esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Experimental Comparison with Other Deep Learning Models

Table 2: Comparison results of the parameters, training times, and identification accuracies of different deep learning models. Figure 9: The structures of the core components of the RGADNet, the RResNet, and the RDRSN. (**a**) The structure of the residual unit A\({}_{i}\) (**b**) the structure of the residual unit B; (**c**) the structure of the residual shrinkage unit. extracted from the complex signals by using complex-valued structures to achieve better identification performance. The identification accuracy of the DCUnet-GAN is close to that of the CC-GAN, but it is more complex, with more training parameters and longer training times. To evaluate the identification performance of the above models under different SNRs, we constructed a new test set by extracting signal samples of a single SNR (0-10 dB, in 2 dB steps) from the test set of the dynamic noisy signals set. The new test set was input into the different models that have been trained, respectively, and the identification accuracies of the different models under different SNRs are shown in Figure 10.  
From Figure 10, we can see that the identification accuracies of the models based on CNN continuously increase with the increase in the SNR, indicating that their identification performances are significantly affected by the quality of the signals. The identification accuracies of the models based on GANs are essentially maintained at good and stable values when the SNR is greater than 2 dB, which indicates that these models have mitigated the impact of noise on identification performance to a certain extent through adversarial training. The CC-GAN has the best identification performance under different SNRs compared with other models based on GANs, indicating that the CC-GAN can better adapt to signals with different SNRs and extract a more ideal distribution of RFF features in dynamic noisy signals. Consequently, the CC-GAN can significantly enhance the robustness and adaptability of the SEI system in dynamic noisy environments and achieve high identification accuracy for individual satellites.
# Bounding the Likelihood of Exceeding Ward Capacity in Stochastic Surgery Scheduling
## 2 Model Development  
The general problem is scheduling a high throughput of in- and out-patient surgeries over a certain time period to minimize the OR overtime while bounding the likelihood of exceeding the limited downstream ward bed capacity. Patient priorities are included in the model as hard constraints, i.e., if a patient requires scheduling within one week, it will be done. We do not consider optimizing the flow of the emergency arrivals as they are treated in a separate flow, in downtime and after hours. Further, we do not consider optimizing the total number of patients scheduled since this requires discriminating between patients and should be the responsibility of the hospital. If the ward beds and ORs are underutilized, the hospital can add more patients to the list of patients to be scheduled.  
In order to solve the problem, we propose a novel two step approach as follows:1. _Operating Room Day Schedule Generation_: An operating room day schedule (ORDS) is a list of patients to be operated on in a particular day by a given operator. We start by extracting all patients belonging to a specific operator. Next, we consider all combinations of these patients subject to practical considerations (e.g., only one ICU patient) and different block lengths. Using Monte Carlo sampling, with historical data for surgery times needed for a given surgery type, we eliminate ORDS that exceed the block length limit with probability \(\delta\).
2. _Ward Combinations Optimization_: Given a fixed number of staffed ward beds, we consider all combinations of patient numbers \(n_{k}\) with the discretized probability of stay \(p_{k}\) for \(k\in|\mathcal{K}|\), where the probability of stay is discretized into \(|\mathcal{K}|\) groups and dependent on the type of surgery performed on the patient. Each such combination is then eliminated if the total patient number exceeds the number of staffed ward beds by a probability \(\Omega\). This is computed using Monte Carlo sampling. Given the set of feasible ORDS and ward combinations, a deterministic mixed-integer programming (MIP) model is solved using a commercial solver. This is followed by a verification of the solution by Monte Carlo sampling using the complete, undiscretized, empirical distribution for the LOS in the ward.  
Both steps will now be described in more detail in Sections 2.1 and 2.2. The second step is then reformulated using a robust formulation described in Section 2.3. The notations used by our models are defined in Appendix A.
## 5 Data Simulation
### LRI1B Simulation
#### 5.4.1 TOF Effect  
Due to the non-zero travel time of the light between the two satellites, the measured range always deviates from the instantaneous range. The range deviation due to media and relativistic effects is a systematic error called the time-of-flight (TOF) effect in Section 3, which can be calculated in an iteration process. In the simulation, we used the analytical expressions in [25] and the error-free orbital data to calculate this error (green trace in Figure 6) during the forward procedure and added it to the error-free instantaneous range. The corresponding light time correction term \(\delta\rho_{\text{TTC}}\) mentioned in Section 3 can be calculated using the simulated GNIIB data with error during the POD procedure though it was not used in the TTL estimation process and has an opposite sign:  
\[\delta\rho_{\text{LTC}}\approx-\delta\rho_{\text{TOF}}. \tag{43}\]
## 3 Developed Solution Approach
### A Novel Bi-Objective Tabu Search  
Local improvements to solutions generated during the initialization and the improvement phases are conducted using a proposed novel bi-objective tabu search (Bi-TS) approach. This Bi-TS utilizes the two neighborhood search functions developed by [PERSON] (2018) to improve the makespan, denoted as \(\mathcal{N}_{1}\) and \(\mathcal{N}_{2}\). To improve the \(MWFT\), two similar neighborhood search functions, denoted as \(\mathcal{N}_{3}\) and \(\mathcal{N}_{4}\), are introduced here. The neighborhood search function \(\mathcal{N}_{1}\) implements moves of operations within \(\tau_{j}\) permutations. It performs moves on critical operations that, by definition, cannot be moved from their current positions in the permutations without affecting the current makespan ([PERSON] and [PERSON], 2017). Accordingly, a critical path is defined as a sequence of consecutive critical operations from start to finish. First, \(\mathcal{N}_{1}\) selects an operation belonging to the critical path, and then it investigates possible moves within the processing sequence of the job to which the selected operation belongs. The feasibility of a move and the quality of the resultant solution are derived by employing simple mathematical relationships. These relationships reduce the required computational time by avoiding the detailed evaluations of the operations' exact start and finish times. Similarly, the neighborhood search function \(\mathcal{N}_{2}\) implements moves of critical operations within and between \(\pi_{m}\) permutations for the machines belonging to the same workstation. The new functions \(\mathcal{N}_{3}\) and \(\mathcal{N}_{4}\) follow the same structure of operation removal-reinsertion mechanism, as in \(\mathcal{N}_{1}\) and \(\mathcal{N}_{2}\), respectively. This similarity allows for a unified tabu structure and tabu list for all neighborhood search moves. In \(\mathcal{N}_{3}\) and \(\mathcal{N}_{4}\), the feasibility conditions proven by [PERSON] (2018) for moves conducted in \(\mathcal{N}_{1}\) and \(\mathcal{N}_{2}\) are employed. Unlike \(\mathcal{N}_{1}\) and \(\mathcal{N}_{2}\), they are applied to critical and non-critical operations. The \(MWFT\) objective values as a result of applying \(\mathcal{N}_{3}\) and \(\mathcal{N}_{4}\) are calculated by modifying the solution's network representation, which was provided in [PERSON] (2018). Since this is a time-consuming process, non-critical operations that belong to the first half of a job or machine sequence are excluded from the search. This exclusion is decided after it is noticed that the  
Figure 6: Solution-recombination process. value of \(MWFT\) generally does not improve by moving operations scheduled early in a given solution. The search structure of the developed Bi-TS approach is demonstrated via the pseudo-code in Algorithm 2. It starts in steps 4 to 7 by initializing the search and iteration variables.
# A Wide-Angle Scanning Leaky-Wave Antenna Based on a Composite Right/Left-Handed Transmission Line
###### Abstract  
This paper presents a frequency-independent, wide-angle scanning leaky-wave antenna (LWA), based on the composite right/left-handed transmission line (CRLH TL). The proposed LWA consists of a coplanar waveguide-grounded (CPWG) structure loaded by varactors. Loaded varactors are used to control the phase constant of the fundamental mode of the LWA by adjusting the applied DC voltage. The LWA has an excellent wide-angle scanning capability, a simple structure, and low cost. Results show that the main beam of an LWA with 20 unit cells can scan from \(-66^{\circ}\) to \(62^{\circ}\) at the operation frequency of3.0 GHz, with a peak gain of 9.9 dBi, and a gain fluctuation of less than 4.9 dB. The operation bandwidth and radiation efficiency are about 13% and over 50%, respectively. A 10-unit cascaded LWA prototype was designed, fabricated, and measured to verify the design concept.  
composite right/left-handed transmission line (CRLH TL); coplanar waveguide ground (CPWG); leaky-wave antenna (LWA); frequency-independent; wide-angle scanning Article  
A wide-Angle scanning Leaky-Wave antenna Based on a Composite Right/Left-Handed Transmission Line  
[PERSON] _et al._ (2020), 10, 1927; doi:10.3390/app10061927
## 5 Conclusions  
We designed a mid-IR narrowband absorber based on a subwavelength fine-structured silicon-gold metagrating. It was constructed of a silicon grating on a stack of a gold film and a substrate of quartz. The grating consisted of two periodically arranged silicon strips in each unit cell. FDTD simulations indicated that perfect absorption of the TM-polarized light at a wavelength of 4.071 \(\upmu\)m can be achieved, with an absorption rate of \(\sim\)99.2% and an absorption bandwidth of \(\sim\)31 nm. The structure can be used as a narrowband mid-IR notch filter operating in the reflection mode. The obtained narrow absorption peak results from the low-loss resonance mode. When the structure is used as a refractive index sensor, its measuring range can be 1.0-2.0 with a quasi linear response. The sensing FOM can be \(\sim\)103, which benefits from the electromagnetic field outside the silicon strips. Although only numerical investigations were carried out in this work, the proposed absorber is simple in structure,  
Figure 8: (**a**) Simulated absorption spectra with different thicknesses of the Cr adhesion layer. (**b**) The same as (**a**) but the adhesion layer is TiO\({}_{2}\).  
Figure 7: Resonance wavelength (red circle) versus refractive index of the surrounding. A linear fit (blue line) is also shown. Inset shows the associated reflection map.  
and is potentially useful in many applications such as optical filtering, spectroscopic sensing, and gas detection.  
Conceptualization, X.H.; software, X.H. and J.J.; formal analysis, X.H.; investigation, X.H.; resources, J.J.; data curation, J.J. and J.Y.; writing--original draft preparation, [PERSON] and S.Z.; writing--review and editing, J.Y.  
This research was funded by National Natural Science Foundation of China, grant number 60907003, 61671455, 61805278; Program for New Century Excellent Talents in University, grant number NCET-12-0142; China Postdoctoral Science Foundation, grant number 2018M633704.  
The authors declare no conflict of interest.
## Materials and Methods
### Development of Three-Axis Accelerometer Sensing System for Safety Helmet Wearing Management
#### 2.2.2 Three-Axis Accelerometer Safety Helmet Sensing System Hardware  
The safety helmet sensor module developed in this study includes a bluetooth low energy (BLE) feature for communication with smartphones, a three-axis accelerometer sensor, and a real-time clock (RTC) time-measuring sensor and is battery-operated.  
As specified in Table 1, A JARDUINO-UNO_BTmini is used in the micro controller unit (MCU) due to its small size. It transmits the three-axis accelerometer and RTC data through Arduino I2C communication with each sensor. The data are transmitted via Bluetooth to the app on the personal smartphone of the worker that is used for data transmission. The three-axis accelerometer sensor uses MMA8452, and the RTC uses DS3231.  
Figure 6 shows the three-axis accelerometer sensing helmet employed in the tests in this study. The sensor module is attached to an ABE-type safety helmet, which is the type that is typically used on construction sites. The total weight of the sensing helmet is 0.491 kg including the increased weight owing to the added sensors, i.e., 0.124 kg. This increased weight due to sensors is regarded as very low in terms of subjective degree of inconvenience [12].
## 2 Materials and Methods
### Dataset and Pre-Processing  
Initially, the shape file of the study area was prepared in the Arc GIS environment from the state map of Rajasthan, Survey of India [36], with the WGS 84 UTM 43N zone projection system and imported into the Google Earth Engine code editor. For the present study, Harmonized Sentinel-2 MSI: Multispectral Instrument, Level-2A collection was used. Sentinel-2 is an imaging mission that supports the Copernicus Land Monitoring studies. These studies include monitoring vegetation, soil, and water cover, as well as the observation of inland waterways and coastal areas. Sentinel-2 has a wide field of view, high resolution, and multiple spectral bands [37]. For the Kharif season, satellite images from April 2020 to September 2020 were considered, while images from October 2020 to March 2021 were selected for the Rabi season. For every 15 days, images were filtered considering less than 10% of cloud cover. The QA band was used to remove cloud cover from the incorporated data [37].  
Figure 1: Location of the study area (Datum: WGS84).
## 3 Methodology
### Fuzzy Image Classification
#### Second Level Segmentation  
[31] presented a supervised method for optimal multi-resolution image segmentation. The method called FbSP optimizer (Fuzzy-based Segmentation Parameter optimizer), offers a fuzzy-based process with which optimized scale, shape, and compactness parameters of multi resolution segmentation are generated. The process is trained using the segments belonging to a specific training building; the information of the training segments such as texture, brightness, stability, rectangular fit, and compactness are used to train FbSP optimizer. Then, new scale, shape, and compactness values are generated for the second level of segmentation. Using the optimal parameters for image re-segmentation, precise building borders are delineated. The output segments have borders fairly fitting the real ones. The problem of this method is the requirement of training data. Therefore, this method is only used for building classification due to its complicated nature. The full description of FbSP optimizer can be found in [32]. After finding the optimized segments for buildings, the next step is to detect building segments out of unclassified ones.
## 3 Effects of F-T Cycles on the Mechanical Properties and Microscopic Parameters
### Effect of F-T Cycles on Particle Morphological Characteristics
#### 3.2.5 Effect of F-T Cycles on Particle Surface Relief Fractal Dimension  
The particle surface undulation had fractal features and could be calculated by the step size method. Assume that the closed curve in Figure 8 is a particle and its trajectory coordinates are obtained by the image processing. If the length of the edge line is measured by a ruler of length \(\varepsilon_{\mathrm{i}}\), then the corresponding ruler number is N (\(\varepsilon_{\mathrm{i}}\)). A series of ruler lengths \(\varepsilon_{\mathrm{1}}\), \(\varepsilon_{\mathrm{2}}\),..., \(\varepsilon_{\mathrm{n}}\) (n \(\rightarrow\infty\)) would correspond to a series of ruler numbers N(\(\varepsilon_{\mathrm{1}}\)), N(\(\varepsilon_{\mathrm{2}}\)),..., N(\(\varepsilon_{\mathrm{n}}\)). The relationship between \(\varepsilon_{\mathrm{i}}\) and N(\(\varepsilon_{\mathrm{i}}\)) is reflected in the double logarithmic coordinates, and the connection curve of each point reflects the degree of particles fluctuating. The steeper the curve, the more significant change has happened in the length of the curve, indicating that the undulation degree of the particles is greater. Hence, the negative slope value of the linear portion about the \(\ln\varepsilon\)-\(\ln\)N(\(\varepsilon\)) curve can be used to characterize the particle surface relief fractal dimension value (D\({}_{\mathrm{pr}}\)) (the same as Equation (1)). The larger the D\({}_{\mathrm{pr}}\) is, the higher the surface undulation will be. The effects of F-T cycles on D\({}_{\mathrm{pr}}\) are displayed in Figure 9.  
Figure 7: The curve of R and F-T cycles.  
Figure 9 displays that the D\({}_{\rm pr}\) of S0 fluctuated between 1.11 and 1.15 under F-T, while the D\({}_{\rm pr}\) of S2 decreased with the increase of F-T cycles, which was smaller than that of S0, meaning that the skeleton structure of S2 not only reduced the surface undulation of particles, but also enhanced the ability of soils to resist deformation after F-T cycles.
# Single and Multivariate Statistics of Jet-Induced Pressure Fluctuations over an Infinite Plate
## 1 Introduction
The objective of the present work is to explore, in a systematic fashion, the dependence of the jet-plate complex flow physics upon the main governing parameters, namely the jet Mach number \(\mathrm{M_{j}}\), the radial distance \(\mathrm{H}\) of the flat plate from the nozzle axis and the spatial location of the pressure transducers on the surface in both the stream-wise (x) and span-wise (y) directions. The pressure signals, measured by a pair of pressure transducers flush-mounted over the plate, are characterized in terms of single-point and two-point statistics in the time and frequency domains. The study is mainly devoted to a very aggressive jet-plate configuration, i.e., with the plate positioned at \(\mathrm{H}/\mathrm{D}=0.75\) from the nozzle axis. For such a small jet-plate distance, the jet plume heavily interacts with the plate, and a turbulent boundary layer (TBL)-like zone develops. A complete characterization of the wall pressure fluctuations in this region is carried out, and the results are compared with those obtained from a less aggressive jet-plate architecture, i.e., \(\mathrm{H}/\mathrm{D}=2\). Furthermore, a wavelet analysis is performed using the continuous wavelet transform (CWT) in order to provide a more complete characterization of the wall pressure signatures induced by the compressible jet over the flat plate. The paper is organized as follows: Section 2 is devoted to the experimental set up and instrumentation description, the results are presented in Section 3 and conclusions are discussed in Section 4.
# Compact and Broadband Microstrip Band-Stop Filters with Single Rectangular Stubs
## 2 Theoretical Calculation  
### Principle of the Broadband Stub  
Figure 1 shows a circuit model of the open circuited stub. In the schematic view in Figure 1a, the line width of the main transmission line is \(W_{0}\), the line length is \(l_{0}\), the characteristic impedance is \(Z_{0}\), the line width of the stub line is \(W_{1}\), the stub length is \(l_{1}\), and the characteristic impedance is \(Z_{1}\). Figure 1b shows the TEM transmission line model of the open circuited stub. The left side shows the equivalent circuit of the open circuited stub, attached parallel to the I/O transmission line, and the right side shows the equivalent lumped element model of the open circuited stub for \(l_{1}<\lambda/4\). The normalized admittance \(y=Y_{\text{in}}/Y_{0}\) of the open circuited stub line is given by  
\[y=\frac{Y_{in}}{Y_{0}}=jb=j\frac{Z_{0}}{Z_{1}}\tan\beta_{1}l_{1} \tag{1}\]  
The circuit of Figure 1a is a three-stage circuit; the transmission coefficient \(S_{21}\) of this circuit is given by the following equation, using the normalized admittance \(y\).  
\[S_{21}=\frac{2}{2+y} \tag{2}\]  
Equation (2) is a complex number, but by rationalizing it to obtain its size, Equation (3) is obtained. By substituting susceptance \(b\) in Equation (1) into Equation (3), then \(\mid S_{21}\mid\) can be found.  
\[\mid S_{21}\mid=\frac{2}{\sqrt{4+b^{2}}} \tag{3}\]  
In order to broaden the stop bandwidth near the quarter-wavelength resonance without multistage or corrugation, it is necessary to enlarge the normalized susceptance \(b\) in Equation (3). The significant parameter decides if it is the ratio of the characteristic impedance \(Z_{1}/Z_{0}\) between the stub line and the main transmission line. As an example, the center frequency 2.5 GHz in the ISM band is selected, but the proposed structure is not limited to this frequency band. Application to other frequency bands is straightforward. Figure 2 shows \(\left|\,S_{21}\,\right|\) and \(\left|\,S_{11}\,\right|\) calculated values when \(Z_{1}/Z_{0}\) is 1/2, 1, and 2. It can be seen that the bandwidth widens if \(Z_{1}/Z_{0}\) is smaller. In this study, the characteristic impedance of the main transmission line is fixed to \(Z_{0}\) = 50 \(\Omega\), so that the impedance matching with the measurement system. The stub becomes a low impedance line with \(W_{1}>W_{0}\) for \(Z_{1}/Z_{0}<1\), and becomes a high impedance line with \(W_{1}<W_{0}\) for \(Z_{1}/Z_{0}>1\). On the other hand, the bandwidth can be explained from quality factor \(Q\) at the quarter-wavelength series resonance [25].  
\[Q=\frac{\omega_{0}L}{R}=\frac{1}{\omega_{0}CR} \tag{4}\]  
Increasing the stop bandwidth is the same as the decrease of \(Q\), so from Equation (4), it is necessary to increase \(C\) and \(R\), or to decrease \(L\) of the resonator. Using a low impedance line with a large line width for the stub is equivalent to increasing the \(C\) of the resonator from the electrostatic analysis; as a result, \(Q\) decreases and becomes broadband.
# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 4 Model
### Parameter Scaling
To ensure the parameters are in a consistent format for each of the dimensionality scaling algorithms, a scale normalisation procedure is applied using Equation (13), where during the training process, the \(p_{min}\) and \(p_{max}\) represent the minimum and maximum values for each parameter (given in Table 1), and \(q_{min}\) and \(q_{max}\) represent 0 and 1. During the implementation process, these values are exchanged such that \(q_{min}\) and \(q_{max}\) represent the minimum and maximum values for each parameter and \(p_{min}\) and \(p_{max}\) represent 0 and 1. \[\rho_{n}=\frac{(p_{n}-q_{min})(p_{max}-p_{min})}{q_{max}-q_{min}}+p_{min} \tag{13}\]  
Additionally, a sorting algorithm was used to place the three mid-band filters in ascending order based on their centre frequency. This prevents normalisation errors due to the frequency ranges, allowing filters to be rearranged by the user.