esa-sceva/satcom-chunk-collection huggingface dataset:
# Options for Performing DNN-Based Causal Speech Denoising Using the U-Net Architecture +
## 2 Related Works and Research Planning  
Early DNN-based speech denoising methods often utilized time-frequency representations to analyze spectral features over time [5]. Recent trends indicate that incorporating phase information can significantly enhance speech quality [10; 11]. As the phase information exists in the raw temporal waveform and its spectral transformation as well, DNN-based phase-aware speech denoising can be carried out straightforwardly using the speech waveform as the input [12; 13; 14]. For the denoising process conducted in the spectral domain [2; 5; 6; 7; 8], the short-time Fourier transform (STFT) presentation with the real and imaginary (or RI for short) components arranged in sequence is most popular.  
DNN-based speech enhancement can be implemented in the form of mapping or masking. The masking approach estimates a suppression gain for each target value [2; 15], while mapping directly predicts the output values [5; 16; 17]. Although the masking approach provides an auxiliary constraint that improves consistency with desired outputs, its advantages diminish as DNNs become more proficient.  
Causality is crucial for real-time applications, as it ensures that DNNs only utilize past and present features. Two common methods for implementing causal DNN-based speech denoising include recurrent networks [18; 19] (such as long short-term memory, LSTM [20]) and frame-buffering techniques that compile past frame data into a buffer to guide the DNN during denoising [21; 22].  
A well-defined loss function is essential for training DNNs in speech denoising, as it quantifies the alignment between the DNN's predictions and target outputs. Here, the goal is to minimize noise while preserving original speech characteristics, balancing the trade-off between denoising and potential speech distortion. Recent advancements in loss functions that optimize both magnitude and phase spectra show promise [10]. Additionally, power compression has proven beneficial in enhancing denoising performance [23].  
In this study, we adopt the frame-buffering approach to construct two representative causal U-Nets (classical and advanced) to evaluate the most suitable processing domain for speech denoising. The classical U-Net utilizes multi-level layers of conventional convolution and activation, while the advanced U-Net employs global local former blocks (GLFB) as introduced in [24]. After identifying the optimal domain, we will develop domain-specific loss functions tailored to the domain's characteristics.
# Design of Mega-Constellations for Global Uniform Coverage with Inter-Satellite Links
## 4 Simulation and Results
### Simulation Studies and Results on Mega-Constellation Design
In conclusion, the mixed Walker  
Figure 8: Comparison of the distribution of sub-satellite points density along latitude zones in the mixed Walker constellation and Walker constellation. **(a)** Distribution of sub-satellite points density along latitude zones of a Walker constellation with 5625 satellites; **(b)** Distribution of sub-satellite points density along latitude zones of a mixed Walker constellation with 5686 satellites. constellation designed in this paper has less variability in coverage performance across the globe than the Walker constellation and offers significant global coverage strengths.
# Synchrotron X-ray Irradiation of a Rat's Head Model: Monte Carlo Study of Chromatic Gel Dosimetry
###### Abstract  
Accurate treatment planning in radiotherapy essentially decreases damage to healthy tissue surrounding the tumor. Due to plans to use a direct, highly collimated, narrow beam with high intensity to treat small area tumors, researchers have studied microbeam radiation therapy extensively. Using a synchrotron beam as the radiation source may help to limit damage, but treatment planning using computerized simulations and dosimetry is still necessary to achieve optimal results. For this purpose, PDA-gel dosimeters were developed and their sensitivity around a 150 keV induced synchrotron X-ray radiation beam was examined via Monte Carlo simulations using the EGS5 code system. The microbeam development is now at the animal study stage. In this study, we simulate the irradiation of a rat's brain. The simulation results obtained spectra for two types of PDA-gel dosimeters that were compared with the spectrum obtained in a modelized brain tumor of a rat. Additionally, percentage depth dose curves were calculated for the brain tissue and the two gels. Correction equations for the dosimeters were obtained from the dose-difference plots. For further references, these equations can be used to calculate the actual dose in a brain tumor in a rat. The Monte Carlo simulations demonstrate that PDA-gel dosimeters can be used for treatment planning using synchrotron irradiations.  
X-ray; gel dosimeters; EGS5; Monte Carlo simulations; percentage depth dose; polydiacetylene
## 4 Experimental Results and Discussion
### Failure Modes and Pore Distribution of 3D-Printed CFRCs  
From the above analysis, it can be seen that layer thickness is the most significant factor affecting the maximum tensile stress. In the tensile test, we can see that the bonding between layers is better when the layer thickness is lower. With the decrease in layer thickness, the failure condition changes from inter-layer failure to fracture failure. This change is very obvious in the tensile test, as shown in Figure 6.  
For the specimen with reinforced fiber thickness of 0.2 mm, the failure of the specimen is accompanied by the explosion of the matrix layer, as well as long fiber pull-out and fracture. In the specimen with 0.15 mm fiber print layer thickness, it can be seen that in the range of fracture section, the matrix surface layer and the fiber layer have interlayer  
Figure 5: Analysis diagram of the results of the full factor test: (**a**) Normal diagram; (**b**) Pareto diagram; (**c**) graph of printing layer thickness, printing speed, maximum tensile stress; (**d**) scatter plots between layer thickness and maximum tensile stress.  
Figure 6: Fracture comparison between print specimens with different fiber thickness.  
failure. There are fibers pulled out but the matrix surface layer does not explode. The specimen with 0.125 mm reinforced fiber print layer thickness had partial interlayer failure and very short fiber pull-out in the fracture section. For the specimen with 0.10 mm fiber print layer, the matrix surface layer and reinforced fiber layer only failed between layers with the size of a few bubbles at the fracture, and only a few fibers were pulled out. The specimen with reinforced fiber print layer thickness of 0.05 mm was neatly fractured at the fracture without fiber pulling out, and there was no interlayer failure between the matrix layer and the fiber layer. It can be seen that the smaller the layer thickness, the better the bonding condition between the layers and the stronger the interface.  
It can be seen using the ultra depth-of-field microscopy that the specimen with reinforced fiber layer thickness of 0.05 mm has a great interlayer interface, as shown in Figure 7. This relatively perfect infiltration and interlayer bonding ensures almost no cracking between layers even when it is fractured. At the same time, the 0.2 mm reinforced fiber layer thickness specimen has almost complete separation of the fiber layer from the matrix layer. The 0.125 mm reinforced fiber layer thickness specimen has cracking and gaps between the fiber layers and the matrix layer. In addition, with practical printing, if the fiber layer thickness is lower than 0.05 mm, there will be part of the excess matrix overflowing the fiber layer, seriously affecting the dimensional accuracy of the printed parts. Therefore, the layer thickness of 0.05 mm is the optimal printing layer thickness for independent extrusion printers.
## 3 Design Approach
### Conceptual Design
* No components are placed sideways of the path to the antenna, no signal whatsoever is routed in that area of the board, no perpendicular angles are taken in the paths, especially for sensitive high frequency signals, and the decoupling capacitors with the smallest values are placed as close as possible to the DW1000 on the power track. These design optimizations can also be used for the design of other, future UWB boards. The effect of this optimization is profound, as will be shown in later sections.
# Optimization of AZ91D Process and Corrosion Resistance Using Wire Arc Additive Manufacturing
## 3 Results and Discussion
### Electrochemical Impedance Spectroscopy (EIS)  
EIS graph (see Figure 7) reveals a semi-circular form where its diameter is related to the composition and microstructure. Several authors have described this arc's diameter being associated with corrosion resistance [25; 26]. With that said, AZ91D produced by WAAM had six times larger diameter than the AZ91D produced by casting which means corrosion resistance of AZ91D produced by WAAM was better [27]. This result can be related to the new precipitates (Al5 Mg1 Zn4) and relieve the internal stress by using WAAM process.  
Figure 6: (**a**) XRD spectra of as-received wire, representative peaks of magnesium (a phase), zinc, and Mg18 Al13 precipitates (b phase); (**b**) XRD spectra of a representative printed line, representative peaks of pure magnesium (a phase) and Al5 Mg11 Zn4 precipitates (j phase); (**c**) representing the areas from which the elemental mappings were taken; (**d**) Elemental Mapping of zone 1 [24]. Copyright 2018, Cambridge University Press; (**e**) Elemental Mapping of zone 2 [24].
## 5 Experimental Evaluation
### Ablation Study  
We conducted ablation experiments by modifying:  
* Model Size: Compared full GRU model (160 K params) vs. pruned version (90 K params).
* Compression: Tested 8-bit quantized weights vs. floating-point weights.
* Number of FL Rounds: Evaluated convergence under 20, 50, 100, and 200 rounds.  
To further understand the trade-offs between model complexity, communication efficiency, and convergence behavior, we conducted an ablation study by varying the model size, applying compression techniques, and adjusting the number of federated learning rounds. The key results are summarized in Table 3.  
The results of the ablation experiments are also visualized in Figure 4, illustrating how configuration choices impact model accuracy, communication overhead, and convergence speed.  
Results show that model pruning and quantization reduce communication costs at the expense of minimal accuracy loss.
## 2 Related Work  
### Communication Network for Wind Power Farms (WPFs)  
The supervisory control and data acquisition (SCADA) system enables operators to monitor, control and record data of a wind power plant, from a remote place called a central control station [8; 9; 10]. It consists of three main components as shown in Figure 1.  
* Inside the turbine tower, including the wind turbine controller (WTC), remote terminal units (RTU), intelligent electronic devices (IEDs), and sensors. The WTC collects all data using short communication links, and makes them available for processing and transferring to the control center. The closed-circuit television (CCTV) system and the Internet connection may share or use an independent communication network between the wind turbine and the control center.
* Communication network based on Ethernet equipment (gigabit Ethernet switch) is considered, to transfer data between the WTs and the control center. Most WPFs use the same power cable routes for the SCADA communication network as for the power distribution.
* Control center connects individual WTs and meteorological stations to the control center. It enables the operators to manage the behavior of all WTs as a whole. It requires a long distance for data transmission.
## 2 A Novel Filtering Strategy for Reliable Navigation  
Any FDE scheme, by definition, is reliant on statistical tests that intrinsically require an _a priori_ knowledge or assumption of the error statistics. In conventional FDE algorithms, the errors are often assumed to follow a normal distribution. Based on this assumption, a test statistics is computed in order to detect and remove unacceptably biased measurements, more specifically measurements that are statistically unlikely to occur.  
When it comes to GNSS signal degraded environments, however, the error distributions tend to deviate from the assumed normal distribution thus deteriorating the performance of the traditional FDE schemes. The inconsistency between the assumed and the true error statistics often leads to removal of good measurements by the FDE or improper weighting of each measurement. Removing a good measurement in environments where GNSS is already depleted not only degrades the navigation solution but can also make the FDE scheme unavailable due to lack of sufficient redundancy.  
In order to incorporate the GNSS measurements properly in the integrated navigation system, the FDE scheme should operate optimally. However, in harsh environments, FDE schemes face two major challenges, namely non-normality and varying error statistics. This paper, thus, attempts to address these challenges by adapting two approaches:  
* Use of Student's t-distribution for GNSS measurement errors.
* Adaptation of GNSS covariance to the changing GNSS signal conditions.
## 3 Numerical Results
Red dots: obtained from the non-redundant samples: (**a**) \(\varphi\)—component on the cut plane at \(\varphi=0^{\circ}\); (**b**) \(\theta\)—component on the cut plane at \(\varphi=60^{\circ}\); (**c**) \(\varphi\)—component on the cut plane at \(\varphi=60^{\circ}\); (**d**) \(\theta\)—component on the cut plane at \(\varphi=90^{\circ}\). errors relevant to the voltage \(V_{r}\), computed for \(\chi^{\prime}=1.10\), \(\chi=1.05\), \(1.10\), \(1.15\), \(p\), and \(q\) ranging from 2 to 11 and reported in Figure 10a. As can be seen, very low reconstruction errors (about \(-55\) dB) occur for the chosen sampling/OSI parameters. Moreover, the voltage reconstruction in Figure 10b relevant to the meridian at \(\varphi=90^{\circ}\) and obtained from NF samples affected by random errors, as in a real measurement, assesses the robustness of the adopted OSI algorithm. The background noise and the uncertainty on each NF sample are the same as in the previous case. The overall effectiveness is confirmed by the FF reconstruction. The ones reported here refer to cut planes at \(\varphi=90^{\circ}\) and \(\vartheta=90^{\circ}\). As can be seen, very good agreements also occur in this case. Note that the FF reconstruction at \(\vartheta=90^{\circ}\) (see Figure 11b) is particularly significant, since this result can never be obtained using [PERSON]'s NF/FF transformation [15] modified to account for the presence of an infinite PEC ground plane without impairing the reconstruction with a severe truncation error. Figure 10: Relevant to the reconstruction of \(V_{r}\): (**a**) Normalised mean-square errors; (**b**) Amplitude on the meridian at \(\varphi=90^{\circ}\). Blue solid line: exact. Red dots: recovered from the error-affected NF samples. Figure 9: \(V_{r}\) on the meridian at \(\varphi=90^{\circ}\). Blue solid line: exact. Red dots: interpolated from the non-redundant samples: (**a**) Amplitude; (**b**) Phase. It is interesting to compare the number of NF samples collected over the upper hemisphere with that required by [PERSON]'s NF/FF transformation [15] in the presence of an infinite PEC ground plane. This last number is about one-half of that which would be required when the probe can be driven along the whole scan sphere. These comparisons are shown in Table 1 for both reported examples. As can be noted, in both the cases, the numbers of NF samples required by the devised sampling strategy are smaller than those needed by [PERSON]'s NF/FF transformation [15] in the presence of an infinite PEC ground plane, and this reduction reflects in a significant reduction in measurement time. Note that the devised approach also offers the possibility of recovering the NF data of the classical spherical lattice lying on the equator, otherwise zeroed when executing [PERSON]'s NF/FF transformation [15].
# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## 2 Background
* Massive IoT deployment will need the low complexity that can be provided by PLS, or a hybrid approach can be taken, where a cross-layer low-weight mechanism can ensure security. * Post-quantum era: With the advent of quantum computing, encryption algorithms would be more vulnerable to cyberattacks. * Edge intelligence: As satellites can serve as edge computing devices, AI edge intelligence based on PLS can play a larger role. * Physical layer key generation: This utilizes the randomness inherent in wireless channels to establish a shared secret key between communicating devices, e.g., Alice and Bob. * RF-fingerprinting-based authentication: This technique creates a unique fingerprint based on the RF characteristics of the device's communication signal. Several factors, such as signal strength, fading patterns, and noise, can contribute to this fingerprint. The fingerprint is then compared to a pre-stored reference fingerprint for authentication. * Localization-based authentication: This method relies on the physical location of a device to verify its identity. The location is determined by using techniques like GPS, WiFi fingerprinting, or cellular network positioning. If the reported location matches that expected for a legitimate device, authentication is successful. It should be noted that 6G aims for centimeter-level accuracy in positioning. PLS techniques can be useful in various scenarios in 6G and can also be applied to complement upper-layer authentication methods. PLS has some inherent challenges in ISTNs that arise due to (a) correlated channels due to the negligible distance between legitimate users and an eavesdropper and that from the satellite to legitimate users, (b) co-channel interference due to the spectrum being shared by NTNs and TNs, (c) the broadcast nature of satellites, which creates a multi-user multi-eavesdropper scenario, (d) reliability concerns caused by channel estimation errors and hardware impairments [41; 42]. The correlation between the main and wiretap channels also presents challenges for CSI-based schemes. This paper is organized as follows. Section 2 discusses the various performance parameters. Section 3 focuses on some of the existing surveys of ISTNs. In Section 4, we review the PLS techniques from the signal processing viewpoint by dividing them into resource allocation, beamforming, cooperative communication, and physical characteristics. In Section 5, we present future directions. Section 6 concludes the paper. Figure 1: This illustration depicts a comprehensive network infrastructure that combines terrestrial and non-terrestrial components to provide seamless global connectivity. The terrestrial network consists of base stations, backhaul links, and the core network. The non-terrestrial network comprises various satellite constellations, including HAPs, UAVs, LEO, MEO, and GEO satellites. By leveraging the strengths of each component, this integrated network architecture delivers reliable, efficient, and ubiquitous communication services, even in remote or challenging environments.
# Highly Integrated Wideband Transmit/Receive Module for X-Band SAR Applications
## 3 Radio Frequency Design
### Power Divider  
A power divider/combine was designed with the objective of reducing the part count, in particular to halve the number of coaxial connectors necessary for TRM calibration. Basically, the proposed solution employs only one calibration connector for two TRMs. The calibration signal during the receive calibration mode is sent to two TRMs; thus, it should be split equally between the TRMs. Additionally, when performing the calibration of the transmitting path of each TRM, the high-power signal at the output of the SCFE is coupled toward the calibration port through the coupler and the Wilkinson divider, with the side TRM being turned off. The power divider was first designed based on well-known theory [36] by selecting the number of stages as two to achieve the required bandwidth. Then, it was implemented into the circuit simulator [35] for refinement and geometry tuning/optimization. Then, the optimized circuit was implemented into the full-wave simulation environment to verify its performances. The equivalent circuit and the full-wave models are reported in Figure 8, whereas the final results in Figure 9 show the comparison of the relevant S-parameters between the circuit and full-wave responses. Specifically, the return loss at Port 1 and at Port 2 (and 3) are shown in Figure 9a,b, whereas the insertion loss and coupling factor are given in Figure 9c,d, respectively. Good agreement was found, although the insertion loss from the full-wave model was affected by the metal and dielectric losses initially neglected in the circuit simulator. Moreover, the slight differences that can be seen in the frequency range 12-14 GHz in Figure 9a,b are mainly due to the coupling between the microstrips, since the circuit model does not take it into account.
## 4 Result of Implementation  
The development environment for this study was constructed as shown in Table 1. In this study, the software environment for the experiment was developed using Python, 3.10 version,  
Figure 4: LSTM Model structure.  
Figure 5: ELM Model structure.  
the artificial intelligence library used was the PyTorch-based MMDetection API, and the hardware environment was Windows 10 for OS, with an i9-9900 k for CPU, 128 GB of RAM, and 128 GB for the GPU. NVIDIA RTX 6000 was used, and a detailed environment is indicated in Table 1.  
To evaluate the performance of odor analysis, we propose a hybrid neural network architecture called CNN-Transformer, which combines the CNN and Transformer models. The performance of the proposed CNN-Transformer hybrid model is compared and evaluated with other models, including the CNN+LSTM hybrid, CNN, LSTM, and ELM models, as shown in Figure 6.  
Various performance metrics such as accuracy, precision, recall, F1 score, and RMSE are measured to assess the predictive capabilities of each model and compare their performance across these metrics. In this study, we utilize a publicly available dataset of 120,000 odor samples to construct the CNN-Transformer hybrid model and evaluate its performance. By comparing and evaluating the CNN-Transformer hybrid model against the other models, we aim to assess its effectiveness in odor analysis tasks and determine if it outperforms the existing models. The evaluation is based on performance metrics such as accuracy, precision, recall, F1 score, and RMSE, which provide insights into the model's predictive capabilities and performance.