esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Models
#### 3.3.3 SeqGAN  
GAN, as proposed by [28], is a deep learning-based model used for generating realistic data from random input. GAN comprises two deep learning networks, the generator and discriminator, both trained collaboratively. The discriminator \(D\) is tasked to distinguish between real (training data) and fake (generated) data, while the generator \(G\) aims to deceive \(D\) by producing new samples that closely resemble real data, stemming from random input  
Figure 5: Skip-gram model for MalCode2 Vec.  
distribution. GAN training operates on a minimax game framework, where each network strives to maximize its respective benefits. While GANs (conditional GANs, Wasserstein GANs, etc.) have achieved notable success in image generation, they are primarily designed to generate real-valued, continuous data. Therefore, generating sequences of discrete tokens, such as text, poses a significant challenge. The discrete nature of generated data makes it difficult for \(D\) to guide \(G\) on the outputs.  
SeqGAN [29] extends GAN for sequential generation tasks. In this framework, the generator is regarded as a reinforcement learning agent, where each generated token represents a state and the selection of the next token constitutes an action.  
A discriminator is trained to provide rewards for complete sequences, and Monte Carlo search is employed to sample complete sequences.  
The generative model was constructed using LSTM [30], while CNN was used as the discriminator.  
The effectiveness of the GAN model was demonstrated in [31], where a CNN-based ACGAN was applied to grayscale images of malware. This approach notably enhanced the generality of the detector. We utilized the SeqGAN model for malware families, particularly focusing on addressing a few highly imbalanced classes.
## 7 System Performance and Evaluation
### Performance of Web Page  
The web page should be lightweight and scalable for efficient information sharing in real-time. To check the efficiency of our web page, an equivalent simple object access protocol (SOAP)-based remote web-monitoring framework was developed to check flexibility, overhead, and scalability. For comparison purposes, a client requested one or more requests simultaneously, and multi-threading was used to respond to multiple client requests. Figure 12a compares the memory consumption of REST-based and SOAP-based web pages for greater than 50 requests. As the number of requests increases, the message size also increases. Figure 12b shows the comparative analysis of a REST web service application program interface with that of an existing SOAP-based web service framework. For a similar number of requests, the REST-based program interface consumes substantially less time when compared to the SOAP-based web page. The REST outperformed SOAP in terms of request completion time and demonstrated faster responses when compared to SOAP-based web services. Therefore, the REST style web interface is efficient and light weight. Moreover, the SOAP-based web server utilizes more memory as compared to the REST-based webpage.
# Passive Location for 5G OFDM Radiation Sources Based on Virtual Synthetic Aperture
## 4 Resolutions and Satellite Vibration  
### Azimuth Resolution  
The 3 dB width of the matched filtering output is \(\tau_{a}=1/B_{a}\), where \(B_{a}\) is the azimuth bandwidth of the signal. The virtual aperture time is \(T_{s}\), so the bandwidth \(B_{a}=KT_{s}=kv/D\). Thus, the azimuth resolution is:  
\[\rho_{a}=v\tau_{a}=\frac{v}{B_{a}}=\frac{D}{k}. \tag{21}\]  
As a result, the azimuth resolution is constant. However, if we process only part of the received data, the azimuth resolution will change. Suppose that the length of the captured data is \(t_{s}\), the azimuth resolution is:  
\[\rho_{a}(t_{s})=\frac{v}{Kt_{s}}=\frac{v}{t_{s}}\frac{cR_{0}}{v^{2}f_{0}}= \frac{cR_{0}}{vf_{0}t_{s}}. \tag{22}\]  
Therefore, the longer the virtual aperture is, the better the azimuth resolution will be.
## 3 Results
### Differentiation of 3T3-L1 White Headipocytes and immortalized Brown Preadjocytes  
Differentiation into white adipocytes and brown adipocytes was induced, and the differentiated white adipocytes and brown adipocytes were identified by staining fat droplets with Oil-Red-O (Figure 1A,B). In addition, lipid accumulation during the differentiation of brown adipose progenitor cells was confirmed (Figure 1A,B). To confirm the differentiation from immature adipocytes to mature adipocytes, the expression of specific transcriptional regulators was analyzed (Figure 1). Peroxisome proliferatorator-activated receptor gamma (PPAR\(\gamma\)), adipocyte protein 2 (aP2), and CCAAT/enhancer-binding protein alpha (C/EBP\(\alpha\)), which are specific transcription factors for white adipocytes, were identified (Figure 1A). In addition, PR domains containing 16 (PRDM16), PPAR\(\gamma\), peroxisome proliferatorator-activated receptor gamma coactivator 1 alpha (PGC1\(\alpha\)), and uncoupling protein 1 (UCP1), which are specific transcription factors for brown adipocytes, were identified (Figure 1B).  
\begin{table}
\begin{tabular}{c c c} \hline
**Target Gene** & **Forward Primer** & **Reverse Primer** \\ \hline Gapdh & GGGCTCCCAGCTTAGGTTCAT & TTCACACCCGACCTT \\ Ucp1 & AGGATTGGCCCTCTACGACTCA & GCATTCTGACCTTACAGACCT \\ Pgc1\(\alpha\) & TCTGGGTTGATTGAAGTGGTG & TCTGGAACACCGTAGCAAG \\ Ppar\(\gamma\) & TCCATTCACAAAGAGCTGACC & GCCCATGGGGAGTTAGAAAGG \\ Prdm16 & ACAAGCTCTACACGCGATTC & CGTGATATGTTTTTGCCCT \\ aP2 & GCTTTGCCCAAAAGAAAG & ACGCCCAGTTTTGAAGGAAT \\ Cebp\(\alpha\) & TGGACAAGAACACGAACG & ACCTTCTGTTGCGTCTCCCAC \\ \hline \end{tabular}
\end{table}
Table 1: List of primers (sequence 5′\(\rightarrow\)3′) used for qPCR.
## 4 Conclusions  
In this study, in order to investigate the influence of wet compression on the performance of a heat pump system with a 3.5 Kw capacity, we performed an experiment under the ISO 13256-2 test conditions, varying the quality at the compressor inlet by controlling EEV, using R32 as the working fluid. The results demonstrate that the discharge temperature decreased and the refrigerant mass flow rate increased, due to the rising liquid droplets with respect to a decrease in the quality. The heating capacity increased with an increasing EWT of OD HEX and decreasing quality, due to the increasing refrigerant mass flow rate, while it decreased rapidly at a quality of 0.91, 0.88, and 0.84, for EWT of OD HEX at 10 \({}^{\circ}\)C, 5 \({}^{\circ}\)C, and 0 \({}^{\circ}\)C, respectively, due to the loss of the mass flow rate caused by a back flow of liquid refrigerant into the suction chamber. The power input remained almost unchanged during dry compression, while it increased with decreasing quality during wet compression, which was also due to an increase in the mass flow rate, and then sharply decreased due to the loss of the mass flow rate. When the EWT of OD HEX was 10 \({}^{\circ}\)C, 5 \({}^{\circ}\)C, and 0 \({}^{\circ}\)C, the power input increased by a maximum of 10%, 12.7%, and 14.4%, respectively, when compared to that of SH 10. The COP then increased from SH 10 \({}^{\circ}\)C until it reached a maximum value at a quality of ca. 0.94 to 0.90 in the two-phase mixture region, according to the EWT of OD HEX, and then decreased with quality. This indicates that the heating capacity consistently increases more than the power input with decreasing quality, until an abrupt decrease; following this, the power input in the region of wet compression is proportionally larger than the increase in the heating capacity, according to the decreasing quality. The maximum COP thus occurs at specific qualities, i.e., as mentioned above, ca. 0.94 to 0.90. When the EWT of OD HEX was 10 \({}^{\circ}\)C, 5 \({}^{\circ}\)C, and 0 \({}^{\circ}\)C, the COP increased by a maximum of ca. 12.4%, 10.6%, 10.2%, respectively, when compared to that of SH 10 \({}^{\circ}\)C.  
In summary, wet compression by controlling the EEV opening could provide an alternative solution for systems operating at a high compression ratio, as long as care is taken with regards to excessive liquid droplets, which may raise the load of the compressor. In addition, the superheat at the discharge line could be proposed as a proper controlling parameter for adjusting the quality at the suction line by varying the opening of the expansion valve during wet compression.  
[PERSON], [PERSON] conceived and designed the experiments; [PERSON] did necessary calibrations for the measuring devices and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools and wrote the paper.  
The authors declare no conflict of interest.  
Figure 16: Superheat at the discharge line as a function of quality for different EWT of OD HEX.
## 4 Experimental Setup  
The Aurora-4 database [35] is used to evaluate noise robustness of the features created via the proposed RPCA-SPC. Aurora-4 is a medium vocabulary task (a 5000-word vocabulary task) acquired from the Wall Street Journal (WSJ) corpus [36] at 8 kHz and 16 kHz sampling rates. In Aurora-4, 7138 noise-free clean utterances are recorded with a primary microphone to form the clean-training set, and they are also further contaminated to form the multi-training set with or without the secondary channel distortions and any of six different types of additive noise at the SNRs ranging from 10 to 20 dB. The testing data are split into 14 different test sets (Sets 1-14), with each set containing 330 utterances. The utterances in Sets 1-7 are recorded with a single microphone, while different microphones are used to record the utterances in Sets 8-14, which accordingly contain channel distortions relative to those in Sets 1-7. In addition, Sets 2-7 and Sets 9-14 are further contaminated by additive noise of six types at SNRs from 5 to 15 dB. In particular, as for our experiments we adopt the clean-condition training mode to prepare the acoustic models, and all of the utterances used are at the sampling rate of 8 kHz.  
Regarding the speech features, 39-dimensional MFCCs (including 13 static components plus their first- and second-order time derivatives) and 40-dimensional FBANK features serve as the baseline features, and they are further processed by any of mean normalization (MN), mean and variance normalization (MVN), relative spectral (RASTA), and the presented RPCA-SPC. The clean-condition training data is converted to speech features, which are then used to train context-dependent (CD) acoustic models, which further have two different structures, i.e., GMM-HMM and DNN-HMM, in which GMM, DNN, and HMM refer to Gaussian-mixture model, deep-neural network, and hidden Markov model, respectively. Stated in more detail, GMM-HMM and DNN-HMM use GMM and DNN, respectively, to represent each state of the hidden Markov model. As for GMM-HMM, each tri-phone of speech signals and the silence are respectively characterized by a HMM with three states and eight Gaussian mixtures per state and a HMM with three states each having 16 mixtures. On the other hand, seven layers are used for the DNN structure in the DNN-HMMs for tri-phones and silence, having five hidden layers with each layer containing 2048 nodes. A set of trigram language models is created via the reference transcription of training utterances. Finally, the evaluation results are represented using word error rate (WER).  
Figure 5: (**a**) The time series of the eighth logarithmic mel-filter-bank coefficients (FBANK) of a noisy utterance; (**b**) The sparse part of (**a**) derived from RPCA; (**c**) The low-rank part of (**a**) derived from RPCA.
# Optimized Gateway Placement for Interference Cancellation in Transmit-Only LPWA Networks
## 3 Single GW Placement for Two SNs
### Margins  
In this section, we consider some SINR margins to improve the likelihood of correct decoding in the presence of variations in power levels, e.g., due to shadowing. In particular, these margins imply slightly larger or smaller circles; a capture margin and location margin will be suggested.  
1. Capture Margin  
The capture margin is a small amount, \(\epsilon\) added to the capture threshold \(\tau\), then, the SINR of a received packet in a GW, \(\gamma\) is shown in Equation (25).  
\[\gamma=\frac{P_{r}(d_{m})}{\sum_{i\
eq m}P_{r}(d_{i})+N_{0}}\geq(\tau+\epsilon). \tag{25}\]  
As shown in Figure 5, when \(z\) is held fixed while \(\gamma\) grows, that is, while the capture margin grows, it is clear that the radius of the capture circle will diminish. On the other hand, the radius of ICD circle will grow as the capture margin increases and both circles will become the same circle when \(z=\frac{1}{\tau^{2}}\).  
2. Location Margin  
As shown in Figures 4a and 6a, when the GW is inside the shaded region, i.e., it is inside of the capture circle and outside of the ICD circle, then any of the packets transmitted by \(s_{i}\) and \(s_{j}\) in collision could be decoded successfully by this GW. The shape of the shaded region is similar to a crescent. However, considering the uncertainties in power level, we should give some location margin between capture circle and ICD circle for optimized GW location, as shown in Figure 6b.  
Suppose that we give the same value \(\delta\) of location margin to decrease the radii of capture circle and increase the radii of ICD circle, then, if the minimum width of the ICD region is less than \(2\delta\) as in Figure 6a, we will get a real crescent region for optimized GW locations.
## 3 Adaptive LQG-FF Current-Voltage Controller
### Adaptive Gain-Scheduling Parameters for the LQG-FF Controller  
Due to the nonlinear characteristics of the Sepic converter, a method to adjust the controller parameters under any operation condition was designed. The adopted approach is named Gain-scheduling, which is a popular nonlinear technique widely used in aerospace and process control [32]. This method requires the calculation of the state feedback vector and the observer parameters, solving offline the Riccati differential equation for each operating point (\(v_{sc}\) and \(v_{b}\) combinations). Subsequently, intermediate operating values are calculated through a linear function; in this way, the variation of the state feedback vector and the observer gain vector are calculated; these functions must be implemented in a real-time control device.  
The complete control structure, which includes the integration of the different control blocks previously described, is shown in Figure 9. In this solution, the model parameters are adjusted depending on the operating point, thus updating matrices \(A_{m}\), \(B_{m}\), \(\ell\), \(K_{x}\); and four signals are measured: the currents \(i_{sz}\) and \(i_{dc}\), and the voltages \(V_{sc}\) and \(V_{dc}\).  
A reduction in the SoC of a battery decreases the terminal's voltage [33]; similarly, the energy stored in the supercapacitor is calculated as \(C_{sc}\cdot v_{sc}^{2}/2\), and hence the supercapacitor SoC is also reduced when the voltage is decreased. Therefore, the adaptive gain-scheduling method used in this work ensures the correct parameters for any SoC and voltage condition in the battery and supercapacitor.  
Figure 8: Structure of the LQG-FF current–voltage controller.
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 2 Passive Bistatic Radar and Mie Scattering
### Forward Scattering Phenomenology  
FSR depends on the main lobe of scattering power propagating along the same direction as the incident wave, i.e., the forward direction. In general, significant FSR scattering behavior is observed from harmful space debris typically occupying the Mie and optical scattering regions depending on the size of the target radius in comparison to the incident wavelength [31]. Mie region scatter is defined as that which occurs over the range \(0.1\lambda\leq r_{lg}\leq 2\lambda\), while scattering over the range \(r_{lg}>2\lambda\) occupies the optical region.  
Figure 1: General 2D Bistatic Radar geometry.  
Therefore, if choosing the L1 C/A GPS signal to be our IOO with a wavelength of 19 cm, then FSR will be optimal for targets as small as 3.8 cm in diameter. This lower bound would significantly enhance global SDA efforts as the scattering energy is maximized towards the receiver. As a result, the signal-to-noise ratio (SNR) is improved.  
The Mie region FS-RCS and scattering beamwidth provide additional benefits to target detectability and signal processing. Compared to the bistatic scatter cross sections (B-RCS), which occur at \(\beta\approx 90^{\circ}\), it was shown that the FS-RCS could be 4 to 22 dB larger depending on the complexity of the target shape [32]. This also holds true in comparison to the monostatic case for target diameters \(\geq 1.5\lambda\). Larger RCS values increase SNR and heighten correlation peaks. A Mie region pattern study depicts scattering curves with half-power beamwidths (HPBW) increasing inversely to relative target size [33], which further supports the approximation in Equation (2). In addition, it was also shown that FS-RCS was higher compared to the B-RCS for spheres of several sizes ranging from the Mie to the optical regime [33]. It is evident, therefore, that the FS-RCS phenomenon would be beneficial to the detection of the smaller targets that have proven difficult to track.
# A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position
## 4 Experiments and Analysis
### BLE/PDR Integrated System Position Experiment
From the comparison of the curves with the real trajectory in (c) and (d), the proposed method is superior to the classical EKF. The particle filter also performs well in some positions compared to the proposed method. However, the particle filter requires the construction of a large number of particles requiring a heavy computational load. The proposed method is more suitable for real-time localization than the particle filter method. Another comparison of the experimental results of the integrated system was conducted in the 331 test site and the results were shown in Figure 16. From Figure 16, the green line that represents the proposed method is closer to the real track and smoother in some corners compared to other methods. Although the test paths are relatively short because of the extent of the experimental scenarios, the two methods of fusion position have different principles, in which PDR has high instantaneous accuracy but suffers cumulative errors, and the real-time Bluetooth fingerprint method can compensate for this shortcoming based on the robust filter. Therefore, even with the test paths becoming longer, there will be no cumulative error to make the path diverge. We can conclude that the proposed method makes it possible to have similar improvement under different circumstances. Figure 15: The trajectory of three methods in the true position scenario. (**a**) The trajectory and jump points, (**b**) the trajectory in the position scenario, (**c**) the comparison of different fusion methods with trajectory one, (**d**) the comparison of different fusion methods with trajectory two.
## 3 Simulation Results and Discussion
### Cost Function  
There mainly exist three forms of cost functions [18; 27], as follows:  
\[J_{1}=I_{y}, \tag{10}\]  
\[J_{2}=\frac{I_{y}-I_{x}}{I_{y}+I_{x}}, \tag{11}\]  
\[J_{3}=10\lg\left(I_{y}/I_{x}\right) \tag{12}\]  
Among the three cost functions, J\({}_{1}\) is the laser intensity in the y-direction, J\({}_{2}\) is the degree of polarization (DOP), and J\({}_{3}\) is the PER of the output laser. Firstly, the convergence of the RMS-Prop algorithm under different cost functions is simulated. In order that the results are more statistically significant, the program is executed for one thousand times and the calculated data are averaged. When \(\delta\)u = 0.05 and \(\eta\) = 0.4, the simulation results of three cost functions iterating for five hundred times are shown in Figure 2a,b. The horizontal axis in the figure represents the iteration number, and the vertical axes represent DOP and PER, respectively. When J\({}_{1}\) is the cost function, DOP cannot converge to 0.98 within 500 iterations, while it is realized by J\({}_{2}\) and J\({}_{3}\) after 467 and 201 iterations. J\({}_{3}\) has a better convergence performance than the others, which can be seen more obviously from the curve graph of PER. J\({}_{3}\) is chosen as the cost function in our following simulations.  
Figure 2: Convergence curves for different cost functions: (**a**) DOP curves; (**b**) PER curves.
# A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
## 3 The Equivariant Vector Network with Self-Supervised Learning
### The Theory of Equivariant Networks  
To realize the equivariant and universality of point cloud networks, our network adopts an SO(3) equivariant network framework with good performance and portability at the present stage which is called the Vector Neural Network (VNN) [15]. The VNN achieves equivariance on the 3D rotation \(g\in\text{SO}(3)\) by lifting the representation of the neurons in the network from a scalar \(x\in\mathbb{R}^{1}\) to a vector \(v\in\mathbb{R}^{3}\). In this way, the equivariance of the whole network is realized. The network's learned mapping function \(f\) satisfies the following property: for any rotation matrix \(R\in\text{SO}(3)\),  
\[f(\mathcal{V}R;\theta)=f(\mathcal{V};\theta)R,\quad\mathcal{V}\in\mathbb{R}^{ N\times\mathbb{C}\times 3} \tag{1}\]  
holds true.  
The \(\mathcal{V}\) in the formula is the vector feature \(\mathcal{V}=[v_{1},v_{2},v_{3},\dots,v_{N}]\in\mathbb{R}^{N\times\mathbb{C} \times 3}\) generated by the original scalar feature \(\mathcal{X}=[x_{1},x_{2},x_{3},\dots,x_{N}]\in\mathbb{R}^{N\times 3}\) in the data. The original scalar feature \(\mathcal{X}\) in the input sample can be lifted into a vector feature \(\mathcal{V}\) after the vectorization operation in the data processing stage so that information can be transmitted equivariantly in the network. This vector list feature \(\mathcal{V}\) is similar to standard latent representations and can be used to encode entire 3D shapes, parts, or individual points. The number of potential channels \(C^{(d)}\) of \(\mathcal{V}\) can be mapped from layer to layer similarly to a standard neural network:  
\[\mathcal{V}^{(d+1)}=f(\mathcal{V}^{(d)};\theta):\mathbb{R}^{N\times\mathbb{C} ^{(d)}\times 3}\rightarrow\mathbb{R}^{N\times\mathbb{C}^{(d+1)}\times 3} \tag{2}\]  
Through the use of vector neurons, the rotation action in the data sample can be expressed in the network so that the network can not only encode the entire 3D shape but also learn the direction information of it. The theory of vector neurons also inspired us to vectorize the Transformer architecture, which is also a scalar computation architecture, so it can also be vectorized to transfer vector information.