esa-sceva/satcom-chunk-collection huggingface dataset:
# Preprocessing for Unintended Conducted Emissions Classification with ResNet
## 3 Results
### Time vs. Frequency Domain Preprocessing  
With knowledge that electrical equipment produces features in the frequency domain and the prior successful application of frequency-based signal processing techniques with the linear discriminant analysis classifier given by [PERSON] et al. ([3,5]), the first experiment performed was a comparison of classification in the frequency domain versus the time domain. Results from this comparison verified that the ResNet-based classifier trained and tested with frequency domain data trained quicker and provided higher accuracy than the time domain.  
To determine a baseline on the frequency domain classification attempt, the authors performed a simple rectangular windowing prior to the FFT. One way to view the difference in training time and classification performance is shown in Figure 3, which shows the validation classification accuracy as a fraction vs. the training epoch. It demonstrates faster convergence during the training process, as well as a higher performance level in the end (75% accuracy for frequency domain and 11% accuracy for time domain).  
As mentioned earlier, each device in the data set used in this effort were measured for two 10 min periods separated in time. During this effort, the authors observed a significant drop in accuracy that when testing on one of these two collections and training on the other. This drop is a result of the devices or background noise changing between the two  
Figure 3: Classification accuracy vs. training epoch with 15\(\times\) noise added.  
collection period. Their features of interest to the classifier were stationary during a single collect period but not between the collection periods. This drop can be most easily seen in Figure 4. This figure shows about 30% worse classification performance for the case in which the training and validation sets were split across time.  
At epoch 200, the authors began training the ResNet classifier with a very small set of data from the second collection period, which substantially helped the performance of the validation and test of the classifier as measured by the classification accuracy. The amount of data used in the training was only approximately 1 out of 30 of the available data segments from the second collection for each device.  
This experiment implies two principles for this application:  
* Training data must fully represent the variability over time in the UCE signals. Otherwise, a good representation of the breadth of characteristics for each device will not be learned by the ResNet classifier.
* Retraining can improve performance. This example showed the potential to improve classification with continued training on small amounts of additional data in time.  
Additionally, more complex data augmentation methods may be useful in diverting the classifier from focusing on time-dependent features.
# Numerical Simulation of Vacuum Leak Jet and Jet Noise
## 1 Introduction
For supersonic jet noise, [PERSON] [20] summarized three noise generation mechanisms, namely turbulent mixing noise caused by unstable turbulence, broadband shock noise associated with the quasi-periodic shock cell structure, and screech tones excited by the feedback loop. Although there are many theories on jet in a vacuum environment, they mainly focus on the effects of plumes generated by thrusters on spacecrafts, such as force, torque, heat, etc. However, leak jet dynamics is rarely studied. Secondly, there are many studies on the conventional jet noise on the ground, and various forecasting models are also mature. But due to significant changes of density, pressure, and temperature in vacuum environment, the terrestrial jet noise model cannot accurately predict the characteristics of the leak jet noise in vacuum environment. [PERSON]'s group [21; 22] conducted a leak detection test on the ISS and the measured spectrum showed obvious harmonic wave series, which cannot be explained by screen tone theory in supersonic jet noise. Furthermore, the turbulence mixing noise in vacuum environment has not yet been fully understood. Therefore, this paper concentrates on the numerical simulation of a specific leak condition, preliminarily revealing the aerodynamic properties and mechanism of the noise propagation in vacuum environment. Specifically, Section 2 introduces the setup configurations of numerical simulation meanwhile Section 3 presents corresponding analysis of the simulation results. Finally, Section 4 draws the conclusion of the whole paper.
# Low-Light Photodetectors for Fluorescence Microscopy
## 3 Single Point Detectors
### High Resolution of Photon Counting
#### 3.4.3 Other Types of HPDs  
This subsubsection briefly refers to other types of HPDs that were developed by Hamamatsu Photonics (Table 3) and applications of the HPD to single-molecule fluorescence microscopy. These other types of HPDs are cooled HPD and MPPC (SiPM)-incorporated HPDs.  
Table 3 lists the characteristics of these HPDs.  
The cooled HPD, in which the photocathode is cooled by the Peltier element, reduces thermal electronic noise from the photocathode to one-tenth that of the non-cooled HPDs  
Figure 11: Fluorescence lifetime measurement data obtained by an HPD (**a**) and a PMT (**b**). The data were acquired using fluorescein dye solution. Adapted with permission from [13].  
Figure 12: FLIM images acquired by HPD (**a**) and SPAD (**b**). Adapted with permission from [13].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of HPD** & **Feature** & **Possible Application** \\ \hline Photocathode-cooled & Low thermal noise & FCS \\ MPPC (SiPM)-incorporated & Low voltage operation & Easy to install into systems \\ \hline \hline \end{tabular}
\end{table}
Table 3: Other types of HPDs [32; 33; 34; 35].  
(Figure 13). This HPD is supposed to be useful for the detection of extremely low light, including single molecule detection.  
The MPPC-incorporated HPD was developed to solve the difficulties of operating the HPD under high voltages such as 8 kV. The MPPC-incorporated HPD can operate with a lower voltage that operates PMTs due to the MPPC's high gain, and is able to detect single photons. The first prototype of an MPPC (SiPM)-incorporated HPD with a GaAsP photocathode with a diameter of 3 mm and a 25.4-mm (1-inch) bi-alkali photocathode type of MPPC (SiPM)-incorporated HPD were developed by Hamamatsu Photonics, and [PERSON] et al. evaluated its characteristics [33; 34]. As shown in Figure 14, the HPD operates with lower voltages (photocathode voltage \(\sim\)\(-\)3 kV and MPPC bias voltage \(\sim\) +70 V) than those of the current HPD product that incorporates an AD (photocathode voltage \(\sim\)\(-\)8 kV and avalanche photodiode bias voltage \(\sim\)+450 V). The low voltage operation capability facilitates installation of the HPD into various apparatus, including fluorescence microscopes. Recently, Hamamatsu Photonics developed an HPD with a 50.8-mm (2-inch) diameter [35].  
Figure 14: Schematic of multi-pixel photon counters (MPPC)-incorporated HPD.  
Figure 13: Dependence of dark count rate of the developed cooled HPD on the applied Peltier current [32]. Application of 1.2 A Peltier current cooled the HPD to about 283 K at an ambient temperature of 298 K.
# Rectangular Microstrip Array Feed Antenna for C-Band Satellite Communications: Preliminary Results
## 3 Antenna with Reflector: Simulation Results  
We simulate the \(2\times 2\) microstrip array antenna with a \(D=2.4\) m diameter parabolic reflector. Figure 7 shows the simulation configuration consisting of a feeder antenna on the right side and a parabolic reflector on the left side. The radiation emitted from the array antenna is directed to the parabolic reflector so that the beam is reflected and focused on the destination antenna. The diameter is widely used and available to obtain a low-cost system. Directivity can be improved by optimizing the focal point distance of the parabolic antenna (\(F\)). Optimization is carried out by parameterizing the distance from the feeding antenna to the reflector on the main axis. The optimal focal point distance parameter is \(F=1188.3\) mm. It has a good reflection coefficient at a frequency of \(4.148\) GHz with a value of \(-15.64\) dB. The antenna has a bandwidth of \(501.4\) MHz in the frequency range of \(3.7112\)-\(4.2126\) GHz, as shown in Figure 8. The directional radiation pattern at a frequency of \(4.148\) GHz can be seen in the polar E plane and the H plane, as shown in Figures 9 and 10, respectively. The antenna system has a gain of \(33.1\) dBi.  
In particular, the increased gain is followed by a narrowing of the beamwidth, resulting in a distinctive directed beam pattern. In the E-plane, the sidelobe level decreases significantly. Moreover, the E- and H-plane conditions are balanced, as evidenced by the almost identical beamwidth and sidelobe level values. In addition, the performance comparison of the proposed antenna with other designs for C-band is summarized in Table 1. It is obvious that the addition of a reflector to the antenna improves performance in terms of gain.  
Figure 5: H-plane radiation pattern at \(4.148\) GHz.  
Figure 6: Gain comparison of the microstrip array antenna.  
Figure 8: Reflection coefficient of the array antenna combined with a 2.4-m parabolic reflector.  
Figure 7: A simulation configuration schematic.
## 2 Methodology
### Numerical Simulation  
Numerical simulation serves as a powerful computational tool that replicates natural environments, phenomena, and evolutionary processes, enabling researchers to investigate the structural characteristics, morphological features, and kinematic behaviors of real-world prototypes through computer-based modeling. This approach not only complements experimental research and theoretical analysis but also provides valuable insights that address the limitations inherent in physical experimentation.  
Universal Distinct Element Code (UDEC), developed by Itasca Consulting Group, Inc. in the United States, is a two-dimensional discrete element program for processing discontinuous media. The software is a commercial product that is mainly used for analysis in the fields of geotechnical engineering, rock mechanics, mining design, seismic engineering, and more [34; 35].  
In the process of conducting numerical simulations using UDEC, the first step is problem definition and model setup. This involves precisely defining the physical problem, such as determining the dimensions and boundaries of the surrounding rock mass and tunnel geometry in tunnel excavation simulations, and then assigning material properties including elastic modulus, Poisson's ratio, density, and strength parameters for rock elements from laboratory or field data, and specifying joint properties if applicable. Boundary conditions are then set, with displacement fixation at certain boundaries and the application of external loads. Next is model generation, which requires creating a mesh of distinct elements to discretize the problem domain, choosing unstructured meshes for complex geometries and structured ones for simple shapes, and assembling the mesh while assigning proper material and boundary conditions. In numerical simulation setup, an appropriate time step is calculated based on material properties and element size to satisfy the CFL condition, a suitable solver is selected according to the problem type, and convergence criteria related to displacement or force residuals are set. Before running the simulation, the model is initialized with initial stress and displacement states and pre-existing fractures or voids are addressed. During the simulation, UDEC solves the governing equations over time, and key variables are monitored and recorded. Finally, in results analysis and interpretation, the data are visualized using UDEC's tools or other software, relevant parameters are calculated, and a sensitivity analysis is performed by varying input parameters. Overall, each step in UDEC numerical simulation must be carefully executed with accurate data input to ensure reliable results.
###### Abstract  
The rapid increase in the population is expected to result in the approaching of design capacity for many US wastewater treatment plants (WWTPs) over the next decade. WWTPs treat both municipal and industrial wastewater influents, resulting in the production of biosolids after digestion. Biogas, a potential recovered alternative energy source, is also produced as an output from successful anaerobic digestion. More than 7M of dry tons/year of biosolids produced in the US are most often disposed in either landfills or land-applied (~80%). These options are becoming more challenging to implement due to increases in transportation costs and tipping fees, decreases in the availability of landfill/landfarm space, and most importantly, increased regulations. This situation is strongly encouraging WWTPs to find alternatives for the disposal of biosolids. Developing alternative management/disposal options for biosolids are evolving. One of the most attractive alternative option from a sustainability perspective are biorefineries (converts waste to commercial products), which are a fast-growing option given the push toward circular urban source economies (little to no waste generation). Anaerobic digestion has been widely applied in WWTPs to reduce the volume of activated sludge due to its low energy requirements, effective handling of fluctuations due to organic loading rate, relative flexibility with temperature and pH changes, and since biogas is produced that can be transformed into energy. Various pretreatment methods for waste sludges prior to digestion that have been studied to reduce solids production and increase the energetic content of the biogas are presented and discussed. Solids handling and management, which comprises ~60% of the operational cost of a WWTP, is estimated to save more than $100 M annually by achieving at least 20% reduction in the annual production of biosolids within the US. This review incorporates an assessment of various pretreatment methods to optimize the anaerobic digestion of waste sludges with a focus on maximizing both biosolids reduction and biogas quality.  
**Keywords: anaerobic digestion; biosolids volume reduction; biogas production; sustainability of urban waste management**
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 3 Microcontroller Design
### Memory  
The TNODE5 processor is based on 16-bit von-Neumann architecture. It has a shared address space containing the data and program memories. The extended address bus architecture allows access to 1 MB of memory space. The chip integrates 64 kB of the Flash-based program memory and 16 kB of the random access data memory. The integrated sector-erasable Flash is organized in 16 k \(\times\) 44-bit data words. The sector size is variably selectable from 2\({}^{0}\) to 2\({}^{9}\) kbits. The Flash controller implements an error detection and correction (EDAC) algorithm that operates on additional 12 bits of each data word. The implemented EDAC can correct 1-bit errors and detect 2-bit errors, which significantly improves the system reliability. The initial programming of the Flash memory must be performed via I\({}^{2}\)C debug interface of the TNODE5 processor. During the programming operation via the I\({}^{2}\)C debug port the Flash controller is set into the debug mode in which each timing-related function is stalled until the erase/write procedure is finished. Alternatively, the Flash memory can be programmed by the processor core by using the register interfaces of the controller. The programming can be done on a sector-by-sector basis, which is very usefully for an on-the-fly update of software modules.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Bismuth Sulfide Films  
The bismuth sulfide (Bi2S3) films were produced in the presence of 2-methoxyethanol (C3H8O2) to control the solubility of Bi3+ ions. The obtained films showed the orthorhombic phase with a band gap of 1.6 eV. The highest photocurrent density reached 1 mA/cm\({}^{2}\)  
Figure 4: FESEM micrographs of Pb5 films deposited under various precursor concentrations (**a**) 0.1 M, (**b**) 0.15 M, (**c**) 0.2 M, (**d**) 0.25 M, (**e**) 0.3 M at 25,000 magnifications reproduced with permission from [86], Materials Science-Poland, 2016.  
indicating good photon-to-electron conversion properties in photoelectrochemical cells [90]. The annealed films (200 \({}^{\circ}\)C in argon atmosphere) improved the crystallinity when compared to the as-deposited films (amorphous phase). The films showed high absorption coefficients (more than 10\({}^{4}\) cm\({}^{-1}\)) in the visible region [91]. The n-type films were deposited on glass slides at room temperature [92]. The annealing process was carried out at 250 \({}^{\circ}\)C, 30 min in the air. SEM studies revealed that grains completely covered the substrate surfaces. Photosensitivity enhancement could be observed in the annealed films, making these materials very useful in sensor applications. Formation of the films could be carried out (70 deposition cycles) by using bismuth (III) nitrate (Bi(NO\({}_{3}\))\({}_{3}\)), TEA, and sodium sulfide on the stainless steel substrate, as suggested by [PERSON] and co-workers [93]. Based on the XRD pattern, the strong peaks corresponded to the (111) plane with a crystallite size of about 34 nm. SEM images confirmed that porous nanostructures could provide higher kinetics for electrochemical reactions. Experimental results highlighted interconnected nanoparticles caused good energy density (8.89 Wh/kg at 1.4 Ag\({}^{-1}\)), high specific capacitance (289 F/g), and high power density (1147 W/kg at 3.2 Ag\({}^{-1}\)).
## 2 Model Construction and Analysis
### Model Construction
#### 2.1.1 Overall Architecture  
Figure 1 shows the architecture of the improved DCNN model. The model architecture consisted of 15 layers of DCNNs collectively called \"DCN_Fire\". The structural parameters are described below. The first 12 layers of the DCNN model were composed of eight convolutions (Conv 1-8) in the convolutional layer and four maximum pooling layers (Pool 1-4). Forest fire features were extracted from a convolutional layer on a lower level and transferred to a higher level. The maximum pooling layers could extract the changeable part of the image and reduce the output size of the convolutional layer. The last classification layer recognized images of fires according to the previously extracted high-level features. The model structure was suitable for extracting different forest flame features from the training data set.  
Figure 1: The architecture of the DCN_Fire model.
## 3 System Model and Problem Formulation
### Mobile Sink Trajectory Planning  
To plan the trajectory of the mobile sink, we create a precedence graph where the \(x\)-axis is time and the \(y\)-axis is the individual UGVs. Suppose that there are a total of four UGVs represented by \(a\), \(b\), \(c\), and \(d\). Step 1, as seen in the Figure 2, each \(y\) represents a UGV, and, supposing the UGV appears at time \(t\), we then plot a point at the cross. For the same \(y\), we will get many isolated points along the time scale. If a deadline is given, we could count the total number of points (noted as \(n\)) from all of the UGVs. As shown in Figure 2, when the \(x\) reaches 500, the total number of points is 172 (\(n=172\)). In Step 2, we add edges between these points. Suppose that there are two points \(a\) and \(b\), and \(t_{a}\) represents the time that \(a\) appears. \(d_{ab}\) is the time that the mobile sink takes to move from \(a\) to \(b\). If \(t_{b}-t_{a}\geq d_{ab}\), then we draw an edge between \(a\) and \(b\), and add the edge \(e_{ab}\) into the adjacency matrix. To reduce the search space, for each point, it will have a number of edges that is no more than the number of the neighboring UGVs. In other words, a UGV will generate many points, and a point generated by another UGV will build an edge to the nearest feasible one among them.  
A matrix \(M\) is derived from Figure 2. We sort all the points according to their appearance time along the time scale in the figure and number them from 1 to \(n\). The row and column elements of the matrix are both these points where we have  
\[M_{ij}=\left\{\begin{array}{ll}d_{ij},&\text{if }t_{j}-t_{i}\geq d_{ij},\\ -1,&\text{otherwise},\end{array}\right. \tag{2}\]  
\[AdjacencyMatrix=\begin{array}{ll}0&1&2\\ 1&1.5\\ 2&1&-1&1\\ -1&-1&-1\end{array}\right). \tag{3}\]  
An example of the matrix is shown in Equation (3), which is related to a triangle graph where three points are located at the vertexes. If there is a non-negative value at the crossing of two points, it means that the mobile sink can move from one point to another without missing the data collection window. The value is the time needed to travel from one point to another. Any previous work based on TSP can not solve this problem, since the node is only accessible at a certain time, and each cycle is so different.