^{13}\)C /\({}^{12}\)C ratio in the atmosphere CO\({}_{2}\). The stable isotope ratio of CO\({}_{2}\) (\(\delta^{13}\)C) in the atmosphere contains unique information to study the overall balance of surface CO\({}_{2}\) fluxes [92,93]. As reviewed by [PERSON] _et al._[94], C isotopes can be helpful in investigations of the following four aspects at ecosystem and local scales: (i) plant water-use efficiency and the response of plants to changes in precipitation and relative humidity [95-100], (ii) variation in light distribution and stand structure [101-104], (iii) recycling of respired CO\({}_{2}\)[105-109], and (iv) determining the relative contributions of photosynthesis and respiration to the total net ecosystem exchange [110-114].  
Multiple efforts to measure stable C isotopes at both flux towers and flask stations around the world have been achieved. Several new techniques (e.g., automated measurement systems, tunable diode laser (TDL) spectrometer and pulsed quantum cascade laser spectrometer) have also been applied to this investigation [115-117], isotope measurements, however, are still lacking considering the land surface diversity/heterogeneity. This shortage of long-term measurements and of sampling frequency still limits isotopic studies and applications to various spatial/temporal scales.  
Efforts to expand CarbonTracker to assimilate observations of C isotopes (\({}^{13}\)CO\({}_{2}\), \({}^{14}\)CO\({}_{2}\)) and other observation types (eddy-flux measurements, satellite radiances) are underway. Such observations could facilitate attribution of carbon fluxes to specific processes such as fossil fuel burning, biomass burning, or agricultural food and biofuel production [78].
# Deep Learning Logging Sedimentary Microfacies via Improved U-Net
## 2 Materials and Methods
### Overall Implementation Process
#### 2.3.3 Attribute Extension  
Extracting some salient features of logging curves based on physical drive helps to reduce the complexity of data feature extraction and achieve the purpose of improving classification accuracy. The log curve is the geophysical response of the sedimentary body, and the abrupt change point of its shape is the key to dividing different sedimentary microfacies. Feature augmentation combined with domain knowledge can further improve machine learning capabilities [24]. Based on this, this study defined three attributes of the logging curve slope value, azimuth value and variance value, and used them as characteristic curves extracted via physical drives to be involved in the identification of subsequent sedimentary microfacies. When selecting attributes, it is necessary to select attributes that can describe the morphological characteristics of curves such as lithology curves and resistivity curves in each layer section [25]. These three attributes are used to enhance the morphological characteristics of the curve, enhance the \"directivity\" of the logging data and highlight the sudden change points of the logging curve, thus reducing the complexity of data feature extraction.  
1. Slope D refers to the difference between the values of two adjacent logging samples, indicating the rate of change of the curve, which is conducive to highlighting the division point between different sedimentary microfacies and enhancing the morphological characteristics of the curve. This attribute essentially reflects the rate of depositional cycles and expands the characteristics of depositional rhythms. The simplest difference equation can find the change between two points of the curve. By calculating the difference value corresponding to each measurement point, a new slope value property curve can be extended.
2. Azimuth G: Well logging curves have time-series characteristics, and their overall directionality is the key to identifying sedimentary microfacies. A square position is assigned to each measurement point, as a supplement to the attribute \"slope\", which reflects the characteristics of the data value becoming larger or smaller, and can enhance the attribute of the \"directivity\" of the logging data. The log curve composed of \(n\) values \(X=(x_{1},x_{2},\ldots,x_{n})\) and the corresponding azimuth value curve \(G=(g_{1},g_{2},\ldots,g_{n})\) can be obtained with the following formula: \[g_{i}=\left\{\begin{array}{cl}0.1,&if\ x_{i+1}-x_{i}>0.01;\\ -0.1,&if\ x_{i+1}-x_{i}<-0.01;\\ 0,&Otherwise;\end{array}\right.\] (2) \[g_{n}=g_{n-1}.\] (3) where \(g_{i}\) represents the azimuth value corresponding to the value of log data.
3. Variance A can reflect the degree of dispersion of each measurement point relative to the overall data. This can highlight the sudden change point of the well log curve and better realize its stratification. It can be obtained with a simple variance equation.