esa-sceva/satcom-chunk-collection huggingface dataset:
# Special Issue on Wastewater Treatment and Reuse Technologies
## 5 Removal of Heavy Metals  
Acid mine drainage generated by industrial mines is characterized by its highly acidic nature and toxic heavy metal content [20]. With their high solubility as well as non-biodegradable and bio-accumulative properties, heavy metals are a serious threat to human health and the environment. [PERSON] et al. [21] demonstrated the performance of natural zeolite (clinoptiolite) to remove multiple heavy metallic ions commonly found in acid mine drainage in their article \"_An Innovative Dual-Column System for Heavy Metallic Ion Sorption by Natural Zeolite_\". A novel dual-column sorption system was designed to overcome some of the shortcomings of existing column experimental designs. In contrast to most previous single-component sorption set-ups, this study evaluated the simultaneous sorption process by natural zeolite of the five most commonly occurring heavy metallic ions found in acid mine drainage, namely, Pb\({}^{2+}\), Cu\({}^{2+}\), Fe\({}^{3+}\), Ni\({}^{2+}\) and Zn\({}^{2+}\). This paper demonstrated the effectiveness and removal efficiency of the proposed system and provided insights into heavy metal ion selectivity and treatment system breakthroughs.  
Included in this special issue is another article on heavy metal remediation from industrial wastewater by [PERSON] et al. [22]. Phosphate materials, particularly calcium phosphate, have been studied as effective adsorbents for heavy metals from wastewaters and polluted soils. However, there is a lack of understanding of the mechanisms of heavy metal immobilization that involves protonation/deprotonation of surface hydroxyl groups and their interaction with the metal oxyanion. Therefore the article entitled \"_Adsorption of Chromium (VI) on Calcium Phosphate: Mechanisms and Stability Constants of Surface Complexes_\" [22] investigated the mechanisms of complexation of hexavalent chromium with low-crystallized octacalcium phosphate. The obtained results suggested that Cr (VI) adsorption on octacalcium phosphate was of an electrostatic nature in acidic solutions, and of a chemical nature in lower acidic to alkaline solutions.
# An Initial Machine Learning-Based Victim's Scream Detection Analysis for Burning Sites
## 3 Methodology
### Classifier
#### 3.5.2 Long Short-Term Memory (LSTM)  
Long short-term memory (LSTM) networks are a recurrent neural network (RNN), which is efficient in learning order dependence in sequence prediction problems. To address the vanishing gradient problems of some RNN, the LSTM network was introduced to address this issue of RNN [58]. LSTM cells are compiled of multiple gates: input gate (\(i\)), forget gate (\(f\)), output gate (\(o\)). The functionalities of these gates are as follows: forget gate suppresses irrelevant information while letting applicable information pass through, input gate takes part in updating the cell state, and output gates update the value of hidden units.  
We have implemented the built-in LSTM network from the 'Keras' [59] library associated with TensorFlow [60]. Unlike SVM, LSTM requires feeding the features from one sequence of events to the next sequence. Therefore, no averaging function has been applied to the extracted features. Since our audio data had different audio durations, we calculated the median number of frames for all the audio files in the custom dataset by setting a sampling rate of 16 kHz, frame length of 32 ms, and hop length (that is, overlap) of 16 ms in order to make all the audio files to same length. The median number of frames was calculated to be 110, which becomes the time-step for our dataset. The audio files were clipped at 110 frames if they were longer, and zero padding was used as in [61] for the audio files shorter than 110 frames. The next step was to extract sixteen features for each time-step as a sequence of the data. The LSTM unit requires a three-dimensional input to process in the shape of the batch size, time steps, and the number of features. To develop the best-fitted model for the LSTM, we performed experimentation changing the hyperparameters as follows:  
* The number of hidden layers;
* The number of input units (neurons) for each layer;
* Activation function;
* Optimizer;
* Learning rate;
* Batch size;
* Dropout;
* Loss Function.  
We documented the best settings from these experiments in the results and analysis section of LSTM.
## 5 Signal Feature Extraction Methods for Different Vessels
### Experimental Data Verification Based on PE of IMF with Maximum Average Energy  
To validate the universality of the proposed method, 100 samples of different types of signals were taken as test data to compare the effectiveness of three feature extraction methods. The feature parameters extracted include the PE values of the original signals and the PE values of the IMF components with maximum average energy, as shown in Figure 13.  
Analyzing Figure (a)a, there is a noticeable difference between Vessel A and the other two vessels, approximately around 0.2. However, the difference between Vessel B and Vessel C is less than 0.05, making it challenging to distinguish their feature parameters. In Figure (b)b, the entropy values of the IMF components with the maximum average energy for the same vessel are generally maintained at the same level. The PE values between different vessels differ by more than 0.2, indicating effective discrimination of the feature parameters of electric field signals from different vessel axis frequencies.  
For different vessels at different time scales, as analyzed in Section 4.2, the entropy values show the maximum difference at time scales 42 and 43. Therefore, time scales 42 and 43 are selected, and the experimental results are shown in Figure 14.
# On Hybrid Energy Utilization in Wireless Sensor Networks
## 2 Problem Formulation and Markov Decision Process  
### System Model  
The system uses hybrid energy to transmit data packets in a WSN, as shown in Figure 1. Here, the data is periodically reported from each sensor to the sink node. Moreover, two buffers are considered, in which one represents a battery for solar harvested energy and the other indicates a buffer for data packets. In order to achieve reliable communications, a constant energy source is required if the harvested energy in the battery is insufficient. Otherwise, the event of dropping a packet occurs given that the packet buffer is full or the solar harvested energy in the battery is not enough to transmit any packet.  
We study the optimal transmission policy for hybrid energy utilization concerning the battery and packet buffer states, actions, transition probabilities, and cost value for each action. The hybrid energy utilization is formulated as an MDP design framework for simple wireless communication between one sink and one WSN node in order to find the optimal transmission policy.  
The MDP model can be described as \((\mathcal{S},\mathcal{A},\mathcal{P},R)\). Let \(\mathcal{S}\) be the state space which is a composite space of the packet state \(\mathcal{Q}=\{0,\ldots,N_{Q}-1\}\) and battery state \(\mathcal{B}=\{0,\ldots,N_{B}-1\}\); i.e., \(\mathcal{S}=\mathcal{Q}\times\mathcal{B}\), where \(\times\) denotes the Cartesian product. At the state \(S=(i,j)\), it means that there are \(i\) packets and \(j\) available solar energy units in the packet buffer and the battery, respectively. \(\mathcal{A}=\mathcal{A}_{\mathcal{C}}\times\mathcal{A}_{\mathcal{H}}\) is the composite action for transmitting packets using hybrid energy, including the action for transmitting  
Figure 1: System architecture. WSN: wireless sensor network.  
packets using the constant energy source \(\mathcal{A}_{C}=\{0,1,\ldots,N_{A}\}\) and the action for transmitting packets using solar harvested energy \(\mathcal{A}_{\mathcal{H}}=\{0,1,\ldots,N_{A}\}\), where \(N_{A}\) is the maximum allowable action index. We assume that \(\mathcal{P}\) is the state transition probability for different actions, and \(R\) is the reward function to harmonize the cost value of constant energy source usage and the PDP for all actions.
# Prediction of Structural Type for City-Scale Seismic Damage Simulation Based on Machine Learning
## 3 Supervised ML Solution
### Model Training  
The data of 230,683 buildings in Tangshan were used to train the artificial neural network, decision forest and logistic regression models. Azure packages each operation as a component that can be defined and organized through visual programming. The prediction solution can be created efficiently using components. Using the decision forest model as an example, the supervised ML solution was designed using the components, as shown in Figure 3.  
The components in Figure 3 are specified as follows:  
Component 1 (Select Data): Select the uploaded source data, i.e., Tangshan data.  
Component 2 (Select Model): Select \"Multi-class Decision Forest\" model in Azure for the prediction.  
Component 3 (Split Data): Split the source data into training data (i.e., 80% of the source data) and assessment data (i.e., the remaining 20% of the source data).  
Figure 3: Designed supervised machine learning (ML) solution.  
Figure 2: Correlation matrix of the building data.  
Component 4 (Train Model): Train the prediction model using the training data.  
Component 5 (Score Model): Score the prediction results using the assessment data.  
Component 6 (Evaluate Model): Evaluate the accuracy of the prediction model.  
Using the designed supervised ML solution above, the overall accuracy for predicting structural type in Tangshan reaches 98.3%, as shown in Figure 4a. If the artificial neural network and logistic regression models are adopted, the corresponding overall accuracies will be 98.0% and 97.0%, as shown in Figure 4b,c. Therefore, the designed supervised ML solution can predict the structural type of a city with high accuracy.  
Figure 4: Prediction accuracies and confusion matrices for the designed supervised ML solution using (**a**) multi-class decision forest model, (**b**) multi-class neural network model and (**c**) logistic regression model.  
Besides, according to the precisions and recalls in Figure 4, the macro and micro F1 scores of the above three predictions can be calculated to evaluate the performances of different ML models further, as shown in Table 1. The F1 score is simply a way to combine the performance metrics of precision and recall. According to Table 1, the macro and micro F1 scores of the decision forest model is the highest, while that of the logistic regression model is the lowest. In addition, the decision forest model also has the highest accuracy, but the logistic regression has the lowest accuracy. Therefore, although the artificial neural network and logistic regression models also have high accuracy, the decision forest model is recommended for the prediction of structural types in this study, due to the best performance in the above three models.
# A Novel Tempogram Generating Algorithm Based on Matching Pursuit
## 3 Tempogram Based on Matching Pursuit
### Motivation  
A tempogram is generated by extracting periodicity from the novelty curve. ACF can be used to obtain the periodicity from the similarity between the novelty curve itself and the lagged curves. FT can also derive the frequency as the periodicity from the novelty curve by computing Fourier coefficients, in which the coefficients represent the similarity between the novelty curve and the orthogonal Fourier basis at certain frequencies. However, these two methods limit the quantity and the forms of the curves to be compared to some extent. ACF confines the compared curves to a series of lagged curves and FT confines them to a series of the orthogonal basis. As these two approaches confine the quantity and the forms of the compared curves, the matching degree are restricted, and the accuracy of the tempogram is affected correspondingly. We require that the compared curves should match well with the characteristics of the tempo, which can be represented by the beat pulse in the time domain.  
To some extent, the matching idea was explicitly expressed in the papers [29], however, the quantity and the forms of the compared curves were still limited, because their templates were limited on the assumption in which the duple meter were too simple to be used to describe the realistic metrical templates. [PERSON] [32] tried to compensate for the limited template set through the periodicity vector resampling, but it seemed that he still cannot systematically solve the problem. It is far more important that the matching algorithm of all the papers [29; 30; 31; 32], such as cross-correlation and k-NN regression, still need further improvement.  
In this paper, we proposed the tempogram generating algorithm based on MP. The dictionary of MP makes it more flexible to construct the templates or atoms as the similar curves, so that the quantity and the forms of the compared curves can be enriched easily. We constructed a tempo dictionary and designed the atoms in the light of the characteristics of tempo and note onset to ensure higher degree of similarity. Moreover, the matching algorithm of MP is more effective and more systematic to find curves that are similar to the tempo curves. Furthermore, MP makes the tempogram sparser such that it can be applied more effectively. For the tempogram, the fewer the number of big tempo components, in other words, the sparser the coefficients are, the higher the certainty of the tempo will be, and the better the tempogram will be applied certainly. Under the condition of the ideally sparsest tempogram, there would be only one big coefficient attached to the tempo at a given time. It means that there is only one line in the tempogram if the tempo is supposed to be constant.  
To implement the proposed algorithm, we first create the tempo dictionary that will be used for the MP algorithm, and then we process the musical signal through MP to obtain the tempogram.
## 3 Improved Artificial Potential Field Method Based on Joint Space
### The Gradient of the Potential Field Function
This section focuses on the calculation of the shortest distance \(d(q,q_{obs})\) when facing different obstacles. There are two types of obstacles on the factory assembly line usually, which are obstacles with complex shapes and obstacles with regular shapes. The obstacle can be simulated using the ball envelope method [25], as shown in Figures 3 and 4. Figure 4: The shortest distance \(d(q,q_{obs})\) between the point on the joint and an obstacle with a regular shape. Figure 3: The shortest distance \(d(q,q_{obs})\) between the point on the joint and an obstacle with a complex shape. The parameters describing the obstacle are \(\{a,b,c,r\}\), where \(a,b,c\) represent the spatial location coordinates of the obstacle and \(r\) represents the radius of the ball envelope obstacle. For an obstacle with a complex shape, it can be enveloped by a single sphere. According to the conclusion of Section 2, the position configuration coordinates of \(q\) are determined by a series variable of the joint angle, and can be solved by kinematics. Therefore, the coordinates of \(q\) can be expressed as \(q:\{x(\theta),y(\theta),z(\theta)\}\). Then, the shortest distance between the joint of the robot and the obstacle can be expressed as (13):  
\[d(q,q_{obs})=\sqrt{\left(x(\theta)-a\right)^{2}+\left(y(\theta)-b\right)^{2}+ \left(z(\theta)-c\right)^{2}}-r_{1}-r_{2} \tag{13}\]  
where \(r_{1}\) denotes the radius of the enveloping obstacle sphere and \(r_{2}\) denotes the thickness of the joint. For an obstacle with a regular shape, it can be enveloped by a series of spheres whose coordinate parameters are denoted by \(\{a_{i},b_{i},c_{i},r_{i}\}\). The distance between the joint and the nearest sphere is the shortest distance \(d(q,q_{obs})\) between the joint and the obstacle with a regular shape. In order to make the motion of the robotic arm smooth, the manipulator receives repulsive forces from the two sources, one is the nearest ball, and the other is the ball adjacent to the nearest ball. On the left side of Figure 5, ball 2 is closest to the joint, and the robotic arm is subject to the repulsive force of ball 2 and the adjacent ball 1. When the robotic arm moves to the next state, as shown in the right side of Figure 5, ball 3 is closest to the joint and the robot is subject to the repulsive force of ball 3 and the adjacent ball 2. The derivative of Equation (13) can lead to Equation (14):  
\[\
abla d(q,q_{obs})=\frac{(x-a)\frac{\partial x(\theta)}{\partial\theta}+(y-b )\frac{\partial y(\theta)}{\partial\theta}+(z-c)\frac{\partial z(\theta)}{ \partial\theta}}{\sqrt{(x-a)^{2}+(y-b)^{2}+\left(z-c\right)^{2}}} \tag{14}\]  
The analytic formula of the gradient of the repulsive potential field \(\
abla U_{rep}\) can be obtained by substituting Equations (13) and (14) into Equation (10), and it is determined by the series variable of the joint angle. This algorithm has a very high real-time performance because it only needs to calculate the potential field function once per cycle.
## 3 Results and Discussion
The analysis of the LSTM prediction quality by a standalone model are misleading, because (1) the power load prediction is made in conjunction with the ANN, (2) the MSE values of the LSTM models measure the absolute value between the measured and predicted weather conditions rather than how accurate is the predicted weather trend for the power load prediction and (3) the ANN integrates the outputs of multiple weakly predicted weather futures into the final power load prediction. Figure 8: The power load predictions results using only the temperature weather conditions. (**Left**) Predicted power load using the current temperature conditions with the MSE = \(1.20\times 10^{-2}\). (**Right**) Predicted power load using the future LSTM temperature predictions with the MSE = \(1.66\times 10^{-2}\).
# Impact Evaluation of DME Beacons on BeiDou B2a Signal Reception Performance
## 4 Evaluation of B2a Signal Reception Performance
### DME Signal Reception Power Budget  
The signal power budget constitutes a critical component of the interference evaluation framework, necessitating comprehensive characterization of the complete transmission channel from DME beacon emission to receiver acquisition.  
According to the \"MH/T4006.3-1998 Aviation Radio Navigation Equipment Part 3: Rangefinder (DME) Technical Requirements\" [30], the peak output power of DME beacons is specified as 1000 W (90 dBW). The maximum antenna gain is 9 dB (at an elevation angle of 8\({}^{\circ}\)), and the feed line loss \(L_{line}\) is \(-3\) dB.  
The dominant factor in signal power attenuation is space loss in the propagation path, which comprises four key components: free-space path loss, lens effect loss, polarization, and rain attenuation loss. From the radar equation, the free-space path loss of the transmitted signal from DME beacon is calculated as follows:  
\[L_{loss}=10\log\left(\frac{\lambda^{2}}{\left(4\pi R\right)^{2}}\right), \tag{19}\]  
where \(\lambda\) represents the wavelength of the signal emitted by the DME beacon, and \(R\) represents the slant distance from the DME beacon.  
While DME signal propagation is fundamentally limited by line-of-sight constraints, ground station transmissions located slightly beyond the radio horizon remain detectable. This phenomenon occurs due to terrain diffraction effects at the horizon boundary, although additional losses due to this diffraction effect mount rapidly. In high-altitude analysis, in addition to calculating the path loss using Formula (19), additional losses caused by terrain diffraction and multipath effects due to terrain and distance must also be considered. When the terrain on the radio horizon just grazes the propagation path, an additional loss of 7-to-10 dB may occur [13].  
The lens effect loss is the different degree of refraction of radio waves radiated from different elevation angles of the earth's surface, resulting in signal loss. For the DME signal, when the antenna elevation angle is 0\({}^{\circ}\), the lens effect loss \(L_{lens}\) is as shown in Table 1.  
The antenna polarization loss and rain attenuation loss for the L-band signal is generally set to 1 dB [18].  
Within the receiver part, the receiver pre-selection filter insertion loss \(L_{GNSS}\) is a key consideration. When the bandwidth is 20 MHz, the equivalent filter transfer function response characteristics of the two front-end filters and the antenna are as shown in Figure 8[13]. The gain of the receiver antenna for the DME signals was conservatively estimated at 0 dB.  
Through the above analysis, the DME signal power \(P_{k}\) received by the airborne BDS receiver is  
\[P_{k}=89+G_{antenna}-L_{line}-L_{loss}-L_{lens}-L_{GNSS}. \tag{20}\]
## 3 Proposed Method
### Signal Parameterized Estimation Network  
The signal parameterized estimation network takes the input feature map \(U\in\mathbb{R}^{M\times N\times 2}\), outputs a set of signal estimation parameters \(\theta\). \(\theta\) is a set of parameters used for signal transformation. In this paper, we consider the variation caused by time drift, symbol rate conversion, sample rate offset, and centre frequency offset. Time drift involves shifting the signal with the correct initial amount. The symbol rate conversion and sample rate offset can be corrected using the correct sample increment resampling and interpolation. We implement these two changes to approximate the 2D affine transformation in the image.  
\[\left(\begin{array}{ccc}\theta_{11}&\theta_{12}&\theta_{13}\\ \theta_{21}&\theta_{22}&\theta_{23}\end{array}\right) \tag{7}\]  
For centre frequency offset correction, we estimate the phase offset for each sample point to compensate with  
\[n\theta_{3},\,n=0,1,\ldots,N-1 \tag{8}\]  
The phase noise caused by the CFO is compensated by the phase offset of a constant term \(\theta_{4}\).  
\(\theta=f_{est}(U)\) is used to represent the signal parameterized estimation function. Within the scope of this paper, \(\theta\) has eight parameters, including six parameters of the 2D affine transformation and two parameters of phase and carrier frequency recovery.  
We use a deep neural network model to implement \(\theta\). Long short-term memory (LSTM) [25] network is a recurrent neural network suitable for processing and predicting important events with relatively long intervals and delays in time series. LSTM is an effective technique for solving long-order dependency problems. Because of the specificity of radio data, the sampling points are highly in temporal correlation. Different modulation signals have trip points in phase and amplitude. The range of signal contextual information is large, and this problem causes the influence of the input of the hidden layer on the network output to decline as the network loop continues to recurse. Therefore, we use Bi-directional LSTM [26] to extract radio signal modulation features, which is an extended model of LSTM. The Bi-LSTM contains two input sequences, one is the positive sequence and the other is the inverted sample of the input sequence. The two-way cyclic network structure can perform well on sequence classification problems. The architecture of Bi-LSTM presents in Figure 3.  
In the signal parameterized estimation network model, we implemented two Bi-LSTMs, then two fully connected layers. Finally, \(\theta\) is obtained by the linear activation function. We use the appropriate weight initialization, dropout, and regularization tricks to achieve optimal performance under this network model.