esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Wireless Sensor Networks (WSNs) is a group of spatially dispersed autonomous sensor devices, named motes. These motes have a microcontroller, sensors, are powered by AA or AAA batteries, and mainly have the ability to communicate using the IEEE 802.15.4 standard. The motes communicate between them inside the WSN exchanging packets using a multi-hop routing. They use a very low amount of power (below 100 mW). This limits the maximum communication distance between motes within the WSN. Usually, one mote acts as a gateway to other networks and this mote is also called sink or simply Base Station (BS), and the data collected by the sensors of each mote are sent to this mote. The maximum distance between the BS and the nearest mote is below 100 m because of the power limitations of the motes. If the WSN-BS distance is above this boundary, the communication will surely fail. We propose a new technique in order to achieve a long range communication from the WSN, for instance to communicate to a Low Earth Orbit (LEO) satellite. Many proposals in the literature based on Collaborative Beamforming (CB), also known as Distributed or Cooperative Beamforming, for these long range communications are found, however the synchronization of clocks is an almost impossible task given the simplicity and cheapness of the architecture of the motes. To overcome this problem, we propose a new technique, named Stochastic Collaborative Beamforming (SCB), in which we take advantage of the synchronization errors of the clocks. In SCB, it is possible to obtain the adequate time delay that permits the interference or sufficient gain in the direction of the receiver. This gain is obtained from interfering independent signals coming from each mote of the WSN, using a repetition scheme. Although it does not get all the nominal gain that could be obtained in case of a perfect synchronization, it does get a sufficient gain to reach the BS with limited power consumption.  
wireless sensor networks; stochastic beamforming; cooperative beamforming; collaborative beamforming; distributed beamforming +
Footnote †: journal:  
Article
## 2 Flared SBF Antenna
### Numerical Analysis
#### 2.2.2 Effect of the Rim Height  
This parametric study focuses on the effect of the rim height on the gain and impedance bandwidth of the flared SBF antenna. We decided to keep the flaring angle for this section constant at 0f = 20\({}^{\circ}\). We chose this flaring angle because it provided the highest gain and wide bandwidth in the previous section. It is important to note that while the flaring angle is kept constant, \(\mathrm{D_{U}}\) (upper diameter of the rim) still changes because of the changing height.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Hr** & **0f (\({}^{\circ}\))** & **D\({}_{\mathrm{U}}\) (mm)** & **Realized Gain @ 5.5 GHz (dBi)** & **Imp. BW (\%)** & **Min. Cross-Pol. Ratio (dB)** \\ \hline \multirow{6}{*}{0.6 \(\lambda\)} & 0 & 110 & 14.8 & 26.4 & \(-\)24.00 \\ \cline{2-5}  & 10 & 121.5 & 16.3 & 28.1 & \(-\)23.27 \\ \cline{2-5}  & 15 & 127.5 & 16.7 & 55.0 & \(-\)22.15 \\ \cline{2-5}  & 20 & 133.8 & 17.2 & 54.4 & \(-\)21.32 \\ \cline{2-5}  & 30 & 147.8 & 16.0 & 43.1 & \(-\)19.43 \\ \cline{2-5}  & 40 & 165 & 14.3 & 42.4 & \(-\)14.84 \\ \hline \end{tabular}
\end{table}
Table 2: Performance comparison of the flared SBF antenna with different flaring angles (0f).  
15.41 dBi to 17.22 dBi (an increase of 1.81 dB) at 5.6 GHz; the gain at 5.5 GHz also improved from 15.27 dBi to 17.02 dBi (a 1.75 increase). There was no significant change in the realized gain when the rim height was increased further to 0.8 \(\lambda\). While the cross-polarization ratio does not change significantly as the rim height is increased, we can see in Figure 4b that the minimum cross-polarization ratio of 21.32 dB was obtained at 0.6 \(\lambda\).  
Figure 4: Effect of height variation as a function of frequency. **(a)** Realized gain; **(b)** Cross-polarization ratio; **(c)** Reflection coefficient.  
The effect of varying the height of the rim on the reflection coefficient (S11) can be seen in Figure 4c. Increasing the height of the rim from 0.4 \(\lambda\) to 0.6 \(\lambda\) lowered the reflection coefficient result of the antenna, thereby increasing the impedance bandwidth from 26.4% (4.43-5.78 GHz) to 55% (4.35-7.60 GHz). This value dropped to 29.1% (4.38-5.87 GHz) as the height is increased further to 0.8 \(\lambda\). This shows that the rim's height also affects impedance matching.  
Table 3 summarizes the effect of the rim's height on the flared SBF antenna. From this study, we observe that varying the height of the SBF antenna's rim while keeping the flaring angle and other dimensions constant affects the gain and impedance bandwidth result, while the cross-polarization ratio was not significantly affected. The highest gain, widest impedance bandwidth, and lowest cross-polarization ratio were obtained at \(\mathrm{Hr}=0.6\)\(\lambda\). The performance of the antenna was worse at other \(\mathrm{Hr}\) values.
## 1 Introduction
proposed an artificial neural network (ANN) [22] to establish a temperature shift compensated model. [PERSON] et al. developed an extreme learning machine (ELM) to calibrate the pressure shift [23]. Neural networks, however, require a long training time and a large amount of calculation, which is a barrier for marine engineering applications. Therefore, our work proposes a low-cost pressure measurement system with temperature compensation based on embedded technology and rational polynomial fitting to reduce the cost of temperature compensation and increase the flexibility of the compensation system. In this work, a MEMS piezoresistive pressure sensor with ideal performance over a wide effective range by optimizing the micromachining process is designed, simulated, fabricated, and tested. Firstly, the optimization greatly enhances the performance and the stability of the pressure sensor under harsh environment such as high temperature and pressure. Secondly, a novel polyimide anticorrosive layer is fabricated by polyimide film on the top of the micro sensor to prevent it from corrosion by an electrolyte such as seawater. Finally, to compensate the temperature drift, a digital low-cost temperature compensation system is built for this micro pressure sensor.
# A New Low-Cost Compact Antenna for the 2.45 and 5.8 GHz ISM Bands
## 4 Experimental Results
When compared with other antennas in the literature, the antenna proposed in this study has the smallest dimensions (although the orders of magnitude can be considered comparable) but also good performance in terms of gain for both frequency bands, particularly when the least expensive substrate, i.e., FR-4, is used. The antenna presented in [21] has a total volume of 2323 mm\({}^{3}\)(33% less compact) with low gains (1.48 dBi vs. 2.41 dBi and 3.83 dBi vs. 5.22 dBi). The article does not present the results in terms of bandwidth and efficiency. The antenna proposed in [23] has a total volume of 2880 mm\({}^{3}\) (46% less compact), slots with complex geometry (Sierpinski fractal geometry), similar gains, and higher bandwidths (48% on average), but the article does not present the results in terms of efficiency. Note also that the radiation efficiency obtained is high despite the use of a lossy FR-4 substrate and antenna compactness. This is due to its low relative permittivity (4.4) and low tangent losses (0.02), which improve not only radiation efficiency but also the antenna's bandwidth at interest frequencies [16]. Consequently, the proposed dual-band antenna could, therefore, be a very good candidate both for wireless embedded and energy harvesting systems whose RF source operates in the 2.45 and 5.8 GHz bands.
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 3 Results  
We evaluated the proposed TCP-NACK protocol under different configuration environments and distances by using laptops with the modified TCP-NACK protocol under the Ubuntu kernel, and applying the necessary communication standards for each experiment. We choose to evaluate the general Quality of Service (QoS) provisioning parameters from a networking perspective using the \(\overline{\theta}\), the delay, \(\delta\), and jitter metrics [18]. Hence, those parameters were obtained by the intrusive traffic technique, for which we employed the Distributed Internet Traffic Generator (D-ITG) software. D-ITG is a platform capable of producing IPv4 and IPv6 traffic, following stochastic models for packet size and inter departure time [19]. The details of our fulfilled experiments are described next.
# CPM-OFDM Performance over Underwater Acoustic Channels
## 4 Shallow Underwater Acoustic Channel Model
### Ambient Noise  
In a realistic UWA channel, the noise is not white and depends upon various acoustic frequencies. For our simulations, we use an ambient noise model that is a combination of thermal, shipping, wave, and turbulence noise [57]. The power spectral density (PSD) of the total noise is:  
\[N(f)=10\log\biggl{(}10^{\frac{N_{shipping}(f)}{10}}+10^{\frac{N_{turbulence}(f )}{10}}+10^{\frac{N_{wave}(f)}{10}}+10^{\frac{N_{thermal}(f)}{10}}\biggr{)} \tag{17}\]  
The noise is location specific and can be modeled as color noise, and its amplitude is high at both the lower and upper end of the spectrum while minimum around 60 kHz [58].
## 2 Materials and Methods
### Methodology
#### 2.5.4 Comparison of Different ML Methods  
First, the selected four ML methods were trained and tested with the data from the NE1, NE2, and NE3 sites. A comparison of input variable combinations was conducted: 70% of data at three NE sites are randomly selected as the training set and the other 30% as the testing set, and this process runs 1000 times. Then, in order to evaluate ML's robustness, the trained models based on the data from all three NE sites were further tested using the data from the RO1 site. For the other three ML methods (SVM, CNN, and ELM), A0 was also chosen as the control combination. When adding SLN and NMP (A1, A2, A3), a combination was selected for each ML method that results in the highest model accuracy during training and testing in three NE sites for RO1 validation. As a result, each ML method has two combinations of input variables for validation at the RO1 site.
# Ionospheric Science: An Example of the Importance of Diversity in Approaches to Scientific Research
## 2 Historical Background
However, all communications links between satellites and ground stations must cross the ionosphere, and thus, the same as HF, may be subject to disruption by space weather effects in the ionosphere. This applies to all frequencies from very high frequency (VHF) to S-band, thus covering a wide range of satellite applications, today including L-band satcom, GNSS, etc. In addition, radio signals working at these frequencies are widely used for satellite tracking by ground-based radars and for active and passive observations of Earth's surface using satellite-based sensors.
## 4 Results
### Typhoon Center Path Compare  
The sea-level pressure (SLP) data extracted from the WRF simulation results were used to determine the typhoon center by identifying the minimum SLP value. This center location was then compared with data from the CMA data, as shown in Figure 9. The red line represents CMA data, while the blue and green lines correspond to WRF simulations driven by FNL and ERA5 datasets, respectively.  
Figure 7: Wind field simulation results for Typhoon Nepartak at 09:00 on 4 July 2016. (**a**) shows the simulation driven by FNL data using WRF, (**b**) shows the simulation driven by ERA5 data using WRF, (**c**) shows the simulation using the Holland empirical parameter typhoon model, (**d**) shows the simulation using the Jelesnianski empirical parameter typhoon model, and (**e**) shows the SMAP data.  
Figure 8: Wind field simulation results for Typhoon Nepartak at 22:00 on 6 July 2016. (**a**) shows the simulation driven by FNL data using WRF, (**b**) shows the simulation driven by ERA5 data using WRF, (**c**) shows the simulation using the Holland empirical parameter typhoon model, (**d**) shows the simulation using the Jelesnianski empirical parameter typhoon model, and (**e**) shows the SMAP data.  
Figure 9: Comparison of Typhoon Surigae Center Path. (**a**) shows the Typhoon Surigae. (**b**) shows the Typhoon Nepartak. The red line represents CMA recorded data, the green line indicates the WRF simulation driven by ERA5, and the blue line represents the WRF simulation driven by FNL.  
At the early stages of the WRF simulation, when the typhoon was still in the tropical depression or tropical storm phase, its structure had not fully formed. Consequently, determining the typhoon center based on the minimum SLP showed some variability. However, as the typhoon intensified, the central position extracted by this method increasingly aligned with CMA data. For the Typhoon Surigae simulation, both ERA5- and FNL-driven WRF simulations yielded typhoon tracks that closely matched CMA data, demonstrating overall accuracy. For Typhoon Nepartak, however, the ERA5-driven simulation provided a track that more accurately reflected CMA data than the FNL-driven simulation.
## 4 Results and Discussion
### FTIR Analysis  
The FTIR spectrum of chitosan (Figure 3) displayed vibrational peaks at 3360 cm\({}^{-1}\) (O-H and N-H stretch), 2877 cm\({}^{-1}\) (C-H stretch), 1647 cm\({}^{-1}\) (amide), 1561 cm\({}^{-1}\) (N-H bending of amine), 1152 cm\({}^{-1}\) (antisymmetric stretch C-O-C and C-N stretch), and 1032 cm\({}^{-1}\) (skeletal vibration of C-O stretch) [52]. The alginate spectrum showed vibrations at 3600-3000 cm\({}^{-1}\) (O-H stretch), 1600 cm\({}^{-1}\) (COO-antisymmetric stretch), 1412 cm\({}^{-1}\) (COO-symmetric stretch), 3000-2850 cm\({}^{-1}\) (C-H stretching), and 1085-1032 cm\({}^{-1}\) (C-O-C antisymmetric stretch). The peak at 1637 cm\({}^{-1}\) for alginate and chitosan mixture (blank PEC film without TM-nanoemulsion) is assigned to the superposition of the carboxylate group on alginate and the amine/amide groups on chitosan. These results proved that the carboxylate groups (-COO) of alginate react with the protonated chitosan amino groups (-NH3\({}^{+}\)) by electrostatic attraction to form the polyelectrolyte complex. The interactions between amide bonds on chitosan and the protonated carboxylate groups on alginate could also form intermolecular hydrogen bonds between the polymers [53]. The FTIR band between 2955 and 2863 cm\({}^{-1}\) is related to stretching of the thyme oil CH\({}_{3}\) group. Peaks at 1350 and 1250 cm\({}^{-1}\) are attributed to OH in-plane bending and C-O stretching vibrations from the thyme oil phenol group, respectively [54; 55]. The band at 800 cm\({}^{-1}\) is ascribed to out of plane aromatic C-H wagging vibrations [56].
# Sound Field Modeling Method and Key Imaging Technology of an Ultrasonic Phased Array: A Review
## 3 Imaging Technology
### Compressed Sensing of Array Imaging
Currently, designing a high-performance sparse dictionary to guarantee reconstructed images at low compression rates is an attractive research direction. These are major directions to study the method of ultrasonic signal reconstruction based on a sparse dictionary and to analyze the influence of the type of sparse transformation basis on the reconstruction error. [PERSON] et al. (2018) used the Fourier basis as the sparse transform basis to reduce the number of receive channels and the amount of data required for imaging using the sparse dictionary technique. Secondly, the design of the measurement matrix is the  
Figure 9: Schematic diagram of ultrasonic arrays imaging based on compressed sensing. key to the successful implementation of CS theory. A method is proposed to obtain the optimal measurement matrix type for phased array ultrasonic beam compression reconstruction by optimizing the incoherence and column independence of the measurement matrix. The acquisition process of a compressed sensing ultrasonic array signal is shown in Figure 10 [83]. [PERSON] et al. [84] performed incoherent compressive sensing (IncCS) and multiple-measurement-vector (MMV)-based compressive sensing reconstruction of the frequency domain data of aluminum block side borehole images acquired by ultrasonic arrays, respectively. The design of reconstruction algorithms is the core of CS theory. The current reconstruction algorithms can be divided into three categories: convex optimization methods, greedy algorithms, and combinatorial algorithms. [PERSON] et al. [85; 86; 87] proposed a greedy-algorithm-based CS for ultrasonic array signals. In the literature [88], an experimental system is developed for eight-array sparse sampling based on ultrasonic array techniques and CS. [PERSON] et al. [89] found that both compressed sensing and wavelet threshold approaches hold promise for achieving large amounts of compression. Although wavelet transformation can improve the signal-to-noise ratio and reconstruction accuracy, CS can be better. To reduce the amount of calculation in the compression process. [PERSON] et al. [90] proposed a CS-based method for reconstructing ultrasonic array FMC data. This method can realize the reconstruction of ultrasonic full-matrix data under a 75% compression rate. The root-means-square error between the reconstructed data and the actual full matrix data is about 6%.
## 4 Experiments and Results
### Evaluation Metrics
#### 4.3.7 Key Sensitivity  
To assess the sensitivity of the keys, an experiment was drawn: we are encrypting the same image using the two slightly different keys. In the experiment, we are assessing the sensitivity of the initial condition \(x_{0}\) and the control parameter \(\mu\) of the logistic map. The difference is set to \(\Delta=10^{-15}\). Figure 15 shows the results of the experiments. A structural similarity metric is used for comparison of the two resulted ciphers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image** & **Full Encryption** & **Selective Encryption** \\ \hline
256 \(\times\) 256 tank & 31.652340 s & 3.780312 s \\ \hline
256 \(\times\) 256 lena & 30.875214 s & 3.010088 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Computational complexity of selective encryption against full encryption.  
As shown in the experiment, the slightest change in the key results in a huge change in the cipher.
## 3 Results and Discussion
### Effect of Organic Grinding Aids on the Grinding Property of Converter Steel Slag
#### 3.4.3 Effect of GA on the Angle of Repose of Steel Slag Powder  
Powder fluidity is usually characterized by the angle of repose. The smaller the angle of repose, the better the powder fluidity. As shown in Figure 11, it can be seen that the angle of repose of steel slag powder is decreased after adding GA, indicating that GA can improve the fluidity of steel slag powder. The role of GA on the angle of repose is firstly enhanced and then weakened on the whole, which is similar to the role of GA on the sieving residue, and it is reaches maximum effectiveness at 50 min grinding time, i.e., the action effect of GA on powder fluidity is the greatest at 50 min grinding time.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ginding time (min)**} & \multicolumn{4}{c}{**Without GA**} & \multicolumn{4}{c}{**With GA**} \\ \cline{2-10}  & **\textless{3}\(\upmu\)m** & **3-32\(\upmu\)m** & **32-65\(\upmu\)m** & **\textless{65}\(\upmu\)m** & **\textless{3}\(\upmu\)m** & **3-32\(\upmu\)m** & **32-65\(\upmu\)m** & **\textless{65}\(\upmu\)m** \\ \hline
10 & 2.44 & 25.42 & 21.95 & 50.19 & 2.83 & 23.83 & 22.80 & 50.54 \\
20 & 5.66 & 40.06 & 30.84 & 23.45 & 4.81 & 37.44 & 34.14 & 23.62 \\
30 & 9.00 & 44.22 & 32.10 & 14.68 & 7.63 & 47.08 & 32.74 & 12.55 \\
40 & 10.54 & 48.80 & 30.51 & 10.15 & 10.44 & 56.04 & 28.47 & 5.04 \\
50 & 14.87 & 56.99 & 22.65 & 5.49 & 14.32 & 64.21 & 21.10 & 0.38 \\
60 & 16.36 & 53.83 & 20.96 & 8.84 & 15.73 & 64.46 & 18.61 & 1.20 \\
70 & 14.50 & 55.74 & 21.47 & 8.29 & 15.05 & 57.33 & 19.49 & 8.13 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Particle size distribution of steel slag powder with GA under different grinding time (%).  
Figure 11: Repose angle of steel slag powder with GA vs. grinding time.
# TopoSinGAN: Learning a Topology-Aware Generative Model from a Single Image
## 1 Introduction  
Since its inception by [PERSON] et al. in 2014, Generative Adversarial Networks (GANs) have revolutionized image generation [1]. At the core of GANs are two neural networks, a generator and a discriminator, which are trained concurrently using a game theory strategy. This innovative structure enables GANs to produce synthetic images that are nearly indistinguishable from real ones. GANs have been applied in various domains, including unconditional and conditional image synthesis [2], image inpainting [3], and image-to-image translation [4]. They have been particularly valuable in generating training images to address the scarcity of comprehensive datasets in image processing [2, 5, 6, 7, 8, 9]. However, dealing with diverse datasets that include multiple object classes, such as ImageNet [10], remains challenging. This often requires training models tailored to specific tasks, such as super resolution [11], inpainting [3], and retargeting [12]. Training GANs typically demands large datasets, which can be resource-intensive. As a result, the focus can shift to training generative models on a limited number of images, or even a single image to capture unique characteristics and address data scarcity [13].  
The innovative SinGAN [13] provides a unique approach by training a GAN on a single image, focusing on unconditional image generation and harmonization. SinGAN uses a multitiered and multiresolution method, beginning with training at low resolutions such as 26 \(\times\) 35 pixels. As training progresses through various scales, the generator's complexity increases, and the image resolution is enhanced. Each new scale builds on thetraining from previous scales, with only the newly introduced layers being trained. This multiscale architecture is illustrated in Figure 1. Building on this concept, the SinGAN-Seg model [9] was developed to enhance SinGAN by adding a mask channel to the conventional RGB channels. This modification has proven effective, particularly in creating annotated images for medical segmentation tasks.  
Despite their success in generating visually convincing images, these models struggle with ensuring topological integrity in their predictions, which is crucial for applications such as agricultural fields, detailed medical delineations, and robotics. The precise nature of these tasks, whether they involve accurate robotic movements or detailed vascular representations in medical imaging, requires high topological accuracy [14; 15].  
To address this issue, we introduce TopoSinGAN. This model builds on the SinGAN framework and incorporates a novel differentiable topology loss function. The core of this loss function is its ability to identify and count terminals within the generated boundary mask, where a terminal is defined as a boundary pixel with only one neighboring boundary pixel. By reducing the number of these terminal nodes, TopoSinGAN aims to improve topological accuracy. We further validate that this approach is differentiable, making it suitable for optimization through backpropagation.