esa-sceva/satcom-chunk-collection huggingface dataset:
# Bandwidth Enhancement of a V-Band Klystron with Stagger-Tuned Multiple Radial Re-Entrant Cavities
## 3 Results and Analysis
The FT magnitude (normalized to maximum) for the frequency range 59.8 to 60.4 GHz is shown in Figure 12. The bandwidth improvement of the proposed devices is evident from the comparison given in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Intermediate Cavity**} & \multicolumn{5}{c}{**Dimensions (mm)**} & \multicolumn{5}{c}{**Simulation Results**} \\ \cline{2-9}
**Type** & **a** & **w** & **b** & **g** & **c** & **Unloaded Q (Q\({}_{0}\))** & **R/Q (\(\Omega\))** & **f (GHz)** \\ \hline IC1 & 3 & 0.6 & 1.75 & 0.3875 & 0.22 & 1346 & 85.7 & 60.42 \\ \hline IC9 & 3 & 0.6 & 1.6 & 0.38 & 0.22 & 1384 & 89.2 & 60.12 \\ \hline IC8 & 3 & 0.6 & 1.6 & 0.38 & 0.22 & 1384 & 89.2 & 60.12 \\ \hline IC7 & 3 & 0.6 & 1.6 & 0.38 &Figure 10: The \(-3\) dB bandwidth plots of the RF section with (**a**) IC9 tuned at 60.1 GHz, (**b**) IC8 tuned at 60.1 GHz, (**c**) IC7 tuned at 60.1 GHz, (**d**) IC6 tuned at 60.1 GHz, (**e**) IC5 tuned at 60.1 GHz, (**f**) IC4 tuned at 60.1 GHz, (**g**) IC3 tuned at 60.1 GHz, and (**h**) IC2 tuned at 60.1 GHz.
## 2 Experimental Details  
Figure 1 shows the experimental setup for the passively Q-switched (PSQ) Er:Lu\({}_{2}\)O\({}_{3}\) ceramic laser. A compact three mirror, V-shaped cavity design was employed. The resonator comprised a plane pump coupling mirror (M1) with 6.5% transmission at lasing wavelength and high transmission (>90%) at the pump wavelength, a concave dichroic mirror (M2, 50 mm radius of curvature) and a reflection-type SESAM. The concave dichroic mirror was high-reflection coated at the lasing wavelength and anti-reflection coated at the pump wavelength, which was used to filter out the residual pump light and minimize its influence on the SA. The laser output was extracted and separated from the pump light by a dichroic mirror (M3).  
The gain medium was an uncoated 10.6-mm-long, 7 at. % Er:Lu\({}_{2}\)O\({}_{3}\) ceramic, which was wrapped with indium foil and mounted on a water-cooled heat-sink for heat removal. The physical length of the laser resonator was around 55 mm, and the spacing between M1 and M2 was \(\sim\)35 mm. Based on the ABCD matrix analysis, the calculated stable zone for the thermal focal length was in the range of from \(\sim\)120 mm to \(\sim\)5 mm. The pump source used in the experiment was a fiber coupled laser diode, and its center wavelength was locked at 976 nm with a bandwidth less than 0.3 nm. The delivery fiber has a core diameter of 105 \(\upmu\)m and a numerical aperture (NA) of 0.22. The pump light was launched into the gain medium through a telescopic lens system, and the spot diameter was around 360 \(\upmu\)m. Under an absorbed pump power of \(\sim\)10 W, the thermal focal length in the Er:Lu\({}_{2}\)O\({}_{3}\) ceramic was measured to be around 50 mm according to resonator transform circle theory. In the range of 120 mm to 50 mm thermal focal length, the calculated radius of the laser mode in the gain medium varied from \(-\)250 \(\upmu\)m to \(-\)132 \(\upmu\)m.  
The passive Q-switch used in the experiment was a piece of commercial SESAM, which was designed to operate in the wavelength range of 2700-2900 nm (BATOP, model SAM-2800-3-12.7g). The SESAMs have a modulation depth of \(\sim\)2%, a non-saturable loss of \(\sim\)1%, and a relaxation time constant of \(\sim\)10 ps. To measure the laser output power, a thermopile power meter (OPHIR 30A-BB-18) was used in the experiment. The output spectra of the laser output were analyzed using a Fourier Transform Infrared Spectroscopy system (Thorlabs OSA205C) whose resolution was \(\sim\)0.2 nm around \(\sim\)3 \(\upmu\)m.
## 2 Principles  
### Principles of FPI  
The FPI consists of two semi-reflective mirrors separated by a distance of \(d\) with a refractive index of \(n\). Its schematic diagram is shown in Figure 1. A parallel beam with a wavenumber \(\
u\) is incident on the FPI at an angle \(\theta\). After multiple reflections and transmissions, the exit angle of the light remains unchanged. The OPD between adjacent beams is \(\Delta_{FPI}=2 nd\text{cos}\theta\), and the phase difference is \(\delta_{FPI}=4\tau\
u nd\text{cos}\theta\).  
The light beams exiting the FPI are converged onto the image plane by the imaging system, where multi-beam interference occurs. The effect of interference can be regarded as generating a transmission spectrum, with the FPI transmission spectrum \(T_{FPI}\) distributed as follows:  
\[T_{FPI}(\
u)=\frac{(1-R)^{2}}{1+R^{2}-2R\cos(2\pi\
u\Delta_{FPI})}=a_{0}+\sum _{m=1}^{\infty}2a_{m}\cos(m\cdot 2\pi\
u\Delta_{FPI}), \tag{1}\]  
where \(R\) denotes the reflectivity of the FPI reflective surface. The transmittance function \(T_{FPI}\) demonstrates periodicity in the wavenumber dimension and admits a Fourier series expansion. The coefficients are defined as follows: \(a_{0}=(1-R)/(1+R)\) represents the DC  
Figure 1: Schematic diagram of FPI principle [26].  
component coefficient; \(a_{m}=R^{m}(1-R)/(1+R)\) corresponds to the fundamental frequency and higher-order harmonic coefficients.
## 5 Results and Discussion
### Ablation Experiment  
To demonstrate the importance of different components in the STAGCN, five variants are derived for comparison. The five variants of the STAGCN model are explained in detail:  
* Variant-Base: this model only adopts the AGCN to extract spatial features.
* Variant-M: this model adds Bi-Mamba based on Variant-Base to extract temporal features.
* Variant-A: this model adds MTLA based on Variant-Base.
* Variant-M-A: this model adds Bi-Mamba and MTLA based on Variant-Base.
* Variant-Preg: this model uses the predefined adjacency matrix based on the PCC for the GCN, which is used to replace the AGCN in the STAGCN.  
As shown in Figure 11, the proposed STAGCN has better performance than other variants. Based on the experimental results, the following conclusions can be drawn: (1) The predefined graphs rely on manually set adjacency matrices, which may fail to capture dynamic spatial dependencies in multi-sensor data. In contrast, the AGCN dynamically learns spatial relationships, improving feature aggregation. (2) The performance of the model depends on the extraction of both spatial and temporal features. (3) When MTLA is removed, the performance decreases significantly, which means that MTLA is one of the most important components. This is because MTLA plays a crucial role in multi-scale  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Training Samples** & **Validation Samples** & **Testing Samples** & **Batch Size** & **Training Time per Epoch (s)** \\ \hline C1 & 15,096 & 3775 & 9421 & 32 & 34.2 \\ C4 & 15,096 & 3775 & 9421 & 32 & 34.6 \\ C6 & 15,096 & 3775 & 9421 & 32 & 35.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The numbers of the training samples, the validation samples, the testing samples, the batch size, and the mean training time per epoch of the STAGCN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Original Signal**} & \multicolumn{2}{c}{**Denoised Signal**} \\ \cline{2-5}  & **MAE (\(\mu\)m)** & **RMSE (\(\mu\)m)** & **MAE (\(\mu\)m)** & **RMSE (\(\mu\)m)** \\ \hline C1 & 3.54 & 4.52 & 2.32 & 2.84 \\ C4 & 5.18 & 6.75 & 3.37 & 4.24 \\ C6 & 6.86 & 9.21 & 4.52 & 5.89 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effect of signal denoising on model prediction performance.  
feature fusion, dynamically adjusting attention weights to capture spatial, temporal, and channel dependencies effectively.
## 4 Results and Discussion
### Ablation Study  
#### 4.3.1 ASPP Analysis  
Table 3 shows the analysis of the accuracy contribution of ASPP used in the EAR-Net and the change in accuracy, according to the rate. From the table results, it can be seen that ASPP contributes greatly to improving segmentation accuracy. Moreover, the highest accuracy is shown when the rate is set to (6, 12, 18). The lower the rate, the  
Figure 8: Visual comparisons of complex scenes on the Cityscapes validation set. The orange box shows the section where the EAR-Net achieves a more accurate segmentation result than the U-Net.  
Figure 9: Visual comparisons of multiple objects on the Cityscapes validation set. The orange box shows the section where the EAR-Net achieves a more accurate segmentation result than the U-Net.  
narrower the reception area, but there are few checkerboard artifacts because the number of pixels that perform the operation is large. The checkerboard artifact is a phenomenon in which a grid pattern appears on the feature map because adjacent pixels are not processed. Conversely, the higher the rate, the larger the receptive area, but the higher the tendency for checkerboard artifacts. Therefore, it is important to find the optimal rate value, which is achieved in this ablation study.  
Moreover, the number of parameters is shown in Table 3. The difference between the number of parameters of the model with and without the ASPP is approximately 2.5 M. For models with different rates, the number of parameters does not change. This proves that the number of parameters of ASPP is about 2.5 M and that atrous convolution only increases the empty space of the kernel, even if the rates are different.
## 6 Federated Learning for Early Health Prediction
### Federated Learning for Disease and Mortality Prediction
#### 6.2.1 Cardiovascular Diseases  
The authors in [86] introduced an innovative approach to leveraging federated learning for the prediction of cardiovascular diseases in healthcare settings. Their study proposed a hybrid classifier-based framework that combines local models on individual health service providers' data with a centralized classifier, thereby striking a balance between data privacy and model accuracy. By harnessing the power of federated learning, this research aimed to enhance the accuracy of cardiovascular disease predictions while preserving sensitive patient data, making it a promising solution for healthcare institutions seeking to improve patient care and outcomes in a privacy-conscious manner.
## 3 The Proposed Multiple-Signal Joint Estimation Method
### Multiple-Signal Compact Coupled Filter Group Architecture
The state equation can be expressed as  
\[\mathsf{s}_{\text{GNSS6\& 5G}}[t_{k}]=\left[\mathbf{p}^{\text{T}}[t_{k }],\mathbf{v}^{\text{T}}[t_{k}],\rho[t_{k}],\alpha[t_{k}]\right]^{\text{T}} \tag{13}\]  
In the GNSS and 5G joint positioning compact-coupled-filter group architecture, each filter in the first group outputs updated and filtered GNSS and 5G observations into the second group, which are used to modify the measurements of each channel in the receiver at the current time. By combining the estimated value of the previous time, the second group uses the SRURF algorithm to solve the fusion of multiple signal sources' TOA and DOA and gives the joint estimation of the position, speed, and clock offset of the positioning terminal with GNSS and 5G.
## 2 Materials and Methods
### Calibration of Sensors
#### 2.3.2 Salt Calibration  
Five sets of samples with a moisture content of 6% and salt contents of 0.0%, 0.5%, 1.0%, 1.5%, and 2.0% were prepared by washing the natural coarse-grained saline soil. The samples were sealed and stored for 24 h to ensure a uniform distribution of salt and moisture and then dried in an oven. A total of 20 g of dried sample from each group was weighed, 100 g of distilled water was added, and a saline soil solution was prepared at a soil-water ratio of 1:5. After thoroughly stirring and shaking with a glass rod to completely dissolve the salt in the saline soil, the CS655-1--CS655-5 sensor probes were sequentially inserted into the saline soil solution for performing three conductivity tests, and the average value was calculated. The test results were plotted, with the horizontal axis representing the salt content and the vertical axis representing the conductivity, as shown in Figure 6. There was a favorable linear relationship between the conductivity and salt content, with a correlation coefficient greater than 0.99.  
Figure 5: CS655 sensor water content calibration function.
###### Abstract  
_Brassica rapa_, commonly known as the rapeseed plant, is globally recognized for its nutrient-rich composition and oil-packed seeds, earning its distinction as a substantial oil-seed crop. The seed quality, particularly the germination rate, is instrumental in guaranteeing a high-yield rapeseed crop. Given this, the accurate, quantitative determination and selection of germination rates in seed batches prior to sowing is of paramount importance. However, conventional germination tests, employed to determine the average germination rate of seed batches, are marred by substantial time and cost inefficiencies. This study proposes the use of near-infrared spectral analysis as a proficient, non-invasive approach for assessing germination rates in rapeseed seed batches. The research involved artificial aging of seeds procured from a variety of rapeseed strains, resulting in 228 batches with a broad germination rate spectrum of 15.73% to 99.13%. We recorded near-infrared diffuse reflectance spectra and applied a range of strategies for spectral data preprocessing and feature variable selection. Furthermore, we leveraged support vector regression (SVR) modeling to augment the detection methodology. SVR training and detection were conducted using MATLAB, with selected feature wavelengths undergoing rigorous scrutiny and discussion. The results indicated that employing Savitzky-Golay convolution smoothing for spectral preprocessing, along with Synergy interval Partial Least Squares (SiPLS) in conjunction with Random Frog (RF) for the selection of 50 feature wavelength points, yielded optimal germination rate prediction performance within the SVR model. The coefficients of determination (R\({}^{2}\)c) for the training set and (R\({}^{2}\)p) for the testing set were observed to be 0.8559 and 0.8386, respectively, while the Root Mean Square Errors of Calibration (RMSEC) and Prediction (RMSEP) were calculated to be 13.76% and 17.04%. The mechanism of detecting seed vigor through near-infrared spectroscopy was analyzed based on joint variable screening and sensitive variable traceability. Consequently, the SG-SiPLS-RF-SVR model demonstrates its effectiveness in predicting the average germination rate of seed batches, offering a rapid, non-invasive detection method that can be universally applied to various rapeseed strains, thus significantly improving seed production efficiency.  
near-infrared spectroscopy; rapeseed plant seeds; germination rate; feature wavelength selection; support vector machine regression Article
# Underwater Target Recognition Based on Multi-Decision
## 5 Numerical Results
### Multi-Step Decision LOFAR Line Spectrum Enhancement Algorithm Validity Test
It can be seen from Table 5 that the spectral correlation coefficients in the high-resonance components of signals A and B are smaller than their original spectral correlation coefficients. It means we can enhance the degree of difference between the two signals by extracting the high-resonance components of the signal. Energy Distribution of Low Resonant Component Signal  
Figure 6: Percentage of total energy of each frequency band of low-resonance component signal. spectrum has \"breakpoints\", \"broken lines\" or only a short line due to noise interference, the line spectrum enhancement algorithm can still extend and complete the line spectrum. Experimental Verification of Underwater Target Recognition Based on Convolutional Neural Network (CNN)
# Optimization of BP Neural Network Model for Rockburst Prediction under Multiple Influence Factors
## 5 Prediction Results and Analysis
### Analysis
In order to study the influence of sample size on the prediction effect of the BP neural network model, the average and maximum prediction accuracy rates of twenty BP models under different sample sizes were calculated, as shown in Figure 5. It can be seen that with the increase in sample size, the average and maximum prediction accuracy rates both increase nonlinearly. Therefore, the expansion of the sample size is one of the conditions for improving the prediction accuracy of the BP model.
## 4 Discussion
Considering the length of the SDS molecule (\(\sim\)1.7 nm), the layer thickness of the SDS molecule at the toluene-\(d_{8}\)-D\({}_{2}\)O interface (1.23 nm) indicates that the SDS molecules would be in a disordered configuration in the SDS layer of the toluene-\(d_{8}\)-D\({}_{2}\)O sample. Therefore, the difference in the adsorption structure of the SDS molecules between the air- and toluene-\(d_{8}\)-D\({}_{2}\)O interfaces could be attributed to the difference in the adsorption behavior of the SDS molecules depending on the interfacial conditions. However, a rough depth resolution is given by \(2\pi/Q_{\rm max}\)[34], which corresponds to \(\sim\)3 nm in air-liquid sample measurements and \(\sim\)10 nm in liquid-liquid sample measurements. Without any complementary data or special measurement methods, achieving a depth resolution of 1-2 nm using the NR method is extremely difficult. Additionally, the reproducibility of measurements obtained using various equipment and fitting analyses has been investigated in the X-ray reflectometry method [35]. The results revealed an inter-laboratory reproducibility of \(\sim\)1 A and a fitting analysis reproducibility of \(\sim\)0.5 A. In the NR software study, the same data were analyzed using different software [36]. Although these data were analyzed using Aurore and Motofit, starting from the same set of initial parameters, there was a 1-2 A difference in the obtained optimized thickness values. Therefore, these results indicate that it is hard to believe that the reflectometry technique detects 1-2 nm thick layers with a sub-nm resolution. In conclusion, it must be noted that, for the fine estimation of the SDS layer thickness using the NR technique, additional information on the SDS layer thickness must be provided by complementary measurements, such as X-ray reflectometry or contrast variation experiments using deuterium-labeled compounds.
# Widely-Tunable Ring-Resonator Semiconductor Lasers
###### Abstract  
Chip-scale widely-tunable lasers are important for both communication and sensing applications. They have a number of advantages, such as size, weight, and cost compared to mechanically tuned counterparts. Furthermore, they allow for integration in more complex integrated photonic chips to realize added functionality. Here we give an extensive overview of such lasers realized by utilizing ring resonators inside the laser cavity. Use of ring resonators for tuning allows for wide-tunability by exploiting the Vernier effect, and at the same time improves the laser linewidth, as effective cavity length is increased at ring resonance. In this review, we briefly introduce basic concepts of laser tuning using ring resonators. Then, we study a number of laser cavity configurations that utilize two ring resonators, and compare their tuning performance. We introduce a third ring resonator to the laser cavity, study three different cavity configurations utilizing three ring resonators, and select the optimal one, for which we show that laser tuning is straightforward, provided there are monitor photodetectors on-chip. Finally, we give a literature overview showing superior linewidth performance of ring-based widely-tunable lasers.  
semiconductor lasers; cavity resonators; laser tuning; photonic integrated circuits; ring resonators +
Footnote †: journal: Applied Physics Letters
# A Detailed Review on Foam Concrete Composites: Ingredients, Properties, and Microstructure
## 2 Compositions of Foam Concrete
### Fibers  
Fibers are used to improve the mechanical and strength properties of concrete. They are divided into natural and artificial types. Examples include alkali-resistant glass, coir fiber, steel, carbon, palm oil and polypropylene fiber [57]. The use of fibers helps to improve compressive strength and limit crack formation. However, it causes less workability and difficulties for compaction [59].  
In the study in which the hybrid use of polyvinyl alcohol fiber (PVA) and coir fiber was carried out, it was suggested that the use alone for both fiber types should be 0.3%. With the use of PVA fiber, 76% more strength was obtained compared to the control sample and it performed better than coir fiber. The reason for this is that PVA fiber has higher tensile strength [60]. It was reported that polypropylene fiber increases the compressive and splitting tensile strengths [49]. The mechanical properties of sugar cane pulp fiber reinforced foam concretes were improved. However, it increased the water absorption rate [61]. With the use of carbon fiber, the modulus of elasticity increases, and flexural strength improves [62]. Some examples used in other studies are given in Table 3.
## 2 Methodology  
### Systems  
All OCT measurements were performed with a commercially available OCT system (Thorlabs Ganymede, Dachau/Munich, Germany). Operating with a center wavelength of 930 nm and a bandwidth of 154 nm, the Thorlabs Ganymede Spectral Domain OCT has an axial and lateral resolution of 6 \(\upmu\)m and 8 \(\upmu\)m, respectively. The maximum penetration depth is 2.73 mm, which was validated by the manufacturer. All these values hold for a refractive index of \(n=1\). Snapshots using the built-in camera with 480 \(\times\) 640 pixels were taken for orientation. By using an interpolation, these images were resized, in order to have the same dimensionality as the OCT dataset, but not the same resolution (Figure 1) and then compared with a microscopic image taken with a Keyence VHX 2000 microscope (KEYENCE Deutschland GmbH, Neu-Isenburg, Germany).  
For the magnetic inductive measurements, the Fischer(r) Dualscope(r) (Helmut Fischer GmbH, Sindelfingen-Maichingen, Germany) FMP40 used with the FGAB 1.3 probe was utilized. This probe can be used for non-ferrous and isolating materials. The radius of the measuring tip is 0.75 mm, having an accuracy of \(\pm 1\)\(\upmu\)m and a precision of 0.3 \(\upmu\)m for coatings up to 100 \(\upmu\)m thickness.
# Transcranial Direct Current Stimulation in the Treatment of Chronic Knee Pain: A Scoping Review
###### Abstract  
Background: Chronic knee pain in older adults is a prevalent condition that significantly impacts quality of life. Transcranial Direct Current Stimulation (tDCS) has emerged as a potential non-invasive treatment option. This scoping review aims to evaluate the efficacy of tDCS in treating chronic knee pain among older adults. Methods: A comprehensive search of peer-reviewed articles was conducted, focusing on randomized controlled trials and pilot studies. Studies were included if they met specific Population, Concept, and Context (PCC) criteria. The primary outcomes assessed were pain reduction and functional improvement. Results: Eleven studies met the inclusion criteria, with a total of 779 participants. However, the results varied across studies, with some showing minimal differences between active tDCS and sham treatments. Advanced neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS), provided insights into the neuromodulatory effects of tDCS, revealing changes in brain activity related to pain perception. Conclusions: Transcranial Direct Current Stimulation (tDCS) presents a promising avenue for treating chronic knee pain in elderly individuals. However, the current body of research offers mixed results, emphasizing the need for more extensive and standardized studies. Future research should focus on understanding the underlying mechanisms, optimizing treatment protocols, and exploring the long-term effects and safety of tDCS.  
transcranial direct current stimulation (tDCS); chronic knee pain; non-pharmacological treatment; neuromodulation; pain management