esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Radar Jamming Signal Analysis and Modeling
### Deception Jamming
#### 2.3.4 Interrupted Sampling Repeater Jamming  
Interrupted Sampling Repeater Jamming (ISR) is a jamming technique evolved from CIJ. It is used to avoid the antenna coupling problem in CIJ which requires the jammer antenna to transmit and receive simultaneously.ISRJ is based on time-division transmit and receive operation. By intermittently \"undersampling\" the radar signal, it cleverly utilizes the intrapulse coherence of pulse compression radar to produce multiple coherent false targets near the echo signal, with good deception effect. Under certain conditions, it can also produce suppression jamming, bringing huge challenges to radar detection and tracking. Its mathematical model is as follows:  
\[J(t)=\sum_{n=1}^{N}\frac{1}{\sqrt{\tau}}\text{rect}\bigg{(}\frac{t-\frac{\tau} {2}-(n-1)T_{s}}{T}\bigg{)}e^{j2\pi\big{(}f_{0}t+\frac{\tau}{2}t^{2}\big{)}} \tag{12}\]  
where \(\text{rect}(\cdot)\) is the rectangular function, \(\tau\) is the pulse width of the intermittent sampling, \(T\) is the radar signal pulse width, \(T_{s}\) is the sampling period, so \(\tau/T_{s}\) represents the duty cycle of the intermittent sampling.
## 1 Introduction  
High quality of railway tracks is a prerequisite for reliable railway operation [1; 2; 3; 4; 5]. The infrastructure quality can be defined by functioning components [6; 7; 8; 9] and smooth track geometry [10; 11; 12], which is of particular interest as it determines operational safety to a large degree. The high loads of trains, frequently combined with high velocities, inevitably cause a deterioration of the track geometry [13; 14]. The defining track geometry parameters--longitudinal level, alignment, gauge, cross level, and twist--thus need to be regularly monitored and compared with threshold levels to detect critical spots in the network. Deeper analyses of the individual parameters enable early detections of critical states and planning of preventive maintenance [15; 16; 17]. In contrast, if the objective is to describe the general track condition, a collective consideration of the above-mentioned parameters is reasonable. For this purpose, many track quality indices (TQIs) have been developed [18; 19]. These TQIs combine the recorded measurement signals in different ways, intended to assess the overall track condition by a single value. Such a holistic view of the track quality is particularly useful to chronologically evaluate the parts of or the entire network. Net-wide time series analyses of TQIs demonstrate whether the general quality improves, remains in a steady state, or declines. Establishing a reliable quality index is therefore a predestined step for achieving a network condition overview and for supporting the strategic allocation of resources [20]. A TQI combining multiple signals cannot be used for detailed maintenance planning, however, as the information on why the index indicates poor track quality is lost during the process of aggregating the different input parameters.  
The first part of this paper is devoted to a literature research of existing track quality indices, describing the geometrical track condition. Total of 14 indices that are applied by railway infrastructure managers in different countries are presented, including brief descriptions and the respective mathematical formulas. Subsequently, all the indices are calculated for a real five-kilometer test section. The results are plotted together with the input signals of this section, which visualizes the properties of the different indices.  
In the second part of this paper a new index for track geometry--the track quality index of Graz University of Technology (TUG_TQI)--is presented. This newly developed TQI has the aim of eliminating the inherent weaknesses of existing indices, which the literature study reveals. The TUG_TQI is an evolution of the track quality index of the Federal Railroad Administration (FRA TQI) and incorporates aspects of other indices to form an optimized, universally applicable track quality index. To validate and visualize its positive characteristics, the index is calculated and plotted for the previously introduced test section.
# Process Development of Low-Loss LPCVD Silicon Nitride Waveguides on 8-Inch Wafer
## 3 Fabrication Process
**(a)** wafer start on a 8-inch wafer; **(b)** BOX layer deposition; **(c)** HM deposition and lithography; **(d)** HM etch; **(e)** BOX layer etch; **(f)** BOX layer anneal; **(g)** core layer deposition; **(h)** reverse etch; **(i)** CMP; **(j)** core layer anneal; **(k)** cladding layer deposition. Through this process, we successfully fabricated a 400 nm-thick Si\({}_{3}\)N\({}_{4}\) waveguide, and to further check the waveguide structure, FIB and XSEM were used to inspect the cross-section of the waveguide. The results are shown in Figure 11. The structural parameters of the waveguide basically conform to our design with a smooth sidewall, and a straight angle of 95\({}^{\circ}\) and no void is observed in the gap with 0.2 \(\upmu\)m width, which is critical to devices such as a directional coupler, multiplexer, and splitter. Figure 8: (a) Layout design of the checkerboard stress relief pattern; (b) SEM images of the checkerboard stress relief pattern. Figure 9: SEM images of the etched waveguide pattern in the BOX.
# Wireless Sensor Networks for Big Data Systems
## 1 Introduction
[6] addressed data management issues in WSNs by introducing different algorithms designed for data collection, aggregation, correlation, compression, and prediction; in addition, they showed potential and feasible application scenarios. Figure 1: Big data system based on a WSN. Convergence problems between WSNs/internet-of-things (IoT) and big data are reviewed and analyzed in [7], wherein the following open issues are mentioned: convergence process, security, management of data, interoperability, and hardware/architecture challenges. From the point of view of data usage, data collected from IoT contribute to context-aware computing, such as ubiquitous and pervasive computing. Thus, big data issues in ambient intelligence and WSNs need to be explored well. To achieve this goal, [PERSON] et al. [8] present open issues and provide an insight on IoT for big data. Furthermore, a wireless big data system that deals with massive data in wireless networks is described in [9]. Instead of describing the entire system, the authors focused on wireless infrastructure such as data-aided transmission, data-driven network optimization, and novel applications under layered architecture (as shown in application, network, transmission, and data layers in Figure 2). Further, they discussed three potential application areas: smart grids, internet-of-things (IoT), and drones/unmanned aerial vehicles (UAV), as illustrated in Figure 2.  
Despite the aforementioned research efforts, a comprehensive survey that provides deep insight into most research areas for WSNs as a source for big data remains lacking. To address this gap in the literature, in this paper, we present a comprehensive survey of research that introduces WSNs as a source of big data. In order to analyze the state-of-the-art for this research area, only the most recent papers (published in 2014 or later) addressing WSNs and big data simultaneously have been selected. Even though many existing research studies are applicable to big data systems, not all of them are included here because we focus on only integrated systems. Based on this selection strategy, we describe the potential applications of big data systems based on WSNs in Section 2. Then, we categorize the current research work according to the research areas in Section 3. Open issues in these research areas are introduced and discussed in Section 4. Finally, we conclude the paper in Section 5.
# Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review
## 3 Results
### Meta-Analysis Results  
Six studies reported PSNR values [6; 14; 32; 34; 37; 39] and five reported SSIM values [6; 14; 34; 37; 39], with standard deviations for both the low-quality input images and the generated images. Consequently, these studies were included in a meta-analysis. The meta-analysis revealed a mean increase in the PSNR between the generated and low-quality input images of \(6.60\pm 2.99\) (Figure 4a). The mean increase in the SSIM was \(0.28\pm 0.15\) (Figure 4b). Both increases were statistically significant (\(p=0.00\)). However, high heterogeneity was observed in both meta-analyses (I\({}^{2}=100\%\)), indicating substantial variability among the included studies.  
Figure 3: Visualizations of the obtained (**a**) PSNR and (**b**) SSIM for each dataset in the included studies for both the low-quality input images and the generated images by the proposed algorithm. The color represents the dataset type (in vivo, phantom, or simulation data). Note that some studies are represented by multiple bars, since they evaluated multiple datasets.
## 2 System Overview

Table 1: Throughput comparison base on different image resolution. Figure 2: System overview, CNN: Convolutional Neural Network. The internal structure system diagram of the Faster R-CNN is shown in Figure 3. The scheme is divided into two modules: one is based on the Region Proposal Network (RPN) are a proposal network module, which is used to generate candidate regions; the other is based on Faster R-CNN classification network module, which is used to detect and classify candidate regions generated by the RPN. Firstly, we input the pre-processed line-scan image of the metal shaft, and the image is pre-processed. The convolution feature map is extracted by the shared convolutional neural network; then, the candidate region is quickly generated through the RPN network, and the redundant candidate bounding box is initially eliminated by non-maximum suppression. Then, the candidate bounding box is extracted by the Region of Interest(ROI) pooling layer, and the Softmax multi-classification and bounding box regression are directly output through the fully connected layer in the convolutional neural network; finally, the final output is obtained by the non-maximum suppression fine screening bounding box.
## 4 Discussion
### Methodological Limitations and Implications of the Results
Hysteresis was identified as a contributing factor to the differences observed between remolded and undisturbed samples, as each follows either a drying or wetting path, respectively. However, this study did not quantify the extent to which hysteresis affects the shear strength and elastic modulus. Future research should incorporate microstructural analyses, such as scanning electron microscopy (SEM) or computed tomography (CT) scans, to elucidate the influence of SWCC hysteresis and soil macrostructure on the mechanical behavior differences observed between the reconstituted and undisturbed specimens. Finally, the filter paper method becomes less accurate at high suction levels because of the difficulty in testing specimens with very low water content. For this reason, the SWCC was derived using measurements of water contents as low as 4%. Nonetheless, the inclusion of data from higher suction ranges could have altered the shape of the curve in that domain. The lack of such data may partly explain the lower coefficients of determination observed in the fitted equations that include high suction values, as shown in Figures 16b, 17b, 18b, 19b and 20b, compared with those limited to low suction ranges. Alternative methods, such as pressure plate apparatus or time-domain reflectometry (TDR) sensors, offer more reliable measurements of SWCC under high suction conditions. The pressure plate apparatus provides direct control and measurement of matric suction up to approximately 1500 kPa [62], offering more accurate results and reducing the uncertainty associated with the indirect methods. TDR sensors allow continuous in situ monitoring of soil moisture and suction, supporting the evaluation of suction under natural and variable environmental conditions [7]. However, both techniques require specialized equipment, involve high operational costs owing to the need for frequent calibration, and have long equilibration times. These factors may limit their practical use in studies involving large numbers of samples. Given these considerations, the filter paper method was selected for this study owing to its low cost and ease of implementation.
## 3 Results and Analysis
### Accuracy of AGC
The error \(\Delta_{AGC\_MULT}\) (Figure 8) for an AGC structure with analog multiplier is constant at 0.3 V across the whole measurement range. When the gain control structure is implemented in the microprocessor (Figure 4), the error \(\Delta_{AGC1}\) increases as the input value increases \(V_{\mathit{oip}}\). The error \(\Delta_{AGC1}\) depends on the input value and the error of the operational amplifier (see Figure 8 \(\Delta_{AGC2}\) which uses a more precise amplifier). Type \(B\) uncertainty is used to estimate the uncertainty associated with the AGC measurement results in indirect measurements. This uncertainty is defined by the relationship:  
\[u_{B_{A_{ACC}}}=\sqrt{\sum_{j=1}^{N}\left(\frac{\partial f}{\partial X_{j}} \right)^{2}u_{B_{X_{j}}}^{2}} \tag{5}\]  
where \(u_{B_{A_{ACC}}}\) is the complex uncertainty of the AGC system,\(f\) is the measurement function, and \(X_{j}\) is the input quantity. The components of uncertainty \(B\) with uniform error distribution are defined by the relationship:  
\[u_{B_{x_{j}}}=\frac{\Delta_{x_{j}}}{\sqrt{3}} \tag{6}\]  
where \(\Delta_{x_{j}}\) is limiting error. For example, the uncertainty \(B\) of ADC is computed by \(u_{B_{ADC}}=\frac{\Delta_{ADC}}{\sqrt{3}}\) (where \(\Delta_{ADC}\) is defined by Equation (4)). In the last step, the expanded uncertainty determining the uncertainty range of the measurement result should be estimated based on the relationship:  
\[U=k_{p}u_{B_{A_{AGC}}} \tag{7}\]  
In the calculations, \(k_{p}=2\) was assumed, and assigned a confidence level of \(p=0.95\). The determined uncertainty is presented in Figure 8 (waveform \(U\)). The maximum value for complex uncertainty is equal to 0.95. For this reason, the desired range of the AGC was reduced from 5 to 4 V.  
Figure 8: Total error of the AGC structure based on the microprocessor, where \(\Delta_{AGC\_MULT}\) is the limiting error of AGC structure with an analog multiplier, \(\Delta_{AGC1}\) is the limiting error of the AGC structure implemented on a microprocessor (Figure 4), \(\Delta_{AGC2}\) is the limiting error of the AGC structure implemented on a microprocessor with a more accurate amplifier (Figure 4), U is the uncertainty, and \(V_{\mathit{oip}}\) is the value of the input signal. In the processing of dynamic signals, the actual sampling frequency should be determined. This parameter can be determined based on the dependencies, \(f_{s}=1/(t_{ADC}+f_{fuzzy}+t_{DAC})\), where \(t_{ADC}\), \(t_{DAC}\) are the processing times in the ADC and the DAC, and \(t_{fuzzy}\) is the execution time required for the algorithm to determine the gain. For this reason, it is necessary to optimize the operation of the fuzzy controller and reduce the \(t_{fuzzy}\). The DAC converter should have low a settling time of around 5 \(\upmu\)s.
# A Spectral-Aware Convolutional Neural Network for Pansharpening
## 4 Experiment Results
### Experimental 2--IKONOS Hobart Dataset  
This dataset covers an urban area of Hobart in Australia. A sub-scene with \(256\times 256\) pixels is used for testing in our experiments.  
Table 3 shows the results of the reduced resolution quality assessment on the IKONOS dataset. The pansharpening qualities of various methods vary with the characteristics of the sensor. PCA and CNMF get worse pansharpening results compared to other methods. Unlike the results on the WorldView-2 dataset, GSA is much more preferable among the traditional methods. Such a phenomenon implies the poor robustness of the traditional methods. Similar to the previous dataset, CNN-based methods achieve a significant improvement. Among them, PNN achieves better results compared with DRPNN and DiCNN1, while our SA-PNN also achieves the best performance.  
For visual inspection, Figure 5 shows the pansharpening results yielded by different methods. Similar to the conclusions drawn from Table 3, the results of the traditional methods are unsatisfactory for the spectral distortion and the diffused blur especially in the result of CNMF. Although the results of PNN, DRPNN, and DiCNN1 are superior in term of spatial details, they inevitably suffer from a little spectral distortion, e.g., the red roofs distributed in Figure 5. The result of our SA-PNN is more similar to the ground truth than others, with comparable spatial details and unnoticeable spectral distortion.
## 2 Theoretical Basis
### The Sound Prediction Based on ARM
In the above process, it could be found that the selection of measurement points does not depend on the actual normal velocity distribution on the structural surface, as \(\mathbf{v}\) does not enter into the computations associated with the derivation of the dipole matrices and the power resistance matrix. Regularization methods are applied for Equations (23) and (25) to keep the computation processes stable. It is worth mentioning that the truncation number of ARMs \(L1\) should be selected carefully as it could have a significant influence on the prediction accuracy. When \(L1\) is too small, the computation may not reach the convergence. On the contrary, with the increase of \(L1\),the condition number of the sensing matrix also increases, which could reinforce the prediction errors. In the study [31], [PERSON] proposed a method to select the optimal \(L1\), which could be given as below. Choose \(L3\) measurement points as the test points from the \(L\) measurement points to be selected. Add the test points to the original measurement group and the velocities at these points will be reconstructed with the target \(L2\) points. Then, update \(L1\) from \(1\) to \(L2\). During every time of iteration, obtain the reconstruction error \(\varepsilon_{\mathbf{v}}=(\|\mathbf{v}^{\prime}_{\mathbf{r}}-\mathbf{v}^{\prime}_{\mathbf{0}}\|_{ 2}/\mathbf{v}^{\prime}_{\mathbf{0}})\times 100\%\) at the test points, where \(\mathbf{v}^{\prime}_{\mathbf{r}}\) is the reconstructed velocity and \(\mathbf{v}^{\prime}_{0}\) is the reference velocity. When the reconstruction error comes to the minimum, the corresponding \(L1\) is regarded as the optimal value. Moreover, the method could be applied for the determination of the ARM truncation number in CMESM.
# Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies
## 3 Concurrent Wake-Up Message
### Two Concurrent Senders
Considering Figure 6, Node-1 and Node-2 are usually in an ultra-low-power listening state. To successfully generate a concurrent wake-up message, synchronization of both nodes has to be achieved prior to sending of \(f_{1}\) and \(f_{2}\). Synchronization can be achieved for example by triggering on the wake-up message that is sent by Node-1 to wake up Node-2 or by utilizing the wake-up acknowledgment transmitted by Node-2. The latter method is implemented in this work, as will be discussed in Section 4. Figure 8 depicts Equation (3) graphically, with the horizontal axis representing the real part and the vertical axis representing the imaginary part of \(y(t)\). It can be seen that there exist generally two cases: (a) \(|A_{1}|=|A_{2}|\) and (b) \(|A_{1}|>|A_{2}|\). The case \(|A_{1}|<|A_{2}|\) is the same as Case (b), which may be achieved by reordering \(A_{1}\) and \(A_{2}\) in Equation (3). Furthermore, it can be deduced from the two graphs (a) and (b) in Figure 8 that the beat signal has an amplitude of \(A_{2}\) and \(A_{1}\) resembles an offset that shifts the beat signal from the origin. Figure 9 visualizes Equation (3) in the time domain; \(f_{c}=(f_{1}+f_{2})/2\) is shown as the black curve, and \(f_{b}\) is visualized in red. In Figure 9, the two sinusoidal signals have equal amplitudes \(A_{1}=A_{2}=1\), and in Figure 9, the amplitudes are \(A_{1}=0.7>A_{2}=0.4\). In both figures, the resulting beat (\(f_{b}\)) has amplitude \(2A_{2}\). The amplitude discrepancy between \(f_{b}\) and \(f_{c}\) visible in Figure 9 shows the offset generated by \(A_{1}\). Please note that the signals plotted in Figure 9,b were obtained from frequencies \(f_{1}=868\,\mathrm{MHz}\) and \(f_{2}=868.125\,\mathrm{MHz}\), although \(f_{c}\) appears to have a lower frequency in the plots, which is due to massive undersampling that was done for reasons of illustration. Figure 10 depicts the same wake-up signal as it appears at the input of the wake-up receiver after envelope detection. As discussed above, the signal has amplitude \(2A_{2}\) and frequency \(2f_{b}\). Figure 10b also illustrates the DC-offset of \(A_{1}-A_{2}\) on the wake-up signal in accordance with the expectations from Equation (3). As introduced in Section 2.2, wake-up receivers usually listen to the wireless channel in the kilohertz range, with a data rate of several kilobits per second. Consequently, timing constraints are in the \(\upmu\)s range. As the wake-up signal appears at the receiver like any other wake-up signal, there are no additional components required, and the same hardware can be used to modulate and demodulate the beat signal.
## 3 Memristive Analog Design
### Memristive RF Circuits
#### 3.2.1 High-Performance RF Switches
Although substantial advances in the development of memristive RF switches have been made, several challenges must be addressed to advance the design of more efficient switches. One key challenge is improving the reliability and durability of memristor-based RF switches to ensure long-term performance. Additionally, enhancing the scalability and integration of these switches into complex RF systems is crucial for their widespread adoption in various applications. The endurance of memristive RF switches should be increased to ensure stable device functioning in practical communication and connectivity systems. Furthermore, optimizing the power consumption of memristive RF switches is essential to increasing efficiency and reducing energy usage. Overall, continued research and innovation in the field of memristive RF switch technology are necessary to overcome these challenges and unlock their full potential in modern communication systems.
## 1 Introduction
This implies that the CNN training process could ensure that CNN-based filters capture accurate enough signals without considering neighboring data points. In this sense, CNN-based filters may also reduce filtering uncertainties due to missing data points, as mentioned above. However, this has not been studied in previous research. It remains unclear whether CNN-based filters work well for discontinuous time series. Thus, in this study, with a detailed explanation about the procedure of constructing a 1D-CNN filter, we examine how a 1D-CNN filter can be utilized to address the challenges in filtering time series with missing values. Quasi-biweekly-to-intraseasonal variability is one of the important atmospheric signals affecting South China and Hong Kong [37], so we take meteorological station observations from the Hong Kong Observatory as sample data. The rest of this article is structured as follows: Section 2 introduces the datasets, analysis methods, and evaluation indicators used in the study; Sections 3 and 4 explain the construction process of the 1D-CNN filter and its validity; Section 5 discusses the advantages of the 1D-CNN bandpass filter in dealing with incomplete time series; finally, Section 6 summarizes the key findings of the study and discusses them.