esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Time-Frequency Selection  
To obtain the target time-frequency masks defined as in Equation (7), we propose a target speaker extractor BLSTM network, which is illustrated in Figure 1. The network consists of one BLSTM layer with 512 nodes followed by two fully-connected Feed-Forward (FF) layers, each with 1024 nodes. This part takes the magnitude spectrum of the observed signal as input and is supposed to output the masks of the target speaker. In our experiments, the Short-Time Fourier Transform (STFT) is performed in 512 points, so the input and output dimensions of the network are both 257. For the last layer, a sigmoid activation function is applied, which ensures that the outputs are in the range of [0,1].  
Besides this main network, there is an auxiliary network designed to provide information of the target speaker. The auxiliary network has two fully-connected layers each with 50 nodes and takes a speaker-dependent utterance as input. The frame-level activations of the auxiliary network are averaged over time and serve as utterance-level weights to adapt the second hidden layer outputs of the main network. Mathematically, the computation is expressed as:  
\[L^{(2)}=\text{ReLU}(O^{(aux)}\odot\text{FF}(L^{(1)})) \tag{8}\]  
where \(L^{(l)}\) denotes the activations of the \(l\)-th layer, \(\text{ReLU}(x)=\max(0,x)\) is the non-linear activation function, \(O^{(aux)}=\text{Average}(aux(S_{target}))\) denotes the utterance-averaged weights provided by the auxiliary network, and \(\odot\) denotes element-wise multiplication. The function of this auxiliary network resembles the Learning Hidden Unit Contribution (LHUC) technique for acoustic model adaptation [31] and the speaker-adaptive layer technique for speaker extraction [32]. In these studies,it has been found that the hidden parameter weighting is better at preserving the speaker information than that using the adaptation utterance as an extra input to the main network.  
Given training speech examples, the whole network is optimized under the cross-entropy criterion. The Adam method is applied to schedule the learning process. Weight decay (\(1e^{-5}\)) and weight norm clipping are used to regularize the network parameters. Note that both in the training phase and in the test phase, the neural network is single-channel based, meaning that the target speaker extraction relies only on the spectral characteristics, because the spatial information is not reliable when the competing interference is also speech. Moreover, this setup is flexible, and it is applicable to arbitrary array geometry. In the test phase, we suggest to apply a median pooling on the masks estimated from different channels.
###### Abstract  
This study investigated the influence of ordinary Portland cement (OPC) and reactive and non-reactive mineral additives on the characteristic microstructure and mechanical performance of ultra-high-performance, strain-hardening cementitious composites (UHP-SHCCs). Nine mixes of cementitious composites were considered composed of reactive and non-reactive materials, such as ground granulated blast furnace slag (GGBS), silica fume (SF), cement kiln dust (CKD), and silica flour. Compressive strength and direct tensile tests were performed on the nine mixes cured for 7 d and 28 d. The test result was analyzed based on microstructural inspections, including thermogravimetry and scanning electron microscopy. The test result and analysis showed that the microstructural property of the UHP-SHCC impacted the compressive strength and the tensile behavior and also influenced the fiber-matrix interaction. Although most of the 7 d cured specimens did not exhibit notable strain-hardening behaviors, the specimen containing the CKD exhibited a tensile strength of 11.6 MPa and a very high strain capacity of 7.5%. All the specimens with OPC, silica flour, GGBS, or SF exhibited considerably improved tensile behavior at 28 d. The specimen with only OPC as a binder could achieve the tensile strength of 11.6 MPa and strain capacity of 6.2%.  
UHP-SHCC; mineral additives; curing conditions; mechanical performance; microstructural analysis +
Footnote †: journal: Applied Sciences
# Low-Frequency Square Kilometer Array Pattern Optimization via Convex Programming
## 3 Materials and Methods
### Preprocessing and Optimization (MATLAB)  
Post-simulation data from FEKO were imported into MATLAB for further processing, which included geometric phase correction, polarization projection, and optimization of excitation weights via convex programming.  
* _Geometric Phase Correction_ To account for the relative positions of the antennas within the array, a geometric phase correction was applied. Each antenna's EEP was initially simulated with the origin of the local reference system centered at each element position. Then, to express the field relative to the global center of the array, the following phase transformation was applied to both components of the radiated field: \[\alpha_{o}(\theta,\varphi)=\alpha_{o^{\prime}}(\theta,\varphi)+\frac{2\pi}{ \lambda}\{sin(\theta)[cos(\varphi)\Delta x+sin(\varphi)\Delta y]+cos(\theta) \Delta z\}\] (5)  
Figure 5: 3D representation of the power pattern from the simulated array shown Figure 3. Credits: ICRAR/INAF.  
Here, \(\alpha_{o^{\prime}}\) denotes the original phase when the antenna is centered in its local reference system, and \(\alpha_{o}\) is the corrected phase relative to the array center. The terms \(\Delta x\), \(\Delta y\), and \(\Delta z\) represent the spatial offsets of each antenna from the array center, and \(\lambda\) is the wavelength.
* _Copolar Field Component Extraction_ After applying the phase correction, the copolarized field component was derived using [PERSON]'s formula [27]. The copolar component for each EEP was computed as follows: \[EEP^{COP}(\theta,\varphi)=\mathbf{EEP}(\theta,\varphi)\cdot\left(\sin(\varphi) \hat{\mathbf{i}}_{\theta}+\cos(\varphi)\hat{\mathbf{i}}_{\varphi}\right)\] (6) where \(\hat{\mathbf{i}}_{\theta}\) and \(\hat{\mathbf{i}}_{\varphi}\) represent the unitary vectors associated respectively with elevation and azimuthal angle. This projection yields the field component aligned with the desired polarization direction, which is essential for a consistent evaluation of the power pattern. From a practical point of view, the EEPs were structured into two complex-valued 3D tensors representing the \(\varphi\) and \(\theta\) components, each of size 720 \(\times\) 181 \(\times\) 256 (azimuth \(\times\) elevation \(\times\) element index). The two tensors were then combined through vector summation to yield the vectorial EEPs.
* _Weight Optimization via Convex Programming_ As a preliminary reference, a station pattern was computed using uniform (unitary) weights for all elements. To suppress sidelobes and enhance directivity, a convex optimization problem to maximize the main lobe gain in a target direction while constraining sidelobe levels to remain below a threshold, set to \(-\)25 dB. The optimization was executed using MATLAB 2024b's _fmincon_ function with the Active-Set algorithm, which showed superior performance compared to the Interior-Point method in this application, offering faster convergence and greater stability under the given constraints.
# Optimized Scheduling of IoT Devices in Healthcare Facilities: Balancing Cost and Quality of Care
## 3 Materials and Methods
### Solution Method
```
1:Input: Population size \(pop\_size\), Number of generations \(num\_gen\), Crossover probability \(p_{c}\), Mutation probability \(p_{m}\), Crossover operator \(cross\_op\), Mutation operator \(mut\_op\), Selection method \(sel\_method\)
2:Output: Optimized solutions and logbook of evolutionary process
3:Step 1: Define Genetic Algorithm Components
4: Register the **individual structure**: List of assignments
5: Register the **fitness function**: Multi-objective (min cost \(\gamma\), max quality of care \(\sigma\))
6: Register **genetic operators**: \(cross\_op\), \(mut\_op\), \(sel\_method\)
7: Set NSGA-II parameters: \(pop\_size\), \(num\_gen\), \(p_{c}\), \(p_{m}\)
8:Step 2: Initialize the Population
9: Create an initial population of \(pop\_size\) individuals
10: Each individual is randomly generated with assignments
11:Step 3: Evolutionary Process
12:for generation = 1 to \(num\_gen\)do
13: Evaluate the fitness of each individual in the population
14: Apply NSGA-II selection using \(sel\_method\):
15: Apply Crossover with probability \(p_{c}\) using \(cross\_op\):
16: Apply Mutation with probability \(p_{m}\) using \(mut\_op\):
17: Randomly modify some assignments (change device or time slot)
18: Apply Elitism:
19:endfor
20: Return the final population and logbook containing evolutionary data
```  
**Algorithm 2** Execution of NSGA-II. Solving a multi-objective problem with a primarily single-objective MILP solver like SCIP presents challenges due to the need to handle multiple conflicting objectives simultaneously. While SCIP is designed for single-objective optimization, various techniques can be employed to adapt it for multi-objective problems. One effective approach is the \(\epsilon\)-constraint method, a well-established technique in multi-objective optimization, as described in [34]. This method transforms a multi-objective problem into a series of single-objective optimizations by iteratively reformulating the problem: one objective is selected as the primary function to minimize, while the remaining objectives are converted into constraints with dynamically adjusted bounds. This allows the solver to systematically explore the trade-offs between competing objectives and generate a set of Pareto-optimal solutions. We refer to the method of employing the SCIP solver to solve a bi-objective optimization problem using the \(\epsilon\)-constraint method as 'SCIP 2-OBJ'.
# Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity
## 3 Super-Resolution Imaging for Passive Radar
### Further Discussions
#### 3.3.2 Influence of Measurements  
In CS-based passive radar imaging, accurate frequency estimation can be obtained only when a large \(M\) is employed. In our algorithm, we only require that \(M\geq O\left(K\log\left(N/K\right)\right)\) for Equation (25). In contrast, our investigations (detailed in Section 4) demonstrate that ESPRIT and RELAX algorithms cannot yield accurate frequency estimation for such a small \(M\). This implies that our proposed CS algorithm can achieve better frequency estimation performance than the ESPRIT and RELAX algorithms when limited measurements are available.
# A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
## 3 Experiment and Results
### Target Detection Experiment
#### 3.2.2 Target Detection Using CycleGAN and the Improved Yolov8
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Augmented** & & & & \\
**Side-Scan** & **True\textbackslash{}Predicted** & **Background** & **Plane** & **Ship** \\
**Sonar Image Dataset** & & Ship & 0.04 & 0.02 & 0.94 \\  & Plane & 0.23 & 0.72 & 0.05 \\  & Background & / & 0.31 & 0.69 \\ \hline \multirow{3}{*}{Dataset B} & Ship & 0.08 & 0 & 0.92 \\  & Plane & 0.13 & 0.83 & 0.04 \\  & Background & / & 0.19 & 0.81 \\ \hline \multirow{3}{*}{Dataset C} & Ship & 0.07 & 0 & 0.93 \\  & Plane & 0.09 & 0.91 & 0 \\  & Background & / & 0.35 & 0.65 \\ \hline \multirow{3}{*}{Dataset D} & Ship & 0.03 & 0 & 0.96 \\  & Plane & 0.12 & 0.82 & 0.07 \\  & Background & / & 0.35 & 0.65 \\ \hline \multirow{3}{*}{Dataset E} & Ship & 0.01 & 0.02 & 0.97 \\  & Plane & 0.08 & 0.91 & 0.01 \\  & Background & / & 0.25 & 0.75 \\ \hline \multirow{3}{*}{Dataset F} & Ship & 0.13 & 0.06 & 0.81 \\  & Plane & 0.32 & 0.62 & 0.07 \\ \cline{1-1}  & Background & / & 0.24 & 0.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of confusion matrices of the improved YOLOv8 on augmented datasets A–E. the result is shown in Figure 10. The overall detection accuracy [EMAIL_ADDRESS] was 0.924, which was 2.3% higher than the result (Figure 9) on the initial dataset (Table 1). Moreover, the detection accuracy [EMAIL_ADDRESS] of the ship and plane targets were 0.961 and 0.886, which were 3.5% and 1.1% higher than the results, respectively (Figure 9). These results demonstrated the effectiveness of data augmentation using CycleGAN in improving the target detection accuracy of side-scan sonar images with only small samples.
## 7 Results
### Energy Consumption
#### 7.1.2 Ec and Ea Calculations  
Upon executing the MATLAB code for health condition assessment, employed for determining data priority through fuzzy-based classification, we noted an execution time of 0.35 s. Likewise, the execution of the code for fuzzy-based server allocation yielded an execution time of 0.34 s. Consequently, the cumulative processing time for both classification and allocation processes tallies to 0.35 + 0.34 = 0.69 s.  
The power consumption of the CPU hinges on the configuration of a laptop equipped with an 11 th Gen Intel(R) Core(TM) i7-1165G7 @ 2.80 GHz 1.69 GHz, with a power consumption spectrum spanning 12 to 28 watts. Considering the peak power consumption of 28 watts, equivalent to 100.8 KJ per hour, the energy consumption for running the code within 0.69 s is computed as (100.8 \(\times\) 0.69)/3600 KJ, amounting to 0.019 KJ. This signifies the energy consumed for a single packet categorization, encompassing the determination of priority and its allocation to a specified node.  
Extending this computation to accommodate multiple packets, the energy consumption for 20 packets totals to 0.019 \(\times\) 20 KJ, equivalent to 0.38 KJ. Similarly, for 40 packets, it stands at 0.76 KJ, for 60 packets at 1.14 KJ, for 80 packets at 1.52 KJ, and for 100 packets at 1.9 KJ.  
In summary, the overall energy consumption (Etotal) of the MFHS is tabulated as follows: 0.53 KJ for 20 packets, 1.06 KJ for 40 packets, 1.59 KJ for 60 packets, 2.12 KJ for 80 packets, and 2.65 KJ for 100 packets, as presented in Table 7.  
Based on the results in [15], the energy consumption of FOFSA, ATAT, and OLB using the same packets transfer 20, 40, 60, 80, and 100 are shown in Table 8--please refer to Appendix A for detailed calculations.
# Multi-Angle Fast Neural Tangent Kernel Classifier
## 5 Analysis of Experimental Results
### Multi-Kernel-NTK Robustness Testing
#### 5.3.2 Network Parameters and Multi-Kernel-NTK  
As mentioned in Section 4.4, we investigated the effect of changing the SVM parameter C and NTK parameter L on the computational complexity of NTK, respectively, and selected several datasets from the UCI dataset and tested the robustness of NTK and multi-kernel-NTK models under different penalty parameters C, where the parameter C ranges from 0.004 to 128, and the results are shown in Figures 4 and 5.  
The red line represents the trend of the multi-kernel-NTK model with C, and the black line represents the trend of the original NTK model with C.  
Another interesting trend of the NTK model can be found in Figure 4; the training time of the model increases exponentially as C increases, which is a property not possessed by other kernel functions. Still, our proposed multi-kernel-NTK model can mitigate this trend well and avoid the significant time expenditure that may result from an overly large parameter C. It can be seen from Figure 5 that as C increases, the multi-kernel model obtains better classification accuracy compared with the original model.  
Figure 4: Effect of penalty parameter C on the training time of the model.
# Transient Attitude Motion of TNS-0#2 Nanosatellite during Atmosphere Re-Entry
## 3 Attitude Motion Reconstruction Technique
### Measurements Processing Technique  
The algorithms of the attitude motion determination using on-board sensors measurements are well studied in the literature. There are two main approaches: real-time on-board motion estimation using recursive or local algorithms [20; 21; 22] and post-flight measurements processing for motion reconstruction [23; 24; 25] and for satellite parameters characterization [26; 27]. The first approach is commonly used for active attitude control systems, and the second is more suitable for motion estimation of satellites with passive attitude control systems. The magnetometer measurements processing technique was used for the TNS-0 #2 nanosatellite attitude motion reconstruction. Sun sensors were not used since they are sensitive to the albedo and therefore have very low accuracy. Also, Sun sensors measurements are not available in the shadowed part of the orbit. Nevertheless, Sun sensors are used for the verification of the motion estimation established using magnetometer measurements.  
The problem of the attitude motion reconstruction is formulated as follows. It is necessary to obtain such initial conditions for the attitude motion equations that the difference between the predicted measurements calculated using the measurements model and the actual measurements from the on-board sensor achieves a minimum by the mean square criterion.  
Consider an initial conditions vector consisting of the quaternion vector part \(\mathbf{q}(t=0)\) and angular velocity vector \(\boldsymbol{\omega}(t=0)\):  
\[\boldsymbol{\xi}=[\mathbf{q}(t=0),\boldsymbol{\omega}(t=0)]^{T}\]  
For the given vector \(\boldsymbol{\xi}\) the attitude quaternion \(\Lambda(t=t_{k})\) for any time \(t_{k}\) is obtained by the integration of the motion equations. The quaternion provides the prediction of the magnetometer measurements  
\[\mathbf{b}_{\text{model}}^{k}=\mathbf{A}(\Lambda_{k})\mathbf{b}_{o}^{k}\]  
where \(\mathbf{b}_{\text{model}}^{k}\) is a predicted unit vector of the geomagnetic induction field in the orbital reference frame, and \(\mathbf{b}_{o}^{k}\) is calculated according to IGRF model for a given position of the satellite (obtained using either GPS/GLONASS receiver or TLE and SGP4 model). The problem of the vector \(\boldsymbol{\xi}\), determination reduces to the problem of the following function minimization  
\[F(\boldsymbol{\xi})=\sum_{k=1}^{N}\biggl{(}\biggl{|}\mathbf{b}_{\text{model}}^ {k}-\mathbf{b}_{meas}^{k}\biggr{|}\biggr{)}^{2}\]  
where \(\mathbf{b}_{meas}^{k}\) is a unit magnetometer measurements vector excluding the constant bias. The minimization of the function \(F(\boldsymbol{\xi})\) is carried out using the nonlinear optimization methods.
Several studies using test particles simulation [15; 16; 17] have examined the question of what magnitude of electric filed is required to explain the flux enhancements at lower L-shells. They are based on the assumption of penetration of large-scale electric fields into the inner magnetosphere. It is important to note that this approach may explain nighttime deep injections, although daytime injections are also observed. [PERSON] et al. [15] have demonstrated that a strong electric field is necessary to explain the energetic electron increases in the inner ERB. The authors suggested that the enhanced large-scale electric fields may penetrate deeper into the inner magnetosphere during storm periods. They concluded that the actual field magnitude is stronger than those in the existing electric field models, particularly for \(L\leq 2\), and it is even stronger than in the outer magnetosphere. Modeling by [16] showed that injections below the inner edge \(L=1.2\) during major storms in 2015 is consistent with a rapid enhancement of the convection electric field to 5 mV/m. However, modeling of another event (9 November 2004) showed that the same field was needed, but with westward direction at dayside, expected from an overshielding effect [17]. As discussed above, observation showed that the large-scale westward dayside fields are small. Transient electric fields measured in the equatorial inner magnetosphere and ionosphere were studied with satellite experiments in several papers [20; 21; 29; 30; 31; 32; 33; 34; 35; 36; 37]. The amplitudes of time-varying (inductive) electric fields are quite high. Their characteristic time is about several minutes or less. Inductive electric fields are associated with temporal variations of the magnetic field and, therefore, they are related to the varying solar wind conditions. Modeling of inductive electric fields and its application to simulate the dynamics of energetic particles in the inner magnetosphere is currently under active development [38; 39]. Here we present a comprehensive study of the solar wind parameters accompanying the FEE enhancements observed in the energy range from 30 to 300 keV. The cases of three great geomagnetic storms (super storms) are analyzed in detail. The method of data selection is described in Section 2. The results of analysis are presented in Section 3 and discussed in Section 4. Conclusions are offered in Section 5.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 2 Basic Principle and Functionality  
LiDAR, also called LaDAR or optical radar [24], employs electromagnetic waves in the optical spectrum to measure the distance between the sensing instrument and a target object time. Additionally, by analyzing the interaction of the emitted radiation with the target--such as scattering, absorption, reflection, and fluorescence--one can also infer its physical characteristics [24; 25]. As an active remote sensing technique, LiDAR provides its own illumination source, enabling data acquisition both during the day and at night, including regions of total darkness, such as the prolonged darkness of polar areas [26; 27].
# DCO-MAC: A Hybrid MAC Protocol for Data Collection in Underwater Acoustic Sensor Networks
## 4 Proposed DCO-MAC Protocol  
In the network initialization phase, the position of the sensor nodes is obtained by performing a localization algorithm. The sensor nodes broadcast HELLO packets to exchange position information. After receiving HELLO packets from the neighboring nodes, the sensor nodes calculate the propagation delay with the neighboring nodes according to the position information; the propagation delay is calculated as the ratio of the distance and sound speed. In the system model described in Section 3, a level-1 node belongs to a level-1 sub-network and one of the level-2 sub-networks. In order to avoid the collision, the data packet transmission in two kinds of sub-networks is executed alternately.  
Figure 1: The network topology of the two-tier underwater acoustic sensor network (UASN) for data collection.  
As the traffic load is heavy in the level-1 sub-network, the probability that multiple level-1 nodes simultaneously send control packets to reserve the channel is relatively high, which results in a high collision possibility at the sink. Hence, a RSV-MAC protocol is implemented in the level-1 sub-network. In the RSV-MAC protocol, the sink initiates the transmission of the level-1 sub-network, and schedules the data transmission of the level-1 nodes to avoid the collision. Because of the light traffic load in the level-2 sub-network, the collision probability of the control packets is small. Therefore, a CT-MAC protocol is carried out in the level-2 sub-network, where the level-2 node sends a control packet to reserve the channel before sending the data packets. In addition, in order to support the access of the mobile nodes, extra transmission time is reserved in both the RSV-MAC and CT-MAC protocols.