esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Based on the literature, we expected that EEG signals recorded from controls and adults with MDD would differ more strongly during exposure to emotionally positive material than at rest. Therefore, we collected EEG data while healthy controls and adults with MDD attempted to maximize their emotional responses to positive images and during the resting state. Given prior work linking changes in relative power (i.e., the power difference between a pair of electrodes) to shifts in emotional experience [20], we focused the analysis on relative power features from all possible (a) regional inter-hemispheric, (b) cross-regional inter-hemispheric, and (c) intra-hemisphericelectrode pairs. We then compared leave-one-participant-out cross-validation (LOPO-CV) classification performance based on EEG relative power features and then determined the best relative power feature subsets for the two states considered separately. In addition to feature extraction, MDD detector design is also critical to classification performance. Various machine-learning classifiers have been employed in different MDD studies, including the very simple _k_-NN, LDA, and more sophisticated classifiers like Naive Bayes (NB), logistic regression (LR), and SVM. Two studies have compared these classifiers for detection of MDD based on EEG signals [10; 11]. A comparison of _k_-NN, LDA, and LR [10] showed that LR achieved the best classification accuracy. More recently, [PERSON] et al. [11] reported that SVM outperformed both LR and NB in MDD-control classification. Although these results indicate that SVM is the best classifier for EEG-based MDD detection, the classification performance of SVM could still be improved. One strategy for doing so is based on conformal transformation of a kernel proposed by [PERSON] and [PERSON] [21]. The technique of conformally transforming a kernel enlarges the spatial resolution around the SVMs, separating hyperplane in a kernel-induced feature space, thus improving the generalization ability of SVM. This technique has recently been applied to solve problems in other domains, including EEG-based emotion recognition [22]. In the present study, we introduced this variant of SVM, the conformal kernel SVM (CK-SVM), as the MDD detector and compared it with two commonly-used MDD detectors (LDA and SVM) and a variant of LDA classifier called quadratic discriminant analysis (QDA) [23], which has not yet been tested in previous MDD studies. To preview the results, CK-SVM emerged as superior to the other classifiers for EEG-based MDD detection.
## 2 Materials and Methods
### EONND: Energy Operator Detector with Neural Network Architecture
#### 2.3.3 Fully Connected Layer  
The fully connected layer in a typical CNN is actually a deep neural network (DNN). DNN updates the linear coefficients \(w\) and bias \(b\) of each neuron using a backpropagation algorithm through the training-set data. The training process of neural networks not only tests the ability of engineers to tune the parameters but also requires a large amount of training data with labels. In this paper, instead of the fully connected layer, a sampling layer and a sigmoid layer were used to classify the signal point by point at the sample-level. The sampling layer actually normalizes the output of the pooling layer:  
\[y_{i}=\frac{x_{i}-\min_{(i-2k)<i}(x_{i})}{\max_{(i-2k)<i}(x_{i})-\min_{(i-2k)<i }(x_{i})}, \tag{9}\]  
where \(i\) denotes the current sampling point, and the extreme values are obtained on signal segments in the range \([i-2k,i]\). The sigmoid layer, which is connected to the sampling  
Figure 4: Pooling functions.  
layer, is responsible for classifying the output of the sampling layer. The sigmoid activation function is given in Equation (10):  
\[f(x)=\frac{1}{1+e^{-x}}. \tag{10}\]  
The sigmoid activation function requires a pre-set threshold T, and, when the output of the neuron exceeds T, the output value is fixed to 1 by a hard limiter, indicating that the muscle is activated, while the output in other cases is 0, indicating that the muscle is not activated.  
The complete framework of EONND is described above. Using the raw EMG signal as input, EONND can return a binary output, 0 or 1, for each sampling point. However, after experimental testing, the direct output classification results contained a large number of false alarms. A post-processing step was applied to the output of EONND to reject the erroneous transitions due to the stochastic nature of the EMG signal [20]. Since the shortest interval between muscle contractions is generally considered to be \(>\) 30 ms [11], all the muscle activations lasting less than 30 ms were discarded [34].
# Experimental Study on a Granular Material-Filled Lining in a High Ground-Stress Soft-Rock Tunnel
## 4 Study of the Mechanical Properties of Different Filling Materials
### Regularity Analysis of the Strain of Granular Filling Materials
#### 4.1.2 Analysis of the Compressive Strain Mechanism  
(1) Compressive strain of 8 mm and 4 mm soil particles  
Soil particles have higher compressive strain, mainly due to the larger particle size, the greater inter-particle voids and pores of the particles, and weaker pressure-resistant properties. In the early stage of loading, the compressive strain rate was larger due to large inter-particle voids, a large particle-void ratio (as shown in Figure 7), and particle dislocation (as shown in Figure 8). In the later stages, the inter-particle voids were filled, and crushing and recombination were gradually consolidated, and the strain rate was reduced until the strain limit was reached.  
As can be seen in Figure 9, the crushing effect of the upper layer was more significant than that of the lower layer when soil particles were broken. The reason is that the upper layers absorbed deformation energy and destroyed it during compression, and passed it down layer by layer. The particle size, inter-particle voids and particle pores of 8 mm soil are larger than those of 4 mm soil, so its compressive strain was also larger.  
(2) Compressive strain of ceramics particles  
Ceramsite particles have high porosity due to their internal honeycomb structure, which leads to greater compressive strain (see Figure 6). During the early stages of the compression process, due to the mutual extrusion of loose ceramics under load, interparticle voids and particle pores decreased. This resulted in a higher strain rate. In the later stages, due to the continuous extrusion of particles, the particles were broken, flaky particles filled the voids, and the compressive strain was reduced.  
(3) Compressive strain of coarse sand and fine sand  
As compared with other particles, sand has a higher density, smaller porosity, and a lower coefficient of crushing, resulting in a smaller compressive strain (as shown in  
Figure 8: Particle dislocation before and after compression of soil particles.  
Figure 9: Soil particle breakage in the compression process.  
Figure 6). Sand compression is primarily due to the compression of pores between the particles, and the particles themselves do not deform due to compression. The porosity of coarse sand is significantly higher than that of fine sand, so the compressive strain of coarse sand is greater than that of fine sand.  
In summary, it can be seen that the compressive strain of 8 mm soil was the largest, and its ability to yield to deformation as a granular material-filled lining was the best, which can ensure that the surrounding rock allows its plastic zone to develop under a certain supporting force, release the surrounding rock pressure to the greatest extent, and reduce the force on the lining.
## 4 Mode Scrambling for Few-Mode Fibers
### Deterministic Mode Scrambling
(a) 6-mode fiber; (b) 10-mode fiber. The capacity gain brought by using deterministic MS to a strategy where no scrambler is used is 7.8 bit/s for the 6-mode fiber and 5.2 bit/s for the 10-mode fiber.
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 2 Methodology  
### Integrated Interference Model  
In the practical scenarios of aviation operations, deploying protection requires an integrated modeling approach for all factors. The interference power between an interfering transmitter (\(Tx\)) and a victim receiver (\(Rx\)) is described as Equation (1) [25; 26; 27].  
\[I_{n}=P_{Tx}+G_{Tx}+G_{Rx}-P_{loss}-L \tag{1}\]  
In Equation (1), \(I_{n}\) is the interference power, \(P_{Tx}\) represents the transmission power, \(G_{Tx}\) represents the gain of the \(Tx\) antenna, \(G_{Rx}\) represents the gain of the \(Rx\) antenna, \(P_{loss}\) is the path loss, and \(L\) is other losses.  
\(P_{Tx}\) and \(G_{Tx}\) primarily depend on the type of base station and antenna. Models provided in ref. [28] are mainly applicable to urban scenarios and not suitable for aircraft operation scenarios. In the calculation of vertical prohibited zones, the protection radius may vary with flight altitude. Aircraft may be subjected to radiation interference from base stations at higher altitudes. Therefore, it is necessary to consider the architectural and topographical factors near airports. \(G_{Rx}\) reflects the impact of aircraft attitude changes on interference. The radio altimeter antenna typically has maximum gain in the direction  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **This Paper** & **[3]** & **[17]** & **[18]** & **[20]** & **[24]** & **[25]** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Integrated \\ Interference \\ Model \\ \end{tabular} } & Applicable in aviation & \(\bigcirc\) & \(\bigcirc\) & & & & \\ \cline{2-6}  & RMa, UMa, UMi, & \(\bigcirc\) & & & \(\bigcirc\) & & \(\bigcirc\) \\ \cline{2-6}  & ICAO 6 dB, WCLS, antenna types & & & & & & \\ \cline{2-6}  & Prohibited zones & \(\bigcirc\) & & & & & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Dynamic \\ Aircraft \\ Operational \\ Scenarios \\ \end{tabular} } & Apynamic interference thresholds & \(\bigcirc\) & \(\bigcirc\) & & & \(\bigcirc\) & \(\bigcirc\) & \\ \cline{2-6}  & Vertical prohibited zones & \(\bigcirc\) & & & & \\ \cline{2-6}  & Analyzing LOS and NLOS scenarios & \(\bigcirc\) & & \(\bigcirc\) & \(\bigcirc\) & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Mitigation \\ Measures \\ \end{tabular} } & Applicable for BSs near airports & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & & \(\bigcirc\) & \(\bigcirc\) \\ \cline{2-6}  & Tx power control and angle shutoff & \(\bigcirc\) & & & & \(\bigcirc\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The differences between this paper and previous works.  
perpendicular to the aircraft body. When the aircraft pitches or rolls, the direction of maximum gain changes, making it easier for interference to enter the radio altimeter.
## 5 Experiment and Result
In the context of networking, this metric captures the average duration between a packet leaving its origin and reaching its final endpoint. Figure 5 demonstrates that the ACRP protocol exhibits a reduced end-to-end delay compared to other protocols. Specifically, compared to fuzzy-AODV [22] and QoS-OLSR [17], CBVRP [21], and AMRBC [20], ACRP was able to reduce end-to-end delay more effectively. Additionally, under conditions of both lowest density and highest density, ACRP experiences significant reductions: over 17%, 3%, 3%, and 7% for the former, and an impressive 42%, 38%, 13%, and 20% for the latter. This improvement can be attributed to the presence of common nodes, which facilitate shorter packet travel distances and consequently lead to reduced delays from source to destination. Figure 6 shows the total number of intermediate nodes that a data packet must traverse between the source and destination. Along the data path, each node forms a hop, transferring data from one source to another. When compared to QoS-OLSR [17], kRop [13], and LBC [14] methods, the ACRP method reduced hop count by over 33%, 44%, and 76% at the highest node densities (250), respectively. Due to the fact that this protocol only uses the common member and cluster head to transmit packets, communication between the source and destination does not require sending packets to every member. A decreased hop count indicates shorter transmission distances between nodes, leading to lower energy consumption in relaying messages within the network. Therefore, the reduced energy expenditure per hop contributes to an overall decrease in energy usage across the network. Additionally, extended network longevity is achieved through decreased energy consumption, thereby preserving energy resources. Figure 4: The delivery ratio in different node densities. Figure 7 shows the number of packets that transmit from source to destination (throughput) based on Bit/Sec, and the performance of the ACRP method reached the highest amount when compared to other protocols. It is evident that the ACRP method exhibits superior performance when compared to the CBVRP [21], QoS-OLSR [17], AMRBC [20], and CRLLR [19] approaches. Among the examined methods, ACRP demonstrates superior performance with a 27%, 50%, 54%, and 54% advantage over CBVRP, QoS-OLSR, AMRBC, and CRLLR, respectively, in the scenario with the lowest node numbers (50). Additionally, in the highest density scenario (250), ACRP outperforms other methods by 9%, 46%, 60%, and 16%, respectively. Two factors contribute to this: firstly, as indicated by Equation (7), the proper selection of the cluster head establishes a robust link between the member node and its respective cluster head. Secondly, transmitting data via shared members across clusters has led to an augmentation of this parameter. Figure 5: The end-to-end different node densities. Figure 6: The hop count averages different node densities. Figure 8: The percentage of reachability with different speed. Figure 7: The throughput averages of different node densities.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps Derived from a Combination of Field Biomass, LiDAR Data and Optical and/or Radar Data
#### 3.4.7 Republic of Panama Aboveground Carbon Density Map  
[PERSON] et al. [92] developed a high-resolution nationwide map of aboveground carbon density at 1-ha resolution for the Republic of Panama for the year 2012 using field plot and remote sensing data. They initially performed a maximum likelihood analysis to fit a power-law model using field plot data and GLAS-based tree canopy height, then used the Random Forest and stratification methods, respectively, to generate two national aboveground carbon density maps, and finally compared the maps. Results revealed the accuracy of carbon density estimated by the Random Forest method was more accurate, corresponding to a bias \(<\) 15.3 Mg C ha\({}^{-1}\). The average uncertainty of estimated carbon density from field and LiDAR data by the Random Forest model was 20.5 Mg C ha\({}^{-1}\) at the pixel level.
# Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter
## 2 Methodology Model
### Method of Prediction  
#### 2.3.1 Neural Network Units  
The unique feedback loop structure of Recurrent Neural Network (RNN) can realize the cyclic update of data, so as to realize the time memory function. The time-unfolded computational graph of RNN is shown in Figure 2. The following update equations are applied:  
\[\mathbf{a}^{(t)} =\mathbf{b}+\mathbf{W}\mathbf{h}^{(t-1)}+\mathbf{U}\mathbf{x}^{(t)} \tag{4}\] \[\mathbf{h}^{(t)} =\tan\text{h}(\mathbf{a}^{(t)})\] (5) \[\mathbf{o}^{(t)} =\mathbf{c}+\mathbf{V}\mathbf{h}^{(t)} \tag{6}\]  
The parameters are the bias vectors \(\mathbf{b}\) and \(\mathbf{c}\) along with the weight matrices \(\mathbf{U}\), \(\mathbf{V}\), and \(\mathbf{W}\), respectively, for input-to-hidden, hidden-to-output, and hidden-to-hidden connections.  
Sequence information that is too long is difficult for RNN to process. To solve this problem, the LSTM neural network introduces a separate memory unit \(\mathbf{c}_{t}\) to retain historical information, and controls the information flow in the learning process through the forgetting gate, input gate, and output gate. Its structure is shown in Figure 3. Therefore, LSTM can selectively forget, delete, or update the internal information of the network, so as to achieve a better learning effect on the long-term dependence of the information in the sequence [35]. The compact forms of the equations for an LSTM cell are:  
\[\mathbf{f}_{t} =\sigma_{g}(\mathbf{W}_{f}\mathbf{x}_{t}+\mathbf{U}_{f}\mathbf{h}_{t-1}+\mathbf{b}_{f}) \tag{7}\] \[\mathbf{i}_{t} =\sigma_{g}(\mathbf{W}_{i}\mathbf{x}_{t}+\mathbf{U}_{i}\mathbf{h}_{t-1}+\mathbf{b}_{i})\] (8) \[\mathbf{o}_{t} =\sigma_{g}(\mathbf{W}_{o}\mathbf{x}_{t}+\mathbf{U}_{o}\mathbf{h}_{t-1}+\mathbf{b}_{o})\] (9) \[\mathbf{\widetilde{c}}_{t} =\sigma_{c}(\mathbf{W}_{c}\mathbf{x}_{t}+\mathbf{U}_{c}\mathbf{h}_{t-1}+\mathbf{b}_{c})\] (10) \[\mathbf{c}_{t} =\mathbf{f}_{t}\odot\mathbf{c}_{t-1}+\mathbf{i}_{t}\odot\mathbf{\widetilde{c}}_{t}\] (11) \[\mathbf{h}_{t} =\mathbf{o}_{t}\odot\mathbf{\sigma}_{h}(\mathbf{c}_{t}) \tag{12}\]  
where \(\mathbf{x}_{t}\) is the input vector; and \(\mathbf{f}_{t}\), \(\mathbf{i}_{t}\), and \(\mathbf{o}_{t}\) indicate the activation vector of forget gate, input gate, and output gate, respectively. \(\mathbf{h}_{t}\) indicates the hidden state vector also known as output vector of the LSTM unit. \(\mathbf{\widetilde{c}}_{t}\) indicates cell input activation vector. \(\mathbf{c}_{t}\) indicates cell state vector. \(\mathbf{\sigma}\) denotes the activation function of the gate (normally sigmoid function and hyperbolic tangent function). \(\mathbf{W}\), \(\mathbf{U}\), and \(\mathbf{b}\) are weight matrices and bias vector parameters which need to be learned during training. The operator \(\odot\) (Hadamard product) denotes the element-wise multiplication of elements between two vectors.  
Figure 2: The time-unfolded computational graph of RNN.
## 2 Description of the Proposed Method  
Figure 1 shows a pictorial description of a possible application of the proposed method by both the aggregator and the distribution system operator (DSO). The DSO provides a day-ahead load forecast and grid requirements (voltage deviation, VUF in this paper) to the aggregator and the aggregator collects the possible arrival and departure times of EVs. The aggregator can then provide all this information to the proposed method, which will then provide an optimized schedule of EVs to satisfy both grid and customer benefits.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Related Work**} & \multicolumn{2}{c}{**Customer Objective Function**} & \multicolumn{2}{c}{**System Objective Function**} & \multicolumn{2}{c}{**V2G Technology**} \\ \cline{2-6}  & **Charging Cost** & **Battery Degradation** & **Voltage** & **Voltage** & **V2G in** & **V2G in** \\  & & & **Unbalance** & **Deviation** & **Active Power** & **Reactive Power** \\ \hline
[30] & ✓ & ✓ & - & - & ✓ & - \\
[31] & - & ✓ & - & - & ✓ & - \\
[32] & ✓ & ✓ & - & - & ✓ & - \\
[33] & ✓ & - & - & ✓ & - & - \\
[34] & ✓ & - & ✓ & ✓ & ✓ & - \\
[35] & - & - & ✓ & ✓ & - & ✓ \\
[36] & - & - & ✓ & ✓ & ✓ & - \\
[37] & - & - & - & ✓ & ✓ & ✓ \\
[38] & - & - & ✓ & ✓ & - & - \\ Proposed Method & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of recent works on coordinated charging of EVs.  
A plot of the proposed method to coordinate EVs is shown in Figure 2 and a brief description of this process is as follows:  
1. The first step requires the preparation of the data of EV-user behaviors, e.g., plug-in time, plug-out time, daily distance, initial SOC, wear costs, information of the test systems, such as the residential baseload, dynamic price, and significant parameters of the PSO optimization algorithm.
2. Stage 1 uses the input data from the previous step for further processing and formulates an optimization problem in which the objectives are to minimize VUF, EV charging costs, and battery degradation costs. This optimization problem is then solved using the PSO. The output from Stage 1 provides an optimized power schedule for charging (G2V)/ discharging (V2G) as well as the best costs for customers.
3. Once the electrical schedule for EVs is obtained, Stage 2 will use this information to formulate another optimization problem with the purpose of minimizing voltage deviation. The constraint to this optimization is the reactive power capability of V2 Gs, which can be produced/absorbed by controlling the inverter [39; 40]. Moreover, the reactive power compensation from EVs will not cause any degradation to the battery [41; 42]. Thus, Stage 2 provides a reactive power schedule for EVs.  
Figure 1: Application of the proposed method for customer and grid benefits.  
Figure 2: Proposed method to schedule EVs.
## 2 Measurement Method  
The spectral matching ratios SMR are defined as the short circuit current ratios between two junctions normalized to the short circuit currents produced under the reference condition AM1.5D:  
\[SMR_{ik}(t)=\frac{I_{i}(t)}{I_{k}(t)}\frac{\hat{f}_{k}}{\hat{f}_{i}} \tag{1}\]  
The calculated \(SMR_{ik}(t)\) (SMR for simplicity) is a function of time because the solar spectrum changes during the day, affecting the first term, while the second term is a function only of the spectral responses of the two junctions and of the reference spectrum, so is constant. As a ratio of four currents, the SMR is a dimensionless value.  
There are two methods most frequently used to measure the SMR: by making use of a calibrated spectroradiometer or by using component cells, each having spectral responsivities representative of the actual multi-junction devices [4].  
The latter approach is simpler in terms of operation, because it does not need a complex calibrated (and hence expensive) spectroradiometer system to continuously measure the spectral irradiance. Moreover the three spectral responsivities of the CPV device under test do not have to be measured. However, a good result is guaranteed only if matched component cells are used and after a proper calibration. If this is not the case, this method furnishes an approximate measurement only.  
The present study uses only the spectroradiometer method, and the short circuit currents are calculated by \[J_{i}(t) =\int\Phi_{i}(\lambda)G(t,\;\lambda)d\lambda \tag{2}\] \[J_{i} =\int\Phi_{i}(\lambda)\hat{G}(\lambda)d\lambda \tag{3}\]  
By substituting Equations (2) and (3) into Equation (1) we obtain  
\[SMR_{ik}(t)==\frac{\int\Phi_{i}(\lambda)G(\lambda,t)d\lambda}{\int\Phi_{k}( \lambda)G(\lambda,t)d\lambda}\;\frac{\int\Phi_{k}(\lambda)\hat{G}(\lambda)d \lambda}{\int\Phi_{i}(\lambda)\hat{G}(\lambda)d\lambda} \tag{4}\]  
Equation (4) highlights the remarkable symmetry property of the SMR with respect to all the four functions \(\Phi_{i}(\lambda)\), \(\Phi_{k}(\lambda)\), \(G(\lambda,t)\) and \(\hat{G}(\lambda)\): A linear transformation of one or more of the four functions of form \(f(\lambda)\)  
\[f(\lambda)\Longrightarrow kf(\lambda)\;k\in\mathbb{R}_{0} \tag{5}\]  
has no impact on the _SMR_. In other words, the _SMR_ does not depend on the magnitude of the measured curves, but only on their relative shape.