esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 PAPR-Aware Transmission Strategies
### Pre-Equalization for QAM Modulation  
In this subsection, pre-equalizers for a QAM SWIPT system are addressed as a baseline to evaluate the corresponding pre-equalizers with the proposed multisine PSK SWIPT system.  
Based on (6), the envelope of the received signal applying complex normal symbol input is  
\[A_{\text{QAM}}(t)\leq\left\|\mathbf{x}\circ\mathbf{h}\circ\mathbf{w}\right\|_{1}. \tag{25}\]  
Since \(\phi_{n}\) is a random value, the phase difference \(\left[\phi_{i}\left(t\right)-\phi_{j}\left(t\right)\right]=2\pi\left(i-j\right) \Delta ft+\phi_{i}-\phi_{j}\) has a small probability to achieve zero for all \(n\) at the same time even ensuring \(\angle w_{n}=-\angle h_{n}\). Thus, the PAPR of the signal is a lot smaller than the bound \(\frac{\left\|\mathbf{x}\circ\mathbf{w}\right\|_{1}^{2}}{\left\|\mathbf{x} \circ\mathbf{h}\circ\mathbf{w}\right\|^{2}}\). The maximum PAPR upper-bound is achieved when \(|x_{i}||h_{i}||w_{i}|=|x_{j}||h_{j}||w_{j}|\). While the pre-equalizer \(\mathbf{w}_{PAPR}\) can compensate the channel \(\mathbf{h}\), the symbol amplitude variations cannot be compensated so it is unlikely that the multi-tone QAM signal reaches the peak PAPR \(2N_{t}\).  
The corresponding matched pre-equalizer and balanced pre-equalizer are the same as the ones for multisine PSK based SWIPT system.  
However, the pre-equalizer maximizing the QAM capacity is a water-filling scheme as \(w_{n,\mathcal{C}}=\sqrt{\left(v-\frac{z}{|h_{n}|^{2}}\right)^{+}}\angle h^{*}\) with \(v=1+\frac{(1-\rho)\sigma^{2}+\sigma_{\text{min}}^{2}}{(1-\rho)P_{t}}\left\| \mathbf{\hat{h}}\right\|^{2},\ z=\frac{\left[(1-\rho)\sigma^{2}+\sigma_{\text {min}}^{2}\right]N_{t}}{(1-\rho)P_{t}}\).
# A Novel Computer-Aided-Diagnosis System for Breast Ultrasound Images Based on BI-RADS Categories
## 4 Methods
### Convolutional Neural Network  
Convolutional Neural Network (CNN) [11]: a class of deep artificial neural networks; it has become a popular classification approach in computer vision recently. It consists of an input layer, a convolution layer, a pooling layer, a fully connected layer, and an output layer. A feature map is obtained by processing the input image with the convolutional kernel; weight-sharing is used among neurons in the same convolutional layer. During back propagation, convolution layers get updated to let the network learn the features itself. The weights or bias for every neuron may be modified or updated using gradient descent and backpropagation where the chain rule of calculus is used to adjust the weights that will minimize the loss function.  
In this study, a CNN architecture was implemented on the BUS image in order to validate the classification performance of the model. Before each acquired BUS image was introduced into the model, the size of the image was resized to a spatial resolution of 99 \(\times\) 99 pixels in order to satisfy the input criterion of the proposed CNN model. Because the sample size of the data set is restricted, effective data preprocessing and augmentation is mandatory for medical image datasets, especially for CNN training. In the CNN training stage, only geometric translation and rotation were performed to preserve the shape textures of breast tumors for the final classification. Moreover, all BUS image samples were decomposed into five categories, including background itself (negative sample) and BI-RAD categories 2-5 (positive sample). The illustration of CNN on an input image is shown in Figure 11. The CNN architecture includes an input layer, two convolution layers, and two max-pooling layers, and two fully connected layers for the multi-class classification are also shown in Figure 12. The fully connected layer 1 flattens max-pooling layer 2 into a single vector. The detailed parameters of the proposed CNN architecture are further summarized in Table 5.  
Figure 11: Illustration of CNN on an input image: (**a**) original image with a size of 321 \(\times\) 321; (**b**) sample image cropped on the original image—size was reduced to 99 \(\times\) 99. The cross-shaped is a positive sample (tumor), but the triangular one is a negative sample (background).  
Figure 12: The proposed CNN architecture.
# Electronically Controllable Phase Shifter with Progressive Impedance Transformation at K Band
## 2 Materials and Methods: Phase Shifter Working Principle and Design  
The working principle of the device is based on the combination of two microwave elements: a 3 dB/90 hybrid coupler and a couple of reflective tunable circuits. The reflective circuits are connected to the \(-\)3 dB/90\({}^{\circ}\) and \(-\)3 dB/180\({}^{\circ}\) output ports (ports 3 and 4, respectively), while the isolated port of the 3 dB/90\({}^{\circ}\) coupler becomes the output port (port 2). Figure 1 shows the connection scheme. The input signal (port 1) is conducted through the coupler to ports 3 and 4 with a phase difference of 90\({}^{\circ}\) between them. The signal reflections in ports 3 and 4 steer both signals again towards the coupler, adding the reflective circuit tunable phase variation (\(\Delta\varphi\)). As a result, port 1 receives no signal (opposite phase summation of identical signals) and all the available power is routed to port 2 with a phase shift of \(\Delta\varphi\). For the reflective circuits, we propose the use of a progressive impedance transformation, for enlarging the achievable phase range.
## 4 Results and Discussion
### Verification of the Hypothesis of the Study
#### 4.6.1 Factor Analysis
The global mean of each category presented in Table 8 was used to rank the factors affecting the adoption of IoT in the Silicon Mountain tech ecosystem in order of importance. The results of this ranking are displayed in Table 9. This ranking provides a clear prioritization of the challenges, highlighting which areas require the most immediate attention to improve IoT adoption in the Silicon Mountain technology ecosystem. An analysis of Table 9 shows that the mean scores for all barriers ranged between 3.00 and 4.00. These scores indicate an average level of concern between'moderate' and'serious' on the Likert scale. The most significant challenge identified was standardization and financial Resources, with a mean score of 3.779. This was closely followed by labor shortage in the industry, which had a mean of 3.760. The remaining categories, in descending order of severity, were educational and knowledge challenges, market challenges, government policies, security and data privacy challenges, and labor and power supply.
## 4 Determining Travel Time Indicators and Predictors
### Determining Travel Time Indicators  
After comparing the results for all candidates for travel time indicators, eight were selected for the analysis, and they were obtained by combining the indicators from Table 1 to make the most sense in representing travel time discrepancy and variability. For example, travel time shows extreme deviations as values move closer to the 0 th or 100 th percentile, making these indicators unusable. In other cases, indicators have unreasonably high or low (even negative) values, which also makes no sense in this context.  
The chosen four indicators relative to minimum travel time to be representative, with their values for all segments, are shown in Figure 7: \(v_{10/T}\), \(v_{AV/T}\), \(v_{50/T}\), and \(v_{90/T}\). Zero percent indicates values equal to the minimum travel time. The values are connected for adjacent segments to better distinguish the indicators. The indicator \(v_{10/T}\) could be less suitable for the analysis because its value sometimes is below the minimum travel time. The median value and the arithmetic mean show similar behavior, but in practice, the median is better suited because of the importance of middle-driver performance in timetable construction.  
The other four indicators are the ones that represent travel time variability exclusively, as shown in Figure 8: \(v_{50/10}\), \(v_{90/50}\), \(t_{SD}/t_{AV}\), and \(\Delta_{90/10}\). Zero percent means there is no variability. The values are connected for adjacent segments to better distinguish the indicators. The coefficient of variation shows extreme values at some segments, which makes it less suitable for an indicator than \(\Delta_{90/10}\); however, this indicator could be less suitable because early vehicles and late vehicles might not show similar behavior in real-time conditions.  
Figure 7: Chosen travel time indicators relative to minimum travel time for further analysis for each segment.
## 2 The Structure of the UPQC System
### Shunt Inverter Control Structure  
When current disturbances occur, the series converter will be activated. Figure 2 illustrates the control structure of the series converter. The reference signal is generated in the \(dq\) reference frame. High-order harmonic components are filtered out using a low-pass filter (LPF). Finally, through the inverse park transformation, the reference signal is converted from the \(dq\) coordinate system to the \(abc\) coordinate system, as shown in Figure 3.  
If the load current harmonics are used as the compensation reference, according to [PERSON]'s Current Law, the relationship between the grid source current \(i_{s}\), the load current \(i_{L}\), and the compensation current \(i_{c}\) output by the parallel compensation unit is given by:  
\[i_{L}=i_{s}+i_{c} \tag{4}\]The load current consists of the fundamental sine component \(i_{sf}\), harmonic components \(i_{sh}\), and the fluctuation component of the source current \(\Delta i_{s}\):  
\[i_{L}=i_{sf}+i_{sh}+\Delta i_{s} \tag{5}\]  
The basic function of the parallel compensation unit is to inject a compensation current that is equal in magnitude and opposite in direction to the load harmonic current, thereby ensuring that the grid current retains only the fundamental component. The overall mathematical model of the parallel compensation unit is:  
\[L\frac{di_{c}}{dt}+Ri_{c}=u-u_{s} \tag{6}\]  
where \(L\) represents the equivalent inductance of the system; \(R\) is the equivalent resistance of the parallel compensation unit; \(u\) is the output voltage of the inverter, generated by the controller.  
In addition, the parallel compensation unit not only filters the high-frequency components of the compensation current to ensure the quality of the injected current, but also dynamically responds to the fluctuations in the grid voltage. It compensates for unbalanced currents to some extent, thereby enhancing the robustness of the system.
## 3 Results
### Seasonality of the Other Sites  
For the other sites involved in the Sentinel 1 seasonality analysis, variations in both VH- and VV-polarized backscattered power for the four years are shown in Figure 11 for Hard-pan crust 1 (S3, S5, S7, S11, and S13), Hard-pan crust 2 (S4, S12) and Soft crusts (S2, S8, S9). Images were acquired at local incidence angles ranging from 37\({}^{\circ}\) to 41\({}^{\circ}\) (ascending pass) and from 30\({}^{\circ}\) to 34\({}^{\circ}\) (descending pass). In this section, seasonal, annual, and episodic temporalities were analyzed together.  
As seen in Figure 8, for the other sites described in Figure 11, conditions from 2014 to January 2017 are also represented by \(\sigma^{0}\) constant trend due to the lack of significant rainfalls over that period. The episodic event that occurred on 13 April 2015 is also highlighted here, with a second episodic event registered on 20 April 2016, with a rapidly and isolated increase in \(\sigma^{0}\) for some sites. From January 2017, La Nina wet conditions changed the surface roughness detected at C-band. Over long dry periods, crust development occurred at a slow rate, which resulted in a small slope increase of VV-polarized \(\sigma^{0}\) from May 2015 to May 2016 for Sites S4 and S12 and from July 2016 to February 2017 for Sites S11 and S13. The latest sites surround flooding grounds, which are influenced by salt dissolution and seem to be influenced by those events decreasing \(\sigma^{0}\) during 2017-2018 springtime. Overall, the higher \(\sigma^{0}\) is observed over Sites S3, S5 and S7 with smooth slopes over time. These sites are over elevated grounds which prevented them from flooding and salt dissolution unless heavy rainfalls occurred.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Site**} & **Range** & **Interval** & **Variation** & **Rate** \\  & **(Start Date–End Date)** & **[day]** & **[dB]** & **[dB/month]** \\ \hline \multirow{2}{*}{1} & 23 October 2017–28 December 2017 & 66 & 4.56 & 2.07 \\  & 23 February 2018–27 April 2018 & 72 & 2.55 & 1.06 \\ \hline \multirow{2}{*}{10} & 17 September 2017–15 January 2018 & 120 & 8.80 & 2.20 \\  & 20 February 2018–08 June 2018 & 108 & 3.76 & 1.04 \\  & 18 September 2018–23 December 2018 & 96 & 5.01 & 1.57 \\ \hline \multirow{2}{*}{11} & 22 March 2018–14 June 2018 & 84 & 4.18 & 1.49 \\  & 12 September 2018–29 December 2018 & 108 & 4.71 & 1.31 \\ \hline \multirow{2}{*}{4} & 20 February 2018–21 May 2018 & 90 & 4.10 & 1.37 \\  & 06 September 2018–29 December 2018 & 114 & 3.84 & 1.01 \\ \hline \multirow{2}{*}{6} & 29 September 2017–28 December 2017 & 90 & 3.71 & 1.24 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Monthly rate of increase of VV polarized backscattering coefficient \(\sigma^{0}\) during dry periods.  
In general, Sites S2, S8, and S9 had similar \(\sigma^{0}\) patterns over the four years, being affected by fluctuations in ponded water, increasing its backscattering power in presence of wind-induced roughness.
# Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data
## 4 Results
### Evaluation of Small-Basin-Scale TWSC
#### 4.6.2 Comparison with In Situ Groundwater Level Data  
We also used the in situ groundwater level data of the Ganzhou Hotel well site from January 2007 to December 2010, the Zhangjiawei well site from January 2013 to May 2015, and the Yichun well site from January 2009 to December 2012 to verify the TWSC estimates in this study. We compared the Ground Water Level Change (GWLC) with the TWSC estimate in the corresponding sub-basin indirectly, as shown in Figure 11. As a result, the in situ groundwater level data of the three stations and the TWSC of the corresponding sub-basin show a relatively similar variation. However, due to the lack of long time series in situ data, we only analyzed the groundwater level and the TWSC of the corresponding sub-basin of three stations with relatively short time series. In addition, the correlation coefficients (CC) of GWLC and TWSC for all three stations during the analysis period are greater than 0.4.  
Figure 10: Comparison of TWSC calculated by different methods and CSRM in the Ganjiang River Basin.
# A Selective Dynamic Sampling Back-Propagation Approach for Handling the Two-Class Imbalance Problem
## 6 Experimental Results and Discussion
The use of TL (TL and SMOTE-TL) seems to increase the classification performance, but it does not eliminate too many samples (see Figure 2), except by CNNTL, which we consider to cancel the positive effect of TL by the important training dataset reduction. SMOTE-ENN does not seem to improve the classification performance of SMOTE in spite of including a cleaning step that removes both majority and minority samples. The methods that have achieved the enhancing of the classifier performance are those that only eliminate samples from the majority class. Furthermore, analyzing only the selective samples methods (SL-SMOTE, B-SMOTE, ADASYIN, SPIDER-1 and SPIDER-2), those are the ones in which the more appropriate samples are selected to be over-sampled. It is considered that in the result presented in Figure 2, SL-SMOTE and B-SMOTE obtain the best results, whereas the advantages of ADASYN, SPIDER-1 and SPIDER-2 are not clear (RUS often outperforms these approaches, but without statistical significance; Figure 1). SL-SMOTE, B-SMOTE and the proposed method do not show statistical significance in their classification results, but the number of samples used by SDSA in the training stage is fewer than employed for SL-SMOTE and B-SMOTE (see Figure 2). Focusing on the dynamic sampling approaches' analysis, SDSAO presents a slight advantage in performance than DyS and SDSAS, whereas DOS does not seem to be an attractive method. However, the aim of DOS is to identify a suitable over-sampling rate, whilst reducing the processing time and storage requirements, as well as keeping or increasing the ANN performance, to obtain a trade-off between classification performance and computational cost. SDSA and DyS improve the classification performance, including a selective process, but while DyS tries to reduce the oversampling ratio during the training (i.e., it applies the ROS method in each epoch with different class imbalance ratios; see Section 4), the SDSA only tries to use the \"best samples\" to train the ANN. Dynamic sampling approaches are a very attractive way to deal with a class imbalance problem. They face two important topics: (i) improving the classification performance; and (ii) reducing the classifier computational cost.
# A Single-Bit Incremental Second-Order Delta-Sigma Modulator with Coarse-Fine Input Buffer
###### Abstract  
This paper presents an incremental second-order delta-sigma modulator with a coarse-fine input buffer in 180-nm CMOS. The modulator's architecture was implemented as a second-order cascade of integrators with a feedback structure. The switched-capacitor integrator was operated in discrete time, with high-gain amplifiers required to achieve improved performance during the integration phase. The amplifier comprised rail-to-rail input and gain-boosted cascode intermediate stages, thus achieving a high gain and wide input voltage range. The circuit adopts a coarse-fine buffer for higher performance. The coarse buffer is operated first to enable fast settling through a high slew rate, followed by the fine buffer to satisfy the low-noise and high-accuracy characteristics. The fine buffer has a smaller current consumption with higher power efficiency. The experiment results show that the proposed input buffer achieved a 13.14 effective number of bits and an 80.87 dB signal-to-noise and distortion ratio. The modulator operates a single bit and sampling clock at 125 kHz. The proposed delta-sigma modulator was operated at 1.8 V. The proposed circuit was designed using a standard 0.18-um CMOS process with an active area of 1.06 mm\({}^{2}\). The total current consumption with the coarse-fine buffer was 1.374 mA.  
coarse-fine input buffer; pre-charge buffer; low-noise amplifier; delta-sigma modulator; incremental delta-sigma modulator
## 3 Gaussian Process Regression Model for Radiation Contamination Mapping
### GPR Implementation in Python  
The GPR predictions were done by using the Python Scikit-learn (sklearn) library, which has many built-in functions used for standard regression techniques [25]. Specifically, the sklearn.preprocessing and sklearn.gaussian_regression modules are used in this work. The StandardScaler option within sklearn.preprocessing is used to standardize the features by centering the mean to zero and scaling to unit variance. This allows for a more consistent behavior in the regression due to many of the kernel covariance functions requiring the data to represent a standard normal distribution, such as a Gaussian, with zero mean and unit variance [23; 25]. GPR is performed on the scaled dataset by using the sklearn.gaussian_regression module, and subsequently transformed back to its original scale. The sklearn.gaussian_regression module also contains all the standard kernel covariance functions tested in this work, which include the radial basis function, rational quadratic function, and the Matern function. The GPR analysis gives the output and corresponding standard deviations for each prediction, providing some quantity to assess the confidence in the prediction. A maximum likelihood estimation method for the parameters in the GPR model and kernel correlation function is used at each predicted iteration, and solved by using the built-in limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimizing algorithm [26]. We conducted a case study using a k-fold cross-validation test to (a) demonstrate the validity of using a GPR model for radiation contamination mapping and (b) investigate the optimal kernel for the covariance function that best describes the simulated data, and in turn the underlying physics for gamma-ray transport in air.
# Performance Modeling Framework for IoT-over-Satellite Using Shared Radio Spectrum
## 3 IoT-over-Satellite Performance Modeling
### Terrestrial Interference Model  
By getting the number of IoT device population and the uplink received powers of each measurement point, the average terrestrial interference of a measurement point (n) can be calculated by,  
\[\overline{P_{l,n}}=\overline{\rm N}_{\rm active}P_{r,n}\left(P_{\rm LoS,n} \times L_{\rm orLoS,n}+\left(1-P_{\rm LoS,n}\right)\times L_{\rm orNLoS,n} \right), \tag{12}\]  
where \(P_{\rm r,n}\) is the received power of the satellite, \(P_{\rm NLoS,n}\) and \(P_{\rm LoS,n}\) representing the Probability of Non Line of Sight (NLoS), and Line of Sight (LoS) of the measurement point calculated by (8) and the excessive loss (\(L_{\sigma}\)) in LoS and NLoS situation using Reference [21]. The total terrestrial interference is equal to the sum of the average terrestrial interference terms within the satellite horizon as following,  
\[P_{I}=\sum_{n=1}^{k}\overline{P_{l,n}}, \tag{13}\]  
where \(k\) is equal to the number of measurement points in the satellite coverage area, and the average number of active IoT devices\((\overline{N}_{\rm active})\) will be discussed in the following subsection.