esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Moreover, [PERSON] and [PERSON] [20] pioneered a dual-channel PCNN (DPCNN) model to enhance the selection and diffusion ability of feature information, which was faster and simpler than single channel PCNN coupling judgment. In the DPCNN model, some key parameters affected the quality and calculation efficiency of sub-band coefficients to a certain extent, such as link strength, external excitation, and ignition output value. So, the literature [21; 22; 23; 24; 25] has improved the model parameters of DPCNN. In essence, link strength was usually set as local standard deviation [21], and [PERSON] et al. calculated the fuzzy membership of each pixel to adapt to the link strength of DPCNN, which obtained the fused image with high contrast [22]. [PERSON] and [PERSON] adopted spatial frequency (SF) as the external excitation, which was capable of effectively overcoming the Gibbs phenomenon at the target boundary [23]. The morphological gradient of the sub-band was used as the external excitation value, which could availably judge the detailed edge information of the high frequency sub-band [24]. [PERSON] et al. took the average gradient as the link strength, and modified spatial frequency (MSF) as the external excitation value, which enabled the DPCNN to achieve the extraction of rich details [25]. The above DPCNN model achieves an excellent judgment of sub-band coefficients, but the traditional DPCNN model [20] must wait for all sub-band coefficients to be activated before judging, which may result in false fired pulses. Besides, the ignition output value was set to 1 or 0 in this model [21], which made the active judgment unable to reflect the grade difference. Hence, a method of selecting low and high frequency sub-band coefficients in NSCT domain is proposed for side-scan sonar image fusion of adjacent strips. The frame design is shown in Figure 1.  
In this work, we have made the following progress: (1) SML energy filtering (SMLF) with multiple parameters and technologies, such as relating sum-modified Laplacian energy with visual contrast value (RLV), guide filter and multi-channel filter (MCF), is used to eliminate block flow in low frequency sub-band images; (2) Improved DPCNN (IDPCNN) model is employed to increase the depth and activity of pulse ignition, which can quickly and accurately select high frequency image coefficients; and (3) multiple fusion metrics are applied to evaluate the performance of single and combined fusion criteria. The remaining of this paper is organized as follows. Section 2 introduces the proposed image fusion method and process in detail. The experimental results and analysis are illustrated in Section 3. Finally, the conclusions are presented in Section 4.
# Hyperspectral Data: Efficient and Secure Transmission
## 1 Introduction  
Hyperspectral imaging sensors obtain information from the electromagnetic spectrum of an observed area. Spectral imaging techniques cover a significant portion of the electromagnetic spectrum in which the frequencies are in a range that spans from the ultraviolet to the infrared. A hyperspectral sensor subdivides the spectrum into different spectral channels (referred to as \"bands\"). For such reasons, a hyperspectral image can be considered as a sort of \"datacube\" [1], since such data can be structured in a three-dimensional manner.  
Hyperspectral images are efficiently used in a wide range of real-life and research applications (agriculture, mineralogy, physics, surveillance, etc.). Hyperspectral images are often shared among different entities, sometimes with different purposes (for example different research centres, etc.), in order to carry out conjunct tasks.  
Several scenarios can be drawn in which these data are shared/stored/transmitted for sensitive objectives (e.g., military applications [2; 3], counter-terrorism [4], forensic applications [5], etc.). Thus, an important concern might be to ensure data protection against tampering, which can occur even in very sensitive cases (e.g., target-detection applications, etc.). Since these images need to be efficiently transmitted to a base as soon as they are acquired, and they need to be efficiently stored, we propose a possible framework for efficient transmission and protection.  
The proposed framework relies on two main components: a lossless compression algorithm and a reversible watermarking scheme. Generally, digital watermarking techniques are used for ensuring security, content authentication, and copyright protection (e.g., [6; 7; 8]). Using watermarking techniques, the input data (i.e., the hyperspectral images) might become a sort of \"carrier\" of hidden information, which can carry important data (e.g., [9; 10]). We highlight the key aspects of the proposed framework, in Section 2, and we outline further details for a possible effective implementation, in Section 2.1. Regarding the implementation, we considered a reversible invisible watermarking scheme (Section 2.1.1), designed specifically for hyperspectral images, and the multiband losslesscompression of hyperspectral images (LMBHI) algorithm [11] (reviewed in Section 2.1.2), which is a predictive-based lossless compression algorithm that achieves interesting results.  
Finally, we report and discuss the obtained experimental results in Sections 3 and 4 draws our conclusions.
## 1 Introduction
The objectives of this study were (1) to determine if the species composition of culturable dissimilatory nitrate reducers and denitrifiers in contaminated and uncontaminated soils were as predicted by their tolerance to and/or utilization of the particular contaminants and (2) evaluate whether dissimilatory nitrate-reducing and/or denitrifying bacteria can be more reliably identified using PCR primers specific for a broad suite of NR and DN genes versus screening for the capacity to reduce nitrate and/or nitrite. The PCR products were sequenced in order to determine that the expected genes were amplified. The sites used in this study were located within the Tallgrass Prairie Preserve (TPP) in Osage County, Oklahoma. Mineral rights are owned by the Osage Nation, and oil, together with produced water (brine), has been produced for over 100 years [33]. Accidental spills of oil and/or brine have occurred, and our research on the most effective means of remediation/restoration has been described in detail in previous publications [12; 13; 34; 35; 36; 37]. In this study, bacterial strains were isolated from crude oil- and oil/brine-contaminated and uncontaminated tallgrass prairie soils from TPP near Pawhuska, Oklahoma. Five to 10 years had lapsed between the contamination event and soil collection for isolation of the bacterial strains, yet somewhat elevated levels of petroleum hydrocarbons, Na\({}^{+}\), Cl\({}^{-}\), and NO\({}_{3}\)\({}^{-}\) persisted [13]. The nature of the contaminant was hypothesized to alter the species composition of NR and DN bacteria in certain ways, e.g., we hypothesized that NR and DN bacteria in the crude oil-contaminated sites would contain a greater proportion of strains affiliated with groups, such as \(\gamma\)-Proteobacteria, known to increase in crude-oil contaminated sites [38; 39]. Some strains of \(\gamma\)-Proteobacteria are noted for the ability to use crude oil components as sole carbon sources [40; 41], although the ability to degrade petroleum compounds is also found in many other microbial groups [42; 43]. Since high levels of sodium chloride inhibit certain groups of bacteria [44; 45], although [PERSON] et al. [46] found _napA_ and _narG_ genes in highly saline soil, we hypothesized that the NR and DN bacteria from oil/brine contaminated sites would be biased towards those groups that are salt tolerant, such as _Bacillus_[5] and _Pseudomonas_[6; 47]. Our strains represent some of the diversity of readily culturable NR/DN bacteria present at these sites, but we acknowledge that this is not an exhaustive survey.
## 4 Results and Discussion
The proposed leak detection method is designed to identify leaks which create a change in the normal local pressure profile of the pipe (_i.e._, the sensor 'learns' what is 'normal' for that section of pipe on a daily basis and only warns of a potential leak if the relative pressure value goes outside this normal range). Therefore, the minimum size of a leak which could be detected depends on pipe geometry and material and the operational parameters (pressure managed system, PRV controlled, _etc._). One of the main benefits of the relative pressure change compared to absolute pressure reading is that the results are less affected by error. Environmental factors such as degradation of the clip holding the FSR sensor and gradual loss of the contact pressure due to relaxation of the clip can potentially cause errors in readings. However this will not affect the leak detection method dramatically as it can be compensated for in the leak detection algorithm, _i.e._, this will be gradual and potentially happen over an extended period of time and will be different from the sensor's response to a leak. Furthermore, it is important to realize that for leak detection not only measurements from a single sensor will be investigated, but also the data from neighboring nodes, as shown in the laboratory tests, in order to differentiate between a pressure drop due to a leak and those in the system.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 3 Methodology
### Elements for the Prototype
#### 3.2.1 Comparative Analysis and Selection of Single-Board Computers  
Information was collected on three devices to determine which one best meets the aforementioned requirements. To do this, a comparison was made between the Raspberry Pi 4B, the Arduino Uno, and the ODROID-C4, the details of which are presented in Table 3. This table indicates that the Arduino Uno cannot implement the unified communication service because it lacks a Wi-Fi module and operates solely with the Arduino IDE as its operating system. Although the Raspberry Pi 4B and the ODROID-C4 are suitable devicesfor this purpose, there are significant differences. The ODROID-C4 lacks a built-in Wi-Fi module, requiring the addition of an external module, which would increase the project's cost and complexity. Additionally, the ODROID-C4 is not available in the country where the prototype was designed and implemented (Ecuador), which would increase the price due to shipping expenses. On the other hand, the Raspberry Pi 4B platform offers the necessary features for implementing the IP telephony service, including a high-performance processor, built-in Wi-Fi connectivity, and availability of documentation and installation guides. Due to these advantages, the Raspberry Pi 4B was chosen as the most suitable platform to deploy our T-UAV prototype.
# Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array
## 4 Discussion  
During the actual simulation process, we discovered that the linearity problem between PD current and spike delay cannot be resolved using only a single slope reference signal (scheme B). Figure 13a depicts two signals connected to the comparator in temporal encoding mode, the PD signal and the \(V_{ref}\) signal, and the horizontal axis represents time (normalized by the integration period). The TTFS spike delay generated by this circuit exhibits strong nonlinearity with the input PD, as depicted in Figure 13b.  
In order to further improve the above nonlinear problem, a follow-up plan introduces a segmented-slope ramp wave in \(V_{ref}\), as depicted in Figure 14. In this circuit slice, a slope-enhanced current is introduced according to the time slice of a single integration cycle. A single-slope \(V_{ref}\) signal is substituted for the segmented-slope \(V_{ref}\) signal, and the relationship between the TTFS peak delay and input PD is depicted in Figure 15.  
\begin{table}
\begin{tabular}{c c c} \hline
**Circuit** & **Frequency Encoding** & **Temporal Encoding** \\ \hline Scheme A & 8.4 uA & 0.23 uA \\ Scheme B & 2.9 uA & 2.5 uA \\ \hline \end{tabular}
\end{table}
Table 1: Operating current of the pixel circuit. (CMOS 130 nm process; 1.2 V power supply voltage; 1 nA PD input current; 0 \({}^{\circ}\)C; typical corner).  
\begin{table}
\begin{tabular}{c c c} \hline
**Circuit** & **Input Current** & **Spike Width** \\ \hline Scheme A & 100 pA & 327 ps \\  & 200 pA & 327 ps \\ Scheme A & 500 pA & 327 ps \\  & 1 nA & 327 ps \\  & 10 nA & 328 ps \\  & 100 pA & 496 ps \\  & 200 pA & 522 ps \\ Scheme B & 500 pA & 553 ps \\  & 1 nA & 583 ps \\  & 10 nA & 739 ps \\ \hline \end{tabular}
\end{table}
Table 2: Widths of the generated spikes in each mode (1.2 V power supply voltage; 25 \({}^{\circ}\)C; typical corner).  
Figure 13: (**a**) The PD signal versus the \(V_{ref}\) signal in the temporal encoding mode. (**b**) Linearity analysis of the PD signal and the \(V_{ref}\) signal.  
The non-linear factor has been improved from 0.099 to 0.0045, a 22-fold improvement. The part of the circuit structure that generates the segmented-slope \(V_{ref}\) signal (Figure 12) is a shared circuit for the entire area array and does not occupy the space of a single-pixel readout circuit. In the circuit design, however, we implemented configurable segmented-slope \(V_{ref}\) with memristors. This portion of the circuit will be implemented in future solutions as we are resolving the integration issue between the memristor and conventional CMOS technology.  
Further, after the sensor and network part of the project is completed, we will conduct additional evaluations on the task accuracy and energy efficiency of the entire artificial vision system.
# Advances in Bio-Hydrogen Production: A Critical Review of Pyrolysis Gas Reforming
## 3 Hydrogen Production by Catalytic Reforming of Pyrolysis Volatiles
### Slow Pyrolysis
#### 3.1.3 Steam-to-Biomass Ratio  
Steam is one of the most common reforming agents and can be used for reforming pyrolysis volatiles. The use of steam has important advantages, as highlighted by many authors [73; 74]. Promoting the reforming process and limiting coke deposition on the catalyst surface are among the main reported advantages of using steam in this step of the process [73; 75]. According to research performed by [PERSON] et al. [76], reforming corncol pyrolysis volatiles at 650 \({}^{\circ}\)C over nickel with a Shenli lignite char catalyst showed that introducing steam as a reforming agent increased the gas yield up to 85.1 mmol/g, and this amount was twice as great compared to that of the gas yield in the reforming process without steam. While the hydrogen yield was 21.3 mmol/g without using steam as an agent in the reforming process, hydrogen production was enhanced by 279.8% and reached 61.9 mmol/g [76]. Another research study performed by [PERSON] et al. [77] assessed the effects of using steam and a Ni-exchanged resin char catalyst on reforming corncol pyrolysis volatiles at 650 \({}^{\circ}\)C. Gas yield results demonstrated that the steam reforming process produced 84.5 mmol/g of gas, 80% more than the reforming process without steam. In addition, the hydrogen yield increased to 61.2 mmol/g, which was 37 mmol/g more than that supplied by the non-steam reforming process [77].  
Figure 6: Effect of reforming temperature on hydrogen yield [70; 72; 73].  
The impact of the steam-to-biomass (S/B) ratio is another important factor investigated in the literature. This ratio was considered between 1.8 and 6 for the reforming of wood pellets pyrolysis volatiles at 900 \({}^{\circ}\)C using tire-derived char as a catalyst. According to the results of the study, increasing the S/B ratio from 1.82 to 3.32 led to a 28% gas yield increase. Also, hydrogen production was enhanced from 17.2 mmol/g to 33.6 mmol/g in this range. However, the results of increasing the S/B ratio from 3.32 to 4.32 and then up to 6 demonstrated its negligible effects on gas yield. Hydrogen yield was also evaluated in this range, and the results showed that it was 33.6 mmol/g, 35.1 mmol/g, and 42.8 mmol/g at ratios of 3.32, 4.32, and 6, respectively. Therefore, a higher S/B ratio led to producing more hydrogen, although it did not have a considerable effect on the total gas yield [70]. Taking into account these results, optimizing the S/B ratio performs a key role in designing feasible processes from technical and economic points of view.
# Road Pavement Damage Detection Based on Local Minimum of Grayscale and Feature Fusion
## 1 Introduction
Similarly, based on the local minimum grayscale feature points, a few image blocks of testing images are selected and then input into the trained CNN model. The trained CNN model can output a feature vector for each image block. These feature vectors will be combined to a composite feature vector, which is the feature descriptor of the whole image window. Finally, the composite feature vector is input to the classifier constructed by the SVM, to detect whether there are damaged areas in the image window. The proposed detection method exhibits high accuracy with little cost of computing resources, and our study offers possible solutions to the demand for fast detection at mobile terminals in the future. The methodology used to detect the pavement damage is shown in Figure 1.
# Convolutional Neural Network with an Elastic Matching Mechanism for Time Series Classification
## 4 Experiments
### Evaluation on the UCR Archive
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **FCN** & **EM-FCN** & **ResNet** & **EM-ResNet** & **Inception** & **EM-Inception** \\ \hline ProximalPhalanxOutlineCorrect & 0.9065 & 0.9107 & 0.9196 & 0.9141 & **0.9347** & 0.9244 \\ ProximalPhalanxTW & 0.7610 & 0.7659 & 0.7727 & **0.7902** & 0.7854 & 0.7854 \\ RefrigerationDevices & 0.4965 & 0.5147 & 0.5301 & 0.5360 & 0.5413 & **0.540** \\ ScreenType & **0.6219** & 0.6027 & 0.6155 & 0.5680 & 0.5707 & 0.5680 \\ ShapeItem & 0.7056 & 0.8667 & 0.7822 & 0.9144 & **0.9833** & 0.8833 \\ ShapesAll & 0.8940 & 0.8950 & 0.9263 & 0.9183 & 0.9150 & **0.9367** \\ SmallKitchenAppliances & 0.7771 & 0.7787 & 0.7813 & **0.7920** & 0.7680 & 0.7653 \\ SonyAIBORobSurface1 & 0.9584 & 0.9584 & **0.9607** & 0.9271 & 0.8502 & 0.9534 \\ SonyAIBORobotSurface2 & **0.9803** & 0.9643 & 0.9754 & 0.9664 & 0.9454 & 0.9423 \\ StarLightCurves & 0.9650 & 0.9745 & 0.9723 & 0.9745 & **0.9789** & 0.9492 \\ Strawberry & 0.9751 & 0.9757 & 0.9800 & 0.9703 & **0.9811** & 0.9568 \\ SwedishLeaf & 0.9674 & **0.9776** & 0.9626 & 0.9648 & 0.9472 & 0.9760 \\ Symbols & 0.9554 & 0.9548 & 0.8931 & 0.9759 & **0.9829** & 0.9769 \\ SyntheticControl & 0.9887 & 0.9933 & 0.9967 & **1.0000** & 0.9933 & **1.0000** \\ ToeSegmentation1 & 0.9614 & 0.9561 & 0.9570 & 0.9649 & 0.9561 & **0.9737** \\ ToeSegmentation2 & 0.8892 & 0.8846 & 0.8938 & 0.8923 & **0.9462** & **0.9462** \\ Trace & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** \\ TwoLedECG & 0.9995 & **1.0000** & **1.0000** & **1.0000** & 0.9956 & 0.9991 \\ TwoPatterns & 0.8705 & 0.8758 & **1.0000** & **1.0000** & **1.0000** & **1.0000** \\ UWaveGestureLibraryX & 0.7538 & 0.7831 & 0.7812 & 0.7929 & 0.8130 & **0.8275** \\ UWaveGestureLibraryY & 0.6425 & 0.6801 & 0.6658 & 0.6778 & **0.7501** & 0.7493 \\ UWaveGestureLibraryZ & 0.7267 & 0.7515 & 0.7486 & **0.7607** & 0.7482 & 0.7510 \\ UWaveGestureLibraryAll & 0.8179 & 0.8210 & 0.8608 & 0.8783 & 0.9422 & **0.9764** \\ Wafer & 0.9972 & 0.9982 & 0.9981 & **0.9989** & 0.9982 & 0.9977 \\ Wine & 0.6111 & **0.7963** & 0.7222 & 0.7370 & 0.7593 & **0.7963** \\ WordSynonyms & 0.5611 & 0.5690 & 0.6166 & 0.6395 & 0.7320 & **0.7508** \\ Worms & 0.7818 & 0.8052 & 0.7610 & 0.7273 & 0.7532 & **0.8182** \\ WormsTwoClass & 0.7429 & 0.7532 & 0.7481 & 0.7143 & **0.7922** & 0.6883 \\ Yoga & 0.8372 & 0.8760 & 0.8667 & 0.8720 & 0.9053 & **0.9237** \\ \hline Number of Win & 9 & 17 & 10 & 21 & 24 & **35** \\ AVG-AR & 4.1529 & 3.6235 & 3.4588 & 3.0588 & 2.8941 & **2.7177** \\ AVG-GR & 3.6715 & 3.0460 & 3.0936 & 2.5862 & 2.3412 & **2.1272** \\ MPCE & 0.0515 & 0.0480 & 0.0453 & 0.0443 & 0.0428 & **0.0417** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 8: Critical difference diagram of a pairwise statistical difference comparison of 12 methods on the UCR archive.
## 5 Conclusions  
In this paper, a thermodynamic design software of the S-CO\({}_{2}\) centrifugal compressor (S-CO\({}_{2}\)CPTD) was developed based on the 1-D thermal design method. The 150 kW S-CO\({}_{2}\) centrifugal compressor at the speed of 40,000 rpm was designed by using the developed software. The 3-D aerodynamic analysis and aerodynamic optimization was carried out in the compressor initial design. The concrete conclusions are as follows:  
1. The initial design of compressor was done by the thermodynamic design software of the S-CO\({}_{2}\) centrifugal compressor. The power was 147.72 kW, the total pressure ratio was 1.67 and the total static efficiency was 50.30%. On the basis of the initial design of the compressor, Case A (9 main blades + 9 splitter blades) and Case B (12 main blades) were analyzed. Considering the factors such as performance, machining and economy, the splitter blade structure was not recommended for compressor design in this research.
2. According to the 3-D numerical results of variable initial designs, the software modification was made by adjusting the empirical coefficient, the loss model and the design method. The thermal design software matching the S-CO\({}_{2}\) centrifugal compressor was obtained. It made the design of the S-CO\({}_{2}\) centrifugal compressor intelligent, accurate and efficient.
3. Based on the improved numerical calculation method, the modified compressor design software and the optimized flow passage geometry parameters, the optimal design of S-CO\({}_{2}\) centrifugal compressor was obtained. The 3-D aerodynamic analysis and off-design performance analysis of the optimal design were carried out. The results show that the aerodynamic performance of the compressor was excellent at the low speed requirement of 40,000 rpm. The total pressure ratio was 1.9 and the total static efficiency was 79.54% under the design condition. The S-CO\({}_{2}\) centrifugal compressor of optimal design could run normally in the flow rate range of 5.97-11.05 kg/s. Additionally, it could run efficiently and stably in the range of 5.97-9.52 kg/s.  
Conceptualization, D.S. and Y.X.; investigation, D.S.; methodology, D.S.; resources, Y.X., software, D.S. and Y.X.; supervision, Y.X.; validation Y.X.; writing--original draft preparation, D.S.; writing--review and editing, D.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.