esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks
## 4 System Model  
We consider a multi-cell LTE network. The bandwidth channel on each cell is divided into \(N\) RBs to be allocated to \(M\) actives users attached to each enodeB. The users are randomly distributed in the cells. Users are facing intercell interferences caused by neighboring cells. A central unit collects channel state information from users. We assume perfect knowledge of channel state information. Each user faces different levels of SINR on each RB. User mobility is considered and user speed is 3 km/h, which means channel signals are assumed to be constant over 1 TTI (Time to Transmit interval). RBs are allocated to actives users every TTI. A RB is allocated to only one UE and a UE can be allocated more than one RB.  
Each user has two queues of data. The first queue contains data for first transmission and the second contains data for retransmission. The retransmission queue is considered here because of the perturbed nature of the network since cell edge users are facing weak channel signals. The queues sizes are considered to be finite. Asynchronous adaptive HARQ with 8 stop and wait parallel processes and maximum retransmission number is set to 3 as specify in LTE standard for downlink [37] is considered. One packet is transmitted on each allocated resource block for simplicity.  
The queues and head of line delay (HoL) are updated accordingly. When queues are full of data, older packets are discarded for that user. When maximum HoL delay is reached, older packets are discarded for that user. Packet loss ratio is obtained at the end of the resource allocation process by dividing the total number of discarded packets by the total number of packets that is left the buffers either lost or effectively transmitted. When resource blocks are allocated to a user, the HoL delay of that user is decreased. For users that have not been allocated resources blocks, HoL is increased.  
We consider voice traffic. The assumptions of the system models are follows:  
* There is a fixed data block size (in the case of voice service, it is 40 bytes [37]).
* The data rate is represented by a Poisson distribution (one block every 20 ms for voice service [38]).
* The buffer size has a finite length.
* The process is initialized by allocating random buffer size and HoL delay.
* HARQ (Hybrid Automatic Repeat reQuest) is considered.
* One packet is transmitted on each allocated resource block.
## 8 Discussion  
The analysis process described in Sections 6 and 7 shows that the CWT scalogram algorithm identifies the fault by simply transforming (by CWT process) the whole abnormal data set and then comparing the result with the scalogram of the entire normal data set. The method is applicable with preconditions. The first precondition is that the fault must have already happened; a CWT scalogram algorithm cannot detect the fault signal if the fault does not exist, i.e., the data set recorded stops before a fault occurs. The second precondition is that the occurred fault must be visible from the data set after CWT transform. However, the two preconditions are not established in the trampoline machine situation. The pulse change is weak and basically not detectable in the range of CWT frequencies. Besides, our goal is to predict the fault before it happens. Therefore, the proposed method in this research zooms into the signal by cutting the data into sequential time segments. However, the length of time segments affects \"resolution\" of detecting a fault somewhere in the series of scalograms. Therefore, CWT scalograms and synchrosqueezed scalograms in Figures 6 and 7 are scalograms that contain pulse information of the machine at different time segments. These diagrams still require further processing according to their respective time segments before the data set can be testified as normal or abnormal. This further processing is achieved by observing changing number of pulses as described in this paper.  
Having introduced the proposed data processing method, we also want to validate the result of segmented FFT. We choose the same segmented time interval shown in Figures 6 and 7, i.e., 350 data points with 50 data points overlap. We generate numbers of segmented FFT figures. Figure 14 shows 12 spectrums generated by segmenting the data sets that produced Figures 4 and 5, then we select 6 consecutive segmented FFT spectrums, respectively, from Figures 4, 5 and 14a,f are segmented normal FFT spectrums, while Figure 14g,l are abnormal segmented FFT spectrums. The results are demonstrated below.  
Figure 14: _Cont._Figure 14: Segmented FFT spectrum for both normal and abnormal data sets: (**a–f**) segmented normal FFT spectrums, (**g-l**) segmented abnormal FFT spectrums.  
It is clear from comparing the series of Figure 14 with Figures 6 and 7 that segmented FFT can only show a dominant low frequency. Other frequencies are not distinguishable from the spectrum, which confirms that FFT is not efficacious for trend detection use.
# Automatic Age and Gender Recognition Using Ensemble Learning
## 7 Conclusions and Future Works  
In this study, an ensemble learning based approach is proposed for age and gender classifications using MFCC features extracted from speech signals. For this purpose, five different machine learning algorithms, namely SVM, E-TREE, KNN, RF, and LR, and two different ensembling methods, namely stacking and majority voting, were examined. In the study, a total of 13 models were created, 5 with base-level classifiers and 8 with the ensemble of these classifiers, and performance measurements of each were performed. All classifiers in the study were developed with a dataset consisting of randomly selected speeches from the Turkish subsection of the MCV dataset. In the experiments, it was observed that SVM provides the highest age and gender classification accuracy among the five base-level classifiers. Following SVM, E-TREE shows the next highest accuracy, followed by the RF, KNN, and LR classifiers. After the individual performance evaluations of the base-level classifiers were performed, the performance evaluations of the ensemble models created by majority voting and stacking of these classifiers were carried out. The experimental results obtained in the study show that the stacking-based ensemble models provide superior performance than those based on majority voting. The model created by stacking the predictions of five base-level classifiers with the LR meta-classifier stands out as the model with the highest accuracy. The accuracy of this model on the test dataset is 97.41%, while the highest accuracy achieved by majority voting is 96.72%. The experimental results show that classification models created with both ensemble approaches provide a significant performance increase in age and gender classifications.  
There are various potential areas for future research to explore. One main area is to assess the proposed models using different datasets, including child speech, and to experiment with various modern deep learning architectures like transformer architectures. Furthermore, broadening the diversity of the ensemble by incorporating different deep-learning architectures could be another avenue for future research. Lastly, for age and gender recognition, exploring multi-modal models utilizing features extracted from various data sources, such as facial images, body movements, and speech utterances, could be another direction for future work.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Data Availability Statement: Common Voice Corpus 13.0 Turkish database is available via [[https://commonvoice.mozilla.org/tr/datasets](https://commonvoice.mozilla.org/tr/datasets)]([https://commonvoice.mozilla.org/tr/datasets](https://commonvoice.mozilla.org/tr/datasets)) (accessed on 15 July 2024).
## 4 Path Tracking
### Tracking Scheme  
Let the curvature of Lam\(\acute{\text{e}}\) curve C\(j\) and the trajectory be \(\kappa_{j}\) and \(\kappa_{5}\), respectively. When the DWMR moves along the trajectory at a velocity \(v_{c}\), the robot twist is constrained by  
\[\omega=-\kappa_{5}v_{c} \tag{29}\]  
Based on Equations (10) and (29), when the DWMR moves along the trajectory, the actuated joint rates follow in terms of \(\kappa_{5}\):  
\[\dot{\theta}_{1}=\frac{v_{c}}{r}(1+l\kappa_{5}),\quad\dot{\theta}_{2}=\frac{v _{c}}{r}(1-l\kappa_{5}) \tag{30}\]  
If the DWMR tracks the trajectory at a velocity \(v_{c}(t)\), the time-derivatives of the actuated joint rates are  
\[\ddot{\theta}_{1}=\frac{\dot{v}_{c}}{r}(1+l\kappa_{5})+\frac{v_{c}}{r}l\dot{ \kappa}_{5},\quad\ddot{\theta}_{2}=\frac{\dot{v}_{c}}{r}(1-l\kappa_{5})-\frac {v_{c}}{r}l\kappa_{5} \tag{31}\]  
As shown in Figure 4, when the actuated joint rates and accelerations are obtained, the desired torques can be readily calculated based on the inverse dynamics model of Equation (14). It is noteworthy that although path tracking is first implemented by means of the Lame curve blending technique at the level of kinematic control, it is finally converted into the torque control at the dynamic level. Moreover, this multi-loop vision guidance framework is carried out in the receding-horizon mode, implying multi-step planning but one-step control.
# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 2 Theoretical Background  
Bistatic CYGNSS radars receive the L-band GNSS-R signals that are transmitted by the GPS satellites and subsequently forward-scattered from Earth's surface in the specular direction. This configuration of the CYGNSS and GPS constellations functions as a bistatic radar at L-band, which receives information relevant to the scattering surface properties. SM can be retrieved as part of such information overland as it is the primary determinant of the dielectric constant of the scattering surface. This section provides theoretical background for SM retrieval from bistatic radar observations, potential use of CYGNSS observables in such a task, and related challenges.
## 4 Experimental Setup  
We now investigate the full-band representation of musical instrument sounds and the nonstationarity of the adaptive AM-FM sinusoids from eaQHM. We aim to show that spectral decomposition fails to capture partials at the higher end of the spectrum so full-band quasi-harmonic modeling increases the quality of analysis and resynthesis by capturing sinusoids across the full range of the spectrum. Additionally, we aim to show that adaptive AM-FM sinusoids from eaQHM capture nonstationary partials inside the frame. We compare full-band modeling with eaQHM against the SM [1,20] and EDS estimated with ESPRIT [36] using the same number of partials \(K\). We assume that the musical instrument sounds under investigation can be well represented as quasi-harmonic. Thus, we set \(K_{\max}\) to the highest harmonic number \(k\) below Nyquist frequency \(\
icefrac{{f_{0}}}{{2}}\) or equivalently the highest integer \(K\) that satisfies \(Kf_{0}\leq\
icefrac{{f_{0}}}{{2}}\). The fundamental frequency \(f_{0}\) of all sounds was estimated using the sawtooth waveform inspired pitch estimator (SWIPE) [53] because in the experiments the frame size \(L\), the maximum number of partials \(K_{\max}\), and the full-band harmonic template depend on \(f_{0}\). In the SM, \(K\) is the number of spectral peaks modeled by sinusoids. For EDS, ESPRIT uses \(K\) to determine the separation between the dimension of the signal space (sinusoidal component) and of the residual.  
The SM is considered the baseline for comparison due to the quasi-stationary nature of the sinusoids and the need for spectral decomposition. EDS estimated with ESPRIT is considered the state-of-the-art due to the accurate analysis and synthesis and constant frequency of EDS inside the frame \(m\). We present a comparison of the local and global SRER as a function of \(K\) and \(L\) for the SM and EDS against eaQHM in two experiments. In experiment 1, we vary \(K\) from 2 to \(K_{\max}\) and record the SRER. In experiment 2, we vary \(L\) from \(3T_{0}f_{s}\) to \(8T_{0}f_{s}\) samples and record the SRER, where \(T_{0}=\
icefrac{{1}}{{f_{0}}}\) is the fundamental period. The local SRER is calculated within the first frame \(m=0\), where we expect the attack transients to be. The first frame is centered at the onset with \(\tau=0\) (and the first half is zero-padded), so artifacts such as pre-echo (in the first half of the frame) are also expected to be captured by the local SRER. The global SRER is calculated across all frames, thus considering the whole sound signal \(\hat{x}\left(n\right)\). Next, we describe the musical instrument sounds modeled and the selection of parameter values for the algorithms.
## 2 Nonlinear Mismatches in \(M\)-TIADC
### Mismatch-Induced Error Signal  
By finding the discrete-time Fourier transform (DTFT) of \(y[n]\), the output in the frequency domain is written as [25]:  
\[Y(e^{j\omega})=X(e^{j\omega})+E(e^{j\omega}), \tag{2}\]  
where:  
\[E(e^{j\omega})=\sum_{k=0}^{M-1}E_{k}(e^{j\omega}) \tag{3}\]  
with:  
\[E_{k}(e^{j\omega})=\sum_{l=2}^{L}\frac{1}{(2\pi)^{l-1}}A_{l}(k)X^{*l}\left(e^{ i(\omega-\frac{\omega\alpha}{M}k)}\right) \tag{4}\]  
and:  
\[A_{l}(k)=\frac{1}{M}\alpha_{n,l}e^{-j\frac{2\alpha}{M}mk}, \tag{5}\]  
in which \(X^{*l}(e^{j\omega})\) represents the \(l\)-fold convolution of \(X(e^{j\omega})\).  
The error signal \(E(e^{j\omega})\) can be expressed in matrix form as:  
\[E(e^{j\omega})=\sum_{l=2}^{L}\tilde{E}_{l}=\sum_{l=2}^{L}\tilde{X^{l}}^{T}{ \mathbf{W}}\widetilde{\alpha}_{l} \tag{6}\]  
Figure 1: (**a**) A time-interleaved analog-to-digital converter (ADC) consists of \(M\) sub-ADCs; (**b**) timing diagram of the sampling clocks. MUX, Multiplexer; CLK, Clock.  
with:  
\[\vec{X}^{l}=\frac{1}{(2\pi)^{l-1}}\left[X^{sl}(e^{j\omega})\ X^{sl}(e^{j(\omega- \frac{2\pi}{M})})\ \cdots\ X^{sl}(e^{j(\omega-\frac{2(M-1)\pi}{M})})\right]^{T}, \tag{7}\]  
\[\mathbf{W}=\left[\begin{array}{cccc}1&1&\cdots&1\\ 1&e^{-j\frac{2\pi}{M}}&\cdots&e^{-j\frac{2(M-1)\pi}{M}}\\ \vdots&\vdots&\ddots&\vdots\\ 1&e^{-j\frac{2(M-1)\pi}{M}}&\cdots&e^{-j\frac{2(M-1)(M-1)\pi}{M}}\end{array}\right] \tag{8}\]  
being the discrete Fourier transform (DFT) matrix and:  
\[\vec{\alpha}_{l}=[\alpha_{0,l}\ \alpha_{1,l}\ \cdots\ \alpha_{M-1,l}]^{T} \tag{9}\]  
being the nonlinear coefficients vectors.  
In the next subsection, the error signal \(E(e^{j\omega})\) is represented by the binary Hadamard matrix of order \(M\), and a model for \(M\)-TIADC exploiting the multiplier Hadamard transform (MHT) structure is presented.
# Inertial Parameter Identification in Robotics: A Survey
## 8 Introducing the BIRDy Matlab Toolbox: A Benchmark for Identification of Robot Dynamics
### Implementation Details
#### 9.3.2 ML Implementation  
As with the IDIM-LS methods, two different variations of the ML identification algorithm were evaluated, namely with and without data-filtering. In the proposed implementation of the ML identification method, the values of the diagonal variance matrix \(\mathbf{\sigma}_{k}^{2}\) are set based on variance measurements of the error signal between the measured joint derivatives \(\bar{\mathbf{q}},\bar{\mathbf{q}},\bar{\mathbf{q}}\) and the simulation reference \(\mathbf{q},\dot{\mathbf{q}},\bar{\mathbf{q}}\). Please note that it is usually difficult to obtain the value of \(\sigma_{k}^{2}\) on a real system as the reference quantities \(\mathbf{q},\dot{\mathbf{q}},\bar{\mathbf{q}}\) are not available. The resolution of the nonlinear optimization problem (36) is carried out using the Levenberg-Marquardt algorithm through the _lsqnonlin_ function from the Matlab Optimization Toolbox. The Jacobian matrices \(\mathbf{G}_{k}\) are computed numerically, with finite differences and a tolerance of \(10^{-7}\). The algorithm stops once any of the following criteria is reached:  
* \(\left(\left\|\rho_{ML}^{i+1}\right\|_{2}-\left\|\rho_{ML}^{i}\right\|_{2} \right)/\left\|\rho_{ML}^{i}\right\|_{2}<\text{tol}_{1}\) where \(\mathbf{\rho}_{ML}^{i}\) denotes the error at iteration i, defined following (36) as \(\mathbf{\rho}_{ML}^{i}=\dfrac{1}{2}\sum\limits_{k=1}^{N}\mathbf{\varepsilon}^{ \top}(t_{k})\left(\mathbf{G}_{k}\mathbf{\sigma}_{k}^{2}\mathbf{G}_{k}^{\top}\right)^{-2} \mathbf{\varepsilon}(t_{k})\);
* \(\underset{j}{\max}\left|\left(\hat{\mathbf{\beta}}_{ML}^{i+1}(j)-\hat{\mathbf{\beta}}_ {ML}^{i}(j)\right)/\hat{\mathbf{\beta}}_{IV}^{i}(j)\right|<\text{tol}_{2}\), \(\forall\hat{\mathbf{\beta}}_{ML}^{i}(j)\
eq 0\) with \(j\) the index and \(i\) the iteration number.
* maximum number of iterations: \(i_{max}=10\).  
where the values of the function tolerance \(\text{tol}_{1}\) and step tolerance \(\text{tol}_{2}\) are set according to the guidelines given in [11]. In our case, we have \(\text{tol}_{1}=0.1\%\) and \(\text{tol}_{2}=2.5\%\).
## 2 Machinery Risk Assessment Methodology
### Risk Assessment Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Consequence (C)**} \\ \cline{3-5}
**Estimation of Probability (P) and** & Level of C & 1—minor & 2—serious & 3—severe \\ \cline{2-5}
**Consequence (C), and Risk.** & & & Almost accident, & \\ \cline{3-5} (Step 3, Figure 1) & Description & & & Sick leave more & Severe occupational \\  & & & leave duration max. & than 3 days & injury \\ \hline  & & & & \\ \hline  & Probability (P) & & & & \\ \hline Level of P & Description & & & R = P \(\times\) C \\ \hline
1—low & The harm after exposure to a hazard is & & **R(1)** & **R(2)** & **R(3)** \\  & almost impossible. & & & & \\
2—medium & The harm after exposure to a hazard is & & & & \\  & almost possible. & & & & \\
3—high & The harm after exposure to a hazard is & & & & \\  & almost certain. & & & & \\ \hline Acceptable risk assessment (Risk category) & & & & & \\  & (Step 5, Figure 1) & & & & \\ \hline
**R(1); R(2)** & Negligible & & No measures for risk level reduction are necessary. & \\
**R(3); R(4)** & Acceptable (ALARP) & It is suitable to accept and/or increase the efficiency of measures to reduce the risk & level, by reducing the P level or C level. & \\
**R(6); R(9)** & Inacceptable & It is inevitable to immediately take and increase the efficiency of measures to reduce & the risk level, by reducing the P level and/or C level. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example of a constructed “risk matrix” according to the algorithm for risk assessment (source: own design). Estimation of the probability and consequence level in Table 1 represents a qualitative expression, which does not specify the frequency or probability of harm, e.g., per year. With the methodology applied for the assessment of consequences of severe industrial accidents (e.g., SEVESO III Directive), the consequence is expressed mainly by the number of affected people (within but also out of plant) and the so-called social acceptability (e.g., 1 death per 1000 persons) is evaluated [28; 40; 41].
## 5 Guidance and Control Algorithm
### Rendezvous
#### 5.1.3 Guidance Trajectory Update Strategy  
The primary objective of rendezvous is to minimize the drift rate in the final state by generating a passively safe orbit. Therefore, a PCO with an initial position and velocity was generated, and the final state of the PCO was set as the target state. During half of the orbital period for rendezvous, the initial time and state are updated and a new optimal trajectory is generated for every cycle (120 s), which is the ISL period. As a result, the periodic guidance update strategy not only compensates for the error due to the nonlinearity of dynamics but also prevents the initial estimated error that is contained in the optimal trajectory from gradually increasing as it propagates. The formulation for PCO was obtained from [42]:  
\[\begin{cases}x(t)=\frac{d_{1}}{2}\sin{(nt+\alpha)}\\ y(t)=d_{1}\cos{(nt+\alpha)}+d_{3}\\ z(t)=d_{2}\sin{(nt+\beta)}\end{cases}. \tag{12}\]  
The guidance trajectory of the sampling frequency (\(\mathbf{f}_{S1}\)) was chosen as 2 to 10 min (Figure 6). If it is set to 2 min, the final drift rate may decrease, but it requires a larger thrust owing to its shortened maneuver time. In this study, \(\mathbf{f}_{S1}\) is set as 0.0083 Hz (once per 120 s) because the maximum thrust is smaller than the saturation limit, and the fuel budget is affordable. During orbit control, the target thrust vector should not change frequently; otherwise, the attitude control performance should be degraded. Hence, the thrust vector sampling frequency (\(\mathbf{f}_{S2}\)) is set as 0.25 Hz (once per four seconds) and fired consistently.  
Figure 7 shows the rendezvous simulation results for achieving the PCO. The dynamic models were set as JGM3 70 \(\times\) 70 for gravity, the exponential model for atmospheric drag, and the spherical model for solar radiation pressure. The differential equation was integrated using the ode45 integrator in MATLAB with a step size of 1 s. The simulation settings were identical to those used in this study. In the final controlled state, the error is approximately 0.1 m in the radial and cross-track directions and 1.0 m in the along-track direction. The optimal control input shows discontinuity every 120 s, and the magnitude increases after half the maneuver time. This is because the remaining feasible time is  
reduced and can be eliminated when \(f_{S1}\) is decreased. While the optimal control input changes over time, the sliding mode control input has a similar form to noise under 0.02 mN. Initially, it barely required a cross-track maneuver; thus, the optimal control showed a lower level than the sliding mode control.
## 3 Materials and Methodology
### Deep Learning Models
The output highlights the characteristics of the input data, which is why the output is referred to as a feature map. An activation function further analyzes the convolution result, and down-sampling uses pooling to remove extraneous data. CNNs use rounds of learning to update the kernels/filters so that the feature map can functionally represent the input data. Figure 3 depicts an example of a CNN architecture. Convolutional layers, dropout regularization, a multi-head attention mechanism, a dense layer, the max pooling layer, and the flattening layer are all included in the model. The feature maps acquired from the convolutional layer are down-sampled using max pooling to lower their dimensionality while keeping significant features. The output from the preceding layers is flattened into a vector by the flattening layer, readying it for the fully connected layer. A pair of dense layers, each consisting of 256 and 2 units, is incorporated. The ReLU activation function is employed in the first dense layer, and the SoftMax activation function is used in the final two units of the dense layer to produce probabilities for binary classification. The model is optimized using the Adam optimizer and trained with a categorical cross-entropy loss function for multiclass classification.